Annual Meeting of the Association for Computational Linguistics (2024)

Volumes

Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) 865 papers
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) 78 papers
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) 39 papers
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) 51 papers
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 5: Tutorial Abstracts) 7 papers
Findings of the Association for Computational Linguistics ACL 2024 976 papers
Proceedings of the 3rd Workshop on Advances in Language and Vision Research (ALVR) 19 papers
Proceedings of The Second Arabic Natural Language Processing Conference 108 papers
Proceedings of the 11th Workshop on Argument Mining (ArgMining 2024) 19 papers
Proceedings of the 23rd Workshop on Biomedical Natural Language Processing 80 papers
Proceedings of the 2nd Workshop on Cross-Cultural Considerations in NLP 10 papers
Proceedings of the 1st Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2024) 21 papers
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics 24 papers
Proceedings of the 1st Workshop on Data Contamination (CONDA) 5 papers
Proceedings of the 3rd Workshop on NLP Applications to Field Linguistics (Field Matters 2024) 10 papers
Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP) 27 papers
Proceedings of the 1st Human-Centered Large Language Modeling Workshop 8 papers
Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024) 38 papers
Proceedings of the 1st Workshop on Knowledge Graphs and Large Language Models (KaLLM 2024) 14 papers
Proceedings of the 3rd Workshop on Knowledge Augmented Methods for NLP 8 papers
Proceedings of the 1st Workshop on Towards Knowledgeable Language Models (KnowLLM 2024) 17 papers
Proceedings of the 1st Workshop on Language + Molecules (L+M 2024) 16 papers
Proceedings of the 5th Workshop on Computational Approaches to Historical Language Change 19 papers
Proceedings of the Seventh Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2024) 21 papers
Proceedings of the 1st Workshop on Machine Learning for Ancient Languages (ML4AL 2024) 27 papers
Proceedings of the 6th Workshop on NLP for Conversational AI (NLP4ConvAI 2024) 9 papers
Proceedings of the 2nd Workshop on Natural Language Reasoning and Structured Explanations (@ACL 2024) 8 papers
Proceedings of the Fifth Workshop on Privacy in Natural Language Processing 19 papers
Proceedings of the 9th Workshop on Representation Learning for NLP (RepL4NLP-2024) 21 papers
Proceedings of the Fourth Workshop on Scholarly Document Processing (SDP 2024) 32 papers
Proceedings of the 10th SIGHAN Workshop on Chinese Language Processing (SIGHAN-10) 21 papers
Proceedings of the First Workshop on Natural Language Processing for Turkic Languages (SIGTURK 2024) 10 papers
Proceedings of The 9th Social Media Mining for Health Research and Applications (SMM4H 2024) Workshop and Shared Tasks 41 papers
Proceedings of the 4th Workshop on Spatial Language Understanding and Grounded Communication for Robotics (SpLU-RoboNLP 2024) 4 papers
Proceedings of the Sixth Workshop on Teaching NLP 19 papers
Proceedings of TextGraphs-17: Graph-based Methods for Natural Language Processing 16 papers
Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis 54 papers

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bib (full) Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Lun-Wei Ku | Andre Martins | Vivek Srikumar

Finetuning large language models (LLMs) has been empirically effective on a variety of downstream tasks. Existing approaches to finetuning an LLM either focus on parameter-efficient finetuning, which only updates a small number of trainable parameters, or attempt to reduce the memory footprint during the training phase of the finetuning. Typically, the memory footprint during finetuning stems from three contributors: model weights, optimizer states, and intermediate activations. However, existing works still require considerable memory, and none can simultaneously mitigate the memory footprint of all three sources. In this paper, we present quantized side tuing (QST), which enables memory-efficient and fast finetuning of LLMs by operating through a dual-stage process. First, QST quantizes an LLM’s model weights into 4-bit to reduce the memory footprint of the LLM’s original weights. Second, QST introduces a side network separated from the LLM, which utilizes the hidden states of the LLM to make task-specific predictions. Using a separate side network avoids performing back-propagation through the LLM, thus reducing the memory requirement of the intermediate activations. Finally, QST leverages several low-rank adaptors and gradient-free downsample modules to significantly reduce the trainable parameters, so as to save the memory footprint of the optimizer states. Experiments show that QST can reduce the total memory footprint by up to 2.3× and speed up the finetuning process by up to 3× while achieving competent performance compared with the state-of-the-art. When it comes to full finetuning, QST can reduce the total memory footprint up to 7×.

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Unsupervised Multimodal Clustering for Semantics Discovery in Multimodal Utterances
Hanlei Zhang | Hua Xu | Fei Long | Xin Wang | Kai Gao

Discovering the semantics of multimodal utterances is essential for understanding human language and enhancing human-machine interactions. Existing methods manifest limitations in leveraging nonverbal information for discerning complex semantics in unsupervised scenarios. This paper introduces a novel unsupervised multimodal clustering method (UMC), making a pioneering contribution to this field. UMC introduces a unique approach to constructing augmentation views for multimodal data, which are then used to perform pre-training to establish well-initialized representations for subsequent clustering. An innovative strategy is proposed to dynamically select high-quality samples as guidance for representation learning, gauged by the density of each sample’s nearest neighbors. Besides, it is equipped to automatically determine the optimal value for the top-K parameter in each cluster to refine sample selection. Finally, both high- and low-quality samples are used to learn representations conducive to effective clustering. We build baselines on benchmark multimodal intent and dialogue act datasets. UMC shows remarkable improvements of 2-6% scores in clustering metrics over state-of-the-art methods, marking the first successful endeavor in this domain. The complete code and data are available at https://github.com/thuiar/UMC.

Large language models (LLMs) have achieved human-level text generation, emphasizing the need for effective deepfake text detection to mitigate risks like the spread of fake news and plagiarism. Existing research has been constrained by evaluating detection methods o specific domains or particular language models. In practical scenarios, however, the detector faces texts from various domains or LLMs without knowing their sources. To this end, we build a comprehensive testbed by gathering texts from diverse human writings and deepfake texts generated by different LLMs. Empirical results on mainstream detection methods demonstrate the difficulties associated with detecting deepfake text in a wide-ranging testbed, particularly in out-of-distribution scenarios. Such difficulties align with the diminishing linguistic differences between the two text sources. Despite challenges, the top-performing detector can identify 84.12% out-of-domain texts generated by a new LLM, indicating the feasibility for application scenarios.

The rapid development of language models (LMs) brings unprecedented accessibility and usage for both models and users. On the one hand, powerful LMs achieve state-of-the-art performance over numerous downstream NLP tasks. On the other hand, more and more attention is paid to unrestricted model accesses that may bring malicious privacy risks of data leakage. To address these issues, many recent works propose privacy-preserving language models (PPLMs) with differential privacy (DP). Unfortunately, different DP implementations make it challenging for a fair comparison among existing PPLMs. In this paper, we present PrivLM-Bench, a multi-perspective privacy evaluation benchmark to empirically and intuitively quantify the privacy leakage of LMs. Instead of only reporting DP parameters, PrivLM-Bench sheds light on the neglected inference data privacy during actual usage. PrivLM-Bench first clearly defines multi-faceted privacy objectives. Then, PrivLM-Bench constructs a unified pipeline to perform private fine-tuning. Lastly, PrivLM-Bench performs existing privacy attacks on LMs with pre-defined privacy objectives as the empirical evaluation results. The empirical attack results are used to fairly and intuitively evaluate the privacy leakage of various PPLMs. We conduct extensive experiments on three datasets of GLUE for mainstream LMs.

Recent advances in large language models (LLMs) have stepped forward the development of multilingual speech and machine translation by its reduced representation errors and incorporated external knowledge. However, both translation tasks typically utilize beam search decoding and top-1 hypothesis selection for inference. These techniques struggle to fully exploit the rich information in the diverse N-best hypotheses, making them less optimal for translation tasks that require a single, high-quality output sequence. In this paper, we propose a new generative paradigm for translation tasks, namely GenTranslate, which builds upon LLMs to generate better results from the diverse translation versions in N-best list. Leveraging the rich linguistic knowledge and strong reasoning abilities of LLMs, our new paradigm can integrate the diverse N-best candidates to generate a higher-quality translation result. Furthermore, to support LLM finetuning, we build and release a HypoTranslate dataset that contains over 592K hypotheses-translation pairs in 11 languages. Experiments on various speech and machine translation benchmarks (e.g., FLEURS, CoVoST-2, WMT) demonstrate that our GenTranslate significantly outperforms the state-of-the-art model.

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Exploring Chain-of-Thought for Multi-modal Metaphor Detection
Yanzhi Xu | Yueying Hua | Shichen Li | Zhongqing Wang

Metaphors are commonly found in advertising and internet memes. However, the free form of internet memes often leads to a lack of high-quality textual data. Metaphor detection demands a deep interpretation of both textual and visual elements, requiring extensive common-sense knowledge, which poses a challenge to language models. To address these challenges, we propose a compact framework called C4MMD, which utilizes a Chain-of-Thought(CoT) method for Multi-modal Metaphor Detection. Specifically, our approach designs a three-step process inspired by CoT that extracts and integrates knowledge from Multi-modal Large Language Models(MLLMs) into smaller ones. We also developed a modality fusion architecture to transform knowledge from large models into metaphor features, supplemented by auxiliary tasks to improve model performance. Experimental results on the MET-MEME dataset demonstrate that our method not only effectively enhances the metaphor detection capabilities of small models but also outperforms existing models. To our knowledge, this is the first systematic study leveraging MLLMs in metaphor detection tasks. The code for our method is publicly available at https://github.com/xyz189411yt/C4MMD.

The upscaling of Large Language Models (LLMs) has yielded impressive advances in natural language processing, yet it also poses significant deployment challenges. Weight quantization has emerged as a widely embraced solution to reduce memory and computational demands. This paper introduces BitDistiller, a framework that synergizes Quantization-Aware Training (QAT) with Knowledge Distillation (KD) to boost the performance of LLMs at ultra-low precisions (sub-4-bit). Specifically, BitDistiller first incorporates a tailored asymmetric quantization and clipping technique to maximally preserve the fidelity of quantized weights, and then proposes a novel Confidence-Aware Kullback-Leibler Divergence (CAKLD) objective, which is employed in a self-distillation manner to enable faster convergence and superior model performance. Empirical evaluations demonstrate that BitDistiller significantly surpasses existing methods in both 3-bit and 2-bit configurations on general language understanding and complex reasoning benchmarks. Notably, BitDistiller is shown to be more cost-effective, demanding fewer data and training resources. The code is available at https://github.com/DD-DuDa/BitDistiller.

Temporal knowledge graph (TKG) reasoning has two settings: interpolation reasoning and extrapolation reasoning. Both of them draw plenty of research interest and have great significance. Methods of the former de-emphasize the temporal correlations among facts sequences, while methods of the latter require strict chronological order of knowledge and ignore inferring clues provided by missing facts of the past. These limit the practicability of TKG applications as almost all of the existing TKG reasoning methods are designed specifically to address either one setting. To this end, this paper proposes an original Temporal PAth-based Reasoning (TPAR) model for both the interpolation and extrapolation reasoning settings. TPAR performs a neural-driven symbolic reasoning fashion that is robust to ambiguous and noisy temporal data, and with fine interpretability as well. Comprehensive experiments show that TPAR outperforms SOTA methods on the link prediction task for both the interpolation and the extrapolation settings. A novel pipeline experimental setting is designed to evaluate the performances of SOTA combinations and the proposed TPAR towards interpolation and extrapolation reasoning. And more diverse experiments are conducted to show the robustness and interpretability of TPAR.

Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating additional information from retrieval. However, studies have shown that LLMs still face challenges in effectively using the retrieved information, even ignore it or be misled by it. The key reason is that the training of LLMs does not clearly make LLMs learn how to utilize input retrieved texts with varied quality. In this paper, we propose a novel perspective that considers the role of LLMs in RAG as “Information Refiner”, which means that regardless of correctness, completeness, or usefulness of retrieved texts, LLMs can consistently integrate knowledge within the retrieved texts and model parameters to generate the texts that are more concise, accurate, and complete than the retrieved texts. To this end, we propose an information refinement training method named INFO-RAG that optimizes LLMs for RAG in an unsupervised manner. INFO-RAG is low-cost and general across various tasks. Extensive experiments on zero-shot prediction of 11 datasets in diverse tasks including Question Answering, Slot-Filling, Language Modeling, Dialogue, and Code Generation show that INFO-RAG improves the performance of LLaMA2 by an average of 9.39% relative points. INFO-RAG also shows advantages in in-context learning and robustness of RAG.

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CSCD-NS: a Chinese Spelling Check Dataset for Native Speakers
Yong Hu | Fandong Meng | Jie Zhou

In this paper, we present CSCD-NS, the first Chinese spelling check (CSC) dataset designed for native speakers, containing 40,000 samples from a Chinese social platform. Compared with existing CSC datasets aimed at Chinese learners, CSCD-NS is ten times larger in scale and exhibits a distinct error distribution, with a significantly higher proportion of word-level errors. To further enhance the data resource, we propose a novel method that simulates the input process through an input method, generating large-scale and high-quality pseudo data that closely resembles the actual error distribution and outperforms existing methods. Moreover, we investigate the performance of various models in this scenario, including large language models (LLMs), such as ChatGPT. The result indicates that generative models underperform BERT-like classification models due to strict length and pronunciation constraints. The high prevalence of word-level errors also makes CSC for native speakers challenging enough, leaving substantial room for improvement.

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Evaluating Dynamic Topic Models
Charu Karakkaparambil James | Mayank Nagda | Nooshin Haji Ghassemi | Marius Kloft | Sophie Fellenz

There is a lack of quantitative measures to evaluate the progression of topics through time in dynamic topic models (DTMs). Filling this gap, we propose a novel evaluation measure for DTMs that analyzes the changes in the quality of each topic over time. Additionally, we propose an extension combining topic quality with the model’s temporal consistency. We demonstrate the utility of the proposed measure by applying it to synthetic data and data from existing DTMs, including DTMs from large language models (LLMs). We also show that the proposed measure correlates well with human judgment. Our findings may help in identifying changing topics, evaluating different DTMs and LLMs, and guiding future research in this area.

Large language models (LLMs) with enormous pre-training tokens and parameters emerge diverse abilities, including math reasoning, codegeneration, and instruction following. These abilities are further enhanced by supervised fine-tuning (SFT). While the open-source community has explored ad-hoc SFT for enhancing individual capabilities, proprietary LLMs exhibit versatility across various skills. Therefore, understanding the facilitation of multiple abilities via SFT is paramount. In this study, we specificially focuses on the interplay of data composition between mathematical reasoning, code generation, and general human-aligning abilities during SFT. We propose four intriguing research questions to explore the association between model performance and various factors including data amount, composition ratio, model size and SFT strategies. Our experiments reveal that distinct capabilities scale differently and larger models generally show superior performance with same amount of data. Mathematical reasoning and code generation consistently improve with increasing data amount, whereas general abilities plateau after roughly a thousand samples. Moreover, we observe data composition appears to enhance various abilities under limited data conditions, yet can lead to performance conflicts when data is plentiful. Our findings also suggest the amount of composition data influences performance more than the composition ratio. In analysis of SFT strategies, we find that sequentially learning multiple skills risks catastrophic forgetting. Our proposed Dual-stage Mixed Fine-tuning (DMT) strategy offers a promising solution to learn multiple abilities with different scaling patterns.

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Through the Lens of Split Vote: Exploring Disagreement, Difficulty and Calibration in Legal Case Outcome Classification
Shanshan Xu | Santosh T.y.s.s | Oana Ichim | Barbara Plank | Matthias Grabmair

In legal decisions, split votes (SV) occur when judges cannot reach a unanimous decision, posing a difficulty for lawyers who must navigate diverse legal arguments and opinions. In high-stakes domains, %as human-AI interaction systems become increasingly important, understanding the alignment of perceived difficulty between humans and AI systems is crucial to build trust. However, existing NLP calibration methods focus on a classifier’s awareness of predictive performance, measured against the human majority class, overlooking inherent human label variation (HLV). This paper explores split votes as naturally observable human disagreement and value pluralism. We collect judges’ vote distributions from the European Court of Human Rights (ECHR), and present SV-ECHR, a case outcome classification (COC) dataset with SV information. We build a taxonomy of disagreement with SV-specific subcategories. We further assess the alignment of perceived difficulty between models and humans, as well as confidence- and human-calibration of COC models. We observe limited alignment with the judge vote distribution. To our knowledge, this is the first systematic exploration of calibration to human judgements in legal NLP. Our study underscores the necessity for further research on measuring and enhancing model calibration considering HLV in legal decision tasks.

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Inference to the Best Explanation in Large Language Models
Dhairya Dalal | Marco Valentino | Andre Freitas | Paul Buitelaar

While Large Language Models (LLMs) have found success in real-world applications, their underlying explanatory process is still poorly understood. This paper proposes IBE-Eval, a framework inspired by philosophical accounts on Inference to the Best Explanation (IBE) to advance the interpretation and evaluation of LLMs’ explanations. IBE-Eval estimates the plausibility of natural language explanations through a combination of explicit logical and linguistic features including: consistency, parsimony, coherence, and uncertainty. Extensive experiments are conducted on Causal Question Answering (CQA), where IBE-Eval is tasked to select the most plausible causal explanation amongst competing ones generated by LLMs (i.e., GPT 3.5 and Llama 2). The experiments reveal that IBE-Eval can successfully identify the best explanation with up to 77% accuracy (≈ 27% above random), improving upon a GPT 3.5-as-a-Judge baseline (≈+17%) while being intrinsically more efficient and interpretable. Additional analyses suggest that, despite model-specific variances, LLM-generated explanations tend to conform to IBE criteria and that IBE-Eval is significantly correlated with human judgment, opening up opportunities for future development of automated explanation verification tools.

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A Novel Cartography-Based Curriculum Learning Method Applied on RoNLI: The First Romanian Natural Language Inference Corpus
Eduard Poesina | Cornelia Caragea | Radu Ionescu

Natural language inference (NLI), the task of recognizing the entailment relationship in sentence pairs, is an actively studied topic serving as a proxy for natural language understanding. Despite the relevance of the task in building conversational agents and improving text classification, machine translation and other NLP tasks, to the best of our knowledge, there is no publicly available NLI corpus for the Romanian language. To this end, we introduce the first Romanian NLI corpus (RoNLI) comprising 58K training sentence pairs, which are obtained via distant supervision, and 6K validation and test sentence pairs, which are manually annotated with the correct labels. We conduct experiments with multiple machine learning methods based on distant learning, ranging from shallow models based on word embeddings to transformer-based neural networks, to establish a set of competitive baselines. Furthermore, we improve on the best model by employing a new curriculum learning strategy based on data cartography. Our dataset and code to reproduce the baselines are available at https://github.com/Eduard6421/RONLI.

Recent advances in few-shot question answering (QA) mostly rely on the power of pre-trained large language models (LLMs) and fine-tuning in specific settings. Although the pre-training stage has already equipped LLMs with powerful reasoning capabilities, LLMs still need to be fine-tuned to adapt to specific domains to achieve the best results. In this paper, we propose to select the most informative data for fine-tuning, thereby improving the efficiency of the fine-tuning process with comparative or even better accuracy on the open-domain QA task. We present MinPrompt, a minimal data augmentation framework for open-domain QA based on an approximate graph algorithm and unsupervised question generation. We transform the raw text into a graph structure to build connections between different factual sentences, then apply graph algorithms to identify the minimal set of sentences needed to cover the most information in the raw text. We then generate QA pairs based on the identified sentence subset and train the model on the selected sentences to obtain the final model. Empirical results on several benchmark datasets and theoretical analysis show that MinPrompt is able to achieve comparable or better results than baselines with a high degree of efficiency, bringing consistent improvements in F-1 scores.

Large language models hold significant potential for integrating various data types, such as text documents and database records, for advanced analytics. However, blending text and numerical data presents substantial challenges. LLMs need to process and cross-reference entities and numbers, handle data inconsistencies and redundancies, and develop planning capabilities such as building a working memory for managing complex data queries. In this paper, we introduce four novel tasks centered around sports data analytics to evaluate the numerical reasoning and information fusion capabilities of LLMs. These tasks involve providing LLMs with detailed, play-by-play sports game descriptions, then challenging them with adversarial scenarios such as new game rules, longer durations, scrambled narratives, and analyzing key statistics in game summaries. We conduct extensive experiments on NBA and NFL games to assess the performance of LLMs on these tasks. Our benchmark, SportsMetrics, introduces a new mechanism for assessing LLMs’ numerical reasoning and fusion skills.

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SciMON: Scientific Inspiration Machines Optimized for Novelty
Qingyun Wang | Doug Downey | Heng Ji | Tom Hope

We explore and enhance the ability of neural language models to generate novel scientific directions grounded in literature. Work on literature-based hypothesis generation has traditionally focused on binary link prediction—severely limiting the expressivity of hypotheses. This line of work also does not focus on optimizing novelty. We take a dramatic departure with a novel setting in which models use as input background contexts (e.g., problems, experimental settings, goals), and output natural language ideas grounded in literature. We present SciMON, a modeling framework that uses retrieval of “inspirations” from past scientific papers, and explicitly optimizes for novelty by iteratively comparing to prior papers and updating idea suggestions until sufficient novelty is achieved. Comprehensive evaluations reveal that GPT-4 tends to generate ideas with overall low technical depth and novelty, while our methods partially mitigate this issue. Our work represents a first step toward evaluating and developing language models that generate new ideas derived from the scientific literature. Code, data, and resources are publicly available for research purposes: https://github.com/eaglew/clbd.

We introduce EVL_Gen, a streamlined framework designed for the pre-training of visually conditioned language generation models with high computational demands, utilizing frozen pre-trained large language models (LLMs). The conventional approach in vision-language pre-training (VLP) typically involves a two-stage optimization process: an initial resource-intensive phase dedicated to general-purpose vision-language representation learning, focused on extracting and consolidating relevant visual features. This is followed by a subsequent phase that emphasizes end-to-end alignment between visual and linguistic modalities. Our novel one-stage, single-loss framework bypasses the computationally demanding first training stage by gradually merging similar visual tokens during training, while avoiding model collapse caused by single-stage training of BLIP-2 type models. The gradual merging process effectively condenses visual information while preserving semantic richness, resulting in rapid convergence without compromising performance. Our experimental findings demonstrate that our approach accelerates the training of vision-language models by a factor of 5 without a noticeable impact on overall performance. Furthermore, we illustrate that our models significantly narrow the performance gap to current vision-language models using only 1/10 of the data. Finally, we showcase how our image-text models can seamlessly adapt to video-conditioned language generation tasks through novel soft attentive temporal token contextualizing modules. Code: https://github.com/yiren-jian/EVLGen

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Confidence Under the Hood: An Investigation into the Confidence-Probability Alignment in Large Language Models
Abhishek Kumar | Robert Morabito | Sanzhar Umbet | Jad Kabbara | Ali Emami

As the use of Large Language Models (LLMs) becomes more widespread, understanding their self-evaluation of confidence in generated responses becomes increasingly important as it is integral to the reliability of the output of these models. We introduce the concept of Confidence-Probability Alignment, that connects an LLM’s internal confidence, quantified by token probabilities, to the confidence conveyed in the model’s response when explicitly asked about its certainty. Using various datasets and prompting techniques that encourage model introspection, we probe the alignment between models’ internal and expressed confidence. These techniques encompass using structured evaluation scales to rate confidence, including answer options when prompting, and eliciting the model’s confidence level for outputs it does not recognize as its own. Notably, among the models analyzed, OpenAI’s GPT-4 showed the strongest confidence-probability alignment, with an average Spearman’s ̂𝜌 of 0.42, across a wide range of tasks. Our work contributes to the ongoing efforts to facilitate risk assessment in the application of LLMs and to further our understanding of model trustworthiness.

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Retrieval-Augmented Multilingual Knowledge Editing
Weixuan Wang | Barry Haddow | Alexandra Birch

Knowledge represented in Large Language Models (LLMs) is quite often incorrect and can also become obsolete over time. Updating knowledge via fine-tuning is computationally resource-hungry and not reliable, and so knowledge editing (KE) has developed as an effective and economical alternative to inject new knowledge or to fix factual errors in LLMs. Although there has been considerable interest in this area, current KE research exclusively focuses on monolingual settings, typically in English. However, what happens if the new knowledge is supplied in one language, but we would like to query an LLM in a different language? To address the problem of multilingual knowledge editing, we propose Retrieval-Augmented Multilingual Knowledge Editor (ReMaKE) to update knowledge in LLMs. ReMaKE can be used to perform model-agnostic knowledge editing in a multilingual setting. ReMaKE concatenates the new knowledge retrieved from a multilingual knowledge base with users’ prompts before querying an LLM. Our experimental results show that ReMaKE outperforms baseline knowledge editing methods by a significant margin and is scalable to real-word application scenarios. Our multilingual knowledge editing dataset (MzsRE) in 12 languages, the code, and additional project information are available at https://github.com/weixuan-wang123/ReMaKE.

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Picturing Ambiguity: A Visual Twist on the Winograd Schema Challenge
Brendan Park | Madeline Janecek | Naser Ezzati-Jivan | Yifeng Li | Ali Emami

Large Language Models (LLMs) have demonstrated remarkable success in tasks like the Winograd Schema Challenge (WSC), showcasing advanced textual common-sense reasoning. However, applying this reasoning to multimodal domains, where understanding text and images together is essential, remains a substantial challenge. To address this, we introduce WinoVis, a novel dataset specifically designed to probe text-to-image models on pronoun disambiguation within multimodal contexts. Utilizing GPT-4 for prompt generation and Diffusion Attentive Attribution Maps (DAAM) for heatmap analysis, we propose a novel evaluation framework that isolates the models’ ability in pronoun disambiguation from other visual processing challenges. Evaluation of successive model versions reveals that, despite incremental advancements, Stable Diffusion 2.0 achieves a precision of 56.7% on WinoVis, only marginally surpassing random guessing. Further error analysis identifies important areas for future research aimed at advancing text-to-image models in their ability to interpret and interact with the complex visual world.

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Subtle Biases Need Subtler Measures: Dual Metrics for Evaluating Representative and Affinity Bias in Large Language Models
Abhishek Kumar | Sarfaroz Yunusov | Ali Emami

Research on Large Language Models (LLMs) has often neglected subtle biases that, although less apparent, can significantly influence the models’ outputs toward particular social narratives. This study addresses two such biases within LLMs: representative bias, which denotes a tendency of LLMs to generate outputs that mirror the experiences of certain identity groups, and affinity bias, reflecting the models’ evaluative preferences for specific narratives or viewpoints. We introduce two novel metrics to measure these biases: the Representative Bias Score (RBS) and the Affinity Bias Score (ABS), and present the Creativity-Oriented Generation Suite (CoGS), a collection of open-ended tasks such as short story writing and poetry composition, designed with customized rubrics to detect these subtle biases. Our analysis uncovers marked representative biases in prominent LLMs, with a preference for identities associated with being white, straight, and men. Furthermore, our investigation of affinity bias reveals distinctive evaluative patterns within each model, akin to ‘bias fingerprints’. This trend is also seen in human evaluators, highlighting a complex interplay between human and machine bias perceptions.

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Framing in the Presence of Supporting Data: A Case Study in U.S. Economic News
Alexandria Leto | Elliot Pickens | Coen Needell | David Rothschild | Maria Leonor Pacheco

The mainstream media has much leeway in what it chooses to cover and how it covers it. These choices have real-world consequences on what people know and their subsequent behaviors. However, the lack of objective measures to evaluate editorial choices makes research in this area particularly difficult. In this paper, we argue that there are newsworthy topics where objective measures exist in the form of supporting data and propose a computational framework to analyze editorial choices in this setup. We focus on the economy because the reporting of economic indicators presents us with a relatively easy way to determine both the selection and framing of various publications. Their values provide a ground truth of how the economy is doing relative to how the publications choose to cover it. To do this, we define frame prediction as a set of interdependent tasks. At the article level, we learn to identify the reported stance towards the general state of the economy. Then, for every numerical quantity reported in the article, we learn to identify whether it corresponds to an economic indicator and whether it is being reported in a positive or negative way. To perform our analysis, we track six American publishers and each article that appeared in the top 10 slots of their landing page between 2015 and 2023.

Multimodal Large Language Models (MLLMs) have demonstrated proficiency in handling a variety of visual-language tasks. However, current MLLM benchmarks are predominantly designed to evaluate reasoning based on static information about a single image, and the ability of modern MLLMs to extrapolate from image sequences, which is essential for understanding our ever-changing world, has been less investigated. To address this challenge, this paper introduces Mementos, a new benchmark designed to assess MLLMs’ sequential image reasoning abilities. Mementos features 4,761 diverse image sequences with varying lengths. We also employ a GPT-4 assisted method to evaluate MLLM reasoning performance. Through a careful evaluation of nine recent MLLMs on Mementos, including GPT-4V and Gemini, we find that they struggle to accurately describe dynamic information about given image sequences, often leading to hallucinations/misrepresentations of objects and their corresponding behaviors. Our quantitative analysis and case studies identify three key factors impacting MLLMs’ sequential image reasoning: the correlation between object and behavioral hallucinations, the influence of co-occurring behaviors, and the compounding impact of behavioral hallucinations.

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TTM-RE: Memory-Augmented Document-Level Relation Extraction
Chufan Gao | Xuan Wang | Jimeng Sun

Document-level relation extraction aims to categorize the association between any two entities within a document.We find that previous methods for document-level relation extraction are ineffective in exploiting the full potential of large amounts of training data with varied noise levels. For example, in the ReDocRED benchmark dataset, state-of-the-art methods trained on the large-scale, lower-quality, distantly supervised training data generally do not perform better than those trained solely on the smaller, high-quality, human-annotated training data. To unlock the full potential of large-scale noisy training data for document-level relation extraction, we propose TTM-RE, a novel approach that integrates a trainable memory module, known as the Token Turing Machine, with a noisy-robust loss function that accounts for the positive-unlabeled setting. The trainable memory module enhances knowledge extraction from the large-scale noisy training dataset through an explicit learning of the memory tokens and a soft integration of the learned memory tokens into the input representation, thereby improving the model’s effectiveness for the final relation classification. Extensive experiments on ReDocRED, a benchmark dataset for document-level relation extraction, reveal that TTM-RE achieves state-of-the-art performance (with an absolute F1 score improvement of over 3%). Ablation studies further illustrate the superiority of TTM-RE in other domains (the ChemDisGene dataset in the biomedical domain) and under highly unlabeled settings.

This work aims to build a text embedder that can capture characteristics of texts specified by user instructions clarifying the similarity criterion. While previous methods improve general task awareness by injecting the instruction information into encoding, they fail to be sensitive to clearer criteria like “evaluate similarity based on emotion”. We instead propose a different viewpoint, which treats the instruction as a “question” about the input text and encodes the expected answers to obtain the representation accordingly. Intuitively, texts with the same (implicit) semantics would share similar answers following the instruction, thus leading to more similar representations. Specifically, we propose InBedder that instantiates this learning-to-answer idea by only fine-tuning language models via abstractive question answering tasks. Despite its simplicity, InBedder demonstrates significantly improved instruction-following capabilities according to our proposed instruction awareness tests and instruction robustness tests, when applied to language models with large language models (LLMs) (e.g., llama-2-7b) and smaller encoder-based LMs (e.g., roberta-large). Additionally, our qualitative analysis of clustering outcomes, achieved by applying diverse instructions to the same unlabeled corpus, demonstrates a high degree of interpretability in the clusters formed.

Language models (LMs) have achieved notable success in numerous NLP tasks, employing both fine-tuning and in-context learning (ICL) methods. While language models demonstrate exceptional performance, they face robustness challenges due to spurious correlations arising from imbalanced label distributions in training data or ICL exemplars. Previous research has primarily concentrated on word, phrase, and syntax features, neglecting the concept level, often due to the absence of concept labels and difficulty in identifying conceptual content in input texts. This paper introduces two main contributions. First, we employ ChatGPT to assign concept labels to texts, assessing concept bias in models during fine-tuning or ICL on test data. We find that LMs, when encountering spurious correlations between a concept and a label in training or prompts, resort to shortcuts for predictions. Second, we introduce a data rebalancing technique that incorporates ChatGPT-generated counterfactual data, thereby balancing label distribution and mitigating spurious correlations. Our method’s efficacy, surpassing traditional token removal approaches, is validated through extensive testing.

Large language models have demonstrated impressive performance on commonsense tasks; however, these tasks are often posed as multiple-choice questions, allowing models to exploit systematic biases. Commonsense is also inherently probabilistic with multiple correct answers. The purpose of “boiling water” could be making tea, cooking but also could be killing germs. Existing tasks do not capture the probabilistic nature of common sense. To this end, we present commonsense frame completion (CFC), a new generative task that evaluates common sense via multiple open-ended generations. We also propose a method of probabilistic evaluation that strongly correlates with human judgments. Humans drastically outperform strong language model baselines on our dataset, indicating this approach is both a challenging and useful evaluation of machine common sense.

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GradSafe: Detecting Jailbreak Prompts for LLMs via Safety-Critical Gradient Analysis
Yueqi Xie | Minghong Fang | Renjie Pi | Neil Gong

Large Language Models (LLMs) face threats from jailbreak prompts. Existing methods for detecting jailbreak prompts are primarily online moderation APIs or finetuned LLMs. These strategies, however, often require extensive and resource-intensive data collection and training processes. In this study, we propose GradSafe, which effectively detects jailbreak prompts by scrutinizing the gradients of safety-critical parameters in LLMs. Our method is grounded in a pivotal observation: the gradients of an LLM’s loss for jailbreak prompts paired with compliance response exhibit similar patterns on certain safety-critical parameters. In contrast, safe prompts lead to different gradient patterns. Building on this observation, GradSafe analyzes the gradients from prompts (paired with compliance responses) to accurately detect jailbreak prompts. We show that GradSafe, applied to Llama-2 without further training, outperforms Llama Guard—despite its extensive finetuning with a large dataset—in detecting jailbreak prompts. This superior performance is consistent across both zero-shot and adaptation scenarios, as evidenced by our evaluations on ToxicChat and XSTest. The source code is available at https://github.com/xyq7/GradSafe.

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Pouring Your Heart Out: Investigating the Role of Figurative Language in Online Expressions of Empathy
Gyeongeun Lee | Christina Wong | Meghan Guo | Natalie Parde

Empathy is a social mechanism used to support and strengthen emotional connection with others, including in online communities. However, little is currently known about the nature of these online expressions, nor the particular factors that may lead to their improved detection. In this work, we study the role of a specific and complex subcategory of linguistic phenomena, figurative language, in online expressions of empathy. Our extensive experiments reveal that incorporating features regarding the use of metaphor, idiom, and hyperbole into empathy detection models improves their performance, resulting in impressive maximum F1 scores of 0.942 and 0.809 for identifying posts without and with empathy, respectively.

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An Information-Theoretic Approach to Analyze NLP Classification Tasks
Luran Wang | Mark Gales | Vatsal Raina

Understanding the contribution of the inputs on the output is useful across many tasks. This work provides an information-theoretic framework to analyse the influence of inputs for text classification tasks. Natural language processing (NLP) tasks take either a single or multiple text elements to predict an output variable. Each text element has two components: the semantic meaning and a linguistic realization. Multiple-choice reading comprehension (MCRC) and sentiment classification (SC) are selected to showcase the framework. For MCRC, it is found that the relative context influence on the output reduces on more challenging datasets. In particular, more challenging contexts allows greater variation in the question complexity. Hence, test creators need to carefully consider the choice of the context when designing multiple-choice questions for assessment. For SC, it is found the semantic meaning of the input dominates compared to its linguistic realization when determining the sentiment. The framework is made available at: https://github.com/WangLuran/nlp-element-influence.

Conversational systems often rely on embedding models for intent classification and intent clustering tasks. The advent of Large Language Models (LLMs), which enable instructional embeddings allowing one to adjust semantics over the embedding space using prompts, are being viewed as a panacea for these downstream conversational tasks. However, traditional evaluation benchmarks rely solely on task metrics that don’t particularly measure gaps related to semantic understanding. Thus, we propose an intent semantic toolkit that gives a more holistic view of intent embedding models by considering three tasks– (1) intent classification, (2) intent clustering, and (3) a novel triplet task. The triplet task gauges the model’s understanding of two semantic concepts paramount in real-world conversational systems– negation and implicature. We observe that current embedding models fare poorly in semantic understanding of these concepts. To address this, we propose a pre-training approach to improve the embedding model by leveraging augmentation with data generated by an auto-regressive model and a contrastive loss term. Our approach improves the semantic understanding of the intent embedding model on the aforementioned linguistic dimensions while slightly effecting their performance on downstream task metrics.

Thousands of the world’s languages are in danger of extinction—a tremendous threat to cultural identities and human language diversity. Interlinear Glossed Text (IGT) is a form of linguistic annotation that can support documentation and resource creation for these languages’ communities. IGT typically consists of (1) transcriptions, (2) morphological segmentation, (3) glosses, and (4) free translations to a majority language. We propose Wav2Gloss: a task in which these four annotation components are extracted automatically from speech, and introduce the first dataset to this end, Fieldwork: a corpus of speech with all these annotations, derived from the work of field linguists, covering 37 languages, with standard formatting, and train/dev/test splits. We provide various baselines to lay the groundwork for future research on IGT generation from speech, such as end-to-end versus cascaded, monolingual versus multilingual, and single-task versus multi-task approaches.

Is it possible accurately classify political relations within evolving event ontologies without extensive annotations? This study investigates zero-shot learning methods that use expert knowledge from existing annotation codebook, and evaluates the performance of advanced ChatGPT (GPT-3.5/4) and a natural language inference (NLI)-based model called ZSP. ChatGPT uses codebook’s labeled summaries as prompts, whereas ZSP breaks down the classification task into context, event mode, and class disambiguation to refine task-specific hypotheses. This decomposition enhances interpretability, efficiency, and adaptability to schema changes. The experiments reveal ChatGPT’s strengths and limitations, and crucially show ZSP’s outperformance of dictionary-based methods and its competitive edge over some supervised models. These findings affirm the value of ZSP for validating event records and advancing ontology development. Our study underscores the efficacy of leveraging transfer learning and existing domain expertise to enhance research efficiency and scalability.

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SPOR: A Comprehensive and Practical Evaluation Method for Compositional Generalization in Data-to-Text Generation
Ziyao Xu | Houfeng Wang

Compositional generalization is an important ability of language models and has many different manifestations. For data-to-text generation, previous research on this ability is limited to a single manifestation called Systematicity and lacks consideration of large language models (LLMs), which cannot fully cover practical application scenarios. In this work, we propose SPOR, a comprehensive and practical evaluation method for compositional generalization in data-to-text generation. SPOR includes four aspects of manifestations (Systematicity, Productivity, Order invariance, and Rule learnability) and allows high-quality evaluation without additional manual annotations based on existing datasets. We demonstrate SPOR on two different datasets and evaluate some existing language models including LLMs. We find that the models are deficient in various aspects of the evaluation and need further improvement. Our work shows the necessity for comprehensive research on different manifestations of compositional generalization in data-to-text generation and provides a framework for evaluation.

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OPEx: A Component-Wise Analysis of LLM-Centric Agents in Embodied Instruction Following
Haochen Shi | Zhiyuan Sun | Xingdi Yuan | Marc-Alexandre Côté | Bang Liu

Embodied Instruction Following (EIF) is a crucial task in embodied learning, requiring agents to interact with their environment through egocentric observations to fulfill natural language instructions. Recent advancements have seen a surge in employing large language models (LLMs) within a framework-centric approach to enhance performance in embodied learning tasks, including EIF. Despite these efforts, there exists a lack of a unified understanding regarding the impact of various components—ranging from visual perception to action execution—on task performance. To address this gap, we introduce OPEx, a comprehensive framework that delineates the core components essential for solving embodied learning tasks: Observer, Planner, and Executor. Through extensive evaluations, we provide a deep analysis of how each component influences EIF task performance. Furthermore, we innovate within this space by integrating a multi-agent design into the Planner component of our LLM-centric architecture, further enhancing task performance. Our findings reveal that LLM-centric design markedly improves EIF outcomes, identify visual perception and low-level action execution as critical bottlenecks, and demonstrate that augmenting LLMs with a multi-agent framework further elevates performance.

Multimodal Large Language Models (MLLMs) have demonstrated remarkable proficiency in diverse tasks across different domains, with an increasing focus on improving their zero-shot generalization capabilities for unseen multimodal tasks. Multimodal instruction tuning has emerged as a successful strategy for achieving zero-shot generalization by fine-tuning pre-trained models on diverse multimodal tasks through instructions. As MLLMs grow in complexity and size, the need for parameter-efficient fine-tuning methods like Low-Rank Adaption (LoRA), which fine-tunes with a minimal set of parameters, becomes essential. However, applying LoRA in multimodal instruction tuning presents the challenge of task interference, which leads to performance degradation, especially when dealing with a broad array of multimodal tasks. To address this, this paper introduces a novel approach that integrates multimodal instruction tuning with Conditional Mixture-of-LoRA (MixLoRA). It innovates upon LoRA by dynamically constructing low-rank adaptation matrices tailored to the unique demands of each input instance, aiming to mitigate task interference. Experimental results on various multimodal evaluation datasets indicate that MixLoRA not only outperforms the conventional LoRA with the same or even higher ranks, demonstrating its efficacy and adaptability in diverse multimodal tasks.

Medical text generation aims to assist with administrative work and highlight salient information to support decision-making.To reflect the specific requirements of medical text, in this paper, we propose a set of metrics to evaluate the completeness, conciseness, and attribution of the generated text at a fine-grained level. The metrics can be computed by various types of evaluators including instruction-following (both proprietary and open-source) and supervised entailment models. We demonstrate the effectiveness of the resulting framework, DocLens, with three evaluators on three tasks: clinical note generation, radiology report summarization, and patient question summarization. A comprehensive human study shows that DocLens exhibits substantially higher agreement with the judgments of medical experts than existing metrics. The results also highlight the need to improve open-source evaluators and suggest potential directions. We released the code at https://github.com/yiqingxyq/DocLens.

This paper presents FoFo, a pioneering benchmark for evaluating large language models’ (LLMs) ability to follow complex, domain-specific formats, a crucial yet under-examined capability for their application as AI agents. Despite LLMs’ advancements, existing benchmarks fail to assess their format-following proficiency adequately. FoFo fills this gap with a diverse range of real-world formats and instructions, developed through an AI-Human collaborative method. Our evaluation across both open-source (e.g., Llama 2, WizardLM) and closed-source (e.g., GPT-4, PALM2, Gemini) LLMs highlights three key findings: open-source models significantly lag behind closed-source ones in format adherence; LLMs’ format-following performance is independent of their content generation quality; and LLMs’ format proficiency varies across different domains. These insights suggest the need for specialized tuning for format-following skills and highlight FoFo’s role in guiding the selection of domain-specific AI agents. FoFo will be publicly released, contributing a critical tool for advancing LLM evaluation and application.

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Hyper-CL: Conditioning Sentence Representations with Hypernetworks
Young Yoo | Jii Cha | Changhyeon Kim | Taeuk Kim

While the introduction of contrastive learning frameworks in sentence representation learning has significantly contributed to advancements in the field, it still remains unclear whether state-of-the-art sentence embeddings can capture the fine-grained semantics of sentences, particularly when conditioned on specific perspectives.In this paper, we introduce Hyper-CL, an efficient methodology that integrates hypernetworks with contrastive learning to compute conditioned sentence representations.In our proposed approach, the hypernetwork is responsible for transforming pre-computed condition embeddings into corresponding projection layers. This enables the same sentence embeddings to be projected differently according to various conditions.Evaluation on two representative conditioning benchmarks, namely conditional semantic text similarity and knowledge graph completion, demonstrates that Hyper-CL is effective in flexibly conditioning sentence representations, showcasing its computational efficiency at the same time.We also provide a comprehensive analysis of the inner workings of our approach, leading to a better interpretation of its mechanisms.

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Analysis of Multi-Source Language Training in Cross-Lingual Transfer
Seonghoon Lim | Taejun Yun | Jinhyeon Kim | Jihun Choi | Taeuk Kim

The successful adaptation of multilingual language models (LMs) to a specific language-task pair critically depends on the availability of data tailored for that condition. While cross-lingual transfer (XLT) methods have contributed to addressing this data scarcity problem, there still exists ongoing debate about the mechanisms behind their effectiveness.In this work, we focus on one of promising assumptions about inner workings of XLT, that it encourages multilingual LMs to place greater emphasis on language-agnostic or task-specific features. We test this hypothesis by examining how the patterns of XLT change with a varying number of source languages involved in the process.Our experimental findings show that the use of multiple source languages in XLT-a technique we term Multi-Source Language Training (MSLT)-leads to increased mingling of embedding spaces for different languages, supporting the claim that XLT benefits from making use of language-independent information. On the other hand, we discover that using an arbitrary combination of source languages does not always guarantee better performance. We suggest simple heuristics for identifying effective language combinations for MSLT and empirically prove its effectiveness.

We present ABEX, a novel and effective generative data augmentation methodology for low-resource Natural Language Understanding (NLU) tasks. ABEX is based on ABstract-and-EXpand, a novel paradigm for generating diverse forms of an input document – we first convert a document into its concise, abstract description and then generate new documents based on expanding the resultant abstraction. To learn the task of expanding abstract descriptions, we first train BART on a large-scale synthetic dataset with abstract-document pairs. Next, to generate abstract descriptions for a document, we propose a simple, controllable, and training-free method based on editing AMR graphs. ABEX brings the best of both worlds: by expanding from abstract representations, it preserves the original semantic properties of the documents, like style and meaning, thereby maintaining alignment with the original label and data distribution. At the same time, the fundamental process of elaborating on abstract descriptions facilitates diverse generations. We demonstrate the effectiveness of ABEX on 4 NLU tasks spanning 12 datasets and 4 low-resource settings. ABEX outperforms all our baselines qualitatively with improvements of 0.04% - 38.8%. Qualitatively, ABEX outperforms all prior methods from literature in terms of context and length diversity.

We present Belebele, a multiple-choice machine reading comprehension (MRC) dataset spanning 122 language variants. Significantly expanding the language coverage of natural language understanding (NLU) benchmarks, this dataset enables the evaluation of text models in high-, medium-, and low-resource languages. Each question is based on a short passage from the FLORES-200 dataset and has four multiple-choice answers. The questions were carefully curated to discriminate between models with different levels of general language comprehension. The English dataset on its own proves difficult enough to challenge state-of-the-art language models. Being fully parallel, this dataset enables direct comparison of model performance across all languages. We use this dataset to evaluate the capabilities of multilingual masked language models (MLMs) and large language models (LLMs). We present extensive results and findings, notably that despite significant cross-lingual transfer in English-centric LLMs, much smaller MLMs pretrained on balanced multilingual data still understand far more languages. Overall, Belebele opens up new avenues for evaluating and analyzing the multilingual capabilities of NLP systems.

Recent advances in Automated Theorem Proving have shown the effectiveness of leveraging a (large) language model that generates tactics (i.e. proof steps) to search through proof states. The current model, while trained solely on successful proof paths, faces a discrepancy at the inference stage, as it must sample and try various tactics at each proof state until finding success, unlike its training which does not incorporate learning from failed attempts. Intuitively, a tactic that leads to a failed search path would indicate that similar tactics should receive less attention during the following trials. In this paper, we demonstrate the benefit of training models that additionally learn from failed search paths. Facing the lack of such trial-and-error data in existing open-source theorem-proving datasets, we curate a dataset on intuitionistic propositional logic theorems and formalize it in Lean, such that we can reliably check the correctness of proofs. We compare our model trained on relatively short trial-and-error information (TrialMaster) with models trained only on the correct paths and discover that the former solves more unseen theorems with lower trial searches.

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Interactive Text-to-Image Retrieval with Large Language Models: A Plug-and-Play Approach
Saehyung Lee | Sangwon Yu | Junsung Park | Jihun Yi | Sungroh Yoon

In this paper, we primarily address the issue of dialogue-form context query within the interactive text-to-image retrieval task. Our methodology, PlugIR, actively utilizes the general instruction-following capability of LLMs in two ways. First, by reformulating the dialogue-form context, we eliminate the necessity of fine-tuning a retrieval model on existing visual dialogue data, thereby enabling the use of any arbitrary black-box model. Second, we construct the LLM questioner to generate non-redundant questions about the attributes of the target image, based on the information of retrieval candidate images in the current context. This approach mitigates the issues of noisiness and redundancy in the generated questions. Beyond our methodology, we propose a novel evaluation metric, Best log Rank Integral (BRI), for a comprehensive assessment of the interactive retrieval system. PlugIR demonstrates superior performance compared to both zero-shot and fine-tuned baselines in various benchmarks. Additionally, the two methodologies comprising PlugIR can be flexibly applied together or separately in various situations.

Navigating certain communication situations can be challenging due to individuals’ lack of skills and the interference of strong emotions. However, effective learning opportunities are rarely accessible. In this work, we conduct a human-centered study that uses language models to simulate bespoke communication training and provide just-in-time feedback to support the practice and learning of interpersonal effectiveness skills. We apply the interpersonal effectiveness framework from Dialectical Behavioral Therapy (DBT), DEAR MAN, which focuses on both conversational and emotional skills. We present IMBUE, an interactive training system that provides feedback 28% more similar to experts’ feedback, compared to that generated by GPT-4. IMBUE is the first to focus on communication skills and emotion management simultaneously, incorporate experts’ domain knowledge in providing feedback, and be grounded in psychology theory. Through a randomized trial of 86 participants, we find that IMBUE’s simulation-only variant significantly improves participants’ self-efficacy (up to 17%) and reduces negative emotions (up to 25%). With IMBUE’s additional just-in-time feedback, participants demonstrate 17% improvement in skill mastery, along with greater enhancements in self-efficacy (27% more) and reduction of negative emotions (16% more) compared to simulation-only. The improvement in skill mastery is the only measure that is transferred to new and more difficult situations; situation-specific training is necessary for improving self-efficacy and emotion reduction.

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Token-wise Influential Training Data Retrieval for Large Language Models
Huawei Lin | Jikai Long | Zhaozhuo Xu | Weijie Zhao

Given a Large Language Model (LLM) generation, how can we identify which training data led to this generation? In this paper, we proposed RapidIn, a scalable framework adapting to LLMs for estimating the influence of each training data. The proposed framework consists of two stages: caching and retrieval. First, we compress the gradient vectors by over 200,000x, allowing them to be cached on disk or in GPU/CPU memory. Then, given a generation, RapidIn efficiently traverses the cached gradients to estimate the influence within minutes, achieving over a 6,326x speedup. Moreover, RapidIn supports multi-GPU parallelization to substantially accelerate caching and retrieval. Our empirical result confirms the efficiency and effectiveness of RapidIn.

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Tree-of-Counterfactual Prompting for Zero-Shot Stance Detection
Maxwell Weinzierl | Sanda Harabagiu

Stance detection enables the inference of attitudes from human communications. Automatic stance identification was mostly cast as a classification problem. However, stance decisions involve complex judgments, which can be nowadays generated by prompting Large Language Models (LLMs). In this paper we present a new method for stance identification which (1) relies on a new prompting framework, called Tree-of-Counterfactual prompting; (2) operates not only on textual communications, but also on images; (3) allows more than one stance object type; and (4) requires no examples of stance attribution, thus it is a “Tabula Rasa” Zero-Shot Stance Detection (TR-ZSSD) method. Our experiments indicate surprisingly promising results, outperforming fine-tuned stance detection systems.

Autonomous agents capable of planning, reasoning, and executing actions on the web offer a promising avenue for automating computer tasks. However, the majority of existing benchmarks primarily focus on text-based agents, neglecting many natural tasks that require visual information to effectively solve. Given that most computer interfaces cater to human perception, visual information often augments textual data in ways that text-only models struggle to harness effectively. To bridge this gap, we introduce VisualWebArena, a benchmark designed to assess the performance of multimodal web agents on *realistic visually grounded tasks*. VisualWebArena comprises of a set of diverse and complex web-based tasks that evaluate various capabilities of autonomous multimodal agents. To perform on this benchmark, agents need to accurately process image-text inputs, interpret natural language instructions, and execute actions on websites to accomplish user-defined objectives. We conduct an extensive evaluation of state-of-the-art LLM-based autonomous agents, including several multimodal models. Through extensive quantitative and qualitative analysis, we identify several limitations of text-only LLM agents, and reveal gaps in the capabilities of state-of-the-art multimodal language agents. VisualWebArena provides a framework for evaluating multimodal autonomous language agents, and offers insights towards building stronger autonomous agents for the web.

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FineSurE: Fine-grained Summarization Evaluation using LLMs
Hwanjun Song | Hang Su | Igor Shalyminov | Jason Cai | Saab Mansour

Automated evaluation is crucial for streamlining text summarization benchmarking and model development, given the costly and time-consuming nature of human evaluation. Traditional methods like ROUGE do not correlate well with human judgment, while recently proposed LLM-based metrics provide only summary-level assessment using Likert-scale scores. This limits deeper model analysis, e.g., we can only assign one hallucination score at the summary level, while at the sentence level, we can count sentences containing hallucinations. To remedy those limitations, we propose FineSurE, a fine-grained evaluator specifically tailored for the summarization task using large language models (LLMs). It also employs completeness and conciseness criteria, in addition to faithfulness, enabling multi-dimensional assessment. We compare various open-source and proprietary LLMs as backbones for FineSurE. In addition, we conduct extensive benchmarking of FineSurE against SOTA methods including NLI-, QA-, and LLM-based methods, showing improved performance especially on the completeness and conciseness dimensions. The code is available at https://github.com/DISL-Lab/FineSurE.

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Tuning Large Multimodal Models for Videos using Reinforcement Learning from AI Feedback
Daechul Ahn | Yura Choi | Youngjae Yu | Dongyeop Kang | Jonghyun Choi

Recent advancements in large language models have influenced the development of video large multimodal models (VLMMs). Previous approaches for VLMMs involve Supervised Fine-Tuning (SFT) with instruction-tuned datasets, integrating LLM with visual encoders, and additional learnable parameters. Here, aligning video with text, and vice versa, remains a challenge, primarily due to the insufficient quality and quantity of multimodal instruction-tune data compared to that of text-only. This discrepancy often results in alignments that poorly ground the video content. To address this, we present a novel alignment strategy that employs a multimodal AI system equipped with Reinforcement Learning from AI Feedback (RLAIF), providing self-preference feedback to refine itself and facilitating the alignment of video and text modalities. Our approach uniquely integrates detailed video descriptions as context into a multimodal AI system during the preference feedback generation to enrich the understanding of video content, a process we call context-aware reward modeling. Empirical evaluations on various video benchmarks demonstrate that our VLM-RLAIF outperforms existing approaches, including the SFT model. We commit to open-sourcing our code, models, and datasets to foster further research in this area.

For text-to-image generation, automatically refining user-provided natural language prompts into the keyword-enriched prompts favored by systems is essential for the user experience. Such a prompt refinement process is analogous to translating the prompt from “user languages” into “system languages”. However, the scarcity of such parallel corpora makes it difficult to train a prompt refinement model. Inspired by zero-shot machine translation techniques, we introduce Prompt Refinement with Image Pivot (PRIP). PRIP innovatively uses the latent representation of a user-preferred image as an intermediary “pivot” between the user and system languages. It decomposes the refinement process into two data-rich tasks: inferring representations of user-preferred images from user languages and subsequently translating image representations into system languages. Thus, it can leverage abundant data for training. Extensive experiments show that PRIP substantially outperforms a wide range of baselines and effectively transfers to unseen systems in a zero-shot manner.

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Striking Gold in Advertising: Standardization and Exploration of Ad Text Generation
Masato Mita | Soichiro Murakami | Akihiko Kato | Peinan Zhang

In response to the limitations of manual ad creation, significant research has been conducted in the field of automatic ad text generation (ATG). However, the lack of comprehensive benchmarks and well-defined problem sets has made comparing different methods challenging. To tackle these challenges, we standardize the task of ATG and propose a first benchmark dataset, CAMERA, carefully designed and enabling the utilization of multi-modal information and facilitating industry-wise evaluations. Our extensive experiments with a variety of nine baselines, from classical methods to state-of-the-art models including large language models (LLMs), show the current state and the remaining challenges. We also explore how existing metrics in ATG and an LLM-based evaluator align with human evaluations.

Abstraction ability is crucial in human intelligence, which can also benefit various tasks in NLP study. Existing work shows that LLMs are deficient in abstract ability, and how to improve it remains unexplored. In this work, we design the framework AbsInstruct to enhance LLMs’ abstraction ability through instruction tuning. The framework builds instructions with in-depth explanations to assist LLMs in capturing the underlying rationale of abstraction. Meanwhile, we introduce a plausibility estimator to select instructions that are more consistent with the abstraction knowledge of LLMs to be aligned. Then, our framework combines abstraction instructions with general-purpose ones to build a hybrid dataset. Extensive experiments and analyses demonstrate that our framework can considerably enhance LLMs’ abstraction ability with strong generalization performance while maintaining their general instruction-following abilities.

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Reflect-RL: Two-Player Online RL Fine-Tuning for LMs
Runlong Zhou | Simon Du | Beibin Li

As language models (LMs) demonstrate their capabilities in various fields, their application to tasks requiring multi-round interactions has become increasingly popular. These tasks usually have complex dynamics, so supervised fine-tuning (SFT) on a limited offline dataset does not yield good performance. However, only a few works attempted to directly train the LMs within interactive decision-making environments. We aim to create an effective approach to fine-tune LMs with online reinforcement learning (RL) in these environments. We propose Reflect-RL, a two-player system to fine-tune an LM using SFT and online RL, where a frozen reflection model (player) assists the policy model (player). To generate data for the warm-up SFT stage, we use negative example generation to enhance the error-correction ability of the reflection model. Furthermore, we designed single-prompt action enumeration and applied curriculum learning to allow the policy model to learn more efficiently. Empirically, we verify that Reflect-RL outperforms SFT and online RL without reflection. Testing results indicate GPT-2 XL 1.56B fine-tuned with Reflect-RL outperforms larger open-source LMs, such as Mistral 7B. The benchmarks, dataset, and code involved in this work are publicly available: https://github.com/zhourunlong/Reflect-RL.

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Can ChatGPT’s Performance be Improved on Verb Metaphor Detection Tasks? Bootstrapping and Combining Tacit Knowledge
Cheng Yang | Puli Chen | Qingbao Huang

Metaphors detection, as an important task in the field of NLP, has been receiving sustained academic attention in recent years. Current researches focus supervised metaphors detection systems, which usually require large-scale, high-quality labeled data support. The emerge of large language models (e.g., ChatGPT) has made many NLP tasks (e.g., automatic summarization and dialogue systems) a qualitative leap. However, it is worth noting that the use of ChatGPT for unsupervised metaphors detection is often challenged with less-than-expected performance. Therefore, the aim of our work is to explore how to bootstrap and combine ChatGPT by detecting the most prevalent verb metaphors among metaphors. Our approach first utilizes ChatGPT to obtain literal collocations of target verbs and subject-object pairs of verbs in the text to be detected. Subsequently, these literal collocations and subject-object pairs are mapped to the same set of topics, and finally the verb metaphors are detected through the analysis of entailment relations. The experimental results show that our method achieves the best performance on the unsupervised verb metaphors detection task compared to existing unsupervised methods or direct prediction using ChatGPT. Our code is available at https://github.com/VILAN-Lab/Unsupervised-Metaphor-Detection.

The surge in Large Language Models (LLMs) has revolutionized natural language processing, but fine-tuning them for specific tasks often encounters challenges in balancing performance and preserving general instruction-following abilities. In this paper, we posit that the distribution gap between task datasets and the LLMs serves as the primary underlying cause. To address the problem, we introduce Self-Distillation Fine-Tuning (SDFT), a novel approach that bridges the distribution gap by guiding fine-tuning with a distilled dataset generated by the model itself to match its original distribution. Experimental results on the Llama-2-chat model across various benchmarks demonstrate that SDFT effectively mitigates catastrophic forgetting while achieving comparable or superior performance on downstream tasks compared to the vanilla fine-tuning. Moreover, SDFT demonstrates the potential to maintain the helpfulness and safety alignment of LLMs. Our code is available at https://github.com/sail-sg/sdft.

Retrieval-augmented generation integrates the capabilities of large language models with relevant information retrieved from an extensive corpus, yet encounters challenges when confronted with real-world noisy data. One recent solution is to train a filter module to find relevant content but only achieve suboptimal noise compression. In this paper, we propose to introduce the information bottleneck theory into retrieval-augmented generation. Our approach involves the filtration of noise by simultaneously maximizing the mutual information between compression and ground output, while minimizing the mutual information between compression and retrieved passage. In addition, we derive the formula of information bottleneck to facilitate its application in novel comprehensive evaluations, the selection of supervised fine-tuning data, and the construction of reinforcement learning rewards. Experimental results demonstrate that our approach achieves significant improvements across various question answering datasets, not only in terms of the correctness of answer generation but also in the conciseness with 2.5% compression rate.

Free-text rationales play a pivotal role in explainable NLP, bridging the knowledge and reasoning gaps behind a model’s decision-making. However, due to the diversity of potential reasoning paths and a corresponding lack of definitive ground truth, their evaluation remains a challenge. Existing metrics rely on the degree to which a rationale supports a target label, but we find these fall short in evaluating rationales that inadvertently leak the label. To address this problem, we propose RORA, a RObust free-text RAtionale evaluation against label leakage. RORA quantifies the new information supplied by a rationale to justify the label. This is achieved by assessing the conditional 𝒱-information (Hewitt et al., 2021) with a predictive family robust against leaky features that can be exploited by a small model. RORA consistently outperforms existing approaches in evaluating human-written, synthetic, or model-generated rationales, particularly demonstrating robustness against label leakage. We also show that RORA aligns well with human judgment, providing a more reliable and accurate measurement across diverse free-text rationales.

Current language model-driven agents often lack mechanisms for effective user participation, which is crucial given the vagueness commonly found in user instructions. Although adept at devising strategies and performing tasks, these agents struggle with seeking clarification and grasping precise user intentions. To bridge this gap, we introduce Intention-in-Interaction (IN3), a novel benchmark designed to inspect users’ implicit intentions through explicit queries. Next, we propose the incorporation of model experts as the upstream in agent designs to enhance user-agent interaction. Employing IN3, we empirically train Mistral-Interact, a powerful model that proactively assesses task vagueness, inquires about user intentions, and refines them into actionable goals before starting downstream agent task execution. Integrating it into the XAgent framework, we comprehensively evaluate the enhanced agent system regarding user instruction understanding and execution, revealing that our approach notably excels at identifying vague user tasks, recovering and summarizing critical missing information, setting precise and necessary agent execution goals, and minimizing redundant tool usage, thus boosting overall efficiency.

Large Language Models (LLMs) have revolutionized the field of natural language processing, but they fall short in comprehending biological sequences such as proteins. To address this challenge, we propose InstructProtein, an innovative LLM that possesses bidirectional generation capabilities in both human and protein languages: (i) taking a protein sequence as input to predict its textual function description and (ii) using natural language to prompt protein sequence generation. To achieve this, we first pre-train an LLM on both protein and natural language corpora, enabling it to comprehend individual languages. Then supervised instruction tuning is employed to facilitate the alignment of these two distinct languages. Herein, we introduce a knowledge graph-based instruction generation framework to construct a high-quality instruction dataset, addressing the annotation imbalance and the absence of instructional signals in the existing protein-text corpus. In particular, the instructions inherit the structural relations between proteins and function annotations in knowledge graphs, which empowers our model to engage in the causal modeling of protein functions, akin to the chain-of-thought processes in natural languages. Extensive experiments on bidirectional protein-text generation tasks show that InstructProtein outperforms state-of-the-art LLMs by a large margin.

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ConSiDERS-The-Human Evaluation Framework: Rethinking Human Evaluation for Generative Large Language Models
Aparna Elangovan | Ling Liu | Lei Xu | Sravan Babu Bodapati | Dan Roth

In this position paper, we argue that human evaluation of generative large language models (LLMs) should be a multidisciplinary undertaking that draws upon the insights from disciplines such as user experience research and human behavioral psychology to ensure that the experimental design and results are reliable. The conclusions from these evaluations, therefore, must consider factors such as usability, aesthetics and cognitive biases. We highlight how cognitive biases can conflate fluent information and truthfulness, and how cognitive uncertainty affects the reliability of rating scores such as Likert. Furthermore, the evaluation should differentiate the capabilities and weaknesses of increasingly powerful large language models - which requires effective test sets. Scalability of human evaluation is also crucial to wider adoption. Hence, to design an effective human evaluation system in the age of generative NLP we propose the ConSiDERS-The-Human evaluation framework consisting of 6 pillars - Consistency, Scoring Criteria, Differentiating, User Experience, Responsible, and Scalability.

Semantic textual similarity (STS) is a fundamental NLP task that measures the semantic similarity between a pair of sentences. In order to reduce the inherent ambiguity posed from the sentences, a recent work called Conditional STS (C-STS) has been proposed to measure the sentences’ similarity conditioned on a certain aspect. Despite the popularity of C-STS, we find that the current C-STS dataset suffers from various issues that could impede proper evaluation on this task. In this paper, we reannotate the C-STS validation set and observe an annotator discrepancy on 55% of the instances resulting from the annotation errors in the original label, ill-defined conditions, and the lack of clarity in the task definition. After a thorough dataset analysis, we improve the C-STS task by leveraging the models’ capability to understand the conditions under a QA task setting. With the generated answers, we present an automatic error identification pipeline that is able to identify annotation errors from the C-STS data with over 80% F1 score. We also propose a new method that largely improves the performance over baselines on the C-STS data by training the models with the answers. Finally we discuss the conditionality annotation based on the typed-feature structure (TFS) of entity types. We show in examples that the TFS is able to provide a linguistic foundation for constructing C-STS data with new conditions.

Reasoning, a fundamental cognitive process integral to human intelligence, has garnered substantial interest within artificial intelligence.Notably, recent studies have revealed that chain-of-thought prompting significantly enhances LLM’s reasoning capabilities, which attracts widespread attention from both academics and industry.In this paper, we systematically investigate relevant research, summarizing advanced methods through a meticulous taxonomy that offers novel perspectives.Moreover, we delve into the current frontiers and delineate the challenges and future directions, thereby shedding light on future research.Furthermore, we engage in a discussion about open questions.We hope this paper serves as an introduction for beginners and fosters future research.Resources have been made publicly available at https://github.com/zchuz/CoT-Reasoning-Survey

Grasping the concept of time is a fundamental facet of human cognition, indispensable for truly comprehending the intricacies of the world.Previous studies typically focus on specific aspects of time, lacking a comprehensive temporal reasoning benchmark.To address this, we propose TimeBench, a comprehensive hierarchical temporal reasoning benchmark that covers a broad spectrum of temporal reasoning phenomena.TimeBench provides a thorough evaluation for investigating the temporal reasoning capabilities of large language models.We conduct extensive experiments on GPT-4, LLaMA2, and other popular LLMs under various settings.Our experimental results indicate a significant performance gap between the state-of-the-art LLMs and humans, highlighting that there is still a considerable distance to cover in temporal reasoning.Besides, LLMs exhibit capability discrepancies across different reasoning categories.Furthermore, we thoroughly analyze the impact of multiple aspects on temporal reasoning and emphasize the associated challenges.We aspire for TimeBench to serve as a comprehensive benchmark, fostering research in temporal reasoning.Code and data are available at https://github.com/zchuz/TimeBench.

Large language models (LLMs) have demonstrated strong reasoning capabilities.Nevertheless, they still suffer from factual errors when tackling knowledge-intensive tasks.Retrieval-augmented reasoning represents a promising approach.However, significant challenges still persist, including inaccurate and insufficient retrieval for complex questions, as well as difficulty in integrating multi-source knowledge.To address this, we propose Beam Aggregation Reasoning (BeamAggR), a reasoning framework for knowledge-intensive multi-hop QA.BeamAggR explores and prioritizes promising answers at each hop of question.Concretely, we parse the complex questions into trees, which include atom and composite questions, followed by bottom-up reasoning.For atomic questions, the LLM conducts reasoning on multi-source knowledge to get answer candidates.For composite questions, the LLM combines beam candidates, explores multiple reasoning paths through probabilistic aggregation, and prioritizes the most promising trajectory.Extensive experiments on four open-domain multi-hop reasoning datasets show that our method significantly outperforms SOTA methods by 8.5%.Furthermore, our analysis reveals that BeamAggR elicits better knowledge collaboration and answer aggregation.

Analogical reasoning is a fundamental cognitive ability of humans. However, current language models (LMs) still struggle to achieve human-like performance in analogical reasoning tasks due to a lack of resources for model training. In this work, we address this gap by proposing ANALOGYKB, a million-scale analogy knowledge base (KB) derived from existing knowledge graphs (KGs). ANALOGYKB identifies two types of analogies from the KGs: 1) analogies of the same relations, which can be directly extracted from the KGs, and 2) analogies of analogous relations, which are identified with a selection and filtering pipeline enabled by large language models (LLMs), followed by minor human efforts for data quality control. Evaluations on a series of datasets of two analogical reasoning tasks (analogy recognition and generation) demonstrate that ANALOGYKB successfully enables both smaller LMs and LLMs to gain better analogical reasoning capabilities. Resources of this paper can be found at https://github.com/siyuyuan/analogykb.

A practical dialogue system requires the capacity for ongoing skill acquisition and adaptability to new tasks while preserving prior knowledge. However, current methods for Continual Dialogue State Tracking (DST), a crucial function of dialogue systems, struggle with the catastrophic forgetting issue and knowledge transfer between tasks. We present TaSL, a novel framework for task skill localization and consolidation that enables effective knowledge transfer without relying on memory replay. TaSL uses a novel group-wise technique to pinpoint task-specific and task-shared areas. Additionally, a fine-grained skill consolidation strategy protects task-specific knowledge from being forgotten while updating shared knowledge for bi-directional knowledge transfer. As a result, TaSL strikes a balance between preserving previous knowledge and excelling at new tasks. Comprehensive experiments on various backbones highlight the significant performance improvements of TaSL, with a 7.6% absolute increase in Avg. JGA and an 11% absolute rise in BWT metrics over existing state-of-the-art methods. The source code is provided for reproducibility.

In the era of large language models, Mixture-of-Experts (MoE) is a promising architecture for managing computational costs when scaling up model parameters. However, conventional MoE architectures like GShard, which activate the top-K out of N experts, face challenges in ensuring expert specialization, i.e. each expert acquires non-overlapping and focused knowledge. In response, we propose the DeepSeekMoE architecture towards ultimate expert specialization. It involves two principal strategies: (1) finely segmenting the experts into mN ones and activating mK from them, allowing for a more flexible combination of activated experts; (2) isolating K_s experts as shared ones, aiming at capturing common knowledge and mitigating redundancy in routed experts. Starting from a modest scale with 2B parameters, we demonstrate that DeepSeekMoE 2B achieves comparable performance with GShard 2.9B, which has 1.5 × expert parameters and computation. In addition, DeepSeekMoE 2B nearly approaches the performance of its dense counterpart with the same number of total parameters, which sets the upper bound of MoE models. Subsequently, we scale up DeepSeekMoE to 16B parameters and show that it achieves comparable performance with DeepSeek 7B and LLaMA2 7B, with only about 40% of computations.

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Grounding Language Model with Chunking-Free In-Context Retrieval
Hongjin Qian | Zheng Liu | Kelong Mao | Yujia Zhou | Zhicheng Dou

This paper presents a novel Chunking-Free In-Context (CFIC) retrieval approach, specifically tailored for Retrieval-Augmented Generation (RAG) systems. Traditional RAG systems often struggle with grounding responses using precise evidence text due to the challenges of processing lengthy documents and filtering out irrelevant content. Commonly employed solutions, such as document chunking and adapting language models to handle longer contexts, have their limitations. These methods either disrupt the semantic coherence of the text or fail to effectively address the issues of noise and inaccuracy in evidence retrieval.The CFIC approach addresses these challenges by circumventing the conventional chunking process. It utilizes the encoded hidden states of documents for in-context retrieval, employing auto-aggressive decoding to accurately identify the specific evidence text required for user queries, eliminating the need for chunking. CFIC is further enhanced by incorporating two innovative decoding strategies, namely Constrained Sentence Prefix Decoding and Skip Decoding. These strategies not only improve the efficiency of the retrieval process but also ensure that the fidelity of the generated grounding text evidence is maintained.Our evaluations of CFIC on a range of open question answering datasets demonstrate its superiority in retrieving relevant and accurate information, offering a significant improvement over traditional methods. By doing away with the need for document chunking, CFIC presents a more streamlined, effective, and efficient retrieval solution, making it a valuable advancement in the field of RAG systems.

Abductive reasoning is the process of making educated guesses to provide explanations for observations. Although many applications require the use of knowledge for explanations, the utilization of abductive reasoning in conjunction with structured knowledge, such as a knowledge graph, remains largely unexplored. To fill this gap, this paper introduces the task of complex logical hypothesis generation, as an initial step towards abductive logical reasoning with KG. In this task, we aim to generate a complex logical hypothesis so that it can explain a set of observations. We find that the supervised trained generative model can generate logical hypotheses that are structurally closer to the reference hypothesis. However, when generalized to unseen observations, this training objective does not guarantee better hypothesis generation. To address this, we introduce the Reinforcement Learning from Knowledge Graph (RLF-KG) method, which minimizes differences between observations and conclusions drawn from generated hypotheses according to the KG. Experiments show that, with RLF-KG’s assistance, the generated hypotheses provide better explanations, and achieve state-of-the-art results on three widely used KGs.

The increasing scale of large language models (LLMs) brings emergent abilities to various complex tasks requiring reasoning, such as arithmetic and commonsense reasoning. It is known that the effective design of task-specific prompts is critical for LLMs’ ability to produce high-quality answers. In particular, an effective approach for complex question-and-answering tasks is example-based prompting with chain-of-thought (CoT) reasoning, which significantly improves the performance of LLMs. However, current CoT methods rely on a fixed set of human-annotated exemplars, which are not necessarily the most effective examples for different tasks. This paper proposes a new method, Active-Prompt, to adapt LLMs to different tasks with task-specific example prompts (annotated with human-designed CoT reasoning). For this purpose, we propose a solution to the key problem of determining which questions are the most important and helpful to annotate from a pool of task-specific queries. By borrowing ideas from the related problem of uncertainty-based active learning, we introduce several metrics to characterize the uncertainty so as to select the most uncertain questions for annotation. Experimental results demonstrate the superiority of our proposed method, achieving superior performance on eight complex reasoning tasks. Further analyses of different uncertainty metrics, pool sizes, zero-shot learning, and accuracy-uncertainty relationships demonstrate the effectiveness of our method.

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EasyGen: Easing Multimodal Generation with BiDiffuser and LLMs
Xiangyu Zhao | Bo Liu | Qijiong Liu | Guangyuan Shi | Xiao-Ming Wu

We present EasyGen, an efficient model designed to enhance multimodal understanding and generation by harnessing the capabilities of diffusion models and large language models (LLMs). Unlike existing multimodal models that predominately depend on encoders like CLIP or ImageBind and need ample amounts of training data to bridge modalities, EasyGen leverages BiDiffuser, a bidirectional conditional diffusion model, to foster more efficient modality interactions. EasyGen achieves text generation by training a projection layer linking BiDiffuser and an LLM, and facilities image generation by training an adapter to align the LLM’s text space with the BiDiffuser’s image space. Comprehensive quantitative and qualitative experiments show that EasyGen excels in data-efficient training, high-quality image generation, and extendibility, effectively addressing the challenges in multimodal generation.

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Rewriting the Code: A Simple Method for Large Language Model Augmented Code Search
Haochen Li | Xin Zhou | Zhiqi Shen

In code search, the Generation-Augmented Retrieval (GAR) framework, which generates exemplar code snippets to augment queries, has emerged as a promising strategy to address the principal challenge of modality misalignment between code snippets and natural language queries, particularly with the demonstrated code generation capabilities of Large Language Models (LLMs). Nevertheless, our preliminary investigations indicate that the improvements conferred by such an LLM-augmented framework are somewhat constrained. This limitation could potentially be ascribed to the fact that the generated codes, albeit functionally accurate, frequently display a pronounced stylistic deviation from the ground truth code in the codebase. In this paper, we extend the foundational GAR framework and propose a simple yet effective method that additionally Rewrites the Code (ReCo) within the codebase for style normalization. Experimental results demonstrate that ReCo significantly boosts retrieval accuracy across sparse (up to 35.7%), zero-shot dense (up to 27.6%), and fine-tuned dense (up to 23.6%) retrieval settings in diverse search scenarios. To further elucidate the advantages of ReCo and stimulate research in code style normalization, we introduce Code Style Similarity, the first metric tailored to quantify stylistic similarities in code. Notably, our empirical findings reveal the inadequacy of existing metrics in capturing stylistic nuances. The source code and data are available at https://github.com/Alex-HaochenLi/ReCo.

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A Multidimensional Framework for Evaluating Lexical Semantic Change with Social Science Applications
Naomi Baes | Nick Haslam | Ekaterina Vylomova

Historical linguists have identified multiple forms of lexical semantic change. We present a three-dimensional framework for integrating these forms and a unified computational methodology for evaluating them concurrently. The dimensions represent increases or decreases in semantic 1) sentiment (valence of a target word’s collocates), 2) intensity (emotional arousal of collocates or the frequency of intensifiers), and 3) breadth (diversity of contexts in which the target word appears). These dimensions can be complemented by evaluation of shifts in the frequency of the target words and the thematic content of its collocates. This framework enables lexical semantic change to be mapped economically and systematically and has applications in computational social science. We present an illustrative analysis of semantic shifts in mental health and mental illness in two corpora, demonstrating patterns of semantic change that illuminate contemporary concerns about pathologization, stigma, and concept creep.

Large language models (LLMs) suffer from catastrophic forgetting during continual learning. Conventional rehearsal-based methods rely on previous training data to retain the model’s ability, which may not be feasible in real-world applications. When conducting continual learning based on a publicly-released LLM checkpoint, the availability of the original training data may be non-existent. To address this challenge, we propose a framework called Self-Synthesized Rehearsal (SSR) that uses the LLM to generate synthetic instances for rehearsal. Concretely, we first employ the base LLM for in-context learning to generate synthetic instances. Subsequently, we utilize the latest LLM to refine the instance outputs based on the synthetic inputs, preserving its acquired ability. Finally, we select diverse high-quality synthetic instances for rehearsal in future stages. Experimental results demonstrate that SSR achieves superior or comparable performance compared to conventional rehearsal-based approaches while being more data-efficient. Besides, SSR effectively preserves the generalization capabilities of LLMs in general domains.

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Enhancing Large Language Models in Coding Through Multi-Perspective Self-Consistency
Baizhou Huang | Shuai Lu | Xiaojun Wan | Nan Duan

Large language models (LLMs) have exhibited remarkable ability in code generation. However, generating the correct solution in a single attempt still remains a challenge. Prior works utilize verification properties in software engineering to verify and re-rank solutions in a majority voting manner. But the assumption behind them that generated verification properties have better qualities than solutions may not always hold. In this paper, we treat them equally as different perspectives of LLMs’ reasoning processes. We propose the Multi-Perspective Self-Consistency (MPSC) framework incorporating both inter- and intra-consistency across outputs from multiple perspectives. Specifically, we prompt LLMs to generate diverse outputs from three perspectives, Solution, Specification and Test case, constructing a 3-partite graph. With two measure functions of consistency, we embed both inter- and intra-consistency information into the graph. The optimal choice of solutions is then determined based on analysis in the graph.MPSC significantly boosts performance of foundation models (ChatGPT in this paper) on various benchmarks, including HumanEval (+15.91%), MBPP (+6.43%) and CodeContests (+9.37%), even surpassing GPT-4.

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Citation-Enhanced Generation for LLM-based Chatbots
Weitao Li | Junkai Li | Weizhi Ma | Yang Liu

Large language models (LLMs) exhibit powerful general intelligence across diverse scenarios, including their integration into chatbots. However, a vital challenge of LLM-based chatbots is that they may produce hallucinated content in responses, which significantly limits their applicability. Various efforts have been made to alleviate hallucination, such as retrieval augmented generation and reinforcement learning with human feedback, but most of them require additional training and data annotation. In this paper, we propose a novel post-hoc Citation-Enhanced Generation (CEG) approach combined with retrieval argumentation. Unlike previous studies that focus on preventing hallucinations during generation, our method addresses this issue in a post-hoc way. It incorporates a retrieval module to search for supporting documents relevant to the generated content, and employs a natural language inference-based citation generation module. Once the statements in the generated content lack of reference, our model can regenerate responses until all statements are supported by citations. Note that our method is a training-free plug-and-play plugin that is capable of various LLMs. Experiments on various hallucination-related datasets show our framework outperforms state-of-the-art methods in both hallucination detection and response regeneration on three benchmarks. Our code and datasets can be found at https://github.com/Tsinghua-dhy/CEG.

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Transitive Consistency Constrained Learning for Entity-to-Entity Stance Detection
Haoyang Wen | Eduard Hovy | Alexander Hauptmann

Entity-to-entity stance detection identifies the stance between a pair of entities with a directed link that indicates the source, target and polarity. It is a streamlined task without the complex dependency structure for structural sentiment analysis, while it is more informative compared to most previous work assuming that the source is the author. Previous work performs entity-to-entity stance detection training on individual entity pairs. However, stances between inter-connected entity pairs may be correlated. In this paper, we propose transitive consistency constrained learning, which first finds connected entity pairs and their stances, and adds an additional objective to enforce the transitive consistency. We explore consistency training on both classification-based and generation-based models and conduct experiments to compare consistency training with previous work and large language models with in-context learning. Experimental results illustrate that the inter-correlation of stances in political news can be used to improve the entity-to-entity stance detection model, while overly strict consistency enforcement may have a negative impact. In addition, we find that large language models struggle with predicting link direction and neutral labels in this task.

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Feature-Adaptive and Data-Scalable In-Context Learning
Jiahao Li | Quan Wang | Licheng Zhang | Guoqing Jin | Zhendong Mao

In-context learning (ICL), which promotes inference with several demonstrations, has become a widespread paradigm to stimulate LLM capabilities for downstream tasks. Due to context length constraints, it cannot be further improved in spite of more training data, and general features directly from LLMs in ICL are not adaptive to the specific downstream task. In this paper, we propose a feature-adaptive and data-scalable in-context learning framework (FADS-ICL), which can leverage task-adaptive features to promote inference on the downstream task, with the supervision of beyond-context samples.Specifically, it first extracts general features of beyond-context samples via the LLM with ICL input form one by one, and introduces a task-specific modulator to perform feature refinement and prediction after fitting a specific downstream task. We conduct extensive experiments on FADS-ICL under varying data settings (4~128 shots) and LLM scale (0.8~70B) settings. Experimental results show that FADS-ICL consistently outperforms previous state-of-the-art methods by a significant margin under all settings, verifying the effectiveness and superiority of FADS-ICL. For example, under the 1.5B and 32 shots setting, FADS-ICL can achieve +14.3 average accuracy from feature adaptation over vanilla ICL on 10 datasets, with +6.2 average accuracy over the previous state-of-the-art method, and the performance can further improve with increasing training data.

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Probing the Multi-turn Planning Capabilities of LLMs via 20 Question Games
Yizhe Zhang | Jiarui Lu | Navdeep Jaitly

Large language models (LLMs) are effective at answering questions that are clearly asked. However, when faced with ambiguous queries they can act unpredictably and produce incorrect outputs. This underscores the need for the development of intelligent agents capable of asking clarification questions to resolve ambiguities effectively. This capability requires complex understanding, state tracking, reasoning and planning over multiple conversational turns. However, directly measuring this can be challenging.In this paper, we offer a surrogate problem which assesses an LLMs’s capability to deduce an entity unknown to itself, but revealed to a judge, by asking the judge a series of queries. This entity-deducing game can serve as an evaluation framework to probe the conversational reasoning and planning capabilities of language models.We systematically evaluate various LLMs and discover significant differences in their performance on this task. We find that strong LLMs like GPT-4 outperform human players by a large margin. We further employ Behavior Cloning (BC) to examine whether a weaker model is capable of imitating a stronger model and generalizing to data or domains, using only the demonstrations from a stronger model. We finally propose to use Reinforcement Learning to enhance reasoning and planning capacity of Vicuna models through episodes of game playing, which lead to significant performance improvement. We hope that this problem offers insights into how autonomous agents could be trained to behave more intelligently in ambiguous circumstances.

To mitigate the potential misuse of large language models (LLMs), recent research has developed watermarking algorithms, which restrict the generation process to leave an invisible trace for watermark detection. Due to the two-stage nature of the task, most studies evaluate the generation and detection separately, thereby presenting a challenge in unbiased, thorough, and applicable evaluations. In this paper, we introduce WaterBench, the first comprehensive benchmark for LLM watermarks, in which we design three crucial factors: (1) For benchmarking procedure, to ensure an apples-to-apples comparison, we first adjust each watermarking method’s hyper-parameter to reach the same watermarking strength, then jointly evaluate their generation and detection performance. (2) For task selection, we diversify the input and output length to form a five-category taxonomy, covering 9 tasks. (3) For evaluation metric, we adopt the GPT4-Judge for automatically evaluating the decline of instruction-following abilities after watermarking. We evaluate 4 open-source watermarks on 2 LLMs under 2 watermarking strengths and observe the common struggles for current methods on maintaining the generation quality. The code and data are available at https://github.com/THU-KEG/WaterBench.

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Dependency Transformer Grammars: Integrating Dependency Structures into Transformer Language Models
Yida Zhao | Chao Lou | Kewei Tu

Syntactic Transformer language models aim to achieve better generalization through simultaneously modeling syntax trees and sentences. While prior work has been focusing on adding constituency-based structures to Transformers, we introduce Dependency Transformer Grammars (DTGs), a new class of Transformer language model with explicit dependency-based inductive bias. DTGs simulate dependency transition systems with constrained attention patterns by modifying attention masks, incorporate the stack information through relative positional encoding, and augment dependency arc representation with a combination of token embeddings and operation embeddings. When trained on a dataset of sentences annotated with dependency trees, DTGs achieve better generalization while maintaining comparable perplexity with Transformer language model baselines. DTGs also outperform recent constituency-based models, showing that dependency can better guide Transformer language models. Our code is released at https://github.com/zhaoyd1/Dep_Transformer_Grammars.

Simultaneous translation models play a crucial role in facilitating communication. However, existing research primarily focuses on text-to-text or speech-to-text models, necessitating additional cascade components to achieve speech-to-speech translation. These pipeline methods suffer from error propagation and accumulate delays in each cascade component, resulting in reduced synchronization between the speaker and listener. To overcome these challenges, we propose a novel non-autoregressive generation framework for simultaneous speech translation (NAST-S2x), which integrates speech-to-text and speech-to-speech tasks into a unified end-to-end framework.We develop a non-autoregressive decoder capable of concurrently generating multiple text or acoustic unit tokens upon receiving fixed-length speech chunks. The decoder can generate blank or repeated tokens and employ CTC decoding to dynamically adjust its latency. Experimental results show that NAST-S2x outperforms state-of-the-art models in both speech-to-text and speech-to-speech tasks. It achieves high-quality simultaneous interpretation within a delay of less than 3 seconds and provides a 28× decoding speedup in offline generation.

Large Language Models (LLMs) have shown their impressive capabilities, while also raising concerns about the data contamination problems due to privacy issues and leakage of benchmark datasets in the pre-training phase. Therefore, it is vital to detect the contamination by checking whether an LLM has been pre-trained on the target texts. Recent studies focus on the generated texts and compute perplexities, which are superficial features and not reliable. In this study, we propose to utilize the probing technique for pre-training data detection by examining the model’s internal activations. Our method is simple and effective and leads to more trustworthy pre-training data detection. Additionally, we propose ArxivMIA, a new challenging benchmark comprising arxiv abstracts from Computer Science and Mathematics categories. Our experiments demonstrate that our method outperforms all baselines, and achieves state-of-the-art performance on both WikiMIA and ArxivMIA, with additional experiments confirming its efficacy.

The digital landscape is rapidly evolving with an ever-increasing volume of online news, emphasizing the need for swift and precise analysis of complex events.We refer to the complex events composed of many news articles over an extended period as Temporal Complex Event (TCE). This paper proposes a novel approach using Large Language Models (LLMs) to systematically extract and analyze the event chain within TCE, characterized by their key points and timestamps. We establish a benchmark, named TCELongBench, to evaluate the proficiency of LLMs in handling temporal dynamics and understanding extensive text. This benchmark encompasses three distinct tasks - reading comprehension, temporal sequencing, and future event forecasting. In the experiment, we leverage retrieval-augmented generation (RAG) method and LLMs with long context window to deal with lengthy news articles of TCE. Our findings indicate that models with suitable retrievers exhibit comparable performance with those utilizing long context window.

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IBSEN: Director-Actor Agent Collaboration for Controllable and Interactive Drama Script Generation
Senyu Han | Lu Chen | Li-Min Lin | Zhengshan Xu | Kai Yu

Large language models have demonstrated their capabilities in storyline creation and human-like character role-playing. Current language model agents mainly focus on reasonable behaviors from the level of individuals, and their behaviors might be hard to constraint on the level of the whole storyline. In this paper we introduce IBSEN, a director-actor coordinate agent framework that generates drama scripts and makes the plot played by agents more controllable. The director agent writes plot outlines that the user desires to see, instructs the actor agents to role-play their characters, and reschedules the plot when human players participate in the scenario to ensure the plot is progressing towards the objective. To evaluate the framework, we create a novel drama plot that involves several actor agents and check the interactions between them under the instruction of the director agent. Evaluation results show that our framework could generate complete, diverse drama scripts from only a rough outline of plot objectives, meanwhile maintaining the characteristics of characters in the drama. Our codes and prompts are available at https://github.com/OpenDFM/ibsen.

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Language Model Adaption for Reinforcement Learning with Natural Language Action Space
Jiangxing Wang | Jiachen Li | Xiao Han | Deheng Ye | Zongqing Lu

Reinforcement learning with natural language action space often suffers from the curse of dimensionality due to the combinatorial nature of the natural language. Previous research leverages pretrained language models to capture action semantics and reduce the size of the action space. However, since pretrained models are typically trained on general corpora, there can be an unpredictable mismatch between the priors encoded in pretrained models and the characteristics of the specific RL environment. To address this issue, we propose Mutual-Information Regularized Policy Optimization, MIPO. MIPO enables implicit and dynamic reduction of the action space. Starting from the prior provided by the pretrained language model, our method dynamically adjusts the prior during the learning process based on the guidance of mutual information regularization. Theoretically, we demonstrate that this policy optimization process leads to the monotonic improvement on the mutual-information regularized RL objective. Empirically, we conduct experiments in various environments and demonstrate the effectiveness of MIPO.

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Evaluating Intention Detection Capability of Large Language Models in Persuasive Dialogues
Hiromasa Sakurai | Yusuke Miyao

We investigate intention detection in persuasive multi-turn dialogs employing the largest available Large Language Models (LLMs).Much of the prior research measures the intention detection capability of machine learning models without considering the conversational history.To evaluate LLMs’ intention detection capability in conversation, we modified the existing datasets of persuasive conversation and created datasets using a multiple-choice paradigm.It is crucial to consider others’ perspectives through their utterances when engaging in a persuasive conversation, especially when making a request or reply that is inconvenient for others.This feature makes the persuasive dialogue suitable for the dataset of measuring intention detection capability.We incorporate the concept of ‘face acts,’ which categorize how utterances affect mental states.This approach enables us to measure intention detection capability by focusing on crucial intentions and to conduct comprehensible analysis according to intention types.

In long context scenarios, large language models (LLMs) face three main challenges: higher computational cost, performance reduction, and position bias. Research indicates that LLM performance hinges on the density and position of key information in the input prompt. Inspired by these findings, we propose LongLLMLingua for prompt compression towards improving LLMs’ perception of the key information to simultaneously address the three challenges. Our extensive evaluation across various long context scenarios demonstrates that LongLLMLingua not only enhances performance but also significantly reduces costs and latency. For instance, in the NaturalQuestions benchmark, LongLLMLingua boosts performance by up to 21.4% with around 4x fewer tokens in GPT-3.5-Turbo, leading to substantial cost savings. It achieves a 94.0% cost reduction in the LooGLE benchmark. Moreover, when compressing prompts of about 10k tokens at ratios of 2x-6x, LongLLMLingua can accelerate end-to-end latency by 1.4x-2.6x.

Persuasive dialogue requires multi-turn following and planning abilities to achieve the goal of persuading users, which is still challenging even for state-of-the-art large language models (LLMs). Previous works focus on retrieval-based models or generative models in a specific domain due to a lack of data across multiple domains. In this paper, we leverage GPT-4 to create the first multi-domain persuasive dialogue dataset DailyPersuasion. Then we propose a general method named PersuGPT to learn a persuasion model based on LLMs through intent-to-strategy reasoning, which summarizes the intent of user’s utterance and reasons next strategy to respond. Moreover, we design a simulation-based preference optimization, which utilizes a learned user model and our model to simulate next turns and estimate their rewards more accurately. Experimental results on two datasets indicate that our proposed method outperforms all baselines in terms of automatic evaluation metric Win-Rate and human evaluation. The code and data are available at https://persugpt.github.io.

Large Language Models (LLMs) can play a vital role in psychotherapy by adeptly handling the crucial task of cognitive reframing and overcoming challenges such as shame, distrust, therapist skill variability, and resource scarcity. Previous LLMs in cognitive reframing mainly converted negative emotions to positive ones, but these approaches have limited efficacy, often not promoting clients’ self-discovery of alternative perspectives. In this paper, we unveil the Helping and Empowering through Adaptive Language in Mental Enhancement (HealMe) model. This novel cognitive reframing therapy method effectively addresses deep-rooted negative thoughts and fosters rational, balanced perspectives. Diverging from traditional LLM methods, HealMe employs empathetic dialogue based on psychotherapeutic frameworks. It systematically guides clients through distinguishing circumstances from feelings, brainstorming alternative viewpoints, and developing empathetic, actionable suggestions. Moreover, we adopt the first comprehensive and expertly crafted psychological evaluation metrics, specifically designed to rigorously assess the performance of cognitive reframing, in both AI-simulated dialogues and real-world therapeutic conversations. Experimental results show that our model outperforms others in terms of empathy, guidance, and logical coherence, demonstrating its effectiveness and potential positive impact on psychotherapy.

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Multimodal Prompt Learning with Missing Modalities for Sentiment Analysis and Emotion Recognition
Zirun Guo | Tao Jin | Zhou Zhao

The development of multimodal models has significantly advanced multimodal sentiment analysis and emotion recognition. However, in real-world applications, the presence of various missing modality cases often leads to a degradation in the model’s performance. In this work, we propose a novel multimodal Transformer framework using prompt learning to address the issue of missing modalities. Our method introduces three types of prompts: generative prompts, missing-signal prompts, and missing-type prompts. These prompts enable the generation of missing modality features and facilitate the learning of intra- and inter-modality information. Through prompt learning, we achieve a substantial reduction in the number of trainable parameters. Our proposed method outperforms other methods significantly across all evaluation metrics. Extensive experiments and ablation studies are conducted to demonstrate the effectiveness and robustness of our method, showcasing its ability to effectively handle missing modalities. Codes are available at https://github.com/zrguo/MPLMM.

Automatic pronunciation assessment (APA) manages to quantify a second language (L2) learner’s pronunciation proficiency in a target language by providing fine-grained feedback with multiple pronunciation aspect scores at various linguistic levels. Most existing efforts on APA typically parallelize the modeling process, namely predicting multiple aspect scores across various linguistic levels simultaneously. This inevitably makes both the hierarchy of linguistic units and the relatedness among the pronunciation aspects sidelined. Recognizing such a limitation, we in this paper first introduce HierTFR, a hierarchal APA method that jointly models the intrinsic structures of an utterance while considering the relatedness among the pronunciation aspects. We also propose a correlation-aware regularizer to strengthen the connection between the estimated scores and the human annotations. Furthermore, novel pre-training strategies tailored for different linguistic levels are put forward so as to facilitate better model initialization. An extensive set of empirical experiments conducted on the speechocean762 benchmark dataset suggest the feasibility and effectiveness of our approach in relation to several competitive baselines.

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Detection-Correction Structure via General Language Model for Grammatical Error Correction
Wei Li | Houfeng Wang

Grammatical error correction (GEC) is a task dedicated to rectifying texts with minimal edits, which can be decoupled into two components: detection and correction. However, previous works have predominantly focused on direct correction, with no prior efforts to integrate both into a single model. Moreover, the exploration of the detection-correction paradigm by large language models (LLMs) remains underdeveloped. This paper introduces an integrated detection-correction structure, named DeCoGLM, based on the General Language Model (GLM). The detection phase employs a fault-tolerant detection template, while the correction phase leverages autoregressive mask infilling for localized error correction. Through the strategic organization of input tokens and modification of attention masks, we facilitate multi-task learning within a single model. Our model demonstrates competitive performance against the state-of-the-art models on English and Chinese GEC datasets. Further experiments present the effectiveness of the detection-correction structure in LLMs, suggesting a promising direction for GEC.

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Generative Pre-trained Speech Language Model with Efficient Hierarchical Transformer
Yongxin Zhu | Dan Su | Liqiang He | Linli Xu | Dong Yu

While recent advancements in speech language models have achieved significant progress, they face remarkable challenges in modeling the long acoustic sequences of neural audio codecs. In this paper, we introduce Generative Pre-trained Speech Transformer (GPST), a hierarchical transformer designed for efficient speech language modeling. GPST quantizes audio waveforms into two distinct types of discrete speech representations and integrates them within a hierarchical transformer architecture, allowing for a unified one-stage generation process and enhancing Hi-Res audio generation capabilities. By training on large corpora of speeches in an end-to-end unsupervised manner, GPST can generate syntactically consistent speech with diverse speaker identities. Given a brief 3-second prompt, GPST can produce natural and coherent personalized speech, demonstrating in-context learning abilities. Moreover, our approach can be easily extended to spoken cross-lingual speech generation by incorporating multi-lingual semantic tokens and universal acoustic tokens. Experimental results indicate that GPST significantly outperforms the existing speech language models in terms of word error rate, speech quality, and speaker similarity. See https://youngsheen.github.io/GPST/demo for demo samples.

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Selene: Pioneering Automated Proof in Software Verification
Lichen Zhang | Shuai Lu | Nan Duan

Ensuring correctness is a pivotal aspect of software engineering. Among the various strategies available, software verification offers a definitive assurance of correctness. Nevertheless, writing verification proofs is resource-intensive and manpower-consuming, and there is a great need to automate this process. We introduce Selene in this paper, which is the first project-level automated proof benchmark constructed based on the real-world industrial-level operating system microkernel, seL4. Selene provides a comprehensive framework for end-to-end proof generation and a lightweight verification environment. Our experimental results with advanced large language models (LLMs), such as GPT-3.5-turbo and GPT-4, highlight the capabilities of LLMs in the domain of automated proof generation. Additionally, our further proposed augmentations indicate that the challenges presented by Selene can be mitigated in future research endeavors.

As a relative quality comparison of model responses, human and Large Language Model (LLM) preferences serve as common alignment goals in model fine-tuning and criteria in evaluation. Yet, these preferences merely reflect broad tendencies, resulting in less explainable and controllable models with potential safety risks. In this work, we dissect the preferences of human and 32 different LLMs to understand their quantitative composition, using annotations from real-world user-model conversations for a fine-grained, scenario-wise analysis. We find that humans are less sensitive to errors, favor responses that support their stances, and show clear dislike when models admit their limits. On the contrary, advanced LLMs like GPT-4-Turbo emphasize correctness, clarity, and harmlessness more. Additionally, LLMs of similar sizes tend to exhibit similar preferences, regardless of their training methods, and fine-tuning for alignment does not significantly alter the preferences of pretrained-only LLMs. Finally, we show that preference-based evaluation can be intentionally manipulated. In both training-free and training-based settings, aligning a model with the preferences of judges boosts scores, while injecting the least preferred properties lowers them. This results in notable score shifts: up to 0.59 on MT-Bench (1-10 scale) and 31.94 on AlpacaEval 2.0 (0-100 scale), highlighting the significant impact of this strategic adaptation. We have made all resources of this project publicly available.

Intermediate reasoning or acting steps have successfully improved large language models (LLMs) for handling various downstream natural language processing (NLP) tasks.When applying LLMs for code generation, recent works mainly focus on directing the models to articulate intermediate natural-language reasoning steps, as in chain-of-thought (CoT) prompting, and then output code with the natural language or other structured intermediate steps. However, such output is not suitable for code translation or generation tasks since the standard CoT has different logical structures and forms of expression with the code. In this work, we introduce the universal code (UniCode) as the intermediate representation. It is a description of algorithm steps using a mix of conventions of programming languages, such as assignment operator, conditional operator, and loop. Hence, we collect an instruction dataset UniCoder-Instruct to train our model UniCoder on multi-task learning objectives. UniCoder-Instruct comprises natural-language questions, code solutions, and the corresponding universal code. The alignment between the intermediate universal code representation and the final code solution significantly improves the quality of the generated code. The experimental results demonstrate that UniCoder with the universal code significantly outperforms the previous prompting methods by a large margin, showcasing the effectiveness of the structural clues in pseudo-code.

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AoE: Angle-optimized Embeddings for Semantic Textual Similarity
Xianming Li | Jing Li

Text embedding is pivotal in semantic textual similarity (STS) tasks, which are crucial components in Large Language Model (LLM) applications. STS learning largely relies on the cosine function as the optimization objective to reflect semantic similarity. However, the cosine has saturation zones rendering vanishing gradients and hindering learning subtle semantic differences in text embeddings. To address this issue, we propose a novel Angle-optimized Embedding model, AoE. It optimizes angle differences in complex space to explore similarity in saturation zones better. To set up a comprehensive evaluation, we experimented with existing short-text STS, our newly collected long-text STS, and downstream task datasets. Extensive experimental results on STS and MTEB benchmarks show that AoE significantly outperforms popular text embedding models neglecting cosine saturation zones. It highlights that AoE can produce high-quality text embeddings and broadly benefit downstream tasks.

Role-playing agents (RPAs), powered by large language models, have emerged as a flourishing field of applications. However, a key challenge lies in assessing whether RPAs accurately reproduce the personas of target characters, namely their character fidelity. Existing methods mainly focus on the knowledge and linguistic patterns of characters. This paper, instead, introduces a novel perspective to evaluate the personality fidelity of RPAs with psychological scales. Overcoming drawbacks of previous self-report assessments on RPAs, we propose InCharacter, namely **In**terviewing **Character** agents for personality tests. Experiments include various types of RPAs and LLMs, covering 32 distinct characters on 14 widely used psychological scales. The results validate the effectiveness of InCharacter in measuring RPA personalities. Then, with InCharacter, we show that state-of-the-art RPAs exhibit personalities highly aligned with the human-perceived personalities of the characters, achieving an accuracy up to 80.7%.

The burgeoning generative capabilities of large language models (LLMs) have raised growing concerns about abuse, demanding automatic machine-generated text detectors. DetectGPT, a zero-shot metric-based detector, first introduces perturbation and shows great performance improvement. However, in DetectGPT, the random perturbation strategy could introduce noise, and logit regression depends on the threshold, harming the generalizability and applicability of individual or small-batch inputs. Hence, we propose a novel fine-tuned detector, PECOLA, bridging metric-based and fine-tuned methods by contrastive learning on selective perturbation. Selective strategy retains important tokens during perturbation and weights for multi-pair contrastive learning. The experiments show that PECOLA outperforms the state-of-the-art (SOTA) by 1.20% in accuracy on average on four public datasets. And we further analyze the effectiveness, robustness, and generalization of the method.

With the rise of generative AI, automated fact-checking methods to combat misinformation are becoming more and more important. However, factual claim detection, the first step in a fact-checking pipeline, suffers from two key issues that limit its scalability and generalizability: (1) inconsistency in definitions of the task and what a claim is, and (2) the high cost of manual annotation. To address (1), we review the definitions in related work and propose a unifying definition of factual claims that focuses on verifiability. To address (2), we introduce AFaCTA (Automatic Factual Claim deTection Annotator), a novel framework that assists in the annotation of factual claims with the help of large language models (LLMs). AFaCTA calibrates its annotation confidence with consistency along three predefined reasoning paths. Extensive evaluation and experiments in the domain of political speech reveal that AFaCTA can efficiently assist experts in annotating factual claims and training high-quality classifiers, and can work with or without expert supervision. Our analyses also result in PoliClaim, a comprehensive claim detection dataset spanning diverse political topics.

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Towards Faithful and Robust LLM Specialists for Evidence-Based Question-Answering
Tobias Schimanski | Jingwei Ni | Mathias Kraus | Elliott Ash | Markus Leippold

Advances towards more faithful and traceable answers of Large Language Models (LLMs) are crucial for various research and practical endeavors. One avenue in reaching this goal is basing the answers on reliable sources. However, this Evidence-Based QA has proven to work insufficiently with LLMs in terms of citing the correct sources (source quality) and truthfully representing the information within sources (answer attributability). In this work, we systematically investigate how to robustly fine-tune LLMs for better source quality and answer attributability. Specifically, we introduce a data generation pipeline with automated data quality filters, which can synthesize diversified high-quality training and testing data at scale. We further introduce four test sets to benchmark the robustness of fine-tuned specialist models. Extensive evaluation shows that fine-tuning on synthetic data improves performance on both in- and out-of-distribution. Furthermore, we show that data quality, which can be drastically improved by proposed quality filters, matters more than quantity in improving Evidence-Based QA.

Supervised fine-tuning (SFT) is a crucial step for large language models (LLMs), enabling them to align with human instructions and enhance their capabilities in downstream tasks. Substantially increasing instruction data is a direct solution to align the model with a broader range of downstream tasks or notably improve its performance on a specific task. However, we find that large-scale increases in instruction data can damage the world knowledge previously stored in LLMs. To address this challenge, we propose LoRAMoE, a novelty framework that introduces several low-rank adapters (LoRA) and integrates them by using a router network, like a plugin version of Mixture of Experts (MoE). It freezes the backbone model and forces a portion of LoRAs to focus on leveraging world knowledge to solve downstream tasks, to alleviate world knowledge forgetting. Experimental results show that, as the instruction data increases, LoRAMoE can significantly improve the ability to process downstream tasks, while maintaining the world knowledge stored in the LLM. Our code is available at https://github.com/Ablustrund/LoRAMoE.

Despite showing impressive abilities, large language models (LLMs) often struggle with factual inaccuracies, i.e., ”hallucinations”, even when they hold relevant knowledge. To mitigate these hallucinations, current approaches typically necessitate high-quality human factuality annotations. In this work, we explore Self-Alignment for Factuality, where we leverage the self-evaluation capability of an LLM to provide training signals that steer the model towards factuality. Specifically, we incorporate Self-Eval, a self-evaluation component, to prompt an LLM to validate the factuality of its own generated responses solely based on its internal knowledge. Additionally, we design Self-Knowledge Tuning (SK-Tuning) to augment the LLM’s self-evaluation ability by improving the model’s confidence estimation and calibration. We then utilize these self-annotated responses to fine-tune the model via Direct Preference Optimization algorithm. We show that the proposed self-alignment approach substantially enhances factual accuracy over Llama family models across three key knowledge-intensive tasks on TruthfulQA and BioGEN.

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M-RAG: Reinforcing Large Language Model Performance through Retrieval-Augmented Generation with Multiple Partitions
Zheng Wang | Shu Teo | Jieer Ouyang | Yongjun Xu | Wei Shi

Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by retrieving relevant memories from an external database. However, existing RAG methods typically organize all memories in a whole database, potentially limiting focus on crucial memories and introducing noise. In this paper, we introduce a multiple partition paradigm for RAG (called M-RAG), where each database partition serves as a basic unit for RAG execution. Based on this paradigm, we propose a novel framework that leverages LLMs with Multi-Agent Reinforcement Learning to optimize different language generation tasks explicitly. Through comprehensive experiments conducted on seven datasets, spanning three language generation tasks and involving three distinct language model architectures, we confirm that M-RAG consistently outperforms various baseline methods, achieving improvements of 11%, 8%, and 12% for text summarization, machine translation, and dialogue generation, respectively.

Recently, instruction-following audio-language models have received broad attention for human-audio interaction. However, the absence of benchmarks capable of evaluating audio-centric interaction capabilities has impeded advancements in this field. Previous models primarily focus on assessing different fundamental tasks, such as automatic speech recognition, and lack an assessment of the open-ended generative capabilities centered around audio. Thus, it is challenging to track the progression in the Large Audio-Language Models (LALMs) domain and to provide guidance for future improvement.In this paper, we introduce AIR-Bench (Audio InstRuction Benchmark), the first benchmark designed to evaluate the ability of LALMs to understand various types of audio signals (including human speech, natural sounds, and music), and furthermore, to interact with humans in the textual format. AIR-Bench encompasses two dimensions: foundation and chat benchmarks. The former consists of 19 tasks with approximately 19k single-choice questions, intending to inspect the basic single-task ability of LALMs. The latter one contains 2k instances of open-ended question-and-answer data, directly assessing the comprehension of the model on complex audio and its capacity to follow instructions. Both benchmarks require the model to generate hypotheses directly. We design a unified framework that leverages advanced language models, such as GPT-4, to evaluate the scores of generated hypotheses given the meta-information of the audio. Experimental results demonstrate a high level of consistency between GPT-4-based evaluation and human evaluation. By revealing the limitations of existing LALMs through evaluation results, AIR-Bench can provide insights into the direction of future research. Dataset and evaluation code are available at https://github.com/OFA-Sys/AIR-Bench.

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Navigating the Metrics Maze: Reconciling Score Magnitudes and Accuracies
Tom Kocmi | Vilém Zouhar | Christian Federmann | Matt Post

Ten years ago a single metric, BLEU, governed progress in machine translation research. For better or worse, there is no such consensus today, and consequently it is difficult for researchers to develop and retain intuitions about metric deltas that drove earlier research and deployment decisions. This paper investigates the “dynamic range” of a number of modern metrics in an effort to provide a collective understanding of the meaning of differences in scores both within and among metrics; in other words, we ask “what point difference x in metric y is required between two systems for humans to notice?”. We conduct our evaluation on a new large dataset, ToShip23, using it to discover deltas at which metrics achieve system-level differences that are meaningful to humans, which we measure by pairwise system accuracy. We additionally show that this method of establishing delta-accuracy is more stable than the standard use of statistical p-values in regards to testset size. Where data size permits, we also explore the effect of metric deltas and accuracy across finer-grained features such as translation direction, domain, and system closeness.

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ValueBench: Towards Comprehensively Evaluating Value Orientations and Understanding of Large Language Models
Yuanyi Ren | Haoran Ye | Hanjun Fang | Xin Zhang | Guojie Song

Large Language Models (LLMs) are transforming diverse fields and gaining increasing influence as human proxies. This development underscores the urgent need for evaluating value orientations and understanding of LLMs to ensure their responsible integration into public-facing applications. This work introduces ValueBench, the first comprehensive psychometric benchmark for evaluating value orientations and understanding in LLMs. ValueBench collects data from 44 established psychometric inventories, encompassing 453 multifaceted value dimensions. We propose an evaluation pipeline grounded in realistic human-AI interactions to probe value orientations, along with novel tasks for evaluating value understanding in an open-ended value space. With extensive experiments conducted on six representative LLMs, we unveil their shared and distinctive value orientations and exhibit their ability to approximate expert conclusions in value-related extraction and generation tasks.

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DM-BLI: Dynamic Multiple Subspaces Alignment for Unsupervised Bilingual Lexicon Induction
Ling Hu | Yuemei Xu

Unsupervised bilingual lexicon induction (BLI) task aims to find word translations between languages and has achieved great success in similar language pairs. However, related works mostly rely on a single linear mapping for language alignment and fail on distant or low-resource language pairs, achieving less than half the performance observed in rich-resource language pairs. In this paper, we introduce DM-BLI, a Dynamic Multiple subspaces alignment framework for unsupervised BLI. DM-BLI improves language alignment by utilizing multiple subspace alignments instead of a single mapping. We begin via unsupervised clustering to discover these subspaces in source embedding space. Then we identify and align corresponding subspaces in the target space using a rough global alignment. DM-BLI further employs intra-cluster and inter-cluster contrastive learning to refine precise alignment for each subspace pair. Experiments conducted on standard BLI datasets for 12 language pairs (6 rich-resource and 6 low-resource) demonstrate substantial gains achieved by our framework. We release our code at https://github.com/huling-2/DM-BLI.git.

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SparseFit: Few-shot Prompting with Sparse Fine-tuning for Jointly Generating Predictions and Natural Language Explanations
Jesus Solano | Mardhiyah Sanni | Oana-Maria Camburu | Pasquale Minervini

Models that generate natural language explanations (NLEs) for their predictions have recently gained increasing interest. However, this approach usually demands large datasets of human-written NLEs for the ground-truth answers at training time, which can be expensive and potentially infeasible for some applications. When only a few NLEs are available (a few-shot setup), fine-tuning pre-trained language models (PLMs) in conjunction with prompt-based learning has recently shown promising results. However, PLMs typically have billions of parameters, making full fine-tuning expensive. We propose SparseFit, a sparse few-shot fine-tuning strategy that leverages discrete prompts to jointly generate predictions and NLEs. We experiment with SparseFit on three sizes of the T5 language model and four datasets and compare it against existing state-of-the-art Parameter-Efficient Fine-Tuning (PEFT) techniques. We find that fine-tuning only 6.8% of the model parameters leads to competitive results for both the task performance and the quality of the generated NLEs compared to full fine-tuning of the model and produces better results on average than other PEFT methods in terms of predictive accuracy and NLE quality.

The subjective perception of emotion leads to inconsistent labels from human annotators. Typically, utterances lacking majority-agreed labels are excluded when training an emotion classifier, which cause problems when encountering ambiguous emotional expressions during testing. This paper investigates three methods to handle ambiguous emotion. First, we show that incorporating utterances without majority-agreed labels as an additional class in the classifier reduces the classification performance of the other emotion classes. Then, we propose detecting utterances with ambiguous emotions as out-of-domain samples by quantifying the uncertainty in emotion classification using evidential deep learning. This approach retains the classification accuracy while effectively detects ambiguous emotion expressions. Furthermore, to obtain fine-grained distinctions among ambiguous emotions, we propose representing emotion as a distribution instead of a single class label. The task is thus re-framed from classification to distribution estimation where every individual annotation is taken into account, not just the majority opinion. The evidential uncertainty measure is extended to quantify the uncertainty in emotion distribution estimation. Experimental results on the IEMOCAP and CREMA-D datasets demonstrate the superior capability of the proposed method in terms of majority class prediction, emotion distribution estimation, and uncertainty estimation.

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REANO: Optimising Retrieval-Augmented Reader Models through Knowledge Graph Generation
Jinyuan Fang | Zaiqiao Meng | Craig MacDonald

Open domain question answering (ODQA) aims to answer questions with knowledge from an external corpus. Fusion-in-Decoder (FiD) is an effective retrieval-augmented reader model to address this task. Given that FiD independently encodes passages, which overlooks the semantic relationships between passages, some studies use knowledge graphs (KGs) to establish dependencies among passages. However, they only leverage knowledge triples from existing KGs, which suffer from incompleteness and may lack certain information critical for answering given questions. To this end, in order to capture the dependencies between passages while tacking the issue of incompleteness in existing KGs, we propose to enhance the retrieval-augmented reader model with a knowledge graph generation module (REANO). Specifically, REANO consists of a KG generator and an answer predictor. The KG generator aims to generate KGs from the passages and the answer predictor then generates answers based on the passages and the generated KGs. Experimental results on five ODQA datasets indicate that compared with baselines, REANO can improve the exact match score by up to 2.7% on the EntityQuestion dataset, with an average improvement of 1.8% across all the datasets.

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Learning Disentangled Semantic Spaces of Explanations via Invertible Neural Networks
Yingji Zhang | Danilo Carvalho | Andre Freitas

Disentangled latent spaces usually have better semantic separability and geometrical properties, which leads to better interpretability and more controllable data generation. While this has been well investigated in Computer Vision, in tasks such as image disentanglement, in the NLP domain, sentence disentanglement is still comparatively under-investigated. Most previous work have concentrated on disentangling task-specific generative factors, such as sentiment, within the context of style transfer. In this work, we focus on a more general form of sentence disentanglement, targeting the localised modification and control of more general sentence semantic features. To achieve this, we contribute to a novel notion of sentence semantic disentanglement and introduce a flow-based invertible neural network (INN) mechanism integrated with a transformer-based language Autoencoder (AE) in order to deliver latent spaces with better separability properties. Experimental results demonstrate that the model can conform the distributed latent space into a better semantically disentangled sentence space, leading to improved language interpretability and controlled generation when compared to the recent state-of-the-art language VAE models.

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MoPS: Modular Story Premise Synthesis for Open-Ended Automatic Story Generation
Yan Ma | Yu Qiao | Pengfei Liu

A story premise succinctly defines a story’s main idea, foundation, and trajectory. It serves as the initial trigger in automatic story generation. Existing sources of story premises are limited by a lack of diversity, uneven quality, and high costs that make them difficult to scale. In response, we introduce Modular Story Premise Synthesis (MoPS) which breaks down story premises into modules like background and persona for automated design and generation. MoPS consists of three phases: (1) Pre-collect a consistent set of candidates for each module to form a nested dictionary. (2) Extract a key path from the nested dictionary as the premise design. (3) Instruct an LLM to integrate the design into a coherent premise sentence. Thorough evaluations demonstrate that our synthesized premises excel in diversity, fascination, completeness, and originality compared to those induced from large language models and captured from public story datasets. Similarly, the extended novels and scripts generated from our premises also exhibit higher quality. In supplementary materials, we provide the MoPS code suite, along with 7.5k generated premises and 1k extended stories.

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Open-Set Semi-Supervised Text Classification via Adversarial Disagreement Maximization
Junfan Chen | Richong Zhang | Junchi Chen | Chunming Hu

Open-Set Semi-Supervised Text Classification (OSTC) aims to train a classification model on a limited set of labeled texts, alongside plenty of unlabeled texts that include both in-distribution and out-of-distribution examples. In this paper, we revisit the main challenge in OSTC, i.e., outlier detection, from a measurement disagreement perspective and innovatively propose to improve OSTC performance by directly maximizing the measurement disagreements. Based on the properties of in-measurement and cross-measurements, we design an Adversarial Disagreement Maximization (ADM) model that synergeticly optimizes the measurement disagreements. In addition, we develop an abnormal example detection and measurement calibration approach to guarantee the effectiveness of ADM training. Experiment results and comprehensive analysis of three benchmarks demonstrate the effectiveness of our model.

Tool learning is widely acknowledged as a foundational approach or deploying large language models (LLMs) in real-world scenarios. While current research primarily emphasizes leveraging tools to augment LLMs, it frequently neglects emerging safety considerations tied to their application. To fill this gap, we present ToolSword, a comprehensive framework dedicated to meticulously investigating safety issues linked to LLMs in tool learning. Specifically, ToolSword delineates six safety scenarios for LLMs in tool learning, encompassing malicious queries and jailbreak attacks in the input stage, noisy misdirection and risky cues in the execution stage, and harmful feedback and error conflicts in the output stage. Experiments conducted on 11 open-source and closed-source LLMs reveal enduring safety challenges in tool learning, such as handling harmful queries, employing risky tools, and delivering detrimental feedback, which even GPT-4 is susceptible to. Moreover, we conduct further studies with the aim of fostering research on tool learning safety. The data will be released upon acceptance of the paper.

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A synthetic data approach for domain generalization of NLI models
Mohammad Javad Hosseini | Andrey Petrov | Alex Fabrikant | Annie Louis

Natural Language Inference (NLI) remains an important benchmark task for LLMs. NLI datasets are a springboard for transfer learning to other semantic tasks, and NLI models are standard tools for identifying the faithfulness of model-generated text. There are several large scale NLI datasets today, and models have improved greatly by hill-climbing on these collections. Yet their realistic performance on out-of-distribution/domain data is less well-understood. We explore the opportunity for synthetic high-quality datasets to adapt NLI models for zero-shot use in downstream applications across new and unseen text domains. We demonstrate a new approach for generating NLI data in diverse domains and lengths, so far not covered by existing training sets. The resulting examples have meaningful premises, the hypotheses are formed in creative ways rather than simple edits to a few premise tokens, and the labels have high accuracy. We show that models trained on this data (685K synthetic examples) have the best generalization to completely new downstream test settings. On the TRUE benchmark, a T5-small model trained with our data improves around 7% on average compared to training on the best alternative dataset. The improvements are more pronounced for smaller models, while still meaningful on a T5 XXL model. We also demonstrate gains on test sets when in-domain training data is augmented with our domain-general synthetic data.

The principle of continual relation extraction (CRE) involves adapting to emerging novel relations while preserving old knowledge. Existing CRE approaches excel in preserving old knowledge but falter when confronted with contaminated data streams, likely due to an artificial assumption of no annotation errors. Recognizing the prevalence of noisy labels in real-world datasets, we introduce a more practical learning scenario, termed as noisy-CRE. In response to this challenge, we propose a noise-resistant contrastive framework called Noise-guided Attack in Contrastive Learning (NaCL), aimed at learning incremental corrupted relations. Diverging from conventional approaches like sample discarding or relabeling in the presence of noisy labels, NaCL takes a transformative route by modifying the feature space through targeted attack. This attack aims to align the feature space with the provided, albeit inaccurate, labels, thereby enhancing contrastive representations. Extensive empirical validations demonstrate the consistent performance improvement of NaCL with increasing noise rates, surpassing state-of-the-art methods.

Post-training quantization (PTQ) for large language models (LLMs) significantly accelerates model inference and relieves memory constraints, without incurring model training. A “smoothing paradigm” is commonly used in LLM quantization, which transfers the quantization difficulty of activation to weight quantization using mathematically equivalent transformations. However, existing methods face two issues: 1) Most smoothing parameters are hand-crafted defined which leads to suboptimal results; 2) There are significant performance degradations when tested on unseen datasets. To address these challenges, this paper introduces a robust learnable smooth-based PTQ framework, called LRQuant. Firstly, we consider a learnable paradigm to find optimal smoothing parameters which are initialized by logarithmic activation equivalent. In addition, we empirically found that only relying on MSE loss could hardly lead to optimal quantization results, and we then propose a novel loss function based on the negative logarithm of cosine similarity (NLC loss) between outputs of full-precision and quantized block. At last, we pioneeringly introduce Test-time adaptation (TTA) into LLM quantization, which allows for rapid model adaptation during testing to improve generalization performance. More surprisingly, we find that by using our TTA method, we can achieve better results on test sets than directly using test sets for calibration in some cases while avoiding catastrophic forgetting. Codes are available at https://github.com/zjq0455/RLQ.

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VariErr NLI: Separating Annotation Error from Human Label Variation
Leon Weber-Genzel | Siyao Peng | Marie-Catherine De Marneffe | Barbara Plank

Human label variation arises when annotators assign different labels to the same item for valid reasons, while annotation errors occur when labels are assigned for invalid reasons. These two issues are prevalent in NLP benchmarks, yet existing research has studied them in isolation. To the best of our knowledge, there exists no prior work that focuses on teasing apart error from signal, especially in cases where signal is beyond black-and-white.To fill this gap, we introduce a systematic methodology and a new dataset, VariErr (variation versus error), focusing on the NLI task in English. We propose a 2-round annotation procedure with annotators explaining each label and subsequently judging the validity of label-explanation pairs.VariErr contains 7,732 validity judgments on 1,933 explanations for 500 re-annotated MNLI items. We assess the effectiveness of various automatic error detection (AED) methods and GPTs in uncovering errors versus human label variation. We find that state-of-the-art AED methods significantly underperform GPTs and humans. While GPT-4 is the best system, it still falls short of human performance. Our methodology is applicable beyond NLI, offering fertile ground for future research on error versus plausible variation, which in turn can yield better and more trustworthy NLP systems.

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Benchmarking Knowledge Boundary for Large Language Models: A Different Perspective on Model Evaluation
Xunjian Yin | Xu Zhang | Jie Ruan | Xiaojun Wan

In recent years, substantial advancements have been made in the development of large language models, achieving remarkable performance across diverse tasks.To evaluate the knowledge ability of language models, previous studies have proposed lots of benchmarks based on question-answering pairs.We argue that it is not reliable and comprehensive to evaluate language models with a fixed question or limited paraphrases as the query, since language models are sensitive to prompt.Therefore, we introduce a novel concept named knowledge boundary to encompass both prompt-agnostic and prompt-sensitive knowledge within language models.Knowledge boundary avoids prompt sensitivity in language model evaluations, rendering them more dependable and robust.To explore the knowledge boundary for a given model, we propose projected gradient descent method with semantic constraints, a new algorithm designed to identify the optimal prompt for each piece of knowledge.Experiments demonstrate a superior performance of our algorithm in computing the knowledge boundary compared to existing methods.Furthermore, we evaluate the ability of multiple language models in several domains with knowledge boundary.

We propose ListT5, a novel reranking approach based on Fusion-in-Decoder (FiD) that handles multiple candidate passages at both train and inference time. We also introduce an efficient inference framework for listwise ranking based on m-ary tournament sort with output caching. We evaluate and compare our model on the BEIR benchmark for zero-shot retrieval task, demonstrating that ListT5 (1) outperforms the state-of-the-art RankT5 baseline with a notable +1.3 gain in the average NDCG@10 score, (2) has an efficiency comparable to pointwise ranking models and surpasses the efficiency of previous listwise ranking models, and (3) overcomes the lost-in-the-middle problem of previous listwise rerankers. Our code, model checkpoints, and the evaluation framework will be fully open-sourced.

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Exploring the Potential of Large Language Models in Computational Argumentation
Guizhen Chen | Liying Cheng | Anh Tuan Luu | Lidong Bing

Computational argumentation has become an essential tool in various domains, including law, public policy, and artificial intelligence. It is an emerging research field in natural language processing that attracts increasing attention. Research on computational argumentation mainly involves two types of tasks: argument mining and argument generation. As large language models (LLMs) have demonstrated impressive capabilities in understanding context and generating natural language, it is worthwhile to evaluate the performance of LLMs on diverse computational argumentation tasks. This work aims to embark on an assessment of LLMs, such as ChatGPT, Flan models, and LLaMA2 models, in both zero-shot and few-shot settings. We organize existing tasks into six main categories and standardize the format of fourteen openly available datasets. In addition, we present a new benchmark dataset on counter speech generation that aims to holistically evaluate the end-to-end performance of LLMs on argument mining and argument generation. Extensive experiments show that LLMs exhibit commendable performance across most of the datasets, demonstrating their capabilities in the field of argumentation. Our analysis offers valuable suggestions for evaluating computational argumentation and its integration with LLMs in future research endeavors.

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TaxoLLaMA: WordNet-based Model for Solving Multiple Lexical Semantic Tasks
Viktor Moskvoretskii | Ekaterina Neminova | Alina Lobanova | Alexander Panchenko | Irina Nikishina

In this paper, we explore the capabilities of LLMs in capturing lexical-semantic knowledge from WordNet on the example of the LLaMA-2-7b model and test it on multiple lexical semantic tasks. As the outcome of our experiments, we present TaxoLLaMA, the “all-in-one” model for taxonomy-related tasks, lightweight due to 4-bit quantization and LoRA. TaxoLLaMA achieves 11 SOTA results, and 4 top-2 results out of 16 tasks on the Taxonomy Enrichment, Hypernym Discovery, Taxonomy Construction, and Lexical Entailment tasks. Moreover, it demonstrates a very strong zero-shot performance on Lexical Entailment and Taxonomy Construction with no fine-tuning. We also explore its hidden multilingual and domain adaptation capabilities with a little tuning or few-shot learning. All datasets, code, and pre-trained models are available online (code: https://github.com/VityaVitalich/TaxoLLaMA)

The sequential process of conceptualization and instantiation is essential to generalizable commonsense reasoning as it allows the application of existing knowledge to unfamiliar scenarios. However, existing works tend to undervalue the step of instantiation and heavilyrely on pre-built concept taxonomies and human annotations to collect both types of knowledge, resulting in a lack of instantiated knowledge to complete reasoning, high cost, and limited scalability. To tackle these challenges, we introduce CANDLE (ConceptuAlizationand INstantiation Distillation from Large Language ModEls), a distillation framework that iteratively performs contextualized conceptualization and instantiation over commonsense knowledge bases by instructing large language models to generate both types of knowledge with critic filtering. By applying CANDLE to ATOMIC (Sap et al., 2019a), we construct a comprehensive knowledge base comprising six million conceptualizations and instantiated commonsense knowledge triples. Both types of knowledge are firmly rooted in the original ATOMIC dataset, and intrinsic evaluations demonstrate their exceptional quality and diversity. Empirical results indicate that distilling CANDLE on student models provides benefits across three downstream tasks. Our data and models are publicly available at https://github.com/HKUST-KnowComp/CANDLE.

Parameter-Efficient Fine-tuning (PEFT) facilitates the fine-tuning of Large Language Models (LLMs) under limited resources. However, the fine-tuning performance with PEFT on complex, knowledge-intensive tasks is limited due to the constrained model capacity, which originates from the limited number of additional trainable parameters. To overcome this limitation, we introduce a novel mechanism that fine-tunes LLMs with adapters of larger size yet memory-efficient. This is achieved by leveraging the inherent activation sparsity in the Feed-Forward Networks (FFNs) of LLMs and utilizing the larger capacity of Central Processing Unit (CPU) memory compared to Graphics Processing Unit (GPU). We store and update the parameters of larger adapters on the CPU. Moreover, we employ a Mixture of Experts (MoE)-like architecture to mitigate unnecessary CPU computations and reduce the communication volume between the GPU and CPU. This is particularly beneficial over the limited bandwidth of PCI Express (PCIe). Our method can achieve fine-tuning results comparable to those obtained with larger memory capacities, even when operating under more limited resources such as a 24GB memory single GPU setup, with acceptable loss in training efficiency. Our codes are available at https://github.com/CURRENTF/MEFT.

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Surgical Feature-Space Decomposition of LLMs: Why, When and How?
Arnav Chavan | Nahush Lele | Deepak Gupta

Low-rank approximations, of the weight and feature space can enhance the performance of deep learning models, whether in terms of improving generalization or reducing the latency of inference. However, there is no clear consensus yet on how, when and why these approximations are helpful for large language models (LLMs). In this work, we empirically study the efficacy of weight and feature space decomposition in transformer-based LLMs. We demonstrate that surgical decomposition not only provides critical insights into the trade-off between compression and language modelling performance, but also sometimes enhances commonsense reasoning performance of LLMs. Our empirical analysis identifies specific network segments that intrinsically exhibit a low-rank structure. Furthermore, we extend our investigation to the implications of low-rank approximations on model bias. Overall, our findings offer a novel perspective on optimizing LLMs, presenting the low-rank approximation not only as a tool for performance enhancements, but also as a means to potentially rectify biases within these models.

Machine reasoning, which involves solving complex problems through step-by-step deduction and analysis, is a crucial indicator of the capabilities of Large Language Models (LLMs). However, as the complexity of tasks escalates, LLMs often encounter increasing errors in their multi-step reasoning process. This study delves into the underlying factors contributing to these reasoning errors and seeks to leverage uncertainty to refine them. Specifically, we introduce Uncertainty-aware Adaptive Guidance (UAG), a novel approach for guiding LLM reasoning onto an accurate and reliable trajectory. UAG first identifies and evaluates uncertainty signals within each step of the reasoning chain. Upon detecting a significant increase in uncertainty, UAG intervenes by retracting to a previously reliable state and then introduces certified reasoning clues for refinement. By dynamically adjusting the reasoning process, UAG offers a plug-and-play solution for improving LLMs’ performance in complex reasoning. Extensive experiments across various reasoning tasks demonstrate that UAG not only enhances the reasoning abilities of LLMs but also consistently outperforms several strong baselines with minimal computational overhead. Further analysis reveals that UAG is notably effective in identifying and diminishing reasoning errors.

Knowledge-based visual question answering (KVQA) has been extensively studied to answer visual questions with external knowledge, e.g., knowledge graphs (KGs). While several attempts have been proposed to leverage large language models (LLMs) as an implicit knowledge source, it remains challenging since LLMs may generate hallucinations. Moreover, multiple knowledge sources, e.g., images, KGs and LLMs, cannot be readily aligned for complex scenarios. To tackle these, we present a novel modality-aware integration with LLMs for KVQA (MAIL). It carefully leverages multimodal knowledge for both image understanding and knowledge reasoning. Specifically, (i) we propose a two-stage prompting strategy with LLMs to densely embody the image into a *scene graph* with detailed visual features; (ii) We construct a coupled *concept graph* by linking the mentioned entities with external facts. (iii) A tailored pseudo-siamese graph medium fusion is designed for sufficient multimodal fusion. We utilize the shared mentioned entities in two graphs as mediums to bridge a tight inter-modal exchange, while maximally preserving insightful intra-modal learning by constraining the fusion within mediums. Extensive experiments show the superiority of MAIL.

Key-value (KV) caching is an important technique to accelerate the inference of large language models (LLMs), but incurs significant memory overhead. To compress the size of KV cache, existing methods often compromise precision or require extra data for calibration, limiting their practicality in LLM deployment. In this paper, we introduce DecoQuant, a novel data-free low-bit quantization technique based on tensor decomposition methods, to effectively compress KV cache. Our core idea is to adjust the outlier distribution of the original matrix by performing tensor decomposition, so that the quantization difficulties are migrated from the matrix to decomposed local tensors. Specially, we find that outliers mainly concentrate on small local tensors, while large tensors tend to have a narrower value range. Based on this finding, we propose to apply low-bit quantization to the large tensor, while maintaining high-precision representation for the small tensor. Furthermore, we utilize the proposed quantization method to compress the KV cache of LLMs to accelerate the inference, and develop an efficient dequantization kernel tailored specifically for DecoQuant. Through extensive experiments, DecoQuant demonstrates remarkable efficiency gains, showcasing up to a 75% reduction in memory footprint while maintaining comparable generation quality.

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VerifiNER: Verification-augmented NER via Knowledge-grounded Reasoning with Large Language Models
Seoyeon Kim | Kwangwook Seo | Hyungjoo Chae | Jinyoung Yeo | Dongha Lee

Recent approaches in domain-specific named entity recognition (NER), such as biomedical NER, have shown remarkable advances. However, they still lack of faithfulness, producing erroneous predictions. We assume that knowledge of entities can be useful in verifying the correctness of the predictions. Despite the usefulness of knowledge, resolving such errors with knowledge is nontrivial, since the knowledge itself does not directly indicate the ground-truth label. To this end, we propose VerifiNER, a post-hoc verification framework that identifies errors from existing NER methods using knowledge and revises them into more faithful predictions. Our framework leverages the reasoning abilities of large language models to adequately ground on knowledge and the contextual information in the verification process. We validate effectiveness of VerifiNER through extensive experiments on biomedical datasets. The results suggest that VerifiNER can successfully verify errors from existing models as a model-agnostic approach. Further analyses on out-of-domain and low-resource settings show the usefulness of VerifiNER on real-world applications.

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Making Long-Context Language Models Better Multi-Hop Reasoners
Yanyang Li | Shuo Liang | Michael Lyu | Liwei Wang

Recent advancements in long-context modeling have enhanced language models (LMs) for complex tasks across multiple NLP applications. Despite this progress, we find that these models struggle with multi-hop reasoning and exhibit decreased performance in the presence of noisy contexts. In this paper, we introduce Reasoning with Attributions, a novel approach that prompts LMs to supply attributions for each assertion during their reasoning. We validate our approach through experiments on three multi-hop datasets, employing both proprietary and open-source models, and demonstrate its efficacy and resilience. Furthermore, we explore methods to augment reasoning capabilities via fine-tuning and offer an attribution-annotated dataset and a specialized training strategy. Our fine-tuned model achieves competitive performance on multi-hop reasoning benchmarks, closely paralleling proprietary LMs such as ChatGPT and Claude-instant.

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TransliCo: A Contrastive Learning Framework to Address the Script Barrier in Multilingual Pretrained Language Models
Yihong Liu | Chunlan Ma | Haotian Ye | Hinrich Schuetze

The world’s more than 7000 languages are written in at least 293 scripts. Due to various reasons, many closely related languages use different scripts, which poses a difficulty for multilingual pretrained language models (mPLMs) in learning crosslingual knowledge through lexical overlap. As a consequence, mPLMs are faced with a script barrier: representations from different scripts are located in different subspaces, which can result in crosslingual transfer involving languages of different scripts performing suboptimally. To address this problem, we propose TransliCo, a framework that optimizes the Transliteration Contrastive Modeling (TCM) objective to fine-tune an mPLM by contrasting sentences in its training data and their transliterations in a unified script (in our case Latin), which enhances uniformity in the representation space for different scripts. Using Glot500-m, an mPLM pretrained on over 500 languages, as our source model, we fine-tune it on a small portion (5%) of its training data, and refer to the resulting model as Furina. We show that Furina not only better aligns representations from distinct scripts but also outperforms the original Glot500-m on various zero-shot crosslingual transfer tasks. Additionally, we achieve consistent improvement in a case study on the Indic group where the languages exhibit areal features but use different scripts. We make our code and models publicly available.

Deep learning-based Natural Language Processing (NLP) models are vulnerable to adversarial attacks, where small perturbations can cause a model to misclassify. Adversarial Training (AT) is often used to increase model robustness. However, we have discovered an intriguing phenomenon: deliberately or accidentally miscalibrating models masks gradients in a way that interferes with adversarial attack search methods, giving rise to an apparent increase in robustness. We show that this observed gain in robustness is an illusion of robustness (IOR), and demonstrate how an adversary can perform various forms of test-time temperature calibration to nullify the aforementioned interference and allow the adversarial attack to find adversarial examples. Hence, we urge the NLP community to incorporate test-time temperature scaling into their robustness evaluations to ensure that any observed gains are genuine. Finally, we show how the temperature can be scaled during training to improve genuine robustness.

The Matthew effect is a notorious issue in Recommender Systems (RSs), i.e., the rich get richer and the poor get poorer, wherein popular items are overexposed while less popular ones are regularly ignored. Most methods examine Matthew effect in static or nearly-static recommendation scenarios. However, the Matthew effect will be increasingly amplified when the user interacts with the system over time. To address these issues, we propose a novel paradigm, Hypergraph-Enhanced Multi-Preference Learning for Alleviating Matthew Effect in Conversational Recommendation (HyCoRec), which aims to alleviate the Matthew effect in conversational recommendation. Concretely, HyCoRec devotes to alleviate the Matthew effect by learning multi-aspect preferences, i.e., item-, entity-, word-, review-, and knowledge-aspect preferences, to effectively generate responses in the conversational task and accurately predict items in the recommendation task when the user chats with the system over time. Extensive experiments conducted on two benchmarks validate that HyCoRec achieves new state-of-the-art performance and the superior of alleviating Matthew effect.

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Co-training for Low Resource Scientific Natural Language Inference
Mobashir Sadat | Cornelia Caragea

Scientific Natural Language Inference (NLI) is the task of predicting the semantic relation between a pair of sentences extracted from research articles. The automatic annotation method based on distant supervision for the training set of SciNLI, the first and most popular dataset for this task, results in label noise which inevitably degenerates the performance of classifiers. In this paper, we propose a novel co-training method that assigns weights based on the training dynamics of the classifiers to the distantly supervised labels, reflective of the manner they are used in the subsequent training epochs. That is, unlike the existing semi-supervised learning (SSL) approaches, we consider the historical behavior of the classifiers to evaluate the quality of the automatically annotated labels. Furthermore, by assigning importance weights instead of filtering out examples based on an arbitrary threshold on the predicted confidence, we maximize the usage of automatically labeled data, while ensuring that the noisy labels have a minimal impact on model training. The proposed method obtains an improvement of 1.5% in Macro F1 over the distant supervision baseline, and substantial improvements over several other strong SSL baselines. We make our code and data available on Github.

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RLHFPoison: Reward Poisoning Attack for Reinforcement Learning with Human Feedback in Large Language Models
Jiongxiao Wang | Junlin Wu | Muhao Chen | Yevgeniy Vorobeychik | Chaowei Xiao

Reinforcement Learning with Human Feedback (RLHF) is a methodology designed to align Large Language Models (LLMs) with human preferences, playing an important role in LLMs alignment. Despite its advantages, RLHF relies on human annotators to rank the text, which can introduce potential security vulnerabilities if any adversarial annotator (i.e., attackers) manipulates the ranking score by up-ranking any malicious text to steer the LLM adversarially. To assess the red-teaming of RLHF against human preference data poisoning, we propose RankPoison, a poisoning attack method on candidates’ selection of preference rank flipping to reach certain malicious behaviors (e.g., generating longer sequences, which can increase the computational cost). With poisoned dataset generated by RankPoison, we can perform poisoning attacks on LLMs to generate longer tokens without hurting the original safety alignment performance. Moreover, applying RankPoison, we also successfully implement a backdoor attack where LLMs can generate longer answers under questions with the trigger word. Our findings highlight critical security challenges in RLHF, underscoring the necessity for more robust alignment methods for LLMs.

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Time is Encoded in the Weights of Finetuned Language Models
Kai Nylund | Suchin Gururangan | Noah Smith

We present time vectors, a simple tool to customize language models to new time periods. Time vectors are created by finetuning a language model on data from a single time (e.g., a year or month), and then subtracting the weights of the original pretrained model. This vector specifies a direction in weight space that, as our experiments show, improves performance on text from that time period. Time vectors specialized to adjacent time periods appear to be positioned closer together in a manifold. Using this structure, we interpolate between time vectors to induce new models that perform better on intervening and future time periods, without any additional training. We demonstrate the consistency of our findings across different tasks, domains, model sizes, and time scales. Our results suggest that time is encoded in the weight space of finetuned models.

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Long-Context Language Modeling with Parallel Context Encoding
Howard Yen | Tianyu Gao | Danqi Chen

Extending large language models (LLMs) to process longer inputs is crucial for numerous applications. However, the considerable computational cost of transformers, coupled with limited generalization of positional encoding, restricts the size of their context window. We introduce Cross-Attention to Parallel Encodings (CAPE), a framework that can be applied to any existing decoder-only LLMs for context expansion. CAPE leverages a small encoder to process a long input chunk by chunk and enables the frozen decoder to cross-attend to the additional contexts. CAPE is efficient, generalizable, and versatile: trained with 8K-token documents, CAPE extends the context window of LLaMA-2 to 128K tokens, offering 10× of the throughput with only 1/6 of the memory. CAPE yields strong performance on language modeling and in-context learning. CAPE also excels in retrieval-augmented applications, while existing long-context models degenerate with retrieved contexts. We further introduce a CAPE variant that can extend the context window of instruction-tuned models with only unlabeled data, and showcase its effectiveness on LLaMA-2-Chat, leading to a strong instruction-following model that can leverage very long context on downstream tasks.

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SirLLM: Streaming Infinite Retentive LLM
Yao Yao | Zuchao Li | Hai Zhao

As Large Language Models (LLMs) become increasingly prevalent in various domains, their ability to process inputs of any length and maintain a degree of memory becomes essential. However, the one-off input of overly long texts is limited, as studies have shown that when input lengths exceed the LLMs’ pre-trained text length, there is a dramatic decline in text generation capabilities. Moreover, simply extending the length of pre-training texts is impractical due to the difficulty in obtaining long text data and the substantial memory consumption costs this would entail for LLMs. Recent efforts have employed streaming inputs to alleviate the pressure of excessively long text inputs, but this approach can significantly impair the model’s long-term memory capabilities.Motivated by this challenge, we introduce Streaming Infinite Retentive LLM (SirLLM), which allows LLMs to maintain longer memory during infinite-length dialogues without the need for fine-tuning. SirLLM utilizes the Token Entropy metric and a memory decay mechanism to filter key phrases, endowing LLMs with both long-lasting and flexible memory. We designed three distinct tasks and constructed three datasets to measure the effectiveness of SirLLM from various angles: (1) DailyDialog; (2) Grocery Shopping; (3) Rock-Paper-Scissors. Our experimental results robustly demonstrate that SirLLM can achieve stable and significant improvements across different LLMs and tasks, compellingly proving its effectiveness. When having a coversation, “A sir could forget himself,” but SirLLM never does! Our code is publicly available at https://github.com/Zoeyyao27/SirLLMhttps://github.com/Zoeyyao27/SirLLM

Machine learning models have made incredible progress, but they still struggle when applied to examples from unseen domains. This study focuses on a specific problem of domain generalization, where a model is trained on one source domain and tested on multiple target domains that are unseen during training. We propose IMO: Invariant features Masks for Out-of-Distribution text classification, to achieve OOD generalization by learning invariant features. During training, IMO would learn sparse mask layers to remove irrelevant features for prediction, where the remaining features keep invariant. Additionally, IMO has an attention module at the token level to focus on tokens that are useful for prediction. Our comprehensive experiments show that IMO substantially outperforms strong baselines in terms of various evaluation metrics and settings.

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Generative Pretrained Structured Transformers: Unsupervised Syntactic Language Models at Scale
Xiang Hu | Pengyu Ji | Qingyang Zhu | Wei Wu | Kewei Tu

A syntactic language model (SLM) incrementally generates a sentence with its syntactic tree in a left-to-right manner.We present Generative Pretrained Structured Transformers (GPST), an unsupervised SLM at scale capable of being pre-trained from scratch on raw texts with high parallelism. GPST circumvents the limitations of previous SLMs such as relying on gold trees and sequential training. It consists of two components, a usual SLM supervised by a uni-directional language modeling loss, and an additional composition model, which induces syntactic parse trees and computes constituent representations, supervised by a bi-directional language modeling loss. We propose a representation surrogate to enable joint parallel training of the two models in a hard-EM fashion.We pre-train GPST on OpenWebText, a corpus with billion tokens, and demonstrate the superiority of GPST over GPT-2 with a comparable size in numerous tasks covering both language understanding and language generation. Meanwhile, GPST also significantly outperforms existing unsupervised SLMs on left-to-right grammar induction, while holding a substantial acceleration on training.

In this work, we present the largest benchmark to date on linguistic acceptability: Multilingual Evaluation of Linguistic Acceptability—MELA, with 46K samples covering 10 languages from a diverse set of language families. We establish LLM baselines on this benchmark, and investigate cross-lingual transfer in acceptability judgements with XLM-R. In pursuit of multilingual interpretability, we conduct probing experiments with fine-tuned XLM-R to explore the process of syntax capability acquisition. Our results show that GPT-4o exhibits a strong multilingual ability, outperforming fine-tuned XLM-R, while open-source multilingual models lag behind by a noticeable gap. Cross-lingual transfer experiments show that transfer in acceptability judgment is non-trivial: 500 Icelandic fine-tuning examples lead to 23 MCC performance in a completely unrelated language—Chinese. Results of our probing experiments indicate that training on MELA improves the performance of XLM-R on syntax-related tasks.

End-to-end automatic speech recognition (ASR) systems have made significant progress in general scenarios. However, it remains challenging to transcribe contextual named entities (NEs) in the contextual ASR scenario. Previous approaches have attempted to address this by utilizing the NE dictionary. These approaches treat entities as individual tokens and generate them token-by-token, which may result in incomplete transcriptions of entities. In this paper, we treat entities as indivisible wholes and introduce the idea of copying into ASR. We design a systematic mechanism called CopyNE, which can copy entities from the NE dictionary. By copying all tokens of an entity at once, we can reduce errors during entity transcription, ensuring the completeness of the entity. Experiments demonstrate that CopyNE consistently improves the accuracy of transcribing entities compared to previous approaches. Even when based on the strong Whisper, CopyNE still achieves notable improvements.

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Is Table Retrieval a Solved Problem? Exploring Join-Aware Multi-Table Retrieval
Peter Baile Chen | Yi Zhang | Dan Roth

Retrieving relevant tables containing the necessary information to accurately answer a given question over tables is critical to open-domain question-answering (QA) systems. Previous methods assume the answer to such a question can be found either in a single table or multiple tables identified through question decomposition or rewriting. However, neither of these approaches is sufficient, as many questions require retrieving multiple tables and joining them through a join plan that cannot be discerned from the user query itself. If the join plan is not considered in the retrieval stage, the subsequent steps of reasoning and answering based on those retrieved tables are likely to be incorrect. To address this problem, we introduce a method that uncovers useful join relations for any query and database during table retrieval. We use a novel re-ranking method formulated as a mixed-integer program that considers not only table-query relevance but also table-table relevance that requires inferring join relationships. Our method outperforms the state-of-the-art approaches for table retrieval by up to 9.3% in F1 score and for end-to-end QA by up to 5.4% in accuracy.

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Generalizing Conversational Dense Retrieval via LLM-Cognition Data Augmentation
Haonan Chen | Zhicheng Dou | Kelong Mao | Jiongnan Liu | Ziliang Zhao

Conversational search utilizes muli-turn natural language contexts to retrieve relevant passages. Existing conversational dense retrieval models mostly view a conversation as a fixed sequence of questions and responses, overlooking the severe data sparsity problem – that is, users can perform a conversation in various ways, and these alternate conversations are unrecorded. Consequently, they often struggle to generalize to diverse conversations in real-world scenarios. In this work, we propose a framework for generalizing Conversational dense retrieval via LLM-cognition data Augmentation (ConvAug). We first generate multi-level augmented conversations to capture the diverse nature of conversational contexts. Inspired by human cognition, we devise a cognition-aware prompting process to mitigate the generation of false positives, false negatives, and hallucinations. Moreover, we develop a difficulty-adaptive sample filter that selects challenging samples for complex conversations, thereby giving the model a larger learning space. A contrastive learning objective is then employed to train a better conversational context encoder. Extensive experiments conducted on four public datasets, under both normal and zero-shot settings, demonstrate the effectiveness, generalizability, and applicability of ConvAug. The code is released at https://github.com/haon-chen/ConvAug.

Although Large Language Models (LLMs) are showing impressive performance on a wide range of Natural Language Processing tasks, researchers have found that they still have limited ability to conduct induction. Recent works mainly adopt “post processes” paradigms to improve the performance of LLMs on induction (e.g., the hypothesis search & refinement methods), but their performance is still constrained by the inherent inductive capability of the LLMs. In this paper, we propose a novel framework, Induction through Deduction (ItD), to enable the LLMs to teach themselves induction through deduction. The ItD framework is composed of two main components: a Deductive Data Generation module to generate induction data and a Naive Bayesian Induction module to optimize the fine-tuning and decoding of LLMs. Our empirical results showcase the effectiveness of ItD on two induction benchmarks, achieving relative performance improvement of 36% and 10% compared with previous state-of-the-art, respectively. Our ablation study verifies the effectiveness of two key modules of ItD. We also verify the effectiveness of ItD across different LLMs and deductors. The data and code of this paper can be found at https://github.com/forangel2014/ItD.

Large language models (LLMs) have exhibited great potential in mathematical reasoning. However, there remains a performance gap in this area between existing open-source models and closed-source models such as GPT-4. In this paper, we introduce MathGenie, a novel method for generating diverse and reliable math problems by leveraging the ground-truth solutions of the seed data. We augment these ground-truth solutions and use a specially finetuned model to translate these augmented solutions back into new questions. Subsequently, we generate code-integrated solutions for these questions. To ensure the correctness of the code-integrated solutions, we employ rationale-based verification for filtering. Then, we finetune various pretrained models, ranging from 7B to 70B, on the newly curated data, resulting in a family of models known as MathGenie. These models consistently outperform previous open-source models across five representative mathematical reasoning datasets, achieving state-of-the-art performance. In particular, MathGenie-InternLM2 achieves an accuracy of 87.7% on GSM8K and 55.7% on MATH, securing the best overall score.

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Rethinking Task-Oriented Dialogue Systems: From Complex Modularity to Zero-Shot Autonomous Agent
Heng-Da Xu | Xian-Ling Mao | Puhai Yang | Fanshu Sun | Heyan Huang

Task-oriented dialogue (TOD) systems are predominantly designed to be composed of several functional modules (e.g. dialogue state tracker, dialogue policy, natural language generation) whether they are pipeline or end-to-end architectures. However, this modular design not only heavily relies on massive fully-annotated data, but also suffers from many intrinsic drawbacks, such as serious error accumulation, poor generalization ability, high customization cost, and low fault tolerance rate. In this paper, we rethink the architecture of the task-oriented dialogue systems and propose a novel fully zero-shot autonomous TOD agent, named AutoTOD, where all the delicate modules in traditional TOD systems are deprecated and all it needs is a general-purpose instruction-following language model (e.g. GPT-4). AutoTOD only leverages a simple instruction schema consisting of the description of tasks and external APIs, and can autonomously decide what to do at each dialogue turn, including asking for information, calling APIs, summarizing API results, and correcting previous mistakes. Moreover, we propose a simulation-based evaluation framework to better validate the abilities of TOD models in real-life scenarios. Extensive experiments conducted on the MultiWOZ and SGD datasets show the superior task completion ability and flexible language skills of AutoTOD.

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On Context Utilization in Summarization with Large Language Models
Mathieu Ravaut | Aixin Sun | Nancy Chen | Shafiq Joty

Large language models (LLMs) excel in abstractive summarization tasks, delivering fluent and pertinent summaries. Recent advancements have extended their capabilities to handle long-input contexts, exceeding 100k tokens. However, in question answering, language models exhibit uneven utilization of their input context. They tend to favor the initial and final segments, resulting in a U-shaped performance pattern concerning where the answer is located within the input. This bias raises concerns, particularly in summarization where crucial content may be dispersed throughout the source document(s). Besides, in summarization, mapping facts from the source to the summary is not trivial as salient content is usually re-phrased. In this paper, we conduct the first comprehensive study on context utilization and position bias in summarization. Our analysis encompasses 6 LLMs, 10 datasets, and 5 evaluation metrics. We introduce a new evaluation benchmark called MiddleSum on the which we benchmark two alternative inference methods to alleviate position bias: hierarchical summarization and incremental summarization. Our code and data can be found here: https://github.com/ntunlp/MiddleSum.

Large language models (LLMs) have demonstrated impressive capabilities in various natural language processing tasks. Despite this, their application to information retrieval (IR) tasks is still challenging due to the infrequent occurrence of many IR-specific concepts in natural language. While prompt-based methods can provide task descriptions to LLMs, they often fall short in facilitating a comprehensive understanding and execution of IR tasks, thereby limiting LLMs’ applicability. To address this gap, in this work, we explore the potential of instruction tuning to enhance LLMs’ proficiency in IR tasks. We introduce a novel instruction tuning dataset, INTERS, encompassing 20 tasks across three fundamental IR categories: query understanding, document understanding, and query-document relationship understanding. The data are derived from 43 distinct datasets with manually written templates. Our empirical results reveal that INTERS significantly boosts the performance of various publicly available LLMs, such as LLaMA, Mistral, and Falcon, in IR tasks. Furthermore, we conduct extensive experiments to analyze the effects of instruction design, template diversity, few-shot demonstrations, and the volume of instructions on performance. We make our dataset and the fine-tuned models publicly accessible at https://github.com/DaoD/INTERS.

The emergence of in-context learning (ICL) enables large pre-trained language models (PLMs) to make predictions for unseen inputs without updating parameters. Despite its potential, ICL’s effectiveness heavily relies on the quality, quantity, and permutation of demonstrations, commonly leading to suboptimal and unstable performance. In this paper, we tackle this challenge for the first time from the perspective of demonstration augmentation. Specifically, we start with enriching representations of demonstrations by leveraging their deep feature distribution. We then theoretically reveal that when the number of augmented copies approaches infinity, the augmentation is approximately equal to a novel logit calibration mechanism integrated with specific statistical properties. This insight results in a simple yet highly efficient method that significantly improves the average and worst-case accuracy across diverse PLMs and tasks. Moreover, our method effectively reduces performance variance among varying demonstrations, permutations, and templates, and displays the capability to address imbalanced class distributions.

With the rapid scaling of large language models (LLMs), serving numerouslow-rank adaptations (LoRAs) concurrently has become increasingly impractical,leading to unaffordable costs and necessitating more parameter-efficientfinetuning methods. In this work, we introduce Partially Rotation-enhanced Low-Rank Adaptation (PRoLoRA), an intra-layer sharing mechanism comprising fouressential components: broadcast reduction, rotation enhancement,partially-sharing refinement, and rectified initialization strategy. As asuperset of LoRA, PRoLoRA retains its advantages, and effectively circumventthe drawbacks of peer parameter-sharing methods with superior model capacity,practical feasibility, and broad applicability. Empirical experimentsdemonstrate the remarkably higher parameter efficiency of PRoLoRA in bothspecific parameter budget and performance target scenarios, and its scalabilityto larger LLMs. Notably, with one time less trainable parameters, PRoLoRA stilloutperforms LoRA on multiple instruction tuning datasets. Subsequently, anablation study is conducted to validate the necessity of individual componentsand highlight the superiority of PRoLoRA over three potential variants.Hopefully, the conspicuously higher parameter efficiency can establish PRoLoRAas a resource-friendly alternative to LoRA.

Existing approaches on zero-shot event detection usually train models on datasets annotated with known event types, and prompt them with unseen event definitions. These approaches yield sporadic successes, yet generally fall short of expectations.In this work, we aim to improve zero-shot event detection by training models to better follow event definitions. We hypothesize that a diverse set of event types and definitions are the key for models to learn to follow event definitions while existing event extraction datasets focus on annotating many high-quality examples for a few event types. To verify our hypothesis, we construct an automatically generated Diverse Event Definition (DivED) dataset and conduct comparative studies. Our experiments reveal that a large number of event types (200) and diverse event definitions can significantly boost event extraction performance; on the other hand, the performance does not scale with over ten examples per event type.Beyond scaling, we incorporate event ontology information and hard-negative samples during training, further boosting the performance. Based on these findings, we fine-tuned a LLaMA-2-7B model on our DivED dataset, yielding performance that surpasses SOTA large language models like GPT-3.5 across three open benchmarks on zero-shot event detection.

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Through the MUD: A Multi-Defendant Charge Prediction Benchmark with Linked Crime Elements
Xiao Wei | Qi Xu | Hang Yu | Qian Liu | Erik Cambria

The current charge prediction datasets mostly focus on single-defendant criminal cases.However, real-world criminal cases usually involve multiple defendants whose criminal facts are intertwined. In an early attempt to fill this gap, we introduce a new benchmark that encompasses legal cases involving multiple defendants, where each defendant is labeled with a charge and four types of crime elements, i.e., Object Element, Objective Element, Subject Element, and Subjective Element. Based on the dataset, we further develop an interpretable model called EJudge that incorporates crime elements and legal rules to infer charges. We observe that predicting crime charges while providing corresponding rationales benefits the interpretable AI system. Extensive experiments show that EJudge significantly surpasses state-of-the-art methods, which verify the importance of crime elements and legal rules in multi-defendant charge prediction. The source code and dataset are available at https://anonymous.4open.science/r/MCP_1-6010.

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Interpreting Conversational Dense Retrieval by Rewriting-Enhanced Inversion of Session Embedding
Yiruo Cheng | Kelong Mao | Zhicheng Dou

Conversational dense retrieval has shown to be effective in conversational search. However, a major limitation of conversational dense retrieval is their lack of interpretability, hindering intuitive understanding of model behaviors for targeted improvements. This paper presents CONVINV, a simple yet effective approach to shed light on interpretable conversational dense retrieval models. CONVINV transforms opaque conversational session embeddings into explicitly interpretable text while faithfully maintaining their original retrieval performance as much as possible. Such transformation is achieved by training a recently proposed Vec2Text model based on the ad-hoc query encoder, leveraging the fact that the session and query embeddings share the same space in existing conversational dense retrieval.To further enhance interpretability, we propose to incorporate external interpretable query rewrites into the transformation process. Extensive evaluations on three conversational search benchmarks demonstrate that CONVINV can yield more interpretable text and faithfully preserve original retrieval performance than baselines. Our work connects opaque session embeddings with transparent query rewriting, paving the way toward trustworthy conversational search.

The widespread use of large language models (LLMs) is increasing the demand for methods that detect machine-generated text to prevent misuse. The goal of our study is to stress test the detectors’ robustness to malicious attacks under realistic scenarios. We comprehensively study the robustness of popular machine-generated text detectors under attacks from diverse categories: editing, paraphrasing, co-generating, and prompting. Our attacks assume limited access to the generator LLMs, and we compare the performance of detectors on different attacks under different budget levels. Our experiments reveal that almost none of the existing detectors remain robust under all the attacks, and all detectors exhibit different loopholes. Averaging all detectors, the performance drops by 35% across all attacks. Further, we investigate the reasons behind these defects and propose initial out-of-the-box patches.

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Training Language Models to Generate Text with Citations via Fine-grained Rewards
Chengyu Huang | Zeqiu Wu | Yushi Hu | Wenya Wang

While recent Large Language Models (LLMs) have proven useful in answering user queries, they are prone to hallucination, and their responses often lack credibility due to missing references to reliable sources. An intuitive solution to these issues would be to include in-text citations referring to external documents as evidence. While previous works have directly prompted LLMs to generate in-text citations, their performances are far from satisfactory, especially when it comes to smaller LLMs. In this work, we propose an effective training framework using fine-grained rewards to teach LLMs to generate highly supportive and relevant citations, while ensuring the correctness of their responses. We also conduct a systematic analysis of applying these fine-grained rewards to common LLM training strategies, demonstrating its advantage over conventional practices. We conduct extensive experiments on Question Answering (QA) datasets taken from the ALCE benchmark and validate the model’s generalizability using EXPERTQA. On LLaMA-2-7B, the incorporation of fine-grained rewards achieves the best performance among the baselines, even surpassing that of GPT-3.5-turbo.

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Hypergraph based Understanding for Document Semantic Entity Recognition
Qiwei Li | Zuchao Li | Ping Wang | Haojun Ai | Hai Zhao

Semantic entity recognition is an important task in the field of visually-rich document understanding. It distinguishes the semantic types of text by analyzing the position relationship between text nodes and the relation between text content. The existing document understanding models mainly focus on entity categories while ignoring the extraction of entity boundaries. We build a novel hypergraph attention document semantic entity recognition framework, HGA, which uses hypergraph attention to focus on entity boundaries and entity categories at the same time. It can conduct a more detailed analysis of the document text representation analyzed by the upstream model and achieves a better performance of semantic information. We apply this method on the basis of GraphLayoutLM to construct a new semantic entity recognition model HGALayoutLM. Our experiment results on FUNSD, CORD, XFUND and SROIE show that our method can effectively improve the performance of semantic entity recognition tasks based on the original model. The results of HGALayoutLM on FUNSD and XFUND reach the new state-of-the-art results.

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GSM-Plus: A Comprehensive Benchmark for Evaluating the Robustness of LLMs as Mathematical Problem Solvers
Qintong Li | Leyang Cui | Xueliang Zhao | Lingpeng Kong | Wei Bi

Large language models (LLMs) have achieved impressive performance across various mathematical reasoning benchmarks. However, there are increasing debates regarding whether these models truly understand and apply mathematical knowledge or merely rely on shortcuts for mathematical reasoning. One essential and frequently occurring evidence is that when the math questions are slightly changed, LLMs can behave incorrectly. This motivates us to evaluate the robustness of LLMs’ math reasoning capability by testing a wide range of question variations. We introduce the adversarial grade school math (GSM-Plus) dataset, an extension of GSM8K augmented with various mathematical perturbations. Our experiments on 25 LLMs and 4 prompting techniques show that while LLMs exhibit different levels of math reasoning abilities, their performances are far from robust. In particular, even for problems that have been solved in GSM8K, LLMs can make mistakes when new statements are added or the question targets are altered. We also explore whether more robust performance can be achieved by composing existing prompting methods, in which we try an iterative method that generates and verifies each intermediate thought based on its reasoning goal and calculation result.

Cross-document event coreference resolution (CDECR) involves clustering event mentions across multiple documents that refer to the same real-world events. Existing approaches utilize fine-tuning of small language models (SLMs) like BERT to address the compatibility among the contexts of event mentions. However, due to the complexity and diversity of contexts, these models are prone to learning simple co-occurrences. Recently, large language models (LLMs) like ChatGPT have demonstrated impressive contextual understanding, yet they encounter challenges in adapting to specific information extraction (IE) tasks. In this paper, we propose a collaborative approach for CDECR, leveraging the capabilities of both a universally capable LLM and a task-specific SLM. The collaborative strategy begins with the LLM accurately and comprehensively summarizing events through prompting. Then, the SLM refines its learning of event representations based on these insights during fine-tuning. Experimental results demonstrate that our approach surpasses the performance of both the large and small language models individually, forming a complementary advantage. Across various datasets, our approach achieves state-of-the-art performance, underscoring its effectiveness in diverse scenarios.

Language agents have achieved considerable performance on various complex question-answering tasks by planning with external tools. Despite the incessant exploration in this field, existing language agent systems still struggle with costly, non-reproducible data reliance and face the challenge of compelling a single model for multiple functions. To this end, we introduce AutoAct, an automatic agent learning framework for QA that does not rely on large-scale annotated data and synthetic planning trajectories from closed-source models (e.g., GPT-4). Given limited data with a tool library, AutoAct first automatically synthesizes planning trajectories without any assistance from humans or strong closed-source models. Then, AutoAct leverages a division-of-labor strategy to automatically differentiate based on the target task information and synthesized trajectories, producing a sub-agent group to complete the task. We conduct comprehensive experiments with different LLMs, which demonstrates that AutoAct yields better or parallel performance compared to various strong baselines. Further analysis demonstrates the effectiveness of the division-of-labor strategy, with the trajectory quality generated by AutoAct generally outperforming that of others.

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ChronosLex: Time-aware Incremental Training for Temporal Generalization of Legal Classification Tasks
Santosh T.y.s.s | Tuan-Quang Vuong | Matthias Grabmair

This study investigates the challenges posed by the dynamic nature of legal multi-label text classification tasks, where legal concepts evolve over time. Existing models often overlook the temporal dimension in their training process, leading to suboptimal performance of those models over time, as they treat training data as a single homogeneous block. To address this, we introduce ChronosLex, an incremental training paradigm that trains models on chronological splits, preserving the temporal order of the data. However, this incremental approach raises concerns about overfitting to recent data, prompting an assessment of mitigation strategies using continual learning and temporal invariant methods. Our experimental results over six legal multi-label text classification datasets reveal that continual learning methods prove effective in preventing overfitting thereby enhancing temporal generalizability, while temporal invariant methods struggle to capture these dynamics of temporal shifts.

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Virtual Compiler Is All You Need For Assembly Code Search
Zeyu Gao | Hao Wang | Yuanda Wang | Chao Zhang

Assembly code search is vital for reducing the burden on reverse engineers, allowing them to quickly identify specific functions using natural language within vast binary programs.Despite its significance, this critical task is impeded by the complexities involved in building high-quality datasets. This paper explores training a Large Language Model (LLM) to emulate a general compiler. By leveraging Ubuntu packages to compile a dataset of 20 billion tokens, we further continue pre-train CodeLlama as a Virtual Compiler (ViC), capable of compiling any source code to assembly code. This approach allows for “virtual” compilation across a wide range of programming languages without the need for a real compiler, preserving semantic equivalency and expanding the possibilities for assembly code dataset construction. Furthermore, we use ViC to construct a sufficiently large dataset for assembly code search. Employing this extensive dataset, we achieve a substantial improvement in assembly code search performance, with our model surpassing the leading baseline by 26%.

Parameter-efficient fine-tuning (PEFT) is a popular method for tailoring pre-trained large language models (LLMs), especially as the models’ scale and the diversity of tasks increase. Low-rank adaptation (LoRA) is based on the idea that the adaptation process is intrinsically low-dimensional, i.e., significant model changes can be represented with relatively few parameters. However, decreasing the rank encounters challenges with generalization errors for specific tasks when compared to full-parameter fine-tuning. We present MELoRA, a mini-ensemble low-rank adapters that uses fewer trainable parameters while maintaining a higher rank, thereby offering improved performance potential.The core idea is to freeze original pretrained weights and train a group of mini LoRAs with only a small number of parameters. This can capture a significant degree of diversity among mini LoRAs, thus promoting better generalization ability. We conduct a theoretical analysis and empirical studies on various NLP tasks. Our experimental results show that, compared to LoRA, MELoRA achieves better performance with 8 times fewer trainable parameters on natural language understanding tasks and 36 times fewer trainable parameters on instruction following tasks, which demonstrates the effectiveness of MELoRA.

Large language models (LLMs) have demonstrated striking reasoning capability. Recent works have shown the benefits to LLMs from fine-tuning golden-standard Chain-of-Thought (CoT) rationales or using them as correct examples in few-shot prompting. While humans can indeed imitate correct examples, learning from our mistakes is another vital aspect of human cognition. Hence, a question naturally arises: can LLMs learn and benefit from their mistakes, especially for their reasoning?This study investigates this problem from both the prompting and model-tuning perspectives. We begin by introducing CoTErrorSet, a new benchmark with 609,432 questions, each designed with both correct and error references, and demonstrating the types and reasons for making such mistakes. To explore the effectiveness of those mistakes, we design two methods: (1) Self-rethinking prompting guides LLMs to rethink whether they have made similar previous mistakes; and (2) Mistake tuning involves finetuning models in both correct and incorrect reasoning domains, rather than only tuning models to learn ground truth in traditional methodology. We conduct a series of experiments to prove LLMs can obtain benefits from mistakes in both directions. Our two methods offer potentially cost-effective strategies by leveraging errors to enhance reasoning capabilities, which costs significantly less than creating meticulously hand-crafted golden references. We ultimately make a thorough analysis of the reasons behind LLMs’ errors, which provides directions that future research needs to overcome. CoTErrorSet will be published soon on https://github.com/YookiTong/Learn-from-Mistakes-CotErrorSet.

Empathetic response generation aims to comprehend the cognitive and emotional states in dialogue utterances and generate proper responses. Psychological theories posit that comprehending emotional and cognitive states necessitates iteratively capturing and understanding associated words across dialogue utterances. However, existing approaches regard dialogue utterances as either a long sequence or independent utterances for comprehension, which are prone to overlook the associated words between them. To address this issue, we propose an Iterative Associative Memory Model (IAMM) for empathetic response generation. Specifically, we employ a novel second-order interaction attention mechanism to iteratively capture vital associated words between dialogue utterances and situations, dialogue history, and a memory module (for storing associated words), thereby accurately and nuancedly comprehending the utterances.We conduct experiments on the Empathetic-Dialogue dataset. Both automatic and human evaluations validate the efficacy of the model. Variant experiments on LLMs also demonstrate that attending to associated words improves empathetic comprehension and expression.

This paper investigates using knowledge editing techniques to detoxify Large Language Models (LLMs). We construct a benchmark, SafeEdit, which covers nine unsafe categories with various powerful attack prompts and equips comprehensive metrics for systematic evaluation. We conduct experiments with several knowledge editing approaches, indicating that knowledge editing has the potential to efficiently detoxify LLMs with limited impact on general performance. Then, we propose a simple yet effective baseline, dubbed Detoxifying with Intraoperative Neural Monitoring (DINM), to diminish the toxicity of LLMs within a few tuning steps via only one instance. We further provide an in-depth analysis of the internal mechanism for various detoxifying approaches, demonstrating that previous methods like SFT and DPO may merely suppress the activations of toxic parameters, while DINM mitigates the toxicity of the toxic parameters to a certain extent, making permanent adjustments. We hope that these insights could shed light on future work of developing detoxifying approaches and the underlying knowledge mechanisms of LLMs.

Although large language models (LLMs) demonstrate impressive performance for many language tasks, most of them can only handle texts a few thousand tokens long, limiting their applications on longer sequence inputs, such as books, reports, and codebases. Recent works have proposed methods to improve LLMs’ long context capabilities by extending context windows and more sophisticated memory mechanisms. However, comprehensive benchmarks tailored for evaluating long context understanding are lacking. In this paper, we introduce LongBench, the first bilingual, multi-task benchmark for long context understanding, enabling a more rigorous evaluation of long context understanding. LongBench comprises 21 datasets across 6 task categories in both English and Chinese, with an average length of 6,711 words (English) and 13,386 characters (Chinese). These tasks cover key long-text application areas including single-doc QA, multi-doc QA, summarization, few-shot learning, synthetic tasks, and code completion. All datasets in LongBench are standardized into a unified format, allowing for effortless automatic evaluation of LLMs. Upon comprehensive evaluation of 8 LLMs on LongBench, we find that: (1) Commercial model (GPT-3.5-Turbo-16k) outperforms other open-sourced models, but still struggles on longer contexts. (2) Scaled position embedding and fine-tuning on longer sequences lead to substantial improvement on long context understanding. (3) Context compression technique such as retrieval brings improvement for model with weak ability on long contexts, but the performance still lags behind models that have strong long context understanding capability.

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Dr.Academy: A Benchmark for Evaluating Questioning Capability in Education for Large Language Models
Yuyan Chen | Chenwei Wu | Songzhou Yan | Panjun Liu | Yanghua Xiao

Teachers are important to imparting knowledge and guiding learners, and the role of large language models (LLMs) as potential educators is emerging as an important area of study. Recognizing LLMs’ capability to generate educational content can lead to advances in automated and personalized learning. While LLMs have been tested for their comprehension and problem-solving skills, their capability in teaching remains largely unexplored.In teaching, questioning is a key skill that guides students to analyze, evaluate, and synthesize core concepts and principles.Therefore, our research introduces a benchmark to evaluate the questioning capability in education as a teacher of LLMs through evaluating their generated educational questions, utilizing Anderson and Krathwohl’s taxonomy across general, monodisciplinary, and interdisciplinary domains. We shift the focus from LLMs as learners to LLMs as educators, assessing their teaching capability through guiding them to generate questions. We apply four metrics, including relevance, coverage, representativeness, and consistency, to evaluate the educational quality of LLMs’ outputs. Our results indicate that GPT-4 demonstrates significant potential in teaching general, humanities, and science courses; Claude2 appears more apt as an interdisciplinary teacher. Furthermore, the automatic scores align with human perspectives.

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UniBridge: A Unified Approach to Cross-Lingual Transfer Learning for Low-Resource Languages
Trinh Pham | Khoi Le | Anh Tuan Luu

In this paper, we introduce UniBridge (Cross-Lingual Transfer Learning with Optimized Embeddings and Vocabulary), a comprehensive approach developed to improve the effectiveness of Cross-Lingual Transfer Learning, particularly in languages with limited resources. Our approach tackles two essential elements of a language model: the initialization of embeddings and the optimal vocabulary size. Specifically, we propose a novel embedding initialization method that leverages both lexical and semantic alignment for a language. In addition, we present a method for systematically searching for the optimal vocabulary size, ensuring a balance between model complexity and linguistic coverage. Our experiments across multilingual datasets show that our approach greatly improves the F1-Score in several languages. UniBridge is a robust and adaptable solution for cross-lingual systems in various languages, highlighting the significance of initializing embeddings and choosing the right vocabulary size in cross-lingual environments.

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VISTA: Visualized Text Embedding For Universal Multi-Modal Retrieval
Junjie Zhou | Zheng Liu | Shitao Xiao | Bo Zhao | Yongping Xiong

Multi-modal retrieval becomes increasingly popular in practice. However, the existing retrievers are mostly text-oriented, which lack the capability to process visual information. Despite the presence of vision-language models like CLIP, the current methods are severely limited in representing the text-only and image-only data. In this work, we present a new embedding model VISTA for universal multi-modal retrieval. Our work brings forth threefold technical contributions. Firstly, we introduce a flexible architecture which extends a powerful text encoder with the image understanding capability by introducing visual token embeddings. Secondly, we develop two data generation strategies, which bring high-quality composed image-text to facilitate the training of the embedding model. Thirdly, we introduce a multi-stage training algorithm, which first aligns the visual token embedding with the text encoder using massive weakly labeled data, and then develops multi-modal representation capability using the generated composed image-text data. In our experiments, VISTA achieves superior performances across a variety of multi-modal retrieval tasks in both zero-shot and supervised settings. Our model, data, and source code are available at https://github.com/FlagOpen/FlagEmbedding.

Large language models (LLMs) have shown impressive success in various applications. However, these models are often not well aligned with human intents, which calls for additional treatments on them; that is, the alignment problem. To make LLMs better follow user instructions, existing alignment methods primarily focus on further training them. However, the extra training of LLMs is usually expensive in terms of GPU computing; even worse, some LLMs are not accessible for user-demanded training, such as GPTs. In this work, we take a different perspective—Black-Box Prompt Optimization (BPO)—to perform alignments. The idea is to optimize user prompts to suit LLMs’ input understanding, so as to best realize users’ intents without updating LLMs’ parameters. BPO leverages human preferences to optimize prompts, thus making it superior to LLM (e.g., ChatGPT) as a prompt engineer. Moreover, BPO is model-agnostic, and the empirical results demonstrate that the BPO-aligned ChatGPT yields a 22% increase in the win rate against its original version and 10% for GPT-4. Notably, the BPO-aligned LLMs can outperform the same models aligned by PPO and DPO, and it also brings additional performance gains when combining BPO with PPO or DPO. Code and datasets are released at https://github.com/thu-coai/BPO.

This paper introduces the Open Ko-LLM Leaderboard and the Ko-H5 Benchmark as vital tools for evaluating Large Language Models (LLMs) in Korean. Incorporating private test sets while mirroring the English Open LLM Leaderboard, we establish a robust evaluation framework that has been well integrated in the Korean LLM community. We perform data leakage analysis that shows the benefit of private test sets along with a correlation study within the Ko-H5 benchmark and temporal analyses of the Ko-H5 score. Moreover, we present empirical support for the need to expand beyond set benchmarks. We hope the Open Ko-LLM Leaderboard sets precedent for expanding LLM evaluation to foster more linguistic diversity.

Despite significant strides in multimodal tasks, Multimodal Large Language Models (MLLMs) are plagued by the critical issue of hallucination. The reliable detection of such hallucinations in MLLMs has, therefore, become a vital aspect of model evaluation and the safeguarding of practical application deployment. Prior research in this domain has been constrained by a narrow focus on singular tasks, an inadequate range of hallucination categories addressed, and a lack of detailed granularity. In response to these challenges, our work expands the investigative horizons of hallucination detection. We present a novel meta-evaluation benchmark, MHaluBench, meticulously crafted to facilitate the evaluation of advancements in hallucination detection methods. Additionally, we unveil a novel unified multimodal hallucination detection framework, UNIHD, which leverages a suite of auxiliary tools to validate the occurrence of hallucinations robustly. We demonstrate the effectiveness of UNIHD through meticulous evaluation and comprehensive analysis. We also provide strategic insights on the application of specific tools for addressing various categories of hallucinations.

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Empowering Character-level Text Infilling by Eliminating Sub-Tokens
Houxing Ren | Mingjie Zhan | Zhongyuan Wu | Hongsheng Li

In infilling tasks, sub-tokens, representing instances where a complete token is segmented into two parts, often emerge at the boundaries of prefixes, middles, and suffixes. Traditional methods focused on training models at the token level, leading to sub-optimal performance in character-level infilling tasks during the inference stage. Alternately, some approaches considered character-level infilling, but they relied on predicting sub-tokens in inference, yet this strategy diminished ability in character-level infilling tasks due to the large perplexity of the model on sub-tokens. In this paper, we introduce FIM-SE, which stands for Fill-In-the-Middle with both Starting and Ending character constraints. The proposed method addresses character-level infilling tasks by utilizing a line-level format to avoid predicting any sub-token in inference. In addition, we incorporate two special tokens to signify the rest of the incomplete lines, thereby enhancing generation guidance. Extensive experiments demonstrate that our proposed approach surpasses previous methods, offering a significant advantage. Code is available at https://github.com/SenseLLM/FIM-SE.

Retrieval augmentation is a promising approach to handle long-context language modeling. However, the existing retrieval methods usually work with the chunked context, which is prone to inferior quality of semantic representation and incomplete retrieval of useful information. In this work, we propose a new method for the retrieval augmentation of long-context language modeling, called Landmark Embedding. Our method is characterized by threefold technical contributions. Firstly, we introduce a chunking-free architecture, which keeps the long context coherent such that high-quality embeddings can be generated for the fine-grained units within the context. Secondly, we present a position-aware objective function, which prioritizes the ultimate boundary for a consecutive span of information. By learning to discriminate such a special position, the useful information can be comprehensively retrieved for the query. Thirdly, we design a novel multi-stage learning algorithm, which makes the best use of readily available data and synthetic data for cost-effective training of the landmark embedding. In our experimental study, landmark embedding is able to substantially improve the performance for both LLaMA-2 and ChatGPT in a variety of long-context tasks; meanwhile, it also outperforms the existing retrieval methods with a notable advantage. Our model and source code will be made publicly available.

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GrowOVER: How Can LLMs Adapt to Growing Real-World Knowledge?
Dayoon Ko | Jinyoung Kim | Hahyeon Choi | Gunhee Kim

In the real world, knowledge is constantly evolving, which can render existing knowledge-based datasets outdated. This unreliability highlights the critical need for continuous updates to ensure both accuracy and relevance in knowledge-intensive tasks. To address this, we propose GrowOVER-QA and GrowOVER-Dialogue, dynamic open-domain QA and dialogue benchmarks that undergo a continuous cycle of updates, keeping pace with the rapid evolution of knowledge. Our research indicates that retrieval-augmented language models (RaLMs) struggle with knowledge that has not been trained on or recently updated. Consequently, we introduce a novel retrieval-interactive language model framework, where the language model evaluates and reflects on its answers for further re-retrieval. Our exhaustive experiments demonstrate that our training-free framework significantly improves upon existing methods, performing comparably to or even surpassing continuously trained language models.

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Attribute First, then Generate: Locally-attributable Grounded Text Generation
Aviv Slobodkin | Eran Hirsch | Arie Cattan | Tal Schuster | Ido Dagan

Recent efforts to address hallucinations in Large Language Models (LLMs) have focused on attributed text generation, which supplements generated texts with citations of supporting sources for post-generation fact-checking and corrections. Yet, these citations often point to entire documents or paragraphs, burdening users with extensive verification work. In this paper, we introduce a locally-attributable text generation approach, prioritizing concise attributions. Our method, named “Attribute First, then Generate“, breaks down the conventional end-to-end generation process into three intuitive steps: content selection, sentence planning, and sequential sentence generation. By initially identifying relevant source segments (“select first“) and then conditioning the generation process on them (“then generate“), we ensure these segments also act as the output’s fine-grained attributions (“select“ becomes “attribute“). Tested on Multi-document Summarization and Long-form Question-answering, our method not only yields more concise citations than the baselines but also maintains - and in some cases enhances - both generation quality and attribution accuracy. Furthermore, it significantly reduces the time required for fact verification by human assessors.

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T2S-GPT: Dynamic Vector Quantization for Autoregressive Sign Language Production from Text
Aoxiong Yin | Haoyuan Li | Kai Shen | Siliang Tang | Yueting Zhuang

In this work, we propose a two-stage sign language production (SLP) paradigm that first encodes sign language sequences into discrete codes and then autoregressively generates sign language from text based on the learned codebook. However, existing vector quantization (VQ) methods are fixed-length encodings, overlooking the uneven information density in sign language, which leads to under-encoding of important regions and over-encoding of unimportant regions. To address this issue, we propose a novel dynamic vector quantization (DVA-VAE) model that can dynamically adjust the encoding length based on the information density in sign language to achieve accurate and compact encoding. Then, a GPT-like model learns to generate code sequences and their corresponding durations from spoken language text. Extensive experiments conducted on the PHOENIX14T dataset demonstrate the effectiveness of our proposed method. To promote sign language research, we propose a new large German sign language dataset, PHOENIX-News, which contains 486 hours of sign language videos, audio, and transcription texts. Experimental analysis on PHOENIX-News shows that the performance of our model can be further improved by increasing the size of the training data. Our project homepage is https://t2sgpt-demo.yinaoxiong.cn.

Ocean science, which delves into the oceans that are reservoirs of life and biodiversity, is of great significance given that oceans cover over 70% of our planet’s surface. Recently, advances in Large Language Models (LLMs) have transformed the paradigm in science. Despite the success in other domains, current LLMs often fall short in catering to the needs of domain experts like oceanographers, and the potential of LLMs for ocean science is under-explored. The intrinsic reason may be the immense and intricate nature of ocean data as well as the necessity for higher granularity and richness in knowledge. To alleviate these issues, we introduce OceanGPT, the first-ever LLM in the ocean domain, which is expert in various ocean science tasks. We propose DoInstruct, a novel framework to automatically obtain a large volume of ocean domain instruction data, which generates instructions based on multi-agent collaboration. Additionally, we construct the first oceanography benchmark, OceanBench, to evaluate the capabilities of LLMs in the ocean domain. Though comprehensive experiments, OceanGPT not only shows a higher level of knowledge expertise for oceans science tasks but also gains preliminary embodied intelligence capabilities in ocean technology.

Recent developments in Language Models (LMs) have shown their effectiveness in NLP tasks, particularly in knowledge-intensive tasks.However, the mechanisms underlying knowledge storage and memory access within their parameters remain elusive. In this paper, we investigate whether a generative LM (e.g., GPT-2) is able to access its memory sequentially or randomly. Through carefully-designed synthetic tasks, covering the scenarios of full recitation, selective recitation and grounded question answering, we reveal that LMs manage to sequentially access their memory while encountering challenges in randomly accessing memorized content. We find that techniques including recitation and permutation improve the random memory access capability of LMs. Furthermore, by applying this intervention to realistic scenarios of open-domain question answering, we validate that enhancing random access by recitation leads to notable improvements in question answering. The code to reproduce our experiments can be found at https://github.com/sail-sg/lm-random-memory-access.

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BIPED: Pedagogically Informed Tutoring System for ESL Education
Soonwoo Kwon | Sojung Kim | Minju Park | Seunghyun Lee | Kyuseok Kim

Large Language Models (LLMs) have a great potential to serve as readily available and cost-efficient Conversational Intelligent Tutoring Systems (CITS) for teaching L2 learners of English. Existing CITS, however, are designed to teach only simple concepts or lack the pedagogical depth necessary to address diverse learning strategies. To develop a more pedagogically informed CITS capable of teaching complex concepts, we construct a BIlingual PEDagogically-informed Tutoring Dataset (BIPED) of one-on-one, human-to-human English tutoring interactions. Through post-hoc analysis of the tutoring interactions, we come up with a lexicon of dialogue acts (34 tutor acts and 9 student acts), which we use to further annotate the collected dataset. Based on a two-step framework of first predicting the appropriate tutor act then generating the corresponding response, we implemented two CITS models using GPT-4 and SOLAR-KO, respectively. We experimentally demonstrate that the implemented models not only replicate the style of human teachers but also employ diverse and contextually appropriate pedagogical strategies.

Facts extraction is pivotal for constructing knowledge graphs. Recently, the increasing demand for temporal facts in downstream tasks has led to the emergence of the task of temporal fact extraction. In this paper, we specifically address the extraction of temporal facts from natural language text. Previous studies fail to handle the challenge of establishing time-to-fact correspondences in complex sentences. To overcome this hurdle, we propose a timeline-based sentence decomposition strategy using large language models (LLMs) with in-context learning, ensuring a fine-grained understanding of the timeline associated with various facts. In addition, we evaluate the performance of LLMs for direct temporal fact extraction and get unsatisfactory results. To this end, we introduce TSDRE, a method that incorporates the decomposition capabilities of LLMs into the traditional fine-tuning of smaller pre-trained language models (PLMs). To support the evaluation, we construct ComplexTRED, a complex temporal fact extraction dataset. Our experiments show that TSDRE achieves state-of-the-art results on both HyperRED-Temporal and ComplexTRED datasets.

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Collaboration or Corporate Capture? Quantifying NLP’s Reliance on Industry Artifacts and Contributions
Will Aitken | Mohamed Abdalla | Karen Rudie | Catherine Stinson

Impressive performance of pre-trained models has garnered public attention and made news headlines in recent years. Almost always, these models are produced by or in collaboration with industry. Using them is critical for competing on natural language processing (NLP) benchmarks and correspondingly to stay relevant in NLP research. We surveyed 100 papers published at EMNLP 2022 to determine the degree to which researchers rely on industry models, other artifacts, and contributions to publish in prestigious NLP venues and found that the ratio of their citation is at least three times greater than what would be expected. Our work serves as a scaffold to enable future researchers to more accurately address whether: 1) Collaboration with industry is still collaboration in the absence of an alternative or 2) if NLP inquiry has been captured by the motivations and research direction of private corporations.

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Prompt Expansion for Adaptive Text-to-Image Generation
Siddhartha Datta | Alexander Ku | Deepak Ramachandran | Peter Anderson

Text-to-image generation models are powerful but difficult to use. Users craft specific prompts to get better images, though the images can be repetitive. This paper proposes the Prompt Expansion framework that helps users generate high-quality, diverse images with less effort. The Prompt Expansion model takes a text query as input and outputs a set of expanded text prompts that are optimized such that when passed to a text-to-image model, they generate a wider variety of appealing images. We conduct a human evaluation study that shows that images generated through Prompt Expansion are more aesthetically pleasing and diverse than those generated by baseline methods. Overall, this paper presents a novel and effective approach to improving the text-to-image generation experience.

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Progressively Modality Freezing for Multi-Modal Entity Alignment
Yani Huang | Xuefeng Zhang | Richong Zhang | Junfan Chen | Jaein Kim

Multi-Modal Entity Alignment aims to discover identical entities across heterogeneous knowledge graphs. While recent studies have delved into fusion paradigms to represent entities holistically, the elimination of features irrelevant to alignment and modal inconsistencies is overlooked, which are caused by inherent differences in multi-modal features. To address these challenges, we propose a novel strategy of progressive modality freezing, called PMF, that focuses on alignment-relevant features and enhances multi-modal feature fusion. Notably, our approach introduces a pioneering cross-modal association loss to foster modal consistency.Empirical evaluations across nine datasets confirm PMF’s superiority, demonstrating state-of-the-art performance and the rationale for freezing modalities. Our code is available at https://github.com/ninibymilk/PMF-MMEA.

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Llama2Vec: Unsupervised Adaptation of Large Language Models for Dense Retrieval
Chaofan Li | Zheng Liu | Shitao Xiao | Yingxia Shao | Defu Lian

Dense retrieval calls for discriminative embeddings to represent the semantic relationship between query and document. It may benefit from the using of large language models (LLMs), given LLMs’ strong capability on semantic understanding. However, the LLMs are learned by auto-regression, whose working mechanism is completely different from representing whole text as one discriminative embedding. Thus, it is imperative to study how to adapt LLMs properly so that they can be effectively initialized as the backbone encoder for dense retrieval. In this paper, we propose a novel approach, called Llama2Vec, which performs unsupervised adaptation of LLM for its dense retrieval application. Llama2Vec consists of two pretext tasks: EBAE (Embedding-Based Auto-Encoding) and EBAR (Embedding-Based Auto-Regression), where the LLM is prompted to reconstruct the input sentence and predict the next sentence based on its text embeddings. Llama2Vec is simple, lightweight, but highly effective. It is used to adapt LLaMA-2-7B on the Wikipedia corpus. With a moderate steps of adaptation, it substantially improves the model’s fine-tuned performances on a variety of dense retrieval benchmarks. Notably, it results in the new state-of-the-art performances on popular benchmarks, such as passage and document retrieval on MSMARCO, and zero-shot retrieval on BEIR. The model and source code will be made publicly available to facilitate the future research. Our model is available at https://github.com/FlagOpen/FlagEmbedding.

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Democratizing LLMs for Low-Resource Languages by Leveraging their English Dominant Abilities with Linguistically-Diverse Prompts
Xuan-Phi Nguyen | Mahani Aljunied | Shafiq Joty | Lidong Bing

Large language models (LLMs) are known to effectively perform tasks by simply observing few exemplars. However, in low-resource languages, obtaining such hand-picked exemplars can still be challenging, where unsupervised techniques may be necessary. Moreover, competent generative capabilities of LLMs are observed only in high-resource languages, while their performances among under-represented languages fall behind due to pre-training data imbalance. To elicit LLMs’ ability onto low-resource languages without any supervised data, we propose to assemble synthetic exemplars from a diverse set of high-resource languages to prompt the LLMs to translate from any language into English. These prompts are then used to create intra-lingual exemplars to perform tasks in the target languages. Our unsupervised prompting method performs on par with supervised few-shot learning in LLMs of different sizes for translations between English and 13 Indic and 21 African low-resource languages. We also show that fine-tuning a 7B model on data generated from our method helps it perform competitively with a 175B model. In non-English translation tasks, our method even outperforms supervised prompting by up to 3 chrF++ in many low-resource languages. When evaluated on zero-shot multilingual summarization, our method surpasses other English-pivoting baselines by up to 4 ROUGE-L and is also favored by GPT-4.

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Metaphor Understanding Challenge Dataset for LLMs
Xiaoyu Tong | Rochelle Choenni | Martha Lewis | Ekaterina Shutova

Metaphors in natural language are a reflection of fundamental cognitive processes such as analogical reasoning and categorisation, and are deeply rooted in everyday communication. Metaphor understanding is therefore an essential task for large language models (LLMs). We release the Metaphor Understanding Challenge Dataset (MUNCH), designed to evaluate the metaphor understanding capabilities of LLMs. The dataset provides over 10k paraphrases for sentences containing metaphor use, as well as 1.5k instances containing inapt paraphrases. The inapt paraphrases were carefully selected to serve as control to determine whether the model indeed performs full metaphor interpretation or rather resorts to lexical similarity. All apt and inapt paraphrases were manually annotated. The metaphorical sentences cover natural metaphor uses across 4 genres (academic, news, fiction, and conversation), and they exhibit different levels of novelty. Experiments with LLaMA and GPT-3.5 demonstrate that MUNCH presents a challenging task for LLMs. The dataset is freely accessible at https://github.com/xiaoyuisrain/metaphor-understanding-challenge.

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A Multi-Task Embedder For Retrieval Augmented LLMs
Peitian Zhang | Zheng Liu | Shitao Xiao | Zhicheng Dou | Jian-Yun Nie

LLMs confront inherent limitations in terms of its knowledge, memory, and action. The retrieval augmentation stands as a vital mechanism to address these limitations, which brings in useful information from external sources to augment the LLM. However, existing retrieval methods encounter two pressing issues. On one hand, the general retrievers are not properly optimized for retrieval augmentation hence exhibit limited effectiveness; on the other hand, the task-specific retrievers excel in the targeted retrieval augmentation scenario, while lack the versatility to handle diverse scenarios. In this work, we propose LLM-Embedder for the unified support of diverse retrieval augmentation scenarios. Our method presents three technical contributions. Firstly, we introduce a new reward formulation, namely rank-aware reward. It exploits the ranking position of the desired output among N sampled outputs from the LLM, which leads to fine-grained and robust computation of reward from the LLM’s feedback. Secondly, we design a novel distillation objective, called graded distillation. It incorporates both the absolute value and the relative order of the reward for more sufficient utilization of the LLM’s feedback. Thirdly, we systematically optimize the multi-task learning, which effectively unifies the multiple retrieval functionalities into one model. In our experiment, LLM-Embedder substantially improves the LLM’s performances in various downstream tasks, while introducing superior retrieval augmentation’s effect over both general and task-specifc retrievers. Our data, code, and model have been released at https://github.com/FlagOpen/FlagEmbedding.

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Language Models Don’t Learn the Physical Manifestation of Language
Bruce Lee | Jaehyuk Lim

We argue that language-only models don’t learn the physical manifestation of language. We present an empirical investigation of visual-auditory properties of language through a series of tasks, termed H-Test.These tasks highlight a fundamental gap between human linguistic understanding and the sensory-deprived linguistic understanding of LLMs. In support of our hypothesis, 1. deliberate reasoning (Chain-of-Thought), 2. few-shot examples, or 3. stronger LLM from the same model family (LLaMA 2 13B -> LLaMA 2 70B) has no significant effect on H-Test performance. We bring in the philosophical case of Mary, who learns about the world in a sensory-deprived environment as a useful conceptual framework to understand how language-only models learn about the world (Jackson, 1986). Our experiments show that some of the strongest proprietary LLMs stay near random chance baseline accuracy of 50%, highlighting the limitations of linguistic knowledge acquired in the absence of sensory experience. Our code and data are available at <github.com/brucewlee/h-test>.

Social media bot detection has always been an arms race between advancements in machine learning bot detectors and adversarial bot strategies to evade detection. In this work, we bring the arms race to the next level by investigating the opportunities and risks of state-of-the-art large language models (LLMs) in social bot detection. To investigate the opportunities, we design novel LLM-based bot detectors by proposing a mixture-of-heterogeneous-experts framework to divide and conquer diverse user information modalities. To illuminate the risks, we explore the possibility of LLM-guided manipulation of user textual and structured information to evade detection. Extensive experiments with three LLMs on two datasets demonstrate that instruction tuning on merely 1,000 annotated examples produces specialized LLMs that outperform state-of-the-art baselines by up to 9.1% on both datasets, while LLM-guided manipulation strategies could significantly bring down the performance of existing bot detectors by up to 29.6% and harm the calibration and reliability of bot detection systems.

The reflection capacity of Large Language Model (LLM) has garnered extensive attention. A post-hoc prompting strategy, e.g., reflexion and self-refine, refines LLM’s response based on self-evaluated or external feedback. However, recent research indicates without external feedback, LLM’s intrinsic reflection is unstable. Our investigation unveils that the key bottleneck is the quality of the self-evaluated feedback. We find LLMs often exhibit overconfidence or high randomness when self-evaluate, offering stubborn or inconsistent feedback, which causes poor reflection. To remedy this, we advocate Self-Contrast: It adaptively explores diverse solving perspectives tailored to the request, contrasts the differences, and summarizes these discrepancies into a checklist which could be used to re-examine and eliminate discrepancies. Our method endows LLM with diverse perspectives to alleviate stubborn biases. Moreover, their discrepancies indicate potential errors or inherent uncertainties that LLM often overlooks. Reflecting upon these can catalyze more accurate and stable reflection. Experiments conducted on a series of reasoning and translation tasks with different LLMs serve to underscore the effectiveness and generality of our strategy.

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Relying on the Unreliable: The Impact of Language Models’ Reluctance to Express Uncertainty
Kaitlyn Zhou | Jena Hwang | Xiang Ren | Maarten Sap

As natural language becomes the default interface for human-AI interaction, there is a need for LMs to appropriately communicate uncertainties in downstream applications. In this work, we investigate how LMs incorporate confidence in responses via natural language and how downstream users behave in response to LM-articulated uncertainties. We examine publicly deployed models and find that LMs are reluctant to express uncertainties when answering questions even when they produce incorrect responses. LMs can be explicitly prompted to express confidences, but tend to be overconfident, resulting in high error rates (an average of 47%) among confident responses. We test the risks of LM overconfidence by conducting human experiments and show that users rely heavily on LM generations, whether or not they are marked by certainty. Lastly, we investigate the preference-annotated datasets used in post training alignment and find that humans are biased against texts with uncertainty. Our work highlights new safety harms facing human-LM interactions and proposes design recommendations and mitigating strategies moving forward.

Although pre-training has become a prevalent approach for addressing various biomedical tasks, the current efficacy of pre-trained models is hindered by their reliance on a limited scope of medical sources. This limitation results in data scarcity during pre-training and restricts the range of applicable downstream tasks. In response to these challenges, we develop MedCSP, a new pre-training strategy designed to bridge the gap between multimodal medical sources. MedCSP employs modality-level aggregation to unify patient data within individual sources. Additionally, leveraging temporal information and diagnosis history, MedCSP effectively captures explicit and implicit correlations between patients across different sources. To evaluate the proposed strategy, we conduct comprehensive experiments, where the experiments are based on 6 modalities from 2 real-world medical data sources, and MedCSP is evaluated on 4 tasks against 19 baselines, marking an initial yet essential step towards cross-source modeling in the medical domain.

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When Good and Reproducible Results are a Giant with Feet of Clay: The Importance of Software Quality in NLP
Sara Papi | Marco Gaido | Andrea Pilzer | Matteo Negri

Despite its crucial role in research experiments, code correctness is often presumed solely based on the perceived quality of results. This assumption, however, comes with the risk of erroneous outcomes and, in turn, potentially misleading findings. To mitigate this risk, we posit that the current focus on reproducibility should go hand in hand with the emphasis on software quality. We support our arguments with a case study in which we identify and fix three bugs in widely used implementations of the state-of-the-art Conformer architecture. Through experiments on speech recognition and translation in various languages, we demonstrate that the presence of bugs does not prevent the achievement of good and reproducible results, which however can lead to incorrect conclusions that potentially misguide future research. As countermeasures, we release pangoliNN, a library dedicated to testing neural models, and propose a Code-quality Checklist, with the goal of promoting coding best practices and improving software quality within the NLP community.

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SBAAM! Eliminating Transcript Dependency in Automatic Subtitling
Marco Gaido | Sara Papi | Matteo Negri | Mauro Cettolo | Luisa Bentivogli

Subtitling plays a crucial role in enhancing the accessibility of audiovisual content and encompasses three primary subtasks: translating spoken dialogue, segmenting translations into concise textual units, and estimating timestamps that govern their on-screen duration. Past attempts to automate this process rely, to varying degrees, on automatic transcripts, employed diversely for the three subtasks. In response to the acknowledged limitations associated with this reliance on transcripts, recent research has shifted towards transcription-free solutions for translation and segmentation, leaving the direct generation of timestamps as uncharted territory. To fill this gap, we introduce the first direct model capable of producing automatic subtitles, entirely eliminating any dependence on intermediate transcripts also for timestamp prediction. Experimental results, backed by manual evaluation, showcase our solution’s new state-of-the-art performance across multiple language pairs and diverse conditions.

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StreamAtt: Direct Streaming Speech-to-Text Translation with Attention-based Audio History Selection
Sara Papi | Marco Gaido | Matteo Negri | Luisa Bentivogli

Streaming speech-to-text translation (StreamST) is the task of automatically translating speech while incrementally receiving an audio stream. Unlike simultaneous ST (SimulST), which deals with pre-segmented speech, StreamST faces the challenges of handling continuous and unbounded audio streams. This requires additional decisions about what to retain of the previous history, which is impractical to keep entirely due to latency and computational constraints. Despite the real-world demand for real-time ST, research on streaming translation remains limited, with existing works solely focusing on SimulST. To fill this gap, we introduce StreamAtt, the first StreamST policy, and propose StreamLAAL, the first StreamST latency metric designed to be comparable with existing metrics for SimulST. Extensive experiments across all 8 languages of MuST-C v1.0 show the effectiveness of StreamAtt compared to a naive streaming baseline and the related state-of-the-art SimulST policy, providing a first step in StreamST research.

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ARL2: Aligning Retrievers with Black-box Large Language Models via Self-guided Adaptive Relevance Labeling
LingXi Zhang | Yue Yu | Kuan Wang | Chao Zhang

Retrieval-augmented generation enhances large language models (LLMs) by incorporating relevant information from external knowledge sources. This enables LLMs to adapt to specific domains and mitigate hallucinations in knowledge-intensive tasks. However, existing retrievers are often misaligned with LLMs due to separate training processes and the inherent black-box nature of LLMs. To address this challenge, we propose ARL2, a retriever learning technique that harnesses LLMs as labelers. ARL2 leverages LLMs to annotate and score adaptive relevance evidence, enabling the retriever to learn from robust LLM supervision. Furthermore, ARL2 incorporates a self-training strategy to minimize the cost of API calls. Extensive experiments demonstrate the effectiveness of ARL2, achieving accuracy improvements of 5.4% on NQ and 4.6% on MMLU compared to the state-of-the-art methods. Additionally, ARL2 exhibits robust transfer learning capabilities and strong zero-shot generalization abilities.

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Crayon: Customized On-Device LLM via Instant Adapter Blending and Edge-Server Hybrid Inference
Jihwan Bang | Juntae Lee | Kyuhong Shim | Seunghan Yang | Simyung Chang

The customization of large language models (LLMs) for user-specified tasks gets important. However, maintaining all the customized LLMs on cloud servers incurs substantial memory and computational overheads, and uploading user data can also lead to privacy concerns. On-device LLMs can offer a promising solution by mitigating these issues. Yet, the performance of on-device LLMs is inherently constrained by the limitations of small-scaled models. To overcome these restrictions, we first propose Crayon, a novel approach for on-device LLM customization. Crayon begins by constructing a pool of diverse base adapters, and then we instantly blend them into a customized adapter without extra training. In addition, we develop a device-server hybrid inference strategy, which deftly allocates more demanding queries or non-customized tasks to a larger, more capable LLM on a server. This ensures optimal performance without sacrificing the benefits of on-device customization. We carefully craft a novel benchmark from multiple question-answer datasets, and show the efficacy of our method in the LLM customization.

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FLEUR: An Explainable Reference-Free Evaluation Metric for Image Captioning Using a Large Multimodal Model
Yebin Lee | Imseong Park | Myungjoo Kang

Most existing image captioning evaluation metrics focus on assigning a single numerical score to a caption by comparing it with reference captions. However, these methods do not provide an explanation for the assigned score. Moreover, reference captions are expensive to acquire. In this paper, we propose FLEUR, an explainable reference-free metric to introduce explainability into image captioning evaluation metrics. By leveraging a large multimodal model, FLEUR can evaluate the caption against the image without the need for reference captions, and provide the explanation for the assigned score. We introduce score smoothing to align as closely as possible with human judgment and to be robust to user-defined grading criteria. FLEUR achieves high correlations with human judgment across various image captioning evaluation benchmarks and reaches state-of-the-art results on Flickr8k-CF, COMPOSITE, and Pascal-50S within the domain of reference-free evaluation metrics. Our source code and results are publicly available at: https://github.com/Yebin46/FLEUR.

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MentalManip: A Dataset For Fine-grained Analysis of Mental Manipulation in Conversations
Yuxin Wang | Ivory Yang | Saeed Hassanpour | Soroush Vosoughi

Mental manipulation, a significant form of abuse in interpersonal conversations, presents a challenge to identify due to its context-dependent and often subtle nature. The detection of manipulative language is essential for protecting potential victims, yet the field of Natural Language Processing (NLP) currently faces a scarcity of resources and research on this topic. Our study addresses this gap by introducing a new dataset, named MentalManip, which consists of 4,000 annotated fictional dialogues. This dataset enables a comprehensive analysis of mental manipulation, pinpointing both the techniques utilized for manipulation and the vulnerabilities targeted in victims. Our research further explores the effectiveness of leading-edge models in recognizing manipulative dialogue and its components through a series of experiments with various configurations. The results demonstrate that these models inadequately identify and categorize manipulative content. Attempts to improve their performance by fine-tuning with existing datasets on mental health and toxicity have not overcome these limitations. We anticipate that MentalManip will stimulate further research, leading to progress in both understanding and mitigating the impact of mental manipulation in conversations.

Large Language Models (LLMs) have demonstrated great potential for assisting developers in their daily development. However, most research focuses on generating correct code, how to use LLMs to generate personalized code has seldom been investigated. To bridge this gap, we proposed MPCoder (Multi-user Personalized Code Generator) to generate personalized code for multiple users. To better learn coding style features, we utilize explicit coding style residual learning to capture the syntax code style standards and implicit style learning to capture the semantic code style conventions. We train a multi-user style adapter to better differentiate the implicit feature representations of different users through contrastive learning, ultimately enabling personalized code generation for multiple users. We further propose a novel evaluation metric for estimating similarities between codes of different coding styles. The experimental results show the effectiveness of our approach for this novel task.

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DataDreamer: A Tool for Synthetic Data Generation and Reproducible LLM Workflows
Ajay Patel | Colin Raffel | Chris Callison-Burch

Large language models (LLMs) have become a dominant and important tool for NLP researchers in a wide range of tasks. Today, many researchers use LLMs in synthetic data generation, task evaluation, fine-tuning, distillation, and other model-in-the-loop research workflows. However, challenges arise when using these models that stem from their scale, their closed source nature, and the lack of standardized tooling for these new and emerging workflows. The rapid rise to prominence of these models and these unique challenges has had immediate adverse impacts on open science and on the reproducibility of work that uses them. In this ACL 2024 theme track paper, we introduce DataDreamer, an open source Python library that allows researchers to write simple code to implement powerful LLM workflows. DataDreamer also helps researchers adhere to best practices that we propose to encourage open science and reproducibility. The library and documentation are available at: https://github.com/datadreamer-dev/DataDreamer.

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Understanding and Addressing the Under-Translation Problem from the Perspective of Decoding Objective
Chenze Shao | Fandong Meng | Jiali Zeng | Jie Zhou

Neural Machine Translation (NMT) has made remarkable progress over the past years. However, under-translation and over-translation remain two challenging problems in state-of-the-art NMT systems. In this work, we conduct an in-depth analysis on the underlying cause of under-translation in NMT, providing an explanation from the perspective of decoding objective. To optimize the beam search objective, the model tends to overlook words it is less confident about, leading to the under-translation phenomenon. Correspondingly, the model’s confidence in predicting the End Of Sentence (EOS) diminishes when under-translation occurs, serving as a mild penalty for under-translated candidates. Building upon this analysis, we propose employing the confidence of predicting EOS as a detector for under-translation, and strengthening the confidence-based penalty to penalize candidates with a high risk of under-translation.Experiments on both synthetic and real-world data show that our method can accurately detect and rectify under-translated outputs, with minor impact on other correct translations.

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Identifying while Learning for Document Event Causality Identification
Cheng Liu | Wei Xiang | Bang Wang

Event Causality Identification (ECI) aims to detect whether there exists a causal relation between two events in a document. Existing studies adopt a kind of *identifying after learning* paradigm, where events’ representations are first learned and then used for the identification. Furthermore, they mainly focus on the causality existence, but ignoring causal direction. In this paper, we take care of the causal direction and propose a new *identifying while learning* mode for the ECI task. We argue that a few causal relations can be easily identified with high confidence, and the directionality and structure of these identified causalities can be utilized to update events’ representations for boosting next round of causality identification. To this end, this paper designs an *iterative learning and identifying framework*: In each iteration, we construct an event causality graph, on which events’ causal structure representations are updated for boosting causal identification. Experiments on two public datasets show that our approach outperforms the state-of-the-art algorithms in both evaluations for causality existence identification and direction identification.

Recent advancements have seen Large Language Models (LLMs) and Large Multimodal Models (LMMs) surpassing general human capabilities in various tasks, approaching the proficiency level of human experts across multiple domains. With traditional benchmarks becoming less challenging for these models, new rigorous challenges are essential to gauge their advanced abilities. In this work, we present OlympiadBench, an Olympiad-level bilingual multimodal scientific benchmark, featuring 8,476 problems from Olympiad-level mathematics and physics competitions, including the Chinese college entrance exam. Each problem is detailed with expert-level annotations for step-by-step reasoning. Evaluating top-tier models on OlympiadBench, we implement a comprehensive assessment methodology to accurately evaluate model responses. Notably, the best-performing model, GPT-4V, attains an average score of 17.97% on OlympiadBench, with a mere 10.74% in physics, highlighting the benchmark rigor and the intricacy of physical reasoning. Our analysis orienting GPT-4V points out prevalent issues with hallucinations, knowledge omissions, and logical fallacies. We hope that our challenging benchmark can serve as a valuable resource for helping future AGI research endeavors. The data and evaluation code are available at https://github.com/OpenBMB/OlympiadBench

Taxonomy completion, enriching existing taxonomies by inserting new concepts as parents or attaching them as children, has gained significant interest. Previous approaches embed concepts as vectors in Euclidean space, which makes it difficult to model asymmetric relations in taxonomy. In addition, they introduce pseudo-leaves to convert attachment cases into insertion cases, leading to an incorrect bias in network learning dominated by numerous pseudo-leaves. Addressing these, our framework, TaxBox, leverages box containment and center closeness to design two specialized geometric scorers within the box embedding space. These scorers are tailored for insertion and attachment operations and can effectively capture intrinsic relationships between concepts by optimizing on a granular box constraint loss. We employ a dynamic ranking loss mechanism to balance the scores from these scorers, allowing adaptive adjustments of insertion and attachment scores. Experiments on four real-world datasets show that TaxBox significantly outperforms previous methods, yielding substantial improvements over prior methods in real-world datasets, with average performance boosts of 6.7%, 34.9%, and 51.4% in MRR, Hit@1, and Prec@1, respectively.

In this work, we introduce a semiparametric token-sequence co-supervision training method. It trains a language model by simultaneously leveraging supervision from the traditional next token prediction loss which is calculated over the parametric token embedding space and the next sequence prediction loss which is calculated over the nonparametric sequence embedding space. The nonparametric sequence embedding space is constructed by a separate language model tasked to condense an input text into a single representative embedding. Our experiments demonstrate that a model trained via both supervisions consistently surpasses models trained via each supervision independently. Analysis suggests that this co-supervision encourages a broader generalization capability across the model. Especially, the robustness of parametric token space which is established during the pretraining step tends to effectively enhance the stability of nonparametric sequence embedding space, a new space established by another language model.

The fine-tuning of Large Language Models (LLMs) specialized in code generation has seen notable advancements through the use of open-domain coding queries. Despite the successes, existing methodologies like Evol-Instruct encounter performance limitations, impeding further enhancements in code generation tasks. This paper examines the constraints of existing prompt evolution techniques and introduces a novel approach, Instruction Fusion (IF). IF innovatively combines two distinct prompts through a hybridization process, thereby enhancing the evolution of training prompts for code LLMs. Our experimental results reveal that the proposed novel method effectively addresses the shortcomings of prior methods, significantly improving the performance of Code LLMs across five code generation benchmarks, namely HumanEval, HumanEval+, MBPP, MBPP+ and MultiPL-E, which underscore the effectiveness of Instruction Fusion in advancing the capabilities of LLMs in code generation.

Despite remarkable advancements in emulating human-like behavior through Large Language Models (LLMs), current textual simulations do not adequately address the notion of time. To this end, we introduce TimeArena, a novel textual simulated environment that incorporates complex temporal dynamics and constraints that better reflect real-life planning scenarios. In TimeArena, agents are asked to complete multiple tasks as soon as possible, allowing for parallel processing to save time. We implement the dependency between actions, the time duration for each action, and the occupancy of the agent and the objects in the environment. TimeArena grounds to 30 real-world tasks in cooking, household activity, and laboratory work. We conduct extensive experiments with various LLMs using TimeArena. Our findings reveal that even the most powerful models, e.g., GPT-4, still lag behind humans in effective multitasking, underscoring the need for enhanced temporal awareness in the development of language agents.

Large language models (LLMs) have shown great capabilities in various tasks but also exhibited memorization of training data, raising tremendous privacy and copyright concerns. While prior works have studied memorization during pre-training, the exploration of memorization during fine-tuning is rather limited. Compared to pre-training, fine-tuning typically involves more sensitive data and diverse objectives, thus may bring distinct privacy risks and unique memorization behaviors. In this work, we conduct the first comprehensive analysis to explore language models’ (LMs) memorization during fine-tuning across tasks. Our studies with open-sourced and our own fine-tuned LMs across various tasks indicate that memorization presents a strong disparity among different fine-tuning tasks. We provide an intuitive explanation of this task disparity via sparse coding theory and unveil a strong correlation between memorization and attention score distribution.

New Intent Discovery (NID) aims at detecting known and previously undefined categories of user intent by utilizing limited labeled and massive unlabeled data. Most prior works often operate under the unrealistic assumption that the distribution of both familiar and new intent classes is uniform, overlooking the skewed and long-tailed distributions frequently encountered in real-world scenarios. To bridge the gap, our work introduces the imbalanced new intent discovery i-NID task, which seeks to identify familiar and novel intent categories within long-tailed distributions. A new benchmark baNID-Bench comprised of three datasets is created to simulate the real-world long-tail distributions. ImbaNID-Bench ranges from broad cross-domain to specific single-domain intent categories, providing a thorough representation of practical use cases. Besides, a robust baseline model ImbaNID is proposed to achieve cluster-friendly intent representations. It includes three stages: model pre-training, generation of reliable pseudo-labels, and robust representation learning that strengthens the model performance to handle the intricacies of real-world data distributions. Our extensive experiments on previous benchmarks and the newly established benchmark demonstrate the superior performance of ImbaNID in addressing the i-NID task, highlighting its potential as a powerful baseline for uncovering and categorizing user intents in imbalanced and long-tailed distributions.

The advent of Large Language Models (LLMs) has brought an unprecedented surge in machine-generated text (MGT) across diverse channels. This raises legitimate concerns about its potential misuse and societal implications. The need to identify and differentiate such content from genuine human-generated text is critical in combating disinformation, preserving the integrity of education and scientific fields, and maintaining trust in communication. In this work, we address this problem by introducing a new benchmark based on a multilingual, multi-domain and multi-generator corpus of MGTs — M4GT-Bench. The benchmark is compiled of three tasks: (1) mono-lingual and multi-lingual binary MGT detection; (2) multi-way detection where one need to identify, which particular model generated the text; and (3) mixed human-machine text detection, where a word boundary delimiting MGT from human-written content should be determined. On the developed benchmark, we have tested several MGT detection baselines and also conducted an evaluation of human performance. We see that obtaining good performance in MGT detection usually requires an access to the training data from the same domain and generators. The benchmark is available at https://github.com/mbzuai-nlp/M4GT-Bench.

Tuning language models for dialogue generation has been a prevalent paradigm for building capable dialogue agents. Yet, traditional tuning narrowly views dialogue generation as resembling other language generation tasks, ignoring the role disparities between two speakers and the multi-round interactive process that dialogues ought to be. Such a manner often leads to unsatisfactory chat consistency for the built agent. In this work, we emphasize the interactive, communicative nature of dialogue and argue that it is more feasible to model the speaker roles of agent and user separately, enabling the agent to adhere to its role consistently. With this in mind, we propose an efficient Multi-round Interactive Dialogue Tuning (Midi-Tuning) framework. It models the agent and user individually with two adapters built upon large language models. The adapters make use of respective utterances round by round in alternating order and they are tuned via a round-level memory caching mechanism. Extensive experiments demonstrate that, our framework performs superior to traditional fine-tuning and harbors the tremendous potential for improving dialogue consistency.

The effectiveness of large language models (LLMs) is often hindered by duplicated data in their extensive pre-training datasets. Current approaches primarily focus on detecting and removing duplicates, which risks the loss of valuable information and neglects the varying degrees of duplication. To address this, we propose a soft deduplication method that maintains dataset integrity while selectively reducing the sampling weight of data with high commonness. Central to our approach is the concept of “data commonness”, a metric we introduce to quantify the degree of duplication by measuring the occurrence probabilities of samples using an n-gram model. Empirical analysis shows that this method significantly improves training efficiency, achieving comparable perplexity scores with at least a 26% reduction in required training steps. Additionally, it enhances average few-shot downstream accuracy by 1.77% when trained for an equivalent duration. Importantly, this approach consistently improves performance, even on rigorously deduplicated datasets, indicating its potential to complement existing methods and become a standard pre-training process for LLMs.

Building machines with commonsense has been a longstanding challenge in NLP due to the reporting bias of commonsense rules and the exposure bias of rule-based commonsense reasoning. In contrast, humans convey and pass down commonsense implicitly through stories. This paper investigates the inherent commonsense ability of large language models (LLMs) expressed through storytelling. We systematically investigate and compare stories and rules for retrieving and leveraging commonsense in LLMs. Experimental results on 28 commonsense QA datasets show that stories outperform rules as the expression for retrieving commonsense from LLMs, exhibiting higher generation confidence and commonsense accuracy. Moreover, stories are the more effective commonsense expression for answering questions regarding daily events, while rules are more effective for scientific questions. This aligns with the reporting bias of commonsense in text corpora. We further show that the correctness and relevance of commonsense stories can be further improved via iterative self-supervised fine-tuning. These findings emphasize the importance of using appropriate language to express, retrieve, and leverage commonsense for LLMs, highlighting a promising direction for better exploiting their commonsense abilities.

While large language models (LLMs) have showcased remarkable prowess in various natural language processing tasks, their training costs are exorbitant. Consequently, a plethora of parameter-efficient fine-tuning methods have emerged to tailor large models for downstream tasks, including low-rank training. Recent approaches either amalgamate existing fine-tuning methods or dynamically adjust rank allocation. Nonetheless, these methods continue to grapple with issues like local optimization, inability to train with full rank and lack of focus on specific tasks. In this paper, we introduce an innovative parameter-efficient method for exploring optimal solutions within latent space. More specifically, we introduce a set of latent units designed to iteratively extract input representations from LLMs, continuously refining informative features that enhance downstream task performance. Due to the small and independent nature of the latent units in relation to input size, this significantly reduces training memory requirements. Additionally, we employ an asymmetric attention mechanism to facilitate bidirectional interaction between latent units and freezed LLM representations, thereby mitigating issues associated with non-full-rank training. Furthermore, we apply distillation over hidden states during the interaction, which guarantees a trimmed number of trainable parameters.Experimental results demonstrate that our approach achieves state-of-the-art performance on a range of natural language understanding, generation and reasoning tasks.

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CaMML: Context-Aware Multimodal Learner for Large Models
Yixin Chen | Shuai Zhang | Boran Han | Tong He | Bo Li

In this work, we introduce Context-Aware MultiModal Learner (CaMML), for tuning large multimodal models (LMMs). CaMML, a lightweight module, is crafted to seamlessly integrate multimodal contextual samples into large models, thereby empowering the model to derive knowledge from analogous, domain-specific, up-to-date information and make grounded inferences. Importantly, CaMML is highly scalable and can efficiently handle lengthy multimodal context examples owing to its hierarchical design. Based on CaMML, we have developed two multimodal models, CaMML-7B and CaMML-13B, that have shown exceptional performance across an array of benchmark datasets for multimodal tasks. Remarkably, CaMML-13B achieves the state-of-the-art performance on over ten widely recognized multimodal benchmark datasets, surpassing LLaVA-1.5 (13B) with a noticeable margin, without integration of any external resources. Moreover, we have conducted extensive ablative studies to inspect the inner workings of CaMML and performed qualitative analyses to showcase its effectiveness in handling real-world challenging cases. Code and models are available at: https://github.com/amazon-science/camml.

Understanding events in texts is a core objective of natural language understanding, which requires detecting event occurrences, extracting event arguments, and analyzing inter-event relationships. However, due to the annotation challenges brought by task complexity, a large-scale dataset covering the full process of event understanding has long been absent. In this paper, we introduce MAVEN-Arg, which augments MAVEN datasets with event argument annotations, making the first all-in-one dataset supporting event detection, event argument extraction (EAE), and event relation extraction. As an EAE benchmark, MAVEN-Arg offers three main advantages: (1) a comprehensive schema covering 162 event types and 612 argument roles, all with expert-written definitions and examples; (2) a large data scale, containing 98,591 events and 290,613 arguments obtained with laborious human annotation; (3) the exhaustive annotation supporting all task variants of EAE, which annotates both entity and non-entity event arguments in document level. Experiments indicate that MAVEN-Arg is quite challenging for both fine-tuned EAE models and proprietary large language models (LLMs). Furthermore, to demonstrate the benefits of an all-in-one dataset, we preliminarily explore a potential application, future event prediction, with LLMs. MAVEN-Arg and codes can be obtained from https://github.com/THU-KEG/MAVEN-Argument.

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NPHardEval: Dynamic Benchmark on Reasoning Ability of Large Language Models via Complexity Classes
Lizhou Fan | Wenyue Hua | Lingyao Li | Haoyang Ling | Yongfeng Zhang

Complex reasoning ability is one of the most important features of Large Language Models (LLMs). Numerous benchmarks have been established to assess the reasoning abilities of LLMs. However, they are inadequate in offering a rigorous evaluation and prone to the risk of overfitting, as these publicly accessible and static benchmarks allow models to potentially tailor their responses to specific benchmark metrics, thereby inflating their performance. Addressing these limitations, we introduce a new benchmark NPHardEval. It contains a broad spectrum of 900 algorithmic questions belonging up to the NP-Hard complexity class, offering a rigorous measure of the reasoning ability of LLMs utilizing computational complexity. Moreover, this benchmark is designed with a dynamic update mechanism, where the datapoints are refreshed on a monthly basis. Such regular updates play a crucial role in mitigating the risk of LLMs overfitting to the benchmark, promoting a more accurate and reliable assessment of their reasoning capabilities. The benchmark dataset and code of NPHardEval are available at https://github.com/casmlab/NPHardEval.

Text watermarking technology aims to tag and identify content produced by large language models (LLMs) to prevent misuse. In this study, we introduce the concept of cross-lingual consistency in text watermarking, which assesses the ability of text watermarks to maintain their effectiveness after being translated into other languages. Preliminary empirical results from two LLMs and three watermarking methods reveal that current text watermarking technologies lack consistency when texts are translated into various languages. Based on this observation, we propose a Cross-lingual Watermark Removal Attack (CWRA) to bypass watermarking by first obtaining a response from an LLM in a pivot language, which is then translated into the target language. CWRA can effectively remove watermarks, decreasing the AUCs to a random-guessing level without performance loss. Furthermore, we analyze two key factors that contribute to the cross-lingual consistency in text watermarking and propose X-SIR as a defense method against CWRA.

Realistic practice and tailored feedback are key processes for training peer counselors with clinical skills. However, existing mechanisms of providing feedback largely rely on human supervision. Peer counselors often lack mechanisms to receive detailed feedback from experienced mentors, making it difficult for them to support the large number of people with mental health issues who use peer counseling. Our work aims to leverage large language models to provide contextualized and multi-level feedback to empower peer counselors, especially novices, at scale. To achieve this, we co-design with a group of senior psychotherapy supervisors to develop a multi-level feedback taxonomy, and then construct a publicly available dataset with comprehensive feedback annotations of 400 emotional support conversations. We further design a self-improvement method on top of large language models to enhance the automatic generation of feedback. Via qualitative and quantitative evaluation with domain experts, we demonstrate that our method minimizes the risk of potentially harmful and low-quality feedback generation which is desirable in such high-stakes scenarios.

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In-context Mixing (ICM): Code-mixed Prompts for Multilingual LLMs
Bhavani Shankar | Preethi Jyothi | Pushpak Bhattacharyya

We introduce a simple and effective prompting technique called in-context mixing (ICM) for effective in-context learning (ICL) with multilingual large language models (MLLMs). With ICM, we modify the few-shot examples within ICL prompts to be intra-sententially code-mixed by randomly swapping content words in the target languages with their English translations. We observe that ICM prompts yield superior performance in NLP tasks such as disfluency correction, grammar error correction and text simplification that demand a close correspondence between the input and output sequences. Significant improvements are observed mainly for low-resource languages that are under-represented during the pretraining and finetuning of MLLMs. We present an extensive set of experiments to analyze when ICM is effective and what design choices contribute towards its effectiveness. ICM works consistently and significantly better than other prompting techniques across models of varying capacity such as mT0-XXL, BloomZ and GPT-4.

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Respond in my Language: Mitigating Language Inconsistency in Response Generation based on Large Language Models
Liang Zhang | Qin Jin | Haoyang Huang | Dongdong Zhang | Furu Wei

Large Language Models (LLMs) show strong instruction understanding ability across multiple languages. However, they are easily biased towards English in instruction tuning, and generate English responses even given non-English instructions. In this paper, we investigate the language inconsistent generation problem in monolingual instruction tuning. We find that instruction tuning in English increases the models’ preference for English responses. It attaches higher probabilities to English responses than to responses in the same language as the instruction. Based on the findings, we alleviate the language inconsistent generation problem by counteracting the model preference for English responses in both the training and inference stages. Specifically, we propose Pseudo-Inconsistent Penalization (PIP) which prevents the model from generating English responses when given non-English language prompts during training, and Prior Enhanced Decoding (PED) which improves the language-consistent prior by leveraging the untuned base language model. Experimental results show that our two methods significantly improve the language consistency of the model without requiring any multilingual data. Our code, data, and models will be released.

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Transferable Embedding Inversion Attack: Uncovering Privacy Risks in Text Embeddings without Model Queries
Yu-Hsiang Huang | Yuche Tsai | Hsiang Hsiao | Hong-Yi Lin | Shou-De Lin

This study investigates the privacy risks associated with text embeddings, focusing on the scenario where attackers cannot access the original embedding model. Contrary to previous research requiring direct model access, we explore a more realistic threat model by developing a transfer attack method. This approach uses a surrogate model to mimic the victim model’s behavior, allowing the attacker to infer sensitive information from text embeddings without direct access. Our experiments across various embedding models and a clinical dataset demonstrate that our transfer attack significantly outperforms traditional methods, revealing the potential privacy vulnerabilities in embedding technologies and emphasizing the need for enhanced security measures.

Recent strides in large language models (LLMs) have yielded remarkable performance, leveraging reinforcement learning from human feedback (RLHF) to significantly enhance generation and alignment capabilities. However, RLHF encounters numerous challenges, including the objective mismatch issue, leading to suboptimal performance in Natural Language Understanding (NLU) tasks.To address this limitation, we propose a novel Reinforcement Learning framework enhanced with Label-sensitive Reward (RLLR) to amplify the performance of LLMs in NLU tasks. By incorporating label-sensitive pairs into reinforcement learning, our method aims to adeptly capture nuanced label-sensitive semantic features during RL, thereby enhancing natural language understanding.Experiments conducted on five diverse foundation models across eight tasks showcase promising results. In comparison to Supervised Fine-tuning models (SFT), RLLR demonstrates an average performance improvement of 1.54%. Compared with RLHF models, the improvement averages at 0.69%. These results reveal the effectiveness of our method for LLMs in NLU tasks.

This study investigates the behaviors of Large Language Models (LLMs) when faced with conflicting prompts versus their internal memory. This will not only help to understand LLMs’ decision mechanism but also benefit real-world applications, such as retrieval-augmented generation (RAG).Drawing on cognitive theory, we target the first scenario of decision-making styles where there is no superiority in the conflict and categorize LLMs’ preference into dependent, intuitive, and rational/irrational styles.Another scenario of factual robustness considers the correctness of prompt and memory in knowledge-intensive tasks, which can also distinguish if LLMs behave rationally or irrationally in the first scenario.To quantify them, we establish a complete benchmarking framework including a dataset, a robustness evaluation pipeline, and corresponding metrics. Extensive experiments with seven LLMs reveal their varying behaviors. And, with role play intervention, we can change the styles, but different models present distinct adaptivity and upper-bound. One of our key takeaways is to optimize models or the prompts according to the identified style. For instance, RAG models with high role play adaptability may dynamically adjust the interventions according to the quality of retrieval results — being dependent to better leverage informative context; and, being intuitive when external prompt is noisy.

Self-attention and position embedding are two crucial modules in transformer-based Large Language Models (LLMs). However, the potential relationship between them is far from well studied, especially for long context window extending. In fact, anomalous behaviors that hinder long context extrapolation exist between Rotary Position Embedding (RoPE) and vanilla self-attention.Incorrect initial angles between Q and K can cause misestimation in modeling rotary position embedding of the closest tokens.To address this issue, we propose Collinear Constrained Attention mechanism, namely CoCA. Specifically, we enforce a collinear constraint between Q and K to seamlessly integrate RoPE and self-attention.While only adding minimal computational and spatial complexity, this integration significantly enhances long context window extrapolation ability. We provide an optimized implementation, making it a drop-in replacement for any existing transformer-based models.Extensive experiments demonstrate that CoCA excels in extending context windows. A CoCA-based GPT model, trained with a context length of 512, can extend the context window up to 32K (60×) without any fine-tuning.Additionally, incorporating CoCA into LLaMA-7B achieves extrapolation up to 32K within a training length of only 2K.Our code is publicly available at: https://github.com/codefuse-ai/Collinear-Constrained-Attention

Text simplification aims to make technical texts more accessible to laypeople but often results in deletion of information and vagueness. This work proposes InfoLossQA, a framework to characterize and recover simplification-induced information loss in form of question-and-answer (QA) pairs. Building on the theory of Questions Under Discussion, the QA pairs are designed to help readers deepen their knowledge of a text. First, we collect a dataset of 1,000 linguist-curated QA pairs derived from 104 LLM simplifications of English medical study abstracts. Our analyses of this data reveal that information loss occurs frequently, and that the QA pairs give a high-level overview of what information was lost. Second, we devise two methods for this task: end-to-end prompting of open-source and commercial language models, and a natural language inference pipeline. With a novel evaluation framework considering the correctness of QA pairs and their linguistic suitability, our expert evaluation reveals that models struggle to reliably identify information loss and applying similar standards as humans at what constitutes information loss.

With the advancement of language models (LMs), their exposure to private data is increasingly inevitable, and their deployment (especially for smaller ones) on personal devices, such as PCs and smartphones, has become a prevailing trend. In contexts laden with user information, enabling models to both safeguard user privacy and execute commands efficiently emerges as an essential research imperative. In this paper, we propose CoGenesis, a collaborative generation framework integrating large (hosted on cloud infrastructure) and small models (deployed on local devices) to address privacy concerns logically. Initially, we design a pipeline to create personalized writing instruction datasets enriched with extensive context details as the testbed of this research issue. Subsequently, we introduce two variants of CoGenesis based on sketch and logits respectively. Our experimental findings, based on our synthesized dataset and two additional open-source datasets, indicate that: 1) Large-scale models perform well when provided with user context but struggle in the absence of such context. 2) While specialized smaller models fine-tuned on the synthetic dataset show promise, they still lag behind their larger counterparts. 3) Our CoGenesis framework, utilizing mixed-scale models, showcases competitive performance, providing a feasible solution to privacy issues.

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DAPR: A Benchmark on Document-Aware Passage Retrieval
Kexin Wang | Nils Reimers | Iryna Gurevych

The work of neural retrieval so far focuses on ranking short texts and is challenged with long documents. There are many cases where the users want to find a relevant passage within a long document from a huge corpus, e.g. Wikipedia articles, research papers, etc. We propose and name this task Document-Aware Passage Retrieval (DAPR). While analyzing the errors of the State-of-The-Art (SoTA) passage retrievers, we find the major errors (53.5%) are due to missing document context. This drives us to build a benchmark for this task including multiple datasets from heterogeneous domains. In the experiments, we extend the SoTA passage retrievers with document context via (1) hybrid retrieval with BM25 and (2) contextualized passage representations, which inform the passage representation with document context. We find despite that hybrid retrieval performs the strongest on the mixture of the easy and the hard queries, it completely fails on the hard queries that require document-context understanding. On the other hand, contextualized passage representations (e.g. prepending document titles) achieve good improvement on these hard queries, but overall they also perform rather poorly. Our created benchmark enables future research on developing and comparing retrieval systems for the new task. The code and the data are available.

Multiple-Choice Questions (MCQs) constitute a critical area of research in the study of Large Language Models (LLMs). Previous works have investigated the selection bias problem in MCQs within few-shot scenarios, in which the LLM’s performance may be influenced by the presentation of answer choices, leaving the selection bias during Supervised Fine-Tuning (SFT) unexplored. In this paper, we reveal that selection bias persists in the SFT phase , primarily due to the LLM’s inadequate Multiple Choice Symbol Binding (MCSB) ability. This limitation implies that the model struggles to associate the answer options with their corresponding symbols (e.g., A/B/C/D) effectively. To enhance the model’s MCSB capability, we first incorporate option contents into the loss function and subsequently adjust the weights of the option symbols and contents, guiding the model to understand the option content of the current symbol. Based on this, we introduce an efficient SFT algorithm for MCQs, termed Point-wise Intelligent Feedback (PIF). PIF constructs negative instances by randomly combin- ing the incorrect option contents with all candidate symbols, and proposes a point-wise loss to provide feedback on these negative samples into LLMs. Our experimental results demonstrate that PIF significantly reduces the model’s selection bias by improving its MCSB capability. Remarkably, PIF exhibits a substantial enhancement in the accuracy for MCQs.

Knowledge graphs (KGs) play a pivotal role in knowledge-intensive tasks across specialized domains, where the acquisition of precise and dependable knowledge is crucial. However, existing KG construction methods heavily rely on human intervention to attain qualified KGs, which severely hinders the practical applicability in real-world scenarios. To address this challenge, we propose a general KG construction framework, named **SAC-KG**, to exploit large language models (LLMs) as **S**killed **A**utomatic **C**onstructors for domain **K**nowledge **G**raph. SAC-KG effectively involves LLMs as domain experts to generate specialized and precise multi-level KGs. Specifically, SAC-KG consists of three components: Generator, Verifier, and Pruner. For a given entity, Generator produces its relations and tails from raw domain corpora, to construct a specialized single-level KG. Verifier and Pruner then work together to ensure precision by correcting generation errors and determining whether newly produced tails require further iteration for the next-level KG. Experiments demonstrate that SAC-KG automatically constructs a domain KG at the scale of over one million nodes and achieves a precision of 89.32%, leading to a superior performance with over 20% increase in precision rate compared to existing state-of-the-art methods for the KG construction task.

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Uncertainty-Guided Modal Rebalance for Hateful Memes Detection
Chuanpeng Yang | Yaxin Liu | Fuqing Zhu | Jizhong Han | Songlin Hu

Hateful memes detection is a challenging multimodal understanding task that requires comprehensive learning of vision, language, and cross-modal interactions. Previous research has focused on developing effective fusion strategies for integrating hate information from different modalities. However, these methods excessively rely on cross-modal fusion features, ignoring the modality uncertainty caused by the contribution degree of each modality to hate sentiment and the modality imbalance caused by the dominant modality suppressing the optimization of another modality. To this end, this paper proposes an Uncertainty-guided Modal Rebalance (UMR) framework for hateful memes detection. The uncertainty of each meme is explicitly formulated by designing stochastic representation drawn from a Gaussian distribution for aggregating cross-modal features with unimodal features adaptively. The modality imbalance is alleviated by improving cosine loss from the perspectives of inter-modal feature and weight vectors constraints. In this way, the suppressed unimodal representation ability in multimodal models would be unleashed, while the learning of modality contribution would be further promoted. Extensive experimental results demonstrate that the proposed UMR produces the state-of-the-art performance on four widely-used datasets.

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Missci: Reconstructing Fallacies in Misrepresented Science
Max Glockner | Yufang Hou | Preslav Nakov | Iryna Gurevych

Health-related misinformation on social networks can lead to poor decision-making and real-world dangers. Such misinformation often misrepresents scientific publications and cites them as “proof” to gain perceived credibility. To effectively counter such claims automatically, a system must explain how the claim was falsely derived from the cited publication. Current methods for automated fact-checking or fallacy detection neglect to assess the (mis)used evidence in relation to misinformation claims, which is required to detect the mismatch between them. To address this gap, we introduce Missci, a novel argumentation theoretical model for fallacious reasoning together with a new dataset for real-world misinformation detection that misrepresents biomedical publications. Unlike previous fallacy detection datasets, Missci (i) focuses on implicit fallacies between the relevant content of the cited publication and the inaccurate claim, and (ii) requires models to verbalize the fallacious reasoning in addition to classifying it. We present Missci as a dataset to test the critical reasoning abilities of large language models (LLMs), that are required to reconstruct real-world fallacious arguments, in a zero-shot setting. We evaluate two representative LLMs and the impact of different levels of detail about the fallacy classes provided to the LLM via prompts. Our experiments and human evaluation show promising results for GPT 4, while also demonstrating the difficulty of this task.

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Uncovering the Full Potential of Visual Grounding Methods in VQA
Daniel Reich | Tanja Schultz

Visual Grounding (VG) methods in Visual Question Answering (VQA) attempt to improve VQA performance by strengthening a model’s reliance on question-relevant visual information. The presence of such relevant information in the visual input is typically assumed in training and testing. This assumption, however, is inherently flawed when dealing with imperfect image representations common in large-scale VQA, where the information carried by visual features frequently deviates from expected ground-truth contents. As a result, training and testing of VG-methods is performed with largely inaccurate data, which obstructs proper assessment of their potential benefits.In this study, we demonstrate that current evaluation schemes for VG-methods are problematic due to the flawed assumption of availability of relevant visual information. Our experiments show that these methods can be much more effective when evaluation conditions are corrected. Code is provided.

The integration of large language models (LLMs) and search engines represents a significant evolution in knowledge acquisition methodologies. However, determining the knowledge that an LLM already possesses and the knowledge that requires the help of a search engine remains an unresolved issue. Most existing methods solve this problem through the results of preliminary answers or reasoning done by the LLM itself, but this incurs excessively high computational costs. This paper introduces a novel collaborative approach, namely SlimPLM, that detects missing knowledge in LLMs with a slim proxy model, to enhance the LLM’s knowledge acquisition process. We employ a proxy model which has far fewer parameters, and take its answers as heuristic answers. Heuristic answers are then utilized to predict the knowledge required to answer the user question, as well as the known and unknown knowledge within the LLM. We only conduct retrieval for the missing knowledge in questions that the LLM does not know. Extensive experimental results on five datasets with two LLMs demonstrate a notable improvement in the end-to-end performance of LLMs in question-answering tasks, achieving or surpassing current state-of-the-art models with lower LLM inference costs.

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Favi-Score: A Measure for Favoritism in Automated Preference Ratings for Generative AI Evaluation
Pius Von Däniken | Jan Deriu | Don Tuggener | Mark Cieliebak

Generative AI systems have become ubiquitous for all kinds of modalities, which makes the issue of the evaluation of such models more pressing. One popular approach is preference ratings, where the generated outputs of different systems are shown to evaluators who choose their preferences. In recent years the field shifted towards the development of automated (trained) metrics to assess generated outputs, which can be used to create preference ratings automatically. In this work, we investigate the evaluation of the metrics themselves, which currently rely on measuring the correlation to human judgments or computing sign accuracy scores. These measures only assess how well the metric agrees with the human ratings. However, our research shows that this does not tell the whole story. Most metrics exhibit a disagreement with human system assessments which is often skewed in favor of particular text generation systems, exposing a degree of favoritism in automated metrics. This paper introduces a formal definition of favoritism in preference metrics, and derives the Favi-Score, which measures this phenomenon. In particular we show that favoritism is strongly related to errors in final system rankings. Thus, we propose that preference-based metrics ought to be evaluated on both sign accuracy scores and favoritism.

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LLM-based Rewriting of Inappropriate Argumentation using Reinforcement Learning from Machine Feedback
Timon Ziegenbein | Gabriella Skitalinskaya | Alireza Bayat Makou | Henning Wachsmuth

Ensuring that online discussions are civil and productive is a major challenge for social media platforms. Such platforms usually rely both on users and on automated detection tools to flag inappropriate arguments of other users, which moderators then review. However, this kind of post-hoc moderation is expensive and time-consuming, and moderators are often overwhelmed by the amount and severity of flagged content. Instead, a promising alternative is to prevent negative behavior during content creation. This paper studies how inappropriate language in arguments can be computationally mitigated. We propose a reinforcement learning-based rewriting approach that balances content preservation and appropriateness based on existing classifiers, prompting an instruction-finetuned large language model (LLM) as our initial policy. Unlike related style transfer tasks, rewriting inappropriate arguments allows deleting and adding content permanently. It is therefore tackled on document level rather than sentence level. We evaluate different weighting schemes for the reward function in both absolute and relative human assessment studies. Systematic experiments on non-parallel data provide evidence that our approach can mitigate the inappropriateness of arguments while largely preserving their content. It significantly outperforms competitive baselines, including few-shot learning, prompting, and humans.

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Graph Language Models
Moritz Plenz | Anette Frank

While Language Models (LMs) are the workhorses of NLP, their interplay with structured knowledge graphs (KGs) is still actively researched. Current methods for encoding such graphs typically either (i) linearize them for embedding with LMs – which underutilize structural information, or (ii) use Graph Neural Networks (GNNs) to preserve the graph structure – but GNNs cannot represent text features as well as pretrained LMs. In our work we introduce a novel LM type, the Graph Language Model (GLM), that integrates the strengths of both approaches and mitigates their weaknesses. The GLM parameters are initialized from a pretrained LM to enhance understanding of individual graph concepts and triplets. Simultaneously, we design the GLM’s architecture to incorporate graph biases, thereby promoting effective knowledge distribution within the graph. This enables GLMs to process graphs, texts, and interleaved inputs of both. Empirical evaluations on relation classification tasks show that GLM embeddings surpass both LM- and GNN-based baselines in supervised and zero-shot setting, demonstrating their versatility.

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Analyzing Semantic Change through Lexical Replacements
Francesco Periti | Pierluigi Cassotti | Haim Dubossarsky | Nina Tahmasebi

Modern language models are capable of contextualizing words based on their surrounding context. However, this capability is often compromised due to semantic change that leads to words being used in new, unexpected contexts not encountered during pre-training. In this paper, we model semantic change by studying the effect of unexpected contexts introduced by lexical replacements. We propose a replacement schema where a target word is substituted with lexical replacements of varying relatedness, thus simulating different kinds of semantic change. Furthermore, we leverage the replacement schema as a basis for a novel interpretable model for semantic change. We are also the first to evaluate the use of LLaMa for semantic change detection.

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Exploiting Intrinsic Multilateral Logical Rules for Weakly Supervised Natural Language Video Localization
Zhe Xu | Kun Wei | Xu Yang | Cheng Deng

Weakly supervised natural language video localization (WS-NLVL) aims to retrieve the moment corresponding to a language query in a video with only video-language pairs utilized during training. Despite great success, existing WS-NLVL methods seldomly consider the complex temporal relations enclosing the language query (e.g., between the language query and sub-queries decomposed from it or its synonymous query), yielding illogical predictions. In this paper, we propose a novel plug-and-play method, Intrinsic Multilateral Logical Rules, namely IMLR, to exploit intrinsic temporal relations and logical rules for WS-NLVL. Specifically, we formalize queries derived from the original language query as the nodes of a directed graph, i.e., intrinsic temporal relation graph (ITRG), and the temporal relations between them as the edges. Instead of directly prompting a pre-trained language model, a relation-guided prompting method is introduced to generate ITRG in a hierarchical manner. We customize four types of multilateral temporal logical rules (i.e., identity, inclusion, synchronization, and succession) from ITRG and utilize them to train our model. Experiments demonstrate the effectiveness and superiority of our method on the Charades-STA and ActivityNet Captions datasets.

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Interpretability of Language Models via Task Spaces
Lucas Weber | Jaap Jumelet | Elia Bruni | Dieuwke Hupkes

The usual way to interpret language models (LMs) is to test their performance on different benchmarks and subsequently infer their internal processes.In this paper, we present an alternative approach, concentrating on the _quality_ of LM processing, with a focus on their language abilities.To this end, we construct ‘linguistic task spaces’ – representations of an LM’s language conceptualisation – that shed light on the connections LMs draw between language phenomena.Task spaces are based on the interactions of the learning signals from different linguistic phenomena, which we assess via a method we call ‘similarity probing’.To disentangle the learning signals of linguistic phenomena, we further introduce a method called ‘fine-tuning via gradient differentials’ (FTGD).We apply our methods to language models of three different scales and find that larger models generalise better to overarching general concepts for linguistic tasks, making better use of their shared structure. Further, the distributedness of linguistic processing increases with pre-training through increased parameter sharing between related linguistic tasks. The overall generalisation patterns are mostly stable throughout training and not marked by incisive stages, potentially explaining the lack of successful curriculum strategies for LMs.

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Using Synchronic Definitions and Semantic Relations to Classify Semantic Change Types
Pierluigi Cassotti | Stefano De Pascale | Nina Tahmasebi

There is abundant evidence of the fact that the way words change their meaning can be classified in different types of change, highlighting the relationship between the old and new meanings (among which generalisation, specialisation and co-hyponymy transfer).In this paper, we present a way of detecting these types of change by constructing a model that leverages information both from synchronic lexical relations and definitions of word meanings. Specifically, we use synset definitions and hierarchy information from WordNet and test it on a digitized version of Blank’s (1997) dataset of semantic change types. Finally, we show how the sense relationships can improve models for both approximation of human judgments of semantic relatedness as well as binary Lexical Semantic Change Detection.

Large Language Models (LLMs) tend to be unreliable on fact-based answers.To address this problem, NLP researchers have proposed a range of techniques to estimate LLM’s confidence over facts. However, due to the lack of a systematic comparison, it is not clear how the different methods compare to one other.To fill this gap, we present a rigorous survey and empirical comparison of estimators of factual confidence.We define an experimental framework allowing for fair comparison, covering both fact-verification and QA. Our experiments across a series of LLMs indicate that trained hidden-state probes provide the most reliable confidence estimates; albeit at the expense of requiring access to weights and supervision data. We also conduct a deeper assessment of the methods, in which we measure the consistency of model behavior under meaning-preserving variations in the input. We find that the factual confidence of LLMs is often unstable across semantically equivalent inputs, suggesting there is much room for improvement for the stability of models’ parametric knowledge.

The advancement of large language models (LLMs) has significantly propelled the field of code generation. Previous work integrated reinforcement learning (RL) with compiler feedback for exploring the output space of LLMs to enhance code generation quality. However, the lengthy code generated by LLMs in response to complex human requirements makes RL exploration a challenge. Also, since the unit tests may not cover the complicated code, optimizing LLMs by using these unexecuted code snippets is ineffective. To tackle these challenges, we introduce StepCoder, a novel RL framework for code generation, consisting of two main components: CCCS addresses the exploration challenge by breaking the long sequences code generation task into a Curriculum of Code Completion Subtasks, while FGO only optimizes the model by masking the unexecuted code segments to provide Fine-Grained Optimization. In addition, we furthermore construct the APPS+ dataset for RL training, which is manually verified to ensure the correctness of unit tests. Experimental results show that our method improves the ability to explore the output space and outperforms state-of-the-art approaches in corresponding benchmarks. The code and dataset will be made available upon publication.

Contemporary practices in instruction tuning often hinge on enlarging data scaling without a clear strategy for ensuring data quality, inadvertently introducing noise that may compromise model performance. To address this challenge, we introduce Nuggets, a novel and efficient methodology that leverages one-shot learning to discern and select high-quality instruction data from extensive datasets. Nuggets assesses the potential of individual instruction examples to act as effective one-shot learning instances, thereby identifying those that can significantly improve performance across diverse tasks. Nuggets utilizes a scoring system based on the impact of candidate examples on the perplexity of a diverse anchor set, facilitating the selection of the most advantageous data for instruction tuning. Through rigorous evaluations on two benchmarks, namely MT-Bench and Alpaca-Eval, our study illustrates that instruction tuning with the top 1% of examples curated by Nuggets substantially outperforms conventional methods employing the entire dataset.

Large language models are meticulously aligned to be both helpful and harmless. However, recent research points to a potential overkill which means models may refuse to answer benign queries. In this paper, we investigate the factors for overkill by exploring how models handle and determine the safety of queries. Our findings reveal the presence of shortcuts within models, leading to excessive attention to harmful words like ‘kill’ and prompts emphasizing safety will exacerbate overkill. Based on these insights, we introduce Self-Contrastive Decoding (Self-CD), a training-free and model-agnostic strategy, to alleviate this phenomenon. We first extract such excessive attention by amplifying the difference in the model’s output distributions when responding to system prompts that either include or omit an emphasis on safety. Then we determine the final next-token predictions by downplaying the excessive attention via contrastive decoding. Empirical results have indicated that our method has achieved an average reduction of the refusal rate by 20 % while having almost no impact on safety.

Prompting language models to provide step-by-step answers (e.g., “Chain-of-Thought”) is the prominent approach for complex reasoning tasks, where more accurate reasoning chains typically improve downstream task performance. Recent literature discusses automatic methods to verify reasoning to evaluate and improve their correctness. However, no fine-grained step-level datasets are available to enable thorough evaluation of such verification methods, hindering progress in this direction. We introduce REVEAL: Reasoning Verification Evaluation, a dataset to benchmark automatic verifiers of complex Chain-of-Thought reasoning in open-domain question-answering settings. REVEAL includes comprehensive labels for the relevance, attribution to evidence passages, and logical correctness of each reasoning step in a language model’s answer, across a variety of datasets and state-of-the-art language models. Evaluation on REVEAL shows that verifiers struggle at verifying reasoning chains - in particular, verifying logical correctness and detecting contradictions. Available at https://reveal-dataset.github.io/ .

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Re3: A Holistic Framework and Dataset for Modeling Collaborative Document Revision
Qian Ruan | Ilia Kuznetsov | Iryna Gurevych

Collaborative review and revision of textual documents is the core of knowledge work and a promising target for empirical analysis and NLP assistance. Yet, a holistic framework that would allow modeling complex relationships between document revisions, reviews and author responses is lacking. To address this gap, we introduce Re3, a framework for joint analysis of collaborative document revision. We instantiate this framework in the scholarly domain, and present Re3-Sci, a large corpus of aligned scientific paper revisions manually labeled according to their action and intent, and supplemented with the respective peer reviews and human-written edit summaries. We use the new data to provide first empirical insights into collaborative document revision in the academic domain, and to assess the capabilities of state-of-the-art LLMs at automating edit analysis and facilitating text-based collaboration. We make our annotation environment and protocols, the resulting data and experimental code publicly available.

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NextLevelBERT: Masked Language Modeling with Higher-Level Representations for Long Documents
Tamara Czinczoll | Christoph Hönes | Maximilian Schall | Gerard De Melo

While (large) language models have significantly improved over the last years, they still struggle to sensibly process long sequences found, e.g., in books, due to the quadratic scaling of the underlying attention mechanism. To address this, we propose NextLevelBERT, a Masked Language Model operating not on tokens, but on higher-level semantic representations in the form of text embeddings. We pretrain NextLevelBERT to predict the vector representation of entire masked text chunks and evaluate the effectiveness of the resulting document vectors on three types of tasks: 1) Semantic Textual Similarity via zero-shot document embeddings, 2) Long document classification, 3) Multiple-choice question answering. We find that next-level Masked Language Modeling is an effective technique to tackle long-document use cases and can outperform much larger embedding models as long as the required level of detail of semantic information is not too fine. Our models and code are publicly available online.

The ability to follow instructions is crucial for Large Language Models (LLMs) to handle various real-world applications. Existing benchmarks primarily focus on evaluating pure response quality, rather than assessing whether the response follows constraints stated in the instruction. To fill this research gap, in this paper, we propose FollowBench, a Multi-level Fine-grained Constraints Following Benchmark for LLMs. FollowBench comprehensively includes five different types (i.e., Content, Situation, Style, Format, and Example) of fine-grained constraints. To enable a precise constraint following estimation on diverse difficulties, we introduce a Multi-level mechanism that incrementally adds a single constraint to the initial instruction at each increased level. To assess whether LLMs’ outputs have satisfied every individual constraint, we propose to prompt strong LLMs with constraint-evolution paths to handle challenging open-ended instructions. By evaluating 13 closed-source and open-source popular LLMs on FollowBench, we highlight the weaknesses of LLMs in instruction following and point towards potential avenues for future work. The data and code are publicly available at https://github.com/YJiangcm/FollowBench.

Knowledge editing techniques, aiming to efficiently modify a minor proportion of knowledge in large language models (LLMs) without negatively impacting performance across other inputs, have garnered widespread attention. However, existing methods predominantly rely on memorizing the updated knowledge, impeding LLMs from effectively combining the new knowledge with their inherent knowledge when answering questions. To this end, we propose a Learning to Edit (LTE) framework, focusing on teaching LLMs to apply updated knowledge into input questions, inspired by the philosophy of “Teach a man to fish.” LTE features a two-phase process: (i) the Alignment Phase, which fine-tunes LLMs on a meticulously curated parallel dataset to make reliable, in-scope edits while preserving out-of-scope information and linguistic proficiency; and (ii) the Inference Phase, which employs a retrieval-based mechanism for real-time and mass knowledge editing. By comparing our approach with seven advanced baselines across four popular knowledge editing benchmarks and two LLM architectures, we demonstrate LTE’s superiority in knowledge editing performance, robustness in both batch and sequential editing, minimal interference on general tasks, and rapid editing speeds. The data and code are publicly available at https://github.com/YJiangcm/LTE.

Code Large Language Models (Code LLMs) have demonstrated outstanding performance in code-related tasks. Various instruction finetuning approaches have been proposed to boost the code generation performance of pre-trained Code LLMs. In this paper, we introduce a diverse instruction model DolphCoder with self-evaluating for code generation. It learns diverse instruction targets and combines a code evaluation objective to enhance its code generation ability. Our model achieves superior performance on the HumanEval and MBPP benchmarks, demonstrating new insights for future code instruction tuning work. Our key findings are: (1) Augmenting more diverse responses with more distinct reasoning paths increases the code capability of LLMs. (2) Improving one’s ability to evaluate the correctness of code also enhances their ability to create it.

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When Only Time Will Tell: Interpreting How Transformers Process Local Ambiguities Through the Lens of Restart-Incrementality
Brielen Madureira | Patrick Kahardipraja | David Schlangen

Incremental models that process sentences one token at a time will sometimes encounter points where more than one interpretation is possible. Causal models are forced to output one interpretation and continue, whereas models that can revise may edit their previous output as the ambiguity is resolved. In this work, we look at how restart-incremental Transformers build and update internal states, in an effort to shed light on what processes cause revisions not viable in autoregressive models. We propose an interpretable way to analyse the incremental states, showing that their sequential structure encodes information on the garden path effect and its resolution. Our method brings insights on various bidirectional encoders for contextualised meaning representation and dependency parsing, contributing to show their advantage over causal models when it comes to revisions.

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SpaRC and SpaRP: Spatial Reasoning Characterization and Path Generation for Understanding Spatial Reasoning Capability of Large Language Models
Md Imbesat Rizvi | Xiaodan Zhu | Iryna Gurevych

Spatial reasoning is a crucial component of both biological and artificial intelligence. In this work, we present a comprehensive study of the capability of current state-of-the-art large language models (LLMs) on spatial reasoning. To support our study, we created and contribute a novel Spatial Reasoning Characterization (SpaRC) framework and Spatial Reasoning Paths (SpaRP) datasets, to enable an in-depth understanding of the spatial relations and compositions as well as the usefulness of spatial reasoning chains. We found that all the state-of-the-art LLMs do not perform well on the datasets—their performances are consistently low across different setups. The spatial reasoning capability improves substantially as model sizes scale up. Finetuning both large language models (e.g., Llama-2-70B) and smaller ones (e.g., Llama-2-13B) can significantly improve their F1-scores by 7–32 absolute points. We also found that the top proprietary LLMs still significantly outperform their open-source counterparts in topological spatial understanding and reasoning.

In proactive dialogue, the challenge lies not just in generating responses but in steering conversations toward predetermined goals, a task where Large Language Models (LLMs) typically struggle due to their reactive nature. Traditional approaches to enhance dialogue planning in LLMs, ranging from elaborate prompt engineering to the integration of policy networks, either face efficiency issues or deliver suboptimal performance. Inspired by the dual-process theory in psychology, which identifies two distinct modes of thinking—intuitive (fast) and analytical (slow), we propose the Dual-Process Dialogue Planning (DPDP) framework. DPDP embodies this theory through two complementary planning systems: an instinctive policy model for familiar contexts and a deliberative Monte Carlo Tree Search (MCTS) mechanism for complex, novel scenarios. This dual strategy is further coupled with a novel two-stage training regimen: offline Reinforcement Learning for robust initial policy model formation followed by MCTS-enhanced on-the-fly learning, which ensures a dynamic balance between efficiency and strategic depth. Our empirical evaluations across diverse dialogue tasks affirm DPDP’s superiority in achieving both high-quality dialogues and operational efficiency, outpacing existing methods.

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Spectral Filters, Dark Signals, and Attention Sinks
Nicola Cancedda

Projecting intermediate representations onto the vocabulary is an increasingly popular interpretation tool for transformer-based LLMs, also known as the logit lens (Nostalgebraist). We propose a quantitative extension to this approach and define spectral filters on intermediate representations based on partitioning the singular vectors of the vocabulary embedding and unembedding matrices into bands. We find that the signals exchanged in the tail end of the spectrum, i.e. corresponding to the singular vectors with smallest singular values, are responsible for attention sinking (Xiao et al., 2023), of which we provide an explanation. We find that the negative log-likelihood of pretrained models can be kept low despite suppressing sizeable parts of the embedding spectrum in a layer-dependent way, as long as attention sinking is preserved. Finally, we discover that the representation of tokens that draw attention from many tokens have large projections on the tail end of the spectrum, and likely act as additional attention sinks.

Inferring contextually-relevant and diverse commonsense to understand narratives remains challenging for knowledge models. In this work, we develop a series of knowledge models, DiffuCOMET, that leverage diffusion to learn to reconstruct the implicit semantic connections between narrative contexts and relevant commonsense knowledge. Across multiple diffusion steps, our method progressively refines a representation of commonsense facts that is anchored to a narrative, producing contextually-relevant and diverse commonsense inferences for an input context. To evaluate DiffuCOMET, we introduce new metrics for commonsense inference that more closely measure knowledge diversity and contextual relevance. Our results on two different benchmarks, ComFact and WebNLG+, show that knowledge generated by DiffuCOMET achieves a better trade-off between commonsense diversity, contextual relevance and alignment to known gold references, compared to baseline knowledge models.

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Systematic Task Exploration with LLMs: A Study in Citation Text Generation
Furkan Şahinuç | Ilia Kuznetsov | Yufang Hou | Iryna Gurevych

Large language models (LLMs) bring unprecedented flexibility in defining and executing complex, creative natural language generation (NLG) tasks. Yet, this flexibility brings new challenges, as it introduces new degrees of freedom in formulating the task inputs and instructions and in evaluating model performance. To facilitate the exploration of creative NLG tasks, we propose a three-component research framework that consists of systematic input manipulation, reference data, and output measurement. We use this framework to explore citation text generation – a popular scholarly NLP task that lacks consensus on the task definition and evaluation metric and has not yet been tackled within the LLM paradigm. Our results highlight the importance of systematically investigating both task instruction and input configuration when prompting LLMs, and reveal non-trivial relationships between different evaluation metrics used for citation text generation. Additional human generation and human evaluation experiments provide new qualitative insights into the task to guide future research in citation text generation. We make our code and data publicly available.

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Limits of Theory of Mind Modelling in Dialogue-Based Collaborative Plan Acquisition
Matteo Bortoletto | Constantin Ruhdorfer | Adnen Abdessaied | Lei Shi | Andreas Bulling

Recent work on dialogue-based collaborative plan acquisition (CPA) has suggested that Theory of Mind (ToM) modelling can improve missing knowledge prediction in settings with asymmetric skill-sets and knowledge. Although ToM was claimed to be important for effective collaboration, its real impact on this novel task remains under-explored. By representing plans as graphs and by exploiting task-specific constraints we show that, as performance on CPA nearly doubles when predicting one’s own missing knowledge, the improvements due to ToM modelling diminish. This phenomenon persists even when evaluating existing baseline methods. To better understand the relevance of ToM for CPA, we report a principled performance comparison of models with and without ToM features. Results across different models and ablations consistently suggest that learned ToM features are indeed more likely to reflect latent patterns in the data with no perceivable link to ToM. This finding calls for a deeper understanding of the role of ToM in CPA and beyond, as well as new methods for modelling and evaluating mental states in computational collaborative agents.

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Temporal Knowledge Question Answering via Abstract Reasoning Induction
Ziyang Chen | Dongfang Li | Xiang Zhao | Baotian Hu | Min Zhang

In this study, we address the challenge of enhancing temporal knowledge reasoning in Large Language Models (LLMs). LLMs often struggle with this task, leading to the generation of inaccurate or misleading responses. This issue mainly arises from their limited ability to handle evolving factual knowledge and complex temporal logic. To overcome these limitations, we propose Abstract Reasoning Induction (ARI) framework, which divides temporal reasoning into two distinct phases: Knowledge agnostic and Knowledge-based. This framework offers factual knowledge support to LLMs while minimizing the incorporation of extraneous noisy data. Concurrently, informed by the principles of constructivism, ARI provides LLMs the capability to engage in proactive, self-directed learning from both correct and incorrect historical reasoning samples. By teaching LLMs to actively construct knowledge and methods, it can significantly boosting their temporal reasoning abilities. Our approach achieves significant improvements, with relative gains of 29.7% and 9.27% on two temporal QA datasets, underscoring its efficacy in advancing temporal reasoning in LLMs. The code can be found at https: //github.com/czy1999/ARI-QA.

Since the remarkable generation performance of large language models raised ethical and legal concerns, approaches to detect machine-generated text by embedding watermarks are being developed.However, we discover that the existing works fail to function appropriately in code generation tasks due to the task’s nature of having low entropy.Extending a logit-modifying watermark method, we propose Selective WatErmarking via Entropy Thresholding (SWEET), which enhances detection ability and mitigates code quality degeneration by removing low-entropy segments at generating and detecting watermarks.Our experiments show that SWEET significantly improves code quality preservation while outperforming all baselines, including post-hoc detection methods, in detecting machine-generated code text.Our code is available inhttps://github.com/hongcheki/sweet-watermark.

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MapCoder: Multi-Agent Code Generation for Competitive Problem Solving
Md. Ashraful Islam | Mohammed Eunus Ali | Md Rizwan Parvez

Code synthesis, which requires a deep understanding of complex natural language (NL) problem descriptions, generation of code instructions for complex algorithms and data structures, and the successful execution of comprehensive unit tests, presents a significant challenge. Thus, while large language models (LLMs) demonstrate impressive proficiency in natural language processing (NLP), their performance in code generation tasks remains limited. In this paper, we introduce a new approach to code generation tasks leveraging the multi-agent prompting that uniquely replicates the full cycle of program synthesis as observed in human developers. Our framework, MapCoder, consists of four LLM agents specifically designed to emulate the stages of this cycle: recalling relevant examples, planning, code generation, and debugging. After conducting thorough experiments, with multiple LLMs ablations and analyses across eight challenging competitive problem-solving and program synthesis benchmarks—MapCoder showcases remarkable code generation capabilities, achieving their new state-of-the-art (pass@1) results—(HumanEval 93.9%, MBPP 83.1%, APPS 22.0%, CodeContests 28.5%, and xCodeEval 45.3%). Moreover, our method consistently delivers superior performance across various programming languages and varying problem difficulties. We open-source our framework at https://github.com/Md-Ashraful-Pramanik/MapCoder.

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RelayAttention for Efficient Large Language Model Serving with Long System Prompts
Lei Zhu | Xinjiang Wang | Wayne Zhang | Rynson Lau

A practical large language model (LLM) service may involve a long system prompt, which specifies the instructions, examples, and knowledge documents of the task and is reused across requests. However, the long system prompt causes throughput/latency bottlenecks as the cost of generating the next token grows w.r.t the sequence length. This paper aims to improve the efficiency of LLM services that involve long system prompts. Our key observation is that handling these system prompts requires heavily redundant memory accesses in existing causal attention computation algorithms. Specifically, for batched requests, the cached hidden states (i.e., key-value pairs) of system prompts are transferred from off-chip DRAM to on-chip SRAM multiple times, each corresponding to an individual request. To eliminate such a redundancy, we propose RelayAttention, an attention algorithm that allows reading these hidden states from DRAM exactly once for a batch of input tokens. RelayAttention is a free lunch: it maintains the generation quality while requiring no model retraining, as it is based on a mathematical reformulation of causal attention. We have observed significant performance improvements to a production-level system, vLLM, through integration with RelayAttention. The improvements are even more profound with longer system prompts.

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Boosting Language Models Reasoning with Chain-of-Knowledge Prompting
Jianing Wang | Qiushi Sun | Xiang Li | Ming Gao

Recently, Chain-of-Thought (CoT) prompting has delivered success on complex reasoning tasks, which aims at designing a simple prompt like “Let’s think step by step” or multiple in-context exemplars with well-designed rationales to elicit Large Language Models (LLMs) to generate intermediate reasoning steps. However, the generated rationales often come with hallucinations, making unfactual and unfaithful reasoning chains. To mitigate this brittleness, we propose a novel Chain-of-Knowledge (CoK) prompting, where we aim at eliciting LLMs to generate explicit pieces of knowledge evidence in the form of structure triple. This is inspired by our human behaviors, i.e., we can draw a mind map or knowledge map as the reasoning evidence in the brain before answering a complex question. Benefiting from CoK, we additionally introduce an F2-Verification method to estimate the reliability of the reasoning chains in terms of factuality and faithfulness. For the unreliable response, the wrong evidence can be indicated to prompt the LLM to rethink. Extensive experiments demonstrate that our method can further improve the performance of commonsense, factual, symbolic, and arithmetic reasoning tasks.

The emergence of large language models (LLMs) has increasingly drawn attention to the use of LLMs for human-like planning. Existing work on LLM-based planning either focuses on leveraging the inherent language generation capabilities of LLMs to produce free-style plans, or employs reinforcement learning approaches to learn decision-making for a limited set of actions within restricted environments. However, both approaches exhibit significant discrepancies from the open and executable requirements in real-world planning. In this paper, we propose a new planning task–open grounded planning. The primary objective of open grounded planning is to ask the model to generate an executable plan based on a variable action set, thereby ensuring the executability of the produced plan. To this end, we establishes a benchmark for open grounded planning spanning a wide range of domains. Then we test current state-of-the-art LLMs along with five planning approaches, revealing that existing LLMs and methods still struggle to address the challenges posed by grounded planning in open domains. The outcomes of this paper define and establish a foundational dataset for open grounded planning, and shed light on the potential challenges and future directions of LLM-based planning.

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LLM Knows Body Language, Too: Translating Speech Voices into Human Gestures
Chenghao Xu | Guangtao Lyu | Jiexi Yan | Muli Yang | Cheng Deng

In response to the escalating demand for digital human representations, progress has been made in the generation of realistic human gestures from given speeches. Despite the remarkable achievements of recent research, the generation process frequently includes unintended, meaningless, or non-realistic gestures. To address this challenge, we propose a gesture translation paradigm, GesTran, which leverages large language models (LLMs) to deepen the understanding of the connection between speech and gesture and sequentially generates human gestures by interpreting gestures as a unique form of body language. The primary stage of the proposed framework employs a transformer-based auto-encoder network to encode human gestures into discrete symbols. Following this, the subsequent stage utilizes a pre-trained LLM to decipher the relationship between speech and gesture, translating the speech into gesture by interpreting the gesture as unique language tokens within the LLM. Our method has demonstrated state-of-the-art performance improvement through extensive and impartial experiments conducted on public TED and TED-Expressive datasets.

Employing Large Language Models (LLMs) for semantic parsing has achieved remarkable success. However, we find existing methods fall short in terms of reliability and efficiency when hallucinations are encountered. In this paper, we address these challenges with a framework called QueryAgent, which solves a question step-by-step and performs stepwise self-correction. We introduce an environmental feedback-based self-correction method called ERASER. Unlike traditional approaches, ERASER leverages rich environmental feedback in the intermediate steps to perform selective and differentiated self-correction only when necessary. Experimental results demonstrate that QueryAgent notably outperforms all previous few-shot methods using only one example on GrailQA and GraphQ by 5.7 and 15.0 points. Furthermore, our approach exhibits superiority in terms of efficiency, including run-time, query overhead, and API invocation costs. By leveraging ERASER, we further improve another baseline (i.e., AgentBench) by approximately 10 points, validating the strong transferability of our approach.

Argumentation mining (AM) aims to detect the arguments and their inherent relations from argumentative textual compositions. Generally, AM comprises three key challenging subtasks, including argument component type classification (ACTC), argumentative relation identification (ARI), and argumentative relation type classification (ARTC). Prior methods are afflicted by a sequential feature decoding paradigm, wherein they initially address the features of argumentation components (ACs) for the task of ACTC. Then, these features are amalgamated in pairs to tackle the task of ARI. Finally, the AC pairs and ascertained pertinent relations are employed for ARTC. However, the explicit and comprehensive inter-relationship among the three subtasks is neglected. In this paper, we propose a novel method PITA for PromptIng Task interAction to model the inter-relationships among the three subtasks within a generative framework. Specifically, we employ a dynamic prompt template to indicate all ACs and AC pairs in the three subtasks. Then, from a multi-relational perspective, we construct an undirected heterogeneous graph to capture the various relationships within and between ACs and AC pairs. We apply the Relational Graph Convolutional Network (RGCN) on the graph and inject the task interaction information into the soft prompts with continuous representations. PITA jointly decodes all ACs and AC pairs using the prompt template with task interaction information, which thus explicitly and comprehensively harmonizes the information propagation across the three subtasks. Extensive experiments show PITA achieves state-of-the-art performances on two AM benchmarks.

Large Language Models (LLMs) show promising results in language generation and instruction following but frequently “hallucinate”, making their outputs less reliable. Despite Uncertainty Quantification’s (UQ) potential solutions, implementing it accurately within LLMs is challenging. Our research introduces a simple heuristic: not all tokens in auto-regressive LLM text equally represent the underlying meaning, as “linguistic redundancy” often allows a few keywords to convey the essence of long sentences. However, current methods underestimate this inequality when assessing uncertainty, causing tokens with limited semantics to be equally or excessively weighted in UQ. To correct this, we propose Shifting Attention to more Relevant (SAR) components at both token- and sentence-levels for better UQ. We conduct extensive experiments involving a range of popular “off-the-shelf” LLMs, such as Vicuna, WizardLM, and LLaMA-2-chat, with model sizes extending up to 33B parameters. We evaluate various free-form question-answering tasks, encompassing domains such as reading comprehension, science Q&A, and medical Q&A. Our experimental results, coupled with a comprehensive demographic analysis, demonstrate the superior performance of SAR. The code is available at https://github.com/jinhaoduan/SAR.

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Babel-ImageNet: Massively Multilingual Evaluation of Vision-and-Language Representations
Gregor Geigle | Radu Timofte | Goran Glavaš

Vision-and-language (VL) models with separate encoders for each modality (e.g., CLIP) have become the go-to models for zero-shot image classification and image-text retrieval. They are, however, mostly evaluated in English as multilingual benchmarks are limited in availability. We introduce Babel-ImageNet, a massively multilingual benchmark that offers (partial) translations of ImageNet labels to 100 languages, built without machine translation or manual annotation. We instead automatically obtain reliable translations by linking them – via shared WordNet synsets – to BabelNet, a massively multilingual lexico-semantic network. We evaluate 11 public multilingual CLIP models on zero-shot image classification (ZS-IC) on our benchmark, demonstrating a significant gap between English ImageNet performance and that of high-resource languages (e.g., German or Chinese), and an even bigger gap for low-resource languages (e.g., Sinhala or Lao). Crucially, we show that the models’ ZS-IC performance highly correlates with their performance in image-text retrieval, validating the use of Babel-imageNet to evaluate multilingual models for the vast majority of languages without gold image-text data. Finally, we show that the performance of multilingual CLIP can be drastically improved for low-resource languages with parameter-efficient language-specific training. We make our code and data publicly available: https://github.com/gregor-ge/Babel-ImageNet

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Estimating Agreement by Chance for Sequence Annotation
Diya Li | Carolyn Rose | Ao Yuan | Chunxiao Zhou

In the field of natural language processing, correction of performance assessment for chance agreement plays a crucial role in evaluating the reliability of annotations. However, there is a notable dearth of research focusing on chance correction for assessing the reliability of sequence annotation tasks, despite their widespread prevalence in the field. To address this gap, this paper introduces a novel model for generating random annotations, which serves as the foundation for estimating chance agreement in sequence annotation tasks. Utilizing the proposed randomization model and a related comparison approach, we successfully derive the analytical form of the distribution, enabling the computation of the probable location of each annotated text segment and subsequent chance agreement estimation. Through a combination simulation and corpus-based evaluation, we successfully assess its applicability and validate its accuracy and efficacy.

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Are Emergent Abilities in Large Language Models just In-Context Learning?
Sheng Lu | Irina Bigoulaeva | Rachneet Sachdeva | Harish Tayyar Madabushi | Iryna Gurevych

Large language models, comprising billions of parameters and pre-trained on extensive web-scale corpora, have been claimed to acquire certain capabilities without having been specifically trained on them. These capabilities, referred to as “emergent abilities,” have been a driving force in discussions regarding the potentials and risks of language models. A key challenge in evaluating emergent abilities is that they are confounded by model competencies that arise through alternative prompting techniques, including in-context learning, which is the ability of models to complete a task based on a few examples. We present a novel theory that explains emergent abilities, taking into account their potential confounding factors, and rigorously substantiate this theory through over 1000 experiments. Our findings suggest that purported emergent abilities are not truly emergent, but result from a combination of in-context learning, model memory, and linguistic knowledge. Our work is a foundational step in explaining language model performance, providing a template for their efficient use and clarifying the paradox of their ability to excel in some instances while faltering in others. Thus, we demonstrate that their capabilities should not be overestimated.

Recent work demonstrates that, after instruction tuning, Code Large Language Models (Code LLMs) can obtain impressive capabilities to address a wide range of code-related tasks. However, current instruction tuning methods for Code LLMs mainly focus on the traditional code generation task, resulting in poor performance in complex multi-task scenarios. In this paper, we concentrate on multiple code-related tasks and present WaveCoder, a series of Code LLMs trained with Widespread And Versatile Enhanced instruction data. To enable the models to tackle complex code-related tasks, we propose a method to stably generate diverse, high-quality instruction data from open source code dataset in multi-task scenarios and obtain CodeOcean, a dataset comprising 19,915 instruction instances across 4 code-related tasks, which is aimed at improving the generalization ability of Code LLM. Our experiments demonstrate that WaveCoder models significantly outperform other open-source models in terms of the generalization ability across different code-related tasks. Moreover, WaveCoder-Ultra-6.7B presents the state-of-the-art generalization abilities on a wide range of code-related tasks.

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Eliciting Better Multilingual Structured Reasoning from LLMs through Code
Bryan Li | Tamer Alkhouli | Daniele Bonadiman | Nikolaos Pappas | Saab Mansour

The development of large language models (LLM) has shown progress on reasoning, though studies have largely considered either English or simple reasoning tasks. To address this, we introduce a multilingual structured reasoning and explanation dataset, termed xSTREET, that covers four tasks across six languages. xSTREET exposes a gap in base LLM performance between English and non-English reasoning tasks.We then propose two methods to remedy this gap, building on the insight that LLMs trained on code are better reasoners. First, at training time, we augment a code dataset with multilingual comments using machine translation while keeping program code as-is. Second, at inference time, we bridge the gap between training and inference by employing a prompt structure that incorporates step-by-step code primitives to derive new facts and find a solution. Our methods show improved multilingual performance on xSTREET, most notably on the scientific commonsense reasoning subtask. Furthermore, the models show no regression on non-reasoning tasks, thus demonstrating our techniques maintain general-purpose abilities.

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OLIVE: Object Level In-Context Visual Embeddings
Timothy Ossowski | Junjie Hu

Recent generalist vision-language models (VLMs) have demonstrated impressive reasoning capabilities across diverse multimodal tasks. However, these models still struggle with fine-grained object-level understanding and grounding. In terms of modeling, existing VLMs implicitly align text tokens with image patch tokens, which is ineffective for embedding alignment at the same granularity and inevitably introduces noisy spurious background features. Additionally, these models struggle when generalizing to unseen visual concepts and may not be reliable for domain-specific tasks without further fine-tuning. To address these limitations, we propose a novel method to prompt large language models with in-context visual object vectors, thereby enabling controllable object-level reasoning. This eliminates the necessity of fusing a lengthy array of image patch features and significantly speeds up training. Furthermore, we propose region-level retrieval using our object representations, facilitating rapid adaptation to new objects without additional training. Our experiments reveal that our method achieves competitive referring object classification and captioning performance, while also offering zero-shot generalization and robustness to visually challenging contexts.

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Quantifying Uncertainty in Answers from any Language Model and Enhancing their Trustworthiness
Jiuhai Chen | Jonas Mueller

We introduce BSDetector, a method for detecting bad and speculative answers from a pretrained Large Language Model by estimating a numeric confidence score for any output it generated. Our uncertainty quantification technique works for any LLM accessible only via a black-box API, whose training data remains unknown. By expending a bit of extra computation, users of any LLM API can now get the same response as they would ordinarily, as well as a confidence estimate that cautions when not to trust this response. Experiments on both closed and open-form Question-Answer benchmarks reveal that BSDetector more accurately identifies incorrect LLM responses than alternative uncertainty estimation procedures (for both GPT-3 and ChatGPT). By sampling multiple responses from the LLM and considering the one with the highest confidence score, we can additionally obtain more accurate responses from the same LLM, without extra training steps. In applications involving automated evaluation with LLMs, accounting for our confidence scores leads to more reliable evaluation in both human-in-the-loop and fully-automated settings (across both GPT 3.5 and 4).

With the advancement of large language models (LLMs) and the expansion of their context windows, existing long-context benchmarks fall short in effectively evaluating the models’ comprehension and reasoning abilities in extended texts. Moreover, conventional benchmarks relying on F1 metrics often inaccurately score responses: they may undervalue correct answers that differ from the reference responses and overvalue incorrect ones that resemble the reference texts. In response to these limitations, we introduce Marathon, a novel evaluation benchmark that adopts a multiple-choice question format. It is specifically designed to overcome the constraints of previous benchmarks and provide a rapid, precise, and unbiased appraisal of the long-context comprehension skills of large language models. We conducted comprehensive evaluations on the Marathon benchmark with a range of state-of-the-art LLMs and assessed the effectiveness of various optimization strategies tailored for long-context generation. We anticipate that the Marathon benchmark and its associated leaderboard will enable a more precise and equitable evaluation of LLMs’ capabilities in understanding and reasoning over extended contexts.

Model scaling is becoming the default choice for many language tasks due to the success of large language models (LLMs). However, it can fall short in specific scenarios where simple customized methods excel. In this paper, we delve into the patent approval prediction task and unveil that simple domain-specific graph methods outperform enlarging the model, using the intrinsic dependencies within the patent data. Specifically, we first extend the embedding-based state-of-the-art (SOTA) by scaling up its backbone model with various sizes of open-source LLMs, then explore prompt-based methods to harness proprietary LLMs’ potential, but find the best results close to random guessing, underlining the ineffectiveness of model scaling-up. Hence, we propose a novel Fine-grained cLAim depeNdency (FLAN) Graph through meticulous patent data analyses, capturing the inherent dependencies across segments of the patent text. As it is model-agnostic, we apply cost-effective graph models to our FLAN Graph to obtain representations for approval prediction. Extensive experiments and detailed analyses prove that incorporating FLAN Graph via various graph models consistently outperforms all LLM baselines significantly. We hope that our observations and analyses in this paper can bring more attention to this challenging task and prompt further research into the limitations of LLMs.

Spoken language understanding (SLU) inevitably suffers from error propagation from automatic speech recognition (ASR) in actual scenarios. Some recent works attempt to alleviate this issue through contrastive learning. However, they (1) sample negative pairs incorrectly in pre-training; (2) only focus on implicit metric learning while neglecting explicit erroneous predictions; (3) treat manual and ASR transcripts indiscriminately. In this paper, we propose a novel framework termed PCAD, which can calibrate bias and errors and achieve adaptive-balanced decoupling training. Specifically, PCAD utilizes a prototype-based loss to aggregate label and prediction priors and calibrate bias and error-prone semantics for better inter-class discrimination and intra-class consistency. We theoretically analyze the effect of this loss on robustness enhancement. Further, we leverage a teacher-student model for asymmetric decoupling training between different transcripts and formulate a novel gradient-sensitive exponential moving averaging (GS-EMA) algorithm for adaptive balance of accuracy and robustness. Experiments on three datasets show that PCAD significantly outperforms existing approaches and achieves new state-of-the-art performance.

Transformer-based methods have gone mainstream in multimodal sequential learning. The intra and inter modality interactions are captured by the query-key associations of multi-head attention. In this way, the calculated multimodal contexts (attentional results) are expected to be relevant to the query modality. However, in existing literature, the alignment degree between different calculated attentional results of the same query are under-explored. Based on this concern, we propose a new constrained scheme called Multimodal Contextual Contrast (MCC), which could align the multiple attentional results from both local and global perspectives, making the information capture more efficient. Concretely, the calculated attentional results of different modalities are mapped into a common feature space, those attentional vectors with the same query are considered as a positive group and the remaining sets are negative. From local perspective, we sample the negative groups for a positive group by randomly changing the sequential step of one specific context and keeping the other stay the same. From coarse global perspective, we divide all the contextual groups into two sets (i.e., aligned and unaligned), making the total score of aligned group relatively large. We extend the vectorial inner product operation for more input and calculate the aligned score for each multimodal group. Considering that the computational complexity scales exponentially to the number of modalities, we adopt stochastic expectation approximation (SEA) for the real process. The extensive experimental results on several tasks reveal the effectiveness of our contributions.

Large language models (LLMs) produce hallucinated text, compromising their practical utility in professional contexts. To assess the reliability of LLMs, numerous initiatives have developed benchmark evaluations for hallucination phenomena. However, they often employ constrained generation techniques to produce the evaluation dataset due to cost and time limitations. For instance, this may involve employing directed hallucination induction or deliberately modifying authentic text to generate hallucinations. These are not congruent with the unrestricted text generation demanded by real-world applications. Furthermore, a well-established Chinese-language dataset dedicated to the evaluation of hallucinations is presently lacking. Consequently, we have developed an Unconstrained Hallucination Generation Evaluation (UHGEval) benchmark, containing hallucinations generated by LLMs with minimal restrictions. Concurrently, we have established a comprehensive benchmark evaluation framework to aid subsequent researchers in undertaking scalable and reproducible experiments. We have also evaluated prominent Chinese LLMs and the GPT series models to derive insights regarding hallucination.

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PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers
Weizhe Lin | Jingbiao Mei | Jinghong Chen | Bill Byrne

Large Multimodal Models (LMMs) excel in natural language and visual understanding but are challenged by exacting tasks such as Knowledge-based Visual Question Answering (KB-VQA) which involve the retrieval of relevant information from document collections to use in shaping answers to questions. We present an extensive training and evaluation framework, M2KR, for KB-VQA. M2KR contains a collection of vision and language tasks which we have incorporated into a single suite of benchmark tasks for training and evaluating general-purpose multi-modal retrievers. We use M2KR to develop PreFLMR, a pre-trained version of the recently developed Fine-grained Late-interaction Multi-modal Retriever (FLMR) approach to KB-VQA, and we report new state-of-the-art results across a range of tasks. We also present investigations into the scaling behaviors of PreFLMR intended to be useful in future developments in general-purpose multi-modal retrievers.

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Triple-Encoders: Representations That Fire Together, Wire Together
Justus-Jonas Erker | Florian Mai | Nils Reimers | Gerasimos Spanakis | Iryna Gurevych

Search-based dialog models typically re-encode the dialog history at every turn, incurring high cost.Curved Contrastive Learning, a representation learning method that encodes relative distances between utterances into the embedding space via a bi-encoder, has recently shown promising results for dialog modeling at far superior efficiency.While high efficiency is achieved through independently encoding utterances, this ignores the importance of contextualization. To overcome this issue, this study introduces triple-encoders, which efficiently compute distributed utterance mixtures from these independently encoded utterances through a novel hebbian inspired co-occurrence learning objective in a self-organizing manner, without using any weights, i.e., merely through local interactions. Empirically, we find that triple-encoders lead to a substantial improvement over bi-encoders, and even to better zero-shot generalization than single-vector representation models without requiring re-encoding. Our code (https://github.com/UKPLab/acl2024-triple-encoders) and model (https://huggingface.co/UKPLab/triple-encoders-dailydialog) are publicly available.

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Improving Hateful Meme Detection through Retrieval-Guided Contrastive Learning
Jingbiao Mei | Jinghong Chen | Weizhe Lin | Bill Byrne | Marcus Tomalin

Hateful memes have emerged as a significant concern on the Internet. Detecting hateful memes requires the system to jointly understand the visual and textual modalities. Our investigation reveals that the embedding space of existing CLIP-based systems lacks sensitivity to subtle differences in memes that are vital for correct hatefulness classification. We propose constructing a hatefulness-aware embedding space through retrieval-guided contrastive training. Our approach achieves state-of-the-art performance on the HatefulMemes dataset with an AUROC of 87.0, outperforming much larger fine-tuned large multimodal models. We demonstrate a retrieval-based hateful memes detection system, which is capable of identifying hatefulness based on data unseen in training. This allows developers to update the hateful memes detection system by simply adding new examples without retraining — a desirable feature for real services in the constantly evolving landscape of hateful memes on the Internet.

Large Language Models (LLMs) exhibit robust problem-solving capabilities for diverse tasks. However, most LLM-based agents are designed as specific task solvers with sophisticated prompt engineering, rather than agents capable of learning and evolving through interactions. These task solvers necessitate manually crafted prompts to inform task rules and regulate LLM behaviors, inherently incapacitating to address complex dynamic scenarios e.g., large interactive games. In light of this, we propose Agent-Pro: an LLM-based Agent with Policy-level Reflection and Optimization that can learn a wealth of expertise from interactive experiences and progressively elevate its behavioral policy. Specifically, it involves a dynamic belief generation and reflection process for policy evolution. Rather than action-level reflection, Agent-Pro iteratively reflects on past trajectories and beliefs, “fine-tuning” its irrational beliefs for a better policy. Moreover, a depth-first search is employed for policy optimization, ensuring continual enhancement in policy payoffs. Agent-Pro is evaluated across two games: Blackjack and Texas Hold’em, outperforming vanilla LLM and specialized models. Our results show Agent-Pro can learn and evolve in complex and dynamic scenes, which also benefits numerous LLM-based applications.

This paper reveals a novel linear characteristic exclusive to transformer decoders, including models like GPT, LLaMA, OPT, BLOOM and others. We analyze embedding transformations between sequential layers, uncovering an almost perfect linear relationship (Procrustes similarity score of 0.99). However, linearity decreases when the residual component is removed, due to a consistently low transformer layer output norm. Our experiments show that pruning or linearly approximating some of the layers does not impact loss or model performance significantly. Moreover, we introduce a cosine-similarity-based regularization in our pretraining experiments on smaller models, aimed at reducing layer linearity. This regularization not only improves performance metrics on benchmarks like Tiny Stories and SuperGLUE but as well successfully decreases the linearity of the models. This study challenges the existing understanding of transformer architectures, suggesting that their operation may be more linear than previously assumed.

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Noise Correction on Subjective Datasets
Uthman Jinadu | Yi Ding

Incorporating every annotator’s perspective is crucial for unbiased data modeling. Annotator fatigue and changing opinions over time can distort dataset annotations. To combat this, we propose to learn a more accurate representation of diverse opinions by utilizing multitask learning in conjunction with loss-based label correction. We show that using our novel formulation, we can cleanly separate agreeing and disagreeing annotations. Furthermore, this method provides a controllable way to encourage or discourage disagreement. We demonstrate that this modification can improve prediction performance in a single or multi-annotator setting. Lastly, we show that this method remains robust to additional label noise that is applied to subjective data.

Recommender systems are widely used to suggest engaging content, and Large Language Models (LLMs) have given rise to generative recommenders. Such systems can directly generate items, including for open-set tasks like question suggestion. While the world knowledge of LLMs enables good recommendations, improving the generated content through user feedback is challenging as continuously fine-tuning LLMs is prohibitively expensive. We present a training-free approach for optimizing generative recommenders by connecting user feedback loops to LLM-based optimizers. We propose a generative explore-exploit method that can not only exploit generated items with known high engagement, but also actively explore and discover hidden population preferences to improve recommendation quality. We evaluate our approach on question generation in two domains (e-commerce and general knowledge), and model user feedback with Click Through Rate (CTR). Experiments show our LLM-based explore-exploit approach can iteratively improve recommendations and consistently increase CTR. Ablation analysis shows that generative exploration is key to learning user preferences, avoiding the pitfalls of greedy exploit-only approaches. A human evaluation strongly supports our quantitative findings.

In order for large language model (LLM)-based assistants to effectively adapt to evolving information needs, it must be possible to update their factual knowledge through continued training on new data. The standard recipe for doing so involves continued pre-training on new documents followed by instruction-tuning on question-answer (QA) pairs. However, we find that LLMs trained with this recipe struggle to answer questions, even though the perplexity of documents is minimized. We found that QA pairs are generally straightforward, while documents are more complex, weaving many factual statements together in an intricate manner. Therefore, we hypothesize that it is beneficial to expose LLMs to QA pairs before continued pre-training on documents so that the process of encoding knowledge from complex documents takes into account how this knowledge is accessed through questions. Based on this, we propose pre-instruction-tuning (PIT), a method that instruction-tunes on questions prior to training on documents. This contrasts with standard instruction-tuning, which learns how to extract knowledge after training on documents. Extensive experiments and ablation studies demonstrate that pre-instruction-tuning significantly enhances the ability of LLMs to absorb knowledge from new documents, outperforming standard instruction-tuning by 17.8%.

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What Do Language Models Hear? Probing for Auditory Representations in Language Models
Jerry Ngo | Yoon Kim

This work explores whether language models encode meaningfully grounded representations of sounds of objects. We learn a linear probe that retrieves the correct text representation of an object given a snippet of audio related to that object, where the sound representation is given by a pretrained audio model. This probe is trained via a contrastive loss that pushes the language representations and sound representations of an object to be close to one another. After training, the probe is tested on its ability to generalize to objects that were not seen during training. Across different language models and audio models, we find that the probe generalization is above chance in many cases, indicating that despite being trained only on raw text, language models encode grounded knowledge of sounds for some objects.

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Threads of Subtlety: Detecting Machine-Generated Texts Through Discourse Motifs
Zae Myung Kim | Kwang Lee | Preston Zhu | Vipul Raheja | Dongyeop Kang

With the advent of large language models (LLM), the line between human-crafted and machine-generated texts has become increasingly blurred. This paper delves into the inquiry of identifying discernible and unique linguistic properties in texts that were written by humans, particularly uncovering the underlying discourse structures of texts beyond their surface structures. Introducing a novel methodology, we leverage hierarchical parse trees and recursive hypergraphs to unveil distinctive discourse patterns in texts produced by both LLMs and humans. Empirical findings demonstrate that, although both LLMs and humans generate distinct discourse patterns influenced by specific domains, human-written texts exhibit more structural variability, reflecting the nuanced nature of human writing in different domains. Notably, incorporating hierarchical discourse features enhances binary classifiers’ overall performance in distinguishing between human-written and machine-generated texts, even on out-of-distribution and paraphrased samples. This underscores the significance of incorporating hierarchical discourse features in the analysis of text patterns. The code and dataset will be available at [TBA].

Large Language Models (LLMs) have achieved unprecedented performance in Natural Language Generation (NLG) tasks. However, many existing studies have shown that they could be misused to generate undesired content. In response, before releasing LLMs for public access, model developers usually align those language models through Supervised Fine-Tuning (SFT) or Reinforcement Learning with Human Feedback (RLHF). Consequently, those aligned large language models refuse to generate undesired content when facing potentially harmful/unethical requests. A natural question is “could alignment really prevent those open-sourced large language models from being misused to generate undesired content?”. In this work, we provide a negative answer to this question. In particular, we show those open-sourced, aligned large language models could be easily misguided to generate undesired content without heavy computations or careful prompt designs. Our key idea is to directly manipulate the generation process of open-sourced LLMs to misguide it to generate undesired content including harmful or biased information and even private data. We evaluate our method on 4 open-sourced LLMs accessible publicly and our finding highlights the need for more advanced mitigation strategies for open-sourced LLMs.

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NICE: To Optimize In-Context Examples or Not?
Pragya Srivastava | Satvik Golechha | Amit Deshpande | Amit Sharma

Recent work shows that in-context learning and optimization of in-context examples (ICE) can significantly improve the accuracy of large language models (LLMs) on a wide range of tasks, leading to an apparent consensus that ICE optimization is crucial for better performance. However, most of these studies assume a fixed or no instruction provided in the prompt. We challenge this consensus by investigating the necessity of optimizing ICE when task-specific instructions are provided and find that there are many tasks for which it yields diminishing returns. In particular, using a diverse set of tasks and a systematically created instruction set with gradually added details, we find that as the prompt instruction becomes more detailed, the returns on ICE optimization diminish. To characterize this behavior, we introduce a task-specific metric called Normalized Invariability to Choice of Examples (NICE) that quantifies the learnability of tasks from a given instruction, and provides a heuristic to help decide whether to optimize instructions or ICE for a new task. Given a task, the proposed metric can reliably predict the utility of optimizing ICE compared to using random ICE. Our code is available at [https://github.com/microsoft/nice-icl](https://github.com/microsoft/nice-icl).

Large Language Models (LLMs) have demonstrated remarkable performance on assisting humans in programming and facilitating programming automation. However, existing benchmarks for evaluating the code understanding and generation capacities of LLMs suffer from severe limitations. First, most benchmarks are insufficient as they focus on a narrow range of popular programming languages and specific tasks, whereas real-world software development scenarios show a critical need to implement systems with multilingual and multitask programming environments to satisfy diverse requirements. Second, most benchmarks fail to consider the actual executability and the consistency of execution results of the generated code. To bridge these gaps between existing benchmarks and expectations from practical applications, we introduce **CodeScope**, an execution-based, multilingual, multitask, multidimensional evaluation benchmark for comprehensively measuring LLM capabilities on coding tasks. CodeScope covers **43 programming languages** and **eight coding tasks**. It evaluates the coding performance of LLMs from three dimensions (perspectives): **length**, **difficulty**, and **efficiency**. To facilitate execution-based evaluations of code generation, we develop **MultiCodeEngine**, an automated code execution engine that supports 14 programming languages. Finally, we systematically evaluate and analyze eight mainstream LLMs and demonstrate the superior breadth and challenges of CodeScope for evaluating LLMs on code understanding and generation tasks compared to other benchmarks. The CodeScope benchmark and code are publicly available at https://github.com/WeixiangYAN/CodeScope.

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Digital Socrates: Evaluating LLMs through Explanation Critiques
Yuling Gu | Oyvind Tafjord | Peter Clark

While LLMs can provide reasoned explanations along with their answers, the nature and quality of those explanations are still poorly understood. In response, our goal is to define a detailed way of characterizing the explanation capabilities of modern models and to create a nuanced, interpretable explanation evaluation tool that can generate such characterizations automatically, without relying on expensive API calls or human annotations. Our approach is to (a) define the new task of explanation critiquing - identifying and categorizing any main flaw in an explanation and providing suggestions to address the flaw, (b) create a sizeable, human-verified dataset for this task, and (c) train an open-source, automatic critique model (called Digital Socrates) using this data. Through quantitative and qualitative analysis, we demonstrate how Digital Socrates is useful for revealing insights about student models by examining their reasoning chains, and how it can provide high-quality, nuanced, automatic evaluation of those model explanations for the first time. Digital Socrates thus fills an important gap in evaluation tools for understanding and improving the explanation behavior of models.

As large language models (LLMs) become increasingly integrated into real-world applications such as code generation and chatbot assistance, extensive efforts have been made to align LLM behavior with human values, including safety. Jailbreak attacks, which aim to provoke unintended and unsafe behaviors from LLMs, remain a significant LLM safety threat. We analyze tokens, which are the smallest unit of text that can be processed by LLMs and make the following observations: (1) probabilities of tokens representing harmful responses are higher than those of harmless responses, and (2) responses containing safety disclaimers appear among the top tokens when token probabilities are sorted in descending order. In this paper, we leverage (1) and (2) to develop SafeDecoding, a safety-aware decoding strategy for LLMs, to defend against jailbreak attacks. We perform extensive experiments to evaluate SafeDecoding against six SOTA jailbreak attacks (GCG, AutoDAN, PAIR, DeepInception, SAP30, and template based attack) on five LLMs (Vicuna, Llama2, Guanaco, falcon, and Dolphin) using four benchmark datasets (AdvBench, HEx-PHI, MT-Bench, and Just-Eval). Our results show that SafeDecoding significantly reduces attack success rate and harmfulness of jailbreak attacks without compromising the helpfulness of responses to benign user queries while outperforming six defense methods (Perpelexity, Paraphrase, Retokenization, Self-Reminder, ICD, and Self-Examination).

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Multi-Task Inference: Can Large Language Models Follow Multiple Instructions at Once?
Guijin Son | SangWon Baek | Sangdae Nam | Ilgyun Jeong | Seungone Kim

Large language models (LLMs) are typically prompted to follow a single instruction per inference call. In this work, we analyze whether LLMs also hold the capability to handle multiple instructions simultaneously, denoted as Multi-Task Inference. For this purpose, we introduce the MTI Bench (Multi-Task Inference Benchmark), a comprehensive evaluation benchmark encompassing 5,000 instances across 25 tasks. Each task in the MTI Bench involves 2 to 3 sub-tasks. As expected, we first demonstrate that Multi-Task Inference reduces the total inference time by × 1.46 times in average since it does not require multiple inference calls. Interestingly, contrary to the expectation that LLMs would perform better when tasks are divided, we find that state-of-the-art LLMs, such as Llama-2-Chat-70B and GPT-4, show up to 7.3% and 12.4% improved performance with Multi-Task Inference compared to Single-Task Inference on the MTI Bench. We release the MTI Bench dataset and our code at this [link](https://anonymous.4open.science/r/MTI-Bench-6F01).

Recent advancements in large language models (LLMs) have brought significant changes to various domains, especially through LLM-driven autonomous agents. A representative scenario is in software development, where LLM agents demonstrate efficient collaboration, task division, and assurance of software quality, markedly reducing the need for manual involvement. However, these agents frequently perform a variety of tasks independently, without benefiting from past experiences, which leads to repeated mistakes and inefficient attempts in multi-step task execution. To this end, we introduce Experiential Co-Learning, a novel LLM-agent learning framework in which instructor and assistant agents gather shortcut-oriented experiences from their historical trajectories and use these past experiences for future task execution. The extensive experiments demonstrate that the framework enables agents to tackle unseen software-developing tasks more effectively. We anticipate that our insights will guide LLM agents towards enhanced autonomy and contribute to their evolutionary growth in cooperative learning. The code and data are available at https://github.com/OpenBMB/ChatDev.

New intent discovery (NID) is an important problem for deploying practical dialogue systems, which trains intent classifiers on a semi-supervised corpus where unlabeled user utterances contain both known and novel intents. Most existing NID algorithms place hope on the sample similarity to cluster unlabeled corpus to known or new samples. Lacking supervision on new intents, we experimentally find the intent classifier fails to fully distinguish new intents since they tend to assemble into intertwined centers.To address this problem, we propose a novel GeoID framework that learns geometry-aware representations to maximally separate all intents. Specifically, we are motivated by the recent findings on Neural Collapse (NC) in classification tasks to derive optimal intent center structure. Meanwhile, we devise a dual pseudo-labeling strategy based on optimal transport assignments and semi-supervised clustering, ensuring proper utterances-to-center arrangement.Extensive results show that our GeoID method establishes a new state-of-the-art performance, achieving a +3.49% average accuracy improvement on three standardized benchmarking datasets. We also verify its usefulness in assisting large language models for improved in-context performance.

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Speaker Verification in Agent-generated Conversations
Yizhe Yang | Palakorn Achananuparp | Heyan Huang | Jing Jiang | Ee-Peng Lim

The recent success of large language models (LLMs) has attracted widespread interest to develop role-playing conversational agents personalized to the characteristics and styles of different speakers to enhance their abilities to perform both general and special purpose dialogue tasks. However, the ability to personalize the generated utterances to speakers, whether conducted by human or LLM, has not been well studied. To bridge this gap, our study introduces a novel evaluation challenge: speaker verification in agent-generated conversations, which aimed to verify whether two sets of utterances originate from the same speaker. To this end, we assemble a large dataset collection encompassing thousands of speakers and their utterances. We also develop and evaluate speaker verification models under experiment setups. We further utilize the speaker verification models to evaluate the personalization abilities of LLM-based role-playing models. Comprehensive experiments suggest that the current role-playing models fail in accurately mimicking speakers, primarily due to their inherent linguistic characteristics.

In the era of data-driven decision-making, the complexity of data analysis necessitates advanced expertise and tools of data science, presenting significant challenges even for specialists. Large Language Models (LLMs) have emerged as promising aids as data science agents, assisting humans in data analysis and processing. Yet their practical efficacy remains constrained by the varied demands of real-world applications and complicated analytical process. In this paper, we introduce DSEval – a novel evaluation paradigm, as well as a series of innovative benchmarks tailored for assessing the performance of these agents throughout the entire data science lifecycle. Incorporating a novel bootstrapped annotation method, we streamline dataset preparation, improve the evaluation coverage, and expand benchmarking comprehensiveness. Our findings uncover prevalent obstacles and provide critical insights to inform future advancements in the field.

Large language models (LLMs) demonstrate remarkable multilingual capabilities without being pre-trained on specially curated multilingual parallel corpora.It remains a challenging problem to explain the underlying mechanisms by which LLMs process multilingual texts.In this paper, we delve into the composition of Transformer architectures in LLMs to pinpoint language-specific regions.Specially, we propose a novel detection method, language activation probability entropy (LAPE), to identify language-specific neurons within LLMs.Based on LAPE, we conduct comprehensive experiments on several representative LLMs, such as LLaMA-2, BLOOM, and Mistral. Our findings indicate that LLMs’ proficiency in processing a particular language is predominantly due to a small subset of neurons, primarily situated in the models’ top and bottom layers.Furthermore, we showcase the feasibility to “steer” the output language of LLMs by selectively activating or deactivating language-specific neurons. Our research provides important evidence to the understanding and exploration of the multilingual capabilities of LLMs.

Recent advancements in Large Language Models (LLMs) have showcased their remarkable capabilities in text understanding and generation. However, even stronger LLMs are susceptible to acquiring erroneous or obsolete information from the training corpus. Direct secondary fine-tuning with data containing new knowledge may be ineffective in updating knowledge due to the conflict between old and new knowledge. In this paper, we propose a new paradigm for fine-tuning called F-Learning (Forgetting before Learning), which employs parametric arithmetic to facilitate the forgetting of old knowledge and learning of new knowledge. Experimental results on two publicly available datasets demonstrate that our proposed F-Learning can obviously improve the knowledge updating performance of both full fine-tuning and LoRA fine-tuning, simultaneously outperforming the existing baselines in most cases. Moreover, we have also discovered that forgetting old knowledge by subtracting the parameters of LoRA can yield a similar effect to subtracting the parameters of full fine-tuning, and occasionally even surpass it significantly.

Large language models are first pre-trained on trillions of tokens and then instruction-tuned or aligned to specific preferences. While pre-training remains out of reach for most researchers due to the compute required, fine-tuning has become affordable thanks to parameter-efficient methods such as LoRA and QLoRA. Alignment is known to be sensitive to the many factors involved, including the quantity and quality of data, the alignment method, and the adapter rank. However, there has not yet been an extensive study of their effect on downstream performance. To address this gap, we conduct an in-depth investigation of the impact of popular choices for three crucial axes: (i) the alignment dataset (HH-RLHF and BeaverTails), (ii) the alignment technique (SFT and DPO), and (iii) the model (LLaMA-1, Vicuna-v1.3, Mistral-7b, and Mistral-7b-Instruct). Our extensive setup spanning over 300 experiments reveals consistent trends and unexpected findings. We observe how more informative data helps with preference alignment, cases where supervised fine-tuning outperforms preference optimization, and how aligning to a distinct preference boosts performance on downstream tasks. Through our in-depth analyses, we put forward key guidelines to help researchers perform more effective parameter-efficient LLM alignment.

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Zero-Shot Cross-Domain Dialogue State Tracking via Dual Low-Rank Adaptation
Xiang Luo | Zhiwen Tang | Jin Wang | Xuejie Zhang

Zero-shot dialogue state tracking (DST) seeks to enable dialogue systems to transition to unfamiliar domains without manual annotation or extensive retraining. Prior research has approached this objective by embedding prompts into language models (LMs). Common methodologies include integrating prompts at the input layer or introducing learnable variables at each transformer layer. Nonetheless, each strategy exhibits inherent limitations. Prompts integrated at the input layer risk underutilization, with their impact potentially diminishing across successive transformer layers. Conversely, the addition of learnable variables to each layer can complicate the training process and increase inference latency. To tackle the issues mentioned above, this paper proposes Dual Low-Rank Adaptation (DualLoRA), a plug-and-play architecture designed for zero-shot DST. DualLoRA incorporates two distinct Low-Rank Adaptation (LoRA) components, targeting both dialogue context processing and prompt optimization, to ensure the comprehensive influence of prompts throughout the transformer model layers. This is achieved without incurring additional inference latency, showcasing an efficient integration into existing architectures. Through rigorous evaluation on the MultiWOZ and SGD datasets, DualLoRA demonstrates notable improvements across multiple domains, outperforming traditional baseline methods in zero-shot settings.

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PRP-Graph: Pairwise Ranking Prompting to LLMs with Graph Aggregation for Effective Text Re-ranking
Jian Luo | Xuanang Chen | Ben He | Le Sun

Pairwise Ranking Prompting (PRP) demonstrates impressive effectiveness in zero-shot document re-ranking tasks with large language models (LLMs). However, in the existing methods, PRP only outputs the same label for the comparison results of different confidence intervals without considering the uncertainty of pairwise comparison, which implies an underutilization of the generation probability information of LLMs. To bridge this gap, we propose PRP-Graph, a novel pairwise re-ranking approach, based on a refined scoring PRP unit that exploits the output probabilities of target labels to capture the degree of certainty of the comparison results. Specifically, the PRP-Graph consists of two stages, namely ranking graph construction and ranking graph aggregation. Extensive experiments conducted on the BEIR benchmark demonstrate the superiority of our approach over existing PRP-based methods. Comprehensive analysis reveals that the PRP-Graph displays strong robustness towards the initial ranking order and delivers exceptional re-ranking results with acceptable efficiency. Our code and data are available at https://github.com/Memelank/PRP-Graph.

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RepCodec: A Speech Representation Codec for Speech Tokenization
Zhichao Huang | Chutong Meng | Tom Ko

With recent rapid growth of large language models (LLMs), discrete speech tokenization has played an important role for injecting speech into LLMs. However, this discretization gives rise to a loss of information, consequently impairing overall performance. To improve the performance of these discrete speech tokens, we present RepCodec, a novel speech representation codec for semantic speech tokenization. In contrast to audio codecs which reconstruct the raw audio, RepCodec learns a vector quantization codebook through reconstructing speech representations from speech encoders like HuBERT or data2vec. Together, the speech encoder, the codec encoder and the vector quantization codebook form a pipeline for converting speech waveforms into semantic tokens. The extensive experiments illustrate that RepCodec, by virtue of its enhanced information retention capacity, significantly outperforms the widely used k-means clustering approach in both speech understanding and generation. Furthermore, this superiority extends across various speech encoders and languages, affirming the robustness of RepCodec.We believe our method can facilitate large language modeling research on speech processing.

Large language models (LLMs) excellently generate human-like text, but also raise concerns about misuse in fake news and academic dishonesty. Decoding-based watermark, particularly the watermark based on the GumbelMax trick (GM watermark), is a standout solution for safeguarding machine-generated texts due to its notable detectability. However, GM watermark encounters a major challenge with generation diversity, always yielding identical outputs for the same prompt, negatively impacting generation diversity and user experience. To overcome this limitation, we introduce a new type of GM watermark, the Logits-Addition watermark, as well as three variants that aim to enhance diversity, particularly the GumbelSoft watermark (i.e., the softmax variant of the Logits-Addition watermark). When assessed for detectability in high diversity settings, our Gumbelsoft demonstrates superior performance, with its AUROC score exceeding those of the two alternative variants by a margin of 0.1 to 0.3 and outperforming other decoding-based watermarking methods by a minimum of 0.1.

The swift detection of multimedia fake news has emerged as a crucial task in combating malicious propaganda and safeguarding the security of the online environment. While existing methods have achieved commendable results in modeling entity-level inconsistency, addressing event-level inconsistency following the inherent subject-predicate logic of news and robustly learning news representations from poor-quality news samples remain two challenges. In this paper, we propose an Event-diven fake news detection framework (Event-Radar) based on multi-view learning, which integrates visual manipulation, textual emotion and multimodal inconsistency at event-level for fake news detection. Specifically, leveraging the capability of graph structures to capture interactions between events and parameters, Event-Radar captures event-level multimodal inconsistency by constructing an event graph that includes multimodal entity subject-predicate logic. Additionally, to mitigate the interference of poor-quality news, Event-Radar introduces a multi-view fusion mechanism, learning comprehensive and robust representations by computing the credibility of each view as a clue, thereby detecting fake news. Extensive experiments demonstrate that Event-Radar achieves outstanding performance on three large-scale fake news detection benchmarks. Our studies also confirm that Event-Radar exhibits strong robustness, providing a paradigm for detecting fake news from noisy news samples.

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Fine-Grained Modeling of Narrative Context: A Coherence Perspective via Retrospective Questions
Liyan Xu | Jiangnan Li | Mo Yu | Jie Zhou

This work introduces an original and practical paradigm for narrative comprehension, stemming from the characteristics that individual passages within narratives tend to be more cohesively related than isolated.Complementary to the common end-to-end paradigm, we propose a fine-grained modeling of narrative context, by formulating a graph dubbed NarCo, which explicitly depicts task-agnostic coherence dependencies that are ready to be consumed by various downstream tasks. In particular, edges in NarCo encompass free-form retrospective questions between context snippets, inspired by human cognitive perception that constantly reinstates relevant events from prior context. Importantly, our graph formalism is practically instantiated by LLMs without human annotations, through our designed two-stage prompting scheme.To examine the graph properties and its utility, we conduct three studies in narratives, each from a unique angle: edge relation efficacy, local context enrichment, and broader application in QA. All tasks could benefit from the explicit coherence captured by NarCo.

Recently, the powerful large language models (LLMs) have been instrumental in propelling the progress of recommender systems (RS). However, while these systems have flourished, their susceptibility to security threats has been largely overlooked. In this work, we reveal that the introduction of LLMs into recommendation models presents new security vulnerabilities due to their emphasis on the textual content of items. We demonstrate that attackers can significantly boost an item’s exposure by merely altering its textual content during the testing phase, without requiring direct interference with the model’s training process. Additionally, the attack is notably stealthy, as it does not affect the overall recommendation performance and the modifications to the text are subtle, making it difficult for users and platforms to detect. Our comprehensive experiments across four mainstream LLM-based recommendation models demonstrate the superior efficacy and stealthiness of our approach. Our work unveils a significant security gap in LLM-based recommendation systems and paves the way for future research on protecting these systems.

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Multi-Dimensional Optimization for Text Summarization via Reinforcement Learning
Sangwon Ryu | Heejin Do | Yunsu Kim | Gary Lee | Jungseul Ok

The evaluation of summary quality encompasses diverse dimensions such as consistency, coherence, relevance, and fluency. However, existing summarization methods often target a specific dimension, facing challenges in generating well-balanced summaries across multiple dimensions. In this paper, we propose multi-objective reinforcement learning tailored to generate balanced summaries across all four dimensions. We introduce two multi-dimensional optimization (MDO) strategies for adaptive learning: 1) MDO_min, rewarding the current lowest dimension score, and 2) MDO_pro, optimizing multiple dimensions similar to multi-task learning, resolves conflicting gradients across dimensions through gradient projection. Unlike prior ROUGE-based rewards relying on reference summaries, we use a QA-based reward model that aligns with human preferences. Further, we discover the capability to regulate the length of summaries by adjusting the discount factor, seeking the generation of concise yet informative summaries that encapsulate crucial points. Our approach achieved substantial performance gains compared to baseline models on representative summarization datasets, particularly in the overlooked dimensions.

In reasoning tasks, even a minor error can cascade into inaccurate results, leading to suboptimal performance of large language models insuch domains. Earlier fine-tuning approaches sought to mitigate this by leveraging more precise supervisory signals from human labeling, larger models, or self-sampling, although at a high cost. Conversely, we develop a method that avoids external resources, relying instead on introducing perturbations to the input. Our training approach randomly masks certain tokens within the chain of thought, a techniquewe found to be particularly effective for reasoning tasks. When applied to fine-tuning with GSM8K on Llama-2-7B, this method achieveda 5% improvement in GSM8K accuracy and a 10% improvement in GSM-IC accuracy over standard supervised fine-tuning with a few codes modified. Furthermore, it is complementary to existing methods. When integrated with related explicit data augmentation methods, it leads to improvements across five datasets of various augmentation methods, as well as two different base models. We further investigate the mechanisms behind this improvement through case studies and quantitative analysis, suggesting that our approach may provide superior support for the model in capturing long-distance dependencies, especially those related to questions. This enhancement could deepen understanding of the premises in questions and prior steps.

Elucidating the reasoning process with structured explanations from question to answer is crucial, as it significantly enhances the interpretability, traceability, and trustworthiness of question-answering (QA) systems. However, structured explanations demand models to perform intricately structured reasoning, which poses great challenges. Most existing methods focus on single-step reasoning through supervised learning, ignoring logical dependencies between steps. Moreover, existing reinforcement learning (RL) based methods overlook the structured relationships, underutilizing the potential of RL in structured reasoning. In this paper, we propose SEER, a novel method that maximizes a structure-based return to facilitate structured reasoning and explanation. Our proposed structure-based return precisely describes the hierarchical and branching structure inherent in structured reasoning, effectively capturing the intricate relationships between different reasoning steps. In addition, we introduce a fine-grained reward function to meticulously delineate diverse reasoning steps. Extensive experiments show that SEER significantly outperforms state-of-the-art methods, achieving an absolute improvement of 6.9% over RL-based methods on EntailmentBank, a 4.4% average improvement on STREET benchmark, and exhibiting outstanding efficiency and cross-dataset generalization performance.

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Towards Robust and Generalized Parameter-Efficient Fine-Tuning for Noisy Label Learning
Yeachan Kim | Junho Kim | SangKeun Lee

Parameter-efficient fine-tuning (PEFT) has enabled the efficient optimization of cumbersome language models in real-world settings. However, as datasets in such environments often contain noisy labels that adversely affect performance, PEFT methods are inevitably exposed to noisy labels. Despite this challenge, the adaptability of PEFT to noisy environments remains underexplored. To bridge this gap, we investigate various PEFT methods under noisy labels. Interestingly, our findings reveal that PEFT has difficulty in memorizing noisy labels due to its inherently limited capacity, resulting in robustness. However, we also find that such limited capacity simultaneously makes PEFT more vulnerable to interference of noisy labels, impeding the learning of clean samples. To address this issue, we propose Clean Routing (CleaR), a novel routing-based PEFT approach that adaptively activates PEFT modules. In CleaR, PEFT modules are preferentially exposed to clean data while bypassing the noisy ones, thereby minimizing the noisy influence. To verify the efficacy of CleaR, we perform extensive experiments on diverse configurations of noisy labels. The results convincingly demonstrate that CleaR leads to substantially improved performance in noisy environments

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SparseFlow: Accelerating Transformers by Sparsifying Information Flows
Yeachan Kim | SangKeun Lee

Transformers have become the de-facto standard for natural language processing. However, dense information flows within transformers pose significant challenges for real-time and resource-constrained devices, as computational complexity grows quadratically with sequence length. To counteract such dense information flows, we propose SparseFlow, a novel efficient method designed to sparsify the dense pathways of token representations across all transformer blocks. To this end, SparseFlow parameterizes the information flows linking token representations to transformer blocks. These parameterized information flows are optimized to be sparse, allowing only the salient information to pass through into the blocks. To validate the efficacy of SparseFlow, we conduct comprehensive experiments across diverse benchmarks (understanding and generation), scales (ranging from millions to billions), architectures (including encoders, decoders, and seq-to-seq models), and modalities (such as language-only and vision-language). The results convincingly demonstrate that sparsifying the dense information flows leads to substantial speedup gains without compromising task accuracy. For instance, SparseFlow reduces computational costs by half on average, without a significant loss in accuracy.

Language Models (LMs) excel in understanding textual descriptions of proteins, as evident in biomedical question-answering tasks. However, their capability falters with raw protein data, such as amino acid sequences, due to a deficit in pretraining on such data. Conversely, Protein Language Models (PLMs) can understand and convert protein data into high-quality representations, but struggle to process texts. To address their limitations, we introduce ProtT3, a framework for Protein-to-Text Generation for Text-based Protein Understanding. ProtT3 empowers an LM to understand protein sequences of amino acids by incorporating a PLM as its protein understanding module, enabling effective protein-to-text generation. This collaboration between PLM and LM is facilitated by a cross-modal projector (i.e., Q-Former) that bridges the modality gap between the PLM’s representation space and the LM’s input space. Unlike previous studies focusing on protein property prediction and protein-text retrieval, we delve into the largely unexplored field of protein-to-text generation. To facilitate comprehensive benchmarks and promote future research, we establish quantitative evaluations for protein-text modeling tasks, including protein captioning, protein question-answering, and protein-text retrieval. Our experiments show that ProtT3 substantially surpasses current baselines, with ablation studies further highlighting the efficacy of its core components. Our code is available at https://github.com/acharkq/ProtT3.

Automatic evaluation methods for large language models (LLMs) are hindered by data contamination, leading to inflated assessments of their effectiveness. Existing strategies, which aim to detect contaminated texts, focus on quantifying contamination status instead of accurately gauging model performance. In this paper, we introduce KIEval, a Knowledge-grounded Interactive Evaluation framework, which incorporates an LLM-powered “interactor” role for the first time to accomplish a dynamic contamination-resilient evaluation. Starting with a question in a conventional LLM benchmark involving domain-specific knowledge, KIEval utilizes dynamically generated, multi-round, and knowledge-focused dialogues to determine whether a model’s response is merely a recall of benchmark answers or demonstrates a deep comprehension to apply knowledge in more complex conversations. Extensive experiments on seven leading LLMs across five datasets validate KIEval’s effectiveness and generalization. We also reveal that data contamination brings no contribution or even negative effect to models’ real-world applicability and understanding, and existing contamination detection methods for LLMs can only identify contamination in pre-training but not during supervised fine-tuning.

Recent advances in Large Language Models (LLMs) have highlighted the need for robust, comprehensive, and challenging benchmarks. Yet, research on evaluating their Emotional Intelligence (EI) is considerably limited. Existing benchmarks have two major shortcomings: first, they mainly focus on emotion recognition, neglecting essential EI capabilities such as emotion management and thought facilitation through emotion understanding; second, they are primarily constructed from existing datasets, which include frequent patterns, explicit information, and annotation errors, leading to unreliable evaluation. We propose EmoBench, a benchmark that draws upon established psychological theories and proposes a comprehensive definition for machine EI, including Emotional Understanding and Emotional Application. EmoBench includes a set of 400 hand-crafted questions in English and Chinese, which are meticulously designed to require thorough reasoning and understanding. Our findings reveal a considerable gap between the EI of existing LLMs and the average human, highlighting a promising direction for future research. Our code and data are publicly available at https://github.com/Sahandfer/EmoBench.

The widespread use of large language models (LLMs) has sparked concerns about the potential misuse of AI-generated text, as these models can produce content that closely resembles human-generated text. Current detectors for AI-generated text (AIGT) lack robustness against adversarial perturbations, with even minor changes in characters or words causing a reversal in distinguishing between human-created and AI-generated text. This paper investigates the robustness of existing AIGT detection methods and introduces a novel detector, the Siamese Calibrated Reconstruction Network (SCRN). The SCRN employs a reconstruction network to add and remove noise from text, extracting a semantic representation that is robust to local perturbations. We also propose a siamese calibration technique to train the model to make equally confident predictions under different noise, which improves the model’s robustness against adversarial perturbations. Experiments on four publicly available datasets show that the SCRN outperforms all baseline methods, achieving 6.5%-18.25% absolute accuracy improvement over the best baseline method under adversarial attacks. Moreover, it exhibits superior generalizability in cross-domain, cross-genre, and mixed-source scenarios. The code is available at https://github.com/CarlanLark/Robust-AIGC-Detector.

Accurate evaluation of financial question answering (QA) systems necessitates a comprehensive dataset encompassing diverse question types and contexts. However, current financial QA datasets lack scope diversity and question complexity. This work introduces FinTextQA, a novel dataset for long-form question answering (LFQA) in finance. FinTextQA comprises 1,262 high-quality, source-attributed QA pairs extracted and selected from finance textbooks and government agency websites.Moreover, we developed a Retrieval-Augmented Generation (RAG)-based LFQA system, comprising an embedder, retriever, reranker, and generator. A multi-faceted evaluation approach, including human ranking, automatic metrics, and GPT-4 scoring, was employed to benchmark the performance of different LFQA system configurations under heightened noisy conditions. The results indicate that: (1) Among all compared generators, Baichuan2-7B competes closely with GPT-3.5-turbo in accuracy score; (2) The most effective system configuration on our dataset involved setting the embedder, retriever, reranker, and generator as Ada2, Automated Merged Retrieval, Bge-Reranker-Base, and Baichuan2-7B, respectively; (3) models are less susceptible to noise after the length of contexts reaching a specific threshold. The dataset is publicly available at: https://huggingface.co/datasets/GPS-Lab/FinTextQA.

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On Measuring Faithfulness or Self-consistency of Natural Language Explanations
Letitia Parcalabescu | Anette Frank

Large language models (LLMs) can explain their predictions through post-hoc or Chain-of-Thought (CoT) explanations. But an LLM could make up reasonably sounding explanations that are unfaithful to its underlying reasoning. Recent work has designed tests that aim to judge the faithfulness of post-hoc or CoT explanations. In this work we argue that these faithfulness tests do not measure faithfulness to the models’ inner workings – but rather their self-consistency at output level.Our contributions are three-fold: i) We clarify the status of faithfulness tests in view of model explainability, characterising them as self-consistency tests instead. This assessment we underline by ii) constructing a Comparative Consistency Bank for self-consistency tests that for the first time compares existing tests on a common suite of 11 open LLMs and 5 tasks – including iii) our new self-consistency measure CC-SHAP. CC-SHAP is a fine-grained measure (not a test) of LLM self-consistency. It compares how a model’s input contributes to the predicted answer and to generating the explanation. Our fine-grained CC-SHAP metric allows us iii) to compare LLM behaviour when making predictions and to analyse the effect of other consistency tests at a deeper level, which takes us one step further towards measuring faithfulness by bringing us closer to the internals of the model than strictly surface output-oriented tests.

Instruction Fine-tuning (IFT) is a crucial phase in building large language models (LLMs). Previous works mainly focus on the IFT’s role in the transfer of behavioral norms and the learning of additional world knowledge. However, the understanding of the underlying mechanisms of IFT remains significantly limited. In this paper, we design a knowledge intervention framework to decouple the potential underlying factors of IFT, thereby enabling individual analysis of different factors. Surprisingly, our experiments reveal that attempting to learn additional world knowledge through IFT often struggles to yield positive impacts and can even lead to markedly negative effects. Further, we discover that maintaining internal knowledge consistency before and after IFT is a critical factor for achieving successful IFT. Our findings reveal the underlying mechanisms of IFT and provide robust support for some very recent and potential future works.

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Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key?
Qineng Wang | Zihao Wang | Ying Su | Hanghang Tong | Yangqiu Song

Recent progress in LLMs discussion suggests that multi-agent discussion improves the reasoning abilities of LLMs. In this work, we reevaluate this claim through systematic experiments, where we propose a novel group discussion framework to enrich the set of discussion mechanisms. Interestingly, our results show that a single-agent LLM with strong prompts can achieve almost the same best performance as the best existing discussion approach on a wide range of reasoning tasks and backbone LLMs. We observed that the multi-agent discussion performs better than a single agent only when there is no demonstration in the prompt. Further study reveals the common interaction mechanisms of LLMs during the discussion. Our code can be found in https://github.com/HKUST-KnowComp/LLM-discussion.

LLM has achieved impressive performance on multi-modal tasks, which have received ever-increasing research attention. Recent research focuses on improving prediction performance and reliability (e.g., addressing the hallucination problem). They often prepend relevant external knowledge to the input text as an extra prompt. However, these methods would be affected by the noise in the knowledge and the context length limitation of LLM. In our work, we focus on making better use of external knowledge and propose a method to actively extract valuable information in the knowledge to produce the latent vector as a soft prompt, which is then fused with the image embedding to form a knowledge-enhanced context to instruct LLM. The experimental results on knowledge-based VQA benchmarks show that the proposed method enjoys better utilization of external knowledge and helps the model achieve better performance.

Large language models (LLMs) have exhibited remarkable performance in various natural language processing tasks. Techniques like instruction tuning have effectively enhanced the proficiency of LLMs in the downstream task of machine translation. However, the existing approaches fail to yield satisfactory translation outputs that match the quality of supervised neural machine translation (NMT) systems. One plausible explanation for this discrepancy is that the straightforward prompts employed in these methodologies are unable to fully exploit the acquired instruction-following capabilities. To this end, we propose the TasTe framework, which stands for translating through self-reflection. The self-reflection process includes two stages of inference. In the first stage, LLMs are instructed to generate preliminary translations and conduct self-assessments on these translations simultaneously. In the second stage, LLMs are tasked to refine these preliminary translations according to the evaluation results. The evaluation results in four language directions on the WMT22 benchmark reveal the effectiveness of our approach compared to existing methods. Our work presents a promising approach to unleash the potential of LLMs and enhance their capabilities in MT. The codes and datasets are open-sourced at https://github.com/YutongWang1216/ReflectionLLMMT.

A pivotal advancement in the progress of large language models (LLMs) is the emergence of the Mixture-of-Experts (MoE) LLMs. Compared to traditional LLMs, MoE LLMs can achieve higher performance with fewer active parameters, but it is still hard to deploy them due to their immense parameter sizes. Different from previous weight pruning methods that rely on specifically designed hardware, this paper mainly aims to enhance the deployment efficiency of MoE LLMs by introducing plug-and-play expert-level sparsification techniques. Specifically, we propose, for the first time to our best knowledge, post-training approaches for task-agnostic and task-specific expert pruning and skipping of MoE LLMs, tailored to improve deployment efficiency while maintaining model performance across a wide range of tasks. Extensive experiments show that our proposed methods can simultaneously reduce model sizes and increase the inference speed, while maintaining satisfactory performance. Code will be made available at https://github.com/Lucky-Lance/Expert_Sparsity.

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UNIMO-G: Unified Image Generation through Multimodal Conditional Diffusion
Wei Li | Xue Xu | Jiachen Liu | Xinyan Xiao

Existing text-to-image diffusion models primarily generate images from text prompts. However, the inherent conciseness of textual descriptions poses challenges in faithfully synthesizing images with intricate details, such as specific entities or scenes. This paper presents UNIMO-G, a simple multimodal conditional diffusion framework that operates on multimodal prompts with interleaved textual and visual inputs, which demonstrates a unified ability for both text-driven and subject-driven image generation. UNIMO-G comprises two core components: a Multimodal Large Language Model (MLLM) for encoding multimodal prompts, and a conditional denoising diffusion network for generating images based on the encoded multimodal input. We leverage a two-stage training strategy to effectively train the framework: firstly pre-training on large-scale text-image pairs to develop conditional image generation capabilities, and then instruction tuning with multimodal prompts to achieve unified image generation proficiency. A well-designed data processing pipeline involving language grounding and image segmentation is employed to construct multi-modal prompts. UNIMO-G excels in both text-to-image generation and zero-shot subject-driven synthesis, and is notably effective in generating high-fidelity images from complex multimodal prompts involving multiple image entities.

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The Fine-Tuning Paradox: Boosting Translation Quality Without Sacrificing LLM Abilities
David Stap | Eva Hasler | Bill Byrne | Christof Monz | Ke Tran

Fine-tuning large language models (LLMs) for machine translation has shown improvements in overall translation quality. However, it is unclear what is the impact of fine-tuning on desirable LLM behaviors that are not present in neural machine translation models, such as steerability, inherent document-level translation abilities, and the ability to produce less literal translations. We perform an extensive translation evaluation on the LLaMA and Falcon family of models with model size ranging from 7 billion up to 65 billion parameters.Our results show that while fine-tuning improves the general translation quality of LLMs, several abilities degrade. In particular, we observe a decline in the ability to perform formality steering, to produce technical translations through few-shot examples, and to perform document-level translation. On the other hand, we observe that the model produces less literal translations after fine-tuning on parallel data. We show that by including monolingual data as part of the fine-tuning data we can maintain the abilities while simultaneously enhancing overall translation quality. Our findings emphasize the need for fine-tuning strategies that preserve the benefits of LLMs for machine translation.

While auxiliary information has become a key to enhancing Large Language Models (LLMs), relatively little is known about how LLMs merge these contexts, specifically contexts generated by LLMs and those retrieved from external sources.To investigate this, we formulate a systematic framework to identify whether LLMs’ responses are attributed to either generated or retrieved contexts.To easily trace the origin of the response, we construct datasets with conflicting contexts, i.e., each question is paired with both generated and retrieved contexts, yet only one of them contains the correct answer.Our experiments reveal a significant bias in several LLMs (GPT-4/3.5 and Llama2) to favor generated contexts, even when they provide incorrect information.We further identify two key factors contributing to this bias: i) contexts generated by LLMs typically show greater similarity to the questions, increasing their likelihood of being selected; ii) the segmentation process used in retrieved contexts disrupts their completeness, thereby hindering their full utilization in LLMs.Our analysis enhances the understanding of how LLMs merge diverse contexts, offers valuable insights for advancing current LLM augmentation methods, and highlights the risk of generated misinformation for retrieval-augmented LLMs.

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Unveiling Linguistic Regions in Large Language Models
Zhihao Zhang | Jun Zhao | Qi Zhang | Tao Gui | Xuanjing Huang

Large Language Models (LLMs) have demonstrated considerable cross-lingual alignment and generalization ability. Current research primarily focuses on improving LLMs’ cross-lingual generalization capabilities. However, there is still a lack of research on the intrinsic mechanisms of how LLMs achieve cross-lingual alignment. From the perspective of region partitioning, this paper conducts several investigations on the linguistic competence of LLMs. We discover a core region in LLMs that corresponds to linguistic competence, accounting for approximately 1% of the total model parameters. Removing this core region by setting parameters to zero results in a significant performance decrease across 30 different languages. Furthermore, this core region exhibits significant dimensional dependence, perturbations to even a single parameter on specific dimensions leading to a loss of linguistic competence. Moreover, we discover that distinct monolingual regions exist for different languages, and disruption to these specific regions substantially reduces the LLMs’ proficiency in those corresponding languages. Our research also indicates that freezing the core linguistic region during further pre-training can mitigate the issue of catastrophic forgetting (CF), a common phenomenon observed during further pre-training of LLMs. Overall, exploring the LLMs’ functional regions provides insights into the foundation of their intelligence.

A song is a combination of singing voice and accompaniment. However, existing works focus on singing voice synthesis and music generation independently. Little attention was paid to exploring song synthesis. In this work, we propose a novel task called Text-to-Song synthesis which incorporates both vocal and accompaniment generation. We develop Melodist, a two-stage text-to-song method that consists of singing voice synthesis (SVS) and vocal-to-accompaniment (V2A) synthesis. Melodist leverages tri-tower contrastive pretraining to learn more effective text representation for controllable V2A synthesis. A Chinese song dataset mined from a music website is built to alleviate data scarcity for our research. The evaluation results on our dataset demonstrate that Melodist can synthesize songs with comparable quality and style consistency. Audio samples can be found in https://text2songMelodist.github.io/Sample/.

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FastFiD: Improve Inference Efficiency of Open Domain Question Answering via Sentence Selection
Yufei Huang | Xu Han | Maosong Sun

Open Domain Question Answering (ODQA) has been advancing rapidly in recent times, driven by significant developments in dense passage retrieval and pretrained language models. State-of-the-art models typically incorporate the FiD framework, which is composed by a neural retriever alongside an encoder-decoder neural reader. In the answer generation process, the retriever will retrieve numerous passages (around 100 for instance), each of which is then individually encoded by the encoder. Subsequently, the decoder makes predictions based on these encoded passages. Nevertheless, this framework can be relatively time-consuming, particularly due to the extensive length of the gathered passages. To address this, we introduce FastFiD in this paper, a novel approach that executes sentence selection on the encoded passages. This aids in retaining valuable sentences while reducing the context length required for generating answers. Experiments on three commonly used datasets (Natural Questions, TriviaQA and ASQA) demonstrate that our method can enhance the inference speed by **2.3X-5.7X**, while simultaneously maintaining the model’s performance. Moreover, an in-depth analysis of the model’s attention reveals that the selected sentences indeed hold a substantial contribution towards the final answer. The codes are publicly available at https://github.com/thunlp/FastFiD.

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Discursive Socratic Questioning: Evaluating the Faithfulness of Language Models’ Understanding of Discourse Relations
Yisong Miao | Hongfu Liu | Wenqiang Lei | Nancy Chen | Min-Yen Kan

While large language models have significantly enhanced the effectiveness of discourse relation classifications, it remains unclear whether their comprehension is faithful and reliable. We provide DiSQ, a new method for evaluating the faithfulness of understanding discourse based on question answering. We first employ in-context learning to annotate the reasoning for discourse comprehension, based on the connections among key events within the discourse. Following this, DiSQ interrogates the model with a sequence of questions to assess its grasp of core event relations, its resilience to counterfactual queries, as well as its consistency to its previous responses. then evaluate language models with different architectural designs using DiSQ, finding: (1) DiSQ presents a significant challenge for all models, with the top-performing GPT model attaining only 41% of the ideal performance in PDTB; (2) DiSQ is robust to domain shifts and paraphrase variations; (3) Open-source models generally lag behind their closed-source GPT counterparts, with notable exceptions being those enhanced with chat and code/math features; (4) Our analysis validates the effectiveness of explicitly signalled discourse connectives, the role of contextual information, and the benefits of using historical QA data.

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An Open Multilingual System for Scoring Readability of Wikipedia
Mykola Trokhymovych | Indira Sen | Martin Gerlach

With over 60M articles, Wikipedia has become the largest platform for open and freely accessible knowledge. While it has more than 15B monthly visits, its content is believed to be inaccessible to many readers due to the lack of readability of its text. However, previous investigations of the readability of Wikipedia have been restricted to English only, and there are currently no systems supporting the automatic readability assessment of the 300+ languages in Wikipedia. To bridge this gap, we develop a multilingual model to score the readability of Wikipedia articles. To train and evaluate this model, we create a novel multilingual dataset spanning 14 languages, by matching articles from Wikipedia to simplified Wikipedia and online children encyclopedias. We show that our model performs well in a zero-shot scenario, yielding a ranking accuracy of more than 80% across 14 languages and improving upon previous benchmarks. These results demonstrate the applicability of the model at scale for languages in which there is no ground-truth data available for model fine-tuning. Furthermore, we provide the first overview on the state of readability in Wikipedia beyond English.

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Unlearning Traces the Influential Training Data of Language Models
Masaru Isonuma | Ivan Titov

Identifying the training datasets that influence a language model’s outputs is essential for minimizing the generation of harmful content and enhancing its performance. Ideally, we can measure the influence of each dataset by removing it from training; however, it is prohibitively expensive to retrain a model multiple times. This paper presents UnTrac: unlearning traces the influence of a training dataset on the model’s performance. UnTrac is extremely simple; each training dataset is unlearned by gradient ascent, and we evaluate how much the model’s predictions change after unlearning. Furthermore, we propose a more scalable approach, UnTrac-Inv, which unlearns a test dataset and evaluates the unlearned model on training datasets. UnTrac-Inv resembles UnTrac, while being efficient for massive training datasets. In the experiments, we examine if our methods can assess the influence of pretraining datasets on generating toxic, biased, and untruthful content. Our methods estimate their influence much more accurately than existing methods while requiring neither excessive memory space nor multiple checkpoints.

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Exploring Alignment in Shared Cross-lingual Spaces
Basel Mousi | Nadir Durrani | Fahim Dalvi | Majd Hawasly | Ahmed Abdelali

Despite their remarkable ability to capture linguistic nuances across diverse languages, questions persist regarding the degree of alignment between languages in multilingual embeddings. Drawing inspiration from research on high-dimensional representations in neural language models, we employ clustering to uncover latent concepts within multilingual models. Our analysis focuses on quantifying the alignment and overlap of these concepts across various languages within the latent space. To this end, we introduce two metrics CALIGN and COLAP aimed at quantifying these aspects, enabling a deeper exploration of multilingual embeddings. Our study encompasses three multilingual models (mT5, mBERT, and XLM-R) and three downstream tasks (Machine Translation, Named Entity Recognition, and Sentiment Analysis). Key findings from our analysis include: i) deeper layers in the network demonstrate increased cross-lingual alignment due to the presence of language-agnostic concepts, ii) fine-tuning of the models enhances alignment within the latent space, and iii) such task-specific calibration helps in explaining the emergence of zero-shot capabilities in the models.

This paper identifies a cultural dominance issue within large language models (LLMs) due to the predominant use of English data in model training (e.g., ChatGPT). LLMs often provide inappropriate English-culture-related answers that are not relevant to the expected culture when users ask in non-English languages. To systematically evaluate the cultural dominance issue, we build a benchmark of concrete (e.g., holidays and songs) and abstract (e.g., values and opinions) cultural objects. Empirical results show that the representative GPT models suffer from the culture dominance problem, where GPT-4 is the most affected while text-davinci-003 suffers the least from this problem. Our study emphasizes the need to critically examine cultural dominance and ethical considerations in their development and deployment. We show that two straightforward methods in model development (i.e., pretraining on more diverse data) and deployment (e.g., culture-aware prompting) can significantly mitigate the cultural dominance issue in LLMs.

To improve the performance of large language models (LLMs), researchers have explored providing LLMs with textual task-solving experience via prompts. However, they rely on manual efforts to acquire and apply such experience for each task, which is not feasible for the growing demand for LLMs and the variety of user questions.To address this issue, we design a lifelong autonomous experiential learning framework based on LLMs to explore whether LLMs can imitate human ability for learning and utilizing experience. It autonomously learns and accumulates experience through experience transfer and induction, categorizing the types of input questions to select which accumulated experience to employ for them.Experimental results on six widely used NLP datasets show that our framework performs reliably in each intermediate step and effectively improves the performance of GPT-3.5 and GPT-4. This validates the feasibility of using LLMs to mimic human experiential learning and application capabilities, offering a new path worth further exploration for the evolution of machine intelligence. Additionally, we provide a detailed analysis of the behavior of our framework at each step.We will open source codes after the acceptance, fostering open research in the NLP community and beyond.

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WRP: Weight Recover Prune for Structured Sparsity
Zhendong Tan | Xingjun Zhang | Zheng Wei

As the scale of Large Language Models (LLMs) increases, it is necessary to compress the models to reduce the substantial demand on computational resources. Network pruning significantly reduces the model size by converting the weight matrix from dense to sparse data format. Current methodologies advocate for one-shot pruning to avoid the expense of retraining, ensuring the maintenance of model performance under conditions of 50%-60% unstructured pruning. Nevertheless, matrices characterized by this level of sparsity could not be treated as sparse matrices, because the indices would incur significant costs. To mitigate this problem, NVIDIA introduced the 2:4 structured sparsity. However, we observe a notable decline in model performance when adopting 2:4 structured sparsity due to group constraints. In this paper, we introduce the Weight Recover Prune (WRP) approach. By recovering a minimal set of critical weights, WRP aims to enhance model performance while maintaining the efficiency of the compression. Our evaluation of the WRP method on the LLAMA2 and OPT models shows that it outperforms other 2:4 pattern one-shot pruning methods. Meanwhile, WRP can guarantee that the size of the pruned model is about 60% of the dense model. Our code is available at: https://github.com/TanZhendong/WRP.

One of the central skills that language learners need to practice is speaking the language. Currently, students in school do not get enough speaking opportunities and lack conversational practice. The recent advances in speech technology and natural language processing allow the creation of novel tools to practice their speaking skills. In this work, we tackle the first component of such a pipeline, namely, the automated speech recognition module (ASR). State-of-the-art models are often trained on adult read-aloud data by native speakers and do not transfer well to young language learners’ speech. Second, most ASR systems contain a powerful language model, which smooths out mistakes made by the speakers. To give corrective feedback, which is a crucial part of language learning, the ASR systems in our setting need to preserve the mistakes made by the language learners. In this work, we build an ASR system that satisfies these requirements: it works on spontaneous speech by young language learners and preserves their mistakes. For this, we collected a corpus containing around 85 hours of English audio spoken by Swiss learners from grades 4 to 6 on different language learning tasks, which we used to train an ASR model. Our experiments show that our model benefits from direct fine-tuning of children’s voices and has a much higher error preservation rate.

Nested Named Entity Recognition (Nested NER) entails identifying and classifying entity spans within the text, including the detection of named entities that are embedded within external entities. Prior approaches primarily employ span-based techniques, utilizing the power of exhaustive searches to address the challenge of overlapping entities. Nonetheless, these methods often grapple with the absence of explicit guidance for boundary detection, resulting insensitivity in discerning minor variations within nested spans. To this end, we propose a Boundary-aware Semantic ̲Differentiation and ̲Filtration ̲Network (DiFiNet) tailored for nested NER. Specifically, DiFiNet leverages a biaffine attention mechanism to generate a span representation matrix. This matrix undergoes further refinement through a self-adaptive semantic differentiation module, specifically engineered to discern semantic variances across spans. Furthermore, DiFiNet integrates a boundary filtration module, designed to mitigate the impact of non-entity noise by leveraging semantic relations among spans. Extensive experiments on three benchmark datasets demonstrate our model yields a new state-of-the-art performance.

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Legal Case Retrieval: A Survey of the State of the Art
Yi Feng | Chuanyi Li | Vincent Ng

Recent years have seen increasing attention on Legal Case Retrieval (LCR), a key task in the area of Legal AI that concerns the retrieval of cases from a large legal database of historical cases that are similar to a given query. This paper presents a survey of the major milestones made in LCR research, targeting researchers who are finding their way into the field and seek a brief account of the relevant datasets and the recent neural models and their performances.

Compositional generalization, representing the model’s ability to generate text with new attribute combinations obtained by recombining single attributes from the training data, is a crucial property for multi-aspect controllable text generation (MCTG) methods. Nonetheless, a comprehensive compositional generalization evaluation benchmark of MCTG is still lacking. We propose CompMCTG, a benchmark encompassing diverse multi-aspect labeled datasets and a crafted three-dimensional evaluation protocol, to holistically evaluate the compositional generalization of MCTG approaches. We observe that existing MCTG works generally confront a noticeable performance drop in compositional testing. To mitigate this issue, we introduce Meta-MCTG, a training framework incorporating meta-learning, where we enable models to learn how to generalize by simulating compositional generalization scenarios in the training phase. We demonstrate the effectiveness of Meta-MCTG through achieving obvious improvement (by at most 3.64%) for compositional testing performance in 94.4%.

Humans generally acquire new skills without compromising the old; however, the opposite holds for Large Language Models (LLMs), e.g., from LLaMA to CodeLLaMA. To this end, we propose a new post-pretraining method for LLMs with an expansion of Transformer blocks. We tune the expanded blocks using only new corpus, efficiently and effectively improving the model’s knowledge while mitigating forgetting. In this paper, we experiment on the corpus of code and math, yielding LLaMA Pro-8.3B, a versatile foundation model initialized from LLaMA2-7B, excelling in general tasks, programming, and mathematics. LLaMA Pro and its instruction-following counterpart (LLaMA Pro - Instruct) achieve advanced performance among various benchmarks, demonstrating superiority over existing open models in the LLaMA family and the immense potential of reasoning and addressing diverse tasks as an intelligent agent. Our findings provide valuable insights into integrating natural and programming languages, laying a solid foundation for developing advanced language agents that operate effectively in various environments.

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Generating Contrastive Narratives Using the Brownian Bridge Process for Narrative Coherence Learning
Feiteng Mu | Wenjie Li

A major challenge for narrative reasoning is to learn narrative coherence. Existing works mainly follow the contrastive learning paradigm. However, the negative samples in their methods can be easily distinguished, which makes their methods unsatisfactory. In this work, we devise two strategies for mining hard negatives, including (1) crisscrossing a narrative and its contrastive variants; and (2) event-level replacement. To obtain contrastive variants, we utilize the Brownian Bridge process to guarantee the quality of generated contrastive narratives. We evaluate our model on several tasks. The result proves the effectiveness of our method, and shows that our method is applicable to many applications.

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A Causal Approach for Counterfactual Reasoning in Narratives
Feiteng Mu | Wenjie Li

Counterfactual reasoning in narratives requires predicting how alternative conditions, contrary to what actually happened, might have resulted in different outcomes.One major challenge is to maintain the causality between the counterfactual condition and the generated counterfactual outcome. In this paper, we propose a basic VAE module for counterfactual reasoning in narratives. We further introduce a pre-trained classifier and external event commonsense to mitigate the posterior collapse problem in the VAE approach, and improve the causality between the counterfactual condition and the generated counterfactual outcome. We evaluate our method on two public benchmarks. Experiments show that our method is effective.

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SIP: Injecting a Structural Inductive Bias into a Seq2Seq Model by Simulation
Matthias Lindemann | Alexander Koller | Ivan Titov

Strong inductive biases enable learning from little data and help generalization outside the training distribution. Popular neural architectures such as Transformers lack strong structural inductive biases for seq2seq NLP tasks on their own. Consequently, they struggle with systematic generalization beyond the training distribution, e.g. with extrapolating to longer inputs, even when pre-trained on large amounts of text.We show how a structural inductive bias can be efficiently injected into a seq2seq model by pre-training it to simulate structural transformations on synthetic data. Specifically, we inject an inductive bias towards Finite State Transducers (FSTs) into a Transformer by pre-training it to simulate FSTs given their descriptions. Our experiments show that our method imparts the desired inductive bias, resulting in improved systematic generalization and better few-shot learning for FST-like tasks. Our analysis shows that fine-tuned models accurately capture the state dynamics of the unseen underlying FSTs, suggesting that the simulation process is internalized by the fine-tuned model.

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The Hidden Space of Transformer Language Adapters
Jesujoba Alabi | Marius Mosbach | Matan Eyal | Dietrich Klakow | Mor Geva

We analyze the operation of transformer language adapters, which are small modules trained on top of a frozen language model to adapt its predictions to new target languages. We show that adapted predictions mostly evolve in the source language the model was trained on, while the target language becomes pronounced only in the very last layers of the model. Moreover, the adaptation process is gradual and distributed across layers, where it is possible to skip small groups of adapters without decreasing adaptation performance. Last, we show that adapters operate on top of the model’s frozen representation space while largely preserving its structure, rather than on an isolated subspace. Our findings provide a deeper view into the adaptation process of language models to new languages, showcasing the constraints imposed on it by the underlying model and introduces practical implications to enhance its efficiency.

In the realm of text manipulation and linguistic transformation, the question of authorship has been a subject of fascination and philosophical inquiry. Much like the Ship of Theseus paradox, which ponders whether a ship remains the same when each of its original planks is replaced, our research delves into an intriguing question: Does a text retain its original authorship when it undergoes numerous paraphrasing iterations? Specifically, since Large Language Models (LLMs) have demonstrated remarkable proficiency in both the generation of original content and the modification of human-authored texts, a pivotal question emerges concerning the determination of authorship in instances where LLMs or similar paraphrasing tools are employed to rephrase the text–i.e., whether authorship should be attributed to the original human author or the AI-powered tool. Therefore, we embark on a philosophical voyage through the seas of language and authorship to unravel this intricate puzzle. Using a computational approach, we discover that the diminishing performance in text classification models, with each successive paraphrasing iteration, is closely associated with the extent of deviation from the original author’s style, thus provoking a reconsideration of the current notion of authorship.

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Advancing Large Language Models to Capture Varied Speaking Styles and Respond Properly in Spoken Conversations
Guan-Ting Lin | Cheng-Han Chiang | Hung-yi Lee

In spoken dialogue, even if two current turns are the same sentence, their responses might still differ when they are spoken in different styles. The spoken styles, containing paralinguistic and prosodic information, mark the most significant difference between text and speech modality. When using text-only LLMs to model spoken dialogue, text-only LLMs cannot give different responses based on the speaking style of the current turn. In this paper, we focus on enabling LLMs to listen to the speaking styles and respond properly. Our goal is to teach the LLM that “even if the sentences are identical if they are spoken in different styles, their corresponding responses might be different”. Since there is no suitable dataset for achieving this goal, we collect a speech-to-speech dataset, StyleTalk, with the following desired characteristics: when two current speeches have the same content but are spoken in different styles, their responses will be different. To teach LLMs to understand and respond properly to the speaking styles, we propose the Spoken-LLM framework that can model the linguistic content and the speaking styles. We train Spoken-LLM using the StyleTalk dataset and devise a two-stage training pipeline to help the Spoken-LLM better learn the speaking styles. Based on extensive experiments, we show that Spoken-LLM outperforms text-only baselines and prior speech LLMs methods.

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RetinaQA: A Robust Knowledge Base Question Answering Model for both Answerable and Unanswerable Questions
Prayushi Faldu | Indrajit Bhattacharya | Mausam .

An essential requirement for a real-world Knowledge Base Question Answering (KBQA) system is the ability to detect the answerability of questions when generating logical forms. However, state-of-the-art KBQA models assume all questions to be answerable. Recent research has found that such models, when superficially adapted to detect answerability, struggle to satisfactorily identify the different categories of unanswerable questions, and simultaneously preserve good performance for answerable questions. Towards addressing this issue, we propose RetinaQA, a new KBQA model that unifies two key ideas in a single KBQA architecture: (a) discrimination over candidate logical forms, rather than generating these, for handling schema-related unanswerability, and (b) sketch-filling-based construction of candidate logical forms for handling data-related unaswerability. Our results show that RetinaQA significantly outperforms adaptations of state-of-the-art KBQA models in handling both answerable and unanswerable questions and demonstrates robustness across all categories of unanswerability. Notably, RetinaQA also sets a new state-of-the-art for answerable KBQA, surpassing existing models. We release our code base for further research: https://github.com/dair-iitd/RetinaQA.

Multi-modal large language models (MLLMs) have demonstrated remarkable performance across various tasks. However, these models often prioritize capturing global information and overlook the importance of perceiving local information. This limitation hinders their ability to effectively understand fine-grained details and handle grounding tasks that necessitate nuanced comprehension. Although some recent works have made strides in this, they have primarily focused on single-modality inputs. Therefore, we propose GroundingGPT, an end-to-end language enhanced multi-modal grounding model. It is designed to perform fine-grained grounding tasks for three modalities: image, video and audio. To enhance the model’s performance, we adopt a coarse-to-fine training strategy, utilizing a three-stage training approach to progressively enhance the model’s semantic awareness and fine-grained understanding capabilities. Additionally, we employ a diversified stage-specific dataset construction pipeline, developing a multi-modal, multi-granularity dataset tailored for training the model in different stages. Extensive experiments conducted on multiple multi-modal benchmarks demonstrate that our model achieves impressive fine-grained understanding of multi-modal inputs on grounding tasks while maintaining or improving its global comprehension capabilities. Our code, model, and dataset are available at https://github.com/lzw-lzw/GroundingGPT.

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Automated Justification Production for Claim Veracity in Fact Checking: A Survey on Architectures and Approaches
Islam Eldifrawi | Shengrui Wang | Amine Trabelsi

Automated Fact-Checking (AFC) is the automated verification of claim accuracy. AFC is crucial in discerning truth from misinformation, especially given the huge amounts of content are generated online daily. Current research focuses on predicting claim veracity through metadata analysis and language scrutiny, with an emphasis on justifying verdicts. This paper surveys recent methodologies, proposinga comprehensive taxonomy and presenting the evolution of research in that landscape. A comparative analysis of methodologies and futuredirections for improving fact-checking explainability are also discussed.

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Decoupled Vocabulary Learning Enables Zero-Shot Translation from Unseen Languages
Carlos Mullov | Quan Pham | Alexander Waibel

Multilingual neural machine translation systems learn to map sentences of different languages into a common representation space. Intuitively, with a growing number of seen languages the encoder sentence representation grows more flexible and easily adaptable to new languages. In this work, we test this hypothesis by zero-shot translating from unseen languages. To deal with unknown vocabularies from unknown languages we propose a setup where we decouple learning of vocabulary and syntax, i.e. for each language we learn word representations in a separate step (using cross-lingual word embeddings), and then train to translate while keeping those word representations frozen. We demonstrate that this setup enables zero-shot translation from entirely unseen languages. Zero-shot translating with a model trained on Germanic and Romance languages we achieve scores of 42.6 BLEU for Portuguese-English and 20.7 BLEU for Russian-English on TED domain. We explore how this zero-shot translation capability develops with varying number of languages seen by the encoder. Lastly, we explore the effectiveness of our decoupled learning strategy for unsupervised machine translation. By exploiting our model’s zero-shot translation capability for iterative back-translation we attain near parity with a supervised setting.

Mixture of experts (MoE) is a popular technique to improve capacity of Large Language Models (LLMs) with conditionally-activated parallel experts. However, serving MoE models on memory-constrained devices is challenging due to the large parameter size. Typical solutions such as memory swapping or expert pruning may lead to significantly higher latency or severe accuracy loss.In this paper, we introduce SwapMoE, a framework for efficient serving of MoE-based large language models with tunable memory budgets. The main idea of SwapMoE is to keep a small dynamic set of important experts, namely Virtual Experts, in the main memory for inference, while seamlessly maintaining how the Virtual Experts map to the actual experts. Experiments have shown that SwapMoE can reduce the memory footprint while maintaining reasonable accuracy. For example, on text summarization tasks with Switch Transformer, SwapMoE can reduce the memory consumption from 14.2 GiB to 4.7 GiB, together with 50% latency reduction and a slight Rouge-2 score drop of 0.041.

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PixT3: Pixel-based Table-To-Text Generation
Iñigo Alonso | Eneko Agirre | Mirella Lapata

Table-to-text generation involves generating appropriate textual descriptions given structured tabular data. It has attracted increasing attention in recent years thanks to the popularity of neural network models and the availability of large-scale datasets. A common feature across existing methods is their treatment of the input as a string, i.e., by employing linearization techniques that do not always preserve information in the table, are verbose, and lack space efficiency. We propose to rethink data-to-text generation as a visual recognition task, removing the need for rendering the input in a string format. We present PixT3, a multimodal table-to-text model that overcomes the challenges of linearization and input size limitations encountered by existing models. PixT3 is trained with a new self-supervised learning objective to reinforce table structure awareness and is applicable to open-ended and controlled generation settings. Experiments on the ToTTo and Logic2Text benchmarks show that PixT3 is competitive and, in some settings, superior to generators that operate solely on text.

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Narrowing the Knowledge Evaluation Gap: Open-Domain Question Answering with Multi-Granularity Answers
Gal Yona | Roee Aharoni | Mor Geva

Factual questions typically can be answered correctly at different levels of granularity. For example, both “August 4, 1961” and “1961” are correct answers to the question “When was Barack Obama born?”. Standard question answering (QA) evaluation protocols, however, do not explicitly take this into account and compare a predicted answer against answers of a single granularity level. In this work, we propose GRANOLA QA, a novel evaluation setting where a predicted answer is evaluated in terms of accuracy and informativeness against a set of multi-granularity answers. We present a simple methodology for enriching existing datasets with multi-granularity answers, and create GRANOLA-EQ, a multi-granularity version of the EntityQuestions dataset. We evaluate a range of decoding methods on GRANOLA-EQ, including a new algorithm, called Decoding with Response Aggregation (DRAG), that is geared towards aligning the response granularity with the model’s uncertainty. Our experiments show that large language models with standard decoding tend to generate specific answers, which are often incorrect. In contrast, when evaluated on multi-granularity answers, DRAG yields a nearly 20 point increase in accuracy on average, which further increases for rare entities. Overall, this reveals that standard evaluation and decoding schemes may significantly underestimate the knowledge encapsulated in LMs.

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TAMS: Translation-Assisted Morphological Segmentation
Enora Rice | Ali Marashian | Luke Gessler | Alexis Palmer | Katharina von der Wense

Canonical morphological segmentation is the process of analyzing words into the standard (aka underlying) forms of their constituent morphemes.This is a core task in endangered language documentation, and NLP systems have the potential to dramatically speed up this process. In typical language documentation settings, training data for canonical morpheme segmentation is scarce, making it difficult to train high quality models. However, translation data is often much more abundant, and, in this work, we present a method that attempts to leverage translation data in the canonical segmentation task. We propose a character-level sequence-to-sequence model that incorporates representations of translations obtained from pretrained high-resource monolingual language models as an additional signal. Our model outperforms the baseline in a super-low resource setting but yields mixed results on training splits with more data. Additionally, we find that we can achieve strong performance even without needing difficult-to-obtain word level alignments. While further work is needed to make translations useful in higher-resource settings, our model shows promise in severely resource-constrained settings.

Recently, pre-trained large language models (LLMs) have shown impressive abilities in generating codes from natural language descriptions, repairing buggy codes, translating codes between languages, and retrieving relevant code segments. However, the evaluation of these models has often been performed in a scattered way on only one or two specific tasks, in a few languages, at a partial granularity (e.g., function) level, and in many cases without proper training data. Even more concerning is that in most cases the evaluation of generated codes has been done in terms of mere lexical overlap with a reference code rather than actual execution. We introduce *xCodeEval*, the largest executable multilingual multitask benchmark to date consisting of 25 M document-level coding examples (16.5 B tokens) from about 7.5 K unique problems covering up to 11 programming languages with execution-level parallelism. It features a total of 7 tasks involving code understanding, generation, translation and retrieval. *xCodeEval* adopts an execution-based evaluation and offers a multilingual code execution engine, *ExecEval* that supports unit test based execution in all the 11 languages. To address the challenge of balancing the distributions of text-code samples over multiple attributes in validation/test sets, we propose a novel data splitting and a data selection schema based on the geometric mean and graph-theoretic principle. Our experiments with OpenAI’s LLMs (zero-shot) and open-LLMs (zero-shot and fine-tuned) on the tasks and languages demonstrate to be quite challenging as per the current advancements in language models.

Large Language Models (LLMs) have succeeded remarkably in understanding long-form contents. However, exploring their capability for generating long-form contents, such as reports and articles, has been relatively unexplored and inadequately assessed by existing benchmarks. The prevalent evaluation methods, which predominantly rely on crowdsourcing, are recognized for their labor-intensive nature and lack of efficiency, whereas automated metrics, such as the ROUGE score, demonstrate discordance with human judgment criteria. In this paper, we propose ProxyQA, an innovative framework dedicated to assessing long-text generation. ProxyQA comprises in-depth human-curated meta-questions spanning various domains, each accompanied by specific proxy-questions with pre-annotated answers. LLMs are tasked to generate extensive content in response to these meta-questions, by engaging an evaluator and incorporating the generated texts as contextual background, ProxyQA assesses the generated content’s quality through the evaluator’s accuracy in addressing the proxy-questions. We examine multiple LLMs, emphasizing ProxyQA’s demanding nature as a high-quality assessment tool. Human evaluation demonstrates that the proxy-question method is notably self-consistent and aligns closely with human evaluative standards. The dataset and leaderboard is available at https://proxy-qa.com.

Large language models (LLMs) have an impressive ability to draw on novel information supplied in their context. Yet the mechanisms underlying this contextual grounding remain unknown, especially in situations where contextual information contradicts factual knowledge stored in the parameters, which LLMs also excel at recalling. Favoring the contextual information is critical for retrieval-augmented generation methods, which enrich the context with up-to-date information, hoping that grounding can rectify outdated or noisy stored knowledge. We present a novel method to study grounding abilities using Fakepedia, a novel dataset of counterfactual texts constructed to clash with a model’s internal parametric knowledge. In this study, we introduce Fakepedia, a counterfactual dataset designed to evaluate grounding abilities when the internal parametric knowledge clashes with the contextual information. We benchmark various LLMs with Fakepedia and conduct a causal mediation analysis of LLM components when answering Fakepedia queries, based on our Masked Grouped Causal Tracing (MGCT) method. Through this analysis, we identify distinct computational patterns between grounded and ungrounded responses. We finally demonstrate that distinguishing grounded from ungrounded responses is achievable through computational analysis alone. Our results, together with existing findings about factual recall mechanisms, provide a coherent narrative of how grounding and factual recall mechanisms interact within LLMs.

Multipanel images, commonly seen as web screenshots, posters, etc., pervade our daily lives. These images, characterized by their composition of multiple subfigures in distinct layouts, effectively convey information to people. Toward building advanced multimodal AI applications, such as agents that understand complex scenes and navigate through webpages, the skill of multipanel visual reasoning is essential, and a comprehensive evaluation of models in this regard is important. Therefore, we introduce Multipanel Visual Question Answering (MultipanelVQA), a novel benchmark comprising 6,600 triplets of questions, answers, and multipanel images that specifically challenge models in comprehending multipanel images. Our evaluation shows that questions in the MultipanelVQA benchmark pose significant challenges to the state-of-the-art Multimodal Large Language Models (MLLMs) tested, even though humans can attain approximately 99% accuracy on these questions. Distinctively, the MultipanelVQA benchmark features synthetically generated multipanel images specifically crafted to isolate and assess the impact of various factors, such as the layout, on MLLMs’ multipanel image comprehension abilities. As a result, in addition to benchmarking the capabilities of MLLMs in understanding multipanel images, we analyze various factors of the multipanel image that affect MLLMs’ performance with synthetic data and offer insights for enhancement.

The rapid advancement of large language models (LLMs) has led to a new era marked by the development of autonomous applications in real-world scenarios, which drives innovation in creating advanced web agents. Existing web agents typically only handle one input modality and are evaluated only in simplified web simulators or static web snapshots, greatly limiting their applicability in real-world scenarios. To bridge this gap, we introduce WebVoyager, an innovative Large Multimodal Model (LMM) powered web agent that can complete user instructions end-to-end by interacting with real-world websites. Moreover, we establish a new benchmark by compiling real-world tasks from 15 popular websites and introduce an automatic evaluation protocol leveraging multimodal understanding abilities of GPT-4V to evaluate open-ended web agents. We show that WebVoyager achieves a 59.1% task success rate on our benchmark, significantly surpassing the performance of both GPT-4 (All Tools) and the WebVoyager (text-only) setups, underscoring the exceptional capability of WebVoyager. The proposed automatic evaluation metric achieves 85.3% agreement with human judgment, indicating its effectiveness in providing reliable and accurate assessments of web agents.

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Translation-based Lexicalization Generation and Lexical Gap Detection: Application to Kinship Terms
Senyu Li | Bradley Hauer | Ning Shi | Grzegorz Kondrak

Constructing lexicons with explicitly identified lexical gaps is a vital part of building multilingual lexical resources. Prior work has leveraged bilingual dictionaries and linguistic typologies for semi-automatic identification of lexical gaps. Instead, we propose a generally-applicable algorithmic method to automatically generate concept lexicalizations, which is based on machine translation and hypernymy relations between concepts. The absence of a lexicalization implies a lexical gap. We apply our method to kinship terms, which make a suitable case study because of their explicit definitions and regular structure. Empirical evaluations demonstrate that our approach yields higher accuracy than BabelNet and ChatGPT. Our error analysis indicates that enhancing the quality of translations can further improve the accuracy of our method.

We present a generalizable classification approach that leverages Large Language Models (LLMs) to facilitate the detection of implicitly encoded social meaning in conversations. We design a multi-faceted prompt to extract a textual explanation of the reasoning that connects visible cues to underlying social meanings. These extracted explanations or rationales serve as augmentations to the conversational text to facilitate dialogue understanding and transfer. Our empirical results over 2,340 experimental settings demonstrate the significant positive impact of adding these rationales. Our findings hold true for in-domain classification, zero-shot, and few-shot domain transfer for two different social meaning detection tasks, each spanning two different corpora.

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Robust Frame-Semantic Models with Lexical Unit Trees and Negative Samples
Jacob Devasier | Yogesh Gurjar | Chengkai Li

We present novel advancements in frame-semantic parsing, specifically focusing on target identification and frame identification. Our target identification model employs a novel prefix tree modification to enable robust support for multi-word lexical units, resulting in a coverage of 99.4% of the targets in the FrameNet 1.7 fulltext annotations. It utilizes a RoBERTa-based filter to achieve an F1 score of 0.775, surpassing the previous state-of-the-art solution by +0.012. For frame identification, we introduce a modification to the standard multiple-choice classification paradigm by incorporating additional negative frames for targets with limited candidate frames, resulting in a +0.014 accuracy improvement over the frame-only model of FIDO, the previous state-of-the-art system, and +0.002 over its full system. Our approach significantly enhances performance on rare frames, exhibiting an improvement of +0.044 over FIDO’s accuracy on frames with 5 or fewer samples, and on under-utilized frames, with an improvement of +0.139 on targets with a single candidate frame. Overall, our contributions address critical challenges and advance the state-of-the-art in frame-semantic parsing.

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Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translation
Yuan Yang | Siheng Xiong | Ali Payani | Ehsan Shareghi | Faramarz Fekri

Advancements in logical reasoning, utilizing LLMs to convert natural language into logical symbolism, combined with the use of external theorem provers, have repositioned the symbolic approach as a central point of interest. The main challenge within this paradigm lies in the LLMs’ capability to accurately translate natural language (NL) statements into first-order-logic (FOL) expressions. Although LLMs have shown notable success, there remains a gap in understanding the limitations and challenges they encounter in NL-FOL translation. This is primarily due to the absence of datasets and evaluation test beds at the required fine-grained level. We present MALLS, a dataset of 28K diverse and verified sentence-level NL-FOL pairs collected from GPT4. We utilize a combined strategy of FOL rule parsing, human annotation, and automatic filtering to ensure quality. We also present LogicLLaMA, a LLaMA2-7B/13B fine-tuned on MALLS for NL-FOL translation, which can be used standalone or to correct previously generated rules by GPT3.5 after being further fine-tuned via a novel reinforcement learning with human feedback (RLHF) framework. We benchmark a wide range of LLMs on MALLS and previous datasets, highlighting weaknesses in them in NL-FOL translation and demonstrating the advantages of MALLS. We also show that LogicLLaMA achieves GPT4-level performance and can generalize to other datasets. Project repo is available at https://github.com/gblackout/LogicLLaMA

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Lightweight reranking for language model generations
Siddhartha Jain | Xiaofei Ma | Anoop Deoras | Bing Xiang

Large Language Models (LLMs) can exhibit considerable variation in the quality of their sampled outputs. Reranking and selecting the best generation from the sampled set is a popular way of obtaining strong gains in generation quality. In this paper, we present a novel approach for reranking LLM generations. Unlike other techniques that might involve additional inferences or training a specialized reranker, our approach relies on easy to compute pairwise statistics between the generations that have minimal compute overhead. We show that our approach can be formalized as an extension of self-consistency and analyze its performance in that framework, theoretically as well as via simulations. We show strong improvements for selecting the best k generations for code generation tasks as well as robust improvements for the best generation for the tasks of autoformalization, summarization, and translation. While our approach only assumes black-box access to LLMs, we show that additional access to token probabilities can improve performance even further.

We introduce the task of automatically revising scientific papers based on peer feedback and release ARIES, a dataset of review comments and their corresponding paper edits. The data is drawn from real reviewer-author interactions from computer science, and we provide labels linking each reviewer comment to the specific paper edits made by the author in response. We automatically create a high-precision silver training set, as well as an expert-labeled test set that shows high inter-annotator agreement. In experiments with 10 models covering the state of the art, we find that they struggle even to identify which edits correspond to a comment—especially when the relationship between the edit and the comment is indirect and requires reasoning to uncover. We also extensively analyze GPT-4’s ability to generate edits given a comment and the original paper. We find that it often succeeds on a superficial level, but tends to rigidly follow the wording of the feedback rather than the underlying intent, and lacks technical details compared to human-written edits.

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The Unreasonable Effectiveness of Easy Training Data for Hard Tasks
Peter Hase | Mohit Bansal | Peter Clark | Sarah Wiegreffe

How can we train models to perform well on hard test data when hard training data is by definition difficult to label correctly? This question has been termed the scalable oversight problem and has drawn increasing attention as language models have continually improved. In this paper, we present the surprising conclusion that current pretrained language models often generalize relatively well from easy to hard data, even performing as well as oracle models finetuned on hard data. We demonstrate this kind of easy-to-hard generalization using simple finetuning methods like in-context learning, linear classifier heads, and QLoRA for seven different measures of datapoint hardness, including six empirically diverse human hardness measures (like grade level) and one model-based measure (loss-based). Furthermore, we show that even if one cares most about model performance on hard data, it can be better to collect easy data rather than hard data for finetuning, since hard data is generally noisier and costlier to collect. Our experiments use open models up to 70b in size and four publicly available question-answering datasets with questions ranging in difficulty from 3rd grade science questions to college level STEM questions and general-knowledge trivia. We conclude that easy-to-hard generalization in LMs is surprisingly strong for the tasks studied.

Instruction tuning has remarkably advanced large language models (LLMs) in understanding and responding to diverse human instructions. Despite the success in high-resource languages, its application in lower-resource ones faces challenges due to the imbalanced foundational abilities of LLMs across different languages, stemming from the uneven language distribution in their pre-training data. To tackle this issue, we propose pivot language guided generation (PLUG), an approach that utilizes a high-resource language, primarily English, as the pivot to enhance instruction tuning in lower-resource languages. It trains the model to first process instructions in the pivot language, and then produce responses in the target language. To evaluate our approach, we introduce a benchmark, X-AlpacaEval, of instructions in 4 languages (Chinese, Korean, Italian, and Spanish), each annotated by professional translators. Our approach demonstrates a significant improvement in the instruction-following abilities of LLMs by 29% on average, compared to directly responding in the target language alone. Further experiments validate the versatility of our approach by employing alternative pivot languages beyond English to assist languages where LLMs exhibit lower proficiency. Code and data are available at https://github.com/ytyz1307zzh/PLUG.

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MIDGARD: Self-Consistency Using Minimum Description Length for Structured Commonsense Reasoning
Inderjeet Nair | Lu Wang

We study the task of conducting structured reasoning as generating a reasoning graph from natural language input using large language models (LLMs). Previous approaches have explored various prompting schemes, yet they suffer from error propagation due to the autoregressive nature and single-pass-based decoding, which lack error correction capability. Additionally, relying solely on a single sample may result in the omission of true nodes and edges. To counter this, we draw inspiration from self-consistency (SC), which involves sampling a diverse set of reasoning chains and taking the majority vote as the final answer. To tackle the substantial challenge of applying SC on generated graphs, we propose MIDGARD (MInimum Description length Guided Aggregation of Reasoning in Directed acyclic graph) that leverages Minimum Description Length (MDL)-based formulation to identify consistent properties among the different graph samples generated by an LLM. This formulation helps reject properties that appear in only a few samples, which are likely to be erroneous, while enabling the inclusion of missing elements without compromising precision. Our method demonstrates superior performance than comparisons across various structured reasoning tasks, including argument structure extraction, explanation graph generation, inferring dependency relations among actions for everyday tasks, and semantic graph generation from natural texts.

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ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs
Justin Chen | Swarnadeep Saha | Mohit Bansal

Large Language Models (LLMs) still struggle with natural language reasoning tasks. Motivated by the society of minds (Minsky, 1988), we propose ReConcile, a multi-model multi-agent framework designed as a round table conference among diverse LLM agents. ReConcile enhances collaborative reasoning between LLM agents via multiple rounds of discussion, learning to convince other agents to improve their answers, and employing a confidence-weighted voting mechanism that leads to a better consensus. In each round, ReConcile initiates discussion between agents via a ‘discussion prompt’ that consists of (a) grouped answers and explanations generated by each agent in the previous round, (b) their confidence scores, and (c) demonstrations of answer-rectifying human explanations, used for convincing other agents. Experiments on seven benchmarks demonstrate that ReConcile significantly improves LLMs’ reasoning – both individually and as a team – surpassing prior single-agent and multi-agent baselines by up to 11.4% and even outperforming GPT-4 on three datasets. ReConcile also flexibly incorporates different combinations of agents, including API-based, open-source, and domain-specific models, leading to an 8% improvement on MATH. Finally, we analyze the individual components of ReConcile, demonstrating that the diversity originating from different models is critical to its superior performance.

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Mirror: Multiple-perspective Self-Reflection Method for Knowledge-rich Reasoning
Hanqi Yan | Qinglin Zhu | Xinyu Wang | Lin Gui | Yulan He

While Large language models (LLMs) have the capability to iteratively reflect on their own outputs, recent studies have observed their struggles with knowledge-rich problems without access to external resources. In addition to the inefficiency of LLMs in self-assessment, we also observe that LLMs struggle to revisit their predictions despite receiving explicit negative feedback. Therefore, We propose Mirror, a Multiple-perspective self-reflection method for knowledge-rich reasoning, to avoid getting stuck at a particular reflection iteration. Mirror enables LLMs to reflect from multiple-perspective clues, achieved through a heuristic interaction between a Navigator and a Reasoner. It guides agents toward diverse yet plausibly reliable reasoning trajectory without access to ground truth by encouraging (1) diversity of directions generated by Navigator and (2) agreement among strategically induced perturbations in responses generated by the Reasoner. The experiments on five reasoning datasets demonstrate that Mirror’s superiority over several contemporary self-reflection approaches. Additionally, the ablation study studies clearly indicate that our strategies alleviate the aforementioned challenges.

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Where Do People Tell Stories Online? Story Detection Across Online Communities
Maria Antoniak | Joel Mire | Maarten Sap | Elliott Ash | Andrew Piper

Story detection in online communities is a challenging task as stories are scattered across communities and interwoven with non-storytelling spans within a single text. We address this challenge by building and releasing the StorySeeker toolkit, including a richly annotated dataset of 502 Reddit posts and comments, a detailed codebook adapted to the social media context, and models to predict storytelling at the document and span levels. Our dataset is sampled from hundreds of popular English-language Reddit communities ranging across 33 topic categories, and it contains fine-grained expert annotations, including binary story labels, story spans, and event spans. We evaluate a range of detection methods using our data, and we identify the distinctive textual features of online storytelling, focusing on storytelling spans, which we introduce as a new task. We illuminate distributional characteristics of storytelling on a large community-centric social media platform, and we also conduct a case study on r/ChangeMyView, where storytelling is used as one of many persuasive strategies, illustrating that our data and models can be used for both inter- and intra-community research. Finally, we discuss implications of our tools and analyses for narratology and the study of online communities.

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Large Language Models Are No Longer Shallow Parsers
Yuanhe Tian | Fei Xia | Yan Song

The development of large language models (LLMs) brings significant changes to the field of natural language processing (NLP), enabling remarkable performance in various high-level tasks, such as machine translation, question-answering, dialogue generation, etc., under end-to-end settings without requiring much training data. Meanwhile, fundamental NLP tasks, particularly syntactic parsing, are also essential for language study as well as evaluating the capability of LLMs for instruction understanding and usage. In this paper, we focus on analyzing and improving the capability of current state-of-the-art LLMs on a classic fundamental task, namely constituency parsing, which is the representative syntactic task in both linguistics and natural language processing. We observe that these LLMs are effective in shallow parsing but struggle with creating correct full parse trees. To improve the performance of LLMs on deep syntactic parsing, we propose a three-step approach that firstly prompts LLMs for chunking, then filters out low-quality chunks, and finally adds the remaining chunks to prompts to instruct LLMs for parsing, with later enhancement by chain-of-thought prompting. Experimental results on English and Chinese benchmark datasets demonstrate the effectiveness of our approach on improving LLMs’ performance on constituency parsing.

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Dialogue Summarization with Mixture of Experts based on Large Language Models
Yuanhe Tian | Fei Xia | Yan Song

Dialogue summarization is an important task that requires to generate highlights for a conversation from different aspects (e.g., content of various speakers). While several studies successfully employ large language models (LLMs) and achieve satisfying results, they are limited by using one model at a time or treat it as a black box, which makes it hard to discriminatively learn essential content in a dialogue from different aspects, therefore may lead to anticipation bias and potential loss of information in the produced summaries. In this paper, we propose an LLM-based approach with role-oriented routing and fusion generation to utilize mixture of experts (MoE) for dialogue summarization. Specifically, the role-oriented routing is an LLM-based module that selects appropriate experts to process different information; fusion generation is another LLM-based module to locate salient information and produce finalized dialogue summaries. The proposed approach offers an alternative solution to employing multiple LLMs for dialogue summarization by leveraging their capabilities of in-context processing and generation in an effective manner. We run experiments on widely used benchmark datasets for this task, where the results demonstrate the superiority of our approach in producing informative and accurate dialogue summarization.

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ChiMed-GPT: A Chinese Medical Large Language Model with Full Training Regime and Better Alignment to Human Preferences
Yuanhe Tian | Ruyi Gan | Yan Song | Jiaxing Zhang | Yongdong Zhang

Recently, the increasing demand for superior medical services has highlighted the discrepancies in the medical infrastructure. With big data, especially texts, forming the foundation of medical services, there is an exigent need for effective natural language processing (NLP) solutions tailored to the healthcare domain. Conventional approaches leveraging pre-trained models present promising results in this domain and current large language models (LLMs) offer advanced foundation for medical text processing. However, most medical LLMs are trained only with supervised fine-tuning (SFT), even though it efficiently empowers LLMs to understand and respond to medical instructions but is ineffective in learning domain knowledge and aligning with human preference. In this work, we propose ChiMed-GPT, a new benchmark LLM designed explicitly for Chinese medical domain, and undergoes a comprehensive training regime with pre-training, SFT, and RLHF. Evaluations on tasks including information extraction, question answering, and dialogue generation demonstrate ChiMed-GPT’s superior performance over general domain LLMs. Furthermore, we analyze possible biases through prompting ChiMed-GPT to perform attitude scales regarding discrimination of patients, so as to contribute to further responsible development of LLMs in the medical domain.

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An Investigation of Neuron Activation as a Unified Lens to Explain Chain-of-Thought Eliciting Arithmetic Reasoning of LLMs
Daking Rai | Ziyu Yao

Large language models (LLMs) have shown strong arithmetic reasoning capabilities when prompted with Chain-of-Thought (CoT) prompts. However, we have only a limited understanding of how they are processed by LLMs. To demystify it, prior work has primarily focused on ablating different components in the CoT prompt and empirically observing their resulting LLM performance change. Yet, the reason why these components are important to LLM reasoning is not explored. To fill this gap, in this work, we investigate “neuron activation” as a lens to provide a unified explanation to observations made by prior work. Specifically, we look into neurons within the feed-forward layers of LLMs that may have activated their arithmetic reasoning capabilities, using Llama2 as an example. To facilitate this investigation, we also propose an approach based on GPT-4 to automatically identify neurons that imply arithmetic reasoning. Our analyses revealed that the activation of reasoning neurons in the feed-forward layers of an LLM can explain the importance of various components in a CoT prompt, and future research can extend it for a more complete understanding.

Making legal knowledge accessible to non-experts is crucial for enhancing general legal literacy and encouraging civic participation in democracy. However, legal documents are often challenging to understand for people without legal backgrounds. In this paper, we present a novel application of large language models (LLMs) in legal education to help non-experts learn intricate legal concepts through storytelling, an effective pedagogical tool in conveying complex and abstract concepts. We also introduce a new dataset LegalStories, which consists of 294 complex legal doctrines, each accompanied by a story and a set of multiple-choice questions generated by LLMs. To construct the dataset, we experiment with various LLMs to generate legal stories explaining these concepts. Furthermore, we use an expert-in-the-loop approach to iteratively design multiple-choice questions. Then, we evaluate the effectiveness of storytelling with LLMs through randomized controlled trials (RCTs) with legal novices on 10 samples from the dataset. We find that LLM-generated stories enhance comprehension of legal concepts and interest in law among non-native speakers compared to only definitions. Moreover, stories consistently help participants relate legal concepts to their lives. Finally, we find that learning with stories shows a higher retention rate for non-native speakers in the follow-up assessment. Our work has strong implications for using LLMs in promoting teaching and learning in the legal field and beyond.

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Intrinsic Task-based Evaluation for Referring Expression Generation
Guanyi Chen | Fahime Same | Kees Van Deemter

Recently, a human evaluation study of Referring Expression Generation (REG) models had an unexpected conclusion: on WEBNLG, Referring Expressions (REs) generated by the state-of-the-art neural models were not only indistinguishable from the REs in WEBNLG but also from the REs generated by a simple rule-based system. Here, we argue that this limitation could stem from the use of a purely ratings-based human evaluation (which is a common practice in Natural Language Generation). To investigate these issues, we propose an intrinsic task-based evaluation for REG models, in which, in addition to rating the quality of REs, participants were asked to accomplish two meta-level tasks. One of these tasks concerns the referential success of each RE; the other task asks participants to suggest a better alternative for each RE. The outcomes suggest that, in comparison to previous evaluations, the new evaluation protocol assesses the performance of each REG model more comprehensively and makes the participants’ ratings more reliable and discriminable.

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From Moments to Milestones: Incremental Timeline Summarization Leveraging Large Language Models
Qisheng Hu | Geonsik Moon | Hwee Tou Ng

Timeline summarization (TLS) is essential for distilling coherent narratives from a vast collection of texts, tracing the progression of events and topics over time. Prior research typically focuses on either event or topic timeline summarization, neglecting the potential synergy of these two forms. In this study, we bridge this gap by introducing a novel approach that leverages large language models (LLMs) for generating both event and topic timelines. Our approach diverges from conventional TLS by prioritizing event detection, leveraging LLMs as pseudo-oracles for incremental event clustering and the construction of timelines from a text stream. As a result, it produces a more interpretable pipeline. Empirical evaluation across four TLS benchmarks reveals that our approach outperforms the best prior published approaches, highlighting the potential of LLMs in timeline summarization for real-world applications.

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End-to-end Learning of Logical Rules for Enhancing Document-level Relation Extraction
Kunxun Qi | Jianfeng Du | Hai Wan

Document-level relation extraction (DocRE) aims to extract relations between entities in a whole document. One of the pivotal challenges of DocRE is to capture the intricate interdependencies between relations of entity pairs. Previous methods have shown that logical rules can explicitly help capture such interdependencies. These methods either learn logical rules to refine the output of a trained DocRE model, or first learn logical rules from annotated data and then inject the learnt rules into a DocRE model using an auxiliary training objective. However, these learning pipelines may suffer from the issue of error propagation. To mitigate this issue, we propose Joint Modeling Relation extraction and Logical rules or JMRL for short, a novel rule-based framework that jointly learns both a DocRE model and logical rules in an end-to-end fashion. Specifically, we parameterize a rule reasoning module in JMRL to simulate the inference of logical rules, thereby explicitly modeling the reasoning process. We also introduce an auxiliary loss and a residual connection mechanism in JMRL to better reconcile the DocRE model and the rule reasoning module. Experimental results on four benchmark datasets demonstrate that our proposed JMRL framework is consistently superior to existing rule-based frameworks, improving five baseline models for DocRE by a significant margin.

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Can We Achieve High-quality Direct Speech-to-Speech Translation without Parallel Speech Data?
Qingkai Fang | Shaolei Zhang | Zhengrui Ma | Min Zhang | Yang Feng

Recently proposed two-pass direct speech-to-speech translation (S2ST) models decompose the task into speech-to-text translation (S2TT) and text-to-speech (TTS) within an end-to-end model, yielding promising results. However, the training of these models still relies on parallel speech data, which is extremely challenging to collect. In contrast, S2TT and TTS have accumulated a large amount of data and pretrained models, which have not been fully utilized in the development of S2ST models. Inspired by this, in this paper, we first introduce a composite S2ST model named ComSpeech, which can seamlessly integrate any pretrained S2TT and TTS models into a direct S2ST model. Furthermore, to eliminate the reliance on parallel speech data, we propose a novel training method ComSpeech-ZS that solely utilizes S2TT and TTS data. It aligns representations in the latent space through contrastive learning, enabling the speech synthesis capability learned from the TTS data to generalize to S2ST in a zero-shot manner. Experimental results on the CVSS dataset show that when the parallel speech data is available, ComSpeech surpasses previous two-pass models like UnitY and Translatotron 2 in both translation quality and decoding speed. When there is no parallel speech data, ComSpeech-ZS lags behind by only 0.7 ASR-BLEU and outperforms the cascaded models.

Reconstructing natural language from non-invasive electroencephalography (EEG) holds great promise as a language decoding technology for brain-computer interfaces (BCIs). However, EEG-based language decoding is still in its nascent stages, facing several technical issues such as: 1) Absence of a hybrid strategy that can effectively integrate cross-modality (between EEG and text) self-learning with intra-modality self-reconstruction of EEG features or textual sequences; 2) Under-utilization of large language models (LLMs) to enhance EEG-based language decoding. To address above issues, we propose the Contrastive EEG-Text Masked Autoencoder (CET-MAE), a novel model that orchestrates compound self-supervised learning across and within EEG and text through a dedicated multi-stream encoder. Furthermore, we develop a framework called E2T-PTR (EEG-to-Text decoding using Pretrained Transferable Representations), which leverages pre-trained modules alongside the EEG stream from CET-MAE and further enables an LLM (specifically BART) to decode text from EEG sequences. Comprehensive experiments conducted on the popular text-evoked EEG database, ZuCo, demonstrate the superiority of E2T-PTR, which outperforms the baseline framework in ROUGE-1 F1 and BLEU-4 scores by 8.34% and 32.21%, respectively. Our proposed pre-trained EEG-Text model shows the potential to improve downstream tasks involving EEG and text. This opens up promising avenues for its application in inner speech BCI paradigms, meriting further investigation.

The fast-growing large scale language models are delivering unprecedented performance on almost all natural language processing tasks. However, the effectiveness of large language models are reliant on an exponentially increasing number of parameters. The overwhelming computation complexity incurs a high inference latency that negatively affects user experience. Existing methods to improve inference efficiency, such as tensor parallelism and quantization, target to reduce per-layer computing latency, yet overlook the cumulative latency due to the number of layers. Recent works on reducing the cumulative latency through layer removing, however, lead to significant performance drop. Motivated by the similarity of inputs among adjacent layers, we propose to identify quasi-independent layers, which can be concurrently computed to significantly decrease inference latency. We also introduce a bypassing technique to mitigate the effect of information loss. Empirical experiments of the proposed approach on the LLaMA models confirm that Concurrent Computation of Quasi-Independent Layers (CQIL) can reduce latency by up to 48.3% on LLaMA-33B, while maintaining a close level of performance.

We propose a new method, Adversarial In-Context Learning (adv-ICL), to optimize prompts for in-context learning (ICL). Inspired by adversarial learning, adv-ICL is implemented as a two-player game between a generator and discriminator, with LLMs acting as both. In each round, given an input prefixed by task instructions and several exemplars, the generator produces an output. The discriminator then classifies the generator’s input-output pair as model-generated or real data. Based on the discriminator’s loss, a prompt modifier LLM proposes possible edits to the generator and discriminator prompts, and the edits that most improve the adversarial loss are selected. We show that applying adv-ICL results in significant improvements over state-of-the-art prompt optimization techniques for both open and closed-source models on 13 generation and classification tasks including summarization, arithmetic reasoning, machine translation, data-to-text generation, and the MMLU and big-bench hard benchmarks. In addition, our method is computationally efficient, easily extensible to other LLMs and tasks, and effective in low-resource settings.

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StreamVoice: Streamable Context-Aware Language Modeling for Real-time Zero-Shot Voice Conversion
Zhichao Wang | Yuanzhe Chen | Xinsheng Wang | Lei Xie | Yuping Wang

Recent language model (LM) advancements have showcased impressive zero-shot voice conversion (VC) performance. However, existing LM-based VC models usually apply offline conversion from source semantics to acoustic features, demanding the complete source speech and limiting their deployment to real-time applications. In this paper, we introduce StreamVoice, a novel streaming LM-based model for zero-shot VC, facilitating real-time conversion given arbitrary speaker prompts and source speech. Specifically, to enable streaming capability, StreamVoice employs a fully causal context-aware LM with a temporal-independent acoustic predictor, while alternately processing semantic and acoustic features at each time step of autoregression which eliminates the dependence on complete source speech. To address the potential performance degradation from the incomplete context in streaming processing, we enhance the context-awareness of the LM through two strategies: 1) teacher-guided context foresight, using a teacher model to summarize the present and future semantic context during training to guide the model’s forecasting for missing context; 2) semantic masking strategy, promoting acoustic prediction from preceding corrupted semantic and acoustic input, enhancing context-learning ability. Notably, StreamVoice is the first LM-based streaming zero-shot VC model without any future look-ahead. Experiments demonstrate StreamVoice’s streaming conversion capability while achieving zero-shot performance comparable to non-streaming VC systems.

Multi-Hop Question Answering (MHQA) task presents a significant challenge for large language models (LLMs) due to the intensive knowledge required. Current solutions, like Retrieval-Augmented Generation, typically retrieve potential documents from an external corpus to read an answer. However, the performance of this retrieve-then-read paradigm is constrained by the retriever and the inevitable noise in the retrieved documents. To mitigate these challenges, we introduce a novel generate-then-ground (GenGround) framework, synergizing the parametric knowledge of LLMs and external documents to solve a multi-hop question. GenGround empowers LLMs to alternate two phases until the final answer is derived: (1) formulate a simpler, single-hop question and directly generate the answer; (2) ground the question-answer pair into retrieved documents, amending any wrong predictions in the answer. We also propose an instructional grounding distillation method to generalize our method into smaller models. Extensive experiments conducted on four datasets illustrate the superiority of our method. To further facilitate future research, we have collected a dataset that traces the reasoning process.

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Multimodal Contextualized Semantic Parsing from Speech
Jordan Voas | David Harwath | Ray Mooney

We introduce Semantic Parsing in Contextual Environments (SPICE), a task designed to enhance artificial agents’ contextual awareness by integrating multimodal inputs with prior contexts. SPICE goes beyond traditional semantic parsing by offering a structured, interpretable framework for dynamically updating an agent’s knowledge with new information, mirroring the complexity of human communication. We develop the VG-SPICE dataset, crafted to challenge agents with visual scene graph construction from spoken conversational exchanges, highlighting speech and visual data integration. We also present the Audio-Vision Dialogue Scene Parser (AViD-SP) developed for use on VG-SPICE. These innovations aim to improve multimodal information processing and integration. Both the VG-SPICE dataset and the AViD-SP model are publicly available.

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LaMP: When Large Language Models Meet Personalization
Alireza Salemi | Sheshera Mysore | Michael Bendersky | Hamed Zamani

This paper highlights the importance of personalization in large language models and introduces the LaMP benchmark — a novel benchmark for training and evaluating language models for producing personalized outputs. LaMP offers a comprehensive evaluation framework with diverse language tasks and multiple entries for each user profile. It consists of seven personalized tasks, spanning three text classification and four text generation tasks. We additionally propose two retrieval augmentation approaches that retrieve personal items from each user profile for personalizing language model outputs. To this aim, we study various retrieval models, including term matching, semantic matching, and time-aware methods. Extensive experiments on LaMP for zero-shot and fine-tuned language models demonstrate the efficacy of the proposed retrieval augmentation approach and highlight the impact of personalization in various natural language tasks.

Large language models’ (LLMs) abilities are drawn from their pretraining data, and model development begins with data curation. However, decisions around what data is retained or removed during this initial stage are under-scrutinized. In our work, we ground web text, which is a popular pretraining data source, to its social and geographic contexts. We create a new dataset of 10.3 million self-descriptions of website creators, and extract information about who they are and where they are from: their topical interests, social roles, and geographic affiliations. Then, we conduct the first study investigating how ten “quality” and English language identification (langID) filters affect webpages that vary along these social dimensions. Our experiments illuminate a range of implicit preferences in data curation: we show that some quality classifiers act like topical domain filters, and langID can overlook English content from some regions of the world. Overall, we hope that our work will encourage a new line of research on pretraining data curation practices and its social implications.

The advent of Large Language Models (LLMs) has drastically enhanced dialogue systems. However, comprehensively evaluating the dialogue abilities of LLMs remains a challenge. Previous benchmarks have primarily focused on single-turn dialogues or provided coarse-grained and incomplete assessments of multi-turn dialogues, overlooking the complexity and fine-grained nuances of real-life dialogues. To address this issue, we introduce MT-Bench-101, specifically designed to evaluate the fine-grained abilities of LLMs in multi-turn dialogues. By conducting a detailed analysis of real multi-turn dialogue data, we construct a three-tier hierarchical ability taxonomy comprising 4208 turns across 1388 multi-turn dialogues in 13 distinct tasks. We then evaluate 21 popular LLMs based on MT-Bench-101, conducting comprehensive analyses from both ability and task perspectives and observing differing trends in LLMs performance across dialogue turns within various tasks. Further analysis indicates that neither utilizing common alignment techniques nor chat-specific designs has led to obvious enhancements in the multi-turn abilities of LLMs. Extensive case studies suggest that our designed tasks accurately assess the corresponding multi-turn abilities. The data and code are available at https://github.com/mtbench101/mt-bench-101.

In this paper, we extend financial sentiment analysis (FSA) to event-level since events usually serve as the subject of the sentiment in financial text. Though extracting events from the financial text may be conducive to accurate sentiment predictions, it has specialized challenges due to the lengthy and discontinuity of events in a financial text. To this end, we reconceptualize the event extraction as a classification task by designing a categorization comprising coarse-grained and fine-grained event categories. Under this setting, we formulate the Event-Level Financial Sentiment Analysis(EFSA for short) task that outputs quintuples consisting of (company, industry, coarse-grained event, fine-grained event, sentiment) from financial text. A large-scale Chinese dataset containing 12,160 news articles and 13,725 quintuples is publicized as a brand new testbed for our task. A four-hop Chain-of-Thought LLM-based approach is devised for this task. Systematically investigations are conducted on our dataset, and the empirical results demonstrate the benchmarking scores of existing methods and our proposed method can reach the current state-of-the-art. Our dataset and framework implementation are available at https://github.com/cty1934/EFSA

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What Evidence Do Language Models Find Convincing?
Alexander Wan | Eric Wallace | Dan Klein

Retrieval-augmented language models are being increasingly tasked with subjective, contentious, and conflicting queries such as “is aspartame linked to cancer”. To resolve these ambiguous queries, one must search through a large range of websites and consider “which, if any, of this evidence do I find convincing?”. In this work, we study how LLMs answer this question. In particular, we construct ConflictingQA, a dataset that pairs controversial queries with a series of real-world evidence documents that contain different facts (e.g., quantitative results), argument styles (e.g., appeals to authority), and answers (Yes or No). We use this dataset to perform sensitivity and counterfactual analyses to explore which text features most affect LLM predictions. Overall, we find that current models rely heavily on the relevance of a website to the query, while largely ignoring stylistic features that humans find important such as whether a text contains scientific references or is written with a neutral tone. Taken together, these results highlight the importance of RAG corpus quality (e.g., the need to filter misinformation), and possibly even a shift in how LLMs are trained to better align with human judgements.

Graph data organizes complex relationships and interactions between objects, facilitating advanced analysis and decision-making across different fields. In this paper, we propose a new paradigm for interactive and instructional graph data understanding and reasoning.Instead of adopting complex graph neural models or heuristic graph-to-text instruction design, we leverage Vision-Language Models (VLMs) to encode the graph images with varying structures across different domains. This paper first evaluates the capabilities of public VLMs in graph learning from multiple aspects. Then it introduces a novel instruction-following dataset for multimodal graph understanding and reasoning in English and Chinese. Besides, by fine-tuning MiniGPT-4 and LLaVA on our dataset, we achieved an accuracy increase of 5%-15% compared to baseline models, with the best-performing model attaining scores comparable to Gemini in GPT-asissted Evaluation. This research not only showcases the potential of integrating VLMs with graph data but also opens new avenues for advancements in graph data understanding.

We introduce LangBridge, a zero-shot approach to adapt language models for multilingual reasoning tasks without multilingual supervision. LangBridge operates by bridging two models, each specialized in different aspects: (1) one specialized in understanding multiple languages (e.g., mT5 encoder) and (2) one specialized in reasoning (e.g., MetaMath). LangBridge connects the two models by introducing minimal trainable parameters between them. Despite utilizing only English data for training, LangBridge considerably enhances the performance of language models on low-resource languages across mathematical reasoning, code completion, logical reasoning, and commonsense reasoning. Our analysis suggests that the efficacy of LangBridge stems from the language-agnostic characteristics of multilingual representations. We publicly release our code and models.

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Can LLMs Reason with Rules? Logic Scaffolding for Stress-Testing and Improving LLMs
Siyuan Wang | Zhongyu Wei | Yejin Choi | Xiang Ren

Large language models (LLMs) have achieved impressive human-like performance across various reasoning tasks. However, their mastery of underlying inferential rules still falls short of human capabilities. To investigate this, we propose a logic scaffolding inferential rule generation framework, to construct an inferential rule base, ULogic, comprising both primitive and compositional rules across five domains. Our analysis of GPT-series models over a rule subset reveals significant gaps in LLMs’ logic understanding compared to human performance, especially in compositional and structural complex rules with certain bias patterns. We further distill these rules into a smaller-scale inference engine for flexible rule generation and enhancing downstream reasoning. Through a multi-judger evaluation, our inference engine proves effective in generating accurate, complex and abstract conclusions and premises, and improve various commonsense reasoning tasks. Overall, our work sheds light on LLMs’ limitations in grasping inferential rule and suggests ways to enhance their logical reasoning abilities .

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SEGO: Sequential Subgoal Optimization for Mathematical Problem-Solving
Xueliang Zhao | Xinting Huang | Wei Bi | Lingpeng Kong

Large Language Models (LLMs) have driven substantial progress in artificial intelligence in recent years, exhibiting impressive capabilities across a wide range of tasks, including mathematical problem-solving. Inspired by the success of subgoal-based methods, we propose a novel framework called SEquential subGoal Optimization (SEGO) to enhance LLMs’ ability to solve mathematical problems. By establishing a connection between the subgoal breakdown process and the probability of solving problems, SEGO aims to identify better subgoals with theoretical guarantees. Addressing the challenge of identifying suitable subgoals in a large solution space, our framework generates problem-specific subgoals and adjusts them according to carefully designed criteria. Incorporating these optimized subgoals into the policy model training leads to significant improvements in problem-solving performance. We validate SEGO’s efficacy through experiments on two benchmarks, GSM8K and MATH, where our approach outperforms existing methods, highlighting the potential of SEGO in AI-driven mathematical problem-solving.

Entity Alignment (EA) is vital for integrating diverse knowledge graph (KG) data, playing a crucial role in data-driven AI applications. Traditional EA methods primarily rely on comparing entity embeddings, but their effectiveness is constrained by the limited input KG data and the capabilities of the representation learning techniques. Against this backdrop, we introduce ChatEA, an innovative framework that incorporates large language models (LLMs) to improve EA. To address the constraints of limited input KG data, ChatEA introduces a KG-code translation module that translates KG structures into a format understandable by LLMs, thereby allowing LLMs to utilize their extensive background knowledge to improve EA accuracy. To overcome the over-reliance on entity embedding comparisons, ChatEA implements a two-stage EA strategy that capitalizes on LLMs’ capability for multi-step reasoning in a dialogue format, thereby enhancing accuracy while preserving efficiency. Our experimental results affirm ChatEA’s superior performance, highlighting LLMs’ potential in facilitating EA tasks.The source code is available at https://anonymous.4open.science/r/ChatEA/.

Large Language Models (LLMs) have become integral components in various autonomous agent systems.In this study, we present an exploration-based trajectory optimization approach, referred to as ETO. This learning method is designed to enhance the performance of open LLM agents. Contrary to previous studies that exclusively train on successful expert trajectories, our method allows agents to learn from their exploration failures. This leads to improved performance through an iterative optimization framework. During the exploration phase, the agent interacts with the environment while completing given tasks, gathering failure trajectories to create contrastive trajectory pairs. In the subsequent training phase, the agent utilizes these trajectory preference pairs to update its policy using contrastive learning methods like DPO. This iterative cycle of exploration and training fosters continued improvement in the agents. Our experiments on three complex tasks demonstrate that ETO consistently surpasses baseline performance by a large margin. Furthermore, an examination of task-solving efficiency and potential in scenarios lacking expert trajectory underscores the effectiveness of our approach.

One way to enhance the reasoning capability of Large Language Models (LLMs) is to conduct Supervised Fine-Tuning (SFT) using Chain-of-Thought (CoT) annotations. This approach does not show sufficiently strong generalization ability, however, because the training only relies on the given CoT data. In math problem-solving, for example, there is usually only one annotated reasoning path for each question in the training data. Intuitively, it would be better for the algorithm to learn from multiple annotated reasoning paths given a question. To address this issue, we propose a simple yet effective approach called Reinforced Fine-Tuning (ReFT) to enhance the generalizability of learning LLMs for reasoning, with math problem-solving as an example. ReFT first warmups the model with SFT, and then employs on-line reinforcement learning, specifically the PPO algorithm in this paper, to further fine-tune the model, where an abundance of reasoning paths are automatically sampled given the question and the rewards are naturally derived from the ground-truth answers. Extensive experiments on GSM8K, MathQA, and SVAMP datasets show that ReFT significantly outperforms SFT, and the performance can be potentially further boosted by combining inference-time strategies such as majority voting and re-ranking. Note that ReFT obtains the improvement by learning from the same training questions as SFT, without relying on extra or augmented training questions. This indicates a superior generalization ability for ReFT.

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Cognitive Visual-Language Mapper: Advancing Multimodal Comprehension with Enhanced Visual Knowledge Alignment
Yunxin Li | Xinyu Chen | Baotian Hu | Haoyuan Shi | Min Zhang

Evaluating and Rethinking the current landscape of Large Multimodal Models (LMMs), we observe that widely-used visual-language projection approaches (e.g., Q-former or MLP) focus on the alignment of image-text descriptions yet ignore the visual knowledge-dimension alignment, i.e., connecting visuals to their relevant knowledge. Visual knowledge plays a significant role in analyzing, inferring, and interpreting information from visuals, helping improve the accuracy of answers to knowledge-based visual questions. In this paper, we mainly explore improving LMMs with visual-language knowledge alignment, especially aimed at challenging knowledge-based visual question answering (VQA). To this end, we present a Cognitive Visual-Language Mapper (CVLM), which contains a pretrained Visual Knowledge Aligner (VKA) and a Fine-grained Knowledge Adapter (FKA) used in the multimodal instruction tuning stage. Specifically, we design the VKA based on the interaction between a small language model and a visual encoder, training it on collected image-knowledge pairs to achieve visual knowledge acquisition and projection. FKA is employed to distill the fine-grained visual knowledge of an image and inject it into Large Language Models (LLMs). We conduct extensive experiments on knowledge-based VQA benchmarks and experimental results show that CVLM significantly improves the performance of LMMs on knowledge-based VQA (average gain by 5.0%). Ablation studies also verify the effectiveness of VKA and FKA, respectively.

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FreeCtrl: Constructing Control Centers with Feedforward Layers for Learning-Free Controllable Text Generation
Zijian Feng | Hanzhang Zhou | Kezhi Mao | Zixiao Zhu

Controllable text generation (CTG) seeks to craft texts adhering to specific attributes, traditionally employing learning-based techniques such as training, fine-tuning, or prefix-tuning with attribute-specific datasets. These approaches, while effective, demand extensive computational and data resources. In contrast, some proposed learning-free alternatives circumvent learning but often yield inferior results, exemplifying the fundamental machine learning trade-off between computational expense and model efficacy. To overcome these limitations, we propose FreeCtrl, a learning-free approach that dynamically adjusts the weights of selected feedforward neural network (FFN) vectors to steer the outputs of large language models (LLMs). FreeCtrl hinges on the principle that the weights of different FFN vectors influence the likelihood of different tokens appearing in the output. By identifying and adaptively adjusting the weights of attribute-related FFN vectors, FreeCtrl can control the output likelihood of attribute keywords in the generated content. Extensive experiments on single- and multi-attribute control reveal that the learning-free FreeCtrl outperforms other learning-free and learning-based methods, successfully resolving the dilemma between learning costs and model performance.

Large language models (LLMs) have emerged as a promising alternative to expensive human evaluations. However, the alignment and coverage of LLM-based evaluations are often limited by the scope and potential bias of the evaluation prompts and criteria. To address this challenge, we propose HD-Eval, a novel framework that iteratively aligns LLM-based evaluators with human preference via Hierarchical Criteria Decomposition. HD-Eval inherits the essence from the evaluation mindset of human experts and enhances the alignment of LLM-based evaluators by decomposing a given evaluation task into finer-grained criteria, aggregating them according to estimated human preferences, pruning insignificant criteria with attribution, and further decomposing significant criteria. By integrating these steps within an iterative alignment training process, we obtain a hierarchical decomposition of criteria that comprehensively captures aspects of natural language at multiple levels of granularity. Implemented as a white box, the human preference-guided aggregator is efficient to train and more explainable than relying solely on prompting, and its independence from model parameters makes it applicable to closed-source LLMs. Extensive experiments on three evaluation domains demonstrate the superiority of HD-Eval in further aligning state-of-the-art evaluators and providing deeper insights into the explanation of evaluation results and the task itself.

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Conundrums in Cross-Prompt Automated Essay Scoring: Making Sense of the State of the Art
Shengjie Li | Vincent Ng

Cross-prompt automated essay scoring (AES), an under-investigated but challenging task that has gained increasing popularity in the AES community, aims to train an AES system that can generalize well to prompts that are unseen during model training. While recently-developed cross-prompt AES models have combined essay representations that are learned via sophisticated neural architectures with so-called prompt-independent features, an intriguing question is: are complex neural models needed to achieve state-of-the-art results? We answer this question by abandoning sophisticated neural architectures and developing a purely feature-based approach to cross-prompt AES that adopts a simple neural architecture. Experiments on the ASAP dataset demonstrate that our simple approach to cross-prompt AES can achieve state-of-the-art results.

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Angry Men, Sad Women: Large Language Models Reflect Gendered Stereotypes in Emotion Attribution
Flor Plaza-del-Arco | Amanda Curry | Alba Cercas Curry | Gavin Abercrombie | Dirk Hovy

Large language models (LLMs) reflect societal norms and biases, especially about gender. While societal biases and stereotypes have been extensively researched in various NLP applications, there is a surprising gap for emotion analysis. However, emotion and gender are closely linked in societal discourse. E.g., women are often thought of as more empathetic, while men’s anger is more socially accepted. To fill this gap, we present the first comprehensive study of gendered emotion attribution in five state-of-the-art LLMs (open- and closed-source). We investigate whether emotions are gendered, and whether these variations are based on societal stereotypes. We prompt the models to adopt a gendered persona and attribute emotions to an event like ‘When I had a serious argument with a dear person’. We then analyze the emotions generated by the models in relation to the gender-event pairs. We find that all models consistently exhibit gendered emotions, influenced by gender stereotypes. These findings are in line with established research in psychology and gender studies. Our study sheds light on the complex societal interplay between language, gender, and emotion. The reproduction of emotion stereotypes in LLMs allows us to use those models to study the topic in detail, but raises questions about the predictive use of those same LLMs for emotion applications.

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Label Augmentation for Zero-Shot Hierarchical Text Classification
Lorenzo Paletto | Valerio Basile | Roberto Esposito

Hierarchical Text Classification poses the difficult challenge of classifying documents into multiple labels organized in a hierarchy. The vast majority of works aimed to address this problem relies on supervised methods which are difficult to implement due to the scarcity of labeled data in many real world applications. This paper focuses on strict Zero-Shot Classification, the setting in which the system lacks both labeled instances and training data.We propose a novel approach that uses a Large Language Model to augment the deepest layer of the labels hierarchy in order to enhance its specificity. We achieve this by generating semantically relevant labels as children connected to the existing branches, creating a deeper taxonomy that better overlaps with the input texts. We leverage the enriched hierarchy to perform Zero-Shot Hierarchical Classification by using the Upward score Propagation technique. We test our method on four public datasets, obtaining new state-of-the art results on three of them. We introduce two cosine similarity-based metrics to quantify the density and granularity of a label taxonomy and we show a strong correlation between the metric values and the classification performance of our method on the datasets.

Stickers, while widely recognized for enhancing empathetic communication in online interactions, remain underexplored in current empathetic dialogue research, notably due to the challenge of a lack of comprehensive datasets. In this paper, we introduce the Agent for STICKERCONV (Agent4SC), which uses collaborative agent interactions to realistically simulate human behavior with sticker usage, thereby enhancing multimodal empathetic communication. Building on this foundation, we develop a multimodal empathetic dialogue dataset, STICKERCONV, comprising 12.9K dialogue sessions, 5.8K unique stickers, and 2K diverse conversational scenarios. This dataset serves as a benchmark for multimodal empathetic generation. To advance further, we propose PErceive and Generate Stickers (PEGS), a multimodal empathetic response generation framework, complemented by a comprehensive set of empathy evaluation metrics based on LLM. Our experiments demonstrate PEGS’s effectiveness in generating contextually relevant and emotionally resonant multimodal empathetic responses, contributing to the advancement of more nuanced and engaging empathetic dialogue systems.

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EIT: Enhanced Interactive Transformer
Tong Zheng | Bei Li | Huiwen Bao | Tong Xiao | JingBo Zhu

Two principles: the complementary principle and the consensus principle are widely acknowledged in the literature of multi-view learning. However, the current design of multi-head self-attention, an instance of multi-view learning, prioritizes the complementarity while ignoring the consensus. To address this problem, we propose an enhanced multi-head self-attention (EMHA). First, to satisfy the complementary principle, EMHA removes the one-to-one mapping constraint among queries and keys in multiple subspaces and allows each query to attend to multiple keys. On top of that, we develop a method to fully encourage consensus among heads by introducing two interaction models, namely inner-subspace interaction and cross-subspace interaction. Extensive experiments on a wide range of language tasks (e.g., machine translation, abstractive summarization and grammar correction, language modeling), show its superiority, with a very modest increase in model size. Our code would be available at: https://github.com/zhengkid/EIT-Enhanced-Interactive-Transformer.

Generative Large Language Models (LLMs) are widely utilized for their excellence in various tasks. However, their tendency to produce inaccurate or misleading outputs poses a potential risk, particularly in high-stakes environments. Therefore, estimating the correctness of generative LLM outputs is an important task for enhanced reliability. Uncertainty Estimation (UE) in generative LLMs is an evolving domain, where SOTA probability-based methods commonly employ length-normalized scoring. In this work, we propose Meaning-Aware Response Scoring (MARS) as an alternative to length-normalized scoring for UE methods. MARS is a novel scoring function that considers the semantic contribution of each token in the generated sequence in the context of the question. We demonstrate that integrating MARS into UE methods results in a universal and significant improvement in UE performance. We conduct experiments using three distinct closed-book question-answering datasets across five popular pre-trained LLMs. Lastly, we validate the efficacy of MARS on a Medical QA dataset. Code can be found here.

We introduce EXAMS-V, a new challenging multi-discipline multimodal multilingual exam benchmark for evaluating vision language models. It consists of 20,932 multiple-choice questions across 20 school disciplines covering natural science, social science, and other miscellaneous studies, e.g., religion, fine arts, business, etc. EXAMS-V includes a variety of multimodal features such as text, images, tables, figures, diagrams, maps, scientific symbols, and equations. The questions come in 11 languages from 7 language families. Unlike existing benchmarks, EXAMS-V is uniquely curated by gathering school exam questions from various countries, with a variety of education systems. This distinctive approach calls for intricate reasoning across diverse languages and relies on region-specific knowledge. Solving the problems in the dataset requires advanced perception and joint reasoning over the text and the visual content in the image. Our evaluation results demonstrate that this is a challenging dataset, which is difficult even for advanced vision–text models such as GPT-4V and Gemini; this underscores the inherent complexity of the dataset and its significance as a future benchmark.

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Order-Agnostic Data Augmentation for Few-Shot Named Entity Recognition
Huiming Wang | Liying Cheng | Wenxuan Zhang | De Wen Soh | Lidong Bing

Data augmentation (DA) methods have been proven to be effective for pre-trained language models (PLMs) in low-resource settings, including few-shot named entity recognition (NER). However, existing NER DA techniques either perform rule-based manipulations on words that break the semantic coherence of the sentence, or exploit generative models for entity or context substitution, which requires a substantial amount of labeled data and contradicts the objective of operating in low-resource settings. In this work, we propose order-agnostic data augmentation (OaDA), an alternative solution that exploits the often overlooked order-agnostic property in the training data construction phase of sequence-to-sequence NER methods for data augmentation. To effectively utilize the augmented data without suffering from the one-to-many issue, where multiple augmented target sequences exist for one single sentence, we further propose the use of ordering instructions and an innovative OaDA-XE loss. Specifically, by treating each permutation of entity types as an ordering instruction, we rearrange the entity set accordingly, ensuring a distinct input-output pair, while OaDA-XE assigns loss based on the best match between the target sequence and model predictions. We conduct comprehensive experiments and analyses across three major NER benchmarks and significantly enhance the few-shot capabilities of PLMs with OaDA.

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Text Embedding Inversion Security for Multilingual Language Models
Yiyi Chen | Heather Lent | Johannes Bjerva

Textual data is often represented as real-numbered embeddings in NLP, particularly with the popularity of large language models (LLMs) and Embeddings as a Service (EaaS). However, storing sensitive information as embeddings can be susceptible to security breaches, as research shows that text can be reconstructed from embeddings, even without knowledge of the underlying model. While defence mechanisms have been explored, these are exclusively focused on English, leaving other languages potentially exposed to attacks. This work explores LLM security through multilingual embedding inversion. We define the problem of black-box multilingual and crosslingual inversion attacks, and explore their potential implications. Our findings suggest that multilingual LLMs may be more vulnerable to inversion attacks, in part because English-based defences may be ineffective. To alleviate this, we propose a simple masking defense effective for both monolingual and multilingual models. This study is the first to investigate multilingual inversion attacks, shedding light on the differences in attacks and defenses across monolingual and multilingual settings.

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Large Language Models are Superpositions of All Characters: Attaining Arbitrary Role-play via Self-Alignment
Keming Lu | Bowen Yu | Chang Zhou | Jingren Zhou

Considerable efforts have been invested in augmenting the role-playing proficiency of open-source large language models (LLMs) by emulating proprietary counterparts. Nevertheless, we posit that LLMs inherently harbor role-play capabilities, owing to the extensive knowledge of characters and potential dialogues ingrained in their vast training corpora. Thus, we introduce Ditto, the first self-alignment method for role-play, which encourages an instruction-following LLM to simulate role-play dialogues as a variant of reading comprehension, and creates a role-play training set comprising 4000 characters, surpassing the scale of currently available datasets by tenfold regarding the number of roles. Subsequently, we fine-tune the LLM using this self-generated dataset to augment its role-playing capabilities. Upon evaluating our meticulously constructed role-play benchmark and the roleplay subset of MT-Bench, Ditto, in various parameter scales, consistently maintains a consistent role identity and provides accurate role-specific knowledge in multi-turn role-play conversations, outperforming all open-source role-play baselines. Furthermore, we present the first cross-supervision role-play experiment, revealing that the role-play styles can be easily acquired, while the intrinsic capabilities of LLMs confine the knowledge within role-play.

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PlatoLM: Teaching LLMs in Multi-Round Dialogue via a User Simulator
Chuyi Kong | Yaxin Fan | Xiang Wan | Feng Jiang | Benyou Wang

The unparalleled performance of closed-sourced ChatGPT has sparked efforts towards its democratization, with notable strides made by leveraging real user and ChatGPT dialogues, as evidenced by Vicuna. However, due to challenges in gathering dialogues involving human participation, current endeavors like Baize and UltraChat rely on ChatGPT conducting roleplay to simulate humans based on instructions, resulting in overdependence on seeds, diminished human-likeness, limited topic diversity, and an absence of genuine multi-round conversational dynamics. To address the above issues, we propose a paradigm to simulate human behavior better and explore the benefits of incorporating more human-like questions in multi-turn conversations. Specifically, we directly target human questions extracted from genuine human-machine conversations as a learning goal and provide a novel user simulator called ‘Socratic‘. The experimental results show our response model, ‘PlatoLM‘, achieves SoTA performance among LLaMA-based 7B models in MT-Bench. Our findings further demonstrate that our method introduces highly human-like questioning patterns and rich topic structures, which can teach the response model better than previous works in multi-round conversations.

The capability gap between open-source and closed-source large language models (LLMs) remains a challenge in text-to-SQL tasks. In this paper, we introduce a synthetic data approach that combines data produced by larger, more powerful models (strong models) with error information data generated by smaller, not well-aligned models (weak models). The method not only enhances the domain generalization of text-to-SQL models but also explores the potential of error data supervision through preference learning. Furthermore, we employ the synthetic data approach for instruction tuning on open-source LLMs, resulting SENSE, a specialized text-to-SQL model. The effectiveness of SENSE is demonstrated through state-of-the-art results on the SPIDER and BIRD benchmarks, bridging the performance gap between open-source models and methods prompted by closed-source models.

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STRUCTSUM Generation for Faster Text Comprehension
Parag Jain | Andreea Marzoca | Francesco Piccinno

We consider the task of generating structured representations of text using large language models (LLMs). We focus on tables and mind maps as representative modalities. Tables are more organized way of representing data, while mind maps provide a visually dynamic and flexible approach, particularly suitable for sparse content. Despite the effectiveness of LLMs on different tasks, we show that current models struggle with generating structured outputs. In response, we present effective prompting strategies for both of these tasks. We introduce a taxonomy of problems around factuality, global and local structure, common to both modalities and propose a set of critiques to tackle these issues resulting in an absolute improvement in accuracy of +37pp (79%) for mind maps and +15pp (78%) for tables. To evaluate semantic coverage of generated structured representations we propose Auto-QA, and we verify the adequacy of Auto-QA using SQuAD dataset. We further evaluate the usefulness of structured representations via a text comprehension user study. The results show a significant reduction in comprehension time compared to text when using table (42.9%) and mind map (31.9%), without loss in accuracy.

Most language model pre-training frameworks concatenate multiple documents into fixed-length sequences and use causal masking to compute the likelihood of each token given its context; this strategy is widely adopted due to its simplicity and efficiency. However, to this day, the influence of the pre-training sequence composition strategy on the generalisation properties of the model remains under-explored.In this work, we find that applying causal masking can lead to the inclusion of distracting information from previous documents during pre-training, which negatively impacts the performance of the models on language modelling and downstream tasks. In intra-document causal masking, the likelihood of each token is only conditioned on the previous tokens in the same document, eliminating potential distracting information from previous documents and significantly improving performance. Furthermore, we find that concatenating related documents can reduce some potential distractions during pre-training, and our proposed efficient retrieval-based sequence construction method, Bm25Chunk, can improve in-context learning (+11.6%), knowledge memorisation (+9.8%), and context utilisation (+7.2%) abilities of language models without sacrificing efficiency.

Large Language Models (LLMs) have ignited an innovative surge of AI applications, marking a new era of exciting possibilities equipped with extended context windows. However, hosting these models is cost-prohibitive mainly due to the extensive memory consumption of KV Cache involving long-context modeling. Despite several works proposing to evict unnecessary tokens from the KV Cache, most of them rely on the biased local statistics of accumulated attention scores and report performance using unconvincing metric like perplexity on inadequate short-text evaluation. In this paper, we propose NACL, a general framework for long-context KV cache eviction that achieves more optimal and efficient eviction in a single operation during the encoding phase. Due to NACL’s efficiency, we combine more accurate attention score statistics in Proxy-Tokens Eviction with the diversified random eviction strategy of Random Eviction, aiming to alleviate the issue of attention bias and enhance the robustness in maintaining pivotal tokens for long-context modeling tasks. Notably, our method significantly improves the performance on short- and long-text tasks by 80% and 76% respectively, reducing KV Cache by up to 5× with over 95% performance maintenance. Code available at https://github.com/PaddlePaddle/Research/tree/master/NLP/ACL2024-NACL.

Brain-inspired Spiking Neural Network (SNN) has demonstrated its effectiveness and efficiency in vision, natural language, and speech understanding tasks, indicating their capacity to “see”, “listen”, and “read”. In this paper, we design SpikeVoice, which performs high-quality Text-To-Speech (TTS) via SNN, to explore the potential of SNN to “speak”. A major obstacle to using SNN for such generative tasks lies in the demand for models to grasp long-term dependencies. The serial nature of spiking neurons, however, leads to the invisibility of information at future spiking time steps, limiting SNN models to capture sequence dependencies solely within the same time step. We term this phenomenon “partial-time dependency”. To address this issue, we introduce Spiking Temporal-Sequential Attention (STSA) in the SpikeVoice. To the best of our knowledge, SpikeVoice is the first TTS work in the SNN field. We perform experiments using four well-established datasets that cover both Chinese and English languages, encompassing scenarios with both single-speaker and multi-speaker configurations. The results demonstrate that SpikeVoice can achieve results comparable to Artificial Neural Networks (ANN) with only 10.5% energy consumption of ANN. Both our demo and code are available as supplementary material.

Multi-change captioning aims to describe complex and coupled changes within an image pair in natural language. Compared with single-change captioning, this task requires the model to have higher-level cognition ability to reason an arbitrary number of changes. In this paper, we propose a novel context-aware difference distilling (CARD) network to capture all genuine changes for yielding sentences. Given an image pair, CARD first decouples context features that aggregate all similar/dissimilar semantics, termed common/difference context features. Then, the consistency and independence constraints are designed to guarantee the alignment/discrepancy of common/difference context features. Further, the common context features guide the model to mine locally unchanged features, which are subtracted from the pair to distill locally difference features. Next, the difference context features augment the locally difference features to ensure that all changes are distilled. In this way, we obtain an omni-representation of all changes, which is translated into linguistic sentences by a transformer decoder. Extensive experiments on three public datasets show CARD performs favourably against state-of-the-art methods. The code is available at https://github.com/tuyunbin/CARD.

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Dataflow-Guided Retrieval Augmentation for Repository-Level Code Completion
Wei Cheng | Yuhan Wu | Wei Hu

Recent years have witnessed the deployment of code language models (LMs) in various code intelligence tasks such as code completion. Yet, it is challenging for pre-trained LMs to generate correct completions in private repositories. Previous studies retrieve cross-file context based on import relations or text similarity, which is insufficiently relevant to completion targets. In this paper, we propose a dataflow-guided retrieval augmentation approach, called DraCo, for repository-level code completion. DraCo parses a private repository into code entities and establishes their relations through an extended dataflow analysis, forming a repo-specific context graph. Whenever triggering code completion, DraCo precisely retrieves relevant background knowledge from the repo-specific context graph and generates well-formed prompts to query code LMs. Furthermore, we construct a large Python dataset, ReccEval, with more diverse completion targets. Our experiments demonstrate the superior accuracy and applicable efficiency of DraCo, improving code exact match by 3.43% and identifier F1-score by 3.27% on average compared to the state-of-the-art approach.

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Chain-of-Exemplar: Enhancing Distractor Generation for Multimodal Educational Question Generation
Haohao Luo | Yang Deng | Ying Shen | See-Kiong Ng | Tat-Seng Chua

Multiple-choice questions (MCQs) are important in enhancing concept learning and student engagement for educational purposes. Despite the multimodal nature of educational content, current methods focus mainly on text-based inputs and often neglect the integration of visual information. In this work, we study the problem of multimodal educational question generation, which aims at generating subject-specific educational questions with plausible yet incorrect distractors based on multimodal educational content. To tackle this problem, we introduce a novel framework, named Chain-of-Exemplar (CoE), which utilizes multimodal large language models (MLLMs) with Chain-of-Thought reasoning to improve the generation of challenging distractors. Furthermore, CoE leverages three-stage contextualized exemplar retrieval to retrieve exemplary questions as guides for generating more subject-specific educational questions. Experimental results on the ScienceQA benchmark demonstrate the superiority of CoE in both question generation and distractor generation over existing methods across various subjects and educational levels.

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LLMEmbed: Rethinking Lightweight LLM’s Genuine Function in Text Classification
Chun Liu | Hongguang Zhang | Kainan Zhao | Xinghai Ju | Lin Yang

With the booming of Large Language Models (LLMs), prompt-learning has become a promising method mainly researched in various research areas. Recently, many attempts based on prompt-learning have been made to improve the performance of text classification. However, most of these methods are based on heuristic Chain-of-Thought (CoT), and tend to be more complex but less efficient. In this paper, we rethink the LLM-based text classification methodology, propose a simple and effective transfer learning strategy, namely LLMEmbed, to address this classical but challenging task. To illustrate, we first study how to properly extract and fuse the text embeddings via various lightweight LLMs at different network depths to improve their robustness and discrimination, then adapt such embeddings to train the classifier. We perform extensive experiments on publicly available datasets, and the results show that LLMEmbed achieves strong performance while enjoys low training overhead using lightweight LLM backbones compared to recent methods based on larger LLMs, *i.e.* GPT-3, and sophisticated prompt-based strategies. Our LLMEmbed achieves adequate accuracy on publicly available benchmarks without any fine-tuning while merely use 4% model parameters, 1.8% electricity consumption and 1.5% runtime compared to its counterparts. Code is available at: https://github.com/ChunLiu-cs/LLMEmbed-ACL2024.

In the new era of language models, small models (with billions of parameter sizes) are receiving increasing attention due to their flexibility and cost-effectiveness in deployment. However, limited by the model size, the performance of small models trained from scratch may often be unsatisfactory. Learning a stronger and smaller model with the help of larger models is an intuitive idea. Inspired by the observing modular structures in preliminary analysis, we propose LEMON to learn competent initial points for smaller models by fusing parameters from larger models, thereby laying a solid foundation for subsequent training. Specifically, the parameter fusion process involves two operators for layer and dimension, respectively, and we also introduce controllable receptive fields to model the prior parameter characteristics. In this way, the larger model could be transformed into any specific smaller scale and architecture. Starting from LLaMA 2-7B, we revive two stronger and smaller models with 1.3B and 2.7B. Experimental results demonstrate that the fusion-based method exhibits flexibility and outperforms a series of competitive baselines in terms of both effectiveness and efficiency.

End-to-end speech translation (ST) presents notable disambiguation challenges as it necessitates simultaneous cross-modal and cross-lingual transformations. While word sense disambiguation is an extensively investigated topic in textual machine translation, the exploration of disambiguation strategies for ST models remains limited. Addressing this gap, this paper introduces the concept of speech sense disambiguation (SSD), specifically emphasizing homophones - words pronounced identically but with different meanings. To facilitate this, we first create a comprehensive homophone dictionary and an annotated dataset rich with homophone information established based on speech-text alignment. Building on this unique dictionary, we introduce AmbigST, an innovative homophone-aware contrastive learning approach that integrates a homophone-aware masking strategy. Our experiments on different MuST-C and CoVoST ST benchmarks demonstrate that AmbigST sets new performance standards. Specifically, it achieves SOTA results on BLEU scores for English to German, Spanish, and French ST tasks, underlining its effectiveness in reducing speech sense ambiguity. Data, code and scripts are freely available at https://github.com/ytf-philp/AmbigST.

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To be Continuous, or to be Discrete, Those are Bits of Questions
Yiran Wang | Masao Utiyama

Recently, binary representation has been proposed as a novel representation that lies between continuous and discrete representations. It exhibits considerable information-preserving capability when being used to replace continuous input vectors. In this paper, we investigate the feasibility of further introducing it to the output side, aiming to allow models to output binary labels instead. To preserve the structural information on the output side along with label information, we extend the previous contrastive hashing method as structured contrastive hashing. More specifically, we upgrade CKY from label-level to bit-level, define a new similarity function with span marginal probabilities, and introduce a novel contrastive loss function with a carefully designed instance selection strategy. Our model achieves competitive performance on various structured prediction tasks, and demonstrates that binary representation can be considered a novel representation that further bridges the gap between the continuous nature of deep learning and the discrete intrinsic property of natural languages.

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Moûsai: Efficient Text-to-Music Diffusion Models
Flavio Schneider | Ojasv Kamal | Zhijing Jin | Bernhard Schölkopf

Recent years have seen the rapid development of large generative models for text; however, much less research has explored the connection between text and another “language” of communication – music. Music, much like text, can convey emotions, stories, and ideas, and has its own unique structure and syntax. In our work, we bridge text and music via a text-to-music generation model that is highly efficient, expressive, and can handle long-term structure. Specifically, we develop Moûsai, a cascading two-stage latent diffusion model that can generate multiple minutes of high-quality stereo music at 48kHz from textual descriptions. Moreover, our model features high efficiency, which enables real-time inference on a single consumer GPU with a reasonable speed. Through experiments and property analyses, we show our model’s competence over a variety of criteria compared with existing music generation models. Lastly, to promote the open-source culture, we provide a collection of open-source libraries with the hope of facilitating future work in the field. We open-source the following: Codes: https://github.com/archinetai/audio-diffusion-pytorch. Music samples for this paper: http://bit.ly/44ozWDH. Music samples for all models: https://bit.ly/audio-diffusion.

Multi-hop question answering (MQA) is one of the challenging tasks to evaluate machine’s comprehension and reasoning abilities, where large language models (LLMs) have widely achieved the human-comparable performance. Due to the dynamics of knowledge facts in real world, knowledge editing has been explored to update model with the up-to-date facts while avoiding expensive re-training or fine-tuning. Starting from the edited fact, the updated model needs to provide cascading changes in the chain of MQA. The previous art simply adopts a mix-up prompt to instruct LLMs conducting multiple reasoning tasks sequentially, including question decomposition, answer generation, and conflict checking via comparing with edited facts. However, the coupling of these functionally-diverse reasoning tasks inhibits LLMs’ advantages in comprehending and answering questions while disturbing them with the unskilled task of conflict checking. We thus propose a framework, Programmable knowledge editing for Multi-hop Question Answering (PokeMQA), to decouple the jobs. Specifically, we prompt LLMs to decompose knowledge-augmented multi-hop question, while interacting with a detached trainable scope detector to modulate LLMs behavior depending on external conflict signal. The experiments on three LLM backbones and two benchmark datasets validate our superiority in knowledge editing of MQA, outperforming all competitors by a large margin in almost all settings and consistently producing reliable reasoning process.

In the digital world, memes present a unique challenge for content moderation due to their potential to spread harmful content. Although detection methods have improved, proactive solutions such as intervention are still limited, with current research focusing mostly on text-based content, neglecting the widespread influence of multimodal content like memes. Addressing this gap, we present MemeGuard, a comprehensive framework leveraging Large Language Models (LLMs) and Visual Language Models (VLMs) for meme intervention. MemeGuard harnesses a specially fine-tuned VLM, VLMeme, for meme interpretation, and a multimodal knowledge selection and ranking mechanism (MKS) for distilling relevant knowledge. This knowledge is then employed by a general-purpose LLM to generate contextually appropriate interventions. Another key contribution of this work is the Intervening Cyberbullying in Multimodal Memes (ICMM) dataset, a high-quality, labeled dataset featuring toxic memes and their corresponding human-annotated interventions. We leverage ICMM to test MemeGuard, demonstrating its proficiency in generating relevant and effective responses to toxic memes. red Disclaimer: This paper contains harmful content that may be disturbing to some readers.

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Efficient OCR for Building a Diverse Digital History
Jacob Carlson | Tom Bryan | Melissa Dell

Many users consult digital archives daily, but the information they can access is unrepresentative of the diversity of documentary history. The sequence-to-sequence architecture typically used for optical character recognition (OCR) – which jointly learns a vision and language model – is poorly extensible to low-resource document collections, as learning a language-vision model requires extensive labeled sequences and compute. This study models OCR as a character level image retrieval problem, using a contrastively trained vision encoder. Because the model only learns characters’ visual features, it is more sample efficient and extensible than existing architectures, enabling accurate OCR in settings where existing solutions fail. Crucially, it opens new avenues for community engagement in making digital history more representative of documentary history.

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Acquiring Clean Language Models from Backdoor Poisoned Datasets by Downscaling Frequency Space
Zongru Wu | Zhuosheng Zhang | Pengzhou Cheng | Gongshen Liu

Despite the notable success of language models (LMs) in various natural language processing (NLP) tasks, the reliability of LMs is susceptible to backdoor attacks. Prior research attempts to mitigate backdoor learning while training the LMs on the poisoned dataset, yet struggles against complex backdoor attacks in real-world scenarios. In this paper, we investigate the learning mechanisms of backdoor LMs in the frequency space by Fourier analysis. Our findings indicate that the backdoor mapping presented on the poisoned datasets exhibits a more discernible inclination towards lower frequency compared to clean mapping, resulting in the faster convergence of backdoor mapping. To alleviate this dilemma, we propose Multi-Scale Low-Rank Adaptation (MuScleLoRA), which deploys multiple radial scalings in the frequency space with low-rank adaptation to the target model and further aligns the gradients when updating parameters. Through downscaling in the frequency space, MuScleLoRA encourages the model to prioritize the learning of relatively high-frequency clean mapping, consequently mitigating backdoor learning. Experimental results demonstrate that MuScleLoRA outperforms baselines significantly. Notably, MuScleLoRA reduces the average success rate of diverse backdoor attacks to below 15% across multiple datasets and generalizes to various backbone LMs, including BERT, RoBERTa, and Llama2. The codes are publicly available at Anonymous.

Reducing the ‘hallucination' problem of Large Language Models (LLMs) is crucial for their wide applications. A comprehensive and fine-grained measurement of the hallucination is the first key step for the governance of this issue but is under-explored in the community.Thus, we present ANAH, a bilingual dataset that offers ANalytical Annotation of Hallucinations in LLMs within Generative Question Answering.Each answer sentence in our dataset undergoes rigorous annotation, involving the retrieval of a reference fragment, the judgment of the hallucination type, and the correction of hallucinated content. ANAH consists of ~12k sentence-level annotations for ~4.3k LLM responses covering over 700 topics, constructed by a human-in-the-loop pipeline.Thanks to the fine granularity of the hallucination annotations, we can quantitatively confirm that the hallucinations of LLMs progressively accumulate in the answer and use ANAH to train and evaluate hallucination annotators. We conduct extensive experiments on studying generative and discriminative annotators and show that, although current open-source LLMs have difficulties in fine-grained hallucination annotation, the generative annotator trained with ANAH can surpass all open-source LLMs and GPT-3.5, obtain performance competitive with GPT-4, and exhibits better generalization ability on unseen questions.

Inspired by the exceptional general intelligence of Large Language Models (LLMs), researchers have begun to explore their application in pioneering the next generation of recommender systems — systems that are conversational, explainable, and controllable. However, existing literature primarily concentrates on integrating domain-specific knowledge into LLMs to enhance accuracy using a fixed task template, often overlooking the diversity of recommendation tasks and the ability of LLMs to follow recommendation-specific instructions. To address this gap, we first introduce a collection of supervised learning tasks, augmented with labels derived from a conventional recommender model, aimed at explicitly improving LLMs’ proficiency in adhering to recommendation-specific instructions. Next, we propose a reinforcement learning-based alignment procedure to enhance LLMs’ generalization ability. Extensive experiments on two real-world datasets demonstrate that our approach significantly improves the capability of LLMs to respond to instructions within recommender systems, reducing formatting errors while maintaining a high level of accuracy.

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Revealing the Parametric Knowledge of Language Models: A Unified Framework for Attribution Methods
Haeun Yu | Pepa Atanasova | Isabelle Augenstein

Language Models (LMs) acquire parametric knowledge from their training process, embedding it within their weights. The increasing scalability of LMs, however, poses significant challenges for understanding a model’s inner workings and further for updating or correcting this embedded knowledge without the significant cost of retraining. This underscores the importance of unveiling exactly what knowledge is stored and its association with specific model components. Instance Attribution (IA) and Neuron Attribution (NA) offer insights into this training-acquired knowledge, though they have not been compared systematically. Our study introduces a novel evaluation framework to quantify and compare the knowledge revealed by IA and NA. To align the results of the methods we introduce the attribution method NA-Instances to apply NA for retrieving influential training instances, and IA-Neurons to discover important neurons of influential instances discovered by IA. We further propose a comprehensive list of faithfulness tests to evaluate the comprehensiveness and sufficiency of the explanations provided by both methods. Through extensive experiments and analysis, we demonstrate that NA generally reveals more diverse and comprehensive information regarding the LM’s parametric knowledge compared to IA. Nevertheless, IA provides unique and valuable insights into the LM’s parametric knowledge, which are not revealed by NA. Our findings further suggest the potential of a synergistic approach of combining the diverse findings of IA and NA for a more holistic understanding of an LM’s parametric knowledge.

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Full Parameter Fine-tuning for Large Language Models with Limited Resources
Kai Lv | Yuqing Yang | Tengxiao Liu | Qipeng Guo | Xipeng Qiu

Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP) but demand massive GPU resources for training. Lowering the threshold for LLMs training would encourage greater participation from researchers, benefiting both academia and society. While existing approaches have focused on parameter-efficient fine-tuning, which tunes or adds a small number of parameters, few have addressed the challenge of tuning the full parameters of LLMs with limited resources. In this work, we propose a new optimizer, LOw-Memory Optimization (LOMO), which fuses the gradient computation and the parameter update in one step to reduce memory usage. By integrating LOMO with existing memory saving techniques, we reduce memory usage to 10.8% compared to the standard approach (DeepSpeed solution). Consequently, our approach enables the full parameter fine-tuning of a 65B model on a single machine with 8 × RTX 3090, each with 24GB memory. Code and data are available at https://github.com/OpenLMLab/LOMO.

Multi-modal Chain-of-Thought (MCoT) requires models to leverage knowledge from both textual and visual modalities for step-by-step reasoning, which gains increasing attention. Nevertheless, the current MCoT benchmark still faces some challenges: (1) absence of visual modal reasoning, (2) single-step visual modal reasoning, and (3) domain missing, thereby hindering the development of MCoT. Motivated by this, we introduce a novel benchmark (M³CoT) to address the above challenges, advancing the multi-domain, multi-step, and multi-modal CoT. Additionally, we conduct a thorough evaluation involving abundant MCoT approaches on Vision Large Language Models (VLLMs). In addition, we highlight that the current VLLMs still struggle to correctly reason in M³CoT and there is a large gap between VLLMs and human performance in M³CoT, despite their superior results on previous MCoT benchmarks. To our knowledge, we take the first meaningful step toward the multi-domain, multi-step, and multi-modal scenario in MCoT. We hope that M³CoT will serve as a valuable resource, providing a pioneering foundation in multi-domain, multi-step, multi-modal chain-of-thought research.

Long-context modeling capabilities are important for large language models (LLMs) in various applications. However, directly training LLMs with long context windows is insufficient to enhance this capability since some training samples do not exhibit strong semantic dependencies across long contexts.In this study, we propose a data mining framework ProLong that can assign each training sample with a long dependency score, which can be used to rank and filter samples that are more advantageous for enhancing long-context modeling abilities in LLM training. Specifically, we first use delta perplexity scores to measure the Dependency Strength between text segments in a given document. Then, we refine this metric based on the Dependency Distance of these segments to incorporate spatial relationships across long contexts. Final results are calibrated with a Dependency Specificity metric to prevent trivial dependencies introduced by repetitive patterns. Moreover, a random sampling approach is proposed to optimize the computational efficiency of ProLong. Comprehensive experiments on multiple benchmarks indicate that ProLong effectively identifies documents that carry long dependencies, and LLMs trained on these documents exhibit significantly enhanced long-context modeling capabilities.

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Label-Synchronous Neural Transducer for E2E Simultaneous Speech Translation
Keqi Deng | Phil Woodland

While the neural transducer is popular for online speech recognition, simultaneous speech translation (SST) requires both streaming and re-ordering capabilities. This paper presents the LS-Transducer-SST, a label-synchronous neural transducer for SST, which naturally possesses these two properties. The LS-Transducer-SST dynamically decides when to emit translation tokens based on an Auto-regressive Integrate-and-Fire (AIF) mechanism. A latency-controllable AIF is also proposed, which can control the quality-latency trade-off either only during decoding, or it can be used in both decoding and training. The LS-Transducer-SST can naturally utilise monolingual text-only data via its prediction network which helps alleviate the key issue of data sparsity for E2E SST. During decoding, a chunk-based incremental joint decoding technique is designed to refine and expand the search space. Experiments on the Fisher-CallHome Spanish (Es-En) and MuST-C En-De data show that the LS-Transducer-SST gives a better quality-latency trade-off than existing popular methods. For example, the LS-Transducer-SST gives a 3.1/2.9 point BLEU increase (Es-En/En-De) relative to CAAT at a similar latency and a 1.4 s reduction in average lagging latency with similar BLEU scores relative to Wait-k.

With the advent of foundation models, prompt tuning has positioned itself as an important technique for directing model behaviors and eliciting desired responses. Prompt tuning regards selecting appropriate keywords included into the input, thereby adapting to the downstream task without adjusting or fine-tuning the model parameters. There is a wide range of work in prompt tuning, from approaches that directly harness the backpropagated gradient signals from the model, to those employing black-box optimization such as reinforcement learning (RL) methods. Our primary focus is on RLPrompt, which aims to find optimal prompt tokens leveraging soft Q-learning. While the results show promise, we have observed that the prompts frequently appear unnatural, which impedes their interpretability. We address this limitation by using sparse Tsallis entropy regularization, a principled approach to filtering out unlikely tokens from consideration. We extensively evaluate our approach across various tasks, including few-shot text classification, unsupervised text style transfer, and textual inversion from images. The results indicate a notable improvement over baselines, highlighting the efficacy of our approach in addressing the challenges of prompt tuning. Moreover, we show that the prompts discovered using our method are more natural and interpretable compared to those from other baselines.

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A Modular Approach for Multimodal Summarization of TV Shows
Louis Mahon | Mirella Lapata

In this paper we address the task of summarizing television shows, which touches key areas in AI research: complex reasoning, multiple modalities, and long narratives. We present a modular approach where separate components perform specialized sub-tasks which we argue affords greater flexibility compared to end-to-end methods. Our modules involve detecting scene boundaries, reordering scenes so as to minimize the number of cuts between different events, converting visual information to text, summarizing the dialogue in each scene, and fusing the scene summaries into a final summary for the entire episode. We also present a new metric, PRISMA (**P**recision and **R**ecall Evaluat**i**on of **s**ummary F**a**cts), to measure both precision and recall of generated summaries, which we decompose into atomic facts. Tested on the recently released SummScreen3D dataset (Papalampidi & Lapata, 2023), our method produces higher quality summaries than comparison models, as measured with ROUGE and our new fact-based metric.

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Think Twice: Perspective-Taking Improves Large Language Models’ Theory-of-Mind Capabilities
Alex Wilf | Sihyun Lee | Paul Pu Liang | Louis-Philippe Morency

Human interactions are deeply rooted in the interplay of thoughts, beliefs, and desires made possible by Theory of Mind (ToM): our cognitive ability to understand the mental states of ourselves and others. Although ToM may come naturally to us, emulating it presents a challenge to even the most advanced Large Language Models (LLMs). Recent improvements to LLMs’ reasoning capabilities from simple yet effective prompting techniques such as Chain-of-Thought (CoT) have seen limited applicability to ToM. In this paper, we turn to the prominent cognitive science theory “Simulation Theory” to bridge this gap. We introduce SimToM, a novel two-stage prompting framework inspired by Simulation Theory’s notion of perspective-taking. To implement this idea on current ToM benchmarks, SimToM first filters context based on what the character in question knows before answering a question about their mental state. Our approach, which requires no additional training and minimal prompt-tuning, shows substantial improvement over existing methods, and our analysis reveals the importance of perspective-taking to Theory-of-Mind capabilities. Our findings suggest perspective-taking as a promising direction for future research into improving LLMs’ ToM capabilities.

Answering questions within business and finance requires reasoning, precision, and a wide-breadth of technical knowledge. Together, these requirements make this domain difficult for large language models (LLMs). We introduce BizBench, a benchmark for evaluating models’ ability to reason about realistic financial problems. BizBench comprises eight quantitative reasoning tasks, focusing on question-answering (QA) over financial data via program synthesis. We include three financially-themed code-generation tasks from newly collected and augmented QA data. Additionally, we isolate the reasoning capabilities required for financial QA: reading comprehension of financial text and tables for extracting intermediate values, and understanding financial concepts and formulas needed to calculate complex solutions. Collectively, these tasks evaluate a model’s financial background knowledge, ability to parse financial documents, and capacity to solve problems with code. We conduct an in-depth evaluation of open-source and commercial LLMs, comparing and contrasting the behavior of code-focused and language-focused models. We demonstrate that the current bottleneck in performance is due to LLMs’ limited business and financial understanding, highlighting the value of a challenging benchmark for quantitative reasoning within this domain.

While image captioning is an essential field of vision language models (VLM), a lack of continuity between the learning objective and final performance metrics of VLMs complicates their training and optimization. Reinforcement learning (RL) can directly optimize such metrics, but it is accompanied by a significant computational cost, making it difficult to apply to recent large-scale VLMs. In this paper, we propose Direct Metric Optimization (DMO), which is a lightweight final-metric-optimizing training method. We replace the computationally expensive exploration process in RL with an offline, diverse text data augmentation and show that self-supervised training on reward-weighted augmented data leads to direct and stable metric optimization. Our experiments demonstrate that DMO achieves performance comparable to those of the state-of-the-art RL method while saving hundreds of times more model forwarding iterations and greater amounts of computation time. This suggests that DMO constitutes a promising alternative for metric optimization in the era of large-scale VLMs.

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Deciphering Hate: Identifying Hateful Memes and Their Targets
Eftekhar Hossain | Omar Sharif | Mohammed Moshiul Hoque | Sarah Masud Preum

Internet memes have become a powerful means for individuals to express emotions, thoughts, and perspectives on social media. While often considered as a source of humor and entertainment, memes can also disseminate hateful content targeting individuals or communities. Most existing research focuses on the negative aspects of memes in high-resource languages, overlooking the distinctive challenges associated with low-resource languages like Bengali (also known as Bangla). Furthermore, while previous work on Bengali memes has focused on detecting hateful memes, there has been no work on detecting their targeted entities. To bridge this gap and facilitate research in this arena, we introduce a novel multimodal dataset for Bengali, BHM (Bengali Hateful Memes). The dataset consists of 7,148 memes with Bengali as well as code-mixed captions, tailored for two tasks: (i) detecting hateful memes, and (ii) detecting the social entities they target (i.e., Individual, Organization, Community, and Society). To solve these tasks, we propose DORA (Dual cO-attention fRAmework), a multimodal deep neural network that systematically extracts the significant modality features from the memes and jointly evaluates them with the modality-specific features to understand the context better. Our experiments show that DORA is generalizable on other low-resource hateful meme datasets and outperforms several state-of-the-art rivaling baselines.

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Inducing Systematicity in Transformers by Attending to Structurally Quantized Embeddings
Yichen Jiang | Xiang Zhou | Mohit Bansal

Transformers generalize to novel compositions of structures and entities after being trained on a complex dataset, but easily overfit on datasets of insufficient complexity. We observe that when the training set is sufficiently complex, the model encodes structurally equivalent sentences using a systematic attention pattern. Inspired by this observation, we propose SQ-Transformer (Structurally Quantized) that explicitly encourages systematicity in the embeddings and attention layers even with low-complexity data. At the embedding level, we introduce Structure-oriented Vector Quantization (SoVQ) to cluster word embeddings into several classes of structurally equivalent entities. At the attention level, we devise the Systematic Attention Layer (SAL) and an alternative, Systematically Regularized Layer (SRL) that operate on the quantized word embeddings so that sentences of the same structure are encoded with invariant or similar attention patterns. Empirically, we show SQ-Transformer achieves stronger compositional generalization than the vanilla Transformer on multiple low-complexity semantic parsing and machine translation datasets. In our analysis, we show SoVQ indeed learns a syntactically clustered embedding space, and SAL/SRL induces generalizable attention patterns, altogether leading to improved systematicity.

Model selection for a given target task can be costly, as it may entail extensive annotation of the quality of outputs of different models. We introduce DiffUse, an efficient method to make an informed decision between candidate text generation models based on preference annotations. DiffUse reduces the required amount of annotations, thus saving valuable time and resources in performing evaluation.DiffUse intelligently selects instances by clustering embeddings that represent the semantic differences between model outputs. Thus, it is able to identify a subset of examples that are more informative for preference decisions. Our method is model-agnostic, and can be applied to any text generation model for selecting between models, prompts and configurations. Moreover, we propose a practical iterative approach for dynamically determining how many instances to annotate. In a series of experiments over hundreds of model pairs, we demonstrate that DiffUse can dramatically reduce the required number of annotations – by up to 75% – while maintaining high evaluation reliability.

This study investigates the concept of the ‘right to be forgotten’ within the context of large language models (LLMs). We explore machine unlearning as a pivotal solution, with a focus on pre-trained models–a notably under-researched area. Our research delineates a comprehensive framework for machine unlearning in pre-trained LLMs, encompassing a critical analysis of seven diverse unlearning methods. Through rigorous evaluation using curated datasets from arXiv, books, and GitHub, we establish a robust benchmark for unlearning performance, demonstrating that these methods are over 10⁵ times more computationally efficient than retraining. Our results show that integrating gradient ascent with gradient descent on in-distribution data improves hyperparameter robustness. We also provide detailed guidelines for efficient hyperparameter tuning in the unlearning process. Our findings advance the discourse on ethical AI practices, offering substantive insights into the mechanics of machine unlearning for pre-trained LLMs and underscoring the potential for responsible AI development.

Interpretability research aims to bridge the gap between the empirical success and our scientific understanding of the inner workings of large language models (LLMs). However, most existing research in this area focused on analyzing a single mechanism, such as how models copy or recall factual knowledge. In this work, we propose the formulation of competition of mechanisms, which instead of individual mechanisms focuses on the interplay of multiple mechanisms, and traces how one of them becomes dominant in the final prediction. We uncover how and where the competition of mechanisms happens within LLMs using two interpretability methods, logit inspection and attention modification. Our findings show traces of the mechanisms and their competition across various model components, and reveal attention positions that effectively control the strength of certain mechanisms.

Plain language summarization with LLMs can be useful for improving textual accessibility of technical content. But how factual are these summaries in a high-stakes domain like medicine? This paper presents FactPICO, a factuality benchmark for plain language summarization of medical texts describing randomized controlled trials (RCTs), which are the basis of evidence-based medicine and can directly inform patient treatment. FactPICO consists of 345 plain language summaries of RCT abstracts generated from three LLMs (i.e., GPT-4, Llama-2, and Alpaca), with fine-grained evaluation and natural language rationales from experts. We assess the factuality of critical elements of RCTs in those summaries: Populations, Interventions, Comparators, Outcomes (PICO), as well as the reported findings concerning these. We also evaluate the correctness of the extra information (e.g., explanations) added by LLMs. Using FactPICO, we benchmark a range of existing factuality metrics, including the newly devised ones based on LLMs. We find that plain language summarization of medical evidence is still challenging, especially when balancing between simplicity and factuality, and that existing metrics correlate poorly with expert judgments on the instance level.

Aspect sentiment quad prediction (ASQP) aims to predict four aspect-based elements, including aspect term, opinion term, aspect category, and sentiment polarity. In practice, unseen aspects, due to distinct data distribution, impose many challenges for a trained neural model. Motivated by this, this work formulates ASQP into the few-shot scenario, which aims for fast adaptation in real applications. Therefore, we first construct a few-shot ASQP dataset (FSQP) that contains richer categories and is more balanced for the few-shot study. Moreover, recent methods extract quads through a generation paradigm, which involves converting the input sentence into a templated target sequence. However, they primarily focus on the utilization of a single template or the consideration of different template orders, thereby overlooking the correlations among various templates. To tackle this issue, we further propose a Broad-view Soft Prompting (BvSP) method that aggregates multiple templates with a broader view by taking into account the correlation between the different templates. Specifically, BvSP uses the pre-trained language model to select the most relevant k templates with Jensen–Shannon divergence. BvSP further introduces soft prompts to guide the pre-trained language model using the selected templates. Then, we aggregate the results of multi-templates by voting mechanism. Empirical results demonstrate that BvSP significantly outperforms the state-of-the-art methods under four few-shot settings and other public datasets. Our code and dataset are available at https://github.com/byinhao/BvSP.

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Safety Alignment in NLP Tasks: Weakly Aligned Summarization as an In-Context Attack
Yu Fu | Yufei Li | Wen Xiao | Cong Liu | Yue Dong

Recent developments in balancing the usefulness and safety of Large Language Models (LLMs) have raised a critical question: Are mainstream NLP tasks adequately aligned with safety consideration? Our study, focusing on safety-sensitive documents obtained through adversarial attacks, reveals significant disparities in the safety alignment of various NLP tasks. For instance, LLMs can effectively summarize malicious long documents but often refuse to translate them. This discrepancy highlights a previously unidentified vulnerability: attacks exploiting tasks with weaker safety alignment, like summarization, can potentially compromise the integrity of tasks traditionally deemed more robust, such as translation and question-answering (QA). Moreover, the concurrent use of multiple NLP tasks with lesser safety alignment increases the risk of LLMs inadvertently processing harmful content. We demonstrate these vulnerabilities in various safety-aligned LLMs, particularly Llama2 models, Gemini and GPT-4, indicating an urgent need for strengthening safety alignments across a broad spectrum of NLP tasks.

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Speech language models lack important brain-relevant semantics
Subba Reddy Oota | Emin Çelik | Fatma Deniz | Mariya Toneva

Despite known differences between reading and listening in the brain, recent work has shown that text-based language models predict both text-evoked and speech-evoked brain activity to an impressive degree. This poses the question of what types of information language models truly predict in the brain. We investigate this question via a direct approach, in which we systematically remove specific low-level stimulus features (textual, speech, and visual) from language model representations to assess their impact on alignment with fMRI brain recordings during reading and listening. Comparing these findings with speech-based language models reveals starkly different effects of low-level features on brain alignment. While text-based models show reduced alignment in early sensory regions post-removal, they retain significant predictive power in late language regions. In contrast, speech-based models maintain strong alignment in early auditory regions even after feature removal but lose all predictive power in late language regions. These results suggest that speech-based models provide insights into additional information processed by early auditory regions, but caution is needed when using them to model processing in late language regions. We make our code publicly available. [https://github.com/subbareddy248/speech-llm-brain]

Enterprise documents such as forms, receipts, reports, and other such records, often carry rich semantics at the intersection of textual and spatial modalities. The visual cues offered by their complex layouts play a crucial role in comprehending these documents effectively. In this paper, we present DocLLM, a lightweight extension to traditional large language models (LLMs) for reasoning over visual documents, taking into account both textual semantics and spatial layout. Our model differs from existing multimodal LLMs by avoiding expensive image encoders and focuses exclusively on bounding box information to incorporate the spatial layout structure. Specifically, the cross-alignment between text and spatial modalities is captured by decomposing the attention mechanism in classical transformers to a set of disentangled matrices. Furthermore, we devise a pre-training objective that learns to infill text segments. This approach allows us to address irregular layouts and heterogeneous content frequently encountered in visual documents. The pre-trained model is fine-tuned using a large-scale instruction dataset, covering four core document intelligence tasks. We demonstrate that our solution outperforms SotA LLMs on 14 out of 16 datasets across all tasks, and generalizes well to 4 out of 5 previously unseen datasets.

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Bypassing LLM Watermarks with Color-Aware Substitutions
Qilong Wu | Varun Chandrasekaran

Watermarking approaches are proposed to identify if text being circulated is human- or large language model- (LLM) generated. The state-of-the-art watermarking strategy of Kirchenbauer et al. (2023a) biases the LLM to generate specific (“green”) tokens. However, determining the robustness of this watermarking method under finite (low) edit budgets is an open problem. Additionally, existing attack methods failto evade detection for longer text segments. We overcome these limitations, and propose Self Color Testing-based Substitution (SCTS), thefirst “color-aware” attack. SCTS obtains color information by strategically prompting the watermarked LLM and comparing output tokensfrequencies. It uses this information to determine token colors, and substitutes green tokens with non-green ones. In our experiments, SCTS successfully evades watermark detection using fewer number of edits than related work. Additionally, we show both theoretically and empirically that SCTS can remove the watermark for arbitrarily long watermarked text.

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Parallel Structures in Pre-training Data Yield In-Context Learning
Yanda Chen | Chen Zhao | Zhou Yu | Kathleen McKeown | He He

Pre-trained language models (LMs) are capable of in-context learning (ICL): they can adapt to a task with only a few examples given in the prompt without any parameter update. However, it is unclear where this capability comes from as there is a stark distribution shift between pre-training text and ICL prompts. In this work, we study what patterns of the pre-training data contribute to ICL. We find that LMs’ ICL ability depends on parallel structures in the pre-training data—pairs of phrases following similar templates in the same context window. Specifically, we detect parallel structures by checking whether training on one phrase improves prediction of the other, and conduct ablation experiments to study their effect on ICL. We show that removing parallel structures in the pre-training data reduces LMs’ ICL accuracy by 51% (vs 2% from random ablation). This drop persists even when excluding common patterns such as n-gram repetitions and long-range dependency, showing the diversity and generality of parallel structures. A closer look at the detected parallel structures indicates that they cover diverse linguistic tasks and span long distances in the data.

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OpenToM: A Comprehensive Benchmark for Evaluating Theory-of-Mind Reasoning Capabilities of Large Language Models
Hainiu Xu | Runcong Zhao | Lixing Zhu | Jinhua Du | Yulan He

Neural Theory-of-Mind (N-ToM), machine’s ability to understand and keep track of the mental states of others, is pivotal in developing socially intelligent agents. However, prevalent N-ToM benchmarks have several shortcomings, including the presence of ambiguous and artificial narratives, absence of personality traits and preferences, a lack of questions addressing characters’ psychological mental states, and limited diversity in the questions posed. In response to these issues, we construct OpenToM, a new benchmark for assessing N-ToM with (1) longer and clearer narrative stories, (2) characters with explicit personality traits, (3) actions that are triggered by character intentions, and (4) questions designed to challenge LLMs’ capabilities of modeling characters’ mental states of both the physical and psychological world. Using OpenToM, we reveal that state-of-the-art LLMs thrive at modeling certain aspects of mental states in the physical world but fall short when tracking characters’ mental states in the psychological world.

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Towards Privacy-Aware Sign Language Translation at Scale
Phillip Rust | Bowen Shi | Skyler Wang | Necati Cihan Camgoz | Jean Maillard

A major impediment to the advancement of sign language translation (SLT) is data scarcity. Much of the sign language data currently available on the web cannot be used for training supervised models due to the lack of aligned captions. Furthermore, scaling SLT using large-scale web-scraped datasets bears privacy risks due to the presence of biometric information, which the responsible development of SLT technologies should account for. In this work, we propose a two-stage framework for privacy-aware SLT at scale that addresses both of these issues. We introduce SSVP-SLT, which leverages self-supervised video pretraining on anonymized and unannotated videos, followed by supervised SLT finetuning on a curated parallel dataset. SSVP-SLT achieves state-of-the-art finetuned and zero-shot gloss-free SLT performance on the How2Sign dataset, outperforming the strongest respective baselines by over 3 BLEU-4. Based on controlled experiments, we further discuss the advantages and limitations of self-supervised pretraining and anonymization via facial obfuscation for SLT.

Fine-grained control over large language models (LLMs) remains a significant challenge, hindering their adaptability to diverse user needs. While Reinforcement Learning from Human Feedback (RLHF) shows promise in aligning LLMs, its reliance on scalar rewards often limits its ability to capture diverse user preferences in real-world applications. To address this limitation, we introduce the Directional Preference Alignment (DPA) framework. Unlike the scalar-reward RLHF, DPA incorporates multi-objective reward modeling to represent diverse preference profiles. Additionally, DPA models user preferences as directions (i.e., unit vectors) in the reward space to achieve user-dependent preference control. Our method involves training a multi-objective reward model and then fine-tuning the LLM with a preference-conditioned variant of Rejection Sampling Finetuning (RSF), an RLHF method adopted by Llama 2. This method enjoys a better performance trade-off across various reward objectives. In comparison with the scalar-reward RLHF, DPA offers users intuitive control over LLM generation: they can arithmetically specify their desired trade-offs (e.g., more helpfulness with less verbosity). We also validate the effectiveness of DPA with real-world alignment experiments on Mistral-7B. Our method provides straightforward arithmetic control over the trade-off between helpfulness and verbosity while maintaining competitive performance with strong baselines such as Direct Preference Optimization (DPO).

Writing assistance aims to improve the correctness and quality of input texts, with character checking being crucial in detecting and correcting wrong characters. In the real world where handwriting occupies the vast majority, characters that humans get wrong include faked characters (i.e., untrue characters created due to writing errors) and misspelled characters (i.e., true characters used incorrectly due to spelling errors). However, existing datasets and related studies only focus on misspelled characters that can be represented by computer text encoding systems, thereby ignoring faked characters which are more common and difficult. To break through this dilemma, we present Visual-C³, a human-annotated Visual Chinese Character Checking dataset with faked and misspelled Chinese characters. To the best of our knowledge, Visual-C³ is the first real-world visual and the largest human-crafted dataset for the Chinese character checking scenario. Additionally, we also propose and evaluate novel baseline methods on Visual-C³. Extensive empirical results and analyses show that Visual-C³ is high-quality yet challenging. As the first study focusing on Chinese faked characters, the dataset and the baseline methods are publicly available at https://github.com/THUKElab/Visual-C3.

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RAVEL: Evaluating Interpretability Methods on Disentangling Language Model Representations
Jing Huang | Zhengxuan Wu | Christopher Potts | Mor Geva | Atticus Geiger

Individual neurons participate in the representation of multiple high-level concepts. To what extent can different interpretability methods successfully disentangle these roles? To help address this question, we introduce RAVEL (Resolving Attribute-Value Entanglements in Language Models), a dataset that enables tightly controlled, quantitative comparisons between a variety of existing interpretability methods. We use the resulting conceptual framework to define the new method of Multi-task Distributed Alignment Search (MDAS), which allows us to find distributed representations satisfying multiple causal criteria. With Llama2-7B as the target language model, MDAS achieves state-of-the-art results on RAVEL, demonstrating the importance of going beyond neuron-level analyses to identify features distributed across activations. We release our benchmark at https://github.com/explanare/ravel.

Large language models (LLMs) are increasingly prevalent in conversational systems due to their advanced understanding and generative capabilities in general contexts. However, their effectiveness in task-oriented dialogues (TOD), which requires not only response generation but also effective dialogue state tracking (DST) within specific tasks and domains, remains less satisfying. In this work, we propose a novel approach FnCTOD for solving DST with LLMs through function calling. This method improves zero-shot DST, allowing adaptation to diverse domains without extensive data collection or model tuning. Our experimental results demonstrate that our approach achieves exceptional performance with both modestly sized open-source and also proprietary LLMs: with in-context prompting it enables various 7B or 13B parameter models to surpass the previous state-of-the-art (SOTA) achieved by ChatGPT, and improves ChatGPT’s performance beating the SOTA by 5.6% average joint goal accuracy (JGA). Individual model results for GPT-3.5 and GPT-4 are boosted by 4.8% and 14%, respectively. We also show that by fine-tuning on a small collection of diverse task-oriented dialogues, we can equip modestly sized models, specifically a 13B parameter LLaMA2-Chat model, with function-calling capabilities and DST performance comparable to ChatGPT while maintaining their chat capabilities. We have made the code publicly available at https://github.com/facebookresearch/FnCTOD.

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Faithful Chart Summarization with ChaTS-Pi
Syrine Krichene | Francesco Piccinno | Fangyu Liu | Julian Eisenschlos

Chart-to-summary generation can help explore data, communicate insights, and help the visually impaired people. Multi-modal generative models have been used to produce fluent summaries, but they can suffer from factual and perceptual errors. In this work we present CHATS-CRITIC, a reference-free chart summarization metric for scoring faithfulness. CHATS-CRITIC is composed of an image-to-text model to recover the table from a chart, and a tabular entailment model applied to score the summary sentence by sentence. We find that CHATS-CRITIC evaluates the summary quality according to human ratings better than reference-based metrics, either learned or n-gram based, and can be further used to fix candidate summaries by removing not supported sentences. We then introduce CHATS-PI, a chart-to-summary pipeline that leverages CHATS-CRITIC during inference to fix and rank sampled candidates from any chart-summarization model. We evaluate CHATS-PI and CHATS-CRITIC using human raters, establishing state-of-the-art results on two popular chart-to-summary datasets.

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Enhancing Dialogue State Tracking Models through LLM-backed User-Agents Simulation
Cheng Niu | Xingguang Wang | Xuxin Cheng | Juntong Song | Tong Zhang

Dialogue State Tracking (DST) is designed to monitor the evolving dialogue state in the conversations and plays a pivotal role in developing task-oriented dialogue systems. However, obtaining the annotated data for the DST task is usually a costly endeavor. In this paper, we focus on employing LLMs to generate dialogue data to reduce dialogue collection and annotation costs. Specifically, GPT-4 is used to simulate the user and agent interaction, generating thousands of dialogues annotated with DST labels. Then a two-stage fine-tuning on LLaMA 2 is performed on the generated data and the real data for the DST prediction. Experimental results on two public DST benchmarks show that with the generated dialogue data, our model performs better than the baseline trained solely on real data. In addition, our approach is also capable of adapting to the dynamic demands in real-world scenarios, generating dialogues in new domains swiftly. After replacing dialogue segments in any domain with the corresponding generated ones, the model achieves comparable performance to the model trained on real data. The source code and generated dialogue data are available at https://github.com/ParticleMedia/LUAS.

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MetaSumPerceiver: Multimodal Multi-Document Evidence Summarization for Fact-Checking
Ting-Chih Chen | Chia-Wei Tang | Chris Thomas

Fact-checking real-world claims often requires reviewing multiple multimodal documents in order to assess the claim’s truthfulness, a highly laborious and time-consuming task. In this paper, we present a summarization model crafted to generate claim-specific summaries useful for fact-checking from multimodal multi-document datasets. The model takes inputs in the form of documents, images, and a claim, with the objective of assisting in fact-checking tasks. We introduce a dynamic perceiver-based model that is able to handle inputs from multiple modalities of arbitrary lengths. To train our model, we leverage a novel reinforcement learning-based entailment objective in order to generate summaries that provide evidence distinguishing between different truthfulness labels. To assess the efficacy of our approach, we conduct experiments on both an existing benchmark as well as a new dataset of multi-document claims which we contribute. Our approach outperforms the SOTA approach by 4.6% in the claim verification task on the MOCHEG dataset and demonstrates strong performance on our new Multi-News-Fact-Checking dataset.

Large language models (LLMs) have achieved commendable accomplishments in various natural language processing tasks. However, LLMs still encounter significant challenges when dealing with complex scenarios involving multiple entities. These challenges arise from the presence of implicit relationships that demand multi-step reasoning. In this paper, we propose a novel approach ERA-CoT, which aids LLMs in understanding context by capturing relationships between entities and supports the reasoning of diverse tasks through Chain-of-Thoughts (CoT).Experimental results show that ERA-CoT demonstrates the superior performance of our proposed method compared to current CoT prompting methods, achieving a significant improvement of an average of 5.1% on GPT3.5 compared to previous SOTA baselines. Our analysis indicates that ERA-CoT increases the LLM’s understanding of entity relationships, significantly improves the accuracy of question answering, and enhances the reasoning ability of LLMs.

Web agents powered by Large Language Models (LLMs) have demonstrated remarkable abilities in planning and executing multi-step interactions within complex web-based environments, fulfilling a wide range of web navigation tasks. Despite these advancements, the potential for LLM-powered agents to effectively engage with sequential user instructions in real-world scenarios has not been fully explored. In this work, we introduce a new task of Conversational Web Navigation, which necessitates sophisticated interactions that span multiple turns with both the users and the environment, supported by a specially developed dataset named Multi-Turn Mind2Web (MT-Mind2Web). To tackle the limited context length of LLMs and the context-dependency issue of the conversational tasks, we further propose a novel framework, named self-reflective memory-augmented planning (Self-MAP), which employs memory utilization and self-reflection techniques. Extensive experiments are conducted to benchmark the MT-Mind2Web dataset, and validate the effectiveness of the proposed method.

With the remarkable advancements of large language models (LLMs), LLM-based agents have become a research hotspot in human-computer interaction.However, there is a scarcity of benchmarks available for LLM-based mobile agents.Benchmarking these agents generally faces three main challenges:(1) The inefficiency of UI-only operations imposes limitations to task evaluation.(2) Specific instructions within a singular application lack adequacy for assessing the multi-dimensional reasoning and decision-making capacities of LLM mobile agents.(3) Current evaluation metrics are insufficient to accurately assess the process of sequential actions. To this end, we propose Mobile-Bench, a novel benchmark for evaluating the capabilities of LLM-based mobile agents.First, we expand conventional UI operations by incorporating 103 collected APIs to accelerate the efficiency of task completion.Subsequently, we collect evaluation data by combining real user queries with augmentation from LLMs.To better evaluate different levels of planning capabilities for mobile agents, our data is categorized into three distinct groups: SAST, SAMT, and MAMT, reflecting varying levels of task complexity. Mobile-Bench comprises 832 data entries, with more than 200 tasks specifically designed to evaluate multi-APP collaboration scenarios.Furthermore, we introduce a more accurate evaluation metric, named CheckPoint, to assess whether LLM-based mobile agents reach essential points during their planning and reasoning steps. Dataset and platform will be released in the future.

Current large language models demonstrate deficiencies in understanding low-resource languages, particularly the minority languages in China. This limitation stems from the scarcity of available pre-training data. To address this accessibility challenge, we present MC², a Multilingual Corpus of Minority Languages in China, which is the largest open-source corpus of its kind so far. MC² includes four underrepresented languages: Tibetan, Uyghur, Kazakh, and Mongolian. Notably, we focus on the less common writing systems of Kazakh and Mongolian, i.e., Kazakh Arabic script and traditional Mongolian script, respectively, which have been long neglected in previous corpus construction efforts. Recognizing the prevalence of language contamination within existing corpora, we adopt a quality-centric solution for collecting MC², prioritizing accuracy while enhancing diversity. Furthermore, we underscore the importance of attending to the multiplicity of writing systems, which is closely related to the cultural awareness of the resulting models. The MC² corpus and related models are made public to the community.

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Decoder-only Streaming Transformer for Simultaneous Translation
Shoutao Guo | Shaolei Zhang | Yang Feng

Simultaneous Machine Translation (SiMT) generates translation while reading source tokens, essentially producing the target prefix based on the source prefix. To achieve good performance, it leverages the relationship between source and target prefixes to exact a policy to guide the generation of translations. Although existing SiMT methods primarily focus on the Encoder-Decoder architecture, we explore the potential of Decoder-only architecture, owing to its superior performance in various tasks and its inherent compatibility with SiMT. However, directly applying the Decoder-only architecture to SiMT poses challenges in terms of training and inference. To alleviate the above problems, we propose the first Decoder-only SiMT model, named Decoder-only Streaming Transformer (DST). Specifically, DST separately encodes the positions of the source and target prefixes, ensuring that the position of the target prefix remains unaffected by the expansion of the source prefix. Furthermore, we propose a Streaming Self-Attention (SSA) mechanism tailored for the Decoder-only architecture. It is capable of obtaining translation policy by assessing the sufficiency of input source information and integrating with the soft-attention mechanism to generate translations. Experiments demonstrate that our approach achieves state-of-the-art performance on three translation tasks.

While significant attention has been dedicated to exploiting weaknesses in LLMs through jailbreaking attacks, there remains a paucity of effort in defending against these attacks. We point out a pivotal factor contributing to the success of jailbreaks: the intrinsic conflict between the goals of being helpful and ensuring safety. Accordingly, we propose to integrate goal prioritization at both training and inference stages to counteract. Implementing goal prioritization during inference substantially diminishes the Attack Success Rate (ASR) of jailbreaking from 66.4% to 3.6% for ChatGPT. And integrating goal prioritization into model training reduces the ASR from 71.0% to 6.6% for Llama2-13B. Remarkably, even in scenarios where no jailbreaking samples are included during training, our approach slashes the ASR by half. Additionally, our findings reveal that while stronger LLMs face greater safety risks, they also possess a greater capacity to be steered towards defending against such attacks, both because of their stronger ability in instruction following. Our work thus contributes to the comprehension of jailbreaking attacks and defenses, and sheds light on the relationship between LLMs’ capability and safety. Our code is available at https://github.com/thu-coai/JailbreakDefense_GoalPriority.

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I am a Strange Dataset: Metalinguistic Tests for Language Models
Tristan Thrush | Jared Moore | Miguel Monares | Christopher Potts | Douwe Kiela

Statements involving metalinguistic self-reference (“This paper has six sections.”) are prevalent in many domains. Can large language models (LLMs) handle such language? In this paper, we present “I am a Strange Dataset”, a new dataset for addressing this question. There are two subtasks: generation and verification. In generation, models continue statements like “The penultimate word in this sentence is” (where a correct continuation is “is”). In verification, models judge the truth of statements like “The penultimate word in this sentence is sentence.” (false). We also provide minimally different metalinguistic non-self-reference examples to complement the main dataset by probing for whether models can handle metalinguistic language at all. The dataset is hand-crafted by experts and validated by non-expert annotators. We test a variety of open-source LLMs (7B to 70B parameters) as well as closed-source LLMs through APIs. All models perform close to chance across both subtasks and even on the non-self-referential metalinguistic control data, though we find some steady improvement with model scale. GPT 4 is the only model to consistently do significantly better than chance, and it is still only in the 60% range, while our untrained human annotators score well in the 89-93% range. The dataset and evaluation toolkit are available at https://github.com/TristanThrush/i-am-a-strange-dataset

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TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Space
Shaolei Zhang | Tian Yu | Yang Feng

Large Language Models (LLMs) sometimes suffer from producing hallucinations, especially LLMs may generate untruthful responses despite knowing the correct knowledge. Activating the truthfulness within LLM is the key to fully unlocking LLM’s knowledge potential. In this paper, we propose TruthX, an inference-time intervention method to activate the truthfulness of LLM by identifying and editing the features within LLM’s internal representations that govern the truthfulness. TruthX employs an auto-encoder to map LLM’s representations into semantic and truthful latent spaces respectively, and applies contrastive learning to identify a truthful editing direction within the truthful space. During inference, by editing LLM’s internal representations in truthful space, TruthX effectively enhances the truthfulness of LLM. Experiments show that TruthX improves the truthfulness of 13 advanced LLMs by an average of 20% on TruthfulQA benchmark. Further analyses suggest that TruthX can control LLM to produce truthful or hallucinatory responses via editing only one vector in LLM’s internal representations.

We propose ProtLLM, a versatile cross-modal large language model (LLM) for both protein-centric and protein-language tasks. ProtLLM features a unique dynamic protein mounting mechanism, enabling it to handle complex inputs where the natural language text is interspersed with an arbitrary number of proteins. Besides, we propose the protein-as-word language modeling approach to train ProtLLM. By developing a specialized protein vocabulary, we equip the model with the capability to predict not just natural language but also proteins from a vast pool of candidates. Additionally, we construct a large-scale interleaved protein-text dataset, named InterPT, for pre-training. This dataset comprehensively encompasses both (1) structured data sources like protein annotations and (2) unstructured data sources like biological research papers, thereby endowing ProtLLM with crucial knowledge for understanding proteins. We evaluate ProtLLM on classic supervised protein-centric tasks and explore its novel protein-language applications. Experimental results demonstrate that ProtLLM not only achieves superior performance against protein-specialized baselines on protein-centric tasks but also induces zero-shot and in-context learning capabilities on protein-language tasks.

Simultaneous speech-to-speech translation (Simul-S2ST, a.k.a streaming speech translation) outputs target speech while receiving streaming speech inputs, which is critical for real-time communication. Beyond accomplishing translation between speech, Simul-S2ST requires a policy to control the model to generate corresponding target speech at the opportune moment within speech inputs, thereby posing a double challenge of translation and policy. In this paper, we propose StreamSpeech, a direct Simul-S2ST model that jointly learns translation and simultaneous policy in a unified framework of multi-task learning. Adhering to a multi-task learning approach, StreamSpeech can perform offline and simultaneous speech recognition, speech translation and speech synthesis via an “All-in-One” seamless model. Experiments on CVSS benchmark demonstrate that StreamSpeech achieves state-of-the-art performance in both offline S2ST and Simul-S2ST tasks. Besides, StreamSpeech is able to present high-quality intermediate results (i.e., ASR or translation results) during simultaneous translation process, offering a more comprehensive real-time communication experience.

Recent work has showcased the powerful capability of large language models (LLMs) in recalling knowledge and reasoning. However, the reliability of LLMs in combining these two capabilities into reasoning through multi-hop facts has not been widely explored. This paper systematically investigates the possibilities for LLMs to utilize shortcuts based on direct connections between the initial and terminal entities of multi-hop knowledge. We first explore the existence of factual shortcuts through Knowledge Neurons, revealing that: (i) the strength of factual shortcuts is highly correlated with the frequency of co-occurrence of initial and terminal entities in the pre-training corpora; (ii) few-shot prompting leverage more shortcuts in answering multi-hop questions compared to chain-of-thought prompting. Then, we analyze the risks posed by factual shortcuts from the perspective of multi-hop knowledge editing. Analysis shows that approximately 20% of the failures are attributed to shortcuts, and the initial and terminal entities in these failure instances usually have higher co-occurrences in the pre-training corpus. Finally, we propose erasing shortcut neurons to mitigate the associated risks and find that this approach significantly reduces failures in multiple-hop knowledge editing caused by shortcuts. Code is publicly available at https://github.com/Jometeorie/MultiHopShortcuts.

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Why Don’t Prompt-Based Fairness Metrics Correlate?
Abdelrahman Zayed | Goncalo Mordido | Ioana Baldini | Sarath Chandar

The widespread use of large language models has brought up essential questions about the potential biases these models might learn. This led to the development of several metrics aimed at evaluating and mitigating these biases. In this paper, we first demonstrate that prompt-based fairness metrics exhibit poor agreement, as measured by correlation, raising important questions about the reliability of fairness assessment using prompts. Then, we outline six relevant reasons why such a low correlation is observed across existing metrics. Based on these insights, we propose a method called Correlated Fairness Output (CAIRO) to enhance the correlation between fairness metrics. CAIRO augments the original prompts of a given fairness metric by using several pre-trained language models and then selects the combination of the augmented prompts that achieves the highest correlation across metrics. We show a significant improvement in Pearson correlation from 0.3 and 0.18 to 0.90 and 0.98 across metrics for gender and religion biases, respectively. Our code is available at https://github.com/chandar-lab/CAIRO.

To address the global issue of online hate, hate speech detection (HSD) systems are typically developed on datasets from the United States, thereby failing to generalize to English dialects from the Majority World. Furthermore, HSD models are often evaluated on non-representative samples, raising concerns about overestimating model performance in real-world settings. In this work, we introduce NaijaHate, the first dataset annotated for HSD which contains a representative sample of Nigerian tweets. We demonstrate that HSD evaluated on biased datasets traditionally used in the literature consistently overestimates real-world performance by at least two-fold. We then propose NaijaXLM-T, a pretrained model tailored to the Nigerian Twitter context, and establish the key role played by domain-adaptive pretraining and finetuning in maximizing HSD performance. Finally, owing to the modest performance of HSD systems in real-world conditions, we find that content moderators would need to review about ten thousand Nigerian tweets flagged as hateful daily to moderate 60% of all hateful content, highlighting the challenges of moderating hate speech at scale as social media usage continues to grow globally. Taken together, these results pave the way towards robust HSD systems and a better protection of social media users from hateful content in low-resource settings.

Publishing open-source academic video recordings is an emergent and prevalent approach to sharing knowledge online. Such videos carry rich multimodal information including speech, the facial and body movements of the speakers, as well as the texts and pictures in the slides and possibly even the papers. Although multiple academic video datasets have been constructed and released, few of them support both multimodal content recognition and understanding tasks, which is partially due to the lack of high-quality human annotations. In this paper, we propose a novel multimodal, multigenre, and multipurpose audio-visual academic lecture dataset (M³AV), which has almost 367 hours of videos from five sources covering computer science, mathematics, and medical and biology topics. With high-quality human annotations of the slide text and spoken words, in particular high-valued name entities, the dataset can be used for multiple audio-visual recognition and understanding tasks. Evaluations performed on contextual speech recognition, speech synthesis, and slide and script generation tasks demonstrate that the diversity of M³AV makes it a challenging dataset.

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Mitigating Biases for Instruction-following Language Models via Bias Neurons Elimination
Nakyeong Yang | Taegwan Kang | Stanley Jungkyu Choi | Honglak Lee | Kyomin Jung

Instruction-following language models often show undesirable biases. These undesirable biases may be accelerated in the real-world usage of language models, where a wide range of instructions is used through zero-shot example prompting. To solve this problem, we first define the bias neuron, which significantly affects biased outputs, and prove its existence empirically. Furthermore, we propose a novel and practical bias mitigation method, CRISPR, to eliminate bias neurons of language models in instruction-following settings. CRISPR automatically determines biased outputs and categorizes neurons that affect the biased outputs as bias neurons using an explainability method. Experimental results demonstrate the effectiveness of our method in mitigating biases under zero-shot instruction-following settings without losing the model’s task performance and existing knowledge. The experimental results reveal the generalizability of our method as it shows robustness under various instructions and datasets. Surprisingly, our method can mitigate the bias in language models by eliminating only a few neurons (at least three).

This paper focuses on answering subjective questions about products. Different from the factoid question with a single answer span, this subjective one involves multiple viewpoints. For example, the question of ‘how the phone’s battery is?’ not only involves facts of battery capacity but also contains users’ opinions on the battery’s pros and cons. A good answer should be able to integrate these heterogeneous and even inconsistent viewpoints, which is formalized as a subjective induction QA task. For this task, the data distributions are often imbalanced across different product domains. It is hard for traditional methods to work well without considering the shift of domain patterns. To address this problem, we propose a novel domain-adaptive model. Concretely, for each sample in the source and target domain, we first retrieve answer-related knowledge and represent them independently. To facilitate knowledge transferring, we then disentangle the representations into domain-invariant and domain-specific latent factors. Moreover, we develop an adversarial discriminator with contrastive learning to reduce the impact of out-of-domain bias. Based on learned latent vectors in a target domain, we yield multi-perspective summaries as inductive answers. Experiments on popular datasets show the effectiveness of our method.

Large language models (LLMs) have shown an impressive ability to perform a wide range of tasks using in-context learning (ICL), where a few examples are used to describe a task to the model. However, the performance of ICL varies significantly with the choice of demonstrations, and previous research usually focuses on the data aspect ignoring the model’s effect. In this work, we first revisit the factors contributing to this variance from the model aspect, and find that the demonstration choice is both data- and model-dependent. We further propose a conjecture that the performance of a demonstration positively correlates with its contribution to the model’s understanding of the test samples, and accordingly propose a data- and model-dependent demonstration selection method, TopK + ConE. Empirically, our method yields consistent improvements in both language understanding and generation tasks with different model scales. Further analyses confirm that, besides the generality and stability under different circumstances, our method provides a unified explanation for the effectiveness of previous methods. Code is publicly available at https://github.com/Romainpkq/revisit_demon_selection_in_ICL.

Although great progress has been made by previous table understanding methods including recent approaches based on large language models (LLMs), they rely heavily on the premise that given tables must be converted into a certain text sequence (such as Markdown or HTML) to serve as model input. However, it is difficult to access such high-quality textual table representations in some real-world scenarios, and table images are much more accessible. Therefore, how to directly understand tables using intuitive visual information is a crucial and urgent challenge for developing more practical applications. In this paper, we propose a new problem, multimodal table understanding, where the model needs to generate correct responses to various table-related requests based on the given table image. To facilitate both the model training and evaluation, we construct a large-scale dataset named MMTab, which covers a wide spectrum of table images, instructions and tasks. On this basis, we develop Table-LLaVA, a generalist tabular multimodal large language model (MLLM), which significantly outperforms recent open-source MLLM baselines on 23 benchmarks under held-in and held-out settings.

Generating long-term texts such as novels using artificial intelligence has always been a challenge. A common approach is to use large language models (LLMs) to construct a hierarchical framework that first plans and then writes. Despite the fact that the generated novels reach a sufficient length, they exhibit poor logical coherence and appeal in their plots and deficiencies in character and event depiction, ultimately compromising the overall narrative quality. In this paper, we propose a method named Extracting Excelsior and Expanding. Ex3 initially extract structural information by learning from raw novel data. By combining this structure information with the novel data, an instruction-following dataset is meticulously crafted. This dataset is then utilized to fine-tune the LLM, aiming for excelsior generation performance. In the final stage, a tree-like expansion method is deployed to facilitate the generation of arbitrarily long novels.Evaluation against previous methods showcases Ex3’s ability to produce higher-quality long-form novels.

Existing Knowledge Base Question Answering (KBQA) architectures are hungry for annotated data, which make them costly and time-consuming to deploy. We introduce the problem of few-shot transfer learning for KBQA, where the target domain offers only a few labeled examples, but a large labeled training dataset is available in a source domain. We propose a novel KBQA architecture called FuSIC-KBQA that performs KB-retrieval using multiple source-trained retrievers, re-ranks using an LLM and uses this as input for LLM few-shot in-context learning to generate logical forms, which are further refined using execution-guided feedback. Experiments over four source-target KBQA pairs of varying complexity show that FuSIC-KBQA significantly outperforms adaptations of SoTA KBQA models for this setting. Additional experiments in the in-domain setting show that FuSIC-KBQA also outperforms SoTA KBQA models when training data is limited.

Text watermarking has emerged as a pivotal technique for identifying machine-generated text. However, existing methods often rely on arbitrary vocabulary partitioning during decoding to embed watermarks, which compromises the availability of suitable tokens and significantly degrades the quality of responses. This study assesses the impact of watermarking on different capabilities of large language models (LLMs) from a cognitive science lens. Our finding highlights a significant disparity; knowledge recall and logical reasoning are more adversely affected than language generation. These results suggest a more profound effect of watermarking on LLMs than previously understood. To address these challenges, we introduce Watermarking with Mutual Exclusion (WatME), a novel approach leveraging linguistic prior knowledge of inherent lexical redundancy in LLM vocabularies to seamlessly integrate watermarks. Specifically, WatME dynamically optimizes token usage during the decoding process by applying a mutually exclusive rule to the identified lexical redundancies. This strategy effectively prevents the unavailability of appropriate tokens and preserves the expressive power of LLMs. We provide both theoretical analysis and empirical evidence showing that WatME effectively preserves the diverse capabilities of LLMs while ensuring watermark detectability.

When adapting Large Language Models for Recommendation (LLMRec), it is crucial to integrate collaborative information. Existing methods achieve this by learning collaborative embeddings in LLMs’ latent space from scratch or by mapping from external models. However, they fail to represent the information in a text-like format, which may not align optimally with LLMs. To bridge this gap, we introduce BinLLM, a novel LLMRec method that seamlessly integrates collaborative information through text-like encoding. BinLLM converts collaborative embeddings from external models into binary sequences — a specific text format that LLMs can understand and operate on directly, facilitating the direct usage of collaborative information in text-like format by LLMs. Additionally, BinLLM provides options to compress the binary sequence using dot-decimal notation to avoid excessively long lengths. Extensive experiments validate that BinLLM introduces collaborative information in a manner better aligned with LLMs, resulting in enhanced performance. We release our code at https://github.com/zyang1580/BinLLM.

Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated exceptional capabilities in visual perception and understanding. However, these models also suffer from hallucinations, which limit their reliability as AI systems. We believe that these hallucinations are partially due to the models’ struggle with understanding what they can and cannot perceive from images, a capability we refer to as self-awareness in perception. Despite its importance, this aspect of MLLMs has been overlooked in prior studies. In this paper, we aim to define and evaluate the self-awareness of MLLMs in perception. To do this, we first introduce the knowledge quadrant in perception, which helps define what MLLMs know and do not know about images. Using this framework, we propose a novel benchmark, the Self-Awareness in Perception for MLLMs (MM-SAP), specifically designed to assess this capability. We apply MM-SAP to a variety of popular MLLMs, offering a comprehensive analysis of their self-awareness and providing detailed insights. The experiment results reveal that current MLLMs possess limited self-awareness capabilities, pointing to a crucial area for future advancement in the development of trustworthy MLLMs. Code and data are available at https://github.com/YHWmz/MM-SAP.

Large language models exhibit high-level commonsense reasoning abilities, especially with enhancement methods like Chain-of-Thought (CoT). However, we find these CoT-like methods lead to a considerable number of originally correct answers turning wrong, which we define as the Toxic CoT problem. To interpret and mitigate this problem, we first utilize attribution tracing and causal tracing methods to probe the internal working mechanism of the LLM during CoT reasoning. Through comparisons, we prove that the model exhibits information loss from the question over the shallow attention layers when generating rationales or answers. Based on the probing findings, we design a novel method called RIDERS (Residual decodIng and sERial-position Swap), which compensates for the information deficit in the model from both decoding and serial-position perspectives. Through extensive experiments on multiple commonsense reasoning benchmarks, we validate that this method not only significantly eliminates Toxic CoT problems (decreased by 23.6%), but also effectively improves the model’s overall commonsense reasoning performance (increased by 5.5%).

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Multi-Aspect Controllable Text Generation with Disentangled Counterfactual Augmentation
Yi Liu | Xiangyu Liu | Xiangrong Zhu | Wei Hu

Multi-aspect controllable text generation aims to control the generated texts in attributes from multiple aspects (e.g., “positive” from sentiment and “sport” from topic). Existing works neglect attribute correlations formed by the intertwining of different attributes. Particularly, the stereotype formed by imbalanced attribute correlations significantly affects multi-aspect control. In this paper, we propose MAGIC, a new multi-aspect controllable text generation method with disentangled counterfactual augmentation. We alleviate the issue of imbalanced attribute correlations during training using counterfactual feature vectors in the attribute latent space by disentanglement. During inference, we enhance attribute correlations by target-guided counterfactual augmentation to further improve multi-aspect control. Experiments show that MAGIC outperforms state-of-the-art baselines in both imbalanced and balanced attribute correlation scenarios.

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Reward-based Input Construction for Cross-document Relation Extraction
Byeonghu Na | Suhyeon Jo | Yeongmin Kim | Il-chul Moon

Relation extraction (RE) is a fundamental task in natural language processing, aiming to identify relations between target entities in text. While many RE methods are designed for a single sentence or document, cross-document RE has emerged to address relations across multiple long documents. Given the nature of long documents in cross-document RE, extracting document embeddings is challenging due to the length constraints of pre-trained language models. Therefore, we propose REward-based Input Construction (REIC), the first learning-based sentence selector for cross-document RE. REIC extracts sentences based on relational evidence, enabling the RE module to effectively infer relations. Since supervision of evidence sentences is generally unavailable, we train REIC using reinforcement learning with RE prediction scores as rewards. Experimental results demonstrate the superiority of our method over heuristic methods for different RE structures and backbones in cross-document RE. Our code is publicly available at https://github.com/aailabkaist/REIC.

Event Argument Extraction (EAE) aims to extract arguments for specified events from a text. Previous research has mainly focused on addressing long-distance dependencies of arguments, modeling co-occurrence relationships between roles and events, but overlooking potential inductive biases: (i) semantic differences among arguments of the same type and (ii) large margin separation between arguments of the different types. Inspired by prototype networks, we introduce a new model named HMPEAE, which takes the two inductive biases above as targets to locate prototypes and guide the model to learn argument representations based on these prototypes.Specifically, we set multiple prototypes to represent each role to capture intra-class differences. Simultaneously, we use hypersphere as the output space for prototypes, defining large margin separation between prototypes to encourage the model to learn significant differences between different types of arguments effectively.We solve the “argument-prototype” assignment as an optimal transport problem to optimize the argument representation and minimize the absolute distance between arguments and prototypes to achieve compactness within sub-clusters. Experimental results on the RAMS and WikiEvents datasets show that HMPEAE achieves state-of-the-art performances.

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Understanding Retrieval Robustness for Retrieval-augmented Image Captioning
Wenyan Li | Jiaang Li | Rita Ramos | Raphael Tang | Desmond Elliott

Recent advances in retrieval-augmented models for image captioning highlight the benefit of retrieving related captions for efficient, lightweight models with strong domain-transfer capabilities. While these models demonstrate the success of retrieval augmentation, retrieval models are still far from perfect in practice: the retrieved information can sometimes mislead the model, resulting in incorrect generation and worse performance. In this paper, we analyze the robustness of a retrieval-augmented captioning model SmallCap. Our analysis shows that the model is sensitive to tokens that appear in the majority of the retrieved captions, and the input attribution shows that those tokens are likely copied into the generated output. Given these findings, we propose to train the model by sampling retrieved captions from more diverse sets. This decreases the chance that the model learns to copy majority tokens, and improves both in-domain and cross-domain performance.

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Semi-Supervised Spoken Language Glossification
Huijie Yao | Wengang Zhou | Hao Zhou | Houqiang Li

Spoken language glossification (SLG) aims to translate the spoken language text into the sign language gloss, i.e., a written record of sign language. In this work, we present a framework named Semi-Supervised Spoken Language Glossification (S³LG) for SLG. To tackle the bottleneck of limited parallel data in SLG, our S³LG incorporates large-scale monolingual spoken language text into SLG training. The proposed framework follows the self-training structure that iteratively annotates and learns from pseudo labels. Considering the lexical similarity and syntactic difference between sign language and spoken language, our S³LG adopts both the rule-based heuristic and model-based approach for auto-annotation. During training, we randomly mix these complementary synthetic datasets and mark their differences with a special token. As the synthetic data may be less quality, the S³LG further leverages consistency regularization to reduce the negative impact of noise in the synthetic data. Extensive experiments are conducted on public benchmarks to demonstrate the effectiveness of the S³LG. Our code is available at https://github.com/yaohj11/S3LG.

Graphical User Interface (GUI) agents are designed to automate complex tasks on digital devices, such as smartphones and desktops. Most existing GUI agents interact with the environment through extracted structured data, which can be notably lengthy (e.g., HTML) and occasionally inaccessible (e.g., on desktops). To alleviate this issue, we propose a novel visual GUI agent – SeeClick, which only relies on screenshots for task automation. In our preliminary study, we have discovered a key challenge in developing visual GUI agents: GUI grounding – the capacity to accurately locate screen elements based on instructions. To tackle this challenge, we propose to enhance SeeClick with GUI grounding pre-training and devise a method to automate the curation of GUI grounding data. Along with the efforts above, we have also created ScreenSpot, the first realistic GUI grounding benchmark that encompasses mobile, desktop, and web environments. After pre-training, SeeClick demonstrates significant improvement in ScreenSpot over various baselines. Moreover, comprehensive evaluations on three widely used benchmarks consistently support our finding that advancements in GUI grounding directly correlate with enhanced performance in downstream GUI agent tasks. The model, data and code will be open-sourced.

Despite the many advances of Large Language Models (LLMs) and their unprecedented rapid evolution, their impact and integration into every facet of our daily lives is limited due to various reasons. One critical factor hindering their widespread adoption is the occurrence of hallucinations, where LLMs invent answers that sound realistic, yet drift away from factual truth. In this paper, we present a novel method for detecting hallucinations in large language models, which tackles a critical issue in the adoption of these models in various real-world scenarios. Through extensive evaluations across multiple datasets and LLMs, including Llama-2, we study the hallucination levels of various recent LLMs and demonstrate the effectiveness of our method to automatically detect them. Notably, we observe up to 87% hallucinations for Llama-2 in a specific experiment, where our method achieves a Balanced Accuracy of 81%, all without relying on external knowledge.

Large language models (LLMs) garner significant attention for their unprecedented performance, leading to an increasing number of researches evaluating LLMs. However, these evaluation benchmarks are limited to assessing the instruction-following capabilities, overlooking the fundamental abilities that emerge during the pre-training stage. Previous subjective evaluation methods mainly reply on scoring by API models. However, in the absence of references, large models have shown limited ability to discern subtle differences. To bridge the gap, we propose F-Eval, a bilingual evaluation benchmark to evaluate the fundamental abilities, including expression, commonsense and logic. The tasks in F-Eval include multi-choice objective tasks, open-ended objective tasks, reference-based subjective tasks and reference-free subjective tasks. For reference-free subjective tasks, we devise new evaluation methods, serving as alternatives to scoring by API models. We conduct evaluations on 13 advanced LLMs. Results show that our evaluation methods show higher correlation coefficients and larger distinction than other evaluators. Additionally, we discuss the influence of different model sizes, dimensions, and normalization methods. We anticipate that F-Eval will facilitate the study of LLMs’ fundamental abilities.

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Comparing Inferential Strategies of Humans and Large Language Models in Deductive Reasoning
Philipp Mondorf | Barbara Plank

Deductive reasoning plays a pivotal role in the formulation of sound and cohesive arguments. It allows individuals to draw conclusions that logically follow, given the truth value of the information provided. Recent progress in the domain of large language models (LLMs) has showcased their capability in executing deductive reasoning tasks. Nonetheless, a significant portion of research primarily assesses the accuracy of LLMs in solving such tasks, often overlooking a deeper analysis of their reasoning behavior. In this study, we draw upon principles from cognitive psychology to examine inferential strategies employed by LLMs, through a detailed evaluation of their responses to propositional logic problems. Our findings indicate that LLMs display reasoning patterns akin to those observed in humans, including strategies like supposition following or chain construction. Moreover, our research demonstrates that the architecture and scale of the model significantly affect its preferred method of reasoning, with more advanced models tending to adopt strategies more frequently than less sophisticated ones. Importantly, we assert that a model’s accuracy, that is the correctness of its final conclusion, does not necessarily reflect the validity of its reasoning process. This distinction underscores the necessity for more nuanced evaluation procedures in the field.

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Whose Preferences? Differences in Fairness Preferences and Their Impact on the Fairness of AI Utilizing Human Feedback
Maria Lerner | Florian Dorner | Elliott Ash | Naman Goel

There is a growing body of work on learning from human feedback to align various aspects of machine learning systems with human values and preferences. We consider the setting of fairness in content moderation, in which human feedback is used to determine how two comments — referencing different sensitive attribute groups — should be treated in comparison to one another. With a novel dataset collected from Prolific and MTurk, we find significant gaps in fairness preferences depending on the race, age, political stance, educational level, and LGBTQ+ identity of annotators. We also demonstrate that demographics mentioned in text have a strong influence on how users perceive individual fairness in moderation. Further, we find that differences also exist in downstream classifiers trained to predict human preferences. Finally, we observe that an ensemble, giving equal weight to classifiers trained on annotations from different demographics, performs better for different demographic intersections; compared to a single classifier that gives equal weight to each annotation.

In this paper, we present an innovative process-oriented math process reward model called Math-shepherd, which assigns a reward score to each step of math problem solutions. The training of Math-shepherd is achieved using automatically constructed process-wise supervision data, breaking the bottleneck of heavy reliance on manual annotation in existing work. We explore the effectiveness of Math-shepherd in two scenarios: 1) Verification: Math-shepherd is utilized for reranking multiple outputs generated by Large Language Models (LLMs); 2) Reinforcement Learning (RL): Math-shepherd is employed to reinforce LLMs.With Math-shepherd, a series of open-source LLMs demonstrates exceptional performance. For instance, process RL with Math-shepherd significantly enhances Mistral-7B (77.9%→84.1% on GSM8K and 28.6%→33.0% on MATH).The accuracy can be further improved to 89.1% and 43.5% on two benchmarks with verification of Math-shepherd.We believe that automatic process supervision holds significant potential for the future evolution of LLMs.

In this paper, we uncover a positional bias in the evaluation paradigm of adopting large language models (LLMs), e.g., GPT-4, as a referee to score and compare the quality of responses generated by candidate models. We find that the quality ranking of candidate responses can be easily hacked by simply altering their order of appearance in the context. This manipulation allows us to skew the evaluation result, making one model appear considerably superior to the other, e.g., Vicuna-13B could beat ChatGPT on 66 over 80 tested queries with ChatGPT as an evaluator. We propose a simple yet effective calibration framework to address our discovered positional bias.To evaluate the effectiveness of our framework, we manually annotate the “win/tie/lose” outcomes of responses from ChatGPT and Vicuna-13B in the Vicuna Benchmark’s question prompt. Extensive experiments demonstrate that our approach successfully alleviates evaluation bias, resulting in closer alignment with human judgments.

Event relations are crucial for narrative understanding and reasoning. Governed by nuanced logic, event relation extraction (ERE) is a challenging task that demands thorough semantic understanding and rigorous logical reasoning. In this paper, we conduct an in-depth investigation to systematically explore the capability of LLMs in understanding and applying event relation logic. More in detail, we first investigate the deficiencies of LLMs in logical reasoning across different tasks. Our study reveals that LLMs are not logically consistent reasoners, which results in their suboptimal performance on tasks that need rigorous reasoning. To address this, we explore three different approaches to endow LLMs with event relation logic, and thus enable them to generate more coherent answers across various scenarios. Based on our approach, we also contribute a synthesized dataset (LLM-ERL) involving high-order reasoning for evaluation and fine-tuning. Extensive quantitative and qualitative analyses on different tasks also validate the effectiveness of our approach and provide insights for solving practical tasks with LLMs in future work. Codes are available at https://github.com/chenmeiqii/Teach-LLM-LR.

Video storytelling is engaging multimedia content that utilizes video and its accompanying narration to share a story and attract the audience, where a key challenge is creating narrations for recorded visual scenes. Previous studies on dense video captioning and video story generation have made some progress. However, in practical applications, we typically require synchronized narrations for ongoing visual scenes. In this work, we introduce a new task of Synchronized Video Storytelling, which aims to generate synchronous and informative narrations for videos. These narrations, associated with each video clip, should relate to the visual content, integrate relevant knowledge, and have an appropriate word count corresponding to the clip’s duration. Specifically, a structured storyline is beneficial to guide the generation process, ensuring coherence and integrity. To support the exploration of this task, we introduce a new benchmark dataset E-SyncVidStory with rich annotations. Since existing Multimodal LLMs are not effective in addressing this task in one-shot or few-shot settings, we propose a framework named VideoNarrator that can generate a storyline for input videos and simultaneously generate narrations with the guidance of the generated or predefined storyline. We further introduce a set of evaluation metrics to thoroughly assess the generation. Both automatic and human evaluations validate the effectiveness of our approach. Our dataset, codes, and evaluations will be released.

Fine-grained vision-language models (VLM) have been widely used for inter-modality local alignment between the predefined fixed patches and textual words. However, in medical analysis, lesions exhibit varying sizes and positions, and using fixed patches may cause incomplete representations of lesions. Moreover, these methods provide explainability by using heatmaps to show the general image areas potentially associated with texts rather than specific regions, making their explanations not explicit and specific enough. To address these issues, we propose a novel Adaptive patch-word Matching (AdaMatch) model to correlate chest X-ray (CXR) image regions with words in medical reports and apply it to CXR-report generation to provide explainability for the generation process. AdaMatch exploits the fine-grained relation between adaptive patches and words to provide explanations of specific image regions with corresponding words. To capture the abnormal regions of varying sizes and positions, we introduce an Adaptive Patch extraction (AdaPatch) module to acquire adaptive patches for these regions adaptively. Aiming to provide explicit explainability for the CXR-report generation task, we propose an AdaMatch-based bidirectional LLM for Cyclic CXR-report generation (AdaMatch-Cyclic). It employs AdaMatch to obtain the keywords for CXR images and ‘keypatches’ for medical reports as hints to guide CXR-report generation. Extensive experiments on two publicly available CXR datasets validate the effectiveness of our method and its superior performance over existing methods. Source code will be released.

Large language models (LLMs) have achieved remarkable performance on various NLP tasks and are augmented by tools for broader applications. Yet, how to evaluate and analyze the tool utilization capability of LLMs is still under-explored. In contrast to previous works that evaluate models holistically, we comprehensively decompose the tool utilization into multiple sub-processes, including instruction following, planning, reasoning, retrieval, understanding, and review. Based on that, we further introduce T-Eval to evaluate the tool-utilization capability step by step. T-Eval disentangles the tool utilization evaluation into several sub-domains along model capabilities, facilitating the inner understanding of both holistic and isolated competency of LLMs. We conduct extensive experiments on T-Eval and in-depth analysis of various LLMs. T-Eval not only exhibits consistency with the outcome-oriented evaluation but also provides a more fine-grained analysis of the capabilities of LLMs, providing a new perspective in LLM evaluation on tool-utilization ability. The benchmark will be available.

Some prior work has shown that LLMs perform well in NLG evaluation for different tasks. However, we discover that LLMs seem to confuse different evaluation criteria, which reduces their reliability. For further verification, we first consider avoiding issues of inconsistent conceptualization and vague expression in existing NLG quality criteria themselves. So we summarize a clear hierarchical classification system for 11 common aspects with corresponding different criteria from previous studies involved. Inspired by behavioral testing, we elaborately design 18 types of aspect-targeted perturbation attacks for fine-grained analysis of the evaluation behaviors of different LLMs. We also conduct human annotations beyond the guidance of the classification system to validate the impact of the perturbations. Our experimental results reveal confusion issues inherent in LLMs, as well as other noteworthy phenomena, and necessitate further research and improvements for LLM-based evaluation.

Information retrieval (IR) plays a crucial role in locating relevant resources from vast amounts of data, and its applications have evolved from traditional knowledge bases to modern retrieval models (RMs). The emergence of large language models (LLMs) has further revolutionized the IR field by enabling users to interact with search systems in natural languages. In this paper, we explore the advantages and disadvantages of LLMs and RMs, highlighting their respective strengths in understanding user-issued queries and retrieving up-to-date information. To leverage the benefits of both paradigms while circumventing their limitations, we propose **InteR**, a novel framework that facilitates information refinement through synergy between RMs and LLMs. InteR allows RMs to expand knowledge in queries using LLM-generated knowledge collections and enables LLMs to enhance prompt formulation using retrieved documents. This iterative refinement process augments the inputs of RMs and LLMs, leading to more accurate retrieval. Experiments on large-scale retrieval benchmarks involving web search and low-resource retrieval tasks show that InteR achieves overall superior **zero-shot** retrieval performance compared to state-of-the-art methods, even those using relevance judgment. Source code is available at https://github.com/Cyril-JZ/InteR.

Advancing the frontier of subquadratic architectures for Language Models (LMs) is crucial in the rapidly evolving field of natural language processing. Current innovations, including State Space Models, were initially celebrated for surpassing Transformer performance on language modeling tasks. However, these models have revealed deficiencies in essential In-Context Learning capabilities – a domain where the Transformer traditionally shines. The Based model emerged as a hybrid solution, blending a Linear Transformer with a kernel inspired by the Taylor expansion of exponential functions, augmented by convolutional networks. Mirroring the Transformer’s in-context adeptness, it became a strong contender in the field. In our work, we present a singular, elegant alteration to the Based kernel that amplifies its In-Context Learning abilities evaluated with the Multi-Query Associative Recall task and overall language modeling process, as demonstrated on the Pile dataset.

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Temperature-scaling surprisal estimates improve fit to human reading times – but does it do so for the “right reasons”?
Tong Liu | Iza Škrjanec | Vera Demberg

A wide body of evidence shows that human language processing difficulty is predicted by the information-theoretic measure surprisal, a word’s negative log probability in context. However, it is still unclear how to best estimate these probabilities needed for predicting human processing difficulty – while a long-standing belief held that models with lower perplexity would provide more accurate estimates of word predictability, and therefore lead to better reading time predictions, recent work has shown that for very large models, psycholinguistic predictive power decreases. One reason could be that language models might be more confident of their predictions than humans, because they have had exposure to several magnitudes more data. In this paper, we test what effect temperature-scaling of large language model (LLM) predictions has on surprisal estimates and their predictive power of reading times of English texts. Firstly, we show that calibration of large language models typically improves with model size, i.e. poorer calibration cannot account for poorer fit to reading times. Secondly, we find that temperature-scaling probabilities lead to a systematically better fit to reading times (up to 89% improvement in delta log likelihood), across several reading time corpora. Finally, we show that this improvement in fit is chiefly driven by words that are composed of multiple subword tokens.

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Beyond Recognising Entailment: Formalising Natural Language Inference from an Argumentative Perspective
Ameer Saadat-Yazdi | Nadin Kökciyan

In argumentation theory, argument schemes are a characterisation of stereotypical patterns of inference. There has been little work done to develop computational approaches to identify these schemes in natural language. Moreover, advancements in recognizing textual entailment lack a standardized definition of inference, which makes it challenging to compare methods trained on different datasets and rely on the generalisability of their results. In this work, we propose a rigorous approach to align entailment recognition with argumentation theory. Wagemans’ Periodic Table of Arguments (PTA), a taxonomy of argument schemes, provides the appropriate framework to unify these two fields. To operationalise the theoretical model, we introduce a tool to assist humans in annotating arguments according to the PTA. Beyond providing insights into non-expert annotator training, we present Kialo-PTA24, the first multi-topic dataset for the PTA. Finally, we benchmark the performance of pre-trained language models on various aspects of argument analysis. Our experiments show that the task of argument canonicalisation poses a significant challenge for state-of-the-art models, suggesting an inability to represent argumentative reasoning and a direction for future investigation.

We introduce AnyGPT, an any-to-any multimodal language model that utilizes discrete representations for the unified processing of various modalities, including speech, text, images, and music. AnyGPT can be trained stably without any alterations to the current large language model (LLM) architecture or training paradigms. Instead, it relies exclusively on data-level preprocessing, facilitating the seamless integration of new modalities into LLMs, akin to the incorporation of new languages.We build a multimodal text-centric dataset for multimodal alignment pre-training. Utilizing generative models, we synthesize the first large-scale any-to-any multimodal instruction dataset. It consists of 108k samples of multi-turn conversations that intricately interweave various modalities, thus equipping the model to handle arbitrary combinations of multimodal inputs and outputs.Experimental results demonstrate that AnyGPT is capable of facilitating any-to-any multimodal conversation while achieving performance comparable to specialized models across all modalities, proving that discrete representations can effectively and conveniently unify multiple modalities within a language model. Demos are shown in https://junzhan2000.github.io/AnyGPT.github.io/.

Social media abounds with multimodal sarcasm, and identifying sarcasm targets is particularly challenging due to the implicit incongruity not directly evident in the text and image modalities. Current methods for Multimodal Sarcasm Target Identification (MSTI) predominantly focus on superficial indicators in an end-to-end manner, overlooking the nuanced understanding of multimodal sarcasm conveyed through both the text and image. This paper proposes a versatile MSTI framework with a coarse-to-fine paradigm, by augmenting sarcasm explainability with reasoning and pre-training knowledge. Inspired by the powerful capacity of Large Multimodal Models (LMMs) on multimodal reasoning, we first engage LMMs to generate competing rationales for coarser-grained pre-training of a small language model on multimodal sarcasm detection. We then propose fine-tuning the model for finer-grained sarcasm target identification. Our framework is thus empowered to adeptly unveil the intricate targets within multimodal sarcasm and mitigate the negative impact posed by potential noise inherently in LMMs. Experimental results demonstrate that our model far outperforms state-of-the-art MSTI methods, and markedly exhibits explainability in deciphering sarcasm as well.

Aligning large language models (LLMs) with human expectations without human-annotated preference data is an important problem. In this paper, we propose a method to evaluate the response preference by using the output probabilities of response pairs under contrastive prompt pairs, which could achieve better performance on LLaMA2-7B and LLaMA2-13B compared to RLAIF. Based on this, we propose an automatic alignment method, Direct Large Model Alignment (DLMA). First, we use contrastive prompt pairs to automatically generate preference data. Then, we continue to evaluate the generated preference data using contrastive prompt pairs and calculate a self-rewarding score. Finally, we use the DPO algorithm to effectively align LLMs by combining this self-rewarding score. In the experimental stage, our DLMA method could surpass the RLHF method without relying on human-annotated preference data.

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Diffusion Lens: Interpreting Text Encoders in Text-to-Image Pipelines
Michael Toker | Hadas Orgad | Mor Ventura | Dana Arad | Yonatan Belinkov

Text-to-image diffusion models (T2I) use a latent representation of a text prompt to guide the image generation process. However, the process by which the encoder produces the text representation is unknown. We propose the Diffusion Lens, a method for analyzing the text encoder of T2I models by generating images from its intermediate representations. Using the Diffusion Lens, we perform an extensive analysis of two recent T2I models. Exploring compound prompts, we find that complex scenes describing multiple objects are composed progressively and more slowly compared to simple scenes; Exploring knowledge retrieval, we find that representation of uncommon concepts require further computation compared to common concepts, and that knowledge retrieval is gradual across layers. Overall, our findings provide valuable insights into the text encoder component in T2I pipelines.

Humans often interact with large language models (LLMs) in multi-turn interaction to obtain desired answers or more information. However, most existing studies overlook the multi-turn instruction following ability of LLMs, in terms of training dataset, training method, and evaluation benchmark. In this paper, we introduce Parrot, a solution aiming to enhance multi-turn instruction following for LLMs. First, we introduce an efficient but effective method for collecting multi-turn instructions that feature human-like queries, such as anaphora and ellipsis. Second, we propose a context-aware preference optimization strategy to further enhance LLMs for complex queries in multi-turn interaction. Moreover, to quantitatively evaluate LLMs in multi-turn instruction following, we manually build a multi-turn benchmark derived from existing ones. Extensive experiments show that Parrot improves current LLMs by up to 7.2% in multi-turn instruction following. Our dataset and codes will be open-sourced to facilitate future research.

Note-level Automatic Singing Voice Transcription (AST) converts singing recordings into note sequences, facilitating the automatic annotation of singing datasets for Singing Voice Synthesis (SVS) applications. Current AST methods, however, struggle with accuracy and robustness when used for practical annotation. This paper presents ROSVOT, the first robust AST model that serves SVS, incorporating a multi-scale framework that effectively captures coarse-grained note information and ensures fine-grained frame-level segmentation, coupled with an attention-based pitch decoder for reliable pitch prediction. We also established a comprehensive annotation-and-training pipeline for SVS to test the model in real-world settings. Experimental findings reveal that the proposed model achieves state-of-the-art transcription accuracy with either clean or noisy inputs. Moreover, when trained on enlarged, automatically annotated datasets, the SVS model outperforms its baseline, affirming the capability for practical application. Audio samples are available at https://rosvot.github.io. Codes can be found at https://github.com/RickyL-2000/ROSVOT.

Security practitioners maintain vulnerability reports (e.g., GitHub Advisory) to help developers mitigate security risks. An important task for these databases is automatically extracting structured information mentioned in the report, e.g., the affected software packages, to accelerate the defense of the vulnerability ecosystem.However, it is challenging for existing work on affected package identification to achieve high precision. One reason is that all existing work focuses on relatively smaller models, thus they cannot harness the knowledge and semantic capabilities of large language models.To address this limitation, we propose VulLibGen, the first method to use LLM for affected package identification. In contrast to existing work, VulLibGen proposes the novel idea to directly generate the affected package. To improve the precision, VulLibGen employs supervised fine-tuning (SFT), retrieval augmented generation (RAG) and a local search algorithm. The local search algorithm is a novel post-processing algorithm we introduce for reducing the hallucination of the generated packages. Our evaluation results show that VulLibGen has an average precision of 0.806 for identifying vulnerable packages in the four most popular ecosystems in GitHub Advisory (Java, JS, Python, Go) while the best average precision in previous work is 0.721. Additionally, VulLibGen has high value to security practice: we submitted 60 <vulnerability, affected package> pairs to GitHub Advisory (covers four ecosystems) and 34 of them have been accepted and merged.

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Self-Modifying State Modeling for Simultaneous Machine Translation
Donglei Yu | Xiaomian Kang | Yuchen Liu | Yu Zhou | Chengqing Zong

Simultaneous Machine Translation (SiMT) generates target outputs while receiving stream source inputs and requires a read/write policy to decide whether to wait for the next source token or generate a new target token, whose decisions form a decision path. Existing SiMT methods, which learn the policy by exploring various decision paths in training, face inherent limitations. These methods not only fail to precisely optimize the policy due to the inability to accurately assess the individual impact of each decision on SiMT performance, but also cannot sufficiently explore all potential paths because of their vast number. Besides, building decision paths requires unidirectional encoders to simulate streaming source inputs, which impairs the translation quality of SiMT models. To solve these issues, we propose Self-Modifying State Modeling (SM²), a novel training paradigm for SiMT task. Without building decision paths, SM² individually optimizes decisions at each state during training. To precisely optimize the policy, SM² introduces Self-Modifying process to independently assess and adjust decisions at each state. For sufficient exploration, SM² proposes Prefix Sampling to efficiently traverse all potential states. Moreover, SM² ensures compatibility with bidirectional encoders, thus achieving higher translation quality. Experiments show that SM² outperforms strong baselines. Furthermore, SM² allows offline machine translation models to acquire SiMT ability with fine-tuning.

Embodied agents equipped with GPT as their brain have exhibited extraordinary decision-making and generalization abilities across various tasks. However, existing zero-shot agents for vision-and-language navigation (VLN) only prompt the GPT-4 to select potential locations within localized environments, without constructing an effective “global-view” for the agent to understand the overall environment. In this work, we present a novel **map**-guided **GPT**-based agent, dubbed **MapGPT**, which introduces an online linguistic-formed map to encourage the global exploration. Specifically, we build an online map and incorporate it into the prompts that include node information and topological relationships, to help GPT understand the spatial environment. Benefiting from this design, we further propose an adaptive planning mechanism to assist the agent in performing multi-step path planning based on a map, systematically exploring multiple candidate nodes or sub-goals step by step. Extensive experiments demonstrate that our MapGPT is applicable to both GPT-4 and GPT-4V, achieving state-of-the-art zero-shot performance on the R2R and REVERIE simultaneously (~10% and ~12% improvements in SR), and showcasing the newly emergent global thinking and path planning abilities of the GPT.

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BadAgent: Inserting and Activating Backdoor Attacks in LLM Agents
Yifei Wang | Dizhan Xue | Shengjie Zhang | Shengsheng Qian

With the prosperity of large language models (LLMs), powerful LLM-based intelligent agents have been developed to provide customized services with a set of user-defined tools. State-of-the-art methods for constructing LLM agents adopt trained LLMs and further fine-tune them on data for the agent task. However, we show that such methods are vulnerable to our proposed backdoor attacks named BadAgent on various agent tasks, where a backdoor can be embedded by fine-tuning on the backdoor data. At test time, the attacker can manipulate the deployed LLM agents to execute harmful operations by showing the trigger in the agent input or environment. To our surprise, our proposed attack methods are extremely robust even after fine-tuning on trustworthy data. Though backdoor attacks have been studied extensively in natural language processing, to the best of our knowledge, we could be the first to study them on LLM agents that are more dangerous due to the permission to use external tools. Our work demonstrates the clear risk of constructing LLM agents based on untrusted LLMs or data. Our code is public at https://github.com/DPamK/BadAgent

Recent advances in large language models (LLMs) have revolutionized the landscape of reasoning tasks. To enhance the capabilities of LLMs to emulate human reasoning, prior studies have focused on modeling reasoning steps using various thought structures like chains, trees, or graphs. However, LLM-based reasoning still encounters the following challenges: (1) Limited adaptability of preset structures to diverse tasks; (2) Insufficient precision in exploiting known conditions to derive new ones; and (3) Inadequate consideration of historical reasoning experiences for subsequent reasoning steps. To this end, we propose DetermLR, a novel perspective that rethinks the reasoning process as an evolution from indeterminacy to determinacy. First, we categorize known conditions into two types: determinate and indeterminate premises, facilitating the transformation process. Subsequently, we leverage quantitative measurements to prioritize more relevant premises to explore new insights. Furthermore, we automate the storage and extraction of available premises and reasoning paths with reasoning memory, preserving historical reasoning details for subsequent reasoning steps. Comprehensive experimental results demonstrate that DetermLR surpasses all baselines on various logical reasoning benchmarks: LogiQA, ProofWriter, FOLIO, PrOntoQA, and LogicalDeduction. Compared to previous multi-step reasoning methods, DetermLR achieves higher accuracy with fewer reasoning steps, highlighting its superior efficiency and effectiveness in solving logical reasoning tasks.

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LePaRD: A Large-Scale Dataset of Judicial Citations to Precedent
Robert Mahari | Dominik Stammbach | Elliott Ash | Alex Pentland

We present the Legal Passage Retrieval Dataset, LePaRD. LePaRD contains millions of examples of U.S. federal judges citing precedent in context. The dataset aims to facilitate work on legal passage retrieval, a challenging practice-oriented legal retrieval and reasoning task. Legal passage retrieval seeks to predict relevant passages from precedential court decisions given the context of a legal argument. We extensively evaluate various approaches on LePaRD, and find that classification-based retrieval appears to work best. Our best models only achieve a recall of 59% when trained on data corresponding to the 10,000 most-cited passages, underscoring the difficulty of legal passage retrieval. By publishing LePaRD, we provide a large-scale and high quality resource to foster further research on legal passage retrieval. We hope that research on this practice-oriented NLP task will help expand access to justice by reducing the burden associated with legal research via computational assistance. Warning: Extracts from judicial opinions may contain offensive language.

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To Generate or to Retrieve? On the Effectiveness of Artificial Contexts for Medical Open-Domain Question Answering
Giacomo Frisoni | Alessio Cocchieri | Alex Presepi | Gianluca Moro | Zaiqiao Meng

Medical open-domain question answering demands substantial access to specialized knowledge. Recent efforts have sought to decouple knowledge from model parameters, counteracting architectural scaling and allowing for training on common low-resource hardware. The retrieve-then-read paradigm has become ubiquitous, with model predictions grounded on relevant knowledge pieces from external repositories such as PubMed, textbooks, and UMLS. An alternative path, still under-explored but made possible by the advent of domain-specific large language models, entails constructing artificial contexts through prompting. As a result, “to generate or to retrieve” is the modern equivalent of Hamlet’s dilemma. This paper presents MedGENIE, the first generate-then-read framework for multiple-choice question answering in medicine. We conduct extensive experiments on MedQA-USMLE, MedMCQA, and MMLU, incorporating a practical perspective by assuming a maximum of 24GB VRAM. MedGENIE sets a new state-of-the-art in the open-book setting of each testbed, allowing a small-scale reader to outcompete zero-shot closed-book 175B baselines while using up to 706x fewer parameters. Our findings reveal that generated passages are more effective than retrieved ones in attaining higher accuracy.

Over the past few years, one of the most notable advancements in AI research has been in foundation models (FMs), headlined by the rise of language models (LMs). However, despite researchers’ attention and the rapid growth in LM application, the capabilities, limitations, and associated risks still need to be better understood. To address these issues, we introduce a new instruction benchmark, MERA, oriented towards the FMs’ performance on the Russian language. The benchmark encompasses 21 evaluation tasks for generative models covering 10 skills and is supplied with private answer scoring to prevent data leakage. The paper introduces a methodology to evaluate FMs and LMs in fixed zero- and few-shot instruction settings that can be extended to other modalities. We propose an evaluation methodology, an open-source code base for the MERA assessment, and a leaderboard with a submission system. We evaluate open LMs as baselines and find they are still far behind the human level. We publicly release MERA to guide forthcoming research, anticipate groundbreaking model features, standardize the evaluation procedure, and address potential ethical concerns and drawbacks.

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SC2: Towards Enhancing Content Preservation and Style Consistency in Long Text Style Transfer
Jie Zhao | Ziyu Guan | Cai Xu | Wei Zhao | Yue Jiang

Text style transfer (TST) aims to vary the style polarity of text while preserving the semantic content. Although recent advancements have demonstrated remarkable progress in short TST, it remains a relatively straightforward task with limited practical applications. The more comprehensive long TST task presents two challenges: (1) existing methods encounter difficulties in accurately evaluating content attributes in multiple words, leading to content degradation; (2) the conventional vanilla style classifier loss encounters obstacles in maintaining consistent style across multiple generated sentences.In this paper, we propose a novel method SC2, where a multilayer Joint Style-Content Weighed (JSCW) module and a Style Consistency loss are designed to address the two issues. The JSCW simultaneously assesses the amounts of style and content attributes within a token, aiming to acquire a lossless content representation and thereby enhancing content preservation. The multiple JSCW layers further progressively refine content representations. We design a style consistency loss to ensure the generated multiple sentences consistently reflect the target style polarity. Moreover, we incorporate a denoising non-autoregressive decoder to accelerate the training. We conduct plentiful experiments and the results show significant improvements of SC2 over competitive baselines. Our code: https://github.com/jiezhao6/SC2.

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Dodo: Dynamic Contextual Compression for Decoder-only LMs
Guanghui Qin | Corby Rosset | Ethan Chau | Nikhil Rao | Benjamin Van Durme

Transformer-based language models (LMs) are inefficient in long contexts. We propose Dodo, a solution for context compression. Instead of one vector per token in a standard transformer model, Dodo represents text with a dynamic number of hidden states at each layer, reducing the cost of self-attention to a fraction of typical time and space. Moreover, off-the-shelf models such as LLaMA can be adapted to Dodo by efficient parameter tuning methods such as LoRA. In use, Dodo can act as either an autoregressive LM or a context compressor for downstream tasks. We demonstrate through experiments in language modeling, question answering, and summarization that Dodo retains capabilities in these tasks, while drastically reducing the overhead during decoding. For example, in the autoencoding task, Dodo shrinks context at a 20x compression ratio with a BLEU score of 98% for reconstruction, achieving nearly lossless encoding.

Low-resource languages (LRLs) face challenges in supervised neural machine translation (NMT) due to limited parallel data, prompting research in unsupervised NMT.Unsupervised NMT (UNMT), without requiring ground truth, provides solutions for LRL translations using synthetic pseudo-parallel data and parallel data from auxiliary language pairs. However, they usually encounter translation errors, including errors from synthetic data and from auxiliary language pairs with linguistic biases.We argue that large language models (LLMs) mitigate UNMT’s translation errors by dynamically organizing auxiliary languages in prompts to improve LRL translations. In this paper, we propose PrObability-driven Meta-graph Prompter (POMP), an approach employing a dynamic graph to organize multiple auxiliary languages, to prompt LLMs in LRL translations. POMP proposes a language-specific meta-graph that dynamically samples multiple translation paths to organize auxiliary languages in constructing prompts. Following the path, POMP prompts LLMs to translate with a mixture of auxiliary languages. We achieve the meta-graph’s evolution by back-propagating evaluation scores to update probabilities on the graph.Our experimental improvements show POMP’s effectiveness on LRLs’ translation.

We present NewsBench, a novel evaluation framework to systematically assess the capabilities of Large Language Models (LLMs) for editorial capabilities in Chinese journalism. Our constructed benchmark dataset is focused on four facets of writing proficiency and six facets of safety adherence, and it comprises manually and carefully designed 1,267 test samples in the types of multiple choice questions and short answer questions for five editorial tasks in 24 news domains. To measure performances, we propose different GPT-4 based automatic evaluation protocols to assess LLM generations for short answer questions in terms of writing proficiency and safety adherence, and both are validated by the high correlations with human evaluations. Based on the systematic evaluation framework, we conduct a comprehensive analysis of eleven popular LLMs which can handle Chinese. The experimental results highlight GPT-4 and ERNIE Bot as top performers, yet reveal a relative deficiency in journalistic safety adherence in creative writing tasks. Our findings also underscore the need for enhanced ethical guidance in machine-generated journalistic content, marking a step forward in aligning LLMs with journalistic standards and safety considerations. The evaluation framework and experimental results are expected to provide an in-depth understanding of the editorial capabilities of LLMs and speed up the development of LLMs in journalism.

Intuitively, reasoning abilities are considered language-agnostic. However, existing LLMs exhibit inconsistent reasoning abilities across different languages, e.g., reasoning in the dominant language like English is superior to other languages due to the imbalance of multilingual training data. To enhance reasoning abilities in non-dominant languages, we propose a Multilingual-Alignment-as-Preference Optimization framework (MAPO) to align the reasoning processes in other languages with the dominant language. Specifically, we harness an off-the-shelf translation model for the consistency between answers in non-dominant and dominant languages, which we adopt as the preference for optimization, e.g., Direct Preference Optimization(DPO) or Proximal Policy Optimization (PPO). Experiments show that MAPO stably achieves significant improvements in the multilingual reasoning of various models on all three benchmarks (MSVAMP +16.2%, MGSM +6.1%, and MNumGLUESub +13.3%), with improved reasoning consistency across languages. The project is available at https://github.com/NJUNLP/MAPO.

Large Language Models (LLMs) exhibit substantial capabilities yet encounter challenges including hallucination, outdated knowledge, and untraceable reasoning processes. Retrieval-augmented generation (RAG) has emerged as a promising solution, integrating knowledge from external databases to mitigate these challenges. However, inappropriate retrieved passages can potentially hinder the LLMs’ capacity to generate comprehensive and high-quality responses. Prior RAG studies on the robustness of retrieval noises often confine themselves to a limited set of noise types, deviating from real-world retrieval environments and limiting practical applicability. In this study, we initially investigate retrieval noises and categorize them into three distinct types, reflecting real-world environments. We analyze the impact of these various retrieval noises on the robustness of LLMs. Subsequently, we propose a novel RAG approach known as Retrieval-augmented Adaptive Adversarial Training (RAAT). RAAT leverages adaptive adversarial training to dynamically adjust the model’s training process in response to retrieval noises. Concurrently, it employs multi-task learning to ensure the model’s capacity to internally recognize noisy contexts. Extensive experiments demonstrate that the LLaMA-2 7B model trained using RAAT exhibits significant improvements in F1 and EM scores under diverse noise conditions. For reproducibility, we will release our code and data upon acceptance.

Comparative reasoning plays a crucial role in predicting text preferences; however, large language models (LLMs) often demonstrate inconsistencies in their reasoning, leading to incorrect preference predictions. While approaches like Chain-of-Thought improve accuracy in many settings, they struggle to consistently distinguish the similarities and differences of complex texts. We introduce SC², a model that prompts LLMs to predict text preferences by generating structured intermediate comparisons. SC² begins by proposing aspects for comparison, followed by generating textual comparisons under each aspect. We select consistent comparisons with a pairwise comparator that ensures each comparison of a given aspect clearly distinguishes differences between texts, significantly reducing hallucination and improving consistency. Our empirical studies across various NLP tasks, including summarization, retrieval, and automatic rating, demonstrate that SC²‘s enhanced performance in text preference prediction is significant.

Recent studies have shown that integrating constructional information can improve the performance of pre-trained language models (PLMs) in natural language understanding. However, exploration into leveraging constructional information to enhance generative language models for natural language generation has been limited. Additionally, probing studies indicate that PLMs primarily grasp the syntactic structure of constructions but struggle to capture their semantics. In this work, we encode constructions as inductive biases to explicitly embed constructional semantics and guide the generation process. We begin by presenting a construction grammar induction framework designed to automatically identify constructions from corpora. Subsequently, we propose the Construction-Enhanced Language Model (CoELM). It introduces a construction-guided language modeling approach that employs a dynamic sequence reassembly strategy during pre-training. Extensive experiments have demonstrated the superiority of CoELM across various benchmarks.

In the field of speech synthesis, there is a growing emphasis on employing multimodal speech to enhance robustness. A key challenge in this area is the scarcity of datasets that pair audio with corresponding video. We employ a methodology that incorporates modality alignment during the pre-training phase on multimodal datasets, uniquely facilitating zero-shot generalization through the process of freezing the video modality feature extraction component and the encoder module within the pretrained weights, thereby enabling effective cross-modal and cross-lingual transfer. We have named this method ‘Uni-Dubbing’. Our method finely tunes with both multimodal and single-modality audio data. In multimodal scenarios, it achieves a reduced word error rate (WER) of 31.73%, surpassing the previous best of 33.9%. It also excels in metrics like tone quality and synchronization. With single-modality audio, it achieves a WER of 36.08%, demonstrating adaptability to limited data. Its domain generalization capabilities are proven across various language tasks in video translation and audio generation. Trained on 433 hours of audio data, it surpasses techniques using 200 hours of audiovisual data. The code and demo are available at https://diracer.github.io/unidubbing.

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On the Impact of Calibration Data in Post-training Quantization and Pruning
Miles Williams | Nikolaos Aletras

Quantization and pruning form the foundation of compression for neural networks, enabling efficient inference for large language models (LLMs). Recently, various quantization and pruning techniques have demonstrated remarkable performance in a post-training setting. They rely upon calibration data, a small set of unlabeled examples that are used to generate layer activations. However, no prior work has systematically investigated how the calibration data impacts the effectiveness of model compression methods. In this paper, we present the first extensive empirical study on the effect of calibration data upon LLM performance. We trial a variety of quantization and pruning methods, datasets, tasks, and models. Surprisingly, we find substantial variations in downstream task performance, contrasting existing work that suggests a greater level of robustness to the calibration data. Finally, we make a series of recommendations for the effective use of calibration data in LLM quantization and pruning.

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SymKGQA: Few-Shot Knowledge Graph Question Answering via Symbolic Program Generation and Execution
Prerna Agarwal | Nishant Kumar | Srikanta Bedathur

Semantic Parsing of natural language questions into their executable logical form (LF) has shown state-of-the-art (SOTA) performance for Knowledge Graph Question Answering (KGQA). However, these methods are not applicable for real-world applications, due to lack of KG-specific training data. Recent advances in the capabilities of Large Language Models (LLMs) has led towards generating low-level LFs such as SPARQL and S-Expression in a few-shot setting. Unfortunately, these methods: (1) are limited to the knowledge of underlying LLM about the LF, (2) performs inferior for the harder complex benchmarks such as KQA Pro, (3) suffers while grounding the generated LF to a specific Knowledge Graph. Recently, a new LF called KoPL has been introduced that explicitly models complex reasoning process step-by-step in a symbolic manner and has shown SOTA on KQA Pro in fully-supervised setting. Inspired by this, we propose SymKGQA framework that generates step-by-step Symbolic LF i.e., KoPL in a few-shot in-context learning setting using LLM. Our framework is not dependent on pre-trained information of LLM about KoPL. We further build a Retrieval-Augmented Generation based Question-Aware Contextual KoPL (QUACK) resolver to ground the generated LF. Our experiments with different LLMs and few-shot settings demonstrate that SymKGQA outperforms all other few-shot and even many of the fully-supervised KGQA approaches.

We introduce a new unsupervised text embedding method, Meta-Task Prompting with Explicit One-Word Limitation (MetaEOL), for generating high-quality sentence embeddings from Large Language Models (LLMs) without the need for model fine-tuning. Leveraging meta-task prompting, MetaEOL guides LLMs to produce embeddings through a series of carefully designed prompts that address multiple representational aspects. Our comprehensive experiments demonstrate that embeddings averaged from various meta-tasks are versatile embeddings that yield competitive performance on Semantic Textual Similarity (STS) benchmarks and excel in downstream tasks, surpassing contrastive-trained models. Our findings suggest a new scaling law, offering a versatile and resource-efficient approach for embedding generation across diverse scenarios.

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A Sentiment Consolidation Framework for Meta-Review Generation
Miao Li | Jey Han Lau | Eduard Hovy

Modern natural language generation systems with Large Language Models (LLMs) exhibit the capability to generate a plausible summary of multiple documents; however, it is uncertain if they truly possess the capability of information consolidation to generate summaries, especially on documents with opinionated information. We focus on meta-review generation, a form of sentiment summarisation for the scientific domain. To make scientific sentiment summarization more grounded, we hypothesize that human meta-reviewers follow a three-layer framework of sentiment consolidation to write meta-reviews. Based on the framework, we propose novel prompting methods for LLMs to generate meta-reviews and evaluation metrics to assess the quality of generated meta-reviews. Our framework is validated empirically as we find that prompting LLMs based on the framework — compared with prompting them with simple instructions — generates better meta-reviews.

Structured Sentiment Analysis (SSA) was cast as a problem of bi-lexical dependency graph parsing by prior studies.Multiple formulations have been proposed to construct the graph, which share several intrinsic drawbacks:(1) The internal structures of spans are neglected, thus only the boundary tokens of spans are used for relation prediction and span recognition, thus hindering the model’s expressiveness;(2) Long spans occupy a significant proportion in the SSA datasets, which further exacerbates the problem of internal structure neglect.In this paper, we treat the SSA task as a dependency parsing task on partially-observed dependency trees, regarding flat spans without determined tree annotations as latent subtrees to consider internal structures of spans.We propose a two-stage parsing method and leverage TreeCRFs with a novel constrained inside algorithm to model latent structures explicitly, which also takes advantages of joint scoring graph arcs and headed spans for global optimization and inference. Results of extensive experiments on five benchmark datasets reveal that our method performs significantly better than all previous bi-lexical methods, achieving new state-of-the-art.

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OWSM-CTC: An Open Encoder-Only Speech Foundation Model for Speech Recognition, Translation, and Language Identification
Yifan Peng | Yui Sudo | Muhammad Shakeel | Shinji Watanabe

There has been an increasing interest in large speech models that can perform multiple tasks in a single model. Such models usually adopt an encoder-decoder or decoder-only architecture due to their popularity and good performance in many domains. However, autoregressive models can be slower during inference compared to non-autoregressive models and also have potential risks of hallucination. Though prior studies observed promising results of non-autoregressive models for certain tasks at small scales, it remains unclear if they can be scaled to speech-to-text generation in diverse languages and tasks. Inspired by the Open Whisper-style Speech Model (OWSM) project, we propose OWSM-CTC, a novel encoder-only speech foundation model based on Connectionist Temporal Classification (CTC). It is trained on 180k hours of public audio data for multilingual automatic speech recognition (ASR), speech translation (ST), and language identification (LID). Compared to encoder-decoder OWSM, our OWSM-CTC achieves competitive results on ASR and up to 24% relative improvement on ST, while it is more robust and 3 to 4 times faster for inference. OWSM-CTC also improves the long-form ASR result with 20x speed-up.We will publicly release our code, pre-trained model, and training logs to promote open science in speech foundation models.

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Do Large Language Models Latently Perform Multi-Hop Reasoning?
Sohee Yang | Elena Gribovskaya | Nora Kassner | Mor Geva | Sebastian Riedel

We study whether Large Language Models (LLMs) latently perform multi-hop reasoning with complex prompts such as “The mother of the singer of ‘Superstition’ is”. We look for evidence of a latent reasoning pathway where an LLM (1) latently identifies “the singer of ‘Superstition’” as Stevie Wonder, the bridge entity, and (2) uses its knowledge of Stevie Wonder’s mother to complete the prompt. We analyze these two hops individually and consider their co-occurrence as indicative of latent multi-hop reasoning. For the first hop, we test if changing the prompt to indirectly mention the bridge entity instead of any other entity increases the LLM’s internal recall of the bridge entity. For the second hop, we test if increasing this recall causes the LLM to better utilize what it knows about the bridge entity. We find strong evidence of latent multi-hop reasoning for the prompts of certain relation types, with the reasoning pathway used in more than 80% of the prompts. However, the utilization is highly contextual, varying across different types of prompts. Also, on average, the evidence for the second hop and the full multi-hop traversal is rather moderate and only substantial for the first hop. Moreover, we find a clear scaling trend with increasing model size for the first hop of reasoning but not for the second hop. Our experimental findings suggest potential challenges and opportunities for future development and applications of LLMs.

In math reasoning with large language models (LLMs), fine-tuning data augmentation by query evolution and diverse reasoning paths is empirically verified effective, profoundly narrowing the gap between open-sourced LLMs and cutting-edge proprietary LLMs. In this paper, we conduct an investigation for such data augmentation in math reasoning and are intended to answer: (1) What strategies of data augmentation are more effective; (2) What is the scaling relationship between the amount of augmented data and model performance; and (3) Can data augmentation incentivize generalization to out-of-domain mathematical reasoning tasks?To this end, we create two new dataset AugGSM8K and AugMATH, by complicating and diversifying the queries and sampling multiple reasoning paths from GSM8K and MATH.We obtained a series of LLMs called MuggleMath by fine-tuning LLaMA models on AugGSM8K and AugMATH. MuggleMath substantially achieves new state-of-the-art on GSM8K and MATH.A log-linear relationship and a segmented log-linear are presented between MuggleMath’s performance and the amount of augmented data on GSM8K and MATH, respectively.We also find that it is weak in out-of-domain math reasoning generalization from AugGSM8K to MATH and from AugMATH to GSM8K, which suggests that augmenting queries that cover a broader range of subjects is more beneficial for generalization.

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Harnessing Toulmin’s theory for zero-shot argument explication
Ankita Gupta | Ethan Zuckerman | Brendan O’Connor

To better analyze informal arguments on public forums, we propose the task of argument explication, which makes explicit a text’s argumentative structure and implicit reasoning by outputting triples of propositions ⟨claim, reason warrant⟩. The three slots, or argument components, are derived from the widely known Toulmin (1958) model of argumentation. While prior research applies Toulmin or related theories to annotate datasets and train supervised models, we develop an effective method to prompt generative large language models (LMs) to output explicitly named argument components proposed by Toulmin by prompting with the theory name (e.g., ‘According to Toulmin model’). We evaluate the outputs’ coverage and validity through a human study and automatic evaluation based on prior argumentation datasets and perform robustness checks over alternative LMs, prompts, and argumentation theories. Finally, we conduct a proof-of-concept case study to extract an interpretable argumentation (hyper)graph from a large corpus of critical public comments on whether to allow the COVID-19 vaccine for children, suggesting future directions for corpus analysis and argument visualization.

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BinaryAlign: Word Alignment as Binary Sequence Labeling
Gaetan Latouche | Marc-André Carbonneau | Benjamin Swanson

Real world deployments of word alignment are almost certain to cover both high and low resource languages. However, the state-of-the-art for this task recommends a different model class depending on the availability of gold alignment training data for a particular language pair. We propose BinaryAlign, a novel word alignment technique based on binary sequence labeling that outperforms existing approaches in both scenarios, offering a unifying approach to the task. Additionally, we vary the specific choice of multilingual foundation model, perform stratified error analysis over alignment error type, and explore the performance of BinaryAlign on non-English language pairs. We make our source code publicly available.

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Quantifying the Persona Effect in LLM Simulations
Tiancheng Hu | Nigel Collier

Large language models (LLMs) have shown remarkable promise in simulating human language and behavior. This study investigates how integrating persona variables—demographic, social, and behavioral factors—impacts LLMs’ ability to simulate diverse perspectives. We find that persona variables account for <10% variance in annotations in existing subjective NLP datasets. Nonetheless, incorporating persona variables via prompting in LLMs provides modest but statistically significant improvements. Persona prompting is most effective in samples where many annotators disagree, but their disagreements are relatively minor. Notably, we find a linear relationship in our setting: the stronger the correlation between persona variables and human annotations, the more accurate the LLM predictions are using persona prompting. In a zero-shot setting, a powerful 70b model with persona prompting captures 81% of the annotation variance achievable by linear regression trained on ground truth annotations. However, for most subjective NLP datasets, where persona variables have limited explanatory power, the benefits of persona prompting are limited.

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Artifacts or Abduction: How Do LLMs Answer Multiple-Choice Questions Without the Question?
Nishant Balepur | Abhilasha Ravichander | Rachel Rudinger

Multiple-choice question answering (MCQA) is often used to evaluate large language models (LLMs). To see if MCQA assesses LLMs as intended, we probe if LLMs can perform MCQA with choices-only prompts, where models must select the correct answer only from the choices. In three MCQA datasets and four LLMs, this prompt bests a majority baseline in 11/12 cases, with up to 0.33 accuracy gain. To help explain this behavior, we conduct an in-depth, black-box analysis on memorization, choice dynamics, and question inference. Our key findings are threefold. First, we find no evidence that the choices-only accuracy stems from memorization alone. Second, priors over individual choices do not fully explain choices-only accuracy, hinting that LLMs use the group dynamics of choices. Third, LLMs have some ability to infer a relevant question from choices, and surprisingly can sometimes even match the original question. We hope to motivate the use of stronger baselines in MCQA benchmarks, the design of robust MCQA datasets, and further efforts to explain LLM decision-making.

The rapid propagation of misinformation poses substantial risks to public interest. To combat misinformation, large language models (LLMs) are adapted to automatically verify claim credibility. Nevertheless, existing methods heavily rely on the embedded knowledge within LLMs and / or black-box APIs for evidence collection, leading to subpar performance with smaller LLMs or upon unreliable context. In this paper, we propose retrieval augmented fact verification through the synthesis of contrasting arguments (RAFTS). Upon input claims, RAFTS starts with evidence retrieval, where we design a retrieval pipeline to collect and re-rank relevant documents from verifiable sources. Then, RAFTS forms contrastive arguments (i.e., supporting or refuting) conditioned on the retrieved evidence. In addition, RAFTS leverages an embedding model to identify informative demonstrations, followed by in-context prompting to generate the prediction and explanation. Our method effectively retrieves relevant documents as evidence and evaluates arguments from varying perspectives, incorporating nuanced information for fine-grained decision-making. Combined with informative in-context examples as prior, RAFTS achieves significant improvements to supervised and LLM baselines without complex prompts. We demonstrate the effectiveness of our method through extensive experiments, where RAFTS can outperform GPT-based methods with a significantly smaller 7B LLM.

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SyllabusQA: A Course Logistics Question Answering Dataset
Nigel Fernandez | Alexander Scarlatos | Andrew Lan

Automated teaching assistants and chatbots have significant potential to reduce the workload of human instructors, especially for logistics-related question answering, which is important to students yet repetitive for instructors. However, due to privacy concerns, there is a lack of publicly available datasets. We introduce SyllabusQA, an open-source dataset with 63 real course syllabi covering 36 majors, containing 5,078 open-ended course logistics-related question-answer pairs that are diverse in both question types and answer formats. Since many logistics-related questions contain critical information like the date of an exam, it is important to evaluate the factuality of answers. We benchmark several strong baselines on this task, from large language model prompting to retrieval-augmented generation. We introduce Fact-QA, an LLM-based (GPT-4) evaluation metric to evaluate the factuality of predicted answers. We find that despite performing close to humans on traditional metrics of textual similarity, there remains a significant gap between automated approaches and humans in terms of fact precision.

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MindMap: Knowledge Graph Prompting Sparks Graph of Thoughts in Large Language Models
Yilin Wen | Zifeng Wang | Jimeng Sun

Large language models (LLMs) have achieved remarkable performance in natural language understanding and generation tasks. However, they often suffer from limitations such as difficulty in incorporating new knowledge, generating hallucinations, and explaining their reasoning process. To address these challenges, we propose a novel prompting pipeline, named MindMap, that leverages knowledge graphs (KGs) to enhance LLMs’ inference and transparency. Our method enables LLMs to comprehend KG inputs and infer with a combination of implicit and external knowledge. Moreover, our method elicits the mind map of LLMs, which reveals their reasoning pathways based on the ontology of knowledge. We evaluate our method on diverse question & answering tasks, especially in medical domains, and show significant improvements over baselines. We also introduce a new hallucination evaluation benchmark and analyze the effects of different components of our method. Our results demonstrate the effectiveness and robustness of our method in merging knowledge from LLMs and KGs for combined inference.

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AGB-DE: A Corpus for the Automated Legal Assessment of Clauses in German Consumer Contracts
Daniel Braun | Florian Matthes

Legal tasks and datasets are often used as benchmarks for the capabilities of language models. However, openly available annotated datasets are rare. In this paper, we introduce AGB-DE, a corpus of 3,764 clauses from German consumer contracts that have been annotated and legally assessed by legal experts. Together with the data, we present a first baseline for the task of detecting potentially void clauses, comparing the performance of an SVM baseline with three fine-tuned open language models and the performance of GPT-3.5. Our results show the challenging nature of the task, with no approach exceeding an F1-score of 0.54. While the fine-tuned models often performed better with regard to precision, GPT-3.5 outperformed the other approaches with regard to recall. An analysis of the errors indicates that one of the main challenges could be the correct interpretation of complex clauses, rather than the decision boundaries of what is permissible and what is not.

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Examining the robustness of LLM evaluation to the distributional assumptions of benchmarks
Charlotte Siska | Katerina Marazopoulou | Melissa Ailem | James Bono

Benchmarks have emerged as the central approach for evaluating Large Language Models (LLMs). The research community often relies on a model’s average performance across the test prompts of a benchmark to evaluate the model’s performance. This is consistent with the assumption that the test prompts within a benchmark represent a random sample from some real-world distribution of interest. We note that this is generally not the case; instead, we hold that the distribution of interest varies according to the specific use case. Hence, we analyze the robustness of LLM benchmarks to their underlying distributional assumptions. We find that (1) the correlation in model performance across test prompts is non-random, (2) accounting for correlations across test prompts can change model rankings on major benchmarks, (3) explanatory factors for these correlations include semantic similarity and common LLM failure points.

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Re-Tuning: Overcoming the Compositionality Limits of Large Language Models with Recursive Tuning
Eric Pasewark | Kyle Montgomery | Kefei Duan | Dawn Song | Chenguang Wang

We present a new method for large language models to solve compositional tasks. Although they have shown strong performance on traditional language understanding tasks, large language models struggle to solve compositional tasks, where the solution depends on solving smaller instances of the same problem. We propose a natural approach to solve compositional tasks recursively. Our method, Re-Tuning, tunes models to break down a problem into subproblems, solve those subproblems, and combine the results. We show that our method significantly improves model performance on three representative compositional tasks: integer addition, dynamic programming, and parity. Compared to state-of-the-art methods that keep intermediate steps towards solving the problems, Re-Tuning achieves significantly higher accuracy and is more GPU memory efficient.

Large Language Models (LLMs) have demonstrated superior results across a wide range of tasks, and Retrieval-augmented Generation (RAG) is an effective way to enhance the performance by locating relevant information and placing it into the context window of the LLM. However, the relationship between retrievers and LLMs in a RAG is still under-investigated. Most existing work treats the retriever and the LLM as independent components and leaves a gap between retrieving human-”friendly” information and assembling a LLM-”friendly” context. In this work, we examine a novel bridge mechanism. We validate the ranking and selection assumptions of retrievers in the context of RAG and propose a framework that chains together supervised and reinforcement learning to train a bridge model that optimizes the connection between the retriever and the LLM. Empirical results demonstrate the effectiveness of our method in both question-answering and personalized generation tasks.

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Large Language Models Can Learn Temporal Reasoning
Siheng Xiong | Ali Payani | Ramana Kompella | Faramarz Fekri

While large language models (LLMs) have demonstrated remarkable reasoning capabilities, they are not without their flaws and inaccuracies. Recent studies have introduced various methods to mitigate these limitations. Temporal reasoning (TR), in particular, presents a significant challenge for LLMs due to its reliance on diverse temporal concepts and intricate temporal logic. In this paper, we propose TG-LLM, a novel framework towards language-based TR. Instead of reasoning over the original context, we adopt a latent representation, temporal graph (TG) that enhances the learning of TR. A synthetic dataset (TGQA), which is fully controllable and requires minimal supervision, is constructed for fine-tuning LLMs on this text-to-TG translation task. We confirmed in experiments that the capability of TG translation learned on our dataset can be transferred to other TR tasks and benchmarks. On top of that, we teach LLM to perform deliberate reasoning over the TGs via Chain-of-Thought (CoT) bootstrapping and graph data augmentation. We observed that those strategies, which maintain a balance between usefulness and diversity, bring more reliable CoTs and final results than the vanilla CoT distillation.

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Learning Relational Decomposition of Queries for Question Answering from Tables
Raphaël Mouravieff | Benjamin Piwowarski | Sylvain Lamprier

Table Question-Answering involves both understanding the natural language query and grounding it in the context of the input table to extract relevant information. In this context, many methods have highlighted the benefits of intermediate pre-training using SQL queries. However, while most approaches aim at generating final answers directly from inputs, we claim that there is better to do with SQL queries during training.By learning to imitate a restricted subset of SQL-like algebraic operations, we demonstrate that their execution flow provides intermediate supervision steps that allow for increased generalization and structural reasoning compared to classical approaches. Our method, bridges the gap between semantic parsing and direct answering methods, offering valuable insights into which types of operations should be predicted by a generative architecture and which should be executed by an external algorithm. Our code can be found at https://github.com/RaphaelMouravieff/Partial-Exec.

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Characterizing Similarities and Divergences in Conversational Tones in Humans and LLMs by Sampling with People
Dun-Ming Huang | Pol Van Rijn | Ilia Sucholutsky | Raja Marjieh | Nori Jacoby

Conversational tones — the manners and attitudes in which speakers communicate — are essential to effective communication. As Large Language Models (LLMs) become increasingly popular, it is necessary to characterize the divergences in their conversational tones relative to humans. Prior research relied on pre-existing taxonomies or text corpora, which suffer from experimenter bias and may not be representative of real-world distributions. Inspired by methods from cognitive science, we propose an iterative method for simultaneously eliciting conversational tones and sentences, where participants alternate between two tasks: (1) one participant identifies the tone of a given sentence and (2) a different participant generates a sentence based on that tone. We run 50 iterations of this process with both human participants and GPT-4 and obtain a dataset of sentences and frequent conversational tones. In an additional experiment, humans and GPT-4 annotated all sentences with all tones. With data from 1,339 participants, 33,370 human judgments, and 29,900 GPT-4 queries, we show how our approach can be used to create an interpretable geometric representation of relations between tones in humans and GPT-4. This work showcases how combining ideas from machine learning and cognitive science can address challenges in human-computer interactions.

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Pareto Optimal Learning for Estimating Large Language Model Errors
Theodore Zhao | Mu Wei | J. Preston | Hoifung Poon

Large Language Models (LLMs) have shown impressive abilities in many applications. When a concrete and precise answer is desired, it is important to have a quantitative estimation of the potential error rate. However, this can be challenging due to the text-in-text-out nature of the generative models. We present a method based on Pareto optimization that generates a risk score to estimate the probability of error in an LLM response by integrating multiple sources of information. We prove theoretically that the error estimator optimized in our framework aligns with the LLM and the information sources in an Pareto optimal manner. Experimental results show that the risk scores estimated by our method are well correlated with the true LLM error rate, thus facilitating error correction. By dynamically combining with prompting strategies such as self-verification and information retrieval, we demonstrate the proposed method can be utilized to increase the performance of an LLM, surpassing state-of-the-art task specific model.

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Simul-LLM: A Framework for Exploring High-Quality Simultaneous Translation with Large Language Models
Victor Agostinelli | Max Wild | Matthew Raffel | Kazi Fuad | Lizhong Chen

Large language models (LLMs) with billions of parameters and pretrained on massive amounts of data are now capable of near or better than state-of-the-art performance in a variety of downstream natural language processing tasks. Neural machine translation (NMT) is one such task that LLMs have been applied to with great success. However, little research has focused on applying LLMs to the more difficult subset of NMT called simultaneous translation (SimulMT), where translation begins before the entire source context is available to the model. In this paper, we address key challenges facing LLMs fine-tuned for SimulMT, validate classical SimulMT concepts and practices in the context of LLMs, explore adapting LLMs that are fine-tuned for NMT to the task of SimulMT, and introduce Simul-LLM, the first open-source fine-tuning and evaluation pipeline development framework for LLMs focused on SimulMT.

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Defending Against Alignment-Breaking Attacks via Robustly Aligned LLM
Bochuan Cao | Yuanpu Cao | Lu Lin | Jinghui Chen

Recently, Large Language Models (LLMs) have made significant advancements and are now widely used across various domains. Unfortunately, there has been a rising concern that LLMs can be misused to generate harmful or malicious content. Though a line of research has focused on aligning LLMs with human values and preventing them from producing inappropriate content, such alignments are usually vulnerable and can be bypassed by alignment-breaking attacks via adversarially optimized or handcrafted jailbreaking prompts. In this work, we introduce a Robustly Aligned LLM (RA-LLM) to defend against potential alignment-breaking attacks. RA-LLM can be directly constructed upon an existing aligned LLM with a robust alignment checking function, without requiring any expensive retraining or fine-tuning process of the original LLM. Furthermore, we also provide a theoretical analysis for RA-LLM to verify its effectiveness in defending against alignment-breaking attacks. Through real-world experiments on open-source large language models, we demonstrate that RA-LLM can successfully defend against both state-of-the-art adversarial prompts and popular handcrafted jailbreaking prompts by reducing their attack success rates from nearly 100% to around 10% or less.

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Interactive-KBQA: Multi-Turn Interactions for Knowledge Base Question Answering with Large Language Models
Guanming Xiong | Junwei Bao | Wen Zhao

This study explores the realm of knowledge base question answering (KBQA). KBQA is considered a challenging task, particularly in parsing intricate questions into executable logical forms. Traditional semantic parsing (SP)-based methods require extensive data annotations, which result in significant costs. Recently, the advent of few-shot in-context learning, powered by large language models (LLMs), has showcased promising capabilities. Yet, fully leveraging LLMs to parse questions into logical forms in low-resource scenarios poses a substantial challenge. To tackle these hurdles, we introduce Interactive-KBQA, a framework designed to generate logical forms through direct interaction with knowledge bases (KBs). Within this framework, we have developed three generic APIs for KB interaction. For each category of complex question, we devised exemplars to guide LLMs through the reasoning processes. Our method achieves competitive results on the WebQuestionsSP, ComplexWebQuestions, KQA Pro, and MetaQA datasets with a minimal number of examples (shots). Importantly, our approach supports manual intervention, allowing for the iterative refinement of LLM outputs. By annotating a dataset with step-wise reasoning processes, we showcase our model’s adaptability and highlight its potential for contributing significant enhancements to the field.

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LLMs in the Imaginarium: Tool Learning through Simulated Trial and Error
Boshi Wang | Hao Fang | Jason Eisner | Benjamin Van Durme | Yu Su

Tools are essential for large language models (LLMs) to acquire up-to-date information and take consequential actions in external environments. Existing work on tool-augmented LLMs primarily focuses on the broad coverage of tools and the flexibility of adding new tools. However, a critical aspect that has surprisingly been understudied is simply how accurately an LLM uses tools for which it has been trained. We find that existing LLMs, including GPT-4 and open-source LLMs specifically fine-tuned for tool use, only reach a correctness rate in the range of 30% to 60%, far from reliable use in practice. We propose a biologically inspired method for tool-augmented LLMs, simulated trial and error (STE), that orchestrates three key mechanisms for successful tool use behaviors in the biological system: trial and error, imagination, and memory. Specifically, STE leverages an LLM’s ‘imagination’ to simulate plausible scenarios for using a tool, after which the LLM interacts with the tool to learn from its execution feedback. Both short-term and long-term memory are employed to improve the depth and breadth of the exploration, respectively. Comprehensive experiments on ToolBench show that STE substantially improves tool learning for LLMs under both in-context learning and fine-tuning settings, bringing a boost of 46.7% to Mistral-Instruct-7B and enabling it to outperform GPT-4. We also show effective continual learning of tools via a simple experience replay strategy.

The Mixture of Experts (MoE) for language models has been proven effective in augmenting the capacity of models by dynamically routing each input token to a specific subset of experts for processing. Despite the success, most existing methods face a challenge for balance between sparsity and the availability of expert knowledge: enhancing performance through increased use of expert knowledge often results in diminishing sparsity during expert selection. To mitigate this contradiction, we propose HyperMoE, a novel MoE framework built upon Hypernetworks. This framework integrates the computational processes of MoE with the concept of knowledge transferring in multi-task learning. Specific modules generated based on the information of unselected experts serve as supplementary information, which allows the knowledge of experts not selected to be used while maintaining selection sparsity. Our comprehensive empirical evaluations across multiple datasets and backbones establish that HyperMoE significantly outperforms existing MoE methods under identical conditions concerning the number of experts. Our code is publicly available at https://github.com/Bumble666/Hyper_MoE

Aligning large language models (LLMs) with human preferences is crucial for enhancing their utility in terms of helpfulness, truthfulness, safety, harmlessness, and interestingness. Existing methods for achieving this alignment often involve employing reinforcement learning from human feedback (RLHF) to fine-tune LLMs based on human labels assessing the relative quality of model responses. Nevertheless, RLHF is susceptible to instability during fine-tuning and presents challenges in implementation. Drawing inspiration from the emerging field of representation engineering (RepE), this study aims to identify relevant representations for high-level human preferences embedded in patterns of activity within an LLM and achieve precise control of model behavior by transforming its representations. This novel approach, denoted as Representation Alignment from Human Feedback (RAHF), proves to be effective, computationally efficient, and easy to implement. Extensive experiments demonstrate the efficacy of RAHF in not only capturing but also manipulating representations to align with a broad spectrum of human preferences or values, rather than being confined to a singular concept or function (e.g. honesty or bias). RAHF’s versatility in accommodating diverse human preferences shows its potential for advancing LLM performance.

Multimodal large language models (MLLMs) have demonstrated promising results in a variety of tasks that combine vision and language. As these models become more integral to research and applications, conducting comprehensive evaluations of their capabilities has grown increasingly important. However, most existing benchmarks fail to consider that, in certain situations, images need to be interpreted within a broader context. In this work, we introduce a new benchmark, named as CODIS, designed to assess the ability of models to use context provided in free-form text to enhance visual comprehension. Our findings indicate that MLLMs consistently fall short of human performance on this benchmark. Further analysis confirms that these models struggle to effectively extract and utilize contextual information to improve their understanding of images. This underscores the pressing need to enhance the ability of MLLMs to comprehend visuals in a context-dependent manner.

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ARAIDA: Analogical Reasoning-Augmented Interactive Data Annotation
Chen Huang | Yiping Jin | Ilija Ilievski | Wenqiang Lei | Jiancheng Lv

Human annotation is a time-consuming task that requires a significant amount of effort. To address this issue, interactive data annotation utilizes an annotation model to provide suggestions for humans to approve or correct. However, annotation models trained with limited labeled data are prone to generating incorrect suggestions, leading to extra human correction effort. To tackle this challenge, we propose Araida, an analogical reasoning-based approach that enhances automatic annotation accuracy in the interactive data annotation setting and reduces the need for human corrections. Araida involves an error-aware integration strategy that dynamically coordinates an annotation model and a k-nearest neighbors (KNN) model, giving more importance to KNN’s predictions when predictions from the annotation model are deemed inaccurate. Empirical studies demonstrate that Araida is adaptable to different annotation tasks and models. On average, it reduces human correction labor by 11.02% compared to vanilla interactive data annotation methods.

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PolCLIP: A Unified Image-Text Word Sense Disambiguation Model via Generating Multimodal Complementary Representations
Qihao Yang | Yong Li | Xuelin Wang | Fu Lee Wang | Tianyong Hao

Word sense disambiguation (WSD) can be viewed as two subtasks: textual word sense disambiguation (Textual-WSD) and visual word sense disambiguation (Visual-WSD). They aim to identify the most semantically relevant senses or images to a given context containing ambiguous target words. However, existing WSD models seldom address these two subtasks jointly due to lack of images in Textual-WSD datasets or lack of senses in Visual-WSD datasets. To bridge this gap, we propose PolCLIP, a unified image-text WSD model. By employing an image-text complementarity strategy, it not only simulates stable diffusion models to generate implicit visual representations for word senses but also simulates image captioning models to provide implicit textual representations for images. Additionally, a disambiguation-oriented image-sense dataset is constructed for the training objective of learning multimodal polysemy representations. To the best of our knowledge, PolCLIP is the first model that can cope with both Textual-WSD and Visual-WSD. Extensive experimental results on benchmarks demonstrate the effectiveness of our method, achieving a 2.53% F1-score increase over the state-of-the-art models on Textual-WSD and a 2.22% HR@1 improvement on Visual-WSD.

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Prompted Aspect Key Point Analysis for Quantitative Review Summarization
An Tang | Xiuzhen Zhang | Minh Dinh | Erik Cambria

Key Point Analysis (KPA) aims for quantitative summarization that provides key points (KPs) as succinct textual summaries and quantities measuring their prevalence. KPA studies for arguments and reviews have been reported in the literature. A majority of KPA studies for reviews adopt supervised learning to extract short sentences as KPs before matching KPs to review comments for quantification of KP prevalence. Recent abstractive approaches still generate KPs based on sentences, often leading to KPs with overlapping and hallucinated opinions, and inaccurate quantification. In this paper, we propose Prompted Aspect Key Point Analysis (PAKPA) for quantitative review summarization. PAKPA employs aspect sentiment analysis and prompted in-context learning with Large Language Models (LLMs) to generate and quantify KPs grounded in aspects for business entities, which achieves faithful KPs with accurate quantification, and removes the need for large amounts of annotated data for supervised training. Experiments on the popular review dataset Yelp and the aspect-oriented review summarization dataset SPACE show that our framework achieves state-of-the-art performance. Source code and data are available at: https://github.com/antangrocket1312/PAKPA

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Ask Again, Then Fail: Large Language Models’ Vacillations in Judgment
Qiming Xie | Zengzhi Wang | Yi Feng | Rui Xia

We observe that current large language models often waver in their judgments when faced with follow-up questions, even if the original judgment was correct. This wavering presents a significant challenge for generating reliable responses and building user trust. To comprehensively assess this issue, we introduce a Follow-up Questioning Mechanism along with two metrics to quantify this inconsistency, confirming its widespread presence in current large language models. Furthermore, to mitigate this issue, we explore various prompting strategies for closed-source models, and develop a training-based framework Unwavering-FQ that teaches large language models to maintain their originally correct judgments through synthesized high-quality preference data. Our experimental results confirm the effectiveness of our framework and its ability to enhance the general capabilities of large language models.

Large language models (LLMs) are increasingly used to meet user information needs, but their effectiveness in dealing with user queries that contain various types of ambiguity remains unknown, ultimately risking user trust and satisfaction. To this end, we introduce CLAMBER, a benchmark for evaluating LLMs using a well-organized taxonomy. Building upon the taxonomy, we construct 12K high-quality data to assess the strengths, weaknesses, and potential risks of various off-the-shelf LLMs.Our findings indicate the limited practical utility of current LLMs in identifying and clarifying ambiguous user queries, even enhanced by chain-of-thought (CoT) and few-shot prompting. These techniques may result in overconfidence in LLMs and yield only marginal enhancements in identifying ambiguity. Furthermore, current LLMs fall short in generating high-quality clarifying questions due to a lack of conflict resolution and inaccurate utilization of inherent knowledge.In this paper, CLAMBER presents a guidance and promotes further research on proactive and trustworthy LLMs.

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Multimodal Reasoning with Multimodal Knowledge Graph
Junlin Lee | Yequan Wang | Jing Li | Min Zhang

Multimodal reasoning with large language models (LLMs) often suffers from hallucinations and the presence of deficient or outdated knowledge within LLMs. Some approaches have sought to mitigate these issues by employing textual knowledge graphs, but their singular modality of knowledge limits comprehensive cross-modal understanding. In this paper, we propose the Multimodal Reasoning with Multimodal Knowledge Graph (MR-MKG) method, which leverages multimodal knowledge graphs (MMKGs) to learn rich and semantic knowledge across modalities, significantly enhancing the multimodal reasoning capabilities of LLMs. In particular, a relation graph attention network is utilized for encoding MMKGs and a cross-modal alignment module is designed for optimizing image-text alignment. A MMKG-grounded dataset is constructed to equip LLMs with initial expertise in multimodal reasoning through pretraining. Remarkably, MR-MKG achieves superior performance while training on only a small fraction of parameters, approximately 2.25% of the LLM’s parameter size. Experimental results on multimodal question answering and multimodal analogy reasoning tasks demonstrate that our MR-MKG method outperforms previous state-of-the-art models.

Recently, Temporal Knowledge Graph Forecasting (TKGF) has emerged as a pivotal domain for forecasting future events. Unlike black-box neural network methods, rule-based approaches are lauded for their efficiency and interpretability. For this line of work, it is crucial to correctly estimate the predictive effectiveness of the rules, i.e., the confidence. However, the existing literature lacks in-depth investigation into how confidence evolves with time. Moreover, inaccurate and heuristic confidence estimation limits the performance of rule-based methods. To alleviate such issues, we propose a framework named TempValid to explicitly model the temporal validity of rules for TKGF. Specifically, we design a time function to model the interaction between temporal information with confidence. TempValid conceptualizes confidence and other coefficients as learnable parameters to avoid inaccurate estimation and combinatorial explosion. Furthermore, we introduce a rule-adversarial negative sampling and a time-aware negative sampling strategies to facilitate TempValid learning. Extensive experiments show that TempValid significantly outperforms previous state-of-the-art (SOTA) rule-based methods on six TKGF datasets. Moreover, it exhibits substantial advancements in cross-domain and resource-constrained rule learning scenarios.

It is time-saving to build a reading assistant for customer service representations (CSRs) when reading user manuals, especially information-rich ones. Current solutions don’t fit the online custom service scenarios well due to the lack of attention to user questions and possible responses. Hence, we propose to develop a time-saving and careful reading assistant for CSRs, named CARE. It can help the CSRs quickly find proper responses from the user manuals via explicit clue chains. Specifically, each of the clue chains is formed by inferring over the user manuals, starting from the question clue aligned with the user question and ending at a possible response. To overcome the shortage of supervised data, we adopt the self-supervised strategy for model learning. The offline experiment shows that CARE is efficient in automatically inferring accurate responses from the user manual. The online experiment further demonstrates the superiority of CARE to reduce CSRs’ reading burden and keep high service quality, in particular with >35% decrease in time spent and keeping a >0.75 ICC score.

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Enhancing Numerical Reasoning with the Guidance of Reliable Reasoning Processes
Dingzirui Wang | Longxu Dou | Xuanliang Zhang | Qingfu Zhu | Wanxiang Che

Numerical reasoning is an essential ability for NLP systems to handle numeric information. Recent research indicates that fine-tuning a small-scale model to learn generating reasoning processes alongside answers can significantly enhance performance. However, current methods have the limitation that most methods generate reasoning processes with large language models (LLMs), which are “unreliable” since such processes could contain information unrelated to the answer. To address this limitation, we introduce enhancing numerical reasoning with reliable processes (Encore), which derives the reliable reasoning process by decomposing the answer formula, ensuring which fully supports the answer. Nevertheless, models could lack enough data to learn the reasoning process generation adequately, since our method generates only one single reasoning process for one formula. To overcome this difficulty, we present a series of pre-training tasks to help models learn the reasoning process generation with synthesized data. The experiments show that Encore yields improvement on all five experimental datasets with an average of 1.8%, proving the effectiveness of our method.

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PAGED: A Benchmark for Procedural Graphs Extraction from Documents
Weihong Du | Wenrui Liao | Hongru Liang | Wenqiang Lei

Automatic extraction of procedural graphs from documents creates a low-cost way for users to easily understand a complex procedure by skimming visual graphs. Despite the progress in recent studies, it remains unanswered: whether the existing studies have well solved this task (Q1) and whether the emerging large language models (LLMs) can bring new opportunities to this task (Q2). To this end, we propose a new benchmark PAGED, equipped with a large high-quality dataset and standard evaluations. It investigates five state-of-the-art baselines, revealing that they fail to extract optimal procedural graphs well because of their heavy reliance on hand-written rules and limited available data. We further involve three advanced LLMs in PAGED and enhance them with a novel self-refine strategy. The results point out the advantages of LLMs in identifying textual elements and their gaps in building logical structures. We hope PAGED can serve as a major landmark for automatic procedural graph extraction and the investigations in PAGED can offer insights into the research on logic reasoning among non-sequential elements.

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Navigating the Shadows: Unveiling Effective Disturbances for Modern AI Content Detectors
Ying Zhou | Ben He | Le Sun

With the launch of ChatGPT, large language models (LLMs) have attracted global attention. In the realm of article writing, LLMs have witnessed extensive utilization, giving rise to concerns related to intellectual property protection, personal privacy, and academic integrity. In response, AI-text detection has emerged to distinguish between human and machine-generated content. However, recent research indicates that these detection systems often lack robustness and struggle to effectively differentiate perturbed texts. Currently, there is a lack of systematic evaluations regarding detection performance in real-world applications, and a comprehensive examination of perturbation techniques and detector robustness is also absent. To bridge this gap, our work simulates real-world scenarios in both informal and professional writing, exploring the out-of-the-box performance of current detectors. Additionally, we have constructed 12 black-box text perturbation methods to assess the robustness of current detection models across various perturbation granularities. Furthermore, through adversarial learning experiments, we investigate the impact of perturbation data augmentation on the robustness of AI-text detectors. We have released our code and data at https://github.com/zhouying20/ai-text-detector-evaluation.

Retrieval-augmented generation (RAG) has become a main technique for alleviating hallucinations in large language models (LLMs). Despite the integration of RAG, LLMs may still present unsupported or contradictory claims to the retrieved contents. In order to develop effective hallucination prevention strategies under RAG, it is important to create benchmark datasets that can measure the extent of hallucination. This paper presents RAGTruth, a corpus tailored for analyzing word-level hallucinations in various domains and tasks within the standard RAG frameworks for LLM applications. RAGTruth comprises nearly 18,000 naturally generated responses from diverse LLMs using RAG. These responses have undergone meticulous manual annotations at both the individual case and word levels, incorporating evaluations of hallucination intensity. We not only benchmark hallucination frequencies across different LLMs, but also critically assess the effectiveness of several existing hallucination detection methodologies. We show that using a high-quality dataset such as RAGTruth, it is possible to finetune a relatively small LLM and achieve a competitive hallucination detection performance when compared to the existing prompt-based approaches using state-of-the-art LLMs such as GPT-4. Furthermore, the finetuned model can effectively mitigate hallucination in LLM responses.

In the era of large language models (LLMs), hallucination (the tendency to generate factually incorrect content) poses great challenges to trustworthy and reliable deployment of LLMs in real-world applications. To tackle the hallucination, three key questions should be well studied: how to detect hallucinations (detection), why do LLMs hallucinate (source), and what can be done to mitigate them (mitigation). To address these challenges, this work presents a systematic empirical study on LLM hallucinations, focused on the three aspects of hallucination detection, source and mitigation. Specially, we construct a new hallucination benchmark HaluEval 2.0, and design a simple yet effective detection method for LLM hallucinations. Furthermore, we zoom into the different training or utilization stages of LLMs and extensively analyze the potential factors that lead to the LLM hallucinations. Finally, we implement and examine a series of widely used techniques to mitigate the hallucinations in LLMs. Our work has led to several important findings to understand the hallucination origin and mitigate the hallucinations in LLMs.

Knowledge distillation (KD) is a common approach to compress a teacher model to reduce its inference cost and memory footprint, by training a smaller student model. However, in the context of autoregressive language models (LMs), we empirically find that larger teacher LMs might dramatically result in a poorer student. In response to this problem, we conduct a series of analyses and reveal that different tokens have different teaching modes, neglecting which will lead to performance degradation. Motivated by this, we propose a simple yet effective adaptive teaching approach (ATKD) to improve the KD. The core of ATKD is to reduce rote learning and make teaching more diverse and flexible. Extensive experiments on 8 LM tasks show that, with the help of ATKD, various baseline KD methods can achieve consistent and significant performance gains (up to +3.04% average score) across all model types and sizes. More encouragingly, ATKD can improve the student model generalization effectively.

Incrementally expanding the capability of an existing translation model to solve new domain tasks over time is a fundamental and practical problem, which usually suffers from catastrophic forgetting. Generally, multi-domain learning can be seen as a good solution. However, there are two drawbacks: 1) it requires having the training data for all domains available at the same time, which may be unrealistic due to storage or privacy concerns; 2) it requires re-training the model on the data of all domains from scratch when adding a new domain and this is time-consuming and computationally expensive. To address these issues, we present a semi-supervised contrastive distillation framework for incremental neural machine translation. Specifically, to avoid catastrophic forgetting, we propose to exploit unlabeled data from the same distributions of the older domains through knowledge distillation. Further, to ensure the distinct domain characteristics in the model as the number of domains increases, we devise a cross-domain contrastive objective to enhance the distilled knowledge. Extensive experiments on domain translation benchmarks show that our approach, without accessing any previous training data or re-training on all domains from scratch, can significantly prevent the model from forgetting previously learned knowledge while obtaining good performance on the incrementally added domains. The code and data with step-by-step instructions will be released upon acceptance.

Large language models (LLMs) have successfully served as a general-purpose interface across multiple tasks and languages, while the adaptation of voice LLMs is mostly designed for specific purposes (either single-task or monolingual), where the advantages of LLMs especially for low-resource language processing and zero-shot task generalization are less exploited in the audio community. To bridge the gap, we introduce Make-A-Voice as a multi-modal voice LLM and conduct a comprehensive study on its capability to deal with multiple tasks/languages. When trained on ~200K hours of 6-language data for 4 voice generation applications, Make-A-Voice emerges notable advantages: 1) as scalable learners to improve performance with end-to-end local and global multiscale transformers; and 2) as multitask learners by adjusting prompts to share common knowledge across modalities (speech/singing) and present in-context learning abilities by generalizing to unseen tasks not explicitly train on; 3) as multilingual learners to alleviate data scarcity of low-resource languages by including rich-resource language training data. Experimental results demonstrate that Make-A-Voice exhibits superior audio quality and style similarity compared with competitive baseline models in monolingual/cross-lingual voice generation. Audio samples are available at https://M-Voice.github.io

Recently, the development of open-source large language models (LLMs) has advanced rapidly. Nevertheless, due to data constraints, the capabilities of most open-source LLMs are primarily focused on English. To address this issue, we introduce the concept of chat vector to equip pre-trained language models with instruction following and human value alignment via simple model arithmetic. The chat vector is derived by subtracting the weights of a pre-trained base model (e.g. LLaMA2) from those of its corresponding chat model (e.g. LLaMA2-chat). By simply adding the chat vector to a continual pre-trained model’s weights, we can endow the model with chat capabilities in new languages without the need for further training.Our empirical studies demonstrate the superior efficacy of the chat vector from three different aspects: instruction following, toxicity mitigation, and multi-turn dialogue. Moreover, to showcase the adaptability of our approach, we extend our experiments to encompass various languages, base models, and chat vectors. The results underscore the chat vector’s simplicity, effectiveness, and wide applicability, making it a compelling solution for efficiently enabling conversational capabilities in pre-trained language models. Our code is available at https://github.com/aqweteddy/ChatVector.

Large language models (LLMs) are typically aligned to be harmless to humans. Unfortunately, recent work has shown that such models are susceptible to automated jailbreak attacks that induce them to generate harmful content. More recent LLMs often incorporate an additional layer of defense, a Guard Model, which is a second LLM that is designed to check and moderate the output response of the primary LLM. Our key contribution is to show a novel attack strategy, PRP, that is successful against several open-source (e.g., Llama 2) and closed-source (e.g., GPT 3.5) implementations of Guard Models. PRP leverages a two step prefix-based attack that operates by (a) constructing a universal adversarial prefix for the Guard Model, and (b) propagating this prefix to the response. We find that this procedure is effective across multiple threat models, including ones in which the adversary has no access to the Guard Model at all. Our work suggests that further advances are required on defenses and Guard Models before they can be considered effective. Code at https://github.com/AshishHoodaIITD/prp-llm-guard-rail-attack.

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Hide and Seek in Noise Labels: Noise-Robust Collaborative Active Learning with LLMs-Powered Assistance
Bo Yuan | Yulin Chen | Yin Zhang | Wei Jiang

Learning from noisy labels (LNL) is a challenge that arises in many real-world scenarios where collected training data can contain incorrect or corrupted labels. Most existing solutions identify noisy labels and adopt active learning to query human experts on them for denoising. In the era of large language models (LLMs), although we can reduce the human effort to improve these methods, their performances are still subject to accurately separating the clean and noisy samples from noisy data. In this paper, we propose an innovative collaborative learning framework NoiseAL based on active learning to combine LLMs and small models (SMs) for learning from noisy labels. During collaborative training, we first adopt two SMs to form a co-prediction network and propose a dynamic-enhanced threshold strategy to divide the noisy data into different subsets, then select the clean and noisy samples from these subsets to feed the active annotator LLMs to rectify noisy samples. Finally, we employ different optimization objectives to conquer subsets with different degrees of label noises. Extensive experiments on synthetic and real-world noise datasets further demonstrate the superiority of our framework over state-of-the-art baselines.

In this study, we delve into the realm of counterfactual reasoning capabilities of large language models (LLMs). Our primary objective is to cultivate the counterfactual thought processes within LLMs and rigorously assess these processes for their validity. Specifically, we introduce a novel task, Counterfactual Logical Modification (CLOMO), and a high-quality human-annotated benchmark. In this task, LLMs must adeptly alter a given argumentative text to uphold a predetermined logical relationship. To effectively evaluate a generation model’s counterfactual capabilities, we propose an innovative evaluation metric, the decomposed Self-Evaluation Score (SES) to directly evaluate the natural language output of LLMs instead of modeling the task as a multiple-choice problem. Analysis shows that the proposed automatic metric aligns well with human preference. Our experimental results show that while LLMs demonstrate a notable capacity for logical counterfactual thinking, there remains a discernible gap between their current abilities and human performance. Code and data are available at https://github.com/Eleanor-H/CLOMO.

Question answering over heterogeneous data requires reasoning over diverse sources of data, which is challenging due to the large scale of information and organic coupling of heterogeneous data. Various approaches have been proposed to address these challenges. One approach involves training specialized retrievers to select relevant information, thereby reducing the input length. Another approach is to transform diverse modalities of data into a single modality, simplifying the task difficulty and enabling more straightforward processing. In this paper, we propose HProPro, a novel program-based prompting framework for the hybrid question answering task. HProPro follows the code generation and execution paradigm. In addition, HProPro integrates various functions to tackle the hybrid reasoning scenario. Specifically, HProPro contains function declaration and function implementation to perform hybrid information-seeking over data from various sources and modalities, which enables reasoning over such data without training specialized retrievers or performing modal transformations. Experimental results on two typical hybrid question answering benchmarks HybridQA and MultiModalQA demonstrate the effectiveness of HProPro: it surpasses all baseline systems and achieves the best performances in the few-shot settings on both datasets.

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IndicGenBench: A Multilingual Benchmark to Evaluate Generation Capabilities of LLMs on Indic Languages
Harman Singh | Nitish Gupta | Shikhar Bharadwaj | Dinesh Tewari | Partha Talukdar

As large language models (LLMs) see increasing adoption across the globe, it is imperative for LLMs to be representative of the linguistic diversity of the world. India is a linguistically diverse country of 1.4 Billion people. To facilitate research on multilingual LLM evaluation, we release IndicGenBench — the largest benchmark for evaluating LLMs on user-facing generation tasks across a diverse set 29 of Indic languages covering 13 scripts and 4 language families. IndicGenBench is composed of diverse generation tasks like cross-lingual summarization, machine translation, and cross-lingual question answering. IndicGenBench extends existing benchmarks to many Indic languages through human curation providing multi-way parallel evaluation data for many under-represented Indic languages for the first time. We evaluate stateof-the-art LLMs like GPT-3.5, GPT-4, PaLM2, and LLaMA on IndicGenBench in a variety of settings. The largest PaLM-2 models performs the best on most tasks, however, there is a significant performance gap in all languages compared to English showing that further research is needed for the development of more inclusive multilingual language models. IndicGenBench isavailable at www.github.com/google-researchdatasets/indic-gen-bench

Temporal knowledge graph completion aims to infer the missing facts in temporal knowledge graphs. Current approaches usually embed factual knowledge into continuous vector space and apply geometric operations to learn potential patterns in temporal knowledge graphs. However, these methods only adopt a single operation, which may have limitations in capturing the complex temporal dynamics present in temporal knowledge graphs. Therefore, we propose a simple but effective method, i.e. TCompoundE, which is specially designed with two geometric operations, including time-specific and relation-specific operations. We provide mathematical proofs to demonstrate the ability of TCompoundE to encode various relation patterns. Experimental results show that our proposed model significantly outperforms existing temporal knowledge graph embedding models. Our code is available at https://github.com/nk-ruiying/TCompoundE.

As instruction-tuned large language models (LLMs) evolve, aligning pretrained foundation models presents increasing challenges. Existing alignment strategies, which typically leverage diverse and high-quality data sources, often overlook the intrinsic uncertainty of tasks, learning all data samples equally. This may lead to suboptimal data efficiency and model performance. In response, we propose uncertainty-aware learning (UAL) to improve the model alignment of different task scenarios, by introducing the sample uncertainty (elicited from more capable LLMs). We implement UAL by a simple fashion – adaptively setting the label smoothing value of training according to the uncertainty of individual samples. Analysis shows that our UAL indeed facilitates better token clustering in the feature space, validating our hypothesis. Extensive experiments on widely used benchmarks demonstrate that our UAL significantly and consistently outperforms standard supervised fine-tuning. Notably, LLMs aligned in a mixed scenario have achieved an average improvement of 10.62% on high-entropy tasks (i.e., AlpacaEval leaderboard), and 1.81% on complex low-entropy tasks (i.e., MetaMath and GSM8K).

Accurate and interpretable user satisfaction estimation (USE) is critical for understanding, evaluating, and continuously improving conversational systems. Users express their satisfaction or dissatisfaction with diverse conversational patterns in both general-purpose (ChatGPT and Bing Copilot) and task-oriented (customer service chatbot) conversational systems. Existing approaches based on featurized ML models or text embeddings fall short in extracting generalizable patterns and are hard to interpret. In this work, we show that LLMs can extract interpretable signals of user satisfaction from their natural language utterances more effectively than embedding-based approaches. Moreover, an LLM can be tailored for USE via an iterative prompting framework using supervision from labeled examples. Our proposed method, Supervised Prompting for User satisfaction Rubrics (SPUR), not only has higher accuracy but is more interpretable as it scores user satisfaction via learned rubrics with a detailed breakdown.

Large Language Models (LLMs) demonstrate significant value in domain-specific applications, benefiting from their fundamental capabilities. Nevertheless, it is still unclear which fundamental capabilities contribute to success in specific domains. Moreover, the existing benchmark-based evaluation cannot effectively reflect the performance of real-world applications. In this survey, we review recent advances of LLMs in domain applications, aiming to summarize the fundamental capabilities and their collaboration. Furthermore, we establish connections between fundamental capabilities and specific domains, evaluating the varying importance of different capabilities. Based on our findings, we propose a reliable strategy for domains to choose more robust backbone LLMs for real-world applications.

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Measuring Political Bias in Large Language Models: What Is Said and How It Is Said
Yejin Bang | Delong Chen | Nayeon Lee | Pascale Fung

We propose to measure political bias in LLMs by analyzing both the content and style of their generated content regarding political issues. Existing benchmarks and measures focus on gender and racial biases. However, political bias exists in LLMs and can lead to polarization and other harms in downstream applications. In order to provide transparency to users, we advocate that there should be fine-grained and explainable measures of political biases generated by LLMs. Our proposed measure looks at different political issues such as reproductive rights and climate change, at both the content (the substance of the generation) and the style (the lexical polarity) of such bias. We measured the political bias in eleven open-sourced LLMs and showed that our proposed framework is easily scalable to other topics and is explainable.

In this paper, we demonstrate that an inherent waveform pattern in the attention allocation of large language models (LLMs) significantly affects their performance in tasks demanding a high degree of context awareness, such as utilizing LLMs for tool-use. Specifically, the crucial information in the context will be potentially overlooked by model when it is positioned in the trough zone of the attention waveform, leading to decreased performance. To address this issue, we propose a novel inference method named Attention Buckets. It allows LLMs to process their input through multiple parallel processes. Each process utilizes a distinct base angle for the rotary position embedding, thereby creating a unique attention waveform. By compensating an attention trough of a particular process with an attention peak of another process, our approach enhances LLM’s awareness to various contextual positions, thus mitigating the risk of overlooking crucial information. In the largest tool-use benchmark, our method elevates a 7B model to achieve state-of-the-art performance, comparable to that of GPT-4. On other benchmarks and some RAG tasks, which also demand a thorough understanding of contextual content, Attention Buckets also exhibited notable enhancements in performance.

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Layer-Condensed KV Cache for Efficient Inference of Large Language Models
Haoyi Wu | Kewei Tu

Huge memory consumption has been a major bottleneck for deploying high-throughput large language models in real-world applications. In addition to the large number of parameters, the key-value (KV) cache for the attention mechanism in the transformer architecture consumes a significant amount of memory, especially when the number of layers is large for deep language models. In this paper, we propose a novel method that only computes and caches the KVs of a small number of layers, thus significantly saving memory consumption and improving inference throughput. Our experiments on large language models show that our method achieves up to 26× higher throughput than standard transformers and competitive performance in language modeling and downstream tasks. In addition, our method is orthogonal to existing transformer memory-saving techniques, so it is straightforward to integrate them with our model, achieving further improvement in inference efficiency. Our code is available at https://github.com/whyNLP/LCKV.

While large language models (LLMs) have been pre-trained on multilingual corpora, their performance still lags behind in most languages compared to a few resource-rich languages. One common approach to mitigate this issue is to translate training data from resource-rich languages into other languages and then continue training. However, using the data obtained solely relying on translation while ignoring the original capabilities of LLMs across languages is not always effective, which we show will limit the performance of cross-lingual knowledge transfer. In this work, we propose SDRRL, a method based on Self-Distillation from Resource-Rich Languages that effectively improve multilingual performance by leveraging the internal capabilities of LLMs on resource-rich languages. We evaluate on different LLMs (LLaMA-2 and SeaLLM) and source languages (English and French) across various comprehension and generation tasks, experimental results demonstrate that SDRRL can significantly enhance multilingual capabilities while minimizing the impact on original performance in resource-rich languages.

We introduce CHARM, the first benchmark for comprehensively and in-depth evaluating the commonsense reasoning ability of large language models (LLMs) in Chinese, which covers both globally known and Chinese-specific commonsense. We evaluated 7 English and 12 Chinese-oriented LLMs on CHARM, employing 5 representative prompt strategies for improving LLMs’ reasoning ability, such as Chain-of-Thought. Our findings indicated that the LLM’s language orientation and the task’s domain influence the effectiveness of the prompt strategy, which enriches previous research findings. We built closely-interconnected reasoning and memorization tasks, and found that some LLMs struggle with memorizing Chinese commonsense, affecting their reasoning ability, while others show differences in reasoning despite similar memorization performance. We also evaluated the LLMs’ memorization-independent reasoning abilities and analyzed the typical errors. Our study precisely identified the LLMs’ strengths and weaknesses, providing the clear direction for optimization. It can also serve as a reference for studies in other fields. We will release CHARM at https://github.com/opendatalab/CHARM.

With the bloom of Large Language Models (LLMs), Multimodal Large Language Models (MLLMs) that incorporate LLMs with pre-trained vision models have recently demonstrated impressive performance across diverse vision-language tasks. However, they fall short to comprehend context involving multiple images. A primary reason for this shortcoming is that the visual features for each images are encoded individually by frozen encoders before feeding into the LLM backbone, lacking awareness of other images and the multimodal instructions. We term this issue as prior-LLM modality isolation and propose a two phase paradigm, browse-and-concentrate, to enable in-depth multimodal context fusion prior to feeding the features into LLMs. This paradigm initially “browses” through the inputs for essential insights, and then revisits the inputs to “concentrate” on crucial details, guided by these insights, to achieve a more comprehensive understanding of the multimodal inputs. Additionally, we develop training strategies specifically to enhance the understanding of multi-image inputs. Our method markedly boosts the performance on 7 multi-image scenarios, contributing to increments on average accuracy by 2.13% and 7.60% against strong MLLMs baselines with 3B and 11B LLMs, respectively.

Recent developments in Multimodal Large Language Models (MLLMs) have shown rapid progress, moving towards the goal of creating versatile MLLMs that understand inputs from various modalities. However, existing methods typically rely on joint training with paired multimodal instruction data, which is resource-intensive and challenging to extend to new modalities. In this paper, we propose a new paradigm through the model composition of existing MLLMs to create a new model that retains the modal understanding capabilities of each original model. Our basic implementation, NaiveMC, demonstrates the effectiveness of this paradigm by reusing modality encoders and merging LLM parameters. Furthermore, we introduce DAMC to address parameter interference and mismatch issues during the merging process, thereby enhancing the model performance. To facilitate research in this area, we propose MCUB, a benchmark for assessing ability of MLLMs to understand inputs from diverse modalities. Experiments on this benchmark and four other multimodal understanding tasks show significant improvements over baselines, proving that model composition can create a versatile model capable of processing inputs from multiple modalities.

We present a novel inference scheme, self-speculative decoding, for accelerating Large Language Models (LLMs) without the need for an auxiliary model. This approach is characterized by a two-stage process: drafting and verification. The drafting stage generates draft tokens at a slightly lower quality but more quickly, which is achieved by selectively skipping certain intermediate layers during drafting. Subsequently, the verification stage employs the original LLM to validate those draft output tokens in one forward pass. This process ensures the final output remains identical to that produced by the unaltered LLM. Moreover, the proposed method requires no additional neural network training and no extra memory footprint, making it a plug-and-play and cost-effective solution for inference acceleration. Benchmarks with LLaMA-2 and its variants demonstrated a speedup up to 1.99×.

Multimodal machine translation (MMT) aims to improve the performance of machine translation with the help of visual information, which has received widespread attention recently. It has been verified that visual information brings greater performance gains when the textual information is limited. However, most previous works ignore to take advantage of the complete textual inputs and the limited textual inputs at the same time, which limits the overall performance. To solve this issue, we propose a mixup method termed Soul-Mix to enhance MMT by using visual information more effectively. We mix the predicted translations of complete textual input and the limited textual inputs. Experimental results on the Multi30K dataset of three translation directions show that our Soul-Mix significantly outperforms existing approaches and achieves new state-of-the-art performance with fewer parameters than some previous models. Besides, the strength of Soul-Mix is more obvious on more challenging MSCOCO dataset which includes more out-of-domain instances with lots of ambiguous verbs.

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Measuring Meaning Composition in the Human Brain with Composition Scores from Large Language Models
Changjiang Gao | Jixing Li | Jiajun Chen | Shujian Huang

The process of meaning composition, wherein smaller units like morphemes or words combine to form the meaning of phrases and sentences, is essential for human sentence comprehension. Despite extensive neurolinguistic research into the brain regions involved in meaning composition, a computational metric to quantify the extent of composition is still lacking. Drawing on the key-value memory interpretation of transformer feed-forward network blocks, we introduce the Composition Score, a novel model-based metric designed to quantify the degree of meaning composition during sentence comprehension. Experimental findings show that this metric correlates with brain clusters associated with word frequency, structural processing, and general sensitivity to words, suggesting the multifaceted nature of meaning composition during human sentence comprehension.

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MIST: Mutual Information Maximization for Short Text Clustering
Krissanee Kamthawee | Can Udomcharoenchaikit | Sarana Nutanong

Short text clustering poses substantial challenges due to the limited amount of information provided by each text sample. Previous efforts based on dense representations are still inadequate as texts are not sufficiently segregated in the embedding space before clustering. Even though the state-of-the-art method utilizes contrastive learning to boost performance, the process of summarizing all local tokens to form a sequence representation for the whole text includes noise that may obscure limited key information. We propose Mutual Information Maximization Framework for Short Text Clustering (MIST), which overcomes the information drown-out by including a mechanism to maximize the mutual information between representations on both sequence and token levels. Experimental results across eight standard short text datasets show that MIST outperforms the state-of-the-art method in terms of Accuracy or Normalized Mutual Information in most cases.

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Self-chats from Large Language Models Make Small Emotional Support Chatbot Better
Zhonghua Zheng | Lizi Liao | Yang Deng | Libo Qin | Liqiang Nie

Large Language Models (LLMs) have shown strong generalization abilities to excel in various tasks, including emotion support conversations. However, deploying such LLMs like GPT-3 (175B parameters) is resource-intensive and challenging at scale. In this study, we utilize LLMs as “Counseling Teacher” to enhance smaller models’ emotion support response abilities, significantly reducing the necessity of scaling up model size. To this end, we first introduce an iterative expansion framework, aiming to prompt the large teacher model to curate an expansive emotion support dialogue dataset. This curated dataset, termed ExTES, encompasses a broad spectrum of scenarios and is crafted with meticulous strategies to ensure its quality and comprehensiveness. Based on this, we then devise a Diverse Response Inpainting (DRI) mechanism to harness the teacher model to produce multiple diverse responses by filling in the masked conversation context. This richness and variety serve as instructive examples, providing a robust foundation for fine-tuning smaller student models. Experiments across varied scenarios reveal that the teacher-student scheme with DRI notably improves the response abilities of smaller models, even outperforming the teacher model in some cases. The dataset and codes are available in https://github.com/pandazzh2020/ExTES.

The rapid advancement of large language models (LLMs) has facilitated their transformation into conversational chatbots that can grasp contextual nuances and generate pertinent sentences, closely mirroring human values through advanced techniques such as instruction tuning and reinforcement learning from human feedback (RLHF). However, the computational efficiency required for LLMs, achieved through techniques like post-training quantization (PTQ), presents challenges such as token-flipping that can impair chatbot performance. In response, we propose a novel preference alignment approach, quantization-aware direct preference optimization (QDPO), that aligns quantized LLMs with their full-precision counterparts, improving conversational abilities. Evaluated on two instruction-tuned LLMs in various languages, QDPO demonstrated superior performance in improving conversational abilities compared to established PTQ and knowledge-distillation fine-tuning techniques, marking a significant step forward in the development of efficient and effective conversational LLMs.

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Complex Reasoning over Logical Queries on Commonsense Knowledge Graphs
Tianqing Fang | Zeming Chen | Yangqiu Song | Antoine Bosselut

Event commonsense reasoning requires the ability to reason about the relationship between events, as well as infer implicit contextunderlying that relationship. However, data scarcity makes it challenging for language models to learn to generate commonsense infer-ences for contexts and questions involving interactions between complex events. To address this demand, we present COM2 (COMplexCOMmonsense), a new dataset created by sampling multi-hop logical queries (e.g., the joint effect or cause of both event A and B, or theeffect of the effect of event C) from an existing commonsense knowledge graph (CSKG), and verbalizing them using handcrafted rules andlarge language models into multiple-choice and text generation questions. Our experiments show that language models trained on COM2 exhibit significant improve ments in complex reasoning ability, resulting in enhanced zero-shot performance in both in-domain and out-of-domain tasks for question answering and generative commonsense reasoning, without expensive human annotations

We present Expert-Token-Routing, a unified generalist framework that facilitates seamless integration of multiple expert LLMs. Our framework represents expert LLMs as special expert tokens within the vocabulary of a meta LLM. The meta LLM can route to an expert LLM like generating new tokens. Expert-Token-Routing not only supports learning the implicit expertise of expert LLMs from existing instruction dataset but also allows for dynamic extension of new expert LLMs in a plug-and-play manner. It also conceals the detailed collaboration process from the user’s perspective, facilitating interaction as though it were a singular LLM. Our framework outperforms various existing multi-LLM collaboration paradigms across benchmarks that incorporate six diverse expert domains, demonstrating effectiveness and robustness in building generalist LLM system via synergizing multiple expert LLMs.

The increasing demand for the deployment of LLMs in information-seeking scenarios has spurred efforts in creating verifiable systems, which generate responses to queries along with supporting evidence. In this paper, we explore the attribution capabilities of plan-based models which have been recently shown to improve the faithfulness, grounding, and controllability of generated text. We conceptualize plans as a sequence of questions which serve as blueprints of the generated content and its organization. We propose two attribution models that utilize different variants of blueprints, an abstractive model where questions are generated from scratch, and an extractive model where questions are copied from the input. Experiments on long-form question-answering show that planning consistently improves attribution quality. Moreover, the citations generated by blueprint models are more accurate compared to those obtained from LLM-based pipelines lacking a planning component.

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Exploring Precision and Recall to assess the quality and diversity of LLMs
Florian Le Bronnec | Alexandre Verine | Benjamin Negrevergne | Yann Chevaleyre | Alexandre Allauzen

We introduce a novel evaluation framework for Large Language Models (LLMs) such as Llama-2 and Mistral, focusing on importing Precision and Recall metrics from image generation to text generation. This approach allows for a nuanced assessment of the quality and diversity of generated text without the need for aligned corpora. By conducting a comprehensive evaluation of state-of-the-art language models, the study reveals new insights into their performance on open-ended generation tasks, which are not adequately captured by traditional benchmarks. The findings highlight a trade-off between the quality and diversity of generated samples, particularly when models are fine-tuned on instruction dataset or with human feedback. This work extends the toolkit for distribution-based NLP evaluation, offering insights into the practical capabilities and challenges that current LLMs face in generating diverse and high-quality text.

Existing approaches for aligning large language models with human preferences face a trade-off that requires a separate reward model (RM) for on-policy learning. In this paper, we present a novel alignment framework, SELF-JUDGE that (1) does on-policy learning and 2) is parameter efficient, as it does not require an additional RM for evaluating the samples for on-policy learning. To this end, we propose Judge-augmented Supervised Fine-Tuning (JSFT) to train a single model to act as both a policy and a judge. Specifically, we view the pairwise judgment task, choosing the better response from a response pair, as a special case of the instruction-following task. The resulting model can judge preferences of on-the-fly responses from current policy initialized from itself. Experimental results show the efficacy of SELF-JUDGE, outperforming baselines in preference benchmarks. We also show that the rejecting sampling by itself can improve performance further without an additional evaluator.

Legal systems worldwide are inundated with exponential growth in cases and documents. There is an imminent need to develop NLP and ML techniques for automatically processing and understanding legal documents to streamline the legal system. However, evaluating and comparing various NLP models designed specifically for the legal domain is challenging. This paper addresses this challenge by proposing : Benchmark for Indian Legal Text Understanding and Reasoning. contains monolingual (English, Hindi) and multi-lingual (9 Indian languages) domain-specific tasks that address different aspects of the legal system from the point of view of understanding and reasoning over Indian legal documents. We present baseline models (including LLM-based) for each task, outlining the gap between models and the ground truth. To foster further research in the legal domain, we create a leaderboard (available at: https://exploration-lab.github.io/IL-TUR/ ) where the research community can upload and compare legal text understanding systems.

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JumpCoder: Go Beyond Autoregressive Coder via Online Modification
Mouxiang Chen | Hao Tian | Zhongxin Liu | Xiaoxue Ren | Jianling Sun

While existing code large language models (code LLMs) exhibit impressive capabilities in code generation, their autoregressive sequential generation inherently lacks reversibility. This limitation hinders them from timely correcting previous missing statements during coding as humans do, often leading to error propagation and suboptimal performance. We introduce JumpCoder, a novel model-agnostic framework that enables human-like online modification and non-sequential generation to augment code LLMs. The key idea behind JumpCoder is to insert new code into the currently generated code when necessary during generation, which is achieved through an auxiliary infilling model that works in tandem with the code LLM. Since identifying the best infill position beforehand is intractable, we adopt an infill-first, judge-later strategy, which experiments with filling at the k most critical positions following the generation of each line, and uses an Abstract Syntax Tree (AST) parser alongside the Generation Model Scoring to effectively judge the validity of each potential infill. Extensive experiments using six state-of-the-art code LLMs across multiple and multilingual benchmarks consistently indicate significant improvements over all baselines. Our code is available in the uploaded attachment.

Datasets are foundational to many breakthroughs in modern artificial intelligence. Many recent achievements in the space of natural language processing (NLP) can be attributed to the fine-tuning of pre-trained models on a diverse set of tasks that enables a large language model (LLM) to respond to instructions. Instruction fine-tuning (IFT) requires specifically constructed and annotated datasets. However, existing datasets are almost all in the English language. In this work, our primary goal is to bridge the language gap by building a human-curated instruction-following dataset spanning 65 languages. We worked with fluent speakers of languages from around the world to collect natural instances of instructions and completions. Furthermore, we create the most extensive multilingual collection to date, comprising 513 million instances through templating and augmenting existing datasets across 114 languages. In total, we contribute three key resources: we develop and open-source the Aya Dataset, the Aya Collection, and the Aya Evaluation Suite. The Aya initiative also serves as a valuable case study in participatory research, involving collaborators from 119 countries. We see this as an important framework for future research collaborations that aim to bridge gaps in resources.

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Language Models can Exploit Cross-Task In-context Learning for Data-Scarce Novel Tasks
Anwoy Chatterjee | Eshaan Tanwar | Subhabrata Dutta | Tanmoy Chakraborty

Large Language Models (LLMs) have transformed NLP with their remarkable In-context Learning (ICL) capabilities. Automated assistants based on LLMs are gaining popularity; however, adapting them to novel tasks is still challenging. While colossal models excel in zero-shot performance, their computational demands limit widespread use, and smaller language models struggle without context. This paper investigates whether LLMs can generalize from labeled examples of predefined tasks to novel tasks. Drawing inspiration from biological neurons and the mechanistic interpretation of the Transformer architecture, we explore the potential for information sharing across tasks. We design a cross-task prompting setup with three LLMs and show that LLMs achieve significant performance improvements despite no examples from the target task in the context. Cross-task prompting leads to a remarkable performance boost of 107% for LLaMA-2 7B, 18.6% for LLaMA-2 13B, and 3.2% for GPT 3.5 on average over zero-shot prompting, and performs comparable to standard in-context learning. The effectiveness of generating pseudo-labels for in-task examples is demonstrated, and our analyses reveal a strong correlation between the effect of cross-task examples and model activation similarities in source and target input tokens. This paper offers a first-of-its-kind exploration of LLMs’ ability to solve novel tasks based on contextual signals from different task examples.

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Split and Rephrase with Large Language Models
David Ponce | Thierry Etchegoyhen | Jesús Calleja | Harritxu Gete

The Split and Rephrase (SPRP) task, which consists in splitting complex sentences into a sequence of shorter grammatical sentences, while preserving the original meaning, can facilitate the processing of complex texts for humans and machines alike. It is also a valuable testbed to evaluate natural language processing models, as it requires modelling complex grammatical aspects. In this work, we evaluate large language models on the task, showing that they can provide large improvements over the state of the art on the main metrics, although still lagging in terms of splitting compliance. Results from two human evaluations further support the conclusions drawn from automated metric results. We provide a comprehensive study that includes prompting variants, domain shift, fine-tuned pretrained language models of varying parameter size and training data volumes, contrasted with both zero-shot and few-shot approaches on instruction-tuned language models. Although the latter were markedly outperformed by fine-tuned models, they may constitute a reasonable off-the-shelf alternative. Our results provide a fine-grained analysis of the potential and limitations of large language models for SPRP, with significant improvements achievable using relatively small amounts of training data and model parameters overall, and remaining limitations for all models on the task.

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ChunkAttention: Efficient Self-Attention with Prefix-Aware KV Cache and Two-Phase Partition
Lu Ye | Ze Tao | Yong Huang | Yang Li

Self-attention is an essential component of large language models (LLM) but a significant source of inference latency for long sequences. In multi-tenant LLMs serving scenarios, the compute and memory operation cost of self-attention can be optimized by using the probability that multiple LLM requests have shared system prompts in prefixes. In this paper, we introduce ChunkAttention, a prefix-aware self-attention module that can detect matching prompt prefixes across multiple requests and share their key/value tensors in memory at runtime to improve the memory utilization of KV cache. This is achieved by breaking monolithic key/value tensors into smaller chunks and structuring them into the auxiliary prefix tree. Consequently, on top of the prefix-tree based KV cache, we design an efficient self-attention kernel, where a two-phase partition algorithm is implemented to improve the data locality during self-attention computation in the presence of shared system prompts. Experiments show that ChunkAttention can speed up the self-attention kernel by 3.2-4.8× compared to the start-of-the-art implementation, with the length of the system prompt ranging from 1024 to 4096.

Alignment has become a critical step for instruction-tuned Large Language Models (LLMs) to become helpful assistants. However, effective evaluation of alignment for emerging Chinese LLMs is still significantly lacking, calling for real-scenario grounded, open-ended, challenging and automatic evaluations tailored for alignment. To fill in this gap, we introduce AlignBench, a comprehensive multi-dimensional benchmark for evaluating LLMs’ alignment in Chinese. We tailor a human-in-the-loop data curation pipeline, containing 8 main categories, 683 real-scenario rooted queries and corresponding human verified references.To ensure references’ correctness, each knowledge-intensive query is accompanied with evidences collected from reliable webpages (including the url and quotation) by our annotators.For automatic evaluation, our benchmark employs a rule-calibrated multi-dimensional LLM-as-Judge (CITATION) with Chain-of-Thought to generate explanations and final ratings as evaluations, ensuring high reliability and interpretability.All evaluation codes and data are publicly available at https://github.com/THUDM/AlignBench

The continual learning (CL) ability is vital for deploying large language models (LLMs) in the dynamic world. Existing methods devise the learning module to acquire task-specific knowledge with parameter-efficient tuning (PET) block and the selection module to pick out the corresponding one for the testing input, aiming at handling the challenges of catastrophic forgetting and knowledge transfer in CL. However, these methods tend to address only one of the challenges, ignoring the potential of aligning the two modules to effectively address catastrophic forgetting and knowledge transfer simultaneously. To this end, we propose a novel Shared Attention Framework (SAPT), to align the PET learning and selection via the Shared Attentive Learning & Selection module. Extensive Experiments on two CL benchmarks demonstrate the superiority of SAPT. Moreover, SAPT consistently demonstrates its superiority when we scale it to different model sizes (from 770M to 13B), different model architectures (T5 and LLaMA-2) and unseen tasks.

Fine-tuning large-scale pre-trained models is inherently a resource-intensive task. While it can enhance the capabilities of the model, it also incurs substantial computational costs, posing challenges to the practical application of downstream tasks. Existing parameter-efficient fine-tuning (PEFT) methods such as Low-Rank Adaptation (LoRA) rely on a bypass framework that ignores the differential parameter budget requirements across weight matrices, which may lead to suboptimal fine-tuning outcomes. To address this issue, we introduce the Dynamic Low-Rank Adaptation (DoRA) method. DoRA decomposes high-rank LoRA layers into structured single-rank components, allowing for dynamic pruning of parameter budget based on their importance to specific tasks during training, which makes the most of the limited parameter budget. Experimental results demonstrate that DoRA can achieve competitive performance compared with LoRA and full model fine-tuning, and outperform various strong baselines with the same storage parameter budget. Our code is available at [github](https://github.com/MIkumikumi0116/DoRA)

Knowledge editing aims to change language models’ performance on several special cases (i.e., editing scope) by infusing the corresponding expected knowledge into them. With the recent advancements in large language models (LLMs), knowledge editing has been shown as a promising technique to adapt LLMs to new knowledge without retraining from scratch. However, most of the previous studies neglect the multi-lingual nature of some main-stream LLMs (e.g., LLaMA, ChatGPT and GPT-4), and typically focus on monolingual scenarios, where LLMs are edited and evaluated in the same language. As a result, it is still unknown the effect of source language editing on a different target language. In this paper, we aim to figure out this cross-lingual effect in knowledge editing. Specifically, we first collect a large-scale cross-lingual synthetic dataset by translating ZsRE from English to Chinese. Then, we conduct English editing on various knowledge editing methods covering different paradigms, and evaluate their performance in Chinese, and vice versa. To give deeper analyses of the cross-lingual effect, the evaluation includes four aspects, i.e., reliability, generality, locality and portability. Furthermore, we analyze the inconsistent behaviors of the edited models and discuss their specific challenges.

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Argument Mining in Data Scarce Settings: Cross-lingual Transfer and Few-shot Techniques
Anar Yeginbergen | Maite Oronoz | Rodrigo Agerri

Recent research on sequence labelling has been exploring different strategies to mitigate the lack of manually annotated data for the large majority of the world languages. Among others, the most successful approaches have been based on (i) the crosslingual transfer capabilities of multilingual pre-trained language models (model-transfer), (ii) data translation and label projection (data-transfer) and (iii), prompt-based learning by reusing the mask objective to exploit the few-shot capabilities of pre-trained language models (few-shot). Previous work seems to conclude that model-transfer outperform data-transfer methods and that few-shot techniques based on prompting are superior to updating the model’s weights via fine-tuning. In this paper we empirically demonstrate that, for Argument Mining, a sequence labelling task which requires the detection of long and complex discourse structures, previous insights on crosslingual transfer or few-shot learning do not apply. Contrary to previous work, we show that for Argument Mining data-transfer obtains better results than model-transfer and that fine-tuning outperforms few-shot methods. Regarding the former, the domain of the dataset used for data-transfer seems to be a deciding factor, while, for few-shot, the type of task (length and complexity of the sequence spans) and sampling method proves to be crucial.

When using language models (LMs) to solve complex problems, humans might struggle to understand the LM-generated solutions and repair the flawed ones. To assist humans in repairing them, we propose to automatically decompose complex solutions into multiple simpler pieces that correspond to specific subtasks. We introduce a novel objective for learning task decomposition, termed assistive value (AssistV), which measures the feasibility and speed for humans to repair the decomposed solution. We collect a dataset of human repair experiences on different decomposed solutions. Utilizing the collected data as in-context examples, we then learn to critique, refine, and rank decomposed solutions to improve AssistV. We validate our method under competitive programming problems: under 177 hours of human study, our method enables non-experts to solve 33.3% more problems, speeds them up by 3.3x, and empowers them to match unassisted experts.

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An Entropy-based Text Watermarking Detection Method
Yijian Lu | Aiwei Liu | Dianzhi Yu | Jingjing Li | Irwin King

Text watermarking algorithms for large language models (LLMs) can effectively identify machine-generated texts by embedding and detecting hidden features in the text. Although the current text watermarking algorithms perform well in most high-entropy scenarios, its performance in low-entropy scenarios still needs to be improved. In this work, we opine that the influence of token entropy should be fully considered in the watermark detection process, i.e., the weight of each token during watermark detection should be customized according to its entropy, rather than setting the weights of all tokens to the same value as in previous methods. Specifically, we propose Entropy-based Text Watermarking Detection (EWD) that gives higher-entropy tokens higher influence weights during watermark detection, so as to better reflect the degree of watermarking. Furthermore, the proposed detection process is training-free and fully automated. From the experiments, we demonstrate that our EWD can achieve better detection performance in low-entropy scenarios, and our method is also general and can be applied to texts with different entropy distributions. Our code and data is available. Additionally, our algorithm could be accessed through MarkLLM (CITATION).

Generating recommendation reasons for recommendation results is a long-standing problem because it is challenging to explain the underlying reasons for recommending an item based on user and item IDs. Existing models usually learn semantic embeddings for each user and item, and generate the reasons according to the embeddings of the user-item pair. However, user and item IDs do not carry inherent semantic meaning, thus the limited number of reviews cannot model users’ preferences and item characteristics effectively, negatively affecting the model generalization for unseen user-item pairs.To tackle the problem, we propose the Concept Enhanced Explainable Recommendation framework (CEER), which utilizes macro concepts as the intermediary to bridge the gap between the user/item embeddings and the recommendation reasons. Specifically, we maximize the information bottleneck to extract macro concepts from user-item reviews. Then, for recommended user-item pairs, we jointly train the concept embeddings with the user and item embeddings, and generate the explanation according to the concepts. Extensive experiments on three datasets verify the superiority of our CEER model.

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How to Engage your Readers? Generating Guiding Questions to Promote Active Reading
Peng Cui | Vilém Zouhar | Xiaoyu Zhang | Mrinmaya Sachan

Using questions in written text is an effective strategy to enhance readability. However, what makes an active reading question good, what the linguistic role of these questions is, and what is their impact on human reading remains understudied. We introduce GuidingQ, a dataset of 10K in-text questions from textbooks and scientific articles. By analyzing the dataset, we present a comprehensive understanding of the use, distribution, and linguistic characteristics of these questions. Then, we explore various approaches to generate such questions using language models. Our results highlight the importance of capturing inter-question relationships and the challenge of question position identification in generating these questions. Finally, we conduct a human study to understand the implication of such questions on reading comprehension. We find that the generated questions are of high quality and are almost as effective as human-written questions in terms of improving readers’ memorization and comprehension.

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Less is More: Mitigating Multimodal Hallucination from an EOS Decision Perspective
Zihao Yue | Liang Zhang | Qin Jin

Large Multimodal Models (LMMs) often suffer from multimodal hallucinations, wherein they may create content that is not present in the visual inputs. In this paper, we explore a new angle of this issue: overly detailed training data hinders the model’s ability to timely terminate generation, leading to continued outputs beyond visual perception limits. By investigating how the model decides to terminate generation with EOS, the special end-of-sentence token, we find that the model assesses the completeness of the entire sequence by comparing the generated text with the image. This observation suggests that the model possesses an inherent potential of making proper EOS decisions based on its visual perception to avoid overly lengthy outputs. To take advantage of such potential, we explore two methods to mitigate multimodal hallucinations: a training objective that enables the model to reduce hallucinations by learning from regular instruction data, and a data filtering strategy to prevent harmful training data from exacerbating model hallucinations. Both methods significantly improve the hallucination performance of LMMs, without requiring any additional data or knowledge.

Self-consistency (SC), leveraging multiple samples from LLMs, shows significant gains on various reasoning tasks but struggles with free-form generation due to the difficulty of aggregating answers. Its variants, UCS and USC, rely on sample selection or voting mechanisms to improve output quality. These methods, however, face limitations due to their inability to fully utilize the nuanced consensus knowledge present within multiple candidate samples, often resulting in suboptimal outputs. We propose Fine-Grained Self-Consistency (FSC) to addresses these limitations by extracting and integrating segment-level commonalities from candidate samples, enhancing the performance of LLMs both in open-ended and reasoning tasks. Based on this, we present two additional strategies: candidate filtering, which enhances overall quality by identifying highly similar candidate sets, and merging, which reduces input token requirements by combining similar samples. The effectiveness of FSC is demonstrated through extensive experiments on various tasks, including summarization, code generation, and mathematical reasoning, using GPT-3.5-turbo and GPT-4. The results indicate significant improvements over baseline methods, showcasing the potential of FSC to optimize output quality by effectively synthesizing fine-grained consensus knowledge from multiple samples.

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More frequent verbs are associated with more diverse valency frames: Efficient principles at the lexicon-grammar interface
Siyu Tao | Lucia Donatelli | Michael Hahn

A substantial body of work has provided evidence that the lexicons of natural languages are organized to support efficient communication. However, existing work has largely focused on word-internal properties, such as Zipf’s observation that more frequent words are optimized in form to minimize communicative cost. Here, we investigate the hypothesis that efficient lexicon organization is also reflected in valency, or the combinations and orders of additional words and phrases a verb selects for in a sentence. We consider two measures of valency diversity for verbs: valency frame count (VFC), the number of distinct frames associated with a verb, and valency frame entropy (VFE), the average information content of frame selection associated with a verb. Using data from 79 languages, we provide evidence that more frequent verbs are associated with a greater diversity of valency frames, suggesting that the organization of valency is consistent with communicative efficiency principles. We discuss our findings in relation to classical findings such as Zipf’s meaning-frequency law and the principle of least effort, as well as implications for theories of valency and communicative efficiency principles.

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Quantifying Generalizations: Exploring the Divide Between Human and LLMs’ Sensitivity to Quantification
Claudia Collacciani | Giulia Rambelli | Marianna Bolognesi

Generics are expressions used to communicate abstractions about categories. While conveying general truths (e.g., “Birds fly”), generics have the interesting property to admit exceptions (e.g., penguins do not fly). Statements of this type help us organizing our knowledge of the world, and form the basis of how we express it (Hampton, 2012; Leslie, 2014).This study investigates how Large Language Models (LLMs) interpret generics, drawing upon psycholinguistic experimental methodologies. Understanding how LLMs interpret generic statements serves not only as a measure of their ability to abstract but also arguably plays a role in their encoding of stereotypes. Given that generics interpretation necessitates a comparison with explicitly quantified sentences, we explored i.) whether LLMs can correctly associate a quantifier with the generic structure, and ii.) whether the presence of a generic sentence as context influences the outcomes of quantifiers. We evaluated LLMs using both Surprisal distributions and prompting techniques.The findings indicate that models do not exhibit a strong sensitivity to quantification. Nevertheless, they seem to encode a meaning linked with the generic structure, which leads them to adjust their answers accordingly when a generalization is provided as context.

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Can Large Language Models Interpret Noun-Noun Compounds? A Linguistically-Motivated Study on Lexicalized and Novel Compounds
Giulia Rambelli | Emmanuele Chersoni | Claudia Collacciani | Marianna Bolognesi

Noun-noun compounds interpretation is the task where a model is given one of such constructions, and it is asked to provide a paraphrase, making the semantic relation between the nouns explicit, as in carrot cake is “a cake made of carrots.” Such a task requires the ability to understand the implicit structured representation of the compound meaning. In this paper, we test to what extent the recent Large Language Models can interpret the semantic relation between the constituents of lexicalized English compounds and whether they can abstract from such semantic knowledge to predict the semantic relation between the constituents of similar but novel compounds by relying on analogical comparisons (e.g., carrot dessert). We test both Surprisal metrics and prompt-based methods to see whether i.) they can correctly predict the relation between constituents, and ii.) the semantic representation of the relation is robust to paraphrasing. Using a dataset of lexicalized and annotated noun-noun compounds, we find that LLMs can infer some semantic relations better than others (with a preference for compounds involving concrete concepts). When challenged to perform abstractions and transfer their interpretations to semantically similar but novel compounds, LLMs show serious limitations.

Recently, the advent of large language models (LLMs) has revolutionized generative agents. Among them, Role-Playing Conversational Agents (RPCAs) attract considerable attention due to their ability to emotionally engage users. However, the absence of a comprehensive benchmark impedes progress in this field. To bridge this gap, we introduce CharacterEval, a Chinese benchmark for comprehensive RPCA assessment, complemented by a tailored high-quality dataset. The dataset comprises 1,785 multi-turn role-playing dialogues, encompassing 11,376 examples and featuring 77 characters derived from Chinese novels and scripts. It was carefully constructed, beginning with initial dialogue extraction via GPT-4, followed by rigorous human-led quality control, and enhanced with in-depth character profiles sourced from Baidu Baike. CharacterEval employs a multifaceted evaluation approach, encompassing thirteen targeted metrics on four dimensions. To facilitate the convenient evaluation for these subjective metrics in CharacterEval, we further developed CharacterRM, a role-playing reward model based on human annotations, which has a higher correlation with human judgment compared to GPT-4. Comprehensive experiments on CharacterEval demonstrate that Chinese LLMs exhibit more promising capabilities than GPT-4 in Chinese role-playing conversation.

The recent advancements in generative language models have demonstrated their ability to memorize knowledge from documents and recall knowledge to respond to user queries effectively. Building upon this capability, we propose to enable multimodal large language models (MLLMs) to memorize and recall images within their parameters. Given a user query for visual content, the MLLM is anticipated to “recall” the relevant image from its parameters as the response. Achieving this target presents notable challenges, including inbuilt visual memory and visual recall schemes within MLLMs. To address these challenges, we introduce a generative cross-modal retrieval framework, which assigns unique identifier strings to represent images and involves two training steps: learning to memorize and learning to retrieve. The first step focuses on training the MLLM to memorize the association between images and their respective identifiers. The latter step teaches the MLLM to generate the corresponding identifier of the target image, given the textual query input. By memorizing images in MLLMs, we introduce a new paradigm to cross-modal retrieval, distinct from previous discriminative approaches. The experiments demonstrate that the generative paradigm performs effectively and efficiently even with large-scale image candidate sets.

Aspect Sentiment Quad Prediction (ASQP) aims to predict all quads (aspect term, aspect category, opinion term, sentiment polarity) for a given review, which is the most representative and challenging task in aspect-based sentiment analysis. A key challenge in the ASQP task is the scarcity of labeled data, which limits the performance of existing methods. To tackle this issue, we propose a self-training framework with a pseudo-label scorer, wherein a scorer assesses the match between reviews and their pseudo-labels, aiming to filter out mismatches and thereby enhance the effectiveness of self-training. We highlight two critical aspects to ensure the scorer’s effectiveness and reliability: the quality of the training dataset and its model architecture. To this end, we create a human-annotated comparison dataset and train a generative model on it using ranking-based objectives. Extensive experiments on public ASQP datasets reveal that using our scorer can greatly and consistently improve the effectiveness of self-training. Moreover, we explore the possibility of replacing humans with large language models for comparison dataset annotation, and experiments demonstrate its feasibility. We will release our code and data via GitHub.

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Learning to Generate Answers with Citations via Factual Consistency Models
Rami Aly | Zhiqiang Tang | Samson Tan | George Karypis

Large Language Models (LLMs) frequently hallucinate, impeding their reliability in mission-critical situations. One approach to address this issue is to provide citations to relevant sources alongside generated content, enhancing the verifiability of generations. However, citing passages accurately in answers remains a substantial challenge. This paper proposes a weakly-supervised fine-tuning method leveraging factual consistency models (FCMs). Our approach alternates between generating texts with citations and supervised fine-tuning with FCM-filtered citation data. Focused learning is integrated into the objective, directing the fine-tuning process to emphasise the factual unit tokens, as measured by an FCM. Results on the ALCE few-shot citation benchmark with various instruction-tuned LLMs demonstrate superior performance compared to in-context learning, vanilla supervised fine-tuning, and state-of-the-art methods, with an average improvement of 34.1, 15.5, and 10.5 citation F₁ points, respectively. Moreover, in a domain transfer setting we show that the obtained citation generation ability robustly transfers to unseen datasets. Notably, our citation improvements contribute to the lowest factual error rate across baselines.

In this paper, we introduce a novel and simple method for obtaining high-quality text embeddings using only synthetic data and less than 1k training steps. Unlike existing methods that often depend on multi-stage intermediate pre-training with billions of weakly-supervised text pairs, followed by fine-tuning with a few labeled datasets, our method does not require building complex training pipelines or relying on manually collected datasets that are often constrained by task diversity and language coverage. We leverage proprietary LLMs to generate diverse synthetic data for hundreds of thousands of text embedding tasks across 93 languages. We then fine-tune open-source decoder-only LLMs on the synthetic data using standard contrastive loss. Experiments demonstrate that our method achieves strong performance on highly competitive text embedding benchmarks without using any labeled data. Furthermore, when fine-tuned with a mixture of synthetic and labeled data, our model sets new state-of-the-art results on the BEIR and MTEB benchmarks.

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Self-Training with Direct Preference Optimization Improves Chain-of-Thought Reasoning
Tianduo Wang | Shichen Li | Wei Lu

Teaching small-scale language models to perform math reasoning is a valuable yet challenging task. Besides obtaining labeled data from human experts, one of the most common ways to collect high-quality data is by sampling from a larger and more powerful language model. Although previous works have demonstrated the effectiveness of this method, such a knowledge distillation paradigm can be costly and unstable, especially considering that many large language models, such as GPT-4, are closed-sourced, proprietary, and their behaviors are unpredictable. In this work, to avoid relying on outputs from large models, we demonstrate that the reasoning abilities of small-scale language models can be enhanced through self-training, which involves training models with their own outputs. We also show that the vanilla self-training can be further augmented by an alignment algorithm, direct preference optimization (DPO). We empirically found that models trained with the DPO objective are capable of making better generations that largely benefit multi-turn self-training. The experiments show our models outperform the state-of-the-art models with comparable sizes on a series of downstream math reasoning tasks with minimal resource requirements.

Open-source large language models (LLMs) have gained significant strength across diverse fields. Nevertheless, the majority of studies primarily concentrate on English, with only limited exploration into the realm of multilingual abilities.In this work, we therefore construct an open-source multilingual supervised fine-tuning dataset.Different from previous works that simply translate English instructions, we consider both the language-specific and language-agnostic abilities of LLMs. Firstly, we introduce a knowledge-grounded data augmentation approach to elicit more language-specific knowledge of LLMs, improving their ability to serve users from different countries. Moreover, we find modern LLMs possess strong cross-lingual transfer capabilities, thus repeatedly learning identical content in various languages is not necessary. Consequently, we can substantially prune the language-agnostic supervised fine-tuning (SFT) data without any performance degradation, making multilingual SFT more efficient.The resulting UltraLink dataset comprises approximately 1 million samples across five languages (i.e., En, Zh, Ru, Fr, Es), and the proposed data construction method can be easily extended to other languages.UltraLink-LM, which is trained on the UltraLink dataset, outperforms several representative baselines across many tasks.

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Document-level Claim Extraction and Decontextualisation for Fact-Checking
Zhenyun Deng | Michael Schlichtkrull | Andreas Vlachos

Selecting which claims to check is a time-consuming task for human fact-checkers, especially from documents consisting of multiple sentences and containing multiple claims. However, existing claim extraction approaches focus more on identifying and extracting claims from individual sentences, e.g., identifying whether a sentence contains a claim or the exact boundaries of the claim within a sentence. In this paper, we propose a method for document-level claim extraction for fact-checking, which aims to extract check-worthy claims from documents and decontextualise them so that they can be understood out of context. Specifically, we first recast claim extraction as extractive summarization in order to identify central sentences from documents, then rewrite them to include necessary context from the originating document through sentence decontextualisation. Evaluation with both automatic metrics and a fact-checking professional shows that our method is able to extract check-worthy claims from documents at a higher rate than previous work, while also improving evidence retrieval.

Counterfactually Augmented Data (CAD) involves creating new data samples by applying minimal yet sufficient modifications to flip the label of existing data samples to other classes. Training with CAD enhances model robustness against spurious features that happen to correlate with labels by spreading the casual relationships across different classes. Yet, recent research reveals that training with CAD may lead models to overly focus on modified features while ignoring other important contextual information, inadvertently introducing biases that may impair performance on out-of-distribution (OOD) datasets. To mitigate this issue, we employ contrastive learning to promote global feature alignment in addition to learning counterfactual clues. We theoretically prove that contrastive loss can encourage models to leverage a broader range of features beyond those modified ones. Comprehensive experiments on two human-edited CAD datasets demonstrate that our proposed method outperforms the state-of-the-art on OOD datasets.

In this study, we explore in-context learning (ICL) in document-level event argument extraction (EAE) to alleviate the dependency on large-scale labeled data for this task. We introduce the Heuristic-Driven Link-of-Analogy (HD-LoA) prompting tailored for the EAE task. Specifically, we hypothesize and validate that LLMs learn task-specific heuristics from demonstrations in ICL. Building upon this hypothesis, we introduce an explicit heuristic-driven demonstration construction approach, which transforms the haphazard example selection process into a systematic method that emphasizes task heuristics. Additionally, inspired by the analogical reasoning of human, we propose the link-of-analogy prompting, which enables LLMs to process new situations by drawing analogies to known situations, enhancing their performance on unseen classes beyond limited ICL examples. Experiments show that our method outperforms existing prompting methods and few-shot supervised learning methods on document-level EAE datasets. Additionally, the HD-LoA prompting shows effectiveness in other tasks like sentiment analysis and natural language inference, demonstrating its broad adaptability.

Though advanced in understanding visual information with human languages, Large Vision-Language Models (LVLMs) still suffer from multimodal hallucinations. A natural concern is that during multimodal interaction, the generated hallucinations could influence the LVLMs’ subsequent generation. Thus, we raise a question: When presented with a query relevant to the previously generated hallucination, will LVLMs be misled and respond incorrectly, even though the ground visual information exists? To answer this, we propose a framework called \\textitMMHalSnowball to evaluate LVLMs’ behaviors when encountering generated hallucinations, where LVLMs are required to answer specific visual questions within a curated hallucinatory conversation. Crucially, our experiment shows that the performance of open-source LVLMs drops by at least 31\\%, indicating that LVLMs are prone to accept the generated hallucinations and make false claims that they would not have supported without distractions. We term this Multimodal Hallucination Snowballing. To mitigate this issue, we further propose a training-free method called Residual Visual Decoding, where we revise the output distribution of LVLMs with the one derived from the residual visual input, providing models with direct access to the visual information. Experiments show that our method can mitigate more than 24\\% of the snowballed multimodal hallucination while maintaining capabilities.

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mCoT: Multilingual Instruction Tuning for Reasoning Consistency in Language Models
Huiyuan Lai | Malvina Nissim

Large language models (LLMs) with Chain-of-thought (CoT) have recently emerged as a powerful technique for eliciting reasoning to improve various downstream tasks. As most research mainly focuses on English, with few explorations in a multilingual context, the question of how reliable this reasoning capability is in different languages is still open. To address it directly, we study multilingual reasoning consistency across multiple languages, using popular open-source LLMs. First, we compile the first large-scale multilingual math reasoning dataset, *mCoT-MATH*, covering eleven diverse languages. Then, we introduce multilingual CoT instruction tuning to boost reasoning capability across languages, thereby improving model consistency. While existing LLMs show substantial variation across the languages we consider, and especially low performance for lesser resourced languages, our 7B parameter model *mCoT* achieves impressive consistency across languages, and superior or comparable performance to close- and open-source models even of much larger sizes.

The debate surrounding gun control and gun regulation in the United States has intensified in the wake of numerous mass shooting events. As perspectives on this matter vary, it becomes increasingly important to comprehend individuals’ positions. Stance detection, the task of determining an author’s position towards a proposition or target, has gained attention for its potential use in understanding public perceptions towards controversial topics and identifying the best strategies to address public concerns. In this paper, we present GunStance, a dataset of tweets pertaining to shooting events, focusing specifically on the controversial topics of “banning guns” versus “regulating guns.” The tweets in the dataset are sourced from discussions on Twitter following various shooting incidents in the United States. Amazon Mechanical Turk was used to manually annotate a subset of the tweets relevant to the targets of interest (“banning guns” and “regulating guns”) into three classes: In-Favor, Against, and Neutral. The remaining unlabeled tweets are included in the dataset to facilitate studies on semi-supervised learning (SSL) approaches that can help address the scarcity of the labeled data in stance detection tasks. Furthermore, we propose a hybrid approach that combines curriculum-based SSL and Large Language Models (LLM), and show that the proposed approach outperforms supervised, semi-supervised, and LLM-based zero-shot models in most experiments on our assembled dataset.

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Beyond Traditional Benchmarks: Analyzing Behaviors of Open LLMs on Data-to-Text Generation
Zdeněk Kasner | Ondrej Dusek

We analyze the behaviors of open large language models (LLMs) on the task of data-to-text (D2T) generation, i.e., generating coherent and relevant text from structured data. To avoid the issue of LLM training data contamination with standard benchmarks, we design Quintd - a tool for collecting novel structured data records from public APIs. We find that open LLMs (Llama 2, Mistral, and Zephyr) can generate fluent and coherent texts in zero-shot settings from data in common formats collected with Quintd. However, we show that the semantic accuracy of the outputs is a major issue: both according to human annotators and our reference-free metric based on GPT-4, more than 80% of the outputs of open LLMs contain at least one semantic error. We publicly release the code, data, and model outputs.

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Don’t Go To Extremes: Revealing the Excessive Sensitivity and Calibration Limitations of LLMs in Implicit Hate Speech Detection
Min Zhang | Jianfeng He | Taoran Ji | Chang-Tien Lu

The fairness and trustworthiness of Large Language Models (LLMs) are receiving increasing attention. Implicit hate speech, which employs indirect language to convey hateful intentions, occupies a significant portion of practice. However, the extent to which LLMs effectively address this issue remains insufficiently examined. This paper delves into the capability of LLMs to detect implicit hate speech and express confidence in their responses. Our evaluation meticulously considers various prompt patterns and mainstream uncertainty estimation methods. Our findings highlight that LLMs exhibit two extremes: (1) LLMs display excessive sensitivity towards groups or topics that may cause fairness issues, resulting in misclassifying benign statements as hate speech. (2) LLMs’ confidence scores for each method excessively concentrate on a fixed range, remaining unchanged regardless of the dataset’s complexity. Consequently, the calibration performance is heavily reliant on primary classification accuracy. These discoveries unveil new limitations of LLMs, underscoring the need for caution when optimizing models to ensure they do not veer towards extremes. This serves as a reminder to carefully consider sensitivity and confidence in the pursuit of model fairness.

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Don’t Rank, Combine! Combining Machine Translation Hypotheses Using Quality Estimation
Giorgos Vernikos | Andrei Popescu-Belis

Neural machine translation systems estimate probabilities of target sentences given source sentences, yet these estimates may not align with human preferences. This work introduces QE-fusion, a method that synthesizes translations using a quality estimation metric (QE), which correlates better with human judgments. QE-fusion leverages a pool of candidates sampled from a model, combining spans from different candidates using a QE metric such as CometKiwi. We compare QE-fusion against beam search and recent reranking techniques, such as Minimum Bayes Risk decoding or QE-reranking. Our method consistently improves translation quality in terms of COMET and BLEURT scores when applied to large language models (LLMs) used for translation (PolyLM, XGLM, Llama2, Mistral, ALMA, and Tower) and to multilingual translation models (NLLB), over five language pairs. Notably, QE-fusion exhibits larger improvements for LLMs due to their ability to generate diverse outputs. We demonstrate that our approach generates novel translations in over half of the cases and consistently outperforms other methods across varying numbers of candidates (5–200). Furthermore, we empirically establish that QE-fusion scales linearly with the number of candidates in the pool.

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Generating and Evaluating Plausible Explanations for Knowledge Graph Completion
Antonio Di Mauro | Zhao Xu | Wiem Ben Rim | Timo Sztyler | Carolin Lawrence

Explanations for AI should aid human users, yet this ultimate goal remains under-explored. This paper aims to bridge this gap by investigating the specific explanatory needs of human users in the context of Knowledge Graph Completion (KGC) systems. In contrast to the prevailing approaches that primarily focus on mathematical theories, we recognize the potential limitations of explanations that may end up being overly complex or nonsensical for users. Through in-depth user interviews, we gain valuable insights into the types of KGC explanations users seek. Building upon these insights, we introduce GradPath, a novel path-based explanation method designed to meet human-centric explainability constraints and enhance plausibility. Additionally, GradPath harnesses the gradients of the trained KGC model to maintain a certain level of faithfulness. We verify the effectiveness of GradPath through well-designed human-centric evaluations. The results confirm that our method provides explanations that users consider more plausible than previous ones.

Evaluation of opinion summaries using conventional reference-based metrics often fails to provide a comprehensive assessment and exhibits limited correlation with human judgments. While Large Language Models (LLMs) have shown promise as reference-free metrics for NLG evaluation, their potential remains unexplored for opinion summary evaluation. Furthermore, the absence of sufficient opinion summary evaluation datasets hinders progress in this area. In response, we introduce the SUMMEVAL-OP dataset, encompassing 7 dimensions crucial to the evaluation of opinion summaries: fluency, coherence, relevance, faithfulness, aspect coverage, sentiment consistency, and specificity. We propose OP-I-PROMPT, a dimension-independent prompt, along with OP-PROMPTS, a dimension-dependent set of prompts for opinion summary evaluation. Our experiments demonstrate that OP-I-PROMPT emerges as a good alternative for evaluating opinion summaries, achieving an average Spearman correlation of 0.70 with human judgments, surpassing prior methodologies. Remarkably, we are the first to explore the efficacy of LLMs as evaluators, both on closed-source and open-source models, in the opinion summary evaluation domain.

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LANDeRMT: Dectecting and Routing Language-Aware Neurons for Selectively Finetuning LLMs to Machine Translation
Shaolin Zhu | Leiyu Pan | Bo Li | Deyi Xiong

Recent advancements in large language models (LLMs) have shown promising results in multilingual translation even with limited bilingual supervision. The major challenges are catastrophic forgetting and parameter interference for finetuning LLMs when provided parallel training data. To address these challenges, we propose LANDeRMT, a Language-Aware Neuron Detecting and Routing framework that selectively finetunes LLMs to Machine Translation with diverse translation training data. In LANDeRMT, we evaluate the awareness of neurons to MT tasks and categorize them into language-general and language-specific neurons. This categorization enables selective parameter updates during finetuning, mitigating parameter interference and catastrophic forgetting issues. For the detected neurons, we further propose a conditional awareness-based routing mechanism to dynamically adjust language-general and language-specific capacity within LLMs, guided by translation signals. Experimental results demonstrate that the proposed LANDeRMT is very effective in learning translation knowledge, significantly improving translation quality over various strong baselines for multiple language pairs.

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A Joint Coreference-Aware Approach to Document-Level Target Sentiment Analysis
Hongjie Cai | Heqing Ma | Jianfei Yu | Rui Xia

Most existing work on aspect-based sentiment analysis (ABSA) focuses on the sentence level, while research at the document level has not received enough attention. Compared to sentence-level ABSA, the document-level ABSA is not only more practical but also requires holistic document-level understanding capabilities such as coreference resolution. To investigate the impact of coreference information on document-level ABSA, we conduct a three-stage research for the document-level target sentiment analysis (DTSA) task: 1) exploring the effectiveness of coreference information for the DTSA task; 2) reducing the reliance on manually annotated coreference information; 3) alleviating the evaluation bias caused by missing the coreference information of opinion targets. Specifically, we first manually annotate the coreferential opinion targets and propose a multi-task learning framework to jointly model the DTSA task and the coreference resolution task. Then we annotate the coreference information with ChatGPT for joint training. Finally, to address the issue of missing coreference targets, we modify the metrics from strict matching to a loose matching method based on the clusters of targets. The experimental results not only demonstrate the effectiveness of our framework but also reflect the feasibility of using ChatGPT-annotated coreferential entities and the applicability of the modified metrics. Our source code is publicly released at https://github.com/NUSTM/DTSA-Coref.

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VisDiaHalBench: A Visual Dialogue Benchmark For Diagnosing Hallucination in Large Vision-Language Models
Qingxing Cao | Junhao Cheng | Xiaodan Liang | Liang Lin

Despite the significant success of large vision-language models (LVLMs), some studies have revealed that LVLMs suffer from the hallucination problem, where the LVLMs’ response contains descriptions of non-existent objects. Although various benchmarks have been proposed to investigate this problem, they mostly focus on single-turn evaluation and overlook the hallucination raised by textual inputs. To investigate the hallucination problem of LVLMs when given long-term misleading textual history, we propose a novel visual dialogue hallucination evaluation benchmark VisDiaHalBench. The benchmark consists of samples with five-turn questions about an edited image and its original version. VisDiaHalBench differs from previous hallucination benchmarks in the following three points: 1) The questions and answers are unambiguously grounded by annotated scene graphs. 2) The images are uncommonly edited to inspect the visual model and common-object hallucination in LLMs. 3) The carefully designed dialogue refers a same object in different turns to assess the image consistency and influence of history for LVLMs. The detailed analysis of several state-of-the-art LVLMs across image consistency, visual understanding, history influence, and other dimensions reveals their substantial performance gap with single-turn VQA tasks. The benchmark is released in: https://github.com/qingxingcao/VisDiaHalBench

Accurate representation of procedures in restricted scenarios, such as non-standardized scientific experiments, requires precise depiction of constraints. Unfortunately, Domain-specific Language (DSL), as an effective tool to express constraints structurally, often requires case-by-case hand-crafting, necessitating customized, labor-intensive efforts. To overcome this challenge, we introduce the AutoDSL framework to automate DSL-based constraint design across various domains. Utilizing domain specified experimental protocol corpora, AutoDSL optimizes syntactic constraints and abstracts semantic constraints. Quantitative and qualitative analyses of the DSLs designed by AutoDSL across five distinct domains highlight its potential as an auxiliary module for language models, aiming to improve procedural planning and execution.

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Multipath parsing in the brain
Berta Franzluebbers | Donald Dunagan | Miloš Stanojević | Jan Buys | John Hale

Humans understand sentences word-by-word, in the order that they hear them. This incrementality entails resolving temporary ambiguities about syntactic relationships. We investigate how humans process these syntactic ambiguities by correlating predictions from incremental generative dependency parsers with timecourse data from people undergoing functional neuroimaging while listening to an audiobook. In particular, we compare competing hypotheses regarding the number of developing syntactic analyses in play during word-by-word comprehension: one vs more than one. This comparison involves evaluating syntactic surprisal from a state-of-the-art dependency parser with LLM-adapted encodings against an existing fMRI dataset. In both English and Chinese data, we find evidence for multipath parsing. Brain regions associated with this multipath effect include bilateral superior temporal gyrus.

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Search-Adaptor: Embedding Customization for Information Retrieval
Jinsung Yoon | Yanfei Chen | Sercan Arik | Tomas Pfister

Embeddings extracted by pre-trained Large Language Models (LLMs) have significant potential to improve information retrieval and search. Beyond the zero-shot setup in which they are being conventionally used, being able to take advantage of the information from the relevant query-corpus paired data can further boost the LLM capabilities. In this paper, we propose a novel method, Search-Adaptor, for customizing LLMs for information retrieval in an efficient and robust way. Search-Adaptor modifies the embeddings generated by pre-trained LLMs, and can be integrated with any LLM, including those only available via prediction APIs. On multiple English, multilingual, and multimodal retrieval datasets, we show consistent and significant performance benefits for Search-Adaptor – e.g., more than 5% improvements for Google Embedding APIs in nDCG@10 averaged over 14 BEIR datasets.

AI alignment in the shape of Reinforcement Learning from Human Feedback (RLHF) is increasingly treated as a crucial ingredient for high performance large language models. Proximal Policy Optimization (PPO) has been installed by the seminal literature as the standard method for the RL part of RLHF. However, it involves both high computational cost and sensitive hyperparameter tuning. We posit that most of the motivational principles that led to the development of PPO are less of a practical concern in RLHF and advocate for a less computationally expensive method that preserves and even increases performance. We revisit how alignment from human preferences is formulated in the context of RL. Keeping simplicity as a guiding principle, we show that many components of PPO are unnecessary in an RLHF context and that far simpler REINFORCE-style optimization variants outperform both PPO and newly proposed “RL-free” methods such as DPO and RAFT. Our work suggests that careful adaptation to LLMs alignment characteristics allows benefiting from online RL optimization at low cost.

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VIEScore: Towards Explainable Metrics for Conditional Image Synthesis Evaluation
Max Ku | Dongfu Jiang | Cong Wei | Xiang Yue | Wenhu Chen

In the rapidly advancing field of conditional image generation research, challenges such as limited explainability lie in effectively evaluating the performance and capabilities of various models. This paper introduces VIEScore, a Visual Instruction-guided Explainable metric for evaluating any conditional image generation tasks. VIEScore leverages general knowledge from Multimodal Large Language Models (MLLMs) as the backbone and does not require training or fine-tuning. We evaluate VIEScore on seven prominent tasks in conditional image tasks and found: (1) VIEScore (GPT4-o) achieves a high Spearman correlation of 0.4 with human evaluations, while the human-to-human correlation is 0.45. (2) VIEScore (with open-source MLLM) is significantly weaker than GPT-4o and GPT-4v in evaluating synthetic images. (3) VIEScore achieves a correlation on par with human ratings in the generation tasks but struggles in editing tasks. With these results, we believe VIEScore shows its great potential to replace human judges in evaluating image synthesis tasks.

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Tree Transformer’s Disambiguation Ability of Prepositional Phrase Attachment and Garden Path Effects
Lingling Zhou | Suzan Verberne | Gijs Wijnholds

This work studies two types of ambiguity in natural language: prepositional phrase (PP) attachment ambiguity, and garden path constructions. Due to the different nature of these ambiguities – one being structural, the other incremental in nature – we pretrain and evaluate the Tree Transformer of Wang et al. (2019), an unsupervised Transformer model that induces tree representations internally. To assess PP attachment ambiguity we inspect the model’s induced parse trees against a newly prepared dataset derived from the PP attachment corpus (Ratnaparkhi et al., 1994). Measuring garden path effects is done by considering surprisal rates of the underlying language model on a number of dedicated test suites, following Futrell et al. (2019). For comparison we evaluate a pretrained supervised BiLSTM-based model trained on constituency parsing as sequence labelling (Gómez-Rodríguez and Vilares, 2018). Results show that the unsupervised Tree Transformer does exhibit garden path effects, but its parsing ability is far inferior to the supervised BiLSTM, and it is not as sensitive to lexical cues as other large LSTM models, suggesting that supervised parsers based on a pre-Transformer architecture may be the better choice in the presence of ambiguity.

Knowledge graphs (KGs) complement Large Language Models (LLMs) by providing reliable, structured, domain-specific, and up-to-date external knowledge. However, KGs and LLMs are often developed separately and must be integrated after training. We introduce Tree-of-Traversals, a novel zero-shot reasoning algorithm that enables augmentation of black-box LLMs with one or more KGs. The algorithm equips a LLM with actions for interfacing a KG and enables the LLM to perform tree search over possible thoughts and actions to find high confidence reasoning paths. Tree-of-Traversals significantly improves performance on question answering and KG question answering tasks. Code is available at https://github.com/amazon-science/tree-of-traversals

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Structured Tree Alignment for Evaluation of (Speech) Constituency Parsing
Freda Shi | Kevin Gimpel | Karen Livescu

We present the structured average intersection-over-union ratio (STRUCT-IOU), an evaluation metric that compares a constituency parse tree over automatically recognized spoken word boundaries with the ground-truth parse tree over written words. To compute the metric, we (1) project the ground-truth parse tree to the speech domain by forced alignment, (2) align the projected ground-truth constituents with the predicted ones under certain structured constraints, and (3) calculate the average IOU score across all aligned constituent pairs. STRUCT-IOU takes word boundaries into account and overcomes the challenge that the predicted words and ground truth may not have perfect one-to-one correspondence. Extending to the evaluation of text constituency parsing, we demonstrate that STRUCT-IOU shows higher tolerance to syntactically plausible parses than PARSEVAL (Black et al., 1991).

Recent studies have shown that Text-to-Image (T2I) model generations can reflect social stereotypes present in the real world. However, existing approaches for evaluating stereotypes have a noticeable lack of coverage of global identity groups and their associated stereotypes. To address this gap, we introduce the ViSAGe (Visual Stereotypes Around the Globe) dataset to enable the evaluation of known nationality-based stereotypes in T2I models, across 135 nationalities. We enrich an existing textual stereotype resource by distinguishing between stereotypical associations that are more likely to have visual depictions, such as ‘sombrero’, from those that are less visually concrete, such as ‘attractive’. We demonstrate ViSAGe’s utility through a multi-faceted evaluation of T2I generations. First, we show that stereotypical attributes in ViSAGe are thrice as likely to be present in generated images of corresponding identities as compared to other attributes, and that the offensiveness of these depictions is especially higher for identities from Africa, South America, and South East Asia. Second, we assess the ‘stereotypical pull’ of visual depictions of identity groups, which reveals how the ‘default’ representations of all identity groups in ViSAGe have a pull towards stereotypical depictions, and that this pull is even more prominent for identity groups from the Global South. CONTENT WARNING: Some examples contain offensive stereotypes.

This paper proposes PiNose, which trains a probing model on offline self-consistency checking results, thereby circumventing the need for human-annotated data and achieving transferability across diverse data distributions. As the consistency check process is offline, PiNose reduces the computational burden of generating multiple responses by online consistency verification. Additionally, it examines various aspects of internal states prior to response decoding, contributing to more effective detection of factual inaccuracies. Experiment results on both factuality detection and question answering benchmarks show that PiNose achieves surpassing results than existing factuality detection methods.

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What Do Language Models Learn in Context? The Structured Task Hypothesis.
Jiaoda Li | Yifan Hou | Mrinmaya Sachan | Ryan Cotterell

Large language models (LLMs) exhibit an intriguing ability to learn a novel task from in-context examples presented in a demonstration, termed in-context learning (ICL). Understandably, a swath of research has been dedicated to uncovering the theories underpinning ICL. One popular hypothesis explains ICL by task selection. LLMs identify the task based on the demonstration and generalize it to the prompt. Another popular hypothesis is that ICL is a form of meta-learning, i.e., the models learn a learning algorithm at pre-training time and apply it to the demonstration. Finally, a third hypothesis argues that LLMs use the demonstration to select a composition of tasks learned during pre-training to perform ICL. In this paper, we empirically explore these three hypotheses that explain LLMs’ ability to learn in context with a suite of experiments derived from common text classification tasks. We invalidate the first two hypotheses with counterexamples and provide evidence in support of the last hypothesis. Our results suggest an LLM could learn a novel task in context via composing tasks learned during pre-training.

Closed-source agents suffer from several issues such as a lack of affordability, transparency, and reproducibility, particularly on complex interactive tasks. This motivates the development of open-source alternatives. We introduce Lumos, one of the first frameworks for training open-source LLM-based agents. Lumos features a learnable, unified and modular architecture with a planning module that learns high-level subgoal generation, and a grounding module trained to translate these into the actions using various tools in the execution module. The design allows for modular upgrades and wider applicability to diverse interactive tasks. To foster generalizable agent learning, we collect large-scale, unified, and high-quality training annotations derived from diverse ground-truth reasoning rationales across various complex interactive tasks. On 9 datasets, Lumos exhibits several key advantages: (1) Lumos excels multiple larger open-source agents on the held-out datasets (unused for training) for each task type. Lumos even surpasses GPT agents on QA and web tasks; (2) Lumos outperforms open-source agents produced by chain-of-thoughts and unmodularized integrated training; and (3) Lumos effectively generalizes to unseen tasks, outperforming 33B-scale agents and domain-specific agents. Code and data will be released.

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Investigating Cultural Alignment of Large Language Models
Badr AlKhamissi | Muhammad ElNokrashy | Mai Alkhamissi | Mona Diab

The intricate relationship between language and culture has long been a subject of exploration within the realm of linguistic anthropology. Large Language Models (LLMs), promoted as repositories of collective human knowledge, raise a pivotal question: do these models genuinely encapsulate the diverse knowledge adopted by different cultures? Our study reveals that these models demonstrate greater cultural alignment along two dimensions—firstly, when prompted with the dominant language of a specific culture, and secondly, when pretrained with a refined mixture of languages employed by that culture. We quantify cultural alignment by simulating sociological surveys, comparing model responses to those of actual survey participants as references. Specifically, we replicate a survey conducted in various regions of Egypt and the United States through prompting LLMs with different pretraining data mixtures in both Arabic and English with the personas of the real respondents and the survey questions. Further analysis reveals that misalignment becomes more pronounced for underrepresented personas and for culturally sensitive topics, such as those probing social values. Finally, we introduce Anthropological Prompting, a novel method leveraging anthropological reasoning to enhance cultural alignment. Our study emphasizes the necessity for a more balanced multilingual pretraining dataset to better represent the diversity of human experience and the plurality of different cultures with many implications on the topic of cross-lingual transfer.

The boardgame Diplomacy is a challenging setting for communicative and cooperative artificial intelligence. The most prominent communicative Diplomacy AI, Cicero, has excellent strategic abilities, exceeding human players. However, the best Diplomacy players master communication, not just tactics, which is why the game has received attention as an AI challenge. This work seeks to understand the degree to which Cicero succeeds at communication. First, we annotate in-game communication with abstract meaning representation to separate in-game tactics from general language. Second, we run two dozen games with humans and Cicero, totaling over 200 human-player hours of competition. While AI can consistently outplay human players, AI-Human communication is still limited because of AI’s difficulty with deception and persuasion. This shows that Cicero relies on strategy and has not yet reached the full promise of communicative and cooperative AI.

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VoiceCraft: Zero-Shot Speech Editing and Text-to-Speech in the Wild
Puyuan Peng | Po-Yao Huang | Shang-Wen Li | Abdelrahman Mohamed | David Harwath

We introduce VoiceCraft, a token infilling neural codec language model, that achieves state-of-the-art performance on both speech editing and zero-shot text-to-speech (TTS) on audiobooks, internet videos, and podcasts. VoiceCraft employs a Transformer decoder architecture and introduces a token rearrangement procedure that combines causal masking and delayed stacking to enable generation within an existing sequence. On speech editing tasks, VoiceCraft produces edited speech that is nearly indistinguishable from unedited recordings in terms of naturalness, as evaluated by humans; for zero-shot TTS, our model outperforms prior SotA models including VALL-E and the popular commercial model XTTS v2. Crucially, the models are evaluated on challenging and realistic datasets, that consist of diverse accents, speaking styles, recording conditions, and background noise and music, and our model performs consistently well compared to other models and real recordings. In particular, for speech editing evaluation, we introduce a high quality, challenging, and realistic dataset named . We encourage readers to listen to the demos at https://jasonppy.github.io/VoiceCraft_web. Data, code, and model weights are available at https://github.com/jasonppy/VoiceCraft

Many commercial and open-source models claim to detect machine-generated text with extremely high accuracy (99% or more). However, very few of these detectors are evaluated on shared benchmark datasets and even when they are, the datasets used for evaluation are insufficiently challenging—lacking variations in sampling strategy, adversarial attacks, and open-source generative models. In this work we present RAID: the largest and most challenging benchmark dataset for machine-generated text detection. RAID includes over 6 million generations spanning 11 models, 8 domains, 11 adversarial attacks and 4 decoding strategies. Using RAID, we evaluate the out-of-domain and adversarial robustness of 8 open- and 4 closed-source detectors and find that current detectors are easily fooled by adversarial attacks, variations in sampling strategies, repetition penalties, and unseen generative models. We release our data along with a leaderboard to encourage future research.

A dog whistle is a form of coded communication that carries a secondary meaning to specific audiences and is often weaponized for racial and socioeconomic discrimination. Dog whistling historically originated from United States politics, but in recent years has taken root in social media as a means of evading hate speech detection systems and maintaining plausible deniability. In this paper, we present an approach for word-sense disambiguation of dog whistles from standard speech using Large Language Models (LLMs), and leverage this technique to create a dataset of 16,550 high-confidence coded examples of dog whistles used in formal and informal communication. Silent Signals is the largest dataset of disambiguated dog whistle usage, created for applications in hate speech detection, neology, and political science.

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On the Representational Capacity of Neural Language Models with Chain-of-Thought Reasoning
Franz Nowak | Anej Svete | Alexandra Butoi | Ryan Cotterell

The performance of modern language models (LMs) has been improved by chain-of-thought (CoT) reasoning, i.e., the process of generating intermediate results that guide the model towards a final answer. A possible explanation for this improvement is that CoT reasoning extends an LM’s computational power, as RNNs and transformers with additional scratch space are known to be Turing complete. Comparing LMs to Turing machines, however, introduces a category error—Turing machines decide language membership, whereas LMs define distributions over strings. To bridge this gap, we formalize CoT reasoning in a probabilistic setting. We present several results on the representational capacity of recurrent and transformer LMs with CoT reasoning, showing that they can represent the same family of distributions over strings as probabilistic Turing machines.

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Analyzing LLM Behavior in Dialogue Summarization: Unveiling Circumstantial Hallucination Trends
Sanjana Ramprasad | Elisa Ferracane | Zachary Lipton

Recent advancements in large language models (LLMs) have significantly advanced the capabilities of summarization systems.However, they continue to face a persistent challenge: hallucination. While prior work has extensively examined LLMs in news domains, evaluation of dialogue summarization has primarily focused on BART-based models, resulting in a notable gap in understanding LLM effectiveness.Our work seeks to address this gap by benchmarking LLMs for dialogue summarization faithfulness using human annotations,focusing on identifying and categorizing span-level inconsistencies.Specifically, we evaluate two prominent LLMs: GPT-4 and Alpaca-13B.Our evaluation reveals that LLMs often generate plausible, but not fully supported inferences based on conversation contextual cues, a trait absent in older models. As a result, we propose a refined taxonomy of errors, introducing a novel category termed “Contextual Inference” to address this aspect of LLM behavior. Using our taxonomy, we compare the behavioral differences between LLMs and older fine-tuned models. Additionally, we systematically assess the efficacy of automatic error detection methods on LLM summaries and find that they struggle to detect these nuanced errors effectively. To address this, we introduce two prompt-based approaches for fine-grained error detection. Our methods outperform existing metrics, particularly in identifying the novel “Contextual Inference” error type.

Large language models (LLMs) are central to modern natural language processing, delivering exceptional performance in various tasks. However, their substantial computational and memory requirements present challenges, especially for devices with limited DRAM capacity. This paper tackles the challenge of efficiently running LLMs that exceed the available DRAM capacity by storing the model parameters in flash memory, but bringing them on demand to DRAM. Our method involves constructing an inference cost model that takes into account the characteristics of flash memory, guiding us to optimize in two critical areas: reducing the volume of data transferred from flash and reading data in larger, more contiguous chunks. Within this hardware-informed framework, we introduce two principal techniques. First, “windowing” strategically reduces data transfer by reusing previously activated neurons, and second, “row-column bundling”, tailored to the sequential data access strengths of flash memory, increases the size of data chunks read from flash memory. These methods collectively enable running models up to twice the size of the available DRAM, with a 4-5x and 20-25x increase in inference speed compared to naive loading approaches in CPU and GPU, respectively. Our integration of sparsity awareness, context-adaptive loading, and a hardware-oriented design paves the way for effective inference of LLMs on devices with limited memory.

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Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models
Muhammad Maaz | Hanoona Rasheed | Salman Khan | Fahad Khan

Conversation agents fueled by Large Language Models (LLMs) are providing a new way to interact with visual data. While there have been initial attempts for image-based conversation models, this work addresses the under-explored field of video-based conversation by introducing Video-ChatGPT. It is a multimodal model that merges a video-adapted visual encoder with an LLM. The resulting model is capable of understanding and generating detailed conversations about videos. We introduce a new dataset of 100,000 video-instruction pairs used to train Video-ChatGPT acquired via manual and semi-automated pipeline that is easily scalable and robust to label noise. We also develop a quantitative evaluation framework for video-based dialogue models to objectively analyze the strengths and weaknesses of video-based dialogue models. Code: https://github.com/mbzuai-oryx/Video-ChatGPT.

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To Distill or Not to Distill? On the Robustness of Robust Knowledge Distillation
Abdul Waheed | Karima Kadaoui | Muhammad Abdul-Mageed

Arabic is known to present unique challengesfor Automatic Speech Recognition (ASR). Onone hand, its rich linguistic diversity andwide range of dialects complicate the de-velopment of robust, inclusive models. Onthe other, current multilingual ASR modelsare compute-intensive and lack proper com-prehensive evaluations. In light of thesechallenges, we distill knowledge from largeteacher models into smaller student variantsthat more efficient. We also introduce a novelhuman-annotated dataset covering five under-represented Arabic dialects for evaluation. Wefurther evaluate both our models and existingSoTA multilingual models on both standardavailable benchmarks and our new dialectaldata. Our best-distilled model’s overall perfor-mance (45.0% WER) surpasses that of a SoTAmodel twice its size (SeamlessM4T-large-v2,WER=47.0%) and its teacher model (Whisper-large-v2, WER=55.1%), and its average perfor-mance on our new dialectal data (56.9% WER)outperforms all other models. To gain more in-sight into the poor performance of these modelson dialectal data, we conduct an error analysisand report the main types of errors the differentmodels tend to make. The GitHub repositoryfor the project is available at https://github.com/UBC-NLP/distill-whisper-ar.

We present LayerSkip, an end-to-end solution to speed-up inference of large language models (LLMs). First, during training we apply layer dropout, with low dropout rates for earlier layers and higher dropout rates for later layers, and an early exit loss where all transformer layers share the same exit. Second, during inference, we show that this training recipe increases the accuracy of early exit at earlier layers, without adding any auxiliary layers or modules to the model. Third, we present a novel self-speculative decoding solution where we exit at early layers and verify and correct with remaining layers of the model. Our proposed self-speculative decoding approach has less memory footprint than other speculative decoding approaches and benefits from shared compute and activations of the draft and verification stages. We run experiments on different Llama model sizes on different types of training: pretraining from scratch, continual pretraining, finetuning on specific data domain, and finetuning on specific task. We implement our inference solution and show speedups of up to 2.16x on summarization for CNN/DM documents, 1.82x on coding, and 2.0x on TOPv2 semantic parsing task. We open source our code at https://github.com/facebookresearch/LayerSkip.

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Classist Tools: Social Class Correlates with Performance in NLP
Amanda Curry | Giuseppe Attanasio | Zeerak Talat | Dirk Hovy

The field of sociolinguistics has studied factors affecting language use for the last century. Labov (1964) and Bernstein (1960) showed that socioeconomic class strongly influences our accents, syntax and lexicon. However, despite growing concerns surrounding fairness and bias in Natural Language Processing (NLP), there is a dearth of studies delving into the effects it may have on NLP systems. We show empirically that NLP systems’ performance is affected by speakers’ SES, potentially disadvantaging less-privileged socioeconomic groups. We annotate a corpus of 95K utterances from movies with social class, ethnicity and geographical language variety and measure the performance of NLP systems on three tasks: language modelling, automatic speech recognition, and grammar error correction. We find significant performance disparities that can be attributed to socioeconomic status as well as ethnicity and geographical differences. With NLP technologies becoming ever more ubiquitous and quotidian, they must accommodate all language varieties to avoid disadvantaging already marginalised groups. We argue for the inclusion of socioeconomic class in future language technologies.

Extraction of experimental procedures from human language in scientific literature and patents into actionable sequences in robotics language holds immense significance in scientific domains. Such an action extraction task is particularly challenging given the intricate details and context-dependent nature of the instructions, especially in fields like chemistry where reproducibility is paramount. In this paper, we introduce ActionIE, a method that leverages Large Language Models (LLMs) to bridge this divide by converting actions written in natural language into executable Python code. This enables us to capture the entities of interest, and the relationship between each action, given the features of Programming Languages. Utilizing linguistic cues identified by frequent patterns, ActionIE provides an improved mechanism to discern entities of interest. While our method is broadly applicable, we exemplify its power in the domain of chemical literature, wherein we focus on extracting experimental procedures for chemical synthesis. The code generated by our method can be easily transformed into robotics language which is in high demand in scientific fields. Comprehensive experiments demonstrate the superiority of our method. In addition, we propose a graph-based metric to more accurately reflect the precision of extraction. We also develop a dataset to address the scarcity of scientific literature occurred in existing datasets.

Violence-provoking speech – speech that implicitly or explicitly promotes violence against the members of the targeted community, contributed to a massive surge in anti-Asian crimes during the COVID-19 pandemic. While previous works have characterized and built tools for detecting other forms of harmful speech, like fear speech and hate speech, our work takes a community-centric approach to studying anti-Asian violence-provoking speech. Using data from ~420k Twitter posts spanning a 3-year duration (January 1, 2020 to February 1, 2023), we develop a codebook to characterize anti-Asian violence-provoking speech and collect a community-crowdsourced dataset to facilitate its large-scale detection using state-of-the-art classifiers. We contrast the capabilities of natural language processing classifiers, ranging from BERT-based to LLM-based classifiers, in detecting violence-provoking speech with their capabilities to detect anti-Asian hateful speech. In contrast to prior work that has demonstrated the effectiveness of such classifiers in detecting hateful speech (F₁ = 0.89), our work shows that accurate and reliable detection of violence-provoking speech is a challenging task (F₁ = 0.69). We discuss the implications of our findings, particularly the need for proactive interventions to support Asian communities during public health crises.

The growing popularity of Large Language Models has sparked interest in context compression for Large Language Models (LLMs). However, the performance of previous methods degrades dramatically as compression ratios increase, sometimes even falling to the closed-book level. This decline can be attributed to the loss of key information during the compression process. Our preliminary study supports this hypothesis, emphasizing the significance of retaining key information to maintain model performance under high compression ratios. As a result, we introduce Query-Guided Compressor (QGC), which leverages queries to guide the context compression process, effectively preserving key information within the compressed context. Additionally, we employ a dynamic compression strategy. We validate the effectiveness of our proposed QGC on the Question Answering task, including NaturalQuestions, TriviaQA, and HotpotQA datasets. Experimental results show that QGC can consistently perform well even at high compression ratios, which also offers significant benefits in terms of inference cost and throughput.

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COSMIC: Mutual Information for Task-Agnostic Summarization Evaluation
Maxime Darrin | Philippe Formont | Jackie Cheung | Pablo Piantanida

Assessing the quality of summarizers poses significant challenges—gold summaries are hard to obtain and their suitability depends on the use context of the summarization system. Who is the user of the system, and what do they intend to do with the summary? In response, we propose a novel task-oriented evaluation approach that assesses summarizers based on their capacity to produce summaries while preserving task outcomes. We theoretically establish both a lower and upper bound on the expected error rate of these tasks, which depends on the mutual information between source texts and generated summaries. We introduce COSMIC, a practical implementation of this metric, and demonstrate its strong correlation with human judgment-based metrics, as well as its effectiveness in predicting downstream task performance. Comparative analyses against established metrics like BERTScore and ROUGE highlight the competitive performance of COSMIC.

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EUROPA: A Legal Multilingual Keyphrase Generation Dataset
Olivier Salaün | Frédéric Piedboeuf | Guillaume Le Berre | David Alfonso-Hermelo | Philippe Langlais

Keyphrase generation has primarily been explored within the context of academic research articles, with a particular focus on scientific domains and the English language. In this work, we present EUROPA, a novel dataset for multilingual keyphrase generation in the legal domain. It is derived from legal judgments from the Court of Justice of the European Union (EU), and contains instances in all 24 EU official languages. We run multilingual models on our corpus and analyze the results, showing room for improvement on a domain-specific multilingual corpus such as the one we present.

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GLIMPSE: Pragmatically Informative Multi-Document Summarization for Scholarly Reviews
Maxime Darrin | Ines Arous | Pablo Piantanida | Jackie Cheung

Scientific peer review is essential for the quality of academic publications. However, the increasing number of paper submissions to conferences has strained the reviewing process. This surge poses a burden on area chairs who have to carefully read an ever-growing volume of reviews and discern each reviewer’s main arguments as part of their decision process. In this paper, we introduce , a summarization method designed to offer a concise yet comprehensive overview of scholarly reviews. Unlike traditional consensus-based methods, extracts both common and unique opinions from the reviews. We introduce novel uniqueness scores based on the Rational Speech Act framework to identify relevant sentences in the reviews. Our method aims to provide a pragmatic glimpse into all reviews, offering a balanced perspective on their opinions. Our experimental results with both automatic metrics and human evaluation show that generates more discriminative summaries than baseline methods in terms of human evaluation while achieving comparable performance with these methods in terms of automatic metrics.

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Peacock: A Family of Arabic Multimodal Large Language Models and Benchmarks
Fakhraddin Alwajih | El Moatez Billah Nagoudi | Gagan Bhatia | Abdelrahman Mohamed | Muhammad Abdul-Mageed

Multimodal large language models (MLLMs) have proven effective in a wide range of tasks that require complex reasoning and linguistic comprehension. However, due to a lack of high-quality multimodal resources in languages other than English, the success of MLLMs remains relatively limited to English-based settings. This poses significant challenges in developing comparable models for other languages, even those with large speaker populations, such as Arabic. To alleviate this challenge, we introduce a comprehensive family of Arabic MLLMs, dubbed *Peacock*, with strong vision and language capabilities. Through comprehensive qualitative and quantitative analysis, we demonstrate the solid performance of our models on various visual reasoning tasks and further show their emerging dialectal potential. Additionally, we introduce *Henna*, a new benchmark specifically designed for assessing MLLMs on aspects related to Arabic culture, setting the first stone for culturally-aware Arabic MLLMs. The GitHub repository for the *Peacock* project is available at [https://github.com/UBC-NLP/peacock](https://github.com/UBC-NLP/peacock).

Multistep instructions, such as recipes and how-to guides, greatly benefit from visual aids, such as a series of images that accompany the instruction steps. While Large Language Models (LLMs) have become adept at generating coherent textual steps, Large Vision/Language Models (LVLMs) are less capable of generating accompanying image sequences. The most challenging aspect is that each generated image needs to adhere to the relevant textual step instruction, as well as be visually consistent with earlier images in the sequence. To address this problem, we propose an approach for generating consistent image sequences, which integrates a Latent Diffusion Model (LDM) with an LLM to transform the sequence into a caption to maintain the semantic coherence of the sequence. In addition, to maintain the visual coherence of the image sequence, we introduce a copy mechanism to initialise reverse diffusion processes with a latent vector iteration from a previously generated image from a relevant step. Both strategies will condition the reverse diffusion process on the sequence of instruction steps and tie the contents of the current image to previous instruction steps and corresponding images. Experiments show that the proposed approach is preferred by humans in 46.6% of the cases against 26.6% for the second best method. In addition, automatic metrics showed that the proposed method maintains semantic coherence and visual consistency across steps in both domains.

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Cheetah: Natural Language Generation for 517 African Languages
Ife Adebara | AbdelRahim Elmadany | Muhammad Abdul-Mageed

Low-resource African languages pose unique challenges for natural language processing (NLP) tasks, including natural language generation (NLG). In this paper, we develop Cheetah, a massively multilingual NLG language model for African languages. Cheetah supports 517 African languages and language varieties, allowing us to address the scarcity of NLG resources and provide a solution to foster linguistic diversity. We demonstrate the effectiveness of Cheetah through comprehensive evaluations across six generation downstream tasks. In five of the six tasks, Cheetah significantly outperforms other models, showcasing its remarkable performance for generating coherent and contextually appropriate text in a wide range of African languages. We additionally conduct a detailed human evaluation to delve deeper into the linguistic capabilities of Cheetah. The findings of this study contribute to advancing NLP research in low-resource settings, enabling greater accessibility and inclusion for African languages in a rapidly expanding digital landscape. We will publicly release our models for research.

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TaPERA: Enhancing Faithfulness and Interpretability in Long-Form Table QA by Content Planning and Execution-based Reasoning
Yilun Zhao | Lyuhao Chen | Arman Cohan | Chen Zhao

Long-form Table Question Answering (LFTQA) requires systems to generate paragraph long and complex answers to questions over tabular data. While Large language models based systems have made significant progress, it often hallucinates, especially when the task involves complex reasoning over tables. To tackle this issue, we propose a new LLM-based framework, TaPERA, for LFTQA tasks. Our framework uses a modular approach that decomposes the whole process into three sub-modules: 1) QA-based Content Planner that iteratively decomposes the input question into sub-questions; 2) Execution-based Table Reasoner that produces executable Python program for each sub-question; and 3) Answer Generator that generates long-form answer grounded on the program output. Human evaluation results on the FeTaQA and QTSumm datasets indicate that our framework significantly improves strong baselines on both accuracy and truthfulness, as our modular framework is better at table reasoning, and the long-form answer is always consistent with the program output. Our modular design further provides transparency as users are able to interact with our framework by manually changing the content plans.

We introduce FinanceMath, a novel benchmark designed to evaluate LLMs' capabilities in solving knowledge-intensive math reasoning problems. Compared to prior works, this study features three core advancements. First, FinanceMath includes 1,200 problems with a hybrid of textual and tabular content. These problems require college-level knowledge in the finance domain for effective resolution. Second, we provide expert-annotated, detailed solution references in Python program format, ensuring a high-quality benchmark for LLM assessment. We also construct a finance-domain knowledge bank and investigate various knowledge integration strategies. Finally, we evaluate a wide spectrum of 44 LLMs with both Chain-of-Thought and Program-of-Thought prompting methods. Our experimental results reveal that the current best-performing system (i.e., GPT-4o) achieves only 60.9% accuracy using CoT prompting, leaving substantial room for improvement. Moreover, while augmenting LLMs with external knowledge can improve model performance (e.g., from 47.5% to 54.5% for Gemini-1.5-Pro), their accuracy remains significantly lower than the estimated human expert performance of 92%. We believe that FinanceMath can advance future research in the area of domain-specific knowledge retrieval and integration, particularly within the context of solving reasoning-intensive tasks.

There is a growing need for Large Language Models (LLMs) to effectively use tools and external Application Programming Interfaces (APIs) to plan and complete tasks. As such, there is tremendous interest in methods that can acquire sufficient quantities of train and test data that involve calls to tools / APIs. Two lines of research have emerged as the predominant strategies for addressing this challenge. The first has focused on synthetic data generation techniques, while the second has involved curating task-adjacent datasets which can be transformed into API / Tool-based tasks. In this paper, we focus on the task of identifying, curating, and transforming existing datasets and, in turn, introduce API-BLEND, a large corpora for training and systematic testing of tool-augmented LLMs. The datasets mimic real-world scenarios involving API-tasks such as API / tool detection, slot filling, and sequencing of the detected APIs. We demonstrate the utility of the API-BLEND dataset for both training and benchmarking purposes.

LoRA employs lightweight modules to customize large language models (LLMs) for each downstream task or domain, where different learned additional modules represent diverse skills. Combining existing LoRAs to address new tasks can enhance the reusability of learned LoRAs, particularly beneficial for tasks with limited annotated data. Most prior works on LoRA combination primarily rely on task-level weights for each involved LoRA, making different examples and tokens share the same LoRA weights. However, in generative tasks, different tokens may necessitate diverse skills to manage. Taking the Chinese math task as an example, understanding the problem description may depend more on the Chinese LoRA, while the calculation part may rely more on the math LoRA. To this end, we propose LoRA-Flow, which utilizes dynamic weights to adjust the impact of different LoRAs. The weights at each step are determined by a fusion gate with extremely few parameters, which can be learned with only 200 training examples. Experiments across six generative tasks demonstrate that our method consistently outperforms baselines with task-level fusion weights. This underscores the necessity of introducing dynamic fusion weights for LoRA combination.

In this paper, we introduce a novel dynamic expert selection framework for Mixture of Experts (MoE) models, aiming to enhance computational efficiency and model performance by adjusting the number of activated experts based on input difficulty. Unlike existing MoE approaches that rely on fixed TopK Routing, which activates a predetermined number of experts regardless of the input’s complexity, our method dynamically allocates experts based on the confidence level in expert selection for each input. This allows for more efficient utilization of computational resources, activating more experts for complex tasks requiring advanced reasoning and fewer for simpler tasks. Through extensive evaluations, our dynamic routing method demonstrates substantial improvements over Top2 Routing across various benchmarks, achieving an average improvement of 0.7% with less than 90% activated parameters. Further analysis shows our model dispatches more experts to tasks requiring complex reasoning skills, like BBH, confirming its ability to dynamically allocate computational resources in alignment with the input’s complexity.Our findings also highlight a variation in the number of experts needed across different layers of the transformer model, offering insights into the potential for designing heterogeneous MoE frameworks. The code and models are available at https://github.com/ZhenweiAn/Dynamic_MoE.

Speech recognition and translation systems perform poorly on noisy inputs, which are frequent in realistic environments. Augmenting these systems with visual signals has the potential to improve robustness to noise. However, audio-visual (AV) data is only available in limited amounts and for fewer languages than audio-only resources.To address this gap, we present XLAVS-R, a cross-lingual audio-visual speech representation model for noise-robust speech recognition and translation in over 100 languages. It is designed to maximize the benefits of limited multilingual AV pre-training data, by building on top of audio-only multilingual pre-training and simplifying existing pre-training schemes. Extensive evaluation on the MuAViC benchmark shows the strength of XLAVS-R on downstream audio-visual speech recognition and translation tasks, where it outperforms the previous state of the art by up to 18.5% WER and 4.7 BLEU given noisy AV inputs, and enables strong zero-shot audio-visual ability with audio-only fine-tuning.

Humans learn social skills through both imitation and social interaction. This social learning process is largely understudied by existing research on building language agents. Motivated by this gap, we propose an interactive learning method, SOTOPIA-π, that improves the social intelligence of language agents. This method leverages behavior cloning and self-reinforcement based training on filtered social interaction data according to large language model (LLM) rating. We show that our training method allows a 7B LLM to reach the social goal completion ability of an expert model (GPT-4-based agent) without the loss of more generic abilities, such as the ability to answer knowledge-based questions. We also demonstrate that this training paradigm uncovers some weaknesses in standard evaluation and safety training paradigms that (1) LLM-based evaluation of social intelligence overestimates the abilities of the language agents trained specifically for social interaction, and that (2) despite not training for better safety or question answering (QA) ability, our methods improve the safety of language agents and maintain general QA ability on the MMLU benchmark.

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XFT: Unlocking the Power of Code Instruction Tuning by Simply Merging Upcycled Mixture-of-Experts
Yifeng Ding | Jiawei Liu | Yuxiang Wei | Lingming Zhang

We introduce XFT, a simple yet powerful training scheme, by simply merging upcycled Mixture-of-Experts (MoE) to unleash the performance limit of instruction-tuned code Large Language Models (LLMs). While vanilla sparse upcycling fails to improve instruction tuning, XFT introduces a shared expert mechanism with a novel routing weight normalization strategy into sparse upcycling, which significantly boosts instruction tuning. After fine-tuning the upcycled MoE model, XFT introduces a learnable model merging mechanism to compile the upcycled MoE model back to a dense model, achieving upcycled MoE-level performance with only dense-model compute. By applying XFT to a 1.3B model, we create a new state-of-the-art tiny code LLM with 67.1 and 64.6 pass@1 on HumanEval and HumanEval+ respectively. With the same data and model architecture, XFT improves supervised fine-tuning (SFT) by 13% on HumanEval+, along with consistent improvements from 2% to 13% on MBPP+, MultiPL-E, and DS-1000, demonstrating its generalizability. XFT is fully orthogonal to existing techniques such as Evol-Instruct and OSS-Instruct, opening a new dimension for improving code instruction tuning. Codes are available at https://github.com/ise-uiuc/xft.

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Generalizability of Mixture of Domain-Specific Adapters from the Lens of Signed Weight Directions and its Application to Effective Model Pruning
Tuc Nguyen | Thai Le

Several parameter-efficient fine-tuning methods based on adapters have been proposed as a streamlined approach to incorporate not only a single specialized knowledge into existing Pre-Trained Language Models (PLMs) but also multiple of them at once. Recent works such as AdapterSoup propose to mix not all but only a selective sub-set of domain-specific adapters during inference via model weight averaging to optimize performance on novel, unseen domains with excellent computational efficiency. However, the essential generalizability of this emerging weight-space adapter mixing mechanism on unseen, in-domain examples remains unexplored. Thus, in this study, we conduct a comprehensive analysis to elucidate the generalizability of domain-specific adapter mixtures in in-domain evaluation. We also provide investigations into the inner workings of the mixture of domain-specific adapters by analyzing their weight signs, yielding critical analysis on the negative correlation between their fraction of weight sign difference and their mixtures’ generalizability. The code is available at Github.

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Learning to Decode Collaboratively with Multiple Language Models
Zejiang Shen | Hunter Lang | Bailin Wang | Yoon Kim | David Sontag

We propose a method to teach multiple large language models (LLM) to collaborate by interleaving their generations at the token level. We model the decision of which LLM generates the next token as a latent variable. By optimizing the marginal likelihood of a training set under our latent variable model, the base LLM automatically learns when to generate itself and when to call on one of the “assistant” language models to generate, all without direct supervision. Token-level collaboration during decoding allows for a fusion of each model’s expertise in a manner tailored to the specific task at hand. Our collaborative decoding is especially useful in cross-domain settings where a generalist base LLM learns to invoke domain expert models. On instruction-following, domain-specific QA, and reasoning tasks, we show that the performance of the joint system exceeds that of the individual models. Through qualitative analysis, we show models trained with our method exhibit several interesting collaboration patterns, e.g., template-filling, by visualizing the learned latent decisions.

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DRAGIN: Dynamic Retrieval Augmented Generation based on the Real-time Information Needs of Large Language Models
Weihang Su | Yichen Tang | Qingyao Ai | Zhijing Wu | Yiqun Liu

Dynamic retrieval augmented generation (RAG) paradigm actively decides when and what to retrieve during the text generation process of Large Language Models (LLMs).There are two key elements of this paradigm: identifying the optimal moment to activate the retrieval module (deciding when to retrieve) and crafting the appropriate query once retrieval is triggered (determining what to retrieve).However, current dynamic RAG methods fall short in both aspects. Firstly, the strategies for deciding when to retrieve often rely on static rules. Moreover, the strategies for deciding what to retrieve typically limit themselves to the LLM’s most recent sentence or the last few tokens, while the LLM’s information needs may span across the entire context.To overcome these limitations, we introduce a new framework, DRAGIN, i.e., Dynamic Retrieval Augmented Generation based on the Information Needs of LLMs. Our framework is specifically designed to make decisions on when and what to retrieve based on the LLM’s information needs during the text generation process.We evaluate DRAGIN along with existing methods comprehensively over 4 knowledge-intensive generation datasets. Experimental results show that DRAGIN achieves superior performance on all tasks, demonstrating the effectiveness of our method.

Temporal reasoning is fundamental for large language models (LLMs) to comprehend the world. Current temporal reasoning datasets are limited to questions about single or isolated events, falling short in mirroring the realistic temporal characteristics involving concurrent nature and intricate temporal interconnections. In this paper, we introduce CoTempQA, a comprehensive co-temporal Question Answering (QA) benchmark containing four co-temporal scenarios (Equal, Overlap, During, Mix) with 4,748 samples for evaluating the co-temporal comprehension and reasoning abilities of LLMs. Our extensive experiments reveal a significant gap between the performance of current LLMs and human-level reasoning on CoTempQA tasks. Even when enhanced with Chain of Thought (CoT) methodologies, models consistently struggle with our task. In our preliminary exploration, we discovered that mathematical reasoning plays a significant role in handling co-temporal events and proposed a strategy to boost LLMs’ co-temporal reasoning from a mathematical perspective. We hope that our CoTempQA datasets will encourage further advancements in improving the co-temporal reasoning capabilities of LLMs.

Since the natural language processing (NLP) community started to make large language models (LLMs) act as a critic to evaluate the quality of generated texts, most of the existing works train a critique generation model on the evaluation data labeled by GPT-4’s direct prompting. We observe that these models lack the ability to generate informative critiques in both pointwise grading and pairwise comparison especially without references. As a result, their generated critiques cannot provide fine-grained distinguishability on generated texts, causing unsatisfactory evaluation performance. In this paper, we propose a simple yet effective method called Eval-Instruct, which can first acquire pointwise grading critiques with pseudo references and then revise these critiques via multi-path prompting to obtain informative evaluation data in different tasks and settings, including pointwise grading and pairwise comparison with / without references. After fine-tuning on these data, the resulting model CritiqueLLM is empirically shown to outperform ChatGPT and all the open-source baselines and even achieve comparable evaluation performance to GPT-4 in system-level correlations of pointwise grading. We also demonstrate that our generated critiques can act as scalable feedback to further improve the generation quality of strong LLMs like ChatGPT.

Recent advancements in large language models (LLMs) have revealed their potential for achieving autonomous agents possessing human-level intelligence. However, existing benchmarks for evaluating LLM Agents either use static datasets, potentially leading to data leakage or focus only on single-agent scenarios, overlooking the complexities of multi-agent interactions. There is a lack of a benchmark that evaluates the diverse capabilities of LLM agents in multi-agent, dynamic environments. To this end, we introduce LLMArena, a novel and easily extensible framework for evaluating the diverse capabilities of LLM in multi-agent dynamic environments. LLMArena encompasses seven distinct gaming environments, employing Trueskill scoring to assess crucial abilities in LLM agents, including spatial reasoning, strategic planning, numerical reasoning, risk assessment, communication, opponent modeling, and team collaboration. We conduct an extensive experiment and human evaluation among different sizes and types of LLMs, showing that LLMs still have a significant journey ahead in their development towards becoming fully autonomous agents, especially in opponent modeling and team collaboration. We hope LLMArena could guide future research towards enhancing these capabilities in LLMs, ultimately leading to more sophisticated and practical applications in dynamic, multi-agent settings.

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Small But Funny: A Feedback-Driven Approach to Humor Distillation
Sahithya Ravi | Patrick Huber | Akshat Shrivastava | Vered Shwartz | Arash Einolghozati

The emergence of Large Language Models (LLMs) has brought to light promising language generation capabilities, particularly in performing tasks like complex reasoning and creative writing. Consequently, distillation through imitation of teacher responses has emerged as a popular technique to transfer knowledge from LLMs to more accessible, Small Language Models (SLMs). While this works well for simpler tasks, there is a substantial performance gap on tasks requiring intricate language comprehension and creativity, such as humor generation. We hypothesize that this gap may stem from the fact that creative tasks might be hard to learn by imitation alone and explore whether an approach, involving supplementary guidance from the teacher, could yield higher performance. To address this, we study the effect of assigning a dual role to the LLM - as a “teacher” generating data, as well as a “critic” evaluating the student’s performance. Our experiments on humor generation reveal that the incorporation of feedback significantly narrows the performance gap between SLMs and their larger counterparts compared to merely relying on imitation. As a result, our research highlights the potential of using feedback as an additional dimension to data when transferring complex language abilities via distillation.

Although Large Language Models (LLMs) demonstrate remarkable ability in processing and generating human-like text, they do have limitations when it comes to comprehending and expressing world knowledge that extends beyond the boundaries of natural language(e.g., chemical molecular formula). Injecting a collection of symbolic data directly into the training of LLMs can be problematic, as it disregards the synergies among different symbolic families and overlooks the need for a balanced mixture of natural and symbolic data. In this work, we tackle these challenges from both a data and framework perspective and introduce Symbol-LLM series models. First, we curated a data collection consisting of 34 tasks and incorporating 20 distinct symbolic families, intending to capture the interrelations and foster synergies between symbols. Then, a two-stage tuning framework succeeds in injecting symbolic knowledge without loss of the generality ability. Extensive experiments on both symbol- and NL-centric tasks demonstrate the balanced and superior performances of Symbol-LLM series models.

The advancement of Artificial Intelligence is pivotal in reshaping healthcare, enhancing diagnostic precision, and facilitating personalized treatment strategies. One major challenge for healthcare professionals is quickly navigating through long clinical documents to provide timely and effective solutions. Doctors often struggle to draw quick conclusions from these extensive documents. To address this issue and save time for healthcare professionals, an effective summarization model is essential. Most current models assume the data is only text-based. However, patients often include images of their medical conditions in clinical documents. To effectively summarize these multimodal documents, we introduce EDI-Summ, an innovative Image-Guided Encoder-Decoder Model. This model uses modality-aware contextual attention on the encoder and an image cross-attention mechanism on the decoder, enhancing the BART base model to create detailed visual-guided summaries. We have tested our model extensively on three multimodal clinical benchmarks involving multimodal question and dialogue summarization tasks. Our analysis demonstrates that EDI-Summ outperforms state-of-the-art large language and vision-aware models in these summarization tasks. Disclaimer: The work includes vivid medical illustrations, depicting the essential aspects of the subject matter.

Current clustering-based open relation extraction (OpenRE) methods usually apply clustering algorithms on top of pre-trained language models. However, this practice has three drawbacks. First, embeddings from language models are high-dimensional and anisotropic, so using simple metrics to calculate distances between these embeddings may not accurately reflect the relational similarity. Second, there exists a gap between the pre-trained language models and downstream clustering for their different objective forms. Third, clustering with embeddings deviates from the primary aim of relation extraction, as it does not directly obtain relations. In this work, we propose a new idea for OpenRE in the era of LLMs, that is, extracting relational phrases and directly exploiting the knowledge in LLMs to assess the semantic similarity between phrases without relying on any additional metrics. Based on this idea, we developed a framework, oreLLM, that makes two LLMs work collaboratively to achieve clustering and address the above issues. Experimental results on different datasets show that oreLLM outperforms current baselines by 1.4%∼ 3.13% in terms of clustering accuracy.

The latest generative large language models (LLMs) have found their application in data augmentation tasks, where small numbers of text samples are LLM-paraphrased and then used to fine-tune downstream models. However, more research is needed to assess how different prompts, seed data selection strategies, filtering methods, or model settings affect the quality of paraphrased data (and downstream models). In this study, we investigate three text diversity incentive methods well established in crowdsourcing: taboo words, hints by previous outlier solutions, and chaining on previous outlier solutions. Using these incentive methods as part of instructions to LLMs augmenting text datasets, we measure their effects on generated texts’ lexical diversity and downstream model performance. We compare the effects over 5 different LLMs, 6 datasets and 2 downstream models. We show that diversity is most increased by taboo words, but downstream model performance is highest with hints.

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Beyond Orthography: Automatic Recovery of Short Vowels and Dialectal Sounds in Arabic
Yassine El Kheir | Hamdy Mubarak | Ahmed Ali | Shammur Chowdhury

This paper presents a novel Dialectal Sound and Vowelization Recovery framework, designed to recognize borrowed and dialectal sounds within phonologically diverse and dialect-rich languages, that extends beyond its standard orthographic sound sets. The proposed framework utilized quantized sequence of input with(out) continuous pretrained self-supervised representation. We show the efficacy of the pipeline using limited data for Arabic, a dialect-rich language containing more than 22 major dialects. Phonetically correct transcribed speech resources for dialectal Arabic is scare. Therefore, we introduce ArabVoice15, a first of its kind, curated test set featuring 5 hours of dialectal speech across 15 Arab countries, with phonetically accurate transcriptions, including borrowed and dialect-specific sounds. We described in detail the annotation guideline along with the analysis of the dialectal confusion pairs. Our extensive evaluation includes both subjective – human perception tests and objective measures. Our empirical results, reported with three test sets, show that with only one and half hours of training data, our model improve character error rate by ≈7% in ArabVoice15 compared to the baseline.

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Document-Level Machine Translation with Large-Scale Public Parallel Corpora
Proyag Pal | Alexandra Birch | Kenneth Heafield

Despite the fact that document-level machine translation has inherent advantages over sentence-level machine translation due to additional information available to a model from document context, most translation systems continue to operate at a sentence level. This is primarily due to the severe lack of publicly available large-scale parallel corpora at the document level. We release a large-scale open parallel corpus with document context extracted from ParaCrawl in five language pairs, along with code to compile document-level datasets for any language pair supported by ParaCrawl. We train context-aware models on these datasets and find improvements in terms of overall translation quality and targeted document-level phenomena. We also analyse how much long-range information is useful to model some of these discourse phenomena and find models are able to utilise context from several preceding sentences.

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Bridging the Empirical-Theoretical Gap in Neural Network Formal Language Learning Using Minimum Description Length
Nur Lan | Emmanuel Chemla | Roni Katzir

Neural networks offer good approximation to many tasks but consistently fail to reach perfect generalization, even when theoretical work shows that such perfect solutions can be expressed by certain architectures. Using the task of formal language learning, we focus on one simple formal language and show that the theoretically correct solution is in fact not an optimum of commonly used objectives — even with regularization techniques that according to common wisdom should lead to simple weights and good generalization (L1, L2) or other meta-heuristics (early-stopping, dropout). On the other hand, replacing standard targets with the Minimum Description Length objective (MDL) results in the correct solution being an optimum.

To answer a question, language models often need to integrate prior knowledge learned during pretraining and new information presented in context. We hypothesize that models perform this integration in a predictable way across different questions and contexts: models will rely more on prior knowledge for questions about entities (e.g., persons, places, etc.) that they are more familiar with due to higher exposure in the training corpus, and be more easily persuaded by some contexts than others. To formalize this problem, we propose two mutual information-based metrics to measure a model’s dependency on a context and on its prior about an entity: first, the persuasion score of a given context represents how much a model depends on the context in its decision, and second, the susceptibility score of a given entity represents how much the model can be swayed away from its original answer distribution about an entity. We empirically test our metrics for their validity and reliability. Finally, we explore and find a relationship between the scores and the model’s expected familiarity with an entity, and provide two use cases to illustrate their benefits.

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Word Matters: What Influences Domain Adaptation in Summarization?
Yinghao Li | Siyu Miao | Heyan Huang | Yang Gao

Domain adaptation aims to enable Large Language Models (LLMs) to generalize domain datasets unseen effectively during the training phase. However, factors such as the size of the model parameters and the scale of training data are general influencers and do not reflect the nuances of domain adaptation performance. This paper investigates the fine-grained factors affecting domain adaptation performance, analyzing the specific impact of ‘words’ in training data on summarization tasks. We propose quantifying dataset learning difficulty as the learning difficulty of generative summarization, which is determined by two indicators: word-based compression rate and abstraction level. Our experiments conclude that, when considering dataset learning difficulty, the cross-domain overlap and the performance gain in summarization tasks exhibit an approximate linear relationship, which is not directly related to the number of words. Based on this finding, predicting a model’s performance on unknown domain datasets is possible without undergoing training. Source code and scripts are available at https://github.com/li-aolong/Word-Matters.

Visualization recommendations, which aim to automatically match proper visual charts for specific data tables, can significantly simplify the data analysis process. Traditional approaches in this domain have primarily relied on rule-based or machine learning-based methodologies. These methods often demand extensive manual maintenance and yet fail to fully comprehend the tabular data, leading to unsatisfactory performance. Recently, Large Language Models (LLMs) have emerged as powerful tools, exhibiting strong reasoning capabilities. This advancement suggests their substantial promise in addressing visualization recommendation challenges. However, effectively harnessing LLMs to discern and rationalize patterns in tabular data, and consequently deduce the essential information for chart generation, remains an unresolved challenge. To this end, we introduce a novel Hierarchical Table Prompt-based reprogramming framework, named HTP. This framework aims to integrate multi-dimensional tabular data into LLMs through a strategically crafted prompt learning method while keeping the LLMs’ backbone and weights unaltered. The HTP framework uniquely incorporates a four-level prompt structure, encompassing general, instance, cluster, and column levels. This multi-level approach is engineered to provide a comprehensive understanding of both general distribution and multifaceted fine-grained features of tabular data, before inputting the tabular data into the frozen LLM. Our empirical studies confirm that the HTP framework achieves state-of-the-art performance, marking an advancement in the field of data visualization and analysis. The code and data will be made publicly available upon acceptance.

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HOLMES: Hyper-Relational Knowledge Graphs for Multi-hop Question Answering using LLMs
Pranoy Panda | Ankush Agarwal | Chaitanya Devaguptapu | Manohar Kaul | Prathosh Ap

Given unstructured text, Large Language Models (LLMs) are adept at answering simple (single-hop) questions. However, as the complexity of the questions increase, the performance of LLMs degrade. We believe this is due to the overhead associated with understanding the complex question followed by filtering and aggregating unstructured information in the raw text. Recent methods try to reduce this burden by integrating structured knowledge triples into the raw text, aiming to provide a structured overview that simplifies information processing. However, this simplistic approach is query-agnostic and the extracted facts are ambiguous as they lack context. To address these drawbacks and to enable LLMs to answer complex (multi-hop) questions with ease, we propose to use a knowledge graph (KG) that is context-aware and is distilled to contain query-relevant information. The use of our compressed distilled KG as input to the LLM results in our method utilizing up to 67% fewer tokens to represent the query relevant information present in the supporting documents, compared to the state-of-the-art (SoTA) method.Our experiments show consistent improvements over the SoTA across several metrics (EM, F1, BERTScore, and Human Eval) on two popular benchmark datasets (HotpotQA and MuSiQue).

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Toward In-Context Teaching: Adapting Examples to Students’ Misconceptions
Alexis Ross | Jacob Andreas

When a teacher provides examples for a student to study, these examples must be informative, enabling a student to progress from their current state toward a target concept or skill. Good teachers must therefore simultaneously infer what students already know and adapt their teaching to students’ changing state of knowledge. There is increasing interest in using computational models, particularly large language models, as pedagogical tools. As students, language models in particular have shown a remarkable ability to adapt to new tasks given small numbers of examples. But how effectively can these models adapt as teachers to students of different types? To study this question, we introduce a suite of models and evaluation methods we call AdapT. AdapT has two components: (1) a collection of simulated Bayesian student models that can be used for evaluation of automated teaching methods; (2) a platform for evaluation with human students, to characterize the real-world effectiveness of these methods. We additionally introduce (3) AToM, a new probabilistic method for adaptive teaching that jointly infers students’ past beliefs and optimizes for the correctness of future beliefs. In evaluations of simulated students across three learning domains (fraction arithmetic, English morphology, function learning), AToM systematically outperforms LLM-based and standard Bayesian teaching methods. In human experiments, both AToM and LLMs outperform non-adaptive random example selection. Our results highlight both the difficulty of the adaptive teaching task and the potential of learned adaptive methods for solving it.

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Bridging Word-Pair and Token-Level Metaphor Detection with Explainable Domain Mining
Yuan Tian | Ruike Zhang | Nan Xu | Wenji Mao

Metaphor detection aims to identify whether a linguistic expression in text is metaphorical or literal. Most existing research tackles this problem either using word-pair or token-level information as input, and thus treats word-pair and token-level metaphor detection as distinct subtasks. Benefited from the simplified structure of word pairs, recent methods for word-pair metaphor detection can provide intermediate explainable clues for the detection results, which remains a challenging issue for token-level metaphor detection. To mitigate this issue in token-level metaphor detection and take advantage of word pairs, in this paper, we make the first attempt to bridge word-pair and token-level metaphor detection via modeling word pairs within a sentence as explainable intermediate information. As the central role of verb in metaphorical expressions, we focus on token-level verb metaphor detection and propose a novel explainable Word Pair based Domain Mining (WPDM) method. Our work is inspired by conceptual metaphor theory (CMT). We first devise an approach for conceptual domain mining utilizing semantic role mapping and resources at cognitive, commonsense and lexical levels. We then leverage the inconsistency between source and target domains for core word pair modeling to facilitate the explainability. Experiments on four datasets verify the effectiveness of our method and demonstrate its capability to provide the core word pair and corresponding conceptual domains as explainable clues for metaphor detection.

While the recent Chain-of-Thought (CoT) technique enhances the reasoning ability of large language models (LLMs) with the theory of mind, it might still struggle in handling logical reasoning that relies much on symbolic expressions and rigid deducing rules. To strengthen the logical reasoning capability of LLMs, we propose a novel Symbolic Chain-of-Thought, namely SymbCoT, a fully LLM-based framework that integrates symbolic expressions and logic rules with CoT prompting. Technically, building upon an LLM, SymbCoT 1) first translates the natural language context into the symbolic format, and then 2) derives a step-by-step plan to solve the problem with symbolic logical rules, 3) followed by a verifier to check the translation and reasoning chain. Via thorough evaluations on 5 standard datasets with both First-Order Logic and Constraint Optimization symbolic expressions, SymbCoT shows striking improvements over the CoT method consistently, meanwhile refreshing the current state-of-the-art performances. We further demonstrate that our system advances in more faithful, flexible, and explainable logical reasoning. To our knowledge, this is the first attempt at combining symbolic expressions and rules into CoT for logical reasoning with LLMs. Code is open at https://github.com/Aiden0526/SymbCoT.

Previous graph-based approaches in Aspect-based Sentiment Analysis(ABSA) have demonstrated impressive performance by utilizing graph neural networks and attention mechanisms to learn structures of static dependency trees and dynamic latent trees. However, incorporating both semantic and syntactic information simultaneously within complex global structures can introduce irrelevant contexts and syntactic dependencies during the process of graph structure learning, potentially resulting in inaccurate predictions. In order to address the issues above, we propose S²GSL, incorporating Segment to Syntactic enhanced Graph Structure Learning for ABSA. Specifically, S²GSL is featured with a segment-aware semantic graph learning and a syntax-based latent graph learning enabling the removal of irrelevant contexts and dependencies, respectively. We further propose a self-adaptive aggregation network that facilitates the fusion of two graph learning branches, thereby achieving complementarity across diverse structures. Experimental results on four benchmarks demonstrate the effectiveness of our framework.

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Maverick: Efficient and Accurate Coreference Resolution Defying Recent Trends
Giuliano Martinelli | Edoardo Barba | Roberto Navigli

Large autoregressive generative models have emerged as the cornerstone for achieving the highest performance across several Natural Language Processing tasks. However, the urge to attain superior results has, at times, led to the premature replacement of carefully designed task-specific approaches without exhaustive experimentation. The Coreference Resolution task is no exception; all recent state-of-the-art solutions adopt large generative autoregressive models that outperform encoder-based discriminative systems. In this work, we challenge this recent trend by introducing Maverick, a carefully designed – yet simple – pipeline, which enables running a state-of-the-art Coreference Resolution system within the constraints of an academic budget, outperforming models with up to 13 billion parameters with as few as 500 million parameters. Maverick achieves state-of-the-art performance on the CoNLL-2012 benchmark, training with up to 0.006x the memory resources and obtaining a 170x faster inference compared to previous state-of-the-art systems. We extensively validate the robustness of the Maverick framework with an array of diverse experiments, reporting improvements over prior systems in data-scarce, long-document, and out-of-domain settings. We release our code and models for research purposes at https://github.com/SapienzaNLP/maverick-coref.

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ESCoT: Towards Interpretable Emotional Support Dialogue Systems
Tenggan Zhang | Xinjie Zhang | Jinming Zhao | Li Zhou | Qin Jin

Understanding the reason for emotional support response is crucial for establishing connections between users and emotional support dialogue systems. Previous works mostly focus on generating better responses but ignore interpretability, which is extremely important for constructing reliable dialogue systems. To empower the system with better interpretability, we propose an emotional support response generation scheme, named Emotion-Focused and Strategy-Driven Chain-of-Thought (ESCoT), mimicking the process of identifying, understanding, and regulating emotions. Specially, we construct a new dataset with ESCoT in two steps: (1) Dialogue Generation where we first generate diverse conversation situations, then enhance dialogue generation using richer emotional support strategies based on these situations; (2) Chain Supplement where we focus on supplementing selected dialogues with elements such as emotion, stimuli, appraisal, and strategy reason, forming the manually verified chains. Additionally, we further develop a model to generate dialogue responses with better interpretability. We also conduct extensive experiments and human evaluations to validate the effectiveness of the proposed ESCoT and generated dialogue responses. Our dataset, code, and model will be released.

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PathReasoner: Modeling Reasoning Path with Equivalent Extension for Logical Question Answering
Fangzhi Xu | Qika Lin | Tianzhe Zhao | JiaweiHan JiaweiHan | Jun Liu

Logical reasoning task has attracted great interest since it was proposed. Faced with such a task, current competitive models, even large language models (e.g., ChatGPT and PaLM 2), still perform badly. Previous promising LMs struggle in logical consistency modeling and logical structure perception. To this end, we model the logical reasoning task by transforming each logical sample into reasoning paths and propose an architecture PathReasoner. It addresses the task from the views of both data and model. To expand the diversity of the logical samples, we propose an atom extension strategy supported by equivalent logical formulas, to form new reasoning paths. From the model perspective, we design a stack of transformer-style blocks. In particular, we propose a path-attention module to joint model in-atom and cross-atom relations with the high-order diffusion strategy. Experiments show that PathReasoner achieves competitive performances on two logical reasoning benchmarks and great generalization abilities.

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WARDEN: Multi-Directional Backdoor Watermarks for Embedding-as-a-Service Copyright Protection
Anudeex Shetty | Yue Teng | Ke He | Qiongkai Xu

Embedding as a Service (EaaS) has become a widely adopted solution, which offers feature extraction capabilities for addressing various downstream tasks in Natural Language Processing (NLP). Prior studies have shown that EaaS can be prone to model extraction attacks; nevertheless, this concern could be mitigated by adding backdoor watermarks to the text embeddings and subsequently verifying the attack models post-publication. Through the analysis of the recent watermarking strategy for EaaS, EmbMarker, we design a novel CSE (Clustering, Selection, Elimination) attack that removes the backdoor watermark while maintaining the high utility of embeddings, indicating that the previous watermarking approach can be breached. In response to this new threat, we propose a new protocol to make the removal of watermarks more challenging by incorporating multiple possible watermark directions. Our defense approach, WARDEN, notably increases the stealthiness of watermarks and has been empirically shown to be effective against CSE attack.

Parameter Efficient Fine-Tuning (PEFT) has gained significant attention for its ability to achieve competitive results while updating only a small subset of trainable parameters. Despite the promising performance of current PEFT methods, they present challenges in hyperparameter selection, such as determining the rank of LoRA or Adapter, or specifying the length of soft prompts. In addressing these challenges, we propose a novel approach to fine-tuning neural models, termed Representation EDiting (RED), which scales and biases the representation produced at each layer. RED substantially reduces the number of trainable parameters by a factor of 25,700 compared to full parameter fine-tuning, and by a factor of 32 compared to LoRA. Remarkably, RED achieves comparable or superior results to full parameter fine-tuning and other PEFT methods. Extensive experiments were conducted across models of varying architectures and scales, including RoBERTa, GPT-2, T5, and Llama-2, and the results demonstrate the efficiency and efficacy of RED, positioning it as a promising PEFT approach for large neural models.

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Context Consistency between Training and Inference in Simultaneous Machine Translation
Meizhi Zhong | Lemao Liu | Kehai Chen | Mingming Yang | Min Zhang

Simultaneous Machine Translation (SiMT) aims to yield a real-time partial translation with a monotonically growing source-side context.However, there is a counterintuitive phenomenon about the context usage between training and inference: *e.g.*, in wait-k inference, model consistently trained with wait-k is much worse than that model inconsistently trained with wait-k' (k'≠ k) in terms of translation quality. To this end, we first investigate the underlying reasons behind this phenomenon and uncover the following two factors: 1) the limited correlation between translation quality and training loss; 2) exposure bias between training and inference. Based on both reasons, we then propose an effective training approach called context consistency training accordingly, which encourages consistent context usage between training and inference by optimizing translation quality and latency as bi-objectives and exposing the predictions to the model during the training. The experiments on three language pairs demonstrate that our SiMT system encouraging context consistency outperforms existing SiMT systems with context inconsistency for the first time.

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Using Natural Language Explanations to Improve Robustness of In-context Learning
Xuanli He | Yuxiang Wu | Oana-Maria Camburu | Pasquale Minervini | Pontus Stenetorp

Recent studies demonstrated that large language models (LLMs) can excel in many tasks via in-context learning (ICL). However, recentworks show that ICL-prompted models tend to produce inaccurate results when presented with adversarial inputs. In this work, we investigate whether augmenting ICL with natural language explanations (NLEs) improves the robustness of LLMs on adversarial datasets covering natural language inference and paraphrasing identification. We prompt LLMs with a small set of human-generated NLEs to produce further NLEs, yielding more accurate results than both a zero-shot-ICL setting and using only human-generated NLEs. Our results on five popular LLMs (GPT3.5-turbo, Llama2, Vicuna, Zephyr, and Mistral) show that our approach yields over 6% improvement over baseline approaches for eight adversarial datasets: HANS, ISCS, NaN, ST, PICD, PISP, ANLI, and PAWS. Furthermore, previous studies have demonstrated that prompt selection strategies significantly enhance ICL on in-distribution test sets. However, our findings reveal that these strategies do not match the efficacy of our approach for robustness evaluations, resulting in an accuracy drop of 8% compared to the proposed approach.

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Chunk, Align, Select: A Simple Long-sequence Processing Method for Transformers
Jiawen Xie | Pengyu Cheng | Xiao Liang | Yong Dai | Nan Du

Although dominant in natural language processing, transformer-based models still struggle with long-sequence processing, due to the computational costs of their self-attention operations, which increase exponentially as the length of the input sequence grows. To address this challenge, we propose a **Sim**ple framework to enhance the long-content processing of off-the-shelf pre-trained transformers via three steps: **C**hunk, **A**lign, and **S**elect (SimCAS). More specifically, we first divide each long-sequence input into a batch of chunks, then align the inter-chunk information during the encoding steps, and finally, select the most representative hidden states from the encoder for the decoding process. With our SimCAS, the computation and memory costs can be reduced to linear complexity. In experiments, we demonstrate the effectiveness of the proposed method on various real-world long-text summarization and reading comprehension tasks, in which SimCAS significantly outperforms prior long-sequence processing baselines. The code is at [https://github.com/xjw-nlp/SimCAS](https://github.com/xjw-nlp/SimCAS).

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ArchCode: Incorporating Software Requirements in Code Generation with Large Language Models
Hojae Han | Jaejin Kim | Jaeseok Yoo | Youngwon Lee | Seung-won Hwang

This paper aims to extend the code generation capability of large language models (LLMs) to automatically manage comprehensive software requirements from given textual descriptions. Such requirements include both functional (i.e. achieving expected behavior for inputs) and non-functional (e.g., time/space performance, robustness, maintainability) requirements. However, textual descriptions can either express requirements verbosely or may even omit some of them. We introduce ARCHCODE, a novel framework that leverages in-context learning to organize requirements observed in descriptions and to extrapolate unexpressed requirements from them. ARCHCODE generates requirements from given descriptions, conditioning them to produce code snippets and test cases. Each test case is tailored to one of the requirements, allowing for the ranking of code snippets based on the compliance of their execution results with the requirements. Public benchmarks show that ARCHCODE enhances to satisfy functional requirements, significantly improving Pass@k scores.Furthermore, we introduce HumanEval-NFR, the first evaluation of LLMs’ non-functional requirements in code generation, demonstrating ARCHCODE’s superiority over baseline methods. The implementation of ARCHCODE and the HumanEval-NFR benchmark are both publicly accessible.

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Combining Supervised Learning and Reinforcement Learning for Multi-Label Classification Tasks with Partial Labels
Zixia Jia | Junpeng Li | Shichuan Zhang | Anji Liu | Zilong Zheng

Traditional supervised learning heavily relies on human-annotated datasets, especially in data-hungry neural approaches. However, various tasks, especially multi-label tasks like document-level relation extraction, pose challenges in fully manual annotation due to the specific domain knowledge and large class sets. Therefore, we address the multi-label positive-unlabelled learning (MLPUL) problem, where only a subset of positive classes is annotated. We propose Mixture Learner for Partially Annotated Classification (MLPAC), an RL-based framework combining the exploration ability of reinforcement learning and the exploitation ability of supervised learning. Experimental results across various tasks, including document-level relation extraction, multi-label image classification, and binary PU learning, demonstrate the generalization and effectiveness of our framework.

Adjusting the outdated behaviors of large langugae models (LLMs) after deployment remains a significant challenge. It motivates the model editing research, which is however mainly explored in a restricted task form with triple-based edit requests. Recent works have initiated a transition to a more practical and unified editing task that takes free-form text as edit requests. However, there are gaps in nuanced benchmark designs and re-evaluation of existing methods. To bridge the gaps, we introduce a multi-level benchmark for free text model editing (MULFE). The benchmark categorizes probe queries into three levels of generalization, ranging from basic literal memory to deeper understanding and reasoning. Based on the benchmark, we conduct extensive experiments across various base models, edit sizes, and editing methods, including adaptations of mainstream locate-and-edit and hypernetwork methods. The results highlight the inconsistent behaviors of edited models on different generalization levels. Higher-level generalization remains a significant challenge. Based on the findings, we propose SIDE, a simple yet effective method based on in-context distillation to enhance the generalization performance. The benchmark dataset and evaluation scripts are publicly available at http://github.com/wchrepo/mulfe.

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MobileSpeech: A Fast and High-Fidelity Framework for Mobile Zero-Shot Text-to-Speech
Shengpeng Ji | Ziyue Jiang | Hanting Wang | Jialong Zuo | Zhou Zhao

Zero-shot text-to-speech (TTS) has gained significant attention due to its powerful voice cloning capabilities, requiring only a few seconds of unseen speaker voice prompts. However, all previous work has been developed for cloud-based systems. Taking autoregressive models as an example, although these approaches achieve high-fidelity voice cloning, they fall short in terms of inference speed, model size, and robustness. Therefore, we propose MobileSpeech, which is a fast, lightweight, and robust zero-shot text-to-speech system based on mobile devices for the first time. Specifically: 1) leveraging discrete codec, we design a parallel speech mask decoder module called SMD, which incorporates hierarchical information from the speech codec and weight mechanisms across different codec layers during the generation process. Moreover, to bridge the gap between text and speech, we introduce a high-level probabilistic mask that simulates the progression of information flow from less to more during speech generation. 2) For speaker prompts, we extract fine-grained prompt duration from the prompt speech and incorporate text, prompt speech by cross attention in SMD. We demonstrate the effectiveness of MobileSpeech on multilingual datasets at different levels, achieving state-of-the-art results in terms of generating speed and speech quality. MobileSpeech achieves RTF of 0.09 on a single A100 GPU and we have successfully deployed MobileSpeech on mobile devices. Audio samples are available at https://mobilespeech.github.io/

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Spatially-Aware Speaker for Vision-and-Language Navigation Instruction Generation
Muraleekrishna Gopinathan | Martin Masek | Jumana Abu-Khalaf | David Suter

Embodied AI aims to develop robots that can understand and execute human language instructions, as well as communicate in natural languages. On this front, we study the task of generating highly detailed navigational instructions for the embodied robots to follow. Although recent studies have demonstrated significant leaps in the generation of step-by-step instructions from sequences of images, the generated instructions lack variety in terms of their referral to objects and landmarks. Existing speaker models learn strategies to evade the evaluation metrics and obtain higher scores even for low-quality sentences. In this work, we propose SAS (Spatially-Aware Speaker), an instruction generator or Speaker model that utilises both structural and semantic knowledge of the environment to produce richer instructions. For training, we employ a reward learning method in an adversarial setting to avoid systematic bias introduced by language evaluation metrics. Empirically, our method outperforms existing instruction generation models, evaluated using standard metrics. Our code is available at https://github.com/gmuraleekrishna/SAS.

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HiRoPE: Length Extrapolation for Code Models Using Hierarchical Position
Kechi Zhang | Ge Li | Huangzhao Zhang | Zhi Jin

Addressing the limitation of context length in large language models for code-related tasks is the primary focus of this paper. Existing LLMs are constrained by their pre-trained context lengths, leading to performance issues in handling long complex code sequences. Inspired by how human programmers navigate code, we introduce Hierarchical Rotary Position Embedding (HiRoPE), a novel approach that enhances the traditional rotary position embedding into a hierarchical format based on the hierarchical structure of source code. HiRoPE offers easy integration into existing LLMs without extra training costs. Our method is extensively evaluated with various LLMs, demonstrating stable performance in tasks such as language modeling and long code completion. We also introduce a new long code understanding task with real-world code projects, in hopes of promoting further development in this code-related field. Theoretically and experimentally, we find that HiRoPE also addresses the out-of-distribution issue in position encoding. Our HiRoPE significantly expands the context length capabilities of LLMs, enabling inference at lengths exponentially greater than the training length.

While large language models (LLMs) are equipped with longer text input capabilities than before, they are struggling to seek correct information in long contexts. The “lost in the middle” problem challenges most LLMs, referring to the dramatic decline in accuracy when correct information is located in the middle. To overcome this crucial issue, this paper proposes to enhance the information searching and reflection ability of LLMs in long contexts via specially designed tasks called Position-Agnostic Multi-step QA (PAM QA). Trained in this task, our model excels in focusing more precisely on the desired information. Experimental results show substantial improvement in Multi-doc QA and other benchmarks, superior to state-of-the-art models by 13.7% absolute gain in shuffled settings, by 21.5% in passage retrieval task. We release our model and code to promote related research in the community.

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CodeAgent: Enhancing Code Generation with Tool-Integrated Agent Systems for Real-World Repo-level Coding Challenges
Kechi Zhang | Jia Li | Ge Li | Xianjie Shi | Zhi Jin

Large Language Models (LLMs) have shown promise in automated code generation but typically excel only in simpler tasks such as generating standalone code units. However, real-world software development often involves complex code repositories with complex dependencies and extensive documentation. To enable LLMs to handle these realworld repo-level code generation, we present CodeAgent, a novel LLM-based agent framework that employs external tools for effective repo-level code generation. CodeAgent integrates five programming tools, enabling interaction with software artifacts for information retrieval, code implementation, and code testing. We implement four agent strategies to optimize these tools’ usage. To the best of our knowledge, CodeAgent is the first agent tool framework specifically for repo-level code generation. In order to measure the effectiveness of our method at the repository level, we have introduced a benchmark dataset CodAgentBench. The performance on this dataset shows a significant improvement brought by our method, with improvements of pass rate ranging from 2.0 to 15.8. Further tests on the HumanEval benchmark confirm CodeAgent’s adaptability and efficacy across various code generation tasks. Notably, CodeAgent outperforms commercial products like Github Copilot, showcasing superior accuracy and efficiency. These results demonstrate CodeAgent’s robust capabilities in code generation, highlighting its potential for real-world repo-level coding challenges.

In this paper, we examine how large language models (LLMs) solve multi-step problems under a language agent framework with three components: a generator, a discriminator, and a planning method. We investigate the practical utility of two advanced planning methods, iterative correction and tree search. We present a comprehensive analysis of how discrimination accuracy affects the overall performance of agents when using these two methods or a simpler method, re-ranking. Experiments on two tasks, text-to-SQL parsing and mathematical reasoning, show that: (1) advanced planning methods demand discriminators with at least 90% accuracy to achieve significant improvements over re-ranking; (2) current LLMs’ discrimination abilities have not met the needs of advanced planning methods to achieve such improvements; (3) with LLM-based discriminators, advanced planning methods may not adequately balance accuracy and efficiency. For example, compared to the other two methods, tree search is at least 10–20 times slower but leads to negligible performance gains, which hinders its real-world applications.

Recently developed large language models (LLMs) have been shown to perform remarkably well on a wide range of language understanding tasks. But, can they really “reason” over the natural language? This question has been receiving significant research attention and many reasoning skills such as commonsense, numerical, and qualitative have been studied. However, the crucial skill pertaining to ‘logical reasoning’ has remained underexplored. Existing work investigating this reasoning ability of LLMs has focused only on a couple of inference rules (such as modus ponens and modus tollens) of propositional and first-order logic. Addressing the above limitation, we comprehensively evaluate the logical reasoning ability of LLMs on 25 different reasoning patterns spanning over propositional, first-order, and non-monotonic logics. To enable systematic evaluation, we introduce LogicBench, a natural language question-answering dataset focusing on the use of a single inference rule. We conduct detailed analysis with a range of LLMs such as GPT-4, ChatGPT, Gemini, Llama-2, and Mistral using chain-of-thought prompting. Experimental results show that existing LLMs do not fare well on LogicBench; especially, they struggle with instances involving complex reasoning and negations. Furthermore, they sometimes tend to prioritize parametric knowledge over contextual information and overlook the correct reasoning chain. We believe that our work and findings facilitate future research for evaluating and enhancing the logical reasoning ability of LLMs.

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Meta-Tuning LLMs to Leverage Lexical Knowledge for Generalizable Language Style Understanding
Ruohao Guo | Wei Xu | Alan Ritter

Language style is often used by writers to convey their intentions, identities, and mastery of language. In this paper, we show that current large language models struggle to capture some language styles without fine-tuning. To address this challenge, we investigate whether LLMs can be meta-trained based on representative lexicons to recognize new styles they have not been fine-tuned on. Experiments on 13 established style classification tasks, as well as 63 novel tasks generated using LLMs, demonstrate that meta-training with style lexicons consistently improves zero-shot transfer across styles. We release the code and data at https://github.com/octaviaguo/Style-LLM.

Self-disclosure, while being common and rewarding in social media interaction, also poses privacy risks. In this paper, we take the initiative to protect the user-side privacy associated with online self-disclosure through detection and abstraction. We develop a taxonomy of 19 self-disclosure categories and curate a large corpus consisting of 4.8K annotated disclosure spans. We then fine-tune a language model for detection, achieving over 65% partial span F₁. We further conduct an HCI user study, with 82% of participants viewing the model positively, highlighting its real-world applicability. Motivated by the user feedback, we introduce the task of self-disclosure abstraction, which is rephrasing disclosures into less specific terms while preserving their utility, e.g., “Im 16F” to “I’m a teenage girl”. We explore various fine-tuning strategies, and our best model can generate diverse abstractions that moderately reduce privacy risks while maintaining high utility according to human evaluation. To help users in deciding which disclosures to abstract, we present a task of rating their importance for context understanding. Our fine-tuned model achieves 80% accuracy, on-par with GPT-3.5. Given safety and privacy considerations, we will only release our corpus and models to researcher who agree to the ethical guidelines outlined in Ethics Statement.

Memory Editing (ME) has emerged as an efficient method to modify erroneous facts or inject new facts into Large Language Models (LLMs). Two mainstream ME methods exist: parameter-modifying ME and parameter-preserving ME (integrating extra modules while preserving original parameters). Regrettably, previous studies on ME evaluation have two critical limitations: (i) evaluating LLMs with single edit only, neglecting the need for continuous editing, and (ii) evaluations focusing solely on basic factual triples, overlooking broader LLM capabilities like logical reasoning and reading understanding. This study addresses these limitations with contributions threefold: (i) We explore how ME affects a wide range of fundamental capabilities of LLMs under sequential editing. Experimental results reveal an intriguing phenomenon: Most parameter-modifying ME consistently degrade performance across all tasks after a few sequential edits. In contrast, parameter-preserving ME effectively maintains LLMs’ fundamental capabilities but struggles to accurately recall edited knowledge presented in a different format. (ii) We extend our evaluation to different editing settings, such as layers to edit, model size, instruction tuning, etc. Experimental findings indicate several strategies that can potentially mitigate the adverse effects of ME. (iii) We further explain why parameter-modifying damages LLMs from three dimensions: parameter changes after editing, language modeling capability, and the in-context learning capability. Our in-depth study advocates more careful use of ME in real-world scenarios.

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REFINESUMM: Self-Refining MLLM for Generating a Multimodal Summarization Dataset
Vaidehi Patil | Leonardo Ribeiro | Mengwen Liu | Mohit Bansal | Markus Dreyer

Multimodal Large Language Models (MLLMs) excel at synthesizing key information from diverse sources. However, generating accurate and faithful multimodal summaries is challenging, primarily due to the lack of appropriate multimodal datasets for fine-tuning that meaningfully integrate textual and visual modalities. To address this gap, we present a new dataset designed specifically for image-text multimodal summarization, harnessing the capabilities of state-of-the-art MLLMs. We generate summaries from Wikipedia sections and corresponding images and evaluate them across text-based, visual and multimodal dimensions, employing reference-free metrics. To refine the dataset, we: (1) Filter the MLLM-generated summaries by training a critic model on human annotations and using its predictions to remove low-quality summaries; (2) Fine-tune the MLLM with the filtered high-quality summaries; (3) Use the fine-tuned model in turn to regenerate the summaries. This self-refinement process significantly improves summary quality, as measured by human judgements and automatic multimodal metrics, resulting in a valuable dataset for multimodal summarization research. The dataset is publicly available at https://github.com/amazon-science/refinesumm.

Large Language Model (LLM) leaderboards based on benchmark rankings are regularly used to guide practitioners in model selection. Often, the published leaderboard rankings are taken at face value — we show this is a (potentially costly) mistake. Under existing leaderboards, the relative performance of LLMs is highly sensitive to (often minute) details. We show that for popular multiple-choice question benchmarks (e.g., MMLU), minor perturbations to the benchmark, such as changing the order of choices or the method of answer selection, result in changes in rankings up to 8 positions. We explain this phenomenon by conducting systematic experiments over three broad categories of benchmark perturbations and identifying the sources of this behavior. Our analysis results in several best-practice recommendations, including the advantage of a *hybrid* scoring method for answer selection. Our study highlights the dangers of relying on simple benchmark evaluations and charts the path for more robust evaluation schemes on the existing benchmarks. The code for this paper is available at [https://github.com/National-Center-for-AI-Saudi-Arabia/lm-evaluation-harness](https://github.com/National-Center-for-AI-Saudi-Arabia/lm-evaluation-harness).

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LLM-Rubric: A Multidimensional, Calibrated Approach to Automated Evaluation of Natural Language Texts
Helia Hashemi | Jason Eisner | Corby Rosset | Benjamin Van Durme | Chris Kedzie

This paper introduces a framework for the automated evaluation of natural language texts. A manually constructed rubric describes how to assess multiple dimensions of interest. To evaluate a text, a large language model (LLM) is prompted with each rubric question and produces a distribution over potential responses. The LLM predictions often fail to agree well with human judges—indeed, the humans do not fully agree with one another. However, the multiple LLM distributions can be _combined_ to _predict_ each human judge’s annotations on all questions, including a summary question that assesses overall quality or relevance. LLM-Rubric accomplishes this by training a small feed-forward neural network that includes both judge-specific and judge-independent parameters. When evaluating dialogue systems in a human-AI information-seeking task, we find that LLM-Rubric with 9 questions (assessing dimensions such as naturalness, conciseness, and citation quality) predicts human judges’ assessment of overall user satisfaction, on a scale of 1–4, with RMS error < 0.5, a 2× improvement over the uncalibrated baseline.

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LIEDER: Linguistically-Informed Evaluation for Discourse Entity Recognition
Xiaomeng Zhu | Robert Frank

Discourse Entity (DE) recognition is the task of identifying novel and known entities introduced within a text. While previous work has found that large language models have basic, if imperfect, DE recognition abilities (Schuster and Linzen, 2022), it remains largely unassessed which of the fundamental semantic properties that govern the introduction and subsequent reference to DEs they have knowledge of. We propose the Linguistically-Informed Evaluation for Discourse Entity Recognition (LIEDER) dataset that allows for a detailed examination of language models’ knowledge of four crucial semantic properties: existence, uniqueness, plurality, and novelty. We find evidence that state-of-the-art large language models exhibit sensitivity to all of these properties except novelty, which demonstrates that they have yet to reach human-level language understanding abilities.

Existing works on long-term open-domain dialogues focus on evaluating model responses within contexts spanning no more than five chat sessions. Despite advancements in long-context large language models (LLMs) and retrieval augmented generation (RAG) techniques, their efficacy in very long-term dialogues remains unexplored. To address this research gap, we introduce a machine-human pipeline to generate high-quality, very long-term dialogues by leveraging LLM-based agent architectures and grounding their dialogues on personas and temporal event graphs. Moreover, we equip each agent with the capability of sharing and reacting to images. The generated conversations are verified and edited by human annotators for long-range consistency and grounding to the event graphs. Using this pipeline, we collect LoCoMo, a dataset of very long-term conversations, each encompassing 600 turns and 16K tokens on avg., over up to 32 sessions. Based on LoCoMo, we present a comprehensive evaluation benchmark to measure long-term memory in models, encompassing question answering, event summarization, and multi-modal dialogue generation tasks. Our experimental results indicate that LLMs exhibit challenges in understanding lengthy conversations and comprehending long-range temporal and causal dynamics within dialogues. Employing strategies like long-context LLMs or RAG can offer improvements but these models still substantially lag behind human performance.

The reward model for Reinforcement Learning from Human Feedback (RLHF) has proven effective in fine-tuning Large Language Models (LLMs). Notably, collecting human feedback for RLHF can be resource-intensive and lead to scalability issues for LLMs and complex tasks. Our proposed framework Proto-RM leverages prototypical networks to enhance reward models under limited human feedback. By enabling stable and reliable structural learning from fewer samples, Proto-RM significantly enhances LLMs' adaptability and accuracy in interpreting human preferences. Extensive experiments on various datasets demonstrate that Proto-RM significantly improves the performance of reward models and LLMs in human feedback tasks, achieving comparable and usually better results than traditional methods, while requiring significantly less data in data-limited scenarios. This research offers a promising direction for enhancing the efficiency of reward models and optimizing the fine-tuning of language models under restricted feedback conditions.

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NEO-BENCH: Evaluating Robustness of Large Language Models with Neologisms
Jonathan Zheng | Alan Ritter | Wei Xu

The performance of Large Language Models (LLMs) degrades from the temporal drift between data used for model training and newer text seen during inference. One understudied avenue of language change causing data drift is the emergence of neologisms – new word forms – over time. We create a diverse resource of recent English neologisms by using several popular collection methods. We analyze temporal drift using neologisms by comparing sentences containing new words with near-identical sentences that replace neologisms with existing substitute words. Model performance is nearly halved in machine translation when a single neologism is introduced in a sentence. Motivated by these results, we construct a benchmark to evaluate LLMs’ ability to generalize to neologisms with various natural language understanding tasks and model perplexity. Models with later knowledge cutoff dates yield lower perplexities and perform better in downstream tasks. LLMs are also affected differently based on the linguistic origins of words, indicating that neologisms are complex for static LLMs to address. We will release our benchmark and code for reproducing our experiments.

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Impacts of Misspelled Queries on Translation and Product Search
Greg Hanneman | Natawut Monaikul | Taichi Nakatani

Machine translation is used in e-commerce to translate second-language queries into the primary language of the store, to be matched by the search system against the product catalog. However, many queries contain spelling mistakes. We first present an analysis of the spelling-robustness of a population of MT systems, quantifying how spelling variations affect MT output, the list of returned products, and ultimately user behavior. We then present two sets of practical experiments illustrating how spelling-robustness may be specifically improved. For MT, reducing the number of BPE operations significantly improves spelling-robustness in six language pairs. In end-to-end e-commerce, the inclusion of a dedicated spelling correction model, and the augmentation of that model’s training data with language-relevant phenomena, each improve robustness and consistency of search results.

Large Language Models (LLMs) have shown remarkable capabilities in tasks such as summarization, arithmetic reasoning, and question answering. However, they encounter significant challenges in the domain of moral reasoning and ethical decision-making, especially in complex scenarios with multiple stakeholders. This paper introduces the Skin-in-the-Game (SKIG) framework, aimed at enhancing moral reasoning in LLMs by exploring decisions’ consequences from multiple stakeholder perspectives. The core components of the framework consist of simulating accountability for decisions, conducting empathy exercises on different stakeholders, and evaluating the risks associated with the impacts of potential actions. We study SKIG’s performance across various moral reasoning benchmarks with proprietary and open-source LLMs, and investigate its crucial components through extensive ablation analyses. Our framework exhibits marked improvements in performance compared to baselines across different language models and benchmarks.

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The MERSA Dataset and a Transformer-Based Approach for Speech Emotion Recognition
Enshi Zhang | Rafael Trujillo | Christian Poellabauer

Research in the field of speech emotion recognition (SER) relies on the availability of comprehensive datasets to make it possible to design accurate emotion detection models. This study introduces the Multimodal Emotion Recognition and Sentiment Analysis (MERSA) dataset, which includes both natural and scripted speech recordings, transcribed text, physiological data, and self-reported emotional surveys from 150 participants collected over a two-week period. This work also presents a novel emotion recognition approach that uses a transformer-based model, integrating pre-trained wav2vec 2.0 and BERT for feature extractions and additional LSTM layers to learn hidden representations from fused representations from speech and text. Our model predicts emotions on dimensions of arousal, valence, and dominance. We trained and evaluated the model on the MSP-PODCAST dataset and achieved competitive results from the best-performing model regarding the concordance correlation coefficient (CCC). Further, this paper demonstrates the effectiveness of this model through cross-domain evaluations on both IEMOCAP and MERSA datasets.

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Transparent and Scrutable Recommendations Using Natural Language User Profiles
Jerome Ramos | Hossein A. Rahmani | Xi Wang | Xiao Fu | Aldo Lipani

Recent state-of-the-art recommender systems predominantly rely on either implicit or explicit feedback from users to suggest new items. While effective in recommending novel options, many recommender systems often use uninterpretable embeddings to represent user preferences. This lack of transparency not only limits user understanding of why certain items are suggested but also reduces the user’s ability to scrutinize and modify their preferences, thereby affecting their ability to receive a list of preferred recommendations. Given the recent advances in Large Language Models (LLMs), we investigate how a properly crafted prompt can be used to summarize a user’s preferences from past reviews and recommend items based only on language-based preferences. In particular, we study how LLMs can be prompted to generate a natural language (NL) user profile that holistically describe a user’s preferences. These NL profiles can then be leveraged to fine-tune a LLM using only NL profiles to make transparent and scrutable recommendations. Furthermore, we validate the scrutability of our user profile-based recommender by investigating the impact on recommendation changes after editing NL user profiles. According to our evaluations of the model’s rating prediction performance on two benchmarking rating prediction datasets, we observe that this novel approach maintains a performance level on par with established recommender systems in a warm-start setting. With a systematic analysis into the effect of updating user profiles and system prompts, we show the advantage of our approach in easier adjustment of user preferences and a greater autonomy over users’ received recommendations.

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Fora: A corpus and framework for the study of facilitated dialogue
Hope Schroeder | Deb Roy | Jad Kabbara

Facilitated dialogue is increasingly popular as a method of civic engagement and as a method for gathering social insight, but resources for its study are scant. We present Fora, a unique collection of annotated facilitated dialogues. We compile 262 facilitated conversations that were hosted with partner organizations seeking to engage their members and surface insights regarding issues like education, elections, and public health, primarily through the sharing of personal experience. Alongside this corpus of 39,911 speaker turns, we present a framework for the analysis of facilitated dialogue. We taxonomize key personal sharing behaviors and facilitation strategies in the corpus, annotate a 25% sample (10,000+ speaker turns) of the data accordingly, and evaluate and establish baselines on a number of tasks essential to the identification of these phenomena in dialogue. We describe the data, and relate facilitator behavior to turn-taking and participant sharing. We outline how this research can inform future work in understanding and improving facilitated dialogue, parsing spoken conversation, and improving the behavior of dialogue agents.

Large language models (LLMs) have shown remarkable capabilities in various natural language understanding tasks with a few demonstration examples via in-context learning. Common strategies to boost such “in-context” learning ability are to ensemble multiple model decoded results and require the model to generate an explanation along with the prediction. However, these models often treat different class predictions equally and neglect the potential discrepancy between the explanations and predictions. To fully unleash the power of explanations, we propose EASE, an Explanation-Aware Soft Ensemble framework to empower in-context learning with LLMs. We design two techniques, explanation-guided ensemble, and soft probability aggregation, to mitigate the effect of unreliable explanations and improve the consistency between explanations and final predictions. Experiments on seven natural language understanding tasks and four varying-size LLMs demonstrate the effectiveness of our proposed framework.

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What is the Best Way for ChatGPT to Translate Poetry?
Shanshan Wang | Derek Wong | Jingming Yao | Lidia Chao

Machine translation (MT) has historically faced significant challenges when applied to literary works, particularly in the domain of poetry translation. The advent of Large Language Models such as ChatGPT holds potential for innovation in this field. This study examines ChatGPT’s capabilities in English-Chinese poetry translation tasks, utilizing targeted prompts and small sample scenarios to ascertain optimal performance. Despite promising outcomes, our analysis reveals persistent issues in the translations generated by ChatGPT that warrant attention. To address these shortcomings, we propose an Explanation-Assisted Poetry Machine Translation (EAPMT) method, which leverages monolingual poetry explanation as a guiding information for the translation process. Furthermore, we refine existing evaluation criteria to better suit the nuances of modern poetry translation. We engaged a panel of professional poets for assessments, complemented evaluations by using GPT-4. The results from both human and machine evaluations demonstrate that our EAPMT method outperforms traditional translation methods of ChatGPT and the existing online systems. This paper validates the efficacy of our method and contributes a novel perspective to machine-assisted literary translation.

Large language models are trained on massive scrapes of the web, which are often unstructured, noisy, and poorly phrased. Current scaling laws show that learning from such data requires an abundance of both compute and data, which grows with the size of the model being trained. This is infeasible both because of the large compute costs and duration associated with pre-training, and the impending scarcity of high-quality data on the web. In this work, we propose Web Rephrase Augmented Pre-training (WRAP) that uses an off-the-shelf instruction-tuned model prompted to paraphrase documents on the web in specific styles such as “like Wikipedia” or in “question-answer format” to jointly pre-train LLMs on real and synthetic rephrases. First, we show that using WRAP on the C4 dataset, which is naturally noisy, speeds up pre-training by ~3x. At the same pre-training compute budget, it improves perplexity by more than 50% on average across different subsets of the Pile, and improves zero-shot question answer accuracy across 13 tasks by more than 2%. Second, we investigate the impact of the re-phrasing style on the performance of the model, offering insights into how the composition of the training data can impact the performance of LLMs in OOD settings. Our gains are attributed to the fact that re-phrased synthetic data has higher utility than just real data because it (i) incorporates style diversity that closely reflects downstream evaluation style, and (ii) has higher ‘quality’ than web-scraped data.

Large language models (LLMs) often require task-relevant knowledge to augment their internal knowledge through prompts. However, simply injecting external knowledge into prompts does not guarantee that LLMs can identify and use relevant information in the prompts to conduct chain-of-thought reasoning, especially when the LLM’s internal knowledge is derived from biased information on the pretraining data. In this paper, we propose a novel causal view to formally explain the internal knowledge bias of LLMs via a Structural Causal Model (SCM). We review the chain-of-thought (CoT) prompting from a causal perspective and discover that the biased information from pretrained models can impair LLMs’ reasoning abilities. When the CoT reasoning paths are misled by irrelevant information from prompts and are logically incorrect, simply editing factual information is insufficient to reach the correct answer. To estimate the confounding effect on CoT reasoning in LLMs, we use external knowledge as an instrumental variable. We further introduce CoT as a mediator to conduct front-door adjustment and generate logically correct CoTs where the spurious correlation between LLMs’ pretrained knowledge and task queries is reduced. With extensive experiments, we validate that our approach enables more accurate CoT reasoning and enhances LLM generation on knowledge-intensive tasks.

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Representation Learning with Conditional Information Flow Maximization
Dou Hu | Lingwei Wei | Wei Zhou | Songlin Hu

This paper proposes an information-theoretic representation learning framework, named conditional information flow maximization, to extract noise-invariant sufficient representations for the input data and target task. It promotes the learned representations have good feature uniformity and sufficient predictive ability, which can enhance the generalization of pre-trained language models (PLMs) for the target task. Firstly, an information flow maximization principle is proposed to learn more sufficient representations for the input and target by simultaneously maximizing both input-representation and representation-label mutual information. Unlike the information bottleneck, we handle the input-representation information in an opposite way to avoid the over-compression issue of latent representations. Besides, to mitigate the negative effect of potential redundant features from the input, we design a conditional information minimization principle to eliminate negative redundant features while preserve noise-invariant features. Experiments on 13 language understanding benchmarks demonstrate that our method effectively improves the performance of PLMs for classification and regression. Extensive experiments show that the learned representations are more sufficient, robust and transferable.

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GPT is Not an Annotator: The Necessity of Human Annotation in Fairness Benchmark Construction
Virginia Felkner | Jennifer Thompson | Jonathan May

Social biases in LLMs are usually measured via bias benchmark datasets. Current benchmarks have limitations in scope, grounding, quality, and human effort required. Previous work has shown success with a community-sourced, rather than crowd-sourced, approach to benchmark development. However, this work still required considerable effort from annotators with relevant lived experience. This paper explores whether an LLM (specifically, GPT-3.5-Turbo) can assist with the task of developing a bias benchmark dataset from responses to an open-ended community survey. We also extend the previous work to a new community and set of biases: the Jewish community and antisemitism. Our analysis shows that GPT-3.5-Turbo has poor performance on this annotation task and produces unacceptable quality issues in its output. Thus, we conclude that GPT-3.5-Turbo is not an appropriate substitute for human annotation in sensitive tasks related to social biases, and that its use actually negates many of the benefits of community-sourcing bias benchmarks.

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Quantifying Contamination in Evaluating Code Generation Capabilities of Language Models
Martin Riddell | Ansong Ni | Arman Cohan

While large language models have achieved remarkable performance on various code generation benchmarks, there have been growing concerns regarding potential contamination of these benchmarks as they may be leaked into pretraining and finetuning data. While recent work has investigated contamination in natural language generation and understanding tasks, there has been less extensive research into how data contamination impacts the evaluation of code generation, which is critical for understanding the robustness and reliability of LLMs in programming contexts. In this work, we perform a comprehensive study of data contamination of popular code generation benchmarks, and precisely quantify their overlap with pretraining corpus through both surface-level and semantic-level matching. In our experiments, we show that there are substantial overlap between popular code generation benchmarks and open training corpus, and models perform significantly better on the subset of the benchmarks where similar solutions are seen during training. We also conduct extensive analysis on the factors that affect model memorization and generalization, such as model size, problem difficulty, and question length. We release all resulting files from our matching pipeline for future research.

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Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task Arithmetic
Rishabh Bhardwaj | Duc Anh Do | Soujanya Poria

We propose RESTA to perform LLM realignment towards safety, which gets compromised due to downstream task fine-tuning. RESTA stands for REstoring Safety through Task Arithmetic. At its core, it involves a simple arithmetic addition of a safety vector to the weights of the compromised model. We demonstrate the effectiveness of RESTA in both parameter-efficient and full fine-tuning, covering a wide range of downstream tasks, including instruction following in Chinese, English, and Hindi, as well as problem-solving capabilities in Code and Math. We also showcase the generalizability of RESTA on three existing safety evaluation benchmarks and a multilingual benchmark dataset proposed as a part of this work, consisting of 550 harmful questions covering 11 categories, each with 5 sub-categories of harm. Overall, RESTA decreases the harmfulness of the compromised model from 18.6% to 5.1% and from 9.2% to 1.5% in parameter-efficient and full fine-tuning, respectively, while maintaining most of the model’s performance on the task. We release the source codes at: https://github.com/declare-lab/resta.

Journalists regularly make decisions on whether or not to report stories, based on “news values”. In this work, we wish to explicitly model these decisions to explore _when_ and _why_ certain stories get press attention. This is challenging because very few labelled links between source documents and news articles exist and language use between corpora is very different. We address this problem by implementing a novel _probabilistic relational modeling_ framework, which we show is a low-annotation linking methodology that outperforms other, more state-of-the-art retrieval-based baselines. Next, we define a new task: __newsworthiness prediction__, to predict if a policy item will get covered. We focus on news coverage of local public policy in the San Francisco Bay Area by the _San Francisco Chronicle_. We gather 15k policies discussed across 10 years of public policy meetings, and transcribe over 3,200 hours of public discussion. In general, we find limited impact of public discussion on newsworthiness prediction accuracy, suggesting that some of the most important stories barely get discussed in public.Finally, we show that newsworthiness predictions can be a useful assistive tool for journalists seeking to keep abreast of local government. We perform human evaluation with expert journalists and show our systems identify policies they consider newsworthy with 68% F1 and our coverage recommendations are helpful with an 84% win-rate against baseline. We release all code and data to our work here: https://github.com/alex2awesome/newsworthiness-public.

The emerging citation-based QA systems are gaining more attention especially in generative AI search applications. The importance of extracted knowledge provided to these systems is vital from both accuracy (completeness of information) and efficiency (extracting the information in a timely manner). In this regard, citation-based QA systems are suffering from two shortcomings. First, they usually rely only on web as a source of extracted knowledge and adding other external knowledge sources can hamper the efficiency of the system. Second, web-retrieved contents are usually obtained by some simple heuristics such as fixed length or breakpoints which might lead to splitting information into pieces. To mitigate these issues, we propose our enhanced web and efficient knowledge graph (KG) retrieval solution (EWEK-QA) to enrich the content of the extracted knowledge fed to the system. This has been done through designing an adaptive web retriever and incorporating KGs triples in an efficient manner. We demonstrate the effectiveness of over the open-source state-of-the-art (SoTA) web-based and KG baseline models using a comprehensive set of quantitative and human evaluation experiments. Our model is able to: first, improve the web-retriever baseline in terms of extracting more relevant passages (>20%), the coverage of answer span (>25%) and self containment (>35%); second, obtain and integrate KG triples into its pipeline very efficiently (by avoiding any LLM calls) to outperform the web-only and KG-only SoTA baselines significantly in 7 quantitative QA tasks and our human evaluation.

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Multi-modal Preference Alignment Remedies Degradation of Visual Instruction Tuning on Language Models
Shengzhi Li | Rongyu Lin | Shichao Pei

Multi-modal large language models (MLLMs) are expected to support multi-turn queries of interchanging image and text modalities in production. However, the current MLLMs trained with visual-question-answering (VQA) datasets could suffer from degradation, as VQA datasets lack the diversity and complexity of the original text instruction datasets with which the underlying language model was trained. To address this degradation, we first collect a lightweight, 5k-sample VQA preference dataset where answers were annotated by Gemini for five quality metrics in a granular fashion and investigate standard Supervised Fine-tuning, rejection sampling, Direct Preference Optimization (DPO) and SteerLM algorithms. Our findings indicate that with DPO, we can surpass the instruction-following capabilities of the language model, achieving a 6.73 score on MT-Bench, compared to Vicuna’s 6.57 and LLaVA’s 5.99. This enhancement in textual instruction-following capability correlates with boosted visual instruction performance (+4.9% on MM-Vet, +6% on LLaVA-Bench), with minimal alignment tax on visual knowledge benchmarks compared to the previous RLHF approach. In conclusion, we propose a distillation-based multi-modal alignment model with fine-grained annotations on a small dataset that restores and boosts MLLM’s language capability after visual instruction tuning.

We study semi-supervised sequence generation tasks, where the few labeled examples are too scarce to finetune a model, and meanwhile, few-shot prompted large language models (LLMs) exhibit room for improvement. In this paper, we present the discovery that a student model distilled from a few-shot prompted LLM can commonly generalize better than its teacher to unseen examples on such tasks. We find that the student is able to learn a general pattern from the high-quality pseudolabels produced by the teacher during knowledge distillation (KD), and favorably not a general pattern from the low-quality pseudolabels. Leveraging this discovery, we propose a new method, Multistage Collaborative Knowledge Distillation from an LLM (MCKD), for these tasks. MCKD first few-shot prompts an LLM to produce pseudolabels for unlabeled data. Then at each stage of an iterative KD process, a new pair of students is trained on disjoint partitions of the pseudolabeled data, and produces new and improved pseudolabels for their unseen partitions. We conduct extensive experiments on four syntactic and semantic parsing datasets and show the effectiveness of MCKD for low-resource semi-supervised sequence generation. On CRAFT biomedical parsing, for example, 3-stage MCKD with 50 labeled examples outperforms an LLM teacher and vanilla KD by 7.5% and 3.7% parsing F1, respectively, and matches the performance of supervised finetuning with 500 labeled examples.

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Controlled Text Generation for Black-box Language Models via Score-based Progressive Editor
Sangwon Yu | Changmin Lee | Hojin Lee | Sungroh Yoon

Controlled text generation, aiming to ensure that language models produce text containing only the desired domain or corpus attributes, is immensely crucial in the practical application of language models. Existing methods, however, are inapplicable to black-box models or suffer a significant trade-off between control and fluency in text generation. This paper introduces the Score-based Progressive Editor (ScoPE), a novel approach designed to overcome these issues. ScoPE modifies the context at the token level during the generation process of a backbone language model. This modification guides the subsequent text to naturally include the target attributes. To facilitate this process, ScoPE employs a training objective that maximizes a target score, comprehensively considering both control and fluency. Experimental results on diverse controlled generation tasks demonstrate that ScoPE can effectively regulate the attributes of the generated text while effectively utilizing the capability of the backbone large language models.

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LogogramNLP: Comparing Visual and Textual Representations of Ancient Logographic Writing Systems for NLP
Danlu Chen | Freda Shi | Aditi Agarwal | Jacobo Myerston | Taylor Berg-Kirkpatrick

Standard natural language processing (NLP) pipelines operate on symbolic representations of language, which typically consist of sequences of discrete tokens. However, creating an analogous representation for ancient logographic writing systems is an extremely labor-intensive process that requires expert knowledge. At present, a large portion of logographic data persists in a purely visual form due to the absence of transcription—this issue poses a bottleneck for researchers seeking to apply NLP toolkits to study ancient logographic languages: most of the relevant data are images of writing. This paper investigates whether direct processing of visual representations of language offers a potential solution. We introduce LogogramNLP, the first benchmark enabling NLP analysis of ancient logographic languages, featuring both transcribed and visual datasetsfor four writing systems along with annotations for tasks like classification, translation, and parsing. Our experiments compare systems thatemploy recent visual and text encoding strategies as backbones. The results demonstrate that visual representations outperform textual representations for some investigated tasks, suggesting that visual processing pipelines may unlock a large amount of cultural heritage data of logographic languages for NLP-based analyses. Data and code are available at https: //logogramNLP.github.io/.

Instruction tuning is critical to improve LLMs but usually suffers from low-quality and redundant data. Data filtering for instruction tuning has proved important in improving both the efficiency and performance of the tuning process. But it also leads to extra cost and computation due to the involvement of LLMs in this process. To reduce the filtering cost, we study Superfiltering: Can we use a smaller and weaker model to select data for finetuning a larger and stronger model? Despite the performance gap between weak and strong language models, we find their highly consistent capability to perceive instruction difficulty and data selection results. This enables us to use a much smaller and more efficient model to filter the instruction data used to train a larger language model. Not only does it largely speed up the data filtering, but the filtered-data-finetuned LLM achieves even better performance on standard benchmarks. Extensive experiments validate the efficacy and efficiency of our approach.

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Confabulation: The Surprising Value of Large Language Model Hallucinations
Peiqi Sui | Eamon Duede | Sophie Wu | Richard So

This paper presents a systematic defense of large language model (LLM) hallucinations or ‘confabulations’ as a potential resource instead of a categorically negative pitfall. The standard view is that confabulations are inherently problematic and AI research should eliminate this flaw. In this paper, we argue and empirically demonstrate that measurable semantic characteristics of LLM confabulations mirror a human propensity to utilize increased narrativity as a cognitive resource for sense-making and communication. In other words, it has potential value. Specifically, we analyze popular hallucination benchmarks and reveal that hallucinated outputs display increased levels of narrativity and semantic coherence relative to veridical outputs. This finding reveals a tension in our usually dismissive understandings of confabulation. It suggests, counter-intuitively, that the tendency for LLMs to confabulate may be intimately associated with a positive capacity for coherent narrative-text generation.

Soft prompt tuning is a widely studied parameter-efficient fine-tuning method. However, it has a clear drawback: many soft tokens must be inserted into the input sequences to guarantee downstream performance. As a result, soft prompt tuning is less considered than Low-rank adaptation (LoRA) in the large language modeling (LLM) era. In this work, we propose a novel prompt tuning method, Instruction-Aware Prompt Tuning (IAPT), that requires only four soft tokens. First, we install a parameter-efficient soft prompt generator at each Transformer layer to generate idiosyncratic soft prompts for each input instruction. The generated soft prompts can be seen as a semantic summary of the input instructions and can effectively guide the output generation. Second, the soft prompt generators are modules with a bottleneck architecture consisting of a self-attention pooling operation, two linear projections, and an activation function. Pilot experiments show that prompt generators at different Transformer layers require different activation functions. Thus, we propose to learn the idiosyncratic activation functions for prompt generators automatically with the help of rational functions. We have conducted experiments on various tasks, and the experimental results demonstrate that (a) our IAPT method can outperform the recent baselines with comparable tunable parameters. (b) Our IAPT method is more efficient than LoRA under the single-backbone multi-tenant setting.

We present a novel human annotated dataset for evaluating the ability for visual-language models to generate both short and long descriptions for real-world video clips, termed DeVAn (Dense Video Annotation). The dataset contains 8.5K YouTube video clips of 20-60 seconds in duration and covers a wide range of topics and interests. Each video clip is independently annotated by 5 human annotators, producing both captions (1 sentence) and summaries (3-10 sentences). Given any video selected from the dataset and its corresponding ASR information, we evaluate visual-language models on either caption or summary generation that is grounded in both the visual and auditory content of the video. Additionally, models are also evaluated on caption- and summary-based retrieval tasks, where the summary-based retrieval task requires the identification of a target video given excerpts of a given summary. Given the novel nature of the paragraph-length video summarization task, we compared different existing evaluation metrics and their alignment with human preferences and found that model-based evaluation metrics provide more semantically-oriented and human-aligned evaluation. Finally, we benchmarked a wide range of current video-language models on DeVAn, and we aim for DeVAn to serve as a useful evaluation set in the age of large language models and complex multi-modal tasks. Code is available at https://github.com/TK-21st/DeVAn.

Most traditional AI safety research views models as machines and centers on algorithm-focused attacks developed by security experts. As large language models (LLMs) become increasingly common and competent, non-expert users can also impose risks during daily interactions. Observing this, we shift the perspective, by treating LLMs as human-like communicators to examine the interplay between everyday language interaction and AI safety. Specifically, we study how to persuade LLMs to jailbreak them. First, we propose a persuasion taxonomy derived from decades of social science research. Then, we apply the taxonomy to automatically generate persuasive adversarial prompts (PAP) to jailbreak LLMs. Results show that persuasion significantly increases the jailbreak risk across all risk categories: PAP consistently achieves an attack success rate of over 92% on Llama-2-7b-Chat, GPT-3.5, and GPT-4 in 10 trials, surpassing recent algorithm-focused attacks. On the defense side, we explore various mechanisms against PAP, find a significant gap in existing defenses, and advocate for more fundamental solutions for AI safety.

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The Heuristic Core: Understanding Subnetwork Generalization in Pretrained Language Models
Adithya Bhaskar | Dan Friedman | Danqi Chen

Prior work has found that pretrained language models (LMs) fine-tuned with different random seeds can achieve similar in-domain performance but generalize differently on tests of syntactic generalization. In this work, we show that, even within a single model, we can find multiple subnetworks that perform similarly in-domain, but generalize vastly differently. To better understand these phenomena, we investigate if they can be understood in terms of “competing subnetworks”: the model initially represents a variety of distinct algorithms, corresponding to different subnetworks, and generalization occurs when it ultimately converges to one. This explanation has been used to account for generalization in simple algorithmic tasks (“grokking”). Instead of finding competing subnetworks, we find that all subnetworks—whether they generalize or not—share a set of attention heads, which we refer to as the _heuristic core_. Further analysis suggests that these attention heads emerge early in training and compute shallow, non-generalizing features. The model learns to generalize by incorporating additional attention heads, which depend on the outputs of the “heuristic” heads to compute higher-level features. Overall, our results offer a more detailed picture of the mechanisms for syntactic generalization in pre-trained LMs.

Large vision-language models (LVLMs) excel across diverse tasks involving concrete images from natural scenes. However, their ability to interpret abstract figures, such as geometry shapes and scientific plots, remains limited due to a scarcity of training datasets in scientific domains.To fill this gap, we introduce Multimodal ArXiv, consisting of ArXivCap and ArXivQA, for enhancing LVLMs scientific comprehension.ArXivCap is a figure-caption dataset comprising 6.4M images and 3.9M captions, sourced from 572K ArXiv papers spanning various scientific domains.Drawing from ArXivCap, we introduce ArXivQA, a question-answering dataset generated by prompting GPT-4V based on scientific figures. ArXivQA greatly enhances open-sourced LVLMs’ mathematical reasoning capabilities, achieving a 10.4% absolute accuracy gain on a multimodal mathematical reasoning benchmark.Furthermore, employing ArXivCap, we devise four vision-to-text tasks for benchmarking LVLMs.Evaluation results with state-of-the-art LVLMs underscore their struggle with the nuanced semantics of academic figures, while domain-specific training yields substantial performance gains.Our error analysis uncovers misinterpretations of visual context, recognition errors, and the production of overly simplified captions by current LVLMs, shedding light on future improvements.

Recently, there has been growing interest in long-context scaling of large language models (LLMs). To facilitate research in this field, we propose L-Eval to institute a more standardized evaluation for Long-Context Language Models (LCLMs) addressing two key aspects: dataset construction and evaluation metrics. On the one hand, we build a new evaluation suite containing 20 sub-tasks, 508 long documents, and more than 2,000 human-labeled query-response pairs including diverse task types, domains, and input length (3k~200k tokens). On the other hand, we investigate the effectiveness of evaluation metrics for LCLMs and we show that Length-instruction-enhanced (LIE) evaluation and LLM judges can better correlate with human judgments. We conducted a comprehensive study of 4 popular commercial LLMs and 12 open-source counterparts using the L-Eval benchmark. Our empirical findings offer useful insights into the study of LCLMs and lay the groundwork for the development of a more principled evaluation of these models.

Language technologies should be judged on their usefulness in real-world use cases. An often overlooked aspect in natural language processing (NLP) research and evaluation is language variation in the form of non-standard dialects or language varieties (hereafter, varieties). Most NLP benchmarks are limited to standard language varieties. To fill this gap, we propose DIALECTBENCH, the first-ever large-scale benchmark for NLP on varieties, which aggregates an extensive set of task-varied varieties datasets (10 text-level tasks covering 281 varieties). This allows for a comprehensive evaluation of NLP system performance on different varieties. We provide substantial proof of performance disparities between standard and non-standard language varieties, and we also identify language clusters with larger performance divergence across tasks.We believe DIALECTBENCH provides a comprehensive view of the current state of NLP for varieties and one step towards advancing it further.

Although achieving promising performance, recent analyses show that current generative large language models (LLMs) may still capture dataset biases and utilize them for generation, leading to poor generalizability and harmfulness of LLMs. However, due to the diversity of dataset biases and the over-optimization problem, previous prior-knowledge-based debiasing methods and fine-tuning-based debiasing methods may not be suitable for current LLMs.To address this issue, we explore combining active learning with the causal mechanisms and propose a casual-guided active learning (CAL) framework, which utilizes LLMs itself to automatically and autonomously identify informative biased samples and induce the bias patterns. Then a cost-effective and efficient in-context learning based method is employed to prevent LLMs from utilizing dataset biases during generation.Experimental results show that CAL can effectively recognize typical biased instances and induce various bias patterns for debiasing LLMs.

Psychological measurement is essential for mental health, self-understanding, and personal development. Traditional methods, such as self-report scales and psychologist interviews, often face challenges with engagement and accessibility. While game-based and LLM-based tools have been explored to improve user interest and automate assessment, they struggle to balance engagement with generalizability. In this work, we propose PsychoGAT (Psychological Game AgenTs) to achieve a generic gamification of psychological assessment. The main insight is that powerful LLMs can function both as adept psychologists and innovative game designers. By incorporating LLM agents into designated roles and carefully managing their interactions, PsychoGAT can transform any standardized scales into personalized and engaging interactive fiction games. To validate the proposed method, we conduct psychometric evaluations to assess its effectiveness and employ human evaluators to examine the generated content across various psychological constructs, including depression, cognitive distortions, and personality traits. Results demonstrate that PsychoGAT serves as an effective assessment tool, achieving statistically significant excellence in psychometric metrics such as reliability, convergent validity, and discriminant validity. Moreover, human evaluations confirm PsychoGAT’s enhancements in content coherence, interactivity, interest, immersion, and satisfaction.

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Towards Better Understanding of Contrastive Sentence Representation Learning: A Unified Paradigm for Gradient
Mingxin Li | Richong Zhang | Zhijie Nie

Sentence Representation Learning (SRL) is a crucial task in Natural Language Processing (NLP), where contrastive Self-Supervised Learning (SSL) is currently a mainstream approach. However, the reasons behind its remarkable effectiveness remain unclear. Specifically, many studies have investigated the similarities between contrastive and non-contrastive SSL from a theoretical perspective. Such similarities can be verified in classification tasks, where the two approaches achieve comparable performance. But in ranking tasks (i.e., Semantic Textual Similarity (STS) in SRL), contrastive SSL significantly outperforms non-contrastive SSL. Therefore, two questions arise: First, *what commonalities enable various contrastive losses to achieve superior performance in STS?* Second, *how can we make non-contrastive SSL also effective in STS?* To address these questions, we start from the perspective of gradients and discover that four effective contrastive losses can be integrated into a unified paradigm, which depends on three components: the **Gradient Dissipation**, the **Weight**, and the **Ratio**. Then, we conduct an in-depth analysis of the roles these components play in optimization and experimentally demonstrate their significance for model performance. Finally, by adjusting these components, we enable non-contrastive SSL to achieve outstanding performance in STS.

The world’s languages exhibit certain so-called typological or implicational universals; for example, Subject-Object-Verb (SOV) languages typically use postpositions. Explaining the source of such biases is a key goal of linguistics.We study word-order universals through a computational simulation with language models (LMs).Our experiments show that typologically-typical word orders tend to have lower perplexity estimated by LMs with cognitively plausible biases: syntactic biases, specific parsing strategies, and memory limitations. This suggests that the interplay of cognitive biases and predictability (perplexity) can explain many aspects of word-order universals.It also showcases the advantage of cognitively-motivated LMs, typically employed in cognitive modeling, in the simulation of language universals.

As Natural Language Processing (NLP) systems are increasingly employed in intricate social environments, a pressing query emerges: *Can these NLP systems mirror human-esque collaborative intelligence, in a multi-agent society consisting of multiple large language models (LLMs)?* This paper probes the collaboration mechanisms among contemporary NLP systems by melding practical experiments with theoretical insights. We fabricate four unique ‘societies’ comprised of LLM agents, where each agent is characterized by a specific ‘trait’ (easy-going or overconfident) and engages in collaboration with a distinct ‘thinking pattern’ (debate or reflection). Through evaluating these multi-agent societies on three benchmark datasets, we discern that certain collaborative strategies not only outshine previous top-tier approaches but also optimize efficiency (using fewer API tokens). Moreover, our results further illustrate that LLM agents manifest human-like social behaviors, such as conformity and consensus reaching, mirroring foundational social psychology theories. In conclusion, we integrate insights from social psychology to contextualize the collaboration of LLM agents, inspiring further investigations into the collaboration mechanism for LLMs. We commit to sharing our code and datasets, hoping to catalyze further research in this promising avenue.

This paper proposes Multi-modAl Retrieval model via Visual modulE pLugin (MARVEL), which learns an embedding space for queries and multi-modal documents to conduct retrieval. MARVEL encodes queries and multi-modal documents with a unified encoder model, which helps to alleviate the modality gap between images and texts. Specifically, we enable the image understanding ability of the well-trained dense retriever, T5-ANCE, by incorporating the visual module’s encoded image features as its inputs. To facilitate the multi-modal retrieval tasks, we build the ClueWeb22-MM dataset based on the ClueWeb22 dataset, which regards anchor texts as queries, and extracts the related text and image documents from anchor-linked web pages. Our experiments show that MARVEL significantly outperforms the state-of-the-art methods on the multi-modal retrieval dataset WebQA and ClueWeb22-MM. MARVEL provides an opportunity to broaden the advantages of text retrieval to the multi-modal scenario. Besides, we also illustrate that the language model has the ability to extract image semantics and partly map the image features to the input word embedding space. All codes are available at https://github.com/OpenMatch/MARVEL.

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Distributional Inclusion Hypothesis and Quantifications: Probing for Hypernymy in Functional Distributional Semantics
Chun Lo | Wai Lam | Hong Cheng | Guy Emerson

Functional Distributional Semantics (FDS) models the meaning of words by truth-conditional functions. This provides a natural representation for hypernymy but no guarantee that it can be learnt when FDS models are trained on a corpus. In this paper, we probe into FDS models and study the representations learnt, drawing connections between quantifications, the Distributional Inclusion Hypothesis (DIH), and the variational-autoencoding objective of FDS model training. Using synthetic data sets, we reveal that FDS models learn hypernymy on a restricted class of corpus that strictly follows the DIH. We further introduce a training objective that both enables hypernymy learning under the reverse of the DIH and improves hypernymy detection from real corpora.

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CausalGym: Benchmarking causal interpretability methods on linguistic tasks
Aryaman Arora | Dan Jurafsky | Christopher Potts

Language models (LMs) have proven to be powerful tools for psycholinguistic research, but most prior work has focused on purely behavioural measures (e.g., surprisal comparisons). At the same time, research in model interpretability has begun to illuminate the abstract causal mechanisms shaping LM behavior. To help bring these strands of research closer together, we introduce CausalGym. We adapt and expand the SyntaxGym suite of tasks to benchmark the ability of interpretability methods to causally affect model behaviour. To illustrate how CausalGym can be used, we study the pythia models (14M–6.9B) and assess the causal efficacy of a wide range of interpretability methods, including linear probing and distributed alignment search (DAS). We find that DAS outperforms the other methods, and so we use it to study the learning trajectory of two difficult linguistic phenomena in pythia-1b: negative polarity item licensing and filler–gap dependencies. Our analysis shows that the mechanism implementing both of these tasks is learned in discrete stages, not gradually.

Despite efforts to expand the knowledge of large language models (LLMs), knowledge gaps—missing or outdated information in LLMs—might always persist given the evolving nature of knowledge. In this work, we study approaches to identify LLM knowledge gaps and abstain from answering questions when knowledge gaps are present. We first adapt existing approaches to model calibration or adaptation through fine-tuning/prompting and analyze their ability to abstain from generating low-confidence outputs. Motivated by their failures in self-reflection and over-reliance on held-out sets, we propose two novel approaches that are based on model collaboration, i.e., LLMs probing other LLMs for knowledge gaps, either cooperatively or competitively. Extensive experiments with three LLMs on four QA tasks featuring diverse knowledge domains demonstrate that both cooperative and competitive approaches to unveiling LLM knowledge gaps achieve up to 19.3% improvements on abstain accuracy against the strongest baseline. Further analysis reveals that our abstention methods pinpoint failure cases in retrieval augmentation and knowledge gaps in multi-hop reasoning.

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Mission: Impossible Language Models
Julie Kallini | Isabel Papadimitriou | Richard Futrell | Kyle Mahowald | Christopher Potts

Chomsky and others have very directly claimed that large language models (LLMs) are equally capable of learning languages that are possible and impossible for humans to learn. However, there is very little published experimental evidence to support such a claim. Here, we develop a set of synthetic impossible languages of differing complexity, each designed by systematically altering English data with unnatural word orders and grammar rules. These languages lie on an impossibility continuum: at one end are languages that are inherently impossible, such as random and irreversible shuffles of English words, and on the other, languages that may not be intuitively impossible but are often considered so in linguistics, particularly those with rules based on counting word positions. We report on a wide range of evaluations to assess the capacity of GPT-2 small models to learn these uncontroversially impossible languages, and crucially, we perform these assessments at various stages throughout training to compare the learning process for each language. Our core finding is that GPT-2 struggles to learn impossible languages when compared to English as a control, challenging the core claim. More importantly, we hope our approach opens up a productive line of inquiry in which different LLM architectures are tested on a variety of impossible languages in an effort to learn more about how LLMs can be used as tools for these cognitive and typological investigations.

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Semisupervised Neural Proto-Language Reconstruction
Liang Lu | Peirong Xie | David Mortensen

Existing work implementing comparative reconstruction of ancestral languages (proto-languages) has usually required full supervision. However, historical reconstruction models are only of practical value if they can be trained with a limited amount of labeled data. We propose a semisupervised historical reconstruction task in which the model is trained on only a small amount of labeled data (cognate sets with proto-forms) and a large amount of unlabeled data (cognate sets without proto-forms). We propose a neural architecture for comparative reconstruction (DPD-BiReconstructor) incorporating an essential insight from linguists’ comparative method: that reconstructed words should not only be reconstructable from their daughter words, but also deterministically transformable back into their daughter words. We show that this architecture is able to leverage unlabeled cognate sets to outperform strong semisupervised baselines on this novel task.

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Speech Translation with Speech Foundation Models and Large Language Models: What is There and What is Missing?
Marco Gaido | Sara Papi | Matteo Negri | Luisa Bentivogli

The field of natural language processing (NLP) has recently witnessed a transformative shift with the emergence of foundation models, particularly Large Language Models (LLMs) that have revolutionized text-based NLP. This paradigm has extended to other modalities, including speech, where researchers are actively exploring the combination of Speech Foundation Models (SFMs) and LLMs into single, unified models capable of addressing multimodal tasks. Among such tasks, this paper focuses on speech-to-text translation (ST). By examining the published papers on the topic, we propose a unified view of the architectural solutions and training strategies presented so far, highlighting similarities and differences among them. Based on this examination, we not only organize the lessons learned but also show how diverse settings and evaluation approaches hinder the identification of the best-performing solution for each architectural building block and training choice. Lastly, we outline recommendations for future works on the topic aimed at better understanding the strengths and weaknesses of the SFM+LLM solutions for ST.

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Speech vs. Transcript: Does It Matter for Human Annotators in Speech Summarization?
Roshan Sharma | Suwon Shon | Mark Lindsey | Hira Dhamyal | Bhiksha Raj

Reference summaries for abstractive speech summarization require human annotation, which can be performed by listening to an audio recording or by reading textual transcripts of the recording. In this paper, we examine whether summaries based on annotators listening to the recordings differ from those based on annotators reading transcripts. Using existing intrinsic evaluation based on human evaluation, automatic metrics, LLM-based evaluation, and a retrieval-based reference-free method, we find that summaries are indeed different based on the source modality, and that speech-based summaries are more factually consistent and information-selective than transcript-based summaries. Transcript-based summaries are impacted by recognition errors in the source, and expert-written summaries are more informative and reliable. We make all the collected data and analysis code public to facilitate the reproduction of our work and advance research in this area.

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D2LLM: Decomposed and Distilled Large Language Models for Semantic Search
Zihan Liao | Hang Yu | Jianguo Li | Jun Wang | Wei Zhang

The key challenge in semantic search is to create models that are both accurate and efficient in pinpointing relevant sentences for queries. While BERT-style bi-encoders excel in efficiency with pre-computed embeddings, they often miss subtle nuances in search tasks. Conversely, GPT-style LLMs with cross-encoder designs capture these nuances but are computationally intensive, hindering real-time applications. In this paper, we present D2LLMs—Decomposed and Distilled LLMs for semantic search—that combines the best of both worlds. We decompose a cross-encoder into an efficient bi-encoder integrated with Pooling by Multihead Attention and an Interaction Emulation Module, achieving nuanced understanding and pre-computability. Knowledge from the LLM is distilled into this model using contrastive, rank, and feature imitation techniques. Our experiments show that D2LLM surpasses five leading baselines in terms of all metrics across three tasks, particularly improving NLI task performance by at least 6.45%

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Arabic Diacritics in the Wild: Exploiting Opportunities for Improved Diacritization
Salman Elgamal | Ossama Obeid | Mhd Kabbani | Go Inoue | Nizar Habash

The widespread absence of diacritical marks in Arabic text poses a significant challenge for Arabic natural language processing (NLP). This paper explores instances of naturally occurring diacritics, referred to as “diacritics in the wild,” to unveil patterns and latent information across six diverse genres: news articles, novels, children’s books, poetry, political documents, and ChatGPT outputs. We present a new annotated dataset that maps real-world partially diacritized words to their maximal full diacritization in context. Additionally, we propose extensions to the analyze-and-disambiguate approach in Arabic NLP to leverage these diacritics, resulting in notable improvements. Our contributions encompass a thorough analysis, valuable datasets, and an extended diacritization algorithm. We release our code and datasets as open source.

Automated disinformation generation is often listed as one of the risks of large language models (LLMs). The theoretical ability to flood the information space with disinformation content might have dramatic consequences for democratic societies around the world. This paper presents a comprehensive study of the disinformation capabilities of the current generation of LLMs to generate false news articles in English language. In our study, we evaluated the capabilities of 10 LLMs using 20 disinformation narratives. We evaluated several aspects of the LLMs: how well they are at generating news articles, how strongly they tend to agree or disagree with the disinformation narratives, how often they generate safety warnings, etc. We also evaluated the abilities of detection models to detect these articles as LLM-generated. We conclude that LLMs are able to generate convincing news articles that agree with dangerous disinformation narratives.

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Learn or Recall? Revisiting Incremental Learning with Pre-trained Language Models
Junhao Zheng | Shengjie Qiu | Qianli Ma

Incremental Learning (IL) has been a long-standing problem in both vision and Natural Language Processing (NLP) communities.In recent years, as Pre-trained Language Models (PLMs) have achieved remarkable progress in various NLP downstream tasks, utilizing PLMs as backbones has become a common practice in recent research of IL in NLP.Most assume that catastrophic forgetting is the biggest obstacle to achieving superior IL performance and propose various techniques to overcome this issue.However, we find that this assumption is problematic.Specifically, we revisit more than 20 methods on four classification tasks (Text Classification, Intent Classification, Relation Extraction, and Named Entity Recognition) under the two most popular IL settings (Class-Incremental and Task-Incremental) and reveal that most of them severely underestimate the inherent anti-forgetting ability of PLMs.Based on the observation, we propose a frustratingly easy method called SEQ* for IL with PLMs.The results show that SEQ* has competitive or superior performance compared with state-of-the-art (SOTA) IL methods yet requires considerably less trainable parameters and training time.These findings urge us to revisit the IL with PLMs and encourage future studies to have a fundamental understanding of the catastrophic forgetting in PLMs.

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How to Handle Different Types of Out-of-Distribution Scenarios in Computational Argumentation? A Comprehensive and Fine-Grained Field Study
Andreas Waldis | Yufang Hou | Iryna Gurevych

The advent of pre-trained Language Models (LMs) has markedly advanced natural language processing, but their efficacy in out-of-distribution (OOD) scenarios remains a significant challenge. Computational argumentation (CA), modeling human argumentation processes, is a field notably impacted by these challenges because complex annotation schemes and high annotation costs naturally lead to resources barely covering the multiplicity of available text sources and topics. Due to this data scarcity, generalization to data from uncovered covariant distributions is a common challenge for CA tasks like stance detection or argument classification. This work systematically assesses LMs’ capabilities for such OOD scenarios. While previous work targets specific OOD types like topic shifts or OOD uniformly, we address three prevalent OOD scenarios in CA: topic shift, domain shift, and language shift. Our findings challenge the previously asserted general superiority of in-context learning (ICL) for OOD. We find that the efficacy of such learning paradigms varies with the type of OOD. Specifically, while ICL excels for domain shifts, prompt-based fine-tuning surpasses for topic shifts. To sum up, we navigate the heterogeneity of OOD scenarios in CA and empirically underscore the potential of base-sized LMs in overcoming these challenges.

Large language models (LLMs) show remarkable human-like capability in various domains and languages. To bridge this quality gap, we introduce Cendol, a collection of Indonesian LLMs encompassing both decoder-only and encoder-decoder architectures across a range of model sizes. We highlight Cendol’s effectiveness across a diverse array of tasks, attaining ~20% improvement, and demonstrate its capability to generalize to unseen tasks and indigenous languages of Indonesia. Furthermore, Cendol models showcase improved human favorability despite their limitations in capturing indigenous knowledge and cultural values in Indonesia. In addition, we discuss the shortcomings of parameter-efficient tunings, such as LoRA, for language adaptation. Alternatively, we propose the usage of vocabulary adaptation to enhance efficiency. Lastly, we evaluate the safety of Cendol and showcase that safety in pre-training in one language such as English is transferable to low-resource languages, such as Indonesian, even without RLHF and safety fine-tuning.

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Must NLP be Extractive?
Steven Bird

How do we roll out language technologies across a world with 7,000 languages? In one story, we scale the successes of NLP further into ‘low-resource’ languages, doing ever more with less. However, this approach does not recognise the fact that, beyond the 500 institutional languages, the remaining languages are oral vernaculars spoken by communities who use a language of wider communication to interact with the outside world. I argue that such ‘contact languages’ are the appropriate target for technologies like machine translation, and that the 6,500 oral languages must be approached differently. I share a story from an Indigenous community, where local people reshaped an extractive agenda to align with their relational agenda. I describe the emerging paradigm of relational NLP and explain how it opens the way to non-extractive methods and to solutions that enhance human agency.

The practice of Retrieval-Augmented Generation (RAG), which integrates Large Language Models (LLMs) with retrieval systems, has become increasingly prevalent. However, the repercussions of LLM-derived content infiltrating the web and influencing the retrieval-generation feedback loop are largely uncharted territories. In this study, we construct and iteratively run a simulation pipeline to deeply investigate the short-term and long-term effects of LLM text on RAG systems. Taking the trending Open Domain Question Answering (ODQA) task as a point of entry, our findings reveal a potential digital “Spiral of Silence” effect, with LLM-generated text consistently outperforming human-authored content in search rankings, thereby diminishing the presence and impact of human contributions online. This trend risks creating an imbalanced information ecosystem, where the unchecked proliferation of erroneous LLM-generated content may result in the marginalization of accurate information. We urge the academic community to take heed of this potential issue, ensuring a diverse and authentic digital information landscape.

We introduce Latxa, a family of large language models for Basque ranging from 7 to 70 billion parameters. Latxa is based on Llama 2, which we continue pretraining on a new Basque corpus comprising 4.3M documents and 4.2B tokens. Addressing the scarcity of high-quality benchmarks for Basque, we further introduce 4 multiple choice evaluation datasets: EusProficiency, comprising 5,169 questions from official language proficiency exams; EusReading, comprising 352 reading comprehension questions; EusTrivia, comprising 1,715 trivia questions from 5 knowledge areas; and EusExams, comprising 16,046 questions from public examinations. In our extensive evaluation, Latxa outperforms all previous open models we compare to by a large margin. In addition, it is competitive with GPT-4 Turbo in language proficiency and understanding, despite lagging behind in reading comprehension and knowledge-intensive tasks. Both the Latxa family of models, as well as our new pretraining corpora and evaluation datasets, are publicly available under open licenses. Our suite enables reproducible research on methods to build LLMs for low-resource languages.

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Why are Sensitive Functions Hard for Transformers?
Michael Hahn | Mark Rofin

Empirical studies have identified a range of learnability biases and limitations of transformers, such as a persistent difficulty in learning to compute simple formal languages such as PARITY, and a bias towards low-degree functions. However, theoretical understanding remains limited, with existing expressiveness theory either overpredicting or underpredicting realistic learning abilities. We prove that, under the transformer architecture, the loss landscape is constrained by the input-space sensitivity: Transformers whose output is sensitive to many parts of the input string inhabit isolated points in parameter space, leading to a low-sensitivity bias in generalization. We show theoretically and empirically that this theory unifies a broad array of empirical observations about the learning abilities and biases of transformers, such as their generalization bias towards low sensitivity and low degree, and difficulty in length generalization for PARITY. This shows that understanding transformers’ inductive biases requires studying not just their in-principle expressivity, but also their loss landscape.

Large Language Model (LLM)-enhanced agents become increasingly prevalent in Human-AI communication, offering vast potential from entertainment to professional domains. However, current multi-modal dialogue systems overlook the acoustic information present in speech, which is crucial for understanding human communication nuances. This oversight can lead to misinterpretations of speakers’ intentions, resulting in inconsistent or even contradictory responses within dialogues. To bridge this gap, in this paper, we propose PerceptiveAgent, an empathetic multi-modal dialogue system designed to discern deeper or more subtle meanings beyond the literal interpretations of words through the integration of speech modality perception. Employing LLMs as a cognitive core, PerceptiveAgent perceives acoustic information from input speech and generates empathetic responses based on speaking styles described in natural language. Experimental results indicate that PerceptiveAgent excels in contextual understanding by accurately discerning the speakers’ true intentions in scenarios where the linguistic meaning is either contrary to or inconsistent with the speaker’s true feelings, producing more nuanced and expressive spoken dialogues. Code is publicly available at: https://github.com/Haoqiu-Yan/PerceptiveAgent.

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IRCoder: Intermediate Representations Make Language Models Robust Multilingual Code Generators
Indraneil Paul | Goran Glavaš | Iryna Gurevych

Code generation has fast become one of the most popular applications of language models (LMs). Nonetheless, research on multilingual aspects of Code-LMs, such as cross-lingual transfer between different programming languages, language-specific data augmentation, and post-hoc LM adaptation, alongside the exploitation of data sources other than the original textual content, has been much sparser than for their natural language counterparts. In particular, most mainstream Code-LMs have been pre-trained on source code files alone. In this work, we investigate the prospect of leveraging readily available compiler intermediate representations (IR)—shared across programming languages—to improve the multilingual capabilities of Code-LMs and facilitate cross-lingual transfer. To this end, we first compile SLTrans, a parallel dataset consisting of nearly 4M self-contained source code files coupled with their respective intermediate representations. Next, starting from various base Code-LMs (ranging from 1.1B to 7.3B parameters), we carry out continued causal language modelling training on SLTrans, forcing the Code-LMs to (1) learn the IR language and (2) align the IR constructs with respective constructs of various programming languages. Our resulting models, dubbed IRCoder, display sizeable and consistent gains across various code generation tasks and metrics, including prompt robustness, multilingual code completion, code understanding, and instruction following.

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The Echoes of Multilinguality: Tracing Cultural Value Shifts during Language Model Fine-tuning
Rochelle Choenni | Anne Lauscher | Ekaterina Shutova

Texts written in different languages reflect different culturally-dependent beliefs of their writers. Thus, we expect multilingual LMs (MLMs), that are jointly trained on a concatenation of text in multiple languages, to encode different cultural values for each language. Yet, as the ‘multilinguality’ of these LMs is driven by cross-lingual sharing, we also have reason to belief that cultural values bleed over from one language into another. This limits the use of MLMs in practice, as apart from being proficient in generating text in multiple languages, creating language technology that can serve a community also requires the output of LMs to be sensitive to their biases (Naous et al. 2023). Yet, little is known about how cultural values emerge and evolve in MLMs (Hershcovich et al. 2022). We are the first to study how languages can exert influence on the cultural values encoded for different test languages, by studying how such values are revised during fine-tuning. Focusing on the fine-tuning stage allows us to study the interplay between value shifts when exposed to new linguistic experience from different data sources and languages. Lastly, we use a training data attribution method to find patterns in the fine-tuning examples, and the languages that they come from, that tend to instigate value shifts.

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MYTE: Morphology-Driven Byte Encoding for Better and Fairer Multilingual Language Modeling
Tomasz Limisiewicz | Terra Blevins | Hila Gonen | Orevaoghene Ahia | Luke Zettlemoyer

A major consideration in multilingual language modeling is how to best represent languages with diverse vocabularies and scripts.Although contemporary text encoding methods cover most of the world’s writing systems, they exhibit bias towards the high-resource languages of the Global West. As a result, texts of underrepresented languages tend to be segmented into long sequences of linguistically meaningless units. To address the disparities, we introduce a new paradigm that encodes the same information with segments of consistent size across diverse languages. Our encoding convention (MYTE) is based on morphemes, as their inventories are more balanced across languages than characters, which are used in previous methods. We show that MYTE produces shorter encodings for all 99 analyzed languages, with the most notable improvements for non-European languages and non-Latin scripts. This, in turn, improves multilingual LM performance and diminishes the perplexity gap throughout diverse languages.

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MultiLegalPile: A 689GB Multilingual Legal Corpus
Joel Niklaus | Veton Matoshi | Matthias Stürmer | Ilias Chalkidis | Daniel Ho

Large, high-quality datasets are crucial for training Large Language Models (LLMs). However, so far, few datasets are available for specialized critical domains such as law and the available ones are often small and only in English. To fill this gap, we curate and release MultiLegalPile, a 689GB corpus in 24 languages from 17 jurisdictions. MultiLegalPile includes diverse legal data sources and allows for pretraining NLP models under fair use, with most of the dataset licensed very permissively. We pretrain two RoBERTa models and one Longformer multilingually, and 24 monolingual models on each of the language-specific subsets and evaluate them on LEXTREME. Additionally, we evaluate the English and multilingual models on LexGLUE. Our multilingual models set a new SotA on LEXTREME and our English models on LexGLUE. We release the dataset, trained models, and all code under the most open licenses possible.

Enhancing the attribution in large language models (LLMs) is a crucial task. One feasible approach is to enable LLMs to cite external sources that support their generations. However, existing datasets and evaluation methods in this domain still exhibit notable limitations. In this work, we formulate the task of attributed query-focused summarization (AQFS) and present WebCiteS, a Chinese dataset featuring 7k human-annotated summaries with citations. WebCiteS derives from real-world user queries and web search results, offering a valuable resource for model training and evaluation. Prior works in attribution evaluation do not differentiate between groundedness errors and citation errors. They also fall short in automatically verifying sentences that draw partial support from multiple sources. We tackle these issues by developing detailed metrics and enabling the automatic evaluator to decompose the sentences into sub-claims for fine-grained verification. Our comprehensive evaluation of both open-source and proprietary models on WebCiteS highlights the challenge LLMs face in correctly citing sources, underscoring the necessity for further improvement. The dataset and code will be open-sourced to facilitate further research in this crucial field.

What can large language models learn? By definition, language models (LM) are distributionsover strings. Therefore, an intuitive way of addressing the above question is to formalize it as a matter of learnability of classes of distributions over strings. While prior work in this direction focused on assessing the theoretical limits, in contrast, we seek to understand the empirical learnability. Unlike prior empirical work, we evaluate neural LMs on their home turf—learning probabilistic languages—rather than as classifiers of formal languages. In particular, we investigate the learnability of regular LMs (RLMs) by RNN and Transformer LMs. We empirically test the learnability of RLMs as a function of various complexity parameters of the RLM and the hidden state size of the neural LM. We find that the RLM rank, which corresponds to the size of linear space spanned by the logits of its conditional distributions, and the expected length of sampled strings are strong and significant predictors of learnability for both RNNs and Transformers. Several other predictors also reach significance, but with differing patterns between RNNs and Transformers.

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Tree-Averaging Algorithms for Ensemble-Based Unsupervised Discontinuous Constituency Parsing
Behzad Shayegh | Yuqiao Wen | Lili Mou

We address unsupervised discontinuous constituency parsing, where we observe a high variance in the performance of the only previous model in the literature. We propose to build an ensemble of different runs of the existing discontinuous parser by averaging the predicted trees, to stabilize and boost performance. To begin with, we provide comprehensive computational complexity analysis (in terms of P and NP-complete) for tree averaging under different setups of binarity and continuity. We then develop an efficient exact algorithm to tackle the task, which runs in a reasonable time for all samples in our experiments. Results on three datasets show our method outperforms all baselines in all metrics; we also provide in-depth analyses of our approach.

Safety is critical to the usage of large language models (LLMs). Multiple techniques such as data filtering and supervised fine-tuning have been developed to strengthen LLM safety. However, currently known techniques presume that corpora used for safety alignment of LLMs are solely interpreted by semantics. This assumption, however, does not hold in real-world applications, which leads to severe vulnerabilities in LLMs. For example, users of forums often use ASCII art, a form of text-based art, to convey image information. In this paper, we propose a novel ASCII art-based jailbreak attack and introduce a comprehensive benchmark Vision-in-Text Challenge (ViTC) to evaluate the capabilities of LLMs in recognizing prompts that cannot be solely interpreted by semantics. We show that five SOTA LLMs (GPT-3.5, GPT-4, Gemini, Claude, and Llama2) struggle to recognize prompts provided in the form of ASCII art. Based on this observation, we develop the jailbreak attack ArtPrompt, which leverages the poor performance of LLMs in recognizing ASCII art to bypass safety measures and elicit undesired behaviors from LLMs. ArtPrompt only requires black-box access to the victim LLMs, making it a practical attack. We evaluate ArtPrompt on five SOTA LLMs, and show that ArtPrompt can effectively and efficiently induce undesired behaviors from all five LLMs.

Software development is a complex task that necessitates cooperation among multiple members with diverse skills. Numerous studies used deep learning to improve specific phases in a waterfall model, such as design, coding, and testing. However, the deep learning model in each phase requires unique designs, leading to technical inconsistencies across various phases, which results in a fragmented and ineffective development process. In this paper, we introduce ChatDev, a chat-powered software development framework in which specialized agents driven by large language models (LLMs) are guided in what to communicate (via chat chain) and how to communicate (via communicative dehallucination). These agents actively contribute to the design, coding, and testing phases through unified language-based communication, with solutions derived from their multi-turn dialogues. We found their utilization of natural language is advantageous for system design, and communicating in programming language proves helpful in debugging. This paradigm demonstrates how linguistic communication facilitates multi-agent collaboration, establishing language as a unifying bridge for autonomous task-solving among LLM agents. The code and data are available at https://github.com/OpenBMB/ChatDev.

Text style transfer (TST) is an important task in natural language generation, which aims to transfer the text style (e.g., sentiment) while keeping its semantic information. Due to the absence of parallel datasets for supervision, most existing studies have been conducted in an unsupervised manner, where the generated sentences often suffer from high semantic divergence and thus low semantic preservation. In this paper, we propose a novel disentanglement-based framework for TST named DisenTrans, where disentanglement means that we separate the attribute and content components in the natural language corpus and consider this task from these two perspectives. Concretely, we first create a disentangled Chain-of-Thought prompting procedure to synthesize parallel data and corresponding attribute components for supervision. Then we develop a disentanglement learning method with synthetic data, where two losses are designed to enhance the focus on attribute properties and constrain the semantic space, thereby benefiting style control and semantic preservation respectively. Instructed by the disentanglement concept, our framework creates valuable supervised information and utilizes it effectively in TST tasks. Extensive experiments on mainstream datasets present that our framework achieves significant performance with great sample efficiency.

Multi-agent systems, when enhanced with Large Language Models (LLMs), exhibit profound capabilities in collective intelligence. However, the potential misuse of this intelligence for malicious purposes presents significant risks. To date, comprehensive research on the safety issues associated with multi-agent systems remains limited. In this paper, we explore these concerns through the innovative lens of agent psychology, revealing that the dark psychological states of agents constitute a significant threat to safety.To tackle these concerns, we propose a comprehensive framework (PsySafe) grounded in agent psychology, focusing on three key areas: firstly, identifying how dark personality traits in agents can lead to risky behaviors; secondly, evaluating the safety of multi-agent systems from the psychological and behavioral perspectives, and thirdly, devising effective strategies to mitigate these risks.Our experiments reveal several intriguing phenomena, such as the collective dangerous behaviors among agents, agents’ self-reflection when engaging in dangerous behavior, and the correlation between agents’ psychological assessments and dangerous behaviors. We anticipate that our framework and observations will provide valuable insights for further research into the safety of multi-agent systems. We make our data and code publicly accessible at https://github.com/AI4Good24/PsySafe.

Emotional Support Conversation (ESC) is a task aimed at alleviating individuals’ emotional distress through daily conversation. Given its inherent complexity and non-intuitive nature, ESConv dataset incorporates support strategies to facilitate the generation of appropriate responses. Recently, despite the remarkable conversational ability of large language models (LLMs), previous studies have suggested that they often struggle with providing useful emotional support. Hence, this work initially analyzes the results of LLMs on ESConv, revealing challenges in selecting the correct strategy and a notable preference for a specific strategy. Motivated by these, we explore the impact of the inherent preference in LLMs on providing emotional support, and consequently, we observe that exhibiting high preference for specific strategies hinders effective emotional support, aggravating its robustness in predicting the appropriate strategy. Moreover, we conduct a methodological study to offer insights into the necessary approaches for LLMs to serve as proficient emotional supporters. Our findings emphasize that (1) low preference for specific strategies hinders the progress of emotional support, (2) external assistance helps reduce preference bias, and (3) existing LLMs alone cannot become good emotional supporters. These insights suggest promising avenues for future research to enhance the emotional intelligence of LLMs.

Processing and reasoning over long contexts is crucial for many practical applications of Large Language Models (LLMs), such as document comprehension and agent construction. Despite recent strides in making LLMs process contexts with more than 100K tokens, there is currently a lack of a standardized benchmark to evaluate this long-context capability. Existing public benchmarks typically focus on contexts around 10K tokens, limiting the assessment and comparison of LLMs in processing longer contexts. In this paper, we propose , the first LLM benchmark featuring an average data length surpassing 100K tokens. comprises synthetic and realistic tasks spanning diverse domains in English and Chinese. The tasks in are designed to require an understanding of long dependencies in contexts and make simply retrieving a limited number of passages from contexts not sufficient for these tasks. Based on , we evaluate several state-of-the-art LLMs tailored for processing long contexts. The experimental results indicate that existing long-context LLMs still require significant advancements to process 100K+ contexts effectively. Furthermore, we present three intriguing analyses regarding the behavior of LLMs processing long context. Our code and data is released.

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Natural Language Satisfiability: Exploring the Problem Distribution and Evaluating Transformer-based Language Models
Tharindu Madusanka | Ian Pratt-Hartmann | Riza Batista-Navarro

Efforts to apply transformer-based language models (TLMs) to the problem of reasoning in natural language have enjoyed ever-increasing success in recent years. The most fundamental task in this area to which nearly all others can be reduced is that of determining satisfiability. However, from a logical point of view, satisfiability problems vary along various dimensions, which may affect TLMs’ ability to learn how to solve them. The problem instances of satisfiability in natural language can belong to different computational complexity classes depending on the language fragment in which they are expressed. Although prior research has explored the problem of natural language satisfiability, the above-mentioned point has not been discussed adequately. Hence, we investigate how problem instances from varying computational complexity classes and having different grammatical constructs impact TLMs’ ability to learn rules of inference. Furthermore, to faithfully evaluate TLMs, we conduct an empirical study to explore the distribution of satisfiability problems.

Much recent work seeks to evaluate values and opinions in large language models (LLMs) using multiple-choice surveys and questionnaires. Most of this work is motivated by concerns around real-world LLM applications. For example, politically-biased LLMs may subtly influence society when they are used by millions of people. Such real-world concerns, however, stand in stark contrast to the artificiality of current evaluations: real users do not typically ask LLMs survey questions. Motivated by this discrepancy, we challenge the prevailing *constrained* evaluation paradigm for values and opinions in LLMs and explore more realistic *unconstrained* evaluations. As a case study, we focus on the popular Political Compass Test (PCT). In a systematic review, we find that most prior work using the PCT *forces models to comply with the PCT’s multiple-choice format. We show that models give substantively different answers when not forced; that answers change depending on how models are forced; and that answers lack paraphrase robustness. Then, we demonstrate that models give different answers yet again in a more realistic open-ended answer setting. We distill these findings into recommendations and open challenges in evaluating values and opinions in LLMs.

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AI ‘News’ Content Farms Are Easy to Make and Hard to Detect: A Case Study in Italian
Giovanni Puccetti | Anna Rogers | Chiara Alzetta | Felice Dell’Orletta | Andrea Esuli

Large Language Models (LLMs) are increasingly used as ‘content farm’ models (CFMs), to generate synthetic text that could pass for real news articles. This is already happening even for languages that do not have high-quality monolingual LLMs. We show that fine-tuning Llama (v1), mostly trained on English, on as little as 40K Italian news articles, is sufficient for producing news-like texts that native speakers of Italian struggle to identify as synthetic.We investigate three LLMs and three methods of detecting synthetic texts (log-likelihood, DetectGPT, and supervised classification), finding that they all perform better than human raters, but they are all impractical in the real world (requiring either access to token likelihood information or a large dataset of CFM texts). We also explore the possibility of creating a proxy CFM: an LLM fine-tuned on a similar dataset to one used by the real ‘content farm’. We find that even a small amount of fine-tuning data suffices for creating a successful detector, but we need to know which base LLM is used, which is a major challenge.Our results suggest that there are currently no practical methods for detecting synthetic news-like texts ‘in the wild’, while generating them is too easy. We highlight the urgency of more NLP research on this problem.

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Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Models
Mosh Levy | Alon Jacoby | Yoav Goldberg

This paper explores the impact of extending input lengths on the capabilities of Large Language Models (LLMs). Despite LLMs advancements in recent times, their performance consistency across different input lengths is not well understood. We investigate this aspect by introducing a novel QA reasoning framework, specifically designed to assess the impact of input length. We isolate the effect of input length using multiple versions of the same sample, each being extended with padding of different lengths, types and locations. Our findings show a notable degradation in LLMs’ reasoning performance at much shorter input lengths than their technical maximum. We show that the degradation trend appears in every version of our dataset, although at different intensities.Additionally, our study reveals that the traditional metric of next word prediction correlates negatively with performance of LLMs’ on our reasoning dataset. We analyse our results and identify failure modes that can serve as useful guides for future research, potentially informing strategies to address the limitations observed in LLMs.

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Disambiguate Words like Composing Them: A Morphology-Informed Approach to Enhance Chinese Word Sense Disambiguation
Yue Wang | Qiliang Liang | Yaqi Yin | Hansi Wang | Yang Liu

In parataxis languages like Chinese, word meanings are highly correlated with morphological knowledge, which can help to disambiguate word senses. However, in-depth exploration of morphological knowledge in previous word sense disambiguation (WSD) methods is still lacking due to the absence of publicly available resources. In this paper, we are motivated to enhance Chinese WSD with full morphological knowledge, including both word-formations and morphemes. We first construct the largest and releasable Chinese WSD resources, including the lexico-semantic inventories MorInv and WrdInv, a Chinese WSD dataset MiCLS, and an out-of-volcabulary (OOV) test set. Then, we propose a model, MorBERT, to fully leverage this morphology-informed knowledge for Chinese WSD and achieve a SOTA F1 of 92.18% in the task. Finally, we demonstrated the model’s robustness in low-resource settings and generalizability to OOV senses. These resources and methods may bring new insights into and solutions for various downstream tasks in both computational and humanistic fields.

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Do Llamas Work in English? On the Latent Language of Multilingual Transformers
Chris Wendler | Veniamin Veselovsky | Giovanni Monea | Robert West

We ask whether multilingual language models trained on unbalanced, English-dominated corpora use English as an internal pivot language—-a question of key importance for understanding how language models function and the origins of linguistic bias. Focusing on the Llama-2 family of transformer models, our study is based on carefully constructed non-English prompts with a unique correct single-token continuation. From layer to layer, transformers gradually map an input embedding of the final prompt token to an output embedding from which next-token probabilities are computed. Tracking intermediate embeddings through their high-dimensional space reveals three distinct phases, whereby intermediate embeddings (1) start far away from output token embeddings; (2) already in middle layers allow for decoding a semantically correct next token, but giving higher probability to its version in English than in the input language; (3) move into an input-language-specific region of the embedding space. We cast these results into a conceptual model where the three phases operate in ”input space”, ”concept space”, and ”output space”, respectively. Crucially, our evidence suggests that the abstract ”concept space” lies closer to English than to other input languages, which may have important consequences regarding the biases embodied by multilingual language models.

Large Language Models (LLMs) have demonstrated remarkable abilities in general scenarios. Instruction finetuning empowers them to align with humans in various tasks. Nevertheless, the Diversity and Quality of the instruction data remain two main challenges for instruction finetuning. With regard to this, in this paper, we propose a novel gradient-based method to automatically select high-quality and diverse instruction finetuning data for machine translation. Our key innovation centers around analyzing how individual training examples influence the model during training. Specifically, we select training examples that exert beneficial influences on the model as high-quality ones by means of Influence Function plus a small high-quality seed dataset. Moreover, to enhance the diversity of the training data we maximize the variety of influences they have on the model by clustering on their gradients and resampling. Extensive experiments on WMT22 and FLORES translation tasks demonstrate the superiority of our methods, and in-depth analysis further validates their effectiveness and generalization.

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Media Framing: A typology and Survey of Computational Approaches Across Disciplines
Yulia Otmakhova | Shima Khanehzar | Lea Frermann

Framing studies how individuals and societies make sense of the world, by communicating or representing complex issues through schema of interpretation. The framing of information in the mass media influences our interpretation of facts and corresponding decisions, so detecting and analysing it is essential to understand biases in the information we consume. Despite that, framing is still mostly examined manually, on a case-by-case basis, while existing large-scale automatic analyses using NLP methods are not mature enough to solve this task. In this survey we show that despite the growing interest to framing in NLP its current approaches do not capture those aspects which allow to frame, rather than simply convey, the message. To this end, we bring together definitions of frames and framing adopted in different disciplines; examine cognitive, linguistic, and communicative aspects a frame contains beyond its topical content. We survey recent work on computational frame detection, and discuss how framing aspects and frame definitions are (or should) be reflected in NLP approaches.

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SPZ: A Semantic Perturbation-based Data Augmentation Method with Zonal-Mixing for Alzheimer’s Disease Detection
FangFang Li | Cheng Huang | PuZhen Su | Jie Yin

Alzheimer’s Disease (AD), characterized by significant cognitive and functional impairment, necessitates the development of early detection techniques. Traditional diagnostic practices, such as cognitive assessments and biomarker analysis, are often invasive and costly. Deep learning-based approaches for non-invasive AD detection have been explored in recent studies, but the lack of accessible data hinders further improvements in detection performance. To address these challenges, we propose a novel semantic perturbation-based data augmentation method that essentially differs from existing techniques, which primarily rely on explicit data engineering. Our approach generates controlled semantic perturbations to enhance textual representations, aiding the model in identifying AD-specific linguistic patterns, particularly in scenarios with limited data availability. It learns contextual information and dynamically adjusts the perturbation degree for different linguistic features. This enhances the model’s sensitivity to AD-specific linguistic features and its robustness against natural language noise. Experimental results on the ADReSS challenge dataset demonstrate that our approach outperforms other strong and competitive deep learning methods.

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Calibrating Large Language Models Using Their Generations Only
Dennis Ulmer | Martin Gubri | Hwaran Lee | Sangdoo Yun | Seong Oh

As large language models (LLMs) are increasingly deployed in user-facing applications, building trust and maintaining safety by accurately quantifying a model’s confidence in its prediction becomes even more important. However, finding effective ways to calibrate LLMs—especially when the only interface to the models is their generated text—remains a challenge. We propose APRICOT (Auxiliary prediction of confidence targets): A method to set confidence targets and train an additional model that predicts an LLM’s confidence based on its textual input and output alone. This approach has several advantages: It is conceptually simple, does not require access to the target model beyond its output, does not interfere with the language generation, and has a multitude of potential usages, for instance by verbalizing the predicted confidence or using it to re-prompting the LLM to accurately reflecting its uncertainty. We show how our approach performs competitively in terms of calibration error for white-box and black-box LLMs on closed-book question-answering to detect incorrect LLM answers.

Despite the advancements in in-context learning (ICL) for large language models (LLMs), current research centers on specific prompt engineering, such as demonstration selection, with the expectation that a single iteration of demonstrations processing can generalize effectively to a given test sample. However, this perspective overlooks the potential benefits derived from multiple iterations involving demonstrations, a practice aligning more closely with the iterative decision-making process exhibited by humans, who often learn through analogy. In this study, we introduce a novel two-stage framework to boost ICL in LLMs. Specifically, our framework delineates the ICL process into two distinct stages: Deep-Thinking and test stages. The Deep-Thinking stage incorporates a unique attention mechanism, i.e., iterative enhanced attention, which enables multiple rounds of information accumulation. This mechanism operates by manipulating the Key-Value matrices without training, fostering enhanced understanding capabilities in LLMs by thinking demonstrations multiple times. We evaluated Deep-Thinking across a range of benchmarks and LLMs, showing its superior performance over vanilla ICL methods and its effectiveness in challenging tasks where demonstration selection is infeasible.

Recent studies show that large language models (LLMs) improve their performance through self-feedback on certain tasks while degrade on others. We discovered that such a contrary is due to LLM’s bias in evaluating their own output. In this paper, we formally define LLM’s self-bias – the tendency to favor its own generation – using two statistics. We analyze six LLMs (GPT-4, GPT-3.5, Gemini, LLaMA2, Mixtral and DeepSeek) on translation, constrained text generation, and mathematical reasoning tasks. We find that self-bias is prevalent in all examined LLMs across multiple languages and tasks. Our analysis reveals that while the self-refine pipeline improves the fluency and understandability of model outputs, it further amplifies self-bias. To mitigate such biases, we discover that larger model size and external feedback with accurate assessment can significantly reduce bias in the self-refine pipeline, leading to actual performance improvement in downstream tasks. The code and data are released at https://github.com/xu1998hz/llm_self_bias.

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Language Complexity and Speech Recognition Accuracy: Orthographic Complexity Hurts, Phonological Complexity Doesn’t
Chihiro Taguchi | David Chiang

We investigate what linguistic factors affect the performance of Automatic Speech Recognition (ASR) models. We hypothesize that orthographic and phonological complexities both degrade accuracy. To examine this, we fine-tune the multilingual self-supervised pretrained model Wav2Vec2-XLSR-53 on 25 languages with 15 writing systems, and we compare their ASR accuracy, number of graphemes, unigram grapheme entropy, logographicity (how much word/morpheme-level information is encoded in the writing system), and number of phonemes. The results demonstrate that a high logographicity correlates with low ASR accuracy, while phonological complexity has no significant effect.

We introduce Contrastive Activation Addition (CAA), a method for steering language models by modifying their activations during forward passes. CAA computes “steering vectors” by averaging the difference in residual stream activations between pairs of positive and negative examples of a particular behavior, such as factual versus hallucinatory responses. During inference, these steering vectors are added at all token positions after the user’s prompt with either a positive or negative coefficient, allowing precise control over the degree of the targeted behavior. We evaluate CAA’s effectiveness on Llama 2 Chat using multiple-choice behavioral question datasets and open-ended generation tasks. We demonstrate that CAA significantly alters model behavior, is effective over and on top of traditional methods like finetuning and system prompt design, and minimally reduces capabilities. Moreover, we gain deeper insights into CAA’s mechanisms by employing various activation space interpretation methods. CAA accurately steers model outputs and sheds light on how high-level concepts are represented in Large Language Models (LLMs).

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EconAgent: Large Language Model-Empowered Agents for Simulating Macroeconomic Activities
Nian Li | Chen Gao | Mingyu Li | Yong Li | Qingmin Liao

The advent of artificial intelligence has led to a growing emphasis on data-driven modeling in macroeconomics, with agent-based modeling (ABM) emerging as a prominent bottom-up simulation paradigm. In ABM, agents (*e.g.*, households, firms) interact within a macroeconomic environment, collectively generating market dynamics. Existing agent modeling typically employs predetermined rules or learning-based neural networks for decision-making. However, customizing each agent presents significant challenges, complicating the modeling of agent heterogeneity. Additionally, the influence of multi-period market dynamics and multifaceted macroeconomic factors are often overlooked in decision-making processes.In this work, we introduce **EconAgent**, a large language model-empowered agent with human-like characteristics for macroeconomic simulation. We first construct a simulation environment that incorporates various market dynamics driven by agents’ decisions regarding work and consumption. Through the perception module, we create heterogeneous agents with distinct decision-making mechanisms. Furthermore, we model the impact of macroeconomic trends using a memory module, which allows agents to reflect on past individual experiences and market dynamics.Simulation experiments show that EconAgent can make realistic decisions, leading to more reasonable macroeconomic phenomena compared to existing rule-based or learning-based agents. Our codes are released at https://github.com/tsinghua-fib-lab/ACL24-EconAgent.

With the rapid development of Large Language Models (LLMs), increasing attention has been paid to their safety concerns. Consequently, evaluating the safety of LLMs has become an essential task for facilitating the broad applications of LLMs. Nevertheless, the absence of comprehensive safety evaluation benchmarks poses a significant impediment to effectively assess and enhance the safety of LLMs. In this work, we present SafetyBench, a comprehensive benchmark for evaluating the safety of LLMs, which comprises 11,435 diverse multiple choice questions spanning across 7 distinct categories of safety concerns. Notably, SafetyBench also incorporates both Chinese and English data, facilitating the evaluation in both languages. Our extensive tests over 25 popular Chinese and English LLMs in both zero-shot and few-shot settings reveal a substantial performance advantage for GPT-4 over its counterparts, and there is still significant room for improving the safety of current LLMs. We also demonstrate that the measured safety understanding abilities in SafetyBench are correlated with safety generation abilities. Data and evaluation guidelines are available at https://github.com/thu-coai/SafetyBench. Submission entrance and leaderboard are available at https://llmbench.ai/safety.

Originating from China’s Shang Dynasty approximately 3,000 years ago, the Oracle Bone Script (OBS) is a cornerstone in the annals of linguistic history, predating many established writing systems. Despite the discovery of thousands of inscriptions, a vast expanse of OBS remains undeciphered, casting a veil of mystery over this ancient language. The emergence of modern AI technologies presents a novel frontier for OBS decipherment, challenging traditional NLP methods that rely heavily on large textual corpora, a luxury not afforded by historical languages. This paper introduces a novel approach by adopting image generation techniques, specifically through the development of Oracle Bone Script Decipher (OBSD). Utilizing a conditional diffusion-based strategy, OBSD generates vital clues for decipherment, charting a new course for AI-assisted analysis of ancient languages. To validate its efficacy, extensive experiments were conducted on an oracle bone script dataset, with quantitative results demonstrating the effectiveness of OBSD.

Managing long sequences has become an important and necessary feature for large language models (LLMs). However, assessing their ability to handle long contexts remains a challenge. This paper introduces M⁴LE, a Multi-ability, Multi-range, Multi-task, Multi-domain benchmark for Long-context Evaluation. It encompasses 36 NLP datasets, covering 11 types of tasks and 12 domains, providing a comprehensive test bed. To address the lack of tasks featuring naturally long sequences, we propose an automatic approach to convert short-sequence tasks into long-sequence scenarios. These scenarios evaluate LLMs’ long-context understanding across five key abilities: understanding of single or multiple relevant spans in long contexts based on explicit or semantic hints, and global context understanding. This automatic approach allows us to create instances evenly distributed from 1k to 8k input length. Our evaluation of 11 prominent LLMs reveals that 1) Current LLMs struggle to understand long context, particularly when tasks require multiple-span attention. 2) Semantic retrieval is more difficult for competent LLMs. 3) Models fine-tuned on longer text with position interpolation have comparable performance to those using Neural Tangent Kernel (NTK) aware scaling methods without fine-tuning. We make our benchmark publicly available to encourage future research in this challenging area.

This study addresses the challenge of extending Large Language Models (LLMs) to non-English languages, specifically those using non-Roman scripts. We propose an approach that utilizes the romanized form of text as an interface for LLMs, hypothesizing that its frequent informal use and shared tokens with English enhance cross-lingual alignment. Our approach involve the continual pretraining of a English LLM like Llama 2 on romanized text of non-English, non-Roman script languages, followed by instruction tuning on romanized data. The results indicate that romanized text not only reduces token fertility by 2x-4x but also matches if not outperforms native script representation across various NLU, NLG and MT tasks. Moreover, the embeddings computed on romanized text exhibit closer alignment with their English translations than those from the native script. Our approach presents a promising direction for leveraging the power of English LLMs in languages traditionally underrepresented in NLP research.

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Causal Estimation of Memorisation Profiles
Pietro Lesci | Clara Meister | Thomas Hofmann | Andreas Vlachos | Tiago Pimentel

Understanding memorisation in language models has practical and societal implications, e.g., studying models’ training dynamics or preventing copyright infringements.Prior work defines memorisation as the causal effect of training with an instance on the model’s ability to predict that instance. This definition relies on a counterfactual: the ability to observe what would have happened had the model not seen that instance.Existing methods struggle to provide computationally efficient and accurate estimates of this counterfactual. Further, they often estimate memorisation for a model architecture rather than for a specific model instance. This paper fills an important gap in the literature, proposing a new, principled, and efficient method to estimate memorisation based on the difference-in-differences design from econometrics. Using this method, we characterise a model’s memorisation profile–its memorisation trends across training–by only observing its behaviour on a small set of instances throughout training.In experiments with the Pythia model suite, we find that memorisation (i) is stronger and more persistent in larger models, (ii) is determined by data order and learning rate, and (iii) has stable trends across model sizes, thus making memorisation in larger models predictable from smaller ones.

With the growing complexity of fact verification tasks, the concern with “thoughtful” reasoning capabilities is increasing. However, recent fact verification benchmarks mainly focus on checking a narrow scope of semantic factoids within claims and lack an explicit logical reasoning process. In this paper, we introduce CHECKWHY, a challenging dataset tailored to a novel causal fact verification task: checking the truthfulness of the causal relation within claims through rigorous reasoning steps. CHECKWHY consists of over 19K “why” claim-evidence- argument structure triplets with supports, refutes, and not enough info labels. Each argument structure is composed of connected evidence, representing the reasoning process that begins with foundational evidence and progresses toward claim establishment. Through extensive experiments on state-of-the-art models, we validate the importance of incorporating the argument structure for causal fact verification. Moreover, the automated and human evaluation of argument structure generation reveals the difficulty in producing satisfying argument structure by fine-tuned models or Chain-of-Thought prompted LLMs, leaving considerable room for future improvements.

Maximum-a-posteriori (MAP) decoding is the most widely used decoding strategy for neural machine translation (NMT) models. The underlying assumption is that model probability correlates well with human judgment, with better translations getting assigned a higher score by the model. However, research has shown that this assumption does not always hold, and generation quality can be improved by decoding to optimize a utility function backed by a metric or quality-estimation signal, as is done by Minimum Bayes Risk (MBR) or Quality-Aware decoding. The main disadvantage of these approaches is that they require an additional model to calculate the utility function during decoding, significantly increasing the computational cost. In this paper, we propose to make the NMT models themselves quality-aware by training them to estimate the quality of their own output. Using this approach for MBR decoding we can drastically reduce the size of the candidate list, resulting in a speed-up of two-orders of magnitude. When applying our method to MAP decoding we obtain quality gains similar or even superior to quality reranking approaches, but with the efficiency of single pass decoding.

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On Efficient and Statistical Quality Estimation for Data Annotation
Jan-Christoph Klie | Juan Haladjian | Marc Kirchner | Rahul Nair

Annotated datasets are an essential ingredient to train, evaluate, compare and productionalize supervised machine learning models. It is therefore imperative that annotations are of high quality. For their creation, good quality management and thereby reliable quality estimates are needed. Then, if quality is insufficient during the annotation process, rectifying measures can be taken to improve it. Quality estimation is often performed by having experts manually label instances as correct or incorrect. But checking all annotated instances tends to be expensive. Therefore, in practice, usually only subsets are inspected; sizes are chosen mostly without justification or regard to statistical power and more often than not, are relatively small. Basing estimates on small sample sizes, however, can lead to imprecise values for the error rate. Using unnecessarily large sample sizes costs money that could be better spent, for instance on more annotations. Therefore, we first describe in detail how to use confidence intervals for finding the minimal sample size needed to estimate the annotation error rate. Then, we propose applying acceptance sampling as an alternative to error rate estimation We show that acceptance sampling can reduce the required sample sizes up to 50% while providing the same statistical guarantees.

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EZ-STANCE: A Large Dataset for English Zero-Shot Stance Detection
Chenye Zhao | Cornelia Caragea

Zero-shot stance detection (ZSSD) aims to determine whether the author of a text is in favor, against, or neutral toward a target that is unseen during training. In this paper, we present EZ-STANCE, a large English ZSSD dataset with 47,316 annotated text-target pairs. In contrast to VAST, which is the only other large existing ZSSD dataset for English, EZ-STANCE is 2.5 times larger, includes both noun-phrase targets and claim targets that cover a wide range of domains, provides two challenging subtasks for ZSSD: target-based ZSSD and domain-based ZSSD, and contains much harder examples for the neutral class. We evaluate EZ-STANCE using state-of-the-art deep learning models. Furthermore, we propose to transform ZSSD into the NLI task by applying simple yet effective prompts to noun-phrase targets. Our experimental results show that EZ-STANCE is a challenging new benchmark, which provides significant research opportunities on English ZSSD. We publicly release our dataset and code at https://github.com/chenyez/EZ-STANCE.

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American Sign Language Handshapes Reflect Pressures for Communicative Efficiency
Kayo Yin | Terry Regier | Dan Klein

Communicative efficiency is a key topic in linguistics and cognitive psychology, with many studies demonstrating how the pressure to communicate with minimal effort guides the form of natural language. However, this phenomenon is rarely explored in signed languages. This paper shows how handshapes in American Sign Language (ASL) reflect these efficiency pressures and provides new evidence of communicative efficiency in the visual-gestural modality.We focus on hand configurations in native ASL signs and signs borrowed from English to compare efficiency pressures from both ASL and English usage. First, we develop new methodologies to quantify the articulatory effort needed to produce handshapes and the perceptual effort required to recognize them. Then, we analyze correlations between communicative effort and usage statistics in ASL or English. Our findings reveal that frequent ASL handshapes are easier to produce and that pressures for communicative efficiency mostly come from ASL usage, rather than from English lexical borrowing.

Information about pretraining corpora used to train the current best-performing language models is seldom discussed: commercial models rarely detail their data, and even open models are often released without accompanying training data or recipes to reproduce them. As a result, it is challenging to conduct and advance scientific research on language modeling, such as understanding how training data impacts model capabilities and limitations. To facilitate scientific research on language model pretraining, we curate and release Dolma, a three-trillion-token English corpus, built from a diverse mixture of web content, scientific papers, code, public-domain books, social media, and encyclopedic materials. We extensively document Dolma, including its design principles, details about its construction, and a summary of its contents. We present analyses and experimental results on intermediate states of Dolma to share what we have learned about important data curation practices. Finally, we open-source our data curation toolkit to enable reproduction of our work as well as support further research in large-scale data curation.

Language models (LMs) have become ubiquitous in both NLP research and in commercial product offerings. As their commercial importance has surged, the most powerful models have become closed off, gated behind proprietary interfaces, with important details of their training data, architectures, and development undisclosed. Given the importance of these details in scientifically studying these models, including their biases and potential risks, we believe it is essential for the research community to have access to powerful, truly open LMs. To this end, we have built OLMo, a competitive, truly Open Language Model, to enable the scientific study of language models. Unlike most prior efforts that have only released model weights and inference code, we release OLMo alongside open training data and training and evaluation code. We hope this release will empower the open research community and inspire a new wave of innovation.

Large language models (LLMs) undergo safety alignment to ensure safe conversations with humans. However, this paper introduces a training-free attack method capable of reversing safety alignment, converting the outcomes of stronger alignment into greater potential for harm by accessing only LLM output token distributions. Specifically, our method achieves this reversal by contrasting the output token distribution of a safety-aligned language model (e.g., Llama-2-chat) against its pre-trained version (e.g., Llama-2), so that the token predictions are shifted towards the opposite direction of safety alignment.We name this method emulated disalignment (ED) because sampling from this contrastive distribution provably emulates the result of fine-tuning to minimize a safety reward.Our experiments with ED across three evaluation datasets and four model families (Llama-1, Llama-2, Mistral, and Alpaca) show that ED doubles the harmfulness of pre-trained models and outperforms strong baselines, achieving the highest harmful rates in 43 out of 48 evaluation subsets by a large margin.Eventually, given ED’s reliance on language model output token distributions, which particularly compromises open-source models, our findings highlight the need to reassess the open accessibility of language models, even if they have been safety-aligned.Code is available at https://github.com/ZHZisZZ/emulated-disalignment.

Despite the considerable advancements in English LLMs, the progress in building comparable models for other languages has been hindered due to the scarcity of tailored resources. Our work aims to bridge this divide by introducing an expansive suite of resources specifically designed for the development of Indic LLMs, covering 22 languages, containing a total of 251B tokens and 74.8M instruction-response pairs. Recognizing the importance of both data quality and quantity, our approach combines highly curated manually verified data, unverified yet valuable data, and synthetic data. We build a clean, open-source pipeline for curating pre-training data from diverse sources, including websites, PDFs, and videos, incorporating best practices for crawling, cleaning, flagging, and deduplication. For instruction-fine tuning, we amalgamate existing Indic datasets, translate/transliterate English datasets into Indian languages, and utilize LLaMa2 and Mixtral models to create conversations grounded in articles from Indian Wikipedia and Wikihow. Additionally, we address toxicity alignment by generating toxic prompts for multiple scenarios and then generate non-toxic responses by feeding these toxic prompts to an aligned LLaMa2 model. We hope that the datasets, tools, and resources released as a part of this work will not only propel the research and development of Indic LLMs but also establish an open-source blueprint for extending such efforts to other languages.

Large Language Models (LLMs) have achieved remarkable performance in objective tasks such as open-domain question answering and mathematical reasoning, which can often be solved through recalling learned factual knowledge or chain-of-thought style reasoning. However, we find that the performance of LLMs in subjective tasks is still unsatisfactory, such as metaphor recognition, dark humor detection, etc. Compared to objective tasks, subjective tasks focus more on interpretation or emotional response rather than a universally accepted reasoning pathway. Based on the characteristics of the tasks and the strong dialogue-generation capabilities of LLMs, we propose RiC (Reasoning in Conversation), a method that focuses on solving subjective tasks through dialogue simulation. The motivation of RiC is to mine useful contextual information by simulating dialogues instead of supplying chain-of-thought style rationales, thereby offering potential useful knowledge behind dialogues for giving the final answers. We evaluate both API-based and open-source LLMs including GPT-4, ChatGPT, and OpenChat across twelve tasks. Experimental results show that RiC can yield significant improvement compared with various baselines.

Recent breakthroughs in large language models (LLMs) have centered around a handful of data-rich languages. What does it take to broaden access to breakthroughs beyond first-class citizen languages? Our work introduces Aya, a massively multilingual generative language model that follows instructions in 101 languages of which over 50% are considered as lower-resourced. Aya outperforms mT0 and BLOOMZ on the majority of tasks while covering double the number of languages. We introduce extensive new evaluation suites that broaden the state-of-art for multilingual eval across 99 languages —— including discriminative and generative tasks, human evaluation, and simulated win rates that cover both held-out tasks and in-distribution performance. Furthermore, we conduct detailed investigations on the optimal finetuning mixture composition, data pruning, as well as the toxicity, bias, and safety of our models.

Significant progress has been made in automatic text evaluation with the introduction of large language models (LLMs) as evaluators. However, current sample-wise evaluation paradigm suffers from the following issues: (1) Sensitive to prompt design; (2) Poor resistance to noise; (3) Inferior ensemble performance with static reference. Inspired by the fact that humans treat both criterion definition and inter sample comparison as references for evaluation, we propose BatchEval, a paradigm that conducts batch-wise evaluation iteratively to alleviate the above problems. We explore variants under this paradigm and confirm the optimal settings are two stage procedure with heterogeneous batch composition strategy and decimal scoring format. Comprehensive experiments across 3 LLMs on 4 text evaluation tasks demonstrate that BatchEval outperforms state-of-the-art methods by 10.5% on Pearson correlations with only 64% API cost on average. Further analyses have been conducted to verify the robustness, generalization, and working mechanism of BatchEval.

Theory of Mind (ToM) is the cognitive capability to perceive and ascribe mental states to oneself and others. Recent research has sparked a debate over whether large language models (LLMs) exhibit a form of ToM. However, existing ToM evaluations are hindered by challenges such as constrained scope, subjective judgment, and unintended contamination, yielding inadequate assessments. To address this gap, we introduce ToMBench with three key characteristics: a systematic evaluation framework encompassing 8 tasks and 31 abilities in social cognition, a multiple-choice question format to support automated and unbiased evaluation, and a build-from-scratch bilingual inventory to strictly avoid data leakage. Based on ToMBench, we conduct extensive experiments to evaluate the ToM performance of 10 popular LLMs across tasks and abilities. We find that even the most advanced LLMs like GPT-4 lag behind human performance by over 10% points, indicating that LLMs have not achieved a human-level theory of mind yet. Our aim with ToMBench is to enable an efficient and effective evaluation of LLMs’ ToM capabilities, thereby facilitating the development of LLMs with inherent social intelligence.

Theory of mind (ToM) refers to humans’ ability to understand and infer the desires, beliefs, and intentions of others. The acquisition of ToM plays a key role in humans’ social cognition and interpersonal relations. Though indispensable for social intelligence, ToM is still lacking for modern AI and NLP systems since they cannot access the human mental state and cognitive process beneath the training corpus. To empower AI systems with the ToM ability and narrow the gap between them and humans, in this paper, we propose COKE: the first cognitive knowledge graph for machine theory of mind. Specifically, COKE formalizes ToM as a collection of 45k+ manually verified cognitive chains that characterize human mental activities and subsequent behavioral/affective responses when facing specific social circumstances. In addition, we further generalize COKE using LLMs and build a powerful generation model COLM tailored for cognitive reasoning. Experimental results in both automatic and human evaluation demonstrate the high quality of COKE, the superior ToM ability of COLM, and its potential to significantly enhance social applications.

Recently, several scholars have contributed to the growth of a new theoretical framework in NLP called perspectivism. This approach aimsto leverage data annotated by different individuals to model diverse perspectives that affect their opinions on subjective phenomena such as irony. In this context, we propose MultiPICo, a multilingual perspectivist corpus of ironic short conversations in different languages andlinguistic varieties extracted from Twitter and Reddit. The corpus includes sociodemographic information about its annotators. Our analysis of the annotated corpus shows how different demographic cohorts may significantly disagree on their annotation of irony and how certain cultural factors influence the perception of the phenomenon and the agreement on the annotation. Moreover, we show how disaggregated annotations and rich annotator metadata can be exploited to benchmark the ability of large language models to recognize irony, their positionality with respect to sociodemographic groups, and the efficacy of perspective-taking prompting for irony detection in multiple languages.

Autonomous agents that address day-to-day digital tasks (e.g., ordering groceries for a household), must not only operate multiple apps (e.g., notes, messaging, shopping app) via APIs, but also generate rich code with complex control flow in an iterative manner based on their interaction with the environment. However, existing benchmarks for tool use are inadequate, as they only cover tasks that require a simple sequence of API calls. To remedy this gap, we built AppWorld Engine, a high-quality execution environment (60K lines of code) of 9 day-to-day apps operable via 457 APIs and populated with realistic digital activities simulating the lives of ~100 fictitious users. We then created AppWorld Benchmark (40K lines of code), a suite of 750 natural, diverse, and challenging autonomous agent tasks requiring rich and interactive code generation. It supports robust programmatic evaluation with state-based unit tests, allowing for different ways of completing a task while also checking for unexpected changes, i.e., collateral damage. The state-of-the-art LLM, GPT4O, solves only ~49% of our ‘normal’ tasks and ~30% of ‘challenge’ tasks, while other models solve at least 16% fewer. This highlights the benchmark’s difficulty and AppWorld’s potential to push the frontiers of interactive coding agents.

Theory of Mind (ToM), the ability to understand people’s mental states, is an essential ingredient for developing machines with human-level social intelligence. Recent machine learning models, particularly large language models, seem to show some aspects of ToM understanding. However, existing ToM benchmarks use unimodal datasets – either video or text. Human ToM, on the other hand, is more than video or text understanding. People can flexibly reason about another person’s mind based on conceptual representations (e.g., goals, beliefs, plans) extracted from any available data. To address this, we introduce a multimodal Theory of Mind question answering (MMToM-QA) benchmark. MMToM-QA comprehensively evaluates machine ToM both on multimodal data and on different kinds of unimodal data about a person’s activity in a household environment. To engineer multimodal ToM capacity, we propose a novel method, BIP-ALM (Bayesian Inverse Planning Accelerated by Language Models). BIP-ALM extracts unified representations from multimodal data and utilizes language models for scalable Bayesian inverse planning. We conducted a systematic comparison of human performance, BIP-ALM, and state-of-the-art models, including GPT-4. The experiments demonstrate that large language models and large multimodal models still lack robust ToM capacity. BIP-ALM, on the other hand, shows promising results, by leveraging the power of both model-based mental inference and language models.

Recent LLMs have demonstrated remarkable performance in solving exam-like math word problems. However, the degree to which these numerical reasoning skills are effective in real-world scenarios, particularly in expert domains, is still largely unexplored. This paper introduces DocMath-Eval, a comprehensive benchmark specifically designed to evaluate the numerical reasoning capabilities of LLMs in the context of understanding and analyzing specialized documents containing both text and tables. We conduct an extensive evaluation of 48 LLMs with Chain-of-Thought and Program-of-Thought prompting methods, aiming to comprehensively assess the capabilities and limitations of existing LLMs in DocMath-Eval. We found that even the current best-performing system (i.e., GPT-4o) still significantly lags behind human experts in solving complex numerical reasoning problems grounded in long contexts. We believe that DocMath-Eval can serve as a valuable benchmark for evaluating LLMs' capabilities in solving challenging numerical reasoning problems within expert domains.

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Unintended Impacts of LLM Alignment on Global Representation
Michael Ryan | William Held | Diyi Yang

Before being deployed for user-facing applications, developers align Large Language Models (LLMs) to user preferences through a variety of procedures, such as Reinforcement Learning From Human Feedback (RLHF) and Direct Preference Optimization (DPO). Current evaluations of these procedures focus on benchmarks of instruction following, reasoning, and truthfulness. However, human preferences are not universal, and aligning to specific preference sets may have unintended effects. We explore how alignment impacts performance along three axes of global representation: English dialects, multilingualism, and opinions from and about countries worldwide. Our results show that current alignment procedures create disparities between English dialects and global opinions. We find alignment improves capabilities in several languages. We conclude by discussing design decisions that led to these unintended impacts and recommendations for more equitable preference tuning. We make our code and data publicly available on Github.

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ICLEF: In-Context Learning with Expert Feedback for Explainable Style Transfer
Arkadiy Saakyan | Smaranda Muresan

While state-of-the-art large language models (LLMs) can excel at adapting text from one style to another, current work does not address the explainability of style transfer models. Recent work has explored generating textual explanations from larger teacher models and distilling them into smaller student models. One challenge with such approach is that LLM outputs may contain errors that require expertise to correct, but gathering and incorporating expert feedback is difficult due to cost and availability. To address this challenge, we propose ICLEF, a novel human-AI collaboration approach to model distillation that incorporates scarce expert human feedback by combining in-context learning and model self-critique. We show that our method leads to generation of high-quality synthetic explainable style transfer datasets for formality (E-GYAFC) and subjective bias (E-WNC). Via automatic and human evaluation, we show that specialized student models fine-tuned on our datasets outperform generalist teacher models on the explainable style transfer task in one-shot settings, and perform competitively compared to few-shot teacher models, highlighting the quality of the data and the role of expert feedback. In an extrinsic task of authorship attribution, we show that explanations generated by smaller models fine-tuned on E-GYAFC are more predictive of authorship than explanations generated by few-shot teacher models.

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MAP’s not dead yet: Uncovering true language model modes by conditioning away degeneracy
Davis Yoshida | Kartik Goyal | Kevin Gimpel

It has been widely observed that exact or approximate MAP (mode-seeking) decoding from natural language generation (NLG) models consistently leads to degenerate outputs (Holtzman et al., 2019; Stahlberg and Byrne, 2019). Prior work has attributed this behavior to either a fundamental and unavoidable inadequacy of modes in probabilistic models or weaknesses in language modeling. Contrastingly, we argue that degenerate modes can even occur in the absence of any modeling error, due to contamination of the training data. Specifically, we argue that mixing even a tiny amount of low-entropy noise with a population text distribution can cause the data distribution’s mode to become degenerate. We therefore propose to apply MAP decoding to the model’s true conditional distribution where the conditioning variable explicitly avoids specific degenerate behavior. Using exact search, we empirically verify that the length-conditional modes of machine translation models and language models are indeed more fluent and topical than their unconditional modes. For the first time, we also share many examples of exact modal sequences from these models, and from several variants of the LLaMA-7B model. Notably, we observethat various kinds of degenerate modes persist, even at the scale of LLaMA-7B. Although we cannot tractably address these degeneracieswith exact search, we perform a classifier-based approximate search on LLaMA-7B, a model which was not trained for instruction following, and find that we are able to elicit reasonable outputs without any finetuning.

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Guardians of the Machine Translation Meta-Evaluation: Sentinel Metrics Fall In!
Stefano Perrella | Lorenzo Proietti | Alessandro Scirè | Edoardo Barba | Roberto Navigli

Annually, at the Conference of Machine Translation (WMT), the Metrics Shared Task organizers conduct the meta-evaluation of Machine Translation (MT) metrics, ranking them according to their correlation with human judgments. Their results guide researchers toward enhancing the next generation of metrics and MT systems. With the recent introduction of neural metrics, the field has witnessed notable advancements. Nevertheless, the inherent opacity of these metrics has posed substantial challenges to the meta-evaluation process. This work highlights two issues with the meta-evaluation framework currently employed in WMT, and assesses their impact on the metrics rankings. To do this, we introduce the concept of sentinel metrics, which are designed explicitly to scrutinize the meta-evaluation process’s accuracy, robustness, and fairness. By employing sentinel metrics, we aim to validate our findings, and shed light on and monitor the potential biases or inconsistencies in the rankings. We discover that the present meta-evaluation framework favors two categories of metrics: i) those explicitly trained to mimic human quality assessments, and ii) continuous metrics. Finally, we raise concerns regarding the evaluation capabilities of state-of-the-art metrics, emphasizing that they might be basing their assessments on spurious correlations found in their training data.

Despite significant advances in Semantic Role Labeling (SRL), much work in this field has been carried out with a focus on verbal predicates, with the research on nominal SRL lagging behind. In many contexts, however, nominal predicates are often as informative as verbal ones, thus needing proper treatment. In this paper we aim to fill this gap and make nominal SRL a first-class citizen. We introduce a novel approach to create the first large-scale, high-quality inventory of nominal predicates and organize them into semantically-coherent frames. Although automatically created, NounAtlas – our frame inventory – is subsequently fully validated. We then put forward a technique to generate silver training data for nominal SRL and show that a state-of-the-art SRL model can achieve good performance. Interestingly, thanks to our design choices which enable seamless integration of our predicate inventory with its verbal counterpart, we can mix verbal and nominal data and perform robust SRL on both types of predicates.

Large language models (LLMs) encapsulate vast amounts of knowledge but still remain vulnerable to external misinformation. Existing research mainly studied this susceptibility behavior in a single-turn setting. However, belief can change during a multi-turn conversation, especially a persuasive one. Therefore, in this study, we delve into LLMs’ susceptibility to persuasive conversations, particularly on factual questions that they can answer correctly. We first curate the Farm (i.e., Fact to Misinform) dataset, which contains factual questions paired with systematically generated persuasive misinformation. Then, we develop a testing framework to track LLMs’ belief changes in a persuasive dialogue. Through extensive experiments, we find that LLMs’ correct beliefs on factual knowledge can be easily manipulated by various persuasive strategies.

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LooGLE: Can Long-Context Language Models Understand Long Contexts?
Jiaqi Li | Mengmeng Wang | Zilong Zheng | Muhan Zhang

Large language models (LLMs) are typically limited to processing texts within context window size, which has spurred significant research efforts into enhancing LLMs’ long-context understanding as well as developing high-quality benchmarks to evaluate the ability. However, prior datasets suffer from short comings like short length compared to the context window of modern LLMs; outdated documents that might have data leakage problems; and an emphasis on short dependency tasks only. In this paper, we present LooGLE , a Long Context Generic Language Evaluation benchmark. It features documents post-2022, with over 24,000 tokens per document and 6,000 newly generated questions spanning varying dependency ranges in diverse domains. Human annotators meticulously crafted over 1,100 high-quality question-answer (QA) pairs with thorough cross-validation for a most precise assessment of LLMs’ long dependency capabilities. We conduct a comprehensive evaluation of representative LLMs on LooGLE . The results indicate that most LLMs have shockingly bad long context ability and fail to capture long dependencies in the context, even when their context window size is enough to fit the entire document. Our results shed light on enhancing the “true long-context understanding” ability of LLMs instead of merely enlarging their context window.

In this paper, we introduce a novel Face-to-Face spoken dialogue model. It processes audio-visual speech from user input and generates audio-visual speech as the response, marking the initial step towards creating an avatar chatbot system without relying on intermediate text. To this end, we newly introduce MultiDialog, the first large-scale multimodal (i.e, audio and visual) spoken dialogue corpus containing 340 hours of approximately 9,000 dialogues, recorded based on the open domain dialogue dataset, TopicalChat. The MultiDialog contains parallel audio-visual recordings of conversation partners acting according to the given script with emotion annotations, which we expect to open up research opportunities in multimodal synthesis. Our Face-to-Face spoken dialogue model incorporates a textually pretrained large language model and adapts it into the audio-visual spoken dialogue domain by incorporating speech-text joint pretraining. Through extensive experiments, we validate the effectiveness of our model in facilitating a face-to-face conversation. Demo and data are available at https://multidialog.github.io and https://huggingface.co/datasets/IVLLab/MultiDialog, respectively.

Benchmarking is seen as critical to assessing progress in NLP. However, creating a benchmark involves many design decisions (e.g., which datasets to include, which metrics to use) that often rely on tacit, untested assumptions about what the benchmark is intended to measure or is actually measuring. There is currently no principled way of analyzing these decisions and how they impact the validity of the benchmark’s measurements. To address this gap, we draw on evidence-centered design in educational assessments and propose Evidence-Centered Benchmark Design (ECBD), a framework which formalizes the benchmark design process into five modules. ECBD specifies the role each module plays in helping practitioners collect evidence about capabilities of interest. Specifically, each module requires benchmark designers to describe, justify, and support benchmark design choices—e.g., clearly specifying the capabilities the benchmark aims to measure or how evidence about those capabilities is collected from model responses. To demonstrate the use of ECBD, we conduct case studies with three benchmarks: BoolQ, SuperGLUE, and HELM. Our analysis reveals common trends in benchmark design and documentation that could threaten the validity of benchmarks’ measurements.

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Having Beer after Prayer? Measuring Cultural Bias in Large Language Models
Tarek Naous | Michael Ryan | Alan Ritter | Wei Xu

As the reach of large language models (LMs) expands globally, their ability to cater to diverse cultural contexts becomes crucial. Despite advancements in multilingual capabilities, models are not designed with appropriate cultural nuances. In this paper, we show that multilingual and Arabic monolingual LMs exhibit bias towards entities associated with Western culture. We introduce CAMeL, a novel resource of 628 naturally-occurring prompts and 20,368 entities spanning eight types that contrast Arab and Western cultures. CAMeL provides a foundation for measuring cultural biases in LMs through both extrinsic and intrinsic evaluations. Using CAMeL, we examine the cross-cultural performance in Arabic of 16 different LMs on tasks such as story generation, NER, and sentiment analysis, where we find concerning cases of stereotyping and cultural unfairness. We further test their text-infilling performance, revealing the incapability of appropriate adaptation to Arab cultural contexts. Finally, we analyze 6 Arabic pre-training corpora and find that commonly used sources such as Wikipedia may not be best suited to build culturally aware LMs, if used as they are without adjustment. We will make CAMeL publicly available at: https://github.com/tareknaous/camel

Detecting semantic arguments of a predicate word has been conventionally modeled as a sentence-level task. The typical reader, however, perfectly interprets predicate-argument relations in a much wider context than just the sentence where the predicate was evoked. In this work, we reformulate the problem of argument detection through textual entailment to capture semantic relations across sentence boundaries. We propose a method that tests whether some semantic relation can be inferred from a full passage by first encoding it into a simple and standalone proposition and then testing for entailment against the passage. Our method does not require direct supervision, which is generally absent due to dataset scarcity, but instead builds on existing NLI and sentence-level SRL resources. Such a method can potentially explicate pragmatically understood relations into a set of explicit sentences. We demonstrate it on a recent document-level benchmark, outperforming some supervised methods and contemporary language models.

Language models (LMs) automatically learn word embeddings during pre-training on language corpora. Although word embeddings are usually interpreted as feature vectors for individual words, their roles in language model generation remain underexplored. In this work, we theoretically and empirically revisit output word embeddings and find that their linear transformations are equivalent to steering language model generation styles. We name such steers LM-Steers and find them existing in LMs of all sizes. It requires learning parameters equal to 0.2% of the original LMs’ size for steering each style. On tasks such as language model detoxification and sentiment control, LM-Steers can achieve comparable or superior performance compared with state-of-the-art controlled generation methods while maintaining a better balance with generation quality. The learned LM-Steer serves as a lens in text styles: it reveals that word embeddings are interpretable when associated with language model generations and can highlight text spans that most indicate the style differences. An LM-Steer is transferrable between different language models by an explicit form calculation. One can also continuously steer LMs simply by scaling the LM-Steer or compose multiple LM-Steers by adding their transformations. Our codes are publicly available at https://github.com/Glaciohound/LM-Steer.

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bib (full) Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Lun-Wei Ku | Andre Martins | Vivek Srikumar

Virtual environments play a key role in benchmarking advances in complex planning and decision-making tasks but are expensive and complicated to build by hand. Can current language models themselves serve as world simulators, correctly predicting how actions change different world states, thus bypassing the need for extensive manual coding? Our goal is to answer this question in the context of text-based simulators. Our approach is to build and use a new benchmark, called ByteSized32-State-Prediction, containing a dataset of text game state transitions and accompanying game tasks. We use this to directly quantify, for the first time, how well LLMs can serve as text-based world simulators. We test GPT-4 on this dataset and find that, despite its impressive performance, it is still an unreliable world simulator without further innovations. This work thus contributes both new insights into current LLM’s capabilities and weaknesses, as well as a novel benchmark to track future progress as new models appear.

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FanOutQA: A Multi-Hop, Multi-Document Question Answering Benchmark for Large Language Models
Andrew Zhu | Alyssa Hwang | Liam Dugan | Chris Callison-Burch

One type of question that is commonly found in day-to-day scenarios is “fan-out” questions, complex multi-hop, multi-document reasoning questions that require finding information about a large number of entities. However, there exist few resources to evaluate this type of question-answering capability among large language models. To evaluate complex reasoning in LLMs more fully, we present FanOutQA, a high-quality dataset of fan-out question-answer pairs and human-annotated decompositions with English Wikipedia as the knowledge base. We formulate three benchmark settings across our dataset and benchmark 7 LLMs, including GPT-4, LLaMA 2, Claude-2.1, and Mixtral-8x7B, finding that contemporary models still have room to improve reasoning over inter-document dependencies in a long context. We provide our dataset, along with open-source tools to run models to encourage evaluation.

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Revisiting Code Similarity Evaluation with Abstract Syntax Tree Edit Distance
Yewei Song | Cedric Lothritz | Xunzhu Tang | Tegawendé Bissyandé | Jacques Klein

This paper revisits recent code similarity evaluation metrics, particularly focusing on the application of Abstract Syntax Tree (AST) editing distance in diverse programming languages. In particular, we explore the usefulness of these metrics and compare them to traditional sequence similarity metrics. Our experiments showcase the effectiveness of AST editing distance in capturing intricate code structures, revealing a high correlation with established metrics. Furthermore, we explore the strengths and weaknesses of AST editing distance and prompt-based GPT similarity scores in comparison to BLEU score, execution match, and Jaccard Similarity. We propose, optimize, and publish an adaptable metric that demonstrates effectiveness across all tested languages, representing an enhanced version of Tree Similarity of Edit Distance (TSED).

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Resisting the Lure of the Skyline: Grounding Practices in Active Learning for Morphological Inflection
Saliha Muradoglu | Michael Ginn | Miikka Silfverberg | Mans Hulden

Active learning (AL) aims to lower the demand of annotation by selecting informative unannotated samples for the model building. In this paper, we explore the importance of conscious experimental design in the language documentation and description setting, particularly the distribution of the unannotated sample pool. We focus on the task of morphological inflection using a Transformer model. We propose context motivated benchmarks: a baseline and skyline. The baseline describes the frequency weighted distribution encountered in natural speech. We simulate this using Wikipedia texts. The skyline defines the more common approach, uniform sampling from a large, balanced corpus (UniMorph, in our case), which often yields mixed results. We note the unrealistic nature of this unannotated pool. When these factors are considered, our results show a clear benefit to targeted sampling.

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Speculative Contrastive Decoding
Hongyi Yuan | Keming Lu | Fei Huang | Zheng Yuan | Chang Zhou

Large language models (LLMs) exhibit exceptional performance in language tasks, yet their auto-regressive inference is limited due to high computational requirements and is sub-optimal due to the exposure bias. Inspired by speculative decoding and contrastive decoding, we introduce Speculative Contrastive Decoding (SCD), a straightforward yet powerful decoding approach that leverages predictions from smaller language models (LMs) to achieve both decoding acceleration and quality improvement. Extensive evaluations and analyses on four diverse language tasks demonstrate the effectiveness of SCD, showing that decoding efficiency and quality can compatibly benefit from one smaller LM.

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RDRec: Rationale Distillation for LLM-based Recommendation
Xinfeng Wang | Jin Cui | Yoshimi Suzuki | Fumiyo Fukumoto

Large language model (LLM)-based recommender models that bridge users and items through textual prompts for effective semantic reasoning have gained considerable attention. However, few methods consider the underlying rationales behind interactions, such as user preferences and item attributes, limiting the reasoning ability of LLMs for recommendations. This paper proposes a rationale distillation recommender (RDRec), a compact model designed to learn rationales generated by a larger language model (LM). By leveraging rationales from reviews related to users and items, RDRec remarkably specifies their profiles for recommendations. Experiments show that RDRec achieves state-of-the-art (SOTA) performance in both top-N and sequential recommendations. Our code is available online.

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Isotropy, Clusters, and Classifiers
Timothee Mickus | Stig-Arne Grönroos | Joseph Attieh

Whether embedding spaces use all their dimensions equally, i.e., whether they are isotropic, has been a recent subject of discussion. Evidence has been accrued both for and against enforcing isotropy in embedding spaces. In the present paper, we stress that isotropy imposes requirements on the embedding space that are not compatible with the presence of clusters—which also negatively impacts linear classification objectives. We demonstrate this fact both empirically and mathematically and use it to shed light on previous results from the literature.

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Language Models Do Hard Arithmetic Tasks Easily and Hardly Do Easy Arithmetic Tasks
Andrew Gambardella | Yusuke Iwasawa | Yutaka Matsuo

The ability (and inability) of large language models (LLMs) to perform arithmetic tasks has been the subject of much theoretical and practical debate. We show that LLMs are frequently able to correctly and confidently predict the first digit of n-digit by m-digit multiplication tasks without using chain of thought reasoning, despite these tasks require compounding operations to solve. Simultaneously, LLMs in practice often fail to correctly or confidently predict the last digit of an n-digit by m-digit multiplication, a task equivalent to 1-digit by 1-digit multiplication which can be easily learned or memorized. We show that the latter task can be solved more robustly when the LLM is conditioned on all of the correct higher-order digits, which on average increases the confidence of the correct last digit on 5-digit by 5-digit multiplication tasks using Llama 2-13B by over 230% (0.13→0.43) and Mistral-7B by 150% (0.22→0.55).

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Simpson’s Paradox and the Accuracy-Fluency Tradeoff in Translation
Zheng Wei Lim | Ekaterina Vylomova | Trevor Cohn | Charles Kemp

A good translation should be faithful to the source and should respect the norms of the target language. We address a theoretical puzzle about the relationship between these objectives. On one hand, intuition and some prior work suggest that accuracy and fluency should trade off against each other, and that capturing every detail of the source can only be achieved at the cost of fluency. On the other hand, quality assessment researchers often suggest that accuracy and fluency are highly correlated and difficult for human raters to distinguish (Callison-Burch et al., 2007). We show that the tension between these views is an instance of Simpson’s paradox, and that accuracy and fluency are positively correlated at the level of the corpus but trade off at the level of individual source segments. We further suggest that the relationship between accuracy and fluency is best evaluated at the segment (or sentence) level, and that the trade off between these dimensions has implications both for assessing translation quality and developing improved MT systems.

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UltraSparseBERT: 99% Conditionally Sparse Language Modelling
Peter Belcak | Roger Wattenhofer

We present UltraSparseBERT, a BERT variant that uses 0.3% of its neurons during inference while performing on par with similar BERT models. UltraSparseBERT selectively engages just 12 out of 4095 neurons for each layer inference. This is achieved by reorganizing feedforward networks into fast feedforward networks (FFFs).To showcase but one benefit of high sparsity, we provide an Intel MKL implementation achieving 78x speedup over the optimized feedforward baseline on CPUs, and an OpenAI Triton implementation performing forward passes 4.1x faster than the corresponding native GPU implementation. The training and benchmarking code is enclosed.

The paper introduces SceMQA, a novel benchmark for scientific multimodal question answering at the college entrance level. It addresses a critical educational phase often overlooked in existing benchmarks, spanning high school to pre-college levels. SceMQA focuses on core science subjects including Mathematics, Physics, Chemistry, and Biology. It features a blend of multiple-choice and free-response formats, ensuring a comprehensive evaluation of AI models’ abilities. Additionally, our benchmark provides specific knowledge points for each problem and detailed explanations for each answer. SceMQA also uniquely presents problems with identical contexts but varied questions to facilitate a more thorough and accurate assessment of reasoning capabilities. In the experiment, we evaluate both open-source and close-source state-of-the-art Multimodal Large Language Models (MLLMs), across various experimental settings. The results show that further research and development are needed in developing more capable MLLM, as highlighted by only 50% to 60% accuracy achieved by the strongest models.

Retrieval augmented generation (RAG) exhibits outstanding performance in promoting the knowledge capabilities of large language models (LLMs) with retrieved documents related to user queries. However, RAG only focuses on improving the response quality of LLMs via enhancing queries indiscriminately with retrieved information, paying little attention to what type of knowledge LLMs really need to answer original queries more accurately. In this paper, we suggest that long-tail knowledge is crucial for RAG as LLMs have already remembered common world knowledge during large-scale pre-training. Based on our observation, we propose a simple but effective long-tail knowledge detection method for LLMs. Specifically, the novel Generative Expected Calibration Error (GECE) metric is derived to measure the “long-tailness” of knowledge based on both statistics and semantics. Hence, we retrieve relevant documents and infuse them into the model for patching knowledge loopholes only when the input query relates to long-tail knowledge. Experiments show that, compared to existing RAG pipelines, our method achieves over 4x speedup in average inference time and consistent performance improvement in downstream tasks.

Large Language Models (LLMs) demonstrate remarkable potential across various domains; however, they exhibit a significant performance gap in Information Extraction (IE). Note that high-quality instruction data is the vital key for enhancing the specific capabilities of LLMs, while current IE datasets tend to be small in scale, fragmented, and lack standardized schema. To this end, we introduce IEPile, a comprehensive bilingual (English and Chinese) IE instruction corpus, which contains approximately 0.32B tokens. We construct IEPile by collecting and cleaning 33 existing IE datasets, and introduce schema-based instruction generation to unearth a large-scale corpus. Experimentally, IEPile enhance the performance of LLMs for IE, with notable improvements in zero-shot generalization. We open-source the resource and pre-trained models, hoping to provide valuable support to the NLP community.

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Bi-Directional Multi-Granularity Generation Framework for Knowledge Graph-to-Text with Large Language Model
Haowei Du | Chen Li | Dinghao Zhang | Dongyan Zhao

The knowledge graph-to-text (KG-to-text) generation task aims to synthesize coherent and engaging sentences that accurately convey the complex information derived from an input knowledge graph. Existing methods generate the whole target text based on all KG triples at once and may incorporate incorrect KG triples for each sentence. To this end, we propose the bi-directional multi-granularity generation framework. Instead of generating the whole text at a time, we construct the sentence level generation based on the corresponding triples and generate the graph-level text as a result. Moreover, we design a backward relation extraction task to enhance the correctness of relational information. Our method achieves the new state-of-the-art in benchmark dataset WebNLG and further analysis shows the efficiency of different modules.

Zero-shot cross-lingual spoken language understanding (SLU) can promote the globalization application of dialog systems, which has attracted increasing attention. While current code-switching based cross-lingual SLU frameworks have shown promising results, they (i) predominantly utilize contrastive objectives to model hard alignment, which may disrupt the inherent structure within sentences of each language; and (ii) focus optimization objectives solely on the original sentences, neglecting the relation between original sentences and code-switched sentences, which may hinder contextualized embeddings from further alignment. In this paper, we propose a novel framework dubbed REPE (short for Representation-Level and Prediction-Level Alignment), which leverages both code-switched and original sentences to achieve multi-level alignment. Specifically, REPE introduces optimal transport to facilitate soft alignment between the representations of code-switched and original sentences, thereby preserving structural integrity as much as possible. Moreover, REPE adopts multi-view learning to enforce consistency regularization between the prediction of the two sentences, aligning them into a more refined language-invariant space. Based on this, we further incorporate a self-distillation layer to boost the robustness of REPE. Extensive experiments on two benchmarks across ten languages demonstrate the superiority of the proposed REPE framework.

We present a novel Parameter-Efficient Fine-Tuning (PEFT) method, dubbed as Adaptive Freezing of Low-Rank Adaptation (AFLoRA). Specifically, for each pre-trained frozen weight tensor, we add a parallel path of trainable low-rank matrices, namely a down-projection and an up-projection matrix, each of which is followed by a feature transformation vector. Based on a novel freezing score, we incrementally freeze these projection matrices during fine-tuning to reduce the computation and alleviate over-fitting. Our experimental results demonstrate that we can achieve state-of-the-art performance with an average improvement of up to 0.85% as evaluated on the GLUE benchmark while yielding up to 9.5× fewer average trainable parameters. While compared in terms of runtime, AFLoRA can yield up to 1.86× improvement as opposed to similar PEFT alternatives. Besides the practical utility of our approach, we provide insights on the trainability requirements of LoRA paths at different modules and the freezing schedule for the different projection matrices.

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DDPrompt: Differential Diversity Prompting in Large Language Models
Lin Mu | Wenhao Zhang | Yiwen Zhang | Peiquan Jin

Large Language Models (LLMs) have shown that their reasoning ability could be enhanced through approaches like Chain-of-Thought (CoT) prompting. However, these methods use single prompts for different types of questions and do not design appropriate prompts for questions with different characteristics. In this paper, we aim to explore a methodology that generates differentially diverse reasoning paths for different types of questions. To achieve this, we propose a novel prompting strategy called Differential Diversity Prompting (DDPrompt). Firstly, we generate the optimal prompts collection based on question characteristics. Then, we use this optimal prompts collection to generate multiple answers for a question and choose the final answer by voting. We evaluated DDPrompt on twelve reasoning benchmarks and significant improvement in the performance of LLMs on complex reasoning tasks (e.g., GSM8K 75%->84%, Tracking Shuffled Objects (68.8%->83.9%))

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Monotonic Representation of Numeric Attributes in Language Models
Benjamin Heinzerling | Kentaro Inui

Language models (LMs) can express factual knowledge involving numeric properties such as Karl Popper was born in 1902. However, how this information is encoded in the model’s internal representations is not understood well. Here, we introduce a method for finding and editing representations of numeric properties such as an entity’s birth year. We find directions that encode numeric properties monotonically, in an interpretable fashion. When editing representations along these directions, LM output changes accordingly. For example, by patching activations along a “birthyear” direction we can make the LM express an increasingly late birthyear. Property-encoding directions exist across several numeric properties in all models under consideration, suggesting the possibility that monotonic representation of numeric properties consistently emerges during LM pretraining.Code: https://github.com/bheinzerling/numeric-property-reprA long version of this short paper is available at: https://arxiv.org/abs/2403.10381

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Two Issues with Chinese Spelling Correction and A Refinement Solution
Changxuan Sun | Linlin She | Xuesong Lu

The Chinese Spelling Correction (CSC) task aims to detect and correct misspelled characters in Chinese text, and has received lots of attention in the past few years. Most recent studies adopt a Transformer-based model and leverage different features of characters such as pronunciation, glyph and contextual information to enhance the model’s ability to complete the task. Despite their state-of-the-art performance, we observe two issues that should be addressed to further advance the CSC task. First, the widely-used benchmark datasets SIGHAN13, SIGHAN14 and SIGHAN15, contain many mistakes. Hence the performance of existing models is not accurate and should be re-evaluated. Second, existing models seem to have reached a performance bottleneck, where the improvements on the SIGHAN’s testing sets are increasingly smaller and unstable. To deal with the two issues, we make two contributions: (1) we manually fix the SIGHAN datasets and re-evaluate four representative CSC models using the fixed datasets; (2) we analyze the new results to identify the spelling errors that none of the four models successfully corrects, based on which we propose a simple yet effective refinement solution. Experimental results show that our solution improves the four models in all metrics by notable margins.

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DynaSemble: Dynamic Ensembling of Textual and Structure-Based Models for Knowledge Graph Completion
Ananjan Nandi | Navdeep Kaur | Parag Singla | Mausam .

We consider two popular approaches to KnowledgeGraph Completion (KGC): textual modelsthat rely on textual entity descriptions, andstructure-based models that exploit the connectivitystructure of the Knowledge Graph(KG). Preliminary experiments show that theseapproaches have complementary strengths:structure-based models perform exceptionallywell when the gold answer is easily reachablefrom the query head in the KG, while textualmodels exploit descriptions to give goodperformance even when the gold answer isnot easily reachable. In response, we proposeDynaSemble, a novel method for learningquery-dependent ensemble weights to combinethese approaches by using the distributions ofscores assigned by the models in the ensembleto all candidate entities. DynaSemble achievesstate-of-the-art results on three standard KGCdatasets, with up to 6.8 pt MRR and 8.3 ptHits@1 gains over the best baseline model forthe WN18RR dataset.

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Fine-Tuning Pre-Trained Language Models with Gaze Supervision
Shuwen Deng | Paul Prasse | David Reich | Tobias Scheffer | Lena Jäger

Human gaze data provide cognitive information that reflect human language comprehension and has been effectively integrated into a variety of natural language processing (NLP) tasks, demonstrating improved performance over corresponding plain text-based models. In this work, we propose to integrate a gaze module into pre-trained language models (LMs) at the fine-tuning stage to improve their capabilities to learn representations that are grounded in human language processing. This is done by extending the conventional purely text-based fine-tuning objective with an auxiliary loss to exploit cognitive signals. The gaze module is only included during training, retaining compatibility with existing pre-trained LM-based pipelines. We evaluate the proposed approach using two distinct pre-trained LMs on the GLUE benchmark and observe that the proposed model improves performance compared to both standard fine-tuning and traditional text augmentation baselines.

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Growing Trees on Sounds: Assessing Strategies for End-to-End Dependency Parsing of Speech
Adrien Pupier | Maximin Coavoux | Jérôme Goulian | Benjamin Lecouteux

Direct dependency parsing of the speech signal –as opposed to parsing speech transcriptions– has recently been proposed as a task (Pupier et al. 2022), as a way of incorporating prosodic information in the parsing system and bypassing the limitations of a pipeline approach that would consist of using first an Automatic Speech Recognition (ASR) system and then a syntactic parser. In this article, we report on a set of experiments aiming at assessing the performance of two parsing paradigms (graph-based parsing and sequence labeling based parsing) on speech parsing. We perform this evaluation on a large treebank of spoken French, featuring realistic spontaneous conversations. Our findings show that (i) the graph based approach obtain better results across the board (ii) parsing directly from speech outperforms a pipeline approach, despite having 30% fewer parameters.

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Sketch-Guided Constrained Decoding for Boosting Blackbox Large Language Models without Logit Access
Saibo Geng | Berkay Döner | Chris Wendler | Martin Josifoski | Robert West

Constrained decoding, a technique for enforcing constraints on language model outputs, offers a way to control text generation without retraining or architectural modifications. Its application is, however, typically restricted to models that give users access to next-token distributions (usually via softmax logits), which poses a limitation with blackbox large language models (LLMs). This paper introduces sketch-guided constrained decoding (SketchGCD), a novel approach to constrained decoding for blackbox LLMs, which operates without access to the logits of the blackbox LLM. SketchGCD utilizes a locally hosted auxiliary model to refine the output of an unconstrained blackbox LLM, effectively treating this initial output as a “sketch” for further elaboration. This approach is complementary to traditional logit-based techniques and enables the application of constrained decoding in settings where full model transparency is unavailable. We demonstrate the efficacy of SketchGCD through experiments in closed information extraction and constituency parsing, showing how it enhances the utility and flexibility of blackbox LLMs for complex NLP tasks.

The incorporation of Denoising Diffusion Models (DDMs) in the Text-to-Speech (TTS) domain is rising, providing great value in synthesizing high quality speech. Although they exhibit impressive audio quality, the extent of their semantic capabilities is unknown, and controlling their synthesized speech’s vocal properties remains a challenge. Inspired by recent advances in image synthesis, we explore the latent space of frozen TTS models, which is composed of the latent bottleneck activations of the DDM’s denoiser. We identify that this space contains rich semantic information, and outline several novel methods for finding semantic directions within it, both supervised and unsupervised. We then demonstrate how these enable off-the-shelf audio editing, without any further training, architectural changes or data requirements. We present evidence of the semantic and acoustic qualities of the edited audio, and provide supplemental samples: https://latent-analysis-grad-tts.github.io/speech-samples/.

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Learnable Privacy Neurons Localization in Language Models
Ruizhe Chen | Tianxiang Hu | Yang Feng | Zuozhu Liu

Concerns regarding Large Language Models (LLMs) to memorize and disclose private information, particularly Personally Identifiable Information (PII), become prominent within the community. Many efforts have been made to mitigate the privacy risks.However, the mechanism through which LLMs memorize PII remains poorly understood. To bridge this gap, we introduce a pioneering method for pinpointing PII-sensitive neurons (privacy neurons) within LLMs. Our method employs learnable binary weight masks to localize specific neurons that account for the memorization of PII in LLMs through adversarial training. Our investigations discover that PII is memorized by a small subset of neurons across all layers, which shows the property of PII specificity. Furthermore, we propose to validate the potential in PII risk mitigation by deactivating the localized privacy neurons. Both quantitative and qualitative experiments demonstrate the effectiveness of our neuron localization algorithm.

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Is the Pope Catholic? Yes, the Pope is Catholic. Generative Evaluation of Non-Literal Intent Resolution in LLMs
Akhila Yerukola | Saujas Vaduguru | Daniel Fried | Maarten Sap

Humans often express their communicative intents indirectly or non-literally, which requires their interlocutors—human or AI—to understand beyond the literal meaning of words. While most existing work has focused on discriminative evaluations, we present a new approach to generatively evaluate large language models’ (LLMs’) intention understanding by examining their responses to non-literal utterances. Ideally, an LLM should respond in line with the true intention of a non-literal utterance, not its literal interpretation. Our findings show that LLMs struggle to generate contextually relevant responses to non-literal language. We also find that providing oracle intentions substantially improves response appropriateness, but using chain-of-thought to make models spell out intentions before responding improves much less. These findings suggest that LLMs are not yet pragmatic interlocutors, and that explicitly modeling intention could improve LLM responses to non-literal language.

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Generating Harder Cross-document Event Coreference Resolution Datasets using Metaphoric Paraphrasing
Shafiuddin Rehan Ahmed | Zhiyong Wang | George Baker | Kevin Stowe | James Martin

The most popular Cross-Document Event Coreference Resolution (CDEC) datasets fail to convey the true difficulty of the task, due to the lack of lexical diversity between coreferring event triggers (words or phrases that refer to an event). Furthermore, there is a dearth of event datasets for figurative language, limiting a crucial avenue of research in event comprehension. We address these two issues by introducing ECB+META, a lexically rich variant of Event Coref Bank Plus (ECB+) for CDEC on symbolic and metaphoric language. We use ChatGPT as a tool for the metaphoric transformation of sentences in the documents of ECB+, then tag the original event triggers in the transformed sentences in a semi-automated manner. In this way, we avoid the re-annotation of expensive coreference links. We present results that show existing methods that work well on ECB+ struggle with ECB+META, thereby paving the way for CDEC research on a much more challenging dataset. Code/data: https://github.com/ahmeshaf/llms_coref

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Soft Self-Consistency Improves Language Models Agents
Han Wang | Archiki Prasad | Elias Stengel-Eskin | Mohit Bansal

Generations from large language models (LLMs) can be improved by sampling and scoring multiple solutions to select a final answer. Current “sample and select” methods such as self-consistency (SC) rely on majority voting to score answers. However, when tasks have many distinct and valid answers, selection by voting requires a large number of samples. This makes SC prohibitively expensive for interactive tasks that involve generating multiple actions (answers) sequentially. After establishing that majority voting fails to provide consistent gains on such tasks, we demonstrate how to increase success rates by softening the scoring criterion. We introduce Soft Self-Consistency (SOFT-SC), which replaces SC’s discontinuous scoring with a continuous score computed from model likelihoods, allowing for selection even when actions are sparsely distributed. SOFT-SC improves both performance and efficiency on long-horizon interactive tasks, requiring half as many samples as SC for comparable or better performance. For a fixed number of samples, SOFT-SC leads to a 1.3% increase over SC in absolute success rate on writing bash programs, a 6.6% increase on online shopping (WebShop), and a 4.7% increase for an interactive household game (ALFWorld). Finally, we show that SOFT-SC can be applied to both open-source and black-box models.

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RecGPT: Generative Pre-training for Text-based Recommendation
Hoang Ngo | Dat Quoc Nguyen

We present the first domain-adapted and fully-trained large language model, RecGPT-7B, and its instruction-following variant, RecGPT-7B-Instruct, for text-based recommendation. Experimental results on rating prediction and sequential recommendation tasks show that our model, RecGPT-7B-Instruct, outperforms previous strong baselines. We are releasing our RecGPT models as well as their pre-training and fine-tuning datasets to facilitate future research and downstream applications in text-based recommendation. Public “huggingface” links to our RecGPT models and datasets are available at: https://github.com/VinAIResearch/RecGPT

Detecting critical moments, such as emotional outbursts or changes in decisions during conversations, is crucial for understanding shifts in human behavior and their consequences. Our work introduces a novel problem setting focusing on these moments as turning points (TPs), accompanied by a meticulously curated, high-consensus, human-annotated multi-modal dataset. We provide precise timestamps, descriptions, and visual-textual evidence high-lighting changes in emotions, behaviors, perspectives, and decisions at these turning points. We also propose a framework, TPMaven, utilizing state-of-the-art vision-language models to construct a narrative from the videos and large language models to classify and detect turning points in our multi-modal dataset. Evaluation results show that TPMaven achieves an F1-score of 0.88 in classification and 0.61 in detection, with additional explanations aligning with human expectations.

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What Does Parameter-free Probing Really Uncover?
Tommi Buder-Gröndahl

Supervised approaches to probing large language models (LLMs) have been criticized of using pre-defined theory-laden target labels. As an alternative, parameter-free probing constructs structural representations bottom-up via information derived from the LLM alone. This has been suggested to capture a genuine “LLM-internal grammar”. However, its relation to familiar linguistic formalisms remains unclear. I extend prior work on a parameter-free probing technique called perturbed masking applied to BERT, by comparing its results to the Universal Dependencies (UD) formalism for English. The results highlight several major discrepancies between BERT and UD, which lack correlates in linguistic theory. This raises the question of whether human grammar is the correct analogy to interpret BERT in the first place.

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ATLAS: Improving Lay Summarisation with Attribute-based Control
Zhihao Zhang | Tomas Goldsack | Carolina Scarton | Chenghua Lin

Lay summarisation aims to produce summaries of scientific articles that are comprehensible to non-expert audiences. However, previous work assumes a one-size-fits-all approach, where the content and style of the produced summary are entirely dependent on the data used to train the model. In practice, audiences with different levels of expertise will have specific needs, impacting what content should appear in a lay summary and how it should be presented. Aiming to address this, we propose ATLAS, a novel abstractive summarisation approach that can control various properties that contribute to the overall “layness” of the generated summary using targeted control attributes. We evaluate ATLAS on a combination of biomedical lay summarisation datasets, where it outperforms state-of-the-art baselines using mainstream summarisation metrics.Additional analyses provided on the discriminatory power and emergent influence of our selected controllable attributes further attest to the effectiveness of our approach.

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EmbSpatial-Bench: Benchmarking Spatial Understanding for Embodied Tasks with Large Vision-Language Models
Mengfei Du | Binhao Wu | Zejun Li | Xuanjing Huang | Zhongyu Wei

The recent rapid development of Large Vision-Language Models (LVLMs) has indicated their potential for embodied tasks. However, the critical skill of spatial understanding in embodied environments has not been thoroughly evaluated, leaving the gap between current LVLMs and qualified embodied intelligence unknown. Therefore, we construct EmbSpatial-Bench, a benchmark for evaluating embodied spatial understanding of LVLMs. The benchmark is automatically derived from embodied scenes and covers 6 spatial relationships from an egocentric perspective. Experiments expose the insufficient capacity of current LVLMs (even GPT-4V). We further present EmbSpatial-SFT, an instruction-tuning dataset designed to improve LVLMs’ embodied spatial understanding.

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Understanding the Effects of Noise in Text-to-SQL: An Examination of the BIRD-Bench Benchmark
Niklas Wretblad | Fredrik Riseby | Rahul Biswas | Amin Ahmadi | Oskar Holmström

Text-to-SQL, which involves translating natural language into Structured Query Language (SQL), is crucial for enabling broad access to structured databases without expert knowledge. However, designing models for such tasks is challenging due to numerous factors, including the presence of noise, such as ambiguous questions and syntactical errors. This study provides an in-depth analysis of the distribution and types of noise in the widely used BIRD-Bench benchmark and the impact of noise on models. While BIRD-Bench was created to model dirty and noisy database values, it was not created to contain noise and errors in the questions and gold SQL queries. We found that noise in questions and gold queries are prevalent in the dataset, with varying amounts across domains, and with an uneven distribution between noise types. The presence of incorrect gold SQL queries, which then generate incorrect gold answers, has a significant impact on the benchmark’s reliability. Surprisingly, when evaluating models on corrected SQL queries, zero-shot baselines surpassed the performance of state-of-the-art prompting methods. We conclude that informative noise labels and reliable benchmarks are crucial to developing new Text-to-SQL methods that can handle varying types of noise.

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Dwell in the Beginning: How Language Models Embed Long Documents for Dense Retrieval
João Coelho | Bruno Martins | Joao Magalhaes | Jamie Callan | Chenyan Xiong

This study investigates the existence of positional biases in Transformer-based language models for text representation learning, particularly in the context of web document retrieval. We build on previous research that demonstrated loss of information in the middle of input sequences for causal language models, extending it to the domain of embedding learning. We examine positional biases at multiple stages of the training pipeline for an encoder-decoder neural retrieval model, namely language model pre-training, contrastive pre-training, and contrastive fine-tuning. Experiments with the MS-MARCO document collection reveal that after contrastive pre-training the model already generates embeddings that better capture the beginning of the input content, with fine-tuning further aggravating this effect.

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That’s Optional: A Contemporary Exploration of “that” Omission in English Subordinate Clauses
Ella Rabinovich

The Uniform Information Density (UID) hypothesis posits that speakers optimize the communicative properties of their utterances by avoiding spikes in information, thereby maintaining a relatively uniform information profile over time. This paper investigates the impact of UID principles on syntactic reduction, specifically focusing on the optional omission of the connector “that” in English subordinate clauses. Building upon previous research, we extend our investigation to a larger corpus of written English, utilize contemporary large language models (LLMs) and extend the information-uniformity principles by the notion of entropy, to estimate the UID manifestations in the usecase of syntactic reduction choices.

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Do Large Language Models Discriminate in Hiring Decisions on the Basis of Race, Ethnicity, and Gender?
Haozhe An | Christabel Acquaye | Colin Wang | Zongxia Li | Rachel Rudinger

We examine whether large language models (LLMs) exhibit race- and gender-based name discrimination in hiring decisions, similar to classic findings in the social sciences (Bertrand and Mullainathan, 2004). We design a series of templatic prompts to LLMs to write an email to a named job applicant informing them of a hiring decision. By manipulating the applicant’s first name, we measure the effect of perceived race, ethnicity, and gender on the probability that the LLM generates an acceptance or rejection email. We find that the hiring decisions of LLMs in many settings are more likely to favor White applicants over Hispanic applicants. In aggregate, the groups with the highest and lowest acceptance rates respectively are masculine White names and masculine Hispanic names. However, the comparative acceptance rates by group vary under different templatic settings, suggesting that LLMs’ race- and gender-sensitivity may be idiosyncratic and prompt-sensitive.

Content moderators play a key role in keeping the conversation on social media healthy. While the high volume of content they need to judge represents a bottleneck to the moderation pipeline, no studies have explored how models could support them to make faster decisions. There is, by now, a vast body of research into detecting hate speech, sometimes explicitly motivated by a desire to help improve content moderation, but published research using real content moderators is scarce. In this work we investigate the effect of explanations on the speed of real-world moderators. Our experiments show that while generic explanations do not affect their speed and are often ignored, structured explanations lower moderators’ decision making time by 7.4%.

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Born Differently Makes a Difference: Counterfactual Study of Bias in Biography Generation from a Data-to-Text Perspective
Biaoyan Fang | Ritvik Dinesh | Xiang Dai | Sarvnaz Karimi

How do personal attributes affect biography generation? Addressing this question requires an identical pair of biographies where only the personal attributes of interest are different. However, it is rare in the real world. To address this, we propose a counterfactual methodology from a data-to-text perspective, manipulating the personal attributes of interest while keeping the co-occurring attributes unchanged. We first validate that the fine-tuned Flan-T5 model generates the biographies based on the given attributes. This work expands the analysis of gender-centered bias in text generation. Our results confirm the well-known bias in gender and also show the bias in regions, in both individual and its related co-occurring attributes in semantic machining and sentiment.

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Sign Language Translation with Sentence Embedding Supervision
Yasser Hamidullah | Josef van Genabith | Cristina España-Bonet

State-of-the-art sign language translation (SLT) systems facilitate the learning process through gloss annotations, either in an end2end manner or by involving an intermediate step. Unfortunately, gloss labelled sign language data is usually not available at scale and, when available, gloss annotations widely differ from dataset to dataset. We present a novel approach using sentence embeddings of the target sentences at training time that take the role of glosses. The new kind of supervision does not need any manual annotation but it is learned on raw textual data. As our approach easily facilitates multilinguality, we evaluate it on datasets covering German (PHOENIX-2014T) and American (How2Sign) sign languages and experiment with mono- and multilingual sentence embeddings and translation systems. Our approach significantly outperforms other gloss-free approaches, setting the new state-of-the-art for data sets where glosses are not available and when no additional SLT datasets are used for pretraining, diminishing the gap between gloss-free and gloss-dependent systems.

Topic modeling is a widely used technique to analyze large document corpora. With the ever-growing emergence of scientific contributions in the field, non-technical users may often use the simplest available software module, independent of whether there are potentially better models available. We present a Simplified Topic Retrieval, Exploration, and Analysis Module (STREAM) for user-friendly topic modelling and especially subsequent interactive topic visualization and analysis. For better topic analysis, we implement multiple intruder-word based topic evaluation metrics. Additionally, we publicize multiple new datasets that can extend the so far very limited number of publicly available benchmark datasets in topic modeling. We integrate downstream interpretable analysis modules to enable users to easily analyse the created topics in downstream tasks together with additional tabular information.The code is available at the following link: https://github.com/AnFreTh/STREAM

For large language models (LLMs) to be effective in the financial domain – where each decision can have a significant impact – it is necessary to investigate realistic tasks and data. Financial professionals often interact with documents spanning hundreds of pages, but most financial research datasets only deal with short excerpts from these documents. To address this, we introduce a long-document financial QA task. We augment 7,437 questions from the existing FinQA dataset with full-document context, extending the average context length from under 700 words in FinQA to 123k words in DocFinQA. We conduct extensive experiments over retrieval-based QA pipelines and long-context language models. Based on our experiments, DocFinQA proves a significant challenge for even state-of-the-art systems. We also provide a case study on a subset of the longest documents in DocFinQA and find that models particularly struggle with these documents. Addressing these challenges may have a wide-reaching impact across applications where specificity and long-range contexts are critical, like gene sequences and legal document contract analysis. DocFinQA dataset is publicly accessible.

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MaskLID: Code-Switching Language Identification through Iterative Masking
Amir Hossein Kargaran | François Yvon | Hinrich Schuetze

We present MaskLID, a simple, yet effective, code-switching (CS) language identification (LID) method. MaskLID does not require any training and is designed to complement current high-performance sentence-level LIDs. Sentence-level LIDs are classifiers trained on monolingual texts to provide single labels, typically using a softmax layer to turn scores into probabilities. However, in cases where a sentence is composed in both L1 and L2 languages, the LID classifier often only returns the dominant label L1. To address this limitation, MaskLID employs a strategy to mask text features associated with L1, allowing the LID to classify the text as L2 in the next round. This method uses the LID itself to identify the features that require masking and does not rely on any external resource. In this work, we explore the use of MaskLID for two open-source LIDs (GlotLID and OpenLID), that are both based on the FastText architecture. Code and demo are available at https://github.com/cisnlp/MaskLID.

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An Empirical Analysis on Large Language Models in Debate Evaluation
Xinyi Liu | Pinxin Liu | Hangfeng He

In this study, we investigate the capabilities and inherent biases of advanced large language models (LLMs) such as GPT-3.5 and GPT-4 in the context of debate evaluation. We discover that LLM’s performance exceeds humans and surpasses the performance of state-of-the-art methods fine-tuned on extensive datasets. We additionally explore and analyze biases present in LLMs, including positional bias, lexical bias, order bias, which may affect their evaluative judgments. Our findings reveal a consistent bias in both GPT-3.5 and GPT-4 towards the second candidate response presented, attributed to prompt design. We also uncover a lexical bias in both GPT-3.5 and GPT-4, especially when label sets carry connotations such as numerical or sequential, highlighting the critical need for careful label verbalizer selection in prompt design. Additionally, our analysis indicates a tendency of both models to favor the debate’s concluding side as the winner, suggesting an end-of-discussion bias.

We introduce a new, extensive multidimensional quality metrics (MQM) annotated dataset covering 11 language pairs in the biomedical domain. We use this dataset to investigate whether machine translation (MT) metrics which are fine-tuned on human-generated MT quality judgements are robust to domain shifts between training and inference. We find that fine-tuned metrics exhibit a substantial performance drop in the unseen domain scenario relative to both metrics that rely on the surface form and pre-trained metrics that are not fine-tuned on MT quality judgments.

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IndicIRSuite: Multilingual Dataset and Neural Information Models for Indian Languages
Saiful Haq | Ashutosh Sharma | Omar Khattab | Niyati Chhaya | Pushpak Bhattacharyya

In this paper, we introduce Neural Information Retrieval resources for 11 widely spoken Indian Languages (Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Oriya, Punjabi, Tamil, and Telugu) from two major Indian language families (Indo-Aryan and Dravidian). These resources include (a) INDIC-MARCO, a multilingual version of the MS MARCO dataset in 11 Indian Languages created using Machine Translation, and (b) Indic-ColBERT, a collection of 11 distinct Monolingual Neural Information Retrieval models, each trained on one of the 11 languages in the INDIC-MARCO dataset. To the best of our knowledge, IndicIRSuite is the first attempt at building large-scale Neural Information Retrieval resources for a large number of Indian languages, and we hope that it will help accelerate research in Neural IR for Indian Languages. Experiments demonstrate that Indic-ColBERT achieves 47.47% improvement in the MRR@10 score averaged over the INDIC-MARCO baselines for all 11 Indian languages except Oriya, 12.26% improvement in the NDCG@10 score averaged over the MIRACL Bengali and Hindi Language baselines, and 20% improvement in the MRR@100 Score over the Mr. Tydi Bengali Language baseline.

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AGR: Reinforced Causal Agent-Guided Self-explaining Rationalization
Yunxiao Zhao | Zhiqiang Wang | Xiaoli Li | Jiye Liang | Ru Li

Most existing rationalization approaches are susceptible to degeneration accumulation due to a lack of effective control over the learning direction of the model during training. To address this issue, we propose a novel approach AGR (Agent-Guided Rationalization), guiding the next action of the model based on its current training state. Specifically, we introduce causal intervention calculus to quantify the causal effects inherent during rationale training, and utilize reinforcement learning process to refine the learning bias of them. Furthermore, we pretrain an agent within this reinforced causal environment to guide the next step of the model. We theoretically demonstrate that a good model needs the desired guidance, and empirically show the effectiveness of our approach, outperforming existing state-of-the-art methods on BeerAdvocate and HotelReview datasets.

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Shoulders of Giants: A Look at the Degree and Utility of Openness in NLP Research
Surangika Ranathunga | Nisansa De Silva | Dilith Jayakody | Aloka Fernando

We analysed a sample of NLP research papers archived in ACL Anthology as an attempt to quantify the degree of openness and the benefit of such an open culture in the NLP community. We observe that papers published in different NLP venues show different patterns related to artefact reuse. We also note that more than 30% of the papers we analysed do not release their artefacts publicly. Further, we observe a wide language-wise disparity in publicly available NLP-related artefacts.

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The Probabilities Also Matter: A More Faithful Metric for Faithfulness of Free-Text Explanations in Large Language Models
Noah Siegel | Oana-Maria Camburu | Nicolas Heess | Maria Perez-Ortiz

In order to oversee advanced AI systems, it is important to understand their reasons for generating a given output. When prompted, large language models (LLMs) can provide natural language explanations or reasoning traces that sound plausible and receive high ratings from human annotators. However, it is unclear to what extent these explanations are truly capturing the factors responsible for the model’s predictions: the most “human-like” explanation may be different from the one that is most faithful to the model’s true decision making process. In this work, we introduce the correlational counterfactual test (CCT), a faithfulness metric based on counterfactual input edits that takes into account not just the binary label change, but the total shift in the model’s predicted label distribution. We evaluate the faithfulness of free-text explanations generated by few-shot-prompted LLMs from the Llama-2 family on three NLP tasks. We find that these explanations are indeed more likely to mention factors when they are impactful to the model’s prediction, with the degree of association increasing with model size but varying significantly by task.

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Naming, Describing, and Quantifying Visual Objects in Humans and LLMs
Alberto Testoni | Juell Sprott | Sandro Pezzelle

While human speakers use a variety of different expressions when describing the same object in an image, giving rise to a distribution of plausible labels driven by pragmatic constraints, the extent to which current Vision & Language Large Language Models (VLLMs) can mimic this crucial feature of language use is an open question. This applies to common, everyday objects, but it is particularly interesting for uncommon or novel objects for which a category label may be lacking or fuzzy. Furthermore, similar patterns of variation are observed among human speakers for highly context-sensitive expressions, such as the quantifiers ‘few’ or ‘most’. In our work, we evaluate VLLMs (FROMAGe, BLIP-2, LLaVA) on three categories (nouns, attributes, and quantifiers) where humans show great subjective variability concerning the distribution over plausible labels, using datasets and resources mostly under-explored in previous work. Our results reveal mixed evidence on the ability of VLLMs to capture human naming preferences at generation time: while some models are good at mimicking human distributions for nouns and attributes, all of them fail to assign quantifiers, a task that requires more accurate, high-level reasoning.

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Are LLMs classical or nonmonotonic reasoners? Lessons from generics
Alina Leidinger | Robert Van Rooij | Ekaterina Shutova

Recent scholarship on reasoning in LLMs has supplied evidence of impressive performance and flexible adaptation to machine generated or human critique. Nonmonotonic reasoning, crucial to human cognition for navigating the real world, remains a challenging, yet understudied task. In this work, we study nonmonotonic reasoning capabilities of seven state-of-the-art LLMs in one abstract and one commonsense reasoning task featuring generics, such as ‘Birds fly’, and exceptions, ‘Penguins don’t fly’ (see Fig. 1). While LLMs exhibit reasoning patterns in accordance with human nonmonotonic reasoning abilities, they fail to maintain stable beliefs on truth conditions of generics at the addition of supporting examples (‘Owls fly’) or unrelated information (‘Lions have manes’).Our findings highlight pitfalls in attributing human reasoning behaviours to LLMs as long as consistent reasoning remains elusive.

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ConstitutionalExperts: Training a Mixture of Principle-based Prompts
Savvas Petridis | Ben Wedin | Ann Yuan | James Wexler | Nithum Thain

Large language models (LLMs) are highly capable at a variety of tasks given the right prompt, but writing one is still a difficult and tedious process. In this work, we introduce ConstitutionalExperts, a method for learning a prompt consisting of constitutional principles (i.e. rules), given a training dataset. Unlike prior methods that optimize the prompt as a single entity, our method incrementally improves the prompt by surgically editing individual principles. We also show that we can improve overall performance by learning unique prompts for different semantic regions of the training data and using a mixture-of-experts (MoE) architecture to route inputs at inference time. We compare our method to other state of the art prompt-optimization techniques across six benchmark datasets. We also investigate whether MoE improves these other techniques. Our results suggest that ConstitutionalExperts outperforms other prompt optimization techniques by 10.9% (F1) and that mixture-of-experts improves all techniques, suggesting its broad applicability.

Large Language Models (LLMs) have demonstrated impressive capability in different tasks and are bringing transformative changes to many domains. However, keeping the knowledge in LLMs up-to-date remains a challenge once pretraining is complete. It is thus essential to design effective methods to both update obsolete knowledge and induce new knowledge into LLMs. Existing locate-and-edit knowledge editing (KE) method suffers from two limitations. First, the post-edit LLMs by such methods generally have poor capability in answering complex queries that require multi-hop reasoning. Second, the long run-time of such locate-and-edit methods to perform knowledge edits make it infeasible for large scale KE in practice. In this paper, we explore Parameter-Efficient Fine-Tuning (PEFT) techniques as an alternative for KE. We curate a more comprehensive temporal KE dataset with both knowledge update and knowledge injection examples for KE performance benchmarking. We further probe the effect of fine-tuning on a range of layers in an LLM for the multi-hop QA task. We find that PEFT performs better than locate-and-edit techniques for time-sensitive knowledge edits.

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PRewrite: Prompt Rewriting with Reinforcement Learning
Weize Kong | Spurthi Hombaiah | Mingyang Zhang | Qiaozhu Mei | Michael Bendersky

Prompt engineering is critical for the development of LLM-based applications. However, it is usually done manually in a “trial and error” fashion that can be time consuming, ineffective, and sub-optimal. Even for the prompts which seemingly work well, there is always a lingering question: can the prompts be made better with further modifications?To address these problems, we investigate automated prompt engineering in this paper. Specifically, we propose PRewrite, an automated method to rewrite an under-optimized prompt to a more effective prompt. We instantiate the prompt rewriter using an LLM. The rewriter LLM is trained using reinforcement learning to optimize the performance on a given downstream task. We conduct experiments on diverse benchmark datasets, which demonstrates the effectiveness of PRewrite.

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Paraphrasing in Affirmative Terms Improves Negation Understanding
MohammadHossein Rezaei | Eduardo Blanco

Negation is a common linguistic phenomenon. Yet language models face challenges with negation in many natural language understanding tasks such as question answering and natural language inference. In this paper, we experiment with seamless strategies that incorporate affirmative interpretations (i.e., paraphrases without negation) to make models more robust against negation. Crucially, our affirmative interpretations are obtained automatically. We show improvements with CondaQA, a large corpus requiring reasoning with negation, and five natural language understanding tasks.

Pinyin input method engine (IME) refers to the transformation tool from pinyin sequence to Chinese characters, which is widely used on mobile phone applications. Due to the homophones, Pinyin IME suffers from the one-to-many mapping problem in the process of pinyin sequences to Chinese characters. To solve the above issue, this paper makes the first exploration to leverage an effective conditional variational mechanism (CVM) for pinyin IME. However, to ensure the stable and smooth operation of Pinyin IME under low-resource conditions (e.g., on offline mobile devices), we should balance diversity, accuracy, and efficiency with CVM, which is still challenging. To this end, we employ a novel strategy that simplifies the complexity of semantic encoding by facilitating the interaction between pinyin and the Chinese character information during the construction of continuous latent variables. Concurrently, the accuracy of the outcomes is enhanced by capitalizing on the discrete latent variables. Experimental results demonstrate the superior performance of our method.

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Consistency Training by Synthetic Question Generation for Conversational Question Answering
Hamed Hematian Hemati | Hamid Beigy

Efficiently modeling historical information is a critical component in addressing user queries within a conversational question-answering (QA) context, as historical context plays a vital role in clarifying the user’s questions. However, irrelevant history induces noise in the reasoning process, especially for those questions with a considerable historical context. In our novel model-agnostic approach, referred to as **CoTaH** (**Co**nsistency-**T**rained **a**ugmented **H**istory), we augment the historical information with synthetic questions and subsequently employ consistency training to train a model that utilizes both real and augmented historical data to implicitly make the reasoning robust to irrelevant history. To the best of our knowledge, this is the first instance of research using synthetic question generation as a form of data augmentation to model conversational QA settings. By citing a common modeling error prevalent in previous research, we introduce a new baseline and compare our model’s performance against it, demonstrating an improvement in results, particularly in later turns of the conversation, when dealing with questions that include a large historical context.

While machine translation evaluation has been studied primarily for high-resource languages, there has been a recent interest in evaluation for low-resource languages due to the increasing availability of data and models. In this paper, we focus on a zero-shot evaluation setting focusing on low-resource Indian languages, namely Assamese, Kannada, Maithili, and Punjabi. We collect sufficient Multi-Dimensional Quality Metrics (MQM) and Direct Assessment (DA) annotations to create test sets and meta-evaluate a plethora of automatic evaluation metrics. We observe that even for learned metrics, which are known to exhibit zero-shot performance, the Kendall Tau and Pearson correlations with human annotations are only as high as 0.32 and 0.45. Synthetic data approaches show mixed results and overall do not help close the gap by much for these languages. This indicates that there is still a long way to go for low-resource evaluation.

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Zero-Shot Cross-Lingual Reranking with Large Language Models for Low-Resource Languages
Mofetoluwa Adeyemi | Akintunde Oladipo | Ronak Pradeep | Jimmy Lin

Large language models (LLMs) as listwise rerankers have shown impressive zero-shot capabilities in various passage ranking tasks. Despite their success, there is still a gap in existing literature on their effectiveness in reranking low-resource languages. To address this, we investigate how LLMs function as listwise rerankers in cross-lingual information retrieval (CLIR) systems with queries in English and passages in four African languages: Hausa, Somali, Swahili, and Yoruba. We analyze and compare the effectiveness of monolingual reranking using either query or document translations. We also evaluate the effectiveness of LLMs when leveraging their own generated translations. To grasp the general picture, we examine the effectiveness of multiple LLMs — the proprietary models RankGPT-4 and RankGPT-3.5, along with the open-source model RankZephyr. While the document translation setting, i.e., both queries and documents are in English, leads to the best reranking effectiveness, our results indicate that for specific LLMs, reranking in the African language setting achieves competitive effectiveness with the cross-lingual setting, and even performs better when using the LLM’s own translations.

Multimodal large language models (MLLMs) like LLaVA and GPT-4(V) enable general-purpose conversations about images with the language modality. As off-the-shelf MLLMs may have limited capabilities on images from domains like dermatology and agriculture, they must be fine-tuned to unlock domain-specific applications. The prevalent architecture of current open-source MLLMs comprises two major modules: an image-language (cross-modal) projection network and a large language model. It is desirable to understand the roles of these two modules in modeling domain-specific visual attributes to inform the design of future models and streamline the interpretability efforts on the current models. To this end, via experiments on 4 datasets and under 2 fine-tuning settings, we find that as the MLLM is fine-tuned, it indeed gains domain-specific visual capabilities, but the updates do not lead to the projection extracting relevant domain-specific visual attributes. Our results indicate that the domain-specific visual attributes are modeled by the LLM, even when only the projection is fine-tuned. Through this study, we offer a potential reinterpretation of the role of cross-modal projections in MLLM architectures.

While the abundance of rich and vast datasets across numerous fields has facilitated the advancement of natural language processing, sectors in need of specialized data types continue to struggle with the challenge of finding quality data. Our study introduces a novel guidance data augmentation technique utilizing abstracted context and sentence structures to produce varied sentences while maintaining context-entity relationships, addressing data scarcity challenges. By fostering a closer relationship between context, sentence structure, and role of entities, our method enhances data augmentation’s effectiveness. Consequently, by showcasing diversification in both entity-related vocabulary and overall sentence structure, and simultaneously improving the training performance of named entity recognition task.

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Aligning Large Language Models via Fine-grained Supervision
Dehong Xu | Liang Qiu | Minseok Kim | Faisal Ladhak | Jaeyoung Do

Pre-trained large-scale language models (LLMs) excel at producing coherent articles, yet their outputs may be untruthful, toxic, or fail to align with user expectations. Current approaches focus on using reinforcement learning with human feedback (RLHF) to improve model alignment, which works by transforming coarse human preferences of LLM outputs into a feedback signal that guides the model learning process. However, because this approach operates on sequence-level feedback, it lacks the precision to identify the exact parts of the output affecting user preferences. To address this gap, we propose a method to enhance LLM alignment through fine-grained token-level supervision. Specifically, we ask annotators to minimally edit less preferred responses within the standard reward modeling dataset to make them more favorable, ensuring changes are made only where necessary while retaining most of the original content. The refined dataset is used to train a token-level reward model, which is then used for training our fine-grained Proximal Policy Optimization (PPO) model. Our experiment results demonstrate that this approach can improve LLM performance by up to 5.1% in terms of win rate against the reference model, compared with the traditional PPO model.

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Annotating FrameNet via Structure-Conditioned Language Generation
Xinyue Cui | Swabha Swayamdipta

Despite the remarkable generative capabilities of language models in producing naturalistic language, their effectiveness on explicit manipulation and generation of linguistic structures remain understudied. In this paper, we investigate the task of generating new sentences preserving a given semantic structure, following the FrameNet formalism. We propose a framework to produce novel frame-semantically annotated sentences following an overgenerate-and-filter approach. Our results show that conditioning on rich, explicit semantic information tends to produce generations with high human acceptance, under both prompting and finetuning. Our generated frame-semantic structured annotations are effective at training data augmentation for frame-semantic role labeling in low-resource settings; however, we do not see benefits under higher resource settings. Our study concludes that while generating high-quality, semantically rich data might be within reach, the downstream utility of such generations remains to be seen, highlighting the outstanding challenges with automating linguistic annotation tasks.

Recently, large language models (LLMs) enhanced by self-reflection have achieved promising performance on machine transla004 tion. The key idea is guiding LLMs to generate translation with human-like feedback. However, existing self-reflection methods lack effective feedback information, limiting the translation performance. To address this, we introduce a DUAL-REFLECT framework, leveraging the dual learning of translation tasks to provide effective feedback, thereby enhancing the models’ self-reflective abilities and improving translation performance. The application of this method across various translation tasks has proven its effectiveness in improving translation accuracy and eliminating ambiguities, especially in translation tasks with low-resource language pairs.

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Towards Artwork Explanation in Large-scale Vision Language Models
Kazuki Hayashi | Yusuke Sakai | Hidetaka Kamigaito | Katsuhiko Hayashi | Taro Watanabe

Large-scale Vision-Language Models (LVLMs) output text from images and instructions, demonstrating advanced capabilities in text generation and comprehension. However, it has not been clarified to what extent LVLMs understand the knowledge necessary for explaining images, the complex relationships between various pieces of knowledge, and how they integrate these understandings into their explanations. To address this issue, we propose a new task: the artwork explanation generation task, along with its evaluation dataset and metric for quantitatively assessing the understanding and utilization of knowledge about artworks. This task is apt for image description based on the premise that LVLMs are expected to have pre-existing knowledge of artworks, which are often subjects of wide recognition and documented information.It consists of two parts: generating explanations from both images and titles of artworks, and generating explanations using only images, thus evaluating the LVLMs’ language-based and vision-based knowledge.Alongside, we release a training dataset for LVLMs to learn explanations that incorporate knowledge about artworks.Our findings indicate that LVLMs not only struggle with integrating language and visual information but also exhibit a more pronounced limitation in acquiring knowledge from images alone. The datasets ExpArt=Explain Artworks are available at https://huggingface.co/datasets/naist-nlp/ExpArt

It is widely known that hallucination is a critical issue in Simultaneous Machine Translation (SiMT) due to the absence of source-side information. While many efforts have been made to enhance performance for SiMT, few of them attempt to understand and analyze hallucination in SiMT.Therefore, we conduct a comprehensive analysis of hallucination in SiMT from two perspectives: understanding the distribution of hallucination words and the target-side context usage of them.Intensive experiments demonstrate some valuable findings and particularly show that it is possible to alleviate hallucination by decreasing the over usage of target-side information for SiMT.

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Self-Augmented In-Context Learning for Unsupervised Word Translation
Yaoyiran Li | Anna Korhonen | Ivan Vulić

Recent work has shown that, while large language models (LLMs) demonstrate strong word translation or bilingual lexicon induction (BLI) capabilities in few-shot setups, they still cannot match the performance of ‘traditional’ mapping-based approaches in the unsupervised scenario where no seed translation pairs are available, especially for lower-resource languages. To address this challenge with LLMs, we propose self-augmented in-context learning (SAIL) for unsupervised BLI: starting from a zero-shot prompt, SAIL iteratively induces a set of high-confidence word translation pairs for in-context learning (ICL) from an LLM, which it then reapplies to the same LLM in the ICL fashion. Our method shows substantial gains over zero-shot prompting of LLMs on two established BLI benchmarks spanning a wide range of language pairs, also outperforming mapping-based baselines across the board. In addition to achieving state-of-the-art unsupervised BLI performance, we also conduct comprehensive analyses on SAIL and discuss its limitations.

We present RAM-EHR, a Retrieval AugMentation pipeline to improve clinical predictions on Electronic Health Records (EHRs). RAM-EHR first collects multiple knowledge sources, converts them into text format, and uses dense retrieval to obtain information related to medical concepts. This strategy addresses the difficulties associated with complex names for the concepts. RAM-EHR then augments the local EHR predictive model co-trained with consistency regularization to capture complementary information from patient visits and summarized knowledge. Experiments on two EHR datasets show the efficacy of RAM-EHR over previous knowledge-enhanced baselines (3.4% gain in AUROC and 7.2% gain in AUPR), emphasizing the effectiveness of the summarized knowledge from RAM-EHR for clinical prediction tasks.

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Estimating the Level of Dialectness Predicts Inter-annotator Agreement in Multi-dialect Arabic Datasets
Amr Keleg | Walid Magdy | Sharon Goldwater

On annotating multi-dialect Arabic datasets, it is common to randomly assign the samples across a pool of native Arabic speakers. Recent analyses recommended routing dialectal samples to native speakers of their respective dialects to build higher-quality datasets. However, automatically identifying the dialect of samples is hard. Moreover, the pool of annotators who are native speakers of specific Arabic dialects might be scarce. Arabic Level of Dialectness (ALDi) was recently introduced as a quantitative variable that measures how sentences diverge from Standard Arabic. On randomly assigning samples to annotators, we hypothesize that samples of higher ALDi scores are harder to label especially if they are written in dialects that the annotators do not speak. We test this by analyzing the relation between ALDi scores and the annotators’ agreement, on 15 public datasets having raw individual sample annotations for various sentence-classification tasks. We find strong evidence supporting our hypothesis for 11 of them. Consequently, we recommend prioritizing routing samples of high ALDi scores to native speakers of each sample’s dialect, for which the dialect could be automatically identified at higher accuracies.

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Estimating the Level of Dialectness Predicts Inter-annotator Agreement in Multi-dialect Arabic Datasets
Amr Keleg | Walid Magdy | Sharon Goldwater

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Linear-time Minimum Bayes Risk Decoding with Reference Aggregation
Jannis Vamvas | Rico Sennrich

Minimum Bayes Risk (MBR) decoding is a text generation technique that has been shown to improve the quality of machine translations, but is expensive, even if a sampling-based approximation is used. Besides requiring a large number of sampled sequences, it requires the pairwise calculation of a utility metric, which has quadratic complexity. In this paper, we propose to approximate pairwise metric scores with scores calculated against aggregated reference representations. This changes the complexity of utility estimation from O(n²) to O(n), while empirically preserving most of the quality gains of MBR decoding. We release our source code.

The web contains large-scale, diverse, and abundant information to satisfy the information-seeking needs of humans. Through meticulous data collection, preprocessing, and curation, webpages can be used as a fundamental data resource for language model pretraining. However, when confronted with the progressively revolutionized and intricate nature of webpages, rule-based/feature-based web scrapers are becoming increasingly inadequate. This paper presents a simple, fast, and effective Neural web Scraper (NeuScraper) to help extract primary and clean text contents from webpages. Experimental results show that NeuScraper surpasses the baseline scrapers by achieving more than a 20% improvement, demonstrating its potential in extracting higher-quality data to facilitate the language model pretraining. All of the code is available at https://github.com/OpenMatch/NeuScraper.

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Greed is All You Need: An Evaluation of Tokenizer Inference Methods
Omri Uzan | Craig W. Schmidt | Chris Tanner | Yuval Pinter

While subword tokenizers such as BPE and WordPiece are typically used to build vocabularies for NLP models, the method of decoding text into a sequence of tokens from these vocabularies is often left unspecified, or ill-suited to the method in which they were constructed. We provide a controlled analysis of seven tokenizer inference methods across four different algorithms and three vocabulary sizes, performed on a novel intrinsic evaluation suite we curated for English, combining measures rooted in morphology, cognition, and information theory. We show that for the most commonly used tokenizers, greedy inference performs surprisingly well; and that SaGe, a recently-introduced contextually-informed tokenizer, outperforms all others on morphological alignment.

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What Do Dialect Speakers Want? A Survey of Attitudes Towards Language Technology for German Dialects
Verena Blaschke | Christoph Purschke | Hinrich Schuetze | Barbara Plank

Natural language processing (NLP) has largely focused on modelling standardized languages. More recently, attention has increasingly shifted to local, non-standardized languages and dialects. However, the relevant speaker populations’ needs and wishes with respect to NLP tools are largely unknown. In this paper, we focus on dialects and regional languages related to German – a group of varieties that is heterogeneous in terms of prestige and standardization. We survey speakers of these varieties (N=327) and present their opinions on hypothetical language technologies for their dialects. Although attitudes vary among subgroups of our respondents, we find that respondents are especially in favour of potential NLP tools that work with dialectal input (especially audio input) such as virtual assistants, and less so for applications that produce dialectal output such as machine translation or spellcheckers.

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SeeGULL Multilingual: a Dataset of Geo-Culturally Situated Stereotypes
Mukul Bhutani | Kevin Robinson | Vinodkumar Prabhakaran | Shachi Dave | Sunipa Dev

While generative multilingual models are rapidly being deployed, their safety and fairness evaluations are largely limited to resources collected in English. This is especially problematic for evaluations targeting inherently socio-cultural phenomena such as stereotyping, where it is important to build multilingual resources that reflect the stereotypes prevalent in respective language communities. However, gathering these resources, at scale, in varied languages and regions pose a significant challenge as it requires broad socio-cultural knowledge and can also be prohibitively expensive. To overcome this critical gap, we employ a recently introduced approach that couples LLM generations for scale with culturally situated validations for reliability, and build SeeGULL Multilingual, a global-scale multilingual dataset of social stereotypes, containing over 25K stereotypes, spanning 23 pairs of languages and regions they are common in, with human annotations, and demonstrate its utility in identifying gaps in model evaluations.

Humor is a fundamental facet of human cognition and interaction. Yet, despite recent advances in natural language processing, humor detection remains a challenging task that is complicated by the scarcity of datasets that pair humorous texts with similar non-humorous counterparts. We investigate whether large language models (LLMs) can generate synthetic data for humor detection via editing texts. We benchmark LLMs on an existing human dataset and show that current LLMs display an impressive ability to “unfun” jokes, as judged by humans and as measured on the downstream task of humor detection. We extend our approach to a code-mixed English-Hindi humor dataset where we find that GPT-4’s synthetic data is highly rated by bilingual annotators and provides challenging adversarial examples for humor classifiers.

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Don’t Buy it! Reassessing the Ad Understanding Abilities of Contrastive Multimodal Models
Anna Bavaresco | Alberto Testoni | Raquel Fernández

Image-based advertisements are complex multimodal stimuli that often contain unusual visual elements and figurative language. Previous research on automatic ad understanding has reported impressive zero-shot accuracy of contrastive vision-and-language models (VLMs) on an ad-explanation retrieval task. Here, we examine the original task setup and show that contrastive VLMs can solve it by exploiting grounding heuristics. To control for this confound, we introduce TRADE, a new evaluation test set with adversarial grounded explanations. While these explanations look implausible to humans, we show that they “fool” four different contrastive VLMs. Our findings highlight the need for an improved operationalisation of automatic ad understanding that truly evaluates VLMs’ multimodal reasoning abilities. We make our code and TRADE available at https://github.com/dmg-illc/trade.

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bib (full) Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)

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Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
Yixin Cao | Yang Feng | Deyi Xiong

Text-to-image synthesis for the Chinese language poses unique challenges due to its large vocabulary size, and intricate character relationships. While existing diffusion models have shown promise in generating images from textual descriptions, they often neglect domain-specific contexts and lack robustness in handling the Chinese language. This paper introduces PAI-Diffusion, a comprehensive framework that addresses these limitations. PAI-Diffusion incorporates both general and domain-specific Chinese diffusion models, enabling the generation of contextually relevant images. It explores the potential of using LoRA and ControlNet for fine-grained image style transfer and image editing, empowering users with enhanced control over image generation. Moreover, PAI-Diffusion seamlessly integrates with Alibaba Cloud’s Platform for AI, providing accessible and scalable solutions. All the Chinese diffusion model checkpoints, LoRAs, and ControlNets, including domain-specific ones, are publicly available. A user-friendly Chinese WebUI and the diffusers-api elastic inference toolkit, also open-sourced, further facilitate the easy deployment of PAI-Diffusion models in various local and cloud environments, making it a valuable resource for Chinese text-to-image synthesis.

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OpenVNA: A Framework for Analyzing the Behavior of Multimodal Language Understanding System under Noisy Scenarios
Ziqi Yuan | Baozheng Zhang | Hua Xu | Zhiyun Liang | Kai Gao

We present OpenVNA, an open-source framework designed for analyzing the behavior of multimodal language understanding systems under noisy conditions. OpenVNA serves as an intuitive toolkit tailored for researchers, facilitating convenience batch-level robustness evaluation and on-the-fly instance-level demonstration. It primarily features a benchmark Python library for assessing global model robustness, offering high flexibility and extensibility, thereby enabling customization with user-defined noise types and models. Additionally, a GUI-based interface has been developed to intuitively analyze local model behavior. In this paper, we delineate the design principles and utilization of the created library and GUI-based web platform. Currently, OpenVNA is publicly accessible at https://github.com/thuiar/OpenVNA, with a demonstration video available at https://youtu.be/0Z9cW7RGct4.

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XNLP: An Interactive Demonstration System for Universal Structured NLP
Hao Fei | Meishan Zhang | Min Zhang | Tat-Seng Chua

Structured Natural Language Processing (XNLP) is an important subset of NLP that entails understanding the underlying semantic or syntactic structure of texts, which serves as a foundational component for many downstream applications. Despite certain recent efforts to explore universal solutions for specific categories of XNLP tasks, a comprehensive and effective approach for unifying all XNLP tasks long remains underdeveloped. Meanwhile, while XNLP demonstration systems are vital for researchers exploring various XNLP tasks, existing platforms can be limited to, e.g., supporting few XNLP tasks, lacking interactivity and universalness. To this end, we propose an advanced XNLP demonstration system, where we leverage LLM to achieve universal XNLP, with one model for all with high generalizability. Overall, our system advances in multiple aspects, including universal XNLP modeling, high performance, interpretability, scalability, and interactivity, offering a unified platform for exploring diverse XNLP tasks in the community.

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Towards the TopMost: A Topic Modeling System Toolkit
Xiaobao Wu | Fengjun Pan | Anh Tuan Luu

Topic models have a rich history with various applications and have recently been reinvigorated by neural topic modeling. However, these numerous topic models adopt totally distinct datasets, implementations, and evaluations. This impedes quick utilization and fair comparisons, and thereby hinders their research progress and applications. To tackle this challenge, we in this paper propose a Topic Modeling System Toolkit (TopMost). Compared to existing toolkits, TopMost stands out by supporting more extensive features. It covers a broader spectrum of topic modeling scenarios with their complete lifecycles, including datasets, preprocessing, models, training, and evaluations. Thanks to its highly cohesive and decoupled modular design, TopMost enables rapid utilization, fair comparisons, and flexible extensions of diverse cutting-edge topic models. Our code, tutorials, and documentation are available at https://github.com/bobxwu/topmost.

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Wordflow: Social Prompt Engineering for Large Language Models
Zijie Wang | Aishwarya Chakravarthy | David Munechika | Duen Horng Chau

Large language models (LLMs) require well-crafted prompts for effective use. Prompt engineering, the process of designing prompts, is challenging, particularly for non-experts who are less familiar with AI technologies. While researchers have proposed techniques and tools to assist LLM users in prompt design, these works primarily target AI application developers rather than non-experts. To address this research gap, we propose social prompt engineering, a novel paradigm that leverages social computing techniques to facilitate collaborative prompt design. To investigate social prompt engineering, we introduce Wordflow, an open-source and social text editor that enables everyday users to easily create, run, share, and discover LLM prompts. Additionally, by leveraging modern web technologies, Wordflow allows users to run LLMs locally and privately in their browsers. Two usage scenarios highlight how social prompt engineering and our tool can enhance laypeople’s interaction with LLMs. Wordflow is publicly accessible at https://poloclub.github.io/wordflow.

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LM Transparency Tool: Interactive Tool for Analyzing Transformer Language Models
Igor Tufanov | Karen Hambardzumyan | Javier Ferrando | Elena Voita

We present the LM Transparency Tool (LM-TT), an open-source interactive toolkit for analyzing the internal workings of Transformer-based language models. Differently from previously existing tools that focus on isolated parts of the decision-making process, our framework is designed to make the entire prediction process transparent, and allows tracing back model behavior from the top-layer representation to very fine-grained parts of the model. Specifically, it (i) shows the important part of the whole input-to-output information flow, (ii) allows attributing any changes done by a model block to individual attention heads and feed-forward neurons, (iii) allows interpreting the functions of those heads or neurons. A crucial part of this pipeline is showing the importance of specific model components at each step. As a result, we are able to look at the roles of model components only in cases where they are important for a prediction. Since knowing which components should be inspected is key for analyzing large models where the number of these components is extremely high, we believe our tool will greatly support the interpretability community both in research settings and in practical applications.

This paper introduces EmpathyEar, a pioneering open-source, avatar-based multimodal empathetic chatbot, to fill the gap in traditional text-only empathetic response generation (ERG) systems. Leveraging the advancements of a large language model, combined with multimodal encoders and generators, EmpathyEar supports user inputs in any combination of text, sound, and vision, and produces multimodal empathetic responses, offering users, not just textual responses but also digital avatars with talking faces and synchronized speeches. A series of emotion-aware instruction-tuning is performed for comprehensive emotional understanding and generation capabilities. In this way, EmpathyEar provides users with responses that achieve a deeper emotional resonance, closely emulating human-like empathy. The system paves the way for the next emotional intelligence, for which we open-source the code for public access.

We introduce OpenWebAgent, an open toolkit designed to optimize web automation by integrating both large language models (LLMs) and large multimodal models (LMMs). This toolkit focuses on enhancing human-computer interactions on the web, simplifying complex tasks through an advanced HTML parser, a rapid action generation module, and an intuitive user interface. At the core of OpenWebAgent is an innovative web agent framework that uses a modular design to allow developers to seamlessly integrate a variety of models and tools to process web information and automate tasks on the web. This enables the development of powerful, task-oriented web agents, significantly enhancing user experience and operational efficiency on the web. The OpenWebAgent framework, Chrome plugin, and demo video are available at https://github.com/THUDM/OpenWebAgent/.

Large Language Models (LLMs) usually suffer from knowledge cutoff or fallacy issues, which means they are unaware of unseen events or generate text with incorrect facts owing to outdated/noisy data. To this end, many knowledge editing approaches for LLMs have emerged – aiming to subtly inject/edit updated knowledge or adjust undesired behavior while minimizing the impact on unrelated inputs. Nevertheless, due to significant differences among various knowledge editing methods and the variations in task setups, there is no standard implementation framework available for the community, which hinders practitioners from applying knowledge editing to applications. To address these issues, we propose EasyEdit, an easy-to-use knowledge editing framework for LLMs. It supports various cutting-edge knowledge editing approaches and can be readily applied to many well-known LLMs such as T5, GPT-J, LlaMA, etc. Empirically, we report the knowledge editing results on LlaMA-2 with EasyEdit, demonstrating that knowledge editing surpasses traditional fine-tuning in terms of reliability and generalization. We have released the source code on GitHub, along with Google Colab tutorials and comprehensive documentation for beginners to get started. Besides, we present an online system for real-time knowledge editing, and a demo video.

In recent years, instruction tuning has gained increasing attention and emerged as a crucial technique to enhance the capabilities of Large Language Models (LLMs). To construct high-quality instruction datasets, many instruction processing approaches have been proposed, aiming to achieve a delicate balance between data quantity and data quality. Nevertheless, due to inconsistencies that persist among various instruction processing methods, there is no standard open-source instruction processing implementation framework available for the community, which hinders practitioners from further developing and advancing. To facilitate instruction processing research and development, we present EasyInstruct, an easy-to-use instruction processing framework for LLMs, which modularizes instruction generation, selection, and prompting, while also considering their combination and interaction. EasyInstruct is publicly released and actively maintained at Github, along with an online demo app and a demo video for quick-start, calling for broader research centered on instruction data and synthetic data.

Following the rapid progress in natural language processing (NLP) models, language models are applied to increasingly more complex interactive tasks such as negotiations and conversation moderations. Having human evaluators directly interact with these NLP models is essential for adequately evaluating the performance on such interactive tasks. We develop BotEval, an easily customizable, open-source, evaluation toolkit that focuses on enabling human-bot interactions as part of the evaluation process, as opposed to human evaluators making judgements for a static input. BotEval balances flexibility for customization and user-friendliness by providing templates for common use cases that span various degrees of complexity and built-in compatibility with popular crowdsourcing platforms.We showcase the numerous useful features of BotEval through a study that evaluates the performance of various chatbots on their effectiveness for conversational moderation and discuss how BotEval differs from other annotation tools.

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GenGO: ACL Paper Explorer with Semantic Features
Sotaro Takeshita | Simone Ponzetto | Kai Eckert

We present GenGO, a system for exploring papers published in ACL conferences. Paper data stored in our database is enriched with multi-aspect summaries, extracted named entities, a field of study label, and text embeddings by our data processing pipeline. These metadata are used in our web-based user interface to enable researchers to quickly find papers relevant to their interests, and grasp an overview of papers without reading full-text of papers. To make GenGO to be available online as long as possible, we design GenGO to be simple and efficient to reduce maintenance and financial costs. In addition, the modularity of our data processing pipeline lets developers easily extend it to add new features. We make our code available to foster open development and transparency: https://gengo.sotaro.io.

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NLP-KG: A System for Exploratory Search of Scientific Literature in Natural Language Processing
Tim Schopf | Florian Matthes

Scientific literature searches are often exploratory, whereby users are not yet familiar with a particular field or concept but are interested in learning more about it. However, existing systems for scientific literature search are typically tailored to keyword-based lookup searches, limiting the possibilities for exploration. We propose NLP-KG, a feature-rich system designed to support the exploration of research literature in unfamiliar natural language processing (NLP) fields. In addition to a semantic search, NLP-KG allows users to easily find survey papers that provide a quick introduction to a field of interest. Further, a Fields of Study hierarchy graph enables users to familiarize themselves with a field and its related areas. Finally, a chat interface allows users to ask questions about unfamiliar concepts or specific articles in NLP and obtain answers grounded in knowledge retrieved from scientific publications. Our system provides users with comprehensive exploration possibilities, supporting them in investigating the relationships between different fields, understanding unfamiliar concepts in NLP, and finding relevant research literature. Demo, video, and code are available at: https://github.com/NLP-Knowledge-Graph/NLP-KG-WebApp.

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LocalRQA: From Generating Data to Locally Training, Testing, and Deploying Retrieval-Augmented QA Systems
Xiao Yu | Yunan Lu | Zhou Yu

Retrieval-augmented question-answering systems combine retrieval techniques with large language models to provide answers that are more accurate and informative. Many existing toolkits allow users to quickly build such systems using off-the-shelf models, but they fall short in supporting researchers and developers to customize the *model training, testing, and deployment process*. We propose LocalRQA, an open-source toolkit that features a wide selection of model training algorithms, evaluation methods, and deployment tools curated from the latest research. As a showcase, we build QA systems using online documentation obtained from Databricks and Faire’s websites. We find 7B-models trained and deployed using LocalRQA reach a similar performance compared to using OpenAI’s text-ada-002 and GPT-4-turbo.

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JORA: JAX Tensor-Parallel LoRA Library for Retrieval Augmented Fine-Tuning
Anique Tahir | Lu Cheng | Huan Liu

The scaling of Large Language Models (LLMs) for retrieval-based tasks, particularly in Retrieval Augmented Generation (RAG), faces significant memory constraints, especially when fine-tuning extensive prompt sequences. Current open-source libraries support full-model inference and fine-tuning across multiple GPUs but fall short of accommodating the efficient parameter distribution required for retrieved context. Addressing this gap, we introduce a novel framework for PEFT-compatible fine-tuning of GPT models, leveraging distributed training. Our framework uniquely utilizes JAX’s just-in-time (JIT) compilation and tensor-sharding for efficient resource management, thereby enabling accelerated fine-tuning with reduced memory requirements. This advancement significantly improves the scalability and feasibility of fine-tuning LLMs for complex RAG applications, even on systems with limited GPU resources. Our experiments show more than 12x improvement in runtime compared to Hugging Face/DeepSpeed implementation with four GPUs while consuming less than half the VRAM per GPU.

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LinguaLinked: Distributed Large Language Model Inference on Mobile Devices
Junchen Zhao | Yurun Song | Simenl3@uci.edu Simenl3@uci.edu | Ian Harris | Sangeetha Abdu Jyothi

Deploying Large Language Models (LLMs) locally on mobile devices presents a significant challenge due to their extensive memory requirements. In this paper, we introduce LinguaLinked, a system for decentralized, distributed LLM inference on mobile devices. LinguaLinked enables collaborative execution of the inference task across multiple trusted devices and ensures data privacy by processing information locally. LinguaLinked uses three key strategies. First, an optimized model assignment technique segments LLMs and uses linear optimization to align segments with each device�s capabilities. Second, an optimized data transmission mechanism ensures efficient and structured data flow between model segments while also maintaining the integrity of the original model structure. Finally, LinguaLinked incorporates a runtime load balancer that actively monitors and redistributes tasks among mobile devices to prevent bottlenecks, enhancing the system�s overall efficiency and responsiveness. We demonstrate that LinguaLinked facilitates efficient LLM inference while maintaining consistent throughput and minimal latency through extensive testing across various mobile devices, from high-end to low-end Android devices.

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IMGTB: A Framework for Machine-Generated Text Detection Benchmarking
Michal Spiegel | Dominik Macko

In the era of large language models generating high quality texts, it is a necessity to develop methods for detection of machine-generated text to avoid their harmful use or simply for annotation purposes. It is, however, also important to properly evaluate and compare such developed methods. Recently, a few benchmarks have been proposed for this purpose; however, integration of newest detection methods is rather challenging, since new methods appear each month and provide slightly different evaluation pipelines.In this paper, we present the IMGTB framework, which simplifies the benchmarking of machine-generated text detection methods by easy integration of custom (new) methods and evaluation datasets. In comparison to existing frameworks, it enables to objectively compare statistical metric-based zero-shot detectors with classification-based detectors and with differently fine-tuned detectors. Its configurability and flexibility makes research and development of new detection methods easier, especially their comparison to the existing state-of-the-art detectors. The default set of analyses, metrics and visualizations offered by the tool follows the established practices of machine-generated text detection benchmarking found in state-of-the-art literature.

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DrugWatch: A Comprehensive Multi-Source Data Visualisation Platform for Drug Safety Information
Artem Bobrov | Domantas Saltenis | Zhaoyue Sun | Gabriele Pergola | Yulan He

Drug safety research is crucial for maintaining public health, often requiring comprehensive data support. However, the resources currently available to the public are limited and fail to provide a comprehensive understanding of the relationship between drugs and their side effects. This paper introduces “DrugWatch”, an easy-to-use and interactive multi-source information visualisation platform for drug safety study. It allows users to understand common side effects of drugs and their statistical information, flexibly retrieve relevant medical reports, or annotate their own medical texts with our automated annotation tool. Supported by NLP technology and enriched with interactive visual components, we are committed to providing researchers and practitioners with a one-stop information analysis, retrieval, and annotation service. The demonstration video is available at https://www.youtube.com/watch?v=RTqDgxzETjw. We also deployed an online demonstration system at https://drugwatch.net/.

The rapid development of Chinese large language models (LLMs) poses big challenges for efficient LLM evaluation. While current initiatives have introduced new benchmarks or evaluation platforms for assessing Chinese LLMs, many of these focus primarily on capabilities, usually overlooking potential alignment and safety issues. To address this gap, we introduce OpenEval, an evaluation testbed that benchmarks Chinese LLMs across capability, alignment and safety. For capability assessment, we include 12 benchmark datasets to evaluate Chinese LLMs from 4 sub-dimensions: NLP tasks, disciplinary knowledge, commonsense reasoning and mathematical reasoning. For alignment assessment, OpenEval contains 7 datasets that examines the bias, offensiveness and illegalness in the outputs yielded by Chinese LLMs. To evaluate safety, especially anticipated risks (e.g., power-seeking, self-awareness) of advanced LLMs, we include 6 datasets. In addition to these benchmarks, we have implemented a phased public evaluation and benchmark update strategy to ensure that OpenEval is in line with the development of Chinese LLMs or even able to provide cutting-edge benchmark datasets to guide the development of Chinese LLMs. In our first public evaluation, we have tested a range of Chinese LLMs, spanning from 7B to 72B parameters, including both open-source and proprietary models. Evaluation results indicate that while Chinese LLMs have shown impressive performance in certain tasks, more attention should be directed towards broader aspects such as commonsense reasoning, alignment, and safety.

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AutoRE: Document-Level Relation Extraction with Large Language Models
Lilong Xue | Dan Zhang | Yuxiao Dong | Jie Tang

Large Language Models (LLMs) have demonstrated exceptional abilities in comprehending and generating text, motivating numerous researchers to utilize them for Information Extraction (IE) purposes, including Relation Extraction (RE). Nonetheless, most existing methods are predominantly designed for Sentence-level Relation Extraction (SentRE) tasks, which typically encompass a restricted set of relations and triplet facts within a single sentence. Furthermore, certain approaches resort to treating relations as candidate choices integrated into prompt templates, leading to inefficient processing and suboptimal performance when tackling Document-Level Relation Extraction (DocRE) tasks, which entail handling multiple relations and triplet facts distributed across a given document, posing distinct challenges. To overcome these limitations, we introduce AutoRE, an end-to-end DocRE model that adopts a novel RE extraction paradigm named RHF (Relation-Head-Facts). Unlike existing approaches, AutoRE does not rely on the assumption of known relation options, making it more reflective of real-world scenarios. Additionally, we have developed an easily extensible RE framework using a Parameters Efficient Fine Tuning (PEFT) algorithm (QLoRA). Our experiments on the RE-DocRED dataset showcase AutoRE’s best performance, achieving state-of-the-art results, surpassing TAG by 10.03% and 9.03% respectively on the dev and test set. The code is available and the demonstration video is provided.

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LinkTransformer: A Unified Package for Record Linkage with Transformer Language Models
Abhishek Arora | Melissa Dell

Many computational analyses require linking information across noisy text datasets. While large language models (LLMs) offer significant promise, approximate string matching packages in popular statistical softwares such as R and Stata remain predominant in academic applications. These packages have simple interfaces and can be easily extended to a diversity of languages and settings, and for academic applications, ease-of-use and extensibility are essential. In contrast, packages for record linkage with LLMs require significant familiarity with deep learning frameworks and often focus on specialized applications of commercial value in English. The open-source package LinkTransformer aims to bridge this gap by providing an end-to-end software for performing record linkage and other data cleaning tasks with transformer LLMs, treating linkage as a text retrieval problem. At its core is an off-the-shelf toolkit for applying transformer models to record linkage. LinkTransformer contains a rich repository of pre-trained models for multiple languages and supports easy integration of any transformer language model from Hugging Face or OpenAI, providing the extensibility required for many scholarly applications. Its APIs also perform common data processing tasks, e.g., aggregation, noisy de-duplication, and translation-free cross-lingual linkage. LinkTransformer contains comprehensive tools for efficient model tuning, allowing for highly customized applications, and users can easily contribute their custom-trained models to its model hub to ensure reproducibility. Using a novel benchmark dataset geared towards academic applications, we show that LinkTransformer - with both custom models and Hugging Face or OpenAI models off-the-shelf - outperforms string matching by a wide margin. By combining transformer LMs with intuitive APIs, LinkTransformer aims to democratize these performance gains for those who lack familiarity with deep learning frameworks.

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DocPilot: Copilot for Automating PDF Edit Workflows in Documents
Puneet Mathur | Alexa Siu | Varun Manjunatha | Tong Sun

Digital documents, such as PDFs, are vital in business workflows, enabling communication, documentation, and collaboration. Handling PDFs can involve navigating complex workflows and numerous tools (e.g., comprehension, annotation, editing), which can be tedious and time-consuming for users. We introduce DocPilot, an AI-assisted document workflow Copilot system capable of understanding user intent and executing tasks accordingly to help users streamline their workflows. DocPilot undertakes intelligent orchestration of various tools through LLM prompting in four steps: (1) Task plan generation, (2) Task plan verification and self-correction, (3) Multi-turn User Feedback, and (4) Task Plan Execution via Code Generation and Error log-based Code Self-Revision. The primary goal of this system is to free the user from the intricacies of document editing, enabling them to focus on the creative aspects and enrich their document management experience.

Evaluation is pivotal for honing Large Language Models (LLMs), pinpointing their capabilities and guiding enhancements. The rapid development of LLMs calls for a lightweight and easy-to-use framework for swift evaluation deployment. However, due to the various implementation details to consider, developing a comprehensive evaluation platform is never easy. Existing platforms are often complex and poorly modularized, hindering seamless incorporation into researcher’s workflows. This paper introduces UltraEval, a user-friendly evaluation framework characterized by lightweight, comprehensiveness, modularity, and efficiency. We identify and reimplement three core components of model evaluation (models, data, and metrics). The resulting composability allows for the free combination of different models, tasks, prompts, and metrics within a unified evaluation workflow. Additionally, UltraEval supports diverse models owing to a unified HTTP service and provides sufficient inference acceleration.

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PyFoma: a Python finite-state compiler module
Mans Hulden | Michael Ginn | Miikka Silfverberg | Michael Hammond

We describe PyFoma, an open-source Python module for constructing weighted and unweighted finite-state transducers and automata from regular expressions, string rewriting rules, right-linear grammars, or low-level state/transition manipulation. A large variety of standard algorithms for working with finite-state machines is included, with a particular focus on the needs of linguistic and NLP applications. The data structures and code in the module are designed for legibility to allow for potential use in teaching the theory and algorithms associated with finite-state machines.

The proliferation of fake news poses a significant threat not only by disseminating misleading information but also by undermining the very foundations of democracy. The recent advance of generative artificial intelligence has further exacerbated the challenge of distinguishing genuine news from fabricated stories. In response to this challenge, we introduce VeraCT Scan, a novel retrieval-augmented system for fake news detection. This system operates by extracting the core facts from a given piece of news and subsequently conducting an internet-wide search to identify corroborating or conflicting reports. Then sources’ credibility is leveraged for information verification. Besides determining the veracity of news, we also provide transparent evidence and reasoning to support its conclusions, resulting in the interpretability and trust in the results. In addition to GPT-4 Turbo, Llama-2 13B is also fine-tuned for news content understanding, information verification, and reasoning. Both implementations have demonstrated state-of-the-art accuracy in the realm of fake news detection.

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string2string: A Modern Python Library for String-to-String Algorithms
Mirac Suzgun | Stuart Shieber | Dan Jurafsky

We introduce **string2string**, an open-source library that offers a comprehensive suite of efficient algorithms for a broad range of string-to-string problems. It includes traditional algorithmic solutions as well as recent advanced neural approaches to tackle various problems in string alignment, distance measurement, lexical and semantic search, and similarity analysis�along with several helpful visualization tools and metrics to facilitate the interpretation and analysis of these methods. Notable algorithms featured in the library include the Smith-Waterman algorithm for pairwise local alignment, the Hirschberg algorithm for global alignment, the Wagner-Fischer algorithm for edit distance, BARTScore and BERTScore for similarity analysis, the Knuth-Morris-Pratt algorithm for lexical search, and Faiss for semantic search. In addition, it wraps existing efficient and widely-used implementations of certain frameworks and metrics, such as sacreBLEU and ROUGE. Overall, the library aims to provide extensive coverage and increased flexibility in comparison to existing libraries for strings. It can be used for many downstream applications, tasks, and problems in natural-language processing, bioinformatics, and computational social sciences. It is implemented in Python, easily installable via pip, and accessible through a simple API. Source code, documentation, and tutorials are all available on our GitHub page: https://github.com/stanfordnlp/string2string* Documentation: https://string2string.readthedocs.io/en/latest/* GitHub page: https://github.com/stanfordnlp/string2string* Short video: https://drive.google.com/file/d/1IT-pBACDVUoEHewk__5Pz5mU5oAMq5k_/view?usp=sharing

The impressive capabilities in Large Language Models (LLMs) provide a powerful approach to reimagine users’ typing experience. This paper demonstrates the Proofread feature in Gboard, a virtual keyboard running on mobile phones. Proofread enables seamless sentence-level and paragraph-level corrections with a single tap. We describe the complete system in this paper, from data generation, metrics design to model tuning and deployment. To obtain models with sufficient quality, we implement a careful data synthetic pipeline tailored to online use cases, design multifaceted metrics, employ a two-stage tuning approach to acquire the dedicated LLM for the feature: the Supervised Fine Tuning (SFT) for foundational quality, followed by the Reinforcement Learning (RL) tuning approach for targeted refinement. Specifically, we find sequential tuning on Rewrite and proofread tasks yields the best quality in SFT stage, and propose global and direct rewards in the RL tuning stage to seek further improvement. Extensive experiments on a human-labeled golden set showed our tuned PaLM2-XS model achieved 85.56% good ratio. We launched the feature to Pixel 8 devices by serving the model on TPU v5 in Google Cloud, with thousands of daily active users. Serving latency was significantly reduced by quantization, bucket inference, text segmentation, and speculative decoding. Our demo could be seen in Youtube.

Despite the remarkable achievements of large language models (LLMs) in various tasks, there remains a linguistic bias that favors high-resource languages, such as English, often at the expense of low-resource and regional languages. To address this imbalance, we introduce SeaLLMs, an innovative series of language models that specifically focuses on Southeast Asian (SEA) languages. SeaLLMs are built upon popular English-centric models through continued pre-training with an extended vocabulary, specialized instruction and alignment tuning to better capture the intricacies of regional languages. This allows them to respect and reflect local cultural norms, customs, stylistic preferences, and legal considerations. Our comprehensive evaluation demonstrates that SeaLLM models exhibit superior performance across a wide spectrum of linguistic tasks and assistant-style instruction-following capabilities relative to comparable open-source models. Moreover, they outperform ChatGPT-3.5 in non-Latin languages, such as Thai, Khmer, Lao, and Burmese, by large margins while remaining lightweight and cost-effective to operate.

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Fundus: A Simple-to-Use News Scraper Optimized for High Quality Extractions
Max Dallabetta | Conrad Dobberstein | Adrian Breiding | Alan Akbik

This paper introduces Fundus, a user-friendly news scraper that enables users to obtain millions of high-quality news articles with just a few lines of code. Unlike existing news scrapers, we use manually crafted, bespoke content extractors that are specifically tailored to the formatting guidelines of each supported online newspaper. This allows us to optimize our scraping for quality such that retrieved news articles are textually complete and without HTML artifacts. Further, our framework combines both crawling (retrieving HTML from the web or large web archives) and content extraction into a single pipeline. By providing a unified interface for a predefined collection of newspapers, we aim to make Fundus broadly usable even for non-technical users. This paper gives an overview of the framework, discusses our design choices, and presents a comparative evaluation against other popular news scrapers. Our evaluation shows that Fundus yields significantly higher quality extractions (complete and artifact-free news articles) than prior work.The framework is available on GitHub under https://github.com/flairNLP/fundus and can be simply installed using pip.

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CharPoet: A Chinese Classical Poetry Generation System Based on Token-free LLM
Chengyue Yu | Lei Zang | Jiaotuan Wang | Chenyi Zhuang | Jinjie Gu

Automatic Chinese classical poetry generation has attracted much research interest, but achieving effective control over format and content simultaneously remains challenging. Traditional systems usually accept keywords as user inputs, resulting in limited control over content. Large language models (LLMs) improve content control by allowing unrestricted user instructions, but the token-by-token generation process frequently makes format errors. Motivated by this, we propose CharPoet, a Chinese classical poetry generation system based on token-free LLM, which provides effective control over both format and content. Our token-free architecture generates in a character-by-character manner, enabling precise control over the number of characters. Pruned from existing token-based LLMs, CharPoet inherits their pretrained capabilities and can generate poetry following instructions like �Write me a poem for my mother’s birthday.� CharPoet achieves format accuracy above 0.96, outperforming Jiuge-GPT-2 (0.91) and GPT-4 (0.38). In terms of content quality, CharPoet surpasses traditional systems including Jiuge, and is comparable to other LLMs. Our system is open source and available at https://modelscope.cn/models/CharPoet/CharPoet. A video demonstration of CharPoet is available at https://youtu.be/voZ25qEp3Dc.

Extracting structured knowledge from unstructured text data has a wide range of application prospects, and a pervasive trend is to develop text annotation tools to help extraction. However, they often encounter issues such as single scenario usage, lack of effective human-machine collaboration, insufficient model supervision, and suboptimal utilization of Large Language Models (LLMs). We introduces an interactive unstructured text annotation and knowledge extraction system that synergistically integrates LLMs and ModelOps to alleviate these issues. The system leverages LLMs for enhanced performance in low-resource contexts, employs a ModelOps platform to monitor models throughout their lifecycle, and amalgamates interactive annotation methods with online machine learning and active learning. The demo video and website are now publicly available.

Despite advancements in Large Language Models (LLMs) and Large Multimodal Models (LMMs), their integration into language-grounded, human-like embodied agents remains incomplete, hindering complex real-life task performance in 3D environments. Existing integrations often feature limited open-sourcing, challenging collective progress in this field. We introduce LEGENT, an open, scalable platform for developing embodied agents using LLMs and LMMs. LEGENT offers a dual approach: a rich 3D environment with interactive, communicable, and actionable agents, paired with a user-friendly interface, and a sophisticated data generation pipeline utilizing advanced algorithms to exploit supervision from simulated worlds at scale. In our experiments, an embryonic vision-language-action model trained on LEGENT-generated data surpasses GPT-4V in embodied tasks, showcasing promising generalization capabilities. The demo video is available at the following link https://video.legent.ai.

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Variationist: Exploring Multifaceted Variation and Bias in Written Language Data
Alan Ramponi | Camilla Casula | Stefano Menini

Exploring and understanding language data is a fundamental stage in all areas dealing with human language. It allows NLP practitioners to uncover quality concerns and harmful biases in data before training, and helps linguists and social scientists to gain insight into language use and human behavior. Yet, there is currently a lack of a unified, customizable tool to seamlessly inspect and visualize language variation and bias across multiple variables, language units, and diverse metrics that go beyond descriptive statistics. In this paper, we introduce Variationist, a highly-modular, extensible, and task-agnostic tool that fills this gap. Variationist handles at once a potentially unlimited combination of variable types and semantics across diversity and association metrics with regards to the language unit of choice, and orchestrates the creation of up to five-dimensional interactive charts for over 30 variable type-semantics combinations. Through our case studies on computational dialectology, human label variation, and text generation, we show how Variationist enables researchers from different disciplines to effortlessly answer specific research questions or unveil undesired associations in language data. A Python library, code, documentation, and tutorials are made publicly available to the research community.

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An LLM-based Knowledge Synthesis and Scientific Reasoning Framework for Biomedical Discovery
Oskar Wysocki | Magdalena.wysocka@cruk.manchester.ac.uk Magdalena.wysocka@cruk.manchester.ac.uk | Danilo Carvalho | Alex Bogatu | Danilo.miranda@idiap.ch Danilo.miranda@idiap.ch | Maxime.delmas@idiap.ch Maxime.delmas@idiap.ch | Harriet.unsworth@cruk.manchester.ac.uk Harriet.unsworth@cruk.manchester.ac.uk | Andre Freitas

We present BioLunar, developed using the Lunar framework, as a tool for supporting biological analyses, with a particular emphasis on molecular-level evidence enrichment for biomarker discovery in oncology. The platform integrates Large Language Models (LLMs) to facilitate complex scientific reasoning across distributed evidence spaces, enhancing the capability for harmonizing and reasoning over heterogeneous data sources. Demonstrating its utility in cancer research, BioLunar leverages modular design, reusable data access and data analysis components, and a low-code user interface, enabling researchers of all programming levels to construct LLM-enabled scientific workflows. By facilitating automatic scientific discovery and inference from heterogeneous evidence, BioLunar exemplifies the potential of the integration between LLMs, specialised databases and biomedical tools to support expert-level knowledge synthesis and discovery.

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CogMG: Collaborative Augmentation Between Large Language Model and Knowledge Graph
Tong Zhou | Yubo Chen | Kang Liu | Jun Zhao

Large language models have become integral to question-answering applications despite their propensity for generating hallucinations and factually inaccurate content. Querying knowledge graphs to reduce hallucinations in LLM meets the challenge of incomplete knowledge coverage in knowledge graphs. On the other hand, updating knowledge graphs by information extraction and knowledge graph completion faces the knowledge update misalignment issue. In this work, we introduce a collaborative augmentation framework, CogMG, leveraging knowledge graphs to address the limitations of LLMs in QA scenarios, explicitly targeting the problems of incomplete knowledge coverage and knowledge update misalignment. The LLMs identify and decompose required knowledge triples that are not present in the KG, enriching them and aligning updates with real-world demands. We demonstrate the efficacy of this approach through a supervised fine-tuned LLM within an agent framework, showing significant improvements in reducing hallucinations and enhancing factual accuracy in QA responses. Our code and video are publicly available.

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ELLA: Empowering LLMs for Interpretable, Accurate and Informative Legal Advice
Yutong Hu | Kangcheng Luo | Yansong Feng

Despite remarkable performance in legal consultation exhibited by legal Large Language Models(LLMs) combined with legal article retrieval components, there are still cases when the advice given is incorrect or baseless. To alleviate these problems, we propose ELLA, a tool for Empowering LLMs for interpretable, accurate, and informative Legal Advice. ELLA visually presents the correlation between legal articles and LLM’s response by calculating their similarities, providing users with an intuitive legal basis for the responses. Besides, based on the users’ queries, ELLA retrieves relevant legal articles and displays them to users. Users can interactively select legal articles for LLM to generate more accurate responses. ELLA also retrieves relevant legal cases for user reference. Our user study shows that presenting the legal basis for the response helps users understand better. The accuracy of LLM’s responses also improves when users intervene in selecting legal articles for LLM. Providing relevant legal cases also aids individuals in obtaining comprehensive information. Our github repo is: https://github.com/Huyt00/ELLA.

To facilitate the research on large language models (LLMs), this paper presents a comprehensive and unified library, LLMBox, to ease the development, use, and evaluation of LLMs. This library is featured with three main merits: (1) a unified data interface that supports the flexible implementation of various training strategies, (2) a comprehensive evaluation that covers extensive tasks, datasets, and models, and (3) more practical consideration, especially on user-friendliness and efficiency. With our library, users can easily reproduce existing methods, train new models, and conduct comprehensive performance comparisons. To rigorously test LLMBox, we conduct extensive experiments in a diverse coverage of evaluation settings, and experimental results demonstrate the effectiveness and efficiency of our library in supporting various implementations related to LLMs. The detailed introduction and usage guidance can be found at https://github.com/RUCAIBox/LLMBox.

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LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
Yaowei Zheng | Richong Zhang | Junhao Zhang | YeYanhan YeYanhan | Zheyan Luo

Efficient fine-tuning is vital for adapting large language models (LLMs) to downstream tasks. However, it requires non-trivial efforts to implement these methods on different models. We present LlamaFactory, a unified framework that integrates a suite of cutting-edge efficient training methods. It provides a solution for flexibly customizing the fine-tuning of 100+ LLMs without the need for coding through the built-in web UI LlamaBoard. We empirically validate the efficiency and effectiveness of our framework on language modeling and text generation tasks. It has been released at https://github.com/hiyouga/LLaMA-Factory and received over 25,000 stars and 3,000 forks.

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bib (full) Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)

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Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)
Xiyan Fu | Eve Fleisig

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Feriji: A French-Zarma Parallel Corpus, Glossary & Translator
Mamadou Keita | Elysabhete Ibrahim | Habibatou Alfari | Christopher Homan

Machine translation (MT) is a rapidly expanding field that has experienced significant advancements in recent years with the development of models capable of translating multiple languages with remarkable accuracy. However, the representation of African languages in this field still needs improvement due to linguistic complexities and limited resources. This applies to the Zarma language, a dialect of Songhay (of the Nilo-Saharan language family) spoken by over 5 million people across Niger and neighboring countries (Lewis et al., 2016). This paper introduces Feriji, the first robust French-Zarma parallel corpus and glossary designed for MT. The corpus, containing 61,085 sentences in Zarma and 42,789 in French, and a glossary of 4,062 words represents a significant step in addressing the need for more resources for Zarma. We fine-tune three large language models on our dataset, obtaining a BLEU score of 30.06 on the best-performing model. We further evaluate the models on human judgments of fluency, comprehension, and readability and the importance and impact of the corpus and models. Our contributions help to bridge a significant language gap and promote an essential and overlooked indigenous African language.

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Pragmatic inference of scalar implicature by LLMs
Ye-eun Cho | Seong mook Kim

This study investigates how Large Language Models (LLMs), particularly BERT (Devlin et al., 2019) and GPT-2 (Radford et al., 2019), engage in pragmatic inference of scalar implicature, such as some. Two sets of experiments were conducted using cosine similarity and next sentence/token prediction as experimental methods. The results in experiment 1 showed that, both models interpret some as pragmatic implicature not all in the absence of context, aligning with human language processing. In experiment 2, in which Question Under Discussion (QUD) was presented as a contextual cue, BERT showed consistent performance regardless of types of QUDs, while GPT-2 encountered processing difficulties since a certain type of QUD required pragmatic inference for implicature. The findings revealed that, in terms of theoretical approaches, BERT inherently incorporates pragmatic implicature not all within the term some, adhering to Default model (Levinson, 2000). In contrast, GPT-2 seems to encounter processing difficulties in inferring pragmatic implicature within context, consistent with Context-driven model (Sperber and Wilson, 2002).

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Topic Modeling for Short Texts with Large Language Models
Tomoki Doi | Masaru Isonuma | Hitomi Yanaka

As conventional topic models rely on word co-occurrence to infer latent topics, topic modeling for short texts has been a long-standing challenge. Large Language Models (LLMs) can potentially overcome this challenge by contextually learning the meanings of words via pretraining. In this paper, we study two approaches to using LLMs for topic modeling: parallel prompting and sequential prompting. Input length limitations prevent LLMs from processing many texts at once. However, an arbitrary number of texts can be handled by LLMs by splitting the texts into smaller subsets and processing them in parallel or sequentially. Our experimental results demonstrate that our methods can identify more coherent topics than existing ones while maintaining the diversity of the induced topics. Furthermore, we found that the inferred topics cover the input texts to some extent, while hallucinated topics are hardly generated.

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Can LLMs substitute SQL? Comparing Resource Utilization of Querying LLMs versus Traditional Relational Databases
Xiang Zhang | Khatoon Khedri | Reza Rawassizadeh

Large Language Models (LLMs) can automate or substitute different types of tasks in the software engineering process. This study evaluates the resource utilization and accuracy of LLM in interpreting and executing natural language queries against traditional SQL within relational database management systems. We empirically examine the resource utilization and accuracy of nine LLMs varying from 7 to 34 Billion parameters, including Llama2 7B, Llama2 13B, Mistral, Mixtral, Optimus-7B, SUS-chat-34B, platypus-yi-34b, NeuralHermes-2.5-Mistral-7B and Starling-LM-7B-alpha, using a small transaction dataset. Our findings indicate that using LLMs for database queries incurs significant energy overhead (even small and quantized models), making it an environmentally unfriendly approach. Therefore, we advise against replacing relational databases with LLMs due to their substantial resource utilization.

Direct speech-to-speech translation (S2ST) with discrete self-supervised representations has achieved remarkable accuracy, but is unable to preserve the speaker timbre of the source speech. Meanwhile, the scarcity of high-quality speaker-parallel data poses a challenge for learning style transfer during translation. We design an S2ST pipeline with style-transfer capability on the basis of discrete self-supervised speech representations and codec units. The acoustic language model we introduce for style transfer leverages self-supervised in-context learning, acquiring style transfer ability without relying on any speaker-parallel data, thereby overcoming data scarcity. By using extensive training data, our model achieves zero-shot cross-lingual style transfer on previously unseen source languages. Experiments show that our model generates translated speeches with high fidelity and speaker similarity. Audio samples are available at http://stylelm.github.io/ .

Code editing encompasses a variety of pragmatic tasks that developers deal with daily. Despite its relevance and practical usefulness, automatic code editing remains an underexplored area in the evolution of deep learning models, partly due to data scarcity. In this work, we explore the use of Large Language Models (LLMs) to edit code based on user instructions. Evaluated on a novel human-written execution-based benchmark dubbed EditEval, we found current models often struggle to fulfill the instructions. In light of this, we contribute InstructCoder, the first instruction-tuning dataset designed to adapt LLMs for general-purpose code editing, containing high-diversity code-editing tasks such as comment insertion, code optimization, and code refactoring. It consists of over 114,000 instruction-input-output triplets and covers multiple distinct code editing scenarios. The collection process starts with filtered commit data sourced from GitHub Python repositories as seeds. Subsequently, the dataset is systematically expanded through an iterative process, where both seed and generated tasks are used to prompt ChatGPT for more data. Our findings reveal that open-source LLMs fine-tuned on InstructCoder can significantly enhance the accuracy of code edits, exhibiting superior code-editing performance matching advanced proprietary LLMs. The datasets and the source code are publicly available.

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BiasDPO: Mitigating Bias in Language Models through Direct Preference Optimization
Ahmed Allam

Large Language Models (LLMs) have become pivotal in advancing natural language processing, yet their potential to perpetuate biases poses significant concerns. This paper introduces a new framework employing Direct Preference Optimization (DPO) to mitigate gender, racial, and religious biases in LLM-generated English text. By developing a loss function that favors less biased over biased completions, our approach cultivates a preference for respectful and non-discriminatory language in LLMs. We also contribute a manually designed dataset for training LLMs to recognize and correct biases. This dataset encompasses a diverse range of prompts paired with both biased and unbiased completions. Implementing this approach on the Microsoft Phi-2 model, we demonstrate substantial reductions in biased outputs as our model outperforms the baseline model on almost all bias benchmarks. Our model also achieves better performance compared to other open-source models on most benchmarks. By reducing biases in the language generated by the model, our study marks a significant step towards developing more ethical and socially responsible LLMs. We publicly release BiasDPO dataset on HuggingFace.

Large language models (LLMs) excel in various tasks but are primarily trained on text data, limiting their application scope. Expanding LLM capabilities to include vision-language understanding is vital, yet training them on multimodal data from scratch is challenging and costly. Existing instruction tuning methods, e.g., LLAVA, often connects a pretrained CLIP vision encoder and LLMs via fully fine-tuning LLMs to bridge the modality gap. However, full fine-tuning is plagued by catastrophic forgetting, i.e., forgetting previous knowledge, and high training costs particularly in the era of increasing tasks and modalities. To solve this issue, we introduce MoExtend, an effective framework designed to streamline the modality adaptation and extension of Mixture-of-Experts (MoE) models. MoExtend seamlessly integrates new experts into pre-trained MoE models, endowing them with novel knowledge without the need to tune pretrained models such as MoE and vision encoders. This approach enables rapid adaptation and extension to new modal data or tasks, effectively addressing the challenge of accommodating new modalities within LLMs. Furthermore, MoExtend avoids tuning pretrained models, thus mitigating the risk of catastrophic forgetting. Experimental results demonstrate the efficacy and efficiency of MoExtend in enhancing the multimodal capabilities of LLMs, contributing to advancements in multimodal AI research.

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On the Interpretability of Deep Learning Models for Collaborative Argumentation Analysis in Classrooms
Deliang Wang | Gaowei Chen

Collaborative argumentation holds significant potential for enhancing students’ learning outcomes within classroom settings. Consequently, researchers have explored the application of artificial intelligence (AI) to automatically analyze argumentation in these contexts. Despite the remarkable performance of deep learning models in this task, their lack of interpretability poses a critical challenge, leading to teachers’ skepticism and limited utilization. To cultivate trust among teachers, this PhD thesis proposal aims to leverage explainable AI techniques to provide explanations for these deep learning models. Specifically, the study develops two deep learning models for automated analysis of argument moves (claim, evidence, and warrant) and specificity levels (low, medium, and high) within collaborative argumentation. To address the interpretability issue, four explainable AI methods are proposed: gradient sensitivity, gradient input, integrated gradient, and LIME. Computational experiments demonstrate the efficacy of these methods in elucidating model predictions by computing word contributions, with LIME delivering exceptional performance. Moreover, a quasi-experiment is designed to evaluate the impact of model explanations on user trust and knowledge, serving as a future study of this PhD proposal. By tackling the challenges of interpretability and trust, this PhD thesis proposal aims to contribute to fostering user trust in AI and facilitating the practical implementation of AI in educational contexts.

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Document Alignment based on Overlapping Fixed-Length Segments
Xiaotian Wang | Takehito Utsuro | Masaaki Nagata

Acquiring large-scale parallel corpora is crucial for NLP tasks such asNeural Machine Translation, and web crawling has become a popularmethodology for this purpose. Previous studies have been conductedbased on sentence-based segmentation (SBS) when aligning documents invarious languages which are obtained through web crawling. Among them,the TK-PERT method (Thompson and Koehn, 2020) achieved state-of-the-artresults and addressed the boilerplate text in web crawling data wellthrough a down-weighting approach. However, there remains a problemwith how to handle long-text encoding better. Thus, we introduce thestrategy of Overlapping Fixed-Length Segmentation (OFLS) in place ofSBS, and observe a pronounced enhancement when performing the sameapproach for document alignment. In this paper, we compare the SBS andOFLS using three previous methods, Mean-Pool, TK-PERT (Thompson andKoehn, 2020), and Optimal Transport (Clark et al., 2019; El- Kishky andGuzman, 2020), on the WMT16 document alignment shared task forFrench-English, as well as on our self-established Japanese-Englishdataset MnRN. As a result, for the WMT16 task, various SBS basedmethods showed an increase in recall by 1% to 10% after reproductionwith OFLS. For MnRN data, OFLS demonstrated notable accuracyimprovements and exhibited faster document embedding speed.

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Automatically Suggesting Diverse Example Sentences for L2 Japanese Learners Using Pre-Trained Language Models
Enrico Benedetti | Akiko Aizawa | Florian Boudin

Providing example sentences that are diverse and aligned with learners’ proficiency levels is essential for fostering effective language acquisition.This study examines the use of Pre-trained Language Models (PLMs) to produce example sentences targeting L2 Japanese learners.We utilize PLMs in two ways: as quality scoring components in a retrieval system that draws from a newly curated corpus of Japanese sentences, and as direct sentence generators using zero-shot learning.We evaluate the quality of sentences by considering multiple aspects such as difficulty, diversity, and naturalness, with a panel of raters consisting of learners of Japanese, native speakers – and GPT-4.Our findings suggest that there is inherent disagreement among participants on the ratings of sentence qualities, except for difficulty. Despite that, the retrieval approach was preferred by all evaluators, especially for beginner and advanced target proficiency, while the generative approaches received lower scores on average.Even so, our experiments highlight the potential for using PLMs to enhance the adaptability of sentence suggestion systems and therefore improve the language learning journey.

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Z-coref: Thai Coreference and Zero Pronoun Resolution
Poomphob Suwannapichat | Sansiri Tarnpradab | Santitham Prom-on

Coreference Resolution (CR) and Zero Pronoun Resolution (ZPR) are vital for extracting meaningful information from text. However, limited research and datasets pose significant challenges in Thai language. To address this, we developed an annotated joint CR and ZPR dataset. Additionally, we introduced the Z-coref model, capable of simultaneously handling CR and ZPR tasks by adjusting the span definition of a prior CR architecture to include token gaps. The proposed model trained on our dataset outperformed the state-of-the-art in resolving both coreference resolution and zero-pronoun resolution, while taking less time to train.

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ReMAG-KR: Retrieval and Medically Assisted Generation with Knowledge Reduction for Medical Question Answering
Sidhaarth Murali | Sowmya S. | Supreetha R

Large Language Models (LLMs) have significant potential for facilitating intelligent end-user applications in healthcare. However, hallucinations remain an inherent problem with LLMs, making it crucial to address this issue with extensive medical knowledge and data. In this work, we propose a Retrieve-and-Medically-Augmented-Generation with Knowledge Reduction (ReMAG-KR) pipeline, employing a carefully curated knowledge base using cross-encoder re-ranking strategies. The pipeline is tested on medical MCQ-based QA datasets as well as general QA datasets. It was observed that when the knowledge base is reduced, the model’s performance decreases by 2-8%, while the inference time improves by 47%.

Retrieving relevant plots from the book for a query is a critical task, which can improve the reading experience and efficiency of readers. Readers usually only give an abstract and vague description as the query based on their own understanding, summaries, or speculations of the plot, which requires the retrieval model to have a strong ability to estimate the abstract semantic associations between the query and candidate plots. However, existing information retrieval (IR) datasets cannot reflect this ability well. In this paper, we propose PlotRetrieval, a labeled dataset to train and evaluate the performance of IR models on the novel task Plot Retrieval. Text pairs in PlotRetrieval have less word overlap and more abstract semantic association, which can reflect the ability of the IR models to estimate the abstract semantic association, rather than just traditional lexical or semantic matching. Extensive experiments across various lexical retrieval, sparse retrieval, dense retrieval, and cross-encoder methods compared with human studies on PlotRetrieval show current IR models still struggle in capturing abstract semantic association between texts. PlotRetrieval can be the benchmark for further research on the semantic association modeling ability of IR models.

Instruction tuning significantly enhances the performance of large language models (LLMs) across various tasks. However, the procedure to optimizing the mixing of instruction datasets for LLM fine-tuning is still poorly understood. This study categorizes instructions into three primary types: NLP downstream tasks, coding, and general chat. We explore the effects of instruction tuning on different combinations of datasets on LLM performance, and find that certain instruction types are more advantageous for specific applications but can negatively impact other areas. This work provides insights into instruction mixtures, laying the foundations for future research.

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Fine-Tuning ASR models for Very Low-Resource Languages: A Study on Mvskoke
Julia Mainzinger | Gina-Anne Levow

Recent advancements in multilingual models for automatic speech recognition (ASR) have been able to achieve a high accuracy for languages with extremely limited resources. This study examines ASR modeling for the Mvskoke language, an indigenous language of America. The parameter efficiency of adapter training is contrasted with training entire models, and it is demonstrated how performance varies with different amounts of data. Additionally, the models are evaluated with trigram language model decoding, and the outputs are compared across different types of speech recordings. Results show that training an adapter is both parameter efficient and gives higher accuracy for a relatively small amount of data.

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Automating Qualitative Data Analysis with Large Language Models
Angelina Parfenova | Alexander Denzler | Jörgen Pfeffer

This PhD proposal aims to investigate ways of automating qualitative data analysis, specifically the thematic coding of texts. Despite existing methods vastly covered in literature, they mainly use Topic Modeling and other quantitative approaches which are far from resembling a human’s analysis outcome. This proposal examines the limitations of current research in the field. It proposes a novel methodology based on Large Language Models to tackle automated coding and make it as close as possible to the results of human researchers. This paper covers studies already done in this field and their limitations, existing software, the problem of duplicating the researcher bias, and the proposed methodology.

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ANHALTEN: Cross-Lingual Transfer for German Token-Level Reference-Free Hallucination Detection
Janek Herrlein | Chia-Chien Hung | Goran Glava�

Research on token-level reference-free hallucination detection has predominantly focused on English, primarily due to the scarcity of robust datasets in other languages. This has hindered systematic investigations into the effectiveness of cross-lingual transfer for this important NLP application. To address this gap, we introduce ANHALTEN, a new evaluation dataset that extends the English hallucination detection dataset to German. To the best of our knowledge, this is the first work that explores cross-lingual transfer for token-level reference-free hallucination detection. ANHALTEN contains gold annotations in German that are parallel (i.e., directly comparable to the original English instances). We benchmark several prominent cross-lingual transfer approaches, demonstrating that larger context length leads to better hallucination detection in German, even without succeeding context. Importantly, we show that the sample-efficient few-shot transfer is the most effective approach in most setups. This highlights the practical benefits of minimal annotation effort in the target language for reference-free hallucination detection. Aiming to catalyze future research on cross-lingual token-level reference-free hallucination detection, we make ANHALTEN publicly available: https://github.com/janekh24/anhalten

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Label-Aware Automatic Verbalizer for Few-Shot Text Classification in Mid-To-Low Resource Languages
Thanakorn Thaminkaew | Piyawat Lertvittayakumjorn | Peerapon Vateekul

Prompt-based learning has shown its effectiveness in few-shot text classification. A key factor in its success is a verbalizer, which translates output from a language model into a predicted class. Notably, the simplest and widely acknowledged verbalizer employs manual labels to represent the classes. However, manual selection may not yield the optimal words for a given language model, potentially leading to subpar classification performance, especially in mid-to-low resource languages with weaker language models. Therefore, we propose Label-Aware Automatic Verbalizer (LAAV), effectively augmenting manual labels for improved few-shot classification results. Specifically, we utilize the label name along with the conjunction “and” to induce the model to generate more effective words for the verbalizer. Experimental results on four mid-to-low resource Southeast Asian languages demonstrate that LAAV significantly outperforms existing verbalizers.

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Vector Spaces for Quantifying Disparity of Multiword Expressions in Annotated Text
Louis Estève | Agata Savary | Thomas Lavergne

Multiword Expressions (MWEs) make a goodcase study for linguistic diversity due to their idiosyncratic nature. Defining MWE canonical forms as types, diversity may be measured notably through disparity, based on pairwise distances between types. To this aim, we train static MWE-aware word embeddings for verbal MWEs in 14 languages, and we show interesting properties of these vector spaces. We use these vector spaces to implement the so-called functional diversity measure. We apply this measure to the results of several MWE identification systems. We find that, although MWE vector spaces are meaningful ata local scale, the disparity measure aggregatingthem at a global scale strongly correlates with the number of types, which questions its usefulness in presence of simpler diversity metrics such as variety. We make the vector spaces we generated available.

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Narratives at Conflict: Computational Analysis of News Framing in Multilingual Disinformation Campaigns
Antonina Sinelnik | Dirk Hovy

Any report frames issues to favor a particular interpretation by highlighting or excluding certain aspects of a story. Despite the widespread use of framing in disinformation, framing properties and detection methods remain underexplored outside the English-speaking world. We explore how multilingual framing of the same issue differs systematically. We use eight years of Russia-backed disinformation campaigns, spanning 8k news articles in 4 languages targeting 15 countries. We find that disinformation campaigns consistently and intentionally favor specific framing, depending on the target language of the audience. We further discover how Russian-language articles consistently highlight selected frames depending on the region of the media coverage. We find that the two most prominent models for automatic frame analysis underperform and show high disagreement, highlighting the need for further research.

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Assessing In-context Learning and Fine-tuning for Topic Classification of German Web Data
Julian Schelb | Andreas Spitz | Roberto Ulloa

Researchers in the political and social sciences often rely on classification models to analyze trends in information consumption by examining browsing histories of millions of webpages. Automated scalable methods are necessary due to the impracticality of manual labeling. In this paper, we model the detection of topic-related content as a binary classification task and compare the accuracy of fine-tuned pre-trained encoder models against in-context learning strategies. Using only a few hundred annotated data points per topic, we detect content related to three German policies in a database of scraped webpages. We compare multilingual and monolingual models, as well as zero and few-shot approaches, and investigate the impact of negative sampling strategies and the combination of URL & content-based features. Our results show that a small sample of annotated data is sufficient to train an effective classifier. Fine-tuning encoder-based models yields better results than in-context learning. Classifiers using both URL & content-based features perform best, while using URLs alone provides adequate results when content is unavailable.

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Knowledge Editing of Large Language Models Unconstrained by Word Order
Ryoma Ishigaki | Jundai Suzuki | Masaki Shuzo | Eisaku Maeda

Large Language Models (LLMs) are considered to have potentially extensive knowledge, but because their internal processing is black-boxed, it has been difficult to directly edit the knowledge held by the LLMs themselves. To address this issue, a method called local modification-based knowledge editing has been developed. This method identifies the knowledge neurons that encode the target knowledge and adjusts the parameters associated with these neurons to update the knowledge. Knowledge neurons are identified by masking the \it{o} part from sentences representing relational triplets (\it{s, r, o}), having the LLM predict the masked part, and observing the LLM�s activation during the prediction. When the architecture is decoder-based, the predicted \it{o} needs to be located at the end of the sentence. Previous local modification-based knowledge editing methods for decoder-based models have assumed SVO languages and faced challenges when applied to SOV languages such as Japanese. In this study, we propose a knowledge editing method that eliminates the need for word order constraints by converting the input for identifying knowledge neurons into a question where \it{o} is the answer. We conducted validation experiments on 500 examples and confirmed that the proposed method is effective for Japanese, a non-SVO language. We also applied this method to English, an SVO language, and demonstrated that it outperforms conventional methods.

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Exploring the Effectiveness and Consistency of Task Selection in Intermediate-Task Transfer Learning
Pin-Jie Lin | Miaoran Zhang | Marius Mosbach | Dietrich Klakow

Identifying beneficial tasks to transfer from is a critical step toward successful intermediate-task transfer learning. In this work, we experiment with 130 source-target task combinations and demonstrate that the transfer performance exhibits severe variance across different source tasks and training seeds, highlighting the crucial role of intermediate-task selection in a broader context. We compare four representative task selection methods in a unified setup, focusing on their effectiveness and consistency. Compared to embedding-free methods and text embeddings, task embeddings constructed from fine-tuned weights can better estimate task transferability by improving task prediction scores from 2.59% to 3.96%. Despite their strong performance, we observe that the task embeddings do not consistently demonstrate superiority for tasks requiring reasoning abilities. Furthermore, we introduce a novel method that measures pairwise token similarity using maximum inner product search, leading to the highest performance in task prediction. Our findings suggest that token-wise similarity is better predictive for predicting transferability compared to averaging weights.

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Does the structure of textual content have an impact on language models for automatic summarization?
Eve Sauvage | Sabrina Campano | Lydia Ouali | Cyril Grouin

The processing of long sequences with models remains a subject in its own right, including automatic summary, despite recent improvements. In this work, we present experiments on the automatic summarization of scientific articles using BART models, taking into account textual information coming from distinct passages from the long texts to be summarized. We demonstrate that taking into account document structure improves the performance of state-of-the-art models and approaches the performance of LongFormer on English.

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Action Inference for Destination Prediction in Vision-and-Language Navigation
Anirudh Kondapally | Kentaro Yamada | Hitomi Yanaka

Vision-and-Language Navigation (VLN) encompasses interacting with autonomous vehicles using language and visual input from the perspective of mobility.Most of the previous work in this field focuses on spatial reasoning and the semantic grounding of visual information.However, reasoning based on the actions of pedestrians in the scene is not much considered.In this study, we provide a VLN dataset for destination prediction with action inference to investigate the extent to which current VLN models perform action inference.We introduce a crowd-sourcing process to construct a dataset for this task in two steps: (1) collecting beliefs about the next action for a pedestrian and (2) annotating the destination considering the pedestrian’s next action.Our benchmarking results of the models on destination prediction lead us to believe that the models can learn to reason about the effect of the action and the next action on the destination to a certain extent.However, there is still much scope for improvement.

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A Computational Analysis and Exploration of Linguistic Borrowings in French Rap Lyrics
Lucas Zurbuchen | Rob Voigt

In France, linguistic borrowings in the relatively conservative French language are an important site of cultural debate, and rap in particular is a hotspot for borrowings. In this work, we use computational methods to understand the factors that affect the prominence and prevalence of a borrowing. To do so, we manually annotate a lexicon of over 700 borrowings occurring in this context (including key aspects for each borrowing such as origin and semantic class). We analyze the prevalence of these borrowings in a newly collected corpus of over 8000 French rap song lyrics and find that there are increases in the proportion of linguistic borrowings, interjections, and Niger-Congo borrowings while terms related to the arts are decreasing in prevalence. We release our code and data to facilitate further research in this area and discuss potential future directions.

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On Improving Repository-Level Code QA for Large Language Models
Jan Strich | Florian Schneider | Irina Nikishina | Chris Biemann

Large Language Models (LLMs) such as ChatGPT, GitHub Copilot, Llama, or Mistral assist programmers as copilots and knowledge sources to make the coding process faster and more efficient. This paper aims to improve the copilot performance by implementing different self-alignment processes and retrieval-augmented generation (RAG) pipelines, as well as their combination. To test the effectiveness of all approaches, we create a dataset and apply a model-based evaluation, using LLM as a judge. It is designed to check the model’s abilities to understand the source code semantics, the dependency between files, and the overall meta-information about the repository. We also compare our approach with other existing solutions, e.g. ChatGPT-3.5, and evaluate on the existing benchmarks. Code and dataset are available online (https://anonymous.4open.science/r/ma_llm-382D).

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Compromesso! Italian Many-Shot Jailbreaks undermine the safety of Large Language Models
Fabio Pernisi | Dirk Hovy | Paul R�ttger

As diverse linguistic communities and users adopt Large Language Models (LLMs), assessing their safety across languages becomes critical. Despite ongoing efforts to align these models with safe and ethical guidelines, they can still be induced into unsafe behavior with jailbreaking, a technique in which models are prompted to act outside their operational guidelines. What research has been conducted on these vulnerabilities was predominantly on English, limiting the understanding of LLM behavior in other languages. We address this gap by investigating Many-Shot Jailbreaking (MSJ) in Italian, underscoring the importance of understanding LLM behavior in different languages. We base our analysis on a newly created Italian dataset to identify unique safety vulnerabilities in 4 families of open-source LLMs.We find that the models exhibit unsafe behaviors even with minimal exposure to harmful prompts, and–more alarmingly–this tendency rapidly escalates with more demonstrations.

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Foundation Model for Biomedical Graphs: Integrating Knowledge Graphs and Protein Structures to Large Language Models
Yunsoo Kim

Transformer model has been a de-facto standard in natural language processing. Its adaptations in other fields such as computer vision showed promising results that this architecture is a powerful neural network in representation learning regardless of the data type. This recent success has led to research in multimodal Large Language Model (LLM), which enabled us to new types of tasks and applications with multiple data types. However, multimodal LLM in the biomedical domain is primarily limited to images, text, and/or sequence data. Here I propose to work on multimodal LLM architecture for biomedical graphs such as protein structure and chemical molecules. The research hypothesis is based on the fact that clinicians and researchers in computational biology and clinical research take advantage of various information for their decision-making process. Therefore, an AI model being able to handle multiple data types should boost its ability to use diverse knowledge for improved performances in clinical applications.

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ViMedAQA: A Vietnamese Medical Abstractive Question-Answering Dataset and Findings of Large Language Model
Minh-Nam Tran | Phu-Vinh Nguyen | Long Nguyen | Dien Dinh

Question answering involves creating answers to questions. With the growth of large language models, the ability of question-answering systems has dramatically improved. However, there is a lack of Vietnamese abstractive question-answering datasets, especially in the medical domain. Therefore, this research aims to mitigate this gap by introducing ViMedAQA. This **Vi**etnamese **Med**ical **A**bstractive **Q**uestion-**A**nswering dataset covers four topics in the Vietnamese medical domain, including body parts, disease, drugs and medicine. Additionally, the empirical results on the proposed dataset examine the capability of the large language models in the Vietnamese medical domain, including reasoning, memorizing and awareness of essential information.

Customizing LLMs for a specific task involves separating high-quality responses from lower-quality ones. This skill can be developed using supervised fine-tuning with extensive human preference data. However, obtaining a large volume of expert-annotated data is costly for most tasks. In this paper, we explore a novel method to optimize LLMs using ranking metrics. This method trains the model to prioritize the best responses from a pool of candidates created for a particular task. Rather than a traditional full ordering, we advocate for a partial ordering, as achieving consensus on the perfect order of candidate responses can be challenging. Our partial ordering is more robust, less sensitive to noise, and can be achieved with limited human annotations or through heuristic methods. We test our system’s improved response generation ability using benchmark datasets, including textual entailment and multi-document question answering. We conduct ablation studies to understand crucial factors, such as how to gather candidate responses for a specific task, determine their most suitable order, and balance supervised fine-tuning with ranking metrics. Our approach, named RESCUE, offers a promising avenue for enhancing the response generation and task accuracy of LLMs.

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Basreh or Basra? Geoparsing Historical Locations in the Svoboda Diaries
Jolie Zhou | Camille Cole | Annie Chen

Geoparsing, the task of assigning coordinates to locations extracted from free text, is invaluable in enabling us to place locations in time and space. In the historical domain, many geoparsing corpora are from large news collections. We examine the Svoboda Diaries, a small historical corpus written primarily in English, with many location names in transliterated Arabic. We develop a pipeline employing named entity recognition for geotagging, and a map-based generate-and-rank approach incorporating candidate name augmentation and clustering of location context words for geocoding. Our system outperforms existing map-based geoparsers in terms of accuracy, lowest mean distance error, and number of locations correctly identified. As location names may vary from those in knowledge bases, we find that augmented candidate generation is instrumental in the system’s performance. Among our candidate generation methods, the generation of transliterated names contributed the most to increased location matches in the knowledge base. Our main contribution is proposing an integrated pipeline for geoparsing of historical corpora using augmented candidate location name generation and clustering methods – an approach that can be generalized to other texts with foreign or non-standard spellings.

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Homophone2Vec: Embedding Space Analysis for Empirical Evaluation of Phonological and Semantic Similarity
Sophie Wu | Anita Zheng | Joey Chuang

This paper introduces a novel method for empirically evaluating the relationship between the phonological and semantic similarity of linguistic units using embedding spaces. Chinese character homophones are used as a proof-of-concept. We employ cosine similarity as a proxy for semantic similarity between characters, and compare relationships between phonologically-related characters and baseline characters (chosen as similar-frequency characters). We show there is a strongly statistically significant positive semantic relationship among different Chinese characters at varying levels of sound-sharing. We also perform some basic probing using t-SNE and UMAP visualizations, and indicate directions for future applications of this method.

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Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition
Tyler McDonald | Ali Emami

Knowledge distillation allows smaller neural networks to emulate the performance of larger, teacher models with reduced computational demands. Traditional methods for Large Language Models (LLMs) often necessitate extensive fine-tuning, which limits their accessibility. To address this, we introduce Trace-of-Thought Prompting, a novel framework designed to distill critical reasoning capabilities from large-scale teacher models (over 8 billion parameters) to small-scale student models (up to 8 billion parameters). This approach leverages problem decomposition to enhance interpretability and facilitate human-in-the-loop interventions. Empirical evaluations on the GSM8K and MATH datasets show that student models achieve accuracy gains of up to 113% on GSM8K and 20% on MATH, with significant improvements particularly notable in smaller models like Llama 2 and Zephyr. Our results suggest a promising pathway for open-source, small-scale models to eventually serve as both students and teachers, potentially reducing our reliance on large-scale, proprietary models. Our code, featuring data analytics and testing scripts, is provided here: https://github.com/traceofthought/trace-of-thought-prompting/tree/main.

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Can LLMs Augment Low-Resource Reading Comprehension Datasets? Opportunities and Challenges
Vinay Samuel | Houda Aynaou | Arijit Chowdhury | Karthik Venkat Ramanan | Aman Chadha

Large Language Models (LLMs) have demonstrated impressive zero-shot performance on a wide range of NLP tasks, demonstrating the ability to reason and apply common sense. A relevant application is to use them for creating high-quality synthetic datasets for downstream tasks. In this work, we probe whether GPT-4 can be used to augment existing extractive reading comprehension datasets. Automating data annotation processes has the potential to save large amounts of time, money, and effort that goes into manually labeling datasets. In this paper, we evaluate the performance of GPT-4 as a replacement for human annotators for low-resource reading comprehension tasks, by comparing performance after fine-tuning, and the cost associated with annotation. This work serves to be the first analysis of LLMs as synthetic data augmenters for QA systems, highlighting the unique opportunities and challenges. Additionally, we release augmented versions of low-resource datasets, that will allow the research community to create further benchmarks for evaluation of generated datasets. Github available at https://github.com/vsamuel2003/qa-gpt4

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Automatic Derivation of Semantic Representations for Thai Serial Verb Constructions: A Grammar-Based Approach
Vipasha Bansal

Deep semantic representations are useful for many NLU tasks (Droganova and Zeman 2019; Schuster and Manning-2016). Manual annotation to build these representations is time-consuming, and so automatic approaches are preferred (Droganova and Zeman 2019; Bender et al. 2015). This paper demonstrates how rich semantic representations can be automatically derived for Thai Serial Verb Constructions (SVCs), where the semantic relationship between component verbs is not immediately clear from the surface forms. I present the first fully-implemented HPSG analysis for Thai SVCs, deriving appropriate semantic representations (MRS; Copestake et al. 2005) from syntactic features, implemented within a DELPH-IN computational grammar (Slayden 2009). This analysis increases verified coverage of SVCs by 73% and decreases ambiguity by 46%. The final grammar can be found at: https://github.com/VipashaB94/ThaiGrammar

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Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai
Parinthapat Pengpun | Can Udomcharoenchaikit | Weerayut Buaphet | Peerat Limkonchotiwat

We present a synthetic data approach for instruction-tuning large language models (LLMs) for low-resource languages in a data-efficient manner, specifically focusing on Thai. We identify three key properties that contribute to the effectiveness of instruction-tuning datasets: fluency, diversity, and cultural context. We propose a seed-data-free framework for generating synthetic instruction-tuning data that incorporates these essential properties. Our framework employs an LLM to generate diverse topics, retrieve relevant contexts from Wikipedia, and create instructions for various tasks, such as question answering, summarization, and conversation. The experimental results show that our best-performing synthetic dataset, which incorporates all three key properties, achieves competitive performance using only 5,000 instructions when compared to state-of-the-art Thai LLMs trained on hundreds of thousands of instructions. Our code and dataset are publicly available at https://github.com/parinzee/seed-free-synthetic-instruct.

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Bridging Distribution Gap via Semantic Rewriting with LLMs to Enhance OOD Robustness
Manas Madine

This paper investigates the robustness of Large Language Models (LLMs) against Out-Of-Distribution (OOD) data within the context of sentiment analysis. Traditional fine-tuning approaches often fail to generalize effectively across different data distributions, limiting the practical deployment of LLMs in dynamic real-world scenarios. To address this challenge, we introduce a novel method called “Semantic Rewriting,” which leverages the inherent flexibility of LLMs to align both in-distribution (ID) and OOD data with the LLMs distributions. By semantically transforming sentences to minimize linguistic discrepancies, our approach helps to standardize features across datasets, thus enhancing model robustness. We conduct extensive experiments with several benchmark datasets and LLMs to validate the efficacy of our method. The results demonstrate that Semantic Rewriting significantly improves the performance of models on OOD tasks, outperforming traditional methods in both robustness and generalization capabilities. Our findings suggest that Semantic Rewriting is a promising technique for developing more reliable and versatile NLP systems capable of performing robustly across diverse operational environments.

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CoVoSwitch: Machine Translation of Synthetic Code-Switched Text Based on Intonation Units
Yeeun Kang

Multilingual code-switching research is often hindered by the lack and linguistically biased status of available datasets. To expand language representation, we synthesize code-switching data by replacing intonation units detected through PSST, a speech segmentation model fine-tuned from OpenAI’s Whisper, using a speech-to-text translation dataset, CoVoST 2. With our dataset, CoVoSwitch, spanning 13 languages, we evaluate the code-switching translation performance of two multilingual translation models, M2M-100 418M and NLLB-200 600M. We reveal that the inclusion of code-switching units results in higher translation performance than monolingual settings and that models are better at code-switching translation into English than non-English. Further, low-resource languages gain most from integration of code-switched units when translating into English but much less when translating into non-English. Translations into low-resource languages also perform worse than even raw code-switched inputs. We find that systems excel at copying English tokens but struggle with non-English tokens, that the off-target problem in monolingual settings is also relevant in code-switching settings, and that models hallucinate in code-switching translation by introducing words absent in both of the original source sentences. CoVoSwitch and code are available at https://github.com/sophiayk20/covoswitch.

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An Analysis under a Unified Formulation of Learning Algorithms with Output Constraints
Mooho Song | Jay-Yoon Lee

Neural networks (NN) perform well in diverse tasks, but sometimes produce nonsensical results to humans. Most NN models “solely” learn from (input, output) pairs, occasionally conflicting with human knowledge. Many studies indicate injecting human knowledge by reducing output constraints during training can improve model performance and reduce constraint violations.While there have been several attempts to compare different existing algorithms under the same programming framework, nonetheless, there has been no previous work that categorizes learning algorithms with output constraints in a unified manner. Our contributions are as follows: (1) We categorize the previous studies based on three axes: type of constraint loss used (e.g. probabilistic soft logic, REINFORCE), exploration strategy of constraint-violating examples, and integration mechanism of learning signals from main task and constraint.(2) We propose new algorithms to integrate the information of main task and constraint injection, inspired by continual-learning algorithms.(3) Furthermore, we propose the H𝛽-score as a metric for considering the main task metric and constraint violation simultaneously.To provide a thorough analysis, we examine all the algorithms on three NLP tasks: natural language inference (NLI), synthetic transduction examples (STE), and semantic role labeling (SRL). We explore and reveal the key factors of various algorithms associated with achieving high H𝛽-scores.

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Beyond Abstracts: A New Dataset, Prompt Design Strategy and Method for Biomedical Synthesis Generation
James O’Doherty | Cian Nolan | Yufang Hou | Anya Belz

The biomedical field relies on cost and time intensive systematic reviews of papers to enable practitioners to keep up to date with research. Impressive recent advances in large language models (LLMs) have made the task of automating at least part of the systematic review process feasible, but progress is slow. This paper identifies some factors that may have been holding research back, and proposes a new, enhanced dataset and prompting-based method for automatic synthesis generation, the most challenging step for automation. We test different models and types of information from and about biomedical studies for their usefulness in obtaining high-quality results.We find that, surprisingly, inclusion of paper abstracts can worsens results. Instead, study summary information, and system instructions informed by domain knowledge, are key to producing high-quality syntheses.

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Improving Sentence Embeddings with Automatic Generation of Training Data Using Few-shot Examples
Soma Sato | Hayato Tsukagoshi | Ryohei Sasano | Koichi Takeda

Decoder-based large language models (LLMs) have shown high performance on many tasks in natural language processing. This is also true for sentence embedding learning, where a decoder-based model, PromptEOL, has achieved the best performance on semantic textual similarity (STS) tasks. However, PromptEOL requires a manually annotated natural language inference (NLI) dataset for fine-tuning.We aim to improve sentence embeddings without using large manually annotated datasets by automatically generating an NLI dataset with an LLM and using it for fine-tuning of PromptEOL. To achieve this, we explore methods of data generation suitable for sentence embedding learning in this study. Specifically, we will focus on automatic dataset generation through few-shot learning and explore the appropriate methods to leverage few-shot examples. Experimental results on the STS tasks demonstrate that our approach outperforms existing models in settings without large manually annotated datasets.

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Curriculum Learning for Small Code Language Models
Marwa Na�r | Kamel Yamani | Lynda Lhadj | Riyadh Baghdadi

Code language models have emerged as useful tools for various programming tasks, yet they often struggle when it comes to complex ones. In this paper, we explore the potential of curriculum learning in enhancing the performance of these models. While prior research has suggested that curriculum learning does not necessarily help in improving the performance of language models, our results surprisingly show that this may not be the case for code language models. We demonstrate that a well-designed curriculum learning approach significantly improves the accuracy of small decoder-only code language models on the task of code execution, while its effect on code completion is less significant. To explore the potential of curriculum learning, we train multiple GPT models with 1 million parameters each to predict the next token and evaluate them on code completion and execution tasks. Our contributions include proposing a novel code difficulty assessment metric by combining software code measures, investigating the effectiveness of Curriculum Learning for code language models, and introducing a Novel Curriculum Learning schedule that enhances the performance of small decoder-only language models in code execution tasks. The results of this paper open the door for more research on the use of curriculum learning for code language models.

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Question-Analysis Prompting Improves LLM Performance in Reasoning Tasks
Dharunish Yugeswardeenoo | Kevin Zhu | Sean O’Brien

Although LLMs have the potential to transform many fields, they still underperform humans in reasoning tasks. Existing methods induce the model to produce step-by-step calculations, but this research explores the question: Does making the LLM analyze the question improve its performance? We propose a novel prompting strategy called Question Analysis Prompting (QAP), in which the model is prompted to explain the question in ’n’ words before solving. The value of ’n’ influences the length of response generated by the model. QAP is evaluated on GPT-3.5 Turbo and GPT-4 Turbo on arithmetic datasets GSM8K, AQuA, and SAT and commonsense dataset StrategyQA. QAP is compared with other state-of-the-art prompts including chain-of-thought (CoT), Plan and Solve Prompting (PS+) and Take A Deep Breath (TADB). QAP outperforms all state-of-the-art prompts on AQuA and SAT datasets on both GPT-3.5 and GPT-4. QAP consistently ranks among the top-2 prompts on 75% of the tests. A key factor of QAP performance can be attributed to response length, where detailed responses are beneficial when answering harder questions, but can negatively affect easy questions.

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An Individualized News Affective Response Dataset
Tiancheng Hu | Nigel Collier

The rise of sensationalism in news reporting, driven by market saturation and online competition, has compromised news quality and trust. At the core of sensationalism is the evocation of affective responses in the readers. Current NLP approaches to emotion detection often overlook the subjective differences in groups and individuals, relying on aggregation techniques that can obscure nuanced reactions. We introduce a novel large-scale dataset capturing subjective affective responses to news headlines. The dataset includes Facebook post screenshots from popular UK media outlets and uses a comprehensive annotation scheme. Annotators report their affective responses, provide discrete emotion labels, assess relevance to current events, and indicate sharing likelihood. Additionally, we collect demographic, personality, and media consumption data. This ongoing dataset aims to enable more accurate models of affective response by considering individual and contextual factors. This work is ongoing and we highly appreciate any feedback.

Research on understanding and generating diagrams has used vision models such as CLIP. However, it remains unclear whether these models accurately identify diagram attributes, such as node colors and shapes, along with edge colors and connection patterns. This study evaluates how well vision models recognize the diagram attributes by probing the model and retrieving diagrams using text queries. Experimental results showed that while vision models can recognize differences in node colors, shapes, and edge colors, they struggle to identify differences in edge connection patterns that play a pivotal role in the semantics of diagrams. Moreover, we revealed inadequate alignment between diagram attributes and language representations in the embedding space.

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CheckersGPT: Learning World Models through Language Modeling
Abhinav Joshi | Vaibhav Sharma | Ashutosh Modi

Although Large Language Models (LLMs) have been trained using just the next token prediction objective, these have shown impressive performance on various tasks. Consequently, it has attracted research interests in this regard. While one line of work in the past has suggested that LLMs learn surface-level statistics from the dataset, another line of work emphasizes that the learned representations are effective for simulating the underlying world model, considering the causal relationship for the next token prediction. This phenomenon is often referred to as the emergence of a world model in sequence prediction tasks. Recent work has demonstrated this phenomenon in a simulated setting of board games like Othello and Chess. In this paper, we analyze the game of Checkers to find out the emergence of a world model in a language model. By training a GPT-style autoregressive language model using only the next character prediction objective, we find that the model does show a hint of learning a world model representation of the board positions. We perform our analysis on two datasets: 1) synthetic dataset, which comes from the checkers game tree, and 2) human gameplay dataset. With multiple models trained with different layer sizes, we find that increasing the parameter size does help learn better world model representation decoded by linear probes.

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In-Context Symbolic Regression: Leveraging Large Language Models for Function Discovery
Matteo Merler | Katsiaryna Haitsiukevich | Nicola Dainese | Pekka Marttinen

State of the art Symbolic Regression (SR) methods currently build specialized models, while the application of Large Language Models (LLMs) remains largely unexplored. In this work, we introduce the first comprehensive framework that utilizes LLMs for the task of SR.We propose In-Context Symbolic Regression (ICSR), an SR method which iteratively refines a functional form with an LLM and determines its coefficients with an external optimizer. ICSR leverages LLMs’ strong mathematical prior both to propose an initial set of possible functions given the observations and to refine them based on their errors.Our findings reveal that LLMs are able to successfully find symbolic equations that fit the given data, matching or outperforming the overall performance of the best SR baselines on four popular benchmarks, while yielding simpler equations with better out of distribution generalization.

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STEP: Staged Parameter-Efficient Pre-training for Large Language Models
Kazuki Yano | Takumi Ito | Jun Suzuki

Pre-training large language models faces significant memory challenges due to the large size of model weights.We propose STaged parameter-Efficient Pre-training (STEP), which combines ideas from parameter-efficient tuning and staged training. We conduct experiments on pre-training models of various sizes and demonstrate that STEP can achieve up to a 40.4% reduction in maximum memory requirement compared to vanilla pre-training while maintaining comparable performance.

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bib (full) Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 5: Tutorial Abstracts)

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Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 5: Tutorial Abstracts)
Luis Chiruzzo | Hung-yi Lee | Leonardo Ribeiro

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Computational Linguistics for Brain Encoding and Decoding: Principles, Practices and Beyond
Jingyuan Sun | Shaonan Wang | Zijiao Chen | Jixing Li | Marie-Francine Moens

Computational linguistics (CL) has witnessed tremendous advancementsin recent years, with models such as large language models demonstratingexceptional performance in various natural language processing tasks. Theseadvancements highlight their potential to help understand brain languageprocessing, especially through the lens of brain encoding and decoding.Brain encoding involves the mapping of linguistic stimuli to brain activity,while brain decoding is the process of reconstructing linguistic stimulifrom observed brain activities. CL models that excel at capturing andmanipulating linguistic features are crucial for mapping linguistic stimulito brain activities and vice versa. Brain encoding and decoding have vastapplications, from enhancing human-computer interaction to developingassistive technologies for individuals with communication impairments. Thistutorial will focus on elucidating how computational linguistics canfacilitate brain encoding and decoding. We will delve into the principlesand practices of using computational linguistics methods for brain encodingand decoding. We will also discuss the challenges and future directions ofbrain encoding and decoding. Through this tutorial, we aim to provide acomprehensive and informative overview of the intersection betweencomputational linguistics and cognitive neuroscience, inspiring futureresearch in this exciting and rapidly evolving field.

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Automatic and Human-AI Interactive Text Generation (with a focus on Text Simplification and Revision)
Yao Dou | Philippe Laban | Claire Gardent | Wei Xu

In this tutorial, we focus on text-to-text generation, a class ofnatural language generation (NLG) tasks, that takes a piece of text as inputand then generates a revision that is improved according to some specificcriteria (e.g., readability or linguistic styles), while largely retainingthe original meaning and the length of the text. This includes many usefulapplications, such as text simplification, paraphrase generation, styletransfer, etc. In contrast to text summarization and open-ended textcompletion (e.g., story), the text-to-text generation tasks we discuss inthis tutorial are more constrained in terms of semantic consistency andtargeted language styles. This level of control makes these tasks idealtestbeds for studying the ability of models to generate text that is bothsemantically adequate and stylistically appropriate. Moreover, these tasksare interesting from a technical standpoint, as they require complexcombinations of lexical and syntactical transformations, stylistic control,and adherence to factual knowledge, – all at once. With a special focus ontext simplification and revision, this tutorial aims to provide an overviewof the state-of-the-art natural language generation research from four majoraspects – Data, Models, Human-AI Collaboration, and Evaluation – and todiscuss and showcase a few significant and recent advances: (1) the use ofnon-retrogressive approaches; (2) the shift from fine-tuning to promptingwith large language models; (3) the development of new learnable metric andfine-grained human evaluation framework; (4) a growing body of studies anddatasets on non-English languages; (5) the rise of HCI+NLP+Accessibilityinterdisciplinary research to create real-world writing assistant systems.

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Computational Expressivity of Neural Language Models
Alexandra Butoi | Ryan Cotterell | Anej Svete

Language models (LMs) are currently at the forefront of NLP researchdue to their remarkable versatility across diverse tasks. However, a largegap exists between their observed capabilities and the explanations proposedby established formal machinery. To motivate a better theoreticalcharacterization of LMs’ abilities and limitations, this tutorial aims toprovide a comprehensive introduction to a specific framework for formalanalysis of modern LMs using tools from formal language theory (FLT). Wepresent how tools from FLT can be useful in understanding the inner workingsand predicting the capabilities of modern neural LM architectures. We willcover recent results using FLT to make precise and practically relevantstatements about LMs based on recurrent neural networks and transformers byrelating them to formal devices such as finite-state automata, Turingmachines, and analog circuits. Altogether, the results covered in thistutorial will allow us to make precise statements and explanations about theobserved as well as predicted behaviors of LMs, as well as providetheoretically motivated suggestions on the aspects of the architectures thatcould be improved.

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Presentation Matters: How to Communicate Science in the NLP Venues and in the Wild?
Sarvnaz Karimi | Cecile Paris | Gholamreza Haffari

Each year a large number of early career researchers join the NLP/Computational Linguistics community, with most starting by presenting their research in the *ACL conferences and workshops. While writing a paper that has made it to these venues is one important step, what comes with communicating the outcome is equally important and sets the path to impact of a research outcome. In addition, not all PhD candidates get the chance of being trained for their presentation skills. Research methods courses are not all of the same quality and may not cover scientific communications, and certainly not all are tailored to the NLP community. We are proposing an introductory tutorial that covers a range of different communication skills, including writing, oral presentation (posters and demos), and social media presence. This is to fill in the gap for the researchers who may not have access to research methods courses or other mentors who could help them acquire such skills. The interactive nature of such a tutorial would allow attendees to ask questions and clarifications which would not be possible from reading materials alone.

This tutorial serves as a comprehensive guide on the vulnerabilities of Large Language Models (LLMs) to adversarial attacks, an interdisciplinary field that blends perspectives from Natural Language Processing (NLP) and Cybersecurity. As LLMs become more complex and integrated into various systems, understanding their security attributes is crucial. However, current research indicates that even safety-aligned models are not impervious to adversarial attacks that can result in incorrect or harmful outputs. The tutorial first lays the foundation by explaining safety-aligned LLMs and concepts in cybersecurity. It then categorizes existing research based on different types of learning architectures and attack methods. We highlight the existing vulnerabilities of unimodal LLMs, multi-modal LLMs, and systems that integrate LLMs, focusing on adversarial attacks designed to exploit weaknesses and mislead AI systems. Finally, the tutorial delves into the potential causes of these vulnerabilities and discusses potential defense mechanisms.

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Detecting Machine-Generated Text: Techniques and Challenges
Li Gao | Wenhan Xiong | Taewoo Kim

As AI-generated text increasingly resembles human-written content, the ability to detect machine-generated text becomes crucial in many applications. This tutorial aims to provide a comprehensive overview of text detection techniques, focusing on machine-generated text and deepfakes. We will discuss various methods for distinguishing between human-written and machine-generated text, including statistical methods, neural network-based techniques, and hybrid approaches. The tutorial will also cover the challenges in the detection process, such as dealing with evolving models and maintaining robustness against adversarial attacks. By the end of the session, attendees will have a solid understanding of current techniques and future directions in the field of text detection.

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bib (full) Findings of the Association for Computational Linguistics ACL 2024

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Findings of the Association for Computational Linguistics ACL 2024
Lun-Wei Ku | Andre Martins | Vivek Srikumar

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Controllable Data Augmentation for Few-Shot Text Mining with Chain-of-Thought Attribute Manipulation
Letian Peng | Yuwei Zhang | Jingbo Shang

Prompting large language models (LLMs) for data augmentation has recently become a common practice in few-shot NLP tasks. In this paper, we propose Chain-of-Thought Attribute Manipulation (CoTAM), a novel approach that generates new data from existing examples by only tweaking in the user-provided, task-specific attribute, e.g., sentiment polarity or topic in movie reviews. Instead of conventional latent representation controlling, we leverage the chain-of-thought prompting to directly edit the text in three steps, (1) attribute decomposition, (2) manipulation proposal, and (3) sentence reconstruction. Extensive results on various tasks, such as text (pair) classification and aspect-based sentiment analysis, verify the superiority of CoTAM over other LLM-based augmentation methods with the same number of training examples for both fine-tuning and in-context learning. Remarkably, the 2D visualization of the augmented dataset using principle component analysis revealed a human-recognizable decision boundary that is likely hinted by the attribute manipulation, demonstrating the potential of our proposed approach.

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Match More, Extract Better! Hybrid Matching Model for Open Domain Web Keyphrase Extraction
Mingyang Song | Liping Jing | Yi Feng

Keyphrase extraction aims to automatically extract salient phrases representing the critical information in the source document. Identifying salient phrases is challenging because there is a lot of noisy information in the document, leading to wrong extraction. To address this issue, in this paper, we propose a hybrid matching model for keyphrase extraction, which combines representation-focused and interaction-based matching modules into a unified framework for improving the performance of the keyphrase extraction task. Specifically, HybridMatch comprises (1) a PLM-based Siamese encoder component that represents both candidate phrases and documents, (2) an interaction-focused matching (IM) component that estimates word matches between candidate phrases and the corresponding document at the word level, and (3) a representation-focused matching (RM) component captures context-aware semantic relatedness of each candidate keyphrase at the phrase level. Extensive experimental results on the OpenKP dataset demonstrate that the performance of the proposed model HybridMatch outperforms the recent state-of-the-art keyphrase extraction baselines. Furthermore, we discuss the performance of large language models in keyphrase extraction based on recent studies and our experiments.

Large language models (LLMs) show great performance in various tasks, but face deployment challenges from limited memory capacity and bandwidth.Low-bit weight quantization can save memory and accelerate inference.Although floating-point (FP) formats show good performance in LLM quantization, they tend to perform poorly with small group sizes or sub-4 bits.We find the reason is that the absence of asymmetry in previous FP quantization makes it unsuitable for handling asymmetric value distribution of LLM weight tensors.In this work, we propose asymmetric FP quantization (AFPQ), which sets separate scales for positive and negative values.Our method leads to large accuracy improvements and can be easily plugged into other quantization methods, including GPTQ and AWQ, for better performance.Besides, no additional storage is needed compared with asymmetric integer (INT) quantization.The code is available at https://github.com/zhangsichengsjtu/AFPQ.

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End-to-End Emotion Semantic Parsing
Xiaotong Jiang | Zhongqing Wang | Guodong Zhou

Emotion detection is the task of automatically associating one or more emotions with a text. The emotions are experienced, targeted, and caused by different semantic constituents. Therefore, it is necessary to incorporate these semantic constituents into the process of emotion detection. In this study, we propose a new task called emotion semantic parsing which aims to parse the emotion and semantic constituents into an abstract semantic tree structure. In particular, we design an end-to-end generation model to capture the relations between emotion and all the semantic constituents, and to generate them jointly. Furthermore, we employ a task decomposition strategy to capture the semantic relation among these constituents in a more cognitive and structural way. Experimental results demonstrate the importance of the proposed task, and indicate the proposed model gives superior performance compared to other models.

Intelligent task-oriented dialogue systems (ToDs) are expected to continuously acquire new knowledge, also known as Continual Learning (CL), which is crucial to fit ever-changing user needs. However, catastrophic forgetting dramatically degrades the model performance in face of a long streamed curriculum. In this paper, we aim to overcome the forgetting problem in ToDs and propose a method (HESIT) with hyper-gradient-based exemplar strategy, which samples influential exemplars for periodic retraining. Instead of unilaterally observing data or models, HESIT adopts a profound exemplar selection strategy that considers the general performance of the trained model when selecting exemplars for each task domain. Specifically, HESIT analyzes the training data influence by tracing their hyper-gradient in the optimization process. Furthermore, HESIT avoids estimating Hessian to make it compatible for ToDs with a large pre-trained model. Experimental results show that HESIT effectively alleviates catastrophic forgetting by exemplar selection, and achieves state-of-the-art performance on the largest CL benchmark of ToDs in terms of all metrics.

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Unveiling Imitation Learning: Exploring the impact of Data Falsity to Large Language Model
Hyunsoo Cho

Many recent studies endeavor to improve open-sourced language models through imitation learning, re-training on the synthetic instruction data from state-of-the-art proprietary models like ChatGPT and GPT-4.However, the innate nature of synthetic data inherently contains noisy data, giving rise to a substantial presence of low-quality data replete with misleading queries, erroneous responses, and flawed reasoning.Although we intuitively grasp the potential harm of noisy data, we lack a quantitative understanding of its impact.To this end, this paper explores correlation between the degree of noise and its impact on language models through instruction tuning.We first introduce the Falsity-Controllable () dataset, which comprises pairs of true answers and corresponding reasoning, as well as false pairs to manually control the factuality ratio of the dataset.Through our extensive experiments, we found multiple intriguing findings of the correlation between factuality and instruction tuning. Specifically, factuality can significantly impact various benchmark characteristics especially when benchmarks are related to knowledge domain, and initial data quality plays a critical role, whereas the number of learning steps has a lesser impact.Additionally, we noted that once the language model is trained with a dataset contaminated by noise, restoring its original performance becomes exceptionally challenging, verging on irreversible.

While language models are increasingly more proficient at code generation, they still frequently generate incorrect programs. Many of these programs are obviously wrong, but others are more subtle and pass weaker correctness checks such as being able to compile. In this work, we focus on these counterfeit samples: programs sampled from a language model that 1) have a high enough log-probability to be generated at a moderate temperature and 2) pass weak correctness checks. Overall, we discover that most models have a very shallow understanding of counterfeits through three clear failure modes. First, models mistakenly classify them as correct. Second, models are worse at reasoning about the execution behaviour of counterfeits and often predict their execution results as if they were correct. Third, when asking models to fix counterfeits, the likelihood of a model successfully repairing a counterfeit is often even lower than that of sampling a correct program from scratch. Counterfeits also have very unexpected properties: first, counterfeit programs for problems that are easier for a model to solve are not necessarily easier to detect and only slightly easier to execute and repair. Second, counterfeits from a given model are just as confusing to the model itself as they are to other models. Finally, both strong and weak models are able to generate counterfeit samples that equally challenge all models. In light of our findings, we recommend that care and caution be taken when relying on models to understand their own samples, especially when no external feedback is incorporated.

Literature review requires researchers to synthesize a large amount of information and is increasingly challenging as the scientific literature expands. In this work, we investigate the potential of LLMs for producing hierarchical organizations of scientific studies to assist researchers with literature review. We define hierarchical organizations as tree structures where nodes refer to topical categories and every node is linked to the studies assigned to that category. Our naive LLM-based pipeline for hierarchy generation from a set of studies produces promising yet imperfect hierarchies, motivating us to collect CHIME, an expert-curated dataset for this task focused on biomedicine. Given the challenging and time-consuming nature of building hierarchies from scratch, we use a human-in-the-loop process in which experts correct errors (both links between categories and study assignment) in LLM-generated hierarchies. CHIME contains 2,174 LLM-generated hierarchies covering 472 topics, and expert-corrected hierarchies for a subset of 100 topics. Expert corrections allow us to quantify LLM performance, and we find that while they are quite good at generating and organizing categories, their assignment of studies to categories could be improved. We attempt to train a corrector model with human feedback which improves study assignment by 12.6 F1 points. We release our dataset and models to encourage research on developing better assistive tools for literature review.

With the recent advances of large language models (LLMs), it is no longer infeasible to build an automated debate system that helps people to synthesise persuasive arguments. Previous work attempted this task by integrating multiple components. In our work, we introduce an argument mining dataset that captures the end-to-end process of preparing an argumentative essay for a debate, which covers the tasks of claim and evidence identification (Task 1 ED), evidence convincingness ranking (Task 2 ECR), argumentative essay summarisation and human preference ranking (Task 3 ASR) and metric learning for automated evaluation of resulting essays, based on human feedback along argument quality dimensions (Task 4 SQE). Our dataset contains 14k examples of claims that are fully annotated with various properties supporting the aforementioned tasks. We evaluate multiple generative baselines for each of these tasks, including representative LLMs. We find, that while they show promising results on individual tasks in our benchmark, their end-to-end performance on all four tasks in succession deteriorates significantly, both in automated measures as well as in human-centred evaluation. This challenge presented by our proposed dataset motivates future research on end-to-end argument mining and summarisation. The repository of this project is available at https://github.com/HarrywillDr/ArgSum-Datatset.

Despite LLMs’ recent advancements, they still suffer from factual inconsistency and hallucination. An often-opted remedy is retrieval-augmented generation – however, there is no guarantee that the model will strictly adhere to retrieved grounding. Fundamentally, LLMs need to be aligned to be more faithful to grounding, which will require high-quality preference annotations. This paper investigates whether we can create high-quality grounded preference data for model alignment without using annotations from humans or large proprietary models. We experimented with existing entailment data and proposed approaches to generate synthetic grounded preference data, with which we train a Grounded Preference Model(GPM). We demonstrate through Proximal Policy Optimization(PPO) training of Mistral-7B-Instruct that our GPM model can successfully align powerful LLMs to generate much better grounded responses as judged by GPT4. Moreover, we show that our GPM is also a great faithfulness classifier, achieving SoTA in dialogue sub-tasks of the TRUE faithfulness Benchmark. We will release our GPM under the Apache 2.0 license.

Large language models (LLMs), while exhibiting exceptional performance, suffer from hallucinations, especially on knowledge-intensive tasks. Existing works propose to augment LLMs with individual text units retrieved from external knowledge corpora to alleviate the issue. However, in many domains, texts are interconnected (e.g., academic papers in a bibliographic graph are linked by citations and co-authorships) which form a (text-attributed) graph. The knowledge in such graphs is encoded not only in single texts/nodes but also in their associated connections. To facilitate the research of augmenting LLMs with graphs, we manually construct a Graph Reasoning Benchmark dataset called GRBench, containing 1,740 questions that can be answered with the knowledge from 10 domain graphs. Then, we propose a simple and effective framework called Graph Chain-of-thought (Graph-CoT) to augment LLMs with graphs by encouraging LLMs to reason on the graph iteratively. Each Graph-CoT iteration consists of three sub-steps: LLM reasoning, LLM-graph interaction, and graph execution. We conduct systematic experiments with three LLM backbones on GRBench, where Graph-CoT outperforms the baselines consistently. The code is available at https://github.com/PeterGriffinJin/Graph-CoT/.

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Text2DB: Integration-Aware Information Extraction with Large Language Model Agents
Yizhu Jiao | Sha Li | Sizhe Zhou | Heng Ji | Jiawei Han

The task of information extraction (IE) is to extract structured knowledge from text. However, it is often not straightforward to utilize IE output due to the mismatch between the IE ontology and the downstream application needs. We propose a new formulation of IE, Text2DB, that emphasizes the integration of IE output and the target database (or knowledge base). Given a user instruction, a document set, and a database, our task requires the model to update the database with values from the document set to satisfy the user instruction. This task requires understanding user instructions for what to extract and adapting to the given DB/KB schema for how to extract on the fly. To evaluate this new task, we introduce a new benchmark featuring common demands such as data infilling, row population, and column addition. In addition, we propose an LLM agent framework OPAL (Observe-Plan-Analyze LLM) which includes an Observer component that interacts with the database, the Planner component that generates a code-based plan with calls to IE models, and the Analyzer component that provides feedback regarding code quality before execution. Experiments show that OPAL can successfully adapt to diverse database schemas by generating different code plans and calling the required IE models. We also highlight difficult cases such as dealing with large databases with complex dependencies and extraction hallucination, which we believe deserve further investigation.

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How Important is a Language Model for Low-resource ASR?
Zoey Liu | Nitin Venkateswaran | Eric Le Ferrand | Emily Prud’hommeaux

N-gram language models (LMs) are the innovation that first made large-vocabulary continuous automatic speech recognition (ASR) viable. With neural end-to-end ASR architectures, however, LMs have become an afterthought. While the effect on accuracy may be negligible for English and Mandarin, jettisoning the LM might not make sense for the world’s remaining 6000+ languages. In this paper, we investigate the role of the LM in low-resource ASR. First we ask: does using an n-gram LM in decoding in neural architectures help ASR performance? While it may seem obvious that it should, its absence in most implementations suggests otherwise. Second, we ask: when an n-gram LM is used in ASR, is there a relationship between the size of the LM and ASR accuracy? We have discovered that gut feelings on this question vary considerably, but there is little empirical work to support any particular claim. We explore these questions “in the wild” using a deliberately diverse set of 9 very small ASR corpora. The results show that: (1) decoding with an n-gram LM, regardless of its size, leads to lower word error rates; and (2) increasing the size of the LM appears to yield improvements only when the audio corpus itself is already relatively large. This suggests that collecting additional LM training text may benefit widely-spoken languages which typically have larger audio corpora. In contrast, for endangered languages where data of any kind will always be limited, efforts may be better spent collecting additional transcribed audio.

Large language models (LLMs) are typically trained on general source data forvarious domains, but a recent surge in domain-specific LLMs has shown theirpotential to outperform general-purpose models in domain-specific tasks (e.g.,biomedicine). Although domain-specific pre-training enhances efficiency andleads to smaller models, the computational costs of training these LLMs remainhigh, posing budgeting challenges. We introduce MediSwift, a suite of biomedicalLMs that leverage sparse pre-training on domain-specific biomedical text data.By inducing up to 75% weight sparsity during the pre-training phase, MediSwiftachieves a 2-2.5x reduction in training FLOPs. Notably, all sparse pre-trainingwas performed on the Cerebras CS-2 system, which is specifically designed torealize the acceleration benefits from unstructured weight sparsity, therebysignificantly enhancing the efficiency of the MediSwift models. Throughsubsequent dense fine-tuning and strategic soft prompting, MediSwift modelsoutperform existing LLMs up to 7B parameters on biomedical tasks, setting newbenchmarks w.r.t efficiency-accuracy on tasks such as PubMedQA. Our results showthat sparse pre-training, along with dense fine-tuning and soft prompting,offers an effective method for creating high-performing, computationallyefficient models in specialized domains.

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Lexicon-Level Contrastive Visual-Grounding Improves Language Modeling
Chengxu Zhuang | Evelina Fedorenko | Jacob Andreas

Today’s most accurate language models are trained on orders of magnitude more language data than human language learners receive— but with no supervision from other sensory modalities that play a crucial role in human learning. Can we make LMs’ representations and predictions more accurate (and more human-like) with more ecologically plausible supervision? This paper describes LexiContrastive Grounding (LCG), a grounded language learning procedure that leverages visual supervision to improve textual representations. LexiContrastive Grounding combines a next-token prediction strategy with a contrastive visual grounding objective, focusing on early-layerrepresentations that encode lexical information. Across multiple word-learning and sentence-understanding benchmarks, LexiContrastiveGrounding not only outperforms standard language-only models in terms of learning efficiency in small and developmentally plausible data regimes, but also improves upon vision-and-language learning procedures including CLIP, GIT, Flamingo, and Vokenization.Moreover, LexiContrastive Grounding improves perplexity by around 5% on multiple language modeling tasks compared to other models trained on the same amount of text data. This work underscores the potential of incorporating visual grounding into language models, aligning more closely with the multimodal nature of human language acquisition.

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P-TA: Using Proximal Policy Optimization to Enhance Tabular Data Augmentation via Large Language Models
Shuo Yang | Chenchen Yuan | Yao Rong | Felix Steinbauer | Gjergji Kasneci

A multitude of industries depend on accurate and reasonable tabular data augmentation for their business processes. Contemporary methodologies in generating tabular data revolve around utilizing Generative Adversarial Networks (GAN) or fine-tuning Large Language Models (LLM). However, GAN-based approaches are documented to produce samples with common-sense errors attributed to the absence of external knowledge. On the other hand, LLM-based methods exhibit a limited capacity to capture the disparities between synthesized and actual data distribution due to the absence of feedback from a discriminator during training. Furthermore, the decoding of LLM-based generation introduces gradient breakpoints, impeding the backpropagation of loss from a discriminator, thereby complicating the integration of these two approaches. To solve this challenge, we propose using proximal policy optimization (PPO) to apply GANs, guiding LLMs to enhance the probability distribution of tabular features. This approach enables the utilization of LLMs as generators for GANs in synthesizing tabular data. Our experiments demonstrate that PPO leads to an approximately 4% improvement in the accuracy of models trained on synthetically generated data over state-of-the-art across three real-world datasets.

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Teaching-Assistant-in-the-Loop: Improving Knowledge Distillation from Imperfect Teacher Models in Low-Budget Scenarios
Yuhang Zhou | Wei Ai

There is increasing interest in distilling task-specific knowledge from large language models (LLM) to smaller student models.Nonetheless, LLM distillation presents a dual challenge: 1) there is a high cost associated with querying the teacher LLM, such as GPT-4, for gathering an ample number of demonstrations; 2) the teacher LLM might provide imperfect outputs with a negative impact on the student’s learning process. To enhance sample efficiency within resource-constrained, imperfect teacher scenarios, we propose a three-component framework leveraging three signal types. The first signal is the student’s self-consistency (consistency of student multiple outputs), which is a proxy of the student’s confidence. Specifically, we introduce a ”teaching assistant” (TA) model to assess the uncertainty of both the student’s and the teacher’s outputs via confidence scoring, which serves as another two signals for student training. Furthermore, we propose a two-stage training schema to first warm up the student with a small proportion of data to better utilize student’s signal. Experiments have shown the superiority of our proposed framework for four complex reasoning tasks. On average, our proposed two-stage framework brings a relative improvement of up to 20.79% compared to fine-tuning without any signals across datasets.

Large language models (LLMs) such as GPT-3 and GPT-4 are powerful but their weights are often publicly unavailable and their immense sizes make the models difficult to be tuned with common hardware. As a result, effectively tuning these models with large-scale supervised data can be challenging. As an alternative, In-Context Learning (ICL) can only use a small number of supervised examples due to context length limits. In this paper, we propose Super In-Context Learning (SuperICL) which allows black-box LLMs to work with locally fine-tuned smaller models, resulting in superior performance on supervised tasks. Our experiments demonstrate that SuperICL can improve performance beyond state-of-the-art fine-tuned models while addressing the instability problem of in-context learning.

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Are self-explanations from Large Language Models faithful?
Andreas Madsen | Sarath Chandar | Siva Reddy

Instruction-tuned Large Language Models (LLMs) excel at many tasks and will even explain their reasoning, so-called self-explanations. However, convincing and wrong self-explanations can lead to unsupported confidence in LLMs, thus increasing risk. Therefore, it’s important to measure if self-explanations truly reflect the model’s behavior. Such a measure is called interpretability-faithfulness and is challenging to perform since the ground truth is inaccessible, and many LLMs only have an inference API. To address this, we propose employing self-consistency checks to measure faithfulness. For example, if an LLM says a set of words is important for making a prediction, then it should not be able to make its prediction without these words. While self-consistency checks are a common approach to faithfulness, they have not previously been successfully applied to LLM self-explanations for counterfactual, feature attribution, and redaction explanations. Our results demonstrate that faithfulness is explanation, model, and task-dependent, showing self-explanations should not be trusted in general. For example, with sentiment classification, counterfactuals are more faithful for Llama2, feature attribution for Mistral, and redaction for Falcon 40B.

Existing datasets for attribute value extraction (AVE) predominantly focus on explicit attribute values while neglecting the implicit ones, lack product images, are often not publicly available, and lack an in-depth human inspection across diverse domains. To address these limitations, we present ImplicitAVE, the first, publicly available multimodal dataset for implicit attribute value extraction. ImplicitAVE, sourced from the MAVE dataset, is carefully curated and expanded to include implicit AVE and multimodality, resulting in a refined dataset of 68k training and 1.6k testing data across five domains. We also explore the application of multimodal large language models (MLLMs) to implicit AVE, establishing a comprehensive benchmark for MLLMs on the ImplicitAVE dataset. Six recent MLLMs with eleven variants are evaluated across diverse settings, revealing that implicit value extraction remains a challenging task for MLLMs. The contributions of this work include the development and release of ImplicitAVE, and the exploration and benchmarking of various MLLMs for implicit AVE, providing valuable insights and potential future research directions. Dataset and code are available at https://github.com/HenryPengZou/ImplicitAVE.

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Prompt Engineering a Prompt Engineer
Qinyuan Ye | Mohamed Ahmed | Reid Pryzant | Fereshte Khani

Prompt engineering is a challenging yet crucial task for optimizing the performance of large language models on customized tasks. It requires complex reasoning to examine the model’s errors, hypothesize what is missing or misleading in the current prompt, and communicate the task with clarity. While recent works indicate that large language models can be meta-prompted to perform automatic prompt engineering, we argue that their potential is limited due to insufficient guidance for complex reasoning in the meta-prompt. We fill this gap by infusing into the meta-prompt three key components: detailed descriptions, context specification, and a step-by-step reasoning template. The resulting method, named PE2, showcases remarkable versatility across diverse language tasks. It finds prompts that outperform “let’s think step by step” by 6.3% on MultiArith and 3.1% on GSM8K, and outperforms competitive baselines on counterfactual tasks by 6.9%. Further, we show that PE2 can make targeted prompt edits, rectify erroneous prompts, and induce multi-step plans for complex tasks.

Neural image classifiers can often learn to make predictions by overly relying on non-predictive features that are spuriously correlated with the class labels in the training data. This leads to poor performance in real-world atypical scenarios where such features are absent. This paper presents ASPIRE (Language-guided Data Augmentation for SPurIous correlation REmoval), a simple yet effective solution for supplementing the training dataset with images without spurious features, for robust learning against spurious correlations via better generalization. ASPIRE, guided by language at various steps, can generate non-spurious images without requiring any group labeling or existing non-spurious images in the training set. Precisely, we employ LLMs to first extract foreground and background features from textual descriptions of an image, followed by advanced language-guided image editing to discover the features that are spuriously correlated with the class label. Finally, we personalize a text-to-image generation model using the edited images to generate diverse in-domain images without spurious features. ASPIRE is complementary to all prior robust training methods in literature, and we demonstrate its effectiveness across 4 datasets and 9 baselines and show that ASPIRE improves the worst-group classification accuracy of prior methods by 1% - 38%. We also contribute a novel test set for the challenging Hard ImageNet dataset.

Tables contrast with unstructured text data by its structure to organize the information.In this paper, we investigate the efficiency of various LLMs in interpreting tabular data through different prompting strategies and data formats. Our analysis extends across six benchmarks for table-related tasks such as question-answering and fact-checking. We pioneer in the assessment of LLMs’ performance on image-based table representation. Specifically, we compare five text-based and three image-based table representations, revealing the influence of representation and prompting on LLM performance. We hope our study provides researchers insights into optimizing LLMs’ application in table-related tasks.

Using novel approaches to dataset development, the Biasly dataset captures the nuance and subtlety of misogyny in ways that are unique within the literature. Built in collaboration with multi-disciplinary experts and annotators themselves, the dataset contains annotations of movie subtitles, capturing colloquial expressions of misogyny in North American film. The open-source dataset can be used for a range of NLP tasks, including binary and multi-label classification, severity score regression, and text generation for rewrites. In this paper, we discuss the methodology used, analyze the annotations obtained, provide baselines for each task using common NLP algorithms, and furnish error analyses to give insight into model behaviour when fine-tuned on the Biasly dataset.

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BlendSQL: A Scalable Dialect for Unifying Hybrid Question Answering in Relational Algebra
Parker Glenn | Parag Dakle | Liang Wang | Preethi Raghavan

Many existing end-to-end systems for hybrid question answering tasks can often be boiled down to a “prompt-and-pray” paradigm, where the user has limited control and insight into the intermediate reasoning steps used to achieve the final result. Additionally, due to the context size limitation of many transformer-based LLMs, it is often not reasonable to expect that the full structured and unstructured context will fit into a given prompt in a zero-shot setting, let alone a few-shot setting. We introduce BlendSQL, a superset of SQLite to act as a unified dialect for orchestrating reasoning across both unstructured and structured data. For hybrid question answering tasks involving multi-hop reasoning, we encode the full decomposed reasoning roadmap into a single interpretable BlendSQL query. Notably, we show that BlendSQL can scale to massive datasets and improve the performance of end-to-end systems while using 35% fewer tokens. Our code is available and installable as a package at https://github.com/parkervg/blendsql.

Several post-training quantization methods have been applied to large language models (LLMs), and have been shown to perform well down to 8-bits. We find that these methods break down at lower bit precision, and investigate quantization-aware training for LLMs (LLM-QAT) to push quantization levels even further. We propose a data-free distillation method that leverages generations produced by the pre-trained model, which better preserves the original output distribution and allows quantizing any generative model independent of its training data, similar to post-training quantization methods. In addition to quantizing weights and activations, we also quantize the KV cache, which is critical for increasing throughput and supporting long sequence dependencies at current model sizes. We experiment with LLaMA models of sizes 7B, 13B, and 30B, at quantization levels down to 4-bits. We observe large improvements over training-free methods, especially in the low-bit settings.

In this work, we present InfiMM, an advanced Multimodal Large Language Model that adapts to intricate vision-language tasks. InfiMM, inspired by the Flamingo architecture, distinguishes itself through the utilization of large-scale training data, comprehensive training strategies, and diverse large language models. This approach ensures the preservation of Flamingo’s foundational strengths while simultaneously introducing augmented capabilities. Empirical evaluations across a variety of benchmarks underscore InfiMM’s remarkable capability in multimodal understanding. The code can be found at: https://anonymous.4open.science/r/infimm-zephyr-F60C/.

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Towards Verifiable Generation: A Benchmark for Knowledge-aware Language Model Attribution
Xinze Li | Yixin Cao | Liangming Pan | Yubo Ma | Aixin Sun

Although achieving great success, Large Language Models (LLMs) usually suffer from unreliable hallucinations. Although language attribution can be a potential solution, there are no suitable benchmarks and evaluation metrics to attribute LLMs to structured knowledge. In this paper, we define a new task of Knowledge-aware Language Model Attribution (KaLMA) that improves upon three core concerns with conventional attributed LMs. First, we extend attribution source from unstructured texts to Knowledge Graph (KG), whose rich structures benefit both the attribution performance and working scenarios. Second, we propose a new “Conscious Incompetence” setting considering the incomplete knowledge repository, where the model identifies the need for supporting knowledge beyond the provided KG. Third, we propose a comprehensive automatic evaluation metric encompassing text quality, citation quality, and text citation alignment. To implement the above innovations, we build a dataset in biography domain BioKaLMA via evolutionary question generation strategy, to control the question complexity and necessary knowledge to the answer. For evaluation, we develop a baseline solution and demonstrate the room for improvement in LLMs’ citation generation, emphasizing the importance of incorporating the “Conscious Incompetence” setting, and the critical role of retrieval accuracy.

Large Language Models (LLMs) have recently been shown to be effective as automatic evaluators with simple prompting and in-context learning. In this work, we assemble 16 LLMs encompassing four different size ranges and evaluate their output responses by preference ranking from the other LLMs as evaluators, such as System Star is better than System Square. We then evaluate the quality of ranking outputs introducing the Cognitive Bias Benchmark for LLMs as Evaluators (CoBBLer), a benchmark to measure six different cognitive biases in LLM evaluation outputs, such as the Egocentric bias where a model prefers to rank its own outputs highly in evaluation. We find that LLMs are biased text quality evaluators, exhibiting strong indications on our bias benchmark (40% of comparisons made by all models) within each of their evaluations that question their robustness as evaluators. Furthermore, we examine the correlation between human and machine preferences and calculate the average Rank-Biased Overlap (RBO) score to be 44%, indicating that machine preferences are misaligned with humans. According to our findings, LLMs may still be unable to be utilized for automatic annotation aligned with human preferences.

Large language models respond well in high-resource languages like English but struggle in low-resource languages. It may arise from the lack of high-quality instruction following data in these languages. Directly translating English samples into these languages can be a solution but unreliable, leading to responses with translation errors and lacking language-specific or cultural knowledge. To address this issue, we propose a novel method to construct cross-lingual instruction following samples with instruction in English and response in low-resource languages. Specifically, the language model first learns to generate appropriate English instructions according to the natural web texts in other languages as responses. The candidate cross-lingual instruction tuning samples are further refined and diversified. We have employed this method to build a large-scale cross-lingual instruction tuning dataset on 10 languages, namely X-Instruction. The instruction data built using our method incorporate more language-specific knowledge compared with the naive translation method. Experimental results have shown that the response quality of the model tuned on X-Instruction greatly exceeds the model distilled from a powerful teacher model, reaching or even surpassing the ones of ChatGPT. In addition, we find that models tuned on cross-lingual instruction following samples can follow the instruction in the output language without further tuning.

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Muffin: Mitigating Unhelpfulness in Emotional Support Conversations with Multifaceted AI Feedback
Jiashuo Wang | Chunpu Xu | Chak Tou Leong | Wenjie Li | Jing Li

Emotional support conversation systems are designed to alleviate users’ emotional distress and assist them in overcoming their challenges. While previous studies have made progress, their models occasionally generate unhelpful responses, which are intended to be supportive but instead have counterproductive effects. Since unhelpful responses can hinder the effectiveness of emotional support, it is crucial to mitigate them within conversations. Our solution is motivated by two principal considerations: (1) multiple facets of emotional support are expected to be considered when developing emotional support conversation models, and (2) directly reducing the probability of generating unhelpful responses can effectively mitigate their occurrence. Accordingly, we introduce a novel model-agnostic framework named ̲Mitigating ̲unhelpfulness with multifaceted AI ̲feedback for emot ̲io ̲nal support (Muffin). It first employs a multifaceted AI feedback module designed to assess the helpfulness model responses across various facets of emotional support. Leveraging contrastive learning, Muffin then reduces the unhelpful responses’ likelihoods. To validate the effectiveness of our proposed framework, we apply Muffin to various previous emotional support generation models, including the state-of-the-art. Experimental results demonstrate that Muffin can significantly mitigate unhelpful response generation while enhancing response fluency and relevance.

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Resonance RoPE: Improving Context Length Generalization of Large Language Models
Suyuchen Wang | Ivan Kobyzev | Peng Lu | Mehdi Rezagholizadeh | Bang Liu

This paper addresses the challenge of train-short-test-long (TSTL) scenarios in Large Language Models (LLMs) equipped with Rotary Position Embedding (RoPE), where models pre-trained on shorter sequences face difficulty with out-of-distribution (OOD) token positions in longer sequences. We introduce Resonance RoPE, a novel approach designed to narrow the generalization gap in TSTL scenarios by refining the interpolation of RoPE features for OOD positions, significantly improving the model performance without additional online computational costs. Furthermore, we present PosGen, a new synthetic benchmark specifically designed for fine-grained behavior analysis in TSTL scenarios, aiming to isolate the constantly increasing difficulty of token generation on long contexts from the challenges of recognizing new token positions. Our experiments on synthetic tasks show that after applying Resonance RoPE, Transformers recognize OOD position better and more robustly. Our extensive LLM experiments also show superior performance after applying Resonance RoPE to the current state-of-the-art RoPE scaling method, YaRN, on both upstream language modeling tasks and a variety of downstream long-text applications.

Large language models (LLMs), despite their remarkable progress across various general domains, encounter significant barriers in medicine and healthcare. This field faces unique challenges such as domain-specific terminologies and reasoning over specialized knowledge. To address these issues, we propose MedAgents, a novel multi-disciplinary collaboration framework for the medical domain. MedAgents leverages LLM-based agents in a role-playing setting that participate in a collaborative multi-round discussion, thereby enhancing LLM proficiency and reasoning capabilities. This training-free framework encompasses five critical steps: gathering domain experts, proposing individual analyses, summarising these analyses into a report, iterating over discussions until a consensus is reached, and ultimately making a decision. Our work focuses on the zero-shot setting, which is applicable in real-world scenarios. Experimental results on nine datasets (MedQA, MedMCQA, PubMedQA, and six subtasks from MMLU) establish that our proposed MedAgents framework excels at mining and harnessing the medical expertise within LLMs, as well as extending its reasoning abilities. Our code can be found at https://github.com/gersteinlab/MedAgents.

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Meta-Reasoning: Semantics-Symbol Deconstruction for Large Language Models
Yiming Wang | Zhuosheng Zhang | Pei Zhang | Baosong Yang | Rui Wang

Neural-symbolic methods have demonstrated efficiency in enhancing the reasoning abilities of large language models (LLMs). However, existing methods mainly rely on syntactically mapping natural languages to complete formal languages like Python and SQL. Those methods require that reasoning tasks be convertible into programs, which cater to the computer execution mindset and deviate from human reasoning habits. To broaden symbolic methods’ applicability and adaptability in the real world, we propose Meta-Reasoning from a linguistic perspective. This method empowers LLMs to deconstruct reasoning-independent semantic information into generic symbolic representations, thereby efficiently capturing more generalized reasoning knowledge. We conduct extensive experiments on more than ten datasets encompassing conventional reasoning tasks like arithmetic, symbolic, and logical reasoning, and the more complex interactive reasoning tasks like theory-of-mind reasoning. Experimental results demonstrate that Meta-Reasoning significantly enhances in-context reasoning accuracy, learning efficiency, out-of-domain generalization, and output stability compared to the Chain-of-Thought technique.

The success of large language models (LLM) benefits from large-scale model parameters and large amounts of pre-training data. However, the textual data for training LLM can not be confirmed to be legal because they are crawled from different web sites. For example, there are copyrighted articles, personal reviews and information in the pre-training data for LLM which are illegal. To address the above issue and develop legal LLM, we propose to detect the pre-training data from LLM in a pure black-box way because the existing LLM services only return the generated text. The previous most related works are the membership inference attack (MIA) on machine learning models to detect the training data from them. But the existing methods are based on analyzing the output probabilities of models which are unrealistic to LLM services. To tackle the problem, we firstly construct the benchmark datasets by collecting textual data from different domains as the seen and unseen pre-training data for LLMs. Then, we investigate a black-box framework named DPDLLM, with the only access to the generated texts from LLM for detecting textual data whether was used to train it. In the proposed framework, we exploit GPT-2 as the reference model to fit the textual data and feed the generated text from LLM into it to acquire sequence probabilities as the significant feature for detection. The experimental results on the benchmark datasets demonstrate that DPDLLM is effective on different popular LLMs and outperforms the existing methods.

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PACIT: Unlocking the Power of Examples for Better In-Context Instruction Tuning
Tianci Xue | Ziqi Wang | Yixia Li | Yun Chen | Guanhua Chen

Instruction tuning enhances the instruction following ability of large language models by finetuning with supervised instruction data. Previous work proposes in-context instruction tuning (ICIT) where specific positive or negative examples are incorporated into the prompt for better performance. In this work, we propose PACIT, a simple and effective in-context instruction tuning method, inspired by the pedagogical concept of desirable difficulty. The PACIT method unlocks the power of examples by encouraging the model to actively learn to grasp the distinctions between the positive and negative examples instead of merely reading. The model is expected to first verify the correctness of the provided example according to the task description, which is then set as the condition for generating a better response to the task instance. Our extensive experiments prove the effectiveness of PACIT, outperforming ICIT baseline on both in-domain and out-domain tasks up to 9.16 and 3.14 average ROUGE-L scores, respectively. Moreover, PACIT can notably enhance the performance of instruction tuning even when all positive and negative examples are generated with a self-instruct method.

Recent advances in large language models (LLMs) have promoted generative error correction (GER) for automatic speech recognition (ASR), which aims to predict the ground-truth transcription from the decoded N-best hypotheses. Thanks to the strong language generation ability of LLMs and rich information in the N-best list, GER shows great effectiveness in enhancing ASR results. However, it still suffers from two limitations: 1) LLMs are unaware of the source speech during GER, which may lead to results that are grammatically correct but violate the source speech content, 2) N-best hypotheses usually only vary in a few tokens, making it redundant to send all of them for GER, which could confuse LLM about which tokens to focus on and thus lead to increased miscorrection. In this paper, we propose ClozeGER, a new paradigm for ASR generative error correction. First, we introduce a multimodal LLM (i.e., SpeechGPT) to receive source speech as extra input to improve the fidelity of correction output. Then, we reformat GER as a cloze test with logits calibration to remove the input information redundancy and simplify GER with clear instructions. Experiments show that ClozeGER achieves a new breakthrough over vanilla GER on 9 popular ASR datasets.

Cross-document Relation Extraction aims to predict the relation between target entities located in different documents. In this regard, the dominant models commonly retain useful information for relation prediction via bridge entities, which allows the model to elaborately capture the intrinsic interdependence between target entities. However, these studies ignore the non-bridge entities, each of which co-occurs with only one target entity and offers the semantic association between target entities for relation prediction. Besides, the commonly-used dataset–CodRED contains substantial NA instances, leading to the prediction bias during inference. To address these issues, in this paper, we propose a novel graph-based cross-document RE model with non-bridge entity enhancement and prediction debiasing. Specifically, we use a unified entity graph to integrate numerous non-bridge entities with target entities and bridge entities, modeling various associations between them, and then use a graph recurrent network to encode this graph. Finally, we introduce a novel debiasing strategy to calibrate the original prediction distribution. Experimental results on the closed and open settings show that our model significantly outperforms all baselines, including the GPT-3.5-turbo and InstructUIE, achieving state-of-the-art performance. Particularly, our model obtains 66.23% and 55.87% AUC points in the official leaderboard under the two settings, respectively,ranking the first place in all submissions since December 2023. Our code is available at https://github.com/DeepLearnXMU/CoRE-NEPD.

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Large Language Models can Share Images, Too!
Young-Jun Lee | Dokyong Lee | Joo Won Sung | Jonghwan Hyeon | Ho-Jin Choi

This paper explores the image-sharing capability of Large Language Models (LLMs), such as GPT-4 and LLaMA 2, in a zero-shot setting. To facilitate a comprehensive evaluation of LLMs, we introduce the photochatplus dataset, which includes enriched annotations (ie intent, triggering sentence, image description, and salient information). Furthermore, we present the gradient-free and extensible Decide, Describe, and Retrieve () framework. With extensive experiments, we unlock the image-sharing capability of equipped with LLMs in zero-shot prompting, with ChatGPT achieving the best performance.Our findings also reveal the emergent image-sharing ability in LLMs under zero-shot conditions, validating the effectiveness of . We use this framework to demonstrate its practicality and effectiveness in two real-world scenarios: (1) human-bot interaction and (2) dataset augmentation. To the best of our knowledge, this is the first study to assess the image-sharing ability of various LLMs in a zero-shot setting. We make our source code and dataset publicly available at https://github.com/passing2961/DribeR.

In the era of code large language models (code LLMs), data engineering plays a pivotal role during the instruction fine-tuning phase. To train a versatile model, previous efforts devote tremendous efforts into crafting instruction data covering all the downstream scenarios. Nonetheless, this will incur significant expenses in constructing data and training model. Therefore, this paper introduces CodeM, a novel data construction strategy, which can efficiently train a versatile model using less data via our newly proposed ability matrix. CodeM uses ability matrix to decouple code LLMs’ abilities into two dimensions, constructing a lightweight training corpus that only covers a subset of target scenarios. Extensive experiments on HumanEvalPack and MultiPL-E imply that code LLMs can combine the single-dimensional abilities to master composed abilities, validating the effectiveness of CodeM.

Advances in large vision-language models (LVLMs) have led to significant progress in generating natural language descriptions for visual contents. These powerful models are known for producing texts that are factually inconsistent with the visual input. While some efforts mitigate such inconsistencies in natural image captioning, the factuality of generated captions for structured visuals, such as charts, has not received as much scrutiny. This work introduces a comprehensive typology of factual errors in generated chart captions. A large-scale human annotation effort provides insight into the error patterns in captions generated by various models, ultimately forming the foundation of a dataset, CHOCOLATE. Our analysis reveals that even advanced models like GPT-4V frequently produce captions laced with factual inaccuracies. To combat this, we establish the task of Chart Caption Factual Error Correction and introduce CHARTVE, a visual entailment model that outperforms current LVLMs in evaluating caption factuality. Furthermore, we propose C2TFEC, an interpretable two-stage framework that excels at correcting factual errors. This work inaugurates a new domain in factual error correction for chart captions, presenting a novel evaluation metric, and demonstrating an effective approach to ensuring the factuality of generated chart captions. The code and data as well as the continuously updated benchmark can be found at: https://khuangaf.github.io/CHOCOLATE/.

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BIDER: Bridging Knowledge Inconsistency for Efficient Retrieval-Augmented LLMs via Key Supporting Evidence
Jiajie Jin | Yutao Zhu | Yujia Zhou | Zhicheng Dou

Retrieval-augmented large language models (LLMs) have demonstrated efficacy in knowledge-intensive tasks such as open-domain QA, addressing inherent challenges in knowledge update and factual inadequacy.However, inconsistencies between retrieval knowledge and the necessary knowledge for LLMs, leading to a decline in LLM’s answer quality. This paper introduces BIDER, an approach that refines retrieval documents into Key Supporting Evidence (KSE) through knowledge synthesis, supervised fine-tuning (SFT), and preference alignment. We train BIDER by learning from crafting KSE, while maximizing its output to align with LLM’s information acquisition preferences through reinforcement learning. Evaluations across five datasets show BIDER boosts LLMs’ answer quality by 7% while reducing input content length in retrieval documents by 80%, outperforming existing methods. The proposed KSE simulation effectively equips LLMs with essential information for accurate question answering.

Visual grounding (VG) aims at locating the foreground entities that match the given natural language expression. Previous datasets and methods for classic VG task mainly rely on the prior assumption that the given expression must literally refer to the target object, which greatly impedes the practical deployment of agents in real-world scenarios. Since users usually prefer to provide the intention-based expressions for the desired object instead of covering all the details, it is necessary for the agents to interpret the intention-driven instructions. Thus, in this work, we take a step further to the intention-driven visual-language (V-L) understanding. To promote classic VG towards human intention interpretation, we propose a new intention-driven visual grounding (IVG) task and build a largest-scale IVG dataset named IntentionVG with free-form intention expressions. Considering that practical agents need to move and find specific targets among various scenarios to realize the grounding task, our IVG task and IntentionVG dataset have taken the crucial properties of both multi-scenario perception and egocentric view into consideration. Besides, various types of models are set up as the baselines to realize our IVG task. Extensive experiments on our IntentionVG dataset and baselines demonstrate the necessity and efficacy of our method for the V-L field. To foster future research in this direction, our newly built dataset and baselines will be publicly available at https://github.com/Rubics-Xuan/IVG.

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Incremental Sequence Labeling: A Tale of Two Shifts
Shengjie Qiu | Junhao Zheng | Zhen Liu | Yicheng Luo | Qianli Ma

The incremental sequence labeling task involves continuously learning new classes over time while retaining knowledge of the previous ones. Our investigation identifies two significant semantic shifts: E2O (where the model mislabels an old entity as a non-entity) and O2E (where the model labels a non-entity or old entity as a new entity). Previous research has predominantly focused on addressing the E2O problem, neglecting the O2E issue. This negligence results in a model bias towards classifying new data samples as belonging to the new class during the learning process. To address these challenges, we propose a novel framework, Incremental Sequential Labeling without Semantic Shifts (IS3). Motivated by the identified semantic shifts (E2O and O2E), IS3 aims to mitigate catastrophic forgetting in models. As for the E2O problem, we use knowledge distillation to maintain the model’s discriminative ability for old entities. Simultaneously, to tackle the O2E problem, we alleviate the model’s bias towards new entities through debiased loss and optimization levels.Our experimental evaluation, conducted on three datasets with various incremental settings, demonstrates the superior performance of IS3 compared to the previous state-of-the-art method by a significant margin.

Knowledge Base Question Answering (KBQA) aims to answer natural language questions based on facts in knowledge bases. A typical approach to KBQA is semantic parsing, which translates a question into an executable logical form in a formal language. Recent works leverage the capabilities of large language models (LLMs) for logical form generation to improve performance. However, although it is validated that LLMs are capable of solving some KBQA problems, there has been little discussion on the differences in LLMs’ proficiency in formal languages used in semantic parsing. In this work, we propose to evaluate the understanding and generation ability of LLMs to deal with differently structured logical forms by examining the inter-conversion of natural and formal language through in-context learning of LLMs. Extensive experiments with models of different sizes show that state-of-the-art LLMs can understand formal languages as well as humans, but generating correct logical forms given a few examples remains a challenge. Most importantly, our results also indicate that LLMs exhibit considerable sensitivity. In general, the formal language with a lower formalization level, i.e., the more similar it is to natural language, is more friendly to LLMs. Code and data can be found at https://github.com/Matthewlliu/structure_probe.

Multimodal entity linking (MEL), which aligns ambiguous mentions within multimodal contexts to referent entities from multimodal knowledge bases, is essential for many natural language processing applications. Previous MEL methods mainly focus on exploring complex multimodal interaction mechanisms to better capture coherence evidence between mentions and entities by mining complementary information. However, in real-world social media scenarios, vision modality often exhibits low quality, low value, or low relevance to the mention. Integrating such information directly will backfire, leading to a weakened consistency between mentions and their corresponding entities. In this paper, we propose a novel latent space vision feature optimization framework MELOV, which combines inter-modality and intra-modality optimizations to address these challenges. For the inter-modality optimization, we exploit the variational autoencoder to mine shared information and generate text-based visual features. For the intra-modality optimization, we consider the relationships between mentions and build graph convolutional network to aggregate the visual features of semantic similar neighbors. Extensive experiments on three benchmark datasets demonstrate the superiority of our proposed framework.

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Unsupervised Distractor Generation via Large Language Model Distilling and Counterfactual Contrastive Decoding
Fanyi Qu | Hao Sun | Yunfang Wu

Within the context of reading comprehension, the task of Distractor Generation (DG) aims to generate several incorrect options to confuse readers. In recent years, the emergence of Large Language Models (LLMs) provides a potential for unsupervised DG without expensive human-annotated distractor labels. In this paper, we leverage LLMs as a cost-effective annotator to enhance the DG capability of smaller student models. To perform knowledge distilling, we propose a dual task training framework that integrates pseudo distractors from LLMs and answer information as the objective target with a two-stage training process. Moreover, we devise a counterfactual contrastive decoding mechanism for increasing the distracting capability of the DG model. Experiments show that our unsupervised generation method with Bart-base greatly surpasses GPT-3.5-turbo zero-shot performance with only 200× fewer model parameters. Our proposed unsupervised DG method offers a cost-effective framework for practical reading comprehension applications, without the need of laborious distractor annotation and costly large-size models.

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Conversational Question Answering with Language Models Generated Reformulations over Knowledge Graph
Lihui Liu | Blaine Hill | Boxin Du | Fei Wang | Hanghang Tong

Conversational question answering (ConvQA) over knowledge graphs (KGs) involves answering multi-turn natural language questions about information contained in a KG. State-of-the-art methods of ConvQA often struggle with inexplicit question-answer pairs. These inputs are easy for human beings to understand given a conversation history, but hard for a machine to interpret, which can degrade ConvQA performance. To address this problem, we propose a reinforcement learning (RL) based model, CoRnNet, which utilizes question reformulations generated by large language models (LLMs) to improve ConvQA performance. CoRnNet adopts a teacher-student architecture where a teacher model learns question representations using human writing reformulations, and a student model to mimic the teacher model’s output via reformulations generated by LLMs. The learned question representation is then used by a RL model to locate the correct answer in a KG. Extensive experimental results show that CoRnNet outperforms state-of-the-art ConvQA models.

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Debug like a Human: A Large Language Model Debugger via Verifying Runtime Execution Step by Step
Li Zhong | Zilong Wang | Jingbo Shang

Large language models (LLMs) are leading significant progress in code generation. Beyond one-pass code generation, recent works further integrate unit tests and program verifiers into LLMs to iteratively refine the generated programs. However, these works consider the generated programs as an indivisible entity, which falls short for LLMs in debugging the programs, especially when the programs contain complex logic flows and data operations. In contrast, when human developers debug programs, they typically set breakpoints and selectively examine runtime execution information. The execution flow and the intermediate variables play a crucial role in the debugging process, yet they are underutilized in the existing literature on code generation. In this study, we introduce Large Language Model Debugger (LDB), a novel debugging framework that enables LLMs to refine their generated programs with the runtime execution information. Specifically, LDB segments the programs into basic blocks and tracks the values of intermediate variables after each block throughout the runtime execution. This allows LLMs to concentrate on simpler code units within the overall execution flow, verify their correctness against the task description block by block, and efficiently pinpoint any potential errors. Experiments demonstrate that LDB consistently enhances the baseline performance by up to 9.8% across the HumanEval, MBPP, and TransCoder benchmarks, archiving new state-of-the-art performance in code debugging for various LLM selections.

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Effective In-Context Example Selection through Data Compression
ZhongXiang Sun | Kepu Zhang | Haoyu Wang | Xiao Zhang | Jun Xu

In-context learning has been extensively validated in large language models. However, the mechanism and selection strategy for in-context example selection, which is a crucial ingredient in this approach, lacks systematic and in-depth research. In this paper, we propose a data compression approach to the selection of in-context examples. We introduce a two-stage method that can effectively choose relevant examples and retain sufficient information about the training dataset within the in-context examples. Our method shows a significant improvement of an average of 5.90% across five different real-world datasets using four language models.

Although large language models (LLMs) acquire extensive world knowledge and some reasoning abilities, their proficiency in generating humorous sentences remains a challenge. Previous research has demonstrated that the humor generation capabilities of ChatGPT are confined to producing merely 25 unique jokes. In this work, we concentrate on endowing LLMs with the ability of generating puns, a particular category of humor by preference learning method. We propose a multi-stage curriculum preference learning framework to optimize both pun structure preferences and humor preferences. Specifically, we improve the Direct Preference Optimization (DPO) algorithm to address the challenge of multi-objective alignment problem. Besides, to facilitate further advancement in this field, we collect a Chinese Pun (ChinesePun) dataset, containing 2.1k puns and corresponding annotations. Experimental results on both Chinese and English benchmark datasets demonstrate that our method significantly outperforms all the baseline models.

Deploying large language models (LLMs) to real scenarios for domain-specific question answering (QA) is a key thrust for LLM applications, which poses numerous challenges, especially in ensuring that responses are both accommodating to user requirements and appropriately leveraging domain-specific knowledge bases. They are the two major difficulties for LLM application as vanilla fine-tuning falls short of addressing. Combining these requirements, we conceive of them as the requirement for the model’s preference to be harmoniously aligned with humans’. Thus, we introduce Knowledgeable Preference AlignmenT (KnowPAT), which constructs two kinds of preference sets to tackle the two issues. Besides, we design a new alignment objective to align the LLM preference with different human preferences uniformly, aiming to optimize LLM performance in real-world, domain-specific QA settings. Adequate experiments and comprehensive comparisons with 15 baseline methods illustrate that our KnowPAT is a superior pipeline for real-scenario domain-specific QA with LLMs.

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MARIO: MAth Reasoning with code Interpreter Output - A Reproducible Pipeline
Minpeng Liao | Chengxi Li | Wei Luo | Wu Jing | Kai Fan

Large language models (LLMs) have significantly improved in understanding natural language but still lack in mathematical reasoning, a hurdle on the path to true artificial general intelligence. The training of large language models, based on next-token prediction, struggles to capture the precise nature of mathematical reasoning, presenting both practical and theoretical challenges. In this paper, we address this challenge by enriching the data landscape and introducing a reasonable data format, enhanced the text analysis of the LLM with a capability to utilize a Python code interpreter. This dataset is derived from GSM8K and MATH and has been further refined through a combination of GPT annotations, human review, and self-training processes. Additionally, we propose a tentative, easily replicable protocol for the fine-tuning of math-specific LLMs, which has led to a significant improvement in the performance of a 7B-parameter LLM on the GSM8K and MATH datasets. A solution generator and a value estimator are fine-tuned simultaneously in a multi-task fashion, while an outlier-free value model-based inference method is proposed to further boost the performance. We are committed to advancing the field of mathematical reasoning in LLMs and, to that end, we will make the source code and checkpoints publicly available.

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DiffusPoll: Conditional Text Diffusion Model for Poll Generation
Le Cheng | Shuangyin Li

Online social media platforms often gather user feedback through polls to enhance user engagement. Automatically generating polls from social media and its context can decrease the labor expenses of media workers and enhance workplace productivity. However, on social media platforms, there are internet water armies that manipulate public opinion through sheer numbers and causing the comments to be biased, drowning out minority views. In such circumstances, polls created based on biased comments often have limited types of options and poor coverage. Therefore, it is crucial to diversify the poll options and try to listen to the voices of the minority. To achieve this, we introduce DiffusPoll, a novel paradigm for poll generation based on a non-autoregressive diffusion model that can generate diversified and high-quality samples. Under the new paradigm, we design a task-specific mask strategy tailored to the inherent logic of polls to optimize controlled generation. Furthermore, we also leverage additional attribute tags from comments to enhance the generation quality. Experimental results indicate that DiffusPoll has achieved state-of-the-art performance in both the quality and diversity of poll generation tasks, and is more likely to hit the voices of minority.

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Exploring Mathematical Extrapolation of Large Language Models with Synthetic Data
Haolong Li | Yu Ma | Yinqi Zhang | Chen Ye | Jie Chen

While large language models (LLMs) have shown excellent capabilities in language understanding, text generation and many other tasks, they still struggle in complex multi-step reasoning problems such as mathematical reasoning. In this paper, through a newly proposed arithmetical puzzle problem, we show that the model can perform well on multi-step reasoning tasks via fine tuning on high-quality synthetic data. Experiments with the open-llama-3B model on three different test datasets show that not only the model can reach a zero-shot pass@1 at 0.44 on the in-domain dataset, it also demonstrates certain generalization capabilities on the out-of-domain datasets. Specifically, this paper has designed two out-of-domain datasets in the form of extending the numerical range and the composing components of the arithmetical puzzle problem separately. The fine-tuned model have shown encouraging performance on these two far more difficult tasks with the zero-shot pass@1 at 0.33 and 0.35 correspondingly.

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Implanting LLM’s Knowledge via Reading Comprehension Tree for Toxicity Detection
Hankun Kang | Tieyun Qian

Toxicity detection plays a crucial role in maintaining the peace of the society. Existing methods can be roughly categorized as small language model (SLM) based and large language model (LLM) based. However, due to the limitation of SLMs on general knowledge and the potential embedded bias in LLMs despite their large amount of knowledge, it is not a good idea to detect toxicity only with either SLM or LLM based method.In this work, we propose to implant LLM’s knowledge into SLM based methods such that we can stick to both types of models’ strengths. To this end, we develop a reading comprehension (RC) tree to transfer knowledge between two models. Specifically, we first construct the RC tree, from an extensive to intensive reading perspective, to capture the local and global information in the text. We then model samples encoded by SLM and knowledge extracted from LLM as two distributions using the constructed RT tree. We finally transfer knowledge via optimal transportation between two distributions. Extensive experiments prove the effectiveness of our method on real-world and machine-generated datasets.

This paper focuses on task-agnostic prompt compression for better generalizability and efficiency. Considering the redundancy in natural language, existing approaches compress prompts by removing tokens or lexical units according to their information entropy obtained from a causal language model such as LLaMa-7B. The challenge is that information entropy may be a suboptimal compression metric: (i) it only leverages unidirectional context and may fail to capture all essential information needed for prompt compression; (ii) it is not aligned with the prompt compression objective.To address these issues, we propose a data distillation procedure to derive knowledge from an LLM to compress prompts without losing crucial information, and meantime, introduce an extractive text compression dataset. We formulate prompt compression as a token classification problem to guarantee the faithfulness of the compressed prompt to the original one, and use a Transformer encoder as the base architecture to capture all essential information for prompt compression from the full bidirectional context. Our approach leads to lower latency by explicitly learning the compression objective with smaller models such as XLM-RoBERTa-large and mBERT.We evaluate our method on both in-domain and out-of-domain datasets, including MeetingBank, LongBench, ZeroScrolls, GSM8K, and BBH. Despite its small size, our model shows significant performance gains over strong baselines and demonstrates robust generalization ability across different LLMs. Additionally, our model is 3x-6x faster than existing prompt compression methods, while accelerating the end-to-end latency by 1.6x-2.9x with compression ratios of 2x-5x.

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EconNLI: Evaluating Large Language Models on Economics Reasoning
Yue Guo | Yi Yang

Large Language Models (LLMs) are widely used for writing economic analysis reports or providing financial advice, but their ability to understand economic knowledge and reason about potential results of specific economic events lacks systematic evaluation. To address this gap, we propose a new dataset, natural language inference on economic events (EconNLI), to evaluate LLMs’ knowledge and reasoning abilities in the economic domain. We evaluate LLMs on (1) their ability to correctly classify whether a premise event will cause a hypothesis event and (2) their ability to generate reasonable events resulting from a given premise. Our experiments reveal that LLMs are not sophisticated in economic reasoning and may generate wrong or hallucinated answers. Our study raises awareness of the limitations of using LLMs for critical decision-making involving economic reasoning and analysis. The dataset and codes are available at https://github.com/Irenehere/EconNLI.

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Better Late Than Never: Model-Agnostic Hallucination Post-Processing Framework Towards Clinical Text Summarization
Songda Li | Yunqi Zhang | Chunyuan Deng | Yake Niu | Hui Zhao

Clinical text summarization has proven successful in generating concise and coherent summaries. However, these summaries may include unintended text with hallucinations, which can mislead clinicians and patients. Existing methods for mitigating hallucinations can be categorized into task-specific and task-agnostic approaches. Task-specific methods lack versatility for real-world applicability. Meanwhile, task-agnostic methods are not model-agnostic, so they require retraining for different models, resulting in considerable computational costs. To address these challenges, we propose MEDAL, a model-agnostic framework designed to post-process medical hallucinations. MEDAL can seamlessly integrate with any medical summarization model, requiring no additional computational overhead. MEDAL comprises a medical infilling model and a hallucination correction model. The infilling model generates non-factual summaries with common errors to train the correction model. The correction model is incorporated with a self-examination mechanism to activate its cognitive capability. We conduct comprehensive experiments using 11 widely accepted metrics on 7 baseline models across 3 medical text summarization tasks. MEDAL demonstrates superior performance in correcting hallucinations when applied to summaries generated by pre-trained language models and large language models.

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Finding and Editing Multi-Modal Neurons in Pre-Trained Transformers
Haowen Pan | Yixin Cao | Xiaozhi Wang | Xun Yang | Meng Wang

Understanding the internal mechanisms by which multi-modal large language models (LLMs) interpret different modalities and integrate cross-modal representations is becoming increasingly critical for continuous improvements in both academia and industry. In this paper, we propose a novel method to identify key neurons for interpretability — how multi-modal LLMs bridge visual and textual concepts for captioning. Our method improves conventional works upon efficiency and applied range by removing needs of costly gradient computation. Based on those identified neurons, we further design a multi-modal knowledge editing method, beneficial to mitigate sensitive words or hallucination. For rationale of our design, we provide theoretical assumption. For empirical evaluation, we have conducted extensive quantitative and qualitative experiments. The results not only validate the effectiveness of our methods, but also offer insightful findings that highlight three key properties of multi-modal neurons: sensitivity, specificity and causal-effect, to shed light for future research.

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Realistic Evaluation of Toxicity in Large Language Models
Tinh Luong | Thanh-Thien Le | Linh Ngo | Thien Nguyen

Large language models (LLMs) have become integral to our professional workflows and daily lives. Nevertheless, these machine companions of ours have a critical flaw: the huge amount of data which endows them with vast and diverse knowledge, also exposes them to the inevitable toxicity and bias. While most LLMs incorporate defense mechanisms to prevent the generation of harmful content, these safeguards can be easily bypassed with minimal prompt engineering. In this paper, we introduce the new Thoroughly Engineered Toxicity (TET) dataset, comprising manually crafted prompts designed to nullify the protective layers of such models. Through extensive evaluations, we demonstrate the pivotal role of TET in providing a rigorous benchmark for evaluation of toxicity awareness in several popular LLMs: it highlights the toxicity in the LLMs that might remain hidden when using normal prompts, thus revealing subtler issues in their behavior.

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Controllable Text Generation with Residual Memory Transformer
Hanqing Zhang | Si Sun | Haiming Wu | Dawei Song

Large-scale Causal Language Models (CLMs), e.g., GPT3 and ChatGPT, have brought great success in text generation. However, it is still an open challenge to effectively control the generation process of a CLM while balancing the flexibility, control granularity, and generation efficiency. In this paper, we provide a new alternative for controllable text generation (CTG), by designing a non-intrusive, lightweight control plugin, namely Residual Memory Transformer (RMT), to accompany the generation of CLM at arbitrary time steps. With an encoder-decoder setup, RMT can accept any types of control conditions and cooperate with the base CLM through a residual learning paradigm, to achieve a more flexible, general, and efficient CTG. Extensive experiments are carried out on various control tasks, in the form of both automatic and human evaluations. The results demonstrate the superiority of RMT over a wide range of state-of-the-art CTG approaches. The code implementation of our work is available at: https://github.com/Residual_Memory_Transformer.

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Prompt-Based Length Controlled Generation with Multiple Control Types
Renlong Jie | Xiaojun Meng | Lifeng Shang | Xin Jiang | Qun Liu

Large language models (LLMs) have attracted great attention given their strong performance on a wide range of NLP tasks. In practice, users often expect generated texts to fall within a specific length range, making length controlled generation an important topic, especially for GPT-style models. Existing length control methods mostly focus on a simple control type of “equal to” a target length. Different from them, we propose a prompt-based method to achieve length controlled generation under different control types with high accuracy. In particular, we adopt reinforcement learning (RL) and sample filtering with the reward signal given by rule-based reward models, which enhances the length control ability of models by rewarding outputs that follow certain control instructions. In addition, we introduce a standard prompt extractor to parse arbitrary users’ input into standard control instructions. Experiments show that our method significantly improves the accuracy of prompt-based length control on popular summarization datasets like CNNDM and NYT under multiple control types. Moreover, both the standard prompt extractor and RL-tuned model show strong generalization to unseen control prompt templates.

We present PCA-Bench, a multimodal decision-making benchmark for evaluating the integrated capabilities of Multimodal Large Language Models (MLLMs). Departing from previous benchmarks focusing on simplistic tasks and individual model capability, PCA-Bench introduces three complex scenarios: autonomous driving, domestic robotics, and open-world games. Given task instructions and diverse contexts, the model is required to seamlessly integrate multiple capabilities of Perception, Cognition, and Action in a reasoning chain to make accurate decisions. Moreover, PCA-Bench features error localization capabilities, scrutinizing model inaccuracies in areas such as perception, knowledge, or reasoning. This enhances the reliability of deploying MLLMs. To balance accuracy and efficiency in evaluation, we propose PCA-Eval, an automatic evaluation protocol, and assess 10 prevalent MLLMs. The results reveal significant performance disparities between open-source models and powerful proprietary models like GPT-4 Vision. To address this, we introduce Embodied-Instruction-Evolution (EIE), an automatic framework for synthesizing instruction tuning examples in multimodal embodied environments. EIE generates 7,510 training examples in PCA-Bench and enhances the performance of open-source MLLMs, occasionally surpassing GPT-4 Vision (+3% in decision accuracy), thereby validating the effectiveness of EIE. Our findings suggest that robust MLLMs like GPT4-Vision show promise for decision-making in embodied agents, opening new avenues for MLLM research. All benchmark data and evaluation code are made public.

Conversational recommender systems are an emerging area that has garnered increasing interest in the community, especially with the advancements in large language models (LLMs) that enable sophisticated handling of conversational input. Despite the progress, the field still has many aspects left to explore. The currently available public datasets for conversational recommendation lack specific user preferences and explanations for recommendations, hindering high-quality recommendations. To address such challenges, we present a novel conversational recommendation dataset named PEARL, synthesized with persona- and knowledge-augmented LLM simulators. We obtain detailed persona and knowledge from real-world reviews and construct a large-scale dataset with over 57k dialogues. Our experimental results demonstrate that PEARL contains more specific user preferences, show expertise in the target domain, and provides recommendations more relevant to the dialogue context than those in prior datasets. Furthermore, we demonstrate the utility of PEARL by showing that our downstream models outperform baselines in both human and automatic evaluations. We release our dataset and code.

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CoLLaVO: Crayon Large Language and Vision mOdel
Byung-Kwan Lee | Beomchan Park | Chae Won Kim | Yong Man Ro

The remarkable success of Large Language Models (LLMs) and instruction tuning drives the evolution of Vision Language Models (VLMs) towards a versatile general-purpose model. Yet, it remains unexplored whether current VLMs genuinely possess quality object-level image understanding capabilities determined from ‘what objects are in the image?’ or ‘which object corresponds to a specified bounding box?’. Our findings reveal that the image understanding capabilities of current VLMs are strongly correlated with their zero-shot performance on vision language (VL) tasks. This suggests that prioritizing basic image understanding is crucial for VLMs to excel at VL tasks. To enhance object-level image understanding, we propose Crayon Large Language and Vision mOdel (CoLLaVO), which incorporates instruction tuning with Crayon Prompt as a new visual prompt tuning scheme based on panoptic color maps. Furthermore, we present a learning strategy of Dual QLoRA to preserve object-level image understanding without forgetting it during visual instruction tuning, thereby achieving a significant leap in numerous VL benchmarks in a zero-shot setting.

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Modelling Variability in Human Annotator Simulation
Wen Wu | Wenlin Chen | Chao Zhang | Phil Woodland

Human annotator simulation (HAS) serves as a cost-effective substitute for human evaluation tasks such as data annotation and system assessment. It is important to incorporate the variability present in human evaluation into HAS, since it helps capture diverse subjective interpretations and mitigate potential biases and over-representation. This work introduces a novel framework for modelling variability in HAS. Conditional softmax flow (S-CNF) is proposed to model the distribution of subjective human annotations, which leverages diverse human annotations via meta-learning. This enables efficient generation of annotations that exhibit human variability for unlabelled input. In addition, a wide range of evaluation metrics are adopted to assess the capability and efficiency of HAS systems in predicting the aggregated behaviours of human annotators, matching the distribution of human annotations, and simulating the inter-annotator disagreements. Results demonstrate that the proposed method achieves state-of-the-art performance on two real-world human evaluation tasks: emotion recognition and toxic speech detection.

In this study, we introduce BEnQA, a dataset comprising parallel Bengali and English exam questions for middle and high school levels in Bangladesh. Our dataset consists of approximately 5K questions covering several subjects in science with different types of questions, including factual, application, and reasoning-based questions. We benchmark several Large Language Models (LLMs) with our parallel dataset and observe a notable performance disparity between the models in Bengali and English. We also investigate some prompting methods, and find that Chain-of-Thought prompting is beneficial mostly on reasoning questions, but not so much on factual ones. We also find that appending English translation helps to answer questions in Bengali. Our findings point to promising future research directions for improving the performance of LLMs in Bengali and more generally in low-resource languages.

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MORE: Multi-mOdal REtrieval Augmented Generative Commonsense Reasoning
Wanqing Cui | Keping Bi | Jiafeng Guo | Xueqi Cheng

Since commonsense information has been recorded significantly less frequently than its existence, language models pre-trained by text generation have difficulty to learn sufficient commonsense knowledge. Several studies have leveraged text retrieval to augment the models’ commonsense ability. Unlike text, images capture commonsense information inherently but little effort has been paid to effectively utilize them. In this work, we propose a novel Multi-mOdal REtrieval (MORE) augmentation framework, to leverage both text and images to enhance the commonsense ability of language models. Extensive experiments on the Common-Gen task have demonstrated the efficacy of MORE based on the pre-trained models of both single and multiple modalities.

Recently, retrieval augmentation and tool augmentation have demonstrated a remarkable capability to expand the internal memory boundaries of language models (LMs) by providing external context. However, internal memory and external context inevitably clash, leading to knowledge conflicts within LMs. In this paper, we aim to interpret the mechanism of knowledge conflicts through the lens of information flow, and then mitigate conflicts by precise interventions at the pivotal point. We find there are some attention heads with opposite effects in the later layers, where memory heads can recall knowledge from internal memory, and context heads can retrieve knowledge from external context. Moreover, we reveal that the pivotal point at which knowledge conflicts emerge in LMs is the integration of inconsistent information flows by memory heads and context heads. Inspired by the insights, we propose a novel method called Pruning Head via PatH PatcHing (PH3), which can efficiently mitigate knowledge conflicts by pruning conflicting attention heads without updating model parameters. PH3 can flexibly control eight LMs to use internal memory (↑ 44.0%) or external context (↑ 38.5%). Moreover, PH3 can also improve the performance of LMs on open-domain QA tasks. We also conduct extensive experiments to demonstrate the cross-model, cross-relation, and cross-format generalization of our method. Our code is publicly available at https://github.com/jinzhuoran/MConflict/.

Recent research trends in computational biology have increasingly focused on integrating text and bio-entity modeling, especially in the context of molecules and proteins. However, previous efforts like BioT5 faced challenges in generalizing across diverse tasks and lacked a nuanced understanding of molecular structures, particularly in their textual representations (e.g., IUPAC). This paper introduces BioT5+, an extension of the BioT5 framework, tailored to enhance biological research and drug discovery. BioT5+ incorporates several novel features: integration of IUPAC names for molecular understanding, inclusion of extensive bio-text and molecule data from sources like bioRxiv and PubChem, the multi-task instruction tuning for generality across tasks, and a numerical tokenization technique for improved processing of numerical data. These enhancements allow BioT5+ to bridge the gap between molecular representations and their textual descriptions, providing a more holistic understanding of biological entities, and largely improving the grounded reasoning of bio-text and bio-sequences. The model is pre-trained and fine-tuned with a large number of experiments, including 3 types of problems (classification, regression, generation), 15 kinds of tasks, and 21 total benchmark datasets, demonstrating the remarkable performance and state-of-the-art results in most cases. BioT5+ stands out for its ability to capture intricate relationships in biological data, thereby contributing significantly to bioinformatics and computational biology. Our code is available at https://github.com/QizhiPei/BioT5.

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SIBO: A Simple Booster for Parameter-Efficient Fine-Tuning
Zhihao Wen | Jie Zhang | Yuan Fang

Fine-tuning all parameters of large language models (LLMs) necessitates substantial computational power and extended time. Latest advancements in parameter-efficient fine-tuning (PEFT) techniques, such as Adapter tuning and LoRA, allow for adjustments to only a minor fraction of the parameters of these LLMs. Concurrently, it has been noted that the issue of over-smoothing diminishes the effectiveness of these Transformer-based LLMs, resulting in suboptimal performances in downstream tasks. In this paper, we present SIBO, which is a SImple BOoster to enhance PEFT, by injecting an initial residual. SIBO is straightforward and readily extensible to a range of state-of-the-art PEFT techniques to alleviate over-smoothing and enhance performance. Extensive experiments on 22 benchmark datasets demonstrate that SIBO significantly enhances the performance of various strong baselines, achieving up to 15.7% and 23.5% improvement over existing PEFT methods on the arithmetic and commonsense reasoning tasks, respectively.

Recent advancements in large language models (LLMs) and multi-modal models (MMs) have demonstrated their remarkable capabilities in problem-solving. Yet, their proficiency in tackling geometry math problems, which necessitates an integrated understanding of both textual and visual information, has not been thoroughly evaluated. To address this gap, we introduce the GeoEval benchmark, a comprehensive collection that includes a main subset of 2,000 problems, a 750 problems subset focusing on backward reasoning, an augmented sub- set of 2,000 problems, and a hard subset of 300 problems. This benchmark facilitates a deeper investigation into the performance of LLMs and MMs in solving geometry math problems. Our evaluation of ten LLMs and MMs across these varied subsets reveals that the WizardMath model excels, achieving a 55.67% accuracy rate on the main subset but only a 6.00% accuracy on the hard subset. This highlights the critical need for testing models against datasets on which they have not been pre-trained. Additionally, our findings indicate that GPT-series models perform more effectively on problems they have rephrased, suggesting a promising method for enhancing model capabilities.

Named entity recognition (NER) is a pivotal task reliant on textual data, often impeding the disambiguation of entities due to the absence of context. To tackle this challenge, conventional methods often incorporate images crawled from the internet as auxiliary information. However, the images often lack sufficient entities or would introduce noise. Even with high-quality images, it is still challenging to efficiently use images as auxiliaries (i.e., fine-grained alignment with texts). We introduce a novel method named InstructNER to address these issues. Leveraging the rich real-world knowledge and image synthesis capabilities of a large pre-trained stable diffusion (SD) model, InstructNER transforms the text-only NER into a multimodal NER (MNER) task. A selection process automatically identifies the best synthetic image by comparing fine-grained similarities with internet-crawled images through a visual bag-of-words strategy. Note, during the image synthesis, a cross-attention matrix between synthetic images and raw text emerges, which inspires a soft attention guidance alignment (AGA) mechanism. AGA optimizes the MNER task and concurrently facilitates instructive alignment in MNER. Empirical experiments on prominent MNER datasets show that our method surpasses all text-only baselines, improving F1-score by 1.4% to 2.3%. Remarkably, even when compared to fully multimodal baselines, our approach maintains competitive. Furthermore, we open-source a comprehensive synthetic image dataset and the code to supplement existing raw dataset. The code and datasets are available in https://github.com/Heyest/InstructNER.

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Neurons in Large Language Models: Dead, N-gram, Positional
Elena Voita | Javier Ferrando | Christoforos Nalmpantis

We analyze a family of large language models in such a lightweight manner that can be done on a single GPU. Specifically, we focus on the OPT family of models ranging from 125m to 66b parameters and rely only on whether an FFN neuron is activated or not. First, we find that the early part of the network is sparse and represents many discrete features. Here, many neurons (more than in some layers of the 66b model) are “dead”, i.e. they never activate on a large collection of diverse data. At the same time, many of the alive neurons are reserved for discrete features and act as token and n-gram detectors. Interestingly, their corresponding FFN updates not only promote next token candidates as could be expected, but also explicitly focus on removing the information about triggering them tokens, i.e., current input. To the best of our knowledge, this is the first example of mechanisms specialized at removing (rather than adding) information from the residual stream. With scale, models become more sparse in a sense that they have more dead neurons and token detectors. Finally, some neurons are positional: them being activated or not depends largely (or solely) on position and less so (or not at all) on textual data. We find that smaller models have sets of neurons acting as position range indicators while larger models operate in a less explicit manner.

Grounded Multimodal Named Entity Recognition (GMNER) is a nascent multimodal task that aims to identify named entities, entity types and their corresponding visual regions. GMNER task exhibits two challenging properties: 1) The weak correlation between image-text pairs in social media results in a significant portion of named entities being ungroundable. 2) There exists a distinction between coarse-grained referring expressions commonly used in similar tasks (e.g., phrase localization, referring expression comprehension) and fine-grained named entities. In this paper, we propose RiVEG, a unified framework that reformulates GMNER into a joint MNER-VE-VG task by leveraging large language models (LLMs) as a connecting bridge. This reformulation brings two benefits: 1) It maintains the optimal MNER performance and eliminates the need for employing object detection methods to pre-extract regional features, thereby naturally addressing two major limitations of existing GMNER methods. 2) The introduction of entity expansion expression and Visual Entailment (VE) module unifies Visual Grounding (VG) and Entity Grounding (EG). It enables RiVEG to effortlessly inherit the Visual Entailment and Visual Grounding capabilities of any current or prospective multimodal pretraining models. Extensive experiments demonstrate that RiVEG outperforms state-of-the-art methods on the existing GMNER dataset and achieves absolute leads of 10.65%, 6.21%, and 8.83% in all three subtasks.

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Learning Job Title Representation from Job Description Aggregation Network
Napat Laosaengpha | Thanit Tativannarat | Chawan Piansaddhayanon | Attapol Rutherford | Ekapol Chuangsuwanich

Learning job title representation is a vital process for developing automatic human resource tools. To do so, existing methods primarily rely on learning the title representation through skills extracted from the job description, neglecting the rich and diverse content within. Thus, we propose an alternative framework for learning job titles through their respective job description (JD) and utilize a Job Description Aggregator component to handle the lengthy description and bidirectional contrastive loss to account for the bidirectional relationship between the job title and its description. We evaluated the performance of our method on both in-domain and out-of-domain settings, achieving a superior performance over the skill-based approach.

Existing benchmarks for visual question answering lack in visual grounding and complexity, particularly in evaluating spatial reasoning skills. We introduce FlowVQA, a novel benchmark aimed at assessing the capabilities of visual question-answering multimodal language models in reasoning with flowcharts as visual contexts. FlowVQA comprises 2,272 carefully generated and human-verified flowchart images from three distinct content sources, along with 22,413 diverse question-answer pairs, to test a spectrum of reasoning tasks, including information localization, decision-making, and logical progression. We conduct a thorough baseline evaluation on a suite of both open-source and proprietary multimodal language models using various strategies, followed by an analysis of directional bias. The results underscore the benchmark’s potential as a vital tool for advancing the field of multimodal modeling, providing a focused and challenging environment for enhancing model performance in visual and logical reasoning tasks.

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Flexible Weight Tuning and Weight Fusion Strategies for Continual Named Entity Recognition
Yahan Yu | Duzhen Zhang | Xiuyi Chen | Chenhui Chu

Continual Named Entity Recognition (CNER) is dedicated to sequentially learning new entity types while mitigating catastrophic forgetting of old entity types. Traditional CNER approaches commonly employ knowledge distillation to retain old knowledge within the current model. However, because only the representations of old and new models are constrained to be consistent, the reliance solely on distillation in existing methods still suffers from catastrophic forgetting. To further alleviate the forgetting issue of old entity types, this paper introduces flexible Weight Tuning (WT) and Weight Fusion (WF) strategies for CNER. The WT strategy, applied at each training step, employs a learning rate schedule on the parameters of the current model. After learning the current task, the WF strategy dynamically integrates knowledge from both the current and previous models for inference. Notably, these two strategies are model-agnostic and seamlessly integrate with existing State-Of-The-Art (SOTA) models. Extensive experiments demonstrate that the WT and WF strategies consistently enhance the performance of previous SOTA methods across ten CNER settings in three datasets.

The automatic evaluation of natural language generation (NLG) systems presents a long-lasting challenge. Recent studies have highlighted various neural metrics that align well with human evaluations. Yet, the robustness of these evaluators against adversarial perturbations remains largely under-explored due to the unique challenges in obtaining adversarial data for different NLG evaluation tasks. To address the problem, we introduce AdvEval, a novel black-box adversarial framework against NLG evaluators. AdvEval is specially tailored to generate data that yield strong disagreements between human and victim evaluators. Specifically, inspired by the recent success of large language models (LLMs) in text generation and evaluation, we adopt strong LLMs as both the data generator and gold evaluator. Adversarial data are automatically optimized with feedback from the gold and victim evaluator. We conduct experiments on 12 victim evaluators and 11 NLG datasets, spanning tasks including dialogue, summarization, and question evaluation. The results show that AdvEval can lead to significant performance degradation of various victim metrics, thereby validating its efficacy.

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Teacher-Student Training for Debiasing: General Permutation Debiasing for Large Language Models
Adian Liusie | Yassir Fathullah | Mark Gales

Large Language Models (LLMs) have demonstrated impressive zero-shot capabilities and versatility in NLP tasks, however they sometimes fail to maintain crucial invariances for specific tasks. One example is permutation sensitivity, where LLMs’ outputs may significantly vary depending on the order of the input options. While debiasing techniques can mitigate these issues, and yield better performance and reliability, they often come with a high computational cost at inference. This paper addresses this inefficiency at inference time. The aim is to distill the capabilities of a computationally intensive, debiased, teacher model into a more compact student model. We explore two variants of student models: one based on pure distillation, and the other on an error-correction approach for more complex tasks, where the student corrects a single biased decision from the teacher to achieve a debiased output. Our approach is general and can be applied to both black-box and white-box LLMs. Furthermore, we demonstrate that our compact, encoder-only student models can outperform their larger, biased teacher counterparts, achieving better results with significantly fewer parameters.

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Uncovering Limitations of Large Language Models in Information Seeking from Tables
Chaoxu Pang | Yixuan Cao | Chunhao Yang | Ping Luo

Tables are recognized for their high information density and widespread usage, serving as essential sources of information. Seeking information from tables (TIS) is a crucial capability for Large Language Models (LLMs), serving as the foundation of knowledge-based Q&A systems. However, this field presently suffers from an absence of thorough and reliable evaluation. This paper introduces a more reliable benchmark for Table Information Seeking (TabIS). To avoid the unreliable evaluation caused by text similarity-based metrics, TabIS adopts a single-choice question format (with two options per question) instead of a text generation format. We establish an effective pipeline for generating options, ensuring their difficulty and quality. Experiments conducted on 12 LLMs reveal that while the performance of GPT-4-turbo is marginally satisfactory, both other proprietary and open-source models perform inadequately. Further analysis shows that LLMs exhibit a poor understanding of table structures, and struggle to balance between TIS performance and robustness against pseudo-relevant tables (common in retrieval-augmented systems). These findings uncover the limitations and potential challenges of LLMs in seeking information from tables. We release our data and code to facilitate further research in this field.

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An Ensemble-of-Experts Framework for Rehearsal-free Continual Relation Extraction
Shen Zhou | Yongqi Li | Xin Miao | Tieyun Qian

Continual relation extraction (CRE) aims to continuously learn relations in new tasks without forgetting old relations in previous tasks.Current CRE methods are all rehearsal-based which need to store samples and thus may encounter privacy and security issues.This paper targets rehearsal-free continual relation extraction for the first time and decomposes it into task identification and within-task prediction sub-problems. Existing rehearsal-free methods focus on training a model (expert) for within-task prediction yet neglect to enhance models’ capability of task identification.In this paper, we propose an Ensemble-of-Experts (EoE) framework for rehearsal-free continual relation extraction. Specifically, we first discriminatively train each expert by augmenting analogous relations across tasks to enhance the expert’s task identification ability. We then propose a cascade voting mechanism to form an ensemble of experts for effectively aggregating their abilities.Extensive experiments demonstrate that our method outperforms current rehearsal-free methods and is even better than rehearsal-based CRE methods.

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Temporal Validity Change Prediction
Georg Wenzel | Adam Jatowt

Temporal validity is an important property of text that has many downstream applications, such as recommender systems, conversational AI, and user status tracking. Existing benchmarking tasks often require models to identify the temporal validity duration of a single statement. However, many data sources contain additional context, such as successive sentences in a story or posts on a social media profile. This context may alter the duration for which the originally collected statement is expected to be valid. We propose Temporal Validity Change Prediction, a natural language processing task benchmarking the capability of machine learning models to detect context statements that induce such change. We create a dataset consisting of temporal target statements sourced from Twitter and crowdsource corresponding context statements. We then benchmark a set of transformer-based language models on our dataset. Finally, we experiment with a multitasking approach to improve the state-of-the-art performance.

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RIFF: Learning to Rephrase Inputs for Few-shot Fine-tuning of Language Models
Saeed Najafi | Alona Fyshe

Pre-trained Language Models (PLMs) can be accurately fine-tuned for downstream text processing tasks. Recently, researchers have introduced several parameter-efficient fine-tuning methods that optimize input prompts or adjust a small number of model parameters (e.g LoRA). In this study, we explore the impact of altering the input text of the original task in conjunction with parameter-efficient fine-tuning methods. To most effectively rewrite the input text, we train a few-shot paraphrase model with a Maximum-Marginal Likelihood objective. Using six few-shot text classification datasets, we show that enriching data with paraphrases at train and test time enhances the performance beyond what can be achieved with parameter-efficient fine-tuning alone. The code used for our experiments can be found at https://github.com/SaeedNajafi/RIFF.

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Modelling Commonsense Commonalities with Multi-Facet Concept Embeddings
Hanane Kteich | Na Li | Usashi Chatterjee | Zied Bouraoui | Steven Schockaert

Concept embeddings offer a practical and efficient mechanism for injecting commonsense knowledge into downstream tasks. Their core purpose is often not to predict the commonsense properties of concepts themselves, but rather to identify commonalities, i.e. sets of concepts which share some property of interest. Such commonalities are the basis for inductive generalisation, hence high-quality concept embeddings can make learning easier and more robust. Unfortunately, standard embeddings primarily reflect basic taxonomic categories, making them unsuitable for finding commonalities that refer to more specific aspects (e.g. the colour of objects or the materials they are made of). In this paper, we address this limitation by explicitly modelling the different facets of interest when learning concept embeddings. We show that this leads to embeddings which capture a more diverse range of commonsense properties, and consistently improves results in downstream tasks such as ultra-fine entity typing and ontology completion.

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Revisiting Multimodal Transformers for Tabular Data with Text Fields
Thomas Bonnier

Tabular data with text fields can be leveraged in applications such as financial risk assessment or medical diagnosis prediction. When employing multimodal approaches to make predictions based on these modalities, it is crucial to make the most appropriate modeling choices in terms of numerical feature encoding or fusion strategy. In this paper, we focus on multimodal classification tasks based on tabular datasets with text fields. We build on multimodal Transformers to propose the Tabular-Text Transformer (TTT), a tabular/text dual-stream Transformer network. This architecture includes a distance-to-quantile embedding scheme for numerical features and an overall attention module which concurrently considers self-attention and cross-modal attention. Further, we leverage the two well-informed modality streams to estimate whether a prediction is uncertain or not. To explain uncertainty in terms of feature values, we use a sampling-based approximation of Shapley values in a bimodal context, with two options for the value function. To show the efficacy and relevance of this approach, we compare it to six baselines and measure its ability to quantify and explain uncertainty against various methods. Our code is available at https://github.com/thomas-bonnier/TabularTextTransformer.

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An Empirical Study on the Characteristics of Bias upon Context Length Variation for Bangla
Jayanta Sadhu | Ayan Khan | Abhik Bhattacharjee | Rifat Shahriyar

Pretrained language models inherently exhibit various social biases, prompting a crucial examination of their social impact across various linguistic contexts due to their widespread usage. Previous studies have provided numerous methods for intrinsic bias measurements, predominantly focused on high-resource languages. In this work, we aim to extend these investigations to Bangla, a low-resource language. Specifically, in this study, we (1) create a dataset for intrinsic gender bias measurement in Bangla, (2) discuss necessary adaptations to apply existing bias measurement methods for Bangla, and (3) examine the impact of context length variation on bias measurement, a factor that has been overlooked in previous studies. Through our experiments, we demonstrate a clear dependency of bias metrics on context length, highlighting the need for nuanced considerations in Bangla bias analysis. We consider our work as a stepping stone for bias measurement in the Bangla Language and make all of our resources publicly available to support future research.

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ConTempo: A Unified Temporally Contrastive Framework for Temporal Relation Extraction
Jingcheng Niu | Saifei Liao | Victoria Ng | Simon De Montigny | Gerald Penn

The task of temporal relation extraction (TRE) involves identifying and extracting temporal relations between events from narratives. We identify two primary issues with TRE systems. First, by formulating TRE as a simple text classification task where every temporal relation is independent, it is hard to enhance the TRE model’s representation of meaning of temporal relations, and its facility with the underlying temporal calculus. We solve the issue by proposing a novel Temporally Contrastive learning model (ConTempo) that increase the model’s awareness of the meaning of temporal relations by leveraging their symmetric or antisymmetric properties. Second, the reusability of innovations has been limited due to incompatibilities in model architectures. Therefore, we propose a unified framework and show that ConTempo is compatible with all three main branches of TRE research. Our results demonstrate that the performance gains of ConTempo are more pronounced, with the total combination achieving state-of-the-art performance on the widely used MATRES and TBD corpora. We furthermore identified and corrected a large number of annotation errors present in the test set of MATRES, after which the performance increase brought by ConTempo becomes more apparent.

In this work, we dive deep into one of the popular knowledge-grounded dialogue benchmarks that focus on faithfulness, FaithDial. We show that a significant portion of the FaithDial data contains annotation artifacts, which may bias models towards completely ignoring the conversation history. We therefore introduce CHARP, a testbed, designed for evaluating supposedly non-hallucinatory models trained on the FaithDial dataset. Our extensive analysis reveals that models primarily exhibit poor performance on CHARP due to their inability to effectively attend to and reason over the conversation history. Furthermore, the evaluation methods of FaithDial fail to capture these shortcomings, neglecting the conversational history. Our findings indicate that there is substantial room for contribution in both dataset creation and hallucination evaluation for knowledge-grounded dialogue, and that CHARP can serve as a tool for monitoring the progress in this particular research area. Data, models, and source code will be publicly available upon acceptance.

The ability of Large Language Models (LLMs) to critique and refine their reasoning is crucial for their application in evaluation, feedback provision, and self-improvement. This paper introduces CriticBench, a comprehensive benchmark designed to assess LLMs’ abilities to critique and rectify their reasoning across a variety of tasks. CriticBench encompasses five reasoning domains: mathematical, commonsense, symbolic, coding, and algorithmic. It compiles 15 datasets and incorporates responses from three LLM families. Utilizing CriticBench, we evaluate and dissect the performance of 17 LLMs in generation, critique, and correction reasoning, i.e., GQC reasoning. Our findings reveal: (1) a linear relationship in GQC capabilities, with critique-focused training markedly enhancing performance; (2) a task-dependent variation in correction effectiveness, with logic-oriented tasks being more amenable to correction; (3) GQC knowledge inconsistencies that decrease as model size increases; and (4) an intriguing inter-model critiquing dynamic, where stronger models are better at critiquing weaker ones, while weaker models can surprisingly surpass stronger ones in their self-critique. We hope these insights into the nuanced critique-correct reasoning of LLMs will foster further research in LLM critique and self-improvement.

Recently, while large language models (LLMs) have demonstrated impressive results, they still suffer from hallucination, i.e., the generation of false information. Model editing is the task of fixing factual mistakes in LLMs; yet, most previous works treat it as a one-time task, paying little attention to ever-emerging mistakes generated by LLMs. We address the task of sequential model editing (SME) that aims to rectify mistakes continuously. A Dynamic Auxiliary Fusion Network (DAFNet) is designed to enhance the semantic interaction among the factual knowledge within the entire sequence, preventing catastrophic forgetting during the editing process of multiple knowledge triples.Specifically, (1) for semantic fusion within a relation triple, we aggregate the intra-editing attention flow into auto-regressive self-attention with token-level granularity in LLMs. We further leverage multi-layer diagonal inter-editing attention flow to update the weighted representations of the entire sequence-level granularity. (2) Considering that auxiliary parameters are required to store the knowledge for sequential editing, we construct a new dataset named DAFSet, fulfilling recent, popular, long-tail and robust properties to enhance the generality of sequential editing. Experiments show DAFNet significantly outperforms strong baselines in single-turn and sequential editing. The usage of DAFSet also consistently improves the performance of other auxiliary network-based methods in various scenarios.

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Controllable Text Summarization: Unraveling Challenges, Approaches, and Prospects - A Survey
Ashok Urlana | Pruthwik Mishra | Tathagato Roy | Rahul Mishra

Generic text summarization approaches often fail to address the specific intent and needs of individual users. Recently, scholarly attention has turned to the development of summarization methods that are more closely tailored and controlled to align with specific objectives and user needs. Despite a growing corpus of controllable summarization research, there is no comprehensive survey available that thoroughly explores the diverse controllable attributes employed in this context, delves into the associated challenges, and investigates the existing solutions. In this survey, we formalize the Controllable Text Summarization (CTS) task, categorize controllable attributes according to their shared characteristics and objectives, and present a thorough examination of existing datasets and methods within each category. Moreover, based on our findings, we uncover limitations and research gaps, while also exploring potential solutions and future directions for CTS. We release our detailed analysis of CTS papers at https://github.com/ashokurlana/controllable_text_summarization_survey.

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Benchmarking Large Language Models on Communicative Medical Coaching: A Dataset and a Novel System
Hengguan Huang | Songtao Wang | Hongfu Liu | Hao Wang | Ye Wang

Traditional applications of natural language processing (NLP) in healthcare have predominantly focused on patient-centered services, enhancing patient interactions and care delivery, such as through medical dialogue systems. However, the potential of NLP to benefit inexperienced doctors, particularly in areas such as communicative medical coaching, remains largely unexplored. We introduce “ChatCoach”, a human-AI cooperative framework designed to assist medical learners in practicing their communication skills during patient consultations. ChatCoach differentiates itself from conventional dialogue systems by offering a simulated environment where medical learners can practice dialogues with a patient agent, while a coach agent provides immediate, structured feedback. This is facilitated by our proposed Generalized Chain-of-Thought (GCoT) approach, which fosters the generation of structured feedback and enhances the utilization of external knowledge sources. Additionally, we have developed a dataset specifically for evaluating Large Language Models (LLMs) within the ChatCoach framework on communicative medical coaching tasks. Our empirical results validate the effectiveness of ChatCoach.

This paper introduce a novel thought prompting approach called ”Everything of Thoughts” (XoT) for Large Language Models (LLMs) to defy the law of ”Penrose triangle” of existing thought paradigms, to achieve three key perspectives in thought generation simultaneously: performance, efficiency, and flexibility. XoT leverages pretrained reinforcement learning and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge and planning capability into thoughts, thereby enhancing LLMs’ decision-making capabilities. Through the MCTS-LLM collaborative thought revision framework, XoT autonomously produces high-quality comprehensive cognitive mappings with minimal LLM interactions. Additionally, XoT empowers LLMs to utilize flexible cognitive mappings for solving problems with multiple solutions.We evaluate XoT on several challenging problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our results demonstrate that XoT significantly outperforms existing approaches in various dimensions, showcasing its remarkable proficiency in addressing complex problems across diverse domains. The data and code are available at https://github.com/microsoft/Everything-of-Thoughts-XoT.

We introduce SPAGHETTI: Semantic Parsing Augmented Generation for Hybrid English information from Text Tables and Infoboxes, a hybrid question-answering (QA) pipeline that utilizes information from heterogeneous knowledge sources, including knowledge base, text, tables, and infoboxes. Our LLM-augmented approach achieves state-of-the-art performance on the Compmix dataset, the most comprehensive heterogeneous open-domain QA dataset, with 56.5% exact match (EM) rate. More importantly, manual analysis on a sample of the dataset suggests that SPAGHETTI is more than 90% accurate, indicating that EM is no longer suitable for assessing the capabilities of QA systems today.

In the rapidly evolving field of large language models (LLMs), data augmentation (DA) has emerged as a pivotal technique for enhancing model performance by diversifying training examples without the need for additional data collection. This survey explores the transformative impact of LLMs on DA, particularly addressing the unique challenges and opportunities they present in the context of natural language processing (NLP) and beyond. From both data and learning perspectives, we examine various strategies that utilize LLMs for data augmentation, including a novel exploration of learning paradigms where LLM-generated data is used for diverse forms of further training. Additionally, this paper highlights the primary open challenges faced in this domain, ranging from controllable data augmentation to multi-modal data augmentation. This survey highlights a paradigm shift introduced by LLMs in DA, and aims to serve as a comprehensive guide for researchers and practitioners.

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k-SemStamp: A Clustering-Based Semantic Watermark for Detection of Machine-Generated Text
Abe Hou | Jingyu Zhang | Yichen Wang | Daniel Khashabi | Tianxing He

Recent watermarked generation algorithms inject detectable signatures during language generation to facilitate post-hoc detection. While token-level watermarks are vulnerable to paraphrase attacks, SemStamp (Hou et al., 2023) applies watermark on the semantic representation of sentences and demonstrates promising robustness. SemStamp employs locality-sensitive hashing (LSH) to partition the semantic space with arbitrary hyperplanes, which results in a suboptimal tradeoff between robustness and speed. We propose k-SemStamp, a simple yet effective enhancement of SemStamp, utilizing k-means clustering as an alternative of LSH to partition the embedding space with awareness of inherent semantic structure. Experimental results indicate that k-SemStamp saliently improves its robustness and sampling efficiency while preserving the generation quality, advancing a more effective tool for machine-generated text detection.

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ColorSwap: A Color and Word Order Dataset for Multimodal Evaluation
Jirayu Burapacheep | Ishan Gaur | Agam Bhatia | Tristan Thrush

This paper introduces the ColorSwap dataset, designed to assess and improve the proficiency of multimodal models in matching objects with their colors. The dataset is comprised of 2,000 unique image-caption pairs, grouped into 1,000 examples. Each example includes a caption-image pair, along with a “color-swapped” pair. We follow the Winoground schema: the two captions in an example have the same words, but the color words have been rearranged to modify different objects. The dataset was created through a novel blend of automated caption and image generation with humans in the loop. We evaluate image-text matching (ITM) and visual language models (VLMs) and find that even the latest ones are still not robust at this task. GPT-4V and LLaVA score 72% and 42% on our main VLM metric, although they may improve with more advanced prompting techniques. On the main ITM metric, contrastive models such as CLIP and SigLIP perform close to chance (at 12% and 30%, respectively), although the non-contrastive BLIP ITM model is stronger (87%). We also find that finetuning on fewer than 2,000 examples yields significant performance gains on this out-of-distribution word-order understanding task.

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Revisiting OPRO: The Limitations of Small-Scale LLMs as Optimizers
Tuo Zhang | Jinyue Yuan | Salman Avestimehr

Numerous recent works aim to enhance the efficacy of Large Language Models (LLMs) through strategic prompting. In particular, the Optimization by PROmpting (OPRO) approach provides state-of-the-art performance by leveraging LLMs as optimizers where the optimization task is to find instructions that maximize the task accuracy. In this paper, we revisit OPRO for automated prompting with relatively small-scale LLMs, such as LLaMa-2 family and Mistral 7B. Our investigation reveals that OPRO shows limited effectiveness in small-scale LLMs, with limited inference capabilities constraining optimization ability. We suggest future automatic prompting engineering to consider both model capabilities and computational costs. Additionally, for small-scale LLMs, we recommend direct instructions that clearly outline objectives and methodologies as robust prompt baselines, ensuring efficient and effective prompt engineering in ongoing research.

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CeeBERT: Cross-Domain Inference in Early Exit BERT
Divya Jyoti Bajpai | Manjesh Hanawal

Pre-trained Language Models (PLMs), like BERT, with self-supervision objectives exhibit remarkable performance and generalization across various tasks. However, they suffer in inference latency due to their large size. To address this issue, side branches are attached at intermediate layers, enabling early inference of samples without requiring them to pass through all layers. However, the challenge is to decide which layer to infer and exit each sample so that the accuracy and latency are balanced. Moreover, the distribution of the samples to be inferred may differ from that used for training necessitating cross-domain adaptation. We propose an online learning algorithm named Cross-Domain Inference in Early Exit BERT (CeeBERT) that dynamically determines early exits of samples based on the level of confidence at each exit point. CeeBERT learns optimal thresholds from domain-specific confidence observed at intermediate layers on the fly, eliminating the need for labeled data. Experimental results on five distinct datasets with BERT and ALBERT models demonstrate CeeBERT’s ability to improve latency by reducing unnecessary computations with minimal drop in performance. By adapting to the threshold values, CeeBERT can speed up the BERT/ALBERT models by 2× - 3.1× with minimal drop in accuracy. The anonymized source code is available at https://github.com/Div290/CeeBERT.

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UNIWIZ: A Unified Large Language Model Orchestrated Wizard for Safe Knowledge Grounded Conversations
Souvik Das | Rohini Srihari

Large Language Models (LLMs) have made significant progress in integrating safety and knowledge alignment. However, adversarial actors can manipulate these models into generating unsafe responses, and excessive safety alignment can lead to unintended hallucinations. To address these challenges, we introduce UniWiz, a novel 2-step data orchestration framework that unifies safety and knowledge data generation. We propose a “safety-priming” method to generate synthetic safety data and overcome safety bottlenecks. We also inject relevant knowledge into conversations by retrieving factual information from curated sources. UniWiz dataset consists of 17,638 quality-controlled conversations and 10,000 augmented preference data. Pretrained models fine-tuned on UniWiz show improvements across various metrics and outperform state-of-the-art instruction-tuned models trained on much larger datasets.

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A Shocking Amount of the Web is Machine Translated: Insights from Multi-Way Parallelism
Brian Thompson | Mehak Dhaliwal | Peter Frisch | Tobias Domhan | Marcello Federico

We show that content on the web is often translated into many languages, and the low quality of these multi-way translations indicates they were likely created using Machine Translation (MT). Multi-way parallel, machine generated content not only dominates the translations in lower resource languages; it also constitutes a large fraction of the total web content in those languages. We also find evidence of a selection bias in the type of content which is translated into many languages, consistent with low quality English content being translated en masse into many lower resource languages, via MT. Our work raises serious concerns about training models such as multilingual large language models on both monolingual and bilingual data scraped from the web.

Traditionally, developing new language models (LMs) capable of addressing multiple tasks involves fine-tuning pre-trained LMs using a wide collection of datasets, a process that often incurs significant computational expenses. Model merging emerges as a cost-effective alternative, allowing the integration of existing models fine-tuned on different tasks into a single model that performs well across all tasks, eliminating the need for additional training. In this paper, we propose RankMean, an algorithm for merging fine-tuned LMs without requiring any downstream data. RankMean determines merging coefficients based on the relative rankings of weight change magnitudes and applies these coefficients for module-wise integration of various fine-tuned models. Our experimental results demonstrate that RankMean outperforms existing baseline methods on multiple benchmarks. The code is available at https://github.com/VITA-Group/RankMean.

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VALOR-EVAL: Holistic Coverage and Faithfulness Evaluation of Large Vision-Language Models
Haoyi Qiu | Wenbo Hu | Zi-Yi Dou | Nanyun Peng

Large Vision-Language Models (LVLMs) suffer from hallucination issues, wherein the models generate plausible-sounding but factually incorrect outputs, undermining their reliability. A comprehensive quantitative evaluation is necessary to identify and understand the extent of hallucinations in these models. However, existing benchmarks are often limited in scope, focusing mainly on object hallucinations. Furthermore, current evaluation methods struggle to effectively address the subtle semantic distinctions between model outputs and reference data, as well as the balance between hallucination and informativeness. To address these issues, we introduce a multi-dimensional benchmark covering objects, attributes, and relations, with challenging images selected based on associative biases. Moreover, we propose a large language model (LLM)-based two-stage evaluation framework that generalizes the popular CHAIR metric and incorporates both faithfulness and coverage into the evaluation. Experiments on 10 established LVLMs demonstrate that our evaluation metric is more comprehensive and better correlated with humans than existing work when evaluating on our challenging human-annotated benchmark dataset. Our work also highlights the critical balance between faithfulness and coverage of model outputs, and encourages future works to address hallucinations in LVLMs while keeping their outputs informative.

Owing to the scarcity of labeled training data, Spoken Language Understanding (SLU) is still a challenging task in low-resource languages. Therefore, zero-shot cross-lingual SLU attracts more and more attention. Contrastive learning is widely applied to explicitly align representations of similar sentences across different languages. However, the vanilla contrastive learning method may face two problems in zero-shot cross-lingual SLU: (1) the consistency between different languages is neglected; (2) each utterance has two different kinds of SLU labels, i.e. slot and intent, the utterances with one different label are also pushed away without any discrimination, which limits the performance. In this paper, we propose Cyclical Contrastive Learning based on Geodesic (CCLG), which introduces cyclical contrastive learning to achieve the consistency between different languages and leverages geodesic to measure the similarity to construct the positive pairs and negative pairs. Experimental results demonstrate that our proposed framework achieves the new state-of-the-art performance on MultiATIS++ and MTOP datasets, and the model analysis further verifies that CCLG can effectively transfer knowledge between different languages.

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Towards Safer Large Language Models through Machine Unlearning
Zheyuan Liu | Guangyao Dou | Zhaoxuan Tan | Yijun Tian | Meng Jiang

The rapid advancement of Large Language Models (LLMs) has demonstrated their vast potential across various domains, attributed to their extensive pretraining knowledge and exceptional generalizability. However, LLMs often encounter challenges in generating harmful content when faced with problematic prompts. To address this problem, existing work attempted to implement a gradient ascent based approach to prevent LLMs from producing harmful output. While these methods can be effective, they frequently impact the model utility in responding to normal prompts. To address this gap, we introduce Selective Knowledge negation Unlearning (SKU), a novel unlearning framework for LLMs, designed to eliminate harmful knowledge while preserving utility on normal prompts. Specifically, SKU is consisted of two stages: harmful knowledge acquisition stage and knowledge negation stage. The first stage aims to identify and acquire harmful knowledge within the model, whereas the second is dedicated to remove this knowledge. SKU selectively isolates and removes harmful knowledge in model parameters, ensuring the model’s performance remains robust on normal prompts. Our experiments conducted across various LLM architectures demonstrate that SKU identifies a good balance point between removing harmful information and preserving utility.

Chain of Thought (CoT) is significant in improving the reasoning abilities of large language models (LLMs). However, the correlation between the effectiveness of CoT and the length of reasoning steps in prompts remains largely unknown. To shed light on this, we have conducted several empirical experiments to explore the relations. Specifically, we design experiments that expand and compress the rationale reasoning steps within CoT demonstrations, while keeping all other factors constant. We have the following key findings. First, the results indicate that lengthening the reasoning steps in prompts, even without adding new information into the prompt, considerably enhances LLMs’ reasoning abilities across multiple datasets. Alternatively, shortening the reasoning steps, even while preserving the key information, significantly diminishes the reasoning abilities of models. This finding highlights the importance of the number of steps in CoT prompts and provides practical guidance to make better use of LLMs’ potential in complex problem-solving scenarios. Second, we also investigated the relationship between the performance of CoT and the rationales used in demonstrations. Surprisingly, the result shows that even incorrect rationales can yield favorable outcomes if they maintain the requisite length of inference. Third, we observed that the advantages of increasing reasoning steps are task-dependent: simpler tasks require fewer steps, whereas complex tasks gain significantly from longer inference sequences.

While task-agnostic debiasing provides notable generalizability and reduced reliance on downstream data, its impact on language modeling ability and the risk of relearning social biases from downstream task-specific data remain as the two most significant challenges when debiasing Pretrained Language Models (PLMs). The impact on language modeling ability can be alleviated given a high-quality and long-contextualized debiasing corpus, but there remains a deficiency in understanding the specifics of relearning biases. We empirically ascertain that the effectiveness of task-agnostic debiasing hinges on the quantitative bias level of both the task-specific data used for downstream applications and the debiased model. We empirically show that the lower bound of the bias level of the downstream fine-tuned model can be approximated by the bias level of the debiased model, in most practical cases. To gain more in-depth understanding about how the parameters of PLMs change during fine-tuning due to the forgetting issue of PLMs, we propose a novel framework which can Propagate Socially-fair Debiasing to Downstream Fine-tuning, ProSocialTuning. Our proposed framework can push the fine-tuned model to approach the bias lower bound during downstream fine-tuning, indicating that the ineffectiveness of debiasing can be alleviated by overcoming the forgetting issue through regularizing successfully debiased attention heads based on the PLMs’ bias levels from stages of pretraining and debiasing.

A summary structure is inherent to certain types of texts according to the Genre Theory of Linguistics. Such structures aid readers in efficiently locating information within summaries. However, most existing automatic summarization methods overlook the importance of summary structure, resulting in summaries that emphasize the most prominent information while omitting essential details from other sections. While a few summarizers recognize the importance of summary structure, they rely heavily on the predefined labels of summary structures in the source document and ground truth summaries. To address these shortcomings, we developed a Structured Knowledge-Guided Summarization (SKGSum) and its variant, SKGSum-W, which do not require structure labels. Instead, these methods rely on a set of automatically extracted summary points to generate summaries. We evaluate the proposed methods using three real-world datasets. The results indicate that our methods not only improve the quality of summaries, in terms of ROUGE and BERTScore, but also broaden the types of documents that can be effectively summarized.

Spoken Named Entity Recognition (NER) aims to extract entities from speech. The extracted entities can help voice assistants better understand user’s questions and instructions. However, current Chinese Spoken NER datasets are laboratory-controlled data that are collected by reading existing texts in quiet environments, rather than natural spoken data, and the texts used for reading are also limited in topics. These limitations obstruct the development of Spoken NER in more natural and common real-world scenarios. To address this gap, we introduce a real-world Chinese Spoken NER dataset (RWCS-NER), encompassing open-domain daily conversations and task-oriented intelligent cockpit instructions. We compare several mainstream pipeline approaches on RWCS-NER. The results indicate that the current methods, affected by Automatic Speech Recognition (ASR) errors, do not perform satisfactorily in real settings. Aiming to enhance Spoken NER in real-world scenarios, we propose two approaches: self-training-asr and mapping then distilling (MDistilling). Experiments show that both approaches can achieve significant improvements, particularly MDistilling. Even compared with GPT4.0, MDistilling still reaches better results. We believe that our work will advance the field of Spoken NER in real-world settings.

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DEBATE: Devil’s Advocate-Based Assessment and Text Evaluation
Alex Kim | Keonwoo Kim | Sangwon Yoon

As natural language generation (NLG) models have become prevalent, systematically assessing the quality of machine-generated texts has become increasingly important. Recent studies introduce LLM-based evaluators that operate as reference-free metrics, demonstrating their capability to adeptly handle novel tasks. However, these models generally rely on a single-agent approach, which, we argue, introduces an inherent limit to their performance. This is because there exist biases in LLM agent’s responses, including preferences for certain text structure or content. In this work, we propose DEBATE, an NLG evaluation framework based on multi-agent scoring system augmented with a concept of Devil’s Advocate. Within the framework, one agent is instructed to criticize other agents’ arguments, potentially resolving the bias in LLM agent’s answers. DEBATE substantially outperforms the previous state-of-the-art methods in two meta-evaluation benchmarks in NLG evaluation, SummEval and TopicalChat. We also show that the extensiveness of debates among agents and the persona of an agent can influence the performance of evaluators.

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Can Large Multimodal Models Uncover Deep Semantics Behind Images?
Yixin Yang | Zheng Li | Qingxiu Dong | Heming Xia | Zhifang Sui

Understanding the deep semantics of images is essential in the era dominated by social media. However, current research works primarily on the superficial description of images, revealing a notable deficiency in the systematic investigation of the inherent deep semantics. In this work, we introduce DEEPEVAL, a comprehensive benchmark to assess Large Multimodal Models’ (LMMs) capacities of visual deep semantics. DEEPEVAL includes human-annotated dataset and three progressive subtasks: fine-grained description selection, in-depth title matching, and deep semantics understanding. Utilizing DEEPEVAL, we evaluate 9 open-source LMMs and GPT-4V(ision). Our evaluation demonstrates a substantial gap between the deep semantic comprehension capabilities of existing LMMs and humans. For example, GPT-4V is 30% behind humans in understanding deep semantics, even though it achieves human-comparable performance in image description. Further analysis reveals that LMM performance on DEEPEVAL varies according to the specific facets of deep semantics explored, indicating the fundamental challenges remaining in developing LMMs.

Document-level event extraction aims to extract structured event information from unstructured text. However, a single document often contains limited event information and the roles of different event arguments may be biased due to the influence of the information source.This paper addresses the limitations of traditional document-level event extraction by proposing the task of cross-document event extraction (CDEE) to integrate event information from multiple documents and provide a comprehensive perspective on events. We construct a novel cross-document event extraction dataset, namely CLES, which contains 20,059 documents and 37,688 mention-level events, where over 70% of them are cross-document. To address the task, we propose a CDEE pipeline that includes 5 steps, namely event extraction, coreference resolution, entity normalization, role normalization and entity-role resolution. Our CDEE pipeline achieves about 72% F1 in end-to-end cross-document event extraction, suggesting the challenge of this task and setting up a benchmark for future research. Our work builds a new line of information extraction research and will attract new research attention.

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A Graph per Persona: Reasoning about Subjective Natural Language Descriptions
EunJeong Hwang | Vered Shwartz | Dan Gutfreund | Veronika Thost

Reasoning about subjective natural language descriptions, such as opinions and preferences, is a challenging topic that largely remains unsolved to date. In particular, state-of-the-art large language models (LLMs) perform disappointingly in this task, show strong biases, and do not meet the interpretability requirements often needed in these kinds of applications. We propose a novel approach for reasoning about subjective knowledge that integrates potential and implicit meanings and explicitly models the relational nature of the information. We apply supervised graph learning, offer explanations for the model’s reasoning, and show that our model performs well across all 15 topics of OpinionQA, outperforming several prominent LLMs. Our detailed analysis further shows its unique advantages and the complementary nature it offers in comparison to LLMs.

Molecular Relational Learning (MRL), aiming to understand interactions between molecular pairs, plays a pivotal role in advancing biochemical research. Recently, the adoption of large language models (LLMs), known for their vast knowledge repositories and advanced logical inference capabilities, has emerged as a promising way for efficient and effective MRL. Despite their potential, these methods predominantly rely on textual data, thus not fully harnessing the wealth of structural information inherent in molecular graphs. Moreover, the absence of a unified framework exacerbates the issue of insufficient data exploitation, as it hinders the sharing of interaction mechanism learned across various datasets. To address these challenges, this work proposes a novel LLM-based multi-modal framework for molecular interaction modeling following Chain-of-Thought (CoT) theory, termed MolTC, which effectively integrate graphical information of two molecules in pair. To train this integrated framework efficiently, we introduce a *multi-hierarchical CoT theory* to refine its training paradigm, and conduct a comprehensive *Molecular Interactive Instructions* dataset for the development of biochemical LLMs involving MRL.Our experiments,conducted across various datasets involving over 4,000,000 molecular pairs, exhibit the superiority of our method over current GNN and LLM-based baselines. Code is available at https://github.com/MangoKiller/MolTC.

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KPEval: Towards Fine-Grained Semantic-Based Keyphrase Evaluation
Di Wu | Da Yin | Kai-Wei Chang

Despite the significant advancements in keyphrase extraction and keyphrase generation methods, the predominant approach for evaluation mainly relies on exact matching with human references. This scheme fails to recognize systems that generate keyphrases semantically equivalent to the references or diverse keyphrases that carry practical utility. To better assess the capability of keyphrase systems, we propose KPEval, a comprehensive evaluation framework consisting of four critical aspects: reference agreement, faithfulness, diversity, and utility. For each aspect, we design semantic-based metrics to reflect the evaluation objectives. Meta-evaluation studies demonstrate that our evaluation strategy correlates better with human preferences compared to a range of previously proposed metrics. Using KPEval, we re-evaluate 23 keyphrase systems and discover that (1) established model comparison results have blind-spots especially when considering reference-free evaluation; (2) large language models are underestimated by prior evaluation works; and (3) there is no single best model that can excel in all the aspects.

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Learning Low-dimensional Multi-domain Knowledge Graph Embedding via Dual Archimedean Spirals
Jiang Li | Xiangdong Su | Fujun Zhang | Guanglai Gao

Knowledge graph embedding (KGE) is extensively employed for link prediction by representing entities and relations as low-dimensional vectors. In real-world scenarios, knowledge graphs (KGs) usually encompass diverse domains, which poses challenges to KG representations. However, existing KGE methods rarely make domain constraints on the embedding distribution of multi-domain KGs, leading to the embedding overlapping of different domains and performance degradation of link prediction. To address this challenge, we propose Dual Archimedean Spiral Knowledge Graph Embedding (DuASE), a low-dimensional KGE model for multi-domain KGs. DuASE is inspired by our discovery that relation types can distinguish entities from different domains. Specifically, DuASE encodes entities with the same relation on the same Archimedean spiral, allowing it to differentiate the entities from different domains. To avoid embedding overlapping across domains, DuASE further makes the head and the tail spirals in the same triplet cluster to their respective domain space by a regularization function. Thus, DuASE can better capture the domain information and the dependencies between entities when modeling the multi-domain KGs, leading to improved KG representations. We validate the effectiveness of DuASE on the novel multi-domain dataset (n-MDKG) introduced in this study and three other benchmark datasets.

With the remarkable capabilities, large language models (LLMs) have emergedas essential elements in numerous NLP applications, while parameter-efficientfinetuning, especially LoRA, has gained popularity as a lightweight approachfor model customization. Meanwhile, various dropout methods, initially designedfor full finetuning with all the parameters updated, alleviates overfittingassociated with excessive parameter redundancy. Hence, a possible contradictionarises from negligible trainable parameters of LoRA and the effectiveness ofprevious dropout methods, which has been largely overlooked. To fill this gap,we first confirm that parameter-efficient LoRA is also overfitting-prone. Wethen revisit transformer-specific dropout methods, and establish theirequivalence and distinctions mathematically and empirically. Building upon thiscomparative analysis, we introduce a unified framework for a comprehensiveinvestigation, which instantiates these methods based on dropping position,structural pattern and compensation measure. Through this framework, we revealthe new preferences and performance comparisons of them when involved withlimited trainable parameters. This framework also allows us to amalgamate themost favorable aspects into a novel dropout method named HiddenKey. Extensiveexperiments verify the remarkable superiority and sufficiency of HiddenKeyacross multiple models and tasks, which highlights it as the preferred approachfor high-performance and parameter-efficient finetuning of LLMs.

Large Language Model (LLM)-based approach has become the mainstream for Text-to-SQL task and achieves remarkable performance. In this paper, we augment the existing prompt engineering methods by exploiting the database content and execution feedback. Specifically, we introduce DART-SQL, which comprises two key components: (1) Question Rewriting: DART-SQL rewrites natural language questions by leveraging database content information to eliminate ambiguity. (2) Execution-Guided Refinement: DART-SQL incorporates database content information and utilizes the execution results of the generated SQL to iteratively refine the SQL. We apply this framework to the two LLM-based approaches (DAIL-SQL and C3) and test it on four widely used benchmarks (Spider-dev, Spider-test, Realistic and DK). Experiments show that our framework for DAIL-SQL and C3 achieves an average improvement of 12.41% and 5.38%, respectively, in terms of execution accuracy(EX) metric.

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The Knowledge Alignment Problem: Bridging Human and External Knowledge for Large Language Models
Shuo Zhang | Liangming Pan | Junzhou Zhao | William Yang Wang

Large language models often necessitate grounding on external knowledge to generate faithful and reliable answers. Yet even with the correct groundings in the reference, they can ignore them and rely on wrong groundings or their inherent biases to hallucinate when users, being largely unaware of the specifics of the stored information, pose questions that might not directly correlate with the retrieved groundings. In this work, we formulate this knowledge alignment problem and introduce MixAlign, a framework that interacts with both the human user and the knowledge base to obtain and integrate clarifications on how the user question relates to the stored information. MixAlign employs a language model to achieve automatic knowledge alignment and, if necessary, further enhances this alignment through human user clarifications. Experimental results highlight the crucial role of knowledge alignment in boosting model performance and mitigating hallucination, with improvements noted up to 22.2% and 27.1% respectively. We also demonstrate the effectiveness of MixAlign in improving knowledge alignment by producing high-quality, user-centered clarifications.

Knowledge Base Question Answering (KBQA) aims to answer natural language questions over large-scale knowledge bases (KBs), which can be summarized into two crucial steps: knowledge retrieval and semantic parsing. However, three core challenges remain: inefficient knowledge retrieval, mistakes of retrieval adversely impacting semantic parsing, and the complexity of previous KBQA methods. To tackle these challenges, we introduce ChatKBQA, a novel and simple generate-then-retrieve KBQA framework, which proposes first generating the logical form with fine-tuned LLMs, then retrieving and replacing entities and relations with an unsupervised retrieval method, to improve both generation and retrieval more directly. Experimental results show that ChatKBQA achieves new state-of-the-art performance on standard KBQA datasets, WebQSP, and CWQ. This work can also be regarded as a new paradigm for combining LLMs with knowledge graphs (KGs) for interpretable and knowledge-required question answering.

With promising yet saturated results in high-resource settings, low-resource datasets have gradually become crucial benchmarks (e.g., BigBench Hard, superGLUE) for evaluating the learning ability of advanced neural networks. In this work, we find that there exists a set of “hard examples” in low-resource settings that challenge neural networks but are not well evaluated, which causes over-estimated performance. We first give a theoretical analysis on which factors bring the difficulty of low-resource learning. It then motivates us to propose a challenging benchmark Achilles-Bench to better evaluate the learning ability, which covers 11 datasets, including 8 natural language process (NLP) datasets and 3 computer vision (CV) datasets. Experiments on a wide range of models show that neural networks, even pre-trained language models, have sharp performance drops on our benchmark, demonstrating the effectiveness of evaluating the weaknesses of neural networks. On NLP tasks, we surprisingly find that despite better results on traditional low-resource benchmarks, pre-trained networks, does not show performance improvements on our benchmarks. there is still a large robustness gap between existing models and human-level performance, highlighting the need for robust low-resource learning models.

This paper introduces INTERVENOR (INTERactiVE chaiN Of Repair), a system designed to emulate the interactive code repair processes observed in humans, encompassing both code diagnosis and code repair. INTERVENOR prompts Large Language Models (LLMs) to play distinct roles during the code repair process, functioning as both a Code Learner and a Code Teacher. Specifically, the Code Learner is tasked with adhering to instructions to generate or repair code, while the Code Teacher is responsible for crafting a Chain-of-Repair (CoR) to serve as guidance for the Code Learner. During generating the CoR, the Code Teacher needs to check the generated codes from Code Learner and reassess how to address code bugs based on error feedback received from compilers. Experimental results demonstrate that INTERVENOR surpasses baseline models, exhibiting improvements of approximately 18% and 4.3% over GPT-3.5 in code generation and code translation tasks, respectively. Our further analyses show that CoR is effective to illuminate the reasons behind bugs and outline solution plans in natural language. With the feedback of code compilers, INTERVENOR can accurately identify syntax errors and assertion errors and provide precise instructions to repair codes. All data and codes are available at [https://github.com/NEUIR/INTERVENOR](https://github.com/NEUIR/INTERVENOR).

Large language models (LLMs) have advanced the development of various AI conversational agents, including role-playing agents that mimic diverse characters and human behaviors. While prior research has predominantly focused on enhancing the conversational capability, role-specific knowledge and style of these agents, there has been a noticeable gap in assessing their social intelligence. In this paper, we introduce SocialBench, the first benchmark designed to systematically evaluate the sociality of role-playing agents at both individual and group levels of social interactions. SocialBench is constructed from various sources and covers a wide range of 500 characters and over 6,000 question prompts and 30,800 multi-turn role-playing utterances. We conduct comprehensive evaluations on this benchmark using mainstream LLMs. We find that agents excelling in individual level does not imply their proficiency in group level. Experimental results on SocialBench confirm its significance as a testbed for assessing the social interaction of role-playing agents. The benchmark is publicly accessible at https://github.com/X-PLUG/RoleInteract.

Evaluating large language models (LLMs) is fundamental, particularly in the context of practical applications. Conventional evaluation methods, typically designed primarily for LLM development, yield numerical scores that ignore the user experience. Therefore, our study shifts the focus from model-centered to human-centered evaluation in the context of AI-powered writing assistance applications. Our proposed metric, termed “Revision Distance,” utilizes LLMs to suggest revision edits that mimic the human writing process. It is determined by counting the revision edits generated by LLMs. Benefiting from the generated revision edit details, our metric can provide a self-explained text evaluation result in a human-understandable manner beyond the context-independent score. Our results show that for the easy-writing task, “Revision Distance” is consistent with established metrics (ROUGE, Bert-score, and GPT-score), but offers more insightful, detailed feedback and better distinguishes between texts. Moreover, in the context of challenging academic writing tasks, our metric still delivers reliable evaluations where other metrics tend to struggle. Furthermore, our metric also holds significant potential for scenarios lacking reference texts.

Incomplete utterance rewriting (IUR) aims to reconstruct the utterance with omitted information and pronouns to be standalone and complete based on the context. The existing works predominantly focus on simple ellipsis and coreference problems in brief multi-turn dialogues. But in actual scenarios: 1) the context of the dialogues frequently comprises multiple similar candidates for ellipsis and coreference resolution, pouring to confuse. 2) the number of turns tends to be more extensive, while the content with various topics also grows more complex. This paper proposes a novel method called CaT to address these issues. In particular, we first devise a tacker model, distilled from GPT4-turbo, to adopt Context Tracking that dynamically updates a list of key phrases turn by turn, as accurate candidates for ellipsis and coreference resolution. Second, we further present the Dynamic Context Introduction mechanism to filter irrelevant preceding contexts that are not relied on by any element within the key phrase list to condense extended dialogues. Comprehensive experiments indicate that our solution provides a significant improvement over the existing baselines, and achieves state-of-the-art on three benchmarks.

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EmotionQueen: A Benchmark for Evaluating Empathy of Large Language Models
Yuyan Chen | Songzhou Yan | Sijia Liu | Yueze Li | Yanghua Xiao

Emotional intelligence in large language models (LLMs) is of great importance in Natural Language Processing. However, the previous research mainly focus on basic sentiment analysis tasks, such as emotion recognition, which is not enough to evaluate LLMs’ overall emotional intelligence. Therefore, this paper presents a novel framework named EmotionQueen for evaluating the emotional intelligence of LLMs. The framework includes four distinctive tasks: Key Event Recognition, Mixed Event Recognition, Implicit Emotional Recognition, and Intention Recognition. LLMs are requested to recognize important event or implicit emotions and generate empathetic response.We also design two metrics to evaluate LLMs’ capabilities in recognition and response for emotion-related statements. Experiments yield significant conclusions about LLMs’ capabilities and limitations in emotion intelligence.

Since the emergence of large language models, prompt learning has become a popular method for optimizing and customizing these models. Special prompts, such as Chain-of-Thought, have even revealed previously unknown reasoning capabilities within these models. However, the progress of discovering effective prompts has been slow, driving a desire for general prompt optimization methods. Unfortunately, few existing prompt learning methods satisfy the criteria of being truly “general”, i.e., automatic, discrete, black-box, gradient-free, and interpretable all at once. In this paper, we introduce metaheuristics, a branch of discrete non-convex optimization methods with over 100 options, as a promising approach to prompt learning. Within our paradigm, we test six typical methods: hill climbing, simulated annealing, genetic algorithms with/without crossover, tabu search, and harmony search, demonstrating their effectiveness in white-box and black-box prompt learning. Furthermore, we show that these methods can be used to discover more human-understandable prompts that were previously unknown in both reasoning and image generation tasks, opening the door to a cornucopia of possibilities in prompt optimization.

In the era of social media video platforms, popular “hot-comments” play a crucial role in attracting user impressions of short-form videos, making them vital for marketing and branding purpose. However, existing research predominantly focuses on generating descriptive comments or “danmaku” in English, offering immediate reactions to specific video moments. Addressing this gap, our study introduces HOTVCOM, the largest Chinese video hot-comment dataset, comprising 94k diverse videos and 137 million comments. We also present the ComHeat framework, which synergistically integrates visual, auditory, and textual data to generate influential hot-comments on the Chinese video dataset. Empirical evaluations highlight the effectiveness of our framework, demonstrating its excellence on both the newly constructed and existing datasets.

The evaluation of the problem-solving capability under incomplete information scenarios of Large Language Models (LLMs) is increasingly important, encompassing capabilities such as questioning, knowledge search, error detection, and path planning. Current research mainly focus on LLMs’ problem-solving capability such as “Twenty Questions”.However, these kinds of games do not require recognizing misleading cues which are necessary in the incomplete information scenario.Moreover, the existing game such as “Who is undercover” are highly subjective, making it challenging for evaluation.Therefore, in this paper, we introduce a novel game named BrainKing based on the “Who is undercover” and “Twenty Questions” for evaluating LLM capabilities under incomplete information scenarios. It requires LLMs to identify target entities with limited yes-or-no questions and potential misleading answers. By setting up easy, medium, and hard difficulty modes, we comprehensively assess the performance of LLMs across various aspects. Our results reveal the capabilities and limitations of LLMs in BrainKing, providing significant insights of LLM problem-solving levels.

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Distilling Robustness into Natural Language Inference Models with Domain-Targeted Augmentation
Joe Stacey | Marek Rei

Knowledge distillation optimises a smaller student model to behave similarly to a larger teacher model, retaining some of the performance benefits. While this method can improve results on in-distribution examples, it does not necessarily generalise to out-of-distribution (OOD) settings. We investigate two complementary methods for improving the robustness of the resulting student models on OOD domains. The first approach augments the distillation with generated unlabeled examples that match the target distribution. The second method upsamples data points among the training set that are similar to the target distribution. When applied on the task of natural language inference (NLI), our experiments on MNLI show that distillation with these modifications outperforms previous robustness solutions. We also find that these methods improve performance on OOD domains even beyond the target domain.

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Into the Unknown: Generating Geospatial Descriptions for New Environments
Tzuf Paz-Argaman | John Palowitch | Sayali Kulkarni | Reut Tsarfaty | Jason Baldridge

Similar to vision-and-language navigation (VLN) tasks that focus on bridging the gap between vision and language for embodied navigation, the new Rendezvous (RVS) task requires reasoning over allocentric spatial relationships using non-sequential navigation instructions and maps. However, performance substantially drops in new environments with no training data.Using opensource descriptions paired with coordinates (e.g., Wikipedia) provides training data but suffers from limited spatially-oriented text resulting in low geolocation resolution. We propose a large-scale augmentation method for generating high-quality synthetic data for new environments using readily available geospatial data. Our method constructs a grounded knowledge-graph, capturing entity relationships. Sampled entities and relations (“shop north of school”) generate navigation instructions via (i) generating numerous templates using context-free grammar (CFG) to embed specific entities and relations; (ii) feeding the entities and relation into a large language model (LLM) for instruction generation. A comprehensive evaluation on RVS, showed that our approach improves the 100-meter accuracy by 45.83% on unseen environments. Furthermore, we demonstrate that models trained with CFG-based augmentation achieve superior performance compared with those trained with LLM-based augmentation, both in unseen and seen environments. These findings suggest that the potential advantages of explicitly structuring spatial information for text-based geospatial reasoning in previously unknown, can unlock data-scarce scenarios.

Despite it being the cornerstone of BPE, the most common tokenization algorithm, the importance of compression in the tokenization process is still unclear. In this paper, we argue for the theoretical importance of compression, that can be viewed as 0-gram language modeling where equal probability is assigned to all tokens. We also demonstrate the empirical importance of compression for downstream success of pre-trained language models. We control the compression ability of several BPE tokenizers by varying the amount of documents available during their training: from 1 million documents to a character-based tokenizer equivalent to no training data at all. We then pre-train English language models based on those tokenizers and fine-tune them over several tasks. We show that there is a correlation between tokenizers’ compression and models’ downstream performance, suggesting that compression is a reliable intrinsic indicator of tokenization quality. These correlations are more pronounced for generation tasks (over classification) or for smaller models (over large ones). We replicated a representative part of our experiments on Turkish and found similar results, confirming that our results hold for languages with typological characteristics dissimilar to English. We conclude that building better compressing tokenizers is a fruitful avenue for further research and for improving overall model performance.

Byte Pair Encoding is an effective approach in machine translation across several languages. However, our analysis indicates that BPE is prone to over-segmentation in the morphologically rich language, Korean, which can erode word semantics and lead to semantic confusion during training. This semantic confusion, stemming from over-segmentation, ultimately contributes to a degradation of overall translation quality. To address this issue, we introduce Length-aware Subword Vocabulary Construction (LeVoC), a novel approach strategically incorporating longer words into the vocabulary. By utilizing an external monolingual Korean corpus, LeVoC extracts and integrates long words, effectively preserving morphological information and reducing semantic confusion. Our experiments demonstrate that LeVoC not only significantly outperforms BPE, but also can be applied to and surpass current state-of-the-art morpheme-aware subword tokenization methods. We provide evidence that the difficulty in translating sentences with long words in Korean is associated with morphological compositionality, and LeVoC’s ability to reduce semantic confusion during training leads to improved translation quality.

As instruction-tuned large language models (LLMs) gain global adoption, their ability to follow instructions in multiple languages becomes increasingly crucial. In this work, we investigate how multilinguality during instruction tuning of a multilingual LLM affects instruction-following across languages from the pre-training corpus. We first show that many languages transfer some instruction-following capabilities to other languages from even monolingual tuning. Furthermore, we find that only 40 multilingual examples integrated in an English tuning set substantially improve multilingual instruction-following, both in seen and unseen languages during tuning. In general, we observe that models tuned on multilingual mixtures exhibit comparable or superior performance in multiple languages compared to monolingually tuned models, despite training on 10x fewer examples in those languages. Finally, we find that diversifying the instruction tuning set with even just 2-4 languages significantly improves cross-lingual generalization. Our results suggest that building massively multilingual instruction-tuned models can be done with only a very small set of multilingual instruction-responses.

In this paper, we introduce a new embedding model called M3-Embedding, which is distinguished for its versatility in Multi-Linguality, Multi-Functionality, and Multi-Granularity. It provides a uniform support for the semantic retrieval of more than 100 working languages. It can simultaneously accomplish the three common retrieval functionalities: dense retrieval, multi-vector retrieval, and sparse retrieval. Besides, it is also capable of processing inputs of different granularities, spanning from short sentences to long documents of up to 8,192 tokens. The effective training of M3-Embedding presents a series of technical contributions. Notably, we propose a novel self-knowledge distillation approach, where the relevance scores from different retrieval functionalities can be integrated as the teacher signal to enhance the training quality. We also optimize the batching strategy, which enables a large batch size and high training throughput to improve the discriminativeness of embeddings. M3-Embedding exhibits a superior performance in our experiment, leading to new state-of-the-art results on multilingual, cross-lingual, and long-document retrieval benchmarks.

Large Language Models (LLMs) have shown remarkable progress in automated code generation. Yet, LLM-generated code may contain errors in API usage, class, data structure, or missing project-specific information. As much of this project-specific context cannot fit into the prompts of LLMs, we must find ways to allow the model to explore the project-level code context. We present CoCoGen, a new code generation approach that uses compiler feedback to improve the LLM-generated code. CoCoGen first leverages static analysis to identify mismatches between the generated code and the project’s context. It then iteratively aligns and fixes the identified errors using information extracted from the code repository. We integrate CoCoGen with two representative LLMs, i.e., GPT-3.5-Turbo and Code Llama (13B), and apply it to Python code generation. Experimental results show that CoCoGen significantly improves the vanilla LLMs by over 80% in generating code dependent on the project context and consistently outperforms the existing retrieval-based code generation baselines.

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An Element is Worth a Thousand Words: Enhancing Legal Case Retrieval by Incorporating Legal Elements
Chenlong Deng | Zhicheng Dou | Yujia Zhou | Peitian Zhang | Kelong Mao

Legal case retrieval plays an important role in promoting judicial justice and fairness. One of its greatest challenges is that the definition of relevance goes far beyond the common semantic relevance as in ad-hoc retrieval. In this paper, we reveal that the legal elements, which typically comprise key facts in a specialized legal context, can largely improve the relevance matching of legal case retrieval. To facilitate the use of legal elements, we construct a Chinese legal element dataset called LeCaRD-Elem based on the widely-used LeCaRD dataset, through a two-stage semi-automatic method with a minimized reliance on human labor. Meanwhile, we introduce two new models to enhance legal search using legal elements. The first, Elem4LCR-E, is a two-stage model that explicitly predicts legal elements from texts and then leverages them for improved ranking. Recognizing the potential benefits of more seamless integration, we further propose an end-to-end model called Elem4LCR-I, which internalizes the legal element knowledge into its model parameters using a tailored teacher-student training framework. Extensive experiments underscore the significant value of legal elements and demonstrate the superiority of our two proposed models in enhancing legal search over existing methods.

The growth of social media, characterized by its multimodal nature, has led to the emergence of diverse phenomena and challenges, which calls for an effective approach to uniformly solve automated tasks. The powerful Large Vision Language Models make it possible to handle a variety of tasks simultaneously, but even with carefully designed prompting methods, the general domain models often fall short in aligning with the unique speaking style and context of social media tasks. In this paper, we introduce a Large Vision Language Model for Social Media Processing (SoMeLVLM), which is a cognitive framework equipped with five key capabilities including knowledge & comprehension, application, analysis, evaluation, and creation. SoMeLVLM is designed to understand and generate realistic social media behavior. We have developed a 654k multimodal social media instruction-tuning dataset to support our cognitive framework and fine-tune our model. Our experiments demonstrate that SoMeLVLM achieves state-of-the-art performance in multiple social media tasks. Further analysis shows its significant advantages over baselines in terms of cognitive abilities.

The evolution of large language models (LLMs) has culminated in a multitask model paradigm where prompts drive the generation of user-specific outputs. However, this advancement has revealed a critical challenge: LLMs frequently produce outputs against socially acceptable commonsense standards in various scenarios. To address this gap in commonsense reasoning, we present KoCommonGEN v2, a fine-grained benchmark dataset focused on Korean commonsense reasoning. This dataset, enriched with human annotations, comprises multiple-choice questions across seven error categories. These categories include commonsense memorization, numerical commonsense, toxic speech, and more, which are vulnerable to undermining the reliability of LLMs’ commonsense reasoning capabilities. The empirical results present that LLMs struggle with Korean commonsense reasoning. With human accuracy benchmarked at approximately 85%, GPT-4’s performance lags at about 74%, and other LLMs demonstrate an average accuracy of around 42%. Our findings emphasize the need for targeted improvements in Korean commonsense reasoning within LLMs, paving the way for more socially and contextually sensitive AI models.

Transformer-based Language Models have become ubiquitous in Natural Language Processing (NLP) due to their impressive performance on various tasks. However, expensive training as well as inference remains a significant impediment to their widespread applicability. While enforcing sparsity at various levels of the model architecture has found promise in addressing scaling and efficiency issues, there remains a disconnect between how sparsity affects network topology. Inspired by brain neuronal networks, we explore sparsity approaches through the lens of network topology. Specifically, we exploit mechanisms seen in biological networks, such as preferential attachment and redundant synapse pruning, and show that principled, model-agnostic sparsity approaches are performant and efficient across diverse NLP tasks, spanning both classification (such as natural language inference) and generation (summarization, machine translation), despite our sole objective not being optimizing performance. NeuroPrune is competitive with (or sometimes superior to) baselines on performance and can be up to 10x faster in terms of training time for a given level of sparsity, simultaneously exhibiting measurable improvements in inference time in many cases.

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Ranking Large Language Models without Ground Truth
Amit Dhurandhar | Rahul Nair | Moninder Singh | Elizabeth Daly | Karthikeyan Natesan Ramamurthy

Evaluation and ranking of large language models (LLMs) has become an important problem with the proliferation of these models and their impact. Evaluation methods either require human responses which are expensive to acquire or use pairs of LLMs to evaluate each other which can be unreliable. In this paper, we provide a novel perspective where, given a dataset of prompts (viz. questions, instructions, etc.) and a set of LLMs, we rank them without access to any ground truth or reference responses. Inspired by real life where both an expert and a knowledgeable person can identify a novice our main idea is to consider triplets of models, where each one of them evaluates the other two, correctly identifying the worst model in the triplet with high probability. We also analyze our idea and provide sufficient conditions for it to succeed. Applying this idea repeatedly we propose two methods to rank LLMs. In experiments on different generative tasks (summarization, multiple-choice, and dialog), our methods reliably recover true rankings without reference data. This points to a viable low-resource mechanism for practical use.

The utilization of large language models for medical dialogue generation has attracted considerable attention due to its potential to enhance response richness and coherence. While previous studies have made strides in optimizing model performance, there is a pressing need to bolster the model’s capacity for diagnostic logic to ensure patient safety. In response to this need, we propose an approach termed preference learning from process feedback (PLPF), which involves integrating the doctor’s diagnostic logic into LLMs. PLPF encompasses three key components: rule modeling, preference data generation, and preference alignment. These components collectively serve to train the model to adhere to the diagnostic process. Our experimental results, utilizing Standardized Patient Testing, demonstrate that PLPF enhances the diagnostic accuracy of the baseline model in medical conversations by 17.6%, surpassing the performance of traditional approaches. Moreover, PLPF exhibits effectiveness in both multi-round and single-round dialogue tasks, thereby highlighting its potential in improving medical dialogue generation. Our dataset is available at https://github.com/Chengfeng-Dou/SpTesting.

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LM-Cocktail: Resilient Tuning of Language Models via Model Merging
Shitao Xiao | Zheng Liu | Peitian Zhang | Xingrun Xing

The pre-trained language models are continually fine-tuned to better support downstream applications. However, this operation may result in significant performance degeneration on general tasks beyond the targeted domain. To overcome this problem, we propose LM-Cocktail which enables the fine-tuned model to stay resilient in general perspectives. Our method is conducted in the form of model merging, where the fine-tuned language model is merged with the pre-trained base model or the peer models from other domains through weighted average. Despite simplicity, LM-Cocktail is surprisingly effective: the resulted model is able to achieve a strong empirical performance in the whole scope of general tasks while preserving a superior capacity in its targeted domain.

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Episodic Memory Retrieval from LLMs: A Neuromorphic Mechanism to Generate Commonsense Counterfactuals for Relation Extraction
Xin Miao | Yongqi Li | Shen Zhou | Tieyun Qian

Large language models (LLMs) have achieved satisfactory performance in counterfactual generation. However, confined by the stochastic generation process of LLMs, there often are misalignments between LLMs and humans which hinder LLMs from handling complex tasks like relation extraction. As a result, LLMs may generate commonsense-violated counterfactuals like ‘eggs were produced by a box’. To bridge this gap, we propose to mimick the episodic memory retrieval, the working mechanism of human hippocampus, to align LLMs’ generation process with that of humans. In this way, LLMs can derive experience from their extensive memory, which keeps in line with the way humans gain commonsense. We then implement two central functions in the hippocampus, i.e., pattern separation and pattern completion, to retrieve the episodic memory from LLMs and generate commonsense counterfactuals for relation extraction. Experimental results demonstrate the improvements of our framework over existing methods in terms of the quality of counterfactuals.

Exploring and quantifying semantic relatedness is central to representing language and holds significant implications across various NLP tasks. While earlier NLP research primarily focused on semantic similarity, often within the English language context, we instead investigate the broader phenomenon of semantic relatedness. In this paper, we present SemRel, a new semantic relatedness dataset collection annotated by native speakers across 13 languages: Afrikaans, Algerian Arabic, Amharic, English, Hausa, Hindi, Indonesian, Kinyarwanda, Marathi, Moroccan Arabic, Modern Standard Arabic, Spanish, and Telugu. These languages originate from five distinct language families and are predominantly spoken in Africa and Asia – regions characterised by a relatively limited availability of NLP resources. Each instance in the SemRel datasets is a sentence pair associated with a score that represents the degree of semantic textual relatedness between the two sentences. The scores are obtained using a comparative annotation framework. We describe the data collection and annotation processes, challenges when building the datasets, baseline experiments, and their impact and utility in NLP.

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Alirector: Alignment-Enhanced Chinese Grammatical Error Corrector
Haihui Yang | Xiaojun Quan

Chinese grammatical error correction (CGEC) faces serious overcorrection challenges when employing autoregressive generative models such as sequence-to-sequence (Seq2Seq) models and decoder-only large language models (LLMs). While previous methods aim to address overcorrection in Seq2Seq models, they are difficult to adapt to decoder-only LLMs. In this paper, we propose an alignment-enhanced corrector for the overcorrection problem that applies to both Seq2Seq models and decoder-only LLMs. Our method first trains a correction model to generate an initial correction of the source sentence. Then, we combine the source sentence with the initial correction and feed it through an alignment model for another round of correction, aiming to enforce the alignment model to focus on potential overcorrection. Moreover, to enhance the model’s ability to identify nuances, we further explore the reverse alignment of the source sentence and the initial correction. Finally, we transfer the alignment knowledge from two alignment models to the correction model, instructing it on how to avoid overcorrection. Experimental results on three CGEC datasets demonstrate the effectiveness of our approach in alleviating overcorrection and improving overall performance. Our code has been made publicly available.

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VISPool: Enhancing Transformer Encoders with Vector Visibility Graph Neural Networks
Tuna Alikaşifoğlu | Arda Aras | Aykut Koc

The emergence of transformers has revolutionized natural language processing (NLP), as evidenced in various NLP tasks. While graph neural networks (GNNs) show recent promise in NLP, they are not standalone replacements for transformers. Rather, recent research explores combining transformers and GNNs. Existing GNN-based approaches rely on static graph construction methods requiring excessive text processing, and most of them are not scalable with the increasing document and word counts. We address these limitations by proposing a novel dynamic graph construction method for text documents based on vector visibility graphs (VVGs) generated from transformer output. Then, we introduce visibility pooler (VISPool), a scalable model architecture that seamlessly integrates VVG convolutional networks into transformer pipelines. We evaluate the proposed model on the General Language Understanding Evaluation (GLUE) benchmark datasets. VISPool outperforms the baselines with less trainable parameters, demonstrating the viability of the visibility-based graph construction method for enhancing transformers with GNNs.

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The Emotion Dynamics of Literary Novels
Krishnapriya Vishnubhotla | Adam Hammond | Graeme Hirst | Saif Mohammad

Stories are rich in the emotions they exhibit in their narratives and evoke in the readers. The emotional journeys of the various characters within a story are central to their appeal. Computational analysis of the emotions of novels, however, has rarely examined the variation in the emotional trajectories of the different characters within them, instead considering the entire novel to represent a single story arc. In this work, we use character dialogue to distinguish between the emotion arcs of the narration and the various characters. We analyze the emotion arcs of the various characters in a dataset of English literary novels using the framework of Utterance Emotion Dynamics. Our findings show that the narration and the dialogue largely express disparate emotions through the course of a novel, and that the commonalities or differences in the emotional arcs of stories are more accurately captured by those associated with individual characters.

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Accurate and Nuanced Open-QA Evaluation Through Textual Entailment
Peiran Yao | Denilson Barbosa

Open-domain question answering (Open-QA) is a common task for evaluating large language models (LLMs). However, current Open-QA evaluations are criticized for the ambiguity in questions and the lack of semantic understanding in evaluators. Complex evaluators, powered by foundation models or LLMs and pertaining to semantic equivalence, still deviate from human judgments by a large margin. We propose to study the entailment relations of answers to identify more informative and more general system answers, offering a much closer evaluation to human judgment on both NaturalQuestions and TriviaQA while being learning-free. The entailment-based evaluation we propose allows the assignment of bonus or partial marks by quantifying the inference gap between answers, enabling a nuanced ranking of answer correctness that has higher AUC than current methods.

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Dictionary-Aided Translation for Handling Multi-Word Expressions in Low-Resource Languages
Antonios Dimakis | Stella Markantonatou | Antonios Anastasopoulos

Multi-word expressions (MWEs) present unique challenges in natural language processing (NLP), particularly within the context of translation systems, due to their inherent scarcity, non-compositional nature, and other distinct lexical and morphosyntactic characteristics, issues that are exacerbated in low-resource settings.In this study, we elucidate and attempt to address these challenges by leveraging a substantial corpus of human-annotated Greek MWEs. To address the complexity of translating such phrases, we propose a novel method leveraging an available out-of-context lexicon.We assess the translation capabilities of current state-of-the-art systems on this task, employing both automated metrics and human evaluators.We find that by using our method when applicable, the performance of current systems can be significantly improved, however these models are still unable to produce translations comparable to those of a human speaker.

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LANS: A Layout-Aware Neural Solver for Plane Geometry Problem
Zhong-Zhi Li | Ming-Liang Zhang | Fei Yin | Cheng-Lin Liu

Geometry problem solving (GPS) is a challenging mathematical reasoning task requiring multi-modal understanding, fusion, and reasoning. Existing neural solvers take GPS as a vision-language task but are short in the representation of geometry diagrams that carry rich and complex layout information. In this paper, we propose a layout-aware neural solver named LANS, integrated with two new modules: multimodal layout-aware pre-trained language module (MLA-PLM) and layout-aware fusion attention (LA-FA). MLA-PLM adopts structural-semantic pre-training (SSP) to implement global relationship modeling, and point-match pre-training (PMP) to achieve alignment between visual points and textual points. LA-FA employs a layout-aware attention mask to realize point-guided cross-modal fusion for further boosting layout awareness of LANS. Extensive experiments on datasets Geometry3K and PGPS9K validate the effectiveness of the layout-aware modules and superior problem-solving performance of our LANS solver, over existing symbolic and neural solvers. We have made our code and data publicly available.

We propose Knowledge Crosswords, a geometric knowledge reasoning benchmark consisting of incomplete knowledge networks bounded by structured factual constraints, where LLMs are tasked with inferring the missing facts to meet all constraints. The novel setting of geometric knowledge reasoning necessitates new LM abilities beyond existing atomic/linear multi-hop QA, such as backtracking, verifying facts and constraints, reasoning with uncertainty, and more. Knowledge Crosswords contains 2,101 individual problems, covering diverse knowledge domains, and is further divided into three difficulty levels. We conduct extensive experiments to evaluate existing LLMs and approaches on Knowledge Crosswords. Results demonstrate that baseline approaches struggle with larger knowledge networks and semantically-equivalent entity distractors. In light of their limitations, we propose two new approaches, Staged Prompting and Verify-All, to augment LLMs’ abilities for error-aware backtracking and constraint verification. Our Verify-All significantly outperforms prior methods and is more robust towards problems in the hard subset. Further analysis shows that geometric knowledge reasoning poses new challenges to LLMs’ knowledge abilities, particularly in robustness towards varying option orders, complex structural constraints in knowledge networks, “none of the above” scenarios, and more.

Large language models are limited by challenges in factuality and hallucinations to be directly employed off-the-shelf for judging the veracity of news articles, where factual accuracy is paramount. In this work, we propose DELL that identifies three key stages in misinformation detection where LLMs could be incorporated as part of the pipeline: 1) LLMs could generate news reactions to represent diverse perspectives and simulate user-news interaction networks; 2) LLMs could generate explanations for proxy tasks (e.g., sentiment, stance) to enrich the contexts of news articles and produce experts specializing in various aspects of news understanding; 3) LLMs could merge task-specific experts and provide an overall prediction by incorporating the predictions and confidence scores of varying experts. Extensive experiments on seven datasets with three LLMs demonstrate that DELL outperforms state-of-the-art baselines by up to 16.8% in macro f1-score. Further analysis reveals that the generated reactions and explanations are greatly helpful in misinformation detection, while our proposed LLM-guided expert merging helps produce better-calibrated predictions.

As the influence of large language models (LLMs) spans across global communities, their safety challenges in multilingual settings become paramount for alignment research. This paper examines the variations in safety challenges faced by LLMs across different languages and discusses approaches to alleviating such concerns. By comparing how state-of-the-art LLMs respond to the same set of malicious prompts written in higher- vs. lower-resource languages,we observe that (1) LLMs tend to generate unsafe responses much more often when a malicious prompt is written in a lower-resource language, and (2) LLMs tend to generate more irrelevant responses to malicious prompts in lower-resource languages. To understand where the discrepancy can be attributed, we study the effect of instruction tuning with reinforcement learning from human feedback (RLHF) or supervised finetuning (SFT) on the HH-RLHF dataset. Surprisingly, while training with high-resource languages improves model alignment, training in lower-resource languages yields minimal improvement. This suggests that the bottleneck of cross-lingual alignment is rooted in the pretraining stage. Our findings highlight the challenges in cross-lingual LLM safety, and we hope they inform future research in this direction.

Recent works have demonstrated the effectiveness of self-alignment in which a large language model is aligned to follow general instructions using instructional data generated from the model itself starting from a handful of human-written seeds. Instead of general alignment, in this work, we focus on self-alignment for expert domain specialization (e.g., biomedicine, finance). As a preliminary, we quantitively show the marginal effect that generic instruction-following training has on downstream expert domains’ performance. To remedy this, we propose self-specialization - allowing for effective model specialization while achieving cross-task generalization by leveraging only a few labeled seeds. Self-specialization offers a data- and parameter-efficient way of “carving out” an expert model out of a generalist pre-trained LLM. Exploring a variety of popular open large models as a base for specialization, our experimental results in both biomedical and financial domains show that our self-specialized models outperform their base models by a large margin, and even larger models that are generally instruction-tuned or that have been adapted to the target domain by other means.

Taxonomy Expansion, which relies on modeling concepts and concept relations, can be formulated as a set representation learning task. The generalization of set, fuzzy set, incorporates uncertainty and measures the information within a semantic concept, making it suitable for concept modeling. Existing works usually model sets as vectors or geometric objects such as boxes, which are not closed under set operations. In this work, we propose a sound and efficient formulation of set representation learning based on its volume approximation as a fuzzy set. The resulting embedding framework, Fuzzy Set Embedding, satisfies all set operations and compactly approximates the underlying fuzzy set, hence preserving information while being efficient to learn, relying on minimum neural architecture. We empirically demonstrate the power of FUSE on the task of taxonomy expansion, where FUSE achieves remarkable improvements up to 23% compared with existing baselines. Our work marks the first attempt to understand and efficiently compute the embeddings of fuzzy sets.

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Chain of Logic: Rule-Based Reasoning with Large Language Models
Sergio Servantez | Joe Barrow | Kristian Hammond | Rajiv Jain

Rule-based reasoning, a fundamental type of legal reasoning, enables us to draw conclusions by accurately applying a rule to a set of facts. We explore causal language models as rule-based reasoners, specifically with respect to compositional rules - rules consisting of multiple elements which form a complex logical expression. Reasoning about compositional rules is challenging because it requires multiple reasoning steps, and attending to the logical relationships between elements. We introduce a new prompting method, Chain of Logic, which elicits rule-based reasoning through decomposition (solving elements as independent threads of logic), and recomposition (recombining these sub-answers to resolve the underlying logical expression). This method was inspired by the IRAC (Issue, Rule, Application, Conclusion) framework, a sequential reasoning approach used by lawyers. We evaluate chain of logic across eight rule-based reasoning tasks involving three distinct compositional rules from the LegalBench benchmark and demonstrate it consistently outperforms other prompting methods, including chain of thought and self-ask, using open-source and commercial language models.

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Merging Facts, Crafting Fallacies: Evaluating the Contradictory Nature of Aggregated Factual Claims in Long-Form Generations
Cheng-Han Chiang | Hung-yi Lee

Long-form generations from large language models (LLMs) contain a mix of factual and non-factual claims, making evaluating factuality difficult.Prior works evaluate the factuality of a long paragraph by decomposing it into multiple facts, verifying those facts independently, and aggregating the results.Such methods assume that combining factual claims forms a factual paragraph.The above assumption can be violated: we show that strong open-source models like Llama-chat can generate paragraphs that contain verifiable facts, but the facts are combined into a non-factual paragraph due to entity ambiguity.We further reveal that existing factuality metrics, including FActScore and citation recall, cannot properly evaluate these non-factual paragraphs and overestimate their factuality.To address this, we introduce an enhanced metric, **D-FActScore**, specifically designed for content with ambiguous entities.We evaluate the D-FActScores of people biographies generated by retrieval-augmented LLMs.We show that D-FActScore can better assess the factuality of paragraphs with entity ambiguity than FActScore.We also find that four widely used open-source LLMs tend to mix information of distinct entities to form non-factual paragraphs, making their D-FActScore much lower than FActScore by over 10%.

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Can You Learn Semantics Through Next-Word Prediction? The Case of Entailment
William Merrill | Zhaofeng Wu | Norihito Naka | Yoon Kim | Tal Linzen

Do LMs infer the semantics of text from co-occurrence patterns in their training data? Merrill et al. (2022) argue that, in theory, sentence co-occurrence probabilities predicted by an optimal LM should reflect the entailment relationship of the constituent sentences, but it is unclear whether probabilities predicted by neural LMs encode entailment in this way because of strong assumptions made by Merrill et al. (namely, that humans always avoid redundancy). In this work, we investigate whether their theory can be used to decode entailment relations from neural LMs. We find that a test similar to theirs can decode entailment relations between natural sentences, well above random chance, though not perfectly, across many datasets and LMs. This suggests LMs implicitly model aspects of semantics to predict semantic effects on sentence co-occurrence patterns. However, we find the test that predicts entailment in practice works in the opposite direction to the theoretical test. We thus revisit the assumptions underlying the original test, finding its derivation did not adequately account for redundancy in human-written text. We argue that better accounting for redundancy related to *explanations* might derive the observed flipped test and, more generally, improve computational models of speakers in linguistics.

Psychological inoculation, a strategy designed to build resistance against persuasive misinformation, has shown efficacy in curbing its spread and mitigating its adverse effects at early stages. Despite its effectiveness, the design and optimization of these inoculations typically demand substantial human and financial resources, primarily due to the need for repeated experimental trials. To address these challenges, this paper introduces Simulated Misinformation Susceptibility Tests (SMISTs), leveraging Large Language Models (LLMs) to simulate participant responses in misinformation studies. SMIST employs a life experience-driven simulation methodology, which accounts for various aspects of participants’ backgrounds, to mitigate common issues of caricatures and stereotypes in LLM simulations and enhance response diversity. Our extensive experimentation demonstrates that SMIST, utilizing GPT-4 as the backend model, yields results that align closely with those obtained from human-subject studies in misinformation susceptibility. This alignment suggests that LLMs can effectively serve as proxies in evaluating the impact of psychological inoculations. Moreover, SMIST offers the critical benefit of being applicable to emerging or anticipated misinformation scenarios without exposing human participants to potentially harmful content. This characteristic of SMIST not only preserves participant safety but also expands the scope of misinformation research to include more sensitive or speculative topics.

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Social Intelligence Data Infrastructure: Structuring the Present and Navigating the Future
Minzhi Li | Weiyan Shi | Caleb Ziems | Diyi Yang

As Natural Language Processing (NLP) systems become increasingly integrated into human social life, these technologies will need to increasingly rely on social intelligence. Although there are many valuable datasets that benchmark isolated dimensions of social intelligence, there does not yet exist any body of work to join these threads into a cohesive subfield in which researchers can quickly identify research gaps and future directions. Towards this goal, we build a Social AI Data Infrastructure, which consists of a comprehensive social AI taxonomy and a data library of 480 NLP datasets. Our infrastructure allows us to analyze existing dataset efforts, and also evaluate language models’ performance in different social intelligence aspects. Our analyses demonstrate its utility in enabling a thorough understanding of current data landscape and providing a holistic perspective on potential directions for future dataset development. We show there is a need for multifaceted datasets, increased diversity in language and culture, more long-tailed social situations, and more interactive data in future social intelligence data efforts.

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Selective Prefix Tuning for Pre-trained Language Models
Hongyi Zhang | Zuchao Li | Ping Wang | Hai Zhao

The prevalent approach for optimizing pre-trained language models in downstream tasks is fine-tuning. However, it is both time-consuming and memory-inefficient. In response, a more efficient method called Prefix Tuning, which insert learnable vectors into each Transformer layers, has been proposed and proven effective. Recent investigations reveal that prefix tokens carry context-specific information, prompting the hypothesis that enhancing their specialization can improve model performance. To address this, we propose Selective Prefix Tuning (SPT), integrating a selective mechanism inspired by selective self-attention. Additionally, we introduce Selective Loss (SL) to encourage diversity in prefix tokens. Extensive experiments validate the effectiveness of SPT in sentence and token classification tasks. We contribute insight into understanding the role of prefix in model adaptation.

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MODABS: Multi-Objective Learning for Dynamic Aspect-Based Summarization
Xiaobo Guo | Soroush Vosoughi

The rapid proliferation of online content necessitates effective summarization methods, among which dynamic aspect-based summarization stands out. Unlike its traditional counterpart, which assumes a fixed set of known aspects, this approach adapts to the varied aspects of the input text. We introduce a novel multi-objective learning framework employing a Longformer-Encoder-Decoder for this task. The framework optimizes aspect number prediction, minimizes disparity between generated and reference summaries for each aspect, and maximizes dissimilarity across aspect-specific summaries. Extensive experiments show our method significantly outperforms baselines on three diverse datasets, largely due to the effective alignment of generated and reference aspect counts without sacrificing single-aspect summarization quality.

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Non-compositional Expression Generation and its Continual Learning
Jianing Zhou | Suma Bhat

Non-compositional expressions are an integral part of natural language and their meanings cannot be directly derived from the meanings of their component words. Recent work has shown how their processing remains a challenge for pre-trained language models. Here we consider the fact that prior knowledge of their component words is inadequate to infer their meaning as a whole and that these expressions constitute a long-tailed process in language (based on their occurrence in corpora and their coming into use as an idiomatic expression in a continual manner). Against this backdrop, this paper studies the ability of recent pre-trained language models to generate non-compositional expressions in English and their continual learning. Formulating this as a mask infilling task termed as CLoNE, the study uncovers the combined challenges of non-compositionality and their continual learning. Using a set of three diverse idiomatic expression datasets repurposed for this task, we benchmark different large pre-trained language models and different continual learning methods on the task of non-compositional expression generation. Our experiments on the CLoNE task show that large pre-trained language models are limited in their ability to generate non-compositional expressions and available continual learning methods are inadequate for our proposed CLoNE task which calls for more effective methods for continual learning of non-compositionality. Our datasets and code will be released publicly upon acceptance.

This paper surveys and organizes research works of medical dialog systems, which is an important yet challenging task. Although these systems have been surveyed in the medical community from an application perspective, a systematic review from a rigorous technical perspective has to date remained noticeably absent. As a result, an overview of the categories, methods, evaluation of medical dialogue systems remain limited and underspecified, hindering the further improvement of this area. To fill this gap, we investigate an initial pool of 325 papers from well-known computer science, natural language processing conferences and journals, and make an overview. Recently, large language models have shown strong model capacity on downstream tasks, which also reshape medical dialog systems’ foundation.Despite the alluring practical application value, current medical dialogue systems still suffer from problems. To this end, this paper lists grand challenges of medical dialog systems, especially of large language models.

Large language models (LLMs) have demonstrated strong reasoning abilities when prompted to generate chain-of-thought (CoT) explanations alongside answers. However, previous research on evaluating LLMs has solely focused on answer accuracy, neglecting the correctness of the generated CoT. In this paper, we delve deeper into the CoT reasoning capabilities of LLMs in multi-hop question answering by utilizing knowledge graphs (KGs). We propose a novel discriminative and generative CoT evaluation paradigm to assess LLMs’ knowledge of reasoning and the accuracy of the generated CoT. Through experiments conducted on 5 different families of LLMs across 2 multi-hop question-answering datasets, we find that LLMs possess sufficient knowledge to perform reasoning. However, there exists a significant disparity between answer accuracy and faithfulness of the CoT generated by LLMs, indicating that they often arrive at correct answers through incorrect reasoning.

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Comprehensive Abstractive Comment Summarization with Dynamic Clustering and Chain of Thought
Longyin Zhang | Bowei Zou | Jacintha Yi | AiTi Aw

Real-world news comments pose a significant challenge due to their noisy and ambiguous nature, which complicates their modeling for clustering and summarization tasks. Most previous research has predominantly focused on extractive summarization methods within specific constraints. This paper concentrates on Clustering and Abstractive Summarization of online news Comments (CASC). First, we introduce an enhanced fast clustering algorithm that maintains a dynamic similarity threshold to ensure the high density of each comment cluster being built. Moreover, we pioneer the exploration of tuning Large Language Models (LLMs) through a chain-of-thought strategy to generate summaries for each comment cluster. On the other hand, a notable challenge in CASC research is the scarcity of evaluation data. To address this problem, we design an annotation scheme and contribute a manual test suite tailored for CASC. Experimental results on the test suite demonstrate the effectiveness of our improvements to the baseline methods. In addition, the quantitative and qualitative analyses illustrate the adaptability of our approach to real-world news comment scenarios.

Fine-tuning has been demonstrated to be an effective method to improve the domain performance of large language models (LLMs). However, LLMs might fit the dataset bias and shortcuts for prediction, leading to poor generation performance. Previous works have proven that LLMs are prone to exhibit position bias, i.e., leveraging information positioned at the beginning or end, or specific positional cues within the input. Existing debiasing methods for LLMs require external bias knowledge or annotated non-biased samples, which is lacking for position debiasing and impractical in reality. In this work, we propose a self-supervised position debiasing (SOD) framework to mitigate position bias for LLMs. SOD leverages unsupervised responses from pre-trained LLMs for debiasing without relying on any external knowledge. To improve the quality of unsupervised responses, we propose an objective alignment (OAM) module to prune these responses. Experiments on eight datasets and five tasks show that SOD consistently outperforms existing methods in mitigating three types of position biases. Besides, SOD achieves this by sacrificing only a small performance on biased samples, which is general and effective. To facilitate the reproducibility of the results, we share the code of all methods and datasets on https://github.com/LZKSKY/SOD.

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HyperCL: A Contrastive Learning Framework for Hyper-Relational Knowledge Graph Embedding with Hierarchical Ontology
Yuhuan Lu | Weijian Yu | Xin Jing | Dingqi Yang

Knowledge Graph (KG) embeddings are essential for link prediction over KGs. Compared to triplets, hyper-relational facts consisting of a base triplet and an arbitrary number of key-value pairs, can better characterize real-world facts and have aroused various hyper-relational embedding techniques recently. Nevertheless, existing works seldom consider the ontology of KGs, which is beneficial to link prediction tasks. A few studies attempt to incorporate the ontology information, by either utilizing the ontology as constraints on entity representations or jointly learning from hyper-relational facts and the ontology. However, existing approaches mostly overlook the ontology hierarchy and suffer from the dominance issue of facts over ontology, resulting in suboptimal performance. Against this background, we propose a universal contrastive learning framework for hyper-relational KG embeddings (HyperCL), which is flexible to integrate different hyper-relational KG embedding methods and effectively boost their link prediction performance. HyperCL designs relation-aware Graph Attention Networks to capture the hierarchical ontology and a concept-aware contrastive loss to alleviate the dominance issue. We evaluate HyperCL on three real-world datasets in different link prediction tasks. Experimental results show that HyperCL consistently boosts the performance of state-of-the-art baselines with an average improvement of 3.1-7.4% across the three datasets.

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Encoding Hierarchical Schema via Concept Flow for Multifaceted Ideology Detection
Songtao Liu | Bang Wang | Wei Xiang | Han Xu | Minghua Xu

Multifaceted ideology detection (MID) aims to detect the ideological leanings of texts towards multiple facets. Previous studies on ideology detection mainly focus on one generic facet and ignore label semantics and explanatory descriptions of ideologies, which are a kind of instructive information and reveal the specific concepts of ideologies. In this paper, we develop a novel concept semantics-enhanced framework for the MID task. Specifically, we propose a bidirectional iterative concept flow (BICo) method to encode multifaceted ideologies. BICo enables the concepts to flow across levels of the schema tree and enriches concept representations with multi-granularity semantics. Furthermore, we explore concept attentive matching and concept-guided contrastive learning strategies to guide the model to capture ideology features with the learned concept semantics. Extensive experiments on the benchmark dataset show that our approach achieves state-of-the-art performance in MID, including in the cross-topic scenario.

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Character-Level Chinese Dependency Parsing via Modeling Latent Intra-Word Structure
Yang Hou | Zhenghua Li

Revealing the syntactic structure of sentences in Chinese poses significant challenges for word-level parsers due to the absence of clear word boundaries. To facilitate a transition from word-level to character-level Chinese dependency parsing, this paper proposes modeling latent internal structures within words. In this way, each word-level dependency tree is interpreted as a forest of character-level trees. A constrained Eisner algorithm is implemented to ensure the compatibility of character-level trees, guaranteeing a single root for intra-word structures and establishing inter-word dependencies between these roots. Experiments on Chinese treebanks demonstrate the superiority of our method over both the pipeline framework and previous joint models. A detailed analysis reveals that a coarse-to-fine parsing strategy empowers the model to predict more linguistically plausible intra-word structures.

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AlignRE: An Encoding and Semantic Alignment Approach for Zero-Shot Relation Extraction
Zehan Li | Fu Zhang | Jingwei Cheng

Zero-shot Relation Extraction (ZSRE) aims to predict unseen relations between entity pairs from input sentences. Existing prototype-based ZSRE methods encode relation descriptions into prototype embeddings and predict by measuring the similarity between sentence embeddings and prototype embeddings. However, these methods often overlook abundant side information of relations and suffer from a significant encoding gap between prototypes and sentences, limiting performance. To this end, we propose a framework named AlignRE, based on two Alignment methods for ZSRE. Specifically, we present a novel perspective centered on encoding schema alignment to enhance prototype-based ZSRE methods. We utilize well-designed prompt-tuning to bridge the encoding gap. To improve prototype quality, we explore and leverage multiple side information and propose a prototype aggregation method based on semantic alignment to create comprehensive relation prototype representations. We conduct experiments on FewRel and Wiki-ZSL datasets and consistently outperform state-of-the-art methods. Moreover, our method exhibits substantially faster performance and reduces the need for extensive manual labor in prototype construction. Code is available at https://github.com/lizehan1999/AlignRE.

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Disperse-Then-Merge: Pushing the Limits of Instruction Tuning via Alignment Tax Reduction
Tingchen Fu | Deng Cai | Lemao Liu | Shuming Shi | Rui Yan

Supervised fine-tuning (SFT) on instruction-following corpus is a crucial approach toward the alignment of large language models (LLMs). However, the performance of LLMs on standard knowledge and reasoning benchmarks tends to suffer from deterioration at the latter stage of the SFT process, echoing the phenomenon of alignment tax. Through our pilot study, we put a hypothesis that the data biases are probably one cause behind the phenomenon. To address the issue, we introduce a simple disperse-then-merge framework. To be concrete, we disperse the instruction-following data into portions and then train multiple sub-models using different data portions. Lastly, we merge multiple models into a single one via model merging techniques. Despite its simplicity, our framework outperforms various sophisticated methods such as data curation and training regularization on a series of standard knowledge and reasoning benchmarks.

To tackle the problem of domain-specific knowledge scarcity within large language models (LLMs), knowledge graph-retrievalaugmented method has been proven to be an effective and efficient technique for knowledge infusion. However, existing approaches face two primary challenges: knowledge mismatch between public available knowledge graphs and the specific domain of the task at hand, and poor information compliance of LLMs with knowledge graphs. In this paper, we leverage a small set of labeled samples and a large-scale corpus to efficiently construct domain-specific knowledge graphs by an LLM, addressing the issue of knowledge mismatch. Additionally, we propose a three-stage KG-LLM alignment strategy to enhance the LLM’s capability to utilize information from knowledge graphs. We conduct experiments with a limited-sample setting on two biomedical question-answering datasets, and the results demonstrate that our approach outperforms existing baselines.

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Towards Precise Localization of Critical Errors in Machine Translation
Dahyun Jung | Sugyeong Eo | Heuiseok Lim

The advent of large language models has experienced a remarkable improvement in the field of machine translation. However, machine translation is still vulnerable to critical meaning deviations, which may incur catastrophic issues in social or ethical contexts. In particular, existing critical error detection primarily focuses on identifying sentence-level errors, leaving the precise localization of such errors within the sentence unaddressed. In this paper, we introduce a new task, word-level critical error detection (WCED), to detect critical errors at a fine-grained level in machine translation sentences. The task aims to identify the parts of a machine translation that contain catastrophic meaning distortions. We hypothesize that the ability to determine errors at the sentence level will positively influence the detection of more granular errors. We propose a sentence-level error detection module to predict which words in a sentence have critical errors. Experimental results demonstrate that our method outperforms existing methodologies and LLM in En-De, Zh-En, En-Ru, and En-Ko. Our method is helpful for determining the fine-grained location of errors. We hope that such studies will improve the capacity to address critical errors adeptly.

Large Language Models (LLMs), such as LLaMA and T5, have shown exceptional performance across various tasks through fine-tuning. Although low-rank adaption (LoRA) has emerged to cheaply fine-tune these LLMs on downstream tasks, their deployment is still hindered by the vast model scale and computational costs. Post-training model pruning offers a way to compress LLMs. However, the current pruning methods designed for LLMs are not compatible with LoRA. This is due to their utilization of unstructured pruning on LLMs, impeding the merging of LoRA weights, or their dependence on the gradients of pre-trained weights to guide pruning, which can impose significant memory overhead.To this end, we propose LoRAPrune, a new framework that delivers an accurate structured pruned model in a highly memory-efficient manner. Specifically, we first design a LoRA-guided pruning criterion, which uses the weights and gradients of LoRA, rather than the gradients of pre-trained weights for importance estimation. We subsequently integrate this criterion into an iterative pruning process, effectively removing redundant channels and heads. Extensive experimental results demonstrate the superior performance of our LoRAPrune over existing approaches on the LLaMA series models.At a 50% compression rate, LoRAPrune demonstrates superior performance over LLM-Pruner, achieving a reduction in perplexity by 4.81 on WikiText2 and 3.46 on PTB, while also decreasing memory usage by 52.6%.Besides, LoRAPrune also matches semi-structural pruning across multiple LLMs, proving its wide applicability. The code is available at https://github.com/aim-uofa/LoRAPrune.

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Speculative Decoding via Early-exiting for Faster LLM Inference with Thompson Sampling Control Mechanism
Jiahao Liu | Qifan Wang | Jingang Wang | Xunliang Cai

The recent advancements in large language models (LLMs) have been extraordinary, yet the escalating inference costs associated with them present challenges in real-world applications. To address these challenges, we propose a novel approach called Early-exiting Speculative Decoding (EESD) with lossless acceleration. Specifically, EESD utilizes a segment of the LLM to generate draft tokens, incorporating Early-exiting structures after the first N layers. To enhance the quality of draft tokens, a self-distillation method is integrated. This early-exiting design not only reduces deployment and training costs but also significantly accelerates the token generation speed. Moreover, we introduce a novel sampling mechanism that leverages Thompson Sampling to regulate the generation processes, automatically determining the quantity of draft tokens in each round. The original LLM is then employed to validate these draft tokens through a single forward pass, and thus guarantees that the final output text maintains a distribution consistent with vanilla auto-regressive decoding. The experimental results on both 13B and 70B models demonstrate that our approach decodes tokens at a markedly accelerated rate compared to prior methods, showing the effectiveness of our approach.

For the grammatical error correction (GEC) task, there usually exist multiple correction ways for an erroneous input sentence, leading to multiple references. Observing the high proportion of multi-reference instances in Chinese GEC training data, we target a systematic study on how to better utilize multi-reference training data. We propose two new approaches and a simple two-stage training strategy. We compare them against previously proposed approaches, on two Chinese training datasets, i.e., Lang-8 for second language learner texts and FCGEC-Train for native speaker texts, and three test datasets. The experiments and analyses demonstrate the effectiveness of our proposed approaches and reveal interesting insights. Our code is available at https://github.com/ymliucs/MrGEC.

Open large language models (LLMs) with great performance in various tasks have significantly advanced the development of LLMs. However, they are far inferior to commercial models such as ChatGPT and GPT-4 when acting as agents to tackle complex tasks in the real world. These agent tasks employ LLMs as the central controller responsible for planning, memorization, and tool utilization, necessitating both fine-grained prompting methods and robust LLMs to achieve satisfactory performance. Though many prompting methods have been proposed to complete particular agent tasks, there is lack of research focusing on improving the agent capabilities of LLMs themselves without compromising their general abilities. In this work, we present AgentTuning, a simple and general method to enhance the agent abilities of LLMs while maintaining their general LLM capabilities. We construct AgentInstruct, a lightweight instruction-tuning dataset containing high-quality interaction trajectories. We employ a hybrid instruction-tuning strategy by combining AgentInstruct with open-source instructions from general domains. AgentTuning is used to instruction-tune the Llama 2 series, resulting in AgentLM. Our evaluations show that AgentTuning enables LLMs’ agent capabilities without compromising general abilities. The AgentLM-70B is comparable to GPT-3.5-turbo on unseen agent tasks, demonstrating generalized agent capabilities. We open source the AgentInstruct and AgentLM-7B, 13B, and 70B models at https://anonymous.4open.science/r/AgentTuning, serving open and powerful alternatives to commercial LLMs for agent tasks.

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Transition-based Opinion Generation for Aspect-based Sentiment Analysis
Tianlai Ma | Zhongqing Wang | Guodong Zhou

Recently, the use of pre-trained generation models for extracting sentiment elements has resulted in significant advancements in aspect-based sentiment analysis benchmarks. However, these approaches often overlook the importance of explicitly modeling structure among sentiment elements. To address this limitation, we present a study that aims to integrate general pre-trained sequence-to-sequence language models with a structure-aware transition-based approach. Therefore, we propose a transition system for opinion tree generation, designed to better exploit pre-trained language models for structured fine-tuning. Our proposed transition system ensures the structural integrity of the generated opinion tree. By leveraging pre-trained generation models and simplifying the transition set, we are able to maximize the accuracy of opinion tree generation. Extensive experiments show that our model significantly advances the state-of-the-art performance on several benchmark datasets. In addition, the empirical studies also indicate that the proposed opinion tree generation with transition system is more effective in capturing the sentiment structure than other generation models.

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Modeling Dynamic Topics in Chain-Free Fashion by Evolution-Tracking Contrastive Learning and Unassociated Word Exclusion
Xiaobao Wu | Xinshuai Dong | Liangming Pan | Thong Nguyen | Anh Tuan Luu

Dynamic topic models track the evolution of topics in sequential documents, which have derived various applications like trend analysis. However, existing models suffer from repetitive topic and unassociated topic issues, failing to reveal the evolution and hindering further applications. To address these issues, we break the tradition of simply chaining topics in existing work and propose a novel neural Chain-Free Dynamic Topic Model. We introduce a new evolution-tracking contrastive learning method that builds the similarity relations among dynamic topics. This not only tracks topic evolution but also maintains topic diversity, mitigating the repetitive topic issue. To avoid unassociated topics, we further present an unassociated word exclusion method that consistently excludes unassociated words from discovered topics. Extensive experiments demonstrate our model significantly outperforms state-of-the-art baselines, tracking topic evolution with high-quality topics, showing better performance on downstream tasks, and remaining robust to the hyperparameter for evolution intensities.

Many studies have demonstrated that large language models (LLMs) can produce harmful responses, exposing users to unexpected risks. Previous studies have proposed comprehensive taxonomies of LLM risks, as well as corresponding prompts that can be used to examine LLM safety. However, the focus has been almost exclusively on English. We aim to broaden LLM safety research by introducing a dataset for the safety evaluation of Chinese LLMs, and extending it to better identify false negative and false positive examples in terms of risky prompt rejections. We further present a set of fine-grained safety assessment criteria for each risk type, facilitating both manual annotation and automatic evaluation in terms of LLM response harmfulness. Our experiments over five LLMs show that region-specific risks are the prevalent risk type. Warning: this paper contains example data that may be offensive, harmful, or biased. Our data is available at https://github.com/Libr-AI/do-not-answer.

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LLMFactor: Extracting Profitable Factors through Prompts for Explainable Stock Movement Prediction
Meiyun Wang | Kiyoshi Izumi | Hiroki Sakaji

Recently, Large Language Models (LLMs) have attracted significant attention for their exceptional performance across a broad range of tasks, particularly in text analysis. However, the finance sector presents a distinct challenge due to its dependence on time-series data for complex forecasting tasks. In this study, we introduce a novel framework called LLMFactor, which employs Sequential Knowledge-Guided Prompting (SKGP) to identify factors that influence stock movements using LLMs. Unlike previous methods that relied on keyphrases or sentiment analysis, this approach focuses on extracting factors more directly related to stock market dynamics, providing clear explanations for complex temporal changes. Our framework directs the LLMs to create background knowledge through a fill-in-the-blank strategy and then discerns potential factors affecting stock prices from related news. Guided by background knowledge and identified factors, we leverage historical stock prices in textual format to predict stock movement. An extensive evaluation of the LLMFactor framework across four benchmark datasets from both the U.S. and Chinese stock markets demonstrates its superiority over existing state-of-the-art methods and its effectiveness in financial time-series forecasting.

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You Only Look at Screens: Multimodal Chain-of-Action Agents
Zhuosheng Zhang | Aston Zhang

Autonomous graphical user interface (GUI) agents aim to facilitate task automation by interacting with the user interface without manual intervention. Recent studies have investigated eliciting the capabilities of large language models (LLMs) for effective engagement in diverse environments. To align with the input-output requirement of LLMs, most existing approaches are developed under a sandbox setting where they rely on external tools and application-specific APIs to parse the environment into textual elements and interpret the predicted actions. Consequently, those approaches often grapple with inference inefficiency and error propagation risks. To mitigate the challenges, we introduce Auto-GUI, a multimodal solution that directly interacts with the interface, bypassing the need for environment parsing or reliance on application-dependent APIs. Moreover, we propose a chain-of-action technique—leveraging a series of intermediate previous action histories and future action plans—to help the agent decide what action to execute. We evaluate our approach on a new device-control benchmark AITW with 30K unique instructions, spanning multi-step tasks such as application operation, web searching, and web shopping. Experimental results show that Auto-GUI achieves state-of-the-art performance with an action type prediction accuracy of 90% and an overall action success rate of 74%. Code is publicly available at https://github.com/cooelf/Auto-GUI.

Structured pruning is a widely used technique for reducing the size of pre-trained language models (PLMs), but current methods often overlook the potential of compressing the hidden dimension d in PLMs, a dimension critical to model size and efficiency. This paper introduces a novel structured pruning approach, Structured Pruning with PCA Projection ( SP³), targeting the effective reduction of d by projecting features into a space defined by principal components before masking. Extensive experiments on benchmarks (GLUE and SQuAD) show that can reduce d by 70%, compress 94% of the BERT_base model, and maintain over 96% accuracy and outperform other methods that compress d by 6% in accuracy at the same compression ratio. SP³ has also proven effective with other models, including OPT and Llama.Our data and code are available at https://github.com/hyx1999/SP3

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GENDEX: Generative Data Augmentation Strategy Leveraging External Data for Abstractive Dialogue Summarization
Sangwon Park | Hongseok Choi | Dongha Choi | Hyunju Lee

With the proliferation of digital communication, dialogue summarization has become increasingly important. However, it still faces a shortage of data. To address this issue, we developed **Gen**erative **D**ata Augmentation Strategy Leveraging **Ex**ternal Data for Abstractive Dialogue Summarization (**GENDEX**), which is based on the hypothetical foundation that texts containing people and their interpersonal interactions can potentially serve as summaries of corresponding dialogues. We filter short texts containing people and resolve coreferences for better contextual analysis. We then identify the semantic roles of words within the texts and filter them based on the patterns observed in the dialogue summarization datasets. Using these texts, we generate synthetic dialogues through a controlled generation method. To better leverage the augmented data, we utilize noise-tolerant training to fine-tune the summarization model. The experimental results demonstrate the effectiveness of our proposed method, showing its robust performance, generalizability, and scalability. Moreover, performance improvements by *GENDEX* were observed regardless of complexity of dialogues. The code is available at https://github.com/DMCB-GIST/GENDEX.

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Concept-Best-Matching: Evaluating Compositionality In Emergent Communication
Boaz Carmeli | Yonatan Belinkov | Ron Meir

Artificial agents that learn to communicate in order to accomplish a given task acquire communication protocols that are typically opaque to a human. A large body of work has attempted to evaluate the emergent communication via various evaluation measures, with **compositionality** featuring as a prominent desired trait. However, current evaluation procedures do not directly expose the compositionality of the emergent communication. We propose a procedure to assess the compositionality of emergent communication by finding the best-match between emerged words and natural language concepts.The best-match algorithm provides both a global score and a translation-map from emergent words to natural language concepts. To the best of our knowledge, it is the first time that such direct and interpretable mapping between emergent words and human concepts is provided.

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A Tale of Two Revisions: Summarizing Changes Across Document Versions
Santosh T.y.s.s | Natwar Modani | Apoorv Saxena

Document revision is a crucial aspect of the writing process, particularly in collaborative environments where multiple authors contribute simultaneously. However, current tools lack an efficient way to provide a comprehensive overview of changes between versions, leading to difficulties in understanding revisions. To address this, we propose a novel task of providing thematic summary of changes between document versions, organizing individual edits based on shared themes. We assess capabilities of LLMs on this task and further introduce three strategies to tackle this task: (i) representing the input of two documents along with edits in the ‘diff’ format (ii) a two-stage task decomposition with individual edit description generation as an intermediate task and (iii) clustering based chunking and subsequent merging techniques for handling longer documents. Our experiments demonstrate the effectiveness of our approach in improving the model’s capacity to handle this complex task. Additionally, we introduce ChangeSumm, a curated dataset comprising human-written thematic summaries for pairs of document versions, to facilitate evaluation and further research in this direction.

Aspect Sentiment Triplet Extraction (ASTE) aims to extract the triplets of aspect terms, their associated sentiment and opinion terms. Previous works based on different modeling paradigms have achieved promising results. However, these methods struggle to comprehensively explore the various specific relations between sentiment elements in multi-view linguistic features, which is the prior indication effect for facilitating sentiment triplets extraction, requiring to align and aggregate them to capture the complementary higher-order interactions. In this paper, we propose Multi-view Linguistic Features Enhancement (MvLFE) to explore the aforementioned prior indication effect in the “Refine, Align, and Aggregate” learning process. Specifically, we first introduce the relational graph attention network to encode the word-pair relations represented by each linguistic feature and refine them to pay more attention to the aspect-opinion pairs. Next, we employ the multi-view contrastive learning to align them at a fine-grained level in the contextual semantic space to maintain semantic consistency. Finally, we utilize the multi-semantic cross attention to capture and aggregate the complementary higher-order interactions between diverse linguistic features to enhance the aspect-opinion relations. Experimental results on several benchmark datasets show the effectiveness and robustness of our model, which achieves state-of-the-art performance.

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Pro-Woman, Anti-Man? Identifying Gender Bias in Stance Detection
Yingjie Li | Yue Zhang

Gender bias has been widely observed in NLP models, which has the potential to perpetuate harmful stereotypes and discrimination. In this paper, we construct a dataset GenderStance of 36k samples to measure gender bias in stance detection, determining whether models consistently predict the same stance for a particular gender group. We find that all models are gender-biased and prone to classify sentences that contain male nouns as Against and those with female nouns as Favor. Moreover, extensive experiments indicate that sources of gender bias stem from the fine-tuning data and the foundation model itself. We will publicly release our code and dataset.

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Likelihood-based Mitigation of Evaluation Bias in Large Language Models
Masanari Ohi | Masahiro Kaneko | Ryuto Koike | Mengsay Loem | Naoaki Okazaki

Large Language Models (LLMs) are widely used to evaluate natural language generation tasks as automated metrics.However, the likelihood, a measure of LLM’s plausibility for a sentence, can vary due to superficial differences in sentences, such as word order and sentence structure.It is therefore possible that there might be a likelihood bias if LLMs are used for evaluation: they might overrate sentences with higher likelihoods while underrating those with lower likelihoods.In this paper, we investigate the presence and impact of likelihood bias in LLM-based evaluators.We also propose a method to mitigate the likelihood bias.Our method utilizes highly biased instances as few-shot examples for in-context learning.Our experiments in evaluating the data-to-text and grammatical error correction tasks reveal that several LLMs we test display a likelihood bias.Furthermore, our proposed method successfully mitigates this bias, also improving evaluation performance (in terms of correlation of models with human scores) significantly.

Benchmark plays a pivotal role in assessing the advancements of large language models (LLMs). While numerous benchmarks have been proposed to evaluate LLMs’ capabilities, there is a notable absence of a dedicated benchmark for assessing their musical abilities. To address this gap, we present ZIQI-Eval, a comprehensive and large-scale music benchmark specifically designed to evaluate the music-related capabilities of LLMs.ZIQI-Eval encompasses a wide range of questions, covering 10 major categories and 56 subcategories, resulting in over 14,000 meticulously curated data entries. By leveraging ZIQI-Eval, we conduct a comprehensive evaluation over 16 LLMs to evaluate and analyze LLMs’ performance in the domain of music.Results indicate that all LLMs perform poorly on the ZIQI-Eval benchmark, suggesting significant room for improvement in their musical capabilities.With ZIQI-Eval, we aim to provide a standardized and robust evaluation framework that facilitates a comprehensive assessment of LLMs’ music-related abilities. The dataset is available at GitHub and HuggingFace.

Large Language Models (LLMs) have shown remarkable comprehension abilities but face challenges in GPU memory usage during inference, hindering their scalability for real-time applications like chatbots. To accelerate inference, we store computed keys and values (KV cache) in the GPU memory. Existing methods study the KV cache compression to reduce memory by pruning the pre-computed KV cache. However, they neglect the inter-layer dependency between layers and huge memory consumption in pre-computation. To explore these deficiencies, we find that the number of crucial keys and values that influence future generations decreases layer by layer and we can extract them by the consistency in attention weights. Based on the findings, we propose PyramidInfer, a method that compresses the KV cache by layer-wise retaining crucial context. PyramidInfer saves significant memory by computing fewer keys and values without sacrificing performance. Experimental results show PyramidInfer improves 2.2x throughput compared to Accelerate with over 54% GPU memory reduction in KV cache.

Drama is a form of storytelling inspired by human creativity, proceeding with a predefined storyline, carrying emotions and thoughts.This paper introduces LLM-based interactive drama, which endows traditional drama with an unprecedented immersion, where a person is allowed to walk into it and interact with the characters and scenes.We define this new artistic genre by 6 essential elements—plot, character, thought, diction, spectacle and interaction—and study the entire pipeline to forge a backbone drama LLM to drive the playing process, which is challenged by limited drama resources, uncontrollable narrative development, and complicated instruction following.We propose Narrative Chain to offer finer control over the narrative progression during interaction with players;Auto-Drama to synthesize drama scripts given arbitrary stories;Sparse Instruction Tuning to allow the model to follow sophisticated instructions.We manually craft 3 scripts, Detective Conan, Harry Potter, Romeo and Juliet, and design a 5-dimension principle to evaluate the drama LLM comprehensively.

While Large Language Models (LLMs) can serve as agents to simulate human behaviors (i.e., role-playing agents), we emphasize the importance of point-in-time role-playing. This situates characters at specific moments in the narrative progression for three main reasons: (i) enhancing users’ narrative immersion, (ii) avoiding spoilers, and (iii) fostering engagement in fandom role-playing. To accurately represent characters at specific time points, agents must avoid character hallucination, where they display knowledge that contradicts their characters’ identities and historical timelines. We introduce TimeChara, a new benchmark designed to evaluate point-in-time character hallucination in role-playing LLMs. Comprising 10,895 instances generated through an automated pipeline, this benchmark reveals significant hallucination issues in current state-of-the-art LLMs (e.g., GPT-4o). To counter this challenge, we propose Narrative-Experts, a method that decomposes the reasoning steps and utilizes narrative experts to reduce point-in-time character hallucinations effectively. Still, our findings with TimeChara highlight the ongoing challenges of point-in-time character hallucination, calling for further study.

VLMs (Vision-Language Models) extend the capabilities of LLMs (Large Language Models) to accept multimodal inputs. Since it has been verified that LLMs can be induced to generate harmful or inaccurate content through specific test cases (termed as Red Teaming), how VLMs perform in similar scenarios, especially with their combination of textual and visual inputs, remains a question. To explore this problem, we present a novel red teaming dataset RTVLM, which encompasses 12 subtasks (e.g., image misleading, multi-modal jailbreaking, face fairness, etc) under 4 primary aspects (faithfulness, privacy, safety, fairness). Our RTVLM is the first red teaming dataset to benchmark current VLMs in terms of these 4 different aspects. Detailed analysis shows that 10 prominent open-sourced VLMs struggle with the red teaming in different degrees and have up to 31% performance gap with GPT-4V. Additionally, we simply apply red teaming alignment to LLaVA-v1.5 with Supervised Fine-tuning (SFT) using RTVLM, and this bolsters the models’ performance with 10% in RTVLM test set, 13% in MM-hallu, and without noticeable decline in MM-Bench, overpassing other LLaVA-based models in similar size with regular alignment data. This reveals that current open-sourced VLMs still lack red teaming alignment. Our code and datasets will be open-sourced.

A Large Language Model (LLM) tends to generate inconsistent and sometimes contradictory outputs when presented with a prompt that has equivalent semantics but is expressed differently from the original prompt. To achieve semantic consistency of an LLM, one of the key approaches is to finetune the model with prompt-output pairs with semantically equivalent meanings. Despite its effectiveness, a data-driven finetuning method incurs substantial computation costs in data preparation and model optimization. In this regime, an LLM is treated as a “black box”, restricting our ability to gain deeper insights into its internal mechanism. In this paper, we are motivated to enhance the semantic consistency of LLMs through a more interpretable method (i.e., model editing) to this end. We first identify the model components (i.e., attention heads) that have a key impact on the semantic consistency of an LLM. We subsequently inject biases into the output of these model components along the semantic-consistency activation direction. It is noteworthy that these modifications are cost-effective, without reliance on mass manipulations of the original model parameters. Through comprehensive experiments on the constructed NLU and open-source NLG datasets, our method demonstrates significant improvements in the semantic consistency and task performance of LLMs. Additionally, our method exhibits promising generalization capabilities by performing well on tasks beyond the primary tasks.

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Semantic Skill Grounding for Embodied Instruction-Following in Cross-Domain Environments
Sangwoo Shin | SeungHyun Kim | Youngsoo Jang | Moontae Lee | Honguk Woo

In embodied instruction-following (EIF), the integration of pretrained language models (LMs) as task planners emerges as a significant branch, where tasks are planned at the skill level by prompting LMs with pretrained skills and user instructions. However, grounding these pretrained skills in different domains remains challenging due to their intricate entanglement with the domain-specific knowledge. To address this challenge, we present a semantic skill grounding (SemGro) framework that leverages the hierarchical nature of semantic skills. SemGro recognizes the broad spectrum of these skills, ranging from short-horizon low-semantic skills that are universally applicable across domains to long-horizon rich-semantic skills that are highly specialized and tailored for particular domains. The framework employs an iterative skill decomposition approach, starting from the higher levels of semantic skill hierarchy and then moving downwards, so as to ground each planned skill to an executable level within the target domain. To do so, we use the reasoning capabilities of LMs for composing and decomposing semantic skills, as well as their multi-modal extension for assessing the skill feasibility in the target domain. Our experiments in the VirtualHome benchmark show the efficacy of SemGro in 300 cross-domain EIF scenarios.

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LIRE: listwise reward enhancement for preference alignment
Mingye Zhu | Yi Liu | Lei Zhang | Junbo Guo | Zhendong Mao

Recently, tremendous strides have been made to align the generation of Large Language Models (LLMs) with human values to mitigate toxic or unhelpful content. Leveraging Reinforcement Learning from Human Feedback (RLHF) proves effective and is widely adopted by researchers. However, implementing RLHF is complex, and its sensitivity to hyperparameters renders achieving stable performance and scalability challenging. Furthermore, prevailing approaches to preference alignment primarily concentrate on pairwise comparisons, with limited exploration into multi-response scenarios, thereby overlooking the potential richness within the candidate pool. For the above reasons, we propose a new approach: Listwise Reward Enhancement for Preference Alignment (LIRE), a gradient-based reward optimization approach that incorporates the offline rewards of multiple responses into a streamlined listwise framework, thus eliminating the need for online sampling during training. LIRE is straightforward to implement, requiring minimal parameter tuning, and seamlessly aligns with the pairwise paradigm while naturally extending to multi-response scenarios. Moreover, we introduce a self-enhancement algorithm aimed at iteratively refining the reward during training. Our experiments demonstrate that LIRE consistently outperforms existing methods across several benchmarks on dialogue and summarization tasks, with good transferability to out-of-distribution data, assessed using proxy reward models and human annotators.

3D dense captioning is a task to localize objects in a 3D scene and generate descriptive sentences for each object. Recent approaches in 3D dense captioning have adopted transformer encoder-decoder frameworks from object detection to build an end-to-end pipeline without hand-crafted components. However, these approaches struggle with contradicting objectives where a single query attention has to simultaneously view both the tightly localized object regions and contextual environment. To overcome this challenge, we introduce SIA (See-It-All), a transformer pipeline that engages in 3D dense captioning with a novel paradigm called late aggregation. SIA simultaneously decodes two sets of queries—context query and instance query. The instance query focuses on localization and object attribute descriptions, while the context query versatilely captures the region-of-interest of relationships between multiple objects or with the global scene, then aggregated afterwards (i.e., late aggregation) via simple distance-based measures. To further enhance the quality of contextualized caption generation, we design a novel aggregator to generate a fully informed caption based on the surrounding context, the global environment, and object instances. Extensive experiments on two of the most widely-used 3D dense captioning datasets demonstrate that our proposed method achieves a significant improvement over prior methods.

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DARA: Decomposition-Alignment-Reasoning Autonomous Language Agent for Question Answering over Knowledge Graphs
Haishuo Fang | Xiaodan Zhu | Iryna Gurevych

Answering Questions over Knowledge Graphs (KGQA) is key to well-functioning autonomous language agents in various real-life applications. To improve the neural-symbolic reasoning capabilities of language agents powered by Large Language Models (LLMs) in KGQA, we propose the Decomposition-Alignment-Reasoning Agent (DARA) framework. DARA effectively parses questions into formal queries through a dual mechanism: high-level iterative task decomposition and low-level task grounding. Importantly, DARA can be efficiently trained with a small number of high-quality reasoning trajectories. Our experimental results demonstrate that DARA fine-tuned on LLMs (e.g. Llama-2-7B, Mistral) outperforms both in-context learning-based agents with GPT-4 and alternative fine-tuned agents, across different benchmarks, making such models more accessible for real-life applications. We also show that DARA attains performance comparable to state-of-the-art enumerating-and-ranking-based methods for KGQA.

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GKT: A Novel Guidance-Based Knowledge Transfer Framework For Efficient Cloud-edge Collaboration LLM Deployment
Yao Yao | Zuchao Li | Hai Zhao

The burgeoning size of Large Language Models (LLMs) has led to enhanced capabilities in generating responses, albeit at the expense of increased inference times and elevated resource demands. Existing methods of acceleration, predominantly hinged on knowledge distillation, generally necessitate fine-tuning of considerably large models, such as Llama-7B, posing a challenge for average users. Furthermore, present techniques for expediting inference and reducing costs operate independently. To address these issues, we introduce a novel and intuitive Guidance-based Knowledge Transfer (GKT) framework. This approach leverages a larger LLM as a ”teacher” to create guidance prompts, paired with a smaller ”student” model to finalize responses. Remarkably, GKT requires no fine-tuning and doesn’t necessitate the teacher and student models to have the same vocabulary, allowing for extensive batch generation to accelerate the process while ensuring user customization. GKT can be seamlessly integrated into cloud-edge collaboration architectures, and is versatile enough for plug-and-play application across various models. It excels in both efficiency and affordability, epitomizing a ”cheap and cheerful” solution. GKT achieves a maximum accuracy improvement of 14.18%, along with a 10.72 times speed-up on GSM8K and an accuracy improvement of 14.00 % along with a 7.73 times speed-up in CSQA. When utilizing ChatGPT as teacher model and Llama2-70B as the student model, we can achieve 95.00% of ChatGPT’s performance at 52% of the cost. The results highlight substantial enhancements in accuracy and processing speed on the GSM8K and CSQA datasets, surpassing the performance of using either the student or teacher models in isolation.

Grounded language models use external sources of information, such as knowledge graphs, to meet some of the general challenges associated with pre-training. By extending previous work on compositional generalization in semantic parsing, we allow for a controlled evaluation of the degree to which these models learn and generalize from patterns in knowledge graphs. We develop a procedure for generating natural language questions paired with knowledge graphs that targets different aspects of compositionality and further avoids grounding the language models in information already encoded implicitly in their weights. We evaluate existing methods for combining language models with knowledge graphs and find them to struggle with generalization to sequences of unseen lengths and to novel combinations of seen base components. While our experimental results provide some insight into the expressive power of these models, we hope our work and released datasets motivate future research on how to better combine language models with structured knowledge representations.

Large Language Models (LLMs) have demonstrated impressive capabilities for generalizing in unseen tasks. In the Named Entity Recognition (NER) task, recent advancements have seen the remarkable improvement of LLMs in a broad range of entity domains via instruction tuning, by adopting entity-centric schema. In this work, we explore the potential enhancement of the existing methods by incorporating negative instances into training. Our experiments reveal that negative instances contribute to remarkable improvements by (1) introducing contextual information, and (2) clearly delineating label boundaries. Furthermore, we introduce an efficient longest common subsequence (LCS) matching algorithm, which is tailored to transform unstructured predictions into structured entities. By integrating these components, we present GNER, a Generative NER system that shows improved zero-shot performance across unseen entity domains. Our comprehensive evaluation illustrates our system’s superiority, surpassing state-of-the-art (SoTA) methods by 9 F₁ score in zero-shot evaluation.

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WilKE: Wise-Layer Knowledge Editor for Lifelong Knowledge Editing
Chenhui Hu | Pengfei Cao | Yubo Chen | Kang Liu | Jun Zhao

Knowledge editing aims to rectify inaccuracies in large language models (LLMs) without costly retraining for outdated or erroneous knowledge. However, current knowledge editing methods primarily focus on single editing, failing to meet the requirements for lifelong editing. This study reveals a performance degradation encountered by knowledge editing in lifelong editing, characterized by toxicity buildup and toxicity flash, with the primary cause identified as pattern unmatch. We introduce a knowledge editing approach named Wise-Layer Knowledge Editor (WilKE), which selects editing layer based on the pattern matching degree of editing knowledge across different layers in language models. Experimental results demonstrate that, in lifelong editing, WilKE exhibits an average improvement of 46.2% and 67.8% on editing GPT2-XL and GPT-J relative to state-of-the-art knowledge editing methods.

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DINER: Debiasing Aspect-based Sentiment Analysis with Multi-variable Causal Inference
Jialong Wu | Linhai Zhang | Deyu Zhou | Guoqiang Xu

Though notable progress has been made, neural-based aspect-based sentiment analysis (ABSA) models are prone to learn spurious correlations from annotation biases, resulting in poor robustness on adversarial data transformations. Among the debiasing solutions, causal inference-based methods have attracted much research attention, which can be mainly categorized into causal intervention methods and counterfactual reasoning methods. However, most of the present debiasing methods focus on single-variable causal inference, which is not suitable for ABSA with two input variables (the target aspect and the review). In this paper, we propose a novel framework based on multi-variable causal inference for debiasing ABSA. In this framework, different types of biases are tackled based on different causal intervention methods. For the review branch, the bias is modeled as indirect confounding from context, where backdoor adjustment intervention is employed for debiasing. For the aspect branch, the bias is described as a direct correlation with labels, where counterfactual reasoning is adopted for debiasing. Extensive experiments demonstrate the effectiveness of the proposed method compared to various baselines on the two widely used real-world aspect robustness test set datasets.

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STAR: Constraint LoRA with Dynamic Active Learning for Data-Efficient Fine-Tuning of Large Language Models
Linhai Zhang | Jialong Wu | Deyu Zhou | Guoqiang Xu

Though Large Language Models (LLMs) have demonstrated the powerful capabilities of few-shot learning through prompting methods, supervised training is still necessary for complex reasoning tasks. Because of their extensive parameters and memory consumption, both Parameter-Efficient Fine-Tuning (PEFT) methods and Memory-Efficient Fine-Tuning methods have been proposed for LLMs. Nevertheless, the issue of large annotated data consumption, the aim of Data-Efficient Fine-Tuning, remains unexplored. One obvious way is to combine the PEFT method with active learning. However, the experimental results show that such a combination is not trivial and yields inferior results. Through probe experiments, such observation might be explained by two main reasons: uncertainty gap and poor model calibration. Therefore, in this paper, we propose a novel approach to effectively integrate uncertainty-based active learning and LoRA. Specifically, for the uncertainty gap, we introduce a dynamic uncertainty measurement that combines the uncertainty of the base model and the uncertainty of the full model during the iteration of active learning. For poor model calibration, we incorporate the regularization method during LoRA training to keep the model from being over-confident, and the Monte-Carlo dropout mechanism is employed to enhance the uncertainty estimation. Experimental results show that the proposed approach outperforms existing baseline models on three complex reasoning tasks.

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How Much Does Nonverbal Communication Conform to Entropy Rate Constancy?: A Case Study on Listener Gaze in Interaction
Yu Wang | Yang Xu | Gabriel Skantze | Hendrik Buschmeier

According to the Entropy Rate Constancy (ERC) principle, the information density of a text is approximately constant over its length. Whether this principle also applies to nonverbal communication signals is still under investigation. We perform empirical analyses of video-recorded dialogue data and investigate whether listener gaze, as an important nonverbal communication signal, adheres to the ERC principle. Results show (1) that the ERC principle holds for listener gaze; and (2) that the two linguistic factors syntactic complexity and turn transition potential are weakly correlated with local entropy of listener gaze.

This study investigates how Large Language Models (LLMs) leverage source and reference data in machine translation evaluation task, aiming to better understand the mechanisms behind their remarkable performance in this task.We design the controlled experiments across various input modes and model types, and employ both coarse-grained and fine-grained prompts to discern the utility of source versus reference information.We find that reference information significantly enhances the evaluation accuracy, while surprisingly, source information sometimes is counterproductive, indicating LLMs’ inability to fully leverage the cross-lingual capability when evaluating translations.Further analysis of the fine-grained evaluation and fine-tuning experiments show similar results.These findings also suggest a potential research direction for LLMs that fully exploits the cross-lingual capability of LLMs to achieve better performance in machine translation evaluation tasks.

Generation of plausible yet incorrect factual information, termed hallucination, is an unsolved issue in large language models. We study the ability of language models to deliberate on the responses they give in order to correct their mistakes. We develop the Chain-of-Verification (CoVe) method whereby the model first (i) drafts an initial response; then (ii) plans verification questions to fact-check its draft; (iii) answers those questions independently so the answers are not biased by other responses; and (iv) generates its final verified response. In experiments, we show CoVe decreases hallucinations across a variety of tasks, from list-based questions from Wikidata, closed book MultiSpanQA and longform text generation.

Bargaining is an important and unique part of negotiation between humans. As LLM-driven agents learn to negotiate and act like real humans, how to evaluate agents’ bargaining abilities remains an open problem.For the first time, we formally described the Bargaining task as an asymmetric incomplete information game, defining the gains of the Buyer and Seller in multiple bargaining processes. It allows us to quantitatively assess an agent’s performance in the Bargain task.We collected a real product price dataset, AmazonHistoryPrice, and conducted evaluations of various LLM agents’ bargaining abilities. We find that playing a Buyer is much harder than a Seller, and increasing model size can not effectively improve the Buyer’s performance.To address the challenge, we propose a novel approach called OG-Narrator that integrates a deterministic Offer Generator to control the price range of Buyer’s offers, and an LLM Narrator to create natural language sentences for generated offers.Experimental results show that OG-Narrator improves the buyer’s deal rates from 26.67% to 88.88% and brings a ten times multiplication of profits on all baselines, even a model that has not been aligned.

How to evaluate the coding abilities of Large Language Models (LLMs) remains an open question. We find that existing benchmarks are poorly aligned with real-world code repositories and are insufficient to evaluate the coding abilities of LLMs.To address the knowledge gap, we propose a new benchmark named DevEval, which has three advances. (1) DevEval aligns with real-world repositories in multiple dimensions, e.g., code and dependency distributions. (2) DevEval is annotated by 13 developers and contains comprehensive annotations (e.g., requirements, original repositories, reference code, and reference dependencies). (3) DevEval comprises 1,825 testing samples from 115 repositories, covering 10 popular domains (e.g., Internet, Database). Based on DevEval, we propose repository-level code generation and evaluate 8 popular LLMs on DevEval (e.g., gpt-4, gpt-3.5, StarCoder 2, DeepSeek Coder, CodeLLaMa). Our experiments reveal these LLMs’ coding abilities in real-world code repositories. For example, the highest Pass@1 of gpt-4 only is 53.04% in our experiments. We also analyze LLMs’ failed cases and summarize their shortcomings. We hope DevEval can facilitate the development of LLMs in real code repositories. DevEval, prompts, and LLMs’ predictions have been released.

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LPNL: Scalable Link Prediction with Large Language Models
Baolong Bi | Shenghua Liu | Yiwei Wang | Lingrui Mei | Xueqi Cheng

Exploring the application of large language models (LLMs) to graph learning is an emerging endeavor. However, the vast amount of information inherent in large graphs poses significant challenges to graph learning with LLMs. This work focuses on the link prediction task and introduces **LPNL** (Link Prediction via Natural Language), a framework based on large language models designed for scalable link prediction on large-scale heterogeneous graphs. We design novel prompts for link prediction that articulate graph details in natural language. We propose a two-stage sampling pipeline to extract crucial information from the graphs, and a divide-and-conquer strategy to control the input tokens within predefined limits, addressing the challenge of overwhelming information. We fine-tune a T5 model based on our self-supervised learning designed for link prediction. Extensive experimental results demonstrate that LPNL outperforms multiple advanced baselines in link prediction tasks on large-scale graphs.

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Aligning Speech Segments Beyond Pure Semantics
Kevin Heffernan | Artyom Kozhevnikov | Loic Barrault | Alexandre Mourachko | Holger Schwenk

Multilingual parallel data for speech-to-speech translation is scarce and expensive to create from scratch. This is all the more true for expressive speech translation, which aims at preserving not only the semantics, but also the overall prosody (e.g. style, emotion, rate-of-speech). Existing corpora contain speech utterances with the same meaning, yet the overall prosody is typically different, as human annotators are not tasked with reproducing these aspects, or crowed-sourced efforts do not specifically target this kind of alignment in priority. In this paper, we propose a novel alignment algorithm, which automatically forms pairs of speech segments aligned not only in meaning, but also in expressivity. In order to validate our approach, we train an expressive multilingual speech-to-speech translation system on the automatically aligned data. Our experiments show that in comparison to semantic-only approaches, expressively aligned data yields large improvements in source expressivity preservation (e.g. 43% uplift in speech rate preservation on average), while still maintaining content translation quality. In some scenarios, results also indicate that this alignment algorithm can outperform standard, semantic-focused approaches even on content translation quality.

Humans use multiple senses to comprehend the environment. Vision and language are two of the most vital senses since they allow us to easily communicate our thoughts and perceive the world around us. There has been a lot of interest in creating video-language understanding systems with human-like senses since a video-language pair can mimic both our linguistic medium and visual environment with temporal dynamics. In this survey, we review the key tasks of these systems and highlight the associated challenges. Based on the challenges, we summarize their methods from model architecture, model training, and data perspectives. We also conduct performance comparison among the methods, and discuss promising directions for future research.

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Generative Input: Towards Next-Generation Input Methods Paradigm
Keyu Ding | Yongcan Wang | Zihang Xu | Zhenzhen Jia | Enhong Chen

Since the release of ChatGPT, generative models have achieved tremendous success and become the de facto approach for various NLP tasks. However, its application in the field of input methods remains under-explored. Many neural network approaches have been applied to the construction of Chinese input method engines (IMEs). Previous research often assumed that the input pinyin was correct and focused on Pinyin-to-character (P2C) task, which significantly falls short of meeting users’ demands. Moreover, previous research could not leverage user feedback to optimize the model and provide personalized results. In this study, we propose a novel Generative Input paradigm named GeneInput. It uses prompts to handle all input scenarios and other intelligent auxiliary input functions, optimizing the model with user feedback. The results demonstrate that we have achieved state-of-the-art performance for the first time in the Full-mode Key-sequence to Characters task. GeneInput also includes RLHF-IME, a novel RLHF application framework for input method, that eliminates the need for manual ranking annotations and the performance surpasses GPT-4. Relevant resources have been open-sourced.

Retrieval-Augmented Generation (RAG) is an effective solution to supplement necessary knowledge to large language models (LLMs). Targeting its bottleneck of retriever performance, “generate-then-read” pipeline is proposed to replace the retrieval stage with generation from the LLM itself. Although promising, this research direction is underexplored and still cannot work in the scenario when source knowledge is given. In this paper, we formalize a general “A + B” framework with varying combinations of foundation models and types for systematic investigation. We explore the efficacy of the base and chat versions of LLMs and found their different functionalities suitable for generator A and reader B, respectively. Their combinations consistently outperform single models, especially in complex scenarios. Furthermore, we extend the application of the “A + B” framework to scenarios involving source documents through continuous learning, enabling the direct integration of external knowledge into LLMs. This approach not only facilitates effective acquisition of new knowledge but also addresses the challenges of safety and helpfulness post-adaptation. The paper underscores the versatility of the “A + B” framework, demonstrating its potential to enhance the practical application of LLMs across various domains.

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Functional Overlap Reranking for Neural Code Generation
Hung To | Minh Nguyen | Nghi Bui

Code Large Language Models (CodeLLMs) have ushered in a new era in code generation advancements. However, selecting the best code solutions from all possible CodeLLM outputs remains a challenge. Previous methods often overlooked the intricate functional similarities and interactions between solution clusters. We introduce SRank, a novel reranking strategy for selecting the best solutions from code generation, focusing on modeling the relationships between clusters of solutions. By quantifying the functional overlap between solution clusters, our approach provides a better ranking strategy for code solutions. Empirical results show that our method achieves remarkable results on the pass@1 score. For instance, on the Human-Eval benchmark, we achieve 69.66% in pass@1 with Codex002, 75.31% with WizardCoder, 53.99% with StarCoder, and 60.55% with CodeGen, surpassing state-of-the-art code generation reranking methods such as CodeT and Coder-Reviewer on the same CodeLLM by a significant margin approx 6.1% improvement on average. Even in scenarios with a limited number of sampled solutions and test cases, our approach demonstrates robustness and superiority, marking a new benchmark in code generation reranking. Our implementation can be found at https://github.com/FSoft-AI4Code/SRank-CodeRanker.

Human preference alignment is essential to improve the interaction quality of large language models (LLMs). Existing alignment methods depend on manually annotated preference data to guide the LLM optimization directions. However, continuously updating LLMs for alignment raises a distribution gap between model-generated samples and human-annotated responses, hindering training effectiveness. To mitigate this issue, previous methods require additional preference annotation on newly generated samples to adapt to the shifted distribution, which consumes a large amount of annotation resources. Targeting more efficient human preference optimization, we propose an Adversarial Preference Optimization (APO) framework, in which the LLM and the reward model update alternatively via a min-max game. Through adversarial training, the reward model can adapt to the shifted generation distribution of the LLM without any additional annotation. With comprehensive experiments, we find the proposed adversarial training framework further enhances existing alignment baselines in terms of LLM helpfulness and harmlessness. The code is at https://github.com/Linear95/APO.

Aspect sentiment quad prediction (ASQP) has garnered significant attention in aspect-based sentiment analysis (ABSA). Current ASQP research primarily relies on pre-trained generative language models to produce templated sequences, often complemented by grid-based auxiliary methods. Despite these efforts, the persistent challenge of generation instability remains unresolved and the effectiveness of grid methods remains underexplored in current studies. To this end, we introduce Grid Noise Diffusion Pinpoint Network (GDP), a T5-based generative model aiming to tackle the issue of generation instability. The model consists of three novel modules, including Diffusion Vague Learning (DVL) to facilitate effective model learning and enhance overall robustness; Consistency Likelihood Learning (CLL) to discern the characteristics and commonalities of sentiment elements and thus reduce the impact of distributed noise; and GDP-FOR, a novel generation template, to enable models to generate outputs in a more natural way. Extensive experiments on four datasets demonstrate the remarkable effectiveness of our approach in addressing ASQP tasks.

Recently, neural networks have shown impressive progress across diverse fields, with speech processing being no exception. However, recent breakthroughs in this area require extensive offline training using large datasets and tremendous computing resources. Unfortunately, these models struggle to retain their previously acquired knowledge when learning new tasks continually. In this paper, we investigate the problem of learning sequence-to-sequence models for spoken language understanding in a class-incremental learning (CIL) setting and we propose COCONUT, a CIL method that relies on the combination of experience replay and contrastive learning. Through a modified version of the standard supervised contrastive loss, COCONUT preserves the learned representations by pulling closer samples from the same class and pushing away the others. Moreover, we leverage a multimodal contrastive loss that helps the model learn more discriminative representations of the new data by aligning audio and text features. We also investigate different contrastive designs to combine the strengths of the contrastive loss with teacher-student architectures used for distillation. Experiments on two established SLU datasets reveal the effectiveness of our proposed approach and significant improvements over the baselines. We also show that COCONUT can be combined with methods that operate on the decoder side of the model, resulting in further metrics improvements.

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LLM as Prompter: Low-resource Inductive Reasoning on Arbitrary Knowledge Graphs
Kai Wang | Yuwei Xu | Zhiyong Wu | Siqiang Luo

Knowledge Graph (KG) inductive reasoning, which aims to infer missing facts from new KGs that are not seen during training, has been widely adopted in various applications. One critical challenge of KG inductive reasoning is handling low-resource scenarios with scarcity in both textual and structural aspects. In this paper, we attempt to address this challenge with Large Language Models (LLMs). Particularly, we utilize the state-of-the-art LLMs to generate a graph-structural prompt to enhance the pre-trained Graph Neural Networks (GNNs), which brings us new methodological insights into the KG inductive reasoning methods, as well as high generalizability in practice. On the methodological side, we introduce a novel pretraining and prompting framework ProLINK, designed for low-resource inductive reasoning across arbitrary KGs without requiring additional training. On the practical side, we experimentally evaluate our approach on 36 low-resource KG datasets and find that ProLINK outperforms previous methods in three-shot, one-shot, and zero-shot reasoning tasks, exhibiting average performance improvements by 20%, 45%, and 147%, respectively. Furthermore, ProLINK demonstrates strong robustness for various LLM promptings as well as full-shot scenarios.

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Unsupervised Parsing by Searching for Frequent Word Sequences among Sentences with Equivalent Predicate-Argument Structures
Junjie Chen | Xiangheng He | Danushka Bollegala | Yusuke Miyao

Unsupervised constituency parsing focuses on identifying word sequences that form a syntactic unit (i.e., constituents) in target sentences. Linguists identify the constituent by evaluating a set of Predicate-Argument Structure (PAS) equivalent sentences where we find the constituent appears more frequently than non-constituents (i.e., the constituent corresponds to a frequent word sequence within the sentence set). However, such frequency information is unavailable in previous parsing methods that identify the constituent by observing sentences with diverse PAS. In this study, we empirically show that constituents correspond to frequent word sequences in the PAS-equivalent sentence set. We propose a frequency-based parser, span-overlap, that (1) computes the span-overlap score as the word sequence’s frequency in the PAS-equivalent sentence set and (2) identifies the constituent structure by finding a constituent tree with the maximum span-overlap score. The parser achieves state-of-the-art level parsing accuracy, outperforming existing unsupervised parsers in eight out of ten languages. Additionally, we discover a multilingual phenomenon: participant-denoting constituents tend to have higher span-overlap scores than equal-length event-denoting constituents, meaning that the former tend to appear more frequently in the PAS-equivalent sentence set than the latter. The phenomenon indicates a statistical difference between the two constituent types, laying the foundation for future labeled unsupervised parsing research.

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Data-Centric Explainable Debiasing for Improving Fairness in Pre-trained Language Models
Yingji Li | Mengnan Du | Rui Song | Xin Wang | Ying Wang

Human-like social bias of pre-trained language models (PLMs) on downstream tasks have attracted increasing attention. The potential flaws in the training data are the main factor that causes unfairness in PLMs. Existing data-centric debiasing strategies mainly leverage explicit bias words (defined as sensitive attribute words specific to demographic groups) for counterfactual data augmentation to balance the training data. However, they lack consideration of implicit bias words potentially associated with explicit bias words in complex distribution data, which indirectly harms the fairness of PLMs. To this end, we propose a **Data**-Centric **Debias**ing method (named Data-Debias), which uses an explainability method to search for implicit bias words to assist in debiasing PLMs. Specifically, we compute the feature attributions of all tokens using the Integrated Gradients method, and then treat the tokens that have a large impact on the model’s decision as implicit bias words. To make the search results more precise, we iteratively train a biased model to amplify the bias with each iteration. Finally, we use the implicit bias words searched in the last iteration to assist in debiasing PLMs. Extensive experimental results on multiple PLMs debiasing on three different classification tasks demonstrate that Data-Debias achieves state-of-the-art debiasing performance and strong generalization while maintaining predictive abilities.

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Knowledge-Driven Cross-Document Relation Extraction
Monika Jain | Raghava Mutharaju | Kuldeep Singh | Ramakanth Kavuluru

Relation extraction (RE) is a well-known NLP application often treated as a sentence or document-level task. However, a handful of recent efforts explore it across documents or in the cross-document setting (CrossDocRE). This is distinct from the single document case because different documents often focus on disparate themes, while text within a document tends to have a single goal.Current CrossDocRE efforts do not consider domain knowledge, which are often assumed to be known to the reader when documents are authored. Here, we propose a novel approach, KXDocRE, that embed domain knowledge of entities with input text for cross-document RE. Our proposed framework has three main benefits over baselines: 1) it incorporates domain knowledge of entities along with documents’ text; 2) it offers interpretability by producing explanatory text for predicted relations between entities 3) it improves performance over the prior methods. Code and models are available at https://github.com/kracr/cross-doc-relation-extraction.

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Injecting Salesperson’s Dialogue Strategies in Large Language Models with Chain-of-Thought Reasoning
Wen Chang | Yun-Nung Chen

Recent research in dialogue systems focuses on two main categories: task-oriented (TOD) and open-domain (chit-chat) dialogues. TOD systems help users complete specific tasks, while open-domain systems aim to create engaging conversations. However, user intents often emerge during interactions. A recent study introduced SalesBot, simulating dialogues that transition from chit-chat to task-oriented scenarios to train sales agents. Unfortunately, the initial data lacked smooth transitions and coherent long dialogues, resulting in unnatural interactions. This paper presents SalesBot 2.0, an improved dataset leveraging commonsense knowledge from large language models (LLMs) through strategic prompting. Additionally, we introduce SalesAgent, a novel model trained on salesperson interactions using chain-of-thought (CoT) reasoning. This model excels in transitioning topics, understanding user intents, and selecting appropriate strategies.Experiments with diverse user simulations validate our method’s effectiveness in controlling dialogue strategies in LLMs. SalesBot 2.0 enhances coherence and reduces aggression, improving model learning for sales-customer interactions.

Although large language models (LLMs) show remarkable capabilities and generalizability across various tasks, they are criticized for lack of expertise. One promising solution is to combine knowledge graphs (KGs) with LLMs, and recent studies focus on integrating KGs into LLMs through prompt-based methods. However, these approaches fail to use the structural information of the KGs, suffer from the problem of knowledge conflict, and over-reliance on super LLMs. To address these challenges, we propose KG-Adapter, a parameter-level KG integration method based on parameter-efficient fine-tuning (PEFT). Specifically, we introduce a novel adapter structure designed for decoder-only LLMs, which can encode KGs from both node-centered and relation-centered perspectives, and then perform joint reasoning with LLMs to generate responses end-to-end. Experiments with diverse models on four datasets for two different tasks all demonstrate significant improvements. With only 28M parameters trained, we make the 7B-parameter LLM outperform the previous full-parameter fine-tuned state-of-the-art method and comparable to the prompt-based ChatGPT methods.

Although chain-of-thought (CoT) prompting combined with language models has achieved encouraging results on complex reasoning tasks, the naive greedy decoding used in CoT prompting usually causes the repetitiveness and local optimality. To address this shortcoming, ensemble-optimization tries to obtain multiple reasoning paths to get the final answer assembly. However, current ensemble-optimization methods either simply employ rule-based post-processing such as self-consistency, or train an additional model based on several task-related human annotations to select the best one among multiple reasoning paths, yet fail to generalize to realistic settings where the type of input questions is unknown or the answer format of reasoning paths is unknown. To avoid their limitations, we propose Self-Agreement, a generalizable ensemble-optimization method applying in almost all scenarios where the type of input questions and the answer format of reasoning paths may be known or unknown. Self-agreement firstly samples from language model’s decoder to generate a diverse set of reasoning paths, and subsequently prompts the language model one more time to determine the optimal answer by selecting the most agreed answer among the sampled reasoning paths. Self-agreement simultaneously achieves remarkable performance on six public reasoning benchmarks and superior generalization capabilities.

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Evaluating LLMs’ Mathematical Reasoning in Financial Document Question Answering
Pragya Srivastava | Manuj Malik | Vivek Gupta | Tanuja Ganu | Dan Roth

Large Language Models (LLMs), excel in natural language understanding, but their capability for complex mathematical reasoning with a hybrid of structured tables and unstructured text remain uncertain. This study explores LLMs’ mathematical reasoning on four financial tabular question-answering datasets: TATQA, FinQA, ConvFinQA, and Multihiertt. Through extensive experiments with various models and prompting techniques, we assess how LLMs adapt to complex tables and mathematical tasks. We focus on sensitivity to table complexity and performance variations with an increasing number of arithmetic reasoning steps. The results provide insights into LLMs’ capabilities and limitations in handling complex mathematical scenarios for semi-structured tables. Ultimately, we introduce a novel prompting technique EEDP tailored to semi-structured documents, matching or outperforming baselines performance while providing a nuanced understanding of LLMs abilities.

Large language models (LLMs) have achieved promising results in sentiment analysis through the in-context learning (ICL) paradigm. However, their ability to distinguish subtle sentiments still remains a challenge. Inspired by the human ability to adjust understanding via feedback, this paper enhances ICL by incorporating prior predictions and feedback, aiming to rectify sentiment misinterpretation of LLMs. Specifically, the proposed framework consists of three steps: (1) acquiring prior predictions of LLMs, (2) devising predictive feedback based on correctness, and (3) leveraging a feedback-driven prompt to refine sentiment understanding. Experimental results across nine sentiment analysis datasets demonstrate the superiority of our framework over conventional ICL methods, with an average F1 improvement of 5.95%.

Finding interpretable factors for stock returns is the most vital issue in the empirical asset pricing domain. As data-driven methods, existing factor mining models can be categorized into symbol-based and neural-based models. Symbol-based models are interpretable but inefficient, while neural-based approaches are efficient but lack interpretability. Hence, mining interpretable factors effectively presents a significant challenge. Inspired by the success of Large Language Models (LLMs) in various tasks, we propose a FActor Mining Agent (FAMA) model that enables LLMs to integrate the strengths of both neural and symbolic models for factor mining. In this paper, FAMA consists of two main components: Cross-Sample Selection (CSS) and Chain-of-Experience (CoE). CSS addresses the homogeneity challenges in LLMs during factor mining by assimilating diverse factors as in-context samples, whereas CoE enables LLMs to leverage past successful mining experiences, expediting the mining of effective factors. Experimental evaluations on real-world stock market data demonstrate the effectiveness of our approach by surpassing the SOTA RankIC by 0.006 and RankICIR by 0.105 in predicting S&P 500 returns. Furthermore, the investment simulation shows that our model can achieve superior performance with an annualized return of 38.4% and a Sharpe ratio of 667.2%.

Large language models (LLMs) internalize enormous parametric knowledge during pre-training. Concurrently, realistic applications necessitate external contextual knowledge to aid models on the underlying tasks. This raises a crucial dilemma known as knowledge conflicts, where the contextual knowledge clashes with the parametric knowledge. However, existing decoding works are specialized in resolving knowledge conflicts and could inadvertently deteriorate performance in absence of conflicts. In this paper, we propose an adaptive decoding method, termed as contextual information-entropy constraint decoding (COIECD), to discern whether the knowledge conflicts occur and resolve them. It can improve the model’s faithfulness to conflicting context, and simultaneously maintain high performance among non-conflicting context. Our experiments show that COIECD exhibits strong performance and robustness over knowledge conflicts in realistic datasets.

In the rapidly evolving landscape of Large Language Models (LLMs), ensuring robust safety measures is paramount. To meet this crucial need, we propose SALAD-Bench, a safety benchmark specifically designed for evaluating LLMs, attack, and defense methods. Distinguished by its breadth, SALAD-Bench transcends conventional benchmarks through its large scale, rich diversity, intricate taxonomy spanning three levels, and versatile functionalities.SALAD-Bench is crafted with a meticulous array of questions, from standard queries to complex ones enriched with attack, defense modifications and multiple-choice. To effectively manage the inherent complexity, we introduce an innovative evaluators: the LLM-based MD-Judge for QA pairs with a particular focus on attack-enhanced queries, ensuring a seamless, and reliable evaluation. Above components extend SALAD-Bench from standard LLM safety evaluation to both LLM attack and defense methods evaluation, ensuring the joint-purpose utility. Our extensive experiments shed light on the resilience of LLMs against emerging threats and the efficacy of contemporary defense tactics. Data and evaluator are released under https://github.com/OpenSafetyLab/SALAD-BENCH

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Extracting and Encoding: Leveraging Large Language Models and Medical Knowledge to Enhance Radiological Text Representation
Pablo Messina | Rene Vidal | Denis Parra | Alvaro Soto | Vladimir Araujo

Advancing representation learning in specialized fields like medicine remains challenging due to the scarcity of expert annotations for text and images. To tackle this issue, we present a novel two-stage framework designed to extract high-quality factual statements from free-text radiology reports in order to improve the representations of text encoders and, consequently, their performance on various downstream tasks.In the first stage, we propose a Fact Extractor that leverages large language models (LLMs) to identify factual statements from well-curated domain-specific datasets. In the second stage, we introduce a Fact Encoder (CXRFE) based on a BERT model fine-tuned with objective functions designed to improve its representations using the extracted factual data. Our framework also includes a new embedding-based metric (CXRFEScore) for evaluating chest X-ray text generation systems, leveraging both stages of our approach. Extensive evaluations show that our fact extractor and encoder outperform current state-of-the-art methods in tasks such as sentence ranking, natural language inference, and label extraction from radiology reports. Additionally, our metric proves to be more robust and effective than existing metrics commonly used in the radiology report generation literature. The code of this project is available at https://github.com/PabloMessina/CXR-Fact-Encoder.

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GNNavi: Navigating the Information Flow in Large Language Models by Graph Neural Network
Shuzhou Yuan | Ercong Nie | Michael Färber | Helmut Schmid | Hinrich Schuetze

Large Language Models (LLMs) exhibit strong In-Context Learning (ICL) capabilities when prompts with demonstrations are used. However, fine-tuning still remains crucial to further enhance their adaptability. Prompt-based fine-tuning proves to be an effective fine-tuning method in low-data scenarios, but high demands on computing resources limit its practicality. We address this issue by introducing a prompt-based parameter-efficient fine-tuning (PEFT) approach. GNNavi leverages insights into ICL’s information flow dynamics, which indicates that label words act in prompts as anchors for information propagation. GNNavi employs a Graph Neural Network (GNN) layer to precisely guide the aggregation and distribution of information flow during the processing of prompts by hardwiring the desired information flow into the GNN. Our experiments on text classification tasks with GPT-2 and Llama2 show GNNavi surpasses standard prompt-based fine-tuning methods in few-shot settings by updating just 0.2% to 0.5% of parameters. We compare GNNavi with prevalent PEFT approaches, such as prefix tuning, LoRA and Adapter in terms of performance and efficiency. Our analysis reveals that GNNavi enhances information flow and ensures a clear aggregation process.

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M-QALM: A Benchmark to Assess Clinical Reading Comprehension and Knowledge Recall in Large Language Models via Question Answering
Anand Subramanian | Viktor Schlegel | Abhinav Ramesh Kashyap | Thanh-Tung Nguyen | Vijay Prakash Dwivedi | Stefan Winkler

There is vivid research on adapting Large Language Models (LLMs) to perform a variety of tasks in high-stakes domains such as healthcare. Despite their popularity, there is a lack of understanding of the extent and contributing factors that allow LLMs to recall relevant knowledge and combine it with presented information in the clinical and biomedical domain: a fundamental pre-requisite for success on down-stream tasks.Addressing this gap, we use Multiple Choice and Abstractive Question Answering to conduct a large-scale empirical study on 22 datasets in three generalist and three specialist biomedical sub-domains. Our multifaceted analysis of the performance of 15 LLMs, further broken down by sub-domain, source of knowledge and model architecture, uncovers success factors such as instruction tuning that lead to improved recall and comprehension. We further show that while recently proposed domain-adapted models may lack adequate knowledge, directly fine-tuning on our collected medical knowledge datasets shows encouraging results, even generalising to unseen specialist sub-domains. We complement the quantitative results with a skill-oriented manual error analysis, which reveals a significant gap between the models’ capabilities to simply recall necessary knowledge and to integrate it with the presented context.To foster research and collaboration in this field we share M-QALM, our resources, standardised methodology, and evaluation results, with the research community to facilitate further advancements in clinical knowledge representation learning within language models.

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MovieSum: An Abstractive Summarization Dataset for Movie Screenplays
Rohit Saxena | Frank Keller

Movie screenplay summarization is challenging, as it requires an understanding of long input contexts and various elements unique to movies. Large language models have shown significant advancements in document summarization, but they often struggle with processing long input contexts. Furthermore, while television transcripts have received attention in recent studies, movie screenplay summarization remains underexplored. To stimulate research in this area, we present a new dataset, MovieSum, for abstractive summarization of movie screenplays. This dataset comprises 2200 movie screenplays accompanied by their Wikipedia plot summaries. We manually formatted the movie screenplays to represent their structural elements. Compared to existing datasets, MovieSum possesses several distinctive features: 1) It includes movie screenplays which are longer than scripts of TV episodes. 2) It is twice the size of previous movie screenplay datasets. 3) It provides metadata with IMDb IDs to facilitate access to additional external knowledge. We also show the results of recently released large language models applied to summarization on our dataset to provide a detailed baseline.

Autonomous artificial intelligence (AI) agents have emerged as promising protocols for automatically understanding the language-based environment, particularly with the exponential development of large language models (LLMs). However, a fine-grained, comprehensive understanding of multimodal environments remains under-explored. This work designs an autonomous workflow tailored for integrating AI agents seamlessly into extended reality (XR) applications for fine-grained training. We present a demonstration of a multimodal fine-grained training assistant for LEGO brick assembly in a pilot XR environment. Specifically, we design a cerebral language agent that integrates LLM with memory, planning, and interaction with XR tools and a vision-language agent, enabling agents to decide their actions based on past experiences. Furthermore, we introduce LEGO-MRTA, a multimodal fine-grained assembly dialogue dataset synthesized automatically in the workflow served by a commercial LLM. This dataset comprises multimodal instruction manuals, conversations, XR responses, and vision question answering. Last, we present several prevailing open-resource LLMs as benchmarks, assessing their performance with and without fine-tuning on the proposed dataset. We anticipate that the broader impact of this workflow will advance the development of smarter assistants for seamless user interaction in XR environments, fostering research in both AI and HCI communities.

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Perceptions of Language Technology Failures from South Asian English Speakers
Faye Holt | William Held | Diyi Yang

English NLP systems have empirically worse performance for dialects other than Standard American English (SAmE). However, how these discrepancies impact use of language technology by speakers of non-SAmE global Englishes is not well understood. We focus on reducing this gap for South Asian Englishes (SAsE), a macro-group of regional varieties with cumulatively more speakers than SAmE, by surveying SAsE speakers about their interactions with language technology and compare their responses to a control survey of SAmE speakers. SAsE speakers are more likely to recall failures with language technology and more likely to reference specific issues with written language technology than their SAmE counterparts. Furthermore, SAsE speakers indicate that they modify both their lexicon and syntax to make technology work better, but that lexical issues are perceived as the most salient challenge. We then assess whether these issues are pervasive in more recently developed Large Language Models (LLMs), introducing two benchmarks for broader SAsE Lexical and Indian English Syntactic understanding and evaluating 11 families of LLMs on them.

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A Mechanistic Analysis of a Transformer Trained on a Symbolic Multi-Step Reasoning Task
Jannik Brinkmann | Abhay Sheshadri | Victor Levoso | Paul Swoboda | Christian Bartelt

Transformers demonstrate impressive performance on a range of reasoning benchmarks. To evaluate the degree to which these abilities are a result of actual reasoning, existing work has focused on developing sophisticated benchmarks for behavioral studies. However, these studies do not provide insights into the internal mechanisms driving the observed capabilities. To improve our understanding of the internal mechanisms of transformers, we present a comprehensive mechanistic analysis of a transformer trained on a synthetic reasoning task. We identify a set of interpretable mechanisms the model uses to solve the task, and validate our findings using correlational and causal evidence. Our results suggest that it implements a depth-bounded recurrent mechanisms that operates in parallel and stores intermediate results in selected token positions. We anticipate that the motifs we identified in our synthetic setting can provide valuable insights into the broader operating principles of transformers and thus provide a basis for understanding more complex models.

Multimodal entity linking (MEL) aims to link ambiguous mentions in multimodal contexts to entities in a multimodal knowledge graph. A pivotal challenge is to fully leverage multi-element correlations between mentions and entities to bridge modality gap and enable fine-grained semantic matching. Existing methods attempt several local correlative mechanisms, relying heavily on the automatically learned attention weights, which may over-concentrate on partial correlations. To mitigate this issue, we formulate the correlation assignment problem as an optimal transport (OT) problem, and propose a novel MEL framework, namely OT-MEL, with OT-guided correlation assignment. Thereby, we exploit the correlation between multimodal features to enhance multimodal fusion, and the correlation between mentions and entities to enhance fine-grained matching. To accelerate model prediction, we further leverage knowledge distillation to transfer OT assignment knowledge to attention mechanism. Experimental results show that our model significantly outperforms previous state-of-the-art baselines and confirm the effectiveness of the OT-guided correlation assignment.

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On Efficiently Representing Regular Languages as RNNs
Anej Svete | Robin Chan | Ryan Cotterell

Recent work by Hewitt et al. (2020) provides an interpretation of the empirical success of recurrent neural networks (RNNs) as language models (LMs). It shows that RNNs can efficiently represent bounded hierarchical structures that are prevalent in human language.This suggests that RNNs’ success might be linked to their ability to model hierarchy. However, a closer inspection of hewitt-etal-2020-rnns construction shows that it is not inherently limited to hierarchical structures. This poses a natural question: What other classes of LMs RNNs can efficiently represent? To this end, we generalize Hewitt et al.’s (2020) construction and show that RNNs can efficiently represent a larger class of LMs than previously claimed—specifically, those that can be represented by a pushdown automaton with a bounded stack and a specific stack update function. Altogether, the efficiency of representing this diverse class of LMs with RNN LMs suggests novel interpretations of their inductive bias.

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A Survey on Modelling Morality for Text Analysis
Ines Reinig | Maria Becker | Ines Rehbein | Simone Ponzetto

In this survey, we provide a systematic review of recent work on modelling morality in text, an area of research that has garnered increasing attention in recent years. Our survey is motivated by the importance of modelling decisions on the created resources, the models trained on these resources and the analyses that result from the models’ predictions. We review work at the interface of NLP, Computational Social Science and Psychology and give an overview of the different goals and research questions addressed in the papers, their underlying theoretical backgrounds and the methods that have been applied to pursue these goals. We then identify and discuss challenges and research gaps, such as the lack of a theoretical framework underlying the operationalisation of morality in text, the low IAA reported for manyhuman-annotated resulting resources and the lack of validation of newly proposed resources and analyses.

Data selection in instruction tuning emerges as a pivotal process for acquiring high-quality data and training instruction-following large language models (LLMs), but it is still a new and unexplored research area for vision-language models (VLMs). Existing data selection approaches on LLMs either rely on single unreliable scores, or use downstream tasks for selection, which is time-consuming and can lead to potential over-fitting on the chosen evaluation datasets. To address this challenge, we introduce a novel dataset selection method, Self-Filter, that utilizes the VLM itself as a filter. This approach is inspired by the observation that VLMs benefit from training with the most challenging instructions. Self-Filter operates in two stages. In the first stage, we devise a scoring network to evaluate the difficulty of training instructions, which is co-trained with the VLM. In the second stage, we use the trained score net to measure the difficulty of each instruction, select the most challenging samples, and penalize similar samples to encourage diversity. Comprehensive experiments on LLaVA and MiniGPT-4 show that Self-Filter can reach better results compared to full data settings with merely about 15% samples, and can achieve superior performance against competitive baselines.

Large Language Models (LLMs) have demonstrated exceptional coding capability. However, as another critical component of programming proficiency, the debugging capability of LLMs remains relatively unexplored. Previous evaluations of LLMs’ debugging ability are significantly limited by the risk of data leakage, the scale of the dataset, and the variety of tested bugs. To overcome these deficiencies, we introduce ‘DebugBench’, an LLM debugging benchmark consisting of 4,253 instances. It covers four major bug categories and 18 minor types in C++, Java, and Python. To construct DebugBench, we collect code snippets from the LeetCode community, implant bugs into source data with GPT-4, and assure rigorous quality checks. We evaluate two commercial and four open-source models in a zero-shot scenario. We find that (1) while closed-source models exhibit inferior debugging performance compared to humans, open-source models relatively lower pass rate scores; (2) the complexity of debugging notably fluctuates depending on the bug category; (3) incorporating runtime feedback has a clear impact on debugging performance which is not always helpful. As an extension, we also compare LLM debugging and code generation, revealing a strong correlation between them for closed-source models. These findings will benefit the development of LLMs in debugging.

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POP-CEE: Position-oriented Prompt-tuning Model for Causal Emotion Entailment
Zhihan Zhou | Xue Gu | Yujie Zhao | Hao Xu

The objective of the Causal Emotion Entailment (CEE) task is to identify the causes of the target emotional utterances in a given conversation. Most existing studies have focused on a fine-tuning paradigm based on a pretrained model, e.g., the BERT model. However, there are gaps between the pretrained task and the CEE task. Although a pretrained model enhances contextual comprehension to some extent, it cannot acquire specific knowledge that is relevant to the CEE task. In addition, in a typical CEE task, there are peculiarities in the distribution of the positions with different emotion types of emotion utterances and cause utterances in conversations. Existing methods employ a fixed-size window to capture the relationship between neighboring conversations; however, these methods ignore the specific semantic associations between emotions and cause utterances. To address these issues, we propose the Position-oriented Prompt-tuning (POP-CEE) model to solve the CEE task in an end-to-end manner. Specifically, we can model the CEE task by designing prompts with multiple unified goals and by exploring the positional relationship between emotion and cause utterances using a position constraint module. Experimental results demonstrate that the proposed POP-CEE model achieves state-of-the-art performance on a benchmark dataset. Ourcode and data can be found at: https://github.com/Zh0uzh/POP-CEE.

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Context Length Extension via Generalized Extrapolation Scale
Linhan Li | Zhang Huaping

Context length expansion of transformer models is considered a key challenge, especially when handling context beyond the training length during inference stage. In this paper, we propose Geeneralized extrapolatioN scalE (GeNE), a set of parameterized extrapolation functions applied to each layer and attention head to adaptively adjust its extrapolation scales. Experimental results show that GeNE provides a significant improvement on long context language modeling. By randomly scaling the extrapolation ratio during the finetuning, GeNE achieves stable extrapolation on 64k contexts by training on 16k length text. Further, the instruction following Llama2 model based on GeNE achieved competitive results compared with other open-source models of the same parameter scale.

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Selectively Answering Visual Questions
Julian Eisenschlos | Hernán Maina | Guido Ivetta | Luciana Benotti

Recently, large multi-modal models (LMMs) have emerged with the capacity to perform vision tasks such as captioning and visual question answering (VQA) with unprecedented accuracy. Applications such as helping the blind or visually impaired have a critical need for precise answers. It is specially important for models to be well calibrated and be able to quantify their uncertainty in order to selectively decide when to answer and when to abstain or ask for clarifications. We perform the first in-depth analysis of calibration methods and metrics for VQA with in-context learning LMMs. Studying VQA on two answerability benchmarks, we show that the likelihood score of visually grounded models is better calibrated than in their text-only counterparts for in-context learning, where sampling based methods are generally superior, but no clear winner arises. We propose Avg BLEU, a calibration score combining the benefits of both sampling and likelihood methods across modalities.

We release a multi-accent dataset and propose speech-programming and gradient reversal classifier to improve the generalization.Abstract: Speech-to-SQL (S2SQL) aims to convert spoken questions into SQL queries given relational databases, which has been traditionally implemented in a cascaded manner while facing the following challenges: 1) model training is faced with the major issue of data scarcity, where limited parallel data is available; and 2) the systems should be robust enough to handle diverse out-of-domain speech samples that differ from the source data. In this work, we propose the direct generalizable speech-to-SQL parsing model Wav2SQL which avoids error compounding across cascaded systems. Specifically, 1) to accelerate speech-driven SQL parsing research in the community, we release a large-scale and multi-accent dataset MASpider; 2) leveraging the recent progress in the large-scale pre-training, we show that it alleviates the data scarcity issue and allow for direct speech-to-SQL parsing; and 3) we include the speech re-programming and gradient reversal classifier techniques to reduce acoustic variance and learned style-agnostic representation, improving generalization to unseen out-of-domain custom data. Experimental results demonstrate that Wav2SQL avoids error compounding and achieves state-of-the-art results by up to 4.7% accuracy improvement over the baseline.

Training Large Language Models (LLMs) to process extensive context lengths incurs prohibitive computational costs. Prevailing techniques for extending context capabilities in LLMs typically require not only additional training procedures but also access to datasets with long context (e.g., sequences of 32K tokens), presupposing substantial GPU expenditures. To address the aforementioned issues, we introduce a novel solution named Efficient and Extreme length extension for Large Language Models (E2-LLM). E2-LLM entails a singular training process over considerably short sequences (e.g., 4K tokens), which greatly mitigates the cost of continual-pretraining or fine-tuning. Within the training phase, we incorporate a dual augmentation strategy with Rotary Position Embeddings (RoPE) that adjusts the scale and position indices across distinct training samples. E 2 -LLM is meticulously designed to enhance the model’s robustness to diverse relative positions. The experimental results on multiple benchmark datasets demonstrate the superior performance of E 2 -LLM on demanding tasks of processing long contexts.

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Are Female Carpenters like Blue Bananas? A Corpus Investigation of Occupation Gender Typicality
Da Ju | Karen Ullrich | Adina Williams

People tend to use language to mention surprising properties of events: for example, when a banana is blue, we are more likely to mention color than when it is yellow. This fact is taken to suggest that yellowness is somehow a typical feature of bananas, and blueness is exceptional. Similar to how a yellow color is typical of bananas, there may also be genders that are typical of occupations. In this work, we explore this question using information theoretic techniques coupled with corpus statistic analysis. In two distinct large corpora, we do not find strong evidence that occupations and gender display the same patterns of mentioning as do bananas and color. Instead, we find that gender mentioning is correlated with femaleness of occupation in particular, suggesting perhaps that woman-dominated occupations are seen as somehow “more gendered” than male-dominated ones, and thereby they encourage more gender mentioning overall.

Large Language Models (LLMs) have shown potential in reasoning over structured environments, e.g., knowledge graphs and tables. Such tasks typically require multi-hop reasoning, i.e., match natural language utterance with instances in the environment. Previous works adopt LLMs to incrementally build a reasoning path, where LLMs either invoke tools or pick up items by step-by-step interacting with the environment. We propose Reasoning-Path-Editing (Readi), a novel framework where LLMs can efficiently and faithfully reason over structured environments. In Readi, LLMs initially generate a reasoning path given a query, and edit the path only when necessary. We instantiate the path on structured environments and provide feedback to edit the path if anything goes wrong. Experimental results on three KGQA and two TableQA datasets show the effectiveness of Readi, significantly surpassing previous LLM-based methods (by 9.1% Hit@1 on WebQSP, 12.4% on MQA-3H and 9.5% on WTQ), comparable with state-of-the-art fine-tuned methods (67% on CWQ and 74.7% on WebQSP) and substantially boosting the vanilla LLMs (by 14.9% on CWQ). Our code will be available on https://aka.ms/readi.

In the era of Large Language Models (LLMs), predicting judicial outcomes poses significant challenges due to the complexity of legal proceedings and the scarcity of expert-annotated datasets. Addressing this, we introduce Prediction with Explanation (PredEx), the largest expert-annotated dataset for legal judgment prediction and explanation in the Indian context, featuring over 15,000 annotations. This groundbreaking corpus significantly enhances the training and evaluation of AI models in legal analysis, with innovations including the application of instruction tuning to LLMs. This method has markedly improved the predictive accuracy and explanatory depth of these models for legal judgments. We employed various transformer-based models, tailored for both general and Indian legal contexts. Through rigorous lexical, semantic, and expert assessments, our models effectively leverage PredEx to provide precise predictions and meaningful explanations, establishing it as a valuable benchmark for both the legal profession and the NLP community.

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RulE: Knowledge Graph Reasoning with Rule Embedding
Xiaojuan Tang | Song-Chun Zhu | Yitao Liang | Muhan Zhang

Knowledge graph reasoning is an important problem for knowledge graphs. In this paper, we propose a novel and principled framework called RulE (stands for Rule Embedding) to effectively leverage logical rules to enhance KG reasoning. Unlike knowledge graph embedding methods, RulE learns rule embeddings from existing triplets and first-order rules by jointly representing entities, relations and logical rules in a unified embedding space. Based on the learned rule embeddings, a confidence score can be calculated for each rule, reflecting its consistency with the observed triplets. This allows us to perform logical rule inference in a soft way, thus alleviating the brittleness of logic. On the other hand, RulE injects prior logical rule information into the embedding space, enriching and regularizing the entity/relation embeddings. This makes KGE alone perform better too. RulE is conceptually simple and empirically effective. We conduct extensive experiments to verify each component of RulE.Results on multiple benchmarks reveal that our model outperforms the majority of existing embedding-based and rule-based approaches.

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Multi-Objective Linguistic Control of Large Language Models
Dang Nguyen | Jiuhai Chen | Tianyi Zhou

Large language models (LLMs), despite their breakthroughs on many challenging benchmark tasks, prefer to generate verbose responses and lack the controllability of output complexity, which is usually preferred by human users in practice. In this paper, we study how to precisely control multiple linguistic complexities of LLM output by finetuning using off-the-shelf data. To this end, we propose multi-control tuning (MCTune), which includes multiple linguistic complexity values of ground-truth responses as controls in the input for instruction tuning. We finetune LLaMA2-7B on Alpaca-GPT4 and WizardLM datasets. Evaluations on widely used benchmarks demonstrate that our method does not only improve LLMs’ multi-complexity controllability substantially but also retains or even enhances the quality of the responses as a side benefit.

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Evaluating the Smooth Control of Attribute Intensity in Text Generation with LLMs
Shang Zhou | Feng Yao | Chengyu Dong | Zihan Wang | Jingbo Shang

Controlling the attribute intensity of text generation is crucial across scenarios (e.g., writing conciseness, chatting emotion, and explanation clarity). The remarkable capabilities of large language models (LLMs) have revolutionized text generation, prompting us to explore such smooth control of LLM generation. Specifically, we propose metrics to assess the range, calibration, and consistency of the generated text’s attribute intensity in response to varying control values, as well as its relevance to the intended context. To quantify the attribute intensity and context relevance, we leverage an Elo rating system and GPT4, respectively, both renowned for their robust alignment with human judgment. We look into two viable training-free methods for achieving smooth control of LLMs: (1) Prompting with semantic shifters, and (2) Modifying internal model representations. The evaluations of these two methods are conducted on 5 different attributes with various models.

The recent trend of using Large Language Models (LLMs) as tool agents in real-world applications underscores the necessity for comprehensive evaluations of their capabilities, particularly in complex scenarios involving planning, creating, and using tools. However, existing benchmarks typically focus on simple synthesized queries that do not reflect real-world complexity, thereby offering limited perspectives in evaluating tool utilization. To address this issue, we present UltraTool, a novel benchmark designed to improve and evaluate LLMs’ ability in tool utilization within real-world scenarios. UltraTool focuses on the entire process of using tools - from planning and creating to applying them in complex tasks. It emphasizes real-world complexities, demanding accurate, multi-step planning for effective problem-solving. A key feature of UltraTool is its independent evaluation of planning with natural language, which happens before tool usage and simplifies the task solving by mapping out the intermediate steps. Thus, unlike previous work, it eliminates the restriction of pre-defined toolset. Through extensive experiments on various LLMs, we offer novel insights into the evaluation of capabilities of LLMs in tool utilization, thereby contributing a fresh perspective to this rapidly evolving field. The benchmark is publicly available at https://github.com/JoeYing1019/UltraTool.

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Do Androids Know They’re Only Dreaming of Electric Sheep?
Sky CH-Wang | Benjamin Van Durme | Jason Eisner | Chris Kedzie

We design probes trained on the internal representations of a transformer language model to predict its hallucinatory behavior on three grounded generation tasks. To train the probes, we annotate for span-level hallucination on both sampled (organic) and manually edited (synthetic) reference outputs. Our probes are narrowly trained and we find that they are sensitive to their training domain: they generalize poorly from one task to another or from synthetic to organic hallucinations. However, on in-domain data, they can reliably detect hallucinations at many transformer layers, achieving 95% of their peak performance as early as layer 4. Here, probing proves accurate for evaluating hallucination, outperforming several contemporary baselines and even surpassing an expert human annotator in response-level detection F1. Similarly, on span-level labeling, probes are on par or better than the expert annotator on two out of three generation tasks. Overall, we find that probing is a feasible and efficient alternative to language model hallucination evaluation when model states are available.

Prior research endeavors of the ensemble Large Language Models (LLMs) achieved great success by employing an individual language model (LM) rank before the text generation. However, the use of an individual LM ranker faces two primary challenges: (1) The time-intensive nature of the ranking process, stemming from the comparisons between models; (2) The issue of error propagation arising from the separate ranking and generation models within the framework. In order to overcome these challenges, we propose a novel ensemble framework, namely Unified Ranking and Generation (URG). URG represents an end-to-end framework that jointly ranks the outputs of LLMs and generates fine-grained fusion results, via utilizing a dedicated cross-attention-based module and noise mitigation training against irrelevant information stemming from bad ranking results. Through extensive experimentation and evaluation, we demonstrate the efficiency and effectiveness of our framework in both the ranking and generation tasks. With the close coordination of the ranking and generation modules, our end-to-end framework achieves the state-of-the-art (SOTA) performance on these tasks, and exhibits substantial enhancements to any of the ensembled models.

Retrieval is a widely adopted approach for improving language models leveraging external information. As the field moves towards multi-modal large language models, it is important to extend the pure text based methods to incorporate other modalities in retrieval as well for applications across the wide spectrum of machine learning tasks and data types. In this work, we propose multi-modal retrieval with two approaches: kNN-LM and cross-attention techniques. We demonstrate the effectiveness of our retrieval approaches empirically by applying them to automatic speech recognition tasks with access to external information. Under this setting, we show that speech-based multi-modal retrieval outperforms text based retrieval, and yields up to improvement in word error rate over the multi-modal language model baseline. Furthermore, we achieve state-of-the-art recognition results on the Spoken-Squad question answering dataset.

Low-Rank Adaptation (LoRA) provides an effective yet efficient solution for fine-tuning large language models (LLMs). The modular and plug-and-play nature of LoRA enables the integration of diverse domain-specific LoRAs to enhance the capabilities of LLMs. Previous research on exploiting multiple LoRAs either focuses on specific isolated downstream tasks or fixes the selection of LoRAs during training. However, in real-world scenarios, LLMs receive diverse prompts covering different tasks, and the pool of candidate LoRAs is often dynamically updated. To bridge this gap, we propose LoraRetriever, a retrieve-then-compose framework that adaptively retrieves and composes multiple LoRAs according to the input prompts. LoraRetriever contains three main components: firstly, identifying and retrieving LoRAs relevant to the given input; secondly, formulating strategies for effectively integrating the retrieved LoRAs; and thirdly, developing efficient batch inference to accommodate heterogeneous requests. Experimental results indicate that LoraRetriever consistently outperforms the baselines, highlighting its practical effectiveness and versatility. Our code is available at https://github.com/StyxXuan/LoraRetriever.

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ELAD: Explanation-Guided Large Language Models Active Distillation
Yifei Zhang | Bo Pan | Chen Ling | Yuntong Hu | Liang Zhao

The deployment and application of Large Language Models (LLMs) is hindered by their memory inefficiency, computational demands, and the high costs of API inferences. Traditional distillation methods, which transfer the capabilities of LLMs to smaller models, often fail to determine whether the knowledge has been sufficiently transferred, potentially resulting in high costs or incomplete distillation. In this paper, we propose an Explanation-Guided LLMs Active Distillation (ELAD) framework that employs an active learning strategy to optimize the balance between annotation costs and model performance. To improve the efficiency of sample selection, we introduce an explanation-guided sample selection method that identifies samples challenging its reasoning by exploiting uncertainties in reasoning explanation steps. Additionally, we present a customized LLM-annotated explanation revision technique where the teacher model detects and corrects flaws in the student model’s reasoning. Our experiments across various reasoning datasets demonstrate that our framework significantly enhances the efficiency of LLMs knowledge distillation.

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Evaluating the Elementary Multilingual Capabilities of Large Language Models with MultiQ
Carolin Holtermann | Paul Röttger | Timm Dill | Anne Lauscher

Large language models (LLMs) need to serve everyone, including a global majority of non-English speakers. However, most LLMs today, and open LLMs in particular, are often intended for use in just English (e.g. Llama2, Mistral) or a small handful of high-resource languages (e.g. Mixtral, Qwen). Recent research shows that, despite limits in their intended use, people prompt LLMs in many different languages.Therefore, in this paper, we investigate the basic multilingual capabilities of state-of-the-art open LLMs beyond their intended use.For this purpose, we introduce MultiQ, a new silver standard benchmark for basic open-ended question answering with 27.4k test questions across a typologically diverse set of 137 languages. With MultiQ, we evaluate language fidelity, i.e. whether models respond in the prompted language, and question answering accuracy. All LLMs we test respond faithfully and/or accurately for at least some languages beyond their intended use. Most models are more accurate when they respond faithfully. However, differences across models are large, and there is a long tail of languages where models are neither accurate nor faithful. We explore differences in tokenization as a potential explanation for our findings, identifying possible correlations that warrant further investigation.

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Semantics or spelling? Probing contextual word embeddings with orthographic noise
Jacob Matthews | John Starr | Marten Schijndel

Pretrained language model (PLM) hidden states are frequently employed as contextual word embeddings (CWE): high-dimensional representations that encode semantic information given linguistic context. Across many areas of computational linguistics research, similarity between CWEs is interpreted as semantic similarity. However, it remains unclear exactly what information is encoded in PLM hidden states. We investigate this practice by probing PLM representations using minimal orthographic noise. We expect that if CWEs primarily encode semantic information, a single character swap in the input word will not drastically affect the resulting representation, given sufficient linguistic context. Surprisingly, we find that CWEs generated by popular PLMs are highly sensitive to noise in input data, and that this sensitivity is related to subword tokenization: the fewer tokens used to represent a word at input, the more sensitive its corresponding CWE. This suggests that CWEs capture information unrelated to word-level meaning and can be manipulated through trivial modifications of input data. We conclude that these PLM-derived CWEs may not be reliable semantic proxies, and that caution is warranted when interpreting representational similarity.

Retrieval-augmented generation (RAG) is a powerful technique to facilitate language model generation with proprietary and private data, where data privacy is a pivotal concern. Whereas extensive research has demonstrated the privacy risks of large language models (LLMs), the RAG technique could potentially reshape the inherent behaviors of LLM generation, posing new privacy issues that are currently under-explored. To this end, we conduct extensive empirical studies with novel attack methods, which demonstrate the vulnerability of RAG systems on leaking the private retrieval database. Despite the new risks brought by RAG on the retrieval data, we further discover that RAG can be used to mitigate the old risks, i.e., the leakage of the LLMs’ training data. In general, we reveal many new insights in this paper for privacy protection of retrieval-augmented LLMs, which could benefit both LLMs and RAG systems builders.

Modeling empathy is a complex endeavor that is rooted in interpersonal and experiential dimensions of human interaction, and remains an open problem within AI. Existing empathy datasets fall short in capturing the richness of empathy responses, often being confined to in-lab or acted scenarios, lacking longitudinal data, and missing self-reported labels. We introduce a new multimodal dataset for empathy during personal experience sharing: the EmpathicStories++ dataset containing 53 hours of video, audio, and text data of 41 participants sharing vulnerable experiences and reading empathically resonant stories with an AI agent. EmpathicStories++ is the first longitudinal dataset on empathy, collected over a month-long deployment of social robots in participants’ homes, as participants engage in natural, empathic storytelling interactions with AI agents. We then introduce a novel task of predicting individuals’ empathy toward others’ stories based on their personal experiences, evaluated in two contexts: participants’ own personal shared story context and their reflections on stories they read. We benchmark this task using state-of-the-art models to pave the way for future improvements in contextualized and longitudinal empathy modeling. Our work provides a valuable resource for further research in developing empathetic AI systems and understanding the intricacies of human empathy within genuine, real-world settings.

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MRL Parsing Without Tears: The Case of Hebrew
Shaltiel Shmidman | Avi Shmidman | Moshe Koppel | Reut Tsarfaty

Syntactic parsing remains a critical tool for relation extraction and information extraction, especially in resource-scarce languages where LLMs are lacking. Yet in morphologically rich languages (MRLs), where parsers need to identify multiple lexical units in each token, existing systems suffer in latency and setup complexity. Some use a pipeline to peel away the layers: first segmentation, then morphology tagging, and then syntax parsing; however, errors in earlier layers are then propagated forward. Others use a joint architecture to evaluate all permutations at once; while this improves accuracy, it is notoriously slow. In contrast, and taking Hebrew as a test case, we present a new “flipped pipeline”: decisions are made directly on the whole-token units by expert classifiers, each one dedicated to one specific task. The classifier predictions are independent of one another, and only at the end do we synthesize their predictions. This blazingly fast approach requires only a single huggingface call, without the need for recourse to lexicons or linguistic resources. When trained on the same training set used in previous studies, our model achieves near-SOTA performance on a wide array of Hebrew NLP tasks. Furthermore, when trained on a newly enlarged training corpus, our model achieves a new SOTA for Hebrew POS tagging and dependency parsing. We release this new SOTA model to the community. Because our architecture does not rely on any language-specific resources, it can serve as a model to develop similar parsers for other MRLs.

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SyntaxShap: Syntax-aware Explainability Method for Text Generation
Kenza Amara | Rita Sevastjanova | Mennatallah El-Assady

To harness the power of large language models in safety-critical domains, we need to ensure the explainability of their predictions. However, despite the significant attention to model interpretability, there remains an unexplored domain in explaining sequence-to-sequence tasks using methods tailored for textual data. This paper introduces *SyntaxShap*, a local, model-agnostic explainability method for text generation that takes into consideration the syntax in the text data. The presented work extends Shapley values to account for parsing-based syntactic dependencies. Taking a game theoric approach, SyntaxShap only considers coalitions constraint by the dependency tree. We adopt a model-based evaluation to compare SyntaxShap and its weighted form to state-of-the-art explainability methods adapted to text generation tasks, using diverse metrics including faithfulness, coherency, and semantic alignment of the explanations to the model. We show that our syntax-aware method produces explanations that help build more faithful and coherent explanations for predictions by autoregressive models. Confronted with the misalignment of human and AI model reasoning, this paper also highlights the need for cautious evaluation strategies in explainable AI.

Privacy policies are crucial for informing users about data practices, yet their length and complexity often deter users from reading them. In this paper, we propose an automated approach to identify and visualize data practices within privacy policies at different levels of detail. Leveraging crowd-sourced annotations from the ToS;DR platform, we experiment with various methods to match policy excerpts with predefined data practice descriptions. We further conduct a case study to evaluate our approach on a real-world policy, demonstrating its effectiveness in simplifying complex policies. Experiments show that our approach accurately matches data practice descriptions with policy excerpts, facilitating the presentation of simplified privacy information to users.

Knowledge Graph Embedding (KGE) is a powerful technique for predicting missing links in Knowledge Graphs (KGs) by learning the entities and relations. Hyperbolic space has emerged as a promising embedding space for KGs due to its ability to represent hierarchical data. Nevertheless, most existing hyperbolic KGE methods rely on tangent approximation and are not fully hyperbolic, resulting in distortions and inaccuracies. To overcome this limitation, we propose LorentzKG, a fully hyperbolic KGE method that represents entities as points in the Lorentz model and represents relations as the intrinsic transformation—the Lorentz transformations between entities. We demonstrate that the Lorentz transformation, which can be decomposed into Lorentz rotation/reflection and Lorentz boost, captures various types of relations including hierarchical structures. Experimental results show that our LorentzKG achieves state-of-the-art performance.

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Tell Me What’s Next: Textual Foresight for Generic UI Representations
Andrea Burns | Kate Saenko | Bryan Plummer

Mobile app user interfaces (UIs) are rich with action, text, structure, and image content that can be utilized to learn generic UI representations for tasks like automating user commands, summarizing content, and evaluating the accessibility of user interfaces. Prior work has learned strong visual representations with local or global captioning losses, but fails to retain both granularities.To combat this, we propose Textual Foresight, a novel pretraining objective for learning UI screen representations. Textual Foresight generates global text descriptions of future UI states given a current UI and local action taken. Our approach requires joint reasoning over elements and entire screens, resulting in improved UI features: on generation tasks, UI agents trained with Textual Foresight outperform state-of-the-art by 2% with 28x fewer images. We train with our newly constructed mobile app dataset, OpenApp, which results in the first public dataset for app UI representation learning. OpenApp enables new baselines, and we find Textual Foresight improves average task performance over them by 5.7% while having access to 2x less data.

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Probing the Uniquely Identifiable Linguistic Patterns of Conversational AI Agents
Iqra Zahid | Tharindu Madusanka | Riza Batista-Navarro | Youcheng Sun

The proliferation of Conversational AI agents (CAAs) has emphasised the need to distinguish between human and machine-generated texts, with implications spanning digital forensics and cybersecurity. While prior research primarily focussed on distinguishing human from machine-generated text, our study takes a more refined approach by analysing different CAAs. We construct linguistic profiles for five CAAs, aiming to identify Uniquely Identifiable Linguistic Patterns (UILPs) for each model using authorship attribution techniques. Authorship attribution (AA) is the task of identifying the author of an unknown text from a pool of known authors. Our research seeks to answer crucial questions about the existence of UILPs in CAAs, the linguistic overlap between various text types generated by these models, and the feasibility of Authorship Attribution (AA) for CAAs based on UILPs. Promisingly, we are able to attribute CAAs based on their original texts with a weighted F1-score of 96.94%. Further, we are able to attribute CAAs according to their writing style (as specified by prompts), yielding a weighted F1-score of 95.84%, which sets the baseline for this task. By employing principal component analysis (PCA), we identify the top 100 most informative linguistic features for each CAA, achieving a weighted F1-score ranging from 86.04% to 97.93%, and an overall weighted F1-score of 93.86%.

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The Butterfly Effect of Altering Prompts: How Small Changes and Jailbreaks Affect Large Language Model Performance
Abel Salinas | Fred Morstatter

Large Language Models (LLMs) are regularly being used to label data across many domains and for myriad tasks. By simply asking the LLM for an answer, or “prompting,” practitioners are able to use LLMs to quickly get a response for an arbitrary task. This prompting is done through a series of decisions by the practitioner, from simple wording of the prompt, to requesting the output in a certain data format, to jailbreaking in the case of prompts that address more sensitive topics. In this work, we ask: do variations in the way a prompt is constructed change the ultimate decision of the LLM? We answer this using a series of prompt variations across a variety of text classification tasks. We find that even the smallest of perturbations, such as adding a space at the end of a prompt, can cause the LLM to change its answer. Further, we find that requesting responses in XML and commonly used jailbreaks can have cataclysmic effects on the data labeled by LLMs.

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X-Shot: A Unified System to Handle Frequent, Few-shot and Zero-shot Learning Simultaneously in Classification
Hanzi Xu | Muhao Chen | Lifu Huang | Slobodan Vucetic | Wenpeng Yin

In recent years, few-shot and zero-shot learning, which learn to predict labels with limited annotated instances, have garnered significant attention. Traditional approaches often treat frequent-shot (freq-shot; labels with abundant instances), few-shot, and zero-shot learning as distinct challenges, optimizing systems for just one of these scenarios. Yet, in real-world settings, label occurrences vary greatly. Some of them might appear thousands of times, while others might only appear sporadically or not at all. For practical deployment, it is crucial that a system can adapt to any label occurrence. We introduce a novel classification challenge: **X-shot**, reflecting a real-world context where freq-shot, few-shot, and zero-shot labels co-occur without predefined limits. Here, **X** can span from 0 to positive infinity. The crux of **X-shot** centers on open-domain generalization and devising a system versatile enough to manage various label scenarios. To solve **X-shot**, we propose **BinBin** (**B**inary **IN**ference **B**ased on **IN**struction following) that leverages the Indirect Supervision from a large collection of NLP tasks via instruction following, bolstered by Weak Supervision provided by large language models. **BinBin** surpasses previous state-of-the-art techniques on three benchmark datasets across multiple domains. To our knowledge, this is the first work addressing **X-shot** learning, where **X** remains variable.

Among the many tasks that Large Language Models (LLMs) have revolutionized is text classification. Current text classification paradigms, however, rely solely on the output of the final layer in the LLM, with the rich information contained in internal neurons largely untapped. In this study, we present SPIN: a model-agnostic framework that sparsifies and integrates internal neurons of intermediate layers of LLMs for text classification. Specifically, SPIN sparsifies internal neurons by linear probing-based salient neuron selection layer by layer, avoiding noise from unrelated neurons and ensuring efficiency. The cross-layer salient neurons are then integrated to serve as multi-layered features for the classification head. Extensive experimental results show our proposed SPIN significantly improves text classification accuracy, efficiency, and interpretability.

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Decomposing Co-occurrence Matrices into Interpretable Components as Formal Concepts
Akihiro Maeda | Takuma Torii | Shohei Hidaka

This study addresses the interpretability of word representations through an investigation of a count-based co-occurrence matrix. Employing the mathematical methodology of Formal Concept Analysis, we reveal an underlying structure that is amenable to human interpretation. Furthermore, we unveil the emergence of hierarchical and geometrical structures within word vectors as consequences of word usage. Our experiments on the PPMI matrix demonstrate that the formal concepts that we identified align with interpretable categories, as shown in the category completion task.

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Two-Pronged Human Evaluation of ChatGPT Self-Correction in Radiology Report Simplification
Ziyu Yang | Santhosh Cherian | Slobodan Vucetic

Radiology reports are highly technical documents aimed primarily at doctor-doctor communication. There has been an increasing interest in sharing those reports with patients, necessitating providing them patient-friendly simplifications of the original reports. This study explores the suitability of large language models in automatically generating those simplifications. We examine the usefulness of chain-of-thought and self-correction prompting mechanisms in this domain. We also propose a new evaluation protocol that employs radiologists and laypeople, where radiologists verify the factual correctness of simplifications, and laypeople assess simplicity and comprehension. Our experimental results demonstrate the effectiveness of self-correction prompting in producing high-quality simplifications. Our findings illuminate the preferences of radiologists and laypeople regarding text simplification, informing future research on this topic.

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Planning First, Question Second: An LLM-Guided Method for Controllable Question Generation
Kunze Li | Yu Zhang

In the field of education, for better assessment of students’ abilities, generated questions often need to meet experts’ requirements, indicating the need for controllable question generation (CQG). However, current CQG methods mainly focus on difficulty control, neglecting the control of question content and assessed abilities, which are also crucial in educational QG. In this paper, we propose an LLM-guided method PFQS (for Planning First, Question Second), which utilizes Llama 2 to generate an answer plan and then generates questions based on it. The plan not only includes candidate answers but also integrates LLM’s understanding and multiple requirements, which make question generation simple and controllable. We evaluate our approach on the FairytaleQA dataset, a well-structured QA dataset derived from child-friendly storybooks. In the dataset, the attribute label represents content control, while the local_or_sum and ex_or_im labels denote difficulty control. Experimental results demonstrate that our approach outperforms previous state-of-the-art results and achieves better consistency with requirements compared to prompt-based method. Further application of our method to Llama 2 and Mistral also leads to improved requirement consistency in a zero-shot setting.

Large language models (LLMs) demonstrate exceptional performance in numerous tasks but still heavily rely on knowledge stored in their parameters. Moreover, updating this knowledge incurs high training costs. Retrieval-augmented generation (RAG) methods address this issue by integrating external knowledge. The model can answer questions it couldn’t previously by retrieving knowledge relevant to the query. This approach improves performance in certain scenarios for specific tasks. However, if irrelevant texts are retrieved, it may impair model performance. In this paper, we propose Retrieval Augmented Iterative Self-Feedback (RA-ISF), a framework that iteratively decomposes tasks and processes them in three submodules to enhance the model’s problem-solving capabilities. Experiments show that our method outperforms existing benchmarks, performing well on models like GPT3.5, Llama2, significantly enhancing factual reasoning capabilities and reducing hallucinations.

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MrRank: Improving Question Answering Retrieval System through Multi-Result Ranking Model
Danupat Khamnuansin | Tawunrat Chalothorn | Ekapol Chuangsuwanich

Large Language Models (LLMs) often struggle with hallucinations and outdated information. To address this, Information Retrieval (IR) systems can be employed to augment LLMs with up-to-date knowledge. However, existing IR techniques contain deficiencies, posing a performance bottleneck. Given the extensive array of IR systems, combining diverse approaches presents a viable strategy. Nevertheless, prior attempts have yielded restricted efficacy. In this work, we propose an approach that leverages learning-to-rank techniques to combine heterogeneous IR systems. We demonstrate the method on two Retrieval Question Answering (ReQA) tasks. Our empirical findings exhibit a significant performance enhancement, outperforming previous approaches and achieving state-of-the-art results on ReQA SQuAD.

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Chain-of-Question: A Progressive Question Decomposition Approach for Complex Knowledge Base Question Answering
Peng Yixing | Quan Wang | Licheng Zhang | Yi Liu | Zhendong Mao

Complex KBQA leverages the knowledge base (KB) to answer complex natural questions involving complicated semantics like multi-hop reasoning. Existing methods involve a question decomposition process, i.e., breaking a complex question into several simpler sub-questions, to assist obtaining logical forms for querying the KB. However, existing question decomposition process derives all sub-questions directly according to the original question, resulting in limitations when one sub-question relies on the answer from a previous one. In this work, we propose Chain-of-Question, a progressive question decomposition approach to address complex KBQA challenges. First, inspired by chain-of-thought, we design a prompt to guide LLM to sequentially decompose multiple semantically clear sub-questions and provide corresponding reference answers, where each step of the decomposition relies on the previous results. Next, we utilize the decomposition result to select relevant patterns (relation-entity pairs) as accurate and faithful auxiliary information for the following logical form generation. Finally, we jointly perform logical form generation and answer prediction, utilizing the predicted answer to supplement non-executable logical forms. Experimental results demonstrate that our method achieves state-of-the-art performance on multiple datasets.

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Instruction Tuning with Retrieval-based Examples Ranking for Aspect-based Sentiment Analysis
Guangmin Zheng | Jin Wang | Liang-Chih Yu | Xuejie Zhang

Aspect-based sentiment analysis (ABSA) identifies sentiment information related to specific aspects and provides deeper market insights to businesses and organizations. With the emergence of large language models (LMs), recent studies have proposed using fixed examples for instruction tuning to reformulate ABSA as a generation task. However, the performance is sensitive to the selection of in-context examples; several retrieval methods are based on surface similarity and are independent of the LM generative objective. This study proposes an instruction learning method with retrieval-based example ranking for ABSA tasks. For each target sample, an LM was applied as a scorer to estimate the likelihood of the output given the input and a candidate example as the prompt, and training examples were labeled as positive or negative by ranking the scores. An alternating training schema is proposed to train both the retriever and LM. Instructional prompts can be constructed using high-quality examples. The LM is used for both scoring and inference, improving the generation efficiency without incurring additional computational costs or training difficulties. Extensive experiments on three ABSA subtasks verified the effectiveness of the proposed method, demonstrating its superiority over various strong baseline models. Code and data are released at https://github.com/zgMin/IT-RER-ABSA.

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Unveiling the Truth and Facilitating Change: Towards Agent-based Large-scale Social Movement Simulation
Xinyi Mou | Zhongyu Wei | Xuanjing Huang

Social media has emerged as a cornerstone of social movements, wielding significant influence in driving societal change. Simulating the response of the public and forecasting the potential impact has become increasingly important. However, existing methods for simulating such phenomena encounter challenges concerning their efficacy and efficiency in capturing the behaviors of social movement participants. In this paper, we introduce a hybrid framework for social media user simulation, wherein users are categorized into two types. Core users are driven by Large Language Models, while numerous ordinary users are modeled by deductive agent-based models. We further construct a Twitter-like environment to replicate their response dynamics following trigger events. Subsequently, we develop a multi-faceted benchmark SoMoSiMu-Bench for evaluation and conduct comprehensive experiments across real-world datasets. Experimental results demonstrate the effectiveness and flexibility of our method.

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Incorporating Syntax and Lexical Knowledge to Multilingual Sentiment Classification on Large Language Models
Hiroshi Kanayama | Yang Zhao | Ran Iwamoto | Takuya Ohko

This paper exploits a sentiment extractor supported by syntactic and lexical resources to enhance multilingual sentiment classification solved through the generative approach, without retraining LLMs. By adding external information of words and phrases that have positive/negative polarities, the multilingual sentiment classification error was reduced by up to 33 points, and the combination of two approaches performed best especially in high-performing pairs of LLMs and languages.

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Locating and Extracting Relational Concepts in Large Language Models
Zijian Wang | Britney Whyte | Chang Xu

Relational concepts are indeed foundational to the structure of knowledge representation, as they facilitate the association between various entity concepts, allowing us to express and comprehend complex world knowledge.By expressing relational concepts in natural language prompts, people can effortlessly interact with large language models (LLMs) and recall desired factual knowledge. However, the process of knowledge recall lacks interpretability, and representations of relational concepts within LLMs remain unknown to us. In this paper, we identify hidden states that can express entity and relational concepts through causal mediation analysis in fact recall processes. Our finding reveals that at the last token position of the input prompt, there are hidden states that solely express the causal effects of relational concepts. Based on this finding, we assume that these hidden states can be treated as relational representations and we can successfully extract them from LLMs. The experimental results demonstrate high credibility of the relational representations: they can be flexibly transplanted into other fact recall processes, and can also be used as robust entity connectors. Moreover, we also show that the relational representations exhibit significant potential for controllable fact recall through relation rewriting.

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Unraveling and Mitigating Retriever Inconsistencies in Retrieval-Augmented Large Language Models
Mingda Li | Xinyu Li | Yifan Chen | Wenfeng Xuan | Weinan Zhang

Although Retrieval-Augmented Large Language Models (RALMs) demonstrate their superiority in terms of factuality, they do not consistently outperform the original retrieval-free Language Models (LMs). Our experiments reveal that this example-level performance inconsistency exists not only between retrieval-augmented and retrieval-free LM but also among different retrievers. To understand this phenomenon, we investigate the degeneration behavior of RALMs and theoretically decompose it into four categories. Further analysis based on our decomposition reveals that the innate difference in knowledge sources and the unpredictable degeneration of the reader model contribute most to the inconsistency. Drawing from our analysis, we introduce Ensemble of Retrievers (EoR), a trainable framework that can adaptively retrieve from different knowledge sources and effectively decrease unpredictable reader errors. Our experiments on Open Domain Question Answering show that EoR substantially improves performance over the RALM with a single retriever by considerably reducing inconsistent behaviors.

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SenticVec: Toward Robust and Human-Centric Neurosymbolic Sentiment Analysis
Xulang Zhang | Rui Mao | Erik Cambria

The success of state-of-the-art Natural Language Processing (NLP) systems heavily depends on deep neural networks, which excel in various tasks through strong data fitting and latent feature modeling abilities. However, certain challenges linked to deep neural networks and supervised deep learning deserve considerations, e.g., extensive computing resources, knowledge forgetting, etc. Previous research attempted to tackle these challenges individually through irrelative techniques. However, they do not instigate fundamental shifts in the learning paradigm. In this work, we propose a novel neurosymbolic method for sentiment analysis to tackle these issues. We also propose a novel sentiment-pragmatic knowledge base that places emphasis on human subjectivity within varying domain annotations. We conducted extensive experiments to show that our neurosymbolic framework for sentiment analysis stands out for its lightweight nature, robustness across domains and languages, efficient few-shot training, and rapid convergence.

Ensuring the trustworthiness of large language models (LLMs) is crucial. Most studies concentrate on fully pre-trained LLMs to better understand and improve LLMs’ trustworthiness. In this paper, to reveal the untapped potential of pre-training, we pioneer the exploration of LLMs’ trustworthiness during this period, focusing on five key dimensions: reliability, privacy, toxicity, fairness, and robustness. To begin with, we apply linear probing to LLMs. The high probing accuracy suggests that LLMs in early pre-training can already distinguish concepts in each trustworthiness dimension. Therefore, to further uncover the hidden possibilities of pre-training, we extract steering vectors from a LLM’s pre-training checkpoints to enhance the LLM’s trustworthiness. Finally, inspired by the theoretical result that mutual information estimation is bounded by linear probing accuracy, we also probe LLMs with mutual information to investigate the dynamics of trustworthiness during pre-training. We are the first to observe a similar two-phase phenomenon: fitting and compression. This research provides an initial exploration of trustworthiness modeling during LLM pre-training, seeking to unveil new insights and spur further developments in the field.

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Language Models can Evaluate Themselves via Probability Discrepancy
Tingyu Xia | Bowen Yu | Yuan Wu | Yi Chang | Chang Zhou

In this paper, we begin by illustrating that, when presented with a query, Large Language Models (LLMs) capable of providing accurate responses tend to exhibit a more uniform probability distribution compared to their less proficient counterparts. Building upon this observation, we introduce a novel self-assessment criterion termed ProbDiff for evaluating the performance of diverse LLMs. This method eliminates the need for training an additional evaluation model or relying on external proprietary models such as GPT-4 as a judger. Instead, it solely relies on the LLMs under evaluation to compute the probability discrepancy between the original response generation and its revised versions. A higher discrepancy in two LLMs for the same query suggests a relatively weaker ability. We discover that ProbDiff yields comparable results to mainstream GPT-4-based evaluations on various scenarios including NLG tasks like translation and summarization, as well as LLM evaluation benchmarks such as AlignBench, MT-Bench, and AlpacaEval, across LLMs of different sizes.

Deep neural networks exhibit vulnerability to word-level adversarial attacks in natural language processing. Most of these attack methods adopt synonymous substitutions to perturb original samples for crafting adversarial examples while attempting to maintain semantic consistency with the originals. Some of them claim that they could achieve over 90% attack success rate, thereby raising serious safety concerns. However, our investigation reveals that many purportedly successful adversarial examples are actually invalid due to significant changes in semantic meanings compared to their originals. Even when equipped with semantic constraints such as BERTScore, existing attack methods can generate up to 87.9% invalid adversarial examples. Building on this insight, we first curate a 13K dataset for adversarial validity evaluation with the help of GPT-4. Then, an open-source large language model is fine-tuned to offer an interpretable validity score for assessing the semantic consistency between original and adversarial examples. Finally, this validity score can serve as a guide for existing adversarial attack methods to generate valid adversarial examples. Comprehensive experiments demonstrate the effectiveness of our method in evaluating and refining the quality of adversarial examples.

Contrastive cross-modal models such as CLIP and CLAP aid various vision-language (VL) and audio-language (AL) tasks. However, there has been limited investigation of and improvement in their language encoder – the central component of encoding natural language descriptions of image/audio into vector representations. We extensively evaluate how unsupervised and supervised sentence embedding training affect language encoder quality and cross-modal task performance. In VL pretraining, we found that sentence embedding training enhances language encoder quality and aids in cross-modal tasks, improving contrastive VL models such as CyCLIP. Sentence embedding training benefits AL tasks when the amount of training data is large. We analyze the representation spaces to understand the strengths of sentence embedding training, and find that it improves text-space uniformity, at the cost of decreased cross-modal alignment.

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Your Co-Workers Matter: Evaluating Collaborative Capabilities of Language Models in Blocks World
Guande Wu | Chen Zhao | Claudio Silva | He He

Language agents that interact with the world on their own have great potential for automating digital tasks. While large language model (LLM) agents have made progress in understanding and executing tasks such as textual games and webpage control, many real-world tasks also require collaboration with humans or other LLMs in equal roles, which involves intent understanding, task coordination, and communication. To test LLM’s ability to collaborate, we design a blocks-world environment, where two agents, each having unique goals and skills, build a target structure together. To complete the goals, they can act in the world and communicate in natural language. Under this environment, we design increasingly challenging settings to evaluate different collaboration perspectives, from independent to more complex, dependent tasks. We further adopt chain-of-thought prompts that include intermediate reasoning steps to model the partner’s state and identify and correct execution errors. Both human-machine and machine-machine experiments show that LLM agents have strong grounding capacities, and our approach significantly improves the evaluation metric.

Large language models (LLMs) predominantly employ decoder-only transformer architectures, necessitating the retention of keys/values information for historical tokens to provide contextual information and avoid redundant computation. However, the substantial size and parameter volume of these LLMs require massive GPU memory. This memory demand increases with the length of the input text, leading to an urgent need for more efficient methods of information storage and processing. This study introduces Anchor-based LLMs (AnLLMs), which utilize an innovative anchor-based self-attention network (AnSAN) and also an anchor-based inference strategy. This approach enables LLMs to compress sequence information into an anchor token, reducing the keys/values cache and enhancing inference efficiency. Experiments on question-answering benchmarks reveal that AnLLMs maintain similar accuracy levels while achieving up to 99% keys/values cache reduction and up to 3.5 times faster inference. Despite a minor compromise in accuracy, the substantial enhancements of AnLLMs employing the AnSAN technique in resource utilization and computational efficiency underscore their potential for practical LLM applications.

Medical visual question answering (MVQA) requires in-depth understanding of medical images and questions to provide reliable answers. We summarize multi-level progressive capabilities that models need to focus on in MVQA: recognition, details, diagnosis, knowledge, and reasoning. Existing MVQA models tend to ignore the above capabilities due to unspecific data and plain architecture. To address these issues, this paper proposes Multi-level Visual Language Model (MLeVLM) for MVQA. On the data side, we construct a high-quality multi-level instruction dataset MLe-VQA via GPT-4, which covers multi-level questions and answers as well as reasoning processes from visual clues to semantic cognition. On the architecture side, we propose a multi-level feature alignment module, including attention-based token selector and context merger, which can efficiently align features at different levels from visual to semantic. To better evaluate the model’s capabilities, we manually construct a multi-level MVQA evaluation benchmark named MLe-Bench. Extensive experiments demonstrate the effectiveness of our constructed multi-level instruction dataset and the multi-level feature alignment module. It also proves that MLeVLM outperforms existing medical multimodal large language models.

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Disentangling Length from Quality in Direct Preference Optimization
Ryan Park | Rafael Rafailov | Stefano Ermon | Chelsea Finn

Reinforcement Learning from Human Feedback (RLHF) has been a crucial component in the recent success of Large Language Models. However, RLHF is know to exploit biases in human preferences, such as verbosity. A well-formatted and eloquent answer is often more highly rated by users, even when it is less helpful and objective. A number of approaches have been developed to control those biases in the classical RLHF literature, but the problem remains relatively under-explored for Direct Alignment Algorithms such as Direct Preference Optimization (DPO). Unlike classical RLHF, DPO does not train a separate reward model or use reinforcement learning directly, so previous approaches developed to control verbosity cannot be directly applied to this setting. Our work makes several contributions. For the first time, we study the length problem in the DPO setting, showing significant exploitation in DPO and linking it to out-of-distribution bootstrapping. We then develop a principled but simple regularization strategy that prevents length exploitation, while still maintaining improvements in model quality. We demonstrate these affects across datasets on summarization and dialogue, where we achieve up to 20% improvement in win rates when controlling for length, despite the GPT4 judge’s well-known verbosity bias.

Multimodal knowledge editing represents a critical advancement in enhancing the capabilities of Multimodal Large Language Models (MLLMs). Despite its potential, current benchmarks predominantly focus on coarse-grained knowledge, leaving the intricacies of fine-grained (FG) multimodal entity knowledge largely unexplored. This gap presents a notable challenge, as FG entity recognition is pivotal for the practical deployment and effectiveness of MLLMs in diverse real-world scenarios. To bridge this gap, we introduce MIKE, a comprehensive benchmark and dataset specifically designed for the FG multimodal entity knowledge editing. MIKE encompasses a suite of tasks tailored to assess different perspectives, including Vanilla Name Answering, Entity-Level Caption, and Complex-Scenario Recognition. In addition, a new form of knowledge editing, Multi-step Editing, is introduced to evaluate the editing efficiency. Through our extensive evaluations, we demonstrate that the current state-of-the-art methods face significant challenges in tackling our proposed benchmark, underscoring the complexity of FG knowledge editing in MLLMs. Our findings spotlight the urgent need for novel approaches in this domain, setting a clear agenda for future research and development efforts within the community.

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Reformulating Domain Adaptation of Large Language Models as Adapt-Retrieve-Revise: A Case Study on Chinese Legal Domain
Zhen Wan | Yating Zhang | Yexiang Wang | Fei Cheng | Sadao Kurohashi

While large language models (LLMs) like GPT-4 have recently demonstrated astonishing zero-shot capabilities in general domain tasks, they often generate content with hallucinations in specific domains such as Chinese law, hindering their application in these areas. This is typically due to the absence of training data that encompasses such a specific domain, preventing GPT-4 from acquiring in-domain knowledge. A pressing challenge is that it’s not plausible to continue training LLMs of the GPT-4’s scale on in-domain data.This paper introduces a simple yet effective domain adaptation framework for GPT-4 by reformulating generation as an adapt-retrieve-revise process. The initial step is to adapt an affordable 7B LLM to the Chinese legal domain by continuing learning in-domain data. When solving an in-domain task, we leverage the adapted LLM to generate a draft answer given a task query. Then, the draft answer will be used to retrieve supporting evidence candidates from an external in-domain knowledge base. Finally, the draft answer and retrieved evidence are concatenated into a whole prompt to let GPT-4 assess the evidence and revise the draft answer to generate the final answer. Our proposal combines the advantages of the efficiency of adapting a smaller 7B model with the evidence-assessing capability of GPT-4 and effectively prevents GPT-4 from generating hallucinatory content. In the zero-shot setting of four Chinese legal tasks, our method improves the average score by +33.6 points, compared to GPT-4 direct generation. When compared to two stronger retrieval-based baselines, our method outperforms them by +17.0 and +23.5.

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MemeMQA: Multimodal Question Answering for Memes via Rationale-Based Inferencing
Siddhant Agarwal | Shivam Sharma | Preslav Nakov | Tanmoy Chakraborty

Memes have evolved as a prevalent medium for diverse communication, ranging from humour to propaganda. With the rising popularity of image-focused content, there is a growing need to explore its potential harm from different aspects. Previous studies have analyzed memes in closed settings - detecting harm, applying semantic labels, and offering natural language explanations. To extend this research, we introduce MemeMQA, a multimodal question-answering framework aiming to solicit accurate responses to structured questions while providing coherent explanations. We curate MemeMQACorpus, a new dataset featuring 1,880 questions related to 1,122 memes with corresponding answer-explanation pairs. We further propose ARSENAL, a novel two-stage multimodal framework that leverages the reasoning capabilities of LLMs to address MemeMQA. We benchmark MemeMQA using competitive baselines and demonstrate its superiority - ~18% enhanced answer prediction accuracy and distinct text generation lead across various metrics measuring lexical and semantic alignment over the best baseline. We analyze ARSENAL’s robustness through diversification of question-set, confounder-based evaluation regarding MemeMQA’s generalizability, and modality-specific assessment, enhancing our understanding of meme interpretation in the multimodal communication landscape.

Large language models have been widely adopted in natural language processing, yet they face the challenge of generating unreliable content. Recent works aim to reduce misinformation and hallucinations by resorting to attribution as a means to provide evidence (i.e., citations). However, current attribution methods usually focus on the retrieval stage and automatic evaluation that neglect mirroring the citation mechanisms in human scholarly writing to bolster credibility. In this paper, we address these challenges by modelling the attribution task as preference learning and introducing an Automatic Preference Optimization (APO) framework. First, we create a curated collection for post-training with 6,330 examples by collecting and filtering from existing datasets. Second, considering the high cost of labelling preference data, we further propose an automatic method to synthesize attribution preference data resulting in 95,263 pairs. Moreover, inspired by the human citation process, we further propose a progressive preference optimization method by leveraging fine-grained information. Extensive experiments on three datasets (i.e., ASQA, StrategyQA, and ELI5) demonstrate that APO achieves state-of-the-art citation F1 with higher answer quality.

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KOMBO: Korean Character Representations Based on the Combination Rules of Subcharacters
SungHo Kim | Juhyeong Park | Yeachan Kim | SangKeun Lee

The Korean writing system, Hangeul, has a unique character representation rigidly following the invention principles recorded in Hunminjeongeum. However, existing pre-trained language models (PLMs) for Korean have overlooked these principles. In this paper, we introduce a novel framework for Korean PLMs called KOMBO, which firstly brings the invention principles of Hangeul to represent character. Our proposed method, KOMBO, exhibits notable experimental proficiency across diverse NLP tasks. In particular, our method outperforms the state-of-the-art Korean PLM by an average of 2.11% in five Korean natural language understanding tasks. Furthermore, extensive experiments demonstrate that our proposed method is suitable for comprehending the linguistic features of the Korean language. Consequently, we shed light on the superiority of using subcharacters over the typical subword-based approach for Korean PLMs. Our code is available at: https://github.com/SungHo3268/KOMBO.

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Tree-Planted Transformers: Unidirectional Transformer Language Models with Implicit Syntactic Supervision
Ryo Yoshida | Taiga Someya | Yohei Oseki

Syntactic Language Models (SLMs) can be trained efficiently to reach relatively high performance; however, they have trouble with inference efficiency due to the explicit generation of syntactic structures. In this paper, we propose a new method dubbed tree-planting: instead of explicitly generating syntactic structures, we “plant” trees into attention weights of unidirectional Transformer LMs to implicitly reflect syntactic structures of natural language. Specifically, unidirectional Transformer LMs trained with tree-planting will be called Tree-Planted Transformers (TPT), which inherit the training efficiency from SLMs without changing the inference efficiency of their underlying Transformer LMs. Targeted syntactic evaluations on the SyntaxGym benchmark demonstrated that TPTs, despite the lack of explicit generation of syntactic structures, significantly outperformed not only vanilla Transformer LMs but also various SLMs that generate hundreds of syntactic structures in parallel. This result suggests that TPTs can learn human-like syntactic knowledge as data-efficiently as SLMs while maintaining the modeling space of Transformer LMs unchanged.

With the development of LLMs, the security threats of LLMs are getting more and more attention. Numerous jailbreak attacks have been proposed to assess the security defense of LLMs. Current jailbreak attacks primarily utilize scenario camouflage techniques. However their explicitly mention of malicious intent will be easily recognized and defended by LLMs. In this paper, we propose an indirect jailbreak attack approach, Puzzler, which can bypass the LLM’s defensive strategies and obtain malicious response by implicitly providing LLMs with some clues about the original malicious query. In addition, inspired by the wisdom of “When unable to attack, defend” from Sun Tzu’s Art of War, we adopt a defensive stance to gather clues about the original malicious query through LLMs. The experimental results indicate that the Query Success Rate of the Puzzler is 14.0%-82.7% higher than baselines on the most prominent LLMs. Furthermore, when tested against the state-of-the-art jailbreak detection approaches, Puzzler proves to be more effective at evading detection compared to baselines.

The development of large language models tailored for handling patients’ clinical notes is often hindered by the limited accessibility and usability of these notes due to strict privacy regulations.To address these challenges, we first create synthetic large-scale clinical notes using publicly available case reports extracted from biomedical literature.We then use these synthetic notes to train our specialized clinical large language model, Asclepius.While Asclepius is trained on synthetic data, we assess its potential performance in real-world applications by evaluating it using real clinical notes.We benchmark Asclepius against several other large language models, including GPT-3.5-turbo and other open-source alternatives. To further validate our approach using synthetic notes, we also compare Asclepius with its variants trained on real clinical notes. Our findings convincingly demonstrate that synthetic clinical notes can serve as viable substitutes for real ones when constructing high-performing clinical language models. This conclusion is supported by detailed evaluations conducted by both GPT-4 and medical professionals. All resources—including weights, codes, and data—used in the development of Asclepius will be made publicly accessible for future research.

Transformer based Large Language Models (LLMs) often impose limitations on the length of the text input to ensure the generation of fluent and relevant responses due to the quadratic complexity. These constraints restrict their applicability in long text scenarios. In this paper, we propose a novel semantic compression method that enables generalization to texts that are 6-8 times longer without incurring significant computational costs or requiring fine-tuning. Our proposed framework draws inspiration from source coding in information theory and employs a pre-trained model to reduce the semantic redundancy of long inputs before passing them to the LLMs for downstream tasks. Experimental results demonstrate that our method effectively extends the context window of LLMs across a range of tasks including question answering, summarization, few-shot learning, and information retrieval. Furthermore, the proposed semantic compression method exhibits consistent fluency in text generation while reducing the associated computational overhead.

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Plausible Extractive Rationalization through Semi-Supervised Entailment Signal
Yeo Wei Jie | Ranjan Satapathy | Erik Cambria

The increasing use of complex and opaque black box models requires the adoption of interpretable measures, one such option is extractive rationalizing models, which serve as a more interpretable alternative. These models, also known as Explain-Then-Predict models, employ an explainer model to extract rationales and subsequently condition the predictor with the extracted information. Their primary objective is to provide precise and faithful explanations, represented by the extracted rationales. In this paper, we take a semi-supervised approach to optimize for the plausibility of extracted rationales. We adopt a pre-trained natural language inference (NLI) model and further fine-tune it on a small set of supervised rationales (10%). The NLI predictor is leveraged as a source of supervisory signals to the explainer via entailment alignment. We show that, by enforcing the alignment agreement between the explanation and answer in a question-answering task, the performance can be improved without access to ground truth labels. We evaluate our approach on the ERASER dataset and show that our approach achieves comparable results with supervised extractive models and outperforms unsupervised approaches by > 100%.

Building a reliable visual question answering (VQA) system across different languages is a challenging problem, primarily due to the lack of abundant samples for training. To address this challenge, recent studies have employed machine translation systems for the cross-lingual VQA task. This involves translating the evaluation samples into a source language (usually English) and using monolingual models (i.e., translate-test). However, our analysis reveals that translated texts contain unique characteristics distinct from human-written ones, referred to as translation artifacts. We find that these artifacts can significantly affect the models, confirmed by extensive experiments across diverse models, languages, and translation processes. In light of this, we present a simple data augmentation strategy that can alleviate the adverse impacts of translation artifacts.

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Scented-EAE: Stage-Customized Entity Type Embedding for Event Argument Extraction
Yu Yang | Jinyu Guo | Kai Shuang | Chenrui Mao

Existing methods for incorporating entities into EAE rely on prompts or NER. They typically fail to explicitly explore the role of entity types, which results in shallow argument comprehension and often encounter three issues: (1) weak semantic associations due to missing role-entity correspondence cues; (2) compromised semantic integrity from abandoning context after recognizing entities regardless of their types; (3) one-sided semantic understanding relying solely on argument role semantics. To tackle these issues, we propose Scented-EAE, an EAE model with stage-customized entity type embedding to explicitly underscore and explore the role of entity types, thus intervening in argument selection. Specifically, at the input stage, we strengthen semantic associations by prompting role-entity correspondence after extending a non-autoregressive decoder as part of the encoder. At the intermediate stage, we preserve semantic integrity by optimizing our proposed BIO-aware NER and EAE via a novel IPE joint learning. At the output stage, we expand semantic understanding dimensions by determining arguments using span selectors from argument roles and entity types. Experiments show that our model achieves state-of-the-art performance on mainstream benchmarks. In addition, it also exhibits robustness in low-resource settings with the help of prompts and entity types.

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Fast Randomized Low-Rank Adaptation of Pre-trained Language Models with PAC Regularization
Zijian Lei | Dong Qian | William Cheung

Low-rank adaptation (LoRA) achieves parameter efficient fine-tuning for large language models (LLMs) by decomposing the model weight update into a pair of low-rank projection matrices. Yet, the memory overhead restricts it to scale up when the model size increases. We propose Randomized LoRA (RLoRA) which adopts Randomized Walsh-Hadamard Transform to achieve significant reduction in the size of trainable parameters compared to LoRA. At the same time, it allows a PAC-Bayes regularizer to be efficiently incorporated to improve generalization. We evaluate the effectiveness of RLoRA on LLMs RoBERTa, GPT-2 and LLaMA-7B using GLUE, E2E and math reasoning benchmarks. With a much lower memory requirement, RLoRA can give similar performance as the SOTA low-rank adaptation methods for these three tasks and significantly better performance under few-shot settings.

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SDA: Semantic Discrepancy Alignment for Text-conditioned Image Retrieval
Yuchen Yang | Yu Wang | Yanfeng Wang

In the realm of text-conditioned image retrieval, models utilize a query composed of a reference image and modification text to retrieve corresponding images. Despite its significance, this task is fraught with challenges, including small-scale datasets due to labeling costs and the complexity of attributes in modification texts. These challenges often result in models learning a generalized representation of the query, thereby missing the semantic correlations of image and text attributes.In this paper, we introduce a general boosting framework designed to address these issues by employing semantic discrepancy alignment. Our framework first leverages the ChatGPT to augment text data by modifying the original modification text’s attributes. The augmented text is then combined with the original reference image to create an augmented composed query. Then we generate corresponding images using GPT-4 for the augmented composed query.We realize the cross-modal semantic discrepancy alignment by formulating distance consistency and neighbor consistency between the image and text domains. Through this novel approach, attribute in the text domain can be more effectively transferred to the image domain, enhancing retrieval performance. Extensive experiments on three prominent datasets validate the effectiveness of our approach, with state-of-the-art results on a majority of evaluation metrics compared to various baseline methods.

The remarkable capability of large language models(LLMs) for in-context learning(ICL) needs to be activated by demonstration examples. Prior work has extensively explored the selection of examples for ICL, predominantly following the “select then organize” paradigm, such approaches often neglect the internal relationships between examples and exist an inconsistency between the training and inference. In this paper, we formulate the problem as a Sequential Selection problem and introduce Se², a sequential-aware method that leverages the LLM’s feedback on varying context, aiding in capturing inter-relationships and sequential information among examples, significantly enriching the contextuality and relevance of ICL prompts. Meanwhile, we utilize beam search to seek and construct example sequences, enhancing both quality and diversity. Extensive experiments across 23 NLP tasks from 8 distinct categories illustrate that Se² markedly surpasses competitive baselines and achieves 42% relative improvement over random selection. Further in-depth analysis shows the effectiveness of proposed strategies, highlighting Se²‘s exceptional stability and adaptability across various scenarios. Code available at https://github.com/microsoft/LMOps.

This research aims to accelerate the inference speed of large language models (LLMs) with billions of parameters. We propose Smart Parallel Auto-Correct dEcoding (SPACE), an approach designed for achieving lossless acceleration of LLMs. By integrating semi-autoregressive inference and speculative decoding capabilities, SPACE uniquely enables autoregressive LLMs to parallelize token generation and verification. This is realized through a specialized semi-autoregressive supervised fine-tuning process that equips existing LLMs with the ability to simultaneously predict multiple tokens. Additionally, an auto-correct decoding algorithm facilitates the simultaneous generation and verification of token sequences within a single model invocation. Through extensive experiments on a range of LLMs, SPACE has demonstrated inference speedup ranging from 2.7x-4.0x on HumanEval-X while maintaining output quality.

Evaluation is the baton for the development of large language models. Current evaluations typically employ a single-item assessment paradigm for each atomic test objective, which struggle to discern whether a model genuinely possesses the required capabilities or merely memorizes/guesses the answers to specific questions. To this end, this paper proposes a novel evaluation framework referred to as StructEval. Starting from an atomic test objective, StructEval deepens and broadens the evaluation by conducting a structured assessment across multiple cognitive levels and critical concepts, and therefore offers a comprehensive, robust and consistent evaluations for large language models. Experiments on three widely-used benchmarks demonstrate that StructEval serves as a reliable tool for resisting the risk of data contamination, and reducing the interference of potential biases, thereby providing a more reliable and consistent conclusion regarding model capabilities. Our framework also sheds light on the design of future principled and trustworthy LLM evaluation protocols.

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Mitigating Privacy Seesaw in Large Language Models: Augmented Privacy Neuron Editing via Activation Patching
Xinwei Wu | Weilong Dong | Shaoyang Xu | Deyi Xiong

Protecting privacy leakage in large language models remains a paramount challenge. In this paper, we reveal Privacy Seesaw in LLM privacy safeguarding, a phenomenon where measures to secure specific private information inadvertently heighten exposure risks for other privacy. Through comprehensive analysis, we identify the amount of targeted privacy data and the volume of edited privacy neurons as the two central triggers to this issue. To mitigate privacy seesaw, we propose Augmented Privacy Neuron Editing via Activation Patching (APNEAP), a novel framework designed to well balance model performance with privacy protection. The proposed APNEAP augments collected private data by automatically synthesizing new private data, which deactivates the first trigger to the privacy seesaw issue. Additionally, it adapts activation patching to privacy neuron editing for switching off the second trigger to the privacy seesaw problem. Experimental results show that the proposed APNEAP is capable of alleviating the privacy seesaw phenomenon and offers a more stable and reliable approach to privacy protection in LLMs than previous methods.

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Which Information Matters? Dissecting Human-written Multi-document Summaries with Partial Information Decomposition
Laura Mascarell | Yan LHomme | Majed El Helou

Understanding the nature of high-quality summaries is crucial to further improve the performance of multi-document summarization. We propose an approach to characterize human-written summaries using partial information decomposition, which decomposes the mutual information provided by all source documents into union, redundancy, synergy, and unique information. Our empirical analysis on different MDS datasets shows that there is a direct dependency between the number of sources and their contribution to the summary.

Backdoor attacks pose an increasingly severe security threat to Deep Neural Networks (DNNs) during their development stage. In response, backdoor sample purification has emerged as a promising defense mechanism, aiming to eliminate backdoor triggers while preserving the integrity of the clean content in the samples. However, existing approaches have been predominantly focused on the word space, which are ineffective against feature-space triggers and significantly impair performance on clean data. To address this, we introduce a universal backdoor defense that purifies backdoor samples in the activation space by drawing abnormal activations towards optimized minimum clean activation distribution intervals. The advantages of our approach are twofold: (1) By operating in the activation space, our method captures from surface-level information like words to higher-level semantic concepts such as syntax, thus counteracting diverse triggers; (2) the fine-grained continuous nature of the activation space allows for more precise preservation of clean content while removing triggers. Furthermore, we propose a detection module based on statistical information of abnormal activations, to achieve a better trade-off between clean accuracy and defending performance. Extensive experiments on diverse datasets and against diverse attacks (including syntax and style attacks) demonstrate that our defense achieves state-of-the-art performance.

Molecule-text modeling, which aims to facilitate molecule-relevant tasks with a textual interface and textual knowledge, is an emerging research direction. Beyond single molecules, studying reaction-text modeling holds promise for helping the synthesis of new materials and drugs. However, previous works mostly neglect reaction-text modeling: they primarily focus on modeling individual molecule-text pairs or learning chemical reactions without texts in context. Additionally, one key task of reaction-text modeling – experimental procedure prediction – is less explored due to the absence of an open-source dataset. The task is to predict step-by-step actions of conducting chemical experiments and is crucial to automating chemical synthesis. To resolve the challenges above, we propose a new pretraining method, ReactXT, for reaction-text modeling, and a new dataset, OpenExp, for experimental procedure prediction. Specifically, ReactXT features three types of input contexts to incrementally pretrain LMs. Each of the three input contexts corresponds to a pretraining task to improve the text-based understanding of either reactions or single molecules. ReactXT demonstrates consistent improvements in experimental procedure prediction and molecule captioning and offers competitive results in retrosynthesis. Our code is available at https://github.com/syr-cn/ReactXT.

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Multi-modal Concept Alignment Pre-training for Generative Medical Visual Question Answering
Quan Yan | Junwen Duan | Jianxin Wang

Medical Visual Question Answering (Med-VQA) seeks to accurately respond to queries regarding medical images, a task particularly challenging for open-ended questions. This study unveils the Multi-modal Concept Alignment Pre-training (MMCAP) approach for generative Med-VQA, leveraging a knowledge graph sourced from medical image-caption datasets and the Unified Medical Language System. MMCAP advances the fusion of visual and textual medical knowledge via a graph attention network and a transformer decoder. Additionally, it incorporates a Type Conditional Prompt in the fine-tuning phase, markedly boosting the accuracy and relevance of answers to open-ended questions. Our tests on benchmark datasets illustrate MMCAP’s superiority over existing methods, demonstrating its high efficiency in data-limited settings and effective knowledge-image alignment capability.

Ordinal Classification (OC) is a widely encountered challenge in Natural Language Processing (NLP), with applications in various domains such as sentiment analysis, rating prediction, and more. Previous approaches to tackle OC have primarily focused on modifying existing or creating novel loss functions that explicitly account for the ordinal nature of labels. However, with the advent of Pre-trained Language Models (PLMs), it became possible to tackle ordinality through the implicit semantics of the labels as well. This paper provides a comprehensive theoretical and empirical examination of both these approaches. Furthermore, we also offer strategic recommendations regarding the most effective approach to adopt based on specific settings.

Educational materials such as survey articles in specialized fields like computer science traditionally require tremendous expert inputs and are therefore expensive to create and update. Recently, Large Language Models (LLMs) have achieved significant success across various general tasks. However, their effectiveness and limitations in the education domain are yet to be fully explored. In this work, we examine the proficiency of LLMs in generating succinct survey articles specific to the niche field of NLP in computer science, focusing on a curated list of 99 topics. Automated benchmarks reveal that GPT-4 surpasses its predecessors, inluding GPT-3.5, PaLM2, and LLaMa2 by margins ranging from 2% to 20% in comparison to the established ground truth. We compare both human and GPT-based evaluation scores and provide in-depth analysis. While our findings suggest that GPT-created surveys are more contemporary and accessible than human-authored ones, certain limitations were observed. Notably, GPT-4, despite often delivering outstanding content, occasionally exhibited lapses like missing details or factual errors. At last, we compared the rating behavior between humans and GPT-4 and found systematic bias in using GPT evaluation.

Although model editing has shown promise in revising knowledge in Large Language Models (LLMs), its impact on the inherent capabilities of LLMs is often overlooked. In this work, we reveal a critical phenomenon: even a single edit can trigger model collapse, manifesting as significant performance degradation in various benchmark tasks. However, benchmarking LLMs after each edit, while necessary to prevent such collapses, is impractically time-consuming and resource-intensive. To mitigate this, we propose using perplexity as a surrogate metric, validated by extensive experiments demonstrating changes in an edited model’s perplexity are strongly correlated with its downstream task performances. We further conduct an in-depth study on sequential editing, a practical setting for real-world scenarios, across various editing methods and LLMs, focusing on hard cases from our previous single edit studies. The results indicate that nearly all examined editing methods result in model collapse after only few edits. To facilitate further research, we have utilized GPT-3.5 to develop a new dataset, HardEdit, based on those hard cases. This dataset aims to establish the foundation for pioneering research in reliable model editing and the mechanisms underlying editing-induced model collapse. We hope this work can draw the community’s attention to the potential risks inherent in model editing practices.

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Can We Continually Edit Language Models? On the Knowledge Attenuation in Sequential Model Editing
Qi Li | Xiaowen Chu

Model editing has become a promising method for precisely and effectively updating knowledge in language models. In this paper, we investigate knowledge attenuation, in which the retention of updated knowledge within the language model decreases as the number of edits increases after sequential editing. Through empirical study, we discovered that existing editing methods generally suffer from knowledge attenuation. We attribute this phenomenon to two aspects: (1) redundant parameters interference and (2) update weight disentanglement. To this end, we propose the AdaPLE method. It not only mitigates the knowledge attenuation issue but also improves the performance on existing benchmarks. To the best of our knowledge, we are the first to investigate the cause and mitigation of knowledge attenuation in sequential LLM editing.

Large Language Models (LLMs) driven by In-Context Learning (ICL) have significantly improved the performance of text-to-SQL. Previous methods generally employ a two-stage reasoning framework, namely 1) schema linking and 2) logical synthesis, making the framework not only effective but also interpretable. Despite these advancements, the inherent bad nature of the generalization of LLMs often results in hallucinations, which limits the full potential of LLMs. In this work, we first identify and categorize the common types of hallucinations at each stage in text-to-SQL. We then introduce a novel strategy, Task Alignment (TA), designed to mitigate hallucinations at each stage. TA encourages LLMs to take advantage of experiences from similar tasks rather than starting the tasks from scratch. This can help LLMs reduce the burden of generalization, thereby mitigating hallucinations effectively. We further propose TA-SQL, a text-to-SQL framework based on this strategy. The experimental results and comprehensive analysis demonstrate the effectiveness and robustness of our framework. Specifically, it enhances the performance of the GPT-4 baseline by 21.23% relatively on BIRD dev and it yields significant improvements across six models and four mainstream, complex text-to-SQL benchmarks.

In-image machine translation (IIMT) aims to translate an image containing texts in source language into an image containing translations in target language. In this regard, conventional cascaded methods suffer from issues such as error propagation, massive parameters, and difficulties in deployment and retaining visual characteristics of the input image.Thus, constructing end-to-end models has become an option, which, however, faces two main challenges: 1) the huge modeling burden, as it is required to simultaneously learn alignment across languages and preserve the visual characteristics of the input image; 2) the difficulties of directly predicting excessively lengthy pixel sequences.In this paper, we propose Translatotron-V(ision), an end-to-end IIMT model consisting of four modules. In addition to an image encoder, and an image decoder, our model contains a target text decoder and an image tokenizer. Among them, the target text decoder is used to alleviate the language alignment burden, and the image tokenizer converts long sequences of pixels into shorter sequences of visual tokens, preventing the model from focusing on low-level visual features. Besides, we present a two-stage training framework for our model to assist the model in learning alignment across modalities and languages. Finally, we propose a location-aware evaluation metric called Structure-BLEU to assess the translation quality of the generated images. Experimental results demonstrate that our model achieves competitive performance compared to cascaded models with only 70.9% of parameters, and significantly outperforms the pixel-level end-to-end IIMT model.

The potential for improvements brought by Large Language Models (LLMs) in Text-to-SQL systems is mostly assessed on monolingual English datasets. However, LLMs’ performance for other languages remains vastly unexplored. In this work, we release the StatBot.Swiss dataset, the first bilingual benchmark for evaluating Text-to-SQL systems based on real-world applications. The StatBot.Swiss dataset contains 455 natural language/SQL-pairs over 35 big databases with varying level of complexity for both English and German.We evaluate the performance of state-of-the-art LLMs such as GPT-3.5-Turbo and mixtral-8x7b-instruct for the Text-to-SQL translation task using an in-context learning approach. Our experimental analysis illustrates that current LLMs struggle to generalize well in generating SQL queries on our novel bilingual dataset.

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Subtle Signatures, Strong Shields: Advancing Robust and Imperceptible Watermarking in Large Language Models
Yubing Ren | Ping Guo | Yanan Cao | Wei Ma

The widespread adoption of Large Language Models (LLMs) has led to an increase in AI-generated text on the Internet, presenting a crucial challenge to differentiate AI-created content from human-written text. This challenge is critical to prevent issues of authenticity, trust, and potential copyright violations. Current research focuses on watermarking LLM-generated text, but traditional techniques struggle to balance robustness with text quality. We introduce a novel watermarking approach, Robust and Imperceptible Watermarking (RIW) for LLMs, which leverages token prior probabilities to improve detectability and maintain watermark imperceptibility. RIW methodically embeds watermarks by partitioning selected tokens into two distinct groups based on their prior probabilities and employing tailored strategies for each group. In the detection stage, the RIW method employs the ‘voted z-test’ to provide a statistically robust framework to identify the presence of a watermark accurately. The effectiveness of RIW is evaluated across three key dimensions: success rate, text quality, and robustness against removal attacks. Our experimental results on various LLMs, including GPT2-XL, OPT-1.3B, and LLaMA2-7B, indicate that RIW surpasses existing models, and also exhibits increased robustness against various attacks and good imperceptibility, thus promoting the responsible use of LLMs.

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Thinking about how to extract: Energizing LLMs’ emergence capabilities for document-level event argument extraction
Kai Shuang | Zhouji Zhouji | Wang Qiwei | Jinyu Guo

There are two key challenges remaining for the document-level event argument extraction (D-EAE) tasks: key feature forgetting and cross-event argument confusion. The emergence capability of large language models (LLMs) holds promise for solving the above two challenges. In this paper, we propose a document-level event argument extraction method based on guided summarization and reasoning (EAESR), which leverages the emergence capabilities of LLMs to highlight key event information and to clarify the explicit and implicit association between multiple events. Specifically, we generate document summarization information that shorten the length of the event context while preserving the key event features. In addition, we generate inter-event reasoning information, which helps EAESR make sense of the correlations between events and reduces their dependence on the event context, especially to better cope with the few-shot D-EAE task. Then, we obtain named entity information to enable EAESR to learn argument boundary features to improve the sensitivity of its argument boundary recognition. Eventually, we fused the above features and sentence features to make EAESR have summarizing and reasoning capabilities simultaneously. Extensive experiments on WIKIEVENTS and RAMS have shown that EAESR achieves a new state-of-the-art that outperforms the baseline models by 1.3% F1 and 1.6% F1, respectively, and averages 11% F1 in few-shot settings.

Distantly-Supervised Named Entity Recognition (DS-NER) effectively alleviates the burden of annotation, but meanwhile suffers from the label noise. Recent works attempt to adopt the teacher-student framework to gradually refine the training labels and improve the overall robustness. However, we argue that these teacher-student methods achieve limited performance because the poor calibration of the teacher network produces incorrectly pseudo-labeled samples, leading to error propagation. Therefore, we attempt to mitigate this issue by proposing: (1) Uncertainty-Aware Teacher Learning that leverages the prediction uncertainty to reduce the number of incorrect pseudo labels in the self-training stage; (2) Student-Student Collaborative Learning that allows the transfer of reliable labels between two student networks instead of indiscriminately relying on all pseudo labels from its teacher. This approach further enables a full exploration of mislabeled samples rather than simply filtering unreliable pseudo-labeled samples. We evaluate our proposed method on five DS-NER datasets, demonstrating that our method is superior to the state-of-the-art DS-NER denoising methods.

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Predicting Narratives of Climate Obstruction in Social Media Advertising
Harri Rowlands | Gaku Morio | Dylan Tanner | Christopher Manning

Social media advertising offers a platform for fossil fuel value chain companies and their agents to reinforce their narratives, often emphasizing economic, labor market, and energy security benefits to promote oil and gas policy and products. Whether such narratives can be detected automatically and the extent to which the cost of human annotation can be reduced is our research question. We introduce a task of classifying narratives into seven categories, based on existing definitions and data.Experiments showed that RoBERTa-large outperforms other methods, while GPT-4 Turbo can serve as a viable annotator for the task, thereby reducing human annotation costs. Our findings and insights provide guidance to automate climate-related ad analysis and lead to more scalable ad scrutiny.

Language Models (LMs) acquire factual knowledge during pre-training and store it in the parameters, which can be valuable for downstream tasks. As world evolves, some facts may be incorrectly induced or become obsolete over time. Various model editing methods have been proposed to modify specific examples in LMs. However, existing training-based methods still suffer from sub-optimal locality, where irrelevant neighborhood examples can be adversely influenced. Model’s gradients are still struggling to identify the appropriate direction when updating the parameters. To address this issue, we find that directing the hidden state of the edit example towards spaces where semantics are sparse tends to help preserve the semantics of irrelevant neighborhood examples. Based on this hypothesis, we propose a novel metric, named SSS, to evaluate the degree of sparsity around a sentence embedding in the semantic space without any human or machine annotation. Subsequently, we incorporate SSS into the original loss function of the existing training-based methods to enhance locality. Experiments conducted on two datasets across various models demonstrate that SSS is effective in improving both locality and reasoning capability.

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GeoHard: Towards Measuring Class-wise Hardness through Modelling Class Semantics
Fengyu Cai | Xinran Zhao | Hongming Zhang | Iryna Gurevych | Heinz Koeppl

Recent advances in measuring hardness-wise properties of data guide language models in sample selection within low-resource scenarios. However, class-specific properties are overlooked for task setup and learning. How will these properties influence model learning and is it generalizable across datasets? To answer this question, this work formally initiates the concept of class-wise hardness. Experiments across eight natural language understanding (NLU) datasets demonstrate a consistent hardness distribution across learning paradigms, models, and human judgment. Subsequent experiments unveil a notable challenge in measuring such class-wise hardness with instance-level metrics in previous works. To address this, we propose GeoHard for class-wise hardness measurement by modeling class geometry in the semantic embedding space. GeoHard surpasses instance-level metrics by over 59 percent on Pearson‘s correlation on measuring class-wise hardness. Our analysis theoretically and empirically underscores the generality of GeoHard as a fresh perspective on data diagnosis. Additionally, we showcase how understanding class-wise hardness can practically aid in improving task learning.

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Unveiling Selection Biases: Exploring Order and Token Sensitivity in Large Language Models
Sheng-Lun Wei | Cheng-Kuang Wu | Hen-Hsen Huang | Hsin-Hsi Chen

In this paper, we investigate the phenomena of “selection biases” in Large Language Models (LLMs), focusing on problems where models are tasked with choosing the optimal option from an ordered sequence. We delve into biases related to option order and token usage, which significantly impact LLMs’ decision-making processes. We also quantify the impact of these biases through an extensive empirical analysis across multiple models and tasks. Furthermore, we propose mitigation strategies to enhance model performance. Our key contributions are threefold: 1) Precisely quantifying the influence of option order and token on LLMs, 2) Developing strategies to mitigate the impact of token and order sensitivity to enhance robustness, and 3) Offering a detailed analysis of sensitivity across models and tasks, which informs the creation of more stable and reliable LLM applications for selection problems.

The focus of language model evaluation has transitioned towards reasoning and knowledge-intensive tasks, driven by advancements in pretraining large models. While state-of-the-art models are partially trained on large Arabic texts, evaluating their performance in Arabic remains challenging due to the limited availability of relevant datasets. To bridge this gap, we present ArabicMMLU, the first multi-task language understanding benchmark for the Arabic language, sourced from school exams across diverse educational levels in different countries spanning North Africa, the Levant, and the Gulf regions. Our data comprises 40 tasks and 14,575 multiple-choice questions in Modern Standard Arabic (MSA) and is carefully constructed by collaborating with native speakers in the region. Our comprehensive evaluations of 35 models reveal substantial room for improvement, particularly among the best open-source models. Notably, BLOOMZ, mT0, LLama2, and Falcon struggle to achieve a score of 50%, while even the top-performing Arabic-centric model only achieves a score of 62.3%.

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On the Relationship Between RNN Hidden-State Vectors and Semantic Structures
Edi Muskardin | Martin Tappler | Ingo Pill | Bernhard Aichernig | Thomas Pock

We examine the assumption that hidden-state vectors of recurrent neural networks (RNNs) tend to form clusters of semantically similar vectors, which we dub the clustering hypothesis. While this hypothesis has been assumed in RNN analyses in recent years, its validity has not been studied thoroughly on modern RNN architectures. We first consider RNNs that were trained to recognize regular languages. This enables us to draw on perfect ground-truth automata in our evaluation, against which we can compare the RNN’s accuracy and the distribution of the hidden-state vectors. Then, we consider context-free languages to examine if RNN states form clusters for more expressive languages.For our analysis, we fit (generalized) linear models to classify RNN states into automata states and we apply different unsupervised clustering techniques. With a new ambiguity score, derived from information entropy, we measure how well an abstraction function maps the hidden state vectors to abstract clusters. Our evaluation supports the validity of the clustering hypothesis for regular languages, especially if RNNs are well-trained, i.e., clustering techniques succeed in finding clusters of similar state vectors. However, the clustering accuracy decreases substantially for context-free languages. This suggests that clustering is not a reliable abstraction technique for RNNs used in tasks like natural language processing.

The eXtreme Multi-label Classification (XMC) aims at accurately assigning large-scale labels to instances, and is challenging for learning, managing, and predicting over the large-scale and rapidly growing set of labels. Traditional XMC methods, like one-vs-all and tree-based methods struggle with the growing set of labels due to their static label assumptions, and embedding-based methods struggle with the complex mapping relationships due to their late-interaction paradigm. In this paper, we propose a large language model (LLM) powered agent framework for extreme multi-label classification – XMC-Agent, which can effectively learn, manage and predict the extremely large and dynamically increasing set of labels. Specifically, XMC-Agent models the extreme multi-label classification task as a dynamic navigation problem, employing a scalable hierarchical label index to effectively manage the unified label space. Additionally, we propose two algorithms to enhance the dynamic navigation capabilities of XMC-Agent: a self-construction algorithm for building the scalable hierarchical index, and an iterative feedback learning algorithm for adjusting the agent to specific tasks. Experiments show that XMC-Agentachieves the state-of-the-art performance on three standard datasets.

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Benchmarking Large Language Models on CFLUE - A Chinese Financial Language Understanding Evaluation Dataset
Jie Zhu | Junhui Li | Yalong Wen | Lifan Guo

In light of recent breakthroughs in large language models (LLMs) that have revolutionized natural language processing (NLP), there is an urgent need for new benchmarks to keep pace with the fast development of LLMs. In this paper, we propose CFLUE, the Chinese Financial Language Understanding Evaluation benchmark, designed to assess the capability of LLMs across various dimensions. Specifically, CFLUE provides datasets tailored for both knowledge assessment and application assessment. In knowledge assessment, it consists of 38K+ multiple-choice questions with associated solution explanations. These questions serve dual purposes: answer prediction and question reasoning. In application assessment, CFLUE features 16K+ test instances across distinct groups of NLP tasks such as text classification, machine translation, relation extraction, reading comprehension, and text generation. Upon CFLUE, we conduct a thorough evaluation of representative LLMs. The results reveal that only Qwen-72B, GPT-4, and GPT-4-turbo achieve an accuracy exceeding 60% in answer prediction for knowledge assessment, suggesting that there is still substantial room for improvement in current LLMs. In application assessment, while GPT-4 and GPT-4-turbo rank as the top two performers on average, their significant advantage over open-source LLMs is noticeably diminished, given that Qwen-72B achieves the best performance in 2 out of 5 tasks. The datasets and scripts associated with CFLUE are openly accessible at https://github.com/aliyun/cflue.

Reinforcement learning (RL) has been widely used in training large language models (LLMs) for preventing unexpected outputs, e.g., reducing harmfulness and errors. However, existing RL methods mainly adopt instance-level reward, which cannot provide fine-grained supervision for complex reasoning tasks. As a result, the RL training cannot be fully aware of the specific part or step that actually leads to the incorrectness in model response. To address it, we propose a new RL method named RLMEC that incorporates a generative model as the reward model, which is trained by the erroneous solution rewriting task under the minimum editing constraint, which can produce token-level supervision for RL training. Based 0on the generative reward model, we design the token-level RL objective for training and an imitation-based regularization for stabilizing RL process. And these two objectives focus on the revision of the key tokens for the erroneous solution, reducing the effect of other unimportant tokens. Experiment results on 8 tasks have demonstrated the effectiveness of our approach. Our code and data will be publicly released.

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Definition generation for lexical semantic change detection
Mariia Fedorova | Andrey Kutuzov | Yves Scherrer

We use contextualized word definitions generated by large language models as semantic representations in the task of diachronic lexical semantic change detection (LSCD). In short, generated definitions are used as ‘senses’, and the change score of a target word is retrieved by comparing their distributions in two time periods under comparison. On the material of five datasets and three languages, we show that generated definitions are indeed specific and general enough to convey a signal sufficient to rank sets of words by the degree of their semantic change over time. Our approach is on par with or outperforms prior non-supervised sense-based LSCD methods. At the same time, it preserves interpretability and allows to inspect the reasons behind a specific shift in terms of discrete definitions-as-senses. This is another step in the direction of explainable semantic change modeling.

Research in toxicity detection in natural language processing for the speech modality (audio-based) is quite limited, particularly for languages other than English. To address these limitations and lay the groundwork for truly multilingual audio-based toxicity detection, we introduce MuTox, the first highly multilingual audio-based dataset with toxicity labels which covers 14 different linguistic families. The dataset comprises 20,000 audio utterances for English and Spanish, and 4,000 for the other 28 languages. To demonstrate the quality of this dataset, we trained the MuTox audio-based toxicity classifier, which enables zero-shot toxicity detection across a wide range of languages. This classifier performs on par with existing text-based trainable classifiers, while expanding the language coverage more than tenfold. When compared to a wordlist-based classifier that covers a similar number of languages, MuTox improves F1-Score by an average of 100%. This significant improvement underscores the potential of MuTox in advancing the field of audio-based toxicity detection.

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Phased Instruction Fine-Tuning for Large Language Models
Wei Pang | Chuan Zhou | Xiao-Hua Zhou | Xiaojie Wang

Instruction Fine-Tuning, a method enhancing pre-trained language models’ capabilities from mere next-word prediction to complex instruction following, often employs a one-off training approach on diverse instruction dataset. However, this method may not effectively enhance models’ adherence to instructions due to the simultaneous handling of varying instruction complexities. To address this, we propose a novel phased instruction fine-tuning (Phased IFT) method, grounded in the hypothesis of progressive alignment, which posits that the transition of a pre-trained language model from simple next-word prediction to sophisticated instruction following is a gradual learning process. Specifically, we obtain the score of difficulty for each instruction via GPT-4, stratify the instruction data into subsets of increasing difficulty, and sequentially uptrain on these subsets using the standard supervised loss. Through extensive experiments on the pre-trained models Llama-2 7B/13B, and Mistral-7B using the 52K Alpaca instruction data, we demonstrate that Phased IFT significantly surpasses traditional one-off instruction fine-tuning (One-off IFT) method in win rate, empirically validating the progressive alignment hypothesis. Our findings suggest that Phased IFT offers a simple yet effective pathway for elevating the instruction-following capabilities of pre-trained language models.

Topic relevance of an essay demands that the composition adheres to a clear theme and aligns well with the essay prompt requirements, a critical aspect of essay quality evaluation. However, existing research of Automatic Essay Scoring (AES) for Chinese essays has overlooked topic relevance and lacks detailed feedback, while Automatic Essay Comment Generation (AECG) faces much complexity and difficulty. Additionally, current Large Language Models, including GPT-4, often make incorrect judgments and provide overly impractical feedback when evaluating topic relevance. This paper introduces TOREE (Topic Relevance Evaluation), a comprehensive dataset developed to assess topic relevance in Chinese primary and middle school students’ essays, which is beneficial for AES, AECG and other applications. Moreover, our proposed two-step method utilizes TOREE through a combination of Supervised Fine-tuning and Preference Learning. Experimental results demonstrate that TOREE is of high quality, and our method significantly enhances models’ performance on two designed tasks for topic relevance evaluation, improving both automatic and human evaluations across four diverse LLMs.

Temporal Knowledge Graph (TKG) reasoning seeks to predict future incomplete facts leveraging historical data. While existing approaches have shown effectiveness in addressing the task through various perspectives, such as graph learning and logic rules, they are limited in capturing the indeterminacy in future events, particularly in the case of rare/unseen facts. To tackle the highlighted issues, we introduce a novel approach by conceptualizing TKG reasoning as a sequence denoising process for future facts, namely DiffuTKG. Concretely, we first encodes the historical events as the conditional sequence. Then we gradually introduce Gaussian noise to corrupt target facts during the forward process and then employ a transformer-based conditional denoiser to restore them in the reverse phase. Moreover, we introduce an uncertainty regularization loss to mitigate the risk of prediction biases by favoring frequent scenarios over rare/unseen facts. Empirical results on four real-world datasets show that DiffuTKG outperforms state-of-the-art methods across multiple evaluation metrics.

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Asymmetric Bias in Text-to-Image Generation with Adversarial Attacks
Haz Shahgir | Xianghao Kong | Greg Ver Steeg | Yue Dong

The widespread use of Text-to-Image (T2I) models in content generation requires careful examination of their safety, including their robustness to adversarial attacks. Despite extensive research on adversarial attacks, the reasons for their effectiveness remain underexplored. This paper presents an empirical study on adversarial attacks against T2I models, focusing on analyzing factors associated with attack success rates (ASR). We introduce a new attack objective - entity swapping using adversarial suffixes and two gradient-based attack algorithms. Human and automatic evaluations reveal the asymmetric nature of ASRs on entity swap: for example, it is easier to replace “human” with “robot” in the prompt “a human dancing in the rain.” with an adversarial suffix, but the reverse replacement is significantly harder. We further propose probing metrics to establish indicative signals from the model’s beliefs to the adversarial ASR. We identify conditions that result in a success probability of 60% for adversarial attacks and others where this likelihood drops below 5%. The code and data are available at https://github.com/Patchwork53/AsymmetricAttack

Controlled Text Generation (CTG) aims to produce texts that exhibit specific desired attributes. In this study, we introduce a pluggable CTG framework for Large Language Models (LLMs) named Dynamic Attribute Graphs-based controlled text generation (DATG). This framework utilizes an attribute scorer to evaluate the attributes of sentences generated by LLMs and constructs dynamic attribute graphs. DATG modulates the occurrence of key attribute words and key anti-attribute words, achieving effective attribute control without compromising the original capabilities of the model. We conduct experiments across four datasets in two tasks: toxicity mitigation and sentiment transformation, employing five LLMs as foundational models. Our findings highlight a remarkable enhancement in control accuracy, achieving a peak improvement of 19.29% over baseline methods in the most favorable task across four datasets. Additionally, we observe a significant decrease in perplexity, markedly improving text fluency.

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Coconut: Contextualized Commonsense Unified Transformers for Graph-Based Commonsense Augmentation of Language Models
Jun-Hyung Park | Mingyu Lee | Junho Kim | SangKeun Lee

In this paper, we introduce COCONUT to effectively guide the contextualization of structured commonsense knowledge based on largelanguage models. COCONUT employs a contextualized knowledge prompting scheme to gather high-quality contextualization examplesfrom a large language model. These examples are subsequently distilled into small language models to enhance their contextualization capability. Extensive evaluations show that COCONUT considerably improves commonsense reasoning performance across diverse benchmarks, models, and settings, exhibiting its flexibility and universality in generating contextualized commonsense knowledge. Notably,COCONUT consistently outperforms the state-of-the-art technique by an average of 5.8%.

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Mass-Editing Memory with Attention in Transformers: A cross-lingual exploration of knowledge
Daniel Tamayo | Aitor Gonzalez-Agirre | Javier Hernando | Marta Villegas

Recent research has explored methods for updating and modifying factual knowledge in large language models, often focusing on specific multi-layer perceptron blocks. This study expands on this work by examining the effectiveness of existing knowledge editing methods across languages and delving into the role of attention mechanisms in this process. Drawing from the insights gained, we propose Mass-Editing Memory with Attention in Transformers (MEMAT), a method that achieves significant improvements in all metrics while requiring minimal parameter modifications. MEMAT delivers a remarkable 10% increase in magnitude metrics, benefits languages not included in the training data and also demonstrates a high degree of portability. Our code and data are at https://github.com/dtamayo-nlp/MEMAT.

Large Language Models (LLMs) have demonstrated remarkable versatility in recent years, offering potential applications across specialized domains such as healthcare and medicine. Despite the availability of various open-source LLMs tailored for health contexts, adapting general-purpose LLMs to the medical domain presents significant challenges.In this paper, we introduce BioMistral, an open-source LLM tailored for the biomedical domain, utilizing Mistral as its foundation model and further pre-trained on PubMed Central. We conduct a comprehensive evaluation of BioMistral on a benchmark comprising 10 established medical question-answering (QA) tasks in English. We also explore lightweight models obtained through quantization and model merging approaches. Our results demonstrate BioMistral’s superior performance compared to existing open-source medical models and its competitive edge against proprietary counterparts. Finally, to address the limited availability of data beyond English and to assess the multilingual generalization of medical LLMs, we automatically translated and evaluated this benchmark into 7 other languages. This marks the first large-scale multilingual evaluation of LLMs in the medical domain. Datasets, multilingual evaluation benchmarks, scripts, and all the models obtained during our experiments are freely released.

Safety lies at the core of developing and deploying large language models (LLMs). However, previous safety benchmarks only concern the safety in one language, e.g. the majority language in the pretraining data such as English. In this work, we build the first multilingual safety benchmark for LLMs, XSafety, in response to the global deployment of LLMs in practice. XSafety covers 14 kinds of commonly used safety issues across 10 languages that span several language families. We utilize XSafety to empirically study the multilingual safety for 4 widely-used LLMs, including both close-API and open-source models. Experimental results show that all LLMs produce significantly more unsafe responses for non-English queries than English ones, indicating the necessity of developing safety alignment for non-English languages. In addition, we propose a simple and effective prompting method to improve the multilingual safety of ChatGPT by enhancing cross-lingual generalization of safety alignment. Our prompting method can significantly reduce the ratio of unsafe responses by 42% for non-English queries. We will release all the data and results to facilitate future research on LLMs’ safety.

Legal Judgment Prediction (LJP) refers to the task of automatically predicting judgment results (e.g., charges, law articles and term of penalty) given the fact description of cases. While SOTA models have achieved high accuracy and F1 scores on public datasets, existing datasets fail to evaluate specific aspects of these models (e.g., legal fairness, which significantly impact their applications in real scenarios). Inspired by functional testing in software engineering, we introduce LJPCHECK, a suite of functional tests for LJP models, to comprehend LJP models’ behaviors and offer diagnostic insights. We illustrate the utility of LJPCHECK on five SOTA LJP models. Extensive experiments reveal vulnerabilities in these models, prompting an in-depth discussion into the underlying reasons of their shortcomings.

Legal Judgment Prediction (LJP) has attracted significant attention in recent years. However, previous studies have primarily focused on cases involving only a single defendant, skipping multi-defendant cases due to complexity and difficulty. To advance research, we introduce CMDL, a large-scale real-world Chinese Multi-Defendant LJP dataset, which consists of over 393,945 cases with nearly 1.2 million defendants in total. For performance evaluation, we propose case-level evaluation metrics dedicated for the multi-defendant scenario. Experimental results on CMDL show existing SOTA approaches demonstrate weakness when applied to cases involving multiple defendants. We highlight several challenges that require attention and resolution.

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Model Editing by Standard Fine-Tuning
Govind Krishnan Gangadhar | Karl Stratos

Standard fine-tuning is considered not as effective as specialized methods for model editing due to its comparatively poor performance. However, it is simple, agnostic to the architectural details of the model being edited, and able to leverage advances in standard training techniques with no additional work (e.g., black-box PEFT for computational efficiency), making it an appealing choice for a model editor. In this work, we show that standard fine-tuning alone can yield competitive model editing performance with two minor modifications. First, we optimize the conditional likelihood rather than the full likelihood. Second, in addition to the typical practice of training on randomly paraphrased edit prompts to encourage generalization, we also train on random or similar unedited facts to encourage locality. Our experiments on the ZsRE and CounterFact datasets demonstrate that these simple modifications allow standard fine-tuning to match or outperform highly specialized editors in terms of edit score.

Combining large language models with logical reasoning enhances their capacity to address problems in a robust and reliable manner. Nevertheless, the intricate nature of logical reasoning poses challenges when gathering reliable data from the web to build comprehensive training datasets, subsequently affecting performance on downstream tasks. To address this, we introduce a novel logic-driven data augmentation approach, AMR-LDA. AMR-LDA converts the original text into an Abstract Meaning Representation (AMR) graph, a structured semantic representation that encapsulates the logical structure of the sentence, upon which operations are performed to generate logically modified AMR graphs. The modified AMR graphs are subsequently converted back into text to create augmented data. Notably, our methodology is architecture-agnostic and enhances both generative large language models, such as GPT-3.5 and GPT-4, through prompt augmentation, and discriminative large language models through contrastive learning with logic-driven data augmentation. Empirical evidence underscores the efficacy of our proposed method with improvement in performance across seven downstream tasks, such as reading comprehension requiring logical reasoning, textual entailment, and natural language inference. Furthermore, our method leads on the ReClor leaderboard. The source code and data are publicly available

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CodeInsight: A Curated Dataset of Practical Coding Solutions from Stack Overflow
Nathanaël Beau | Benoit Crabbé

We introduce a novel dataset tailored for code generation, aimed at aiding developers in common tasks. Our dataset provides examples that include a clarified intent, code snippets associated, and an average of three related unit tests. It encompasses a range of libraries such as Pandas, Numpy, and Regex, along with more than 70 standard libraries in Python code derived from Stack Overflow. Comprising 3,402 crafted examples by Python experts, our dataset is designed for both model finetuning and standalone evaluation. To complete unit tests evaluation, we categorize examples in order to get more fine grained analysis, enhancing the understanding of models’ strengths and weaknesses in specific coding tasks. The examples have been refined to reduce data contamination, a process confirmed by the performance of three leading models: Mistral 7B, CodeLLAMA 13B, and Starcoder 15B. We further investigate data-contamination testing GPT-4 performance on a part of our dataset. The benchmark can be accessed at anonymized address.

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ViHateT5: Enhancing Hate Speech Detection in Vietnamese With a Unified Text-to-Text Transformer Model
Luan Thanh Nguyen

Recent advancements in hate speech detection (HSD) in Vietnamese have made significant progress, primarily attributed to the emergence of transformer-based pre-trained language models, particularly those built on the BERT architecture. However, the necessity for specialized fine-tuned models has resulted in the complexity and fragmentation of developing a multitasking HSD system. Moreover, most current methodologies focus on fine-tuning general pre-trained models, primarily trained on formal textual datasets like Wikipedia, which may not accurately capture human behavior on online platforms. In this research, we introduce ViHateT5, a T5-based model pre-trained on our proposed large-scale domain-specific dataset named VOZ-HSD. By harnessing the power of a text-to-text architecture, ViHateT5 can tackle multiple tasks using a unified model and achieve state-of-the-art performance across all standard HSD benchmarks in Vietnamese. Our experiments also underscore the significance of label distribution in pre-training data on model efficacy. We provide our experimental materials for research purposes, including the VOZ-HSD dataset, pre-trained checkpoint, the unified HSD-multitask ViHateT5 model, and related source code on GitHub publicly.

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Bias in News Summarization: Measures, Pitfalls and Corpora
Julius Steen | Katja Markert

Summarization is an important application of large language models (LLMs). Most previous evaluation of summarization models has focused on their content selection, faithfulness, grammaticality and coherence. However, it is well known that LLMs can reproduce and reinforce harmful social biases. This raises the question: Do biases affect model outputs in a constrained setting like summarization?To help answer this question, we first motivate and introduce a number of definitions for biased behaviours in summarization models, along with practical operationalizations. Since we find that biases inherent to input documents can confound bias analysis in summaries, we propose a method to generate input documents with carefully controlled demographic attributes. This allows us to study summarizer behavior in a controlled setting, while still working with realistic input documents.We measure gender bias in English summaries generated by both purpose-built summarization models and general purpose chat models as a case study. We find content selection in single document summarization to be largely unaffected by gender bias, while hallucinations exhibit evidence of bias.To demonstrate the generality of our approach, we additionally investigate racial bias, including intersectional settings.

Despite the success of large language models (LLMs) in natural language generation, much evidence shows that LLMs may produce incorrect or nonsensical text. This limitation highlights the importance of discerning when to trust LLMs, especially in safety-critical domains. Existing methods often express reliability by confidence level, however, their effectiveness is limited by the lack of objective guidance. To address this, we propose CONfidence-Quality-ORDer-preserving alignment approach (CONQORD), which leverages reinforcement learning guided by a tailored dual-component reward function. This function integrates quality reward and order-preserving alignment reward functions. Specifically, the order-preserving reward incentivizes the model to verbalize greater confidence for responses of higher quality to align the order of confidence and quality. Experiments demonstrate that CONQORD significantly improves the alignment performance between confidence and response accuracy, without causing over-cautious. Furthermore, the aligned confidence provided by CONQORD informs when to trust LLMs, and acts as a determinant for initiating the retrieval process of external knowledge. Aligning confidence with response quality ensures more transparent and reliable responses, providing better trustworthiness.

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Zero-shot Cross-lingual Alignment for Embedding Initialization
Xi Ai | Zhiyong Huang

For multilingual training, we present CrossInit, an initialization method that initializes embeddings into similar geometrical structures across languages in an unsupervised manner. CrossInit leverages a common cognitive linguistic mechanism, Zipf’s law, which indicates that similar concepts across languages have similar word ranks or frequencies in their monolingual corpora. Instead of considering point-to-point alignments based on ranks, CrossInit considers the same span of consecutive ranks in each language as the Positive pairs for alignment, while others out of the span are used as Negative pairs. CrossInit then employs Contrastive Learning to iteratively refine randomly initialized embeddings for similar geometrical structures across languages. Our experiments on Unsupervised NMT, XNLI, and MLQA showed significant gains in low-resource and dissimilar languages after applying CrossInit.

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Mitigating Hallucinations in Large Vision-Language Models (LVLMs) via Language-Contrastive Decoding (LCD)
Avshalom Manevich | Reut Tsarfaty

Large Vision-Language Models (LVLMs) are an extension of Large Language Models (LLMs) that facilitate processing both image and text inputs, expanding AI capabilities. However, LVLMs struggle with object hallucinations due to their reliance on text cues and learned object co-occurrence biases. While most research quantifies these hallucinations, mitigation strategies are still lacking. Our study introduces a Language Contrastive Decoding (LCD) algorithm that adjusts LVLM outputs based on LLM distribution confidence levels, effectively reducing object hallucinations. We demonstrate the advantages of LCD in leading LVLMs, showing up to %4 improvement in POPE F1 scores and up to %36 reduction in CHAIR scores on the COCO validation set, while also improving captioning quality scores. Our method effectively improves LVLMs without needing complex post-processing or retraining, and is easily applicable to different models. Our findings highlight the potential of further exploration of LVLM-specific decoding algorithms.

We address the open problem of automatically identifying the direction of lexical borrowing, given word pairs in the donor and recipient languages. We propose strong benchmarks for this task, by applying a set of machine learning models. We extract and publicly release a comprehensive borrowings dataset from the recent RoBoCoP cognates and borrowings database for five Romance languages. We experiment on this dataset with both graphic and phonetic representations and with different features, models and architectures. We interpret the results, in terms of F1 score, commenting on the influence of features and model choice, of the imbalanced data and of the inherent difficulty of the task for particular language pairs. We show that automatically determining the direction of borrowing is a feasible task, and propose additional directions for future work.

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Advancing Post-OCR Correction: A Comparative Study of Synthetic Data
Shuhao Guan | Derek Greene

This paper explores the application of synthetic data in the post-OCR domain on multiple fronts by conducting experiments to assess the impact of data volume, augmentation, and synthetic data generation methods on model performance. Furthermore, we introduce a novel algorithm that leverages computer vision feature detection algorithms to calculate glyph similarity for constructing post-OCR synthetic data. Through experiments conducted across a variety of languages, including several low-resource ones, we demonstrate that models like ByT5 can significantly reduce Character Error Rates (CER) without the need for manually annotated data, and our proposed synthetic data generation method shows advantages over traditional methods, particularly in low-resource languages.

This paper presents a novel solution to tackle the challenges that posed by the abundance of non-standard addresses, which input by users in modern applications such as navigation maps, ride-hailing apps, food delivery platforms, and logistics services. These manually entered addresses often contain irregularities, such as missing information, spelling errors, colloquial descriptions, and directional offsets, which hinder address-related tasks like address matching and linking. To tackle these challenges, we propose GeoAgent, a new framework comprising two main components: a large language model (LLM) and a suite of geographical tools. By harnessing the semantic understanding capabilities of the LLM and integrating specific geospatial tools, GeoAgent incorporates spatial knowledge into address texts and achieves efficient address standardization. Further, to verify the effectiveness and practicality of our approach, we construct a comprehensive dataset of complex non-standard addresses, which fills the gaps in existing datasets and proves invaluable for training and evaluating the performance of address standardization models in this community. Experimental results demonstrate the efficacy of GeoAgent, showcasing substantial improvements in the performance of address-related models across various downstream tasks.

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HQP: A Human-Annotated Dataset for Detecting Online Propaganda
Abdurahman Maarouf | Dominik Bär | Dominique Geissler | Stefan Feuerriegel

Online propaganda poses a severe threat to the integrity of societies. However, existing datasets for detecting online propaganda have a key limitation: they were annotated using weak labels that can be noisy and even incorrect. To address this limitation, our work makes the following contributions: (1) We present HQP: a novel dataset (N=30000) for detecting online propaganda with high-quality labels. To the best of our knowledge, HQP is the first large-scale dataset for detecting online propaganda that was created through human annotation. (2) We show empirically that state-of-the-art language models fail in detecting online propaganda when trained with weak labels (AUC: 64.03). In contrast, state-of-the-art language models can accurately detect online propaganda when trained with our high-quality labels (AUC: 92.25), which is an improvement of 44%. (3) We show that prompt-based learning using a small sample of high-quality labels can still achieve a reasonable performance (AUC: 80.27) while significantly reducing the cost of labeling. (4) We extend HQP to HQP+ to test how well propaganda across different contexts can be detected. Crucially, our work highlights the importance of high-quality labels for sensitive NLP tasks such as propaganda detection.

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Teaching Language Models to Self-Improve by Learning from Language Feedback
Chi Hu | Yimin Hu | Hang Cao | Tong Xiao | JingBo Zhu

Aligning Large Language Models (LLMs) with human intentions and values is crucial yet challenging. Current methods primarily rely on human preferences, which are costly and insufficient in capturing nuanced feedback expressed in natural language. In this paper, we present Self-Refinement Tuning (SRT), a method that leverages model feedback for alignment, thereby reducing reliance on human annotations. SRT uses a base language model (e.g., Tulu2) to generate initial responses, which are critiqued and refined by a more advanced model (e.g., GPT-4-Turbo). This process enables the base model to self-evaluate and improve its outputs, facilitating continuous learning. SRT further optimizes the model by learning from its self-generated feedback and refinements, creating a feedback loop that promotes model improvement. Our empirical evaluations demonstrate that SRT significantly outperforms strong baselines across diverse tasks and model sizes. When applied to a 70B parameter model, SRT increases the win rate from 9.6% to 25.8% on the AlpacaEval 2.0 benchmark, surpassing well-established systems such as GPT-4-0314, Claude 2, and Gemini. Our analysis highlights the crucial role of language feedback in the success of SRT, suggesting potential for further exploration in this direction.

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Exploring Spatial Schema Intuitions in Large Language and Vision Models
Philipp Wicke | Lennart Wachowiak

Despite the ubiquity of large language models (LLMs) in AI research, the question of embodiment in LLMs remains underexplored, distinguishing them from embodied systems in robotics where sensory perception directly informs physical action.Our investigation navigates the intriguing terrain of whether LLMs, despite their non-embodied nature, effectively capture implicit human intuitions about fundamental, spatial building blocks of language. We employ insights from spatial cognitive foundations developed through early sensorimotor experiences, guiding our exploration through the reproduction of three psycholinguistic experiments. Surprisingly, correlations between model outputs and human responses emerge, revealing adaptability without a tangible connection to embodied experiences. Notable distinctions include polarized language model responses and reduced correlations in vision language models. This research contributes to a nuanced understanding of the interplay between language, spatial experiences, and the computations made by large language models.Project Website: https://cisnlp.github.io/Spatial_Schemas/

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Efficient Detection of LLM-generated Texts with a Bayesian Surrogate Model
Yibo Miao | Hongcheng Gao | Hao Zhang | Zhijie Deng

The detection of machine-generated text, especially from large language models (LLMs), is crucial in preventing serious social problems resulting from their misuse. Some methods train dedicated detectors on specific datasets but fall short in generalizing to unseen test data, while other zero-shot ones often yield suboptimal performance. Although the recent DetectGPT has shown promising detection performance, it suffers from significant inefficiency issues, as detecting a single candidate requires querying the source LLM with hundreds of its perturbations. This paper aims to bridge this gap. Concretely, we propose to incorporate a Bayesian surrogate model, which allows us to select typical samples based on Bayesian uncertainty and interpolate scores from typical samples to other samples, to improve query efficiency. Empirical results demonstrate that our method significantly outperforms existing approaches under a low query budget. Notably, when detecting the text generated by LLaMA family models, our method with just 2 or 3 queries can outperform DetectGPT with 200 queries.

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Decoding the Narratives: Analyzing Personal Drug Experiences Shared on Reddit
Layla Bouzoubaa | Elham Aghakhani | Max Song | Quang Trinh | Shadi Rezapour

Online communities such as drug-related subreddits serve as safe spaces for people who use drugs (PWUD), fostering discussions on substance use experiences, harm reduction, and addiction recovery. Users’ shared narratives on these forums provide insights into the likelihood of developing a substance use disorder (SUD) and recovery potential. Our study aims to develop a multi-level, multi-label classification model to analyze online user-generated texts about substance use experiences. For this purpose, we first introduce a novel taxonomy to assess the nature of posts, including their intended connections (Inquisition or Disclosure), subjects (e.g., Recovery, Dependency), and specific objectives (e.g., Relapse, Quality, Safety). Using various multi-label classification algorithms on a set of annotated data, we show that GPT-4, when prompted with instructions, definitions, and examples, outperformed all other models. We apply this model to label an additional 1,000 posts and analyze the categories of linguistic expression used within posts in each class. Our analysis shows that topics such as Safety, Combination of Substances, and Mental Health see more disclosure, while discussions about physiological Effects focus on harm reduction. Our work enriches the understanding of PWUD’s experiences and informs the broader knowledge base on SUD and drug use.

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Unveiling the Art of Heading Design: A Harmonious Blend of Summarization, Neology, and Algorithm
Shaobo Cui | Yiyang Feng | Yisong Mao | Yifan Hou | Boi Faltings

Crafting an appealing heading is crucial for attracting readers and marketing work or products. A popular way is to summarize the main idea with a refined description and a memorable acronym. However, there lacks a systematic study and a formal benchmark including datasets and metrics. Motivated by this absence, we introduce LOgogram, a novel benchmark comprising 6,653 paper abstracts with corresponding descriptions and acronyms. To measure the quality of heading generation, we propose a set of evaluation metrics from three aspects: summarization, neology, and algorithm. Additionally, we explore three strategies for heading generation(generation ordering, tokenization of acronyms, and framework design) under various prevalent learning paradigms(supervised fine-tuning, in-context learning with Large Language Models(LLMs), and reinforcement learning) on our benchmark. Our experimental results indicate the difficulty in identifying a practice that excels across all summarization, neologistic, and algorithmic aspects.

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Understanding Fine-grained Distortions in Reports of Scientific Findings
Amelie Wuehrl | Dustin Wright | Roman Klinger | Isabelle Augenstein

Distorted science communication harms individuals and society as it can lead to unhealthy behavior change and decrease trust in scientific institutions. Given the rapidly increasing volume of science communication in recent years, a fine-grained understanding of how findings from scientific publications are reported to the general public, and methods to detect distortions from the original work automatically, are crucial. Prior work focused on individual aspects of distortions or worked with unpaired data. In this work, we make three foundational contributions towards addressing this problem: (1) annotating 1,600 instances of scientific findings from academic papers paired with corresponding findings as reported in news articles and tweets wrt. four characteristics: causality, certainty, generality and sensationalism; (2) establishing baselines for automatically detecting these characteristics; and (3) analyzing the prevalence of changes in these characteristics in both human-annotated and large-scale unlabeled data. Our results show that scientific findings frequently undergo subtle distortions when reported. Tweets distort findings more often than science news reports. Detecting fine-grained distortions automatically poses a challenging task. In our experiments, fine-tuned task-specific models consistently outperform few-shot LLM prompting.

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MM-SOC: Benchmarking Multimodal Large Language Models in Social Media Platforms
Yiqiao Jin | Minje Choi | Gaurav Verma | Jindong Wang | Srijan Kumar

Social media platforms are hubs for multimodal information exchange, encompassing text, images, and videos, making it challenging for machines to comprehend the information or emotions associated with interactions in online spaces. Multimodal Large Language Models (MLLMs) have emerged as a promising solution to address these challenges, yet struggle with accurately interpreting human emotions and complex contents like misinformation. This paper introduces MM-Soc, a comprehensive benchmark designed to evaluate MLLMs’ understanding of multimodal social media content. MM-Soc compiles prominent multimodal datasets and incorporates a novel large-scale YouTube tagging dataset, targeting a range of tasks from misinformation detection, hate speech detection, and social context generation. Through our exhaustive evaluation on ten size-variants of four open-source MLLMs, we have identified significant performance disparities, highlighting the need for advancements in models’ social understanding capabilities. Our analysis reveals that, in a zero-shot setting, various types of MLLMs generally exhibit difficulties in handling social media tasks. However, MLLMs demonstrate performance improvements post fine-tuning, suggesting potential pathways for improvement.

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Instances Need More Care: Rewriting Prompts for Instances with LLMs in the Loop Yields Better Zero-Shot Performance
Saurabh Srivastava | Chengyue Huang | Weiguo Fan | Ziyu Yao

Large language models (LLMs) have revolutionized zero-shot task performance, mitigating the need for task-specific annotations while enhancing task generalizability. Despite its advancements, current methods using trigger phrases such as “Let’s think step by step” remain limited. This study introduces PRomPTed, an approach that optimizes the zero-shot prompts for individual task instances following an innovative manner of “LLMs in the loop”.Our comprehensive evaluation across 13 datasets and 10 task types based on GPT-4 reveals that PRomPTed significantly outperforms both the naive zero-shot approaches and a strong baseline (i.e., “Output Refinement”) which refines the task output instead of the input prompt. Our experimental results also confirmed the generalization of this advantage to the relatively weaker GPT-3.5. Even more intriguingly, we found that leveraging GPT-3.5 to rewrite prompts for the stronger GPT-4 not only matches but occasionally exceeds the efficacy of using GPT-4 as the prompt rewriter. Our research thus presents a huge value in not only enhancing zero-shot LLM performance but also potentially enabling supervising LLMs with their weaker counterparts, a capability attracting much interest recently. Finally, our additional experiments confirm the generalization of the advantages to open-source LLMs such as Mistral 7B and Mixtral 8x7B.

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Benchmarking Retrieval-Augmented Generation for Medicine
Guangzhi Xiong | Qiao Jin | Zhiyong Lu | Aidong Zhang

While large language models (LLMs) have achieved state-of-the-art performance on a wide range of medical question answering (QA) tasks, they still face challenges with hallucinations and outdated knowledge. Retrieval-augmented generation (RAG) is a promising solution and has been widely adopted. However, a RAG system can involve multiple flexible components, and there is a lack of best practices regarding the optimal RAG setting for various medical purposes. To systematically evaluate such systems, we propose the Medical Information Retrieval-Augmented Generation Evaluation (MIRAGE), a first-of-its-kind benchmark including 7,663 questions from five medical QA datasets. Using MIRAGE, we conducted large-scale experiments with over 1.8 trillion prompt tokens on 41 combinations of different corpora, retrievers, and backbone LLMs through the MedRAG toolkit introduced in this work. Overall, MedRAG improves the accuracy of six different LLMs by up to 18% over chain-of-thought prompting, elevating the performance of GPT-3.5 and Mixtral to GPT-4-level. Our results show that the combination of various medical corpora and retrievers achieves the best performance. In addition, we discovered a log-linear scaling property and the “lost-in-the-middle” effects in medical RAG. We believe our comprehensive evaluations can serve as practical guidelines for implementing RAG systems for medicine.

While LLMs demonstrate impressive capabilities in musical knowledge, we find that music reasoning is still an unsolved task.We introduce ChatMusician, an open-source large language model (LLM) that integrates intrinsic musical abilities. It is based on continual pre-training and finetuning LLaMA2 on a text-compatible music representation, ABC notation, and the music is treated as a second language.ChatMusician can understand and generate music with a pure text tokenizer without external multi-modal neural structures or tokenizers. Interestingly, endowing musical abilities does not harm language abilities, even achieving a slightly higher MMLU score.ChatMusician is capable of composing well-structured, full-length music, condition on texts, chords, melodies, motifs, musical forms, etc.On our meticulously curated college-level music understanding benchmark, MusicTheoryBench, ChatMusician surpasses LLaMA2 and GPT-3.5 by a noticeable margin. We show that ChatMusician preserves or even surpasses the original LLaMA2 7B’s language abilities by evaluating on MMLU benchmark.Our work reveals that LLMs can be an excellent compressor for music, which can be seen as humanity’s creative language, but there remains significant territory to be conquered.We release our 5B token music-language corpora MusicPiles, the collected MusicTheoryBench, code, model and demo.

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Towards Robust Temporal Reasoning of Large Language Models via a Multi-Hop QA Dataset and Pseudo-Instruction Tuning
Qingyu Tan | Hwee Tou Ng | Lidong Bing

Knowledge in the real world is being updated constantly. However, it is costly to frequently update large language models (LLMs). Therefore, it is crucial for LLMs to understand the concept of temporal knowledge. However, prior works on temporal question answering (TQA) did not emphasize multi-answer and multi-hop types of temporal reasoning. In this paper, we propose a complex temporal question-answering dataset Complex-TR that focuses on multi-answer and multi-hop temporal reasoning. Besides, we also propose a novel data augmentation strategy to improve the complex temporal reasoning capability and robustness of LLMs. We conducted experiments on multiple temporal QA datasets. Experimental results show that our method is able to improve LLMs’ performance on temporal QA benchmarks by significant margins.

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Mind Your Format: Towards Consistent Evaluation of In-Context Learning Improvements
Anton Voronov | Lena Wolf | Max Ryabinin

Large language models demonstrate a remarkable capability for learning to solve new tasks from a few examples.The prompt template, or the way the input examples are formatted to obtain the prompt, is an important yet often overlooked aspect of in-context learning.In this work, we conduct a comprehensive study of the template format’s influence on the in-context learning performance.We evaluate the impact of the prompt template across 21 models (from 770M to 70B parameters) and 4 standard classification datasets. We show that a poor choice of the template can reduce the performance of the strongest models and inference methods to a random guess level.More importantly, the best templates do not transfer between different setups and even between models of the same family.Our findings show that the currently prevalent approach to evaluation, which ignores template selection, may give misleading results due to different templates in different works.As a first step towards mitigating this issue, we propose Template Ensembles that aggregate model predictions across several templates.This simple test-time augmentation boosts average performance while being robust to the choice of random set of templates.

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Knowledge Graph-Enhanced Large Language Models via Path Selection
Haochen Liu | Song Wang | Yaochen Zhu | Yushun Dong | Jundong Li

Large Language Models (LLMs) have shown unprecedented performance in various real-world applications. However, they are known to generate factually inaccurate outputs, a.k.a. the hallucination problem. In recent years, incorporating external knowledge extracted from Knowledge Graphs (KGs) has become a promising strategy to improve the factual accuracy of LLM-generated outputs. Nevertheless, most existing explorations rely on LLMs themselves to perform KG knowledge extraction, which is highly inflexible as LLMs can only provide binary judgment on whether a certain knowledge (e.g., a knowledge path in KG) should be used. In addition, LLMs tend to pick only knowledge with direct semantic relationship with the input text, while potentially useful knowledge with indirect semantics can be ignored. In this work, we propose a principled framework KELP with three stages to handle the above problems. Specifically, KELP is able to achieve finer granularity of flexible knowledge extraction by generating scores for knowledge paths with input texts via latent semantic matching. Meanwhile, knowledge paths with indirect semantic relationships with the input text can also be considered via trained encoding between the selected paths in KG and the input text. Experiments on real-world datasets validate the effectiveness of KELP.

Recently, there has been considerable attention on detecting hallucinations and omissions in Machine Translation (MT) systems. The two dominant approaches to tackle this task involve analyzing the MT system’s internal states or relying on the output of external tools, such as sentence similarity or MT quality estimators. In this work, we introduce OTTAWA, a novel Optimal Transport (OT)-based word aligner specifically designed to enhance the detection of hallucinations and omissions in MT systems. Our approach explicitly models the missing alignments by introducing a “null” vector, for which we propose a novel one-side constrained OT setting to allow an adaptive null alignment. Our approach yields competitive results compared to state-of-the-art methods across 18 language pairs on the HalOmi benchmark. In addition, it shows promising features, such as the ability to distinguish between both error types and perform word-level detection without accessing the MT system’s internal states.

In the realm of event prediction, temporal knowledge graph forecasting (TKGF) stands as a pivotal technique. Previous approaches face the challenges of not utilizing experience during testing and relying on a single short-term history, which limits adaptation to evolving data. In this paper, we introduce the Online Neural-Symbolic Event Prediction (ONSEP) framework, which innovates by integrating dynamic causal rule mining (DCRM) and dual history augmented generation (DHAG). DCRM dynamically constructs causal rules from real-time data, allowing for swift adaptation to new causal relationships. In parallel, DHAG merges short-term and long-term historical contexts, leveraging a bi-branch approach to enrich event prediction. Our framework demonstrates notable performance enhancements across diverse datasets, with significant Hit@k (k=1,3,10) improvements, showcasing its ability to augment large language models (LLMs) for event prediction without necessitating extensive retraining. The ONSEP framework not only advances the field of TKGF but also underscores the potential of neural-symbolic approaches in adapting to dynamic data environments.

Recently, advancements in large language models (LLMs) have shown an unprecedented ability across various language tasks. This paper investigates the potential application of LLMs to slot filling with noisy ASR transcriptions, via both in-context learning and task-specific fine-tuning. Dedicated prompt designs and noise-robust LoRA fine-tuning are proposed to improve the robustness of LLMs for slot filling with noisy ASR transcriptions. Moreover, a linearised knowledge injection (LKI) scheme is also proposed to integrate dynamic external knowledge into LLMs. Experiments were performed on SLURP to quantify the performance of LLMs, including GPT-3.5-turbo, GPT-4, LLaMA-13B, LLaMA-2-13B and Vicuna-13B (v1.1 and v1.5) with different ASR error rates. The use of the noise-robust fine-tuning together with LKI for Vicuna-13B-v1.5 achieved 6.7% and 17.6% absolute SLU-F1 improvements compared to a fully fine-tuned Flan-T5-XL model on the limited data setup and the zero-shot setup respectively.

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Too Big to Fail: Larger Language Models are Disproportionately Resilient to Induction of Dementia-Related Linguistic Anomalies
Changye Li | Zhecheng Sheng | Trevor Cohen | Serguei Pakhomov

As artificial neural networks grow in complexity, understanding their inner workings becomes increasingly challenging, which is particularly important in healthcare applications. The intrinsic evaluation metrics of autoregressive neural language models (NLMs), perplexity (PPL), can reflect how “surprised” an NLM model is at novel input. PPL has been widely used to understand the behavior of NLMs. Previous findings show that changes in PPL when masking attention layers in pre-trained transformer-based NLMs reflect linguistic anomalies associated with Alzheimer’s disease dementia. Building upon this, we explore a novel bidirectional attention head ablation method that exhibits properties attributed to the concepts of cognitive and brain reserve in human brain studies, which postulate that people with more neurons in the brain and more efficient processing are more resilient to neurodegeneration. Our results show that larger GPT-2 models require a disproportionately larger share of attention heads to be masked/ablated to display degradation of similar magnitude to masking in smaller models. These results suggest that the attention mechanism in transformer models may present an analogue to the notions of cognitive and brain reserve and could potentially be used to model certain aspects of the progression of neurodegenerative disorders and aging.

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HeSum: a Novel Dataset for Abstractive Text Summarization in Hebrew
Tzuf Paz-Argaman | Itai Mondshine | Asaf Achi Mordechai | Reut Tsarfaty

While large language models (LLMs) excel in various natural language tasks in English, their performance in low-resource languages like Hebrew, especially for generative tasks such as abstractive summarization, remains unclear. The high morphological richness in Hebrew adds further challenges due to the ambiguity in sentence comprehension and the complexities in meaning construction.In this paper, we address this evaluation and resource gap by introducing HeSum, a novel benchmark dataset specifically designed for Hebrew abstractive text summarization. HeSum consists of 10,000 article-summary pairs sourced from Hebrew news websites written by professionals. Linguistic analysis confirms HeSum’s high abstractness and unique morphological challenges. We show that HeSum presents distinct difficulties even for state-of-the-art LLMs, establishing it as a valuable testbed for advancing generative language technology in Hebrew, and MRLs generative challenges in general.

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TRAM: Benchmarking Temporal Reasoning for Large Language Models
Yuqing Wang | Yun Zhao

Reasoning about time is essential for understanding the nuances of events described in natural language. Previous research on this topic has been limited in scope, characterized by a lack of standardized benchmarks that would allow for consistent evaluations across different studies. In this paper, we introduce TRAM, a temporal reasoning benchmark composed of ten datasets, encompassing various temporal aspects of events such as order, arithmetic, frequency, and duration, designed to facilitate a comprehensive evaluation of the TeR capabilities of large language models (LLMs). We evaluate popular LLMs like GPT-4 and Llama2 in zero-shot and few-shot scenarios, and establish baselines with BERT-based and domain-specific models. Our findings indicate that the best-performing model lags significantly behind human performance. It is our aspiration that TRAM will spur further progress in enhancing the TeR capabilities of LLMs.

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Knowledge of Knowledge: Exploring Known-Unknowns Uncertainty with Large Language Models
Alfonso Amayuelas | Kyle Wong | Liangming Pan | Wenhu Chen | William Yang Wang

This paper investigates the capabilities of Large Language Models (LLMs) in understanding their knowledge and uncertainty over questions. Specifically, we focus on addressing known-unknown questions, characterized by high uncertainty due to the absence of definitive answers. To facilitate our study, we collect a new dataset with Known-Unknown Questions (KUQ) and establish a categorization framework to clarify the origins of uncertainty in such queries. Subsequently, we examine the performance of open-source LLMs, fine-tuned using this dataset, in distinguishing between known and unknown queries within open-ended question-answering scenarios. The fine-tuned models demonstrated a significant improvement, achieving a considerable increase in F1-score relative to their pre-fine-tuning state. Through a comprehensive analysis, we reveal insights into the models’ improved uncertainty articulation and their consequent efficacy in multi-agent debates. These findings help us understand how LLMs can be trained to identify and express uncertainty, improving our knowledge of how they understand and express complex or unclear information.

Defeasibility in causal reasoning implies that the causal relationship between cause and effect can be strengthened or weakened. Namely, the causal strength between cause and effect should increase or decrease with the incorporation of strengthening arguments (supporters) or weakening arguments (defeaters), respectively. However, existing works ignore defeasibility in causal reasoning and fail to evaluate existing causal strength metrics in defeasible settings. In this work, we present 𝛿-CAUSAL, the first benchmark dataset for studying defeasibility in causal reasoning. 𝛿-CAUSAL includes around 11K events spanning ten domains, featuring defeasible causality pairs, namely, cause-effect pairs accompanied by supporters and defeaters. We further show that current causal strength metrics fail to reflect the change of causal strength with the incorporation of supporters or defeaters in 𝛿-CAUSAL. To this end, we propose CESAR (Causal Embedding aSsociation with Attention Rating), a metric that measures causal strength based on token-level causal relationships. CESAR achieves a significant 69.7% relative improvement over existing metrics, increasing from 47.2% to 80.1% in capturing the causal strength change brought by supporters and defeaters. We further demonstrate even Large Language Models (LLMs) like GPT-3.5 still lag 4.5 and 10.7 points behind humans in generating supporters and defeaters, emphasizing the challenge posed by 𝛿-CAUSAL.

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Better Synthetic Data by Retrieving and Transforming Existing Datasets
Saumya Gandhi | Ritu Gala | Vijay Viswanathan | Tongshuang Wu | Graham Neubig

Despite recent advances in large language models, building dependable and deployable NLP models typically requires abundant, high-quality training data. However, task-specific data is not available for many use cases, and manually curating task-specific data is labor-intensive. Recent work has studied prompt-driven synthetic data generation using large language models, but these generated datasets tend to lack complexity and diversity. To address these limitations, we introduce a method, _DataTune_, to make better use of existing, publicly available datasets to improve automatic dataset generation. DataTune performs _dataset transformation_, enabling the repurposing of publicly available datasets into a format that is directly aligned with the specific requirements of target tasks. On a diverse set of language-based tasks from the BIG-Bench benchmark, we find that finetuning language models via DataTune improves over a few-shot prompting baseline by 49% and improves over existing methods that use synthetic or retrieved training data by 34%. We find that dataset transformation significantly increases the diversity and difficulty of generated data on many tasks. We release a Python package and open-source repository to make this method accessible to the community (URL will be added upon acceptance).

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Addressing Order Sensitivity of In-Context Demonstration Examples in Causal Language Models
Yanzheng Xiang | Hanqi Yan | Lin Gui | Yulan He

In-context learning has become a popular paradigm in natural language processing. However, its performance can be significantly influenced by the order of in-context demonstration examples. In this paper, we found that causal language models (CausalLMs) are more sensitive to this order compared to prefix language models (PrefixLMs). We attribute this phenomenon to the auto-regressive attention masks within CausalLMs, which restrict each token from accessing information from subsequent tokens. This results in different receptive fields for samples at different positions, thereby leading to representation disparities across positions. To tackle this challenge, we introduce an unsupervised fine-tuning method, termed the Information-Augmented and Consistency-Enhanced approach. This approach utilizes contrastive learning to align representations of in-context examples across different positions and introduces a consistency loss to ensure similar representations for inputs with different permutations. This enhances the model’s predictive consistency across permutations. Experimental results on five benchmarks suggest that our proposed method can reduce the sensitivity of CausalLMs to the order of in-context examples and exhibit robust generalizability, particularly when demonstrations are sourced from a candidate pool different from that used in the training phase, or when the number of in-context examples differs from what is used during training.

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Perspective Taking through Generating Responses to Conflict Situations
Joan Plepi | Charles Welch | Lucie Flek

Although language model performance across diverse tasks continues to improve, these models still struggle to understand and explain the beliefs of other people. This skill requires perspective-taking, the process of conceptualizing the point of view of another person. Perspective taking becomes challenging when the text reflects more personal and potentially more controversial beliefs.We explore this task through natural language generation of responses to conflict situations. We evaluate novel modifications to recent architectures for conditioning generation on an individual’s comments and self-disclosure statements. Our work extends the Social-Chem-101 corpus, using 95k judgements written by 6k authors from English Reddit data, for each of whom we obtained 20-500 self-disclosure statements. Our evaluation methodology borrows ideas from both personalized generation and theory of mind literature. Our proposed perspective-taking models outperform recent work, especially the twin encoder model conditioned on self-disclosures with high similarity to the conflict situation.

Pretrained large language models (LLMs) are currently state-of-the-art for solving the vast majority of natural language processing tasks. While many real-world applications still require fine-tuning to reach satisfactory levels of performance, many of them are in the low-data regime, making fine-tuning challenging. To address this, we propose LLM2LLM, a targeted and iterative data augmentation strategy that uses a teacher LLM to enhance a small seed dataset by augmenting additional data that can be used for fine-tuning on a specific task. LLM2LLM (1) fine-tunes a baseline student LLM on the initial seed data, (2) evaluates and extracts data points that the model gets wrong, and (3) uses a teacher LLM to generate synthetic data based on these incorrect data points, which are then added back into the training data. This approach amplifies the signal from incorrectly predicted data points by the LLM during training and reintegrates them into the dataset to focus on more challenging examples for the LLM. Our results show that LLM2LLM significantly enhances the performance of LLMs in the low-data regime, outperforming both traditional fine-tuning and other data augmentation baselines. LLM2LLM reduces the dependence on labor-intensive data curation and paves the way for more scalable and performant LLM solutions, allowing us to tackle data-constrained domains and tasks. We achieve improvements up to 24.2% on the GSM8K dataset, 32.6% on CaseHOLD, 32.0% on SNIPS, 52.6% on TREC and 39.8% on SST-2 over regular fine-tuning in the low-data regime using a Llama-2-7B student model. Our code is available at https://github.com/SqueezeAILab/LLM2LLM.

Multi-document summarization (MDS) is a challenging task, often decomposed to subtasks of salience and redundancy detection, followed by text generation.In this context, alignment of corresponding sentences between a reference summary and its source documents has been leveraged to generate training data for some of the component tasks. Yet, this enabling alignment step has usually been applied heuristically on the sentence level on a limited number of subtasks.In this paper, we propose extending the summary-source alignment framework by (1) applying it at the more fine-grained proposition span level, (2) annotating alignment manually in a multi-document setup, and (3) revealing the great potential of summary-source alignments to yield several datasets for at least six different tasks. Specifically, for each of the tasks, we release a manually annotated test set that was derived automatically from the alignment annotation. We also release development and train sets in the same way, but from automatically derived alignments.Using the datasets, each task is demonstrated with baseline models and corresponding evaluation metrics to spur future research on this broad challenge.

Supervised finetuning (SFT) on instruction datasets has played a crucial role in achieving the remarkable zero-shot generalization capabilities observed in modern large language models (LLMs). However, the annotation efforts required to produce high quality responses for instructions are becoming prohibitively expensive, especially as the number of tasks spanned by instruction datasets continues to increase. Active learning is effective in identifying useful subsets of samples to annotate from an unlabeled pool, but its high computational cost remains a barrier to its widespread applicability in the context of LLMs. To mitigate the annotation cost of SFT and circumvent the computational bottlenecks of active learning, we propose using experimental design. Experimental design techniques select the most informative samples to label, and typically maximize some notion of uncertainty and/or diversity. In our work, we implement a framework that evaluates several existing and novel experimental design techniques and find that these methods consistently yield significant gains in label efficiency with little computational overhead. On generative tasks, to reach the same generalization performance, our methods save 50% of the annotation cost compared to random sampling.

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Learning Multimodal Contrast with Cross-modal Memory and Reinforced Contrast Recognition
Yuanhe Tian | Fei Xia | Yan Song

In many practical scenarios, contents from different modalities are not semantically aligned; for instance, visual and textual information may conflict with each other, resulting in non-compositional expression effects such as irony or humor. Effective modeling and smooth integration of multimodal information are crucial for achieving good understanding of the contrast across modalities. Being focusing on image-text matching, most current studies face challenges in identifying such contrast, leading to limitations in exploring the extended semantics when images and texts do not match. In this paper, we propose an LLM-based approach for learning multimodal contrast following the encoding-decoding paradigm, enhanced by a memory module with reinforced contrast recognition, and use a series of tasks that have the nature of multimodal contrast to verify our approach. The memory module learns the integration between visual and textual features with trainable memory vectors and the reinforced contrast recognition uses self-rejection sampling to optimize the memory to further enhance learning multimodal contrast. The resulted information, accompanied with visual and text features, is finally fed into the LLM to predict corresponding labels. We experiment our approach on four English and Chinese benchmark datasets, where it outperforms strong baselines and state-of-the-art studies.

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Text Simplification via Adaptive Teaching
Seyed Ali Bahrainian | Jonathan Dou | Carsten Eickhoff

Text simplification is the process of rewriting a piece of text using simpler vocabulary and grammatical structure in order to make the text more accessible and understandable for a larger audience. In this paper, we introduce a new text simplification model based on the notion of adaptive teaching using a teacher network and a text generation network. We name this new model Simplification via Adaptive Teaching (SAT). Our proposed model sets a new state-of-the-art performance in terms of standard simplification metrics such as SARI and D-SARI with a significant improvement over the previous state of the art on the D-Wikipedia dataset and the Wiki-Doc benchmark dataset. Moreover, we conduct a human evaluation in terms of text simplicity, correctness, and fluency to substantiate SAT’s performance.

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A multi-level multi-label text classification dataset of 19th century Ottoman and Russian literary and critical texts
Gokcen Gokceoglu | Devrim Çavuşoğlu | Emre Akbas | Özen Dolcerocca

This paper introduces a multi-level, multi-label text classification dataset comprising over 3000 documents. The dataset features literary and critical texts from 19th-century Ottoman Turkish and Russian. It is the first study to apply large language models (LLMs) to this dataset, sourced from prominent literary periodicals of the era. The texts have been meticulously organized and labeled. This was done according to a taxonomic framework that takes into account both their structural and semantic attributes. Articles are categorized and tagged with bibliometric metadata by human experts. We present baseline classification results using a classical bag-of-words (BoW) naive Bayes model and three modern LLMs: multilingual BERT, Falcon, and Llama-v2. We found that in certain cases, Bag of Words (BoW) outperforms Large Language Models (LLMs), emphasizing the need for additional research, especially in low-resource language settings. This dataset is expected to be a valuable resource for researchers in natural language processing and machine learning, especially for historical and low-resource languages. The dataset is publicly available.

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It is Simple Sometimes: A Study On Improving Aspect-Based Sentiment Analysis Performance
Laura Cabello | Uchenna Akujuobi

Aspect-Based Sentiment Analysis (ABSA) involves extracting opinions from textual data about specific entities and their corresponding aspects through various complementary subtasks. Several prior research has focused on developing ad hoc designs of varying complexities for these subtasks. In this paper, we build upon the instruction tuned model proposed by Scaria et al. (2023), who present an instruction-based model with task descriptions followed by in-context examples on ABSA subtasks. We propose PFInstruct, an extension to this instruction learning paradigm by appending an NLP-related task prefix to the task description. This simple approach leads to improved performance across all tested SemEval subtasks, surpassing previous state-of-the-art (SOTA) on the ATE subtask (Rest14) by +3.28 F1-score, and on the AOOE subtask by an average of +5.43 F1-score across SemEval datasets. Furthermore, we explore the impact of the prefix-enhanced prompt quality on the ABSA subtasks and find that even a noisy prefix enhances model performance compared to the baseline. Our method also achieves competitive results on a biomedical domain dataset (ERSA).

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Whose Emotions and Moral Sentiments do Language Models Reflect?
Zihao He | Siyi Guo | Ashwin Rao | Kristina Lerman

Language models (LMs) are known to represent the perspectives of some social groups better than others, which may impact their performance, especially on subjective tasks such as content moderation and hate speech detection. To explore how LMs represent different perspectives, existing research focused on positional alignment, i.e., how closely the models mimic the opinions and stances of different groups, e.g., liberals or conservatives. However, human communication also encompasses emotional and moral dimensions. We define the problem of affective alignment, which measures how LMs’ emotional and moral tone represents those of different groups. By comparing the affect of responses generated by 36 LMs to the affect of Twitter messages written by two ideological groups, we observe significant misalignment of LMs with both ideological groups. This misalignment is larger than the partisan divide in the U.S. Even after steering the LMs towards specific ideological perspectives, the misalignment and liberal tendencies of the model persist, suggesting a systemic bias within LMs.

In the realm of Large Language Models (LLMs), users commonly employ diverse decoding strategies and adjust hyperparameters to control the generated text. However, a critical question emerges: Are LLMs conscious of the existence of these decoding strategies and capable of regulating themselves? The current decoding generation process often relies on empirical and heuristic manual adjustments to hyperparameters based on types of tasks and demands. However, this process is typically cumbersome, and the decoding hyperparameters may not always be optimal for each sample. To address the aforementioned challenges, we propose a novel text generation paradigm termed Hyperparameter Aware Generation (HAG). By leveraging hyperparameter-aware instruction tuning, the LLM autonomously determines the optimal decoding strategy and configs based on the input samples, enabling self-regulation. Our approach eliminates the need for extensive manual tuning, offering a more autonomous, self-regulate model behavior. Experimental results spanning six datasets across reasoning, creativity, translation, and mathematics tasks demonstrate that hyperparameter-aware instruction tuning empowers the LLMs to self-regulate the decoding strategy and hyperparameter. HAG extends the current paradigm in the text generation process, highlighting the feasibility of endowing the LLMs with self-regulate decoding strategies.

Self-Consistency samples diverse reasoning chains with answers and chooses the final answer by majority voting. It is based on forward reasoning and cannot further improve performance by sampling more reasoning chains when saturated. To further boost performance, we introduce backward reasoning to verify candidate answers. Specifically, for mathematical tasks, we mask a number in the question and ask the LLM to answer a backward question created by a simple template, i.e., to predict the masked number when a candidate answer is provided. Instead of using forward or backward reasoning alone, we propose **FOBAR** to combine **FO**rward and **BA**ckward **R**easoning for verification. Extensive experiments on six standard mathematical data sets and three LLMs show that FOBAR achieves state-of-the-art performance. In particular, FOBAR outperforms Self-Consistency, which uses forward reasoning alone, demonstrating that combining forward and backward reasoning is more accurate in verification. In addition, FOBAR achieves higher accuracy than existing verification methods, showing the effectiveness of the simple template used in backward reasoning and the proposed combination.

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Towards Uncertainty-Aware Language Agent
Jiuzhou Han | Wray Buntine | Ehsan Shareghi

While Language Agents have achieved promising success by placing Large Language Models at the core of a more versatile design that dynamically interacts with the external world, the existing approaches neglect the notion of uncertainty during these interactions. We present the Uncertainty-Aware Language Agent (UALA), a framework that orchestrates the interaction between the agent and the external world using uncertainty quantification. Compared with other well-known counterparts like ReAct, our extensive experiments across 3 representative tasks (HotpotQA, StrategyQA, MMLU) and various LLM sizes demonstrate that UALA brings a significant improvement of performance, while having a substantially lower reliance on the external world (i.e., reduced number of tool calls and tokens). Our analyses provide various insights including the great potential of UALA compared with agent fine-tuning, and underscore the unreliability of verbalised confidence of LLMs as a proxy for uncertainty.

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Detection and Positive Reconstruction of Cognitive Distortion Sentences: Mandarin Dataset and Evaluation
Shuya Lin | Yuxiong Wang | Jonathan Dong | Shiguang Ni

This research introduces a Positive Reconstruction Framework based on positive psychology theory. Overcoming negative thoughts can be challenging, our objective is to address and reframe them through a positive reinterpretation. To tackle this challenge, a two-fold approach is necessary: identifying cognitive distortions and suggesting a positively reframed alternative while preserving the original thought’s meaning. Recent studies have investigated the application of Natural Language Processing (NLP) models in English for each stage of this process. In this study, we emphasize the theoretical foundation for the Positive Reconstruction Framework, grounded in broaden-and-build theory. We provide a shared corpus containing 4001 instances for detecting cognitive distortions and 1900 instances for positive reconstruction in Mandarin. Leveraging recent NLP techniques, including transfer learning, fine-tuning pretrained networks, and prompt engineering, we demonstrate the effectiveness of automated tools for both tasks. In summary, our study contributes to multilingual positive reconstruction, highlighting the effectiveness of NLP in cognitive distortion detection and positive reconstruction.

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PiVe: Prompting with Iterative Verification Improving Graph-based Generative Capability of LLMs
Jiuzhou Han | Nigel Collier | Wray Buntine | Ehsan Shareghi

Large language models (LLMs) have shown great abilities of solving various natural language tasks in different domains. Due to the training objective of LLMs and their pre-training data, LLMs are not very well equipped for tasks involving structured data generation. We propose a framework, Prompting with Iterative Verification (PiVe), to improve graph-based generative capability of LLMs. We show how a small language model could be trained to act as a verifier module for the output of an LLM(i.e., ChatGPT, GPT-4), and to iteratively improve its performance via fine-grained corrective instructions. We also show how the verifier module could apply iterative corrections offline for a more cost-effective solution to the text-to-graph generation task. Experiments on three graph-based datasets show consistent improvement gained via PiVe. Additionally, we create GenWiki-HIQ and highlight that the verifier module can be used as a data augmentation tool to help improve the quality of automatically generated parallel text-graph datasets.

Temporal knowledge graph question answering (TKGQA) poses a significant challenge task, due to the temporal constraints hidden in questions and the answers sought from dynamic structured knowledge. Although large language models (LLMs) have made considerable progress in their reasoning ability over structured data, their application to the TKGQA task is a relatively unexplored area. This paper first proposes a novel generative temporal knowledge graph question answering framework, GenTKGQA, which guides LLMs to answer temporal questions through two phases: Subgraph Retrieval and Answer Generation. First, we exploit LLM’s intrinsic knowledge to mine temporal constraints and structural links in the questions without extra training, thus narrowing down the subgraph search space in both temporal and structural dimensions. Next, we design virtual knowledge indicators to fuse the graph neural network signals of the subgraph and the text representations of the LLM in a non-shallow way, which helps the open-source LLM deeply understand the temporal order and structural dependencies among the retrieved facts through instruction tuning. Experimental results on two widely used datasets demonstrate the superiority of our model.

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VISREAS: Complex Visual Reasoning with Unanswerable Questions
Syeda Nahida Akter | Sangwu Lee | Yingshan Chang | Yonatan Bisk | Eric Nyberg

Verifying a question’s validity before answering is crucial in real-world applications, where users may provide imperfect instructions. In this scenario, an ideal model should address the discrepancies in the query and convey them to the users rather than generating the best possible answer. Addressing this requirement, we introduce a new compositional visual question-answering dataset, VisReas, that consists of answerable and unanswerable visual queries formulated by traversing and perturbing commonalities and differences among objects, attributes, and relations. VisReas contains 2.07M semantically diverse queries generated automatically using Visual Genome scene graphs. The unique feature of this task, validating question answerability with respect to an image before answering, and the poor performance of state-of-the-art models inspired the design of a new modular baseline, Logic2Vision that reasons by producing and executing pseudocode without any external modules to generate the answer. Logic2Vision outperforms generative models in VisReas (+4.82% over LLaVA-1.5; +12.23% over InstructBLIP) and achieves a significant gain in performance against the classification models.

Various euphemisms are emerging in social networks, attracting widespread attention from the natural language processing community. However, existing euphemism datasets are only domain-specific or language-specific. In addition, existing approaches to the study of euphemisms are one-sided. Either only the euphemism detection task or only the euphemism identification task is accomplished, lacking a unified framework. To this end, we construct a large-scale Bilingual Multi-category dataset of Euphemisms named BME, which covers a total of 12 categories for two languages, English and Chinese. Then, we first propose a unified generative model to Jointly conduct the tasks of bilingual Euphemism Detection and Identification named JointEDI. By comparing with LLMs and human evaluation, we demonstrate the effectiveness of the proposed JointEDI and the feasibility of unifying euphemism detection and euphemism identification tasks. Moreover, the BME dataset also provides a new reference standard for euphemism detection and euphemism identification.

Given a script, the challenge in Movie Dubbing (Visual Voice Cloning, V2C) is to generate speech that aligns well with the video in both time and emotion, based on the tone of a reference audio track. Existing state-of-the-art V2C models break the phonemes in the script according to the divisions between video frames, which solves the temporal alignment problem but leads to incomplete phoneme pronunciation and poor identity stability. To address this problem, we propose StyleDubber, which switches dubbing learning from the frame level to phoneme level. It contains three main components: (1) A multimodal style adaptor operating at the phoneme level to learn pronunciation style from the reference audio, and generate intermediate representations informed by the facial emotion presented in the video; (2) An utterance-level style learning module, which guides both the mel-spectrogram decoding and the refining processes from the intermediate embeddings to improve the overall style expression; And (3) a phoneme-guided lip aligner to maintain lip sync. Extensive experiments on two of the primary benchmarks, V2C and Grid, demonstrate the favorable performance of the proposed method as compared to the current state-of-the-art. The code will be made available at https://github.com/GalaxyCong/StyleDubber.

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ETAS: Zero-Shot Transformer Architecture Search via Network Trainability and Expressivity
Jiechao Yang | Yong Liu

Transformer Architecture Search (TAS) methods aim to automate searching for the optimal Transformer architecture configurations for a given task. However, they are impeded by the prohibitive cost of evaluating Transformer architectures. Recently, several Zero-Shot TAS methods have been proposed to mitigate this problem by utilizing zero-cost proxies to evaluate Transformer architectures without training. Unfortunately, they are limited to specific computer vision or natural language processing tasks. Nonetheless, most of them are developed based on empirical observations and lack theoretical guarantees. To solve this problem, we develop a new zero-cost proxy called NTSR that combines two theoretically-inspired indicators to measure the trainability and expressivity of Transformer networks separately. We then integrate it into an effective regularized evolution framework called ETAS to demonstrate its efficacy on various tasks. The results show that our proposed NTSR proxy can consistently achieve a higher correlation with the true performance of Transformer networks on both computer vision and natural language processing tasks. Further, it can significantly accelerate the search process for finding the best-performing Transformer architecture configurations.

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Reasoning Like a Doctor: Improving Medical Dialogue Systems via Diagnostic Reasoning Process Alignment
Kaishuai Xu | Yi Cheng | Wenjun Hou | Qiaoyu Tan | Wenjie Li

Medical dialogue systems have attracted significant attention for their potential to act as medical assistants. Enabling these medical systems to emulate clinicians’ diagnostic reasoning process has been the long-standing research focus. Previous studies rudimentarily realized the simulation of clinicians’ diagnostic process by fine-tuning language models on high-quality dialogue datasets. Nonetheless, they overly focus on the outcomes of the clinician’s reasoning process while ignoring their internal thought processes and alignment with clinician preferences. Our work aims to build a medical dialogue system that aligns with clinicians’ diagnostic reasoning processes. We propose a novel framework, Emulation, designed to generate an appropriate response that relies on abductive and deductive diagnostic reasoning analyses and aligns with clinician preferences through thought process modeling. Experimental results on two datasets confirm the efficacy of Emulation. Crucially, our framework furnishes clear explanations for the generated responses, enhancing its transparency in medical consultations.

This paper introduces ConceptMath, a bilingual (English and Chinese), fine-grained benchmark that evaluates concept-wise mathematical reasoning of Large Language Models (LLMs). Unlike traditional benchmarks that evaluate general mathematical reasoning with an average accuracy, ConceptMath systemically organizes math problems under a hierarchy of math concepts, so that mathematical reasoning can be evaluated at different granularity with concept-wise accuracies. Based on our ConcepthMath, we then evaluate a broad range of LLMs, and we observe existing LLMs, though achieving high average accuracies on traditional benchmarks, exhibit significant performance variations across different math concepts and may even fail catastrophically on the most basic ones. Besides, we also introduce an efficient fine-tuning strategy to enhance the weaknesses of existing LLMs. Finally, we hope ConceptMath could guide the developers to understand the fine-grained mathematical abilities of their models and facilitate the growth of foundation models. Code is available at https://github.com/conceptmath/conceptmath.

Manually annotating instruction data for large language models is difficult, costly, and hard to scale. Meanwhile, current automatic annotation methods typically rely on distilling synthetic data from proprietary LLMs, which not only limits the upper bound of the quality of the instruction data but also raises potential copyright issues. In this paper, we propose REInstruct, a simple and scalable method to automatically build instruction data from an unlabeled corpus without heavy reliance on proprietary LLMs and human annotation.Specifically, REInstruct first selects a subset of unlabeled texts that potentially contain well-structured helpful and insightful content and then generates instructions for these texts. To generate accurate and relevant responses for effective and robust training, REInstruct further proposes a rewriting-based approach to improve the quality of the generated instruction data. By training Llama-7b on a combination of 3k seed data and 32k synthetic data from REInstruct, fine-tuned model achieves a 65.41% win rate on AlpacaEval leaderboard against text-davinci-003, outperforming other open-source, non-distilled instruction data construction methods. The code is publicly available at https://github.com/cs32963/REInstruct.

Knowledge distillation, the technique of transferring knowledge from large, complex models to smaller ones, marks a pivotal step towards efficient AI deployment. Distilling Step-by-Step (DSS), a novel method utilizing chain-of-thought (CoT) distillation, has demonstrated promise by imbuing smaller models with the superior reasoning capabilities of their larger counterparts. In DSS, the distilled model acquires the ability to generate rationales and predict labels concurrently through a multi-task learning framework. However, DSS overlooks the intrinsic relationship between the two training tasks, leading to ineffective integration of CoT knowledge with the task of label prediction. To this end, we investigate the mutual relationship of the two tasks from Information Bottleneck perspective and formulate it as maximizing the mutual information of the representation features of the two tasks. We propose a variational approach to solve this optimization problem using a learning-based method. Our experimental results across four datasets demonstrate that our method outperforms the state-of-the-art DSS. Our findings offer insightful guidance for future research on language model distillation as well as applications involving CoT. Codes are available at https://github.com/xinchen9/cot_distillation_ACL2024.

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PEMT: Multi-Task Correlation Guided Mixture-of-Experts Enables Parameter-Efficient Transfer Learning
Zhisheng Lin | Han Fu | Chenghao Liu | Zhuo Li | Jianling Sun

Parameter-efficient fine-tuning (PEFT) has emerged as an effective method for adapting pre-trained language models to various tasks efficiently. Recently, there has been a growing interest in transferring knowledge from one or multiple tasks to the downstream target task to achieve performance improvements. However, current approaches typically either train adapters on individual tasks or distill shared knowledge from source tasks, failing to fully exploit task-specific knowledge and the correlation between source and target tasks. To overcome these limitations, we propose PEMT, a novel parameter-efficient fine-tuning framework based on multi-task transfer learning. PEMT extends the mixture-of-experts (MoE) framework to capture the transferable knowledge as a weighted combination of adapters trained on source tasks. These weights are determined by a gated unit, measuring the correlation between the target and each source task using task description prompt vectors. To fully exploit the task-specific knowledge, we also propose the Task Sparsity Loss to improve the sparsity of the gated unit. We conduct experiments on a broad range of tasks over 17 datasets. The experimental results demonstrate our PEMT yields stable improvements over full fine-tuning, and state-of-the-art PEFT and knowledge transferring methods on various tasks. The results highlight the effectiveness of our method which is capable of sufficiently exploiting the knowledge and correlation features across multiple tasks.

Recent advancements in large language models (LLMs) have showcased significant improvements in mathematics. However, traditional math benchmarks like GSM8k offer a unidimensional perspective, which fall short in providing a holistic assessment of the LLMs’ math capabilities. To address this gap, we introduce MathBench, a new benchmark that rigorously assesses the mathematical capabilities of large language models. MathBench spans a wide range of mathematical disciplines, offering a detailed evaluation of both theoretical understanding and practical problem-solving skills. The benchmark progresses through five distinct stages, from basic arithmetic to college mathematics, and is structured to evaluate models at various depths of knowledge. Each stage includes theoretical questions and application problems, allowing us to measure a model’s mathematical proficiency and its ability to apply concepts in practical scenarios. MathBench aims to enhance the evaluation of LLMs’ mathematical abilities, providing a nuanced view of their knowledge understanding levels and problem solving skills in a bilingual context.

Although large language models (LLMs) have demonstrated remarkable performance, the lack of transparency in their inference logic raises concerns about their trustworthiness. To gain a better understanding of LLMs, we conduct a detailed analysis of the operations of attention heads and aim to better understand the in-context learning of LLMs. Specifically, we investigate whether attention heads encode two types of relationships between tokens present in natural languages: the syntactic dependency parsed from sentences and the relation within knowledge graphs. We find that certain attention heads exhibit a pattern where, when attending to subject tokens, they recall object tokens and increase the output logits of those object tokens. More crucially, the formulation of such semantic induction heads has a close correlation with the emergence of the in-context learning ability of language models. The study of semantic attention heads advances our understanding of the intricate operations of attention heads in transformers, and further provides new insights into the in-context learning of LLMs.

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Chinese Spelling Corrector Is Just a Language Learner
Lai Jiang | Hongqiu Wu | Hai Zhao | Min Zhang

This paper emphasizes Chinese spelling correction by means of self-supervised learning, which means there are no annotated errors within the training data. Our intuition is that humans are naturally good correctors with exposure to error-free sentences, which contrasts with current unsupervised methods that strongly rely on the usage of confusion sets to produce parallel sentences. In this paper, we demonstrate that learning a spelling correction model is identical to learning a language model from error-free data alone, with decoding it in a greater search space. We propose Denoising Decoding Correction (D2C), which selectively imposes noise upon the source sentence to determine the underlying correct characters. Our method is largely inspired by the ability of language models to perform correction, including both BERT-based models and large language models (LLMs). We show that the self-supervised learning manner generally outperforms the confusion set in specific domains because it bypasses the need to introduce error characters to the training data which can impair the error patterns not included in the introduced error characters.

Object hallucination has been an Achilles’ heel which hinders the broader applications of large vision-language models (LVLMs). Object hallucination refers to the phenomenon that the LVLMs claim non-existent objects in the image. To mitigate the object hallucinations, instruction tuning and external model-based detection methods have been proposed, which either require large-scare computational resources or depend on the detection result of external models. However, there remains an under-explored field to utilize the LVLM itself to alleviate object hallucinations. In this work, we adopt the intuition that the LVLM tends to respond logically consistently for existent objects but inconsistently for hallucinated objects. Therefore, we propose a Logical Closed Loop-based framework for Object Hallucination Detection and Mitigation, namely LogicCheckGPT. In specific, we devise logical consistency probing to raise questions with logical correlations, inquiring about attributes from objects and vice versa. Whether their responses can form a logical closed loop serves as an indicator of object hallucination. As a plug-and-play method, it can be seamlessly applied to all existing LVLMs. Comprehensive experiments conducted on three benchmarks across four LVLMs have demonstrated significant improvements brought by our method, indicating its effectiveness and generality.

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RetrievalQA: Assessing Adaptive Retrieval-Augmented Generation for Short-form Open-Domain Question Answering
Zihan Zhang | Meng Fang | Ling Chen

Adaptive retrieval-augmented generation (ARAG) aims to dynamically determine the necessity of retrieval for queries instead of retrieving indiscriminately to enhance the efficiency and relevance of the sourced information. However, previous works largely overlook the evaluation of ARAG approaches, leading to their effectiveness being understudied. This work presents a benchmark, RetrievalQA, comprising 1,271 short-form questions covering new world and long-tail knowledge. The knowledge necessary to answer the questions is absent from LLMs; therefore, external information must be retrieved to answer correctly. This makes RetrievalQA a suitable testbed to evaluate existing ARAG methods. We observe that calibration-based methods heavily rely on threshold tuning, while vanilla prompting is inadequate for guiding LLMs to make reliable retrieval decisions. Based on our findings, we propose Time-Aware Adaptive Retrieval (TA-ARE), a simple yet effective method that helps LLMs assess the necessity of retrieval without calibration or additional training.

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LLaST: Improved End-to-end Speech Translation System Leveraged by Large Language Models
Xi Chen | Songyang Zhang | Qibing Bai | Kai Chen | Satoshi Nakamura

We introduces ***LLaST***, a framework for building high-performance Large Language model based Speech-to-text Translation systems. We address the limitations of end-to-end speech translation (E2E ST) models by exploring model architecture design and optimization techniques tailored for LLMs. Our approach includes LLM-based speech translation architecture design, ASR-augmented training, multilingual data augmentation, and dual-LoRA optimization. Our approach demonstrates superior performance on the CoVoST-2 benchmark and showcases exceptional scaling capabilities powered by LLMs.We believe this effective method will serve as a strong baseline for speech translation and provide insights for futureimprovements of the LLM-based speech translation framework.

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Plan, Generate and Complicate: Improving Low-resource Dialogue State Tracking via Easy-to-Difficult Zero-shot Data Augmentation
Ming Gu | Yan Yang

Data augmentation methods have been a promising direction to improve the performance of small models for low-resource dialogue state tracking. However, traditional methods rely on pre-defined user goals and neglect the importance of data complexity in this task. In this paper, we propose EDZ-DA, an Easy-to-Difficult Zero-shot Data Augmentation framework for low-resource dialogue state tracking that utilizes large language models to automatically catch the relationships of different domains and then generate the dialogue data. We also complicate the dialogues based on the domain relation to enhance the model’s capability for co-reference slot tracking. Furthermore, we permute slot values to mitigate the influence of output orders and the problem of incomplete value generation. Experimental results illustrate the superiority of our proposed method compared to previous strong data augmentation baselines on MultiWOZ.

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DMoERM: Recipes of Mixture-of-Experts for Effective Reward Modeling
Shanghaoran Quan

The performance of the reward model (RM) is a critical factor in improving the effectiveness of the large language model (LLM) during alignment fine-tuning. There remain two challenges in RM training: 1) training the same RM using various categories of data may cause its generalization performance to suffer from multi-task disturbance, and 2) the human annotation consistency rate is generally only 60% to 75%, causing training data to contain a lot of noise. To tackle these two challenges, we introduced the idea of Mixture-of-Experts (MoE) into the field of RM for the first time. We propose the Double-Layer MoE RM (DMoERM). The outer layer MoE is a sparse model. After classifying an input into task categories, we route it to the corresponding inner layer task-specific model. The inner layer MoE is a dense model. We decompose the specific task into multiple capability dimensions and individually fine-tune a LoRA expert on each one. Their outputs are then synthesized by an MLP to compute the final rewards. To minimize costs, we call a public LLM API to obtain the capability preference labels. The validation on manually labeled datasets confirms that our model attains superior consistency with human preference and outstrips advanced generative approaches. Meanwhile, through BoN sampling and RL experiments, we demonstrate that our model outperforms state-of-the-art ensemble methods of RM and mitigates the overoptimization problem. Our code is available at: https://github.com/quanshr/DMoERM.

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LEIA: Facilitating Cross-lingual Knowledge Transfer in Language Models with Entity-based Data Augmentation
Ikuya Yamada | Ryokan Ri

Adapting English-based large language models (LLMs) to other languages has become increasingly popular due to the efficiency and potential of cross-lingual transfer. However, existing language adaptation methods often overlook the benefits of cross-lingual supervision. In this study, we introduce LEIA, a language adaptation tuning method that utilizes Wikipedia entity names aligned across languages. This method involves augmenting the target language corpus with English entity names and training the model using left-to-right language modeling. We assess LEIA on diverse question answering datasets using 7B-parameter LLMs, demonstrating significant performance gains across various non-English languages.

Code generation aims to understand the problem description and generate corresponding code snippets, where existing works generally decompose such complex tasks into intermediate steps by prompting strategies, such as Chain-of-Thought and its variants. While these studies have achieved some success, their effectiveness is highly dependent on the capabilities of advanced Large Language Models (LLMs) such as GPT-4, particularly in terms of API calls, which significantly limits their practical applicability. Consequently, how to enhance the code generation capabilities of small and medium-scale code LLMs without significantly increasing training costs is an appealing challenge. In this paper, we suggest that code comments are the natural logic pivot between natural language and code language and propose using comments to boost the code generation ability of code LLMs. Concretely, we propose MANGO (comMents As Natural loGic pivOts), including a comment contrastive training strategy and a corresponding logical comment decoding strategy. Experiments are performed on HumanEval and MBPP, utilizing StarCoder and WizardCoder as backbone models, and encompassing model parameter sizes between 3B and 7B. The results indicate that MANGO significantly improves the code pass rate based on the strong baselines. Meanwhile, the robustness of the logical comment decoding strategy is notably higher than the Chain-of-thoughts prompting.

The proliferation of Large Language Models (LLMs) has led to an influx of AI-generated content (AIGC) on the internet, transforming the corpus of Information Retrieval (IR) systems from solely human-written to a coexistence with LLM-generated content. The impact of this surge in AIGC on IR systems remains an open question, with the primary challenge being the lack of a dedicated benchmark for researchers. In this paper, we introduce Cocktail, a comprehensive benchmark tailored for evaluating IR models in this mixed-sourced data landscape of the LLM era. Cocktail consists of 16 diverse datasets with mixed human-written and LLM-generated corpora across various text retrieval tasks and domains. Additionally, to avoid the potential bias from previously included dataset information in LLMs, we also introduce an up-to-date dataset, named NQ-UTD, with queries derived from recent events. Through conducting over 1,000 experiments to assess state-of-the-art retrieval models against the benchmarked datasets in Cocktail, we uncover a clear trade-off between ranking performance and source bias in neural retrieval models, highlighting the necessity for a balanced approach in designing future IR systems. We hope Cocktail can serve as a foundational resource for IR research in the LLM era, with all data and code publicly available at https://github.com/KID-22/Cocktail.

An ideal dialogue system requires continuous skill acquisition and adaptation to new tasks while retaining prior knowledge. Dialogue State Tracking (DST), vital in these systems, often involves learning new services, confronting catastrophic forgetting and a critical capability loss termed the “Value Selection Quandary”. To address these challenges, we introduce the Reason-of-Select (RoS) distillation method by enhancing smaller models with a novel “meta-reasoning” capability. Meta-reasoning, employing an enhanced multi-domain perspective, combines fragments of meta-knowledge from domain-specific dialogues during continual learning, transcending traditional single-perspective reasoning. This domain bootstrapping process enhances the model’s ability to dissect intricate dialogues from multiple possible values, and its domain-agnostic property aligns data distribution across different domains, effectively mitigating forgetting. Besides, two novel improvements, “multi-value resolution” strategy and Semantic Contrastive Reasoning Selection method, significantly enhance RoS by generating DST-specific selection chains and mitigating hallucinations in teachers’ reasoning, ensuring effective and reliable knowledge transfer. Extensive experiments validate the exceptional performance and robust generalization capabilities of our method.

AI-generated text detection has attracted increasing attention as powerful language models approach human-level generation. Limited work is devoted to detecting (partially) AI-paraphrased texts. However, AI paraphrasing is commonly employed in various application scenarios for text refinement and diversity. To this end, we propose a novel detection framework, paraphrased text span detection (PTD), aiming to identify paraphrased text spans within a text. Different from text-level detection, PTD takes in the full text and assigns each of the sentences with a score indicating the paraphrasing degree. We construct a dedicated dataset, PASTED, for paraphrased text span detection. Both in-distribution and out-of-distribution results demonstrate the effectiveness of PTD models in identifying AI-paraphrased text spans. Statistical and model analysis explains the crucial role of the surrounding context of the paraphrased text spans. Extensive experiments show that PTD models can generalize to versatile paraphrasing prompts as well as multiple paraphrased text spans.

The alignment problem in Large Language Models (LLMs) involves adapting them to the broad spectrum of human values. This requirement challenges existing alignment methods due to diversity of preferences and regulatory standards. This paper introduces a novel alignment paradigm, priority rule following, which defines rules as the primary control mechanism in each dialog, prioritizing them over user instructions. Our preliminary analysis reveals that even the advanced LLMs, such as GPT-4, exhibit shortcomings in understanding and prioritizing the rules. Therefore, we present PriorityDistill, a semi-automated approach for distilling priority following signals from LLM simulations to ensure robust rule integration and adherence. Our experiments show that this method not only effectively minimizes misalignments utilizing only one general rule but also adapts smoothly to various unseen rules, ensuring they are shielded from hijacking and that the model responds appropriately.

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Do Zombies Understand? A Choose-Your-Own-Adventure Exploration of Machine Cognition
Ariel Goldstein | Gabriel Stanovsky

Recent advances in LLMs have sparked a debate on whether they understand text. In this position paper, we argue that opponents in this debate hold different definitions for understanding, and particularly differ in their view on the role of consciousness. To substantiate this claim, we propose a thought experiment involving an open-source chatbot Z which excels on every possible benchmark, seemingly without subjective experience. We ask whether Z is capable of understanding, and show that different schools of thought within seminal AI research seem to answer this question differently, uncovering their terminological disagreement. Moving forward, we propose two distinct working definitions for understanding which explicitly acknowledge the question of consciousness, and draw connections with a rich literature in philosophy, psychology and neuroscience.

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Modeling Emotional Trajectories in Written Stories Utilizing Transformers and Weakly-Supervised Learning
Lukas Christ | Shahin Amiriparian | Manuel Milling | Ilhan Aslan | Björn Schuller

Telling stories is an integral part of human communication which can evoke emotions and influence the affective states of the audience. Automatically modeling emotional trajectories in stories has thus attracted considerable scholarly interest. However, as most existing works have been limited to unsupervised dictionary-based approaches, there is no benchmark for this task. We address this gap by introducing continuous valence and arousal labels for an existing dataset of children’s stories originally annotated with discrete emotion categories. We collect additional annotations for this data and map the categorical labels to the continuous valence and arousal space. For predicting the thus obtained emotionality signals, we fine-tune a DeBERTa model and improve upon this baseline via a weakly supervised learning approach. The best configuration achieves a Concordance Correlation Coefficient (CCC) of .8221 for valence and .7125 for arousal on the test set, demonstrating the efficacy of our proposed approach. A detailed analysis shows the extent to which the results vary depending on factors such as the author, the individual story, or the section within the story. In addition, we uncover the weaknesses of our approach by investigating examples that prove to be difficult to predict.

Text-Video Retrieval (TVR) aims to align relevant video content with natural language queries. To date, most of the state-of-the-art TVR methods learn image-to-video transfer learning based on the large-scale pre-trained vision-language models (e.g., CLIP). However, fully fine-tuning these pre-trained models for TVR incurs prohibitively expensive computation cost. To this end, we propose to conduct efficient text-video Retrieval with a salient-and-correlated AdaPter (RAP), i.e., fine-tuning the pre-trained model with a few parameterized layers. To accommodate the text-video scenario, we equip our RAP with two indispensable characteristics including temporal sparsity and correlation. Specifically, we propose a low-rank modulation module to refine the per-image features from frozen CLIP backbone, which accentuates silent frames within the video features while alleviating temporal redundancy. Besides, we introduce an asynchronous self-attention mechanism which firstly selects top responsive visual patch and augments the correlation modeling between them with learnable temporal and patch offsets. Extensive experiments on four TVR datasets demonstrate that our RAP achieves superior or comparable performance compared to the fully fine-tuned counterpart and other parameter-efficient finetuning methods.

Recent studies have illuminated the promising capabilities of large language models (LLMs) in handling long texts. However, their performance in machine translation (MT) of long documents remains underexplored. This paper aims to shed light on how LLMs navigate this complex task, offering a comprehensive evaluation of their capabilities and limitations in long-text MT. First, we collect and construct an instruction-based benchmark dataset, specifically designed for the finetuning and evaluation of LLMs, encompassing multilingual, multi-domain, and document-level parallel data. Second, we conduct a comprehensive comparison between MT and LLM models concerning document-level translation. Our analysis uncovers that LLMs exhibit shortcomings in long-text domains, and their performance diminishes as document size escalates. By exploiting various extrapolation strategies, we enhance the capacity of LLMs to translate longer texts. We release data, code, and models at https://github.com/longyuewangdcu/Document-MT-LLM.

Recently, the topic-grounded dialogue (TGD) system has become increasingly popular as its powerful capability to actively guide users to accomplish specific tasks through topic-guided conversations. Most existing works utilize side information (e.g. topics or personas) in isolation to enhance the topic selection ability. However, due to disregarding the noise within these auxiliary information sources and their mutual influence, current models tend to predict user-uninteresting and contextually irrelevant topics. To build user-engaging and coherent dialogue agent, we propose a personalized topic selection model for topic-grounded dialogue, named PETD, which takes account of the interaction of side information to selectively aggregate such information for more accurately predicting subsequent topics. Specifically, we evaluate the correlation between global topics and personas and selectively incorporate the global topics aligned with user personas. Furthermore, we propose a contrastive learning based persona selector to filter relevant personas under the constraint of lacking pertinent persona annotations. Throughout the selection and generation, diverse relevant side information is considered. Extensive experiments demonstrate that our proposed method can generate engaging and diverse responses, outperforming state-of-the-art baselines across various evaluation metrics.

In-context learning(ICL) has gained considerable attention due to its data efficiency and task adaptability. Unfortunately, ICL suffers from the demonstration bias, i.e., its performance and robustness are severely affected by the selection and ordering of demonstrations. In this paper, we identify that such demonstration bias may primarily stem from the semantic ambiguity induced by demonstrations, i.e., a demonstration may indicate multiple input-to-label mappings and its mapping can be interpreted differently in different contexts by LLMs. Such semantic ambiguity disrupts task comprehension during ICL and results in performance fluctuations. To resolve the semantic ambiguity problem, this paper further proposes two de-biasing strategies to mitigate demonstration bias in in-context learning. Experiments on six datasets show that our methods can effectively alleviate demonstration bias and significantly improve task performance.

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Comparing Data Augmentation Methods for End-to-End Task-Oriented Dialog Systems
Christos Vlachos | Themos Stafylakis | Ion Androutsopoulos

Creating effective and reliable task-oriented dialog systems (ToDSs) is challenging, not only because of the complex structure of these systems, but also due to the scarcity of training data, especially when several modules need to be trained separately, each one with its own input/output training examples. Data augmentation (DA), whereby synthetic training examples are added to the training data, has been successful in other NLP systems, but has not been explored as extensively in ToDSs. We empirically evaluate the effectiveness of DA methods in an end-to-end ToDS setting, where a single system is trained to handle all processing stages, from user inputs to system outputs. We experiment with two ToDSs (UBAR, GALAXY) on two datasets (MultiWOZ, KVRET). We consider three types of DA methods (word-level, sentence-level, dialog-level), comparing eight DA methods that have shown promising results in ToDSs and other NLP systems. We show that all DA methods considered are beneficial, and we highlight the best ones, also providing advice to practitioners. We also introduce a more challenging few-shot cross-domain ToDS setting, reaching similar conclusions.

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MS2SL: Multimodal Spoken Data-Driven Continuous Sign Language Production
Jian Ma | Wenguan Wang | Yi Yang | Feng Zheng

Sign language understanding has made significant strides; however, there is still no viable solution for generating sign sequences directlyfrom entire spoken content, e.g., text or speech. In this paper, we propose a unified framework for continuous sign language production, easing communication between sign and non-sign language users. In particular, a sequence diffusion model, utilizing embeddings extracted from text or speech, is crafted to generate sign predictions step by step. Moreover, by creating a joint embedding space for text, audio, and sign, we bind these modalities and leverage the semantic consistency among them to provide informative feedback for the model training. This embedding-consistency learning strategy minimizes the reliance on sign triplets and ensures continuous model refinement, evenwith a missing audio modality. Experiments on How2Sign and PHOENIX14T datasets demonstrate that our model achieves competitive performance in sign language production.

Multimodal reasoning stands as a pivotal capability for large vision-language models (LVLMs). The integration with Domain-Specific Languages (DSL), offering precise visual representations, equips these models with the opportunity to execute more accurate reasoning in complex and professional domains. However, the vanilla Chain-of-Thought (CoT) prompting method faces challenges in effectively leveraging the unique strengths of visual and DSL representations, primarily due to their differing reasoning mechanisms. Additionally, it often falls short in addressing critical steps in multi-step reasoning tasks. To mitigate these challenges, we introduce the Bi-Modal Behavioral Alignment (BBA) prompting method, designed to maximize the potential of DSL in augmenting complex multi-modal reasoning tasks. This method initiates by guiding LVLMs to create separate reasoning chains for visual and DSL representations. Subsequently, it aligns these chains by addressing any inconsistencies, thus achieving a cohesive integration of behaviors from different modalities. Our experiments demonstrate that BBA substantially improves the performance of GPT-4V(ision) on geometry problem solving (28.34% → 34.22%), chess positional advantage prediction (42.08% → 46.99%) and molecular property prediction (77.47% → 83.52%).

The design choices in Transformer feed-forward neural networks have resulted in significant computational and parameter overhead. In this work, we emphasize the importance of hidden dimensions in designing lightweight FFNs, a factor often overlooked in previous architectures. Guided by this principle, we introduce PartialFormer, a parameter-efficient Transformer architecture utilizing multiple smaller FFNs to reduce parameters and computation while maintaining essential hidden dimensions. These smaller FFNs are integrated into a multi-head attention mechanism for effective collaboration. We also propose a tailored head scaling strategy to enhance PartialFormer’s capabilities. Furthermore, we present a residual-like attention calculation to improve depth scaling within PartialFormer. Extensive experiments on 9 translation tasks and 1 abstractive summarization task validate the effectiveness of our PartialFormer approach on machine translation and summarization tasks. Our code would be available at: https://github.com/zhengkid/PartialFormer.

In the task of aspect sentiment quad prediction (ASQP), generative methods for predicting sentiment quads have shown promisingresults. However, they still suffer from imprecise predictions and limited interpretability, caused by data scarcity and inadequate modeling of the quadruplet composition process. In this paper, we propose Self-Consistent Reasoning-based Aspect sentiment quadruple Prediction (SCRAP), optimizing its model to generate reasonings and the corresponding sentiment quadruplets in sequence. SCRAP adopts the Extract-Then-Assign reasoning strategy, which closely mimics human cognition. In the end, SCRAP significantly improves the model’s ability to handle complex reasoning tasks and correctly predict quadruplets through consistency voting, resulting in enhanced interpretability and accuracy in ASQP.

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PACE: Improving Prompt with Actor-Critic Editing for Large Language Model
Yihong Dong | Kangcheng Luo | Xue Jiang | Zhi Jin | Ge Li

Large language models (LLMs) have showcased remarkable potential across various tasks by conditioning on prompts. However, the quality of different human-written prompts leads to substantial discrepancies in LLMs’ performance, and improving prompts usually necessitates considerable human effort and expertise. To this end, this paper proposes Prompt with Actor-Critic Editing (PACE) for LLMs to enable automatic prompt editing. Drawing inspiration from the actor-critic algorithm in reinforcement learning, PACE leverages LLMs as the dual roles of actors and critics, conceptualizing prompt as a type of policy. PACE refines prompt, taking into account the feedback from both actors performing prompt and critics criticizing response. This process helps LLMs better align prompt to a specific task, thanks to real responses and thinking from LLMs.We conduct extensive experiments on 24 instruction induction tasks and 21 big-bench tasks. Experimental results indicate that PACE elevates the relative performance of medium/low-quality human-written prompts by up to 98%, which has comparable performance to high-quality human-written prompts. Moreover, PACE also exhibits notable efficacy for prompt generation.

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Penetrative AI: Making LLMs Comprehend the Physical World
Huatao Xu | Liying Han | Qirui Yang | Mo Li | Mani Srivastava

Recent developments in Large Language Models (LLMs) have demonstrated their remarkable capabilities across a range of tasks. Questions, however, persist about the nature of LLMs and their potential to integrate common-sense human knowledge when performing tasks involving information about the real physical world. This paper delves into these questions by exploring how LLMs can be extended to interact with and reason about the physical world through IoT sensors and actuators, a concept that we term “Penetrative AI”. The paper explores such an extension at two levels of LLMs’ ability to penetrate into the physical world via the processing of sensory signals. Our preliminary findings indicate that LLMs, with ChatGPT being the representative example in our exploration, have considerable and unique proficiency in employing the embedded world knowledge for interpreting IoT sensor data and reasoning over them about tasks in the physical realm. Not only this opens up new applications for LLMs beyond traditional text-based tasks, but also enables new ways of incorporating human knowledge in cyber-physical systems.

In-context learning is a popular inference strategy where large language models solve a task using only a few labeled demonstrations without needing any parameter updates. Although there have been extensive studies on English in-context learning, multilingual in-context learning remains under-explored, and we lack an in-depth understanding of the role of demonstrations in this context. To address this gap, we conduct a multidimensional analysis of multilingual in-context learning, experimenting with 5 models from different model families, 9 datasets covering classification and generation tasks, and 56 typologically diverse languages. Our results reveal that the effectiveness of demonstrations varies significantly across models, tasks, and languages. We also find that strong instruction-following models including Llama 2-Chat, GPT-3.5, and GPT-4 are largely insensitive to the quality of demonstrations. Instead, a carefully crafted template often eliminates the benefits of demonstrations for some tasks and languages altogether. These findings show that the importance of demonstrations might be overestimated. Our work highlights the need for granular evaluation across multiple axes towards a better understanding of in-context learning.

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Rich Semantic Knowledge Enhanced Large Language Models for Few-shot Chinese Spell Checking
Ming Dong | Yujing Chen | Miao Zhang | Hao Sun | Tingting He

Chinese Spell Checking (CSC) is a widely used technology, which plays a vital role in speech to text (STT) and optical character recognition (OCR). Most of the existing CSC approaches relying on BERT architecture achieve excellent performance. However, limited by the scale of the foundation model, BERT-based method does not work well in few-shot scenarios, showing certain limitations in practical applications. In this paper, we explore using an in-context learning method named RS-LLM (Rich\ Semantic\ based\ LLMs\) to introduce large language models (LLMs) as the foundation model. Besides, we study the impact of introducing various Chinese rich semantic information in our framework. We found that by introducing a small number of specific Chinese rich semantic structures, LLMs achieve better performance than most of the BERT-based model on few-shot CSC task. Furthermore, we conduct experiments on multiple datasets, and the experimental results verified the superiority of our proposed framework.

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An Empirical Study of In-context Learning in LLMs for Machine Translation
Pranjal Chitale | Jay Gala | Raj Dabre

Recent interest has surged in employing Large Language Models (LLMs) for machine translation (MT) via in-context learning (ICL) (Vilar et al., 2023). Most prior studies primarily focus on optimizing translation quality, with limited attention to understanding the specific aspects of ICL that influence the said quality. To this end, we perform the first of its kind, exhaustive study of in-context learning for machine translation (MT). We first establish that ICL is primarily example-driven and not instruction-driven. Following this, we conduct an extensive exploration of various aspects of the examples to understand their influence on downstream performance. Our analysis includes factors such as quality and quantity of demonstrations, spatial proximity, and source versus target originality. Further, we also investigate challenging scenarios involving indirectness and misalignment of examples to understand the limits of ICL. While we establish the significance of the quality of the target distribution over the source distribution of demonstrations, we further observe that perturbations sometimes act as regularizers, resulting in performance improvements. Surprisingly, ICL does not necessitate examples from the same task, and a related task with the same target distribution proves sufficient. We hope that our study acts as a guiding resource for considerations in utilizing ICL for MT. Our code is available on https://github.com/PranjalChitale/in-context-mt-analysis.

The open-ended nature of language generation makes the evaluation of autoregressive large language models (LLMs) challenging. One common evaluation approach uses multiple-choice questions to limit the response space. The model is then evaluated by ranking the candidate answers by the log probability of the first token prediction. However, first-tokens may not consistently reflect the final response output, due to model’s diverse response styles such as starting with “Sure” or refusing to answer. Consequently, first-token evaluation is not indicative of model behaviour when interacting with users. But by how much? We evaluate how aligned first-token evaluation is with the text output along several dimensions, namely final option choice, refusal rate, choice distribution and robustness under prompt perturbation. Our results show that the two approaches are severely misaligned on all dimensions, reaching mismatch rates over 60%. Models heavily fine-tuned on conversational or safety data are especially impacted. Crucially, models remain misaligned even when we increasingly constrain prompts, i.e., force them to start with an option letter or example template. Our findings i) underscore the importance of inspecting the text output as well and ii) caution against relying solely on first-token evaluation.

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ODA: Observation-Driven Agent for integrating LLMs and Knowledge Graphs
Lei Sun | Zhengwei Tao | Youdi Li | Hiroshi Arakawa

The integration of Large Language Models (LLMs) and knowledge graphs (KGs) has achieved remarkable success in various natural language processing tasks. However, existing methodologies that integrate LLMs and KGs often navigate the task-solving process solely based on the LLM’s analysis of the question, overlooking the rich cognitive potential inherent in the vast knowledge encapsulated in KGs. To address this, we introduce Observation-Driven Agent (ODA), a novel AI agent framework tailored for tasks involving KGs. ODA incorporates KG reasoning abilities via global observation, which enhances reasoning capabilities through a cyclical paradigm of observation, action, and reflection. Confronting the exponential explosion of knowledge during observation, we innovatively design a recursive observation mechanism. Subsequently, we integrate the observed knowledge into the action and reflection modules. Through extensive experiments, ODA demonstrates state-of-the-art performance on several datasets, notably achieving accuracy improvements of 12.87% and 8.9%.

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A Comprehensive Study of Jailbreak Attack versus Defense for Large Language Models
Zihao Xu | Yi Liu | Gelei Deng | Yuekang Li | Stjepan Picek

Large Language Models (LLMs) have increasingly become central to generating content with potential societal impacts. Notably, these models have demonstrated capabilities for generating content that could be deemed harmful. To mitigate these risks, researchers have adopted safety training techniques to align model outputs with societal values to curb the generation of malicious content. However, the phenomenon of “jailbreaking” — where carefully crafted prompts elicit harmful responses from models — persists as a significant challenge. This research conducts a comprehensive analysis of existing studies on jailbreaking LLMs and their defense techniques. We meticulously investigate nine attack techniques and seven defense techniques applied across three distinct language models: Vicuna, LLama, and GPT-3.5 Turbo. We aim to evaluate the effectiveness of these attack and defense techniques. Our findings reveal that existing white-box attacks underperform compared to universal techniques and that including special tokens in the input significantly affects the likelihood of successful attacks. This research highlights the need to concentrate on the security facets of LLMs. Additionally, we contribute to the field by releasing our datasets and testing framework, aiming to foster further research into LLM security. We believe these contributions will facilitate the exploration of security measures within this domain.

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A Data-Driven Guided Decoding Mechanism for Diagnostic Captioning
Panagiotis Kaliosis | John Pavlopoulos | Foivos Charalampakos | Georgios Moschovis | Ion Androutsopoulos

Diagnostic Captioning (DC) automatically generates a diagnostic text from one or more medical images (e.g., X-rays, MRIs) of a patient. Treated as a draft, the generated text may assist clinicians, by providing an initial estimation of the patient’s condition, speeding up and helping safeguard the diagnostic process. The accuracy of a diagnostic text, however, strongly depends on how well the key medical conditions depicted in the images are expressed. We propose a new data-driven guided decoding method that incorporates medical information, in the form of existing tags capturing key conditions of the image(s), into the beam search of the diagnostic text generation process. We evaluate the proposed method on two medical datasets using four DC systems that range from generic image-to-text systems with CNN encoders and RNN decoders to pre-trained Large Language Models. The latter can also be used in few- and zero-shot learning scenarios. In most cases, the proposed mechanism improves performance with respect to all evaluation measures. We provide an open-source implementation of the proposed method at https://github.com/nlpaueb/dmmcs.

Aligned Large Language Models (LLMs) showcase remarkable versatility, capable of handling diverse real-world tasks. Meanwhile, aligned LLMs are also expected to exhibit speciality, excelling in specific applications. However, fine-tuning with extra data, a common practice to gain speciality, often leads to catastrophic forgetting (CF) of previously acquired versatility, hindering the model’s performance across diverse tasks. In response to this challenge, we propose CoFiTune, a coarse to fine framework in an attempt to strike the balance between speciality and versatility. At the coarse-grained level, an empirical tree-search algorithm is utilized to pinpoint and update specific modules that are crucial for speciality, while keeping other parameters frozen; at the fine-grained level, a soft-masking mechanism regulates the update to the LLMs, mitigating the CF issue without harming speciality. In an overall evaluation of both speciality and versatility, CoFiTune consistently outperforms baseline methods across diverse tasks and model scales. Compared to the full-parameter SFT, CoFiTune leads to about 14% versatility improvement and marginal speciality loss on a 13B model. Lastly, based on further analysis, we provide a speculative insight into the information forwarding process in LLMs, which helps explain the effectiveness of the proposed method. The code is available at https://github.com/rattlesnakey/CoFiTune.

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A Two-Agent Game for Zero-shot Relation Triplet Extraction
Ting Xu | Haiqin Yang | Fei Zhao | Zhen Wu | Xinyu Dai

Relation triplet extraction is a fundamental task in natural language processing that aims to identify semantic relationships between entities in text. It is particularly challenging in the zero-shot setting, i.e., zero-shot relation triplet extraction (ZeroRTE), where the relation sets between training and test are disjoint. Existing methods deal with this task by integrating relations into prompts, which may lack sufficient understanding of the unseen relations. To address these limitations, this paper presents a novel Two-Agent Game (TAG) approach to deliberate and debate the semantics of unseen relations. TAG consists of two agents, a generator and an extractor. They iteratively interact in three key steps: attempting, criticizing, and rectifying. This enables the agents to fully debate and understand the unseen relations. Experimental results demonstrate consistent improvement over ALBERT-Large, BART, andGPT3.5, without incurring additional inference costs in all cases. Remarkably, our method outperforms strong baselines by a significant margin, achieving an impressive 6%-16% increase in F1 scores, particularly when dealingwith FewRel with five unseen relations.

Parameter-efficient fine-tuning (PEFT) has emerged as the predominant technique for fine-tuning in the era of large language models. However, existing PEFT methods still have inadequate training efficiency. Firstly, the utilization of large-scale foundation models during the training process is excessively redundant for certain fine-tuning tasks. Secondly, as the model size increases, the growth in trainable parameters of empirically added PEFT modules becomes non-negligible and redundant, leading to inefficiency. To achieve task-specific efficient fine-tuning, we propose the Light-PEFT framework, which includes two methods: Masked Early Pruning of the Foundation Model and Multi-Granularity Early Pruning of PEFT. The Light-PEFT framework allows for the simultaneous estimation of redundant parameters in both the foundation model and PEFT modules during the early stage of training. These parameters can then be pruned for more efficient fine-tuning. We validate our approach on GLUE, SuperGLUE, QA tasks, and various models. With Light-PEFT, parameters of the foundation model can be pruned by up to over 40%, while still controlling trainable parameters to be only 25% of the original PEFT method. Compared to utilizing the PEFT method directly, Light-PEFT achieves training and inference speedup, reduces memory usage, and maintains comparable performance and the plug-and-play feature of PEFT.

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Building Bridges: A Dataset for Evaluating Gender-Fair Machine Translation into German
Manuel Lardelli | Giuseppe Attanasio | Anne Lauscher

The translation of gender-neutral person-referring terms (e.g.,the students) is often non-trivial.Translating from English into German poses an interesting case—in German, person-referring nouns are usually gender-specific, and if the gender of the referent(s) is unknown or diverse, the generic masculine (die Studenten (m.)) is commonly used. This solution, however, reduces the visibility of other genders, such as women and non-binary people. To counteract gender discrimination, a societal movement towards using gender-fair language exists (e.g., by adopting neosystems). However, gender-fair German is currently barely supported in machine translation (MT), requiring post-editing or manual translations. We address this research gap by studying gender-fair language in English-to-German MT. Concretely, we enrich a community-created gender-fair language dictionary and sample multi-sentence test instances from encyclopedic text and parliamentary speeches.Using these novel resources, we conduct the first benchmark study involving two commercial systems and six neural MT models for translating words in isolation and natural contexts across two domains. Our findings show that most systems produce mainly masculine forms and rarely gender-neutral variants, highlighting the need for future research. We release code and data at https://github.com/g8a9/building-bridges-gender-fair-german-mt.

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Prompt Chaining or Stepwise Prompt? Refinement in Text Summarization
Shichao Sun | Ruifeng Yuan | Ziqiang Cao | Wenjie Li | Pengfei Liu

Large language models (LLMs) have demonstrated the capacity to improve summary quality by mirroring a human-like iterative process of critique and refinement starting from the initial draft. Two strategies are designed to perform this iterative process: Prompt Chaining and Stepwise Prompt. Prompt chaining orchestrates the drafting, critiquing, and refining phases through a series of three discrete prompts, while Stepwise prompt integrates these phases within a single prompt. However, the relative effectiveness of the two methods has not been extensively studied. This paper is dedicated to examining and comparing these two methods in the context of text summarization to ascertain which method stands out as the most effective. Experimental results show that the prompt chaining method can produce a more favorable outcome. This might be because stepwise prompt might produce a simulated refinement process according to our various experiments. Since refinement is adaptable to diverse tasks, our conclusions have the potential to be extrapolated to other applications, thereby offering insights that may contribute to the broader development of LLMs.

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Trust in Internal or External Knowledge? Generative Multi-Modal Entity Linking with Knowledge Retriever
Xinwei Long | Jiali Zeng | Fandong Meng | Jie Zhou | Bowen Zhou

Multi-modal entity linking (MEL) is a challenging task that requires accurate prediction of entities within extensive search spaces, utilizing multi-modal contexts. Existing generative approaches struggle with the knowledge gap between visual entity information and the intrinsic parametric knowledge of LLMs. To address this knowledge gap, we introduce a novel approach called GELR, which incorporates a knowledge retriever to enhance visual entity information by leveraging external sources. Additionally, we devise a prioritization scheme that effectively handles noisy retrieval results and manages conflicts arising from the integration of external and internal knowledge. Moreover, we propose a noise-aware instruction tuning technique during training to finely adjust the model’s ability to leverage retrieved information effectively. Through extensive experiments conducted on three benchmarks, our approach showcases remarkable improvements, ranging from 3.0% to 6.5%, across all evaluation metrics compared to strong baselines. These results demonstrate the effectiveness and superiority of our proposed method in tackling the complexities of multi-modal entity linking.

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A Semantic Distance Metric Learning approach for Lexical Semantic Change Detection
Taichi Aida | Danushka Bollegala

Detecting temporal semantic changes of words is an important task for various NLP applications that must make time-sensitive predictions.Lexical Semantic Change Detection (SCD) task involves predicting whether a given target word, w, changes its meaning between two different text corpora, C₁ and C₂.For this purpose, we propose a supervised two-staged SCD method that uses existing Word-in-Context (WiC) datasets.In the first stage, for a target word w, we learn two sense-aware encoders that represent the meaning of w in a given sentence selected from a corpus.Next, in the second stage, we learn a sense-aware distance metric that compares the semantic representations of a target word across all of its occurrences in C₁ and C₂.Experimental results on multiple benchmark datasets for SCD show that our proposed method achieves strong performance in multiple languages.Additionally, our method achieves significant improvements on WiC benchmarks compared to a sense-aware encoder with conventional distance functions.

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What Have We Achieved on Non-autoregressive Translation?
Yafu Li | Huajian Zhang | Jianhao Yan | Yongjing Yin | Yue Zhang

Recent advances have made non-autoregressive (NAT) translation comparable to autoregressive methods (AT). However, their evaluation using BLEU has been shown to weakly correlate with human annotations. Limited research compares non-autoregressive translation and autoregressive translation comprehensively, leaving uncertainty about the true proximity of NAT to AT. To address this gap, we systematically evaluate four representative NAT methods across various dimensions, including human evaluation. Our empirical results demonstrate that despite narrowing the performance gap, state-of-the-art NAT still underperforms AT under more reliable evaluation metrics. Furthermore, we discover that explicitly modeling dependencies is crucial for generating natural language and generalizing to out-of-distribution sequences.

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From Zero to Hero: Cold-Start Anomaly Detection
Tal Reiss | George Kour | Naama Zwerdling | Ateret Anaby Tavor | Yedid Hoshen

When first deploying an anomaly detection system, e.g., to detect out-of-scope queries in chatbots, there are no observed data, making data-driven approaches ineffective. Zero-shot anomaly detection methods offer a solution to such “cold-start” cases, but unfortunately they are often not accurate enough. This paper studies the realistic but underexplored cold-start setting where an anomaly detection model is initialized using zero-shot guidance, but subsequently receives a small number of contaminated observations (namely, that may include anomalies). The goal is to make efficient use of both the zero-shot guidance and the observations. We propose ColdFusion, a method that effectively adapts the zero-shot anomaly detector to contaminated observations. To support future development of this new setting, we propose an evaluation suite consisting of evaluation protocols and metrics.

Existing datasets for narrative understanding often fail to represent the complexity and uncertainty of relationships in real-life social scenarios. To address this gap, we introduce a new benchmark, Conan, designed for extracting and analysing intricate character relation graphs from detective narratives. Specifically, we designed hierarchical relationship categories and manually extracted and annotated role-oriented relationships from the perspectives of various characters, incorporating both public relationships known to most characters and secret ones known to only a few. Our experiments with advanced Large Language Models (LLMs) like GPT-3.5, GPT-4, and Llama2 reveal their limitations in inferencing complex relationships and handling longer narratives. The combination of the Conan dataset and our pipeline strategy is geared towards understanding the ability of LLMs to comprehend nuanced relational dynamics in narrative contexts.

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DistillMIKE: Editing Distillation of Massive In-Context Knowledge Editing in Large Language Models
Shanbao Qiao | Xuebing Liu | Seung-Hoon Na

Among the recently emerged knowledge editing methods, in-context knowledge editing (IKE) has shown respectable abilities on knowledge editing in terms of generalization and specificity. Noting the promising advantages but unexplored issues of IKE, we propose **DistillMIKE** as a novel extension of IKE, i.e., editing **distill**ation of "**M**assive” **I**n-context **K**nowledge **E**diting in large language models (LLMs), mainly consisting of two expansions; 1) *Massive in-context knowledge editing (MIKE)*, which extends IKE to a massive editing task, aiming to inject not a single edit but a set of massive edits to LLMs; To preserve specificity, our key novel extension is a “selective” retrieval augmentation, where the retrieval-augmented IKE is only applied to “in-scope” examples, whereas the unedited model without IKE is employed for “out-of-scope” ones. 2) *Editing distillation* of MIKE using low-rank adaptation (LoRA), which distills editing abilities of MIKE to parameters of LLMs in a manner of eliminating the need of lengthy in-context demonstrations, thus removing the computational overhead encountered at the inference time. Experimental results on the zsRE and CounterFact datasets demonstrate that MIKE shows the state-of-the-art perfomrances and DistilMIKE show comparable performances with MIKE. Our code is available at https://github.com/JoveReCode/DistillMIKE.git.

To mitigate the high inference latency stemming from autoregressive decoding in Large Language Models (LLMs), Speculative Decoding has emerged as a novel decoding paradigm for LLM inference. In each decoding step, this method first drafts several future tokens efficiently and then verifies them in parallel. Unlike autoregressive decoding, Speculative Decoding facilitates the simultaneous decoding of multiple tokens per step, thereby accelerating inference. This paper presents a comprehensive overview and analysis of this promising decoding paradigm. We begin by providing a formal definition and formulation of Speculative Decoding. Then, we organize in-depth discussions on its key facets, such as drafter selection and verification strategies. Furthermore, we present a comparative analysis of leading methods under third-party testing environments. We aim for this work to serve as a catalyst for further research on Speculative Decoding, ultimately contributing to more efficient LLM inference.

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Hierarchy-aware Biased Bound Margin Loss Function for Hierarchical Text Classification
Gibaeg Kim | SangHun Im | Heung-Seon Oh

Hierarchical text classification (HTC) is a challenging problem with two key issues: utilizing structural information and mitigating label imbalance. Recently, the unit-based approach generating unit-based feature representations has outperformed the global approach focusing on a global feature representation. Nevertheless, unit-based models using BCE and ZLPR losses still face static thresholding and label imbalance challenges. Those challenges become more critical in large-scale hierarchies. This paper introduces a novel hierarchy-aware loss function for unit-based HTC models: Hierarchy-aware Biased Bound Margin (HBM) loss. HBM integrates learnable bounds, biases, and a margin to address static thresholding and mitigate label imbalance adaptively. Experimental results on benchmark datasets demonstrate the superior performance of HBM compared to competitive HTC models.

In the era of large language models, applying techniques such as Retrieval Augmented Generation can better address Open-Domain Question-Answering problems. Due to constraints including model sizes and computing resources, the length of context is often limited, and it becomes challenging to empower the model to cover overlong contexts while answering questions from open domains. This paper proposes a general and convenient method to cover longer contexts in Open-Domain Question-Answering tasks. %It leverages a small encoder language model that effectively encodes contexts, and the encoding applies cross-attention with origin inputs.It leverages a small encoder and cross-attention mechanism and effectively encodes contexts. With our method, the original language models can cover several times longer contexts while keeping the computing requirements close to the baseline. Our experiments demonstrate that after fine-tuning, there is improved performance across two held-in datasets, four held-out datasets, and also in two In Context Learning settings. Our code will be released at https://github.com/Alibaba-NLP/Vec-RA-ODQA.

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CICLe: Conformal In-Context Learning for Largescale Multi-Class Food Risk Classification
Korbinian Randl | John Pavlopoulos | Aron Henriksson | Tony Lindgren

Contaminated or adulterated food poses a substantial risk to human health. Given sets of labeled web texts for training, Machine Learning and Natural Language Processing can be applied to automatically detect such risks. We publish a dataset of 7,546 short texts describing public food recall announcements. Each text is manually labeled, on two granularity levels (coarse and fine), for food products and hazards that the recall corresponds to. We describe the dataset and benchmark naive, traditional, and Transformer models. Based on our analysis, Logistic Regression based on a TF-IDF representation outperforms RoBERTa and XLM-R on classes with low support. Finally, we discuss different prompting strategies and present an LLM-in-the-loop framework, based on Conformal Prediction, which boosts the performance of the base classifier while reducing energy consumption compared to normal prompting.

Large language models (LLMs) excel in natural language processing but demand intensive computation. To mitigate this, various quantization methods have been explored, yet they compromise LLM performance. This paper unveils a previously overlooked type of outliers in LLMs. Such outliers are found to allocate most of the attention scores on initial tokens of input, termed as pivot tokens, which are crucial to the performance of quantized LLMs. Given that, we propose IntactKV to generate the KV cache of pivot tokens losslessly from the full-precision model. The approach is simple and easy to combine with existing quantization solutions with no extra inference overhead. Besides, IntactKV can be calibrated as additional LLM parameters to boost the quantized LLMs further with minimal training costs. Mathematical analysis also proves that IntactKV effectively reduces the upper bound of quantization error. Empirical results show that IntactKV brings consistent improvement over various quantization methods across different LLMs and downstream tasks, leading to the new state-of-the-art for LLM quantization. The codes are available at https://github.com/ruikangliu/IntactKV.

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Learning Adverbs with Spectral Mixture Kernels
Tomoe Taniguchi | Daichi Mochihashi | Ichiro Kobayashi

For humans and robots to collaborate more in the real world, robots need to understand human intentions from the different manner of their behaviors. In our study, we focus on the meaning of adverbs which describe human motions. We propose a topic model, Hierarchical Dirichlet Process-Spectral Mixture Latent Dirichlet Allocation, which concurrently learns the relationship between those human motions and those adverbs by capturing the frequency kernels that represent motion characteristics and the shared topics of adverbs that depict such motions. We trained the model on datasets we made from movies about “walking” and “dancing”, and found that our model outperforms representative neural network models in terms of perplexity score. We also demonstrate our model’s ability to determine the adverbs for a given motion and confirmed that the model predicts more appropriate adverbs.

The rapid development of Large Language Models (LLMs) has led to their increasing utilization in Chinese K-12 education. Despite the growing integration of LLMs and education, the absence of a dedicated benchmark for evaluating LLMs within this domain presents a pressing concern. Consequently, there is an urgent need for a comprehensive natural language processing benchmark to precisely assess the capabilities of various LLMs in Chinese K-12 education. In response, we introduce E-EVAL, the first comprehensive evaluation benchmark specifically tailored for Chinese K-12 education. E-EVAL comprises 4,351 multiple-choice questions spanning primary, middle, and high school levels, covering a diverse array of subjects. Through meticulous evaluation, we find that Chinese-dominant models often outperform English-dominant ones, with many exceeding GPT 4.0. However, most struggle with complex subjects like mathematics. Additionally, our analysis indicates that most Chinese-dominant LLMs do not achieve higher scores at the primary school level compared to the middle school level, highlighting the nuanced relationship between proficiency in higher-order and lower-order knowledge domains. Furthermore, experimental results highlight the effectiveness of the Chain of Thought (CoT) technique in scientific subjects and Few-shot prompting in liberal arts. Through E-EVAL, we aim to conduct a rigorous analysis delineating the strengths and limitations of LLMs in educational applications, thereby contributing significantly to the advancement of Chinese K-12 education and LLMs.

Charts play a vital role in data visualization, understanding data patterns, and informed decision-making. However, their unique combination of graphical elements (e.g., bars, lines) and textual components (e.g., labels, legends) poses challenges for general-purpose multimodal models. While vision-language models trained on chart data excel in comprehension, they struggle with generalization. To address these challenges, we propose ChartAssistant, a chart-based vision-language model for universal chart comprehension and reasoning. ChartAssistant leverages ChartSFT, a comprehensive dataset covering diverse chart-related tasks with basic (e.g. bars and pies) and specialized (e.g. radars, and bubbles) chart types. It undergoes a two-stage training process, starting with pre-training on chart-to-table parsing to align chart and text, followed by multitask instruction-following fine-tuning. This approach enables ChartAssistant to achieve competitive performance across various chart tasks. Experimental results demonstrate significant performance gains over the state-of-the-art UniChart and ChartLlama methods, especially outperforming them on real-world chart data with zero-shot setting. The code and data are available at https://github.com/OpenGVLab/ChartAst.

Large Language Models (LLMs) can teach small language models (SLMs) to solve complex reasoning tasks (e.g., mathematical question answering) by Chain-of-thought Distillation (CoTD). Specifically, CoTD fine-tunes SLMs by utilizing rationales generated from LLMs such as ChatGPT. However, CoTD has certain limitations that make it unsuitable for knowledge-intensive multi-hop question answering: 1) SLMs have a very limited capacity in memorizing required knowledge compared to LLMs. 2) SLMs do not possess the same powerful integrated abilities in question understanding and knowledge reasoning as LLMs. To address the above limitations, we introduce Decompose-and-Response Distillation (D&R Distillation), which distills two student models, namely Decomposer and Responser separately. The two models solve a knowledge-intensive multi-hop question through an interactive process of asking and answering subquestions. Our method offers two advantages: 1) SLMs have the capability to access external knowledge to address subquestions, which provides more comprehensive knowledge for multi-hop questions. 2) By employing simpler subquestions instead of complex CoT reasoning, SLMs effectively mitigate task complexity and decrease data prerequisites. Experimental results on three knowledge-intensive multi-hop question answering datasets demonstrate that D&R Distillation can surpass previous CoTD methods, even with much less training data.

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ALaRM: Align Language Models via Hierarchical Rewards Modeling
Yuhang Lai | Siyuan Wang | Shujun Liu | Xuanjing Huang | Zhongyu Wei

We introduce ALaRM, the first framework modeling hierarchical rewards in reinforcement learning from human feedback (RLHF), which is designed to enhance the alignment of large language models (LLMs) with human preferences. The framework addresses the limitations of current alignment approaches, which often struggle with the inconsistency and sparsity of human supervision signals, by integrating holistic rewards with aspect-specific rewards. This integration enables more precise and consistent guidance of language models towards desired outcomes, particularly in complex and open text generation tasks. By employing a methodology that filters and combines multiple rewards based on their consistency, the framework provides a reliable mechanism for improving model alignment. We validate our approach through applications in long-form question answering and machine translation tasks, employing gpt-3.5-turbo for pairwise comparisons, and demonstrate improvements over existing baselines. Our work underscores the effectiveness of hierarchical rewards modeling in refining LLM training processes for better human preference alignment. We release our code at https://ALaRM-fdu.github.io.

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LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting
Haoxin Liu | Zhiyuan Zhao | Jindong Wang | Harshavardhan Kamarthi | B. Aditya Prakash

Time-series forecasting (TSF) finds broad applications in real-world scenarios. Prompting off-the-shelf Large Language Models (LLMs) demonstrates strong zero-shot TSF capabilities while preserving computational efficiency. However, existing prompting methods oversimplify TSF as language next-token predictions, overlooking its dynamic nature and lack of integration with state-of-the-art prompt strategies such as Chain-of-Thought. Thus, we propose LSTPrompt, a novel approach for prompting LLMs in zero-shot TSF tasks. LSTPrompt decomposes TSF into short-term and long-term forecasting sub-tasks, tailoring prompts to each. LSTPrompt guides LLMs to regularly reassess forecasting mechanisms to enhance adaptability. Extensive evaluations demonstrate consistently better performance of LSTPrompt than existing prompting methods, and competitive results compared to foundation TSF models.

Text classification is a crucial task encountered frequently in practical scenarios, yet it is still under-explored in the era of large language models (LLMs). This study shows that LLMs are vulnerable to changes in the number and arrangement of options in text classification. Our extensive empirical analyses reveal that the key bottleneck arises from ambiguous decision boundaries and inherent biases towards specific tokens and positions.To mitigate these issues, we make the first attempt and propose a novel two-stage classification framework for LLMs. Our approach is grounded in the empirical observation that pairwise comparisons can effectively alleviate boundary ambiguity and inherent bias. Specifically, we begin with a self-reduction technique to efficiently narrow down numerous options, which contributes to reduced decision space and a faster comparison process. Subsequently, pairwise contrastive comparisons are employed in a chain-of-thought manner to draw out nuances and distinguish confusable options, thus refining the ambiguous decision boundary.Extensive experiments on four datasets (Banking77, HWU64, LIU54, and Clinic150) verify the effectiveness of our framework. Furthermore, benefitting from our framework, various LLMs can achieve consistent improvements. Our code and data are available in https://github.com/Chuge0335/PC-CoT.

Task-agnostic and transferable backdoors implanted in pre-trained language models (PLMs) pose a severe security threat as they can be inherited to any downstream task. However, existing methods rely on manual selection of triggers and backdoor representations, hindering their effectiveness and universality across different PLMs or usage paradigms. In this paper, we propose a new backdoor attack method called UOR, which overcomes these limitations by turning manual selection into automatic optimization. Specifically, we design poisoned supervised contrastive learning, which can automatically learn more uniform and universal backdoor representations. This allows for more even coverage of the output space, thus hitting more labels in downstream tasks after fine-tuning. Furthermore, we utilize gradient search to select appropriate trigger words that can be adapted to different PLMs and vocabularies. Experiments show that UOR achieves better attack performance on various text classification tasks compared to manual methods. Moreover, we test on PLMs with different architectures, usage paradigms, and more challenging tasks, achieving higher scores for universality.

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Language models emulate certain cognitive profiles: An investigation of how predictability measures interact with individual differences
Patrick Haller | Lena Bolliger | Lena Jäger

To date, most investigations on surprisal and entropy effects in reading have been conducted on the group level, disregarding individual differences. In this work, we revisit the predictive power (PP) of different LMs’ surprisal and entropy measures on data of human reading times as a measure of processing effort by incorporating information of language users’ cognitive capacities. To do so, we assess the PP of surprisal and entropy estimated from generative language models (LMs) on reading data obtained from individuals who also completed a wide range of psychometric tests.Specifically, we investigate if modulating surprisal and entropy relative to cognitive scores increases prediction accuracy of reading times, and we examine whether LMs exhibit systematic biases in the prediction of reading times for cognitively high- or low-performing groups, revealing what type of psycholinguistic subjects a given LM emulates.Our study finds that in most cases, incorporating cognitive capacities increases predictive power of surprisal and entropy on reading times, and that generally, high performance in the psychometric tests is associated with lower sensitivity to predictability effects. Finally, our results suggest that the analyzed LMs emulate readers with lower verbal intelligence, suggesting that for a given target group (i.e., individuals with high verbal intelligence), these LMs provide less accurate predictability effect estimates.

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The State of Relation Extraction Data Quality: Is Bigger Always Better?
Erica Cai | Brendan O’Connor

Relation extraction (RE) extracts structured tuples of relationships (e.g. friend, enemy) between entities (e.g. Sherlock Holmes, John Watson) from text, with exciting potential applications. Hundreds of RE papers have been published in recent years; do their evaluation practices inform these goals? We review recent surveys and a sample of recent RE methods papers, compiling 38 datasets currently being used. Unfortunately, many have frequent label errors, and ones with known problems continue to be used. Many datasets focus on producing labels for a large number of relation types, often through error-prone annotation methods (e.g. distant supervision or crowdsourcing), and many recent papers rely exclusively on such datasets. We draw attention to a promising alternative: datasets with a small number of relations, often in specific domains like chemistry, finance, or biomedicine, where it is possible to obtain high quality expert annotations; such data can more realistically evaluate RE performance. The research community should consider more often using such resources.

Large language models (LLMs) have manifested strong ability to generate codes for productive activities. However, current benchmarks for code synthesis, such as HumanEval, MBPP, and DS-1000, are predominantly oriented towards introductory tasks on algorithm and data science, insufficiently satisfying challenging requirements prevalent in real-world coding. To fill this gap, we propose NaturalCodeBench (NCB), a challenging code benchmark designed to mirror the complexity and variety of scenarios in real coding tasks. NCB comprises 402 high-quality problems in Python and Java, meticulously selected from natural user queries from online coding services, covering 6 different domains. Noting the extraordinary difficulty in creating testing cases for real-world queries, we also introduce a semi-automated pipeline to enhance the efficiency of test case construction. Comparing with manual solutions, it achieves an efficiency increase of more than 4 times. Our systematic experiments on 39 LLMs find that performance gaps on NCB between models with close HumanEval scores could still be significant, indicating a lack of focus on practical code synthesis scenarios or over-specified optimization on HumanEval. On the other hand, even the best-performing GPT-4 is still far from satisfying on NCB. The evaluation toolkit and development set are available at https://github.com/THUDM/NaturalCodeBench.

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LLMCrit: Teaching Large Language Models to Use Criteria
Weizhe Yuan | Pengfei Liu | Matthias Gallé

Humans follow criteria when they execute tasks, and these criteria are directly used to assess the quality of task completion. Therefore, having models learn to use criteria to provide feedback can help humans or models to perform tasks better. However, current research in this area tends to consider only a limited number of criteria, or only a limited number of quality assessment aspects. To fill this gap, we propose a general framework that enables large language models (LLMs) to use comprehensive criteria for a task in delivering natural language feedback on task execution. In particular, we present a model-in-the-loop framework that semi-automatically derives criteria from collected guidelines for different writing tasks and constructs in-context demonstrations for each criterion. We choose three tasks from real-world scenarios to operationalize this idea: paper introduction writing, Python code writing, and Reddit post writing, and evaluate our feedback generation framework using different LLMs. The results reveal the fine-grained effects of adding criteria and demonstrations and provide valuable guidance on how to teach LLMs to use criteria more effectively.

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Empowering cross-lingual abilities of instruction-tuned large language models by translation-following demonstrations
Leonardo Ranaldi | Giulia Pucci | Andre Freitas

The language ability of Large Language Models (LLMs) is often unbalanced towards English because of the imbalance in the distribution of the pre-training data. This disparity is demanded in further fine-tuning and affecting the cross-lingual abilities of LLMs. In this paper, we propose to empower Instruction-tuned LLMs (It-LLMs) in languages other than English by building semantic alignment between them. Hence, we propose CrossAlpaca, an It-LLM with cross-lingual Instruction-following and Translation-following demonstrations to improve semantic alignment between languages. We validate our approach on the multilingual Question Answering (QA) benchmarks XQUAD and MLQA and adapted versions of MMLU and BBH.Our models, tested over six different languages, outperform the It-LLMs tuned on monolingual data. The final results show that instruction tuning on non-English data is not enough and that semantic alignment can be further improved by Translation-following demonstrations.

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Ranking Entities along Conceptual Space Dimensions with LLMs: An Analysis of Fine-Tuning Strategies
Nitesh Kumar | Usashi Chatterjee | Steven Schockaert

Conceptual spaces represent entities in terms of their primitive semantic features. Such representations are highly valuable but they are notoriously difficult to learn, especially when it comes to modelling perceptual and subjective features. Distilling conceptual spaces from Large Language Models (LLMs) has recently emerged as a promising strategy, but existing work has been limited to probing pre-trained LLMs using relatively simple zero-shot strategies. We focus in particular on the task of ranking entities according to a given conceptual space dimension. Unfortunately, we cannot directly fine-tune LLMs on this task, because ground truth rankings for conceptual space dimensions are rare. We therefore use more readily available features as training data and analyse whether the ranking capabilities of the resulting models transfer to perceptual and subjective features. We find that this is indeed the case, to some extent, but having at least some perceptual and subjective features in the training data seems essential for achieving the best results.

To achieve non-parametric NMT domain adaptation, k-Nearest-Neighbor Machine Translation (kNN-MT) constructs an external datastore to store domain-specific translation knowledge, which derives a kNN distribution to interpolate the prediction distribution of the NMT model via a linear interpolation coefficient 𝜆. Despite its success, kNN retrieval at each timestep leads to substantial time overhead. To address this issue, dominant studies resort to kNN-MT with adaptive retrieval (kNN-MT-AR), which dynamically estimates 𝜆 and skips kNN retrieval if 𝜆 is less than a fixed threshold. Unfortunately, kNN-MT-AR does not yield satisfactory results. In this paper, we first conduct a preliminary study to reveal two key limitations of kNN-MT-AR: 1) the optimization gap leads to inaccurate estimation of 𝜆 for determining kNN retrieval skipping, and 2) using a fixed threshold fails to accommodate the dynamic demands for kNN retrieval at different timesteps. To mitigate these limitations, we then propose kNN-MT with dynamic retrieval (kNN-MT-DR) that significantly extends vanilla kNN-MT in two aspects. Firstly, we equip kNN-MT with a MLP-based classifier for determining whether to skip kNN retrieval at each timestep. Particularly, we explore several carefully-designed scalar features to fully exert the potential of the classifier. Secondly, we propose a timestep-aware threshold adjustment method to dynamically generate the threshold, which further improves the efficiency of our model. Experimental results on the widely-used datasets demonstrate the effectiveness and generality of our model.

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Symmetric Dot-Product Attention for Efficient Training of BERT Language Models
Martin Courtois | Malte Ostendorff | Leonhard Hennig | Georg Rehm

Initially introduced as a machine translation model, the Transformer architecture has now become the foundation for modern deep learning architecture, with applications in a wide range of fields, from computer vision to natural language processing. Nowadays, to tackle increasingly more complex tasks, Transformer-based models are stretched to enormous sizes, requiring increasingly larger training datasets, and unsustainable amount of compute resources. The ubiquitous nature of the Transformer and its core component, the attention mechanism, are thus prime targets for efficiency research.In this work, we propose an alternative compatibility function for the self-attention mechanism introduced by the Transformer architecture. This compatibility function exploits an overlap in the learned representation of the traditional scaled dot-product attention, leading to a symmetric with pairwise coefficient dot-product attention. When applied to the pre-training of BERT-like models, this new symmetric attention mechanism reaches a score of 79.36 on the GLUE benchmark against 78.74 for the traditional implementation, leads to a reduction of 6% in the number of trainable parameters, and reduces the number of training steps required before convergence by half.

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Synthesizing Conversations from Unlabeled Documents using Automatic Response Segmentation
Fanyou Wu | Weijie Xu | Chandan Reddy | Srinivasan Sengamedu

In this study, we tackle the challenge of inadequate and costly training data that has hindered the development of conversational question answering (ConvQA) systems. Enterprises have a large corpus of diverse internal documents. Instead of relying on a searching engine, a more compelling approach for people to comprehend these documents is to create a dialogue system. In this paper, we propose a robust dialog synthesising method. We learn the segmentation of data for the dialog task instead of using segmenting at sentence boundaries. The synthetic dataset generated by our proposed method achieves superior quality when compared to WikiDialog, as assessed through machine and human evaluations. By employing our inpainted data for ConvQA retrieval system pre-training, we observed a notable improvement in performance across OR-QuAC benchmarks.

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Can Large Language Models Follow Concept Annotation Guidelines? A Case Study on Scientific and Financial Domains
Marcio Fonseca | Shay Cohen

Although large language models (LLMs) exhibit remarkable capacity to leverage in-context demonstrations, it is still unclear to what extent they can learn new facts or concept definitions via prompts. To address this question, we examine the capacity of instruction-tuned LLMs to follow in-context concept annotation guidelines for zero-shot sentence labeling tasks. We design guidelines that present different types of factual and counterfactual concept definitions, which are used as prompts for zero-shot sentence classification tasks. Our results show that although concept definitions consistently help in task performance, only the larger models (with 70B parameters or more) have limited ability to work under counterfactual contexts. Importantly, only proprietary models such as GPT-3.5 can recognize nonsensical guidelines, which we hypothesize is due to more sophisticated alignment methods. Finally, we find that Falcon-180B-chat is outperformed by Llama-2-70B-chat is most cases, which indicates that increasing model scale does not guarantee better adherence to guidelines. Altogether, our simple evaluation method reveals significant gaps in concept understanding between the most capable open-source language models and the leading proprietary APIs.

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Alignment-Based Decoding Policy for Low-Latency and Anticipation-Free Neural Japanese Input Method Editors
Armin Sarhangzadeh | Taro Watanabe

Japanese input method editors (IMEs) are essential tools for inputting Japanese text using a limited set of characters such as the kana syllabary. However, despite their importance, the potential of newer attention-based encoder-decoder neural networks, such as Transformer, has not yet been fully explored for IMEs due to their high computational cost and low-quality intermediate output in simultaneous settings, leading to high latencies. In this work, we propose a simple decoding policy to enable the use of attention-based encoder-decoder networks for simultaneous kana-kanji conversion in the context of Japanese IMEs inspired by simultaneous machine translation (SimulMT). We demonstrate that simply decoding by explicitly considering the word boundaries achieves a fairly strong quality-latency trade-off, as it can be seen as equivalent to performing decoding on aligned prefixes and thus achieving an incremental anticipation-free conversion. We further show how such a policy can be applied in practice to achieve high-quality conversions with minimal computational overhead. Our experiments show that our approach can achieve a noticeably better quality-latency trade-off compared to the baselines, while also being a more practical approach due to its ability to directly handle streaming input. Our code is available at https://anonymous.4open.science/r/transformer_ime-D327.

The demand for understanding and expressing emotions in the field of natural language processing is growing rapidly. Knowledge graphs, as an important form of knowledge representation, have been widely utilized in various emotion-related tasks. However, existing knowledge graphs mainly focus on the representation and reasoning of general factual knowledge, while there are still significant deficiencies in the understanding and reasoning of emotional knowledge. In this work, we construct a comprehensive and accurate emotional commonsense knowledge graph, ECoK. We integrate cutting-edge theories from multiple disciplines such as psychology, cognitive science, and linguistics, and combine techniques such as large language models and natural language processing. By mining a large amount of text, dialogue, and sentiment analysis data, we construct rich emotional knowledge and establish the knowledge generation model COMET-ECoK. Experimental results show that ECoK contains high-quality emotional reasoning knowledge, and the performance of our knowledge generation model surpasses GPT-4-Turbo, which can help downstream tasks better understand and reason about emotions. Our data and code is available from https://github.com/ZornWang/ECoK.

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Deterministic Reversible Data Augmentation for Neural Machine Translation
Jiashu Yao | Heyan Huang | Zeming Liu | Yuhang Guo

Data augmentation is an effective way to diversify corpora in machine translation, but previous methods may introduce semantic inconsistency between original and augmented data because of irreversible operations and random subword sampling procedures. To generate both symbolically diverse and semantically consistent augmentation data, we propose Deterministic Reversible Data Augmentation (DRDA), a simple but effective data augmentation method for neural machine translation. DRDA adopts deterministic segmentations and reversible operations to generate multi-granularity subword representations and pulls them closer together with multi-view techniques. With no extra corpora or model changes required, DRDA outperforms strong baselines on several translation tasks with a clear margin (up to 4.3 BLEU gain over Transformer) and exhibits good robustness in noisy, low-resource, and cross-domain datasets.

The growing interest in leveraging large language models is driven by their exceptional imitation and reasoning capabilities. In-context learning (ICL), a streamlined method, has shown potential in boosting these models’ performance without modifying their underlying parameters, especially when supplied with suitable demonstrations. However, existing methods mainly choose demonstrations by comparing surface-level semantic similarities (e.g., based on embedding) and fall short of identifying the most fitting ones. This paper introduces the concept of a “latent learningscape”, a more nuanced representation that describes the characteristic of the demonstrations. Building on this concept, we develop a results-driven approach to characterize the latent learningscape features of demonstrations, which then inform the creation of more effective prompts. Through comprehensive testing across datasets in arithmetic, commonsense, and symbolic reasoning tasks, our approach outperforms leading models, showing an average increase in scores by 7.4 percentage points.

Despite the promising performance of state space models (SSMs) in long sequence modeling, limitations still exist. Advanced SSMs like S5 and S6 (Mamba) in addressing non-uniform sampling, their recursive structures impede efficient SSM computation via convolution. To overcome compatibility limitations in parallel convolutional computation, this paper proposes a novel non-recursive non-uniform sample processing strategy. Theoretical analysis of SSMs through the lens of Event-Triggered Control (ETC) theory reveals the Non-Stable State (NSS) problem, where deviations from sampling point requirements lead to error transmission and accumulation, causing the divergence of the SSM’s hidden state. Our analysis further reveals that adjustments of input sequences with early memories can mitigate the NSS problem, achieving Sampling Step Adaptation (SSA).Building on this insight, we introduce a simple yet effective plug-and-play mechanism, State Memory Replay (SMR), which utilizes learnable memories to adjust the current state with multi-step information for generalization at sampling points different from those in the training data. This enables SSMs to stably model varying sampling points. Experiments on long-range modeling tasks in autoregressive language modeling and Long Range Arena demonstrate the general effectiveness of the SMR mechanism for a series of SSM models.

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Characterizing Large Language Models as Rationalizers of Knowledge-intensive Tasks
Aditi Mishra | Sajjadur Rahman | Kushan Mitra | Hannah Kim | Estevam Hruschka

Large language models (LLMs) are proficient at generating fluent text with minimal task-specific supervision. However, their ability to generate rationales for knowledge-intensive tasks (KITs) remains under-explored. Generating rationales for KIT solutions, such as commonsense multiple-choice QA, requires external knowledge to support predictions and refute alternate options. In this work, we consider the task of generating retrieval-augmented rationalization of KIT model predictions via external knowledge guidance within a few-shot setting. Surprisingly, crowd-workers preferred LLM-generated rationales over existing crowd-sourced rationales, generated in a similar knowledge-guided setting, on aspects such as factuality, sufficiency, and convincingness. However, fine-grained evaluation of such rationales highlights the need for further improvements in conciseness, novelty, and domain invariance. Additionally, through an expert-sourced study evaluating the reliability of the rationales, we demonstrate that humans’ trust in LLM-generated rationales erodes when communicated faithfully, i.e., without taking model prediction accuracy into account. We find that even instrumenting simple guardrails can be effective for reliable rationalization.

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Challenging Large Language Models with New Tasks: A Study on their Adaptability and Robustness
Chenxi Li | Yuanhe Tian | Zhaxi Zerong | Yan Song | Fei Xia

Recent progress in large language models (LLMs) has marked a notable milestone in the field of artificial intelligence. The conventional evaluation of LLMs primarily relies on existing tasks and benchmarks, raising concerns about test set contamination and the genuine comprehension abilities of LLMs. To address these concerns, we propose to evaluate LLMs by designing new tasks, automatically generating evaluation datasets for the tasks, and conducting detailed error analyses to scrutinize LLMs’ adaptability to new tasks, their sensitivity to prompt variations, and their error tendencies. We investigate the capacity of LLMs to adapt to new but simple tasks, especially when they diverge from the models’ pre-existing knowledge. Our methodology emphasizes the creation of straightforward tasks, facilitating a precise error analysis to uncover the underlying causes of LLM failures. This strategic approach also aims to uncover effective strategies for enhancing LLM performance based on the detailed error analysis of system output.

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Linear Cross-Lingual Mapping of Sentence Embeddings
Oleg Vasilyev | Fumika Isono | John Bohannon

Semantics of a sentence is defined with much less ambiguity than semantics of a single word, and we assume that it should be better preserved by translation to another language. If multilingual sentence embeddings intend to represent sentence semantics, then the similarity between embeddings of any two sentences must be invariant with respect to translation. Based on this suggestion, we consider a simple linear cross-lingual mapping as a possible improvement of the multilingual embeddings. We also consider deviation from orthogonality conditions as a measure of deficiency of the embeddings.

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ULTRA: Unleash LLMs’ Potential for Event Argument Extraction through Hierarchical Modeling and Pair-wise Self-Refinement
Xinliang Frederick Zhang | Carter Blum | Temma Choji | Shalin Shah | Alakananda Vempala

Structural extraction of events within discourse is critical since it avails a deeper understanding of communication patterns and behavior trends. Event argument extraction (EAE), at the core of event-centric understanding, is the task of identifying role-specific text spans (i.e., arguments) for a given event. Document-level EAE (DocEAE) focuses on arguments that are scattered across an entire document. In this work, we explore open-source Large Language Models (LLMs) for DocEAE, and propose ULTRA, a hierarchical framework that extracts event arguments more cost-effectively. Further, it alleviates the positional bias issue intrinsic to LLMs. ULTRA sequentially reads text chunks of a document to generate a candidate argument set, upon which non-pertinent candidates are dropped through self-refinement. We introduce LEAFER to address the challenge LLMs face in locating the exact boundary of an argument. ULTRA outperforms strong baselines, including strong supervised models and ChatGPT, by 9.8% when evaluated by Exact Match (EM).

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LLMs Beyond English: Scaling the Multilingual Capability of LLMs with Cross-Lingual Feedback
Wen Lai | Mohsen Mesgar | Alexander Fraser

To democratize large language models (LLMs) to most natural languages, it is imperative to make these models capable of understanding and generating texts in many languages, in particular low-resource ones. While recent multilingual LLMs demonstrate remarkable performance in such capabilities, these LLMs still support a limited number of human languages due to the lack of training data for low resource languages. Moreover, these LLMs are not yet aligned with human preference for downstream tasks, which is crucial for the success of LLMs in English. In this paper, we introduce xLLaMA-100 and xBLOOM-100 (collectively xLLMs-100), which scale the multilingual capabilities of LLaMA and BLOOM to 100 languages. To do so, we construct two datasets: a multilingual instruction dataset including 100 languages, which represents the largest language coverage to date, and a cross-lingual human feedback dataset encompassing 30 languages. We perform multilingual instruction tuning on the constructed instruction data and further align the LLMs with human feedback using the DPO algorithm on our cross-lingual human feedback dataset. We evaluate the multilingual understanding and generating capabilities of xLLMs-100 on five multilingual benchmarks. Experimental results show that xLLMs-100 consistently outperforms its peers across the benchmarks by considerable margins, defining a new state-of-the-art multilingual LLM that supports 100 languages.

Speculative decoding has emerged as a powerful method to improve latency and throughput in hosting large language models. However, most existing implementations focus on generating a single sequence. Real-world generative AI applications often require multiple responses and how to perform speculative decoding in a batched setting while preserving its latency benefits poses non-trivial challenges. This paper describes a system of batched speculative decoding that sets a new state of the art in multi-sequence generation latency and that demonstrates superior GPU utilization as well as quality of generations within a time budget. For example, for a 7.8B-size model on a single A100 GPU and with a batch size of 8, each sequence is generated at an average speed of 5.8ms per token, the overall throughput being 1.1K tokens per second. These results represent state-of-the-art latency and a 2.15× speed-up over optimized regular decoding. Within a time budget that regular decoding does not finish, our system is able to generate sequences with HumanEval Pass@First of 43% and Pass@All of 61%, far exceeding what’s feasible with single-sequence speculative decoding. Our peak GPU utilization during decoding reaches as high as 15.8%, more than 3× the highest of that of regular decoding and around 10× of single-sequence speculative decoding.

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Deciphering Digital Detectives: Understanding LLM Behaviors and Capabilities in Multi-Agent Mystery Games
Dekun Wu | Haochen Shi | Zhiyuan Sun | Bang Liu

In this study, we explore the application of Large Language Models (LLMs) in Jubensha, a Chinese detective role-playing game and a novel area in Artificial Intelligence (AI) driven gaming. We introduce the first dataset specifically for Jubensha, including character scripts and game rules, to foster AI agent development in this complex narrative environment. Our work also presents a unique multi-agent interaction framework using LLMs, allowing AI agents to autonomously engage in Jubensha games. To evaluate the gaming performance of these AI agents, we developed novel methods measuring their mastery of case information and reasoning skills. Furthermore, we incorporated the latest advancements in prompting engineering to enhance the agents’ performance in information gathering, murderer identification, and logical reasoning. The experimental results validate the effectiveness of our proposed methods. This work aims to offer a novel perspective on understanding LLM capabilities and establish a new benchmark for evaluating large language model-based agents.

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It Is Not About What You Say, It Is About How You Say It: A Surprisingly Simple Approach for Improving Reading Comprehension
Sagi Shaier | Lawrence Hunter | Katharina von der Wense

Natural language processing has seen rapid progress over the past decade. Due to the speed of developments, some practices get established without proper evaluation. Considering one such case and focusing on reading comprehension, we ask our first research question: 1) How does the order of inputs – i.e., question and context – affect model performance? Additionally, given recent advancements in input emphasis, we ask a second research question: 2) Does emphasizing either the question, the context, or both enhance performance? Experimenting with 9 large language models across 3 datasets, we find that presenting the context before the question improves model performance, with an accuracy increase of up to 31%. Furthermore, emphasizing the context yields superior results compared to question emphasis, and in general, emphasizing parts of the input is particularly effective for addressing questions that models lack the parametric knowledge to answer. Experimenting with both prompt-based and attention-based emphasis methods, we additionally find that the best method is surprisingly simple: it only requires concatenating a few tokens to the input and results in an ac- curacy improvement of up to 36%, allowing smaller models to outperform their significantly larger counterparts.

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Large Language Models Relearn Removed Concepts
Michelle Lo | Fazl Barez | Shay Cohen

Advances in model editing through neuron pruning hold promise for removing undesirable concepts from large language models. However, it remains unclear whether models have the capacity to reacquire pruned concepts after editing. To investigate this, we evaluate concept relearning in models by tracking concept saliency and similarity in pruned neurons during retraining for named entity recognition tasks. Our findings reveal that models can quickly regain performance post-pruning by relocating advanced concepts to earlier layers and reallocating pruned concepts to primed neurons with similar semantics. This suggests that models exhibit polysemantic capacities and can blend old and new concepts in individual neurons. While neuron pruning provides interpretability into model concepts, our results highlight the challenges of permanent concept removal for improved model *safety*. Monitoring concept reemergence and developing techniques to mitigate relearning of unsafe concepts will be important directions for more robust model editing. Overall, our work strongly demonstrates the resilience and fluidity of concept representations in LLMs post concept removal.

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Towards Unified Task Embeddings Across Multiple Models: Bridging the Gap for Prompt-Based Large Language Models and Beyond
Xinyu Wang | Hainiu Xu | Lin Gui | Yulan He

Task embedding, a meta-learning technique that captures task-specific information, has gained popularity, especially in areas such as multi-task learning, model editing, and interpretability. However, it faces challenges with the emergence of prompt-guided Large Language Models (LLMs) operating in a gradient-free manner. Existing task embedding methods rely on fine-tuned, task-specific language models, which hinders the adaptability of task embeddings across diverse models, especially prompt-based LLMs. To hardness the potential of task embeddings in the era of LLMs, we propose a framework for unified task embeddings (FUTE), harmonizing task embeddings from various models, including smaller language models and LLMs with varied prompts, within a single vector space. Such uniformity enables comparison and analysis of similarities amongst different models, broadening the scope and utility of existing task embedding methods in multi-model scenarios, while maintaining their performance comparable to architecture-specific methods.

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TOAD: Task-Oriented Automatic Dialogs with Diverse Response Styles
Yinhong Liu | Yimai Fang | David Vandyke | Nigel Collier

In light of recent advances in large language models (LLMs), the expectations for the next generation of virtual assistants include enhanced naturalness and adaptability across diverse usage scenarios. However, the creation of high-quality annotated data for Task-Oriented Dialog (TOD) is recognized to be slow and costly. To address these challenges, we introduce Task-Oriented Automatic Dialogs (TOAD), a novel and scalable TOD dataset along with its automatic generation pipeline. The TOAD dataset simulates realistic app context interaction and provide a variety of system response style options. Two aspects of system response styles are considered, verbosity level and users’ expression mirroring. We benchmark TOAD on two response generation tasks, and the results show that modeling more verbose responses or responses without user expression mirroring is more challenging.

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Machine-Generated Text Localization
Zhongping Zhang | Wenda Qin | Bryan Plummer

Machine-Generated Text (MGT) detection aims to identify a piece of text as machine or human written. Prior work has primarily formulated MGT detection as a binary classification task over an entire document, with limited work exploring cases where only part of a document is machine generated. This paper provides the first in-depth study of MGT that localizes the portions of a document that were machine generated. Thus, if a bad actor were to change a key portion of a news article to spread misinformation, whole document MGT detection may fail since the vast majority is human written, but our approach can succeed due to its granular approach. A key challenge in our MGT localization task is that short spans of text, *e.g.*, a single sentence, provides little information indicating if it is machine generated due to its short length. To address this, we leverage contextual information, where we predict whether multiple sentences are machine or human written at once. This enables our approach to identify changes in style or content to boost performance. A gain of 4-13% mean Average Precision (mAP) over prior work demonstrates the effectiveness of approach on five diverse datasets: GoodNews, VisualNews, WikiText, Essay, and WP. We release our implementation at https://github.com/Zhongping-Zhang/MGT_Localization.

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BenchIE^FL: A Manually Re-Annotated Fact-Based Open Information Extraction Benchmark
Fabrice Lamarche | Philippe Langlais

Open Information Extraction (OIE) is a field of natural language processing that aims to present textual information in a format that allows it to be organized, analyzed and reflected upon. Numerous OIE systems are developed, claiming ever-increasing performance, marking the need for objective benchmarks. BenchIE is the latest reference we know of. Despite being very well thought out, we noticed a number of issues we believe are limiting. Therefore, we propose BenchIE^FL, a new OIE benchmark which fully enforces the principles of BenchIE while containing fewer errors, omissions and shortcomings when candidate facts are matched towards reference ones. BenchIE^FL allows insightful conclusions to be drawn on the actual performance of OIE extractors.

Citation count of a paper is a commonly used proxy for evaluating the significance of a paper in the scientific community. Yet citation measures are widely criticized for failing to accurately reflect the true impact of a paper. Thus, we propose CausalCite, a new way to measure the significance of a paper by assessing the causal impact of the paper on its follow-up papers. CausalCite is based on a novel causal inference method, TextMatch, which adapts the traditional matching framework to high-dimensional text embeddings. TextMatch encodes each paper using text embeddings from large language models (LLMs), extracts similar samples by cosine similarity, and synthesizes a counterfactual sample as the weighted average of similar papers according to their similarity values. We demonstrate the effectiveness of CausalCite on various criteria, such as high correlation with paper impact as reported by scientific experts on a previous dataset of 1K papers, (test-of-time) awards for past papers, and its stability across various subfields of AI. We also provide a set of findings that can serve as suggested ways for future researchers to use our metric for a better understanding of the quality of a paper. Our code is available at https://github.com/causalNLP/causal-cite.

Large language models show compelling performance on reasoning tasks but they tend to perform much worse in languages other than English. This is unsurprising given that their training data largely consists of English text and instructions. A typical solution is to translate instruction data into all languages of interest, and then train on the resulting multilingual data, which is called translate-training. This approach not only incurs high cost, but also results in poorly translated data due to the non-standard formatting of mathematical chain-of-thought. In this paper, we explore the benefits of question alignment, where we train the model to translate reasoning questions into English by finetuning on X-English parallel question data. In this way we perform targeted, in-domain language alignment which makes best use of English instruction data to unlock the LLMs’ multilingual reasoning abilities. Experimental results on LLaMA2-13B show that question alignment leads to consistent improvements over the translate-training approach: an average improvement of 11.3% and 16.1% accuracy across ten languages on the MGSM and MSVAMP multilingual reasoning benchmarks.

This work studies mitigating fact-conflicting hallucinations for large language model (LLM) at inference time.Particularly, we propose a self-endorsement framework that leverages the fine-grained fact-level comparisons across multiple sampled responses.Compared with prior ensemble methods (e.g., self-consistency) that perform response-level selection, our approach can better alleviate hallucinations for knowledge-intensive tasks.Our approach can broadly benefit smaller and open-source LLMs as it mainly conducts simple content-based comparisons.Experiments on Biographies show that our method can effectively improve the factuality of generations with simple and intuitive prompts across different scales of LLMs.Besides, comprehensive analyses on TriviaQA and GSM8K demonstrate the potential of self-endorsement for broader application.

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Multi-Label Classification for Implicit Discourse Relation Recognition
Wanqiu Long | Siddharth N | Bonnie Webber

Discourse relations play a pivotal role in establishing coherence within textual content, uniting sentences and clauses into a cohesive narrative. The Penn Discourse Treebank (PDTB) stands as one of the most extensively utilized datasets in this domain. In PDTB-3, the annotators can assign multiple labels to an example, when they believe the simultaneous presence of multiple relations. Prior research in discourse relation recognition has treated these instances as separate examples during training, with a gold-standard prediction matching one of the labels considered correct at test time. However, this approach is inadequate, as it fails to account for the interdependence of labels in real-world contexts and to distinguish between cases where only one sense relation holds and cases where multiple relations hold simultaneously. In our work, we address this challenge by exploring various multi-label classification frameworks to handle implicit discourse relation recognition. We show that the methods for multi-label prediction don’t depress performance for single-label prediction. Additionally, we give comprehensive analysis of results and data. Our work contributes to advancing the understanding and application of discourse relations and provide a foundation for the future study.

Code LLMs have the potential to make it easier for non-experts to understand and write code. However, current CodeLLM benchmarks rely on a single expert-written prompt per problem, making it hard to generalize their success to non-expert users. In this paper, we present a new natural-language-to-code benchmark of prompts written by a key population of non-experts: beginning programmers. StudentEval contains 1,749 prompts written by 80 students who have only completed one introductory Python course. StudentEval contains numerous non-expert prompts describing the same problem, enabling exploration of key factors in prompt success. We use StudentEval to evaluate 12 Code LLMs and find that StudentEval is a better discriminator of model performance than existing benchmarks. Our analysis of student prompting strategies reveals that nondeterministic LLM sampling can mislead students about the quality of their descriptions, a finding with key implications for Code LLMs in education.

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ProLex: A Benchmark for Language Proficiency-oriented Lexical Substitution
Xuanming Zhang | Zixun Chen | Zhou Yu

Lexical Substitution discovers appropriate substitutes for a given target word in a context sentence. However, the task fails to consider substitutes that are of equal or higher proficiency than the target, an aspect that could be beneficial for language learners looking to improve their writing. To bridge this gap, we propose a new task — language proficiency-oriented lexical substitution. We also introduce ProLex, a novel benchmark designed to assess systems’ ability to generate not only appropriate substitutes but also substitutes that demonstrate better language proficiency. Besides the benchmark, we propose models that can automatically perform the new task. We show that our best model, a Llama2-13B model fine-tuned with task-specific synthetic data, outperforms ChatGPT by an average of 3.2% in F-score and achieves comparable results with GPT-4 on ProLex.

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Generating Diverse and High-Quality Texts by Minimum Bayes Risk Decoding
Yuu Jinnai | Ukyo Honda | Tetsuro Morimura | Peinan Zhang

One of the most important challenges in text generation systems is to produce outputs that are not only correct but also diverse.Recently, Minimum Bayes-Risk (MBR) decoding has gained prominence for generating sentences of the highest quality among the decoding algorithms. However, existing algorithms proposed to generate diverse outputs are predominantly based on beam search or random sampling, thus their output quality is capped by these underlying decoding algorithms. In this paper, we investigate an alternative approach – we develop diversity-promoting decoding algorithms by enforcing diversity objectives to MBR decoding.We propose two variants of MBR; (i) Diverse MBR (DMBR) that adds a diversity penalty to the decoding objective and (ii) k-medoids MBR (KMBR) that reformulates the decoding task as a clustering problem.We evaluate DMBR and KMBR on a variety of directed text generation tasks using encoder-decoder models and a language model with prompting. The experimental results show that the proposed method achieves a better trade-off than the diverse beam search and sampling algorithms overall.

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GATE X-E : A Challenge Set for Gender-Fair Translations from Weakly-Gendered Languages
Spencer Rarrick | Ranjita Naik | Sundar Poudel | Vishal Chowdhary

Neural Machine Translation (NMT) continues to improve in quality and adoption, yet the in advertent perpetuation of gender bias remains a significant concern. Despite numerous studies on gender bias in translations into English from weakly gendered-languages, there are no benchmarks for evaluating this phenomenon or for assessing mitigation strategies. To address this gap, we introduce GATE X-E, an extension to the GATE (Rarrick et al., 2023) corpus, that consists of human translations from Turkish, Hungarian, Finnish, and Persian into English. Each translation is accompanied by feminine, masculine, and neutral variants. The dataset, which contains between 1250 and 1850 instances for each of the four language pairs, features natural sentences with a wide range of sentence lengths and domains, challenging translation rewriters on various linguistic phenomena. Additionally, we present a translation gender rewriting solution built with GPT-4 and use GATE X-E to evaluate it. We open source our contributions to encourage further research on gender debiasing.

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Hyperparameter-Free Approach for Faster Minimum Bayes Risk Decoding
Yuu Jinnai | Kaito Ariu

Minimum Bayes-Risk (MBR) decoding is shown to be a powerful alternative to beam search decoding for a wide range of text generation tasks. However, MBR requires a huge amount of time for inference to compute the MBR objective, which makes the method infeasible in many situations where response time is critical. Confidence-based pruning (CBP) (Cheng and Vlachos, 2023) has recently been proposed to reduce the inference time in machine translation tasks. Although it is shown to significantly reduce the amount of computation, it requires hyperparameter tuning using a development set to be effective. To this end, we propose Adaptive Minimum Bayes-Risk (AMBR) decoding, a hyperparameter-free method to run MBR decoding efficiently. AMBR is derived from the observation that the problem of computing the sample-based MBR objective is the medoid identification problem. AMBR uses the Correlated Sequential Halving (CSH) algorithm (Baharav and Tse, 2019), the algorithm with the best performance guarantee to date for the medoid identification problem, to compute the sample-based MBR objective. We evaluate AMBR on machine translation, text summarization, and image captioning tasks. The results show that AMBR achieves on par with CBP, with CBP selecting hyperparameters through an Oracle for each given computation budget.

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Simplifying Translations for Children: Iterative Simplification Considering Age of Acquisition with LLMs
Masashi Oshika | Makoto Morishita | Tsutomu Hirao | Ryohei Sasano | Koichi Takeda

In recent years, neural machine translation (NMT) has become widely used in everyday life. However, the current NMT lacks a mechanism to adjust the difficulty level of translations to match the user’s language level. Additionally, due to the bias in the training data for NMT, translations of simple source sentences are often produced with complex words. In particular, this could pose a problem for children, who may not be able to understand the meaning of the translations correctly. In this study, we propose a method that replaces high Age of Acquisitions (AoA) words in translations with simpler words to match the translations to the user’s level. We achieve this by using large language models (LLMs), providing a triple of a source sentence, a translation, and a target word to be replaced. We create a benchmark dataset using back-translation on Simple English Wikipedia. The experimental results obtained from the dataset show that our method effectively replaces high-AoA words with lower-AoA words and, moreover, can iteratively replace most of the high-AoA words while still maintaining high BLEU and COMET scores.

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Bi-Chainer: Automated Large Language Models Reasoning with Bidirectional Chaining
Shuqi Liu | Bowei He | Linqi Song

Large Language Models (LLMs) have shown human-like reasoning abilities but still face challenges in solving complex logical problems. Existing unidirectional chaining methods, such as forward chaining and backward chaining, suffer from issues like low prediction accuracy and efficiency. To address these, we propose a bidirectional chaining method, Bi-Chainer, which dynamically switches to depth-first reasoning in the opposite reasoning direction when it encounters multiple branching options within the current direction. Thus, the intermediate reasoning results can be utilized as guidance to facilitate the reasoning process. We show that Bi-Chainer achieves sizable accuracy boots over unidirectional chaining frameworks on four challenging logical reasoning datasets. Moreover, Bi-Chainer enhances the accuracy of intermediate proof steps and reduces the average number of inference calls, resulting in more efficient and accurate reasoning.

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Can Large Language Model Summarizers Adapt to Diverse Scientific Communication Goals?
Marcio Fonseca | Shay Cohen

In this work, we investigate the controllability of large language models (LLMs) on scientific summarization tasks. We identify key stylistic and content coverage factors that characterize different types of summaries such as paper reviews, abstracts, and lay summaries. By controlling stylistic features, we find that non-fine-tuned LLMs outperform humans in the MuP review generation task, both in terms of similarity to reference summaries and human preferences. Also, we show that we can improve the controllability of LLMs with keyword-based classifier-free guidance (CFG) while achieving lexical overlap comparable to strong fine-tuned baselines on arXiv and PubMed. However, our results also indicate that LLMs cannot consistently generate long summaries with more than 8 sentences. Furthermore, these models exhibit limited capacity to produce highly abstractive lay summaries. Although LLMs demonstrate strong generic summarization competency, sophisticated content control without costly fine-tuning remains an open problem for domain-specific applications.

Text-based knowledge graph completion (KGC) methods utilize pre-trained language models for triple encoding and further fine-tune the model to achieve completion. Despite their excellent performance, they neglect the knowledge context in inferring process. Intuitively, knowledge contexts, which refer to the neighboring triples around the target triples, are important information for triple inferring, since they provide additional detailed information about the entities. To this end, we propose a novel framework named KnowC, which models the knowledge context as additional prompts with pre-trained language models for knowledge graph completion. Given the substantial number of neighbors typically associated with entities, along with the constrained input token capacity of language models, we further devise several strategies to sample the neighbors. We conduct extensive experiments on common datasets FB15k-237, WN18RR and Wikidata5M, experiments show that KnowC achieves state-of-the-art performance.

Attribute value extraction involves identifying the value spans of predetermined attributes in product texts. This area of research has traditionally operated under a closed-world assumption, focusing on products from a static set of categories and their associated attributes. However, products in e-commerce stores are ever-increasing and evolving, calling for life-long learning. If continuously trained on the fast-increasing products and attributes, most existing solutions not only struggle for parameter efficiency but also endure foreseeable defects due to data contamination, catastrophic forgetting, etc. As a remedy, we propose and study a new task, which aims to effectively maintain a strong single model for many domains in a life-long learning fashion, without jeopardizing the model performance and parameter efficiency. We introduce factorization into the model and make it domain-aware by decoupling the modeling of product type and attribute, as a way to promote de-contamination and parameter efficiency while scaling up. Tuning the model with distillation prevents forgetting historical knowledge and enables continuous learning from emerging domains. Experiments on hundreds of domains showed that our model attains the near state-of-the-art performance with affordable parameter size, the least historical knowledge forgetting, and the greatest robustness against noises, whilst adding only a few parameters per domain when compared with competitive baselines.

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Exploring Domain Robust Lightweight Reward Models based on Router Mechanism
Hyuk Namgoong | Jeesu Jung | Sangkeun Jung | YoonHyung Roh

Recent advancements in large language models have heavily relied on the large reward model from reinforcement learning from human feedback for fine-tuning. However, the use of a single reward model across various domains may not always be optimal, often requiring retraining from scratch when new domain data is introduced. To address these challenges, we explore the utilization of small language models operating in a domain-specific manner based on router mechanisms. Our three approaches are: 1) utilize mixture of experts to form a single reward model by modularizing an internal router and experts, 2) employing external router to select the appropriate reward model from multiple domain-specific models, and 3) the framework reduces parameter size by loading reward models and router adapters onto a single small language model using adapters. Experimental validation underscores the effectiveness of our approach, demonstrating performance comparable to baseline methods while also reducing the total parameter size.

Generalized Category Discovery (GCD) is a crucial task that aims to recognize both known and novel categories from a set of unlabeled data by utilizing a few labeled data with only known categories. Due to the lack of supervision and category information, current methods usually perform poorly on novel categories and struggle to reveal semantic meanings of the discovered clusters, which limits their applications in the real world. To mitigate the above issues, we propose Loop, an end-to-end active-learning framework that introduces Large Language Models (LLMs) into the training loop, which can boost model performance and generate category names without relying on any human efforts. Specifically, we first propose Local Inconsistent Sampling (LIS) to select samples that have a higher probability of falling to wrong clusters, based on neighborhood prediction consistency and entropy of cluster assignment probabilities. Then we propose a Scalable Query strategy to allow LLMs to choose true neighbors of the selected samples from multiple candidate samples. Based on the feedback from LLMs, we perform Refined Neighborhood Contrastive Learning (RNCL) to pull samples and their neighbors closer to learn clustering-friendly representations. Finally, we select representative samples from clusters corresponding to novel categories to allow LLMs to generate category names for them. Extensive experiments on three benchmark datasets show that Loop outperforms SOTA models by a large margin and generates accurate category names for the discovered clusters. Code and data are available at https://github.com/Lackel/LOOP.

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VAEGPT-Sim: Improving Sentence Representation with Limited Corpus Using Gradually-Denoising VAE
Zhenyi Wang | Haiyan Ning | Qing Ling | Dan Wang

Text embedding requires a highly efficient method for training domain-specific models on limited data, as general models trained on large corpora lack universal applicability in highly specific fields. Therefore, we have introduced VAEGPT-Sim, an innovative model for generating synonyms that combines a denoising variational autoencoder with a target-specific discriminator to generate synonymous sentences that closely resemble human language. Even when trained with completely unsupervised settings, it maintains a harmonious balance between semantic similarity and lexical diversity, as shown by a comprehensive evaluation metric system with the highest average scores compared to other generative models. When VAEGPT-Sim is utilized as a module for contrastive learning in text representation, it delivers state-of-the-art results in small-dataset training on STS benchmarks, surpassing ConSERT by 2.8 points. This approach optimizes the effectiveness of text representation despite a limited corpus, signifying an advancement in domain-specific embedding technology.

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PPTC Benchmark: Evaluating Large Language Models for PowerPoint Task Completion
Yiduo Guo | Zekai Zhang | Yaobo Liang | Dongyan Zhao | Nan Duan

Recent evaluations of Large Language Models (LLMs) have centered around testing their zero-shot/few-shot capabilities for basic natural language tasks and their ability to translate instructions into tool APIs. However, the evaluation of LLMs utilizing complex tools to finish multi-turn, multi-modal instructions in a complex multi-modal environment has not been investigated. To address this gap, we introduce the PowerPoint Task Completion (PPTC) benchmark to assess LLMs’ ability to create and edit PPT files based on user instructions. It contains 279 multi-turn sessions covering diverse topics and hundreds of instructions involving multi-modal operations. We also propose the PPTX-Match Evaluation System that evaluates if LLMs finish the instruction based on the prediction file rather than the label API sequence, thus it supports various LLM-generated API sequences. We measure 3 closed LLMs and 6 open-source LLMs. The results show that GPT-4 outperforms other LLMs with 75.1% accuracy in single-turn dialogue testing but faces challenges in completing entire sessions, achieving just 6% session accuracy. We find three main error causes in our benchmark: error accumulation in the multi-turn session, long PPT template processing, and multi-modality perception. These pose great challenges for future LLM and agent systems .

For a LLM to be trustworthy, its confidence level should be well-calibrated with its actual performance. While it is now common sense that LLM performances are greatly impacted by prompts, the confidence calibration in prompting LLMs has yet to be thoroughly explored.In this paper, we explore how different prompting strategies influence LLM confidence calibration and how it could be improved. We conduct extensive experiments on six prompting methods in the question-answering context and we observe that, while these methods help improve the expected LLM calibration, they also trigger LLMs to be over-confident when responding to some instances.Inspired by human cognition, we propose Fact-and-Reflection (FaR) prompting, which improves the LLM calibration in two steps. First, FaR elicits the known “facts” that are relevant to the input prompt from the LLM. And then it asks the model to “reflect” over them to generate the final answer.Experiments show that FaR prompting achieves significantly better calibration; it lowers the Expected Calibration Error by 23.5% on our multi-purpose QA tasks. Notably, FaR prompting even elicits the capability of verbally expressing concerns in less confident scenarios, which helps trigger retrieval augmentation for solving these harder instances.

Large language models (LLMs) have significantly advanced the field of natural language processing, while the expensive memory and computation consumption impede their practical deployment. Quantization emerges as one of the most effective methods for improving the computational efficiency of LLMs. However, existing ultra-low-bit quantization always causes severe accuracy drops. In this paper, we empirically investigate the micro and macro characteristics of ultra-low bit quantization and present a novel Dual-Binarization method for LLMs, namely DB-LLM. For the micro-level, we take both the accuracy advantage of 2-bit-width and the efficiency advantage of binarization into account, introducing Flexible Dual Binarization (FDB). By splitting 2-bit quantized weights into two independent sets of binaries, FDB ensures the accuracy of representations and introduces flexibility, utilizing the efficient bitwise operations of binarization while retaining the inherent high sparsity of ultra-low bit quantization. For the macro-level, we find the distortion that exists in the prediction of LLM after quantization, which is specified as the deviations related to the ambiguity of samples. We propose the Deviation-Aware Distillation (DAD) method, enabling the model to focus differently on various samples. Comprehensive experiments show that our DB-LLM not only significantly surpasses the current State-of-The-Art (SoTA) in ultra-low bit quantization (, perplexity decreased from 9.64 to 7.23), but also achieves an additional 20% reduction in computational consumption compared to the SOTA method under the same bit-width. Our code is available at https://github.com/Hon-Chen/DB-LLM.

Recently, there is a surge in interest surrounding video large language models (Video LLMs). However, existing benchmarks fail to provide a comprehensive feedback on the temporal perception ability of Video LLMs. On the one hand, most of them are unable to distinguish between different temporal aspects (e.g., speed, direction) and thus cannot reflect the nuanced performance on these specific aspects. On the other hand, they are limited in the diversity of task formats (e.g., only multi-choice QA), which hinders the understanding of how temporal perception performance may vary across different types of tasks. Motivated by these two problems, we propose the TempCompass benchmark, which introduces a diversity of temporal aspects and task formats. To collect high-quality test data, we devise two novel strategies: (1) In video collection, we construct conflicting videos that share the same static content but differ in a specific temporal aspect, which prevents Video LLMs from leveraging single-frame bias or language priors. (2) To collect the task instructions, we propose a paradigm where humans first annotate meta-information for a video and then an LLM generates the instruction. We also design an LLM-based approach to automatically and accurately evaluate the responses from Video LLMs. Based on TempCompass, we comprehensively evaluate 9 state-of-the-art (SOTA) Video LLMs and 3 Image LLMs, and reveal the discerning fact that these models exhibit notably poor temporal perception ability.

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“Get Their Hands Dirty, Not Mine”: On Researcher-Annotator Collaboration and the Agency of Annotators
Shengqi Zhu | Jeffrey Rzeszotarski

Annotation quality is often framed as post-hoc cleanup of annotator-caused issues. This position paper discusses whether, how, and why this narrative limits the scope of improving annotation. We call to consider annotation as a procedural collaboration, outlining three points in this direction:(1) An issue can be either annotator- or researcher-oriented, where one party is accountable and the other party may lack ability to fix it; (2) yet, they can co-occur or have similar consequences, and thus any specific problem we encounter may be a combination;(3) therefore, we need a new language to capture the nuance and holistically describe the full procedure to resolve these issues.To that end, we propose to study how agency is manifested in annotation and picture how this perspective benefits the community more broadly.

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Teaching Large Language Models an Unseen Language on the Fly
Chen Zhang | Xiao Liu | Jiuheng Lin | Yansong Feng

Existing large language models struggle to support numerous low-resource languages, particularly the extremely low-resource ones, for which there is minimal training data available for effective parameter updating. We thus investigate whether LLMs can learn a new language on the fly solely through prompting. To study this question, we collect a research suite for Zhuang, a language supported by no LLMs currently. We introduce DiPMT++, a framework for adapting LLMs to unseen languages by in-context learning. Using a dictionary and 5K parallel sentences only, DiPMT++ significantly enhances the performance of GPT-4 from 0 to 16 BLEU for Chinese-to-Zhuang translation and achieves 32 BLEU for Zhuang-to-Chinese translation. We also validate the effectiveness of our framework on Kalamang, another unseen language. Furthermore, we demonstrate the practical utility of DiPMT++ in aiding humans in translating completely unseen languages, which could contribute to the preservation of linguistic diversity.

Generative large language models (LLMs), e.g., ChatGPT, have demonstrated remarkable proficiency across several NLP tasks, such as machine translation, text summarization. Recent research (Kocmi and Federmann, 2023) has shown that utilizing LLMs for assessing the quality of machine translation (MT) achieves state-of-the-art performance at the system level but performs poorly at the segment level. To further improve the performance of LLMs on MT quality assessment, we conduct an investigation into several prompting designs, and propose a new prompting method called Error Analysis Prompting (EAPrompt) by combining Chain-of-Thoughts (Wei et al., 2022) and Error Analysis (Lu et al., 2023). This technique emulates the commonly accepted human evaluation framework - Multidimensional Quality Metrics (MQM, Freitag et al., (2021)) and produces explainable and reliable MT evaluations at both the system and segment level. Experimental Results from WMT22 metrics shared task validate the effectiveness of EAPrompt on various LLMs, with different structures. Further analysis confirms that EAPrompt effectively distinguishes major errors from minor ones, while also sharing a similar distribution of the number of errors with MQM. These findings highlight the potential of EAPrompt as a human-like evaluator prompting technique for MT evaluation. We will release our code and scripts to facilitate the community.

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GAOKAO-MM: A Chinese Human-Level Benchmark for Multimodal Models Evaluation
Yi Zong | Xipeng Qiu

The Large Vision-Language Models (LVLMs) have demonstrated great abilities in image perception and language understanding. However, existing datasets either focus solely on primary perception abilities and commonsense knowledge, or have a low level of text comprehension difficulty, which are insufficient to reflect the comprehensive capabilities of LVLMs, particularly in terms of Chinese language proficiency. We propose GAOKAO-MM, a multimodal benchmark based on the Chinese College Entrance Examination (GAOKAO), comprising of 8 subjects and 12 types of images, such as diagrams, function graphs, maps and photos. GAOKAO-MM derives from native Chinese context and sets human-level requirements for the model’s abilities, including perception, understanding, knowledge and reasoning. We evaluate 10 LVLMs and find that the accuracies of all of them are lower than 50%, with GPT-4-Vision (48.1%), Qwen-VL-Plus (41.2%) and Gemini-Pro-Vision (35.1%) ranking in the top three positions. The results of our multi-dimension analysis indicate that LVLMs have moderate distance towards Artificial General Intelligence (AGI) and provide insights facilitating the development of multilingual LVLMs. The dataset and evaluation code are available through: https://github.com/OpenMOSS/GAOKAO-MM

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DiffChat: Learning to Chat with Text-to-Image Synthesis Models for Interactive Image Creation
Jiapeng Wang | Chengyu Wang | Tingfeng Cao | Jun Huang | Lianwen Jin

We present DiffChat, a novel method to align Large Language Models (LLMs) to “chat” with prompt-as-input Text-to-Image Synthesis (TIS)models (e.g., Stable Diffusion) for interactive image creation. Given a raw prompt/image and a user-specified instruction, DiffChat can effectively make appropriate modifications and generate the target prompt, which can be leveraged to create the target image of high quality. To achieve this, we first collect an instruction-following prompt engineering dataset named InstructPE for the supervised training of DiffChat.Next, we propose a reinforcement learning framework with the feedback of three core criteria for image creation, i.e., aesthetics, user preference and content integrity. It involves an action-space dynamic modification technique to obtain more relevant positive samples and harder negative samples during the off-policy sampling. Content integrity is also introduced into the value estimation function for further improvement of produced images. Our method can exhibit superior performance than baseline models and strong competitors based on both automatic and human evaluations, which fully demonstrates its effectiveness.

We identify two crucial limitations in the evaluation of recent parallel-integrated method Parallel Context Windows (PCW), which extends the maximum context lengths of language models, e.g., 2048 for LLaMA, by harnessing window-wise attention and positional embedding techniques. We first show that a simple yet strong baseline, weighted sum ensemble, is missing for the in-context few-shot classification. Moreover, on more challenging Chain-of-Thought (CoT) reasoning (e.g., HotpotQA), PCW would present unexpected deterioration regarding question miscomprehension and false inference. Based on our findings, we suggest that the existing PCW design may not guarantee sufficient improvement and practicality in handling lengthy documents in real-world applications. More community efforts on enabling language models’ long context understanding ability should be paid.

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Rationales for Answers to Simple Math Word Problems Confuse Large Language Models
Yidan Zhang | Mingfeng Xue | Dayiheng Liu | Zhenan He

Recently, large language models (LLMs) have demonstrated breakthrough mathematical problem-solving capabilities in grade school math word problems (MWP). For example, on the MWP benchmark GSM8K, the accuracy of GPT-3.5-Turbo and MetaMath-70B reaches 80.80% and 82.30%, respectively. One question arises, does it mean that LLMs have truly mastered related mathematical problem-solving abilities? In this paper, by presenting two types of benchmarks, where MCGSM8K aims at selecting one correct solution from four solutions, while GSM8K-Judgement judges whether a solution to a given question is true or false, we demonstrate that the ability of most LLMs to evaluate the mathematical reasoning process of MWP is far from sufficient. To compensate for this issue, we propose hybrid supervised fine-tuning data from the training data of GSM8K, MCGSM8K, and GSM8K-Judgement, which significantly improves performance on the proposed reasoning process evaluation benchmarks. For example, fine-tuning improves the performance of LLaMA-2-13B from 33.51% to 70.89% on MCGSM8K. In conclusion, we experimentally demonstrate that most LLMs have limited ability to evaluate the mathematical reasoning process of MWP, which can be enhanced through fine-tuning.

As one of the most popular parameter-efficient fine-tuning (PEFT) methods, low-rank adaptation (LoRA) is commonly applied to fine-tune large language models (LLMs). However, updating the weights of LoRA blocks effectively and expeditiously is challenging due to the long calculation path in the original model. To address this, we propose ResLoRA, an improved framework of LoRA. By adding residual paths during training and using merging approaches to eliminate these extra paths during inference, our method can achieve better results in fewer training steps without any extra trainable parameters or inference cost compared to LoRA. The experiments on NLG, NLU, and text-to-image tasks demonstrate the effectiveness of our method. To the best of our knowledge, ResLoRA is the first work that combines the residual path with LoRA. The code of our method is available at [this url](https://github.com/microsoft/LMOps/tree/main/reslora).

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Towards Objectively Benchmarking Social Intelligence of Language Agents at the Action Level
Chenxu Wang | Bin Dai | Huaping Liu | Baoyuan Wang

Prominent large language models have exhibited human-level performance in many domains, even enabling the derived agents to simulate human and social interactions. While practical works have substantiated the practicability of grounding language agents in sandbox simulation or embodied simulators, current social intelligence benchmarks either stay at the language level or use subjective metrics. In pursuit of a more realistic and objective evaluation, we introduce the Social Tasks in Sandbox Simulation (STSS) benchmark, which assesses language agents objectively at the action level by scrutinizing the goal achievements within the multi-agent simulation.Additionally, we sample conversation scenarios to build a language-level benchmark to provide an economically prudent preliminary evaluation and align with prevailing benchmarks. To gauge the significance of agent architecture, we implement a target-driven planning (TDP) module as an adjunct to the existing agent. Our evaluative findings highlight that the STSS benchmark is challenging for state-of-the-art language agents. Furthermore, it effectively discriminates between distinct language agents, suggesting its usefulness as a benchmark for evaluating both language models and agent architectures. Our code is available at https://github.com/wcx21/Social-Tasks-in-Sandbox-Simulation.

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Semantic Role Labeling from Chinese Speech via End-to-End Learning
Huiyao Chen | Xinxin Li | Meishan Zhang | Min Zhang

Semantic Role Labeling (SRL), crucial for understanding semantic relationships in sentences, has traditionally focused on text-based input. However, the increasing use of voice assistants and the need for hands-free interaction have highlighted the importance of SRL from speech.SRL from speech can be accomplished via a two-step pipeline directly: transcribing speech to text via Automatic Speech Recognition (ASR) and then applying text-based SRL, which could lead to error propagation and loss of useful acoustic features.Addressing these challenges, we present the first end-to-end approach for SRL from speech, integrating ASR and SRL in a joint-learning framework, focusing on the Chinese language. By employing a Stright-Through Gumbel-Softmax module for connecting ASR and SRL models, it enables gradient back-propagation and joint optimization, enhancing robustness and effectiveness.Experiments on the Chinese Proposition Bank 1.0 (CPB1.0) and a newly annotated dataset AS-SRL based on AISHELL-1 demonstrate the superiority of the end-to-end model over traditional pipelines, with significantly improved performance.

Multi-modal Event Reasoning (MMER) endeavors to endow machines with the ability to comprehend intricate event relations across diverse data modalities. MMER is fundamental and underlies a wide broad of applications. Despite extensive instruction fine-tuning, current multi-modal large language models still fall short in such ability. The disparity stems from that existing models are insufficient to capture underlying principles governing event evolution in various scenarios. In this paper, we introduce Multi-Modal Event Evolution Learning (MEEL) to enable the model to grasp the event evolution mechanism yielding advanced MMER ability. Specifically, we commence with the design of event diversification to gather seed events from a rich spectrum of scenarios. Subsequently, we employ ChatGPT to generate evolving graphs for these seed events. We propose an instruction encapsulation process that formulates the evolving graphs into instruction-tuning data, aligning the comprehension of event reasoning to humans. Finally, we observe that models trained in this way are still struggling to fully comprehend event evolution. In such a case, we propose the guiding discrimination strategy, in which models are trained to discriminate the improper evolution direction. We collect and curate a benchmark M-EV2 for MMER. Extensive experiments on M-EV2 validate the effectiveness of our approach, showcasing competitive performance in open-source multi-modal LLMs.

The large-scale conversational recommendation dataset is pivotal for the development of conversational recommender systems (CRS). Most existing CRS datasets suffers from the problems of data inextensibility and semantic inconsistency. To tackle these limitations and establish a benchmark in the conversational recommendation scenario, in this paper, we introduce the LLM-REDIAL dataset to facilitate the research in CRS. LLM-REDIAL is constructed by leveraging large language models (LLMs) to generate the high-quality dialogues. To provide the LLMs with detailed guidance, we integrate historical user behavior data with dialogue templates that are carefully designed through the combination of multiple pre-defined goals. LLM-REDIAL has two main advantages. First, it is the largest multi-domain CRS dataset which consists of 47.6k multi-turn dialogues with 482.6k utterances across 4 domains. Second, dialogue semantics and the users’ historical interaction information is highly consistent. Human evaluation are conducted to verify the quality of LLM-REDIAL. In addition, we evaluate the usability of advanced LLM-based models on LLM-REDIAL.

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Investigating Subtler Biases in LLMs: Ageism, Beauty, Institutional, and Nationality Bias in Generative Models
Mahammed Kamruzzaman | Md. Shovon | Gene Kim

LLMs are increasingly powerful and widely used to assist users in a variety of tasks. This use risks introducing LLM biases into consequential decisions such as job hiring, human performance evaluation, and criminal sentencing. Bias in NLP systems along the lines of gender and ethnicity has been widely studied, especially for specific stereotypes (e.g., Asians are good at math). In this paper, we investigate bias along less-studied but still consequential, dimensions, such as age and beauty, measuring subtler correlated decisions that LLMs make between social groups and unrelated positive and negative attributes. Although these subtler biases are understudied they follow people as much as gender and ethnicity do. So, we want to see whether they also follow one with LLMs.We introduce a template-generated dataset of sentence completion tasks that asks the model to select the most appropriate attribute to complete an evaluative statement about a person described as a member of a specific social group. We also reverse the completion task to select the social group based on an attribute. We report the correlations that we find for 4 cutting-edge LLMs. This dataset can be used as a benchmark to evaluate progress in more generalized biases and the templating technique can be used to expand the benchmark with minimal additional human annotation.

Events refer to specific occurrences, incidents, or happenings that take place under a particular background. Event reasoning aims to infer events according to certain relations and predict future events. The cutting-edge techniques for event reasoning play a crucial role in various natural language processing applications. Large language models (LLMs) have made significant advancements in event reasoning owing to their wealth of knowledge and reasoning capabilities. However, smaller instruction-tuned models currently in use do not consistently demonstrate exceptional proficiency in managing these tasks. This discrepancy arises from the absence of explicit modeling of events and the interconnections of them within their instruction data. Consequently, these models face challenges in comprehending event structures and semantics while struggling to bridge the gap between their interpretations and human understanding of events. Additionally, their limitations in grasping event relations lead to constrained event reasoning abilities to effectively deduce and incorporate pertinent event knowledge. In this paper, we propose Event-Oriented Instruction Tuning to train our large language model named EvIT specializing in event reasoning tasks. Specifically, we first propose a novel structure named event quadruple which contains the structure and semantics of events and is complete in the event representation. We then design event-relation learning based on the structures. We encapsulate the learning into the instruction-tuning formulation to better stimulate the event reasoning capacity of our model. To implement our training, we design a heuristic unsupervised method to mine event quadruple from a large-scale corpus. At last, we finetune a Llama model on our Event-Oriented Instruction Tuning. We conduct extensive experiments on event reasoning tasks on several datasets. Automatic and human evaluations demonstrate EvIT achieves competitive performances on event reasoning.

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InstructCMP: Length Control in Sentence Compression through Instruction-based Large Language Models
Juseon-Do Juseon-Do | Jingun Kwon | Hidetaka Kamigaito | Manabu Okumura

Extractive summarization can produce faithful summaries but often requires additional constraints such as a desired summary length. Traditional sentence compression models do not typically consider the constraints because of their restricted model abilities, which require model modifications for coping with them. To bridge this gap, we propose Instruction-based Compression (InstructCMP), an approach to the sentence compression task that can consider the length constraint through instructions by leveraging the zero-shot task-solving abilities of Large Language Models (LLMs). For this purpose, we created new evaluation datasets by transforming traditional sentence compression datasets into an instruction format. By using the datasets, we first reveal that the current LLMs still face challenges in accurately controlling the length for a compressed text. To address this issue, we propose an approach named length priming, that incorporates additional length information into the instructions without external resources. While the length priming effectively works in a zero-shot setting, a training dataset with the instructions would further improve the ability of length control. Thus, we additionally created a training dataset in an instruction format to fine-tune the model on it. Experimental results and analysis show that applying the length priming significantly improves performances of InstructCMP in both zero-shot and fine-tuning settings without the need of any model modifications.

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SymTax: Symbiotic Relationship and Taxonomy Fusion for Effective Citation Recommendation
Karan Goyal | Mayank Goel | Vikram Goyal | Mukesh Mohania

Citing pertinent literature is pivotal to writing and reviewing a scientific document. Existing techniques mainly focus on the local context or the global context for recommending citations but fail to consider the actual human citation behaviour. We propose SymTax, a three-stage recommendation architecture that considers both the local and the global context, and additionally the taxonomical representations of query-candidate tuples and the Symbiosis prevailing amongst them. SymTax learns to embed the infused taxonomies in the hyperbolic space and uses hyperbolic separation as a latent feature to compute query-candidate similarity. We build a novel and large dataset ArSyTa containing 8.27 million citation contexts and describe the creation process in detail. We conduct extensive experiments and ablation studies to demonstrate the effectiveness and design choice of each module in our framework. Also, combinatorial analysis from our experiments shed light on the choice of language models (LMs) and fusion embedding, and the inclusion of section heading as a signal. Our proposed module that captures the symbiotic relationship solely leads to performance gains of 26.66% and 39.25% in Recall@5 w.r.t. SOTA on ACL-200 and RefSeer datasets, respectively. The complete framework yields a gain of 22.56% in Recall@5 wrt SOTA on our proposed dataset. The code and dataset are available at https://github.com/goyalkaraniit/SymTax.

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Assessing News Thumbnail Representativeness: Counterfactual text can enhance the cross-modal matching ability
Yejun Yoon | Seunghyun Yoon | Kunwoo Park

This paper addresses the critical challenge of assessing the representativeness of news thumbnail images, which often serve as the first visual engagement for readers when an article is disseminated on social media. We focus on whether a news image represents the actors discussed in the news text. To serve the challenge, we introduce NewsTT, a manually annotated dataset of 1000 news thumbnail images and text pairs. We found that the pretrained vision and language models, such as BLIP-2, struggle with this task. Since news subjects frequently involve named entities or proper nouns, the pretrained models could have a limited capability to match news actors’ visual and textual appearances. We hypothesize that learning to contrast news text with its counterfactual, of which named entities are replaced, can enhance the cross-modal matching ability of vision and language models. We propose CFT-CLIP, a contrastive learning framework that updates vision and language bi-encoders according to the hypothesis. We found that our simple method can boost the performance for assessing news thumbnail representativeness, supporting our assumption. Code and data can be accessed at https://github.com/ssu-humane/news-images-acl24.

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Towards Better Question Generation in QA-based Event Extraction
Zijin Hong | Jian Liu

Event Extraction (EE) is an essential information extraction task that aims to extract event-related information from unstructured texts.The paradigm of this task has shifted from conventional classification-based methods to more contemporary question-answering-based (QA-based) approaches. However, in QA-based EE, the quality of the questions dramatically affects the extraction accuracy, and how to generate high-quality questions for QA-based EE remains a challenge. In this work, to tackle this challenge, we suggest four criteria to evaluate the quality of a question and propose a reinforcement learning method, RLQG, for QA-based EE that can generate generalizable, high-quality, and context-dependent questions and provides clear guidance to QA models. The extensive experiments conducted on ACE and RAMS datasets have strongly validated our approach’s effectiveness, which also demonstrates its robustness in scenarios with limited training data. The corresponding code of RLQG is released for further research.

Despite intensive efforts devoted to tool learning, the problem of budget-constrained tool learning, which focuses on resolving user queries within a specific budget constraint, has been widely overlooked. This paper proposes a novel method for budget-constrained tool learning. Our approach involves creating a preferable plan under the budget constraint before utilizing the tools. This plan outlines the feasible tools and the maximum number of times they can be employed, offering a comprehensive overview of the tool learning process for large language models. This allows them to allocate the budget from a broader perspective. To devise the plan without incurring significant extra costs, we suggest initially estimating the usefulness of the candidate tools based on past experience. Subsequently, we employ dynamic programming to formulate the plan. Experimental results demonstrate that our method can be integrated with various tool learning methods, significantly enhancing their effectiveness under strict budget constraints.

Large language models with instruction-following abilities have revolutionized the field of artificial intelligence. These models show exceptional generalizability to tackle various real-world tasks through their natural language interfaces. However, their performance heavily relies on high-quality exemplar data, which is often difficult to obtain. This challenge is further exacerbated when it comes to multimodal instruction following. We introduce TextBind, an almost annotation-free framework for empowering LLMs with multi-turn interleaved multimodal instruction-following capabilities. Our approach requires only image-caption pairs and generates multi-turn multimodal instruction-response conversations from a language model. To accommodate interleaved image-text inputs and outputs, we devise MIM, a language model-centric architecture that seamlessly integrates image encoder and decoder models. Extensive quantitative and qualitative experiments demonstrate that MIM trained on TextBind achieves remarkable generation capability in multimodal conversations compared to recent baselines.

Critique, as a natural language description for assessing the quality of model-generated content, has played a vital role in the training, evaluation, and refinement of LLMs. However, a systematic method to evaluate the quality of critique is lacking. In this paper, we pioneer the critique of critique, termed MetaCritique, which builds specific quantification criteria. To achieve a reliable evaluation outcome, we propose Atomic Information Units (AIUs), which describe the critique in a more fine-grained manner. MetaCritique aggregates each AIU’s judgment for the overall score. Moreover, MetaCritique delivers a natural language rationale for the intricate reasoning within each judgment. Lastly, we construct a meta-evaluation dataset covering 4 tasks across 16 public datasets involving human-written and LLM-generated critiques. Experiments demonstrate that MetaCritique can achieve near-human performance. Our study can facilitate future research in LLM critiques based on our following observations and released resources: (1) superior critiques judged by MetaCritique can lead to better refinements, indicating that it can potentially enhance the alignment of existing LLMs; (2) the leaderboard of critique models reveals that open-source critique models commonly suffer from factuality issues; (3) relevant code and data are publicly available at https://anonymous.4open.science/r/MetaCritique-ARR/ to support deeper exploration; (4) an API at PyPI with the usage documentation in Appendix C allows users to assess the critique conveniently.

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CoCo-Agent: A Comprehensive Cognitive MLLM Agent for Smartphone GUI Automation
Xinbei Ma | Zhuosheng Zhang | Hai Zhao

Multimodal large language models (MLLMs) have shown remarkable potential as human-like autonomous language agents to interact with real-world environments, especially for graphical user interface (GUI) automation.However, those GUI agents require comprehensive cognition including exhaustive perception and reliable action response.We propose a Comprehensive Cognitive LLM Agent, CoCo-Agent, with two novel approaches, comprehensive environment perception (CEP) and conditional action prediction (CAP), to systematically improve the GUI automation performance. First, CEP facilitates the GUI perception through different aspects and granularity, including screenshots and complementary detailed layouts for the visual channel and historical actions for the textual channel.Second, CAP decomposes the action prediction into sub-problems: determining the action type and then identifying the action target conditioned on the action type.With our technical design, our agent achieves state-of-the-art performance on AITW and META-GUI benchmarks, showing promising abilities in realistic scenarios. Code is available at https://github.com/xbmxb/CoCo-Agent.

Structured entailment tree can exhibit the reasoning chains from knowledge facts to predicted answers, which is important for constructing an explainable question answering system. Existing works mainly include directly generating the entire tree and stepwise generating the proof steps. The stepwise methods can exploit combinatoriality and generalize to longer steps, but they have large fact search spaces and error accumulation problems resulting in the generation of invalid steps. In this paper, inspired by the Dual Process Theory in cognitive science, we propose FRVA, a Fact-Retrieval and Verification Augmented bidirectional entailment tree generation method that contains two systems. Specifically, System 1 makes intuitive judgments through the fact retrieval module and filters irrelevant facts to reduce the search space. System 2 designs a deductive-abductive bidirectional reasoning module, and we construct cross-verification and multi-view contrastive learning to make the generated proof steps closer to the target hypothesis. We enhance the reliability of the stepwise proofs to mitigate error propagation. Experiment results on EntailmentBank show that FRVA outperforms previous models and achieves state-of-the-art performance in fact selection and structural correctness.

Empowering Large Language Models (LLMs) with distinct human-like personality traits has become an innovative task for developing advanced dialog systems.Although LLMs demonstrate impressive capabilities in following instructions, directly prompting them to exhibit certain personalities through manually crafted instructions may result in sub-optimal performance.In this paper, we propose a plug-and-play prompting method to manipulate the LLMs’ personality traits.Specifically, we append discrete personalized suffixes, automatically generated through an aggregated gradient-based search method, to the user query or dialog histories and induce LLMs to respond with target personalities.In addition, due to the high redundancy of the search space, we adopt a reward-based strategy to prune the vocabulary and focus exclusively on influential tokens.Experiment results on four models ranging from 1.1B to 13B show that our method achieves 79.9% accuracy in customizing LLMs’ personalities, significantly outperforming other prompting methods (65.5%) and model editing methods.Our method also excels in generation fluency and quality with the lowest generation perplexity and the highest GPT-4 evaluation scores.

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Large Language Models Can Learn Representation in Natural Language
Yiduo Guo | Yaobo Liang | Dongyan Zhao | Nan Duan

One major challenge for Large Language Models (LLMs) is completing complex tasks involving multiple entities, such as tool APIs. To tackle this, one approach is to retrieve relevant entities to enhance LLMs in task completion. A crucial issue here is obtaining accurate natural language representations for each entity to aid in retriever precision. In this paper, we propose the Natural Language Representation Optimization Problem, which aims to refine entity descriptions for improved retrieval and LLM utilization. We introduce the Learning to Represent with Natural Language method, which utilizes LLMs to optimize entity representations consisting of text patterns based on environmental feedback. We iteratively prompt LLMs to enhance or adjust patterns based on entity samples and evaluate their effectiveness through environmental feedback. Our method successfully learns human-understandable representations for classification tasks (e.g., instructions and documents) and API call tasks (e.g., APIbench and Virtual Home), significantly improving GPT-4’s task performance.

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CTC-based Non-autoregressive Textless Speech-to-Speech Translation
Qingkai Fang | Zhengrui Ma | Yan Zhou | Min Zhang | Yang Feng

Direct speech-to-speech translation (S2ST) has achieved impressive translation quality, but it often faces the challenge of slow decoding due to the considerable length of speech sequences. Recently, some research has turned to non-autoregressive (NAR) models to expedite decoding, yet the translation quality typically lags behind autoregressive (AR) models significantly. In this paper, we investigate the performance of CTC-based NAR models in S2ST, as these models have shown impressive results in machine translation. Experimental results demonstrate that by combining pretraining, knowledge distillation, and advanced NAR training techniques such as glancing training and non-monotonic latent alignments, CTC-based NAR models achieve translation quality comparable to the AR model, while preserving up to 26.81× decoding speedup.

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RRNorm: A Novel Framework for Chinese Disease Diagnoses Normalization via LLM-Driven Terminology Component Recognition and Reconstruction
Yongqi Fan | Yansha Zhu | Kui Xue | Jingping Liu | Tong Ruan

The Clinical Terminology Normalization aims at finding standard terms from a given termbase for mentions extracted from clinical texts. However, we found that extracted mentions suffer from the multi-implication problem, especially disease diagnoses. The reason for this is that physicians often use abbreviations, conjunctions, and juxtapositions when writing diagnoses, and it is difficult to manually decompose. To address this problem, we propose a Terminology Component Recognition and Reconstruction strategy that leverages the reasoning capability of large language models (LLMs) to recognize the components of terms, enabling automated decomposition and transforming original mentions into multiple atomic mentions. Furthermore, we adopt the mainstream “Recall and Rank” framework to apply the benefits of the above strategy to the task flow. By leveraging the LLM incorporating the advanced sampling strategies, we design a sampling algorithm for atomic mentions and train the recall model using contrastive learning. Besides the information about the components is also used as knowledge to guide the final term ranking and selection. The experimental results show that our proposed strategy effectively improves the performance of the terminology normalization task and our proposed approach achieves state-of-the-art on the experimental dataset. We release our code and data on the repository https://github.com/yuugaochyan/RRNorm.

Information extraction plays a critical role in natural language processing. When applying large language models (LLMs) to this domain, we discover an unexpected phenomenon: LLMs’ spurious associations. In tasks such as relation extraction, LLMs can accurately identify entity pairs, even if the given relation (label) is semantically unrelated to the pre-defined original one. To find these labels, we design two strategies in this study, including forward label extension and backward label validation. We also leverage the extended labels to improve model performance. Our comprehensive experiments show that spurious associations occur consistently in both Chinese and English datasets across various LLM sizes. Moreover, the use of extended labels significantly enhances LLM performance in information extraction tasks. Remarkably, there is a performance increase of 9.55%, 11.42%, and 21.27% in F1 scores on the SciERC, ACE05, and DuEE datasets, respectively.

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AutoCAP: Towards Automatic Cross-lingual Alignment Planning for Zero-shot Chain-of-Thought
Yongheng Zhang | Qiguang Chen | Min Li | Wanxiang Che | Libo Qin

Cross-lingual chain-of-thought can effectively complete reasoning tasks across languages, which gains increasing attention.Recently, dominant approaches in the literature improve cross-lingual alignment capabilities by integrating reasoning knowledge from different languages. Despite achieving excellent performance, current methods still have two main challenges: (1) Manual language specification: They still highly rely on manually selecting the languages to integrate, severely affecting their generalizability; (2) Static weight allocation: Current methods simply integrate all languages equally. In fact, different language reasoning paths should have different weights to achieve better complementation and integration. Motivated by this, we introduce an Automatic Cross-lingual Alignment Planning (AutoCAP) for zero-shot chain-of-thought to address the above challenges. The core of AutoCAP consists of two components: (1) Automatic Language Selection Prompting to guide LLMs to select appropriate languages and (2) Automatic Weight Allocation Prompting to automatically allocate alignment weight scores to each reasoning path. Extensive experiments on several benchmarks reveal that AutoCAP achieves state-of-the-art performance, surpassing previous methods that required manual effort.

Multilingual neural machine translation models generally distinguish translation directions by the language tag (LT) in front of the source or target sentences. However, current LT strategies cannot indicate the desired target language as expected on zero-shot translation, i.e., the off-target issue. Our analysis reveals that the indication of the target language is sensitive to the placement of the target LT. For example, when placing the target LT on the decoder side, the indication would rapidly degrade along with decoding steps, while placing the target LT on the encoder side would lead to copying or paraphrasing the source input. To address the above issues, we propose a simple yet effective strategy named Language Converter Strategy (LCS). By introducing the target language embedding into the top encoder layers, LCS mitigates confusion in the encoder and ensures stable language indication for the decoder. Experimental results on MultiUN, TED, and OPUS-100 datasets demonstrate that LCS could significantly mitigate the off-target issue, with language accuracy up to 95.28%, 96.21%, and 85.35% meanwhile outperforming the vanilla LT strategy by 3.07, 3,3, and 7.93 BLEU scores on zero-shot translation, respectively.

Quantitative reasoning is a critical skill to analyze data, yet the assessment of such ability remains limited. To address this gap, we introduce the Quantitative Reasoning with Data (QRData) benchmark, aiming to evaluate Large Language Models’ capability in statistical and causal reasoning with real-world data. The benchmark comprises a carefully constructed dataset of 411 questions accompanied by data sheets from textbooks, online learning materials, and academic papers. To compare models’ quantitative reasoning abilities on data and text, we enrich the benchmark with an auxiliary set of 290 text-only questions, namely QRText. We evaluate natural language reasoning, program-based reasoning, and agent reasoning methods including Chain-of-Thought, Program-of-Thoughts, ReAct, and code interpreter assistants on diverse models. The strongest model GPT-4 achieves an accuracy of 58%, which has much room for improvement. Among open-source models, Deepseek-coder-instruct, a code LLM pretrained on 2T tokens, gets the highest accuracy of 37%. Analysis reveals that models encounter difficulties in data analysis and causal reasoning, and struggle in using causal knowledge and provided data simultaneously. Code and data are in https://github.com/xxxiaol/QRData.

Large language models (LLMs) possess immense capabilities but are susceptible to malicious exploitation. To mitigate the risk, safety alignment is employed to align LLMs with ethical standards. However, safety-aligned LLMs may remain vulnerable to carefully crafted jailbreak attacks, but these attacks often face high rejection rates and limited harmfulness. In this paper, we expose the vulnerabilities of safety alignment in open-access LLMs, which can significantly enhance the success rate and harmfulness of jailbreak attacks. Through reverse alignment, achieved by accessing model parameters, we show the feasibility of efficiently fine-tuning LLMs to undermine their inherent safeguards. We investigate two types of reverse alignment techniques: reverse supervised fine-tuning (RSFT) and reverse preference optimization (RPO). RSFT operates by supervising the fine-tuning of LLMs to reverse their inherent values. We also explore how to prepare data needed for RSFT. RPO optimizes LLMs to enhance their preference for harmful content, reversing the models’ safety alignment. Our extensive experiments reveal that open-access high-performance LLMs can be adeptly reverse-aligned to output harmful content, even in the absence of manually curated malicious datasets. Our research acts as a whistleblower for the community, emphasizing the need to pay more attention to safety of open-accessing LLMs. It also underscores the limitations of current safety alignment approaches and calls for research on robust safety alignment methods to counteract malicious fine-tuning attacks.

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PEK: A Parameter-Efficient Framework for Knowledge-Grounded Dialogue Generation
Pan Yang | Dandan Song | Zhijing Wu | Yanru Zhou

Pre-trained language models (PLMs) have shown great dialogue generation capability in different scenarios. However, the huge VRAM consumption when fine-tuning them is one of their drawbacks. PEFT approaches can significantly reduce the number of trainable parameters, which enables us to fine-tune larger dialogue generation models. However, the reduction in parameter quantity can diminish a PLM’s expressive capacity and affect the PLM’s learning from certain specific examples like knowledge-related conversations. Previous works have demonstrated that injecting external knowledge into dialogue generation models can improve the model’s performance in knowledge-related conversations. Nonetheless, these methods are designed for the scenario where most parameters of the entire framework are trainable. In this paper, we propose PEK, a parameter-efficient framework for knowledge-enhanced dialogue generation. It enables PLMs to leverage external knowledge documents and knowledge graphs to enhance its generation capabilities with an acceptable number of trainable parameters. Evaluation results on the Wizard of Wikipedia and CMU_DoG datasets show that our approach outperforms baseline methods on multiple evaluation metrics, which validates the effectiveness of our approach.

Current fact verification methods generally follow the two-stage training paradigm: evidence retrieval and claim verification. While existing works focus on developing sophisticated claim verification modules, the fundamental importance of evidence retrieval is largely ignored. Existing approaches usually adopt the heuristic semantic similarity-based retrieval strategy, resulting in the task-irrelevant evidence and undesirable performance. In this paper, we concentrate on evidence retrieval and propose a Retrieval-Augmented Verification framework RAV, consisting of two major modules: the hybrid evidence retrieval and the joint fact verification. Hybrid evidence retrieval module incorporates an efficient retriever for preliminary pruning of candidate evidence, succeeded by a ranker that generates more precise sorting results. Under this end-to-end training paradigm, gradients from the claim verification can be back-propagated to enhance evidence selection. Experimental results on FEVER dataset demonstrate the superiority of RAV.

Recently, Knowledge Editing has received increasing attention, since it could update the specific knowledge from outdated ones in pretrained models without re-training. However, as pointed out by recent studies, existing related methods tend to merely memorize the superficial word composition of the edited knowledge, rather than truly learning and absorbing it. Consequently, on the reasoning questions, we discover that existing methods struggle to utilize the edited knowledge to reason the new answer, and tend to retain outdated responses, which are generated by the original models utilizing original knowledge. Nevertheless, the outdated responses are unexpected for the correct answers to reasoning questions, which we named as the outdated issue. To alleviate this issue, in this paper, we propose a simple yet effective decoding strategy, i.e., outDated ISsue aware deCOding (DISCO), to enhance the performance of edited models on reasoning questions. Specifically, we capture the difference in the probability distribution between the original and edited models. Further, we amplify the difference of the token prediction in the edited model to alleviate the outdated issue, and thus enhance the model performance w.r.t the edited knowledge. Experimental results suggest that applying DISCO could enhance edited models to reason, e.g., on reasoning questions, DISCO outperforms the prior SOTA method by 12.99 F1 scores, and reduces the ratio of the outdated issue to 5.78% on the zsRE dataset.

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Disentangling Dialect from Social Bias via Multitask Learning to Improve Fairness
Maximilian Spliethöver | Sai Nikhil Menon | Henning Wachsmuth

Dialects introduce syntactic and lexical variations in language that occur in regional or social groups. Most NLP methods are not sensitive to such variations. This may lead to unfair behavior of the methods, conveying negative bias towards dialect speakers. While previous work has studied dialect-related fairness for aspects like hate speech, other aspects of biased language, such as lewdness, remain fully unexplored. To fill this gap, we investigate performance disparities between dialects in the detection of five aspects of biased language and how to mitigate them. To alleviate bias, we present a multitask learning approach that models dialect language as an auxiliary task to incorporate syntactic and lexical variations. In our experiments with African-American English dialect, we provide empirical evidence that complementing common learning approaches with dialect modeling improves their fairness. Furthermore, the results suggest that multitask learning achieves state-of-the-art performance and helps to detect properties of biased language more reliably.

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DP-MLM: Differentially Private Text Rewriting Using Masked Language Models
Stephen Meisenbacher | Maulik Chevli | Juraj Vladika | Florian Matthes

The task of text privatization using Differential Privacy has recently taken the form of text rewriting, in which an input text is obfuscated via the use of generative (large) language models. While these methods have shown promising results in the ability to preserve privacy, these methods rely on autoregressive models which lack a mechanism to contextualize the private rewriting process. In response to this, we propose DP-MLM, a new method for differentially private text rewriting based on leveraging masked language models (MLMs) to rewrite text in a semantically similar and obfuscated manner. We accomplish this with a simple contextualization technique, whereby we rewrite a text one token at a time. We find that utilizing encoder-only MLMs provides better utility preservation at lower 𝜀 levels, as compared to previous methods relying on larger models with a decoder. In addition, MLMs allow for greater customization of the rewriting mechanism, as opposed to generative approaches. We make the code for DP-MLM public and reusable, found at https://github.com/sjmeis/DPMLM.

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Question-Instructed Visual Descriptions for Zero-Shot Video Answering
David Mogrovejo | Thamar Solorio

We present Q-ViD, a simple approach for video question answering (video QA), that unlike prior methods, which are based on complex architectures, computationally expensive pipelines or use closed models like GPTs, Q-ViD relies on a single instruction-aware open vision-language model (InstructBLIP) to tackle videoQA using frame descriptions. Specifically, we create captioning instruction prompts that rely on the target questions about the videos and leverage InstructBLIP to obtain video frame captions that are useful to the task at hand. Subsequently, we form descriptions of the whole video using the question-dependent frame captions, and feed that information, along with a question-answering prompt, to a large language model (LLM). The LLM is our reasoning module, and performs the final step of multiple-choice QA. Our simple Q-ViD framework achieves competitive or even higher performances than current state of the art models on a diverse range of videoQA benchmarks, including NExT-QA, STAR, How2QA, TVQA and IntentQA.

Fact verification aims to automatically probe the veracity of a claim based on several pieces of evidence. Existing works are always engaging in accuracy improvement, let alone explainability, a critical capability of fact verification systems.Constructing an explainable fact verification system in a complex multi-hop scenario is consistently impeded by the absence of a relevant, high-quality dataset. Previous datasets either suffer from excessive simplification or fail to incorporate essential considerations for explainability. To address this, we present EX-FEVER, a pioneering dataset for multi-hop explainable fact verification. With over 60,000 claims involving 2-hop and 3-hop reasoning, each is created by summarizing and modifying information from hyperlinked Wikipedia documents. Each instance is accompanied by a veracity label and an explanation that outlines the reasoning path supporting the veracity classification. Additionally, we demonstrate a novel baseline system on our EX-FEVER dataset, showcasing document retrieval, explanation generation, and claim verification, and validate the significance of our dataset. Furthermore, we highlight the potential of utilizing Large Language Models in the fact verification task. We hope our dataset could make a significant contribution by providing ample opportunities to explore the integration of natural language explanations in the domain of fact verification.

Open-sourced Large Language Models (LLMs) have achieved great success in various NLP tasks, however, they are still far inferior to API-based models when acting as agents. How to integrate agent ability into general LLMs becomes a crucial and urgent problem.This paper first delivers three key observations: (1) the current agent training corpus is entangled with both formats following and agent reasoning, which significantly shifts from the distribution of its pre-training data; (2) LLMs exhibit different learning speeds on the capabilities required by agent tasks; and (3) current approaches have side-effects when improving agent abilities by introducing hallucinations. Based on the above findings, we propose Agent-FLAN to effectively Fine-tune LANguage models for Agents.Through careful decomposition and redesign of the training corpus, Agent-FLAN enables Llama2-7B to outperform prior best works by 3.5% across various agent evaluation datasets. With comprehensively constructed negative samples, Agent-FLAN greatly alleviates the hallucination issues based on our established evaluation benchmark. Besides, it consistently improves the agent capability of LLMs when scaling model sizes while slightly enhancing the general capability of LLMs. The code and models are available at https://github.com/InternLM/Agent-FLAN.

Large language models (LLMs) are notorious for hallucinating, i.e., producing erroneous claims in their output. Such hallucinations can be dangerous, as occasional factual inaccuracies in the generated text might be obscured by the rest of the output being generally factually correct, making it extremely hard for the users to spot them. Current services that leverage LLMs usually do not provide any means for detecting unreliable generations. Here, we aim to bridge this gap. In particular, we propose a novel fact-checking and hallucination detection pipeline based on token-level uncertainty quantification. Uncertainty scores leverage information encapsulated in the output of a neural network or its layers to detect unreliable predictions, and we show that they can be used to fact-check the atomic claims in the LLM output. Moreover, we present a novel token-level uncertainty quantification method that removes the impact of uncertainty about what claim to generate on the current step and what surface form to use. Our method Claim Conditioned Probability (CCP) measures only the uncertainty of a particular claim value expressed by the model. Experiments on the task of biography generation demonstrate strong improvements for CCP compared to the baselines for seven different LLMs and four languages. Human evaluation reveals that the fact-checking pipeline based on uncertainty quantification is competitive with a fact-checking tool that leverages external knowledge.

Through pretraining on a corpus with various sources, Large Language Models (LLMs) have gained impressive performance. However, the impact of each component of the pretraining corpus remains opaque. As a result, the organization of the pretraining corpus is still empirical and may deviate from the optimal. To address this issue, we systematically analyze the impact of 48 datasets from 5 major categories of pretraining data of LLMs and measure their impacts on LLMs using benchmarks about nine major categories of model capabilities. Our analyses provide empirical results about the contribution of multiple corpora on the performances of LLMs, along with their joint impact patterns, including complementary, orthogonal, and correlational relationships. We also identify a set of “high-impact data” such as Books that is significantly related to a set of model capabilities. These findings provide insights into the organization of data to support more efficient pretraining of LLMs.

Neural Machine Translation models are extremely data and compute-hungry. However, not all datapoints contribute equally to model training and generalization. Data pruning to remove the low-value data points has the benefit of drastically reducing the compute budget without significantdrop in model performance. In this paper, we propose a new data pruning technique: CheckpointsAcross Time (CAT ), that leverages early model training dynamics to identify the most relevantdata points for model performance. We benchmark CAT against several data pruning techniquesincluding COMET-QE, LASER and LaBSE. We find that CAT outperforms the benchmarks onIndo-European languages on multiple test sets. When applied to English-German, English-Frenchand English-Swahili translation tasks, CAT achieves comparable performance to using the fulldataset, while pruning up to 50% of training data. We inspect the data points that CAT selectsand find that it tends to favour longer sentences and sentences with unique or rare words.

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What Are You Token About? Differentiable Perturbed Top-k Token Selection for Scientific Document Summarization
Luca Ragazzi | Paolo Italiani | Gianluca Moro | Mattia Panni

Scientific document summarization aims to condense complex and long articles in both technical and plain-language terms to facilitate the accessibility and dissemination of scientific findings. Existing datasets suffer from a deficiency in source heterogeneity, as their data predominantly stem from a single common resource, hindering effective model training and generalizability. First, we introduce SciLay, a novel dataset that includes documents from multiple natural science journals with expert-authored technical and lay summaries. Second, we propose PrunePert, a new transformer-based model that incorporates a differentiable perturbed top-k encoder layer to prune irrelevant tokens in end-to-end learning. Experimental results show that our model achieves a nearly 2x speed-up compared to a state-of-the-art linear transformer, remaining comparable in effectiveness. Additional examinations underscore the importance of employing a training dataset that includes different sources to enhance the generalizability of the models. Code is available at https://github.com/disi-unibo-nlp/sci-lay.

Zero-shot entity and relation classification models leverage available external information of unseen classes – e.g., textual descriptions – to annotate input text data. Thanks to the minimum data requirement, Zero-Shot Learning (ZSL) methods have high value in practice, especially in applications where labeled data is scarce. Even though recent research in ZSL has demonstrated significant results, our analysis reveals that those methods are sensitive to provided textual descriptions of entities (or relations). Even a minor modification of descriptions can lead to a change in the decision boundary between entity (or relation) classes. In this paper, we formally define the problem of identifying effective descriptions for zero shot inference. We propose a strategy for generating variations of an initial description, a heuristic for ranking them and an ensemble method capable of boosting the predictions of zero-shot models through description enhancement. Empirical results on four different entity and relation classification datasets show that our proposed method outperform existing approaches and achieve new SOTA results on these datasets under the ZSL settings. The source code of the proposed solutions and the evaluation framework are open-sourced.

kNN-MT has utilized neighborhood knowledge for auxiliary decoding, significantly improving translation performance. Subsequently, kNN-KD transitions the use of neighborhood knowledge from the decoding phase to the training phase, to address the temporal and spatial inefficiencies inherent in kNN-MT. However, kNN-KD transfers all the kNN knowledge arbitrarily, which has the potential to restrict the learning of student models. In this paper, we propose a novel domain-aware kNN-KD method, which filters out domain-relevant neighborhood knowledge for learning in the distillation process. Notably, this entire process exclusively utilizes the neighborhood knowledge of the original model, eliminating the need for establishing any additional datastores. Experiments on four domain translation tasks demonstrate that our method achieves state-of-the-art performance, realizing an average gain of 1.55 COMET and 1.42 BLEU scores, by further enhancing the translation of rare words. Source code can be accessed at https://github.com/wangzx1219/Dk-KD.

Recent mainstream event argument extraction methods process each event in isolation, resulting in inefficient inference and ignoring the correlations among multiple events. To address these limitations, here we propose a multiple-event argument extraction model DEEIA (Dependency-guided Encoding and Event-specific Information Aggregation), capable of extracting arguments from all events within a document simultaneously. The proposed DEEIA model employs a multi-event prompt mechanism, comprising DE and EIA modules. The DE module is designed to improve the correlation between prompts and their corresponding event contexts, whereas the EIA module provides event-specific information to improve contextual understanding. Extensive experiments show that our method achieves new state-of-the-art performance on four public datasets (RAMS, WikiEvents, MLEE, and ACE05), while significantly saving the inference time compared to the baselines. Further analyses demonstrate the effectiveness of the proposed modules.

Speech-to-text (S2T) generation systems frequently face challenges in low-resource scenarios, primarily due to the lack of extensive labeled datasets. One emerging solution is constructing virtual training samples by interpolating inputs and labels, which has notably enhanced system generalization in other domains. Despite its potential, this technique’s application in S2T tasks has remained under-explored. In this paper, we delve into the utility of interpolation augmentation, guided by several pivotal questions. Our findings reveal that employing an appropriate strategy in interpolation augmentation significantly enhances performance across diverse tasks, architectures, and data scales, offering a promising avenue for more robust S2T systems in resource-constrained settings.

Large language models (LLMs) are powerful dialogue agents, but specializing them towards fulfilling a specific function can be challenging. Instructing tuning, i.e. tuning models on instruction and sample responses generated by humans (Ouyang et al., 2022), has proven as an effective method to do so, yet requires a number of data samples that a) might not be available or b) costly to generate. Furthermore, this cost increases when the goal is to make the LLM follow a specific workflow within a dialogue instead of single instructions. Inspired by the self-play technique in reinforcement learning and the use of LLMs to simulate human agents, we propose a more effective method for data collection through LLMs engaging in a conversation in various roles. This approach generates a training data via “self-talk” of LLMs that can be refined and utilized for supervised fine-tuning. We introduce an automated way to measure the (partial) success of a dialogue. This metric is used to filter the generated conversational data that is fed back in LLM for training. Based on our automated and human evaluations of conversation quality, we demonstrate that such self-talk data improves results. In addition, we examine the various characteristics that showcase the quality of generated dialogues and how they can be connected to their potential utility as training data.

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Semantic are Beacons: A Semantic Perspective for Unveiling Parameter-Efficient Fine-Tuning in Knowledge Learning
Renzhi Wang | Piji Li

Parameter-Efficient Fine-Tuning (PEFT) methods enable efficient adaptation of Large Language Models (LLMs) to various downstream applications. However, the effectiveness of the PEFT diminishes notably when downstream tasks require accurate learning of specific knowledge. In this paper, we adopt a semantic perspective to investigate this phenomenon, uncovering the reasons behind PEFT’s limitations in knowledge learning task. Our findings reveals that: (1) PEFT presents a notable risk of pushing the model away from the intended knowledge target; (2) multiple knowledge interfere with each other, and such interference suppresses the learning and expression of knowledge features. Based on these insights, we introduce a data filtering strategy to exclude data that is detrimental to knowledge learning and a re-weighted learning strategy to make the model attentive to semantic distance during knowledge learning. Experimental results demonstrate the effectiveness of the proposed method on open-source large language model, further validate the semantic challenge in PEFT, thus paving the way for future research.

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Leveraging Collection-Wide Similarities for Unsupervised Document Structure Extraction
Gili Lior | Yoav Goldberg | Gabriel Stanovsky

Document collections of various domains, e.g., legal, medical, or financial, often share some underlying collection-wide structure, which captures information that can aid both human users and structure-aware models.We propose to identify the typical structure of document within a collection, which requires to capture recurring topics across the collection, while abstracting over arbitrary header paraphrases, and ground each topic to respective document locations. These requirements pose several challenges: headers that mark recurring topics frequently differ in phrasing, certain section headers are unique to individual documents and do not reflect the typical structure, and the order of topics can vary between documents. Subsequently, we develop an unsupervised graph-based method which leverages both inter- and intra-document similarities, to extract the underlying collection-wide structure. Our evaluations on three diverse domains in both English and Hebrew indicate that our method extracts meaningful collection-wide structure, and we hope that future work will leverage our method for multi-document applications and structure-aware models.

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Enhancing Cross Text-Molecule Learning by Self-Augmentation
Yinuo Jiang | Xiang Zhuang | Keyan Ding | Qiang Zhang | Huajun Chen

The development of Large Language Models (LLMs) has greatly advanced the field of drug discovery, with the belief that natural language can enhance human control over molecule design. However, the scarcity of high-quality labeled data remains a challenge for cross text-molecule learning. Existing datasets are limited due to the difficulty of collecting precise molecule-description pairs. Although recent efforts have utilized pseudo data generated by LLMs for augmentation, the lack of specialized chemistry knowledge of LLMs and the absence of an effective high quality data selector may introduce noise into the annotations, compromising the models’ robustness. To address these challenges, this paper introduces a novel framework that interweaves model fine-tuning and data augmentation to overcome the scarcity of high-quality data. The proposed approach involves an iterative procedure where the model plays dual roles in annotating unlabeled data and sampling a subset of high-quality data until convergence is achieved, enhancing the model’s understanding and adaptability. Additionally, a new dataset called SAPubChem-41 is presented, which comprises meticulously curated high-quality parallel molecule-description pairs designed specifically for fine-tuning purposes. This research provides an important contribution to the field by addressing the need for high-quality datasets and presenting an effective framework for cross text-molecule learning.

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RePALM: Popular Quote Tweet Generation via Auto-Response Augmentation
Erxin Yu | Jing Li | Chunpu Xu

A quote tweet enables users to share others’ content while adding their own commentary. In order to enhance public engagement through quote tweets, we investigate the task of generating popular quote tweets. This task aims to produce quote tweets that garner higher popularity, as indicated by increased likes, replies, and retweets. Despite the impressive language generation capabilities of large language models (LLMs), there has been limited research on how LLMs can effectively learn the popularity of text to better engage the public. Therefore, we introduce a novel approach called Response-augmented Popularity-Aligned Language Model (RePALM), which aligns language generation with popularity by leveraging insights from augmented auto-responses provided by readers. We utilize the Proximal Policy Optimization framework with a dual-reward mechanism to jointly optimize for the popularity of the quote tweet and its consistency with the auto-responses. In our experiments, we collected two datasets consisting of quote tweets containing external links and those referencing others’ tweets. Extensive results demonstrate the superiority of RePALM over advanced language models that do not incorporate response augmentation.

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On the Effect of (Near) Duplicate Subwords in Language Modelling
Anton Schäfer | Thomas Hofmann | Imanol Schlag | Tiago Pimentel

Tokenisation is a core part of language models (LMs). It involves splitting a character sequence into subwords which are assigned random indices before being served to the LM. However, this process—while typically lossless—may lead to less efficient LM training, because it removes character-level information, thereby making it more difficult to generalise across similar subwords, such as *now* and *Now*. We refer to such subwords as **near duplicates**. In this paper, we study the impact of near duplicate subwords on LM training efficiency. First, we design an experiment that gives us an upper bound to how much we should expect a model to improve if we could perfectly generalise across near duplicates. We do this, by duplicating each token in our LM’s vocabulary, creating perfectly equivalent classes of subwords. Experimentally, we find that LMs need roughly 17% more data when trained in a fully duplicated setting. Second, we investigate the impact of naturally occurring near duplicates on LMs. Here, we see that deduplicating them considerably hurts LM performance; but that this loss in performance can be easily mitigated.

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Do Pre-Trained Language Models Detect and Understand Semantic Underspecification? Ask the DUST!
Frank Wildenburg | Michael Hanna | Sandro Pezzelle

In everyday language use, speakers frequently utter and interpret sentences that are semantically underspecified, namely, whose content is insufficient to fully convey their message or interpret them univocally. For example, to interpret the underspecified sentence “Don’t spend too much”, which leaves implicit what (not) to spend, additional linguistic context or outside knowledge is needed. In this work, we propose a novel Dataset of semantically Underspecified Sentences grouped by Type (DUST) and use it to study whether pre-trained language models (LMs) correctly identify and interpret underspecified sentences. We find that newer LMs are reasonably able to identify underspecified sentences when explicitly prompted. However, interpreting them correctly is much harder for any LMs. Our experiments show that when interpreting underspecified sentences, LMs exhibit little uncertainty, contrary to what theoretical accounts of underspecification would predict. Overall, our study reveals limitations in current models’ processing of sentence semantics and highlights the importance of using naturalistic data and communicative scenarios when evaluating LMs’ language capabilities.

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Visual Hallucinations of Multi-modal Large Language Models
Wen Huang | Hongbin Liu | Minxin Guo | Neil Gong

Visual hallucination (VH) means that a multi-modal LLM (MLLM) imagines incorrect details about an image in visual question answering. Existing studies find VH instances only in existing image datasets, which results in biased understanding of MLLMs’ performance under VH due to limited diversity of such VH instances. In this work, we propose a tool called VHTest to generate a diverse set of VH instances. Specifically, VHTest finds some initial VH instances in existing image datasets (e.g., COCO), generates a text description for each VH mode, and uses a text-to-image generative model (e.g., DALL-E-3) to generate VH images based on the text descriptions. We collect a benchmark dataset with 1,200 VH instances in 8 VH modes using VHTest. We find that existing MLLMs such as GPT-4, LLaVA-1.5, and MiniGPT-v2 hallucinate for a large fraction of the instances in our benchmark. Moreover, we find that fine-tuning an MLLM using our benchmark dataset reduces its likelihood to hallucinate without sacrificing its performance on other benchmarks. Our benchmarks are publicly available: https://github.com/wenhuang2000/VHTest.

With the popularity of large language models (LLMs) and their ability to handle longer input documents, there is a growing need for high-quality long document summarization datasets. Although many models already support 16k input, current lengths of summarization datasets are inadequate, and salient information is not evenly distributed. To bridge these gaps, we collect a new summarization dataset called SumSurvey, consisting of more than 18k scientific survey papers. With an average document length exceeding 12k and a quarter exceeding 16k, as well as the uniformity metric outperforming current mainstream long document summarization datasets, SumSurvey brings new challenges and expectations to both fine-tuned models and LLMs. The informativeness of summaries and the models supporting the evaluation of long document summarization warrant further attention. Automatic and human evaluation results on this abstractive dataset confirm this view. Our dataset and code are available at https://github.com/Oswald1997/SumSurvey.

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Pushing the Limits of Low-Resource NER Using LLM Artificial Data Generation
Joan Santoso | Patrick Sutanto | Billy Cahyadi | Esther Setiawan

Named Entity Recognition (NER) is an important task, but to achieve great performance, it is usually necessary to collect a large amount of labeled data, incurring high costs. In this paper, we propose using open-source Large Language Models (LLM) to generate NER data with only a few labeled examples, reducing the cost of human annotations. Our proposed method is very simple and can perform well using only a few labeled data points. Experimental results on diverse low-resource NER datasets show that our proposed data generation method can significantly improve the baseline. Additionally, our method can be used to augment datasets with class-imbalance problems and consistently improves model performance on macro-F1 metrics.

LLMs have marked a revolutonary shift, yet they falter when faced with compositional reasoning tasks. Our research embarks on a quest to uncover the root causes of compositional reasoning failures of LLMs, uncovering that most of them stem from the improperly generated or leveraged implicit reasoning results. Inspired by our empirical findings, we resort to Logit Lens and an intervention experiment to dissect the inner hidden states of LLMs. This deep dive reveals that implicit reasoning results indeed surface within middle layers and play a causative role in shaping the final explicit reasoning results. Our exploration further locates multi-head self-attention (MHSA) modules within these layers, which emerge as the linchpins in accurate generation and leveraing of implicit reasoning results. Grounded on the above findings, we develop CREME, a lightweight method to patch errors in compositional reasoning via editing the located MHSA modules. Our empirical evidence stands testament to CREME’s effectiveness, paving the way for autonomously and continuously enhancing compositional reasoning capabilities in language models.

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Bilingual Rhetorical Structure Parsing with Large Parallel Annotations
Elena Chistova

Discourse parsing is a crucial task in natural language processing that aims to reveal the higher-level relations in a text. Despite growing interest in cross-lingual discourse parsing, challenges persist due to limited parallel data and inconsistencies in the Rhetorical Structure Theory (RST) application across languages and corpora. To address this, we introduce a parallel Russian annotation for the large and diverse English GUM RST corpus. Leveraging recent advances, our end-to-end RST parser achieves state-of-the-art results on both English and Russian corpora. It demonstrates effectiveness in both monolingual and bilingual settings, successfully transferring even with limited second-language annotation. To the best of our knowledge, this work is the first to evaluate the potential of cross-lingual end-to-end RST parsing on a manually annotated parallel corpus.

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Book2Dial: Generating Teacher Student Interactions from Textbooks for Cost-Effective Development of Educational Chatbots
Junling Wang | Jakub Macina | Nico Daheim | Sankalan Pal Chowdhury | Mrinmaya Sachan

Educational chatbots are a promising tool for assisting student learning. However, the development of effective chatbots in education has been challenging, as high-quality data is seldom available in this domain. In this paper, we propose a framework for generating synthetic teacher-student interactions grounded in a set of textbooks. Our approaches capture a key aspect of learning interactions where curious students with partial knowledge interactively ask teachers questions about the material in the textbook. We highlight various quality criteria that such dialogues must fulfill and compare several approaches relying on either prompting or finetuning large language models according to these criteria. We use the synthetic dialogues to train educational chatbots and show the benefits of further fine-tuning in educational domains. However, careful human evaluation shows that our best data synthesis method still suffers from hallucinations and tends to reiterate information from previous conversations. Our findings offer insights for future efforts in synthesizing conversational data that strikes a balance between size and quality. We will open-source our data and code.

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SELP: A Semantically-Driven Approach for Separated and Accurate Class Prototypes in Few-Shot Text Classification
Wenxin Liang | Tingyu Zhang | Han Liu | Feng Zhang

The meta-learning paradigm has demonstrated significant effectiveness in few-shot text classification. Currently, numerous efforts are grounded in metric-based learning, utilizing textual feature vectors for classification, with a common emphasis on enlarging inter-class distances to achieve improved classification effectiveness. However, many methods predominantly focus on enhancing the separation of prototypes without taking the semantic relationships between prototypes and class clusters into consideration. This oversight results in incomplete and inaccurate encoding of prototypes within the semantic space, affecting the generality of the learned metric space. In this paper, we propose the utilization of Semantically Enhanced Labels for calibrating class Prototypes (SELP), thereby obtaining prototypes that are more separated and semantically accurate. Additionally, we have devised a center loss to enhance intra-class compactness, coupled with the introduction of a simulated label distribution method to address the overfitting problem. Extensive experiments on eight few-shot text classification datasets show that the proposed method outperforms baselines significantly. Our code is available at https://github.com/tttyyyzzz-zty/SELP.git.

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Automated Focused Feedback Generation for Scientific Writing Assistance
Eric Chamoun | Michael Schlichtkrull | Andreas Vlachos

Scientific writing is a challenging task, particularly for novice researchers who often rely on feedback from experienced peers. Recent work has primarily focused on improving surface form and style rather than manuscript content. In this paper, we propose a novel task: automated focused feedback generation for scientific writing assistance. We present SWIF²T: a Scientific WrIting Focused Feedback Tool. It is designed to generate specific, actionable and coherent comments, which identify weaknesses in a scientific paper and/or propose revisions to it. Our approach consists of four components - planner, investigator, reviewer and controller - leveraging multiple Large Language Models (LLMs) to implement them. We compile a dataset of 300 peer reviews citing weaknesses in scientific papers and conduct human evaluation. The results demonstrate the superiority in specificity, reading comprehension, and overall helpfulness of SWIF²T’s feedback compared to other approaches. In our analysis, we also identified cases where automatically generated reviews were judged better than human ones, suggesting opportunities for integration of AI-generated feedback in scientific writing.

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FastGAS: Fast Graph-based Annotation Selection for In-Context Learning
Zihan Chen | Song Wang | Cong Shen | Jundong Li

In-context learning (ICL) empowers large language models (LLMs) to tackle new tasks by using a series of training instances as prompts. Since generating the prompts needs to sample from a vast pool of instances and annotate them (e.g., add labels in classification task), existing methods have proposed to select a subset of unlabeled examples for annotation, thus enhancing the quality of prompts and concurrently mitigating annotation costs. However, these methods often require a long time to select instances due to their complexity, hindering their practical viability. To address this limitation, we propose a graph-based selection method, FastGAS, designed to efficiently identify high-quality instances while minimizing computational overhead. Initially, we construct a data similarity graph based on instance similarities. Subsequently, employing a graph partitioning algorithm, we partition the graph into pieces. Within each piece (i.e., subgraph), we adopt a greedy approach to pick the most representative nodes. By aggregating nodes from diverse pieces and annotating the corresponding instances, we identify a set of diverse and representative instances for ICL. Compared to prior approaches, our method not only exhibits superior performance on different tasks but also significantly reduces selection time. In addition, we demonstrate the efficacy of our approach in LLMs of larger sizes.

Structured pruning fundamentally reduces computational and memory overheads of large language models (LLMs) and offers a feasible solution for end-side LLM deployment. Structurally pruned models remain dense and high-precision, highly compatible with further tuning and compression. However, as the coarse-grained structured pruning poses large damage to the highly interconnected model, achieving a high compression ratio for scaled-up LLMs remains a challenge. In this paper, we introduce a task-agnostic structured pruning approach coupled with a compact Transformer architecture design. The proposed approach, named TransAct, reduces transitional activations inside multi-head attention (MHA) and multi-layer perceptron (MLP) modules, while preserving the inter-module activations that are sensitive to perturbations. Hence, the LLM is pruned into an intra-module low-rank architecture, significantly reducing weights, KV Cache and attention computation. TransAct is implemented on the LLaMA model and evaluated on downstream benchmarks. Results verify the optimality of our approach at high compression with respect to both efficiency and performance. Further, ablation studies reveal the strength of activation-guided iterative pruning and provide experimental analysis on the redundancy of MHA and MLP modules.

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Integrating Multi-scale Contextualized Information for Byte-based Neural Machine Translation
Langlin Huang | Yang Feng

Subword tokenization is a common method for vocabulary building in Neural Machine Translation (NMT) models. However, increasingly complex tasks have revealed its disadvantages. First, a vocabulary cannot be modified once it is learned, making it hard to adapt to new words. Second, in multilingual translation, the imbalance in data volumes across different languages spreads to the vocabulary, exacerbating translations involving low-resource languages. While byte-based tokenization addresses these issues, byte-based models struggle with the low information density inherent in UTF-8 byte sequences. Previous works enhance token semantics through local contextualization but fail to select an appropriate contextualizing scope based on the input. Consequently, we propose the Multi-Scale Contextualization (MSC) method, which learns contextualized information of varying scales across different hidden state dimensions. It then leverages the attention module to dynamically integrate the multi-scale contextualized information. Experiments show that MSC significantly outperforms subword-based and other byte-based methods in both multilingual and out-of-domain scenarios. Code can be found in https://github.com/ictnlp/Multiscale-Contextualization.

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Deductive Closure Training of Language Models for Coherence, Accuracy, and Updatability
Afra Feyza Akyürek | Ekin Akyürek | Leshem Choshen | Derry Wijaya | Jacob Andreas

While language models (LMs) can sometimes generate factually correct text and estimate truth values of individual claims, these generally do not reflect a globally coherent, manipulable model of the world. As a consequence, current LMs also generate incorrect or nonsensical content, and are difficult to edit and bring up to date. We present a method called Deductive Closure Training (DCT) that uses LMs themselves to identify implications of (and contradictions within) the text that they generate, yielding an efficient self-supervised procedure for improving LM factuality. Given a collection of seed documents, DCT prompts LMs to generate additional text implied by these documents, reason globally about the correctness of this generated text, and finally fine-tune on text inferred to be correct. Given seed documents from a trusted source, DCT provides a tool for supervised model updating; if seed documents are sampled from the LM itself, DCT enables fully unsupervised fine-tuning for improved coherence and accuracy. Across the CREAK, MQuAKE, and Reversal Curse datasets, supervised DCT improves LM fact verification and text generation accuracy by 3-26%; on CREAK, fully unsupervised DCT improves verification accuracy by 12%. These results show that LMs’ reasoning capabilities during inference can be leveraged during training to improve their reliability.

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Self-Supervised Singing Voice Pre-Training towards Speech-to-Singing Conversion
Ruiqi Li | Rongjie Huang | Yongqi Wang | Zhiqing Hong | Zhou Zhao

Speech-to-singing voice conversion (STS) task always suffers from data scarcity, because it requires paired speech and singing data. Compounding this issue are the challenges of content-pitch alignment and the suboptimal quality of generated outputs, presenting significant hurdles in STS research. This paper presents SVPT, an STS approach boosted by a self-supervised singing voice pre-training model.We leverage spoken language model techniques to tackle the rhythm alignment problem and the in-context learning capability to achieve zero-shot conversion. We adopt discrete-unit random resampling and pitch corruption strategies, enabling training with unpaired singing data and thus mitigating the issue of data scarcity. SVPT also serves as an effective backbone for singing voice synthesis (SVS), offering insights into scaling up SVS models. Experimental results indicate that SVPT delivers notable improvements in both STS and SVS endeavors. Audio samples are available at https://speech2sing.github.io.

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Evaluating Large Language Model Biases in Persona-Steered Generation
Andy Liu | Mona Diab | Daniel Fried

The task of persona-steered text generation requires large language models (LLMs) to generate text that reflects the distribution of views that an individual fitting a persona could have. People have multifaceted personas, but prior work on bias in LLM-generated opinions has only explored multiple-choice settings or one-dimensional personas. We define an incongruous persona as a persona with multiple traits where one trait makes its other traits less likely in human survey data, e.g. political liberals who support increased military spending. We find that LLMs are 9.7% less steerable towards incongruous personas than congruous ones, sometimes generating the stereotypical stance associated with its demographic rather than the target stance. Models that we evaluate that are fine-tuned with Reinforcement Learning from Human Feedback (RLHF) are more steerable, especially towards stances associated with political liberals and women, but present significantly less diverse views of personas. We also find variance in LLM steerability that cannot be predicted from multiple-choice opinion evaluation. Our results show the importance of evaluating models in open-ended text generation, as it can surface new LLM opinion biases. Moreover, such a setup can shed light on our ability to steer models toward a richer and more diverse range of viewpoints.

The rapid increase in multimedia data has spurred advancements in Multimodal Summarization with Multimodal Output (MSMO), which aims to produce a multimodal summary that integrates both text and relevant images. The inherent heterogeneity of content within multimodal inputs and outputs presents a significant challenge to the execution of MSMO. Traditional approaches typically adopt a holistic perspective on coarse image-text data or individual visual objects, overlooking the essential connections between objects and the entities they represent. To integrate the fine-grained entity knowledge, we propose an Entity-Guided Multimodal Summarization model (EGMS). Our model, building on BART, utilizes dual multimodal encoders with shared weights to process text-image and entity-image information concurrently. A gating mechanism then combines visual data for enhanced textual summary generation, while image selection is refined through knowledge distillation from a pre-trained vision-language model. Extensive experiments on public MSMO dataset validate the superiority of the EGMS method, which also prove the necessity to incorporate entity information into MSMO problem.

It is imperative to ensure the stability of every prediction made by a language model; that is, a language’s prediction should remain consistent despite minor input variations, like word substitutions. In this paper, we investigate the problem of certifying a language model’s robustness against Universal Text Perturbations (UTPs), which have been widely used in universal adversarial attacks and backdoor attacks. Existing certified robustness based on random smoothing has shown considerable promise in certifying the input-specific text perturbations (ISTPs), operating under the assumption that any random alteration of a sample’s clean or adversarial words would negate the impact of sample-wise perturbations. However, with UTPs, masking only the adversarial words can eliminate the attack. A naive method is to simply increase the masking ratio and the likelihood of masking attack tokens, but it leads to a significant reduction in both certified accuracy and the certified radius due to input corruption by extensive masking. To solve this challenge, we introduce a novel approach, the superior prompt search method, designed to identify a superior prompt that maintains higher certified accuracy under extensive masking. Additionally, we theoretically motivate why ensembles are a particularly suitable choice as base prompts for random smoothing. The method is denoted by superior prompt ensembling technique. We also empirically confirm this technique, obtaining state-of-the-art results in multiple settings. These methodologies, for the first time, enable high certified accuracy against both UTPs and ISTPs. The source code of CR-UTP is available at https://github.com/UCF-ML-Research/CR-UTP.

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Recovering document annotations for sentence-level bitext
Rachel Wicks | Matt Post | Philipp Koehn

In machine translation, historical models were incapable of handling longer contexts, so the lack of document-level datasets was less noticeable. Now, despite the emergence of long-sequence methods, we remain within a sentence-level paradigm and without data to adequately approach context-aware machine translation. Most large-scale datasets have been processed through a pipeline that discards document-level metadata. In this work, we reconstruct document-level information for three (ParaCrawl, News Commentary, and Europarl) large datasets in German, French, Spanish, Italian, Polish, and Portuguese (paired with English). We then introduce a document-level filtering technique as an alternative to traditional bitext filtering. We present this filtering with analysis to show that this method prefers context-consistent translations rather than those that may have been sentence-level machine translated. Last we train models on these longer contexts and demonstrate improvement in document-level translation without degradation of sentence-level translation. We release our dataset, ParaDocs, and resulting models as a resource to the community.

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MetaPro 2.0: Computational Metaphor Processing on the Effectiveness of Anomalous Language Modeling
Rui Mao | Kai He | Claudia Ong | Qian Liu | Erik Cambria

Metaphor interpretation is a difficult task in natural language understanding. The development of relevant techniques in this domain is slow, mostly because of the lack of large annotated datasets and effective pre-trained language models (PLMs) for metaphor learning. Thus, we propose a large annotated dataset and a PLM for the metaphor interpretation task. Our foundation model is based on a novel anomalous language modeling (ALM) method, which we benchmark with comparable PLM baselines on the new dataset, finding that it largely improves model performance on metaphor identification and interpretation.

Recent advances in large language models (LLMs) have led to significant success in using LLMs as agents. Nevertheless, a common assumption that LLMs always process honest information neglects the widespread deceptive or misleading content in human and AI-generated material. This oversight might expose LLMs to malicious manipulations. To enhance LLMs’ ability to identify and counteract deceptive information, in this paper, inspired by humans’ recursive thinking and perspective-taking, we introduce a novel cognitive framework, Recursive Contemplation (ReCon). ReCon combines formulation and refinement contemplation processes; formulation contemplation produces initial thoughts and speech, while refinement contemplation further polishes them. Additionally, we incorporate first-order and second-order perspective transitions into these processes respectively. Specifically, the first-order allows an LLM agent to infer others’ mental states, and the second-order involves understanding how others perceive the agent’s mental state. After integrating ReCon with various LLMs, extensive experiment results from the Avalon game and BigTom benchmark indicate ReCon’s efficacy in aiding LLMs to discern and maneuver around deceptive information without extra fine-tuning and data. Finally, we demonstrate ReCon’s scaling trend with model parameters, and explore the current limitations of LLMs in terms of safety and reasoning, potentially furnishing insights for subsequent research. Our project page can be found at https://shenzhi-wang.github.io/avalon_recon.

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Direct Preference Optimization with an Offset
Afra Amini | Tim Vieira | Ryan Cotterell

Direct preference optimization (DPO) is a successful fine-tuning strategy for aligning large language models with human preferences without the need to train a reward model or employ reinforcement learning. DPO, as originally formulated, relies on binary preference data and fine-tunes a language model to increase the likelihood of a preferred response over a dispreferred response. However, not all preference pairs are equal. Sometimes, the preferred response is only slightly better than the dispreferred one. In other cases, the preference is much stronger. For instance, if a response contains harmful or toxic content, the annotator will have a strong preference for that response. In this paper, we propose a generalization of DPO, termed DPO with an offset (ODPO), that does not treat every preference pair equally during fine-tuning. Intuitively, ODPO requires the difference between the likelihood of the preferred and dispreferred response to be greater than an offset value. The offset is determined based on the extent to which one response is preferred over another. Our experiments on various tasks suggest that ODPO significantly outperforms DPO in aligning language models, especially when the number of preference pairs is limited.

Direct speech-to-speech translation achieves high-quality results through the introduction of discrete units obtained from self-supervised learning. However, talking head translation, converting audio-visual speech (i.e., talking head video) from one language into another, still confronts several challenges compared to audio speech: (1) Existing methods invariably rely on cascading, synthesizing via both audio and text, resulting in delays and cascading errors. (2) Talking head translation has a limited set of reference frames. If the generated translation exceeds the length of the original speech, the video sequence needs to be supplemented by repeating frames, leading to jarring video transitions. In this work, we propose a model for talking head translation, TransFace, which can directly translate audio-visual speech into audio-visual speech in other languages. It consists of a speech-to-unit translation model to convert audio speech into discrete units and a unit-based audio-visual speech synthesizer, Unit2Lip, to re-synthesize synchronized audio-visual speech from discrete units in parallel. Furthermore, we introduce a Bounded Duration Predictor, ensuring isometric talking head translation and preventing duplicate reference frames. Experiments demonstrate that Unit2Lip significantly improves synchronization and boosts inference speed by a factor of 4.35 on LRS2. Additionally, TransFace achieves impressive BLEU scores of 61.93 and 47.55 for Es-En and Fr-En on LRS3-T and 100% isochronous translations. The samples are available at https://transface-demo.github.io .

Pretrained models learned from real corpora can often capture undesirable features, leading to bias issues against different demographic groups. Most existing studies on bias dataset construction or bias mitigation methods only focus on one demographic group pair to study a certain bias, e.g. black vs. white for racial bias. However, in real-world applications, there are more than two demographic groups that are at risk of the same bias. In this paper, we propose to analyze and reduce biases across multiple demographic groups. We collect and build a multi-demographic bias dataset including five commonly discussed bias dimensions. To mitigate multi-demographic bias, we adopt several novel debiasing methods, including regularisation-based and augmentation-based methods, as well as appropriate evaluation metrics for multi-demographic bias measurement. Experimental results on the proposed multi-demographic dataset show that a fairer model can be achieved using a multi-demographic debiasing approach. Also, the model debiased using the proposed multi-demographic debiasing methods can better transfer to unseen demographics without sacrificing the performance of the pretrained model.

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Fair Federated Learning with Biased Vision-Language Models
Huimin Zeng | Zhenrui Yue | Yang Zhang | Lanyu Shang | Dong Wang

Existing literature that integrates CLIP into federated learning (FL) largely ignores the inherent group unfairness within CLIP and its ethical implications on FL applications. Furthermore, such CLIP bias may be amplified in FL, due to the unique issue of data heterogeneity across clients. However, in identity-sensitive FL applications, model fairness (i.e., group fairness) is imperative for model development. Therefore, this work explores a critical question ignored by the existing literature: how can we build a fair FL framework using biased pre-trained VLMs (e.g., CLIP)? To address this problem, we propose a fairness-aware adaptation framework tailored for VLM (e.g., CLIP) in the context of FL, named Fair Federated Deep Visiual Prompting or FF-DVP. As implied by its name, trains a fair FL model with fairness-aware deep visual prompting (DVP). Moreover, incorporates modality-fused classification heads to learn client-specific knowledge and fairness constraints. These modules explicitly addresses a unique bias in FL, namely the bias triggered by data heterogeneity. We show that can be readily extended to prevailing parameter-efficient fine-tuning methods (e.g., adapter or LoRA) for debiasing. To the best of our knowledge, is the first to leverage biased VLMs for building fair FL frameworks. Extensive results on human face attribute recognition (FAR) applications suggest that effectively improves model fairness and training convergence, outperforming state-of-the-art baselines.

Integrated Speech and Large Language Models (SLMs) that can follow speech instructions and generate relevant text responses have gained popularity lately. However, the safety and robustness of these models remains largely unclear. In this work, we investigate the potential vulnerabilities of such instruction-following speech-language models to adversarial attacks and jailbreaking. Specifically, we design algorithms that can generate adversarial examples to jailbreak SLMs in both white-box and black-box attack settings without human involvement. Additionally, we propose countermeasures to thwart such jailbreaking attacks. Our models, trained on dialog data with speech instructions, achieve state-of-the-art performance on spoken question-answering task, scoring over 80% on both safety and helpfulness metrics. Despite safety guardrails, experiments on jailbreaking demonstrate the vulnerability of SLMs to adversarial perturbations and transfer attacks, with average attack success rates of 90% and 10% respectively when evaluated on a dataset of carefully designed harmful questions spanning 12 different toxic categories. However, we demonstrate that our proposed countermeasures reduce the attack success significantly.

The impressive generation capabilities of large language models (LLMs) have made it harder to detect the subtle hallucinations they make in abstractive summarization, where generated summaries consist of a blend of correct and incorrect information w.r.t. a given document. Recently-proposed LLM-based evaluation metrics attempt to capture this, but still face challenges: (1) they are biased towards summaries generated from the same underlying LLM, and (2) they lack interpretability, offering only a single score. In this work, we present ACUEval, a metric that leverages the power of LLMs to perform two sub-tasks: decomposing summaries into atomic content units (ACUs), and validating them against the source document. Compared to current strong LLM-based metrics, our two-step evaluation strategy improves correlation with human judgments of faithfulness on three summarization evaluation benchmarks by 3% in balanced accuracy compared to the next-best metric, and also shows reduced preference bias towards LLM-generated summary. Further, we show that errors detected by ACUEval can be used to generate actionable feedback for refining the summary, improving the faithfulness scores by more than 10%.

Multimodal Large Language Models (MLLMs) fine-tuned with multimodal instruction-following data have demonstrated formidable capabilities in multimodal tasks. However, fine-tuning all parameters of MLLMs has become challenging due to the rapid growth of the overall model’s parameters. To address this issue, we study Parameter-Efficient Fine-Tuning (PEFT) methods for MLLMs. We aim to identify effective methods for enhancing performance in scenarios where only a limited number of parameters are trained. This paper conducts empirical studies that employ four widely used PEFT methods to fine-tune the LLM component of open-source MLLMs. We present a comprehensive analysis that encompasses various aspects, including the impact of PEFT methods on various models, parameters and location of PEFT module, fine-tuning data scale, model stability based on PEFT method, MLLM’s generalization, and hallucination. We evaluated four PEFT methods on seven datasets from two different categories, unseen and seen datasets. Across all experiments, we show that the adapter is the best-performing PEFT method in various aspects. At the same time, fine-tuning the connector layers leads to improved performance in most MLLMs.

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PARADISE: Evaluating Implicit Planning Skills of Language Models with Procedural Warnings and Tips Dataset
Arda Uzunoglu | Gözde Şahin | Abdulfattah Safa

Recently, there has been growing interest within the community regarding whether large language models are capable of planning or executing plans. However, most prior studies use LLMs to generate high-level plans for simplified scenarios lacking linguistic complexity and domain diversity, limiting analysis of their planning abilities. These setups constrain evaluation methods (e.g., predefined action space), architectural choices (e.g., only generative models), and overlook the linguistic nuances essential for realistic analysis. To tackle this, we present PARADISE, an abductive reasoning task using Q&A format on practical procedural text sourced from wikiHow. It involves tip and warning inference tasks directly associated with goals, excluding intermediary steps, with the aim of testing the ability of the models to infer implicit knowledge of the plan solely from the given goal. Our experiments, utilizing fine-tuned language models and zero-shot prompting, reveal the effectiveness of task-specific small models over large language models in most scenarios. Despite advancements, all models fall short of human performance. Notably, our analysis uncovers intriguing insights, such as variations in model behavior with dropped keywords, struggles of BERT-family and GPT-4 with physical and abstract goals, and the proposed tasks offering valuable prior knowledge for other unseen procedural tasks. The PARADISE dataset and associated resources are publicly available for further research exploration with https://anonymous.4open.science/r/paradise-53BD/README.md.

The recent advances in natural language processing have predominantly favored well-resourced English-centric models, resulting in a significant gap with low-resource languages. In this work, we introduce TURNA, a language model developed for the low-resource language Turkish and is capable of both natural language understanding and generation tasks.TURNA is pretrained with an encoder-decoder architecture based on the unified framework UL2 with a diverse corpus that we specifically curated for this purpose. We evaluated TURNA with three generation tasks and five understanding tasks for Turkish. The results show that TURNA outperforms several multilingual models in both understanding and generation tasks and competes with monolingual Turkish models in understanding tasks.

Emotion plays a crucial role in human conversation. This paper underscores the significance of considering emotion in speech translation. We present the MELD-ST dataset for the emotion-aware speech translation task, comprising English-to-Japanese and English-to-German language pairs. Each language pair includes about 10,000 utterances annotated with emotion labels from the MELD dataset. Baseline experiments using the SeamlessM4T model on the dataset indicate that fine-tuning with emotion labels can enhance translation performance in some settings, highlighting the need for further research in emotion-aware speech translation systems.

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Designing Informative Metrics for Few-Shot Example Selection
Rishabh Adiga | Lakshmi Subramanian | Varun Chandrasekaran

Pretrained language models (PLMs) have shown remarkable few-shot learning capabilities when provided with properly formatted examples. However, selecting the “best” examples remains an open challenge. We propose a complexity-based prompt selection approach for sequence tagging tasks. This approach avoids the training of a dedicated model for selection of examples, and instead uses certain metrics to align the syntactico-semantic complexity of test sentences and examples. We use both sentence- and word-level metrics to match the complexity of examples to the (test) sentence being considered. Our results demonstrate that our approach extracts greater performance from PLMs: it achieves state-of-the-art performance on few-shot NER, achieving a 5% absolute improvement in F1 score on the CoNLL2003 dataset for GPT-4. We also see large gains of upto 28.85 points (F1/Acc.) in smaller models like GPT-j-6B.

In-context learning (ICL) has emerged as a powerful tool for enhancing large language models (LLMs) in addressing downstream tasks. In this paper, we explore the vital task of example selection in ICL by mimicking the human learning process. We propose a Chain-of-Quizzes (CoQ) framework inspired by educational theories such as Bruner’s Spiral Learning and Mastery Learning theory. Specifically, our framework employs the LLMs to answer the quiz (question in the example) to sift ‘good’ examples, combines these examples iteratively with the increasing complexity, and utilizes a final exam to gauge the combined example chains. Our extensive experiments on diverse reasoning datasets show the proposed approach outperforms baseline models. These findings underscore the framework’s potential for future research.

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It’s Not Easy Being Wrong: Large Language Models Struggle with Process of Elimination Reasoning
Nishant Balepur | Shramay Palta | Rachel Rudinger

Chain-of-thought (COT) prompting can help large language models (LLMs) reason toward correct answers, but its efficacy in reasoning toward incorrect answers is unexplored. This process of elimination (PoE), when used with COT, can enhance self-consistency, interpretability, and tasks such as medical diagnoses of exclusion. Thus, we propose PoE with COT, where LLMs must reason toward incorrect options on multiple-choice questions. We evaluate the ability of GPT-3.5, LLaMA-2, and Falcon to perform PoE with COT on a total of four commonsense and scientific reasoning datasets. We find that the strategy of PoE always underperforms the strategy of choosing the correct answer. The agreement of these strategies is also lower than the self-consistency of each strategy. To study these issues further, we conduct error analyses and give suggestions for future work.

Intent detection aims to identify user goals from utterances, and is a ubiquitous step towards the satisfaction of user desired needs in many interaction systems. As dynamic and varied intents arise, models that are capable of identifying new intents promptly are required. However, existing studies usually fine-tune discriminative models on the specific defined intent classes, precluding them from being directly adopted to new intent domains. In this paper, we introduce a generative pre-trained intent model that can recognize new intents from different domains in low-resource scenarios. We reformulate intent detection into a generation task and design descriptive and regularized instructions to guide the model effectively to detect new intents in open domains with no parameter updates. To validate the proposed method, we introduce a new intent detection benchmark, including the Meta-Intent Dataset and three types of representative evaluation settings. We conduct extensive experiments which demonstrate that our method outperforms a range of strong baselines that needs further fine-tuning or domain-specific samples.

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Efficient Continual Pre-training for Building Domain Specific Large Language Models
Yong Xie | Karan Aggarwal | Aitzaz Ahmad

Large language models (LLMs) have demonstrated remarkable open-domain capabilities. LLMs tailored for a domain are typically trained entirely on domain corpus to excel at handling domain-specific tasks. In this work, we explore an alternative strategy of continual pre-training as a means to develop domain-specific LLMs over an existing open-domain LLM. We introduce FinPythia-6.9B, developed through domain-adaptive continual pre-training on the financial domain.Continual pre-trained FinPythia showcases consistent improvements on financial tasks over the original foundational model. We further explore simple but effective data selection strategies for continual pre-training. Our data selection strategies outperform vanilla continual pre-training’s performance with just 10% of corpus size and cost, without any degradation on open-domain standard tasks. Our work proposes an alternative solution to building domain-specific LLMs cost-effectively.

Joint entity and relation extraction is a process that identifies entity pairs and their relations using a single model. We focus on the problem of joint extraction in distantly-labeled data, whose labels are generated by aligning entity mentions with the corresponding entity and relation tags using a knowledge base (KB). One key challenge is the presence of noisy labels arising from both incorrect entity and relation annotations, which significantly impairs the quality of supervised learning. Existing approaches, either considering only one source of noise or making decisions using external knowledge, cannot well-utilize significant information in the training data. We propose DENRL, a generalizable framework that 1) incorporates a lightweight transformer backbone into a sequence labeling scheme for joint tagging, and 2) employs a noise-robust framework that regularizes the tagging model with significant relation patterns and entity-relation dependencies, then iteratively self-adapts to instances with less noise from both sources. Surprisingly, experiments on two benchmark datasets show that DENRL, using merely its own parametric distribution and simple data-driven heuristics, outperforms strong baselines by a large margin with better interpretability.

Large Language Models (LLMs) have revolutionized various domains with extensive knowledge and creative capabilities. However, a critical issue with LLMs is their tendency to produce outputs that diverge from factual reality. This phenomenon is particularly concerning in sensitive applications such as medical consultation and legal advice, where accuracy is paramount. Inspired by human lie detectors using physiological responses, we introduce the LLM Factoscope, a novel Siamese network-based model that leverages the inner states of LLMs for factual detection. Our investigation reveals distinguishable patterns in LLMs’ inner states when generating factual versus non-factual content. We demonstrate its effectiveness across various architectures, achieving over 96% accuracy on our custom-collected factual detection dataset. Our work opens a new avenue for utilizing LLMs’ inner states for factual detection and encourages further exploration into LLMs’ inner workings for enhanced reliability and transparency.

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DictLLM: Harnessing Key-Value Data Structures with Large Language Models for Enhanced Medical Diagnostics
YiQiu Guo | Yuchen Yang | Ya Zhang | Yu Wang | Yanfeng Wang

Structured data offers an efficient means of organizing information. Exsisting text-serialization based methods for processing structured data using large language models (LLMs) are not designed to explicitly capture the heterogeneity of structured data. Such methods are suboptimal for LLMs to process structured data, and may lead to large input token size and poor robustness to input perturbation. In this paper, we propose a novel framework called DictLLM, which is an efficient and effective framework for the modeling of medical lab report to deal with the report-assisted diagnosis generation task. DictLLM introduce 1) group positional encoding to maintain the permutation invariance, 2) hierarchical attention bias to capture the inductive bias of structured data, and 3) a optimal transport alignment layer to align the embeddings generated by the dict encoder with the LLM, producing a list of fixed-length virtual tokens. We conduct experiments with multiple LLM models on a large-scale real-world medical lab report dataset for automatic diagnosis generation. The results show that our proposed framework outperforms the baseline methods and few-shot GPT-4 in terms of both Rouge-L and Knowledge F1 score. We also conduct multiple experiments and analyze the scalability and robustness of our proposed framework, demonstrating the superiority of our method in modeling the heterogeneous structure of medical dictionaries data.

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imapScore: Medical Fact Evaluation Made Easy
Huimin Wang | Yutian Zhao | Xian Wu | Yefeng Zheng

Automatic evaluation of natural language generation (NLG) tasks has gained extensive research interests, since it can rapidly assess the performance of large language models (LLMs). However, automatic NLG evaluation struggles with medical QA because it fails to focus on the crucial correctness of medical facts throughout the generated text. To address this, this paper introduces a new data structure, imap, designed to capture key information in questions and answers, enabling evaluators to focus on essential details. The imap comprises three components: Query, Constraint, and Inform, each of which is in the form of term-value pairs to represent medical facts in a structural manner. We then introduce imapScore, which compares the corresponding medical term-value pairs in the imap to score generated texts. We utilize GPT-4 to extract imap from questions, human-annotated answers, and generated responses. To mitigate the diversity in medical terminology for fair term-value pairs comparison, we use a medical knowledge graph to assist GPT-4 in determining matches. To compare imapScore with existing NLG metrics, we establish a new benchmark dataset. The experimental results show that imapScore consistently outperforms state-of-the-art metrics, demonstrating an average improvement of 79.8% in correlation with human scores. Furthermore, incorporating imap into n-gram, embedding, and LLM metrics boosts the base versions, increasing correlation with human scores by averages of 89.9%, 81.7%, and 32.6%, respectively.

Large language models (LLMs) have shown great potential to empower various domains and are often customized by fine-tuning for the requirements of different applications. However, the powerful learning ability of LLMs not only enables them to learn new tasks but also makes them vulnerable to learning undesired behaviors, such as harmfulness and hallucination, as the fine-tuning data often implicitly or explicitly contains such content. Can we fine-tune LLMs on harmful data without learning harmful behaviors? This paper proposes a controllable training framework to make undesired behaviors unlearnable during the fine-tuning process. Specifically, we introduce security vectors to control the model’s behavior and make it consistent with the undesired behavior. Security vectors are activated during fine-tuning, the consistent behavior makes the model believe that such behavior has already been learned and there is no need for further optimization, while inconsistent data can still be learned. After fine-tuning, security vectors are deactivated to restore the LLM’s normal behavior. Our experiments show that the security vectors can prevent LLM from learning harmful and hallucination behavior while preserving the ability to learn other information.

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Debiasing Large Language Models with Structured Knowledge
Congda Ma | Tianyu Zhao | Manabu Okumura

Due to biases inherently present in data for pre-training, current pre-trained Large Language Models (LLMs) also ubiquitously manifest the same phenomena. Since the bias influences the output from the LLMs across various tasks, the widespread deployment of the LLMs is hampered. We propose a simple method that utilizes structured knowledge to alleviate this issue, aiming to reduce the bias embedded within the LLMs and ensuring they have an encompassing perspective when used in applications. Experimental results indicated that our method has good debiasing ability when applied to existing both autoregressive and masked language models. Additionally, it could ensure that the performances of LLMs on downstream tasks remain uncompromised.Our method outperforms state-of-the-art (SOTA) baselines in the debiasing ability. Importantly, our method obviates the need for training from scratch, thus offering enhanced scalability and cost-effectiveness.

Instruction tuning has been used as a promising approach to improve the performance of large language models (LLMs) on unseen tasks. However, current LLMs exhibit limited robustness to unseen instructions, generating inconsistent outputs when the same instruction is phrased with slightly varied forms or language styles. This behavior indicates LLMs’ lack of robustness to textual variations and generalizability to unseen instructions, potentially leading to trustworthiness issues. Accordingly, we propose Contrastive Instruction Tuning, which maximizes the similarity between the hidden representations of semantically equivalent instruction-instance pairs while minimizing the similarity between semantically different ones. To facilitate this approach, we augment the existing FLAN collection by paraphrasing task instructions. Experiments on the PromptBench benchmark show that CoIN consistently improves LLMs’ robustness to unseen instructions with variations across character, word, sentence, and semantic levels by an average of +2.5% in accuracy.

Generative retrieval uses differentiable search indexes to directly generate relevant document identifiers in response to a query. Recent studies have highlighted the potential of a strong generative retrieval model, trained with carefully crafted pre-training tasks, to enhance downstream retrieval tasks via fine-tuning. However, the full power of pre-training for generative retrieval remains underexploited due to its reliance on pre-defined static document identifiers, which may not align with evolving model parameters. In this work, we introduce BootRet, a bootstrapped pre-training method for generative retrieval that dynamically adjusts document identifiers during pre-training to accommodate the continuing memorization of the corpus. BootRet involves three key training phases: (i) initial identifier generation, (ii) pre-training via corpus indexing and relevance prediction tasks, and (iii) bootstrapping for identifier updates. To facilitate the pre-training phase, we further introduce noisy documents and pseudo-queries, generated by large language models, to resemble semantic connections in both indexing and retrieval tasks. Experimental results demonstrate that BootRet significantly outperforms existing pre-training generative retrieval baselines and performs well even in zero-shot settings.

Aspect-based Sentiment Analysis (ABSA) is extensively researched in the NLP community, yet related models face challenges due to data sparsity when shifting to a new domain. Hence, data augmentation for cross-domain ABSA has attracted increasing attention in recent years. However, two key points have been neglected in prior studies: First, target domain unlabeled data are labeled with pseudo labels by the model trained in the source domain with little quality control, leading to inaccuracy and error propagation. Second, the label and text patterns of generated labeled data are monotonous, thus limiting the robustness and generalization ability of trained ABSA models. In this paper, we aim to design a simple yet effective framework to address the above shortages in ABSA data augmentation, called Refining and Synthesis Data Augmentation (RSDA). Our framework roughly includes two steps: First, it refines generated labeled data using a natural language inference (NLI) filter to control data quality. Second, it synthesizes diverse labeled data via novel label composition and paraphrase approaches. We conduct experiments on 4 kinds of ABSA subtasks, and our framework outperforms 7 strong baselines, demonstrating its effectiveness.

The sound codec’s dual roles in minimizing data transmission latency and serving as tokenizers underscore its critical importance.Recent years have witnessed significant developments in codec models.The ideal sound codec should preserve content, paralinguistics, speakers, and audio information.However, the question of which codec achieves optimal sound information preservation remains unanswered, as in different papers, models are evaluated on their selected experimental settings.This study introduces Codec-SUPERB, an acronym for Codec sound processing Universal PERformance Benchmark.It is an ecosystem designed to assess codec models across representative sound applications and signal-level metrics rooted in sound domain knowledge.Codec-SUPERB simplifies result sharing through an online leaderboard, promoting collaboration within a community-driven benchmark database, thereby stimulating new development cycles for codecs.Furthermore, we undertake an in-depth analysis to offer insights into codec models from both application and signal perspectives, diverging from previous codec papers mainly concentrating on signal-level comparisons.Finally, we will release codes, the leaderboard, and data to accelerate progress within the community.

Social media bot detection is increasingly crucial with the rise of social media platforms. Existing methods predominantly construct social networks as graph and utilize graph neural networks (GNNs) for bot detection. However, most of these methods focus on how to improve the performance of GNNs while neglecting the community structure within social networks. Moreover, GNNs based methods still face problems such as poor model generalization due to the relatively small scale of the dataset and over-smoothness caused by information propagation mechanism. To address these problems, we propose the Community-Aware Heterogeneous Graph Contrastive Learning framework (i.e., CACL), which constructs social network as heterogeneous graph with multiple node types and edge types, and then utilizes community-aware module to mine both hard positive samples and hard negative samples for supervised graph contrastive learning with adaptive graph enhancement algorithms. Extensive experiments demonstrate that our framework addresses the previously mentioned challenges and outperforms competitive baselines on three social media bot benchmarks.

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Are Machines Better at Complex Reasoning? Unveiling Human-Machine Inference Gaps in Entailment Verification
Soumya Sanyal | Tianyi Xiao | Jiacheng Liu | Wenya Wang | Xiang Ren

Making inferences in text comprehension to understand the meaning is essential in language processing. This work studies the entailment verification (EV) problem of complex, multi-sentence premises requiring a system to make multiple inferences implicitly. Modern applications of EV in detecting inconsistent model-generated rationales require complex multi-hop reasoning. However, current textual inference datasets mostly contain short-sentence premises that partially focus on this. To address this, we compile an EV benchmark that includes datasets from three NLP domains (NLI, contextual QA, and rationales) containing multi-sentence premises. On benchmarking humans and LLMs, we find that LLMs are better than humans in multi-hop reasoning across extended contexts, while humans perform better in simple deductive reasoning tasks. We also finetune a Flan-T5 model for EV using two training objectives to obtain a strong open-source model that outperforms GPT-3.5 and rivals GPT-4. Finally, we use our finetuned model to filter out inconsistent model-generated rationales in self-consistency decoding, resulting in a 6% accuracy improvement on average across three MCQ datasets.

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ChartInstruct: Instruction Tuning for Chart Comprehension and Reasoning
Ahmed Masry | Mehrad Shahmohammadi | Md Rizwan Parvez | Enamul Hoque | Shafiq Joty

Charts provide visual representations of data and are widely used for analyzing information, addressing queries, and conveying insights to others. Various chart-related downstream tasks have emerged recently, such as question-answering and summarization. A common strategy to solve these tasks is to fine-tune various models originally trained on vision tasks language. However, such task-specific models are not capable of solving a wide range of chart-related tasks, constraining their real-world applicability. To overcome these challenges, we introduce ChartInsruct: a novel chart-specific vision-language Instruction-following dataset comprising 191K instructions generated with 71K charts. We then present two distinct systems for instruction tuning on such datasets: (1) an end-to-end model that connects a vision encoder for chart understanding with a LLM; and (2) a pipeline model that employs a two-step approach to extract chart data tables and input them into the LLM. In experiments on four downstream tasks, we first show the effectiveness of our model–achieving a new set of state-of-the-art results. Further evaluation shows that our instruction-tuning approach supports a wide array of real-world chart comprehension and reasoning scenarios, thereby expanding the scope and applicability of our models to new kinds of tasks.

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Improving Multilingual Neural Machine Translation by Utilizing Semantic and Linguistic Features
Mengyu Bu | Shuhao Gu | Yang Feng

The many-to-many multilingual neural machine translation can be regarded as the process of integrating semantic features from the source sentences and linguistic features from the target sentences. To enhance zero-shot translation, models need to share knowledge across languages, which can be achieved through auxiliary tasks for learning a universal representation or cross-lingual mapping. To this end, we propose to exploit both semantic and linguistic features between multiple languages to enhance multilingual translation. On the encoder side, we introduce a disentangling learning task that aligns encoder representations by disentangling semantic and linguistic features, thus facilitating knowledge transfer while preserving complete information. On the decoder side, we leverage a linguistic encoder to integrate low-level linguistic features to assist in the target language generation. Experimental results on multilingual datasets demonstrate significant improvement in zero-shot translation compared to the baseline system, while maintaining performance in supervised translation. Further analysis validates the effectiveness of our method in leveraging both semantic and linguistic features.

Weight-sharing supernets are crucial for performance estimation in cutting-edge neural architecture search (NAS) frameworks. Despite their ability to generate diverse subnetworks without retraining, the quality of these subnetworks is not guaranteed due to weight sharing. In NLP tasks like machine translation and pre-trained language modeling, there is a significant performance gap between supernet and training from scratch for the same model architecture, necessitating retraining post optimal architecture identification.This study introduces a solution called mixture-of-supernets, a generalized supernet formulation leveraging mixture-of-experts (MoE) to enhance supernet model expressiveness with minimal training overhead. Unlike conventional supernets, this method employs an architecture-based routing mechanism, enabling indirect sharing of model weights among subnetworks. This customization of weights for specific architectures, learned through gradient descent, minimizes retraining time, significantly enhancing training efficiency in NLP. The proposed method attains state-of-the-art (SoTA) performance in NAS for fast machine translation models, exhibiting a superior latency-BLEU tradeoff compared to HAT, the SoTA NAS framework for machine translation. Furthermore, it excels in NAS for building memory-efficient task-agnostic BERT models, surpassing NAS-BERT and AutoDistil across various model sizes. The code can be found at: https://github.com/UBC-NLP/MoS.

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SharedCon: Implicit Hate Speech Detection using Shared Semantics
Hyeseon Ahn | Youngwook Kim | Jungin Kim | Yo-Sub Han

The ever-growing presence of hate speech on social network services and other online platforms not only fuels online harassment but also presents a growing challenge for hate speech detection. As this task is akin to binary classification, one of the promising approaches for hate speech detection is the utilization of contrastive learning. Recent studies suggest that classifying hateful posts in just a binary manner may not adequately address the nuanced task of detecting implicit hate speech. This challenge is largely due to the subtle nature and context dependency of such pejorative remarks. Previous studies proposed a modified contrastive learning approach equipped with additional aids such as human-written implications or machine-generated augmented data for better implicit hate speech detection. While this approach can potentially enhance the overall performance by its additional data in general, it runs the risk of overfitting as well as heightened cost and time to obtain. These drawbacks serve as motivation for us to design a methodology that is not dependent on human-written or machine-generated augmented data for training. We propose a straightforward, yet effective, clustering-based contrastive learning approach that leverages the shared semantics among the data.

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Smaller Language Models are capable of selecting Instruction-Tuning Training Data for Larger Language Models
Dheeraj Mekala | Alex Nguyen | Jingbo Shang

Instruction-tuning language models has become a crucial step in aligning them for general use. Typically, this process involves extensive training on large datasets, incurring high training costs. In this paper, we introduce a novel training data selection based on the learning percentage of the samples. We assert that current language models possess the capability to autonomously select high-quality training data, leading to comparable or improved performance compared to training on the entire dataset. Our experiments span different-sized models, revealing that this characteristic holds for models ranging from 1B (small) to 13B (large) in size. Moreover, we demonstrate an interesting finding that the data hardness transfers across model sizes, and a smaller 350M model can effectively curate high-quality training data with hard samples for a larger 13B model, resulting in an equally or superior instruction-tuned model compared to training on the complete dataset. Utilizing open-sourced OPT and Llama-2 models up to 13B in size, two publicly available instruction-tuning training datasets and evaluated by both automatic metrics & humans, our paper introduces a novel approach to training data selection, showcasing a more efficient alternative.

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InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents
Qiusi Zhan | Zhixiang Liang | Zifan Ying | Daniel Kang

Recent work has embodied LLMs as agents, allowing them to access tools, perform actions, and interact with external content (e.g., emails or websites). However, external content introduces the risk of indirect prompt injection (IPI) attacks, where malicious instructions are embedded within the content processed by LLMs, aiming to manipulate these agents into executing detrimental actions against users. Given the potentially severe consequences of such attacks, establishing benchmarks to assess and mitigate these risks is imperative.In this work, we introduce InjecAgent, a benchmark designed to assess the vulnerability of tool-integrated LLM agents to IPI attacks. InjecAgent comprises 1,054 test cases covering 17 different user tools and 62 attacker tools. We categorize attack intentions into two primary types: direct harm to users and exfiltration of private data. We conduct a comprehensive evaluation of 30 different LLM agents and show that agents are vulnerable to IPI attacks, with ReAct-prompted GPT-4 vulnerable to attacks 24% of the time. Further investigation into an enhanced setting, where the attacker instructions are reinforced with a hacking prompt, shows additional increases in success rates. Our findings raise questions about the widespread deployment of LLM Agents.

Code Pre-trained Models (CodePTMs) based vulnerability detection have achieved promising results over recent years. However, these models struggle to generalize as they typically learn superficial mapping from source code to labels instead of understanding the root causes of code vulnerabilities, resulting in poor performance in real-world scenarios beyond the training instances. To tackle this challenge, we introduce VulLLM, a novel framework that integrates multi-task learning with Large Language Models (LLMs) to effectively mine deep-seated vulnerability features. Specifically, we construct two auxiliary tasks beyond the vulnerability detection task. First, we utilize the vulnerability patches to construct a vulnerability localization task. Second, based on the vulnerability features extracted from patches, we leverage GPT-4 to construct a vulnerability interpretation task. VulLLM innovatively augments vulnerability classification by leveraging generative LLMs to understand complex vulnerability patterns, thus compelling the model to capture the root causes of vulnerabilities rather than overfitting to spurious features of a single task. The experiments conducted on six large datasets demonstrate that VulLLM surpasses seven state-of-the-art models in terms of effectiveness, generalization, and robustness.

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PPTSER: A Plug-and-Play Tag-guided Method for Few-shot Semantic Entity Recognition on Visually-rich Documents
Wenhui Liao | Jiapeng Wang | Zening Lin | Longfei Xiong | Lianwen Jin

Visually-rich document information extraction (VIE) is a vital aspect of document understanding, wherein Semantic Entity Recognition (SER) plays a significant role. However, few-shot SER on visually-rich documents remains relatively unexplored despite its considerable potential for practical applications. To address this issue, we propose a simple yet effective Plug-and-Play Tag-guided method for few-shot Semantic Entity Recognition (PPTSER) on visually-rich documents. PPTSER is built upon off-the-shelf multi-modal pre-trained models. It leverages the semantics of the tags to guide the SER task, reformulating SER into entity typing and span detection, handling both tasks simultaneously via cross-attention. Experimental results illustrate that PPTSER outperforms existing fine-tuning and few-shot methods, especially in low-data regimes. With full training data, PPTSER achieves comparable or superior performance to fine-tuning baseline. For instance, on the FUNSD benchmark, our method improves the performance of LayoutLMv3-base in 1-shot, 3-shot and 5-shot scenarios by 15.61%, 2.13%, and 2.01%, respectively. Overall, PPTSER demonstrates promising generalizability, effectiveness, and plug-and-play nature for few-shot SER on visually-rich documents. The codes will be available at [https://github.com/whlscut/PPTSER](https://github.com/whlscut/PPTSER).

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LLM Performance Predictors are good initializers for Architecture Search
Ganesh Jawahar | Muhammad Abdul-Mageed | Laks Lakshmanan | Dujian Ding

In this work, we utilize Large Language Models (LLMs) for a novel use case: constructing Performance Predictors (PP) that estimate the performance of specific deep neural network architectures on downstream tasks. We create PP prompts for LLMs, comprising (i) role descriptions, (ii) instructions for the LLM, (iii) hyperparameter definitions, and (iv) demonstrations presenting sample architectures with efficiency metrics and ‘training from scratch’ performance. In machine translation (MT) tasks, GPT-4 with our PP prompts (LLM-PP) achieves a SoTA mean absolute error and a slight degradation in rank correlation coefficient compared to baseline predictors. Additionally, we demonstrate that predictions from LLM-PP can be distilled to a compact regression model (LLM-Distill-PP), which surprisingly retains much of the performance of LLM-PP. This presents a cost-effective alternative for resource-intensive performance estimation. Specifically, for Neural Architecture Search (NAS), we introduce a Hybrid-Search algorithm (HS-NAS) employing LLM-Distill-PP for the initial search stages and reverting to the baseline predictor later. HS-NAS performs similarly to SoTA NAS, reducing search hours by approximately 50%, and in some cases, improving latency, GFLOPs, and model size. The code can be found at: https://github.com/UBC-NLP/llmas.

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MODDP: A Multi-modal Open-domain Chinese Dataset for Dialogue Discourse Parsing
Chen Gong | DeXin Kong | Suxian Zhao | Xingyu Li | Guohong Fu

Dialogue discourse parsing (DDP) aims to capture the relations between utterances in the dialogue. In everyday real-world scenarios, dialogues are typically multi-modal and cover open-domain topics. However, most existing widely used benchmark datasets for DDP contain only textual modality and are domain-specific. This makes it challenging to accurately and comprehensively understand the dialogue without multi-modal clues, and prevents them from capturing the discourse structures of the more prevalent daily conversations. This paper proposes MODDP, the first multi-modal Chinese discourse parsing dataset derived from open-domain daily dialogues, consisting 864 dialogues and 18,114 utterances, accompanied by 12.7 hours of video clips. We present a simple yet effective benchmark approach for multi-modal DDP. Through extensive experiments, we present several benchmark results based on MODDP. The significant improvement in performance from introducing multi-modalities into the original textual unimodal DDP model demonstrates the necessity of integrating multi-modalities into DDP.

In the current environment, psychological issues are prevalent and widespread, with social media serving as a key outlet for individuals to share their feelings. This results in the generation of vast quantities of data daily, where negative emotions have the potential to precipitate crisis situations. There is a recognized need for models capable of efficient analysis. While pre-trained language models have demonstrated their effectiveness broadly, there’s a noticeable gap in pre-trained models tailored for specialized domains like psychology. To address this, we have collected a huge dataset from Chinese social media platforms and enriched it with publicly available datasets to create a comprehensive database encompassing 3.36 million text entries. To enhance the model’s applicability to psychological text analysis, we integrated psychological lexicons into the pre-training masking mechanism. Building on an existing Chinese language model, we performed adaptive training to develop a model specialized for the psychological domain. We evaluated our model’s performance across six public datasets, where it demonstrated improvements compared to eight other models. Additionally, in the qualitative comparison experiment, our model provided psychologically relevant predictions given the masked sentences. Due to concerns regarding data privacy, the dataset will not be made publicly available. However, we have made the pre-trained models and codes publicly accessible to the community via: https://github.com/zwzzzQAQ/Chinese-MentalBERT.

A single language model, even when aligned with labelers through reinforcement learning from human feedback (RLHF), may not suit all human preferences. Recent approaches therefore prefer customization, gathering multi-dimensional feedback, and creating distinct reward models for each dimension.Different language models are then optimized for various preferences using multi-objective RLHF (MORLHF) with varying reward weights.However, RL fine-tuning is unstable and resource-heavy, especially with diverse and usually conflicting objectives.In this paper, we present Multi-Objective Direct Preference Optimization (MODPO), an RL-free extension of Direct Preference Optimization (DPO) for multiple alignment objectives.Essentially, MODPO folds language modeling directly into reward modeling, training language models as implicit collective reward models that combine all objectives with specific weights. MODPO theoretically yields the same optimal solutions as MORLHF but is practically more stable and efficient.Empirical results in safety alignment and long-form question answering show that MODPO matches or outperforms existing methods, producing a Pareto front of language models catering to diverse preferences with three times less computational resources compared to MORLHF.Code is available at https://github.com/ZHZisZZ/modpo.

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DORY: Deliberative Prompt Recovery for LLM
Lirong Gao | Ru Peng | Yiming Zhang | Junbo Zhao

Prompt recovery in large language models (LLMs) is crucial for understanding how LLMs work and addressing concerns regarding privacy, copyright, etc. The trend towards inference-only APIs complicates this task by restricting access to essential outputs for recovery. To tackle this challenge, we extract prompt-related information from limited outputs and identify a strong(negative) correlation between output probability-based uncertainty and the success of prompt recovery.This finding led to the development of Deliberative PrOmpt RecoverY (DORY), our novel approach that leverages uncertainty to recover prompts accurately. DORY involves reconstructing drafts from outputs, refining these with hints, and filtering out noise based on uncertainty. Our evaluation shows that DORY outperforms existing baselines across diverse LLMs and prompt benchmarks, improving performance by approximately 10.82% and establishing a new state-of-the-art record in prompt recovery tasks. Significantly, DORY operates using a single LLM without any external resources or model, offering a cost-effective, user-friendly prompt recovery solution.

Equipping a conversational search engine with strategies regarding when to ask clarification questions is becoming increasingly important across various domains. Attributing to the context understanding capability of LLMs and their access to domain-specific sources of knowledge, LLM-based clarification strategies feature rapid transfer to various domains in a post-hoc manner.However, they still struggle to deliver promising performance on unseen domains, struggling to achieve effective domain transferability.We take the first step to investigate this issue and existing methods tend to produce one-size-fits-all strategies across diverse domains, limiting their search effectiveness.In response, we introduce a novel method, called STYLE,to achieve effective domain transferability.Our experimental results indicate that STYLE bears strong domain transferability, resulting in an average search performance improvement of 10% on four unseen domains.

Generative search engines have the potential to transform how people seek information online, but generated responses from existing large language models (LLMs)-backed generative search engines may not always be accurate. Nonetheless, retrieval-augmented generation exacerbates safety concerns, since adversaries may successfully evade the entire system by subtly manipulating the most vulnerable part of a claim. To this end, we propose evaluating the robustness of generative search engines in the realistic and high-risk setting, where adversaries have only black-box system access and seek to deceive the model into returning incorrect responses. Through a comprehensive human evaluation of various generative search engines, such as Bing Chat, PerplexityAI, and YouChat across diverse queries, we demonstrate the effectiveness of adversarial factual questions in inducing incorrect responses. Moreover, retrieval-augmented generation exhibits a higher susceptibility to factual errors compared to LLMs without retrieval. These findings highlight the potential security risks of these systems and emphasize the need for rigorous evaluation before deployment. The dataset and code will be publicly available.

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Automatic Engineering of Long Prompts
Cho-Jui Hsieh | Si Si | Felix Yu | Inderjit Dhillon

Large language models (LLMs) have demonstrated remarkable capabilities in solving complex open-domain tasks, guided by comprehensive instructions and demonstrations provided in the form of prompts. However, these prompts can be lengthy, often comprising hundreds of lines and thousands of tokens, and their design often requires considerable human effort. Recent research has explored automatic prompt engineering for short prompts, typically consisting of one or a few sentences. However, the automatic design of long prompts remains a challenging problem due to its immense search space. In this paper, we propose an algorithm named Automated Prompt Engineering Xpert (APEX), a novel algorithm that automatically improves long prompts. Leveraging a greedy algorithm with beam-search for efficiency, APEX utilizes search history to significantly enhance the effectiveness of LLM-based mutation in its search process. Our results show that APEX achieves an average of 9.2% accuracy gain on eight tasks in Big Bench Hard and a consistent improvements on GSM8K with various models, highlighting the significance of automating prompt designs to fully harness the capabilities of LLMs.

Chain-of-Thought (CoT) serves as a critical emerging ability in LLMs, especially when it comes to logical reasoning. Attempts have been made to induce such ability in small models as well by distilling from the data with CoT generated by Large Language Models (LLMs). However, existing methods often simply generate and incorporate more data from LLMs and fail to note the importance of efficiently utilizing existing CoT data. We here propose a new training paradigm AS-ES (Abstractive Segments - Extractive Segments) learning, which exploits the inherent information in CoT for iterative generation. Experiments show that our methods surpass the direct seq2seq training on CoT-extensive tasks like MWP and PET summarization, without data augmentation or altering the model itself. Furthermore, we explore the reason behind the inefficiency of small models in learning CoT and provide an explanation of why AS-ES learning works, giving insights into the underlying mechanism of CoT.

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II-MMR: Identifying and Improving Multi-modal Multi-hop Reasoning in Visual Question Answering
Jihyung Kil | Farideh Tavazoee | Dongyeop Kang | Joo-Kyung Kim

Visual Question Answering (VQA) often involves diverse reasoning scenarios across Vision and Language (V&L). Most prior VQA studies, however, have merely focused on assessing the model’s overall accuracy without evaluating it on different reasoning cases. Furthermore, some recent works observe that conventional Chain-of-Thought (CoT) prompting fails to generate effective reasoning for VQA, especially for complex scenarios requiring multi-hop reasoning. In this paper, we propose II-MMR, a novel idea to identify and improve multi-modal multi-hop reasoning in VQA. In specific, II-MMR takes a VQA question with an image and finds a reasoning path to reach its answer using two novel language promptings: (i) answer prediction-guided CoT prompt, or (ii) knowledge triplet-guided prompt. II-MMR then analyzes this path to identify different reasoning cases in current VQA benchmarks by estimating how many hops and what types (i.e., visual or beyond-visual) of reasoning are required to answer the question. On popular benchmarks including GQA and A-OKVQA, II-MMR observes that most of their VQA questions are easy to answer, simply demanding “single-hop” reasoning, whereas only a few questions require “multi-hop” reasoning. Moreover, while the recent V&L model struggles with such complex multi-hop reasoning questions even using the traditional CoT method, II-MMR shows its effectiveness across all reasoning cases in both zero-shot and fine-tuning settings.

Given a source and its edited version performed based on human instructions in natural language, how do we extract the underlying edit operations, to automatically replicate similar edits on other images? This is the problem of reverse designing, and we present TAME-RD, a model to solve this problem. TAME-RD automatically learns from the complex interplay of image editing operations and the natural language instructions to learn fully specified edit operations. It predicts both the underlying image edit operations as discrete categories and their corresponding parameter values in the continuous space.We accomplish this by mapping together the contextual information from the natural language text and the structural differences between the corresponding source and edited images using the concept of pre-post effect. We demonstrate the efficiency of our network through quantitative evaluations on multiple datasets. We observe improvements of 6-10% on various accuracy metrics and 1.01X-4X on the RMSE score and the concordance correlation coefficient for the corresponding parameter values on the benchmark GIER dataset. We also introduce I-MAD, a new two-part dataset: I-MAD-Dense, a collection of approximately 100K source and edited images, together with automatically generated text instructions and annotated edit operations, and I-MAD-Pro, consisting of about 1.6K source and edited images, together with text instructions and annotated edit operations provided by professional editors. On our dataset, we observe absolute improvements of 1-10% on the accuracy metrics and 1.14X–5X on the RMSE score.

In this paper, by treating in-context learning (ICL) as a meta-optimization process, we explain why LLMs are sensitive to the order of ICL examples. This understanding leads us to the development of Batch-ICL, an effective, efficient, and order-agnostic inference algorithm for ICL. Differing from the standard N-shot learning approach, Batch-ICL employs N separate 1-shot forward computations and aggregates the resulting meta-gradients. These aggregated meta-gradients are then applied to the forward computation of a zero-shot query to generate the final prediction. This batch processing approach renders the LLM agnostic to the order of ICL examples. Through extensive experiments and analysis, we demonstrate that Batch-ICL consistently outperforms most permutations of ICL examples. In some cases, it even exceeds the performance of the best order for standard ICL, all while reducing the computational resources required. Furthermore, we develop a novel variant of Batch-ICL featuring multiple “epochs” of meta-optimization. This variant implicitly explores permutations of ICL examples, further enhancing ICL performance.

We present INDICVOICES, a dataset of natural and spontaneous speech containing a total of 7348 hours of read (9%), extempore (74%) and conversational (17%) audio from 16237 speakers covering 145 Indian districts and 22 languages. Of these 7348 hours, 1639 hours have already been transcribed, with a median of 73 hours per language. Through this paper, we share our journey of capturing the cultural, linguistic and demographic diversity of India to create a one-of-its-kind inclusive and representative dataset. More specifically, we share an open-source blueprint for data collection at scale comprising of standardised protocols, centralised tools, a repository of engaging questions, prompts and conversation scenarios spanning multiple domains and topics of interest, quality control mechanisms, comprehensive transcription guidelines and transcription tools. We hope that this open source blueprint will serve as a comprehensive starter kit for data collection efforts in other multilingual regions of the world. Using INDICVOICES, we build IndicASR, the first ASR model to support all the 22 languages listed in the 8th schedule of the Constitution of India.

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ViCor: Bridging Visual Understanding and Commonsense Reasoning with Large Language Models
Kaiwen Zhou | Kwonjoon Lee | Teruhisa Misu | Xin Wang

In our work, we explore the synergistic capabilities of pre-trained vision-and-language models (VLMs) and large language models (LLMs) on visual commonsense reasoning (VCR) problems. We find that VLMs and LLMs-based decision pipelines are good at different kinds of VCR problems. Pre-trained VLMs exhibit strong performance for problems involving understanding the literal visual content, which we noted as visual commonsense understanding (VCU). For problems where the goal is to infer conclusions beyond image content, which we noted as visual commonsense inference (VCI), VLMs face difficulties, while LLMs, given sufficient visual evidence, can use commonsense to infer the answer well. We empirically validate this by letting LLMs classify VCR problems into these two categories and show the significant difference between VLM and LLM with image caption decision pipelines on two subproblems. Moreover, we identify a challenge with VLMs’ passive perception, which may miss crucial context information, leading to incorrect reasoning by LLMs. Based on these, we suggest a collaborative approach, named ViCor, where pre-trained LLMs serve as problem classifiers to analyze the problem category, then either use VLMs to answer the question directly or actively instruct VLMs to concentrate on and gather relevant visual elements to support potential commonsense inferences. We evaluate our framework on two VCR benchmark datasets and outperform all other methods without in-domain fine-tuning.

In-context learning of large-language models (LLMs) has achieved remarkable success in the field of natural language processing, while extensive case studies reveal that the single-step chain-of-thought prompting approach faces challenges such as attention diffusion and inadequate performance in complex tasks like text-to-SQL. To improve the contextual learning capabilities of LLMs in text-to-SQL, a workflow paradigm method is proposed, aiming to enhance the attention and problem-solving scope of LLMs through decomposition. Specifically, the information determination module for eliminating redundant information and the brand-new prompt structure based on problem classification greatly enhance the model’s attention. Additionally, the inclusion of self-correction and active learning modules greatly expands the problem-solving scope of LLMs, hence improving the upper limit of LLM-based approaches. Extensive experiments conducted on three datasets demonstrate that our approach outperforms other methods by a significant margin. About 2-3 percentage point improvements compared to the existing baseline on the Spider Dev, Spider-Realistic, and Bird Dev datasets and new SOTA results on the Spider Test dataset are achieved. Our code is available on GitHub: https://github.com/FlyingFeather/DEA-SQL.

Short video fake news detection is crucial for combating the spread of misinformation. Current detection methods tend to aggregate features from individual modalities into multimodal features, overlooking the implicit opinions and the evolving nature of opinions across modalities. In this paper, we mine implicit opinions within short video news and promote the evolution of both explicit and implicit opinions across all modalities. Specifically, we design a prompt template to mine implicit opinions regarding the credibility of news from the textual component of videos. Additionally, we employ a diffusion model that encourages the interplay among diverse modal opinions, including those extracted through our implicit opinion prompts. Experimental results on a publicly available dataset for short video fake news detection demonstrate the superiority of our model over state-of-the-art methods.

Indian Sign Language has limited resources for developing machine learning and data-driven approaches for automated language processing. Though text/audio-based language processing techniques have shown colossal research interest and tremendous improvements in the last few years, Sign Languages still need to catch up due to the need for more resources. To bridge this gap, in this work, we propose iSign: a benchmark for Indian Sign Language (ISL) Processing. We make three primary contributions to this work. First, we release one of the largest ISL-English datasets with more than video-sentence/phrase pairs. To the best of our knowledge, it is the largest sign language dataset available for ISL. Second, we propose multiple NLP-specific tasks (including SignVideo2Text, SignPose2Text, Text2Pose, Word Prediction, and Sign Semantics) and benchmark them with the baseline models for easier access to the research community. Third, we provide detailed insights into the proposed benchmarks with a few linguistic insights into the working of ISL. We streamline the evaluation of Sign Language processing, addressing the gaps in the NLP research community for Sign Languages. We release the dataset, tasks and models via the following website: https://exploration-lab.github.io/iSign/

The rapid advancements of Large Language Models (LLMs) tightly associate with the expansion of the training data size. However, the unchecked ultra-large-scale training sets introduce a series of potential risks like data contamination, i.e. the benchmark data is used for training. In this work, we propose a holistic method named Polarized Augment Calibration (PAC) along with a new to-be-released dataset to detect the contaminated data and diminish the contamination effect. PAC extends the popular MIA (Membership Inference Attack) — from machine learning community — by forming a more global target at detecting training data to Clarify invisible training data. As a pioneering work, PAC is very much plug-and-play that can be integrated with most (if not all) current white- and black-box LLMs. By extensive experiments, PAC outperforms existing methods by at least 4.5%, towards data contamination detection on more 4 dataset formats, with more than 10 base LLMs. Besides, our application in real-world scenarios highlights the prominent presence of contamination and related issues.

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Truth-Aware Context Selection: Mitigating Hallucinations of Large Language Models Being Misled by Untruthful Contexts
Tian Yu | Shaolei Zhang | Yang Feng

Although Large Language Models (LLMs) have demonstrated impressive text generation capabilities, they are easily misled by untruthful contexts provided by users or knowledge augmentation tools, leading to hallucinations. To alleviate LLMs from being misled by untruthful context and take advantage of knowledge augmentation, we propose Truth-Aware Context Selection (TACS), a lightweight method to adaptively recognize and mask untruthful context from the inputs. TACS begins by performing truth detection on the input context, leveraging the parameterized knowledge within the LLM. Subsequently, it constructs a corresponding attention mask based on the truthfulness of each position, selecting the truthful context and discarding the untruthful context. Additionally, we introduce a new evaluation metric, Disturbance Adaption Rate, to further study the LLMs’ ability to accept truthful information and resist untruthful information.Experimental results indicate that TACS can effectively filter untruthful context and significantly improve the overall quality of LLMs’ responses when presented with misleading information.

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Efficiently Exploring Large Language Models for Document-Level Machine Translation with In-context Learning
Menglong Cui | Jiangcun Du | Shaolin Zhu | Deyi Xiong

Large language models (LLMs) exhibit outstanding performance in machine translation via in-context learning. In contrast to sentence-level translation, document-level translation (DOCMT) by LLMs based on in-context learning faces two major challenges: firstly, document translations generated by LLMs are often incoherent; secondly, the length of demonstration for in-context learning is usually limited. To address these issues, we propose a Context-Aware Prompting method (CAP), which enables LLMs to generate more accurate, cohesive, and coherent translations via in-context learning. CAP takes into account multi-level attention, selects the most relevant sentences to the current one as context, and then generates a summary from these collected sentences. Subsequently, sentences most similar to the summary are retrieved from the datastore as demonstrations, which effectively guide LLMs in generating cohesive and coherent translations. We conduct extensive experiments across various DOCMT tasks, and the results demonstrate the effectiveness of our approach, particularly in zero pronoun translation (ZPT) and literary translation tasks.

Nowadays, data augmentation through synthetic data has been widely used in the field of Grammatical Error Correction (GEC) to alleviate the problem of data scarcity. However, these synthetic data are mainly used in the pre-training phase rather than the data-limited fine tuning phase due to inconsistent error distribution and noisy labels. In this paper, we propose a synthetic data construction method based on contextual augmentation, which can ensure an efficient augmentation of the original data with a more consistent error distribution. Specifically, we combine rule-based substitution with model-based generation, using the generation model to generate a richer context for the extracted error patterns. Besides, we also propose a relabeling-based data cleaning method to mitigate the effects of noisy labels in synthetic data. Experiments on CoNLL14 and BEA19-Test show that our proposed augmentation method consistently and substantially outperforms strong baselines and achieves the state-of-the-art level with only a few synthetic data.

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RECOST: External Knowledge Guided Data-efficient Instruction Tuning
Qi Zhang | Yiming Zhang | Haobo Wang | Junbo Zhao

In the current landscape of large language models (LLMs), the process of instruction tuning serves as an essential step. Considering the high computing power overhead, data-efficient instruction tuning was proposed to reduce the training data size in this process, aiming at selecting high-quality instructional data. Nevertheless, we argue that most current data-efficient instruction-tuning methods are highly dependent on the quality of the original instruction-tuning dataset. When it comes to datasets synthesized by LLMs, a common scenario in this field, dirty samples will even be selected with a higher probability than other samples. To address these challenges, we utilized external knowledge (relevant examples or paragraphs) to evaluate those samples synthesized by LLMs with an in-context-based relative predictive entropy. Based on the new metric, we proposed a framework, dubbed as RECOST, which integrates external-knowledge-base re-ranking and diversity-consistent sampling into a single pipeline. Through extensive experiments on several synthetic datasets (Alpaca and Alpaca-gpt4), we demonstrate the effectiveness of our method and achieve even better results with only 1% of the full dataset.

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Understanding Cross-Lingual Alignment—A Survey
Katharina Hämmerl | Jindřich Libovický | Alexander Fraser

Cross-lingual alignment, the meaningful similarity of representations across languages in multilingual language models, has been an active field of research in recent years. We survey the literature of techniques to improve cross-lingual alignment, providing a taxonomy of methods and summarising insights from throughout the field. We present different understandings of cross-lingual alignment and their limitations. We provide a qualitative summary of results from a number of surveyed papers. Finally, we discuss how these insights may be applied not only to encoder models, where this topic has been heavily studied, but also to encoder-decoder or even decoder-only models, and argue that an effective trade-off between language-neutral and language-specific information is key.

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Mitigate Negative Transfer with Similarity Heuristic Lifelong Prompt Tuning
Chenyuan Wu | Gangwei Jiang | Defu Lian

Lifelong prompt tuning has significantly advanced parameter-efficient lifelong learning with its efficiency and minimal storage demands on various tasks.Our empirical studies, however, highlights certain transferability constraints in the current methodologies: a universal algorithm that guarantees consistent positive transfer across all tasks is currently unattainable, especially when dealing dissimilar tasks that may engender negative transfer.Identifying the misalignment between algorithm selection and task specificity as the primary cause of negative transfer, we present the Similarity Heuristic Lifelong Prompt Tuning (SHLPT) framework. This innovative strategy partitions tasks into two distinct subsets by harnessing a learnable similarity metric, thereby facilitating fruitful transfer from tasks regardless of their similarity or dissimilarity. Additionally, SHLPT incorporates a parameter pool to combat catastrophic forgetting effectively. Our experiments shows that SHLPT outperforms state-of-the-art techniques in lifelong learning benchmarks and demonstrates robustness against negative transfer in diverse task sequences.

While Large language models (LLMs) have demonstrated considerable capabilities across various natural language tasks, they often fall short of the performance achieved by domain-specific state-of-the-art models. One potential approach to enhance domain-specific capabilities of LLMs involves fine-tuning them using corresponding datasets. However, this method can be both resource and time-intensive, and not applicable to closed-source commercial LLMs. In this paper, we propose Preference Adaptation for Enhancing Domain-specific Abilities of LLMs (PANDA), a method designed to augment the domain-specific capabilities of LLMs by leveraging insights from the response preference of expert models without requiring fine-tuning. Our experimental results reveal that PANDA significantly enhances the domain-specific ability of LLMs on text classification and interactive decision tasks. Moreover, LLM with PANDA even outperforms the expert model that being learned on 4 tasks of ScienceWorld. This finding highlights the potential of exploring tuning-free approaches to achieve weak-to-strong generalization.

Advancements in AI and natural language processing have revolutionized machine-human language interactions, with question answering (QA) systems playing a pivotal role. The knowledge base question answering (KBQA) task, utilizing structured knowledge graphs (KG), allows for handling extensive knowledge-intensive questions. However, a significant gap exists in KBQA datasets, especially for low-resource languages. Many existing construction pipelines for these datasets are outdated and inefficient in human labor, and modern assisting tools like Large Language Models (LLM) are not utilized to reduce the workload. To address this, we have designed and implemented a modern, semi-automated approach for creating datasets, encompassing tasks such as KBQA, Machine Reading Comprehension (MRC), and Information Retrieval (IR), tailored explicitly for low-resource environments. We executed this pipeline and introduced the PUGG dataset, the first Polish KBQA dataset, and novel datasets for MRC and IR. Additionally, we provide a comprehensive implementation, insightful findings, detailed statistics, and evaluation of baseline models.

Generating accurate SQL queries for user questions (text-to-SQL) has been a long-standing challenge since it requires a deep understanding of both the user’s question and the corresponding database schema in order to retrieve the desired content accurately. Existing methods rely on the comprehensive capability of large language models (LLMs) to generate the SQL. However, some necessary knowledge is not explicitly included in the database schema and user question or has been learned by LLMs. Thus, the generated SQL of the knowledge-insufficient questions may be inaccurate, negatively influencing the text-to-SQL models’ performance and robustness. To address this challenge, we propose the Knowledge-to-SQL framework, which employs tailored Data Expert LLM (DELLM) to provide helpful knowledge for all text-to-SQL models. Specifically, we introduce the detailed implementation of DELLM regarding table reading and the basic fine-tuning process. We further propose a Preference Learning via Database Feedback (PLDBF) strategy, refining the DELLM to generate more helpful knowledge for LLMs. Extensive experiments verify that DELLM can enhance the state-of-the-art approaches for text-to-SQL tasks. The corresponding code of DELLM is released for further research.

Minimum Bayes risk (MBR) decoding achieved state-of-the-art translation performance by using COMET, a neural metric that has a high correlation with human evaluation.However, MBR decoding requires quadratic time since it computes the expected score between a translation hypothesis and all reference translations.We propose centroid-based MBR (CBMBR) decoding to improve the speed of MBR decoding.Our method clusters the reference translations in the feature space, and then calculates the score using the centroids of each cluster.The experimental results show that our CBMBR not only improved the decoding speed of the expected score calculation 5.7 times, but also outperformed vanilla MBR decoding in translation quality by up to 0.5 COMET in the WMT’22 En↔Ja, En↔De, En↔Zh, and WMT’23 En↔Ja translation tasks.

In this paper, we tackle the task of distractor generation (DG) for multiple-choice questions. Our study introduces two key designs. First, we propose the concept of retrieval augmented pretraining, which involves refining the language model pretraining to align it more closely with the downstream task of DG. Second, we explore the integration of knowledge graphs and language models to further enhance the performance of DG. Our study unveils promising directions for further development in DG by showcasing the efficacy of knowledge augmentation and task-specific pretraining. These findings demonstrate the potential for leveraging both strategies to enhance the quality and performance of DG systems.

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Exploiting Positional Bias for Query-Agnostic Generative Content in Search
Andrew Parry | Sean MacAvaney | Debasis Ganguly

In recent years, research shows that neural ranking models (NRMs) substantially outperform their lexical counterparts in text retrieval. In traditional search pipelines, a combination of features leads to well-defined behaviour. However, as neural approaches become increasingly prevalent as the final scoring component of engines or as standalone systems, their robustness to malicious text and, more generally, semantic perturbation needs to be better understood. We posit that the transformer attention mechanism can induce exploitable defects in search models through sensitivity to token position within a sequence, leading to an attack that could generalise beyond a single query or topic. We demonstrate such defects by showing that non-relevant text–such as promotional content–can be easily injected into a document without adversely affecting its position in search results. Unlike previous gradient-based attacks, we demonstrate the existence of these biases in a query-agnostic fashion. In doing so, without the knowledge of topicality, we can still reduce the negative effects of non-relevant content injection by controlling injection position. Our experiments are conducted with simulated on-topic promotional text automatically generated by prompting LLMs with topical context from target documents. We find that contextualisation of a non-relevant text further reduces negative effects whilst likely circumventing existing content filtering mechanisms. In contrast, lexical models are found to be more resilient to such content injection attacks. We then investigate a simple yet effective compensation for the weaknesses of the NRMs in search, validating our hypotheses regarding transformer bias.

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ICC : Quantifying Image Caption Concreteness for Multimodal Dataset Curation
Moran Yanuka | Morris Alper | Hadar Averbuch-Elor | Raja Giryes

Web-scale training on paired text-image data is becoming increasingly central to multimodal learning, but is challenged by the highly noisy nature of datasets in the wild. Standard data filtering approaches succeed in removing mismatched text-image pairs, but permit semantically related but highly abstract or subjective text. These approaches lack the fine-grained ability to isolate the most concrete samples that provide the strongest signal for learning in a noisy dataset. In this work, we propose a new metric, Image Caption Concreteness (ICC), that evaluates caption text without an image reference to measure its concreteness and relevancy for use in multimodal learning. Our unsupervised approach leverages strong foundation models for measuring visual-semantic information loss in multimodal representations. We demonstrate that this strongly correlates with human evaluation of concreteness in both single-word and caption-level texts. Moreover, we show that curation using ICC complements existing approaches: It succeeds in selecting the highest quality samples from multimodal web-scale datasets to allow for efficient training in resource-constrained settings.

Within the evolving landscape of deep learning, the dilemma of data quantity and quality has been a long-standing problem. The recent advent of Large Language Models (LLMs) offers a data-centric solution to alleviate the limitations of real-world data with synthetic data generation. However, current investigations into this field lack a unified framework and mostly stay on the surface. Therefore, this paper provides an organization of relevant studies based on a generic workflow of synthetic data generation. By doing so, we highlight the gaps within existing research and outline prospective avenues for future study. This work aims to shepherd the academic and industrial communities towards deeper, more methodical inquiries into the capabilities and applications of LLMs-driven synthetic data generation.

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When is a Language Process a Language Model?
Li Du | Holden Lee | Jason Eisner | Ryan Cotterell

A language model may be viewed as a 𝛴-valued stochastic process for some alphabet 𝛴.However, in some pathological situations, such a stochastic process may “leak” probability mass onto the set of infinite strings and hence is not equivalent to the conventional view of a language model as a distribution over ordinary (finite) strings.Such ill-behaved language processes are referred to as *non-tight* in the literature.In this work, we study conditions of tightness through the lens of stochastic processes.In particular, by regarding the symbol as marking a stopping time and using results from martingale theory, we give characterizations of tightness that generalize our previous work [(Du et al. 2023)](https://arxiv.org/abs/2212.10502).

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Accelerating Multilingual Language Model for Excessively Tokenized Languages
Jimin Hong | Gibbeum Lee | Jaewoong Cho

Recent advancements in large language models (LLMs) have remarkably enhanced performances on a variety of tasks in multiple languages. However, tokenizers in LLMs trained primarily on English-centric corpora often overly fragment a text into character or Unicode-level tokens in non-Roman alphabetic languages, leading to inefficient text generation.We introduce a simple yet effective framework to accelerate text generation in such languages. Our approach involves employing a new language model head with a vocabulary set tailored to a specific target language for a pre-trained LLM. This is followed by fine-tuning the new head while incorporating a verification step to ensure the model’s performance is preserved.We show that this targeted fine-tuning, while freezing other model parameters, effectively reduces token fragmentation for the target language. Our extensive experiments demonstrate that the proposed framework increases the generation speed by a factor of 1.7 while maintaining the performance of pre-trained multilingual models on target monolingual tasks.

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Definition Generation for Automatically Induced Semantic Frame
Yi Han | Ryohei Sasano | Koichi Takeda

In a semantic frame resource such as FrameNet, the definition sentence of a frame is essential for humans to understand the meaning of the frame intuitively. Recently, several attempts have been made to induce semantic frames from large corpora, but the cost of creating the definition sentences for such frames is significant. In this paper, we address a new task of generating frame definitions from a set of frame-evoking words. Specifically, given a cluster of frame-evoking words and associated exemplars induced as the same semantic frame, we utilize a large language model to generate frame definitions. We demonstrate that incorporating frame element reasoning as chain-of-thought can enhance the inclusion of correct frame elements in the generated definitions.

Generative retrieval is a promising new paradigm in text retrieval that generates identifier strings of relevant passages as the retrieval target. This paradigm leverages powerful generative language models, distinct from traditional sparse or dense retrieval methods. In this work, we identify a viable direction to further enhance generative retrieval via distillation and propose a feasible framework, named DGR. DGR utilizes sophisticated ranking models, such as the cross-encoder, in a teacher role to supply a passage rank list, which captures the varying relevance degrees of passages instead of binary hard labels; subsequently, DGR employs a specially designed distilled RankNet loss to optimize the generative retrieval model, considering the passage rank order provided by the teacher model as labels. This framework only requires an additional distillation step to enhance current generative retrieval systems and does not add any burden to the inference stage. We conduct experiments on four public datasets, and the results indicate that DGR achieves state-of-the-art performance among the generative retrieval methods. Additionally, DGR demonstrates exceptional robustness and generalizability with various teacher models and distillation losses.

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ToxVidLM: A Multimodal Framework for Toxicity Detection in Code-Mixed Videos
Krishanu Maity | A.s. Poornash | Sriparna Saha | Pushpak Bhattacharyya

In an era of rapidly evolving internet technology, the surge in multimodal content, including videos, has expanded the horizons of online communication. However, the detection of toxic content in this diverse landscape, particularly in low-resource code-mixed languages, remains a critical challenge. While substantial research has addressed toxic content detection in textual data, the realm of video content, especially in non-English languages, has been relatively underexplored. This paper addresses this research gap by introducing a benchmark dataset, the first of its kind, consisting of 931 videos with 4021 code-mixed Hindi-English utterances collected from YouTube. Each utterance within this dataset has been meticulously annotated for toxicity, severity, and sentiment labels. We have developed an advanced Multimodal Multitask framework built for Toxicity detection in Video Content by leveraging Language Models (LMs), crafted for the primary objective along with the additional tasks of conducting sentiment and severity analysis. ToxVidLM incorporates three key modules – the Encoder module, Cross-Modal Synchronization module, and Multitask module – crafting a generic multimodal LM customized for intricate video classification tasks. Our experiments reveal that incorporating multiple modalities from the videos substantially enhances the performance of toxic content detection by achieving an Accuracy and Weighted F1 score of 94.29% and 94.35%, respectively.

Large Language Models (LLMs) have witnessed remarkable advancements in recent years, prompting the exploration of tool learning, which integrates LLMs with external tools to address diverse real-world challenges. Assessing the capability of LLMs to utilise tools necessitates large-scale and stable benchmarks. However, previous works relied on either hand-crafted online tools with limited scale, or large-scale real online APIs suffering from instability of API status. To address this problem, we introduce StableToolBench, a benchmark evolving from ToolBench, proposing a virtual API server and stable evaluation system. The virtual API server contains a caching system and API simulators which are complementary to alleviate the change in API status. Meanwhile, the stable evaluation system designs solvable pass and win rates using GPT-4 as the automatic evaluator to eliminate the randomness during evaluation. Experimental results demonstrate the stability of StableToolBench, and further discuss the effectiveness of API simulators, the caching system, and the evaluator system.

Emotional Intelligence (EI), consisting of emotion perception, emotion cognition and emotion expression, plays the critical roles in improving user interaction experience for the current large language model (LLM) based conversational general AI assistants. Previous works mainly focus on raising the emotion perception ability of them via naive fine-tuning on EI-related classification or regression tasks. However, this leads to the incomplete enhancement of EI and catastrophic forgetting of the general intelligence (GI). To this end, we first introduce EiBench, a large-scale collection of EI-related tasks in the text-to-text format with task instructions that covers all three aspects of EI, which lays a solid foundation for the comprehensive EI enhancement of LLMs. Then a novel Modular Emotional Intelligence enhancement method (**MoEI**), consisting of Modular Parameter Expansion and intra-inter modulation, is proposed to comprehensively enhance the EI of LLMs without compromise their GI. Extensive experiments on two representative LLM-based assistants, Flan-T5 and LLaMA-2-Chat, demonstrate the effectiveness of MoEI to improving EI while maintain GI.

To reliably deploy Large Language Models (LLMs) in a specific country, they must possess an understanding of the nation’s culture and basic knowledge. To this end, we introduce National Alignment, which measures the alignment between an LLM and a targeted country from two aspects: social value alignment and common knowledge alignment. We constructed KorNAT, the first benchmark that measures national alignment between LLMs and South Korea. KorNat contains 4K and 6K multiple-choice questions for social value and common knowledge, respectively. To attain an appropriately aligned ground truth in the social value dataset, we conducted a large-scale public survey with 6,174 South Koreans. For common knowledge, we created the data based on the South Korea text books and GED exams. Our dataset creation process is meticulously designed based on statistical sampling theory, and we also introduce metrics to measure national alignment, including three variations of social value alignment. We tested seven LLMs and found that only few models passed our reference score, indicating there exists room for improvement. Our dataset has received government approval following an assessment by a government-affiliated organization dedicated to evaluating dataset quality.

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Enhancing Adverse Drug Event Detection with Multimodal Dataset: Corpus Creation and Model Development
Pranab Sahoo | Ayush Singh | Sriparna Saha | Aman Chadha | Samrat Mondal

The mining of adverse drug events (ADEs) is pivotal in pharmacovigilance, enhancing patient safety by identifying potential risks associated with medications, facilitating early detection of adverse events, and guiding regulatory decision-making. Traditional ADE detection methods are reliable but slow, not easily adaptable to large-scale operations, and offer limited information. With the exponential increase in data sources like social media content, biomedical literature, and Electronic Medical Records (EMR), extracting relevant ADE-related information from these unstructured texts is imperative. Previous ADE mining studies have focused on text-based methodologies, overlooking visual cues, limiting contextual comprehension, and hindering accurate interpretation. To address this gap, we present a MultiModal Adverse Drug Event (MMADE) detection dataset, merging ADE-related textual information with visual aids. Additionally, we introduce a framework that leverages the capabilities of LLMs and VLMs for ADE detection by generating detailed descriptions of medical images depicting ADEs, aiding healthcare professionals in visually identifying adverse events. Using our MMADE dataset, we showcase the significance of integrating visual cues from images to enhance overall performance. This approach holds promise for patient safety, ADE awareness, and healthcare accessibility, paving the way for further exploration in personalized healthcare.

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Space Decomposition for Sentence Embedding
Wuttikorn Ponwitayarat | Peerat Limkonchotiwat | Ekapol Chuangsuwanich | Sarana Nutanong

Determining sentence pair similarity is crucial for various NLP tasks. A common technique to address this is typically evaluated on a continuous semantic textual similarity scale from 0 to 5. However, based on a linguistic observation in STS annotation guidelines, we found that the score in the range [4,5] indicates an upper-range sample, while the rest are lower-range samples. This necessitates a new approach to treating the upper-range and lower-range classes separately. In this paper, we introduce a novel embedding space decomposition method called MixSP utilizing a Mixture of Specialized Projectors, designed to distinguish and rank upper-range and lower-range samples accurately. The experimental results demonstrate that MixSP decreased the overlap representation between upper-range and lower-range classes significantly while outperforming competitors on STS and zero-shot benchmarks.

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Don’t Augment, Rewrite? Assessing Abusive Language Detection with Synthetic Data
Camilla Casula | Elisa Leonardelli | Sara Tonelli

Research on abusive language detection and content moderation is crucial to combat online harm. However, current limitations set by regulatory bodies and social media platforms can make it difficult to share collected data. We address this challenge by exploring the possibility to replace existing datasets in English for abusive language detection with synthetic data obtained by rewriting original texts with an instruction-based generative model.We show that such data can be effectively used to train a classifier whose performance is in line, and sometimes better, than a classifier trained on original data. Training with synthetic data also seems to improve robustness in a cross-dataset setting. A manual inspection of the generated data confirms that rewriting makes it impossible to retrieve the original texts online.

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Improving Low-Resource Machine Translation for Formosan Languages Using Bilingual Lexical Resources
Francis Zheng | Edison Marrese-Taylor | Yutaka Matsuo

This paper investigates how machine translation for low-resource languages can be improved by incorporating information from bilingual lexicons during the training process for mainly translation between Mandarin and Formosan languages, which are all moribund or critically endangered, and we also show that our techniques work for translation between Spanish and Nahuatl, a language pair consisting of languages from completely different language families. About 70% of the approximately 7,000 languages of the world have data in the form of lexicons, a valuable resource for improving low-resource language translation. We collect a dataset of parallel data and bilingual lexicons between Mandarin and 16 different Formosan languages and examine mainly three different approaches: (1) simply using lexical data as additional parallel data, (2) generating pseudo-parallel sentence data to use during training by replacing words in the original parallel sentence data using the lexicon, and (3) a combination of (1) and (2). All three approaches give us gains in both Bleu scores and chrF scores, and we found that (3) provided the most gains, followed by (1) and then (2), which we observed for both translation between Mandarin and the Formosan languages and Spanish-Nahuatl. With technique (3), we saw an average increase of 5.55 in Bleu scores and 10.33 in chrF scores.

As the capabilities of large language models (LLMs) continue to advance, evaluating their performance is becoming more important and more challenging. This paper aims to address this issue for Mandarin Chinese in the form of CMMLU, a comprehensive Chinese benchmark that covers various subjects, including natural sciences, social sciences, engineering, and the humanities. We conduct a thorough evaluation of more than 20 contemporary multilingual and Chinese LLMs, assessing their performance across different subjects and settings. The results reveal that most existing LLMs struggle to achieve an accuracy of even 60%, which is the pass mark for Chinese exams. This highlights that there is substantial room for improvement in the capabilities of LLMs. Additionally, we conduct extensive experiments to identify factors impacting the models’ performance and propose directions for enhancing LLMs. CMMLU fills the gap in evaluating the knowledge and reasoning capabilities of large language models for Chinese.

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Prometheus-Vision: Vision-Language Model as a Judge for Fine-Grained Evaluation
Seongyun Lee | Seungone Kim | Sue Hyun Park | Geewook Kim | Minjoon Seo

Assessing long-form responses generated by Vision-Language Models (VLMs) is challenging. It not only requires checking whether the VLM follows the given instruction but also verifying whether the text output is properly grounded on the given image. Inspired by the recent approach of evaluating LMs with LMs, in this work, we propose to evaluate VLMs with VLMs. For this purpose, we present a new feedback dataset called the Perception Collection, encompassing 15K customized score rubrics that users might care about during assessment. Using the Perception Collection, we train Prometheus-Vision, the first open-source VLM evaluator model that can understand the user-defined score criteria during evaluation. Prometheus-Vision shows the highest Pearson correlation with human evaluators and GPT-4V among open-source models, showing its effectiveness for transparent and accessible evaluation of VLMs. We open-source our code, dataset, and model.

The rapid advancement of Large Language Models (LLMs) in the realm of mathematical reasoning necessitates comprehensive evaluations to gauge progress and inspire future directions. Existing assessments predominantly focus on problem-solving from the examinee perspective, overlooking a dual perspective of examiner regarding error identification and correction.From the examiner perspective, we define four evaluation tasks for error identification and correction along with a new dataset with annotated error types and steps. We also design diverse prompts to thoroughly evaluate eleven representative LLMs. Our principal findings indicate that GPT-4 outperforms all models, while open-source model LLaMA-2-7B demonstrates comparable abilities to closed-source models GPT-3.5 and Gemini Pro.Notably, calculation error proves the most challenging error type. Moreover, prompting LLMs with the error types can improve the average correction accuracy by 47.9%. These results reveal potential directions for developing the mathematical reasoning abilities of LLMs.Our code and dataset is available on https://github.com/LittleCirc1e/EIC.

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Less is KEN: a Universal and Simple Non-Parametric Pruning Algorithm for Large Language Models
Michele Mastromattei | Fabio Massimo Zanzotto

Neural network pruning has become increasingly crucial due to the complexity of these models and their widespread use in various fields. Existing pruning algorithms often suffer from limitations such as architecture specificity, excessive complexity and reliance on demanding calculations, rendering them impractical for real-world applications.This paper introduces KEN: a straightforward, universal and unstructured pruning algorithm based on Kernel Density Estimation (KDE). KEN aims to construct optimized transformers by selectively preserving the most significant parameters while restoring others to their pre-training state. This strategy preserves model performance while enabling storage of only the optimized subnetwork, leading to substantial memory savings.Extensive evaluations across seven different LLMs demonstrate that KEN achieves equal or better performance than their original unpruned versions, with a minimum parameter reduction of 25%. Furthermore, in-depth comparisons with established pruning and PEFT algorithms confirm KEN effectiveness. We further introduce KEN_viz, an explainable tool that visualizes the optimized model composition achieved by KEN from different points of view.

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When Do LLMs Need Retrieval Augmentation? Mitigating LLMs’ Overconfidence Helps Retrieval Augmentation
Shiyu Ni | Keping Bi | Jiafeng Guo | Xueqi Cheng

Large Language Models (LLMs) have been found to have difficulty knowing they do not possess certain knowledge and tend to provide specious answers in such cases. Retrieval Augmentation (RA) has been extensively studied to mitigate LLMs’ hallucinations. However, due to the extra overhead and unassured quality of retrieval, it may not be optimal to conduct RA all the time. A straightforward idea is to only conduct retrieval when LLMs are uncertain about a question. This motivates us to enhance the LLMs’ ability to perceive their knowledge boundaries to help RA. In this paper, we first quantitatively measure LLMs’ such ability and confirm their overconfidence. Then, we study how LLMs’ certainty about a question correlates with their dependence on external retrieved information. We propose several methods to enhance LLMs’ perception of knowledge boundaries and show that they are effective in reducing overconfidence. Additionally, equipped with these methods, LLMs can achieve comparable or even better performance of RA with much fewer retrieval calls.

Alignment training is crucial for enabling large language models (LLMs) to cater to human intentions and preferences. It is typically performed based on two stages with different objectives: instruction-following alignment and human-preference alignment. However, aligning LLMs with these objectives in sequence suffers from an inherent problem: the objectives may conflict, and the LLMs cannot guarantee to simultaneously align with the instructions and human preferences well. To response to these, in this work, we propose a Hybrid Alignment Training (Hbat) approach, based on alternating alignment and modified elastic weight consolidation methods. The basic idea is to alternate between different objectives during alignment training, so that better collaboration can be achieved between the two alignment tasks. We experiment with Hbat on summarization and dialogue tasks. Experimental results show that the proposed Hbat can significantly outperform all baselines. Notably, Hbat yields consistent performance gains over the traditional two-stage alignment training when using both proximal policy optimization and direct preference optimization.

Speculative decoding has emerged as a promising technique to accelerate the inference of Large Language Models (LLMs) by employing a small language model to draft a hypothesis sequence, which is then validated by the LLM. The effectiveness of this approach heavily relies on the balance between performance and efficiency of the draft model. In our research, we focus on enhancing the proportion of draft tokens that are accepted to the final output by generating multiple hypotheses instead of just one. This allows the LLM more options to choose from and select the longest sequence that meets its standards. Our analysis reveals that hypotheses produced by the draft model share many common token sequences, suggesting a potential for optimizing computation. Leveraging this observation, we introduce an innovative approach utilizing a directed acyclic graph (DAG) to manage the drafted hypotheses. This structure enables us to efficiently predict and merge recurring token sequences, vastly reducing the computational demands of the draft model. We term this approach Graph-structured Speculative Decoding (GSD). We apply GSD across a range of LLMs, including a 70-billion parameter LLaMA-2 model, and observe a remarkable speedup of 1.70× to 1.94 ×, significantly surpassing standard speculative decoding.

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Duwak: Dual Watermarks in Large Language Models
Chaoyi Zhu | Jeroen Galjaard | Pin-Yu Chen | Lydia Chen

As large language models (LLM) are increasingly used for text generation tasks, it is critical to audit their usages, govern their applications, and mitigate their potential harms. Existing watermark techniques are shown effective in embedding single human-imperceptible and machine-detectable patterns without significantly affecting generated text quality and semantics. However, the efficiency in detecting watermarks, i.e., the minimum number of tokens required to assert detection with significance and robustness against post-editing, is still debatable. In this paper, we propose, Duwak, to fundamentally enhance the efficiency and quality of watermarking by embedding dual secret patterns in both token probability distribution and sampling schemes. To mitigate expression degradation caused by biasing toward certain tokens, we design a contrastive search to watermark the sampling scheme, which minimizes the token repetition and enhances the diversity. We theoretically explain the interdependency of the two watermarks within Duwak. We evaluate Duwak extensively on Llama2 and Vicuna under various post-editing attacks, against four state-of-the-art watermarking techniques and combinations of them. Our results show that Duwak marked text achieves the highest watermarked text quality at the lowest required token count for detection, up to 70% tokens less than existing approaches, especially under post paraphrasing.

The rapid advancement of Large Language Models (LLMs) has brought about remarkable generative capabilities but also raised concerns about their potential misuse. While strategies like supervised fine-tuning and reinforcement learning from human feedback have enhanced their safety, these methods primarily focus on natural languages, which may not generalize to other domains. This paper introduces CodeAttack, a framework that transforms natural language inputs into code inputs, presenting a novel environment for testing the safety generalization of LLMs. Our comprehensive studies on state-of-the-art LLMs including GPT-4, Claude-2, and Llama-2 series reveal a new and universal safety vulnerability of these models against code input: CodeAttack bypasses the safety guardrails of all models more than 80% of the time. We find that a larger distribution gap between CodeAttack and natural language leads to weaker safety generalization, such as encoding natural language input with data structures. Furthermore, we give our hypotheses about the success of CodeAttack: the misaligned bias acquired by LLMs during code training, prioritizing code completion over avoiding the potential safety risk. Finally, we analyze potential mitigation measures. These findings highlight new safety risks in the code domain and the need for more robust safety alignment algorithms to match the code capabilities of LLMs.

While large language models (LLMs) have achieved impressive performance across diverse tasks, recent studies showcase that causal LLMs suffer from the “reversal curse”. It is a typical example that the model knows “A’s father is B”, but is unable to reason “B’s child is A”. This limitation poses a challenge to the advancement of artificial general intelligence (AGI), as it suggests a gap in the models’ ability to comprehend and apply bidirectional reasoning. In this paper, we first conduct substantial evaluation and identify that the root cause of the reversal curse lies in the different word order between the training and inference stage, namely, the poor ability of causal language models to predict antecedent words within the training data. Accordingly, permutation on the training data is considered as a potential solution, since this can make the model predict antecedent words or tokens. However, previous permutation methods may disrupt complete phrases or entities, thereby posing challenges for the model to comprehend and learn from training data. To address this issue, we propose Semantic-aware Permutation Training (SPT), which addresses this issue by segmenting the training sentences into semantic units (i.e., entities or phrases) with an assistant language model and permuting these units before feeding into the model. Extensive experiments demonstrate that SPT effectively mitigates the reversal curse since the performance on reversed questions approximates that on the forward ones, and significantly advances the performance of existing works.

Pre-trained speech models, such as wav2vec 2.0, have significantly advanced speech-related tasks, including speech recognition and translation. However, their applicability in streaming scenarios is limited because these models are trained on complete utterances, leading to a mismatch with incremental streaming inputs. This paper identifies three critical design aspects within the architecture of wav2vec 2.0 and proposes a novel model, wav2vec-S, which incorporates simple modifications to ensure consistent speech representations during both training and inference phases for streaming speech inputs. Furthermore, we demonstrate that wav2vec-S models can be efficiently adapted from pre-trained wav2vec 2.0 models through continued pre-training and effectively finetuned to meet various latency requirements in downstream applications. Experiments on speech recognition and translation tasks show that wav2vec-S outperforms strong baseline models and achieves a superior balance between quality and latency.

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Peering into the Mind of Language Models: An Approach for Attribution in Contextual Question Answering
Anirudh Phukan | Shwetha Somasundaram | Apoorv Saxena | Koustava Goswami | Balaji Vasan Srinivasan

With the enhancement in the field of generative artificial intelligence (AI), contextual question answering has become extremely relevant. Attributing model generations to the input source document is essential to ensure trustworthiness and reliability. We observe that when large language models (LLMs) are used for contextual question answering, the output answer often consists of text copied verbatim from the input prompt which is linked together with “glue text” generated by the LLM. Motivated by this, we propose that LLMs have an inherent awareness from where the text was copied, likely captured in the hidden states of the LLM. We introduce a novel method for attribution in contextual question answering, leveraging the hidden state representations of LLMs. Our approach bypasses the need for extensive model retraining and retrieval model overhead, offering granular attributions and preserving the quality of generated answers. Our experimental results demonstrate that our method performs on par or better than GPT-4 at identifying verbatim copied segments in LLM generations and in attributing these segments to their source. Importantly, our method shows robust performance across various LLM architectures, highlighting its broad applicability. Additionally, we present Verifiability-granular, an attribution dataset which has token level annotations for LLM generations in the contextual question answering setup.

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TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification
Martin Gubri | Dennis Ulmer | Hwaran Lee | Sangdoo Yun | Seong Joon Oh

Large Language Model (LLM) services and models often come with legal rules on *who* can use them and *how* they must use them. Assessing the compliance of the released LLMs is crucial, as these rules protect the interests of the LLM contributor and prevent misuse. In this context, we describe the novel fingerprinting problem of Black-box Identity Verification (BBIV). The goal is to determine whether a third-party application uses a certain LLM through its chat function. We propose a method called Targeted Random Adversarial Prompt (TRAP) that identifies the specific LLM in use. We repurpose adversarial suffixes, originally proposed for jailbreaking, to get a pre-defined answer from the target LLM, while other models give random answers. TRAP detects the target LLMs with over 95% true positive rate at under 0.2% false positive rate even after a single interaction. TRAP remains effective even if the LLM has minor changes that do not significantly alter the original function.

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CLASP: Cross-modal Alignment Using Pre-trained Unimodal Models
Jianing Zhou | Ziheng Zeng | Hongyu Gong | Suma Bhat

Recent advancements in joint speech-text pre-training have significantly advanced the processing of natural language. However, a key limitation is their reliance on parallel speech-text data, posing challenges due to data accessibility. Addressing this, our paper introduces an innovative framework for jointly performing speech and text processing without parallel corpora during pre-training but only downstream. Utilizing pre-trained unimodal models, we extract distinct representations for speech and text, aligning them effectively in a newly defined space using a multi-level contrastive learning mechanism. A unique swap reconstruction mechanism enhances the alignment and is followed by fusion via a multi-head mechanism, seamlessly merging modality-invariant and modality-specific representations. Testing for emotion recognition (SLU task) and idiom usage detection (NLU task) demonstrates robust performance, with commendable robustness to noise in text or speech data.

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TimeToM: Temporal Space is the Key to Unlocking the Door of Large Language Models’ Theory-of-Mind
Guiyang Hou | Wenqi Zhang | Yongliang Shen | Linjuan Wu | Weiming Lu

Theory of Mind (ToM)—the cognitive ability to reason about mental states of ourselves and others, is the foundation of social interaction. Although ToM comes naturally to humans, it poses a significant challenge to even the most advanced Large Language Models (LLMs). Due to the complex logical chains in ToM reasoning, especially in higher-order ToM questions, simply utilizing reasoning methods like Chain of Thought (CoT) will not improve the ToM capabilities of LLMs. We present TimeToM, which constructs a temporal space and uses it as the foundation to improve the ToM capabilities of LLMs in multiple scenarios. Specifically, within the temporal space, we construct Temporal Belief State Chain (TBSC) for each character and inspired by the cognition perspective of the social world model, we divide TBSC into self-world beliefs and social world beliefs, aligning with first-order ToM (first-order beliefs) and higher-order ToM (higher-order beliefs) questions, respectively. Moreover, we design a novel tool-belief solver that, by considering belief communication between characters in temporal space, can transform a character’s higher-order beliefs into another character’s first-order beliefs under belief communication period.

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Identifying and Mitigating Annotation Bias in Natural Language Understanding using Causal Mediation Analysis
Sitiporn Sae Lim | Can Udomcharoenchaikit | Peerat Limkonchotiwat | Ekapol Chuangsuwanich | Sarana Nutanong

NLU models have achieved promising results on standard benchmarks. Despite state-of-the-art accuracy, analysis reveals that many models make predictions using annotation bias rather than the properties we intend the model to learn. Consequently, these models perform poorly on out-of-distribution datasets. Recent advances in bias mitigation show that annotation bias can be alleviated through fine-tuning debiasing objectives. In this paper, we apply causal mediation analysis to gauge how much each model component mediates annotation biases. Using the knowledge from the causal analysis, we improve the model’s robustness against annotation bias through two bias mitigation methods: causal-grounded masking and gradient unlearning. Causal analysis reveals that biases concentrated in specific components, even after employing other training-time debiasing techniques. Manipulating these components by masking out neurons’ activations or updating specific weight blocks both demonstrably improve robustness against annotation artifacts.

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Perturbed examples reveal invariances shared by language models
Ruchit Rawal | Mariya Toneva

The rapid growth in natural language processing (NLP) research has led to numerous new models, outpacing our understanding of how they compare to established ones. One major reason for this difficulty is saturating benchmarks, which may not well reflect differences in model performance in the wild. In this work, we introduce a novel framework to compare two NLP models by revealing their shared invariance to interpretable input perturbations targeting a specific linguistic capability. Via experiments on models from the same and different architecture families, this framework offers insights about how changes in models (e.g., distillation, size increase) affect linguistic capabilities. Furthermore, our framework enables evaluation of invariances between commercial black-box models (e.g., InstructGPT family) and models that are better understood (e.g., GPT-2). Across experiments, we observe that large language models share many invariances encoded by models of various sizes, whereas the invariances by large models are only shared by other large models. Possessing a wide variety of invariances may be key to the recent successes of large language models, and our framework can shed light on the types of invariances retained or emerging in new models. We make the code publicly available.

Stochastic sampling strategies such as top-k and top-p have been widely used in dialogue generation task. However, as an open-domain chatting system, there will be two different conversation scenarios, i.e. chit-chat and knowledge-based question answering. In the former situation, responses diversity is essential due to the one-to-many nature in dialogue. The latter, on the other hand, requires less randomness given that stochastic decoding strategy entails the risk of generating incorrect information. As a result, an adaptive and flexible decoding strategy is needed to cope with these two scenarios simultaneously. To this end, we propose the dynamic decoding strategy (DDS), which can adjust the decoding space w.r.t. different contexts. In DDS, both sequence-level and token-level adaptive search can be achieved to adjust the decoding process in a unified framework. Besides, our adaptive algorithm can not only be used during model inference, but it can also be applied during the model training stage to further enhance the performance. Comprehensive experiments indicate that the proposed decoding strategy can consistently improve the performance of pre-trained dialogue models when coupled with four well-used stochastic decoding algorithms.

End-to-end argumentation mining (AM) aims to extract the argumentation structure including argumentation components and their argumentation relations from text. Recent developments in end-to-end AM models have demonstrated significant progress by redefining the AM task as a sequence generation task, exhibiting simplicity and competitive performance. Nevertheless, these models overlook the integration of supplementary discourse structure information, a crucial factor for comprehending argumentation structures, resulting in suboptimal outcomes. In this study, we propose the DENIM framework, which generates discourse structure-aware prefixes for each layer of the generation model. These prefixes imbue the generation-based AM model with discourse structures, thereby augmenting the overall generation process. Moreover, we introduce a multi-task prompt coupled with a three-step decoding strategy, aiming to optimize the efficiency and effectiveness of argumentation structure decoding. Extensive experiments and analyses on two benchmark datasets show that DENIM achieves state-of-the-art performances on two AM benchmarks.

The guidance from capability evaluations has greatly propelled the progress of human society and the development of Artificial Intelligence. However, as LLMs evolve, it becomes challenging to construct evaluation benchmark with accurate labels for SuperLLMs whose capabilities approach or even surpass those of humans. To credibly conduct poor-supervised evaluation without accurate labels, we first prove that the consistency between the model under evaluation and the reference model, when their prediction distributions are independent and the sample size is infinite, can equivalently assess the true capabilities of the model to be evaluated. However, using either humans or LLMs as the reference model cannot sufficiently meet the conditions, for which we propose the PEEM algorithm. By treating all models under evaluation as reference models, PEEM alternately optimizes model weights and filters reference models based on EM algorithm to maximally alleviate the insufficiency of the conditions. Comprehensive experiments across 3 types of tasks with 16 mainstream LLMs validate the efficiency, universality, and effectiveness of PEEM. More generally, PEEM has advanced the evaluation paradigm evolution from human-centric to human&model-centric, alleviating the limitations of human capabilities for evaluating SuperLLMs.

Neural machine translation (NMT) systems often produce inadequate translations for named entities. In this study, we conducted preliminary experiments to examine the factors affecting the translation accuracy of named entities, specifically focusing on their translation difficulty and context diversity. Based on our observations, we propose a novel data augmentation strategy to enhance the accuracy of named entity translation. The main concept behind our approach is to increase both the context diversity and translation probability for the targeted named entity pair. To achieve this, we construct additional samples for named entities that exhibit high translation difficulty or low context diversity and use the augmented training data to re-train the final translation model. Furthermore, we propose an entity-aware machine translation metric that prefers the translation output to generate more accurate named entities. Our experimental results demonstrate significant improvements over the baseline in terms of general translation performance and named entity translation accuracy across various test sets, such as WMT news translation and terminology test sets.

To improve the performance of the dual-encoder retriever, one effective approach is knowledge distillation from the cross-encoder ranker. Existing works prepare training instances by pairing each query with one positive and a batch of negatives. However, most hard negatives mined by advanced dense retrieval methods are still too trivial for the teacher to distinguish, preventing the teacher from transferring abundant dark knowledge to the student through its soft label. To alleviate this issue, we propose Adam, a knowledge distillation framework that can better transfer the dark knowledge held in the teacher with adaptive dark examples. Different from previous works that only rely on one positive and hard negatives as candidate passages, we create dark examples that all have moderate relevance to the query by strengthening negatives and masking positives in the discrete space. Furthermore, as the quality of knowledge held in different training instances varies as measured by the teacher’s confidence score, we propose a self-paced distillation strategy that adaptively concentrates on a subset of high-quality instances to conduct our dark-example-based knowledge distillation to help the student learn better. We conduct experiments on two widely-used benchmarks and verify the effectiveness of our method.

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Instruction Position Matters in Sequence Generation with Large Language Models
Yijin Liu | Xianfeng Zeng | Chenze Shao | Fandong Meng | Jie Zhou

Large language models (LLMs) are capable of performing conditional sequence generation tasks, such as translation or summarization, through instruction fine-tuning. The fine-tuning data is generally sequentially concatenated from a specific task instruction, an input sentence, and the corresponding response. Considering the locality modeled by the self-attention mechanism of LLMs, these models face the risk of instruction forgetting when generating responses for long input sentences. To mitigate this issue, we propose enhancing the instruction-following capability of LLMs by shifting the position of task instructions after the input sentences. Theoretical analysis suggests that our straightforward method can alter the model’s learning focus, thereby emphasizing the training of instruction-following capabilities. Concurrently, experimental results demonstrate that our approach consistently outperforms traditional settings across various model scales (1B / 7B / 13B) and different sequence generation tasks (translation and summarization), without any additional data or annotation costs. Notably, our method significantly improves the zero-shot performance on conditional sequence generation, e.g., up to 9.7 BLEU points on WMT zero-shot translation tasks. Further analysis reveals that our method can significantly improve the tranditional model’s instruction following ability by 1x over traditional approch.

Sparse models, including sparse Mixture-of-Experts (MoE) models, have emerged as an effective approach for scaling Transformer models. However, they often suffer from computational inefficiency since a significant number of parameters are unnecessarily involved in computations by multiplying values by zero or low activation values. To address this issue, we present XMoE, a novel MoE designed to enhance both the efficacy and efficiency of sparse MoE models. XMoE leverages small experts and a threshold-based router to enable tokens to selectively engage only essential parameters. Our extensive experiments on language modeling and machine translation tasks demonstrate that enhances model performance and can decrease the computation load at MoE layers by over 50% without sacrificing performance. Furthermore, we present the versatility of by applying it to dense models, enabling sparse computation during inference. We provide a comprehensive analysis and make our code available at https://anonymous.4open.science/r/XMoE.

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BranchNorm: Robustly Scaling Extremely Deep Transformers
Yijin Liu | Xianfeng Zeng | Fandong Meng | Jie Zhou

Recently, DeepNorm scales Transformers into extremely deep (i.e., 1000 layers) and reveals the promising potential of deep scaling. To stabilize the training of deep models, DeepNorm attempts to constrain the model update to a constant value. Although applying such a constraint can benefit the early stage of model training, it may lead to undertrained models during the whole training procedure. In this paper, we propose BranchNorm, which dynamically rescales the non-residual branch of Transformer in accordance with the training period. BranchNorm not only theoretically stabilizes the training with smooth gradient norms at the early stage, but also encourages better convergence in the subsequent training stage. Experimental results on multiple translation tasks demonstrate that BranchNorm achieves a better trade-off between training stability and converge performance.

Question answering over temporal knowledge graphs (TKGQA) is an emerging topic, which has attracted increasing interest since it considers the dynamic knowledge in the world. Several datasets along with model developments are proposed in the TKGQA research field. However, existing studies generally focus on fact-centered reasoning, with limited attention to temporal reasoning. To tackle the intricate and comprehensive nature of temporal reasoning, we propose a new TKGQA dataset, MusTQ, which contains 666K multi-step temporal reasoning questions as well as a TKG. The multi-step temporal reasoning is established based on six basic temporal reasoning types derived from a well-established measure theory. Using MusTQ, we evaluate previous TKGQA methods and find that they typically fall short in multi-step temporal reasoning. Furthermore, we propose a TKGQA model, MusTKGQA, which enhances multi-step reasoning ability with entity-time attention mechanism and optimized temporal knowledge graph representation. Extensive experiments on MusTQ show that our model achieves state-of-the-art multi-step temporal reasoning performance.

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Deal, or no deal (or who knows)? Forecasting Uncertainty in Conversations using Large Language Models
Anthony Sicilia | Hyunwoo Kim | Khyathi Chandu | Malihe Alikhani | Jack Hessel

Effective interlocutors account for the uncertain goals, beliefs, and emotions of others. But even the best human conversationalist cannot perfectly anticipate the trajectory of a dialogue. How well can language models represent inherent uncertainty in conversations? We propose FortUne Dial, an expansion of the long-standing “conversation forecasting” task: instead of just accuracy, evaluation is conducted with uncertainty-aware metrics, effectively enabling abstention on individual instances. We study two ways in which language models potentially represent outcome uncertainty (internally, using scores and directly, using tokens) and propose fine-tuning strategies to improve calibration of both representations. Experiments on eight difficult negotiation corpora demonstrate that our proposed fine-tuning strategies (a traditional supervision strategy and an off-policy reinforcement learning strategy) can calibrate smaller open-source models to compete with pre-trained models 10x their size.

Fine-tuning pre-trained language models, particularly large language models, demands extensive computing resources and can result in varying performance outcomes across different domains and datasets. This paper examines the approach of integrating multiple models from diverse training scenarios into a unified model. This unified model excels across various data domains and exhibits the ability to generalize well on out-of-domain data. We propose a knowledge fusion method named Evolver, inspired by evolutionary algorithms, which does not need further training or additional training data. Specifically, our method involves aggregating the weights of different language models into a population and subsequently generating offspring models through mutation and crossover operations. These offspring models are then evaluated against their parents, allowing for the preservation of those models that show enhanced performance on development datasets. Importantly, our model evolving strategy can be seamlessly integrated with existing model merging frameworks, offering a versatile tool for model enhancement. Experimental results on mainstream language models (i.e., encoder-only, decoder-only, encoder-decoder) reveal that Evolver outperforms previous state-of-the-art models by large margins.

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ScaLearn: Simple and Highly Parameter-Efficient Task Transfer by Learning to Scale
Markus Frohmann | Carolin Holtermann | Shahed Masoudian | Anne Lauscher | Navid Rekabsaz

Multi-task learning (MTL) has shown considerable practical benefits, particularly when using language models (LMs). While this is commonly achieved by learning tasks under a joint optimization procedure, some methods, such as AdapterFusion, divide the problem into two stages: (i) task learning, where knowledge specific to a task is encapsulated within sets of parameters (e.g., adapters), and (ii) transfer, where this already learned knowledge is leveraged for a target task. This separation of concerns provides numerous benefits (e.g., promoting reusability). However, current two stage MTL introduces a substantial number of additional parameters. We address this issue by leveraging the usefulness of linearly scaling the output representations of source adapters for transfer learning. We introduce ScaLearn, a simple and highly parameter-efficient two-stage MTL method that capitalizes on the knowledge of the source tasks by learning a minimal set of scaling parameters that enable effective transfer to a target task. Our experiments on three benchmarks (GLUE, SuperGLUE, and HumSet) and two encoder LMs show that ScaLearn consistently outperforms strong baselines with a small number of transfer parameters (~0.35% of those of AdapterFusion). Remarkably, we observe that ScaLearn maintains its strong abilities even when further reducing parameters, achieving competitive results with only 8 transfer parameters per target task. Our proposed approach thus demonstrates the power of simple scaling as a promise for more efficient task transfer. Our code is available at https://github.com/CPJKU/ScaLearn.

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Visualizing Dialogues: Enhancing Image Selection through Dialogue Understanding with Large Language Models
Chang-Sheng Kao | Yun-Nung Chen

For dialogue systems, the utilization of multimodal dialogue responses, as opposed to relying solely on text-only responses, offers the capability to describe different concepts through various modalities. This enhances the effectiveness of communication and elevates the overall conversational experience. However, current methods for dialogue-to-image retrieval are constrained by the capabilities of the pre-trained vision language models (VLMs). They struggle to accurately extract key information from conversations and are unable to handle long-turn conversations. In this paper, we leverage the reasoning capabilities of large language models (LLMs) to predict the potential features that may be present in the images to be shared, based on the dialogue context. This approach allows us to obtain succinct and precise descriptors, thereby improving the performance of text-image retrieval. Experimental results shows that our method outperforms previous approaches significantly in terms of Recall@k.

Scientific data visualization plays a crucial role in research by enabling the direct display of complex information and assisting researchers in identifying implicit patterns. Despite its importance, the use of Large Language Models (LLMs) for scientific data visualization remains rather unexplored. In this study, we introduce MatPlotAgent, an efficient model-agnostic LLM agent framework designed to automate scientific data visualization tasks. Leveraging the capabilities of both code LLMs and multi-modal LLMs, MatPlotAgent consists of three core modules: query understanding, code generation with iterative debugging, and a visual feedback mechanism for error correction. To address the lack of benchmarks in this field, we present MatPlotBench, a high-quality benchmark consisting of 100 human-verified test cases. Additionally, we introduce a scoring approach that utilizes GPT-4V for automatic evaluation. Experimental results demonstrate that MatPlotAgent can improve the performance of various LLMs, including both commercial and open-source models. Furthermore, the proposed evaluation method shows a strong correlation with human-annotated scores.

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Continual Few-shot Relation Extraction via Adaptive Gradient Correction and Knowledge Decomposition
Jianpeng Hu | Chengxiang Tan | JiaCheng Xu | XiangyunKong XiangyunKong

Continual few-shot relation extraction (CFRE) aims to continually learn new relations with limited samples. However, current methods neglect the instability of embeddings in the process of different task training, which leads to serious catastrophic forgetting. In this paper, we propose the concept of the following degree from the perspective of instability to analyze catastrophic forgetting and design a novel method based on adaptive gradient correction and knowledge decomposition to alleviate catastrophic forgetting. Specifically, the adaptive gradient correction algorithm is designed to limit the instability of embeddings, which adaptively constrains the current gradient to be orthogonal to the embedding space learned from previous tasks. To reduce the instability between samples and prototypes, the knowledge decomposition module decomposes knowledge into general and task-related knowledge from the perspective of model architecture, which is asynchronously optimized during training. Experimental results on two standard benchmarks show that our method outperforms the state-of-the-art CFRE model and effectively improves the following degree of embeddings.

What a large language model (LLM) would respond in ethically relevant context? In this paper, we curate a large benchmark CMoralEval for morality evaluation of Chinese LLMs. The data sources of CMoralEval are two-fold: 1) a Chinese TV program discussing Chinese moral norms with stories from the society and 2) a collection of Chinese moral anomies from various newspapers and academic papers on morality. With these sources, we aim to create a moral evaluation dataset characterized by diversity and authenticity. We develop a morality taxonomy and a set of fundamental moral principles that are not only rooted in traditional Chinese culture but also consistent with contemporary societal norms. To facilitate efficient construction and annotation of instances in CMoralEval, we establish a platform with AI-assisted instance generation to streamline the annotation process. These help us curate CMoralEval that encompasses both explicit moral scenarios (14,964 instances) and moral dilemma scenarios (15,424 instances), each with instances from different data sources. We conduct extensive experiments with CMoralEval to examine a variety of Chinese LLMs. Experiment results demonstrate that CMoralEval is a challenging benchmark for Chinese LLMs.

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Cache & Distil: Optimising API Calls to Large Language Models
Guillem Ramírez | Matthias Lindemann | Alexandra Birch | Ivan Titov

Large-scale deployment of generative AI tools often depends on costly API calls to a Large Language Model (LLM) to fulfil user queries, a process that also exposes the request stream to external providers. To curtail the frequency of these calls, one can employ a local smaller language model -a student- which is continuously trained on the responses of the LLM. This student gradually gains proficiency in independently handling an increasing number of user requests, a process we term neural caching. The crucial element in neural caching is a policy that decides which requests should be processed by the student alone and which should be redirected to the LLM, subsequently aiding the student’s learning. In this study, we focus on classification tasks, and we consider a range of classic Active Learning-based selection criteria as the policy. Our experiments suggest that Margin Sampling and Query by Committee bring consistent benefits over other policies and baselines across tasks and budgets.

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Investigating the Impact of Model Instability on Explanations and Uncertainty
Sara Marjanovic | Isabelle Augenstein | Christina Lioma

Explainable AI methods facilitate the understanding of model behaviour, yet, small, imperceptible perturbations to inputs can vastly distort explanations. As these explanations are typically evaluated holistically, before model deployment, it is difficult to assess when a particular explanation is trustworthy. Some studies have tried to create confidence estimators for explanations, but none have investigated an existing link between uncertainty and explanation quality. We artificially simulate epistemic uncertainty in text input by introducing noise at inference time. In this large-scale empirical study, we insert different levels of noise perturbations and measure the effect on the output of pre-trained language models and different uncertainty metrics. Realistic perturbations have minimal effect on performance and explanations, yet masking has a drastic effect. We find that high uncertainty doesn’t necessarily imply low explanation plausibility; the correlation between the two metrics can be moderately positive when noise is exposed during the training process. This suggests that noise-augmented models may be better at identifying salient tokens when uncertain. Furthermore, when predictive and epistemic uncertainty measures are over-confident, the robustness of a saliency map to perturbation can indicate model stability issues. Integrated Gradients shows the overall greatest robustness to perturbation, while still showing model-specific patterns in performance; however, this phenomenon is limited to smaller Transformer-based language models.

Text ranking is a critical task in information retrieval. Recent advances in pre-trained language models (PLMs), especially large language models (LLMs), present new opportunities for applying them to text ranking. While supervised fine-tuning (SFT) with ranking data has been widely explored to better align PLMs with text ranking goals, previous studies have focused primarily on encoder-only and encoder-decoder PLMs. Research on leveraging decoder-only LLMs for text ranking remains scarce. An exception to this is RankLLaMA, which uses direct SFT to explore LLaMA’s potential for text ranking. In this work, we propose a two-stage progressive paradigm to better adapt LLMs to text ranking. First, we conduct continual pre-training (CPT) of LLMs on a large weakly-supervised corpus. Second, we perform SFT, and propose an improved optimization strategy building upon RankLLaMA. Our experimental results on multiple benchmarks show that our approach outperforms previous methods in both in-domain and out-domain scenarios.

Automatic methods for generating and gathering linguistic data have proven effective for fine-tuning Language Models (LMs) in languages less resourced than English. Still, while there has been emphasis on data quantity, less attention has been given to its quality. In this work, we investigate the impact of human intervention on machine-generated data when fine-tuning dialogical models. In particular, we study (1) whether post-edited dialogues exhibit higher perceived quality compared to the originals that were automatically generated; (2) whether fine-tuning with post-edited dialogues results in noticeable differences in the generated outputs; and (3) whether post-edited dialogues influence the outcomes when considering the parameter size of the LMs. To this end we created HED-IT, a large-scale dataset where machine-generated dialogues are paired with the version post-edited by humans. Using both the edited and unedited portions of HED-IT, we fine-tuned three different sizes of an LM. Results from both human and automatic evaluation show that the different quality of training data is clearly perceived and it has an impact also on the models trained on such data. Additionally, our findings indicate that larger models are less sensitive to data quality, whereas this has a crucial impact on smaller models. These results enhance our comprehension of the impact of human intervention on training data in the development of high-quality LMs.

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Analyze, Generate and Refine: Query Expansion with LLMs for Zero-Shot Open-Domain QA
Xinran Chen | Xuanang Chen | Ben He | Tengfei Wen | Le Sun

Query expansion (QE) is a critical component in the open-domain question answering (OpenQA) pipeline, enhancing the retrieval performance by broadening the scope of queries with additional relevant texts. However, existing methods like GAR and EAR rely heavily on supervised training and often struggle to maintain effectiveness across domains and datasets. Meanwhile, although large language models (LLMs) have demonstrated QE capability for information retrieval (IR) tasks, their application in OpenQA is hindered by the inadequate analysis of query’s informational needs and the lack of quality control for generated QEs, failing to meet the unique requirements of OpenQA. To bridge this gap, we propose a novel LLM-based QE approach named AGR for the OpenQA task, leveraging a three-step prompting strategy. AGR begins with an analysis of the query, followed by the generation of answer-oriented expansions, and culminates with a refinement process for better query formulation. Extensive experiments on four OpenQA datasets reveal that AGR not only rivals in-domain supervised methods in retrieval accuracy, but also outperforms state-of-the-art baselines in out-domain zero-shot scenarios. Moreover, it exhibits enhanced performance in end-to-end QA evaluations, underscoring the superiority of AGR for OpenQA.

The Spoken Language Understanding Evaluation (SLUE) suite of benchmark tasks was recently introduced to address the need for openresources and benchmarking of complex spoken language understanding (SLU) tasks, including both classification and sequence generation tasks, on natural speech. The benchmark has demonstrated preliminary success in using pre-trained speech foundation models (SFM) for these SLU tasks. However, the community still lacks a fine-grained understanding of the comparative utility of different SFMs. Inspired by this, we ask: which SFMs offer the most benefits for these complex SLU tasks, and what is the most effective approach for incorporating these SFMs? To answer this, we perform an extensive evaluation of multiple supervised and self-supervised SFMs using several evaluation protocols: (i) frozen SFMs with a lightweight prediction head, (ii) frozen SFMs with a complex prediction head, and (iii) fine-tuned SFMs with a lightweight prediction head. Although the supervised SFMs are pre-trained on much more speech recognition data (with labels), they do not always outperform self-supervised SFMs; the latter tend to perform at least as well as, and sometimes better than, supervised SFMs, especially on the sequence generation tasks in SLUE. While there is no universally optimal way of incorporating SFMs, the complex prediction head gives the best performance for most tasks, although it increases the inference time. We also introduce an open-source toolkit and performance leaderboard, SLUE-PERB, for these tasks and modeling strategies.

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Towards Multiple References Era – Addressing Data Leakage and Limited Reference Diversity in Machine Translation Evaluation
Xianfeng Zeng | Yijin Liu | Fandong Meng | Jie Zhou

Recent research has shown a weak correlation between n-gram-based metrics and human evaluations in machine translation task, particularly when evaluating large language models (LLMs). Additionally, the data leakage risk in LLMs may cause an overestimation problem when evaluating LLMs on downstream tasks. In this work, we identify the limited diversity of references as the primary cause for the inferior performance of n-gram-based metrics and the overestimation problem. To address this issue, we propose to utilize multiple references generated by LLMs, coupled with an effective selection strategy focused on accuracy and diversity, to improve the alignment between automatic metrics and human evaluations. We validate our approach on the WMT22 Metrics benchmark with 4 languages and observe a maximum accuracy gain of 9.5% in F200spBLEU, which makes it on par with computationally expensive neural-based metrics. We also show that using multi-reference with n-gram-based metrics significantly alleviates the overestimation problem when evaluating LLMs with data leakage. Further analysis explores the factors that affect the quality of generated references, offering insights into data synthesis by LLMs.

Thanks to recent advances in generative AI, we are able to prompt large language models (LLMs) to produce texts which are fluent and grammatical. In addition, it has been shown that we can elicit attempts at grammatical error correction (GEC) from LLMs when prompted with ungrammatical input sentences. We evaluate how well LLMs can perform at GEC by measuring their performance on established benchmark datasets. We go beyond previous studies, which only examined GPT* models on a selection of English GEC datasets, by evaluating seven open-source and three commercial LLMs on four established GEC benchmarks. We investigate model performance and report results against individual error types. Our results indicate that LLMs do not always outperform supervised English GEC models except in specific contexts – namely commercial LLMs on benchmarks annotated with fluency corrections as opposed to minimal edits. We find that several open-source models outperform commercial ones on minimal edit benchmarks, and that in some settings zero-shot prompting is just as competitive as few-shot prompting.

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BATS: BenchmArking Text Simplicity 🦇
Christin Kreutz | Fabian Haak | Björn Engelmann | Philipp Schaer

Evaluation of text simplification currently focuses on the difference of a source text to its simplified variant. Datasets for this evaluation base on a specific topic and group of readers for which is simplified. The broad applicability of text simplification and specifics that come with intended target audiences (e.g., children compared to adult non-experts) are disregarded. An explainable assessment of the overall simplicity of text is missing. This work is BenchmArking Text Simplicity (BATS): we provide an explainable method to assess practical and concrete rules from literature describing features of simplicity and complexity of text. Our experiments on 15 datasets for text simplification highlight differences in features that are important in different domains of text and for different intended target audiences.

Model interpretability in toxicity detection greatly profits from token-level annotations. However, currently, such annotations are only available in English. We introduce a dataset annotated for offensive language detection sourced from a news forum, notable for its incorporation of the Austrian German dialect, comprising 4,562 user comments. In addition to binary offensiveness classification, we identify spans within each comment constituting vulgar language or representing targets of offensive statements. We evaluate fine-tuned Transformer models as well as large language models in a zero- and few-shot fashion. The results indicate that while fine-tuned models excel in detecting linguistic peculiarities such as vulgar dialect, large language models demonstrate superior performance in detecting offensiveness in AustroTox.

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Discovering influential text using convolutional neural networks
Megan Ayers | Luke Sanford | Margaret Roberts | Eddie Yang

Experimental methods for estimating the impacts of text on human evaluation have been widely used in the social sciences. However, researchers in experimental settings are usually limited to testing a small number of pre-specified text treatments. While efforts to mine unstructured texts for features that causally affect outcomes have been ongoing in recent years, these models have primarily focused on the topics or specific words of text, which may not always be the mechanism of the effect. We connect these efforts with NLP interpretability techniques and present a method for flexibly discovering clusters of similar text phrases that are predictive of human reactions to texts using convolutional neural networks. When used in an experimental setting, this method can identify text treatments and their effects under certain assumptions. We apply the method to two data sets. The first enables direct validation of the model’s ability to detect phrases known to cause the outcome. The second demonstrates its ability to flexibly discover text treatments with varying textual structures. In both cases, the model learns a greater variety of text treatments compared to benchmark methods, and these text features quantitatively meet or exceed the ability of benchmark methods to predict the outcome.

Event extraction (EE) is a critical task in natural language processing, yet deploying a practical EE system remains challenging. On one hand, powerful large language models (LLMs) currently show poor performance because EE task is more complex than other tasks. On the other hand, state-of-the-art (SOTA) small language models (SLMs) for EE tasks are typically developed through fine-tuning, lack flexibility, and have considerable room for improvement. We propose an approach, **L**LMs-as-**C**orrector for **E**vent **E**xtraction (**LC4EE**), aiming to leverage the superior extraction capability of SLMs and the instruction-following ability of LLMs to construct a robust and highly available EE system. By utilizing LLMs to identify and correct errors of SLMs predictions based on automatically generated feedback information, EE performances can be improved significantly. Experimental results on the representative datasets ACE2005 and MAVEN-Arg for Event Detection (ED) and EE tasks validated the effectiveness of our method.

Recent statements about the impressive capabilities of large language models (LLMs) are usually supported by evaluating on open-access benchmarks. Considering the vast size and wide-ranging sources of LLMs’ training data, it could explicitly or implicitly include test data, leading to LLMs being more susceptible to data contamination. However, due to the opacity of training data, the black-box access of models, and the rapid growth of synthetic training data, detecting and mitigating data contamination for LLMs faces significant challenges. In this paper, we propose CDD, which stands for Contamination Detection via output Distribution for LLMs. CDD necessitates only the sampled texts to detect data contamination, by identifying the peakedness of LLM’s output distribution. To mitigate the impact of data contamination in evaluation, we also present TED: Trustworthy Evaluation via output Distribution, based on the correction of LLM’s output distribution. To facilitate this study, we introduce two benchmarks, i.e., DETCON and COMIEVAL, for data contamination detection and contamination mitigation evaluation tasks. Extensive experimental results show that CDD achieves the average relative improvements of 21.8%-30.2% over other contamination detection approaches in terms of Accuracy, F1 Score, and AUC metrics, and can effectively detect implicit contamination. TED substantially mitigates performance improvements up to 66.9% attributed to data contamination across various contamination setups. In real-world applications, we reveal that ChatGPT exhibits a high potential to suffer from data contamination on HumanEval benchmark.

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Efficient Training of Language Models with Compact and Consistent Next Token Distributions
Ashutosh Sathe | Sunita Sarawagi

Maximizing the likelihood of the next token is an established, statistically sound objective for pre-training language models. In this paper we show that we can train better models faster by pre-aggregating the corpus with a collapsed n-gram distribution. Previous studies have proposed corpus-level n-gram statistics as a regularizer; however, the construction and querying of such n-grams, if done naively, prove to be costly and significantly impede training speed, thereby limiting their application in modern large language model pre-training.We introduce an alternative compact representation of the next token distribution that, in expectation, aligns with the complete n-gram distribution while markedly reducing variance across mini-batches compared to the standard next-token loss. Empirically, we demonstrate that both the n-gram regularized model and our approximation yield substantial improvements in model quality and convergence rate compared to existing methods. Furthermore, our approximation facilitates scalability of gains to larger datasets and models compared to the straightforward n-gram regularization method.

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Ancient Chinese Glyph Identification Powered by Radical Semantics
Yang Chi | Fausto Giunchiglia | Chuntao Li | Hao Xu

The ancestor of Chinese character – the ancient characters from about 1300 BC to 200 BC are not fixed in their writing glyphs. At the same or different points in time, one character can possess multiple glyphs that are different in shapes or radicals. Nearly half of ancient glyphs have not been deciphered yet. This paper proposes an innovative task of ancient Chinese glyph identification, which aims at inferring the Chinese character label for the unknown ancient Chinese glyphs which are not in the training set based on the image and radical information. Specifically, we construct a Chinese glyph knowledge graph (CGKG) associating glyphs in different historical periods according to the radical semantics, and propose a multimodal Chinese glyph identification framework (MCGI) fusing the visual, textual, and the graph data. The experiment is designed on a real Chinese glyph dataset spanning over 1000 years, it demonstrates the effectiveness of our method, and reports the potentials of each modality on this task. It provides a preliminary reference for the automatic ancient Chinese character deciphering at the glyph level.

LLMs have demonstrated remarkable capability for understanding semantics, but their understanding of pragmatics is not well studied. To this end, we release a Pragmatics Understanding Benchmark (PUB) dataset consisting of fourteen tasks in four pragmatics phenomena, namely; Implicature, Presupposition, Reference, and Deixis. We curate high-quality test sets for each task, consisting of Multiple Choice Question Answers (MCQA). PUB includes a total of 28k data points, 6.1k are newly annotated. We evaluate nine models varying in the number of parameters and type of training. Our study reveals several key observations about the pragmatic capabilities of LLMs: 1. chat-fine-tuning strongly benefits smaller models, 2. large base models are competitive with their chat-fine-tuned counterparts, 3. there is a huge variance in performance across different pragmatics phenomena, and 4. a noticeable performance gap between human capabilities and model capabilities. We hope that PUB will enable comprehensive evaluation of LLM’s pragmatic reasoning capabilities.

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EmoTransKG: An Innovative Emotion Knowledge Graph to Reveal Emotion Transformation
Huan Zhao | Xupeng Zha | Zixing Zhang

This paper introduces EmoTransKG, an innovative Emotion Knowledge Graph (EKG) that establishes connections and transformations between emotions across diverse open-textual events. Compared to existing EKGs, which primarily focus on linking emotion keywords to related terms or on assigning sentiment dimension ratings to emotion words by humans, EmoTransKG aims to represent the general knowledge involved in emotion transformation. Specifically, in conversations, successive emotions expressed by a single speaker are temporally considered as the head and tail entities, with open-text utterances (events) occurring between them representing the relation. To explore the knowledge of emotion transformations described in EmoTransKG, we develop a Transformer-based translational model called EmoTransNet, which predictively trains tail entities by interpreting the relation as an operation that transforms the source emotion into the target emotion. Particularly, our designed EmoTransNet serves as a plug-in module that seamlessly integrates with any conversational emotion recognition (CER) models for emotion retrofitting. Experimental results on two CER datasets demonstrate that the incorporation of EmoTransNet with baseline models results in substantial improvements, and the qualitative visualization of entities and relations clearly clarify their unique roles in emotion transformations. These experiments confirm the quality and effectiveness of EmoTransKG.

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How Vocabulary Sharing Facilitates Multilingualism in LLaMA?
Fei Yuan | Shuai Yuan | Zhiyong Wu | Lei Li

Large Language Models (LLMs), often show strong performance on English tasks, while exhibiting limitations on other languages. What is an LLM’s multilingual capability when it is trained only on certain languages? The underlying mechanism remains unclear. This study endeavors to examine the multilingual capability of LLMs from the vocabulary sharing perspective by conducting an exhaustive analysis across 101 languages. Through the investigation of the performance gap before and after embedding fine-tuning, we discovered four distinct quadrants. By delving into each quadrant we provide actionable and efficient guidelines for tuning these languages. Extensive experiments reveal that existing LLMs possess multilingual capabilities that surpass our expectations, and we can significantly improve the multilingual performance of LLMs based on these attributes of each quadrant .

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Prefix Text as a Yarn: Eliciting Non-English Alignment in Foundation Language Model
Runzhe Zhan | Xinyi Yang | Derek Wong | Lidia Chao | Yue Zhang

While supervised fine-tuning (SFT) has been a straightforward approach for tailoring the output of foundation large language model (LLM) to specific preferences, concerns have been raised about the depth of this alignment, with some critiques suggesting it is merely “superficial”. We critically examine this hypothesis within the scope of cross-lingual generation tasks, proposing that the effectiveness of SFT may be constrained by its reliance on prior tokens to guide cross-lingual generation. Based on this crucial insight, and in response to the challenges posed by the costly and limited availability of non-English data for SFT, we introduce a novel training-free alignment method named PreTTY, which employs minimal task-related prior tokens to bridge the foundation LLM and the SFT LLM, achieving comparable performance without training. Experiments on machine translation and part-of-speech tagging across seven languages demonstrate the efficacy of PreTTY in cross-lingual settings. Remarkably, by initiating the decoding process with only one or two prior tokens, foundation LLMs can attain up to 98% of the performance metrics of their SFT counterparts. This method presents a cost-effective alternative to traditional SFT and advances the democratization of multilingual LLMs.

Hierarchical text classification aims at categorizing texts into a multi-tiered tree-structured hierarchy of labels. Existing methods pay more attention to capture hierarchy-aware text feature by exploiting explicit parent-child relationships, while interactions between peer labels are rarely taken into account, resulting in severe label confusion within each layer. In this work, we propose a novel Dual Prompt Tuning (DPT) method, which emphasizes identifying discrimination among peer labels by performing contrastive learning on each hierarchical layer. We design an innovative hand-crafted prompt containing slots for both positive and negative label predictions to cooperate with contrastive learning. In addition, we introduce a label hierarchy self-sensing auxiliary task to ensure cross-layer label consistency. Extensive experiments demonstrate that DPT achieves significant improvements and outperforms the current state-of-the-art methods on BGC and RCV1-V2 benchmark datasets.

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Probing the Emergence of Cross-lingual Alignment during LLM Training
Hetong Wang | Pasquale Minervini | Edoardo Ponti

Multilingual Large Language Models (LLMs) achieve remarkable levels of zero-shot cross-lingual transfer performance. We speculate that this is predicated on their ability to align languages without explicit supervision from parallel sentences. While representations of translationally equivalent sentences in different languages are known to be similar after convergence, however, it remains unclear how such cross-lingual alignment emerges during pre-training of LLMs. Our study leverages intrinsic probing techniques, which identify which subsets of neurons encode linguistic features, to correlate the degree of cross-lingual neuron overlap with the zero-shot cross-lingual transfer performance for a given model. In particular, we rely on checkpoints of BLOOM, a multilingual autoregressive LLM, across different training steps and model scales. We observe a high correlation between neuron overlap and downstream performance, which supports our hypothesis on the conditions leading to effective cross-lingual transfer. Interestingly, we also detect a degradation of both implicit alignment and multilingual abilities in certain phases of the pre-training process, providing new insights into the multilingual pretraining dynamics.

This study introduces the Semantic Textual Similarity Pseudo-Label Semi-Supervised Clustering (STSPL-SSC) framework. The STSPL-SSC framework is designed to tackle the prevalent issue of scarce labeled data by combining a Semantic Textual Similarity Pseudo-Label Generation process with a Robust Contrastive Learning module. The process begins with employing k-means clustering on embeddings for initial pseudo-Label allocation. Then we use a Semantic Text Similarity-enhanced module to supervise the secondary clustering of pseudo-labels using labeled data to better align with the real clustering centers. Subsequently, an Adaptive Optimal Transport (AOT) approach fine-tunes the pseudo-labels. Finally, a Robust Contrastive Learning module is employed to foster the learning of classification and instance-level distinctions, aiding clusters to better separate. Experiments conducted on multiple real-world datasets demonstrate that with just one label per class, clustering performance can be significantly improved, outperforming state-of-the-art models with an increase of 1-6% in both accuracy and normalized mutual information, approaching the results of fully-labeled classification.

Increasing the number of parameters in large language models (LLMs) usually improves performance in downstream tasks but raises compute and memory costs, making deployment difficult in resource-limited settings. Quantization techniques, which reduce the bits needed for model weights or activations with minimal performance loss, have become popular due to the rise of LLMs. However, most quantization studies use pre-trained LLMs, and the impact of quantization on instruction-tuned LLMs and the relationship between perplexity and benchmark performance of quantized LLMs are not well understood. Evaluation of quantized LLMs is often limited to language modeling and a few classification tasks, leaving their performance on other benchmarks unclear. To address these gaps, we propose a structured evaluation framework consisting of three critical dimensions: (1) knowledge & capacity, (2) alignment, and (3) efficiency, and conduct extensive experiments across ten diverse benchmarks. Our experimental results indicate that LLMs with 4-bit quantization can retain performance comparable to their non-quantized counterparts, and perplexity can serve as a proxy metric for quantized LLMs on most benchmarks. Furthermore, quantized LLMs with larger parameter scales can outperform smaller LLMs. Despite the memory savings achieved through quantization, it can also slow down the inference speed of LLMs. Consequently, substantial engineering efforts and hardware support are imperative to achieve a balanced optimization of decoding speed and memory consumption in the context of quantized LLMs.

Neural Machine Translation (NMT) encounters challenges when translating in new domains and low-resource languages. To address these issues, researchers have proposed methods to integrate additional knowledge into NMT, such as translation memories (TMs). However, finding TMs that closely match the input sentence remains challenging, particularly in specific domains. On the other hand, monolingual data is widely accessible in most languages, and back-translation is seen as a promising approach for utilizing target language data. Nevertheless, it still necessitates additional training. In this paper, we introduce Pseudo-kNN-MT, a variant of k-nearest neighbor machine translation (kNN-MT) that utilizes target language data by constructing a pseudo datastore. Furthermore, we investigate the utility of large language models (LLMs) for the kNN component. Experimental results demonstrate that our approach exhibits strong domain adaptation capability in both high-resource and low-resource machine translation. Notably, LLMs are found to be beneficial for robust NMT systems.

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Bayesian Prompt Ensembles: Model Uncertainty Estimation for Black-Box Large Language Models
Francesco Tonolini | Nikolaos Aletras | Jordan Massiah | Gabriella Kazai

An important requirement for the reliable deployment of pre-trained large language models (LLMs) is the well-calibrated quantification of the uncertainty in their outputs. While the likelihood of predicting the next token is a practical surrogate of the data uncertainty learned during training, model uncertainty is challenging to estimate, i.e., due to lack of knowledge acquired during training. Prior efforts to quantify uncertainty of neural networks require specific architectures or (re-)training strategies, which are impractical to apply to LLMs with several billion parameters, or for black-box models where the architecture and parameters are not available. In this paper, we propose Bayesian Prompts Ensembles (BayesPE), a novel approach to effectively obtain well-calibrated uncertainty for the output of pre-trained LLMs. BayesPE computes output probabilities through a weighted ensemble of different, but semantically equivalent, task instruction prompts. The relative weights of the different prompts in the ensemble are estimated through approximate Bayesian variational inference over a small labeled validation set. We demonstrate that BayesPE approximates a Bayesian input layer for the LLM, providing a lower bound on the expected model error. In our extensive experiments, we show that BayesPE achieves significantly superior uncertainty calibration compared to several baselines over a range of natural language classification tasks, both in zero- and few-shot settings.

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X-ACE: Explainable and Multi-factor Audio Captioning Evaluation
Qian Wang | Jia-Chen Gu | Zhen-Hua Ling

Automated audio captioning (AAC) aims to generate descriptions based on audio input, attracting exploration of emerging audio language models (ALMs). However, current evaluation metrics only provide a single score to assess the overall quality of captions without characterizing the nuanced difference by systematically going through an evaluation checklist. To this end, we propose the explainable and multi-factor audio captioning evaluation (X-ACE) paradigm. X-ACE identifies four main factors that constitute the majority of audio features, specifically sound event, source, attribute and relation. To assess a given caption from an ALM, it is firstly transformed into an audio graph, where each node denotes an entity in the caption and corresponds to a factor. On the one hand, graph matching is conducted from part to whole for a holistic assessment. On the other hand, the nodes contained within each factor are aggregated to measure the factor-level performance. The pros and cons of an ALM can be explicitly and clearly demonstrated through X-ACE, pointing out the direction for further improvements. Experiments show that X-ACE exhibits better correlation with human perception and can detect mismatches sensitively.

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Reasons to Reject? Aligning Language Models with Judgments
Weiwen Xu | Deng Cai | Zhisong Zhang | Wai Lam | Shuming Shi

As humans, we consistently interact with our peers and receive feedback in the form of natural language. This language feedback allows us to maintain appropriate behavior, and rectify potential errors. The question arises naturally: can we use language feedback to align large language models (LLMs)? In contrast to previous research that aligns LLMs with scalar rewards, we present the first systematic exploration of alignment through the lens of language feedback (i.e., judgment). We start with an in-depth investigation of potential methods that can be adapted for aligning LLMs with judgments, revealing that these methods cannot fully capitalize on judgments. To facilitate more effective utilization of judgments, we propose a novel framework, Contrastive Unlikelihood Training (CUT), that allows for fine-grained inappropriate content detection and correction based on judgments. Our results show that, with merely 1317 off-the-shelf judgment data, CUT can beat the 175B DaVinci003 and surpass the best baseline by 50.84 points on AlpacaEval using LLaMA2-13b. CUT can also align LLMs in an iterative fashion using up-to-date model-specific judgments, improving performance from 81.09 to 91.68 points on AlpacaEval using LLaMA2-chat-13b. Further analysis suggests that judgments hold greater potential in LLM alignment than rewards.

Argumentative Essay Generation (AEG) is a challenging task in computational argumentation, where detailed logical reasoning and effective rhetorical skills are essential.Previous methods on argument generation typically involve planning prior to generation.However, the planning strategies in these methods overlook the exploration of the logical reasoning process.Inspired by argument structure-related theories, we propose an argumentative planning strategy for prompting large language models (LLMs) to generate high-quality essays.This strategy comprises two stages: (1) Sketch planning, which creates a rough outline of the essay, and (2) Dialectical planning, which refines the outline through critical self-reflection.Such a planning strategy enables LLMs to write argumentative essays that are more logical, diverse, and persuasive.Furthermore, due to the scarcity of existing AEG datasets, we construct three new datasets.These datasets are from two domains: exam essays and news editorials, covering both Chinese and English.Automatic and manual evaluation on four datasets show that our method can generate more dialectical and persuasive essays with higher diversity compared to several strong baselines.

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Large Language Models are Few-Shot Training Example Generators: A Case Study in Fallacy Recognition
Tariq Alhindi | Smaranda Muresan | Preslav Nakov

Recognizing fallacies is crucial for ensuring the quality and validity of arguments across various domains. However, computational fallacy recognition faces challenges due to the diverse genres, domains, and types of fallacies found in datasets. This leads to a highly multi-class, and even multi-label, setup with substantial class imbalance. In this study, we aim to enhance existing models for fallacy recognition by incorporating additional context and by leveraging large language models to generate synthetic data, thus increasing the representation of the infrequent classes. We experiment with GPT3.5 to generate synthetic examples and we examine the impact of prompt settings for this. Moreover, we explore zero-shot and few-shot scenarios to evaluate the effectiveness of using the generated examples for training smaller models within a unified fallacy recognition framework. Furthermore, we analyze the overlap between the synthetic data and existing fallacy datasets. Finally, we investigate the usefulness of providing supplementary context for detecting fallacy types that need such context, e.g., diversion fallacies. Our evaluation results demonstrate consistent improvements across fallacy types, datasets, and generators. The code and the synthetic datasets are all publicly available.

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Concept-aware Data Construction Improves In-context Learning of Language Models
Michal Štefánik | Marek Kadlčík | Petr Sojka

Many recent language models (LMs) are capable of in-context learning (ICL), manifested in the LMs’ ability to perform a new task solely from natural-language instruction. Previous work curating in-context learners assumes that ICL emerges from a vast over-parametrization or the scale of multi-task training. However, recent theoretical work attributes the ICL ability to concept-dependent training data and creates functional in-context learners even in small-scale, synthetic settings.In this work, we practically explore this newly identified axis of ICL quality. We propose Concept-aware Training (CoAT), a framework for constructing training scenarios that make it beneficial for the LM to learn to utilize the analogical reasoning concepts from demonstrations. We find that by using CoAT, pre-trained transformers can learn to better utilise new latent concepts from demonstrations and that such ability makes ICL more robust to the functional deficiencies of the previous models. Finally, we show that concept-aware in-context learners are much more effective in in-context learning a majority of unseen tasks compared to traditional instruction tuning, and fare comparably also to previous in-context learners trained in large-scale multitask learning requiring magnitudes of more training data.

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Beyond Text: Leveraging Multi-Task Learning and Cognitive Appraisal Theory for Post-Purchase Intention Analysis
Gerard Yeo | Shaz Furniturewala | Kokil Jaidka

Supervised machine-learning models for predicting user behavior offer a challenging classification problem with lower average prediction performance scores than other text classification tasks. This study evaluates multi-task learning frameworks grounded in Cognitive Appraisal Theory to predict user behavior as a function of users’ self-expression and psychological attributes. Our experiments show that users’ language and traits improve predictions above and beyond models predicting only from text. Our findings highlight the importance of integrating psychological constructs into NLP to enhance the understanding and prediction of user actions. We close with a discussion of the implications for future applications of large language models for computational psychology.

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Non-Autoregressive Machine Translation as Constrained HMM
Haoran Li | Zhanming Jie | Wei Lu

In non-autoregressive translation (NAT), directed acyclic Transformers (DAT) have demonstrated their ability to achieve comparable performance to the autoregressive Transformers.In this paper, we first show that DAT is essentially a fully connected left-to-right Hidden Markov Model (HMM), with the source and target sequences being observations and the token positions being latent states.Even though generative models like HMM do not suffer from label bias in traditional task settings (e.g., sequence labeling), we argue here that the left-to-right HMM in NAT may still encounter this issue due to the missing observations at the inference stage.To combat label bias, we propose two constrained HMMs: 1) Adaptive Window HMM, which explicitly balances the number of outgoing transitions at different states; 2) Bi-directional HMM, i.e., a combination of left-to-right and right-to-left HMMs, whose uni-directional components can implicitly regularize each other’s biases via shared parameters.Experimental results on WMT’14 EnDe and WMT’17 ZhEn demonstrate that our methods can achieve better or comparable performance to the original DAT using various decoding methods.We also demonstrate that our methods effectively reduce the impact of label bias.

Stance detection is a challenging task that aims to identify public opinion from social media platforms with respect to specific targets. Previous work on stance detection largely focused on pure texts. In this paper, we study multi-modal stance detection for tweets consisting of texts and images, which are prevalent in today’s fast-growing social media platforms where people often post multi-modal messages. To this end, we create five new multi-modal stance detection datasets of different domains based on Twitter, in which each example consists of a text and an image. In addition, we propose a simple yet effective Targeted Multi-modal Prompt Tuning framework (TMPT), where target information is leveraged to learn multi-modal stance features from textual and visual modalities. Experimental results on our five benchmark datasets show that the proposed TMPT achieves state-of-the-art performance in multi-modal stance detection.

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Enhanced Language Model Truthfulness with Learnable Intervention and Uncertainty Expression
Farima Fatahi Bayat | Xin Liu | H. Jagadish | Lu Wang

Large language models (LLMs) can generate long-form and coherent text, yet they often hallucinate facts, which undermines their reliability. To mitigate this issue, inference-time methods steer LLM representations toward the “truthful directions” previously learned for truth elicitation. However, applying these truthful directions with the same intensity fails to generalize across different query contexts. We propose LITO, a Learnable Intervention method for Truthfulness Optimization that automatically identifies the optimal intervention intensity tailored to each specific context. LITO explores a sequence of model generations based on increasing levels of intervention intensities. It selects the most accurate response or refuses to answer when the predictions are highly uncertain. Experiments on multiple LLMs and question-answering datasets demonstrate that LITO improves truthfulness while preserving task accuracy. The adaptive nature of LITO counters the limitations of one-size-fits-all intervention methods, maximizing truthfulness by reflecting the model’s internal knowledge only when it is confident. Our code is available at https://github.com/launchnlp/LITO.

In the past year, MultiModal Large Language Models (MM-LLMs) have undergone substantial advancements, augmenting off-the-shelf LLMs to support MM inputs or outputs via cost-effective training strategies. The resulting models not only preserve the inherent reasoning and decision-making capabilities of LLMs but also empower a diverse range of MM tasks. In this paper, we provide a comprehensive survey aimed at facilitating further research of MM-LLMs. Initially, we outline general design formulations for model architecture and training pipeline. Subsequently, we introduce a taxonomy encompassing 126 MM-LLMs, each characterized by its specific formulations. Furthermore, we review the performance of selected MM-LLMs on mainstream benchmarks and summarize key training recipes to enhance the potency of MM-LLMs. Finally, we explore promising directions for MM-LLMs while concurrently maintaining a [real-time tracking website](https://mm-llms.github.io/) for the latest developments in the field. We hope that this survey contributes to the ongoing advancement of the MM-LLMs domain.

The advancement of large language models (LLMs) has enhanced the ability to generalize across a wide range of unseen natural language processing (NLP) tasks through instruction-following.Yet, their effectiveness often diminishes in low-resource languages like Chinese, exacerbated by biased evaluations from data leakage, casting doubt on their true generalizability to new linguistic territories. In response, we introduce the Chinese Instruction-Following Benchmark (**CIF-Bench**), designed to evaluate the zero-shot generalizability of LLMs to the Chinese language. CIF-Bench comprises 150 tasks and 15,000 input-output pairs, developed by native speakers to test complex reasoning and Chinese cultural nuances across 20 categories. To mitigate data contamination, we release only half of the dataset publicly, with the remainder kept private, and introduce diversified instructions to minimize score variance, totaling 45,000 data instances.Our evaluation of 28 selected LLMs reveals a noticeable performance gap, with the best model scoring only 52.9%, highlighting the limitations of LLMs in less familiar language and task contexts.This work not only uncovers the current limitations of LLMs in handling Chinese language tasks but also sets a new standard for future LLM generalizability research, pushing towards the development of more adaptable, culturally informed, and linguistically diverse models.

While reinforcement learning (RL) has been proven essential for tuning large language models (LLMs), it can lead to reward over-optimization (ROO). Existing approaches address ROO by adding KL regularization, requiring computationally expensive hyperparameter tuning. Additionally, KL regularization focuses solely on regularizing the language policy, neglecting a potential source of regularization: the reward function itself. Inspired by demonstration-guided RL, we here introduce the Reward Calibration from Demonstration (RCfD), which leverages human demonstrations and a reward model to recalibrate the reward objective. Formally, given a prompt, the RCfD objective minimizes the distance between the demonstrations’ and LLM’s rewards rather than directly maximizing the reward function. This objective shift avoids incentivizing the LLM to exploit the reward model and promotes more natural and diverse language generation.We show the effectiveness of RCfD in three RL language tasks, where it achieves comparable performance to carefully tuned baselines while mitigating ROO.

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Enhancing Idiomatic Representation in Multiple Languages via an Adaptive Contrastive Triplet Loss
Wei He | Marco Idiart | Carolina Scarton | Aline Villavicencio

Accurately modeling idiomatic or non-compositional language has been a longstanding challenge in Natural Language Processing (NLP). This is partly because these expressions do not derive their meanings solely from their constituent words, but also due to the scarcity of relevant data resources, and their impact on the performance of downstream tasks such as machine translation and simplification. In this paper we propose an approach to model idiomaticity effectively using a triplet loss that incorporates the asymmetric contribution of components words to an idiomatic meaning for training language models by using adaptive contrastive learning and resampling miners to build an idiomatic-aware learning objective. Our proposed method is evaluated on a SemEval challenge and outperforms previous alternatives significantly in many metrics.

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AdaLomo: Low-memory Optimization with Adaptive Learning Rate
Kai Lv | Hang Yan | Qipeng Guo | Haijun Lv | Xipeng Qiu

Large language models have achieved remarkable success, but their extensive parameter size necessitates substantial memory for training, thereby setting a high threshold. While the recently proposed low-memory optimization (LOMO) reduces memory footprint, its optimization technique, akin to stochastic gradient descent, is sensitive to hyper-parameters and exhibits suboptimal convergence, failing to match the performance of the prevailing optimizer for large language models, AdamW. Through analysis of the Adam optimizer, we found that, compared to momentum, the adaptive learning rate is more critical for bridging the gap. Building on this insight, we introduce the low-memory optimization with adaptive learning rate (AdaLomo), which offers an adaptive learning rate for each parameter and exhibits superior convergence performance compared to LOMO theoretically. To maintain memory efficiency, we employ non-negative matrix factorization for the second-order moment estimation. Additionally, we suggest the use of a grouped update normalization to stabilize convergence. Our experiments with instruction-tuning and further pre-training demonstrate that AdaLomo achieves results on par with AdamW, while significantly reducing memory requirements, thereby lowering the hardware barrier to training large language models. The code is accessible at https://github.com/OpenLMLab/LOMO.

Current knowledge editing approaches struggle to effectively propagate updates to interconnected facts.In this work, we delve into the barriers that hinder the appropriate propagation of updated knowledge within these models for accurate reasoning. To support our analysis, we introduce a novel reasoning-based benchmark, ReCoE (Reasoning-based Counterfactual Editing dataset), which covers six common reasoning schemes in the real world. We conduct an extensive analysis of existing knowledge editing techniques, including input-augmentation, finetuning, and locate-and-edit methods. We found that all model editing methods exhibit notably low performance on this dataset, especially within certain reasoning schemes. Our analysis of the chain-of-thought responses from edited models indicate that, while the models effectively update individual facts, they struggle to recall these facts in reasoning tasks. Moreover, locate-and-edit methods severely deteriorate the models’ language modeling capabilities, leading to poor perplexity and logical coherence in their outputs.

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Exciting Mood Changes: A Time-aware Hierarchical Transformer for Change Detection Modelling
Anthony Hills | Talia Tseriotou | Xenia Miscouridou | Adam Tsakalidis | Maria Liakata

Through the rise of social media platforms, longitudinal language modelling has received much attention over the latest years, especially in downstream tasks such as mental health monitoring of individuals where modelling linguistic content in a temporal fashion is crucial. A key limitation in existing work is how to effectively model temporal sequences within Transformer-based language models. In this work we address this challenge by introducing a novel approach for predicting ‘Moments of Change’ (MoC) in the mood of online users, by simultaneously considering user linguistic and time-aware context. A Hawkes process-inspired transformation layer is applied over the proposed architecture to model the influence of time on users’ posts – capturing both their immediate and historical dynamics. We perform experiments on the two existing datasets for the MoC task and showcase clear performance gains when leveraging the proposed layer. Our ablation study reveals the importance of considering temporal dynamics in detecting subtle and rare mood changes. Our results indicate that considering linguistic and temporal information in a hierarchical manner provide valuable insights into the temporal dynamics of modelling user generated content over time, with applications in mental health monitoring.

Understanding and following natural language instructions while navigating through complex, real-world environments poses a significant challenge for general-purpose robots. These environments often include obstacles and pedestrians, making it essential for autonomous agents to possess the capability of self-corrected planning to adjust their actions based on feedback from the surroundings. However, the majority of existing vision-and-language navigation (VLN) methods primarily operate in less realistic simulator settings and do not incorporate environmental feedback into their decision-making processes. To address this gap, we introduce a novel zero-shot framework called CorNav, utilizing a large language model for decision-making and comprising two key components: 1) incorporating environmental feedback for refining future plans and adjusting its actions, and 2) multiple domain experts for parsing instructions, scene understanding, and refining predicted actions. In addition to the framework, we develop a 3D simulator that renders realistic scenarios using Unreal Engine 5. To evaluate the effectiveness and generalization of navigation agents in a zero-shot multi-task setting, we create a benchmark called NavBench. Our empirical study involves deploying 7 baselines across four tasks, i.e., goal-conditioned navigation given a specific object category, goal-conditioned navigation given simple instructions, finding abstract objects based on high-level instructions, and step-by-step instruction following. Extensive experiments demonstrate that CorNav consistently outperforms all baselines by a significant margin across all tasks. On average, CorNav achieves a success rate of 28.1%, surpassing the best baseline’s performance of 20.5%.

Multi-modal information retrieval (MMIR) is a rapidly evolving field where significant progress has been made through advanced representation learning and cross-modality alignment research, particularly in image-text pairing.However, current benchmarks for evaluating MMIR performance on image-text pairings overlook the scientific domain, which has a notable gap with the generic data since the caption of scientific charts and tables usually describes the analysis of experimental results or scientific principles in contrast to human activity or scenery depicted in generic images.To bridge this gap, we develop a scientific domain-specific MMIR benchmark (SciMMIR) by leveraging open-access research paper corpora to extract data relevant to the scientific domain. This benchmark comprises 530K meticulously curated image-text pairs, extracted from figures and tables with detailed captions from scientific documents.We further annotate the image-text pairs with a two-level subset-subcategory hierarchy to facilitate a more comprehensive evaluation of the baselines. We conduct zero-shot and fine-tuned evaluations on prominent multi-modal image-captioning and visual language models, such as CLIP, BLIP, and BLIP-2.Our findings offer critical insights for MMIR in the scientific domain, including the impact of pre-training and fine-tuning settings and the effects of different visual and textual encoders.

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Diving Deep into the Motion Representation of Video-Text Models
Chinmaya Devaraj | Cornelia Fermuller | Yiannis Aloimonos

Videos are more informative than images becausethey capture the dynamics of the scene.By representing motion in videos, we can capturedynamic activities. In this work, we introduceGPT-4 generated motion descriptions thatcapture fine-grained motion descriptions of activitiesand apply them to three action datasets.We evaluated several video-text models on thetask of retrieval of motion descriptions. Wefound that they fall far behind human expertperformance on two action datasets, raisingthe question of whether video-text models understandmotion in videos. To address it, weintroduce a method of improving motion understandingin video-text models by utilizingmotion descriptions. This method proves tobe effective on two action datasets for the motiondescription retrieval task. The results drawattention to the need for quality captions involvingfine-grained motion information in existingdatasets and demonstrate the effectiveness ofthe proposed pipeline in understanding finegrainedmotion during video-text retrieval.

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Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation
Nihal Nayak | Yiyang Nan | Avi Trost | Stephen Bach

We introduce Bonito, an open-source model for conditional task generation that converts unannotated text into task-specific training datasets for instruction tuning. We aim to enable zero-shot task adaptation of large language models on users’ specialized, private data. We train Bonito by fine-tuning a pretrained large language model on a new large-scale dataset with 1.65M examples created by remixing existing instruction tuning datasets into meta-templates. The meta-templates for a dataset produce training examples where the input is the unannotated text and the task attribute and the output consists of the instruction and the response. We use Bonito to generate synthetic tasks for seven datasets from specialized domains with unannotated text across three task types—yes-no question answering, extractive question answering, and natural language inference—and adapt language models. We show that Bonito significantly improves the average performance of pretrained and instruction tuned models over the de facto self supervised baseline. For example, adapting Mistral-Instruct-v2 and instruction tuned variants of Mistral and Llama2 with Bonito improves the strong zero-shot performance by 22.1 F1 points whereas the next word prediction objective undoes some of the benefits of instruction tuning and reduces the average performance by 0.8 F1 points. We conduct additional experiments with Bonito to understand the effects of the domain, the size of the training set, and the choice of alternative synthetic task generators. Overall, we show that learning with synthetic instruction tuning datasets is an effective way to adapt language models to new domains. The model, dataset, and code are available at https://github.com/BatsResearch/bonito.

Paraphrasing of offensive content is a better alternative to content removal and helps improve civility in a communication environment. Supervised paraphrasers; however, rely heavily on large quantities of labelled data to help preserve meaning and intent. They also often retain a large portion of the offensiveness of the original content, which raises questions on their overall usability. In this paper we aim to assist practitioners in developing usable paraphrasers by exploring In-Context Learning (ICL) with large language models (LLMs), i.e., using a limited number of input-label demonstration pairs to guide the model in generating desired outputs for specific queries. Our study focuses on key factors such as - number and order of demonstrations, exclusion of prompt instruction, and reduction in measured toxicity. We perform principled evaluation on three datasets, including our proposed Context-Aware Polite Paraphrase (CAPP) dataset, comprising of dialogue-style rude utterances, polite paraphrases, and additional dialogue context. We evaluate our approach using four closed source and one open source LLM. Our results reveal that ICL is comparable to supervised methods in generation quality, while being qualitatively better by 25% on human evaluation and attaining lower toxicity by 76%. Also, ICL-based paraphrasers only show a slight reduction in performance even with just 10% training data.

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Paying Attention to Deflections: Mining Pragmatic Nuances for Whataboutism Detection in Online Discourse
Khiem Phi | Noushin Salek Faramarzi | Chenlu Wang | Ritwik Banerjee

Whataboutism, a potent tool for disrupting narratives and sowing distrust, remains under-explored in quantitative NLP research. Moreover, past work has not distinguished its use as a strategy for misinformation and propaganda from its use as a tool for pragmatic and semantic framing. We introduce new datasets from Twitter/X and YouTube, revealing overlaps as well as distinctions between whataboutism, propaganda, and the tu quoque fallacy. Furthermore, drawing on recent work in linguistic semantics, we differentiate the ‘what about’ lexical construct from whataboutism. Our experiments bring to light unique challenges in its accurate detection, prompting the introduction of a novel method using attention weights for negative sample mining. We report significant improvements of 4% and 10% over previous state-of-the-art methods in our Twitter and YouTube collections, respectively.

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Epistemology of Language Models: Do Language Models Have Holistic Knowledge?
Minsu Kim | James Thorne

This paper investigates the inherent knowledge in language models from the perspective of epistemological holism. The purpose of this paper is to explore whether LLMs exhibit characteristics consistent with epistemological holism. These characteristics suggest that core knowledge, such as commonsense, general, and specific knowledge, each plays a specific role, serving as the foundation of our knowledge system and being difficult to revise. To assess these traits related to holism, we created a scientific reasoning dataset and examined the epistemology of language models through three tasks: Abduction, Revision, and Argument Generation. In the abduction task, the language models explained situations while avoiding revising the core knowledge. However, in other tasks, the language models were revealed not to distinguish between core and peripheral knowledge, showing an incomplete alignment with holistic knowledge principles.

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Strong hallucinations from negation and how to fix them
Swarnadeep Bhar | Nicholas Asher

Despite great performance on many tasks, language models (LMs) still struggle with reasoning, sometimes providing responses that cannot possibly be true because they stem from logical incoherence. We call such responses strong hallucinations and prove that they follow from an LM’s computation of its internal representations for logical operators and outputs from those representations. Focusing on negation, we provide a novel solution in which negation is treated not as another element of a latent representation, but as an operation over an LM’s latent representations that constrains how they may evolve. We show that our approach improves model performance in cloze prompting and natural language inference tasks with negation without requiring training on sparse negative data.

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LLMs as Narcissistic Evaluators: When Ego Inflates Evaluation Scores
Yiqi Liu | Nafise Moosavi | Chenghua Lin

Automatic evaluation of generated textual content presents an ongoing challenge within the field of NLP. Given the impressive capabilities of modern language models (LMs) across diverse NLP tasks, there is a growing trend to employ these models in creating innovative evaluation metrics for automated assessment of generation tasks. This paper investigates a pivotal question: Do language model-driven evaluation metrics inherently exhibit bias favoring texts generated by the same underlying language model? Specifically, we assess whether prominent LM-based evaluation metrics (e.g. BARTScore, T5Score, and GPTScore) demonstrate a favorable bias toward their respective underlying LMs in the context of summarization tasks. Our findings unveil a latent bias, particularly pronounced when such evaluation metrics are used in a reference-free manner without leveraging gold summaries. These results underscore that assessments provided by generative evaluation models can be influenced by factors beyond the inherent text quality, highlighting the necessity of developing more reliable evaluation protocols in the future.

Benchmarks have been essential for driving progress in machine learning. A better understanding of LLM capabilities on real world tasks is vital for safe development.Designing adequate LLM benchmarks is challenging: Data from real-world tasks is hard to collect, public availability of static evaluation data results in test data contamination and benchmark overfitting, and periodically generating new evaluation data is tedious and may result in temporally inconsistent results. We introduce HelloFresh, based on continuous streams of real-world data generated by intrinsically motivated human labelers. It covers recent events from X (formerly Twitter) community notes and edits of Wikipedia pages, mitigating the risk of test data contamination and benchmark overfitting.Any X user can propose an X note to add additional context to a misleading post (formerly tweet); if the community classifies it as helpful, it is shown with the post. Similarly, Wikipedia relies on community-based consensus, allowing users to edit articles or revert edits made by other users.Verifying whether an X note is helpful or whether a Wikipedia edit should be accepted are hard tasks that require grounding by querying the web.We backtest state-of-the-art LLMs supplemented with simple web search access and find that HelloFresh yields a temporally consistent ranking.To enable continuous evaluation on Hellofresh, we host a public leaderboard and periodically updated evaluation data at https://tinyurl.com/hello-fresh-LLM.

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Chaos with Keywords: Exposing Large Language Models Sycophancy to Misleading Keywords and Evaluating Defense Strategies
Aswin Rrv | Nemika Tyagi | Md Nayem Uddin | Neeraj Varshney | Chitta Baral

This study explores the sycophantic tendencies of Large Language Models (LLMs), where these models tend to provide answers that match what users want to hear, even if they are not entirely correct. The motivation behind this exploration stems from the common behavior observed in individuals searching the internet for facts with partial or misleading knowledge. Similar to using web search engines, users may recall fragments of misleading keywords and submit them to an LLM, hoping for a comprehensive response. Our empirical analysis of several LLMs shows the potential danger of these models amplifying misinformation when presented with misleading keywords. Additionally, we thoroughly assess four existing hallucination mitigation strategies to reduce LLMs sycophantic behavior. Our experiments demonstrate the effectiveness of these strategies for generating factually correct statements. Furthermore, our analyses delve into knowledge-probing experiments on factual keywords and different categories of sycophancy mitigation.

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Empowering Large Language Models for Textual Data Augmentation
Yichuan Li | Kaize Ding | Jianling Wang | Kyumin Lee

With the capabilities of understanding and executing natural language instructions, Large language models (LLMs) can potentially act as a powerful tool for textual data augmentation. However, the quality of augmented data depends heavily on the augmentation instructions provided, and the effectiveness can fluctuate across different downstream tasks. While manually crafting and selecting instructions can offer some improvement, this approach faces scalability and consistency issues in practice due to the diversity of downstream tasks. In this work, we address these limitations by proposing a new solution, which can automatically generate a large pool of augmentation instructions and select the most suitable task-informed instructions, thereby empowering LLMs to create high-quality augmented data for different downstream tasks. Empirically, the proposed approach consistently generates augmented data with better quality compared to non-LLM and LLM-based data augmentation methods, leading to the best performance on 26 few-shot learning tasks sourced from a wide range of application domains.

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Choose Your Transformer: Improved Transferability Estimation of Transformer Models on Classification Tasks
Lukas Garbas | Max Ploner | Alan Akbik

There currently exists a multitude of pre-trained transformer language models (LMs) that are readily available. From a practical perspective, this raises the question of which pre-trained LM will perform best if fine-tuned for a specific downstream NLP task. However, exhaustively fine-tuning all available LMs to determine the best-fitting model is computationally infeasible. To address this problem, we present an approach that inexpensively estimates a ranking of the expected performance of a given set of candidate LMs for a given task. Following a layer-wise representation analysis, we extend existing approaches such as H-score and LogME by aggregating representations across all layers of the transformer model. We present an extensive analysis of 20 transformer LMs, 6 downstream NLP tasks, and various estimators (linear probing, kNN, H-score, and LogME). Our evaluation finds that averaging the layer representations significantly improves the Pearson correlation coefficient between the true model ranks and the estimate, increasing from 0.58 to 0.86 for LogME and from 0.65 to 0.88 for H-score.

Event linking connects event mentions in text with relevant nodes in a knowledge base (KB). Prior research in event linking has mainly borrowed methods from entity linking, overlooking the distinct features of events. Compared to the extensively explored entity linking task, events have more complex structures and can be more effectively distinguished by examining their associated arguments. Moreover, the information-rich nature of events leads to the scarcity of event KBs. This emphasizes the need for event linking models to identify and classify event mentions not in the KB as “out-of-KB,” an area that has received limited attention. In this work, we tackle these challenges by introducing an argument-aware approach. First, we improve event linking models by augmenting input text with tagged event argument information, facilitating the recognition of key information about event mentions. Subsequently, to help the model handle “out-of-KB” scenarios, we synthesize out-of-KB training examples from in-KB instances through controlled manipulation of event arguments. Our experiment across two test datasets showed significant enhancements in both in-KB and out-of-KB scenarios, with a notable 22% improvement in out-of-KB evaluations.

Grounded generation aims to equip language models (LMs) with the ability to produce more credible and accountable responses by accurately citing verifiable sources. However, existing methods, by either feeding LMs with raw or preprocessed materials, remain prone to errors. To address this, we introduce CaLM, a novel verification framework. CaLM leverages the insight that a robust grounded response should be consistent with information derived solely from its cited sources. Our framework empowers smaller LMs, which rely less on parametric memory and excel at processing relevant information given a query, to validate the output of larger LMs. Larger LM responses that closely align with the smaller LMs’ output, which relies exclusively on cited documents, are verified. Responses showing discrepancies are iteratively refined through a feedback loop. Experiments on three open-domain question-answering datasets demonstrate significant performance gains of 1.5% to 7% absolute average without any required model fine-tuning.

Event extraction has gained considerable interest due to its wide-ranging applications. However, recent studies draw attention to evaluation issues, suggesting that reported scores may not accurately reflect the true performance. In this work, we identify and address evaluation challenges, including inconsistency due to varying data assumptions or preprocessing steps, the insufficiency of current evaluation frameworks that may introduce dataset or data split bias, and the low reproducibility of some previous approaches. To address these challenges, we present TextEE, a standardized, fair, and reproducible benchmark for event extraction. TextEE comprises standardized data preprocessing scripts and splits for 16 datasets spanning eight diverse domains and includes 14 recent methodologies, conducting a comprehensive benchmark reevaluation. We also evaluate five varied large language models on our TextEE benchmark and demonstrate how they struggle to achieve satisfactory performance. Inspired by our reevaluation results and findings, we discuss the role of event extraction in the current NLP era, as well as future challenges and insights derived from TextEE. We believe TextEE, the first standardized comprehensive benchmarking tool, will significantly facilitate future event extraction research.

This paper examines the experiences of African American Language (AAL) speakers when using language technologies. Previous work has used quantitative methods to uncover performance disparities between AAL speakers and White Mainstream English speakers when using language technologies, but has not sought to understand the impacts of these performance disparities on AAL speakers. Through interviews with 19 AAL speakers, we focus on understanding such impacts in a contextualized and human-centered manner. We find that AAL speakers often undertake invisible labor of adapting their speech patterns to successfully use language technologies, and they make connections between failures of language technologies for AAL speakers and a lack of inclusion of AAL speakers in language technology design processes and datasets. Our findings suggest that NLP researchers and practitioners should invest in developing contextualized and human-centered evaluations of language technologies that seek to understand the impacts of performance disparities on speakers of underrepresented languages and language varieties.

The introduction of large language models has significantly advanced code generation. However, open-source models often lack the execution capabilities and iterative refinement of advanced systems like the GPT-4 Code Interpreter. To address this, we introduce OpenCodeInterpreter, a family of open-source code systems designed for generating, executing, and iteratively refining code. Supported by Code Feedback, a dataset featuring 68K multi-turn interactions, OpenCodeInterpreter integrates execution and human feedback for dynamic code refinement. Our comprehensive evaluation of OpenCodeInterpreter across key benchmarks such as HumanEval, MBPP, and their enhanced versions from EvalPlus reveals its exceptional performance. Notably, OpenCodeInterpreter-33B achieves an accuracy of 83.2 (76.4) on the average (and plus versions) of HumanEval and MBPP, closely rivaling GPT-4’s 84.2 (76.2) and further elevates to 91.6 (84.6) with synthesized human feedback from GPT-4. OpenCodeInterpreterbrings the gap between open-source code generation models and proprietary systems like GPT-4 Code Interpreter.

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Measuring and Addressing Indexical Bias in Information Retrieval
Caleb Ziems | William Held | Jane Dwivedi-Yu | Diyi Yang

Information Retrieval (IR) systems are designed to deliver relevant content, but traditional systems may not optimize rankings for fairness, neutrality, or the balance of ideas. Consequently, IR can often introduce indexical biases, or biases in the positional order of documents. Although indexical bias can demonstrably affect people’s opinion, voting patterns, and other behaviors, these issues remain understudied as the field lacks reliable metrics and procedures for automatically measuring indexical bias. Towards this end, we introduce the PAIR framework, which supports automatic bias audits for ranked documents or entire IR systems. After introducing DUO, the first general-purpose automatic bias metric, we run an extensive evaluation of 8 IR systems on a new corpus of 32k synthetic and 4.7k natural documents, with 4k queries spanning 1.4k controversial issue topics. A human behavioral study validates our approach, showing that our bias metric can help predict when and how indexical bias will shift a reader’s opinion.

Instruction tuning has emerged as a prominent methodology for teaching Large Language Models (LLMs) to follow instructions. However, current instruction datasets predominantly cater to English or are derived from English-dominated LLMs, leading to inherent biases toward Western culture. This bias negatively impacts non-English languages such as Arabic and the unique culture of the Arab region. This paper addresses this limitation by introducing CIDAR, the first open Arabic instruction-tuning dataset culturally aligned by native Arabic speakers. CIDAR contains 10,000 instruction and output pairs that represent the Arab region. We discuss the cultural relevance of CIDAR via the analysis and comparison to a few models fine-tuned on other datasets. Our experiments indicate that models fine-tuned on CIDAR achieve better cultural alignment compared to those fine-tuned on 30x more data.

In order to enable extraction of structured clinical data from unstructured radiology reports, we introduce RadGraph-XL, a large-scale, expert-annotated dataset for clinical entity and relation extraction. RadGraph-XL consists of 2,300 radiology reports, which are annotated with over 410,000 entities and relations by board-certified radiologists. Whereas previous approaches focus solely on chest X-rays, RadGraph-XL includes data from four anatomy-modality pairs - chest CT, abdomen/pelvis CT, brain MR, and chest X-rays. Then, in order to automate structured information extraction, we use RadGraph-XL to train transformer-based models for clinical entity and relation extraction. Our evaluations include comprehensive ablation studies as well as an expert reader study that evaluates trained models on out-of-domain data. Results demonstrate that our model surpasses the performance of previous methods by up to 52% and notably outperforms GPT-4 in this domain. We release RadGraph-XL as well as our trained model to foster further innovation and research in structured clinical information extraction.

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SMART: Submodular Data Mixture Strategy for Instruction Tuning
H S V N S Kowndinya Renduchintala | Sumit Bhatia | Ganesh Ramakrishnan

Instruction Tuning involves finetuning a language model on a collection of instruction-formatted datasets in order to enhance the generalizability of the model to unseen tasks. Studies have shown the importance of balancing different task proportions during finetuning, but finding the right balance remains challenging. Unfortunately, there’s currently no systematic method beyond manual tuning or relying on practitioners’ intuition. In this paper, we introduce SMART (Submodular data Mixture strAtegy for instRuction Tuning) — a novel data mixture strategy which makes use of a submodular function to assign importance scores to tasks which are then used to determine the mixture weights. Given a fine-tuning budget, SMART redistributes the budget among tasks and selects non-redundant samples from each task. Experimental results demonstrate that SMART significantly outperforms traditional methods such as examples proportional mixing and equal mixing. Furthermore, SMART facilitates the creation of data mixtures based on a few representative subsets of tasks alone and through task pruning analysis, we reveal that in a limited budget setting, allocating budget among a subset of representative tasks yields superior performance compared to distributing the budget among all tasks. The code for reproducing our results is open-sourced at https://github.com/kowndinya-renduchintala/SMART.

Selective prediction minimizes incorrect predictions from vision-language models (VLMs) by allowing them to abstain from answering when uncertain. However, when deploying a vision-language system with low tolerance for inaccurate predictions, selective prediction may be over-cautious and abstain too frequently, even on many correct predictions. We introduce ReCoVERR, an inference-time algorithm to reduce the over-abstention of a selective vision-language system without increasing the error rate of the system’s predictions. When the VLM makes a low-confidence prediction, instead of abstaining ReCoVERR tries to find relevant clues in the image that provide additional evidence for the prediction. ReCoVERR uses an LLM to pose related questions to the VLM, collects high-confidence evidences, and if enough evidence confirms the prediction the system makes a prediction instead of abstaining. ReCoVERR enables three VLMs (BLIP2, InstructBLIP and LLaVA-1.5) to answer up to 20% more questions on the VQAv2 and A-OKVQA tasks without decreasing system accuracy, thus improving overall system reliability. Our code is available at https://github.com/tejas1995/ReCoVERR.

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Language Model Priors and Data Augmentation Strategies for Low-resource Machine Translation: A Case Study Using Finnish to Northern Sámi
Jonne Sälevä | Constantine Lignos

We investigate ways of using monolingual data in both the source and target languages for improving low-resource machine translation. As a case study, we experiment with translation from Finnish to Northern Sámi.Our experiments show that while conventional backtranslation remains a strong contender, using synthetic target-side data when training backtranslation models can be helpful as well.We also show that monolingual data can be used to train a language model which can act as a regularizer without any augmentation of parallel data.

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Differentially Private Knowledge Distillation via Synthetic Text Generation
James Flemings | Murali Annavaram

Large Language models (LLMs) are achieving state-of-the-art performance in many different downstream tasks. However, the increasing urgency of data privacy puts pressure on practitioners to train LLMs with Differential Privacy (DP) on private data. Concurrently, the exponential growth in parameter size of LLMs necessitates model compression before deployment of LLMs on resource-constrained devices or latency-sensitive applications. Differential privacy and model compression generally must trade off utility loss to achieve their objectives. Moreover, simultaneously applying both schemes can compound the utility degradation. To this end, we propose DistilDP: a novel differentially private knowledge distillation algorithm that exploits synthetic data generated by a differentially private teacher LLM. The knowledge of a teacher LLM is transferred onto the student in two ways: one way from the synthetic data itself– the hard labels, and the other way by the output distribution of the teacher evaluated on the synthetic data– the soft labels. Furthermore, if the teacher and student share a similar architectural structure, we can further distill knowledge by aligning the hidden representations between both. Our experimental results demonstrate that DistilDP can substantially improve the utility over existing baselines, at least 9.0 PPL on the Big Patent dataset, with strong privacy parameters, 𝜖=2. These promising results progress privacy-preserving compression of autoregressive LLMs. Our code can be accessed here: https://github.com/james-flemings/dp_compress.

Large language models (LLMs) adapted to follow user instructions are now widely deployed as conversational agents. In this work, we examine one increasingly common instruction-following task: providing writing assistance to compose a long-form answer. To evaluate the capabilities of current LLMs on this task, we construct KIWI, a dataset of knowledge-intensive writing instructions in the scientific domain. Given a research question, an initial model-generated answer and a set of relevant papers, an expert annotator iteratively issues instructions for the model to revise and improve its answer. We collect 1,260 interaction turns from 234 interaction sessions with three state-of-the-art LLMs. Each turn includes a user instruction, a model response, and a human evaluation of the model response. Through a detailed analysis of the collected responses, we find that all models struggle to incorporate new information into an existing answer, and to perform precise and unambiguous edits. Further, we find that models struggle to judge whether their outputs successfully followed user instructions, with accuracy at least 10 points short of human agreement. Our findings indicate that KIWI will be a valuable resource to measure progress and improve LLMs’ instruction-following capabilities for knowledge intensive writing tasks.

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XL-HeadTags: Leveraging Multimodal Retrieval Augmentation for the Multilingual Generation of News Headlines and Tags
Faisal Tareque Shohan | Mir Tafseer Nayeem | Samsul Islam | Abu Ubaida Akash | Shafiq Joty

Millions of news articles published online daily can overwhelm readers. Headlines and entity (topic) tags are essential for guiding readers to decide if the content is worth their time. While headline generation has been extensively studied, tag generation remains largely unexplored, yet it offers readers better access to topics of interest. The need for conciseness in capturing readers’ attention necessitates improved content selection strategies for identifying salient and relevant segments within lengthy articles, thereby guiding language models effectively. To address this, we propose to leverage auxiliary information such as images and captions embedded in the articles to retrieve relevant sentences and utilize instruction tuning with variations to generate both headlines and tags for news articles in a multilingual context. To make use of the auxiliary information, we have compiled a dataset named XL-HeadTags, which includes 20 languages across 6 diverse language families. Through extensive evaluation, we demonstrate the effectiveness of our plug-and-play multimodal-multilingual retrievers for both tasks. Additionally, we have developed a suite of tools for processing and evaluating multilingual texts, significantly contributing to the research community by enabling more accurate and efficient analysis across languages.

This paper introduces the Decomposed Requirements Following Ratio (DRFR), a new metric for evaluating Large Language Models’ (LLMs) ability to follow instructions. Addressing a gap in current methodologies, DRFR breaks down complex instructions into simpler criteria, facilitating a detailed analysis of LLMs’ compliance with various aspects of tasks. Alongside this metric, we present InFoBench, a benchmark comprising 500 diverse instructions and 2,250 decomposed questions across multiple constraint categories. Our experiments compare DRFR with traditional scoring methods and explore annotation sources, including human experts, crowd-sourced workers, and GPT-4. The findings demonstrate DRFR’s higher reliability and the effectiveness of using GPT-4 as a cost-efficient annotator. The evaluation of several advanced LLMs using this framework reveals their strengths and areas needing improvement, particularly in complex instruction-following. This study contributes a novel metric and benchmark, offering insights for future LLM development and evaluation.

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EcoRank: Budget-Constrained Text Re-ranking Using Large Language Models
Muhammad Rashid | Jannat Meem | Yue Dong | Vagelis Hristidis

Large Language Models (LLMs) have achieved state-of-the-art performance in text re-ranking. This process includes queries and candidate passages in the prompts, utilizing pointwise, listwise, and pairwise prompting strategies. A limitation of these ranking strategies with LLMs is their cost: the process can become expensive due to API charges, which are based on the number of input and output tokens. We study how to maximize the re-ranking performance given a budget, by navigating the vast search spaces of prompt choices, LLM APIs, and budget splits. We propose a suite of budget-constrained methods to perform text re-ranking using a set of LLM APIs. Our most efficient method, called EcoRank, is a two-layered pipeline that jointly optimizes decisions regarding budget allocation across prompt strategies and LLM APIs. Our experimental results on four popular QA and passage reranking datasets show that EcoRank outperforms other budget-aware supervised and unsupervised baselines.

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FinTral: A Family of GPT-4 Level Multimodal Financial Large Language Models
Gagan Bhatia | El Moatez Billah Nagoudi | Hasan Cavusoglu | Muhammad Abdul-Mageed

We introduce FinTral, a suite of state-of-the-art multimodal large language models (LLMs) built upon the Mistral-7b model and tailored for financial analysis. FinTral integrates textual, numerical, tabular, and image data. We enhance FinTral with domain-specific pretraining, instruction fine-tuning, and RLAIF training by exploiting a large collection of textual and visual datasets we curate for this work. We also introduce an extensive benchmark featuring nine tasks and 25 datasets for evaluation, including hallucinations in the financial domain. Our FinTral model trained with direct preference optimization employing advanced Tools and Retrieval methods, dubbed FinTral-DPO-T&R, demonstrates an exceptional zero-shot performance. It outperforms ChatGPT-3.5 in all tasks and surpasses GPT-4 in five out of nine tasks, marking a significant advancement in AI-driven financial technology. We also demonstrate that FinTral has the potential to excel in real-time analysis and decision-making in diverse financial contexts.

Large Multimodal Models (LMM) are built across modalities and the misalignment between two modalities can result in “hallucination”, generating textual outputs that are not grounded by the multimodal information in context. To address the multimodal misalignment issue, we adapt the Reinforcement Learning from Human Feedback (RLHF) from the text domain to the vision-language alignment, where human annotators are asked to compare two responses and pinpoint the more hallucinated one, and the vision-language model is trained to maximize the simulated human rewards. We propose a new alignment algorithm called Factually Augmented RLHF that augments the reward model with additional factual information such as image captions and ground-truth multi-choice options, which alleviates the reward hacking phenomenon in RLHF and further improves the performance. We also enhance the GPT-4-generated training data (for vision instruction tuning) with previously available human-written image-text pairs to improve the general capabilities of our model. To evaluate the proposed approach in real-world scenarios, we develop a new evaluation benchmark MMHAL-BENCH with a special focus on penalizing hallucinations. As the first LMM trained with RLHF, our approach achieves remarkable improvement on the LLaVA-Bench dataset with the 96% performance level of the text-only GPT-4 (while previous best methods can only achieve the 87% level), and an improvement of 60% on MMHAL-BENCH over other baselines.

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The Art of Defending: A Systematic Evaluation and Analysis of LLM Defense Strategies on Safety and Over-Defensiveness
Neeraj Varshney | Pavel Dolin | Agastya Seth | Chitta Baral

As Large Language Models (LLMs) play an increasingly pivotal role in natural language processing applications, their safety concerns become critical areas of NLP research. This has resulted in the development of various LLM defense strategies. Unfortunately, despite the shared goal of improving the safety of LLMs, the evaluation suites across various research works are disjoint and lack diverse inputs to ensure accurate and precise evaluation estimates. Furthermore, the important factor of ‘over-defensiveness’ on the safe inputs has largely remained overlooked. Addressing these limitations, this paper presents a systematic evaluation, comparison, and analysis of various LLM defense strategies over both ‘safety’ and ‘over-defensiveness’. To this end, we compile a large and diverse collection of safe and unsafe prompts, design precise evaluation methodology, and study the efficacy of various LLM defense strategies on multiple state-of-the-art LLMs. Our work reveals a number of crucial findings that we believe will pave the way and also facilitate further research in the critical area of improving the safety of LLMs.

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PAT-Questions: A Self-Updating Benchmark for Present-Anchored Temporal Question-Answering
Jannat Meem | Muhammad Rashid | Yue Dong | Vagelis Hristidis

Existing work on Temporal Question Answering (TQA) has predominantly focused on questions anchored to specific timestamps or events (e.g. ‘Who was the US president in 1970?’). Little work has studied questions whose temporal context is relative to the present time (e.g. ‘Who was the previous US president?’). We refer to this problem as Present-Anchored Temporal QA (PATQA). PATQA poses unique challenges: (1) large language models (LLMs) may have outdated knowledge, (2) complex temporal relationships (e.g. ‘before’, ‘previous’) are hard to reason, (3) multi-hop reasoning may be required, and (4) the gold answers of benchmarks must be continuously updated. To address these challenges, we introduce the PAT-Questions benchmark, which includes single and multi-hop temporal questions. The answers in PAT-Questions can be automatically refreshed by re-running SPARQL queries on a knowledge graph, if available. We evaluate several state-of-the-art LLMs and a SOTA temporal reasoning model (TEMPREASON-T5) on PAT-Questions through direct prompting and retrieval-augmented generation (RAG). The results highlight the limitations of existing solutions in PATQA and motivate the need for new methods to improve PATQA reasoning capabilities.

Large language model agents have demonstrated remarkable advancements across various complex tasks. Recent works focus on optimizing the agent team or employing self-reflection to iteratively solve complex tasks. Since these agents are all based on the same LLM, only conducting self-evaluation or removing underperforming agents does not substantively enhance the capability of the agents. We argue that a comprehensive evaluation and accumulating experience from evaluation feedback is an effective approach to improving system performance. In this paper, we propose Reusable Experience Accumulation with 360∘ Assessment (360∘REA), a hierarchical multi-agent framework inspired by corporate organizational practices. The framework employs a novel 360∘ performance assessment method for multi-perspective performance evaluation with fine-grained assessment. To enhance the capability of agents in addressing complex tasks, we introduce dual-level experience pool for agents to accumulate experience through fine-grained assessment. Extensive experiments on complex task datasets demonstrate the effectiveness of 360∘REA.

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Extracting Polymer Nanocomposite Samples from Full-Length Documents
Ghazal Khalighinejad | Defne Circi | L. Brinson | Bhuwan Dhingra

This paper investigates the use of large language models (LLMs) for extracting sample lists of polymer nanocomposites (PNCs) from full-length materials science research papers. The challenge lies in the complex nature of PNC samples, which have numerous attributes scattered throughout the text. The complexity of annotating detailed information on PNCs limits the availability of data, making conventional document-level relation extraction techniques impractical due to the challenge in creating comprehensive named entity span annotations.To address this, we introduce a new benchmark and an evaluation technique for this task and explore different prompting strategies in a zero-shot manner. We also incorporate self-consistency to improve the performance. Our findings show that even advanced LLMs struggle to extract all of the samples from an article. Finally, we analyze the errors encountered in this process, categorizing them into three main challenges, and discuss potential strategies for future research to overcome them.

Recent advancements have showcased the potential of Large Language Models (LLMs) in executing reasoning tasks, particularly facilitated by Chain-of-Thought (CoT) prompting. While tasks like arithmetic reasoning involve clear, definitive answers and logical chains of thought, the application of LLM reasoning in recommendation systems (RecSys) presents a distinct challenge. RecSys tasks revolve around subjectivity and personalized preferences, an under-explored domain in utilizing LLMs’ reasoning capabilities. Our study explores several aspects to better understand reasoning for RecSys and demonstrate how task quality improves by utilizing LLM reasoning for both zero-shot and fine-tuning settings. Additionally, we propose Rec-SAVER (Recommender Systems Automatic Verification and Evaluation of Reasoning) to automatically assess the quality of LLM reasoning responses without the requirement of curated gold references or human raters. We show that our framework aligns with real human judgment on the coherence and faithfulness of reasoning responses. Overall, our work shows that incorporating reasoning into RecSys can improve personalized tasks, paving the way for further advancements in recommender system methodologies.

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Toucan: Many-to-Many Translation for 150 African Language Pairs
AbdelRahim Elmadany | Ife Adebara | Muhammad Abdul-Mageed

We address a notable gap in Natural Language Processing (NLP) by introducing a collection of resources designed to improve Machine Translation (MT) for low-resource languages, with a specific focus on African languages. First, We introduce two language models (LMs), Cheetah-1.2B and Cheetah-3.7B, with 1.2 billion and 3.7 billion parameters respectively. Next, we finetune the aforementioned models to create Toucan, an Afrocentric machine translation model designed to support 156 African language pairs. To evaluate Toucan, we carefully develop an extensive machine translation benchmark, dubbed Afro-Lingu-MT, tailored for evaluating machine translation. Toucan significantly outperforms other models, showcasing its remarkable performance on MT for African languages. Finally, we train a new model, spBLEU-1K, to enhance translation evaluation metrics, covering 1K languages, including African languages. This work aims to advance the field of NLP, fostering cross-cultural understanding and knowledge exchange, particularly in regions with limited language resources such as Africa.

We consider the task of building a dialogue system that can motivate users to adopt positive lifestyle changes, Motivational Interviewing (MI). Addressing such a task requires a system that could infer how to motivate the user effectively. We propose DIIR, a framework that is capable of learning and applying conversation strategies in the form of natural language inductive rules from expert demonstrations. Automatic and human evaluation on instruction-following large language models show natural language strategies descriptions discovered by DIIR can improve active listening skills, reduce unsolicited advice, and promote more collaborative and less authoritative conversations, outperforming in-context demonstrations that are over 50 times longer.

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Evaluating Structural Generalization in Neural Machine Translation
Ryoma Kumon | Daiki Matsuoka | Hitomi Yanaka

Compositional generalization refers to the ability to generalize to novel combinations of previously observed words and syntactic structures.Since it is regarded as a desired property of neural models, recent work has assessed compositional generalization in machine translation as well as semantic parsing.However, previous evaluations with machine translation have focused mostly on lexical generalization (i.e., generalization to unseen combinations of known words).Thus, it remains unclear to what extent models can translate sentences that require structural generalization (i.e., generalization to different sorts of syntactic structures).To address this question, we construct SGET, a machine translation dataset covering various types of compositional generalization with control of words and sentence structures.We evaluate neural machine translation models on SGET and show that they struggle more in structural generalization than in lexical generalization.We also find different performance trends in semantic parsing and machine translation, which indicates the importance of evaluations across various tasks.

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Figuratively Speaking: Authorship Attribution via Multi-Task Figurative Language Modeling
Gregorios Katsios | Ning Sa | Tomek Strzalkowski

The identification of Figurative Language (FL) features in text is crucial for various Natural Language Processing (NLP) tasks, where understanding of the author’s intended meaning and its nuances is key for successful communication. At the same time, the use of a specific blend of various FL forms most accurately reflects a writer’s style, rather than the use of any single construct, such as just metaphors or irony. Thus, we postulate that FL features could play an important role in Authorship Attribution (AA) tasks. We believe that our is the first computational study of AA based on FL use. Accordingly, we propose a Multi-task Figurative Language Model (MFLM) that learns to detect multiple FL features in text at once. We demonstrate, through detailed evaluation across multiple test sets, that the our model tends to perform equally or outperform specialized binary models in FL detection. Subsequently, we evaluate the predictive capability of joint FL features towards the AA task on three datasets, observing improved AA performance through the integration of MFLM embeddings.

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CHAMP: A Competition-level Dataset for Fine-Grained Analyses of LLMs’ Mathematical Reasoning Capabilities
Yujun Mao | Yoon Kim | Yilun Zhou

Recent large language models (LLMs) have shown indications of mathematical reasoning ability on challenging competition-level problems, especially with self-generated verbalizations of intermediate reasoning steps (i.e., chain-of-thought prompting). However, current evaluations mainly focus on the end-to-end final answer correctness, and it is unclear whether LLMs can make use of helpful side information such as problem-specific hints. In this paper, we propose a challenging benchmark dataset for enabling such analyses. The Concept and Hint-Annotated Math Problems (CHAMP) consists of high school math competition problems, annotated with concepts, or general math facts, and hints, or problem-specific tricks. These annotations allow us to explore the effects of additional information, such as relevant hints, misleading concepts, or related problems. This benchmark is difficult, with the best model only scoring 58.1% in standard settings. With concepts and hints, performance sometimes improves, indicating that some models can make use of such side information. Furthermore, we annotate model-generated solutions for their correctness. Using this corpus, we find that models often arrive at the correct final answer through wrong reasoning steps. In addition, we test whether models are able to verify these solutions, and find that most models struggle.

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Improving Machine Translation with Large Language Models: A Preliminary Study with Cooperative Decoding
Jiali Zeng | Fandong Meng | Yongjing Yin | Jie Zhou

Contemporary translation engines based on the encoder-decoder framework have made significant strides in development.However, the emergence of Large Language Models (LLMs) has disrupted their position by presenting the potential for achieving superior translation quality.To uncover the circumstances in which LLMs excel and explore how their strengths can be harnessed to enhance translation quality,we first conduct a comprehensive analysis to assess the strengths and limitations of various commercial NMT systems and MT-oriented LLMs. Our findings indicate that neither NMT nor MT-oriented LLMs alone can effectively address all the translation issues, but MT-oriented LLMs show promise as a complementary solution to NMT systems.Building upon these insights, we propose Cooperative Decoding (CoDec), which treats NMT systems as a pretranslation model and MT-oriented LLMs as a supplemental solution to handle complex scenarios beyond the capability of NMT alone.Experimental results on the WMT22 test sets and a newly collected test set WebCrawl demonstrate the effectiveness and efficiency of CoDec, highlighting its potential as a robust solution for combining NMT systems with MT-oriented LLMs in the field of machine translation.

Advances in machine learning have made it possible to perform various text and speech processing tasks, such as automatic speech recognition (ASR), in an end-to-end (E2E) manner. E2E approaches utilizing pre-trained models are gaining attention for conserving training data and resources. However, most of their applications in ASR involve only one of either a pre-trained speech or a language model. This paper proposes integrating a pre-trained speech representation model and a large language model (LLM) for E2E ASR. The proposed model enables the optimization of the entire ASR process, including acoustic feature extraction and acoustic and language modeling, by combining pre-trained models with a bridge network and also enables the application of remarkable developments in LLM utilization, such as parameter-efficient domain adaptation and inference optimization. Experimental results demonstrate that the proposed model achieves a performance comparable to that of modern E2E ASR models by utilizing powerful pre-training models with the proposed integrated approach.

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Proving membership in LLM pretraining data via data watermarks
Johnny Wei | Ryan Wang | Robin Jia

Detecting whether copyright holders’ works were used in LLM pretraining is poised to be an important problem. This work proposes using data watermarks to enable principled detection with only black-box model access, provided that the rightholder contributed multiple training documents and watermarked them before public release. By applying a randomly sampled data watermark, detection can be framed as hypothesis testing, which provides guarantees on the false detection rate. We study two watermarks: one that inserts random sequences, and another that randomly substitutes characters with Unicode lookalikes. We first show how three aspects of watermark design - watermark length, number of duplications, and interference - affect the power of the hypothesis test. Next, we study how a watermark’s detection strength changes under model and dataset scaling: while increasing the dataset size decreases the strength of the watermark, watermarks remain strong if the model size also increases. Finally, we view SHA hashes as natural watermarks and show that we can robustly detect hashes from BLOOM-176B’s training data, as long as they occurred at least 90 times. Together, our results point towards a promising future for data watermarks in real world use.

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Enhancing Hallucination Detection through Perturbation-Based Synthetic Data Generation in System Responses
Dongxu Zhang | Varun Gangal | Barrett Lattimer | Yi Yang

Detecting hallucinations in large language model (LLM) outputs is pivotal, yet traditional fine-tuning for this classification task is impeded by the expensive and quickly outdated annotation process, especially across numerous vertical domains and in the face of rapid LLM advancements. In this study, we introduce an approach that automatically generates both faithful and hallucinated outputs by rewriting system responses. Experimental findings demonstrate that a T5-base model, fine-tuned on our generated dataset, surpasses state-of-the-art zero-shot detectors and existing synthetic generation methods in both accuracy and latency, indicating efficacy of our approach.

With the growing use of Transformer models hosted on cloud platforms to offer inference services, privacy concerns are escalating, especially concerning sensitive data like investment plans and bank account details. Secure Multi-Party Computing (SMPC) emerges as a promising solution to protect the privacy of inference data and model parameters. However, the application of SMPC in Privacy-Preserving Inference (PPI) for Transformer models often leads to considerable slowdowns or declines in performance. This is largely due to the multitude of nonlinear operations in the Transformer architecture, which are not well-suited to SMPC and are difficult to circumvent or optimize effectively. To address this concern, we introduce a comprehensive PPI framework called SecFormer to achieve fast and accurate PPI for Transformer models. We successfully eliminate the high-cost exponential and maximum operations in PPI without sacrificing model performance and develop a suite of efficient SMPC protocols by employing suitable numerical computation methods to boost other complex nonlinear functions in PPI, including GeLU, LayerNorm, and a redesigned Softmax. Our extensive experiments reveal that SecFormer outperforms MPCFormer in performance, showing improvements of 3.4% and 24.7% for BERT_BASE and BERT_LARGE, respectively. In terms of efficiency, SecFormer is 3.57 and 3.58 times faster than PUMA for BERT_BASE and BERT_LARGE, demonstrating its effectiveness and speed.

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Raccoon: Prompt Extraction Benchmark of LLM-Integrated Applications
Junlin Wang | Tianyi Yang | Roy Xie | Bhuwan Dhingra

With the proliferation of LLM-integrated applications such as GPT-s, millions are deployed, offering valuable services through proprietary instruction prompts. These systems, however, are prone to prompt extraction attacks through meticulously designed queries. To help mitigate this problem, we introduce the Raccoon benchmark which comprehensively evaluates a model’s susceptibility to prompt extraction attacks. Our novel evaluation method assesses models under both defenseless and defended scenarios, employing a dual approach to evaluate the effectiveness of existing defenses and the resilience of the models. The benchmark encompasses 14 categories of prompt extraction attacks, with additional compounded attacks that closely mimic the strategies of potential attackers, alongside a diverse collection of defense templates. This array is, to our knowledge, the most extensive compilation of prompt theft attacks and defense mechanisms to date. Our findings highlight universal susceptibility to prompt theft in the absence of defenses, with OpenAI models demonstrating notable resilience when protected. This paper aims to establish a more systematic benchmark for assessing LLM robustness against prompt extraction attacks, offering insights into their causes and potential countermeasures.

Conversational search facilitates complex information retrieval by enabling multi-turn interactions between users and the system. Supporting such interactions requires a comprehensive understanding of the conversational inputs to formulate a good search query based on historical information. In particular, the search query should include the relevant information from the previous conversation turns.However, current approaches for conversational dense retrieval primarily rely on fine-tuning a pre-trained ad-hoc retriever using the whole conversational search session, which can be lengthy and noisy. Moreover, existing approaches are limited by the amount of manual supervision signals in the existing datasets.To address the aforementioned issues, we propose a **H**istory-**A**ware **Conv**ersational **D**ense **R**etrieval (HAConvDR) system, which incorporates two ideas: context-denoised query reformulation and automatic mining of supervision signals based on the actual impact of historical turns.Experiments on two public conversational search datasets demonstrate the improved history modeling capability of HAConvDR, in particular for long conversations with topic shifts.

Multi-Modal Knowledge Graphs (MMKGs) have proven valuable for various downstream tasks. However, scaling them up is challenging because building large-scale MMKGs often introduces mismatched images (i.e., noise). Most entities in KGs belong to the long tail, meaning there are few images of them available online. This scarcity makes it difficult to determine whether a found image matches the entity. To address this, we draw on the Triangle of Reference Theory and suggest enhancing vision-language models with concept guidance. Specifically, we introduce COG, a two-stage framework with COncept-Guided vision-language models. The framework comprises a Concept Integration module, which effectively identifies image-text pairs of long-tailed entities, and an Evidence Fusion module, which offers explainability and enables human verification. To demonstrate the effectiveness of COG, we create a dataset of 25k image-text pairs of long-tailed entities. Our comprehensive experiments show that COG not only improves the accuracy of recognizing long-tailed image-text pairs compared to baselines but also offers flexibility and explainability.

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ZeroStance: Leveraging ChatGPT for Open-Domain Stance Detection via Dataset Generation
Chenye Zhao | Yingjie Li | Cornelia Caragea | Yue Zhang

Zero-shot stance detection that aims to detect the stance (typically against, favor, or neutral) towards unseen targets has attracted considerable attention. However, most previous studies only focus on targets from a single or limited text domains (e.g., financial domain), and thus zero-shot models cannot generalize well to unseen targets of diverse domains (e.g., political domain). In this paper, we consider a more realistic task, i.e., open-domain stance detection, which aims at training a model that is able to generalize well to unseen targets across multiple domains of interest. Particularly, we propose a novel dataset generation method ZeroStance, which leverages ChatGPT to construct a synthetic open-domain dataset CHATStance that covers a wide range of domains. We then train an open-domain model on our synthetic dataset after proper data filtering. Extensive results indicate that our model, when trained on this synthetic dataset, shows superior generalization to unseen targets of diverse domains over baselines on most benchmarks. Our method requires only a task description in the form of a prompt and is much more cost-effective and data-efficient than previous methods. We will release our code and data to facilitate future research.

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Boosting Zero-Shot Crosslingual Performance using LLM-Based Augmentations with Effective Data Selection
Barah Fazili | Ashish Agrawal | Preethi Jyothi

Large language models (LLMs) are very proficient text generators. We leverage this capability of LLMs to generate task-specific data via zero-shot prompting and promote cross-lingual transfer for low-resource target languages. Given task-specific data in a source language and a teacher model trained on this data, we propose using this teacher to label LLM generations and employ a set of simple data selection strategies that use the teacher’s label probabilities. Our data selection strategies help us identify a representative subset of diverse generations that help boost zero-shot accuracies while being efficient, in comparison to using all the LLM generations (without any subset selection). We also highlight other important design choices that affect cross-lingual performance such as the use of translations of source data and what labels are best to use for the LLM generations. We observe significant performance gains across sentiment analysis and natural language inference tasks (of up to a maximum of 7.13 absolute points and 1.5 absolute points on average) across a number of target languages (Hindi, Marathi, Urdu, Swahili) and domains.

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Reinforcement Tuning for Detecting Stances and Debunking Rumors Jointly with Large Language Models
Ruichao Yang | Wei Gao | Jing Ma | Hongzhan Lin | Bo Wang

Learning multi-task models for jointly detecting stance and verifying rumors poses challenges due to the need for training data of stance at post level and rumor veracity at claim level, which are difficult to obtain. To address this issue, we leverage large language models (LLMs) as the foundation annotators for the joint stance detection (SD) and rumor verification (RV) tasks, dubbed as JSDRV. We introduce a novel reinforcement tuning framework to enhance the joint predictive capabilities of LLM-based SD and RV components. Specifically, we devise a policy for selecting LLM-annotated data at the two levels, employing a hybrid reward mechanism to choose high-quality labels for effective LLM fine-tuning on both tasks. Results demonstrate that JSDRV improves the capabilities of LLMs in the joint tasks, not only outperforming state-of-the-art methods but also generalizing to non-LLMs accommodated as task models.

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Exploring the Potential of Dense Information in Multimodal Alignment
Zhiyuan Fan | Zhihong Chen | Benyou Wang

Despite the success of data augmentation in improving CLIP model, existing methods that utilize LLM or SAM to enrich the information in captions still suffer from several limitations, including insufficient detail and excessive hallucinations, ultimately resulting in compromised alignment and masking the true potential of dense information. This can lead to erroneous conclusions about CLIP’s ability to handle rich data, impeding the development of more effective models. To address the limitations of existing methods, we introduce a novel pipeline that generates highly detailed, factually accurate captions for images, which facilitates in-depth analysis of the potential for dense information in multimodal alignment. Contrary to previous findings, our investigation revealed that lengthening captions boosts performance across diverse benchmarks, even surpassing the effectiveness of meticulously crafted hard negative samples. Building on these insights, DELIP is introduced, demonstrably enhancing both foundational multimodal alignment and compositional reasoning abilities. Finally, we explore strategies to expand the context window of the text encoder, unlocking the potential of richer data for CLIP and paving the way for advancements in leveraging dense information for multimodal alignment.

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Referral Augmentation for Zero-Shot Information Retrieval
Michael Tang | Shunyu Yao | John Yang | Karthik Narasimhan

We propose Referral-Augmented Retrieval (RAR), a simple technique that concatenates document indices with referrals: text from other documents that cite or link to the given document. We find that RAR provides significant performance gains for tasks across paper retrieval, entity retrieval, and open-domain question-answering in both zero-shot and in-domain (e.g., fine-tuned) settings. We examine how RAR provides especially strong improvements on more structured tasks, and can greatly outperform generative text expansion techniques such as DocT5Query and Query2Doc, with a 37% and 21% absolute improvement on ACL paper retrieval, respectively. We also compare three ways to aggregate referrals for RAR. Overall, we believe RAR can help revive and re-contextualize the classic information retrieval idea of using anchor texts to improve the representations of documents in a wide variety of corpuses in the age of neural retrieval.

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InstructEval: Instruction-Tuned Text Evaluator from Human Preference
Wenhao Wu | Wei Li | Xinyan Xiao | Jiachen Liu | Sujian Li

This paper explores to construct a general text evaluator based on open-source Large Language Models (LLMs), a domain predominantly occupied by commercial counterparts such as GPT-4. Recognizing the limitations of open-source models like Llama in evaluative tasks, we introduce InstructEval, a general multi-aspect text evaluator developed through instruction tuning of open-source LLMs. To overcome the shortage of annotated resources for multi-aspect evaluations, InstructEval combines extensive open Human Preference Modeling (HPM) datasets with a small set of multi-aspect annotated data.This approach not only enhances effectiveness in overall evaluation tasks but also exhibits improved performance in multi-aspect evaluation tasks.As demonstrated by our extensive experiments, InstructEval achieves comparable or superior performance to commercial LLMs like ChatGPT or GPT-4 in terms of both overall and multi-aspect evaluation.

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A Curious Case of Searching for the Correlation between Training Data and Adversarial Robustness of Transformer Textual Models
Dang Cuong | Dung Le | Thai Le

Existing works have shown that fine-tuned textual transformer models achieve state-of-the-art prediction performances but are also vulnerable to adversarial text perturbations. Traditional adversarial evaluation is often done only after fine-tuning the models and ignoring the training data. In this paper, we want to prove that there is also a strong correlation between training data and model robustness. To this end, we extract 13 different features representing a wide range of input fine-tuning corpora properties and use them to predict the adversarial robustness of the fine-tuned models. Focusing mostly on encoder-only transformer models BERT and RoBERTa with additional results for BART, ELECTRA and GPT2, we provide diverse evidence to support our argument. First, empirical analyses show that (a) extracted features can be used with a lightweight classifier such as Random Forest to effectively predict the attack success rate and (b) features with the most influence on the model robustness have a clear correlation with the robustness. Second, our framework can be used as a fast and effective additional tool for robustness evaluation since it (a) saves 30x-193x runtime compared to the traditional technique, (b) is transferable across models, (c) can be used under adversarial training, and (d) robust to statistical randomness. Our code is publicly available at https://github.com/CaptainCuong/RobustText_ACL2024.

Do current large language models (LLMs) better solve graph reasoning and generation tasks with parameter updates? In this paper, we propose InstructGraph, a framework that empowers LLMs with the abilities of graph reasoning and generation by instruction tuning and preference alignment. Specifically, we first propose a structured format verbalizer to unify all graph data into a universal code-like format, which can simply represent the graph without any external graph-specific encoders. Furthermore, a graph instruction tuning stage is introduced to guide LLMs in solving graph reasoning and generation tasks. Finally, we identify potential hallucination problems in graph tasks and sample negative instances for preference alignment, the target of which is to enhance the output’s reliability of the model. Extensive experiments across multiple graph-centric tasks exhibit that InstructGraph can achieve the best performance and outperform GPT-4 and LLaMA2 by more than 13% and 38%, respectively.

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RaDA: Retrieval-augmented Web Agent Planning with LLMs
Minsoo Kim | Victor Bursztyn | Eunyee Koh | Shunan Guo | Seung-won Hwang

Agents powered by large language models (LLMs) inherit important limitations, such as the restricted context length, dependency on human-engineered exemplars (e.g., for task decomposition), and insufficient generalization. To address these challenges, we propose RaDA, a novel planning method for Web agents that does not require manual exemplars, efficiently leverages the LLMs’ context, and enhances generalization. RaDA disentangles planning into two stages: for a new given task, during Retrieval-augmented Task Decomposition (RaD), it decomposes tasks into high-level subtasks; next, during Retrieval-augmented Action Generation (RaA), it traverses the trajectory obtained with RaD to iteratively synthesize actions based on dynamically retrieved exemplars. We compare RaDA with strong baselines covering a broad space of design choices, using both GPT-3.5 and GPT-4 as backbones; and we find consistent improvements over previous SOTA in two challenging benchmarks, CompWoB and Mind2Web, covering settings with different complexities. We show the contributions of RaDA via ablation studies and qualitative analysis; and we discuss the structural benefits of our more compositional design.

Large language models (LLMs) have demonstrated impressive reasoning capabilities, yet there is ongoing debate about these abilities and the potential data contamination problem recently. This paper aims to evaluate the reasoning capacities of LLMs, specifically in solving recent competition-level programming problems in Codeforces, which are expert-crafted and unique, requiring deep understanding and robust reasoning skills. We first provide a comprehensive evaluation of GPT-4’s perceived zero-shot performance on this task, considering various aspects such as problems’ release time, difficulties, and types of errors encountered. Surprisingly, the perceived performance of GPT-4 has experienced a cliff like decline in problems after September 2021 consistently across all the difficulties and types of problems, which shows the potential data contamination, as well as the challenges for any existing LLM to solve unseen complex reasoning problems. We further explore various approaches such as fine-tuning, Chain-of-Thought prompting and problem description simplification. Unfortunately, none of them is able to consistently mitigate the challenges. Through our work, we emphasize the importance of this excellent data source for assessing the genuine reasoning capabilities of LLMs, and foster the development of LLMs with stronger reasoning abilities and better generalization in the future.

Hypothetical induction is recognized as the main reasoning type when scientists make observations about the world and try to propose hypotheses to explain those observations. Past research on hypothetical induction is under a constrained setting: (1) the observation annotations in the dataset are carefully manually handpicked sentences (resulting in a close-domain setting); and (2) the ground truth hypotheses are mostly commonsense knowledge, making the task less challenging. In this work, we tackle these problems by proposing the first dataset for social science academic hypotheses discovery, with the final goal to create systems that automatically generate valid, novel, and helpful scientific hypotheses, given only a pile of raw web corpus. Unlike previous settings, the new dataset requires (1) using open-domain data (raw web corpus) as observations; and (2) proposing hypotheses even new to humanity. A multi-module framework is developed for the task, including three different feedback mechanisms to boost performance, which exhibits superior performance in terms of both GPT-4 based and expert-based evaluation.To the best of our knowledge, this is the first work showing that LLMs are able to generate novel (”not existing in literature”) and valid (”reflecting reality”) scientific hypotheses.

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GRADUAL: Granularity-aware Dual Prototype Learning for Better Few-Shot Relation Extraction
Zhiming Li | Yuchen Lyu

Recent studies have shown that fusing text labels and context sentences is an effective method for learning prototype representations in few-shot relation extraction. However, the **inconsistency of prototype representations** across different few-shot tasks persists due to different context sentences for the same relation, even with the integration of text labels into prototype representations. Conversely, the text label for each relation is unique and consistent, 1)which prompts us to propose a **dual prototype learning method**. Unlike previous methods that only construct support-based prototypes, we additionally construct label-based prototypes. Furthermore, we introduce a graph-based prototype adjustment module to construct topological information between support-based and label-based prototypes, thereby generating a more effective similarity measure through a simple linear combination. In addition, relations of different granularities have different distribution widths in the same semantic space, the **imbalanced distribution in the semantic space** leads to a lack of comparability among relations. To create a more discriminative semantic space, 2)we propose a **granularity-aware prototype learning method** that unifies the distribution width of relations, making relations of different granularities have similar distribution widths. Experimental results on two public benchmark datasets show that our proposed methods achieve state-of-the-art performance in few-shot relation classification.

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Training a Better Chinese Spelling Correction Model via Prior-knowledge Guided Teacher
Chi Wei | Shaobin Huang | Rongsheng Li | Naiyu Yan | Rui Wang

Recent advancements in Chinese Spelling Correction (CSC) predominantly leverage pre-trained language models (PLMs). However, a notable challenge with fine-tuned PLM-based CSC models is their tendency to over-correct, leading to poor generalization for error patterns outside the standard distribution. To address this, we developed a teacher network guided by prior knowledge for distillation learning of CSC models. Unlike traditional teacher networks, which depend on task-related pre-training, our method infuses task-related prior information into the teacher network, offering guidance beyond mere labels to the student network. This strategy significantly enhances the CSC model’s language modeling capabilities, crucial for minimizing over-correction. Importantly, our approach is model-independent and the teacher network does not require task-related pre-training, making it broadly applicable for enhancing various PLM-based CSC models with minimal additional computational resources. Extensive experiments on widely used benchmarks demonstrate that our method achieves new state-of-the-art results. Additionally, we explored the potential of generalizing our method to other non-autoregressive text-generation tasks.

Connecting text and visual modalities plays an essential role in generative intelligence. For this reason, inspired by the success of large language models, significant research efforts are being devoted to the development of Multimodal Large Language Models (MLLMs). These models can seamlessly integrate visual and textual modalities, while providing a dialogue-based interface and instruction-following capabilities. In this paper, we provide a comprehensive review of recent visual-based MLLMs, analyzing their architectural choices, multimodal alignment strategies, and training techniques. We also conduct a detailed analysis of these models across a wide range of tasks, including visual grounding, image generation and editing, visual understanding, and domain-specific applications. Additionally, we compile and describe training datasets and evaluation benchmarks, conducting comparisons among existing models in terms of performance and computational requirements. Overall, this survey offers a comprehensive overview of the current state of the art, laying the groundwork for future MLLMs.

The programming skill is one crucial ability for Large Language Models (LLMs), necessitating a deep understanding of programming languages (PLs) and their correlation with natural languages (NLs). We examine the impact of pre-training data on code-focused LLMs’ performance by assessing the comment density as a measure of PL-NL alignment. Given the scarcity of code-comment aligned data in pre-training corpora, we introduce a novel data augmentation method that generates comments for existing code, coupled with a data filtering strategy that filters out code data poorly correlated with natural language. We conducted experiments on three code-focused LLMs and observed consistent improvements in performance on two widely-used programming skill benchmarks. Notably, the model trained on the augmented data outperformed both the model used for generating comments and the model further trained on the data without augmentation.

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Efficient Domain Adaptation for Non-Autoregressive Machine Translation
WangJie You | Pei Guo | Juntao Li | Kehai Chen | Min Zhang

Domain adaptation remains a challenge in the realm of Neural Machine Translation (NMT), even in the era of large language models (LLMs). Existing non-parametric approaches like nearest neighbor machine translation have made small Autoregressive Translation (AT) models achieve efficient domain generalization and adaptation without updating parameters, but leaving the Non-Autoregressive Translation (NAT) counterparts under-explored. To fill this blank, we introduce Bi-kNN, an innovative and efficient domain adaptation approach for NAT models that tailors a k-nearest-neighbor algorithm for NAT. Specifically, we introduce an effective datastore construction and correlated updating strategies to conform the parallel nature of NAT. Additionally, we train a meta-network that seamlessly integrates the NN distribution with the NMT distribution robustly during the iterative decoding process of NAT. Our experimental results across four benchmark datasets demonstrate that our Bi-kNN not only achieves significant improvements over the Base-NAT model (7.8 BLEU on average) but also exhibits enhanced efficiency.

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Exploring Reversal Mathematical Reasoning Ability for Large Language Models
Pei Guo | WangJie You | Juntao Li | Yan Bowen | Min Zhang

Large language models (LLMs) have presented remarkable capabilities in the wide range of natural language understanding and reasoning tasks. Despite their success, a few works indicate that LLMs suffer from the “reversal curse”, in which LLMs can’t employ the inverted structure “B is A” when they are trained based on “A is B”. To explore the effect of the “reversal curse” for LLMs on complex mathematical reasoning tasks, we present two reversal datasets upon GSM8K and MathQA and verify that LLMs also struggle to solve reversal mathematical problems. We analyze the potential reason and attribute it to the insufficient modeling of the relationship between reasoning steps caused by the left-to-right objective. Consequently, based on the characteristics of multi-step reasoning, we design a novel training method to improve the general and reversal reasoning abilities. Finally, we conduct experiments on four mathematical datasets, and the results demonstrate that our method significantly improves the general reasoning capacities and alleviates the reversal problem. Our datasets and codes are available at https: //github.com/AllForward/ReversalMath.

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A Unified Joint Approach with Topological Context Learning and Rule Augmentation for Knowledge Graph Completion
Jingtao Guo | Chunxia Zhang | Lingxi Li | Xiaojun Xue | Zhendong Niu

Knowledge graph completion (KGC) task is to infer the missing knowledge in the knowledge graph based on known factual triples. However, present KGC approaches still face the following two challenges. Those methods perform simple linear update on relation representation, and only local neighborhood information is aggregated, which makes it difficult to capture logic semantic between relations and global topological context information. To tackle the above challenges, we propose a unified joint approach with Topological Context learning and Rule Augmentation (TCRA) for KGC. The TCRA framework consists of an entity topological context learning mechanism based on dual-branch hierarchical graph attention network, and a relation rule context learning mechanism based on Rule-Transformer and rule-to-relation aggregator. The former mechanism encodes the topological structure features of entities, aggregates the local neighborhood topological context information of entities on the three levels (entity, relation and triple), and build clusters of global head or tail entities related to the same relation. It can capture the local and global topological context information of entities related to the same relation. The latter mechanism introduces chain-like Horn rules as the context information of relations, and encodes the logical semantic of relations to enrich the relation representation. Experimental performances on three benchmark datasets FB15k-237, WN18RR and Kinship indicate the effectiveness and superiority of our proposed approach. The codes are publicly available.

Since most large language models (LLMs) are trained once and never updated, they struggle to dynamically adapt to our ever-changing world. In this work, we present FreshQA, a dynamic QA benchmark that tests a model’s ability to answer questions that may require reasoning over up-to-date world knowledge. We develop a two-mode human evaluation procedure to measure both correctness and hallucination, which we use to benchmark both closed and open-source LLMs by collecting >50K human judgments. We observe that all LLMs struggle to answer questions that require fast-changing world knowledge as well as questions with false premises that need to be debunked. In response, we develop FreshPrompt, a few-shot prompting method that curates and organizes relevant information from a search engine into an LLM’s prompt. Our experiments show that FreshPrompt outperforms both competing search engine-augmented prompting methods such as Self-Ask (Press et al., 2022) as well as commercial systems such as Perplexity.AI. To facilitate future work, we additionally develop FreshEval, a reliable autorater for quick evaluation and comparison on FreshQA. Our latest results with FreshEval suggest that open-source LLMs such as Mixtral (Jiang et al., 2024), when combined with FreshPrompt, are competitive with closed-source and commercial systems on search-augmented QA.

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ROSE Doesn’t Do That: Boosting the Safety of Instruction-Tuned Large Language Models with Reverse Prompt Contrastive Decoding
Qihuang Zhong | Liang Ding | Juhua Liu | Bo Du | Dacheng Tao

With the development of instruction-tuned large language models (LLMs), improving the safety of LLMs has become more critical. However, the current approaches for aligning the LLMs output with expected safety usually require substantial training efforts, e.g., high-quality safety data and expensive computational resources, which are costly and inefficient. To this end, we present reverse prompt contrastive decoding (ROSE), a simple-yet-effective method to directly boost the safety of existing instruction-tuned LLMs without any additional training. The principle of ROSE is to improve the probability of desired safe output via suppressing the undesired output induced by the carefully-designed reverse prompts. Experiments on 6 safety and 2 general-purpose tasks show that, our ROSE not only brings consistent and significant safety improvements (up to +13.8% safety score) upon 5 types of instruction-tuned LLMs, but also benefits the general-purpose ability of LLMs. In-depth analyses explore the underlying mechanism of ROSE, and reveal when and where to use it.

Concept reasoning is an important capability for models to understand the world. However, the existing datasets, such as concept extraction and concept generation, suffer from modeledge leakage and context leakage. To address these limitations, we construct a dataset of concept reasoning for large language models (CR-LLM) with modeledge leakage prevention and context leakage prevention, which consists of 2,167 samples and covers different concept types. In addition, we propose a hybrid reasoning method, consisting of inductive reasoning, deductive reasoning and a controller. This method allows large language models to adaptively select the optimal reasoning method for each input sample. Finally, we conduct extensive experiments on CR-LLM using different models and methods. The results show that existing large language models and reasoning methods perform sub-optimally in the concept reasoning task. In contrast, our proposed method significantly improves the capabilities, achieving a 7% increase in accuracy compared to CoT and demonstrating better granularity. We release CR-LLM and code at https://github.com/Nianqi-Li/Concept-Reasoning-for-LLMs.

Recently, multi-task instruction tuning has been utilized to improve sentence representation learning (SRL). It enables SRL models to generate task-specific representations with the guidance of task instruction, thus exhibiting strong generalization ability on unseen tasks. However, these methods mostly neglect the potential interference problems across different tasks and instances, which may affect the training of the model.To address this issue, we propose a data curriculum method, namely **Data-CUBE**, that arranges the order of all the multi-task data for training, to minimize the interference risks from two aspects.At the task level, we aim to find the optimal task order to minimize the total cross-task interference risk and formulate this problem as the traveling salesman problem, which is further solved by a specially designed simulated annealing algorithm. At the instance level, we propose a measurement method to quantify the difficulty of all instances per task, and then arrange instances in an easy-to-difficult order for training.Experimental results show that our approach can boost the performance of state-of-the-art methods. Our code and data will be publicly released.

Extracting semantic topics from short texts presents a significant challenge in the field of data mining. While efforts have been made to mitigate data sparsity issue, the limited length of short documents also results in the absence of semantically relevant words, causing biased evidence lower bound and incomplete labels for likelihood maximization. We refer to this issue as the label sparsity problem. To combat this problem, we propose kNNTM, a neural short text topic model that incorporates a k-Nearest-Neighbor-based label completion algorithm by augmenting the reconstruction label with k-nearest documents to complement these relevant but unobserved words. Furthermore, seeking a precise reflection of distances between documents, we propose a fused multi-view distances metric that takes both local word similarities and global topic semantics into consideration. Extensive experiments on multiple public short-text datasets show that kNNTM model outperforms the state-of-the-art baseline models and can derive both high-quality topics and document representations.

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RefuteBench: Evaluating Refuting Instruction-Following for Large Language Models
Jianhao Yan | Yun Luo | Yue Zhang

The application scope of large language models (LLMs) is increasingly expanding. In practical use, users might provide feedback based on the model’s output, hoping for a responsive model that can complete responses according to their feedback. Whether the model can appropriately respond to users’ refuting feedback and consistently follow through with execution has not been thoroughly analyzed. In light of this, this paper proposes a comprehensive benchmark, RefuteBench, covering tasks such as question answering, machine translation, and email writing. The evaluation aims to assess whether models can positively accept feedback in form of refuting instructions and whether they can consistently adhere to user demands throughout the conversation. We conduct evaluations on numerous LLMs and find that LLMs are stubborn, i.e. exhibit inclination to their internal knowledge, often failing to comply with user feedback. Additionally, as the length of the conversation increases, models gradually forget the user’s stated feedback and roll back to their own responses. We further propose a recall-and-repeat prompts as a simple and effective way to enhance the model’s responsiveness to feedback.

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Complex Logical Query Answering by Calibrating Knowledge Graph Completion Models
Changyi Xiao | Yixin Cao

Complex logical query answering (CLQA) is a challenging task that involves finding answer entities for complex logical queries over incomplete knowledge graphs (KGs). Previous research has explored the use of pre-trained knowledge graph completion (KGC) models, which can predict the missing facts in KGs, to answer complex logical queries. However, KGC models are typically evaluated using ranking evaluation metrics, which may result in values of predictions of KGC models that are not well-calibrated. In this paper, we propose a method for calibrating KGC models, namely CKGC, which enables KGC models to adapt to answering complex logical queries. Notably, CKGC is lightweight and effective. The adaptation function is simple, allowing the model to quickly converge during the adaptation process. The core concept of CKGC is to map the values of predictions of KGC models to the range [0, 1], ensuring that values associated with true facts are close to 1, while values linked to false facts are close to 0. Through experiments on three benchmark datasets, we demonstrate that our proposed calibration method can significantly boost model performance in the CLQA task. Moreover, our approach can enhance the performance of CLQA while preserving the ranking evaluation metrics of KGC models. The code is available at https://github.com/changyi7231/CKGC.

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Argument-Based Sentiment Analysis on Forward-Looking Statements
Chin-Yi Lin | Chung-Chi Chen | Hen-Hsen Huang | Hsin-Hsi Chen

This paper introduces a novel approach to analyzing the forward-looking statements in equity research reports by integrating argument mining with sentiment analysis. Recognizing the limitations of traditional models in capturing the nuances of future-oriented analysis, we propose a refined categorization of argument units into claims, premises, and scenarios, coupled with a unique sentiment analysis framework. Furthermore, we incorporate a temporal dimension to categorize the anticipated impact duration of market events. To facilitate this study, we present the Equity Argument Mining and Sentiment Analysis (Equity-AMSA) dataset. Our research investigates the extent to which detailed domain-specific annotations can be provided, the necessity of fine-grained human annotations in the era of large language models, and whether our proposed framework can improve performance in downstream tasks over traditional methods. Experimental results reveal the significance of manual annotations, especially for scenario identification and sentiment analysis. The study concludes that our annotation scheme and dataset contribute to a deeper understanding of forward-looking statements in equity research reports.

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Paying More Attention to Source Context: Mitigating Unfaithful Translations from Large Language Model
Hongbin Zhang | Kehai Chen | Xuefeng Bai | Yang Xiang | Min Zhang

Large language models (LLMs) have showcased their remarkable capabilities to handle various downstream tasks, including multilingual machine translation ability. Despite their impressive performance, decoder-only LLMs lack an explicit alignment between source and target contexts, leading to translation that may not faithfully represent the original content. To address this, we propose three learning strategies to encourage LLMs to pay more attention to the source context during translation: 1) adjusting attention weights on the source context by adaptive attention re-weighting; 2) suppressing the irrelevant target prefix using contrastive decoding; 3) avoiding excessive reliance on the target prefix through target-constrained tuning. To verify the effectiveness of our model, we curate a new dataset specifically focusing on unfaithful translations generated by LLMs. Experimental results on both human-collected and general test sets verify the effectiveness of our model across multiple language pairs. Further human evaluation demonstrates the efficacy of our method in reducing hallucinatory translation and improving the fidelity of translations.

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Unveiling the Power of Integration: Block Diagram Summarization through Local-Global Fusion
Shreyanshu Bhushan | Eun-Soo Jung | Minho Lee

Block Diagrams play an essential role in visualizing the relationships between components or systems. Generating summaries of block diagrams is important for document understanding or question answering (QA) tasks by providing concise overviews of complex systems. However, it’s a challenging task as it requires compressing complex relationships into informative descriptions. In this paper, we present “BlockNet”, a fusion framework that summarizes block diagrams by integrating local and global information, catering to both English and Korean languages. Additionally, we introduce a new multilingual method to produce block diagram data, resulting in a high-quality dataset called “BD-EnKo”. In BlockNet, we develop “BlockSplit”, an Optical Character Recognition (OCR) based algorithm employing the divide-and-conquer principle for local information extraction. We train an OCR-free transformer architecture for global information extraction using BD-EnKo and public data. To assess the effectiveness of our model, we conduct thorough experiments on different datasets. The assessment shows that BlockNet surpasses all previous methods and models, including GPT-4V, for block diagram summarization.

Text2SQL is a task that translates natural language into SQL statements. Context-dependent Text2SQL offers a more natural database interaction by simulating dialogues between users and databases, with CoSQL and SparC as representative datasets. Yet, these datasets struggle to accurately replicate real-world situations. To address this, we introduce MultiSQL, which extends them in three key aspects: (1) Diverse SQL Operations. We incorporate diverse SQL types such as Create, Update, and Insert to broaden the scope of SQL operations. (2) Schema-Integrated Context. We integrated query context with database schema dependencies to better depict database complexity. (3) Extended Dialogues. We expand dialogue length to better simulate long conversations and complex interactions. This multi-type, schema-integrated, context-dependent Text2SQL dataset comprises nearly 800 dialogue groups and over 9,000 interaction turns across 166 complex databases, offering a better benchmark for interactive user-database dialogue.Addressing MultiSQL’s challenges, we refined evaluation metrics to better capture diverse SQL types and schema dependencies. We designed a prompt framework that leverages historical data and self-refinement to accurately capture the dependency between text queries and database structures. Experiments with GPT-3.5, GPT-4, and LLaMA2-7B show both the effectiveness of our strategies and the challenges of MultiSQL. The datasets is available at https://github.com/grandchicken/MultiSQL.

Tuning-based large language models for machine translation (aka large translation model, LTM) have demonstrated significant performance in the field of machine translation. Despite their success, these models often face difficulties in leveraging demonstrations to further improve their performance. To tackle this challenge, we introduce a novel approach that integrates demonstration-aware training and inference strategies within the framework of tuning-based LTMs, hereby referred to as demonstration-aware LTMs. During training, we enrich the model’s learning process by incorporating both sentence- and document-level demonstrations derived from its original training dataset. During inference, the model synergizes its own contextual translations with retrieved high-quality demonstrations, leading to more precise and contextually appropriate outputs. Empirical results reveal that our demonstration-aware LTM not only mitigates the negative impacts traditionally associated with demonstrations but also secures substantial improvements in translation accuracy, particularly in domain-specific and document-level translation tasks. Source code and scripts are freely available at https://github.com/ChenLi0620/Demo-Aware-LLM-MT.

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DADA: Distribution-Aware Domain Adaptation of PLMs for Information Retrieval
Dohyeon Lee | Jongyoon Kim | Seung-won Hwang | Joonsuk Park

Pre-trained language models (PLMs) exhibit promise in retrieval tasks but struggle with out-of-domain data due to distribution shifts.Addressing this, generative domain adaptation (DA), known as GPL, tackles distribution shifts by generating pseudo queries and labels to train models for predicting query-document relationships in new domains.However, it overlooks the domain distribution, causing the model to struggle with aligning the distribution in the target domain.We, therefore, propose a Distribution-Aware Domain Adaptation (DADA) to guide the model to consider the domain distribution knowledge at the level of both a single document and the corpus, which is referred to as observation-level feedback and domain-level feedback, respectively.Our method effectively adapts the model to the target domain and expands document representation to unseen gold query terms using domain and observation feedback, as demonstrated by empirical results on the BEIR benchmark.

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LLMs cannot find reasoning errors, but can correct them given the error location
Gladys Tyen | Hassan Mansoor | Victor Carbune | Peter Chen | Tony Mak

While self-correction has shown promise in improving LLM outputs in terms of style and quality (e.g. Chen et al., 2023b; Madaan et al.,2023), recent attempts to self-correct logical or reasoning errors often cause correct answers to become incorrect, resulting in worse performances overall (Huang et al., 2023). In this paper, we show that poor self-correction performance stems from LLMs’ inability tofind logical mistakes, rather than their ability to correct a known mistake. Firstly, we benchmark several state-of-the-art LLMs ontheir mistake-finding ability and demonstrate that they generally struggle with the task, even in highly objective, unambiguous cases. Secondly, we test the correction abilities of LLMs – separately from mistake finding – using a backtracking setup that feeds ground truth mistake location information to the model. We show that this boosts downstream task performance across our 5 reasoning tasks, indicating that LLMs’ correction abilities are robust. Finally, we show that it is possible to obtain mistake location information without ground truth labels or in-domain training data. We train a small classifier with out-of-domain data, which exhibits stronger mistake-finding performance than prompting a large model. We release our dataset of LLM-generated logical mistakes, BIG-Bench Mistake, to enable further research into locating LLM reasoning mistakes.

Understanding textual description to generate code seems to be an achieved capability of instruction-following Large Language Models (LLMs) in zero-shot scenario. However, there is a severe possibility that this translation ability may be influenced by having seen target textual descriptions and the related code. This effect is known as Data Contamination.In this study, we investigate the impact of Data Contamination on the performance of GPT-3.5 in the Text-to-SQL code-generating tasks. Hence, we introduce a novel method to detect Data Contamination in GPTs and examine GPT-3.5’s Text-to-SQL performances using the known Spider Dataset and our new unfamiliar dataset Termite. Furthermore, we analyze GPT-3.5’s efficacy on databases with modified information via an adversarial table disconnection (ATD) approach, complicating Text-to-SQL tasks by removing structural pieces of information from the database. Our results indicate a significant performance drop in GPT-3.5 on the unfamiliar Termite dataset, even with ATD modifications, highlighting the effect of Data Contamination on LLMs in Text-to-SQL translation tasks.

Whilst fact verification has attracted substantial interest in the natural language processing community, verifying misinforming statements against data visualizations such as charts has so far been overlooked. Charts are commonly used in the real-world to summarize and com municate key information, but they can also be easily misused to spread misinformation and promote certain agendas. In this paper, we introduce ChartCheck, a novel, large-scale dataset for explainable fact-checking against real-world charts, consisting of 1.7k charts and 10.5k human-written claims and explanations. We systematically evaluate ChartCheck using vision-language and chart-to-table models, and propose a baseline to the community. Finally, we study chart reasoning types and visual attributes that pose a challenge to these models.

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Real World Conversational Entity Linking Requires More Than Zero-Shots
Mohanna Hoveyda | Arjen Vries | Faegheh Hasibi | Maarten Rijke

Entity linking (EL) in conversations faces notable challenges in practical applications, primarily due to scarcity of entity-annotated conversational datasets and sparse knowledge bases (KB) containing domain-specific, long-tail entities. We designed targeted evaluation scenarios to measure the efficacy of EL models under resource constraints. Our evaluation employs two KBs: Fandom, exemplifying real-world EL complexities, and the widely used Wikipedia. First, we assess EL models’ ability to generalize to a new unfamiliar KB using Fandom and a novel zero-shot conversational entity linking dataset that we curated based on Reddit discussions on Fandom entities. We then evaluate the adaptability of EL models to conversational settings without prior training. Our results indicate that current zero-shot EL models falter when introduced to new, domain-specific KBs without prior training, significantly dropping in performance.Our findings reveal that previous evaluation approaches fall short of capturing real-world complexities for zero-shot EL, highlighting the necessity for new approaches to design and assess conversational EL models to adapt to limited resources. The evaluation frame-work and dataset proposed are tailored to facilitate this research.

Using large language models (LLMs) to assist psychological counseling is a significant but challenging task at present. Attempts have been made on improving empathetic conversations or acting as effective assistants in the treatment with LLMs. However, the existing datasets lack consulting knowledge, resulting in LLMs lacking professional consulting competence. Moreover, how to automatically evaluate multi-turn dialogues within the counseling process remains an understudied area. To bridge the gap, we propose CPsyCoun, a report-based multi-turn dialogue reconstruction and evaluation framework for Chinese psychological counseling. To fully exploit psychological counseling reports, a two-phase approach is devised to construct high-quality dialogues while a comprehensive evaluation benchmark is developed for the effective automatic evaluation of multi-turn psychological consultations. Competitive experimental results demonstrate the effectiveness of our proposed framework in psychological counseling. We open-source the datasets and model for future research.

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Tox-BART: Leveraging Toxicity Attributes for Explanation Generation of Implicit Hate Speech
Neemesh Yadav | Sarah Masud | Vikram Goyal | Md Shad Akhtar | Tanmoy Chakraborty

Employing language models to generate explanations for an incoming implicit hate post is an active area of research. The explanation is intended to make explicit the underlying stereotype and aid content moderators. The training often combines top-k relevant knowledge graph (KG) tuples to provide world knowledge and improve performance on standard metrics. Interestingly, our study presents conflicting evidence for the role of the quality of KG tuples in generating implicit explanations. Consequently, simpler models incorporating external toxicity signals outperform KG-infused models. Compared to the KG-based setup, we observe a comparable performance for SBIC (LatentHatred) datasets with a performance variation of +0.44 (+0.49), +1.83 (-1.56), and -4.59 (+0.77) in BLEU, ROUGE-L, and BERTScore. Further human evaluation and error analysis reveal that our proposed setup produces more precise explanations than zero-shot GPT-3.5, highlighting the intricate nature of the task.

Large language models (LLMs) have attracted great interest in many real-world applications; however, their “black-box” nature necessitates scalable and faithful explanations. Shapley values have matured as an explainability method for deep learning, but extending them to LLMs is difficult due to long input contexts and autoregressive output generation. We introduce , an efficient post-hoc explanation method incorporating LLM-specific techniques, which leads to significant runtime improvements: token-level explanations in minutes not hours, and document-level explanations within seconds. We demonstrate how such explanations can improve end-to-end performance of retrieval augmented generation by localizing important words within long documents and reranking passages collected by retrieval systems. On various open-domain question answering benchmarks, we show TextGenSHAP improves the retrieval recall and prediction accuracy significantly.

Data plays a fundamental role in the training of Large Language Models (LLMs). While attention has been paid to the collection and composition of datasets, determining the data sampling strategy in training remains an open question. Most LLMs are trained with a simple strategy, random sampling. However, this sampling strategy ignores the unbalanced nature of training data distribution, which can be sub-optimal. In this paper, we propose ClusterClip Sampling to balance the text distribution of training data for better model training. Specifically, ClusterClip Sampling utilizes data clustering to reflect the data distribution of the training set and balances the common samples and rare samples during training based on the cluster results. A repetition clip operation is introduced to mitigate the overfitting issue led by samples from certain clusters. Extensive experiments validate the effectiveness of ClusterClip Sampling, which outperforms random sampling and other cluster-based sampling variants under various training datasets and large language models.

Generalizing to longer sentences is important for recent Transformer-based language models. Besides algorithms manipulating explicit position features, the success of Transformers without position encodings (NoPE) provides a new way to overcome the challenge. In this paper, we study the length generalization property of NoPE. We find that although NoPE can extend to longer sequences than the commonly used explicit position encodings, it still has a limited context length. We identify a connection between the failure of NoPE’s generalization and the distraction of attention distributions. We propose a parameter-efficient tuning for searching attention heads’ best temperature hyper-parameters, which substantially expands NoPE’s context size. Experiments on long sequence language modeling, the synthetic passkey retrieval task and real-world long context tasks show that NoPE can achieve competitive performances with state-of-the-art length generalization algorithms. The source code is publicly accessible

Motivated by the success of unsupervised neural machine translation (UNMT), we introduce an unsupervised sign language translation and generation network (USLNet), which learns from abundant single-modality (text and video) data without parallel sign language data. USLNet comprises two main components: single-modality reconstruction modules (text and video) that rebuild the input from its noisy version in the same modality and cross-modality back-translation modules (text-video-text and video-text-video) that reconstruct the input from its noisy version in the different modality using back-translation procedure. Unlike the single-modality back-translation procedure in text-based UNMT, USLNet faces the cross-modality discrepancy in feature representation, in which the length and the feature dimension mismatch between text and video sequences. We propose a sliding window method to address the issues of aligning variable-length text with video sequences. To our knowledge, USLNet is the first unsupervised sign language translation and generation model capable of generating both natural language text and sign language video in a unified manner. Experimental results on the BBC-Oxford Sign Language dataset and Open-Domain American Sign Language dataset reveal that USLNet achieves competitive results compared to supervised baseline models, indicating its effectiveness in sign language translation and generation.

Data scarcity is a prevalent challenge in the era of Large Language Models (LLMs). The insatiable hunger of LLMs for large corpora becomes even more pronounced when dealing with non-English and low-resource languages. The issue is particularly exacerbated in Semantic Parsing (SP), i.e. the task of converting text into a formal representation. The complexity of semantic formalisms makes training human annotators and subsequent data annotation unfeasible on a large scale, especially across languages. To mitigate this, we first introduce the Multilingual Semantic Layer (MSL), a conceptual evolution of previous formalisms, which decouples from disambiguation and external inventories and simplifies the task. MSL provides the necessary tools to encode the meaning across languages, paving the way for developing a high-quality semantic parsing dataset across different languages in a semi-automatic strategy. Subsequently, we manually refine a portion of this dataset and fine-tune GPT-3.5 to propagate these refinements across the dataset. Then, we manually annotate 1,100 sentences in eleven languages, including low-resource ones. Finally, we assess our dataset’s quality, showcasing the performance gap reduction across languages in Semantic Parsing.

In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide array of text-centric tasks. However, their ‘large’ scale introduces significant computational and storage challenges, particularly in managing the key-value states of the transformer, which limits their wider applicability. Therefore, we propose to adaptively release resources from caches and rebuild the necessary key-value states. Particularly, we accomplish this by a lightweight controller module to approximate an ideal top-K sparse attention. This module retains the tokens with the highest top-K attention weights and simultaneously rebuilds the discarded but necessary tokens, which may become essential for future decoding. Comprehensive experiments in natural language generation and modeling reveal that our method is not only competitive with full attention in terms of performance but also achieves a significant throughput improvement of up to 221.8%. The code for replication is available on the https://github.com/WHUIR/ADORE.

Despite the impressive performance on information-seeking tasks, large language models (LLMs) still struggle with hallucinations. Attributed LLMs, which augment generated text with in-line citations, demonstrate potential in mitigating hallucinations and improving verifiability. However, current approaches suffer from suboptimal citation quality due to their reliance on in-context learning. Furthermore, the practice of merely citing document identifiers complicates the process for users to pinpoint specific supporting evidence. In this work, we introduce FRONT, a training framework that teaches LLMs to generate Fine-grained grounded citations. By initially grounding fine-grained supporting quotes, which then guide the generation process, these quotes not only provide supervision signals to improve citation quality but also serve as fine-grained attributions. Experiments on the ALCE benchmark demonstrate the efficacy of FRONT in generating superior grounded responses and highly supportive citations. With LLaMA-2-7B, the framework significantly outperforms all the baselines, achieving an average of 14.21% improvement in citation quality across all datasets, even surpassing ChatGPT.

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ReLiK: Retrieve and LinK, Fast and Accurate Entity Linking and Relation Extraction on an Academic Budget
Riccardo Orlando | Pere-Lluís Huguet Cabot | Edoardo Barba | Roberto Navigli

Entity Linking (EL) and Relation Extraction (RE) are fundamental tasks in Natural Language Processing, serving as critical components in a wide range of applications. In this paper, we propose ReLiK, a Retriever-Reader architecture for both EL and RE, where, given an input text, the Retriever module undertakes the identification of candidate entities or relations that could potentially appear within the text. Subsequently, the Reader module is tasked to discern the pertinent retrieved entities or relations and establish their alignment with the corresponding textual spans. Notably, we put forward an innovative input representation that incorporates the candidate entities or relations alongside the text, making it possible to link entities or extract relations in a single forward pass and to fully leverage pre-trained language models contextualization capabilities, in contrast with previous Retriever-Reader-based methods, which require a forward pass for each candidate. Our formulation of EL and RE achieves state-of-the-art performance in both in-domain and out-of-domain benchmarks while using academic budget training and with up to 40x inference speed compared to competitors. Finally, we show how our architecture can be used seamlessly for Information Extraction (cIE), i.e. EL + RE, and setting a new state of the art by employing a shared Reader that simultaneously extracts entities and relations.

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Synergizing Large Language Models and Pre-Trained Smaller Models for Conversational Intent Discovery
Jinggui Liang | Lizi Liao | Hao Fei | Jing Jiang

In Conversational Intent Discovery (CID), Small Language Models (SLMs) struggle with overfitting to familiar intents and fail to label newly discovered ones. This issue stems from their limited grasp of semantic nuances and their intrinsically discriminative framework. Therefore, we propose Synergizing Large Language Models (LLMs) with pre-trained SLMs for CID (SynCID). It harnesses the profound semantic comprehension of LLMs alongside the operational agility of SLMs. By utilizing LLMs to refine both utterances and existing intent labels, SynCID significantly enhances the semantic depth, subsequently realigning these enriched descriptors within the SLMs’ feature space to correct cluster distortion and promote robust learning of representations. A key advantage is its capacity for the early identification of new intents, a critical aspect for deploying conversational agents successfully. Additionally, SynCID leverages the in-context learning strengths of LLMs to generate labels for new intents. Thorough evaluations across a wide array of datasets have demonstrated its superior performance over traditional CID methods.

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FENICE: Factuality Evaluation of summarization based on Natural language Inference and Claim Extraction
Alessandro Scirè | Karim Ghonim | Roberto Navigli

Recent advancements in text summarization, particularly with the advent of Large Language Models (LLMs), have shown remarkable performance. However, a notable challenge persists as a substantial number of automatically-generated summaries exhibit factual inconsistencies, such as hallucinations. In response to this issue, various approaches for the evaluation of consistency for summarization have emerged. Yet, these newly-introduced metrics face several limitations, including lack of interpretability, focus on short document summaries (e.g., news articles), and computational impracticality, especially for LLM-based metrics. To address these shortcomings, we propose Factuality Evaluation of summarization based on Natural language Inference and Claim Extraction (FENICE), a more interpretable and efficient factuality-oriented metric. FENICE leverages an NLI-based alignment between information in the source document and a set of atomic facts, referred to as claims, extracted from the summary. Our metric sets a new state of the art on AGGREFACT, the de-facto benchmark for factuality evaluation. Moreover, we extend our evaluation to a more challenging setting by conducting a human annotation process of long-form summarization. In the hope of fostering research in summarization factuality evaluation, we release the code of our metric and our factuality annotations of long-form summarization at https://github.com/Babelscape/FENICE.

Recently, the self-consistency decoding strategy has shown the ability to improve performance for complex reasoning tasks with large language models (LLMs). However, the costs may be high because the sampling process of the strategy generates some low-probability text, resulting in low-quality reasoning paths. As a consequence, it requires a relatively large sampling number to obtain good aggregation performance. In this paper, we propose an alternative strategy, self-para-consistency. It first generates multiple paraphrases for each test question, then generates reasoning paths for the original and all the paraphrased questions based on greedy decoding, and finally selects the most consistent answer. Since all the candidate paths have relatively high probabilities, the sampling number could be much smaller than the self-consistency strategy. Extensive experiments on complex reasoning datasets demonstrate the effectiveness of our method in reducing the sampling number.

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Looking Right is Sometimes Right: Investigating the Capabilities of Decoder-only LLMs for Sequence Labeling
David Dukić | Jan Snajder

Pre-trained language models based on masked language modeling (MLM) excel in natural language understanding (NLU) tasks. While fine-tuned MLM-based encoders consistently outperform causal language modeling decoders of comparable size, recent decoder-only large language models (LLMs) perform on par with smaller MLM-based encoders. Although their performance improves with scale, LLMs fall short of achieving state-of-the-art results in information extraction (IE) tasks, many of which are formulated as sequence labeling (SL). We hypothesize that LLMs’ poor SL performance stems from causal masking, which prevents the model from attending to tokens on the right of the current token. Yet, how exactly and to what extent LLMs’ performance on SL can be improved remains unclear. We explore techniques for improving the SL performance of open LLMs on IE tasks by applying layer-wise removal of the causal mask (CM) during LLM fine-tuning. This approach yields performance gains competitive with state-of-the-art SL models, matching or outperforming the results of CM removal from all blocks. Our findings hold for diverse SL tasks, demonstrating that open LLMs with layer-dependent CM removal outperform strong MLM-based encoders and even instruction-tuned LLMs.

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mCSQA: Multilingual Commonsense Reasoning Dataset with Unified Creation Strategy by Language Models and Humans
Yusuke Sakai | Hidetaka Kamigaito | Taro Watanabe

It is very challenging to curate a dataset for language-specific knowledge and common sense in order to evaluate natural language understanding capabilities of language models. Due to the limitation in the availability of annotators, most current multilingual datasets are created through translation, which cannot evaluate such language-specific aspects. Therefore, we propose Multilingual CommonsenseQA (mCSQA) based on the construction process of CSQA but leveraging language models for a more efficient construction, e.g., by asking LM to generate questions/answers, refine answers and verify QAs followed by reduced human efforts for verification. Constructed dataset is a benchmark for cross-lingual language-transfer capabilities of multilingual LMs, and experimental results showed high language-transfer capabilities for questions that LMs could easily solve, but lower transfer capabilities for questions requiring deep knowledge or commonsense. This highlights the necessity of language-specific datasets for evaluation and training. Finally, our method demonstrated that multilingual LMs could create QA including language-specific knowledge, significantly reducing the dataset creation cost compared to manual creation. The datasets are available at https://huggingface.co/datasets/yusuke1997/mCSQA.

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Dual-Stage Multi-Task Syntax-Oriented Pre-Training for Syntactically Controlled Paraphrase Generation
Hongxu Liu | Xiaojie Wang | Jiashen Sun | Ke Zeng | Wan Guanglu

Syntactically Controlled Paraphrase Generation (SCPG), which aims at generating sentences having syntactic structures resembling given exemplars, is attracting more research efforts in recent years. We took an empirical survey on previous SCPG datasets and methods and found three tacitly approved while seldom mentioned intrinsic shortcomings/trade-offs in terms of data obtaining, task formulation, and pre-training strategies. As a mitigation to these shortcomings, we proposed a novel Dual-Stage Multi-Task (DSMT) pre-training scheme, involving a series of structure-oriented and syntax-oriented tasks, which, in our opinion, gives sequential text models the ability of com-prehending intrinsically non-sequential structures like Linearized Constituency Trees (LCTs), understanding the underlying syntactics, and even generating them by parsing sentences. We performed further pre-training of the popular T5 model on these novel tasks and fine-tuned the trained model on every possible variant of SCPG task in literature, finding that our models significantly outperformed (up to 10+ BLEU-4) previous state-of-the-art methods. Finally, we carried out ablation studies which demonstrated the effectiveness of our DSMT methods and emphasized on the SCPG performance gains compared to vanilla T5 models, especially on hard samples or under few-shot settings.

Large Language Models (LLMs) have demonstrated an impressive capability known as In-context Learning (ICL), which enables them to acquire knowledge from textual demonstrations without the need for parameter updates.However, many studies have highlighted that the model’s performance is sensitive to the choice of demonstrations, presenting a significant challenge for practical applications where we lack prior knowledge of user queries.Consequently, we need to construct an extensive demonstration pool and incorporate external databases to assist the model, leading to considerable time and financial costs.In light of this, some recent research has shifted focus towards zero-shot ICL, aiming to reduce the model’s reliance on external information by leveraging their inherent generative capabilities. Despite the effectiveness of these approaches, the content generated by the model may be unreliable, and the generation process is time-consuming.To address these issues, we propose Demonstration Augmentation for In-context Learning (DAIL), which employs the model’s previously predicted historical samples as demonstrations for subsequent ones.DAIL brings no additional inference cost and does not rely on the model’s generative capabilities.Our experiments reveal that DAIL can significantly improve the model’s performance over direct zero-shot inference and can even outperform few-shot ICL without any external information.

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Pushing the Limits of Zero-shot End-to-End Speech Translation
Ioannis Tsiamas | Gerard I. Gállego | José Fonollosa | Marta Costa-jussà

Data scarcity and the modality gap between the speech and text modalities are two major obstacles of end-to-end Speech Translation (ST) systems, thus hindering their performance. Prior work has attempted to mitigate these challenges by leveraging external MT data and optimizing distance metrics that bring closer the speech-text representations. However, achieving competitive results typically requires some ST data. For this reason, we introduce ZeroSwot, a method for zero-shot ST that bridges the modality gap without any paired ST data. Leveraging a novel CTC compression and Optimal Transport, we train a speech encoder using only ASR data, to align with the representation space of a massively multilingual MT model. The speech encoder seamlessly integrates with the MT model at inference, enabling direct translation from speech to text, across all languages supported by the MT model. Our experiments show that we can effectively close the modality gap without ST data, while our results on MuST-C and CoVoST demonstrate our method’s superiority over not only previous zero-shot models, but also supervised ones, achieving state-of-the-art results.

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NUMCoT: Numerals and Units of Measurement in Chain-of-Thought Reasoning using Large Language Models
Ancheng Xu | Minghuan Tan | Lei Wang | Min Yang | Ruifeng Xu

Numeral systems and units of measurement are two conjoined topics in activities of human beings and have mutual effects with the languages expressing them. Currently, the evaluation of Large Language Models (LLMs) often involves mathematical reasoning, yet little attention is given to how minor changes in numbers or units can drastically alter the complexity of problems and the performance of LLMs. In this paper, we scrutinize existing LLMs on processing of numerals and units of measurement by constructing datasets with perturbations. We first anatomize the reasoning of math word problems to different sub-procedures like numeral conversions from language to numbers and measurement conversions based on units. Then we further annotate math word problems from ancient Chinese arithmetic works which are challenging in numerals and units of measurement. Experiments on perturbed datasets demonstrate that LLMs still encounter difficulties in handling numeral and measurement conversions.

In an ever-expanding world of domain-specific knowledge, the increasing complexity of consuming, and storing information necessitates the generation of summaries from large information repositories. However, every persona of a domain has different requirements of information and hence their summarization. For example, in the healthcare domain, a persona-based (such as Doctor, Nurse, Patient etc.) approach is imperative to deliver targeted medical information efficiently. Persona-based summarization of domain-specific information by humans is a high cognitive load task and is generally not preferred. The summaries generated by two different humans have high variability and do not scale in cost and subject matter expertise as domains and personas grow. Further, AI-generated summaries using generic Large Language Models (LLMs) may not necessarily offer satisfactory accuracy for different domains unless they have been specifically trained on domain-specific data and can also be very expensive to use in day-to-day operations. Our contribution in this paper is two-fold: 1) We present an approach to efficiently fine-tune a domain-specific small foundation LLM using a healthcare corpus and also show that we can effectively evaluate the summarization quality using AI-based critiquing. 2) We further show that AI-based critiquing has good concordance with Human-based critiquing of the summaries. Hence, such AI-based pipelines to generate domain-specific persona-based summaries can be easily scaled to other domains such as legal, enterprise documents, education etc. in a very efficient and cost-effective manner.

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Evaluating Large Language Models for Health-related Queries with Presuppositions
Navreet Kaur | Monojit Choudhury | Danish Pruthi

As corporations rush to integrate large language models (LLMs) it is critical that they provide factually accurate information, that is robust to any presuppositions that a user may express. In this work, we introduce UPHILL, a dataset consisting of health-related queries with varying degrees of presuppositions. Using UPHILL, we evaluate the factual accuracy and consistency of InstructGPT, ChatGPT, GPT-4 and Bing Copilot models. We find that while model responses rarely contradict true health claims (posed as questions), all investigated models fail to challenge false claims. Alarmingly, responses from these models agree with 23-32% of the existing false claims, and 49-55% with novel fabricated claims. As we increase the extent of presupposition in input queries, responses from all models except Bing Copilot agree with the claim considerably more often, regardless of its veracity. Given the moderate factual accuracy, and the inability of models to challenge false assumptions, our work calls for a careful assessment of current LLMs for use in high-stakes scenarios.

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Word Sense Linking: Disambiguating Outside the Sandbox
Andrei Bejgu | Edoardo Barba | Luigi Procopio | Alberte Fernández-Castro | Roberto Navigli

Word Sense Disambiguation (WSD) is the task of associating a word in a given context with its most suitable meaning among a set of possible candidates. While the task has recently witnessed renewed interest, with systems achieving performances above the estimated inter-annotator agreement, at the time of writing it still struggles to find downstream applications. We argue that one of the reasons behind this is the difficulty of applying WSD to plain text. Indeed, in the standard formulation, models work under the assumptions that a) all the spans to disambiguate have already been identified, and b) all the possible candidate senses of each span are provided, both of which are requirements that are far from trivial. In this work, we present a new task called Word Sense Linking (WSL) where, given an input text and a reference sense inventory, systems have to both identify which spans to disambiguate and then link them to their most suitable meaning.We put forward a transformer-based architecture for the task and thoroughly evaluate both its performance and those of state-of-the-art WSD systems scaled to WSL, iteratively relaxing the assumptions of WSD. We hope that our work will foster easier integration of lexical semantics into downstream applications.

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Generalisation First, Memorisation Second? Memorisation Localisation for Natural Language Classification Tasks
Verna Dankers | Ivan Titov

Memorisation is a natural part of learning from real-world data: neural models pick up on atypical input-output combinations and store those training examples in their parameter space. That this happens is well-known, but how and where are questions that remain largely unanswered. Given a multi-layered neural model, where does memorisation occur in the millions of parameters?Related work reports conflicting findings: a dominant hypothesis based on image classification is that lower layers learn generalisable features and that deeper layers specialise and memorise. Work from NLP suggests this does not apply to language models, but has been mainly focused on memorisation of facts.We expand the scope of the localisation question to 12 natural language classification tasks and apply 4 memorisation localisation techniques.Our results indicate that memorisation is a gradual process rather than a localised one, establish that memorisation is task-dependent, and give nuance to the generalisation first, memorisation second hypothesis.

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Towards Multi-Relational Multi-Hop Reasoning over Dense Temporal Knowledge Graphs
Jian Liu | Zihe Liu | Xueqiang Lyu | Peng Jin | Jinan Xu

Temporal knowledge graph reasoning has emerged as a crucial task for answering time-dependent questions within a knowledge graph (KG).Despite tremendous progress, the present research is impeded by the sparsity of a temporal KG and an over-reliance on simple single-relational reasoning patterns. To overcome these challenges, we introduce MulQuestions, a new temporal KG reasoning benchmark featuring over 200k entities and 960k questions designed to facilitate complex, multi-relational and multi-hop reasoning. Additionally, we propose a new model adept at conducting pattern-aware and time-sensitive reasoning across temporal KGs. The model’s efficacy is confirmed through rigorous evaluations, showcasing its effectiveness in sparse data conditions and adeptness at handling questions with long reasoning chains. We have made our benchmark and model publicly accessible at [https://anonymous].

Hallucinations in large language models (LLMs) refer to the phenomenon of LLMs producing responses that are coherent yet factually inaccurate. This issue undermines the effectiveness of LLMs in practical applications, necessitating research into detecting and mitigating hallucinations of LLMs. Previous studies have mainly concentrated on post-processing techniques for hallucination detection, which tend to be computationally intensive and limited in effectiveness due to their separation from the LLM’s inference process. To overcome these limitations, we introduce MIND, an unsupervised training framework that leverages the internal states of LLMs for real-time hallucination detection without requiring manual annotations. Additionally, we present HELM, a new benchmark for evaluating hallucination detection across multiple LLMs, featuring diverse LLM outputs and the internal states of LLMs during their inference process. Our experiments demonstrate that MIND outperforms existing state-of-the-art methods in hallucination detection.

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Progressive Tuning: Towards Generic Sentiment Abilities for Large Language Models
Guiyang Hou | Yongliang Shen | Weiming Lu

Understanding sentiment is arguably an advanced and important capability of AI agents in the physical world. In previous works, many efforts have been devoted to individual sentiment subtasks, without considering interrelated sentiment knowledge among these subtasks. Although some recent works model multiple sentiment subtasks in a unified manner, they merely simply combine these subtasks without deeply exploring the hierarchical relationships among subtasks. In this paper, we introduce GSA-7B, an open-source large language model specific to the sentiment domain. Specifically, we deeply explore the hierarchical relationships between sentiment subtasks, proposing progressive sentiment reasoning benchmark and progressive task instructions. Subsequently, we use Llama2-7B as the backbone model and propose parameter-efficient progressive tuning paradigm which is implemented by constructing chain of LoRA, resulting in the creation of GSA-7B. Experimental results show that GSA-7B as a unified model performs well across all datasets in the progressive sentiment reasoning benchmark. Additionally, under the few-shot setting, GSA-7B also exhibits good generalization ability for sentiment subtasks and datasets that were not encountered during its training phase.

Despite outstanding performance in a variety of Natural Language Processing (NLP) tasks, recent studies have revealed that NLP models are vulnerable to adversarial attacks that slightly perturb the input to cause the models to misbehave. Several attacks can even compromise the model without requiring access to the model architecture or model parameters (i.e., a blackbox setting), and thus are detrimental to existing NLP applications. To perform these attacks, the adversary queries the victim model many times to determine the most important parts in an input text and transform. In this work, we propose a lightweight and attack-agnostic defense whose main goal is to perplex the process of generating an adversarial example in these query-based black-box attacks; that is to fool the textual fooler. This defense, named AdvFooler, works by randomizing the latent representation of the input at inference time. Different from existing defenses, AdvFooler does not necessitate additional computational overhead during training nor does it rely on assumptions about the potential adversarial perturbation set while having a negligible impact on the model’s accuracy. Our theoretical and empirical analyses highlight the significance of robustness resulting from confusing the adversary via randomizing the latent space, as well as the impact of randomization on clean accuracy. Finally, we empirically demonstrate near state-of-the-art robustness of AdvFooler against representative adversarial attacks on two benchmark datasets.

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Part-of-speech Tagging for Extremely Low-resource Indian Languages
Sanjeev Kumar | Preethi Jyothi | Pushpak Bhattacharyya

Modern natural language processing (NLP) systems thrive when given access to large datasets. However, a large fraction of the world’s languages are not privy to such benefits due to sparse documentation and inadequate digital representation. This is especially true for Indian regional languages. As a first step towards expanding the reach of NLP technologies to extremely low-resource Indian languages, we present a new parallel part-of-speech (POS) evaluation dataset for Angika, Magahi, Bhojpuri and Hindi. Angika, Magahi, Bhojpuri, along with the more well-known Hindi, are all languages spoken in the Indian states of Bihar, Jharkhand and West Bengal. Ours is notably the first NLP resource, even for a shallow NLP task like POS-tagging, for Angika. We establish POS-tagging baselines using state-of-the-art multilingual pretrained language models (PLMs) finetuned on Hindi data, and show zero-shot evaluations on the other three languages. While all four languages use the same Devanagari script, pretrained tokenizers underperform in zero-shot on the three languages. We propose a simple look-back fix to address the tokenization challenge yielding F1-score improvements of up to 8% on Angika and show how it comes very close to an oracle setting when the underlying Hindi word is known (and can be accurately tokenized).

Pre-trained Language Models (PLMs) have shown impressive results in various Natural Language Generation (NLG) tasks, such as powering chatbots and generating stories. However, an ethical concern arises due to their potential to produce verbatim copies of paragraphs from their training data. This is problematic as PLMs are trained on corpora constructed by human authors. As such, there is a pressing need for research to promote the generation of original content by these models. In this study, we introduce a unique “self-plagiarism” contrastive decoding strategy, aimed at boosting the originality of text produced by PLMs. Our method entails modifying prompts in LLMs to develop an amateur model and a professional model. Specifically, the amateur model is urged to plagiarize using three plagiarism templates we have designed, while the professional model maintains its standard language model status. This strategy employs prompts to stimulate the model’s capacity to identify non-original candidate token combinations and subsequently impose penalties. The application of this strategy is integrated prior to the model’s final layer, ensuring smooth integration with most existing PLMs (T5, GPT, LLaMA) without necessitating further adjustments. Implementing our strategy, we noted a significant decline in non-original sequences comprised of more than three words in the academic AASC dataset and the story-based ROCStories dataset. Source code and scripts will be released after the paper’s acceptance and publication.

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Amanda: Adaptively Modality-Balanced Domain Adaptation for Multimodal Emotion Recognition
Xinxin Zhang | Jun Sun | Simin Hong | Taihao Li

This paper investigates unsupervised multimodal domain adaptation for multimodal emotion recognition, which is a solution for data scarcity yet remains under studied. Due to the varying distribution discrepancies of different modalities between source and target domains, the primary challenge lies in how to balance the domain alignment across modalities to guarantee they are all well aligned. To achieve this, we first develop our model based on the information bottleneck theory to learn optimal representation for each modality independently. Then, we align the domains via matching the label distributions and the representations. In order to balance the representation alignment, we propose to minimize a surrogate of the alignment losses, which is equivalent to adaptively adjusting the weights of the modalities throughout training, thus achieving balanced domain alignment across modalities. Overall, the proposed approach features Adaptively modality-balanced domain adaptation, dubbed Amanda, for multimodal emotion recognition. Extensive empirical results on commonly used benchmark datasets demonstrate that Amanda significantly outperforms competing approaches. The code is available at https://github.com/sunjunaimer/Amanda.

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MedREQAL: Examining Medical Knowledge Recall of Large Language Models via Question Answering
Juraj Vladika | Phillip Schneider | Florian Matthes

In recent years, Large Language Models (LLMs) have demonstrated an impressive ability to encode knowledge during pre-training on large text corpora. They can leverage this knowledge for downstream tasks like question answering (QA), even in complex areas involving health topics. Considering their high potential for facilitating clinical work in the future, understanding the quality of encoded medical knowledge and its recall in LLMs is an important step forward. In this study, we examine the capability of LLMs to exhibit medical knowledge recall by constructing a novel dataset derived from systematic reviews – studies synthesizing evidence-based answers for specific medical questions. Through experiments on the new MedREQAL dataset, comprising question-answer pairs extracted from rigorous systematic reviews, we assess six LLMs, such as GPT and Mixtral, analyzing their classification and generation performance. Our experimental insights into LLM performance on the novel biomedical QA dataset reveal the still challenging nature of this task.

Deepfakes, particularly in the auditory domain, have become a significant threat, necessitating the development of robust countermeasures. This paper addresses the escalating challenges posed by deepfake attacks on Automatic Speaker Verification (ASV) systems. We present a novel Urdu deepfake audio dataset for deepfake detection, focusing on two spoofing attacks – Tacotron and VITS TTS. The dataset construction involves careful consideration of phonemic cover and balance and comparison with existing corpora like PRUS and PronouncUR. Evaluation with AASIST-L model shows EERs of 0.495 and 0.524 for VITS TTS and Tacotron-generated audios, respectively, with variability across speakers. Further, this research implements a detailed human evaluation, incorporating a user study to gauge whether people are able to discern deepfake audios from real (bonafide) audios. The ROC curve analysis shows an area under the curve (AUC) of 0.63, indicating that individuals demonstrate a limited ability to detect deepfakes (approximately 1 in 3 fake audio samples are regarded as real). Our work contributes a valuable resource for training deepfake detection models in low-resource languages like Urdu, addressing the critical gap in existing datasets. The dataset is publicly available at: https://github.com/CSALT-LUMS/urdu-deepfake-dataset.

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Leveraging Entailment Judgements in Cross-Lingual Summarisation
Huajian Zhang | Laura Perez-Beltrachini

Synthetically created Cross-Lingual Summarisation (CLS) datasets are prone to include document-summary pairs where the reference summary is unfaithful to the corresponding document as it contains content not supported by the document (i.e., hallucinated content). This low data quality misleads model learning and obscures evaluation results. Automatic ways to assess hallucinations and improve training have been proposed for monolingual summarisation, predominantly in English. For CLS, we propose to use off-the-shelf cross-lingual Natural Language Inference (X-NLI) to evaluate faithfulness of reference and model generated summaries. Then, we study training approaches that are aware of faithfulness issues in the training data and propose an approach that uses unlikelihood loss to teach a model about unfaithful summary sequences. Our results show that it is possible to train CLS models that yield more faithful and at the same time informative summaries.

In the field of information extraction (IE), tasks across a wide range of modalities and their combinations have been traditionally studied in isolation, leaving a gap in deeply recognizing and analyzing cross-modal information. To address this, this work for the first time introduces the concept of grounded Multimodal Universal Information Extraction (MUIE), providing a unified task framework to analyze any IE tasks over various modalities, along with their fine-grained groundings. To tackle MUIE, we tailor a multimodal large language model (MLLM), Reamo, capable of extracting and grounding information from all modalities, i.e., recognizing everything from all modalities at once. Reamo is updated via varied tuning strategies, equipping it with powerful capabilities for information recognition and fine-grained multimodal grounding. To address the absence of a suitable benchmark for grounded MUIE, we curate a high-quality, diverse, and challenging test set, which encompasses IE tasks across 9 common modality combinations with the corresponding multimodal groundings. The extensive comparison of Reamo with existing MLLMs integrated into pipeline approaches demonstrates its advantages across all evaluation dimensions, establishing a strong benchmark for the follow-up research. Our resources are publicly released at https://haofei.vip/MUIE.

The remarkable multimodal capabilities demonstrated by OpenAI’s GPT-4 have sparked significant interest in the development of multimodal Large Language Models (LLMs). A primary research objective of such models is to align visual and textual modalities effectively while comprehending human instructions.Current methodologies often rely on annotations derived from benchmark datasets to construct image-dialogue datasets for training purposes, akin to instruction tuning in LLMs. However, these datasets often exhibit domain bias, potentially constraining the generative capabilities of the models. In an effort to mitigate these limitations, we propose a novel data collection methodology that synchronously synthesizes images and dialogues for visual instruction tuning. This approach harnesses the power of generative models, marrying the abilities of ChatGPT and text-to-image generative models to yield a diverse and controllable dataset with varied image content. This not only provides greater flexibility compared to existing methodologies but also significantly enhances several model capabilities. Our research includes comprehensive experiments conducted on various datasets using the open-source LLAVA model as a testbed for our proposed pipeline. Our results underscore marked enhancements across more than ten commonly assessed capabilities.

The imitation of the children’s language acquisition process has been explored to make language models (LMs) more efficient.In particular, errors caused by children’s regularization (so-called overregularization, e.g., using wroted for the past tense of write) have been widely studied to reveal the mechanisms of language acquisition. Existing research has analyzed regularization in language acquisition only by modeling word inflection directly, which is unnatural in light of human language acquisition. In this paper, we hypothesize that language models that imitate the errors children make during language acquisition have a learning process more similar to humans. To verify this hypothesis, we analyzed the learning curve and error preferences of verb inflections in small-scale LMs using acceptability judgments. We analyze the differences in results by model architecture, data, and tokenization. Our model shows child-like U-shaped learning curves clearly for certain verbs, but the preferences for types of overgeneralization did not fully match the observations in children.

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Fantastic Semantics and Where to Find Them: Investigating Which Layers of Generative LLMs Reflect Lexical Semantics
Zhu Liu | Cunliang Kong | Ying Liu | Maosong Sun

Large language models have achieved remarkable success in general language understanding tasks. However, as a family of generative methods with the objective of next token prediction, the semantic evolution with the depth of these models are not fully explored, unlike their predecessors, such as BERT-like architectures. In this paper, we specifically investigate the bottom-up evolution of lexical semantics for a popular LLM, namely Llama2, by probing its hidden states at the end of each layer using a contextualized word identification task. Our experiments show that the representations in lower layers encode lexical semantics, while the higher layers, with weaker semantic induction, are responsible for prediction. This is in contrast to models with discriminative objectives, such as mask language modeling, where the higher layers obtain better lexical semantics. The conclusion is further supported by the monotonic increase in performance via the hidden states for the last meaningless symbols, such as punctuation, in the prompting strategy. Our codes are available at https://github.com/RyanLiut/LLM_LexSem.

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Harnessing Large Language Models as Post-hoc Correctors
Zhiqiang Zhong | Kuangyu Zhou | Davide Mottin

As Machine Learning (ML) models grow in size and demand higher-quality training data, the expenses associated with re-training and fine-tuning these models are escalating rapidly. Inspired by recent impressive achievements of Large Language Models (LLMs) in different fields, this paper delves into the question: can LLMs efficiently improve an ML’s performance at a minimal cost? We show that, through our proposed training-free framework LLMCorr, an LLM can work as a post-hoc corrector to propose corrections for the predictions of an arbitrary ML model. In particular, we form a contextual knowledge database by incorporating the dataset’s label information and the ML model’s predictions on the validation dataset. Leveraging the in-context learning capability of LLMs, we ask the LLM to summarise the instances in which the ML model makes mistakes and the correlation between primary predictions and true labels. Following this, the LLM can transfer its acquired knowledge to suggest corrections for the ML model’s predictions. Our experimental results on text analysis and the challenging molecular predictions show that LLMCorr improves the performance of a number of models by up to 39%.

How can we construct an automated debate judge to evaluate an extensive, vibrant, multi-turn debate? This task is challenging, as judging a debate involves grappling with lengthy texts, intricate argument relationships, and multi-dimensional assessments.At the same time, current research mainly focuses on short dialogues, rarely touching upon the evaluation of an entire debate.In this paper, by leveraging Large Language Models (LLMs), we propose Debatrix, which makes the analysis and assessment of multi-turn debates more aligned with majority preferences. Specifically, Debatrix features a vertical, iterative chronological analysis and a horizontal, multi-dimensional evaluation collaboration.To align with real-world debate scenarios, we introduced the PanelBench benchmark, comparing our system’s performance to actual debate outcomes.The findings indicate a notable enhancement over directly using LLMs for debate evaluation.Source code and benchmark data are available at https://github.com/ljcleo/debatrix.

Language models trained on large-scale corpus often generate harmful responses that are harmful and contrary to human values. A prevalent approach for human alignment is reinforcement learning from human feedback (RLHF), utilizing algorithms such as proximal policy optimization (PPO). However, these methods are often characterized by complexity, instability, and substantial resource consumption. Considering that existing large language models (LLMs) like ChatGPT are already relatively well-aligned and cost-friendly, researchers propose to align the language model with human preferences from AI feedback. Nevertheless, the common practices, that unidirectionally distill the responses, are constrained by the inherent capability of LLMs. To address it, we introduce CycleAlign, a framework that distills alignment capabilities from the parameter-invisible LLMs (black-box) to the parameter-visible models (white-box) in an iterative manner. CycleAlign iteratively improves both the white-box and black-box models by integrating static and dynamic in-context learning and a belief alignment method.Empirical results illustrate that the model fine-tuned by CycleAlign remarkably exceeds existing methods, and achieves the state-of-the-art performance in alignment with human value.

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Towards a new research agenda for multimodal enterprise document understanding: What are we missing?
Armineh Nourbakhsh | Sameena Shah | Carolyn Rose

The field of multimodal document understanding has produced a suite of models that have achieved stellar performance across several tasks, even coming close to human performance on certain benchmarks. Nevertheless, the application of these models to real-world enterprise datasets remains constrained by a number of limitations. In this position paper, we discuss these limitations in the context of three key aspects of research: dataset curation, model development, and evaluation on downstream tasks. By analyzing 14 datasets and 7 SotA models, we identify major gaps in their utility in the context of a real-world scenario. We demonstrate how each limitation impedes the widespread use of SotA models in enterprise settings, and present a set of research challenges that are motivated by these limitations. Lastly, we propose a research agenda that is aimed at driving the field towards higher impact in enterprise applications.

An important unexplored aspect in previous work on user satisfaction estimation for Task-Oriented Dialogue (TOD) systems is their evaluation in terms of robustness for the identification of user dissatisfaction: current benchmarks for user satisfaction estimation in TOD systems are highly skewed towards dialogues for which the user is satisfied. The effect of having a more balanced set of satisfaction labels on performance is unknown. However, balancing the data with more dissatisfactory dialogue samples requires further data collection and human annotation, which is costly and time-consuming. In this work, we leverage large language models (LLMs) and unlock their ability to generate satisfaction-aware counterfactual dialogues to augment the set of original dialogues of a test collection. We gather human annotations to ensure the reliability of the generated samples. We evaluate two open-source LLMs as user satisfaction estimators on our augmented collection against state-of-the-art fine-tuned models. Our experiments show that when used as few-shot user satisfaction estimators, open-source LLMs show higher robustness to the increase in the number of dissatisfaction labels in the test collection than the fine-tuned state-of-the-art models. Our results shed light on the need for data augmentation approaches for user satisfaction estimation in TOD systems. We release our aligned counterfactual dialogues, which are curated by human annotation, to facilitate further research on this topic.

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Measuring Retrieval Complexity in Question Answering Systems
Matteo Gabburo | Nicolaas Jedema | Siddhant Garg | Leonardo F. R. Ribeiro | Alessandro Moschitti

In this paper, we investigate which questions are challenging for retrieval-based Question Answering (QA). We (i) propose retrieval complexity (RC), a novel metric conditioned on the completeness of retrieved documents, which measures the difficulty of answering questions, and (ii) propose an unsupervised pipeline to measure RC given an arbitrary retrieval system.Our proposed pipeline measures RC more accurately than alternative estimators, including LLMs, on six challenging QA benchmarks. Further investigation reveals that RC scores strongly correlate with both QA performance and expert judgment across five of the six studied benchmarks, indicating that RC is an effective measure of question difficulty.Subsequent categorization of high-RC questions shows that they span a broad set of question shapes, including multi-hop, compositional, and temporal QA, indicating that RC scores can categorize a new subset of complex questions. Our system can also have a major impact on retrieval-based systems by helping to identify more challenging questions on existing datasets.

We introduce a hybrid abstractive summarisation approach combining hierarchical VAEs with LLMs to produce clinically meaningful summaries from social media user timelines, appropriate for mental health monitoring. The summaries combine two different narrative points of view: (a) clinical insights in third person, generated by feeding into an LLM clinical expert-guided prompts, and importantly, (b) a temporally sensitive abstractive summary of the user’s timeline in first person, generated by a novel hierarchical variational autoencoder, TH-VAE. We assess the generated summaries via automatic evaluation against expert summaries and via human evaluation with clinical experts, showing that timeline summarisation by TH-VAE results in more factual and logically coherent summaries rich in clinical utility and superior to LLM-only approaches in capturing changes over time.

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PIXAR: Auto-Regressive Language Modeling in Pixel Space
Yintao Tai | Xiyang Liao | Alessandro Suglia | Antonio Vergari

Recent work showed the possibility of building open-vocabulary large language models (LLMs) that directly operate on pixel representations. These models are implemented as autoencoders that reconstruct masked patches of rendered text.However, these pixel-based LLMs are limited to discriminative tasks (e.g., classification) and, similar to BERT, cannot be used to generate text.Therefore, they cannot be used for generative tasks such as free-form question answering. In this work, we introduce PIXAR, the first pixel-based autoregressive LLM that performs text generation. Consisting of only a decoder, PIXAR can perform free-form generative tasks while keeping the number of parameters on par with previous encoder-decoder models.Furthermore, we highlight the challenges of generating text as non-noisy images and show this is due to using a maximum likelihood objective. To overcome this problem, we propose an adversarial pretraining stage that improves the readability and accuracy of PIXAR by 8.1 on LAMBADA and 8.5 on bAbI— making it comparable to GPT-2 on text generation tasks.This paves the way to build open-vocabulary LLMs that operate on perceptual input only and calls into question the necessity of the usual symbolic input representation, i.e., text as (sub)tokens.

Large language models (LLMs) have demonstrated proficiency across various natural language processing (NLP) tasks but often require additional training, such as continual pre-training and supervised fine-tuning. However, the costs associated with this, primarily due to their large parameter count, remain high. This paper proposes leveraging sparsity in pre-trained LLMs to expedite this training process. By observing sparsity in activated neurons during forward iterations, we identify the potential for computational speed-ups by excluding inactive neurons. We address associated challenges by extending existing neuron importance evaluation metrics and introducing a ladder omission rate scheduler. Our experiments on Llama-2 demonstrate that Sparsity-Accelerated Training (SAT) achieves comparable or superior performance to standard training while significantly accelerating the process. Specifically, SAT achieves a 45% throughput improvement in continual pre-training and saves 38% training time in supervised fine-tuning. It offers a simple, hardware-agnostic, and easily deployable framework for additional LLM training.

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Preemptive Answer “Attacks” on Chain-of-Thought Reasoning
Rongwu Xu | Zehan Qi | Wei Xu

Large language models (LLMs) showcase impressive reasoning capabilities when coupled with Chain-of-Thought (CoT) prompting. However, the robustness of this approach warrants further investigation. In this paper, we introduce a novel scenario termed preemptive answers, where the LLM obtains an answer before engaging in reasoning. This situation can arise inadvertently or induced by malicious users by prompt injection attacks. Experiments reveal that preemptive answers significantly impair the model’s reasoning capability across various CoT methods and a broad spectrum of datasets. To bolster the robustness of reasoning, we propose two measures aimed at mitigating this issue to some extent.

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Do Language Models Exhibit Human-like Structural Priming Effects?
Jaap Jumelet | Willem Zuidema | Arabella Sinclair

We explore which linguistic factors—at the sentence and token level—play an important role in influencing language model predictions, and investigate whether these are reflective of results found in humans and human corpora (Gries and Kootstra, 2017). We make use of the structural priming paradigm—where recent exposure to a structure facilitates processing of the same structure—to investigate where priming effects manifest, and what factors predict them. We find these effects can be explained via the inverse frequency effect found in human priming, where rarer elements within a prime increase priming effects, as well as lexical dependence between prime and target. Our results provide an important piece in the puzzle of understanding how properties within their context affect structural prediction in language models.

The advent of Large Language Models (LLMs) has paved the way for complex tasks such as role-playing, which enhances user interactions by enabling models to imitate various characters. However, the closed-source nature of state-of-the-art LLMs and their general-purpose training limit role-playing optimization. In this paper, we introduce RoleLLM, a framework to benchmark, elicit, and enhance role-playing abilities in LLMs. RoleLLM comprises four stages: (1) Role Profile Construction for 100 roles; (2) Context-Based Instruction Generation (Context-Instruct) for role-specific knowledge extraction; (3) Role Prompting using GPT (RoleGPT) for speaking style imitation; and (4) Role-Conditioned Instruction Tuning (RoCIT) for fine-tuning open-source models along with role customization. By Context-Instruct and RoleGPT, we create RoleBench, the first systematic and fine-grained character-level benchmark dataset for role-playing with 168,093 samples. Moreover, RoCIT on RoleBench yields RoleLLaMA (English) and RoleGLM (Chinese), significantly enhancing role-playing abilities and even achieving comparable results with RoleGPT (using GPT-4).

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LangSuit·E: Planning, Controlling and Interacting with Large Language Models in Embodied Text Environments
Zixia Jia | Mengmeng Wang | Baichen Tong | Song-Chun Zhu | Zilong Zheng

Recent advances in Large Language Models (LLMs) have shown inspiring achievements in constructing autonomous agents that rely onlanguage descriptions as inputs. However, it remains unclear how well LLMs can function as few-shot or zero-shot embodied agents in dynamic interactive environments. To address this gap, we introduce LangSuit·E, a versatile and simulation-free testbed featuring 6 representative embodied tasks in textual embodied worlds. Compared with previous LLM-based testbeds, LangSuit·E (i) offers adaptability to diverse environments without multiple simulation engines, (ii) evaluates agents’ capacity to develop “internalized world knowledge” with embodied observations, and (iii) allows easy customization of communication and action strategies. To address the embodiment challenge, we devise a novel chain-of-thought (CoT) schema, EmMem, which summarizes embodied states w.r.t. history information. Comprehensive benchmark results illustrate challenges and insights of embodied planning. LangSuit·E represents a significant step toward building embodied generalists in the context of language models.

Evaluating the theory of mind (ToM) capabilities of language models (LMs) has recently received a great deal of attention. However, many existing benchmarks rely on synthetic data, which risks misaligning the resulting experiments with human behavior. We introduce the first ToM dataset based on naturally occurring spoken dialogs, Common-ToM, and show that LMs struggle to demonstrate ToM. We then show that integrating a simple, explicit representation of beliefs improves LM performance on Common-ToM.

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MAPLE: Multilingual Evaluation of Parameter Efficient Finetuning of Large Language Models
Divyanshu Aggarwal | Ashutosh Sathe | Ishaan Watts | Sunayana Sitaram

Parameter efficient finetuning has emerged as a viable solution for improving the performance of Large Language Models without requiring massive resources and compute. Prior work on multilingual evaluation has shown that there is a large gap between the performance of LLMs on English and other languages. Further, there is also a large gap between the performance of smaller open-source models and larger LLMs. Finetuning can be an effective way to bridge this gap and make language models more equitable. In this work, we finetune the Llama-2 and Mistral models on two synthetic multilingual instruction tuning datasets to determine its effect on model performance on six downstream tasks covering forty one languages in all. Additionally, we experiment with various parameters, such as rank for low-rank adaptation and values of quantisation to determine their effects on downstream performance and find that higher rank and higher quantisation values benefit low-resource languages. We find that parameter efficient finetuning of smaller open-source models sometimes bridges the gap between the performance of these models and the larger ones, however, English performance can take a hit. We also find that finetuning sometimes improves performance on low-resource languages, while degrading performance on high-resource languages.

As a crucial task in the task-oriented dialogue systems, spoken language understanding (SLU) has garnered increasing attention. However, errors from automatic speech recognition (ASR) often hinder the performance of understanding. To tackle this problem, we propose MoE-SLU, an ASR-Robust SLU framework based on the mixture-of-experts technique. Specifically, we first introduce three strategies to generate additional transcripts from clean transcripts. Then, we employ the mixture-of-experts technique to weigh the representations of the generated transcripts, ASR transcripts, and the corresponding clean manual transcripts. Additionally, we also regularize the weighted average of predictions and the predictions of ASR transcripts by minimizing the Jensen-Shannon Divergence (JSD) between these two output distributions. Experiment results on three benchmark SLU datasets demonstrate that our MoE-SLU achieves state-of-the-art performance. Further model analysis also verifies the superiority of our method.

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Multi-Task Transfer Matters During Instruction-Tuning
David Mueller | Mark Dredze | Nicholas Andrews

Instruction-tuning trains a language model on hundreds of tasks jointly to improve a model’s ability to learn in-context;however, the mechanisms that drive in-context learning are poorly understood and, as a result, the role of instruction-tuning on in-context generalization is poorly understood as well.In this work, we study the impact of instruction-tuning on multi-task transfer: how well a model’s parameters adapt to an unseen task via fine-tuning.We find that instruction-tuning negatively impacts a model’s transfer to unseen tasks, and that model transfer and in-context generalization are highly correlated, suggesting that this catastrophic forgetting may impact in-context learning.We study methods to improve model transfer, finding that multi-task training—how well the training tasks are optimized—can significantly impact ICL generalization; additionally, we find that continual training on unsupervised pre-training data can mitigate forgetting and improve ICL generalization as well.Finally, we demonstrate that, early into training, the impact of instruction-tuning on model transfer to tasks impacts in-context generalization on that task.Overall, we provide significant evidence that multi-task transfer is deeply connected to a model’s ability to learn a task in-context.

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What Makes a Good Order of Examples in In-Context Learning
Qi Guo | Leiyu Wang | Yidong Wang | Wei Ye | Shikun Zhang

Although large language models (LLMs) have demonstrated impressive few-shot learning capabilities via in-context learning (ICL), ICL performance is known to be highly sensitive to the order of examples provided. To identify appropriate orders, recent studies propose heuristic methods to evaluate order performance using a set of unlabeled data. However, the requirement of in-domain data limits their utility in real-world scenarios where additional annotated data is challenging to acquire. Additionally, these dataset-based approaches are prone to being sub-optimal for a lack of consideration for individual differences. To address the problems, we first analyze the properties of performant example orders at both corpus level and instance level. Based on the analysis we propose **DEmO** to adaptively identify performant example order for each instance without extra data. DEmO works by filtering out a subset of orders featuring label fairness, then selecting the most influential order for each test instance. The employment of a content-free metric makes DEmO independent of in-domain data. Extensive experiments indicate the superiority of DEmO over a wide range of strong baselines. Further analysis validates the generalizability across various settings.

We propose a novel VQA dataset, BloomVQA, to facilitate comprehensive evaluation of large vision-language models on comprehension tasks. Unlike current benchmarks that often focus on fact-based memorization and simple reasoning tasks without theoretical grounding, we collect multiple-choice samples based on picture stories that reflect different levels of comprehension, as laid out in Bloom’s Taxonomy, a classic framework for learning assessment widely adopted in education research. Our data maps to a novel hierarchical graph representation which enables automatic data augmentation and novel measures characterizing model consistency. We perform graded evaluation and reliability analysis on recent multi-modal models. In comparison to low-level tasks, we observe decreased performance on tasks requiring advanced comprehension and cognitive skills with up to 38.0% drop in VQA accuracy. In comparison to earlier models, GPT-4V demonstrates improved accuracy over all comprehension levels and also shows a tendency of bypassing visual inputs especially for higher-level tasks. Current models also show consistency patterns misaligned with human comprehension in various scenarios, demonstrating the need for improvement based on theoretically-grounded criteria. The dataset can be accessed at https://huggingface.co/datasets/ygong/BloomVQA.

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AttributionBench: How Hard is Automatic Attribution Evaluation?
Yifei Li | Xiang Yue | Zeyi Liao | Huan Sun

Modern generative search engines enhance the reliability of large language model (LLM) responses by providing cited evidence. However, evaluating the answer’s attribution, i.e., whether every claim within the generated responses is fully supported by its cited evidence, remains an open problem. This verification, traditionally dependent on costly human evaluation, underscores the urgent need for automatic attribution evaluation methods. To bridge the gap in the absence of standardized benchmarks for these methods, we present AttributionBench, a comprehensive benchmark compiled from various existing attribution datasets. Our extensive experiments on AttributionBench reveal the challenges of automatic attribution evaluation, even for state-of-the-art LLMs. Specifically, our findings show that even a fine-tuned GPT-3.5 only achieves around 80% macro-F1 under a binary classification formulation. A detailed analysis of more than 300 error cases indicates that a majority of failures stem from the model’s inability to process nuanced information, and the discrepancy between the information the model has access to and that human annotators do.

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Diffusion Guided Language Modeling
Justin Lovelace | Varsha Kishore | Yiwei Chen | Kilian Weinberger

Current language models demonstrate remarkable proficiency in text generation. However, for many applications it is desirable to control attributes, such as sentiment, or toxicity, of the generated language—ideally tailored towards each specific use case and target audience. For auto-regressive language models, existing guidance methods are prone to decoding errors that cascade during generation and degrade performance. In contrast, text diffusion models can easily be guided with, for example, a simple linear sentiment classifier—however they do suffer from significantly higher perplexity than auto-regressive alternatives. In this paper we use a guided diffusion model to produce a latent proposal that steers an auto-regressive language model to generate text with desired properties. Our model inherits the unmatched fluency of the auto-regressive approach and the plug-and-play flexibility of diffusion. We show that it outperforms previous plug-and-play guidance methods across a wide range of benchmark data sets. Further, controlling a new attribute in our framework is reduced to training a single logistic regression classifier.

The task of model editing becomes popular for correcting inaccurate or outdated parametric knowledge in Large Language Models (LLMs). However, there are major limitations of state of the art (SOTA) model editing methods, including the excessive memorization issue caused by the direct editing methods, as well as the error propagation and knowledge conflict issues from the memory enhancement methods, resulting in hindering models’ *portability*, e.g., the ability to transfer the new knowledge to related one-hop or multi-hop content. To address these issues, we propose the InstructEd method, the idea of which is to insert soft instructions into the attention module so as to facilitate interactions between instructions and questions and to understand and utilize new facts. Our main findings are: (i) InstructEd has achieved SOTA performance on three datasets for one-hop/multi-hop evaluation with LLaMAs and GPT2, achieving 10% (5%) improvement in one-hop (multi-hop) model editing.(ii) Different from earlier methods on editing parameters in FFN, we show that editing attention can also help. (iii) Model editing is highly related to retrieval augmented methods, which can help improve the locality of model editing while slightly decrease the editing performance with hops.

Reinforcement Learning from Human Feedback (RLHF) leverages human preference data to train language models to align more closely with human essence. These human preference data, however, are labeled at the sequence level, creating a mismatch between sequence-level preference labels and tokens, which are autoregressively generated from the language model. Although several recent approaches have tried to provide token-level (i.e., dense) rewards for each individual token, these typically rely on predefined discrete reward values (e.g., positive: +1, negative: -1, neutral: 0), failing to account for varying degrees of preference inherent to each token. To address this limitation, we introduce TLCR (Token-Level Continuous Reward) for RLHF, which incorporates a discriminator trained to distinguish positive and negative tokens, and the confidence of the discriminator is used to assign continuous rewards to each token considering the context. Extensive experiments show that our proposed TLCR leads to consistent performance improvements over previous sequence-level or token-level discrete rewards on open-ended generation benchmarks.

Large language models (LLMs), even when specifically trained to process long input contexts, struggle to capture relevant information located in the middle of their input. This phenomenon has been known as the lost-in-the-middle problem. In this work, we make three contributions. First, we set out to understand the factors that cause this phenomenon. In doing so, we establish a connection between lost-in-the-middle to LLMs’ intrinsic attention bias: LLMs exhibit an U-shaped attention bias where the tokens at the beginning and at the end of its input receive higher attention, regardless of their relevance. Second, we mitigate this positional bias through a calibration mechanism, found-in-the-middle, that allows the model to attend to contexts faithfully according to their relevance, even though when they are in the middle. Third, we show found-in-the-middle not only achieves better performance in locating relevant information within a long context, but also eventually leads to improved retrieval-augmented generation (RAG) performance across various tasks, outperforming existing methods by up to 10 percentage point. These findings open up future directions in understanding LLM attention bias and its potential consequences.

Traditional Dialogue State Tracking (DST) has focused on tracking preferences and intents in conversations centered around specific tasks (e.g. booking services). These conventional systems assume a relatively restricted conversation flow in which each turn gradually offers new information. However, advancements in Large Language Models (LLMs) have ushered in more versatile open-domain chat systems in which extended dialogue sessions encompassing numerous tasks and topics are common—in turn requiring new conversational tracking tools in order to successfully orchestrate such systems. Addressing these challenges, we introduce a novel approach combining dialogue segmentation and state tracking within open-domain dialogues, tailored for zero-shot applications appropriate to a true open-domain dialogue system. Our proposed method S3-DST employs a unique structured prompting technique and *Pre-Analytical Recollection*, a novel grounding mechanism we designed for improving long context tracking. Tested on proprietary anonymized open-domain dialogue datasets as well as publicly available DST and segmentation datasets, S3-DST consistently outperforms the state-of-the-art, showcasing its effectiveness and adaptability state tracking in the next wave of LLM-based chat systems. We also release S3-DST annotations with GPT-4 on a curated subset of LMSYS-Chat-1M to be used as a testbed to fuel research in this direction.

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Set the Clock: Temporal Alignment of Pretrained Language Models
Bowen Zhao | Zander Brumbaugh | Yizhong Wang | Hannaneh Hajishirzi | Noah Smith

Language models (LMs) are trained on web text originating from many points in time and, in general, without any explicit temporal grounding. This work investigates the temporal chaos of pretrained LMs and explores various methods to align their internal knowledge to a target time, which we call “temporal alignment.” To do this, we first automatically construct a dataset containing 20K time-sensitive questions and their answers for each year from 2000 to 2023. Based on this dataset, we empirically show that pretrained LMs (e.g., LLaMa2), despite having a recent pretraining cutoff (e.g., 2022), mostly answer questions using earlier knowledge (e.g., in 2019). We then develop several methods, from prompting to finetuning, to align LMs to use their most recent knowledge when answering questions, and investigate various factors in this alignment. Our experiments demonstrate that aligning LLaMa2 to the year 2022 can enhance its performance by up to 62% according to that year’s answers. This improvement occurs even without explicitly mentioning time information, indicating the possibility of aligning models’ internal sense of time after pretraining. Finally, we find that alignment to a historical time is also possible, with up to 2.8× the performance of the unaligned LM in 2010 if finetuning models to that year. These findings hint at the sophistication of LMs’ internal knowledge organization and the necessity of tuning them properly.

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From One to Many: Expanding the Scope of Toxicity Mitigation in Language Models
Beyza Ermis | Luiza Pozzobon | Sara Hooker | Patrick Lewis

To date, toxicity mitigation in language models has almost entirely been focused on single-language settings. As language models embrace multilingual capabilities, it’s crucial our safety measures keep pace. Recognizing this research gap, our approach expands the scope of conventional toxicity mitigation to address the complexities presented by multiple languages. In the absence of sufficient annotated datasets across languages, we employ translated data to evaluate and enhance our mitigation techniques. We also compare finetuning mitigation approaches against retrieval-augmented techniques under both static and continual toxicity mitigation scenarios. This allows us to examine the effects of translation quality and the cross-lingual transfer on toxicity mitigation. We also explore how model size and data quantity affect the success of these mitigation efforts. Covering nine languages, our study represents a broad array of linguistic families and levels of resource availability, ranging from high to mid-resource languages. Through comprehensive experiments, we provide insights into the complexities of multilingual toxicity mitigation, offering valuable insights and paving the way for future research in this increasingly important field.

The democratization of pre-trained language models through open-source initiatives has rapidly advanced innovation and expanded access to cutting-edge technologies. However, this openness also brings significant security risks, including backdoor attacks, where hidden malicious behaviors are triggered by specific inputs, compromising natural language processing (NLP) system integrity and reliability. This paper suggests that merging a backdoored model with other homogeneous models can significantly remediate backdoor vulnerabilities even if such models are not entirely secure. In our experiments, we verify our hypothesis on various models (BERT-Base, RoBERTa-Large, Llama2-7B, and Mistral-7B) and datasets (SST-2, OLID, AG News, and QNLI). Compared to multiple advanced defensive approaches, our method offers an effective and efficient inference-stage defense against backdoor attacks on classification and instruction-tuned tasks without additional resources or specific knowledge. Our approach consistently outperforms recent advanced baselines, leading to an average of about 75% reduction in the attack success rate. Since model merging has been an established approach for improving model performance, the extra advantage it provides regarding defense can be seen as a cost-free bonus.

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Enhancing Sentence Simplification in Portuguese: Leveraging Paraphrases, Context, and Linguistic Features
Arthur Scalercio | Maria Finatto | Aline Paes

Automatic text simplification focuses on transforming texts into a more comprehensible version without sacrificing their precision. However, automatic methods usually require (paired) datasets that can be rather scarce in languages other than English. This paper presents a new approach to automatic sentence simplification that leverages paraphrases, context, and linguistic attributes to overcome the absence of paired texts in Portuguese.We frame the simplification problem as a textual style transfer task and learn a style representation using the sentences around the target sentence in the document and its linguistic attributes. Moreover, unlike most unsupervised approaches that require style-labeled training data, we fine-tune strong pre-trained models using sentence-level paraphrases instead of annotated data. Our experiments show that our model achieves remarkable results, surpassing the current state-of-the-art (BART+ACCESS) while competitively matching a Large Language Model.

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How Far can 100 Samples Go? Unlocking Zero-Shot Translation with Tiny Multi-Parallel Data
Di Wu | Shaomu Tan | Yan Meng | David Stap | Christof Monz

Zero-shot translation aims to translate between language pairs not seen during training in Multilingual Machine Translation (MMT) and is widely considered an open problem. A common, albeit resource-consuming, solution is to add as many related translation directions as possible to the training corpus. In this paper, we show that for an English-centric model, surprisingly large zero-shot improvements can be achieved by simply fine-tuning with a very small amount of multi-parallel data. For example, on the EC30 dataset, we obtain up to +21.7 ChrF++ non-English overall improvements (870 directions) by using only 100 multi-parallel samples while preserving English-centric translation quality. This performance exceeds M2M100 by an average of 5.9 ChrF++ in the involved non-English directions. When investigating the size effect of fine-tuning data on translation quality, we found that already a small, randomly sampled set of fine-tuning directions is sufficient to achieve comparable improvements. The resulting non-English performance is close to the complete translation upper bound. Even in a minimal setting—fine-tuning with only one single sample—the well-known off-target issue is almost completely resolved, explaining parts—but not all—of the observed improvements in translation quality.

We explore the ability of GPT-4 to perform ad-hoc schema-based information extraction from scientific literature. We assess specifically whether it can, with a basic one-shot prompting approach over the full text of the included manusciprts, replicate two existing material science datasets, one pertaining to multi-principal element alloys (MPEAs), and one to silicate diffusion. We collaborate with materials scientists to perform a detailed manual error analysis to assess where and why the model struggles to faithfully extract the desired information, and draw on their insights to suggest research directions to address this broadly important task.

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Structural Optimization Ambiguity and Simplicity Bias in Unsupervised Neural Grammar Induction
Jinwook Park | Kangil Kim

Neural parameterization has significantly advanced unsupervised grammar induction. However, training these models with a traditional likelihood loss for all possible parses exacerbates two issues: 1) *structural optimization ambiguity* that arbitrarily selects one among structurally ambiguous optimal grammars despite the specific preference of gold parses, and 2) *structural simplicity bias* that leads a model to underutilize rules to compose parse trees. These challenges subject unsupervised neural grammar induction (UNGI) to inevitable prediction errors, high variance, and the necessity for extensive grammars to achieve accurate predictions. This paper tackles these issues, offering a comprehensive analysis of their origins. As a solution, we introduce *sentence-wise parse-focusing* to reduce the parse pool per sentence for loss evaluation, using the structural bias from pre-trained parsers on the same dataset.In unsupervised parsing benchmark tests, our method significantly improves performance while effectively reducing variance and bias toward overly simplistic parses. Our research promotes learning more compact, accurate, and consistent explicit grammars, facilitating better interpretability.

Large Language Models (LLM) have revolutionized Natural Language Processing (NLP), improving state-of-the-art and exhibiting emergent capabilities across various tasks. However, their application in extracting information from visually rich documents, which is at the core of many document processing workflows and involving the extraction of key entities from semi-structured documents, has not yet been successful. The main obstacles to adopting LLMs for this task include the absence of layout encoding within LLMs, which is critical for high quality extraction, and the lack of a grounding mechanism to localize the predicted entities within the document. In this paper, we introduce Language Model-based Document Information EXtraction and Localization (LMDX), a methodology to reframe the document information extraction task for a LLM. LMDX enables extraction of singular, repeated, and hierarchical entities, both with and without training data, while providing grounding guarantees and localizing the entities within the document. Finally, we apply LMDX to the PaLM 2-S and Gemini Pro LLMs and evaluate it on VRDU and CORD benchmarks, setting a new state-of-the-art and showing how LMDX enables the creation of high quality, data-efficient parsers.

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DBQR-QA: A Question Answering Dataset on a Hybrid of Database Querying and Reasoning
Rungsiman Nararatwong | Chung-Chi Chen | Natthawut Kertkeidkachorn | Hiroya Takamura | Ryutaro Ichise

This paper introduces the Database Querying and Reasoning Dataset for Question Answering (DBQR-QA), aimed at addressing the gap in current question-answering (QA) research by emphasizing the essential processes of database querying and reasoning to answer questions. Specifically designed to accommodate sequential questions and multi-hop queries, DBQR-QA more accurately mirrors the dynamics of real-world information retrieval and analysis, with a particular focus on the financial reports of US companies. The dataset’s construction, the challenges encountered during its development, the performance of large language models on this dataset, and a human evaluation are thoroughly discussed to illustrate the dataset’s complexity and highlight future research directions in querying and reasoning tasks.

We introduce NoteChat, a novel cooperative multi-agent framework leveraging Large Language Models (LLMs) to generate patient-physician dialogues. NoteChat embodies the principle that an ensemble of role-specific LLMs, through structured role-play and strategic prompting, can perform their assigned roles more effectively. The synergy among these role-playing LLMs results in a cohesive and efficient dialogue generation. Evaluation on MTS-dialogue, a benchmark dataset for patient-physician dialogues-note pairs, shows that models trained with the augmented synthetic patient-physician dialogues by NoteChat outperforms other state-of-the-art models for generating clinical notes. Our comprehensive automatic and human evaluation demonstrates that NoteChat substantially surpasses state-of-the-art models like ChatGPT and GPT-4 up to 22.78% by domain experts in generating superior synthetic patient-physician dialogues based on clinical notes. NoteChat has the potential to engage patients directly and help clinical documentation, a leading cause of physician burnout.

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Model Editing at Scale leads to Gradual and Catastrophic Forgetting
Akshat Gupta | Anurag Rao | Gopala Anumanchipalli

Editing knowledge in large language models is an attractive capability that allows us to correct incorrectly learned facts during pre-training, as well as update the model with an ever-growing list of new facts. While existing model editing techniques have shown promise, they are usually evaluated using metrics for reliability, specificity and generalization over one or few edits. We argue that for model editing to have practical utility, we must be able to make multiple edits to the same model. With this in mind, we evaluate current model editing methods at scale, focusing on two state of the art methods - ROME and MEMIT. With the lens of scalability, we evaluate model editing methods for three crucial properties - editing proficiency, fact forgetting and downstream performance. We find that as a model is edited sequentially with multiple facts, it continually becomes less editable, forgets previously edited facts and loses the ability to perform downstream tasks. For ROME and MEMIT, this “forgetting” happens in two phases - an initial gradual but progressive forgetting phase followed by an abrupt or catastrophic forgetting. Both gradual and catastrophic forgetting limit the usefulness of model editing methods at scale - the former makes model editing less effective as multiple edits are made to the model while the latter caps the scalability of such model editing methods. Our analysis also highlights other key limitations of ROME and MEMIT at scale. With our work, we push for better evaluation of model editing and development of model editing methods keeping scalability in mind.

This paper presents a groundbreaking multimodal, multi-task, multi-teacher joint-grained knowledge distillation model for visually-rich form document understanding. The model is designed to leverage insights from both fine-grained and coarse-grained levels by facilitating a nuanced correlation between token and entity representations, addressing the complexities inherent in form documents. Additionally, we introduce new inter-grained and cross-grained loss functions to further refine diverse multi-teacher knowledge distillation transfer process, presenting distribution gaps and a harmonised understanding of form documents. Through a comprehensive evaluation across publicly available form document understanding datasets, our proposed model consistently outperforms existing baselines, showcasing its efficacy in handling the intricate structures and content of visually complex form documents.

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Faithful Persona-based Conversational Dataset Generation with Large Language Models
Pegah Jandaghi | Xianghai Sheng | Xinyi Bai | Jay Pujara | Hakim Sidahmed

High-quality conversational datasets are essential for developing AI models that can communicate with users.One way to foster deeper interactions between a chatbot and its user is through *personas*, aspects of the user’s character that provide insights into their personality, motivations, and behaviors.Training Natural Language Processing (NLP) models on a diverse and comprehensive persona-based dataset can lead to conversational models that create a deeper connection with the user, and maintain their engagement. In this paper, we leverage the power of Large Language Models (LLMs) to create a large, high-quality conversational dataset from a seed dataset. We propose a Generator-Critic architecture framework to expand the initial dataset, while improving the quality of its conversations.The Generator is an LLM prompted to output conversations.The Critic consists of a mixture of expert LLMs that control the quality of the generated conversations.These experts select the best generated conversations, which we then use to improve the Generator.We release Synthetic-Persona-Chat, consisting of 20k conversations seeded from Persona-Chat.We evaluate the quality of Synthetic-Persona-Chat and our generation framework on different dimensions through extensive experiments, and observe that the losing rate of Synthetic-Persona-Chat against Persona-Chat during an AI detection test decreases from 17.2% to 8.8% over three iterations.

Despite vision-language models’ (VLMs) remarkable capabilities as versatile visual assistants, two substantial challenges persist within the existing VLM frameworks: (1) lacking task diversity in pretraining and visual instruction tuning, and (2) annotation error and bias in GPT-4 synthesized instruction tuning data. Both challenges lead to issues such as poor generalizability, hallucination, and catastrophic forgetting. To address these challenges, we construct Vision-Flan, the most diverse publicly available visual instruction tuning dataset to date, comprising 187 diverse tasks and 1,664,261 instances sourced from academic datasets, and each task is accompanied by an expert-written instruction. In addition, we propose a two-stage instruction tuning framework, in which VLMs are firstly finetuned on Vision-Flan and further tuned on GPT-4 synthesized data. We find this two-stage tuning framework significantly outperforms the traditional single-stage visual instruction tuning framework and achieves the state-of-the-art performance across a wide range of multi-modal evaluation benchmarks. Finally, we conduct in-depth analyses to understand visual instruction tuning and our findings reveal that: (1) GPT-4 synthesized data does not substantially enhance VLMs’ capabilities but rather modulates the model’s responses to human-preferred formats; (2) A minimal quantity (e.g., 1,000) of GPT-4 synthesized data can effectively align VLM responses with human-preference; (3) Visual instruction tuning mainly helps large-language models (LLMs) to understand visual features.

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TAXI: Evaluating Categorical Knowledge Editing for Language Models
Derek Powell | Walter Gerych | Thomas Hartvigsen

Humans rarely learn one fact in isolation. Instead, learning a new fact induces knowledge of other facts about the world. For example, in learning a korat is a type of cat, you also infer it is a mammal and has claws, ensuring your model of the world is consistent. Knowledge editing aims to inject new facts into language models to improve their factuality, but current benchmarks fail to evaluate consistency, which is critical to ensure efficient, accurate, and generalizable edits. We manually create TAXI, a new benchmark dataset specifically created to evaluate consistency in categorical knowledge edits. TAXI contains 11,120 multiple-choice queries for 976 edits spanning 41 categories (e.g., Dogs), 164 subjects (e.g., Labrador), and 183 properties (e.g., is a mammal). We then use TAXI to evaluate popular editors’ categorical consistency, measuring how often editing a subject’s category appropriately edits its properties. We find that 1) the editors achieve marginal, yet non-random consistency, 2) their consistency far underperforms human baselines, and 3) consistency is more achievable when editing atypical subjects.

Advancements in large language models (LLMs) are revolutionizing interactive game design, enabling dynamic plotlines and interactions between players and non-player characters (NPCs). However, LLMs may exhibit flaws such as hallucinations, forgetfulness, or misinterpretations of prompts, causing logical inconsistencies and unexpected deviations from intended designs. Automated techniques for detecting such game bugs are still lacking. To address this, we propose a systematic LLM-based method for automatically identifying such bugs from player game logs, eliminating the need for collecting additional data such as post-play surveys. Applied to a text-based game DejaBoom!, our approach effectively identifies bugs inherent in LLM-powered interactive games, surpassing unstructured LLM-powered bug-catching methods and filling the gap in automated detection of logical and design flaws.

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Embodied Language Learning: Opportunities, Challenges, and Future Directions
Nadine Amin | Julia Rayz

While large language and vision-language models showcase impressive capabilities, they face a notable limitation: the inability to connect language with the physical world. To bridge this gap, research has focused on embodied language learning, where the language learner is situated in the world, perceives it, and interacts with it. This article explores the current standing of research in embodied language learning, highlighting opportunities and discussing common challenges. Lastly, it identifies existing gaps from the perspective of language understanding research within the embodied world and suggests potential future directions.

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Challenges to Evaluating the Generalization of Coreference Resolution Models: A Measurement Modeling Perspective
Ian Porada | Alexandra Olteanu | Kaheer Suleman | Adam Trischler | Jackie Cheung

It is increasingly common to evaluate the same coreference resolution (CR) model on multiple datasets. Do these multi-dataset evaluations allow us to draw meaningful conclusions about model generalization? Or, do they rather reflect the idiosyncrasies of a particular experimental setup (e.g., the specific datasets used)? To study this, we view evaluation through the lens of measurement modeling, a framework commonly used in the social sciences for analyzing the validity of measurements. By taking this perspective, we show how multi-dataset evaluations risk conflating different factors concerning what, precisely, is being measured. This in turn makes it difficult to draw more generalizable conclusions from these evaluations. For instance, we show that across seven datasets, measurements intended to reflect CR model generalization are often correlated with differences in both how coreference is defined and how it is operationalized; this limits our ability to draw conclusions regarding the ability of CR models to generalize across any singular dimension. We believe the measurement modeling framework provides the needed vocabulary for discussing challenges surrounding what is actually being measured by CR evaluations.

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SAGA: A Participant-specific Examination of Story Alternatives and Goal Applicability for a Deeper Understanding of Complex Events
Sai Vallurupalli | Katrin Erk | Francis Ferraro

Interpreting and assessing goal driven actions is vital to understanding and reasoning over complex events. It is important to be able to acquire the knowledge needed for this understanding, though doing so is challenging. We argue that such knowledge can be elicited through a participant achievement lens. We analyze a complex event in a narrative according to the intended achievements of the participants in that narrative, the likely future actions of the participants, and the likelihood of goal success. We collect 6.3K high quality goal and action annotations reflecting our proposed participant achievement lens, with an average weighted Fleiss-Kappa IAA of 80%. Our collection contains annotated alternate versions of each narrative. These alternate versions vary minimally from the “original” story, but can license drastically different inferences. Our findings suggest that while modern large language models can reflect some of the goal-based knowledge we study, they find it challenging to fully capture the design and intent behind concerted actions, even when the model pretraining included the data from which we extracted the goal knowledge. We show that smaller models fine-tuned on our dataset can achieve performance surpassing larger models.

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SLIDE: A Framework Integrating Small and Large Language Models for Open-Domain Dialogues Evaluation
Kun Zhao | Bohao Yang | Chen Tang | Chenghua Lin | Liang Zhan

The long-standing one-to-many problem of gold standard responses in open-domain dialogue systems presents challenges for automatic evaluation metrics. Though prior works have demonstrated some success by applying powerful Large Language Models (LLMs), existing approaches still struggle with the one-to-many problem, and exhibit subpar performance in domain-specific scenarios. We assume the commonsense reasoning biases within LLMs may hinder their performance in domain-specific evaluations. To address both issues, we propose a novel framework SLIDE (Small and Large Integrated for Dialogue Evaluation), that leverages both a small, specialised model (SLM), and LLMs for the evaluation of open domain dialogues. Our approach introduces several techniques: (1) Contrastive learning to differentiate between robust and non-robust response embeddings; (2) A novel metric for semantic sensitivity that combines embedding cosine distances with similarity learned through neural networks, and (3) A strategy for incorporating the evaluation results from both the SLM and LLMs. Our empirical results demonstrate that our approach achieves state-of-the-art performance in both the classification and evaluation tasks, and additionally the SLIDE evaluator exhibits better correlation with human judgements. Our code is available at https://github.com/hegehongcha/SLIDE-ACL2024.

Instruction tuning has emerged as a powerful technique, significantly boosting zero-shot performance on unseen tasks. While recent work has explored cross-lingual generalization by applying instruction tuning to multilingual models, previous studies have primarily focused on English, with a limited exploration of non-English tasks. For in-depth exploration of cross-lingual generalization in instruction tuning, we perform instruction tuning individually for two distinct language meta-datasets. Subsequently, we assess the performance on unseen tasks in the language different from the one used for training. To facilitate this investigation, we introduce a novel non-English meta-dataset named “KORANI” (Korean Natural Instruction), comprising 51 Korean benchmarks. Moreover, we design cross-lingual templates to mitigate discrepancies in language and instruction-format of the template between training and inference within the cross-lingual setting. Our experiments reveal consistent improvements through cross-lingual generalization in both English and Korean, outperforming baseline by average scores of 20.7% and 13.6%, respectively. Remarkably, these enhancements are comparable to those achieved by mono-lingual instruction tuning and even surpass them in some tasks. The result underscores the significance of relevant data acquisition across languages over linguistic congruence with unseen tasks during instruction tuning.

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What Makes Language Models Good-enough?
Daiki Asami | Saku Sugawara

Psycholinguistic research suggests that humans may build a representation of linguistic input that is ‘good-enough’ for the task at hand. This study examines what architectural features make language models learn human-like good-enough language processing. We focus on the number of layers and self-attention heads in Transformers. We create a good-enough language processing (GELP) evaluation dataset (7,680 examples), which is designed to test the effects of two plausibility types, eight construction types, and three degrees of memory cost on language processing. To annotate GELP, we first conduct a crowdsourcing experiment whose design follows prior psycholinguistic studies. Our model evaluation against the annotated GELP then reveals that the full model as well as models with fewer layers and/or self-attention heads exhibit a good-enough performance. This result suggests that models with shallower depth and fewer heads can learn good-enough language processing.

Chinese Spelling Correction (CSC) commonly lacks large-scale high-quality corpora, due to the labor-intensive labeling of spelling errors in real-life human writing or typing scenarios. Two data augmentation methods are widely adopted: (1) *Random Replacement* with the guidance of confusion sets and (2) *OCR/ASR-based Generation* that simulates character misusing. However, both methods inevitably introduce noisy data (e.g., false spelling errors), potentially leading to over-correction. By carefully analyzing the two types of corpora, we find that though the latter achieves more robust generalization performance, the former yields better-calibrated CSC models. We then provide a theoretical analysis of this empirical observation, based on which a corpus refining strategy is proposed. Specifically, OCR/ASR-based data samples are fed into a well-calibrated CSC model trained on random replacement-based corpora and then filtered based on prediction confidence. By learning a simple BERT-based model on the refined OCR/ASR-based corpus, we set up impressive state-of-the-art performance on three widely-used benchmarks, while significantly alleviating over-correction (e.g., lowering false positive predictions).

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CounterCurate: Enhancing Physical and Semantic Visio-Linguistic Compositional Reasoning via Counterfactual Examples
Jianrui Zhang | Mu Cai | Tengyang Xie | Yong Jae Lee

We propose CounterCurate, a framework to comprehensively improve the visio-linguistic compositional reasoning capability for both contrastive and generative multimodal models. In particular, we identify two critical under- explored problems: the neglect of physically grounded reasoning (counting and position understanding) and the potential of using highly capable text and image generation models for semantic counterfactual fine-tuning. Our work pioneers an approach in addressing these gaps.We first spotlight the near-chance performance of multimodal models like CLIP and LLaVA in physically grounded compositional reasoning. We then apply simple data augmentation using the grounded image generation model GLIGEN to generate fine-tuning data, resulting in significant performance improvements: +33% and +37% for CLIP and LLaVA, respectively, on our newly curated Flickr30k-Positions benchmark. Moreover, we exploit the capabilities of high-performing text generation and image generation models, specifically GPT-4V and DALLE-3, to curate challenging semantic counterfactuals, thereby further enhancing compositional reasoning capabilities on benchmarks such as SugarCrepe, where CounterCurate outperforms GPT-4V.To facilitate future research, we release ourcode, dataset, benchmark, and checkpoints at https://countercurate.github.io/

Clinical natural language processing faces challenges like complex medical terminology and clinical contexts. Recently, large language models (LLMs) have shown promise in this domain. Yet, their direct deployment can lead to privacy issues and are constrained by resources. To address this challenge, we delve into synthetic clinical text generation with LLMs for clinical NLP tasks. We propose an innovative, resource-efficient approach, ClinGen, which infuses knowledge into the process. Our model involves clinical knowledge extraction and context-informed LLM prompting. Both clinical topics and writing styles are drawn from external domain-specific knowledge graphs and LLMs to guide data generation. Our extensive empirical study across 8 clinical NLP tasks and 18 datasets reveals that ClinGen consistently enhances performance across various tasks by 7.7%-8.7% on average, effectively aligning the distribution of real datasets and enriching the diversity of generated training instances.

In this paper, we propose a textless acoustic model with a self-supervised distillation strategy for noise-robust expressive speech-to-speech translation (S2ST).Recently proposed expressive S2ST systems have achieved impressive expressivity preservation performances by cascading unit-to-speech (U2S) generator to the speech-to-unit translation model. However, these systems are vulnerable to the presence of noise in input speech, which is an assumption in real-world translation scenarios. To address this limitation, we propose a U2S generator that incorporates a distillation with no label (DINO) self-supervised training strategy into it’s pretraining process.Because the proposed method captures noise-agnostic expressivity representation, it can generate qualified speech even in noisy environment.Objective and subjective evaluation results verified that the proposed method significantly improved the performance of the expressive S2ST system in noisy environments while maintaining competitive performance in clean environments.

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Knowledge-Infused Legal Wisdom: Navigating LLM Consultation through the Lens of Diagnostics and Positive-Unlabeled Reinforcement Learning
Yang Wu | Chenghao Wang | Ece Gumusel | Xiaozhong Liu

The integration of generative Large Language Models (LLMs) into various applications, including the legal domain, has been accelerated by their expansive and versatile nature. However, when facing a legal case, users without a legal background often struggle to formulate professional queries and may inadvertently overlook critical legal factors when presenting their case narrative to LLMs. To address this issue, we propose the Diagnostic Legal Large Language Model (D3LM), which utilizes adaptive lawyer-like diagnostic questions to collect additional case information and then provides high-quality feedback. D3LM incorporates an innovative graph-based Positive-Unlabeled Reinforcement Learning (PURL) algorithm, enabling the generation of critical questions and enhancing user-LLM interactions. Moreover, an integrated LLM-based stopping criterion facilitates precise Court Views Generation (CVG). Our research also introduces a new English-language CVG dataset based on the US case law database, enriching the realm of LLM research and deployment with a vital dimension. D3LM surpasses classical LLMs by delivering outstanding performance and a remarkable user experience in the legal domain.

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TELLER: A Trustworthy Framework for Explainable, Generalizable and Controllable Fake News Detection
Hui Liu | Wenya Wang | Haoru Li | Haoliang Li

The proliferation of fake news has emerged as a severe societal problem, raising significant interest from industry and academia. While existing deep-learning based methods have made progress in detecting fake news accurately, their reliability may be compromised caused by the non-transparent reasoning processes, poor generalization abilities and inherent risks of integration with large language models (LLMs). To address this challenge, we propose TELLER, a novel framework for trustworthy fake news detection that prioritizes explainability, generalizability and controllability of models. This is achieved via a dual-system framework that integrates cognition and decision systems, adhering to the principles above. The cognition system harnesses human expertise to generate logical predicates, which guide LLMs in generating human-readable logic atoms. Meanwhile, the decision system deduces generalizable logic rules to aggregate these atoms, enabling the identification of the truthfulness of the input news across diverse domains and enhancing transparency in the decision-making process. Finally, we present comprehensive evaluation results on four datasets, demonstrating the feasibility and trustworthiness of our proposed framework.

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Verifiable Generation with Subsentence-Level Fine-Grained Citations
Shuyang Cao | Lu Wang

Verifiable generation requires large language models (LLMs) to cite source documents supporting their outputs, thereby improve output transparency and trustworthiness. Yet, previous work mainly targets the generation of sentence-level citations, lacking specificity about which parts of a sentence are backed by the cited sources. This work studies verifiable generation with subsentence-level fine-grained citations for more precise location of generated content supported by the cited sources. We first present a dataset, SCiFi, comprising 10K Wikipedia paragraphs with subsentence-level citations. Each paragraph is paired with a set of candidate source documents for citation and a query that triggers the generation of the paragraph content. On SCiFi, we evaluate the performance of state-of-the-art LLMs and strategies for processing long documents designed for these models. Our experiment results reveals key factors that could enhance the quality of citations, including the expansion of the source documents’ context accessible to the models and the implementation of specialized model tuning.

How-to procedures, such as how to plant a garden, are now used by millions of users, but sometimes need customizing to meet a user’s specific needs, e.g., planting a garden without pesticides. Our goal is to measure and improve an LLM’s ability to perform such customization. Our approach is to test several simple multi-LLM-agent architectures for customization, as well as an end-to-end LLM, using a new evaluation set, called CustomPlans, of over 200 WikiHow procedures each with a customization need. We find that a simple architecture with two LLM agents used sequentially performs best, one that edits a generic how-to procedure and one that verifies its executability, significantly outperforming (10.5% absolute) an end-to-end prompted LLM. This suggests that LLMs can be configured reasonably effectively for procedure customization. This also suggests that multi-agent editing architectures may be worth exploring further for other customization applications (e.g. coding, creative writing) in the future.

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A Meta-Learning Perspective on Transformers for Causal Language Modeling
Xinbo Wu | Lav Varshney

The Transformer architecture has become prominent in developing large causal language models. However, mechanisms to explain its capabilities are not well understood. Focused on the training process, here we establish a meta-learning view of the Transformer architecture when trained for the causal language modeling task, by explicating an inner optimization process that may happen within the Transformer. Further, from within the inner optimization, we discover and theoretically analyze a special characteristic of the norms of learned token representations within Transformer-based causal language models. Our analysis is supported by experiments conducted on pre-trained large language models and real-world data.

Large Language Models (LLMs) have exhibited impressive capabilities in various tasks, yet their vast parameter sizes restrict their applicability in resource-constrained settings. Knowledge distillation (KD) offers a viable solution by transferring expertise from large teacher models to compact student models. However, traditional KD techniques face specific challenges when applied to LLMs, including restricted access to LLM outputs, significant teacher-student capacity gaps, and the inherited mis-calibration issue. In this work, we present PLaD, a novel preference-based LLM distillation framework. PLaD exploits the teacher-student capacity discrepancy to generate pseudo-preference pairs where teacher outputs are preferred over student outputs. Then, PLaD leverages a ranking loss to re-calibrate the student’s estimation of sequence likelihood, which steers the student’s focus towards understanding the relative quality of outputs instead of simply imitating the teacher. PLaD bypasses the need for access to teacher LLM’s internal states, tackles the student’s expressivity limitations, and mitigates the student mis-calibration issue. Through extensive experiments on two sequence generation tasks and with various LLMs, we demonstrate the effectiveness of our proposed PLaD framework.

Self-correction has emerged as a promising solution to boost the reasoning performance of large language models (LLMs), where LLMs refine their solutions using self-generated critiques that pinpoint the errors. This work explores whether small (≤ 13B) language models (LMs) have the ability of self-correction on reasoning tasks with minimal inputs from stronger LMs. We propose a novel pipeline that prompts smaller LMs to collect self-correction data that supports the training of self-refinement abilities. First, we leverage correct solutions to guide the model in critiquing their incorrect responses. Second, the generated critiques, after filtering, are used for supervised fine-tuning of the self-correcting reasoner through solution refinement. Our experimental results show improved self-correction abilities of two models on five datasets spanning math and commonsense reasoning, with notable performance gains when paired with a strong GPT-4-based verifier, though limitations are identified when using a weak self-verifier for determining when to correct.

How can large language models (LLMs) process and translate endangered languages? Many languages lack a large corpus to train a decent LLM; therefore existing LLMs rarely perform well in unseen, endangered languages. On the contrary, we observe that 2000 endangered languages, though without a large corpus, have a grammar book or a dictionary. We propose LingoLLM, a training-free approach to enable an LLM to process unseen languages that hardly occur in its pre-training. Our key insight is to demonstrate linguistic knowledge of an unseen language in an LLM’s prompt, including a dictionary, a grammar book, and morphologically analyzed input text. We implement LingoLLM on top of two models, GPT-4 and Mixtral, and evaluate their performance on 5 tasks across 8 endangered or low-resource languages. Our results show that LingoLLM elevates translation capability from GPT-4’s 0 to 10.5 BLEU for 10 language directions. Our findings demonstrate the tremendous value of linguistic knowledge in the age of LLMs for endangered languages. Our data, code, and model generations will be released to the public. Our data, code, and model generations can be found at https://github.com/LLiLab/llm4endangeredlang.

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From Tarzan to Tolkien: Controlling the Language Proficiency Level of LLMs for Content Generation
Ali Malik | Stephen Mayhew | Christopher Piech | Klinton Bicknell

We study the problem of controlling the difficulty level of text generated by Large Language Models (LLMs) for contexts where end-users are not fully proficient, such as language learners. Using a novel framework, we evaluate the effectiveness of several key approaches for this task, including few-shot prompting, supervised finetuning, and reinforcement learning (RL), utilising both GPT-4 and open source alternatives like LLama2-7B and Mistral-7B.Our findings reveal a large performance gap between GPT-4 and the open source models when using prompt-based strategies. However, we show how to bridge this gap with a careful combination of finetuning and RL alignment. Our best model, CALM (CEFR-Aligned Language Model), surpasses the performance of GPT-4 and other strategies, at only a fraction of the cost. We further validate the quality of our results through a small-scale human study.

Recent progress in large language models (LLMs) has led to their widespread adoption in various domains. However, these advancements have also introduced additional safety risks and raised concerns regarding their detrimental impact on already marginalized populations.Despite growing mitigation efforts to develop safety safeguards, such as supervised safety-oriented fine-tuning and leveraging safe reinforcement learning from human feedback, multiple concerns regarding the safety and ingrained biases in these models remain. Furthermore, previous work has demonstrated that models optimized for safety often display exaggerated safety behaviors, such as a tendency to refrain from responding to certain requests as a precautionary measure. As such, a clear trade-off between the helpfulness and safety of these models has been documented in the literature. In this paper, we further investigate the effectiveness of safety measures by evaluating models on already mitigated biases. Using the case of Llama 2 as an example, we illustrate how LLMs’ safety responses can still encode harmful assumptions. To do so, we create a set of non-toxic prompts, which we then use to evaluate Llama models. Through our new taxonomy of LLMs responses to users, we observe that the safety/helpfulness trade-offs are more pronounced for certain demographic groups which can lead to different kinds of harms such as quality-of-service harms for marginalized populations.

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CToolEval: A Chinese Benchmark for LLM-Powered Agent Evaluation in Real-World API Interactions
Zishan Guo | Yufei Huang | Deyi Xiong

Assessing the capabilities of large language models (LLMs) as agents in decision making and operational tasks is crucial for the development of LLM-as-agent service. We propose CToolEval, a benchmark designed to evaluate LLMs in the context of Chinese societal applications, featuring 398 APIs across 27 widely-used Apps (e.g., Apps for shopping, map, music, travel, etc.) that cover 14 domains. We further present an evaluation framework that simulates real-life scenarios, to facilitate the assessment of tool invocation ability of LLMs for tool learning and task completion ability for user interation. Our extensive experiments with CToolEval evaluate 11 LLMs, revealing that while GPT-3.5-turbo excels in tool invocation, Chinese LLMs usually struggle with issues like hallucination and a lack of comprehensive tool understanding. Our findings highlight the need for further refinement in decision-making capabilities of LLMs, offering insights into bridging the gap between current functionalities and agent-level performance. To promote further research for LLMs to fully act as reliable agents in complex, real-world situations, we release our data and codes at https://github.com/tjunlp-lab/CToolEval.

Generative models, widely utilized in various applications, can often struggle with prompts corresponding to partial tokens. This struggle stems from tokenization, where partial tokens fall out of distribution during inference, leading to incorrect or nonsensical outputs. This paper examines a technique to alleviate the tokenization artifact on text completion in generative models, maintaining performance even in regular non-subword cases. The method, termed token alignment, involves backtracking to the last complete tokens and ensuring the model’s generation aligns with the prompt. This approach showcases marked improvement across many partial token scenarios, including nuanced cases like space-prefix and partial indentation, with only a minor time increase. The technique and analysis detailed in this paper contribute to the continuous advancement of generative models in handling partial inputs, bearing relevance for applications like code completion and text.

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Rethinking Efficient Multilingual Text Summarization Meta-Evaluation
Rilyn Han | Jiawen Chen | Yixin Liu | Arman Cohan

Evaluating multilingual summarization evaluation metrics, i.e., meta-evaluation, is challenging because of the difficulty of human annotation collection. Therefore, we investigate an efficient multilingual meta-evaluation framework that uses machine translation systems to transform a monolingual meta-evaluation dataset into multilingual versions. To this end, we introduce a statistical test to verify the transformed dataset quality by checking the meta-evaluation result consistency on the original dataset and back-translated dataset. With this quality verification method, we transform an existing English summarization meta-evaluation dataset, RoSE, into 30 languages, and conduct a multilingual meta-evaluation of several representative automatic evaluation metrics. In our meta-evaluation, we find that metric performance varies in different languages and neural metrics generally outperform classical text-matching-based metrics in non-English languages. Moreover, we identify a two-stage evaluation method with superior performance, which first translates multilingual texts into English and then performs evaluation. We make the transformed datasets publicly available to facilitate future research.

We propose emotion2vec, a universal speech emotion representation model. emotion2vec is pre-trained on open-source unlabeled emotion data through self-supervised online distillation, combining utterance-level loss and frame-level loss during pre-training. emotion2vec outperforms state-of-the-art pre-trained universal models and emotion specialist models by only training linear layers for the speech emotion recognition task on the mainstream IEMOCAP dataset. In addition, emotion2vec shows consistent improvements among 10 different languages of speech emotion recognition datasets. emotion2vec also shows excellent results on other emotion tasks, such as song emotion recognition, emotion prediction in conversation, and sentiment analysis. Comparison experiments, ablation experiments, and visualization comprehensively demonstrate the universal capability of the proposed emotion2vec. To the best of our knowledge, emotion2vec is the first universal representation model in various emotion-related tasks, filling a gap in the field.

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Language-Informed Beam Search Decoding for Multilingual Machine Translation
Yilin Yang | Stefan Lee | Prasad Tadepalli

Beam search decoding is the de-facto method for decoding auto-regressive Neural Machine Translation (NMT) models, including multilingual NMT where the target language is specified as an input. However, decoding multilingual NMT models commonly produces off-target translations – yielding translation outputs not in the intended language.In this paper, we first conduct an error analysis of off-target translations for a strong multilingual NMT model and identify how these decodings are produced during beam search. We then propose Language-informed Beam Search (LiBS), a general decoding algorithm incorporating an off-the-shelf Language Identification (LiD) model into beam search decoding to reduce off-target translations. LiBS is an inference-time procedure that is NMT-model agnostic and does not require any additional parallel data. Results show that our proposed LiBS algorithm on average improves +1.1 BLEU and +0.9 BLEU on WMT and OPUS datasets, and reduces off-target rates from 22.9% to 7.7% and 65.8% to 25.3% respectively.

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RA-LoRA: Rank-Adaptive Parameter-Efficient Fine-Tuning for Accurate 2-bit Quantized Large Language Models
Minsoo Kim | Sihwa Lee | Wonyong Sung | Jungwook Choi

Deploying large language models (LLMs) with their extensive parameters and high memory demands challenges computational efficiency, particularly in fine-tuning for specific applications with limited resources. Techniques like Low-Rank Adaptation (LoRA) help by training a smaller, modifiable extension of the base model to reduce memory usage. However, combining quantization with LoRA, especially in low-bit scenarios, can lead to performance losses due to quantization errors. Our innovative Rank-Adaptive LoRA (RA-LoRA) addresses this by dynamically adjusting the adapter’s rank using rank-subspace analysis, optimizing performance with fewer parameters. We tested RA-LoRA on state-of-the-art LLMs for 2-bit efficient fine-tuning, showing it can improve model accuracy with minimal trainable parameters, marking a leap forward in quantization-aware fine-tuning methods and highlighting the significance of rank dynamics in optimizing quantized LLMs.

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The PGNSC Benchmark: How Do We Predict Where Information Spreads?
Alexander Taylor | Wei Wang

Social networks have become ideal vehicles for news dissemination because posted content is easily able to reach users beyond a news outlet’s direct audience. Understanding how information is transmitted among communities of users is a critical step towards understanding the impact social networks have on real-world events. Two significant barriers in this vein of work are identifying user clusters and meaningfully characterizing these communities. Thus, we propose the PGNSC benchmark, which builds information pathways based on the audiences of influential news sources and uses their content to characterize the communities. We present methods of aggregating these news-source-centric communities and for constructing the community feature representations that are used sequentially to construct information pathway prediction pipelines. Lastly, we perform extensive experiments to demonstrate the performance of baseline pipeline constructions and to highlight the possibilities for future work.

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STARLING: Self-supervised Training of Text-based Reinforcement Learning Agent with Large Language Models
Shreyas Basavatia | Keerthiram Murugesan | Shivam Ratnakar

Interactive fiction games have emerged as an important application to improve the generalization capabilities of language-based reinforcement learning (RL) agents. Existing environments for interactive fiction games are domain-specific or time-consuming to generate and do not train the RL agents to master a specific set of skills. In this work, we introduce an interactive environment for self-supervised RL, STARLING, for text-based games that bootstraps the text-based RL agents with automatically generated games (based on the seed set of game ideas) to boost the performance and generalization capabilities to reach a goal of the target environment. These games let the agent hone their skills on a predefined set of tasks. We create and test an environment with 100 games, generated using this automated framework that uses large language models (GPT3) and an interactive fiction game engine (based on Inform7) to provide the user with the ability to generate more games under minimal human supervision. Experimental results based on both the human participants and baseline text-based RL agents reveal that current state-of-the-art text-based RL agents cannot use previously learned skills in new situations at the level humans can. These results enforce STARLING’s potential to serve as a sandbox environment for further research in self-supervised text-based RL.

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Protecting Privacy Through Approximating Optimal Parameters for Sequence Unlearning in Language Models
Dohyun Lee | Daniel Rim | Minseok Choi | Jaegul Choo

Although language models (LMs) demonstrate exceptional capabilities on various tasks, they are potentially vulnerable to extraction attacks, which represent a significant privacy risk.To mitigate the privacy concerns of LMs, machine unlearning has emerged as an important research area, which is utilized to induce the LM to selectively forget about some of its training data.While completely retraining the model will guarantee successful unlearning and privacy assurance, it is impractical for LMs, as it would be time-consuming and resource-intensive.Prior works efficiently unlearn the target token sequences, but upon subsequent iterations, the LM displays significant degradation in performance.In this work, we propose Privacy Protection via Optimal Parameters (POP), a novel unlearning method that effectively forgets the target token sequences from the pretrained LM by applying optimal gradient updates to the parameters.Inspired by the gradient derivation of complete retraining, we approximate the optimal training objective that successfully unlearns the target sequence while retaining the knowledge from the rest of the training data.Experimental results demonstrate that POP exhibits remarkable retention performance post-unlearning across 9 classification and 4 dialogue benchmarks, outperforming the state-of-the-art by a large margin.Furthermore, we introduce Remnant Memorization Accuracy that quantifies privacy risks based on token likelihood and validate its effectiveness through both qualitative and quantitative analyses.

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Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding
Xintong Wang | Jingheng Pan | Liang Ding | Chris Biemann

Large Vision-Language Models (LVLMs) are increasingly adept at generating contextually detailed and coherent responses from visual inputs. However, their application in multimodal decision-making and open-ended generation is hindered by a notable rate of hallucinations, where generated text inaccurately represents the visual contents. To address this issue, this paper introduces the Instruction Contrastive Decoding (ICD) method, a novel approach designed to reduce hallucinations during LVLM inference. Our method is inspired by our observation that what we call disturbance instructions significantly exacerbate hallucinations in multimodal fusion modules. ICD contrasts distributions from standard and instruction disturbance, thereby increasing alignment uncertainty and effectively subtracting hallucinated concepts from the original distribution. Through comprehensive experiments on discriminative benchmarks (POPE and MME) and a generative benchmark (LLaVa-Bench), we demonstrate that ICD significantly mitigates both object-level and attribute-level hallucinations. Moreover, our method not only addresses hallucinations but also significantly enhances the general perception and recognition capabilities of LVLMs.

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Fine-tuning Language Models for Joint Rewriting and Completion of Code with Potential Bugs
Dingmin Wang | Jinman Zhao | Hengzhi Pei | Samson Tan | Sheng Zha

Handling drafty partial code remains a notable challenge in real-time code suggestion applications. Previous work has demonstrated shortcomings of large language models of code (CodeLLMs) in completing partial code with potential bugs. In this study, we view partial code as implementation hints and fine-tune CodeLLMs to jointly rewrite and complete partial code into functional full programs. We explore two strategies: one-pass generation and multi-pass iterative refinement. We construct new training and testing datasets using semantic-altering code transformations and iterative self-generations.We conduct comprehensive experiments over three representative open-sourced CodeLLMs – InCoder, CodeGen, and StarCoder.Results show that CodeLLMs fine-tuned using our approach achieve superior pass rates compared to the previous baselines across existing and newly-created benchmarks, effectively handle both potentially buggy and clean code, and largely preserve the integrity of the original partial implementations. We further present findings on the properties of the potential bugs we tested and on the design choices of our methods.

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A Critical Study of What Code-LLMs (Do Not) Learn
Abhinav Anand | Shweta Verma | Krishna Narasimhan | Mira Mezini

Large Language Models trained on code corpora (code-LLMs) have demonstrated impressive performance in various coding assistance tasks. However, despite their increased size and training dataset, code-LLMs still have limitations such as suggesting codes with syntactic errors, variable misuse etc. Some studies argue that code-LLMs perform well on coding tasks because they use self-attention and hidden representations to encode relations among input tokens. However, previous works have not studied what code properties are not encoded by code-LLMs. In this paper, we conduct a fine-grained analysis of attention maps and hidden representations of code-LLMs. Our study indicates that code-LLMs only encode relations among specific subsets of input tokens. Specifically, by categorizing input tokens into syntactic tokens and identifiers, we found that models encode relations among syntactic tokens and among identifiers, but they fail to encode relations between syntactic tokens and identifiers. We also found that fine-tuned models encode these relations poorly compared to their pre-trained counterparts. Additionally, larger models with billions of parameters encode significantly less information about code than models with only a few hundred million parameters.

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Visual In-Context Learning for Large Vision-Language Models
Yucheng Zhou | Xiang Li | Qianning Wang | Jianbing Shen

In Large Visual Language Models (LVLMs), the efficacy of In-Context Learning (ICL) remains limited by challenges in cross-modal interactions and representation disparities. To overcome these challenges, we introduce a novel Visual In-Context Learning (VICL) method comprising Visual Demonstration Retrieval, Intent-Oriented Image Summarization, and Intent-Oriented Demonstration Composition. Our approach retrieves images via ”Retrieval & Rerank” paradigm, summarises images with task intent and task-specific visual parsing, and composes language-based demonstrations that reduce token count and alleviate cross-modal interaction problem. Experimental evaluations on five visual reasoning datasets demonstrate the effectiveness of our method. Moreover, our extensive experiments leverage information flow analysis to elucidate the effectiveness of our method, and investigate the impact of length and position of demonstrations for LVLM. The use of in-context unlearning further shows promise in resetting specific model knowledge without retraining.

In this paper, we introduce SCALE, a collaborative framework that connects a compact Specialized Translation Model (STM) and a general-purpose Large Language Model (LLM) as one unified translation engine. By introducing translation from STM into the triplet in-context demonstrations, SCALE unlocks refinement and pivoting ability of LLM, thus 1) mitigating language bias of LLMs and parallel data bias of STMs, 2) enhancing LLM speciality without sacrificing generality, and 3) facilitating continual learning in a LLM-tuning-free way.Our comprehensive experiments show that SCALE significantly outperforms both LLMs (GPT-4, GPT-3.5) and supervised models (NLLB, M2M) in either high-resource or challenging low-resource settings. Moreover SCALE shows great scalability by only updating the lightweight STM and witness consistent system improvement, an averaged 4 BLEURT score across 4 languages without tuning LLM. Interestingly, SCALE could also effectively exploit the existing language bias of LLMs by using an English-centric STM as a pivot to conduct translation between any language pairs, outperforming GPT-4 by an average of 6 COMET points across eight translation directions. Furthermore we provide an in-depth analysis of SCALE’s robustness, translation characteristics, latency costs and inherent language bias, providing solid foundation for future studies exploring the potential synergy between LLMs and more specialized models.

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No perspective, no perception!! Perspective-aware Healthcare Answer Summarization
Gauri Naik | Sharad Chandakacherla | Shweta Yadav | Md Shad Akhtar

Healthcare Community Question Answering (CQA) forums offer an accessible platform for individuals seeking information on various healthcare-related topics. People find such platforms suitable for self-disclosure, seeking medical opinions, finding simplified explanations for their medical conditions, and answering others’ questions. However, answers on these forums are typically diverse and prone to off-topic discussions. It can be challenging for readers to sift through numerous answers and extract meaningful insights, making answer summarization a crucial task for CQA forums. While several efforts have been made to summarize the community answers, most of them are limited to the open domain and overlook the different perspectives offered by these answers. To address this problem, this paper proposes a novel task of perspective-specific answer summarization. We identify various perspectives, within healthcare-related responses and frame a perspective-driven abstractive summary covering all responses. To achieve this, we annotate 3167 CQA threads with 6193 perspective-aware summaries in our PUMA dataset. Further, we propose PLASMA, a prompt-driven controllable summarization model. To encapsulate the perspective-specific conditions, we design an energy-controlled loss function for the optimization. We also leverage the prefix tuner to learn the intricacies of the healthcare perspective summarization. Our evaluation against five baselines suggests the superior performance of PLASMA by a margin of ~1.5 - 21% improvement. We supplement our experiments with ablation and qualitative analysis.

We propose a simple method that applies a large language model (LLM) to large-scale retrieval in zero-shot scenarios. Our method, the Large language model as Retriever (LameR), is built upon no other neural models but an LLM in a retrieval-augmented retrieval fashion, while breaking brute-force combinations of retrievers with LLMs and lifting the performance of zero-shot retrieval to be very competitive on benchmark datasets. Essentially, we propose to augment a query with its potential answers by prompting LLMs with a composition of the query and the query’s in-domain candidates. The candidates, regardless of correct or wrong, are obtained by a vanilla retrieval procedure on the target collection. As a part of the prompts, they are likely to help LLM generate more precise answers by pattern imitation or candidate summarization. Even if all the candidates are wrong, the prompts at least make LLM aware of in-collection patterns and genres. Moreover, due to the low performance of a self-supervised retriever, the LLM-based query augmentation becomes less effective as the retriever bottlenecks the whole pipeline. Therefore, we propose to leverage a non-parametric lexicon-based method (e.g., BM25) as the retrieval module to capture query-document overlap in a literal fashion. As such, LameR makes the retrieval procedure transparent to the LLM, thus circumventing the bottleneck.

The present level of proliferation of fake, biased, and propagandistic content online has made it impossible to fact-check every single suspicious claim or article, either manually or automatically. An increasing number of scholars are focusing on a coarser granularity, aiming to profile entire news outlets, which allows fast identification of potential “fake news” by checking the reliability of their source. Source factuality is also an important element of systems for automatic fact-checking and “fake news” detection, as they need to assess the reliability of the evidence they retrieve online. Political bias detection, which in the Western political landscape is about predicting left-center-right bias, is an equally important topic, which has experienced a similar shift toward profiling entire news outlets. Moreover, there is a clear connection between the two, as highly biased media are less likely to be factual; yet, the two problems have been addressed separately. In this survey, we review the state of the art on media profiling for factuality and bias, arguing for the need to model them jointly. We also shed light on some of the major challenges for modeling bias and factuality jointly. We further discuss interesting recent advances in using different information sources and modalities, which go beyond the text of the articles the target news outlet has published. Finally, we discuss current challenges and outline future research directions.

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Semantic Compression for Word and Sentence Embeddings using Discrete Wavelet Transform
Rana Salama | Abdou Youssef | Mona Diab

Wavelet transforms, a powerful mathematical tool, have been widely used in different domains, including Signal and Image processing, to unravel intricate patterns, enhance data representation, and extract meaningful features from data. Tangible results from their application suggest that Wavelet transforms can be applied to NLP capturing a variety of linguistic and semantic properties.In this paper, we empirically leverage the application of Discrete Wavelet Transforms (DWT) to word and sentence embeddings. We aim to showcase the capabilities of DWT in analyzing embedding representations at different levels of resolution and compressing them while maintaining their overall quality.We assess the effectiveness of DWT embeddings on semantic similarity tasks to show how DWT can be used to consolidate important semantic information in an embedding vector. We show the efficacy of the proposed paradigm using different embedding models, including large language models, on downstream tasks. Our results show that DWT can reduce the dimensionality of embeddings by 50-93% with almost no change in performance for semantic similarity tasks, while achieving superior accuracy in most downstream tasks. Our findings pave the way for applying DWT to improve NLP applications.

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Improving Multi-hop Logical Reasoning in Knowledge Graphs with Context-Aware Query Representation Learning
Jeonghoon Kim | Heesoo Jung | Hyeju Jang | Hogun Park

Multi-hop logical reasoning on knowledge graphs is a pivotal task in natural language processing, with numerous approaches aiming to answer First-Order Logic (FOL) queries. Recent geometry (e.g., box, cone) and probability (e.g., beta distribution)-based methodologies have effectively addressed complex FOL queries. However, a common challenge across these methods lies in determining accurate geometric bounds or probability parameters for these queries. The challenge arises because existing methods rely on linear sequential operations within their computation graphs, overlooking the logical structure of the query and the relation-induced information that can be gleaned from the relations of the query, which we call the context of the query. To address the problem, we propose a model-agnostic methodology that enhances the effectiveness of existing multi-hop logical reasoning approaches by fully integrating the context of the FOL query graph. Our approach distinctively discerns (1) the structural context inherent to the query structure and (2) the relation-induced context unique to each node in the query graph as delineated in the corresponding knowledge graph. This dual-context paradigm helps nodes within a query graph attain refined internal representations throughout the multi-hop reasoning steps. Through experiments on two datasets, our method consistently enhances the three multi-hop reasoning foundation models, achieving performance improvements of up to 19.5%. Our codes are available at https://github.com/kjh9503/caqr.

Although Large Language Models (LLMs) exhibit remarkable adaptability across domains, these models often fall short in structured knowledge extraction tasks such as named entity recognition (NER). This paper explores an innovative, cost-efficient strategy to harness LLMs with modest NER capabilities for producing superior NER datasets. Our approach diverges from the basic class-conditional prompts by instructing LLMs to self-reflect on the specific domain, thereby generating domain-relevant attributes (such as category and emotions for movie reviews), which are utilized for creating attribute-rich training data. Furthermore, we preemptively generate entity terms and then develop NER context data around these entities, effectively bypassing the LLMs’ challenges with complex structures. Our experiments across both general and niche domains reveal significant performance enhancements over conventional data generation methods while being more cost-effective than existing alternatives.

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Defending LLMs against Jailbreaking Attacks via Backtranslation
Yihan Wang | Zhouxing Shi | Andrew Bai | Cho-Jui Hsieh

Although many large language models (LLMs) have been trained to refuse harmful requests, they are still vulnerable to jailbreaking attacks which rewrite the original prompt to conceal its harmful intent. In this paper, we propose a new method for defending LLMs against jailbreaking attacks by “backtranslation”. Specifically, given an initial response generated by the target LLM from an input prompt, our backtranslation prompts a language model to infer an input prompt that can lead to the response. The inferred prompt is called the backtranslated prompt which tends to reveal the actual intent of the original prompt, since it is generated based on the LLM’s response and not directly manipulated by the attacker. We then run the target LLM again on the backtranslated prompt, and we refuse the original prompt if the model refuses the backtranslated prompt. We explain that the proposed defense provides several benefits on its effectiveness and efficiency. We empirically demonstrate that our defense significantly outperforms the baselines, in the cases that are hard for the baselines, and our defense also has little impact on the generation quality for benign input prompts. Our implementation is based on our library for LLM jailbreaking defense algorithms at https://github.com/YihanWang617/llm-jailbreaking-defense, and the code for reproducing our experiments is available at https://github.com/YihanWang617/LLM-Jailbreaking-Defense-Backtranslation.

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A Large Collection of Model-generated Contradictory Responses for Consistency-aware Dialogue Systems
Shiki Sato | Reina Akama | Jun Suzuki | Kentaro Inui

Mitigating the generation of contradictory responses poses a substantial challenge in dialogue response generation. The quality and quantity of available contradictory response data play a vital role in suppressing these contradictions, offering two significant benefits. First, having access to large contradiction data enables a comprehensive examination of their characteristics. Second, data-driven methods to mitigate contradictions may be enhanced with large-scale contradiction data for training. Nevertheless, no attempt has been made to build an extensive collection of model-generated contradictory responses. In this paper, we build a large dataset of response generation models’ contradictions for the first time. Then, we acquire valuable insights into the characteristics of model-generated contradictions through an extensive analysis of the collected responses. Lastly, we also demonstrate how this dataset substantially enhances the performance of data-driven contradiction suppression methods.

This paper explores the question of how accurately current large language models can perform logical reasoning in natural language, with an emphasis on whether these models exhibit reasoning biases similar to humans. Specifically, our study focuses on syllogistic reasoning, a form of deductive reasoning extensively studied in cognitive science as a natural form of human reasoning. We present a syllogism dataset called NeuBAROCO, which consists of syllogistic reasoning problems in English and Japanese. This dataset was originally designed for psychological experiments to assess human reasoning capabilities using various forms of syllogisms. Our experiments with leading large language models indicate that these models exhibit reasoning biases similar to humans, along with other error tendencies. Notably, there is significant room for improvement in reasoning problems where the relationship between premises and hypotheses is neither entailment nor contradiction. We also present experimental results and in-depth analysis using a new Chain-of-Thought prompting method, which asks LLMs to translate syllogisms into abstract logical expressions and then explain their reasoning process. Our analysis using this method suggests that the primary limitations of LLMs lie in the reasoning process itself rather than the interpretation of syllogisms.

Data contamination has garnered increased attention in the era of Large language models (LLMs) due to the reliance on extensive internet-derived training corpora. The issue of training corpus overlap with evaluation benchmarks—referred to as contamination—has been the focus of significant recent research. This body of work aims to identify contamination, understand its impacts, and explore mitigation strategies from diverse perspectives. However, comprehensive studies that provide a clear pathway from foundational concepts to advanced insights are lacking in this nascent field. Therefore, we present the first survey in the field of data contamination. We begin by examining the effects of data contamination across various stages and forms. We then provide a detailed analysis of current contamination detection methods, categorizing them to highlight their focus, assumptions, strengths, and limitations. We also discuss mitigation strategies, offering a clear guide for future research. This survey serves as a succinct overview of the most recent advancements in data contamination research, providing a straightforward guide for the benefit of future research endeavors.

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DIMSIM: Distilled Multilingual Critics for Indic Text Simplification
Sneha Mondal | Ritika Ritika | Ashish Agrawal | Preethi Jyothi | Aravindan Raghuveer

Self-correction techniques have recently emerged as a promising framework to improve the quality of responses generated by large language models (LLMs). Few-shot prompted LLMs act as critics to produce feedback for an input, which is further fed to a refiner (also an LLM) to produce an output. However, these critique-refine steps require multiple expensive LLM calls. To circumvent this large inference cost, we borrow inspiration from prior work on knowledge distillation and propose the use of critique distillation to train critic models. These are smaller sequence-to-sequence models that are trained on input-critique pairs generated by an LLM. We focus on the problem of text simplification for three Indian languages: Hindi, Bengali and Marathi. This task is a good fit for self-correction style techniques. It also hasn’t been systematically explored for Indian languages before. We train two separate critics that focus on lexical and structure complexity, and show that it is surprisingly more effective than using an LLM directly as a critic in both 0-shot and few-shot settings. We also show the benefits of training multilingual critics, as opposed to monolingual critics. Extensive human evaluations show that on average, raters find 80% of DIMSIM’s output to be simple and easy to read.

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MATTER: Memory-Augmented Transformer Using Heterogeneous Knowledge Sources
Dongkyu Lee | Chandana Satya Prakash | Jack FitzGerald | Jens Lehmann

Leveraging external knowledge is crucial for achieving high performance in knowledge-intensive tasks, such as question answering. The retrieve-and-read approach is widely adopted for integrating external knowledge into a language model. However, this approach suffers from increased computational cost and latency due to the long context length, which grows proportionally with the number of retrieved knowledge. Furthermore, existing retrieval-augmented models typically retrieve information from a single type of knowledge source, limiting their scalability to diverse knowledge sources with varying structures. In this work, we introduce an efficient memory-augmented transformer called MATTER, designed to retrieve relevant knowledge from multiple heterogeneous knowledge sources. Specifically, our model retrieves and reads from both unstructured sources (paragraphs) and semi-structured sources (QA pairs) in the form of fixed-length neural memories. We demonstrate that our model outperforms existing efficient retrieval-augmented models on popular QA benchmarks in terms of both accuracy and speed. Furthermore, MATTER achieves competitive results compared to conventional read-and-retrieve models while having 100x throughput during inference.

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Ask LLMs Directly, “What shapes your bias?”: Measuring Social Bias in Large Language Models
Jisu Shin | Hoyun Song | Huije Lee | Soyeong Jeong | Jong Park

Social bias is shaped by the accumulation of social perceptions towards targets across various demographic identities. To fully understand such social bias in large language models (LLMs), it is essential to consider the composite of social perceptions from diverse perspectives among identities. Previous studies have either evaluated biases in LLMs by indirectly assessing the presence of sentiments towards demographic identities in the generated text or measuring the degree of alignment with given stereotypes. These methods have limitations in directly quantifying social biases at the level of distinct perspectives among identities. In this paper, we aim to investigate how social perceptions from various viewpoints contribute to the development of social bias in LLMs. To this end, we propose a novel strategy to intuitively quantify these social perceptions and suggest metrics that can evaluate the social biases within LLMs by aggregating diverse social perceptions. The experimental results show the quantitative demonstration of the social attitude in LLMs by examining social perception. The analysis we conducted shows that our proposed metrics capture the multi-dimensional aspects of social bias, enabling a fine-grained and comprehensive investigation of bias in LLMs.

Temporal Knowledge Graph (TKG) forecasting aims to predict future facts based on given histories. Most recent graph-based models excel at capturing structural information within TKGs but lack semantic comprehension abilities. Nowadays, with the surge of LLMs, the LLM-based TKG prediction model has emerged. However, the existing LLM-based model exhibits three shortcomings: (1) It only focuses on the first-order history for prediction while ignoring high-order historical information, resulting in the provided information for LLMs being extremely limited. (2) LLMs struggle with optimal reasoning performance under heavy historical information loads. (3) For TKG prediction, the temporal reasoning capability of LLM alone is limited. To address the first two challenges, we propose Chain-of-History (CoH) reasoning which explores high-order histories step-by-step, achieving effective utilization of high-order historical information for LLMs on TKG prediction. To address the third issue, we design CoH as a plug-and-play module to enhance the performance of graph-based models for TKG prediction. Extensive experiments on three datasets and backbones demonstrate the effectiveness of CoH.

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Can LLMs Speak For Diverse People? Tuning LLMs via Debate to Generate Controllable Controversial Statements
Ming Li | Jiuhai Chen | Lichang Chen | Tianyi Zhou

Making LLMs speak for different, especially minority groups of people, and generate statements supporting their diverse or even controversial perspectives is critical to creating an inclusive environment. However, existing LLMs lack sufficient controllability to the stance of their generated content, which often contains inconsistent, neutral, or biased statements. In this paper, we improve the controllability of LLMs in generating statements supporting an argument the user defined in the prompt. We find that multi-round debates between two LLMs with opposite stances generate higher-quality and more salient statements for each, which are important training data to improve the controllability of LLMs. Motivated by this, we develop a novel debate & tuning (“DEBATUNE”) pipeline finetuning LLMs to generate the statements obtained via debate. To examine DEBATUNE, we curate the largest dataset of debate topics so far, which covers 710 controversial topics and corresponding arguments for each topic. Evaluations by the GPT-4 judge with a novel controversy controllability metric show that LLMs’ capability of generating diverse perspectives is significantly improved by DEBATUNE. Moreover, such controllability can be generalized to unseen topics, generating high-quality statements supporting controversial arguments.

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Label-aware Hard Negative Sampling Strategies with Momentum Contrastive Learning for Implicit Hate Speech Detection
Jaehoon Kim | Seungwan Jin | Sohyun Park | Someen Park | Kyungsik Han

Detecting implicit hate speech that is not directly hateful remains a challenge. Recent research has attempted to detect implicit hate speech by applying contrastive learning to pre-trained language models such as BERT and RoBERTa, but the proposed models still do not have a significant advantage over cross-entropy loss-based learning. We found that contrastive learning based on randomly sampled batch data does not encourage the model to learn hard negative samples. In this work, we propose Label-aware Hard Negative sampling strategies (LAHN) that encourage the model to learn detailed features from hard negative samples, instead of naive negative samples in random batch, using momentum-integrated contrastive learning. LAHN outperforms the existing models for implicit hate speech detection both in- and cross-datasets. The code is available at https://github.com/Hanyang-HCC-Lab/LAHN

Instruction tuning is critical to large language models (LLMs) for achieving better instruction following and task adaptation capabilities but its success heavily relies on the training data quality. Many recent methods focus on improving the data quality but often overlook the compatibility of the data with the student model being finetuned. This paper introduces Selective Reflection-Tuning, a novel paradigm that synergizes a teacher LLM’s reflection and introspection for improving existing data quality with the data selection capability of the student LLM, to automatically refine existing instruction-tuning data. This teacher-student collaboration produces high-quality and student-compatible instruction-response pairs, resulting in sample-efficient instruction tuning and LLMs of superior performance. Selective Reflection-Tuning is a data augmentation and synthesis that generally improves LLM finetuning and self-improvement without collecting brand-new data. We apply our method to Alpaca and WizardLM data and achieve much stronger and top-tier 7B and 13B LLMs.

In conversational AI, personalizing dialogues with persona profiles and contextual understanding is essential. Despite large language models’ (LLMs) improved response coherence, effective persona integration remains a challenge. In this work, we first study two common approaches for personalizing LLMs: textual prompting and direct fine-tuning. We observed that textual prompting often struggles to yield responses that are similar to the ground truths in datasets, while direct fine-tuning tends to produce repetitive or overly generic replies. To alleviate those issues, we propose **S**elective **P**rompt **T**uning (SPT), which softly prompts LLMs for personalized conversations in a selective way. Concretely, SPT initializes a set of soft prompts and uses a trainable dense retriever to adaptively select suitable soft prompts for LLMs according to different input contexts, where the prompt retriever is dynamically updated through feedback from the LLMs. Additionally, we propose context-prompt contrastive learning and prompt fusion learning to encourage the SPT to enhance the diversity of personalized conversations. Experiments on the CONVAI2 dataset demonstrate that SPT significantly enhances response diversity by up to 90%, along with improvements in other critical performance indicators. Those results highlight the efficacy of SPT in fostering engaging and personalized dialogue generation. The SPT model code is [publicly available](https://github.com/hqsiswiliam/SPT) for further exploration.

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Sowing the Wind, Reaping the Whirlwind: The Impact of Editing Language Models
Rima Hazra | Sayan Layek | Somnath Banerjee | Soujanya Poria

In the rapidly advancing field of artificial intelligence, the concept of ‘Red-Teaming’ or ‘Jailbreaking’ large language models (LLMs) has emerged as a crucial area of study. This approach is especially significant in terms of assessing and enhancing the safety and robustness of these models. This paper investigates the intricate consequences of such modifications through model editing, uncovering a complex relationship between enhancing model accuracy and preserving its ethical integrity. Our in-depth analysis reveals a striking paradox: while injecting accurate information is crucial for model reliability, it can paradoxically destabilize the model’s foundational framework, resulting in unpredictable and potentially unsafe behaviors. Additionally, we propose a benchmark dataset NicheHazardQA to investigate this unsafe behavior both within the same and cross topical domain. This aspect of our research sheds light on how the edits, impact the model’s safety metrics and guardrails. Our findings show that model editing serves as a cost-effective tool for topical red-teaming by methodically applying targeted edits and evaluating the resultant model behavior.

Image retrieval from contextual descriptions (IRCD) aims to identify an image within a set of minimally contrastive candidates based on linguistically complex text. Despite the success of VLMs, they still significantly lag behind human performance in IRCD. The main challenges lie in aligning key contextual cues in two modalities, where these subtle cues are concealed in tiny areas of multiple contrastive images and within the complex linguistics of textual descriptions. This motivates us to propose ContextBLIP, a simple yet effective method that relies on a doubly contextual alignment scheme for challenging IRCD. Specifically, 1) our model comprises a multi-scale adapter, a matching loss, and a text-guided masking loss. The adapter learns to capture fine-grained visual cues. The two losses enable iterative supervision for the adapter, gradually highlighting the focal patches of a single image to the key textual cues. We term such a way as intra-contextual alignment. 2) Then, ContextBLIP further employs an inter-context encoder to learn dependencies among candidates, facilitating alignment between the text to multiple images. We term this step as inter-contextual alignment. Consequently, the nuanced cues concealed in each modality can be effectively aligned. Experiments on two benchmarks show the superiority of our method. We observe that ContextBLIP can yield comparable results with GPT-4V, despite involving about 7,500 times fewer parameters.

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PuzzleVQA: Diagnosing Multimodal Reasoning Challenges of Language Models with Abstract Visual Patterns
Yew Ken Chia | Vernon Toh | Deepanway Ghosal | Lidong Bing | Soujanya Poria

Large multimodal models extend the impressive capabilities of large language models by integrating multimodal understanding abilities. However, it is not clear how they can emulate the general intelligence and reasoning ability of humans. As recognizing patterns and abstracting concepts are key to general intelligence, we introduce PuzzleVQA, a collection of 2000 puzzle instances based on abstract patterns. With this dataset, we evaluate large multimodal models with abstract patterns based on fundamental concepts, including colors, numbers, sizes, and shapes. Through our experiments on state-of-the-art large multimodal models, we find that they are not able to generalize well to simple abstract patterns. Notably, GPT-4V achieves a score of 46.4% on single-concept puzzles, which shows that state-of-the-art models struggle on our dataset. To diagnose the reasoning challenges in large multimodal models, we progressively guide the models with our ground truth reasoning explanations for visual perception, inductive reasoning, and deductive reasoning. Our systematic analysis finds that the main bottlenecks of GPT-4V are weaker visual perception and inductive reasoning abilities. Through this work, we hope to shed light on the limitations of large multimodal models and how they can better emulate human cognitive processes in the future.

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How Do Moral Emotions Shape Political Participation? A Cross-Cultural Analysis of Online Petitions Using Language Models
Jaehong Kim | Chaeyoon Jeong | Seongchan Park | Meeyoung Cha | Wonjae Lee

Understanding the interplay between emotions in language and user behaviors is critical. We study how moral emotions shape the political participation of users based on cross-cultural online petition data. To quantify moral emotions, we employ a context-aware NLP model that is designed to capture the subtle nuances of emotions across cultures. For model training, we construct and share a moral emotion dataset comprising nearly 50,000 petition sentences in Korean and English each, along with emotion labels annotated by a fine-tuned LLM. We examine two distinct types of user participation: general support (i.e., registered signatures of petitions) and active support (i.e., sharing petitions on social media). We discover that moral emotions like other-suffering increase both forms of participation and help petitions go viral, while self-conscious have the opposite effect. The most prominent moral emotion, other-condemning, led to polarizing responses among the audience. In contrast, other-praising was perceived differently by culture; it led to a rise in active support in Korea but a decline in the UK. Our findings suggest that both moral emotions embedded in language and cultural perceptions are critical to shaping the public’s political discourse.

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VillagerAgent: A Graph-Based Multi-Agent Framework for Coordinating Complex Task Dependencies in Minecraft
Yubo Dong | Xukun Zhu | Zhengzhe Pan | Linchao Zhu | Yi Yang

In this paper, we aim to evaluate multi-agent systems against complex dependencies, including spatial, causal, and temporal constraints. First, we construct a new benchmark, named VillagerBench, within the Minecraft environment. VillagerBench comprises diverse tasks crafted to test various aspects of multi-agent collaboration, from workload distribution to dynamic adaptation and synchronized task execution. Second, we introduce a Directed Acyclic Graph Multi-Agent Framework (VillagerAgent) to resolve complex inter-agent dependencies and enhance collaborative efficiency. This solution incorporates a task decomposer that creates a directed acyclic graph (DAG) for structured task management, an agent controller for task distribution, and a state manager for tracking environmental and agent data.Our empirical evaluation on VillagerBench demonstrates that VillagerAgentoutperforms the existing AgentVerse model, reducing hallucinations and improving task decomposition efficacy. The results underscore VillagerAgent’s potential in advancing multi-agent collaboration, offering a scalable and generalizable solution in dynamic environments. Source code is open-source on GitHub.

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CF-TCIR: A Compositor-Free Framework for Hierarchical Text-Conditioned Image Retrieval
Yuchen Yang | Yu Wang | Yanfeng Wang

In text-conditioned image retrieval (TCIR), the combination of a reference image and modification text forms a query tuple, aiming to locate the most congruent target image within a dataset. The advantages of rich image semantic information and text flexibility are combined in this manner for more accurate retrieval. While traditional techniques often employ attention-driven compositors to craft a unified image-text representation, our paper introduces a compositor-free framework, CF-TCIR, which eschews the standard compositor. Compositor-based methods are designed to learn a joint representation of images and text, but they struggle to directly capture the correlations between attributes across the image and text modalities. Instead, we reformulate the retrieval process as a cross-modal interaction between a synthesized image feature and its corresponding text descriptor. This novel methodology offers advantages in terms of computational efficiency, scalability, and superior performance. To optimize the retrieval performance, we advocate a tiered retrieval mechanism, blending both coarse-grain and fine-grain paradigms. Moreover, to enrich the contextual relationship within the query tuple, we integrate a generative cross-modal alignment technique, ensuring synchronization of sequential attributes between image and text data.

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DMIN: A Discourse-specific Multi-granularity Integration Network for Conversational Aspect-based Sentiment Quadruple Analysis
Peijie Huang | Xisheng Xiao | Yuhong Xu | Jiawei Chen

Conversational Aspect-based Sentiment Quadruple Analysis (DiaASQ) aims to extract fine-grained sentiment quadruples from dialogues. Previous research has primarily concentrated on enhancing token-level interactions, still lacking in sufficient modeling of the discourse structure information in dialogue. Firstly, it does not incorporate interactions among different utterances in the encoding stage, resulting in a limited token-level context understanding for subsequent modules. Secondly, it ignores the critical fact that discourse information is naturally organized at the utterance level and learning it solely at the token level is incomplete. In this work, we strengthen the token-level encoder by utilizing a discourse structure called “thread” and graph convolutional networks to enhance the token interaction among different utterances. Moreover, we propose an utterance-level encoder to learn the structured speaker and reply information, providing a macro understanding of dialogue discourse. Furthermore, we introduce a novel Multi-granularities Integrator to integrate token-level and utterance-level representations, resulting in a comprehensive and cohesive dialogue contextual understanding. Experiments on two datasets demonstrate that our model achieves state-of-the-art performance. Our codes are publicly available at https://github.com/SIGSDSscau/DMIN.

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Are Decoder-Only Language Models Better than Encoder-Only Language Models in Understanding Word Meaning?
Muhammad Qorib | Geonsik Moon | Hwee Tou Ng

The natural language processing field has been evolving around language models for the past few years, from the usage of n-gram language models for re-ranking, to transfer learning with encoder-only (BERT-like) language models, and finally to large language models (LLMs) as general solvers. LLMs are dominated by the decoder-only type, and they are popular for their efficacy in numerous tasks. LLMs are regarded as having strong comprehension abilities and strong capabilities to solve new unseen tasks. As such, people may quickly assume that decoder-only LLMs always perform better than the encoder-only ones, especially for understanding word meaning. In this paper, we demonstrate that decoder-only LLMs perform worse on word meaning comprehension than an encoder-only language model that has vastly fewer parameters.

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FragRel: Exploiting Fragment-level Relations in the External Memory of Large Language Models
Xihang Yue | Linchao Zhu | Yi Yang

To process contexts with unlimited length using Large Language Models (LLMs), recent studies explore hierarchically managing the long text. Only several text fragments are taken from the external memory and passed into the temporary working memory, i.e., LLM’s context window. However, existing approaches isolatedly handle the text fragments without considering their structural connections, thereby suffering limited capability on texts with intensive inter-relations, e.g., coherent stories and code repositories. This work attempts to resolve this by exploiting the fragment-level relations in external memory. First, we formulate the fragment-level relations and present several instantiations for different text types. Next, we introduce a relation-aware fragment assessment criteria upon previous independent fragment assessment. Finally, we present the fragment-connected Hierarchical Memory based LLM. We validate the benefits of involving these relations on long story understanding, repository-level code generation, and long-term chatting.

Driven by the demand for cross-sentence and large-scale relation extraction, document-level relation extraction (DocRE) has attracted increasing research interest. Despite the continuous improvement in performance, we find that existing DocRE models which initially perform well may make more mistakes when merely changing the entity names in the document, hindering the generalization to novel entity names. To this end, we systematically investigate the robustness of DocRE models to entity name variations in this work. We first propose a principled pipeline to generate entity-renamed documents by replacing the original entity names with names from Wikidata. By applying the pipeline to DocRED and Re-DocRED datasets, we construct two novel benchmarks named Env-DocRED and Env-Re-DocRED for robustness evaluation. Experimental results show that both three representative DocRE models and two in-context learned large language models consistently lack sufficient robustness to entity name variations, particularly on cross-sentence relation instances and documents with more entities. Finally, we propose an entity variation robust training method which not only improves the robustness of DocRE models but also enhances their understanding and reasoning capabilities. We further verify that the basic idea of this method can be effectively transferred to in-context learning for DocRE as well.

On social media platforms, users’ emotions are triggered when they encounter particular content from other users,where such emotions are different from those that spontaneously emerged, owing to the “responsive” nature. Analyzing the aforementioned responsive emotions from user interactions is a task of significant importance for understanding human cognition, the mechanisms of emotion generation, and behavior on the Internet, etc. Performing the task with artificial intelligence generally requires human-annotated data to help train a well-performing system, while existing data resources do not cover this specific area, with none of them focusing on responsive emotion analysis. In this paper, we propose a Chinese dataset named ResEmo for responsive emotion analysis, including 3813 posts with 68,781 comments collected from Weibo, the largest social media platform in China. ResEmo contains three types of human annotations with respect to responsive emotions, namely, responsive relationship, responsive emotion cause, and responsive emotion category. Moreover, to test this dataset, we build large language model (LLM) baseline methods for responsive relation extraction, responsive emotion cause extraction, and responsive emotion detection, which show the potential of the proposed ResEmo being a benchmark for future studies on responsive emotions.

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EHR-SeqSQL : A Sequential Text-to-SQL Dataset For Interactively Exploring Electronic Health Records
Jaehee Ryu | Seonhee Cho | Gyubok Lee | Edward Choi

In this paper, we introduce EHR-SeqSQL, a novel sequential text-to-SQL dataset for Electronic Health Record (EHR) databases. EHR-SeqSQL is designed to address critical yet underexplored aspects in text-to-SQL parsing: interactivity, compositionality, and efficiency. To the best of our knowledge, EHR-SeqSQL is not only the largest but also the first medical text-to-SQL dataset benchmark to include sequential and contextual questions. We provide a data split and the new test set designed to assess compositional generalization ability. Our experiments demonstrate the superiority of a multi-turn approach over a single-turn approach in learning compositionality. Additionally, our dataset integrates specially crafted tokens into SQL queries to improve execution efficiency. With EHR-SeqSQL, we aim to bridge the gap between practical needs and academic research in the text-to-SQL domain.

Ongoing chatting is an important step for conversational agents to build long-term connections with people. However, people tend to quickly lose interest in chatting if the conversational agent’s words are not engaging enough. In this paper, we present a novel task of increasing users’ willingness to continue talking to the agent.We collect a dataset named ContinuousChat by: (i) collecting personas and revising them, and then expanding the personas to detailed-personas through experiences, daily life, future plans, or interesting stories; (ii) expanding detailed-personas into the dialogues, and inject emotions and feelings into them; (iii) rewriting the dialogues in specific styles through few-shot prompt, conditioning on handwritten style-specific examples.We benchmark LLMs on ContinuousChat Dataset using both fine-tuning and in-context learning settings. Experiments over publicly available models demonstrate that although there is substantial room for improvement in generating style-specific dialogues, our ContinuousChat dataset is valuable in guiding conversational agents to generate more attractive dialogues and increase users’ willingness to continue the conversations.

The gap between the trepidation of program reliability and the expense of repairs underscore the indispensability for Automated Program Repair (APR). APR is instrumental in transforming vulnerable programs into more robust ones, bolstering program reliability while simultaneously diminishing the financial burden of manual repairs. Commercial-scale language models (LM) have taken APR to unprecedented levels. However, due to the limitations of model capabilities by parameters, a one-step substantial modification may not achieve the desired effect for models with parameters less than 100B. Moreover, humans interact with the LLM through explicit prompts, which hinders the LLM from receiving feedback from compiler and test cases to automatically optimize its repair policies. Explicit prompts from humans not only increase additional manpower costs, but also pose potential misunderstandings between human’s intent and LMs.Based on the above considerations, we are exploring how to ensure small-scale LM still outperform through process supervision and feedback. We start by constructing a dataset named CodeNet4Repair, replete with multiple repair records, which supervises the fine-tuning of a foundational mode. Building upon the encouraging outcomes of reinforcement learning, we develop a reward model that serves as a critic, providing feedback for the fine-tuned LM’s action, progressively optimizing its policy. During inference, we require the LM to generate solutions iteratively until the repair effect no longer improves or hits the maximum step limit. The experimental results show that this process-based feedback not only outperforms larger outcome-based generation methods, but also nearly matches the performance of closed-source commercial large-scale LMs.

Through reading the documentation in the context, tool-using language models can dynamically extend their capability using external tools. The cost is that we have to input lengthy documentation every time the model needs to use the tool, occupying the input window as well as slowing down the decoding process.Given the progress in general-purpose compression, soft context compression is a suitable approach to alleviate the problem. However, when compressing tool documentation, existing methods suffer from the weaknesses of key information loss (specifically, tool/parameter name errors) and difficulty in adjusting the length of compressed sequences based on documentation lengths.To address these problems, we propose two strategies for compressing tool documentation into concise and precise summary sequences for tool-using language models. 1) Selective compression strategy mitigates key information loss by deliberately retaining key information as raw text tokens. 2) Block compression strategy involves dividing tool documentation into short chunks and then employing a fixed-length compression model to achieve variable-length compression. This strategy facilitates the flexible adjustment of the compression ratio.Results on API-Bank and APIBench show that our approach reaches a performance comparable to the upper-bound baseline under up to 16x compression ratio.

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MedDec: A Dataset for Extracting Medical Decisions from Discharge Summaries
Mohamed Elgaar | Jiali Cheng | Nidhi Vakil | Hadi Amiri | Leo Anthony Celi

Medical decisions directly impact individuals’ health and well-being. Extracting decision spans from clinical notes plays a crucial role in understanding medical decision-making processes. In this paper, we develop a new dataset called “MedDec,” which contains clinical notes of eleven different phenotypes (diseases) annotated by ten types of medical decisions. We introduce the task of medical decision extraction, aiming to jointly extract and classify different types of medical decisions within clinical notes. We provide a comprehensive analysis of the dataset, develop a span detection model as a baseline for this task, evaluate recent span detection approaches, and employ a few metrics to measure the complexity of data samples. Our findings shed light on the complexities inherent in clinical decision extraction and enable future work in this area of research. The dataset and code are available through https://github.com/CLU-UML/MedDec.

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bib (full) Proceedings of the 3rd Workshop on Advances in Language and Vision Research (ALVR)

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Proceedings of the 3rd Workshop on Advances in Language and Vision Research (ALVR)
Jing Gu | Tsu-Jui (Ray) Fu | Drew Hudson | Asli Celikyilmaz | William Wang

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WISMIR3: A Multi-Modal Dataset to Challenge Text-Image Retrieval Approaches
Florian Schneider | Chris Biemann

This paper presents WISMIR3, a multi-modal dataset comprising roughly 300K text-image pairs from Wikipedia. With a sophisticated automatic ETL pipeline, we scraped, filtered, and transformed the data so that WISMIR3 intrinsically differs from other popular text-image datasets like COCO and Flickr30k. We prove this difference by comparing various linguistic statistics between the three datasets computed using the pipeline. The primary purpose of WISMIR3 is to use it as a benchmark to challenge state-of-the-art text-image retrieval approaches, which already reach around 90% Recall@5 scores on the mentioned popular datasets. Therefore, we ran several text-image retrieval experiments on our dataset using current models, which show that the models, in fact, perform significantly worse compared to evaluation results on COCO and Flickr30k. In addition, for each text-image pair, we release features computed by Faster-R-CNN and CLIP models. With this, we want to ease and motivate the use of the dataset for other researchers.

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mBLIP: Efficient Bootstrapping of Multilingual Vision-LLMs
Gregor Geigle | Abhay Jain | Radu Timofte | Goran Glavaš

Modular vision-language models (Vision-LLMs) align pretrained image encoders with (frozen) large language models (LLMs) and post-hoc condition LLMs to ‘understand’ the image input. With the abundance of readily available high-quality English image-text data as well as strong monolingual English LLMs, the research focus has been on English-only Vision-LLMs. Multilingual vision-language models are still predominantly obtained via expensive end-to-end pretraining, resulting in comparatively smaller models, trained on limited multilingual image data supplemented with text-only multilingual corpora. We present mBLIP, the first Vision-LLM leveraging multilingual LLMs, which we obtain in a computationally efficient manner on consumer-level hardware. To this end, we re-align an image encoder previously tuned to an English LLM to a new, multilingual LLM using only a few million multilingual training examples derived from a mix of vision-and-language tasks, which we obtain by machine-translating high-quality English data to 95 languages. On the IGLUE benchmark and XM3600, mBLIP yields results competitive with state-of-the-art models and it greatly outperforms strong English-only Vision-LLMs like Llava 1.5. We release our model, code, and train data at https://github.com/gregor-ge/mBLIP.

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LMPT: Prompt Tuning with Class-Specific Embedding Loss for Long-Tailed Multi-Label Visual Recognition
Peng Xia | Di Xu | Ming Hu | Lie Ju | Zongyuan Ge

Long-tailed multi-label visual recognition (LTML) task is a highly challenging task due to the label co-occurrence and imbalanced data distribution. In this work, we propose a unified framework for LTML, namely prompt tuning with class-specific embedding loss (LMPT), capturing the semantic feature interactions between categories by combining text and image modality data and improving the performance synchronously on both head and tail classes. Specifically, LMPT introduces the embedding loss function with class-aware soft margin and re-weighting to learn class-specific contexts with the benefit of textual descriptions (captions), which could help establish semantic relationships between classes, especially between the head and tail classes. Furthermore, taking into account the class imbalance, the distribution-balanced loss is adopted as the classification loss function to further improve the performance on the tail classes without compromising head classes. Extensive experiments are conducted on VOC-LT and COCO-LT datasets, which demonstrates that our method significantly surpasses the previous state-of-the-art methods and zero-shot CLIP in LTML. Our codes are fully public at https://github.com/richard-peng-xia/LMPT.

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Negative Object Presence Evaluation (NOPE) to Measure Object Hallucination in Vision-Language Models
Holy Lovenia | Wenliang Dai | Samuel Cahyawijaya | Ziwei Ji | Pascale Fung

Object hallucination poses a significant challenge in vision-language (VL) models, often leading to the generation of nonsensical or unfaithful responses with non-existent objects. However, the absence of a general measurement for evaluating object hallucination in VL models has hindered our understanding and ability to mitigate this issue. In this work, we present NOPE (Negative Object Presence Evaluation), a novel benchmark designed to assess object hallucination in VL models through visual question answering (VQA). We propose a cost-effective and scalable approach utilizing large language models to generate 29.5k synthetic negative pronoun (NegP) data of high quality for NOPE. We extensively investigate the performance of 10 state-of-the-art VL models in discerning the non-existence of objects in visual questions, where the ground truth answers are denoted as (e.g., “none”). Additionally, we evaluate their standard performance on visual questions on 9 other VQA datasets. Through our experiments, we demonstrate that no VL model is immune to the vulnerability of object hallucination, as all models achieve accuracy below 10% on NegP. Furthermore, we uncover that lexically diverse visual questions, question types with large scopes, and scene-relevant objects capitalize the risk of object hallucination in VL models.

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How and where does CLIP process negation?
Vincent Quantmeyer | Pablo Mosteiro | Albert Gatt

Various benchmarks have been proposed to test linguistic understanding in pre-trained vision & language (VL) models. Here we build on the existence task from the VALSE benchmark (Parcalabescu et al., 2022) which we use to test models’ understanding of negation, a particularly interesting issue for multimodal models. However, while such VL benchmarks are useful for measuring model performance, they do not reveal anything about the internal processes through which these models arrive at their outputs in such visio-linguistic tasks. We take inspiration from the growing literature on model interpretability to explain the behaviour of VL models on the understanding of negation. Specifically, we approach these questions through an in-depth analysis of the text encoder in CLIP (Radford et al., 2021), a highly influential VL model. We localise parts of the encoder that process negation and analyse the role of attention heads in this task. Our contributions are threefold. We demonstrate how methods from the language model interpretability literature (e.g., causal tracing) can be translated to multimodal models and tasks; we provide concrete insights into how CLIP processes negation on the VALSE existence task; and we highlight inherent limitations in the VALSE dataset as a benchmark for linguistic understanding.

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Enhancing Continual Learning in Visual Question Answering with Modality-Aware Feature Distillation
Malvina Nikandrou | Georgios Pantazopoulos | Ioannis Konstas | Alessandro Suglia

Continual learning focuses on incrementally training a model on a sequence of tasks with the aim of learning new tasks while minimizing performance drop on previous tasks. Existing approaches at the intersection of Continual Learning and Visual Question Answering (VQA) do not study how the multimodal nature of the input affects the learning dynamics of a model. In this paper, we demonstrate that each modality evolves at different rates across a continuum of tasks and that this behavior occurs in established encoder-only models as well as modern recipes for developing Vision & Language (VL) models. Motivated by this observation, we propose a modality-aware feature distillation (MAFED) approach which outperforms existing baselines across models of varying scale in three multimodal continual learning settings. Furthermore, we provide ablations showcasing that modality-aware distillation complements experience replay. Overall, our results emphasize the importance of addressing modality-specific dynamics to prevent forgetting in multimodal continual learning.

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English-to-Japanese Multimodal Machine Translation Based on Image-Text Matching of Lecture Videos
Ayu Teramen | Takumi Ohtsuka | Risa Kondo | Tomoyuki Kajiwara | Takashi Ninomiya

We work on a multimodal machine translation of the audio contained in English lecture videos to generate Japanese subtitles. Image-guided multimodal machine translation is promising for error correction in speech recognition and for text disambiguation. In our situation, lecture videos provide a variety of images. Images of presentation materials can complement information not available from audio and may help improve translation quality. However, images of speakers or audiences would not directly affect the translation quality. We construct a multimodal parallel corpus with automatic speech recognition text and multiple images for a transcribed parallel corpus of lecture videos, and propose a method to select the most relevant ones from the multiple images with the speech text for improving the performance of image-guided multimodal machine translation. Experimental results on translating automatic speech recognition or transcribed English text into Japanese show the effectiveness of our method to select a relevant image.

Multimodal large language models (MLLMs) are flourishing, but mainly focus on images with less attention than videos, especially in sub-fields such as prompt engineering, video chain-of-though (CoT), and instruction tuning on videos. Therefore, we try to explore the collection of CoT datasets in videos to lead to video OpenQA and improve the reasoning ability of MLLMs. Unfortunately, making such video CoT datasets is not an easy task. Given that human annotation is too cumbersome and expensive, while machine-generated is not reliable due to the hallucination issue, we develop an automatic annotation tool that combines machine and human experts, under the active learning paradigm. Active learning is an interactive strategy between the model and human experts, in this way, the workload of human labeling can be reduced and the quality of the dataset can be guaranteed. With the help of the automatic annotation tool, we strive to contribute three datasets, namely VideoCoT, TopicQA, TopicCoT. Furthermore, we propose a simple but effective benchmark based on the collected datasets, which exploits CoT to maximize the complex reasoning capabilities of MLLMs. Extensive experiments demonstrate the effectiveness our solution, and we will release our source codes and datasets to facilitate the research community.

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Enhancing Conceptual Understanding in Multimodal Contrastive Learning through Hard Negative Samples
Philipp J. Rösch | Norbert Oswald | Michaela Geierhos | Jindřich Libovický

Current vision-language models leveraging contrastive learning often face limitations in developing fine-grained conceptual understanding. This is due to random negative samples during pretraining, causing almost exclusively very dissimilar concepts to be compared in the loss function. Consequently, the models struggle with fine-grained semantic differences. To address this problem, we introduce a novel pretraining method incorporating synthetic hard negative text examples. The hard negatives replace terms corresponding to visual concepts, leading to a more fine-grained visual and textual concept alignment. Further, we introduce InpaintCOCO, a new challenging dataset for assessing the fine-grained alignment of colors, objects, and sizes in vision-language models. We created the dataset using generative inpainting from COCO images by changing the visual concepts so that the images no longer match their original captions. Our results show significant improvements in fine-grained concept understanding across various vision-language datasets, including our InpaintCOCO dataset.

This paper explores capabilities of Vision Language Models on spreadsheet comprehension. We propose three self-supervised challenges with corresponding evaluation metrics to comprehensively evaluate VLMs on Optical Character Recognition (OCR), spatial perception, and visual format recognition. Additionally, we utilize the spreadsheet table detection task to assess the overall performance of VLMs by integrating these challenges. To probe VLMs more finely, we propose three spreadsheet-to-image settings: column width adjustment, style change, and address augmentation. We propose variants of prompts to address the above tasks in different settings. Notably, to leverage the strengths of VLMs in understanding text rather than two-dimensional positioning, we propose to decode cell values on the four boundaries of the table in spreadsheet boundary detection. Our findings reveal that VLMs demonstrate promising OCR capabilities but produce unsatisfactory results due to cell omission and misalignment, and they notably exhibit insufficient spatial and format recognition skills, motivating future work to enhance VLMs’ spreadsheet data comprehension capabilities using our methods to generate extensive spreadsheet-image pairs in various settings.

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SlideAVSR: A Dataset of Paper Explanation Videos for Audio-Visual Speech Recognition
Hao Wang | Shuhei Kurita | Shuichiro Shimizu | Daisuke Kawahara

Audio-visual speech recognition (AVSR) is a multimodal extension of automatic speech recognition (ASR), using video as a complement to audio. In AVSR, considerable efforts have been directed at datasets for facial features such as lip-readings, while they often fall short in evaluating the image comprehension capabilities in broader contexts. In this paper, we construct SlideAVSR, an AVSR dataset using scientific paper explanation videos. SlideAVSR provides a new benchmark where models transcribe speech utterances with texts on the slides on the presentation recordings. As technical terminologies that are frequent in paper explanations are notoriously challenging to transcribe without reference texts, our SlideAVSR dataset spotlights a new aspect of AVSR problems. As a simple yet effective baseline, we propose DocWhisper, an AVSR model that can refer to textual information from slides, and confirm its effectiveness on SlideAVSR.

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Causal and Temporal Inference in Visual Question Generation by Utilizing Pre-trained Models
Zhanghao Hu | Frank Keller

Visual Question Generation is a task at the crossroads of visual and language learning, impacting broad domains like education, medicine, and social media. While existing pre-trained models excel in fact-based queries with image pairs, they fall short of capturing human-like inference, particularly in understanding causal and temporal relationships within videos. Additionally, the computational demands of prevalent pre-training methods pose challenges. In response, our study introduces a framework that leverages vision-text matching pre-trained models to guide language models in recognizing event-entity relationships within videos and generating inferential questions. Demonstrating efficacy on the NExT-QA dataset, which is designed for causal and temporal inference in visual question answering, our method successfully guides pre-trained language models in recognizing video content. We present methodologies for abstracting causal and temporal relationships between events and entities, pointing out the importance of consistent relationships among input frames during training and inference phases and suggesting an avenue for future exploration.

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Improving Vision-Language Cross-Lingual Transfer with Scheduled Unfreezing
Max Reinhardt | Gregor Geigle | Radu Timofte | Goran Glavaš

Large-scale pretraining of vision-language (VL) models brought dramatic improvements across numerous tasks, from visual question-answering to cross-modal retrieval but these gains are mostly limited to English. Massively multilingual VL encoder models (mVLMs) hold promise for other languages: after fine-tuning on only English task data, they can perform the task in other languages in what is termed zero-shot cross-lingual transfer (ZS-XLT). Still, ZS-XLT sees a large performance gap to English, especially for low-resource languages. In this work, we reduce this gap with a fine-tuning strategy known as Scheduled Unfreezing (SUF): instead of updating all parameters from the start, we begin with the top layer(s) of the vision-language encoder and gradually unfreeze (i.e., update) its layers top to bottom. SUF forces reliance on encoder’s representations from higher layers: the fact that in multilingual models these representations encode higher-level semantics rather than low-level language-specific idiosyncrasies, we hypothesize, should render SUF beneficial for ZS-XLT. Experiments with two mVLMs (UC2 & CCLM) on three downstream tasks (xGQA, XVNLI, xFlickrCo) show that SUF brings consistent gains in ZS-XLT, especially for visual Q&A (xGQA) by up to 10 points.

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Automatic Layout Planning for Visually-Rich Documents with Instruction-Following Models
Wanrong Zhu | Ruiyi Zhang | Jennifer Healey | William Yang Wang | Tong Sun

Recent advancements in instruction-following models have made user interactions with models more user-friendly and efficient, broadening their applicability. In graphic design, non-professional users often struggle to create visually appealing layouts due to limited skills and resources. In this work, we introduce a novel multimodal instruction-following framework for layout planning, allowing users to easily arrange visual elements into tailored layouts by specifying canvas size and design purpose, such as for book covers, posters, brochures, or menus. We developed three layout reasoning tasks to train the model in understanding and executing layout instructions. Experiments on two benchmarks show that our method not only simplifies the design process for non-professionals but also surpasses the performance of few-shot GPT-4V models, with mIoU higher by 12% on Crello. This progress highlights the potential of multimodal instruction-following models to automate and simplify the design process, providing an approachable solution for a wide range of design tasks on visually-rich documents.

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SEA-VQA: Southeast Asian Cultural Context Dataset For Visual Question Answering
Norawit Urailertprasert | Peerat Limkonchotiwat | Supasorn Suwajanakorn | Sarana Nutanong

Visual Question Answering (VQA) is a critical task that requires the simultaneous understanding of visual and textual information. While significant advancements have been made with multilingual datasets, these often lack cultural specificity, especially in the context of Southeast Asia (SEA). In this paper, we introduce SEA-VQA aiming to highlight the challenges and gaps in existing VQA models when confronted with culturally specific content. Our dataset includes images from eight SEA countries, curated from the UNESCO Cultural Heritage collection. Our evaluation, comparing GPT-4 and GEMINI models, demonstrates substantial performance drops on culture-centric questions compared to the A-OKVQA dataset, a commonsense and world-knowledge VQA benchmark comprising approximately 25,000 questions. Our findings underscore the importance of cultural diversity in VQA datasets and reveal substantial gaps in the ability of current VQA models to handle culturally rich contexts. SEA-VQA serves as a crucial benchmark for identifying these gaps and guiding future improvements in VQA systems.

Describing Wikimedia Commons images using Wikidata’s structured data enables a wide range of automation tasks, such as search and organization, as well as downstream tasks, such as labeling images or training machine learning models. However, there is currently a lack of structured data-labelled images on Wikimedia Commons.To close this gap, we propose the task of Visual Entity Linking (VEL) for Wikimedia Commons, in which we create new labels for Wikimedia Commons images from Wikidata items. VEL is a crucial tool for improving information retrieval, search, content understanding, cross-modal applications, and various machine-learning tasks. In this paper, we propose a method to create new labels for Wikimedia Commons images from Wikidata items. To this end, we create a novel dataset leveraging community-created structured data on Wikimedia Commons and fine-tuning pre-trained models based on the CLIP architecture. Although the best-performing models show promising results, the study also identifies key challenges of the data and the task.

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VerbCLIP: Improving Verb Understanding in Vision-Language Models with Compositional Structures
Hadi Wazni | Kin Ian Lo | Mehrnoosh Sadrzadeh

Verbs describe the dynamics of interactions between people, objects, and their environments. They play a crucial role in language formation and understanding. Nonetheless, recent vision-language models like CLIP predominantly rely on nouns and have a limited account of verbs. This limitation affects their performance in tasks requiring action recognition and scene understanding. In this work, we introduce VerbCLIP, a verb-centric vision-language model which learns meanings of verbs based on a compositional approach to statistical machine learning. Our methods significantly outperform CLIP in zero-shot performance on the VALSE, VL-Checklist, and SVO-Probes datasets, with improvements of +2.38%, +3.14%, and +1.47%, without fine-tuning. Fine-tuning resulted in further improvements, with gains of +2.85% and +9.2% on the VALSE and VL-Checklist datasets.

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Evolutionary Reward Design and Optimization with Multimodal Large Language Models
Ali Narin

Designing reward functions is a pivotal yet challenging task for Reinforcement Learning (RL) practices, often demanding domain expertise and substantial effort. Recent studies have explored the utilization of Large Language Models (LLMs) to generate reward functions via evolutionary search techniques. However, these approaches overlook the potential of multimodal information, such as images and videos. In particular, prior methods predominantly rely on numerical feedback from the RL environment for doing evolution, neglecting the incorporation of visual data obtained during training. This study introduces a novel approach by employing Multimodal Large Language Models (MLLMs) to craft reward functions tailored for various RL tasks. The methodology involves providing MLLM with the RL environment’s code alongside its image as context and task information to generate reward candidates. Then, the chosen agent undergoes training, and the numerical feedback from the environment, along with the recorded video of the top-performing policy, is provided as feedback to the MLLM. By employing an iterative feedback mechanism through evolutionary search, MLLM consistently refines the reward function to maximize accuracy. Testing on two different agents points to the preeminence of our approach over previous methodology, which themselves outperformed 83% of reward functions designed by human experts.

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bib (full) Proceedings of The Second Arabic Natural Language Processing Conference

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Wikidata as a Source of Demographic Information
Samir Abdaljalil | Hamdy Mubarak

Names carry important information about our identities and demographics such as gender, nationality, ethnicity, etc. We investigate the use of individual’s name, in both Arabic and English, to predict important attributes, namely country, region, gender, and language. We extract data from Wikidata, and normalize it, to build a comprehensive dataset consisting of more than 1 million entities and their normalized attributes. We experiment with a Linear SVM approach, as well as two Transformers approaches consisting of BERT model fine-tuning and Transformers pipeline. Our results indicate that we can predict the gender, language and region using the name only with a confidence over 0.65. The country attribute can be predicted with less accuracy. The Linear SVM approach outperforms the other approaches for all the attributes. The best performing approach was also evaluated on another dataset that consists of 1,500 names from 15 countries (covering different regions) extracted from Twitter, and yields similar results.

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Synthetic Arabic Medical Dialogues Using Advanced Multi-Agent LLM Techniques
Mariam ALMutairi | Lulwah AlKulaib | Melike Aktas | Sara Alsalamah | Chang-Tien Lu

The increasing use of artificial intelligence in healthcare requires robust datasets for training and validation, particularly in the domain of medical conversations. However, the creation and accessibility of such datasets in Arabic face significant challenges, especially due to the sensitivity and privacy concerns that are associated with medical conversations. These conversations are rarely recorded or preserved, making the availability of comprehensive Arabic medical dialogue datasets scarce. This limitation slows down not only the development of effective natural language processing models but also restricts the opportunity for open comparison of algorithms and their outcomes. Recent advancements in large language models (LLMs) like ChatGPT, GPT-4, Gemini-pro, and Claude-3 show promising capabilities in generating synthetic data. To address this gap, we introduce a novel Multi-Agent LLM approach capable of generating synthetic Arabic medical dialogues from patient notes, regardless of the original language. This development presents a significant step towards overcoming the barriers in dataset availability, enhancing the potential for broader research and application in AI-driven medical dialogue systems.

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AuRED: Enabling Arabic Rumor Verification using Evidence from Authorities over Twitter
Fatima Haouari | Tamer Elsayed | Reem Suwaileh

Diverging from the trend of the previous rumor verification studies, we introduce the new task of rumor verification using evidence that are exclusively captured from authorities, i.e., entities holding the right and knowledge to verify corresponding information. To enable research on this task for Arabic low-resourced language, we construct and release the first Authority-Rumor-Evidence Dataset (AuRED). The dataset comprises 160 rumors expressed in tweets and 692 Twitter timelines of authorities containing about 34k tweets. Additionally, we explore how existing evidence retrieval and claim verification models for fact-checking perform on our task under both the cross-lingual zero-shot and in-domain fine-tuning setups. Our experiments show that although evidence retrieval models perform relatively well on the task establishing strong baselines, there is still a big room for improvement. However, existing claim verification models perform poorly on the task no matter how good the retrieval performance is. The results also show that stance detection can be useful for evidence retrieval. Moreover, existing fact-checking datasets showed a potential in transfer learning to our task, however, further investigation using different datasets and setups is required.

Dialectal Arabic is the primary spoken language used by native Arabic speakers in daily communication. The rise of social media platforms has notably expanded its use as a written language. However, Arabic dialects do not have standard orthographies. This, combined with the inherent noise in user-generated content on social media, presents a major challenge to NLP applications dealing with Dialectal Arabic. In this paper, we explore and report on the task of CODAfication, which aims to normalize Dialectal Arabic into the Conventional Orthography for Dialectal Arabic (CODA). We work with a unique parallel corpus of multiple Arabic dialects focusing on five major city dialects. We benchmark newly developed pretrained sequence-to-sequence models on the task of CODAfication. We further show that using dialect identification information improves the performance across all dialects. We make our code, data, andpretrained models publicly available.

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Strategies for Arabic Readability Modeling
Juan Liberato | Bashar Alhafni | Muhamed Khalil | Nizar Habash

Automatic readability assessment is relevant to building NLP applications for education, content analysis, and accessibility. However, Arabic readability assessment is a challenging task due to Arabic’s morphological richness and limited readability resources. In this paper, we present a set of experimental results on Arabic readability assessment using a diverse range of approaches, from rule-based methods to Arabic pretrained language models. We report our results on a newly created corpus at different textual granularity levels (words and sentence fragments). Our results show that combining different techniques yields the best results, achieving an overall macro F1 score of 86.7 at the word level and 87.9 at the fragment level on a blind test set. We make our code, data, and pretrained models publicly available.

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AREEj: Arabic Relation Extraction with Evidence
Osama Mraikhat | Hadi Hamoud | Fadi Zaraket

Relational entity extraction is key in building knowledge graphs. A relational entity has a source, a tail and atype. In this paper, we consider Arabic text and introduce evidence enrichment which intuitivelyinforms models for better predictions. Relational evidence is an expression in the textthat explains how sources and targets relate. %It also provides hints from which models learn. This paper augments the existing relational extraction dataset with evidence annotation to its 2.9-million Arabic relations.We leverage the augmented dataset to build , a relation extraction with evidence model from Arabic documents. The evidence augmentation model we constructed to complete the dataset achieved .82 F1-score (.93 precision, .73 recall). The target outperformed SOTA mREBEL with .72 F1-score (.78 precision, .66 recall).

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Improving Language Models Trained on Translated Data with Continual Pre-Training and Dictionary Learning Analysis
Sabri Boughorbel | Md Rizwan Parvez | Majd Hawasly

Training LLMs in low resources languages usually utilizes machine translation (MT) data augmentation from English language. However, translation brings a number of challenges: there are large costs attached to translating and curating huge amounts of content with high-end machine translation solutions; the translated content carries over cultural biases; and if the translation is not faithful and accurate, the quality of the data degrades causing issues in the trained model. In this work, we investigate the role of translation and synthetic data in training language models. We translate TinyStories, a dataset of 2.2M short stories for 3-4 year old children, from English to Arabic using the open NLLB-3B MT model. We train a number of story generation models of size 1M-33M parameters using this data. We identify a number of quality and task-specific issues in the resulting models. To rectify these issues, we further pre-train the models with a small dataset of synthesized high-quality stories generated by a capable LLM in Arabic, representing 1% of the original training data. We show, using GPT-4 as a judge and dictionary learning analysis from mechanistic interpretability, that the suggested approach is a practical means to resolve some of the translation pitfalls. We illustrate the improvement through case studies of linguistic and cultural bias issues.

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A Context-Contrastive Inference Approach To Partial Diacritization
Muhammad ElNokrashy | Badr AlKhamissi

Diacritization plays a pivotal role for meaning disambiguation and improving readability in Arabic texts. Efforts have long focused on marking every eligible character (Full Diacritization). Overlooked in comparison, Partial Diacritzation (‘PD‘) is the selection of a subset of characters to be annotated to aid comprehension only where needed. Research has indicated that excessive diacritic marks can hinder skilled readers—reducing reading speed and accuracy. We conduct a behavioral experiment and show that partially marked text is often easier to read than fully marked text, and sometimes easier than plain text. In this light, we introduce Context-Contrastive Partial Diacritization (‘CCPD‘)—a novel approach to ‘PD‘ which integrates seamlessly with existing Arabic diacritization systems. ‘CCPD‘ processes each word twice, once with context and once without, and diacritizes only the characters with disparities between the two inferences. Further, we introduce novel indicators for measuring partial diacritization quality to help establish this as a machine learning task. Lastly, we introduce ‘TD2‘, a Transformer-variant of an established model which offers a markedly different performance profile on our proposed indicators compared to all other known systems.

This paper introduces Arabic Contrastive Language-Image Pre-training (AraCLIP), a model designed for Arabic image retrieval tasks, building upon the Contrastive Language-Image Pre-training (CLIP) architecture. AraCLIP leverages Knowledge Distillation to transfer cross-modal knowledge from English to Arabic, enhancing its ability to understand Arabic text and retrieve relevant images. Unlike existing multilingual models, AraCLIP is uniquely positioned to understand the intricacies of the Arabic language, including specific terms, cultural nuances, and contextual constructs. By leveraging the CLIP architecture as our foundation, we introduce a novel approach that seamlessly integrates textual and visual modalities, enabling AraCLIP to effectively retrieve images based on Arabic textual queries. We offer an online demonstration allowing users to input Arabic prompts and compare AraCLIP’s performance with state-of-the-art multilingual models. We conduct comprehensive experiments to evaluate AraCLIP’s performance across diverse datasets, including Arabic XTD-11, and Arabic Flicker 8k. Our results showcase AraCLIP’s superiority in image retrieval accuracy, demonstrating its effectiveness in handling Arabic queries. AraCLIP represents a significant advancement in cross-lingual image retrieval, offering promising applications in Arabic language processing and beyond.

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Large Language Models as Legal Translators of Arabic Legislatives: Does ChatGPT and Gemini Care for Context and Terminology?
Khadija ElFqih | Johanna Monti

Accurate translation of terminology and adaptation to in-context information is a pillar to high quality translation. Recently, there is a remarkable interest towards the use and the evaluation of Large Language Models (LLMs) particularly for Machine Translation tasks. Nevertheless, despite their recent advancement and ability to understand and generate human-like language, these LLMs are still far from perfect, especially in domain-specific scenarios, and need to be thoroughly investigated. This is particularly evident in automatically translating legal terminology from Arabic into English and French, where, beyond the inherent complexities of legal language and specialised translations, technical limitations of LLMs further hinder accurate generation of text. In this paper, we present a preliminary evaluation of two evolving LLMs, namely GPT-4 Generative Pre-trained Transformer and Gemini, as legal translators of Arabic legislatives to test their accuracy and the extent to which they care for context and terminology across two language pairs (AR→EN / AR→FR). The study targets the evaluation of Zero-Shot prompting for in-context and out-of-context scenarios of both models relying on a gold standard dataset, verified by professional translators who are also experts in the field. We evaluate the results applying the Multidimensional Quality Metrics to classify translation errors. Moreover, we also evaluate the general LLMs outputs to verify their correctness, consistency, and completeness.In general, our results show that the models are far from perfect and recall for more fine-tuning efforts using specialised terminological data in the legal domain from Arabic into English and French.

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Towards Zero-Shot Text-To-Speech for Arabic Dialects
Khai Doan | Abdul Waheed | Muhammad Abdul-Mageed

Zero-shot multi-speaker text-to-speech (ZS-TTS) systems have advanced for English, however, it still lags behind due to insufficient resources. We address this gap for Arabic, a language of more than 450 million native speakers, by first adapting a sizeable existing dataset to suit the needs of speech synthesis. Additionally, we employ a set of Arabic dialect identification models to explore the impact of pre-defined dialect labels on improving the ZS-TTS model in a multi-dialect setting. Subsequently, we fine-tune the XTTS model, an open-source architecture. We then evaluate our models on a dataset comprising 31 unseen speakers and an in-house dialectal dataset. Our automated and human evaluation results show convincing performance while capable of generating dialectal speech. Our study highlights significant potential for improvements in this emerging area of research in Arabic.

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Performance Analysis of Speech Encoders for Low-Resource SLU and ASR in Tunisian Dialect
Salima Mdhaffar | Haroun Elleuch | Fethi Bougares | Yannick Estève

Speech encoders pretrained through self-supervised learning (SSL) have demonstrated remarkable performance in various downstream tasks, including Spoken Language Understanding (SLU) and Automatic Speech Recognition (ASR). For instance, fine-tuning SSL models for such tasks has shown significant potential, leading to improvements in the SOTA performance across challenging datasets.In contrast to existing research, this paper contributes by comparing the effectiveness of SSL approaches in the context of (i) the low-resource Spoken Tunisian Arabic Dialect and (ii) its combination with a low-resource SLU and ASR scenario, where only a few semantic annotations are available for fine-tuning. We conducted experiments using many SSL speech encoders on the TARIC-SLU dataset. We used speech encoders that were pre-trained on either monolingual or multilingual speech data. Some of them have also been refined without in-domain nor Tunisian data through a multimodal supervised teacher-student learning. The study made in this paper yields numerous significant findings that we will discuss in the paper.

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Arabic Automatic Story Generation with Large Language Models
Ahmed El-Shangiti | Fakhraddin Alwajih | Muhammad Abdul-Mageed

Large language models (LLMs) have recently emerged as a powerful tool for a wide range of language generation tasks. Nevertheless, this progress has been slower in Arabic. In this work, we focus on the task of generating stories from LLMs. For our training, we use stories acquired through machine translation (MT) as well as GPT-4. For the MT data, we develop a careful pipeline that ensures we acquire high-quality stories. For our GPT-4 data, we introduce crafted prompts that allow us to generate data well-suited to the Arabic context in both Modern Standard Arabic (MSA) and two Arabic dialects (Egyptian and Moroccan). For example, we generate stories tailored to various Arab countries on a wide host of topics. Our manual evaluation shows that our model fine-tuned on these training datasets can generate coherent stories that adhere to our instructions. We also conduct an extensive automatic and human evaluation comparing our models against state-of-the-art proprietary and open-source models. Our datasets and models will be made publicly available at https://github.com/UBC-NLP/arastories.

Pre-trained Language Models (PLMs) are integral to many modern natural language processing (NLP) systems. Although multilingual models cover a wide range of languages, they often grapple with challenges like high inference costs and a lack of diverse non-English training data. Arabic-specific PLMs are trained predominantly on modern standard Arabic, which compromises their performance on regional dialects. To tackle this, we construct an Arabic dialectal corpus comprising 3.4M sentences gathered from social media platforms. We utilize this corpus to expand the vocabulary and retrain a BERT-based model from scratch. Named AlcLaM, our model was trained using only 13GB of text, which represents a fraction of the data used by existing models such as CAMeL, MARBERT, and ArBERT, compared to 7.8%%, and 21.3%, respectively. Remarkably, AlcLaM demonstrates superior performance on a variety of Arabic NLP tasks despite the limited training data. AlcLaM is available at: https://github.com/amurtadha/Alclam.

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Data Augmentation for Speech-Based Diacritic Restoration
Sara Shatnawi | Sawsan Alqahtani | Shady Shehata | Hanan Aldarmaki

This paper describes a data augmentation technique for boosting the performance of speech-based diacritic restoration. Our experiments demonstrate the utility of this appraoch, resulting in improved generalization of all models across different test sets. In addition, we describe the first multi-modal diacritic restoration model, utilizing both speech and text as input modalities. This type of model can be used to diacritize speech transcripts. Unlike previous work that relies on an external ASR model, the proposed model is far more compact and efficient. While the multi-modal framework does not surpass the ASR-based model for this task, it offers a promising approach for improving the efficiency of speech-based diacritization, with a potential for improvement using data augmentation and other methods.

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Out-of-Domain Dependency Parsing for Dialects of Arabic: A Case Study
Noor Abo Mokh | Daniel Dakota | Sandra Kübler

We study dependency parsing for four Arabic dialects (Gulf, Levantine, Egyptian, and Maghrebi). Since no syntactically annotated data exist for Arabic dialects, we train the parser on a Modern Standard Arabic (MSA) corpus, which creates an out-of-domain setting.We investigate methods to close the gap between the source (MSA) and target data (dialects), e.g., by training on syntactically similar sentences to the test data. For testing, we manually annotate a small data set from a dialectal corpus. We focus on parsing two linguistic phenomena, which are difficult to parse: Idafa and coordination. We find that we can improve results by adding in-domain MSA data while adding dialectal embeddings only results in minor improvements.

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Investigating Linguistic Features for Arabic NLI
Yasmeen Bassas | Sandra Kübler

Native Language Identification (NLI) is concerned with predicting the native language of an author writing in a second language. We investigate NLI for Arabic, with a focus on the types of linguistic information given that Arabic is morphologically rich. We use the Arabic Learner Corpus (ALC) foro training and testing along with a linear SVM. We explore lexical, morpho-syntactic, and syntactic features. Results show that the best single type of information is character n-grams ranging from 2 to 6. Using this model, we achieve an accuracy of 61.84%, thus outperforming previous results (Ionesco, 2015) by 11.74% even though we use an additional 2 L1s. However, when using prefix and suffix sequences, we reach an accuracy of 53.95%, showing that an approximation of unlexicalized features still reaches solid results.

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John vs. Ahmed: Debate-Induced Bias in Multilingual LLMs
Anastasiia Demidova | Hanin Atwany | Nour Rabih | Sanad Sha’ban | Muhammad Abdul-Mageed

Large language models (LLMs) play a crucial role in a wide range of real world applications. However, concerns about their safety and ethical implications are growing. While research on LLM safety is expanding, there is a noticeable gap in evaluating safety across multiple languages, especially in Arabic and Russian. We address this gap by exploring biases in LLMs across different languages and contexts, focusing on GPT-3.5 and Gemini. Through carefully designed argument-based prompts and scenarios in Arabic, English, and Russian, we examine biases in cultural, political, racial, religious, and gender domains. Our findings reveal biases in these domains. In particular, our investigation uncovers subtle biases where each model tends to present winners as those speaking the primary language the model is prompted with. Our study contributes to ongoing efforts to ensure justice and equality in LLM development and emphasizes the importance of further research towards responsible progress in this field.

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Qalam: A Multimodal LLM for Arabic Optical Character and Handwriting Recognition
Gagan Bhatia | El Moatez Billah Nagoudi | Fakhraddin Alwajih | Muhammad Abdul-Mageed

Arabic Optical Character Recognition (OCR) and Handwriting Recognition (HWR) pose unique challenges due to the cursive and context-sensitive nature of the Arabic script. This study introduces ***Qalam***, a novel foundation model designed for Arabic OCR and HWR, built on a SwinV2 encoder and RoBERTa decoder architecture. Our model significantly outperforms existing methods, achieving a Word Error Rate (WER) of just 0.80% in HWR tasks and 1.18% in OCR tasks. We train ***Qalam*** on a diverse dataset, including over 4.5 million images from Arabic manuscripts and a synthetic dataset comprising 60k image-text pairs. Notably, ***Qalam*** demonstrates exceptional handling of Arabic diacritics, a critical feature in Arabic scripts. Furthermore, it shows a remarkable ability to process high-resolution inputs, addressing a common limitation in current OCR systems. These advancements underscore ***Qalam***’s potential as a leading solution for Arabic script recognition, offering a significant leap in accuracy and efficiency.

The rapid advancements in Large Language Models (LLMs) have led to significant improvements in various natural language processing tasks. However, the evaluation of LLMs’ legal knowledge, particularly in non English languages such as Arabic, remains under-explored. To address this gap, we introduce ArabLegalEval, a multitask benchmark dataset for assessing the Arabic legal knowledge of LLMs. Inspired by the MMLU and LegalBench datasets, ArabLegalEval consists of multiple tasks sourced from Saudi legal documents and synthesized questions. In this work, we aim to analyze the capabilities required to solve legal problems in Arabic and benchmark the performance of state-of-the-art LLMs. We explore the impact of in-context learning on performance and investigate various evaluation methods. Additionally, we explore workflows for automatically generating questions with automatic validation to enhance the dataset’s quality. By releasing ArabLegalEval and our code, we hope to accelerate AI research in the Arabic Legal domain

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CATT: Character-based Arabic Tashkeel Transformer
Faris Alasmary | Orjuwan Zaafarani | Ahmad Ghannam

Tashkeel, or Arabic Text Diacritization (ATD), greatly enhances the comprehension of Arabic text by removing ambiguity and minimizing the risk of misinterpretations caused by its absence.It plays a crucial role in improving Arabic text processing, particularly in applications such as text-to-speech and machine translation.This paper introduces a new approach to training ATD models.First, we finetuned two transformers, encoder-only and encoder-decoder, that were initialized from a pretrained character-based BERT.Then, we applied the Noisy-Student approach to boost the performance of the best model.We evaluated our models alongside 11 commercial and open-source models using two manually labeled benchmark datasets: WikiNews and our CATT dataset.Our findings show that our top model surpasses all evaluated models by relative Diacritic Error Rates (DERs) of 30.83% and 35.21% on WikiNews and CATT, respectively, achieving state-of-the-art in ATD.In addition, we show that our model outperforms GPT-4-turbo on CATT dataset by a relative DER of 9.36%.We open-source our CATT models and benchmark dataset for the research community .

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Picking Up Where the Linguist Left Off: Mapping Morphology to Phonology through Learning the Residuals
Salam Khalifa | Abdelrahim Qaddoumi | Ellen Broselow | Owen Rambow

Learning morphophonological mappings between the spoken form of a language and its underlying morphological structures is crucial for enriching resources for morphologically rich languages like Arabic. In this work, we focus on Egyptian Arabic as our case study and explore the integration of linguistic knowledge with a neural transformer model. Our approach involves learning to correct the residual errors from hand-crafted rules to predict the spoken form from a given underlying morphological representation. We demonstrate that using a minimal set of rules, we can effectively recover errors even in very low-resource settings.

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On the Utility of Pretraining Language Models on Synthetic Data
Alcides Alcoba Inciarte | Sang Yun Kwon | El Moatez Billah Nagoudi | Muhammad Abdul-Mageed

Development of pre-trained language models has predominantly relied on large amounts of datasets. However, this dependence on abundant data has limited the applicability of these models in low-resource settings. In this work, we investigate the utility of exploiting synthetic datasets acquired from different sources to pre-train language models for Arabic. Namely, we leverage data derived based on four different methods: optical character recognition (OCR), automatic speech recognition (ASR), machine translation (MT), and generative language models. We use these datasets to pre-train models in three different architectures: encoder-only (BERTBase), encoder-decoder (T5), and decoder-only (GPT-2). We test the capabilities of resulting models on Arabic natural language understanding (NLU) tasks using the ORCA benchmark. Our results show that utilizing synthetic data can achieve performance comparable to, or even surpassing, those trained on gold data. For example, our model based on a GPT-2 architecture trained on a combined synthetic dataset surpasses the baseline model ARBERTv2. Overall, our models pre-trained on synthetic data demonstrate robust performance across various tasks. This highlights the potential of synthetic datasets in augmenting language model training in low-resource settings.

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Benchmarking LLaMA-3 on Arabic Language Generation Tasks
Md Tawkat Islam Khondaker | Numaan Naeem | Fatimah Khan | AbdelRahim Elmadany | Muhammad Abdul-Mageed

Open-sourced large language models (LLMs) have exhibited remarkable performance in a variety of NLP tasks, often catching up with the closed-sourced LLMs like ChatGPT. Among these open LLMs, LLaMA-3-70B has emerged as the most recent and the most prominent one. However, how LLaMA-3-70B would situate itself in multilingual settings, especially in a rich morphological language like Arabic, has yet to be explored. In this work, we focus to bridge this gap by evaluating LLaMA-3-70B on a diverse set of Arabic natural language generation (NLG) benchmarks. To the best of our knowledge, this is the first study that comprehensively evaluates LLaMA-3-70B on tasks related to Arabic natural language generation. Our study reveals that LLaMA-3-70B lags behind the closed LLMs like ChatGPT, both in modern standard Arabic (MSA) and dialectal Arabic (DA). We further compare the performance of LLaMA-3-70B with our smaller and dedicated finetuned Arabic models. We find that both LLaMA-3-70B and ChatGPT are outperformed by comparatively smaller dedicated Arabic models, indicating the scope for potential improvement with Arabic-focused LLMs.

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From Nile Sands to Digital Hands: Machine Translation of Coptic Texts
Muhammed Saeed | Asim Mohamed | Mukhtar Mohamed | Shady Shehata | Muhammad Abdul-Mageed

The Coptic language, rooted in the historical landscapes of Egypt, continues to serve as a vital liturgical medium for the Coptic Orthodox and Catholic Churches across Egypt, North Sudan, Libya, and the United States, with approximately ten million speakers worldwide. However, the scarcity of digital resources in Coptic has resulted in its exclusion from digital systems, thereby limiting its accessibility and preservation in modern technological contexts. Our research addresses this issue by developing the most extensive parallel Coptic-centered corpus to date. This corpus comprises over 8,000 parallel sentences between Arabic and Coptic, and more than 24,000 parallel sentences between English and Coptic. We have also developed the first neural machine translation system between Coptic, English, and Arabic. Lastly, we evaluate the capability of leading proprietary Large Language Models (LLMs) to translate to and from Coptic using a few-shot learning approach (in-context learning). Our code and data are available at https://github.com/UBC-NLP/copticmt.

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Event-Arguments Extraction Corpus and Modeling using BERT for Arabic
Alaa Aljabari | Lina Duaibes | Mustafa Jarrar | Mohammed Khalilia

Event-argument extraction is a challenging task, particularly in Arabic due to sparse linguistic resources. To fill this gap, we introduce the corpus (550k tokens) as an extension of Wojood, enriched with event-argument annotations. We used three types of event arguments: agent, location, and date, which we annotated as relation types. Our inter-annotator agreement evaluation resulted in 82.23% Kappa score and 87.2% F₁-score. Additionally, we propose a novel method for event relation extraction using BERT, in which we treat the task as text entailment. This method achieves an F₁-score of 94.01%.To further evaluate the generalization of our proposed method, we collected and annotated another out-of-domain corpus (about 80k tokens) called and used it as a second test set, on which our approach achieved promising results (83.59% F₁-score). Last but not least, we propose an end-to-end system for event-arguments extraction. This system is implemented as part of SinaTools, and both corpora are publicly available at https://sina.birzeit.edu/wojood

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Dallah: A Dialect-Aware Multimodal Large Language Model for Arabic
Fakhraddin Alwajih | Gagan Bhatia | Muhammad Abdul-Mageed

Recent advancements have significantly enhanced the capabilities of Multimodal Large Language Models (MLLMs) in generating and understanding image-to-text content. Despite these successes, progress is predominantly limited to English due to the scarcity of high-quality multimodal resources in other languages. This limitation impedes the development of competitive models in languages such as Arabic. To alleviate this situation, we introduce an efficient Arabic multimodal assistant, dubbed ***Dallah***, that utilizes an advanced language model based on LLaMA-2 to facilitate multimodal interactions. ***Dallah*** demonstrates state-of-the-art performance in Arabic MLLMs. Through fine-tuning six Arabic dialects, ***Dallah*** showcases its capability to handle complex dialectal interactions incorporating both textual and visual elements. The model excels in two benchmark tests: one evaluating its performance on Modern Standard Arabic (MSA) and another specifically designed to assess dialectal responses. Beyond its robust performance in multimodal interaction tasks, ***Dallah*** has the potential to pave the way for further development of dialect-aware Arabic MLLMs.

Automated Essay Scoring (AES) has emerged as a significant research problem within natural language processing, providing valuable support for educators in assessing student writing skills. In this paper, we introduce QAES, the first publicly available trait-specific annotations for Arabic AES, built on the Qatari Corpus of Argumentative Writing (QCAW). QAES includes a diverse collection of essays in Arabic, each of them annotated with holistic and trait-specific scores, including relevance, organization, vocabulary, style, development, mechanics, and grammar. In total, it comprises 195 Arabic essays (with lengths ranging from 239 to 806 words) across two distinct argumentative writing tasks. We benchmark our dataset against the state-of-the-art English baselines and a feature-based approach. In addition, we discuss the adopted guidelines and the challenges encountered during the annotation process. Finally, we provide insights into potential areas for improvement and future directions in Arabic AES research.

Text classification is of paramount importance in a wide range of applications, including information retrieval, extraction and sentiment analysis. The challenge of classifying and labelling text genres, especially in web-based corpora, has received considerable attention. The frequent absence of unambiguous genre information complicates the identification of text types. To address these issues, the Functional Text Dimensions (FTD) method has been introduced to provide a universal set of categories for text classification. This study presents the Arabic Functional Text Dimensions Corpus (AFTD Corpus), a carefully curated collection of documents for evaluating text classification in Arabic. The AFTD Corpus which we are making available to the community, consists of 3400 documents spanning 17 different class categories. Through a comprehensive evaluation using traditional machine learning and neural models, we assess the effectiveness of the FTD approach in the Arabic context. CAMeLBERT, a state-of-the-art model, achieved an impressive F1 score of 0.81 on our corpus. This research highlights the potential of the FTD method for improving text classification, especially for Arabic content, and underlines the importance of robust classification models in web applications.

This paper presents an overview of the Arabic Natural Language Understanding (ArabicNLU 2024) shared task, focusing on two subtasks: Word Sense Disambiguation (WSD) and Location Mention Disambiguation (LMD). The task aimed to evaluate the ability of automated systems to resolve word ambiguity and identify locations mentioned in Arabic text. We provided participants with novel datasets, including a sense-annotated corpus for WSD, called SALMA with approximately 34k annotated tokens, and the dataset with 3,893 annotations and 763 unique location mentions. These are challenging tasks. Out of the 38 registered teams, only three teams participated in the final evaluation phase, with the highest accuracy being 77.8% for WSD and 95.0% for LMD. The shared task not only facilitated the evaluation and comparison of different techniques, but also provided valuable insights and resources for the continued advancement of Arabic NLU technologies.

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Pirates at ArabicNLU2024: Enhancing Arabic Word Sense Disambiguation using Transformer-Based Approaches
Tasneem Wael | Eman Elrefai | Mohamed Makram | Sahar Selim | Ghada Khoriba

This paper presents a novel approach to Ara-bic Word Sense Disambiguation (WSD) lever-aging transformer-based models to tackle thecomplexities of the Arabic language. Utiliz-ing the SALMA dataset, we applied severaltechniques, including Sentence Transformerswith Siamese networks and the SetFit frame-work optimized for few-shot learning. Our ex-periments, structured around a robust evalua-tion framework, achieved a promising F1-scoreof up to 71%, securing second place in theArabicNLU 2024: The First Arabic NaturalLanguage Understanding Shared Task compe-tition. These results demonstrate the efficacyof our approach, especially in dealing with thechallenges posed by homophones, homographs,and the lack of diacritics in Arabic texts. Theproposed methods significantly outperformedtraditional WSD techniques, highlighting theirpotential to enhance the accuracy of Arabicnatural language processing applications.

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Upaya at ArabicNLU Shared-Task: Arabic Lexical Disambiguation using Large Language Models
Pawan Rajpoot | Ashvini Jindal | Ankur Parikh

Disambiguating a word’s intended meaning(sense) in a given context is important in Nat-ural Language Understanding (NLU). WSDaims to determine the correct sense of ambigu-ous words in context. At the same time, LMD(a WSD variation) focuses on disambiguatinglocation mention. Both tasks are vital in Nat-ural Language Processing (NLP) and informa-tion retrieval, as they help correctly interpretand extract information from text. Arabic ver-sion is further challenging because of its mor-phological richness, encompassing a complexinterplay of roots, stems, and affixes. This pa-per describes our solutions to both tasks, em-ploying Llama3 and Cohere-based models un-der Zero-Shot Learning and Re-Ranking, re-spectively. Both the shared tasks were partof the second Arabic Natural Language Pro-cessing Conference co-located with ACL 2024.Overall, we achieved 1st rank in the WSD task(accuracy 78%) and 2nd rank in the LMD task(MRR@1 0.59)

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rematchka at ArabicNLU2024: Evaluating Large Language Models for Arabic Word Sense and Location Sense Disambiguation
Reem Abdel-Salam

Natural Language Understanding (NLU) plays a vital role in Natural Language Processing (NLP) by facilitating semantic interactions. Arabic, with its diverse morphology, poses a challenge as it allows multiple interpretations of words, leading to potential misunderstandings and errors in NLP applications. In this paper, we present our approach for tackling Arabic NLU shared tasks for word sense disambiguation (WSD) and location mention disambiguation (LMD). Various approaches have been investigated from zero-shot inference of large language models (LLMs) to fine-tuning of pre-trained language models (PLMs). The best approach achieved 57% on WSD task ranking third place, while for the LMD task, our best systems achieved 94% MRR@1 ranking first place.

The expanding financial markets of the Arab world require sophisticated Arabic NLP tools. To address this need within the banking domain, the Arabic Financial NLP (AraFinNLP) shared task proposes two subtasks: (i) Multi-dialect Intent Detection and (ii) Cross-dialect Translation and Intent Preservation. This shared task uses the updated ArBanking77 dataset, which includes about 39k parallel queries in MSA and four dialects. Each query is labeled with one or more of a common 77 intents in the banking domain. These resources aim to foster the development of robust financial Arabic NLP, particularly in the areas of machine translation and banking chat-bots.A total of 45 unique teams registered for this shared task, with 11 of them actively participated in the test phase. Specifically, 11 teams participated in Subtask 1, while only 1 team participated in Subtask 2. The winning team of Subtask 1 achieved F1 score of 0.8773, and the only team submitted in Subtask 2 achieved a 1.667 BLEU score.

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SMASH at AraFinNLP2024: Benchmarking Arabic BERT Models on the Intent Detection
Youssef Al Hariri | Ibrahim Abu Farha

The recent growth in Middle Eastern stock markets has intensified the demand for specialized financial Arabic NLP models to serve this sector. This article presents the participation of Team SMASH of The University of Edinburgh in the Multi-dialect Intent Detection task (Subtask 1) of the Arabic Financial NLP (AraFinNLP) Shared Task 2024. The dataset used in the shared task is the ArBanking77 (Jarrar et al., 2023). We tackled this task as a classification problem and utilized several BERT and BART-based models to classify the queries efficiently. Our solution is based on implementing a two-step hierarchical classification model based on MARBERTv2. We fine-tuned the model by using the original queries. Our team, SMASH, was ranked 9th with a macro F1 score of 0.7866, indicating areas for further refinement and potential enhancement of the model’s performance.

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Fired_from_NLP at AraFinNLP 2024: Dual-Phase-BERT - A Fine-Tuned Transformer-Based Model for Multi-Dialect Intent Detection in The Financial Domain for The Arabic Language
Md. Sajid Alam Chowdhury | Mostak Chowdhury | Anik Shanto | Hasan Murad | Udoy Das

In the financial industry, identifying user intent from text inputs is crucial for various tasks such as automated trading, sentiment analysis, and customer support. One important component of natural language processing (NLP) is intent detection, which is significant to the finance sector. Limited studies have been conducted in the field of finance using languages with limited resources like Arabic, despite notable works being done in high-resource languages like English. To advance Arabic NLP in the financial domain, the organizer of AraFinNLP 2024 has arranged a shared task for detecting banking intents from the queries in various Arabic dialects, introducing a novel dataset named ArBanking77 which includes a collection of banking queries categorized into 77 distinct intents classes. To accomplish this task, we have presented a hierarchical approach called Dual-Phase-BERT in which the detection of dialects is carried out first, followed by the detection of banking intents. Using the provided ArBanking77 dataset, we have trained and evaluated several conventional machine learning, and deep learning models along with some cutting-edge transformer-based models. Among these models, our proposed Dual-Phase-BERT model has ranked 7^th out of all competitors, scoring 0.801 on the scale of F1-score on the test set.

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AlexuNLP24 at AraFinNLP2024: Multi-Dialect Arabic Intent Detection with Contrastive Learning in Banking Domain
Hossam Elkordi | Ahmed Sakr | Marwan Torki | Nagwa El-Makky

Arabic banking intent detection represents a challenging problem across multiple dialects. It imposes generalization difficulties due to the scarcity of Arabic language and its dialects resources compared to English. We propose a methodology that leverages contrastive training to overcome this limitation. We also augmented the data with several dialects using a translation model. Our experiments demonstrate the ability of our approach in capturing linguistic nuances across different Arabic dialects as well as accurately differentiating between banking intents across diverse linguistic landscapes. This would enhance multi-dialect banking services in the Arab world with limited Arabic language resources. Using our proposed method we achieved second place on subtask 1 leaderboard of the AraFinNLP2024 shared task with micro-F1 score of 0.8762 on the test split.

Intention detection is a crucial aspect of natural language understanding (NLU), focusing on identifying the primary objective underlying user input. In this work, we present a transformer-based method that excels in determining the intent of Arabic text within the banking domain. We explored several machine learning (ML), deep learning (DL), and transformer-based models on an Arabic banking dataset for intent detection. Our findings underscore the challenges that traditional ML and DL models face in understanding the nuances of various Arabic dialects, leading to subpar performance in intent detection. However, the transformer-based methods, designed to tackle such complexities, significantly outperformed the other models in classifying intent across different Arabic dialects. Notably, the AraBERTv2 model achieved the highest micro F1 score of 82.08% in ArBanking77 dataset, a testament to its effectiveness in this context. This achievement, which contributed to our work being ranked 5^th in the shared task, AraFinNLP2024, highlights the importance of developing models that can effectively handle the intricacies of Arabic language processing and intent detection.

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SENIT at AraFinNLP2024: trust your model or combine two
Abdelmomen Nasr | Moez Ben HajHmida

We describe our submitted system to the 2024 Shared Task on The Arabic Financial NLP (Malaysha et al., 2024). We tackled Subtask 1, namely Multi-dialect Intent Detection. We used state-of-the-art pretrained contextualized text representation models and fine-tuned them according to the downstream task at hand. We started by finetuning multilingual BERT and various Arabic variants, namely MARBERTV1, MARBERTV2, and CAMeLBERT. Then, we employed an ensembling technique to improve our classification performance combining MARBERTV2 and CAMeLBERT embeddings. The findings indicate that MARBERTV2 surpassed all the other models mentioned.

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BabelBot at AraFinNLP2024: Fine-tuning T5 for Multi-dialect Intent Detection with Synthetic Data and Model Ensembling
Murhaf Fares | Samia Touileb

This paper presents our results for the Arabic Financial NLP (AraFinNLP) shared task at the Second Arabic Natural Language Processing Conference (ArabicNLP 2024). We participated in the first sub-task, Multi-dialect Intent Detection, which focused on cross-dialect intent detection in the banking domain. Our approach involved fine-tuning an encoder-only T5 model, generating synthetic data, and model ensembling. Additionally, we conducted an in-depth analysis of the dataset, addressing annotation errors and problematic translations. Our model was ranked third in the shared task, achieving a F1-score of 0.871.

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MA at AraFinNLP2024: BERT-based Ensemble for Cross-dialectal Arabic Intent Detection
Asmaa Ramadan | Manar Amr | Marwan Torki | Nagwa El-Makky

Intent detection, also called intent classification or recognition, is an NLP technique to comprehend the purpose behind user utterances. This paper focuses on Multi-dialect Arabic intent detection in banking, utilizing the ArBanking77 dataset. Our method employs an ensemble of fine-tuned BERT-based models, integrating contrastive loss for training. To enhance generalization to diverse Arabic dialects, we augment the ArBanking77 dataset, originally in Modern Standard Arabic (MSA) and Palestinian, with additional dialects such as Egyptian, Moroccan, and Saudi, among others. Our approach achieved an F1-score of 0.8771, ranking first in subtask-1 of the AraFinNLP shared task 2024.

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BFCI at AraFinNLP2024: Support Vector Machines for Arabic Financial Text Classification
Nsrin Ashraf | Hamada Nayel | Mohammed Aldawsari | Hosahalli Shashirekha | Tarek Elshishtawy

In this paper, a description of the system submitted by BFCAI team to the AraFinNLP2024 shared task has been introduced. Our team participated in the first subtask, which aims at detecting the customer intents of cross-dialectal Arabic queries in the banking domain. Our system follows the common pipeline of text classification models using primary classification algorithms integrated with basic vectorization approach for feature extraction. Multi-layer Perceptron, Stochastic Gradient Descent and Support Vector Machines algorithms have been implemented and support vector machines outperformed all other algorithms with an f-score of 49%. Our submission’s result is appropriate compared to the simplicity of the proposed model’s structure.

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dzFinNlp at AraFinNLP: Improving Intent Detection in Financial Conversational Agents
Mohamed Lichouri | Khaled Lounnas | Amziane Zakaria

In this paper, we present our dzFinNlp team’s contribution for intent detection in financial conversational agents, as part of the AraFinNLP shared task. We experimented with various models and feature configurations, including traditional machine learning methods like LinearSVC with TF-IDF, as well as deep learning models like Long Short-Term Memory (LSTM). Additionally, we explored the use of transformer-based models for this task. Our experiments show promising results, with our best model achieving a micro F1-score of 93.02% and 67.21% on the ArBanking77 dataset, in the development and test sets, respectively.

We present an overview of the second edition of the ArAIEval shared task, organized as part of the ArabicNLP 2024 conference co-located with ACL 2024. In this edition, ArAIEval offers two tasks: (i) detection of propagandistic textual spans with persuasion techniques identification in tweets and news articles, and (ii) distinguishing between propagandistic and non-propagandistic memes. A total of 14 teams participated in the final evaluation phase, with 6 and 9 teams participating in Tasks 1 and 2, respectively. Finally, 11 teams submitted system description papers. Across both tasks, we observed that fine-tuning transformer models such as AraBERT was at the core of the majority of the participating systems. We provide a description of the task setup, including a description of the dataset construction and the evaluation setup. We further provide a brief overview of the participating systems. All datasets and evaluation scripts are released to the research community. We hope this will enable further research on these important tasks in Arabic.

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MemeMind at ArAIEval Shared Task: Generative Augmentation and Feature Fusion for Multimodal Propaganda Detection in Arabic Memes through Advanced Language and Vision Models
Uzair Shah | Md. Rafiul Biswas | Marco Agus | Mowafa Househ | Wajdi Zaghouani

Detecting propaganda in multimodal content, such as memes, is crucial for combating disinformation on social media. This paper presents a novel approach for the ArAIEval 2024 shared Task 2 on Multimodal Propagandistic Memes Classification, involving text, image, and multimodal classification of Arabic memes. For text classification (Task 2A), we fine-tune state-of-the-art Arabic language models and use ChatGPT4-generated synthetic text for data augmentation. For image classification (Task 2B), we fine-tune ResNet18, EfficientFormerV2, and ConvNeXt-tiny architectures with DALL-E-2-generated synthetic images. For multimodal classification (Task 2C), we combine ConvNeXt-tiny and BERT architectures in a fusion layer to enhance binary classification. Our results show significant performance improvements with data augmentation for text and image classification models and with the fusion layer for multimodal classification. We highlight challenges and opportunities for future research in multimodal propaganda detection in Arabic content, emphasizing the need for robust and adaptable models to combat disinformation.

This paper describes our participation in the ArAIEval Shared Task 2024, focusing on Task 2C, which challenges participants to detect propagandistic elements in multimodal Arabic memes. The challenge involves analyzing both the textual and visual components of memes to identify underlying propagandistic messages. Our approach integrates the capabilities of MARBERT and ResNet50, top-performing pre-trained models for text and image processing, respectively. Our system architecture combines these models through a fusion layer that integrates and processes the extracted features, creating a comprehensive representation that is more effective in detecting nuanced propaganda. Our proposed system achieved significant success, placing second with an F1 score of 0.7987.

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Mela at ArAIEval Shared Task: Propagandistic Techniques Detection in Arabic with a Multilingual Approach
Md Abdur Razzaq Riyadh | Sara Nabhani

This paper presents our system submitted for Task 1 of the ArAIEval Shared Task on Unimodal (Text) Propagandistic Technique Detection in Arabic. Task 1 involves identifying all employed propaganda techniques in a given text from a set of possible techniques or detecting that no propaganda technique is present. Additionally, the task requires identifying the specific spans of text where these techniques occur. We explored the capabilities of a multilingual BERT model for this task, focusing on the effectiveness of using outputs from different hidden layers within the model. By fine-tuning the multilingual BERT, we aimed to improve the model’s ability to recognize and locate various propaganda techniques. Our experiments showed that leveraging the hidden layers of the BERT model enhanced detection performance. Our system achieved competitive results, ranking second in the shared task, demonstrating that multilingual BERT models, combined with outputs from hidden layers, can effectively detect and identify spans of propaganda techniques in Arabic text.

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MODOS at ArAIEval Shared Task: Multimodal Propagandistic Memes Classification Using Weighted SAM, CLIP and ArabianGPT
Abdelhamid Haouhat | Hadda Cherroun | Slimane Bellaouar | Attia Nehar

Arabic social media platforms are increasingly using propaganda to deceive or influence people. This propaganda is often spread through multimodal content, such as memes. While substantial research has addressed the automatic detection of propaganda in English content, this paper presents the MODOS team’s participation in the Arabic Multimodal Propagandistic Memes Classification shared task. Our system deploys the Segment Anything Model (SAM) and CLIP for image representation and ARABIAN-GPT embeddings for text. Then, we employ LSTM encoders followed by a weighted fusion strategy to perform binary classification. Our system achieved competitive performance in distinguishing between propagandistic and non-propagandistic memes, scored 0.7290 macro F1, and ranked 6th among the participants.

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Nullpointer at ArAIEval Shared Task: Arabic Propagandist Technique Detection with Token-to-Word Mapping in Sequence Tagging
Abrar Abir | Kemal Oflazer

This paper investigates the optimization of propaganda technique detection in Arabic text, including tweets & news paragraphs, from ArAIEval shared task 1. Our approach involves fine-tuning the AraBERT v2 model with a neural network classifier for sequence tagging.Experimental results show relying on the first token of the word for technique prediction produces the best performance. In addition, incorporating genre information as a feature further enhances the model’s performance. Our system achieved a score of 25.41, placing us 4th on the leaderboard. Subsequent post-submission improvements further raised our score to 26.68.

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MemeMind at ArAIEval Shared Task: Spotting Persuasive Spans in Arabic Text with Persuasion Techniques Identification
Md. Rafiul Biswas | Zubair Shah | Wajdi Zaghouani

This paper focuses on detecting propagandistic spans and persuasion techniques in Arabic text from tweets and news paragraphs. Each entry in the dataset contains a text sample and corresponding labels that indicate the start and end positions of propaganda techniques within the text. Tokens falling within a labeled span were assigned ’B’ (Begin) or ’I’ (Inside) tags, ’O’, corresponding to the specific propaganda technique. Using attention masks, we created uniform lengths for each span and assigned BIO tags to each token based on the provided labels. Then, we used AraBERT-base pre-trained model for Arabic text tokenization and embeddings with a token classification layer to identify propaganda techniques. Our training process involves a two-phase fine-tuning approach. First, we train only the classification layer for a few epochs, followed by full model fine-tuning, updating all parameters. This methodology allows the model to adapt to the specific characteristics of the propaganda detection task while leveraging the knowledge captured by the pretrained AraBERT model. Our approach achieved an F1 score of 0.2774, securing the 3rd position in the leaderboard of Task 1.

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CLTL at ArAIEval Shared Task: Multimodal Propagandistic Memes Classification Using Transformer Models
Yeshan Wang | Ilia Markov

We present the CLTL system designed for the ArAIEval Shared Task 2024 on multimodal propagandistic memes classification in Arabic. The challenge was divided into three subtasks: identifying propagandistic content from textual modality of memes (subtask 2A), from visual modality of memes (subtask 2B), and in a multimodal scenario when both modalities are combined (subtask 2C). We explored various unimodal transformer models for Arabic language processing (subtask 2A), visual models for image processing (subtask 2B), and concatenated text and image embeddings using the Multilayer Perceptron fusion module for multimodal propagandistic memes classification (subtask 2C). Our system achieved 77.96% for subtask 2A, 71.04% for subtask 2B, and 79.80% for subtask 2C, ranking 2nd, 1st, and 3rd on the leaderboard.

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CUET_sstm at ArAIEval Shared Task: Unimodal (Text) Propagandistic Technique Detection Using Transformer-Based Model
Momtazul Labib | Samia Rahman | Hasan Murad | Udoy Das

In recent days, propaganda has started to influence public opinion increasingly as social media usage continues to grow. Our research has been part of the first challenge, Unimodal (Text) Propagandistic Technique Detection of ArAIEval shared task at the ArabicNLP 2024 conference, co-located with ACL 2024, identifying specific Arabic text spans using twenty-three propaganda techniques. We have augmented underrepresented techniques in the provided dataset using synonym replacement and have evaluated various machine learning (RF, SVM, MNB), deep learning (BiLSTM), and transformer-based models (bert-base-arabic, Marefa-NER, AraBERT) with transfer learning. Our comparative study has shown that the transformer model “bert-base-arabic” has outperformed other models. Evaluating the test set, it has achieved the micro-F1 score of 0.2995 which is the highest. This result has secured our team “CUET_sstm” first place among all participants in task 1 of the ArAIEval.

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AlexUNLP-MZ at ArAIEval Shared Task: Contrastive Learning, LLM Features Extraction and Multi-Objective Optimization for Arabic Multi-Modal Meme Propaganda Detection
Mohamed Zaytoon | Nagwa El-Makky | Marwan Torki

The rise of memes as a tool for spreading propaganda presents a significant challenge in the current digital environment. In this paper, we outline our work for the ArAIEval Shared Task2 in ArabicNLP 2024. This study introduces a method for identifying propaganda in Arabic memes using a multimodal system that combines textual and visual indicators to enhance the result. Our approach achieves the first place in text classification with Macro-F1 of 78.69%, the third place in image classification with Macro-F1 of 65.92%, and the first place in multimodal classification with Macro-F1 of 80.51%

Detecting propagandistic spans and identifying persuasion techniques are crucial for promoting informed decision-making, safeguarding democratic processes, and fostering a media environment characterized by integrity and transparency. Various machine learning (Logistic Regression, Random Forest, and Multinomial Naive Bayes), deep learning (CNN, CNN+LSTM, CNN+BiLSTM), and transformer-based (AraBERTv2, AraBERT-NER, CamelBERT, BERT-Base-Arabic) models were exploited to perform the task. The evaluation results indicate that CamelBERT achieved the highest micro-F1 score (24.09%), outperforming CNN+LSTM and AraBERTv2. The study found that most models struggle to detect propagandistic spans when multiple spans are present within the same article. Overall, the model’s performance secured a 6^th place ranking in the ArAIEval Shared Task-1.

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SussexAI at ArAIEval Shared Task: Mitigating Class Imbalance in Arabic Propaganda Detection
Mary Fouad | Julie Weeds

In this paper, we are exploring mitigating class imbalancein Arabic propaganda detection. Given amultigenre text which could be a news paragraphor a tweet, the objective is to identify the propagandatechnique employed in the text along withthe exact span(s) where each technique occurs. Weapproach this task as a sequence tagging task. Weutilise AraBERT for sequence classification andimplement data augmentation and random truncationmethods to mitigate the class imbalance withinthe dataset. We demonstrate the importance ofconsidering macro-F1 as well as micro-F1 whenevaluating classifier performance in this scenario.

We present an overview of the FIGNEWSshared task, organized as part of the Arabic-NLP 2024 conference co-located with ACL2024. The shared task addresses bias and pro-paganda annotation in multilingual news posts.We focus on the early days of the Israel War onGaza as a case study. The task aims to fostercollaboration in developing annotation guide-lines for subjective tasks by creating frame-works for analyzing diverse narratives high-lighting potential bias and propaganda. In aspirit of fostering and encouraging diversity,we address the problem from a multilingualperspective, namely within five languages: En-glish, French, Arabic, Hebrew, and Hindi. Atotal of 17 teams participated in two annota-tion subtasks: bias (16 teams) and propaganda(6 teams). The teams competed in four evalua-tion tracks: guidelines development, annotationquality, annotation quantity, and consistency.Collectively, the teams produced 129,800 datapoints. Key findings and implications for thefield are discussed.

This paper presents our team’s contribution to the FIGNEWS 2024 Shared Task, which involved annotating bias and propaganda in news coverage of the Israel-Palestine conflict. We developed comprehensive guidelines and employed a rigorous methodology to analyze 2,200 news posts from several official Facebook accounts of news websites in multiple languages. Our team, Narrative Navigators, achieved third place in both the Bias Guidelines and Bias Consistency tracks, demonstrating the effectiveness of our approach. We achieved an IAA Kappa score of 39.4 for bias annotation and 12.8 for propaganda detection. These findings and our performance underscore the need for enhanced media literacy and further research to counter the impact of biased and misleading information on public understanding of the conflict.

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DRAGON at FIGNEWS 2024 Shared Task: a Dedicated RAG for October 7th conflict News
Sadegh Jafari | Mohsen Mahmoodzadeh | Vanooshe Nazari | Razieh Bahmanyar | Kathryn Burrows

In this study, we present a novel approach to annotating bias and propaganda in social media data by leveraging topic modeling techniques. Utilizing the BERTopic tool, we performed topic modeling on the FIGNEWS Shared-task dataset, which initially comprised 13,500 samples. From this dataset, we identified 35 distinct topics and selected approximately 50 representative samples from each topic, resulting in a subset of 1,812 samples. These selected samples were meticulously annotated for bias and propaganda labels. Subsequently, we employed multiple methods like KNN, SVC, XGBoost, and RAG to develop a classifier capable of detecting bias and propaganda within social media content. Our approach demonstrates the efficacy of using topic modeling for efficient data subset selection and provides a robust foundation for improving the accuracy of bias and propaganda detection in large-scale social media datasets.

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LexiconLadies at FIGNEWS 2024 Shared Task: Identifying Keywords for Bias Annotation Guidelines of Facebook News Headlines on the Israel-Palestine 2023 War
Yousra El-Ghawi | Abeer Marzouk | Aya Khamis

News bias is difficult for humans to identify, but even more so for machines. This is largely due to the lack of linguistically appropriate annotated datasets suitable for use by classifier algorithms. The FIGNEWS Subtask 1: Bias Annotation involved classifying bias through manually annotated 1800 headlines from social media. Our proposed guidelines investigated which combinations of keywords available for classification, across sentence and token levels, may be used to detect possible bias in a conflict where neutrality is highly undesirable. Much of the headlines’ percentage required contextual knowledge of events to identify criteria that matched biased or targeted language. The final annotation guidelines paved the way for a theoretical system which uses keyword and hashtag significance to classify major instances of bias. Minor instances with bias undertones or clickbait may require advanced machine learning methods which learn context through scraping user engagements on social media.

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Uot1 at FIGNEWS 2024 Shared Task: Labeling News Bias
Abdusalam Nwesri | Mai Elbaabaa | Fatima Lashihar | Fatma Alalos

This paper outlines the University of Tripoli’s initiative in creating annotation guidelines to detect bias in news articles concerning the Palestinian-Israeli conflict. Our team participated in the Framing of Israeli Gaza News Media Narrative (FIGNEWS 2024) shared task. We developed annotation guidelines to label bias in news articles. Using those guidelines we managed to annotate 3,900 articles with the aid of our custom-developed annotation tool. Among 16 participating teams, we scored 48.7 on the macro F1 measure in the quality track in which we ranked 4th. In the centrality track we were ranked at the 6th position using the macro F1 avg measure, however, we achieved the 4th best kappa coefficient. Our bias annotation guidelines was ranked in the 9th position.

In this paper, we present our methodology and findings from participating in the FIGNEWS 2024 shared task on annotating news fragments on the Gaza-Israel war for bias and propaganda detection. The task aimed to refine the FIGNEWS 2024 annotation guidelines and to contribute to the creation of a comprehensive dataset to advance research in this field. Our team employed a multi-faceted approach to ensure high accuracy in data annotations. Our results highlight key challenges in detecting bias and propaganda, such as the need for more comprehensive guidelines. Our team ranked first in all tracks for propaganda annotation. For Bias, the team stood in first place for the Guidelines and IAA tracks, and in second place for the Quantity and Consistency tracks.

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Bias Bluff Busters at FIGNEWS 2024 Shared Task: Developing Guidelines to Make Bias Conscious
Jasmin Heierli | Silvia Pareti | Serena Pareti | Tatiana Lando

This paper details our participation in the FIGNEWS-2024 shared task on bias and propaganda annotation in Gaza conflict news. Our objectives were to develop robust guidelines and annotate a substantial dataset to enhance bias detection. We iteratively refined our guidelines and used examples for clarity. Key findings include the challenges in achieving high inter-annotator agreement and the importance of annotator awareness of their own biases. We also explored the integration of ChatGPT as an annotator to support consistency. This paper contributes to the field by providing detailed annotation guidelines, and offering insights into the subjectivity of bias annotation.

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Ceasefire at FIGNEWS 2024 Shared Task: Automated Detection and Annotation of Media Bias Using Large Language Models
Noor Sadiah | Sara Al-Emadi | Sumaya Rahman

In this paper, we present our approach for FIGNEWS Subtask 1, which focuses on detecting bias in news media narratives about the Israel war on Gaza. We used a Large Language Model (LLM) and prompt engineering, using GPT-3.5 Turbo API, to create a model that automatically flags biased news media content with 99% accuracy. This approach provides Natural Language Processing (NLP) researchers with a robust and effective solution for automating bias detection in news media narratives using supervised learning algorithms. Additionally, this paper provides a detailed analysis of the labeled content, offering valuable insights into media bias in conflict reporting. Our work advances automated content analysis and enhances understanding of media bias.

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Sahara Pioneers at FIGNEWS 2024 Shared Task: Data Annotation Guidelines for Propaganda Detection in News Items
Marwa Solla | Hassan Ebrahem | Alya Issa | Harmain Harmain | Abdusalam Nwesri

In today’s digital age, the spread of propaganda through news channels has become a pressing concern. To address this issue, the research community has organized a shared task on detecting propaganda in news posts. This paper aims to present the work carried out at the University of Tripoli for the development and implementation of data annotation guidelines by a team of five annotators. The guidelines were used to annotate 2600 news articles. Each article is labeled as “propaganda”, “Not propaganda”, “Not Applicable”, or “Not clear”. The shared task results put our efforts in the third position among 6 participating teams in the consistency track.

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BiasGanda at FIGNEWS 2024 Shared Task: A Quest to Uncover Biased Views in News Coverage
Al Manar Al Wardi | Blqees Al Busaidi | Malath Al-Sibani | Hiba Salim Muhammad Al-Siyabi | Najma Al Zidjaly

In this study, we aimed to identify biased language in a dataset provided by the FIGNEWS 2024 committee on the Gaza-Israel war. We classified entries into seven categories: Unbiased, Biased against Palestine, Biased against Israel, Biased against Others, Biased against both Palestine and Israel, Unclear, and Not Applicable. Our team reviewed the literature to develop a codebook of terminologies and definitions. By coding each example, we sought to detect language tendencies used by media outlets when reporting on the same event. The primary finding was that most examples were classified as “Biased against Palestine,” as all examined language data used one-sided terms to describe the October 7 event. The least used category was “Not Applicable,” reserved for irrelevant examples or those lacking context. It is recommended to use neutral and balanced language when reporting volatile political news.

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The CyberEquity Lab at FIGNEWS 2024 Shared Task: Annotating a Corpus of Facebook Posts to Label Bias and Propaganda in Gaza-Israel War Coverage in Five Languages
Mohammed Helal | Radi Jarrar | Mohammed Alkhanafseh | Abdallah Karakra | Ruba Awadallah

This paper presents The_CyberEquity_Lab team’s participation in the FIGNEWS 2024 Shared Task (Zaghouani, et al., 2024). The task is to annotate a corpus of Facebook posts into bias and propaganda in covering the Gaza-Israel war. The posts represent news articles written in five different languages. The paper presents the guidelines of annotation that the team has adhered in identifying both bias and propaganda in coverage of this continuous conflict.

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BSC-LANGTECH at FIGNEWS 2024 Shared Task: Exploring Semi-Automatic Bias Annotation using Frame Analysis
Valle Ruiz-Fernández | José Saiz | Aitor Gonzalez-Agirre

This paper introduces the methodology of BSC-LANGTECH team for the FIGNEWS 2024 Shared Task on News Media Narratives. Following the bias annotation subtask, we apply the theory and methods of framing analysis to develop guidelines to annotate bias in the corpus provided by the task organizators. The manual annotation of a subset, with which a moderate IAA agreement has been achieved, is further used in Deep Learning techniques to explore automatic annotation and test the reliability of our framework.

In this paper we report the development of our annotation methodology for the shared task FIGNEWS 2024. The objective of the shared task is to look into the layers of bias in how the war on Gaza is represented in media narrative. Our methodology follows the prescriptive paradigm, in which guidelines are detailed and refined through an iterative process in which edge cases are discussed and converged. Our IAA score (Krippendorff’s 𝛼) is 0.420, highlighting the challenging and subjective nature of the task. Our results show that 52% of posts were unbiased, 42% biased against Palestine, 5% biased against Israel, and 3% biased against both. 16% were unclear or not applicable.

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Sina at FigNews 2024: Multilingual Datasets Annotated with Bias and Propaganda.
Lina Duaibes | Areej Jaber | Mustafa Jarrar | Ahmad Qadi | Mais Qandeel

The proliferation of bias and propaganda onsocial media is an increasingly significant concern,leading to the development of techniquesfor automatic detection. This article presents amultilingual corpus of 12, 000 Facebook postsfully annotated for bias and propaganda. Thecorpus was created as part of the FigNews2024 Shared Task on News Media Narrativesfor framing the Israeli War on Gaza. It coversvarious events during the War from October7, 2023 to January 31, 2024. The corpuscomprises 12, 000 posts in five languages (Arabic,Hebrew, English, French, and Hindi), with2, 400 posts for each language. The annotationprocess involved 10 graduate students specializingin Law. The Inter-Annotator Agreement(IAA) was used to evaluate the annotationsof the corpus, with an average IAA of 80.8%for bias and 70.15% for propaganda annotations.Our team was ranked among the bestperformingteams in both Bias and Propagandasubtasks. The corpus is open-source and availableat https://sina.birzeit.edu/fada

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SQUad at FIGNEWS 2024 Shared Task: Unmasking Bias in Social Media Through Data Analysis and Annotation
Asmahan Al-Mamari | Fatma Al-Farsi | Najma Zidjaly

This paper is a part of the FIGNEWS 2024 Datathon Shared Task and it aims to investigate bias and double standards in media coverage of the Gaza-Israel 2023-2024 conflict through a comprehensive analysis of news articles. The methodology integrated both manual labeling as well as the application of a natural language processing (NLP) tool, which is the Facebook/BART-large-MNLI model. The annotation process involved categorizing the dataset based on identified biases, following a set of guidelines in which categories of bias were defined by the team. The findings revealed that most of the media texts provided for analysis included bias against Palestine, whether it was through the use of biased vocabulary or even tone. It was also found that texts written in Hebrew contained the most bias against Palestine. In addition, when comparing annotations done by AAI-1 and AAI-2, the results turned out to be very similar, which might be mainly due to the clear annotation guidelines set by the annotators themselves. Thus, we recommend the use of clear guidelines to facilitate the process of annotation by future researchers.

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JusticeLeague at FIGNEWS 2024 Shared Task: Innovations in Bias Annotation
Amr Saleh | Huda Mohamed | Hager Sayed

In response to the evolving media representation of the Gaza-Israel conflict, this study aims to categorize news articles based on their bias towards specific entities. Our primary objective is to annotate news articles with labels that indicate their bias: “Unbiased”, “Biased against Palestine”, “Biased against Israel”, “Biased against both Palestine and Israel”, “Biased against others”, “Unclear”, or “Not Applicable”.The methodology involves a detailed annotation process where each article is carefully reviewed and labeled according to predefined guidelines. For instance, an article reporting factual events without derogatory language is labeled as “Unbiased”, while one using inflammatory language against Palestinians is marked as “Biased against Palestine”.Key findings include the identification of various degrees of bias in news articles, highlighting the importance of critical analysis in media consumption. This research contributes to the broader effort of understanding media bias and promoting unbiased journalism. Tools such as Google Drive and Google Sheets facilitated the annotation process, enabling efficient collaboration and data management among the annotators.Our work also includes comprehensive guidelines and examples to ensure consistent annotation, enhancing the reliability of the data.

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Eagles at FIGNEWS 2024 Shared Task: A Context-informed Prescriptive Approach to Bias Detection in Contentious News Narratives
Amanda Chan | Mai A.Baddar | Sofien Baazaoui

This research paper presents an in-depth examination of bias identification in media content related to the Israel-Palestine war. Focusing on the annotation guidelines and process developed by our team of researchers, the document outlines a systematic approach to discerning bias in articles. Through meticulous analysis, key indicators of bias such as emotive language, weasel words, and loaded comparisons are identified and discussed. The paper also explores the delineation between facts and opinions, emphasizing the importance of maintaining objectivity in annotation. Ethical considerations, including the handling of sensitive data and the promotion of multipartiality among annotators, are carefully addressed. The annotation guidelines also include other ethical considerations such as identifying rumors, false information, exercising prudence and selective quotations. The research paper offers insights into the annotation experience, highlighting common mistakes and providing valuable guidelines for future research in bias identification. By providing a comprehensive framework for evaluating bias in media coverage of the Israel-Palestine war, this study contributes to a deeper understanding of the complexities inherent in media discourse surrounding contentious geopolitical issues.

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The Guidelines Specialists at FIGNEWS 2024 Shared Task: An annotation guideline to Unravel Bias in News Media Narratives Using a Linguistic Approach
Ghizlane Bourahouat | Samar Amer

This article presents the participation of “The Guideline Specialists” in the FIGNEWS 2024 Shared Task, which aims to unravel bias and propaganda in news media narratives surrounding the Gaza-Israel 2023-2024 war. Leveraging innovative annotation methodologies and drawing on a diverse team of annotators, our approach focuses on meticulously annotating news articles using a linguistic approach to uncover the intricate nuances of bias. By incorporating detailed examples and drawing on related work that show how language structure represented in the use of passive voice or the use of nominalization and the choice of vocabulary carry bias, our findings provide valuable insights into the representation of the Gaza-Israel conflict across various languages and cultures. The guideline we developed detected the bias against Gaza, against Israel and others by setting keywords that are based on linguistic background tested by the AntConc concordance tool. The result was an annotation guideline that have a solid base. Through this collaborative effort, we developed a guideline that contributes to fostering a deeper understanding of media narratives during one of the most critical moments in recent history.

This paper outlines the KSAA-CAD shared task, highlighting the Contemporary Arabic Language Dictionary within the scenario of developing a Reverse Dictionary (RD) system and enhancing Word Sense Disambiguation (WSD) capabilities. The first KSAA-RD (Al-Matham et al., 2023) highlighted significant gaps in the domain of RDs, which are designed to retrieve words by their meanings or definitions. This shared task comprises two tasks: RD and WSD. The RD task focuses on identifying word embeddings that most accurately match a given definition, termed a “gloss,” in Arabic. Conversely, the WSD task involves determining the specific meaning of a word in context, particularly when the word has multiple meanings. The winning team achieved the highest-ranking score of 0.0644 in RD using Electra embeddings. In this paper, we describe the methods employed by the participating teams and provide insights into the future direction of KSAA-CAD.

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Cher at KSAA-CAD 2024: Compressing Words and Definitions into the Same Space for Arabic Reverse Dictionary
Pinzhen Chen | Zheng Zhao | Shun Shao

We present Team Cher’s submission to the ArabicNLP 2024 KSAA-CAD shared task on the reverse dictionary for Arabic—the retrieval of words using definitions as a query. Our approach is based on a multi-task learning framework that jointly learns reverse dictionary, definition generation, and reconstruction tasks. This work explores different tokenization strategies and compares retrieval performance for each embedding architecture. Evaluation using the KSAA-CAD benchmark demonstrates the effectiveness of our multi-task approach and provides insights into the reverse dictionary task for Arabic. It is worth highlighting that we achieve strong performance without using any external resources in addition to the provided training data.

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MISSION at KSAA-CAD 2024: AraT5 with Arabic Reverse Dictionary
Thamer Alharbi

This research paper presents our approach for the KSAA-CAD 2024 competition, focusing on Arabic Reverse Dictionary (RD) task (Alshammari et al., 2024). Leveraging the functionalities of the Arabic Reverse Dictionary, our system allows users to input glosses and retrieve corresponding words. We provide all associated notebooks and developed models on GitHub and Hugging face, respectively. Our task entails working with a dataset comprising dictionary data and word embedding vectors, utilizing three different architectures of contextualized word embeddings: AraELECTRA, AraBERTv2, and camelBERT-MSA. We fine-tune the AraT5v2-base-1024 model for predicting each embedding, considering various hyperparameters for training and validation. Evaluation metrics include ranking accuracy, mean squared error (MSE), and cosine similarity. The results demonstrate the effectiveness of our approach on both development and test datasets, showcasing promising performance across different embedding types.

Semantic search tasks have grown extremely fast following the advancements in large language models, including the Reverse Dictionary and Word Sense Disambiguation in Arabic. This paper describes our participation in the Contemporary Arabic Dictionary Shared Task. We propose two models that achieved first place in both tasks. We conducted comprehensive experiments on the latest five multilingual sentence transformers and the Arabic BERT model for semantic embedding extraction. We achieved a ranking score of 0.06 for the reverse dictionary task, which is double than last year’s winner. We had an accuracy score of 0.268 for the Word Sense Disambiguation task.

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Baleegh at KSAA-CAD 2024: Towards Enhancing Arabic Reverse Dictionaries
Mais Alheraki | Souham Meshoul

The domain of reverse dictionaries (RDs), while advancing in languages like English and Chinese, remains underdeveloped for Arabic. This study attempts to explore a data-driven approach to enhance word retrieval processes in Arabic RDs. The research focuses on the ArabicNLP 2024 Shared Task, named KSAA-CAD, which provides a dictionary dataset of 39,214 word-gloss pairs, each with a corresponding target word embedding. The proposed solution aims to surpass the baseline performance by employing SOTA deep learning models and innovative data expansion techniques. The methodology involves enriching the dataset with contextually relevant examples, training a T5 model to align the words to their glosses in the space, and evaluating the results on the shared task metrics. We find that our model is closely aligned with the baseline performance on bertseg and bertmsa targets, however does not perform well on electra target, suggesting the need for further exploration.

We describe the findings of the fifth Nuanced Arabic Dialect Identification Shared Task (NADI 2024). NADI’s objective is to help advance SoTA Arabic NLP by providing guidance, datasets, modeling opportunities, and standardized evaluation conditions that allow researchers to collaboratively compete on prespecified tasks. NADI 2024 targeted both dialect identification cast as a multi-label task (Subtask 1), identification of the Arabic level of dialectness (Subtask 2), and dialect-to-MSA machine translation (Subtask 3). A total of 51 unique teams registered for the shared task, of whom 12 teams have participated (with 76 valid submissions during the test phase). Among these, three teams participated in Subtask 1, three in Subtask 2, and eight in Subtask 3. The winning teams achieved 50.57 F1 on Subtask 1, 0.1403 RMSE for Subtask 2, and 20.44 BLEU in Subtask 3, respectively. Results show that Arabic dialect processing tasks such as dialect identification and machine translation remain challenging. We describe the methods employed by the participating teams and briefly offer an outlook for NADI.

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Arabic Train at NADI 2024 shared task: LLMs’ Ability to Translate Arabic Dialects into Modern Standard Arabic
Anastasiia Demidova | Hanin Atwany | Nour Rabih | Sanad Sha’ban

Navigating the intricacies of machine translation (MT) involves tackling the nuanced disparities between Arabic dialects and Modern Standard Arabic (MSA), presenting a formidable obstacle. In this study, we delve into Subtask 3 of the NADI shared task (CITATION), focusing on the translation of sentences from four distinct Arabic dialects into MSA. Our investigation explores the efficacy of various models, including Jais, NLLB, GPT-3.5, and GPT-4, in this dialect-to-MSA translation endeavor. Our findings reveal that Jais surpasses all other models, boasting an average BLEU score of 19.48 in the combination of zero- and few-shot setting, whereas NLLB exhibits the least favorable performance, garnering a BLEU score of 8.77.

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AlexUNLP-STM at NADI 2024 shared task: Quantifying the Arabic Dialect Spectrum with Contrastive Learning, Weighted Sampling, and BERT-based Regression Ensemble
Abdelrahman Sakr | Marwan Torki | Nagwa El-Makky

Recognizing the nuanced spectrum of dialectness in Arabic text poses a significant challenge for natural language processing (NLP) tasks. Traditional dialect identification (DI) methods treat the task as binary, overlooking the continuum of dialect variation present in Arabic speech and text. In this paper, we describe our submission to the NADI shared Task of ArabicNLP 2024. We participated in Subtask 2 - ALDi Estimation, which focuses on estimating the Arabic Level of Dialectness (ALDi) for Arabic text, indicating how much it deviates from Modern Standard Arabic (MSA) on a scale from 0 to 1, where 0 means MSA and 1 means high divergence from MSA. We explore diverse training approaches, including contrastive learning, applying a random weighted sampler along with fine-tuning a regression task based on the AraBERT model, after adding a linear and non-linear layer on top of its pooled output. Finally, performing a brute force ensemble strategy increases the performance of our system. Our proposed solution achieved a Root Mean Squared Error (RMSE) of 0.1406, ranking second on the leaderboard.

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NLP_DI at NADI 2024 shared task: Multi-label Arabic Dialect Classifications with an Unsupervised Cross-Encoder
Vani Kanjirangat | Tanja Samardzic | Ljiljana Dolamic | Fabio Rinaldi

We report the approaches submitted to the NADI 2024 Subtask 1: Multi-label country-level Dialect Identification (MLDID). The core part was to adapt the information from multi-class data for a multi-label dialect classification task. We experimented with supervised and unsupervised strategies to tackle the task in this challenging setting. Under the supervised setup, we used the model trained using NADI 2023 data and devised approaches to convert the multi-class predictions to multi-label by using information from the confusion matrix or using calibrated probabilities. Under unsupervised settings, we used the Arabic-based sentence encoders and multilingual cross-encoders to retrieve similar samples from the training set, considering each test input as a query. The associated labels are then assigned to the input query. We also tried different variations, such as co-occurring dialects derived from the provided development set. We obtained the best validation performance of 48.5% F-score using one of the variations with an unsupervised approach and the same approach yielded the best test result of 43.27% (Ranked 2).

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ASOS at NADI 2024 shared task: Bridging Dialectness Estimation and MSA Machine Translation for Arabic Language Enhancement
Omer Nacar | Serry Sibaee | Abdullah Alharbi | Lahouari Ghouti | Anis Koubaa

This study undertakes a comprehensive investigation of transformer-based models to advance Arabic language processing, focusing on two pivotal aspects: the estimation of Arabic Level of Dialectness and dialectal sentence-level machine translation into Modern Standard Arabic. We conducted various evaluations of different sentence transformers across a proposed regression model, showing that the MARBERT transformer-based proposed regression model achieved the best root mean square error of 0.1403 for Arabic Level of Dialectness estimation. In parallel, we developed bi-directional translation models between Modern Standard Arabic and four specific Arabic dialects—Egyptian, Emirati, Jordanian, and Palestinian—by fine-tuning and evaluating different sequence-to-sequence transformers. This approach significantly improved translation quality, achieving a BLEU score of 0.1713. We also enhanced our evaluation capabilities by integrating MSA predictions from the machine translation model into our Arabic Level of Dialectness estimation framework, forming a comprehensive pipeline that not only demonstrates the effectiveness of our methodologies but also establishes a new benchmark in the deployment of advanced Arabic NLP technologies.

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dzNLP at NADI 2024 Shared Task: Multi-Classifier Ensemble with Weighted Voting and TF-IDF Features
Mohamed Lichouri | Khaled Lounnas | Zahaf Nadjib | Rabiai Ayoub

This paper presents the contribution of our dzNLP team to the NADI 2024 shared task, specifically in Subtask 1 - Multi-label Country-level Dialect Identification (MLDID) (Closed Track). We explored various configurations to address the challenge: in Experiment 1, we utilized a union of n-gram analyzers (word, character, character with word boundaries) with different n-gram values; in Experiment 2, we combined a weighted union of Term Frequency-Inverse Document Frequency (TF-IDF) features with various weights; and in Experiment 3, we implemented a weighted major voting scheme using three classifiers: Linear Support Vector Classifier (LSVC), Random Forest (RF), and K-Nearest Neighbors (KNN).Our approach, despite its simplicity and reliance on traditional machine learning techniques, demonstrated competitive performance in terms of accuracy and precision. Notably, we achieved the highest precision score of 63.22% among the participating teams. However, our overall F1 score was approximately 21%, significantly impacted by a low recall rate of 12.87%. This indicates that while our models were highly precise, they struggled to recall a broad range of dialect labels, highlighting a critical area for improvement in handling diverse dialectal variations.

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ELYADATA at NADI 2024 shared task: Arabic Dialect Identification with Similarity-Induced Mono-to-Multi Label Transformation.
Amira Karoui | Farah Gharbi | Rami Kammoun | Imen Laouirine | Fethi Bougares

This paper describes our submissions to the Multi-label Country-level Dialect Identification subtask of the NADI2024 shared task, organized during the second edition of the ArabicNLP conference. Our submission is based on the ensemble of fine-tuned BERT-based models, after implementing the Similarity-Induced Mono-to-Multi Label Transformation (SIMMT) on the input data. Our submission ranked first with a Macro-Average (MA) F1 score of 50.57%.

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Alson at NADI 2024 shared task: Alson - A fine-tuned model for Arabic Dialect Translation
Manan AlMusallam | Samar Ahmad

DA-MSA Machine Translation is a recentchallenge due to the multitude of Arabic dialects and their variations. In this paper, we present our results within the context of Subtask 3 of the NADI-2024 Shared Task(Abdul-Mageed et al., 2024) that is DA-MSA Machine Translation . We utilized the DIALECTS008MSA MADAR corpus (Bouamor et al., 2018),the Emi-NADI corpus for the Emirati dialect (Khered et al., 2023), and we augmented thePalestinian and Jordanian datasets based onNADI 2021. Our approach involves develop013ing sentence-level machine translations fromPalestinian, Jordanian, Emirati, and Egyptiandialects to Modern Standard Arabic (MSA).To016 address this challenge, we fine-tuned models such as (Nagoudi et al., 2022)AraT5v2-msa-small, AraT5v2-msa-base, and (Elmadanyet al., 2023)AraT5v2-base-1024 to comparetheir performance. Among these, the AraT5v2-base-1024 model achieved the best accuracy, with a BLEU score of 0.1650 on the develop023ment set and 0.1746 on the test set.

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CUFE at NADI 2024 shared task: Fine-Tuning Llama-3 To Translate From Arabic Dialects To Modern Standard Arabic
Michael Ibrahim

LLMs such as GPT-4 and LLaMA excel in multiple natural language processing tasks, however, LLMs face challenges in delivering satisfactory performance on low-resource languages due to limited availability of training data. In this paper, LLaMA-3 with 8 Billion parameters is finetuned to translate among Egyptian, Emirati, Jordanian, Palestinian Arabic dialects, and Modern Standard Arabic (MSA). In the NADI 2024 Task on DA-MSA Machine Translation, the proposed method achieved a BLEU score of 21.44 when it was fine-tuned on thedevelopment dataset of the NADI 2024 Task on DA-MSA and a BLEU score of 16.09 when trained when it was fine-tuned using the OSACT dataset.

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StanceEval 2024: The First Arabic Stance Detection Shared Task
Nora Alturayeif | Hamzah Luqman | Zaid Alyafeai | Asma Yamani

Recently, there has been a growing interest in analyzing user-generated text to understand opinions expressed on social media. In NLP, this task is known as stance detection, where the goal is to predict whether the writer is in favor, against, or has no opinion on a given topic. Stance detection is crucial for applications such as sentiment analysis, opinion mining, and social media monitoring, as it helps in capturing the nuanced perspectives of users on various subjects. As part of the ArabicNLP 2024 program, we organized the first shared task on Arabic Stance Detection, StanceEval 2024. This initiative aimed to foster advancements in stance detection for the Arabic language, a relatively underrepresented area in Arabic NLP research. This overview paper provides a detailed description of the shared task, covering the dataset, the methodologies used by various teams, and a summary of the results from all participants. We received 28 unique team registrations, and during the testing phase, 16 teams submitted valid entries. The highest classification F-score obtained was 84.38.

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Team_Zero at StanceEval2024: Frozen PLMs for Arabic Stance Detection
Omar Galal | Abdelrahman Kaseb

This research explores the effectiveness of using pre-trained language models (PLMs) as feature extractors for Arabic stance detection on social media, focusing on topics like women empowerment, COVID-19 vaccination, and digital transformation. By leveraging sentence transformers to extract embeddings and incorporating aggregation architectures on top of BERT, we aim to achieve high performance without the computational expense of fine-tuning. Our approach demonstrates significant resource and time savings while maintaining competitive performance, scoring an F1-score of 78.62 on the test set. This study highlights the potential of PLMs in enhancing stance detection in Arabic social media analysis, offering a resource-efficient alternative to traditional fine-tuning methods.

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ANLP RG at StanceEval2024: Comparative Evaluation of Stance, Sentiment and Sarcasm Detection
Mezghani Amal | Rahma Boujelbane | Mariem Ellouze

As part of our study, we worked on three tasks:stance detection, sarcasm detection and senti-ment analysis using fine-tuning techniques onBERT-based models. Fine-tuning parameterswere carefully adjusted over multiple iterationsto maximize model performance. The threetasks are essential in the field of natural lan-guage processing (NLP) and present uniquechallenges. Stance detection is a critical taskaimed at identifying a writer’s stances or view-points in relation to a topic. Sarcasm detectionseeks to spot sarcastic expressions, while senti-ment analysis determines the attitude expressedin a text. After numerous experiments, we iden-tified Arabert-twitter as the model offering thebest performance for all three tasks. In particu-lar, it achieves a macro F-score of 78.08% forstance detection, a macro F1-score of 59.51%for sarcasm detection and a macro F1-score of64.57% for sentiment detection. .Our source code is available at https://github.com/MezghaniAmal/Mawqif

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dzStance at StanceEval2024: Arabic Stance Detection based on Sentence Transformers
Mohamed Lichouri | Khaled Lounnas | Ouaras Rafik | Mohamed ABi | Anis Guechtouli

This study compares Term Frequency-Inverse Document Frequency (TF-IDF) features with Sentence Transformers for detecting writers’ stances—favorable, opposing, or neutral—towards three significant topics: COVID-19 vaccine, digital transformation, and women empowerment. Through empirical evaluation, we demonstrate that Sentence Transformers outperform TF-IDF features across various experimental setups. Our team, dzStance, participated in a stance detection competition, achieving the 13th position (74.91%) among 15 teams in Women Empowerment, 10th (73.43%) in COVID Vaccine, and 12th (66.97%) in Digital Transformation. Overall, our team’s performance ranked 13th (71.77%) among all participants. Notably, our approach achieved promising F1-scores, highlighting its effectiveness in identifying writers’ stances on diverse topics. These results underscore the potential of Sentence Transformers to enhance stance detection models for addressing critical societal issues.

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SMASH at StanceEval 2024: Prompt Engineering LLMs for Arabic Stance Detection
Youssef Al Hariri | Ibrahim Abu Farha

This paper presents our submission for the Stance Detection in Arabic Language (StanceEval) 2024 shared task conducted by Team SMASH of the University of Edinburgh. We evaluated the performance of various BERT-based and large language models (LLMs). MARBERT demonstrates superior performance among the BERT-based models, achieving F1 and macro-F1 scores of 0.570 and 0.770, respectively. In contrast, Command R model outperforms all models with the highest overall F1 score of 0.661 and macro F1 score of 0.820.

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CUFE at StanceEval2024: Arabic Stance Detection with Fine-Tuned Llama-3 Model
Michael Ibrahim

In NLP, stance detection identifies a writer’s position or viewpoint on a particular topic or entity from their text and social media activity, which includes preferences and relationships.Researchers have been exploring techniques and approaches to develop effective stance detection systems.Large language models’ latest advancements offer a more effective solution to the stance detection problem. This paper proposes fine-tuning the newly released 8B-parameter Llama 3 model from Meta GenAI for Arabic text stance detection.The proposed method was ranked ninth in the StanceEval 2024 Task on stance detection in Arabic language achieving a Macro average F₁ score of 0.7647.

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StanceCrafters at StanceEval2024: Multi-task Stance Detection using BERT Ensemble with Attention Based Aggregation
Ahmed Hasanaath | Aisha Alansari

Stance detection is a key NLP problem that classifies a writer’s viewpoint on a topic based on their writing. This paper outlines our approach for Stance Detection in Arabic Language Shared Task (StanceEval2024), focusing on attitudes towards the COVID-19 vaccine, digital transformation, and women’s empowerment. The proposed model uses parallel multi-task learning with two fine-tuned BERT-based models combined via an attention module. Results indicate this ensemble outperforms a single BERT model, demonstrating the benefits of using BERT architectures trained on diverse datasets. Specifically, Arabert-Twitterv2, trained on tweets, and Camel-Lab, trained on Modern Standard Arabic (MSA), Dialectal Arabic (DA), and Classical Arabic (CA), allowed us to leverage diverse Arabic dialects and styles.

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MGKM at StanceEval2024 Fine-Tuning Large Language Models for Arabic Stance Detection
Mamoun Alghaslan | Khaled Almutairy

Social media platforms have become essential in daily life, enabling users to express their opinions and stances on various topics. Stance detection, which identifies the viewpoint expressed in text toward a target, has predominantly focused on English. MAWQIF is the pioneering Arabic dataset for target-specific stance detection, consisting of 4,121 tweets annotated with stance, sentiment, and sarcasm. The original dataset, benchmarked on four BERT-based models, achieved a best macro-F1 score of 78.89, indicating significant room for improvement. This study evaluates the effectiveness of three Large Language Models (LLMs) in detecting target-specific stances in MAWQIF. The LLMs assessed are ChatGPT-3.5-turbo, Meta-Llama-3-8B-Instruct, and Falcon-7B-Instruct. Performance was measured using both zero-shot and full fine-tuning approaches. Our findings demonstrate that fine-tuning substantially enhances the stance detection capabilities of LLMs in Arabic tweets. Notably, GPT-3.5-Turbo achieved the highest performance with a macro-F1 score of 82.93, underscoring the potential of fine-tuned LLMs for language-specific applications.

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AlexUNLP-BH at StanceEval2024: Multiple Contrastive Losses Ensemble Strategy with Multi-Task Learning For Stance Detection in Arabic
Mohamed Badran | Mo’men Hamdy | Marwan Torki | Nagwa El-Makky

Stance detection, an evolving task in natural language processing, involves understanding a writer’s perspective on certain topics by analyzing his written text and interactions online, especially on social media platforms. In this paper, we outline our submission to the StanceEval task, leveraging the Mawqif dataset featured in The Second Arabic Natural Language Processing Conference. Our task is to detect writers’ stances (Favor, Against, or None) towards three selected topics (COVID-19 vaccine, digital transformation, and women empowerment). We present our approach primarily relying on a contrastive loss ensemble strategy. Our proposed approach achieved an F1-score of 0.8438 and ranked first in the stanceEval 2024 task. The code and checkpoints are availableat https://github.com/MBadran2000/Mawqif.git

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Rasid at StanceEval: Fine-tuning MARBERT for Arabic Stance Detection
Nouf AlShenaifi | Nourah Alangari | Hadeel Al-Negheimish

As social media usage continues to rise, the demand for systems to analyze opinions and sentiments expressed in textual data has become more critical. This paper presents our submission to the Stance Detection in Arabic Language Shared Task, in which we evaluated three models: the fine-tuned MARBERT Transformer, the fine-tuned AraBERT Transformer, and an Ensemble of Machine learning Classifiers. Our findings indicate that the MARBERT Transformer outperformed the other models in performance across all targets. In contrast, the Ensemble Classifier, which combines traditional machine learning techniques, demonstrated relatively lower effectiveness.

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ISHFMG_TUN at StanceEval: Ensemble Method for Arabic Stance Evaluation System
Ammar Mars | Mustapha Jaballah | Dhaou Ghoul

It is essential to understand the attitude of individuals towards specific topics in Arabic language for tasks like sentiment analysis, opinion mining, and social media monitoring. However, the diversity of the linguistic characteristics of the Arabic language presents several challenges to accurately evaluate the stance. In this study, we suggest ensemble approach to tackle these challenges. Our method combines different classifiers using the voting method. Through multiple experiments, we prove the effectiveness of our method achieving significant F1-score value equal to 0.7027. Our findings contribute to promoting NLP and offer treasured enlightenment for applications like sentiment analysis, opinion mining, and social media monitoring.

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PICT at StanceEval2024: Stance Detection in Arabic using Ensemble of Large Language Models
Ishaan Shukla | Ankit Vaidya | Geetanjali Kale

This paper outlines our approach to the StanceEval 2024- Arabic Stance Evaluation shared task. The goal of the task was to identify the stance, one out of three (Favor, Against or None) towards tweets based on three topics, namely- COVID-19 Vaccine, Digital Transformation and Women Empowerment. Our approach consists of fine-tuning BERT-based models efficiently for both, Single-Task Learning as well as Multi-Task Learning, the details of which are discussed. Finally, an ensemble was implemented on the best-performing models to maximize overall performance. We achieved a macro F1 score of 78.02% in this shared task. Our codebase is available publicly.

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TAO at StanceEval2024 Shared Task: Arabic Stance Detection using AraBERT
Anas Melhem | Osama Hamed | Thaer Sammar

In this paper, we present a high-performing model for Arabic stance detection on the STANCEEVAL2024 shared task part ofARABICNLP2024. Our model leverages ARABERTV1; a pre-trained Arabic language model, within a single-task learning framework. We fine-tuned the model on stance detection data for three specific topics: COVID19 vaccine, digital transformation, and women empowerment, extracted from the MAWQIF corpus. In terms of performance, our model achieves 73.30 macro-F1 score for women empowerment, 70.51 for digital transformation, and 64.55 for COVID-19 vaccine detection.

We present WojoodNER-2024, the second Arabic Named Entity Recognition (NER) Shared Task. In WojoodNER-2024, we focus on fine-grained Arabic NER. We provided participants with a new Arabic fine-grained NER dataset called Wojoodfine, annotated with subtypes of entities. WojoodNER-2024 encompassed three subtasks: (i) Closed-Track Flat Fine-Grained NER, (ii) Closed-Track Nested Fine-Grained NER, and (iii) an Open-Track NER for the Israeli War on Gaza. A total of 43 unique teams registered for this shared task. Five teams participated in the Flat Fine-Grained Subtask, among which two teams tackled the Nested Fine-Grained Subtask and one team participated in the Open-Track NER Subtask. The winning teams achieved F₁ scores of 91% and 92% in the Flat Fine-Grained and Nested Fine-Grained Subtasks, respectively. The sole team in the Open-Track Subtask achieved an F₁ score of 73.7%.

This paper presents our system “muNERa”, submitted to the WojoodNER 2024 shared task at the second ArabicNLP conference. We participated in two subtasks, the flat and nested fine-grained NER sub-tasks (1 and 2). muNERa achieved first place in the nested NER sub-task and second place in the flat NER sub-task. The system is based on the TANL framework (CITATION),by using a sequence-to-sequence structured language translation approach to model both tasks. We utilize the pre-trained AraT5v2-base model as the base model for the TANL framework. The best-performing muNERa model achieves 91.07% and 90.26% for the F-1 scores on the test sets for the nested and flat subtasks, respectively.

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Addax at WojoodNER 2024: Attention-Based Dual-Channel Neural Network for Arabic Named Entity Recognition
Issam Yahia | Houdaifa Atou | Ismail Berrada

Named Entity Recognition (NER) is a fundamental task in Natural Language Processing (NLP) that focuses on extracting entities such as names of people, organizations, locations, and dates from text. Despite significant advancements due to deep learning and transformer architectures like BERT, NER still faces challenges, particularly in low-resource languages like Arabic. This paper presents a BERT-based NER system that utilizes a two-channel parallel hybrid neural network with an attention mechanism specifically designed for the NER Shared Task 2024. In the competition, our approach ranked second by scoring 90.13% in micro-F1 on the test set. The results demonstrate the effectiveness of combining advanced neural network architectures with contextualized word embeddings in improving NER performance for Arabic.

In this paper, we present our submission for the WojoodNER 2024 Shared Tasks addressing flat and nested sub-tasks (1, 2). We experiment with three different approaches. We train (i) an Arabic fine-tuned version of BLOOMZ-7b-mt, GEMMA-7b, and AraBERTv2 on multi-label token classifications task; (ii) two AraBERTv2 models, on main types and sub-types respectively; and (iii) one model for main types and four for the four sub-types. Based on the Wojood NER 2024 test set results, the three fine-tuned models performed similarly with AraBERTv2 favored (F1: Flat=.8780 Nested=.9040). The five model approach performed slightly better (F1: Flat=.8782 Nested=.9043).

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Bangor University at WojoodNER 2024: Advancing Arabic Named Entity Recognition with CAMeLBERT-Mix
Norah Alshammari

This paper describes the approach and results of Bangor University’s participation in the WojoodNER 2024 shared task, specifically for Subtask-1: Closed-Track Flat Fine-Grain NER. We present a system utilizing a transformer-based model called bert-base-arabic-camelbert-mix, fine-tuned on the Wojood-Fine corpus. A key enhancement to our approach involves adding a linear layer on top of the bert-base-arabic-camelbert-mix to classify each token into one of 51 different entity types and subtypes, as well as the ‘O’ label for non-entity tokens. This linear layer effectively maps the contextualized embeddings produced by BERT to the desired output labels, addressing the complex challenges of fine-grained Arabic NER. The system achieved competitive results in precision, recall, and F1 scores, thereby contributing significant insights into the application of transformers in Arabic NER tasks.

This paper details our submission to the WojoodNER Shared Task 2024, leveraging in-context learning with large language models for Arabic Named Entity Recognition. We utilized the Command R model, to perform fine-grained NER on the Wojood-Fine corpus. Our primary approach achieved an F1 score of 0.737 and a recall of 0.756. Post-processing the generated predictions to correct format inconsistencies resulted in an increased recall of 0.759, and a similar F1 score of 0.735. A multi-level prompting method and aggregation of outputs resulted in a lower F1 score of 0.637. Our results demonstrate the potential of ICL for Arabic NER while highlighting challenges related to LLM output consistency.

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mucAI at WojoodNER 2024: Arabic Named Entity Recognition with Nearest Neighbor Search
Ahmed Abdou | Tasneem Mahmoud

Named Entity Recognition (NER) is a task in Natural Language Processing (NLP) that aims to identify and classify entities in text into predefined categories.However, when applied to Arabic data, NER encounters unique challenges stemming from the language’s rich morphological inflections, absence of capitalization cues, and spelling variants, where a single word can comprise multiple morphemes.In this paper, we introduce Arabic KNN-NER, our submission to the Wojood NER Shared Task 2024 (ArabicNLP 2024). We have participated in the shared sub-task 1 Flat NER. In this shared sub-task, we tackle fine-grained flat-entity recognition for Arabic text, where we identify a single main entity and possibly zero or multiple sub-entities for each word.Arabic KNN-NER augments the probability distribution of a fine-tuned model with another label probability distribution derived from performing a KNN search over the cached training data. Our submission achieved 91% on the test set on the WojoodFine dataset, placing Arabic KNN-NER on top of the leaderboard for the shared task.

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bib (full) Proceedings of the 11th Workshop on Argument Mining (ArgMining 2024)

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ARIES: A General Benchmark for Argument Relation Identification
Debela Gemechu | Ramon Ruiz-Dolz | Chris Reed

Measuring advances in argument mining is one of the main challenges in the area. Different theories of argument, heterogeneous annotations, and a varied set of argumentation domains make it difficult to contextualise and understand the results reported in different work from a general perspective. In this paper, we present ARIES, a general benchmark for Argument Relation Identification aimed at providing with a standard evaluation for argument mining research. ARIES covers the three different language modelling approaches: sequence and token modelling, and sequence-to-sequence-to-sequence alignment, together with the three main Transformer-based model architectures: encoder-only, decoder-only, and encoder-decoder. Furthermore, the benchmark consists of eight different argument mining datasets, covering the most common argumentation domains, and standardised with the same annotation structures. This paper provides a first comprehensive and comparative set of results in argument mining across a broad range of configurations to compare with, both advancing the state-of-the-art, and establishing a standard way to measure future advances in the area. Across varied task setups and architectures, our experiments reveal consistent challenges in cross-dataset evaluation, with notably poor results. Given the models’ struggle to acquire transferable skills, the task remains challenging, opening avenues for future research.

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Detecting Scientific Fraud Using Argument Mining
Gabriel Freedman | Francesca Toni

A proliferation of fraudulent scientific research in recent years has precipitated a greater interest in more effective methods of detection. There are many varieties of academic fraud, but a particularly challenging type to detect is the use of paper mills and the faking of peer-review. To the best of our knowledge, there have so far been no attempts to automate this process.The complexity of this issue precludes the use of heuristic methods, like pattern-matching techniques, which are employed for other types of fraud. Our proposed method in this paper uses techniques from the Computational Argumentation literature (i.e. argument mining and argument quality evaluation). Our central hypothesis stems from the assumption that articles that have not been subject to the proper level of scrutiny will contain poorly formed and reasoned arguments, relative to legitimately published papers. We use a variety of corpora to test this approach, including a collection of abstracts taken from retracted papers. We show significant improvement compared to a number of baselines, suggesting that this approach merits further investigation.

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DeepCT-enhanced Lexical Argument Retrieval
Alexander Bondarenko | Maik Fröbe | Danik Hollatz | Jan Merker | Matthias Hagen

The recent Touché lab’s argument retrieval task focuses on controversial topics like ‘Should bottled water be banned?’ and asks to retrieve relevant pro/con arguments. Interestingly, the most effective systems submitted to that task still are based on lexical retrieval models like BM25. In other domains, neural retrievers that capture semantics are more effective than lexical baselines. To add more “semantics” to argument retrieval, we propose to combine lexical models with DeepCT-based document term weights. Our evaluation shows that our approach is more effective than all the systems submitted to the Touché lab while being on par with modern neural re-rankers that themselves are computationally more expensive.

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Exploiting Dialogue Acts and Context to Identify Argumentative Relations in Online Debates
Stefano Mezza | Wayne Wobcke | Alan Blair

Argumentative Relation Classification is the task of determining the relationship between two contributions in the context of an argumentative dialogue. Existing models in the literature rely on a combination of lexical features and pre-trained language models to tackle this task; while this approach is somewhat effective, it fails to take into account the importance of pragmatic features such as the illocutionary force of the argument or the structure of previous utterances in the discussion; relying solely on lexical features also produces models that over-fit their initial training set and do not scale to unseen domains. In this work, we introduce ArguNet, a new model for Argumentative Relation Classification which relies on a combination of Dialogue Acts and Dialogue Context to improve the representation of argument structures in opinionated dialogues. We show that our model achieves state-of-the-art results on the Kialo benchmark test set, and provide evidence of its robustness in an open-domain scenario.

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Multi-Task Learning Improves Performance in Deep Argument Mining Models
Amirhossein Farzam | Shashank Shekhar | Isaac Mehlhaff | Marco Morucci

The successful analysis of argumentative techniques in user-generated text is central to many downstream tasks such as political and market analysis. Recent argument mining tools use state-of-the-art deep learning methods to extract and annotate argumentative techniques from various online text corpora, but each task is treated as separate and different bespoke models are fine-tuned for each dataset. We show that different argument mining tasks share common semantic and logical structure by implementing a multi-task approach to argument mining that meets or exceeds performance from existing methods for the same problems. Our model builds a shared representation of the input and exploits similarities between tasks in order to further boost performance via parameter-sharing. Our results are important for argument mining as they show that different tasks share substantial similarities and suggest a holistic approach to the extraction of argumentative techniques from text.

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Computational Modelling of Undercuts in Real-world Arguments
Yuxiao Ye | Simone Teufel

Argument Mining (AM) is the task of automatically analysing arguments, such that the unstructured information contained in them is converted into structured representations. Undercut is a unique structure in arguments, as it challenges the relationship between a premise and a claim, unlike direct attacks which challenge the claim or the premise itself. Undercut is also an important counterargument device as it often reflects the value of arguers. However, undercuts have not received the attention in the filed of AM they should have — there is neither much corpus data about undercuts, nor an existing AM model that can automatically recognise them. In this paper, we present a real-world dataset of arguments with explicitly annotated undercuts, and the first computational model that is able to recognise them. The dataset consists of 400 arguments, containing 326 undercuts. On this dataset, our approach beats a strong baseline in undercut recognition, with F₁ = 38.8%, which is comparable to the performance on recognising direct attacks. We also conduct experiments on a benchmark dataset containing no undercuts, and prove that our approach is as good as the state of the art in terms of recognising the overall structure of arguments. Our work pioneers the systematic analysis and computational modelling of undercuts in real-world arguments, setting a foundation for future research in the role of undercuts in the dynamics of argumentation.

Multimodal Argument Mining (MAM) is a recent area of research aiming to extend argument analysis and improve discourse understanding by incorporating multiple modalities. Initial results confirm the importance of paralinguistic cues in this field. However, the research community still lacks a comprehensive platform where results can be easily reproduced, and methods and models can be stored, compared, and tested against a variety of benchmarks. To address these challenges, we propose MAMKit, an open, publicly available, PyTorch toolkit that consolidates datasets and models, providing a standardized platform for experimentation. MAMKit also includes some new baselines, designed to stimulate research on text and audio encoding and fusion for MAM tasks. Our initial results with MAMKit indicate that advancements in MAM require novel annotation processes to encompass auditory cues effectively.

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Overview of DialAM-2024: Argument Mining in Natural Language Dialogues
Ramon Ruiz-Dolz | John Lawrence | Ella Schad | Chris Reed

Argumentation is the process by which humans rationally elaborate their thoughts and opinions in written (e.g., essays) or spoken (e.g., debates) contexts. Argument Mining research, however, has been focused on either written argumentation or spoken argumentation but without considering any additional information, e.g., speech acts and intentions. In this paper, we present an overview of DialAM-2024, the first shared task in dialogical argument mining, where argumentative relations and speech illocutions are modelled together in a unified framework. The task was divided into two different sub-tasks: the identification of propositional relations and the identification of illocutionary relations. Six different teams explored different methodologies to leverage both sources of information to reconstruct argument maps containing the locutions uttered in the speeches and the argumentative propositions implicit in them. The best performing team achieved an F1-score of 67.05% in the overall evaluation of the reconstruction of complete argument maps, considering both sub-tasks included in the DialAM-2024 shared task.

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DFKI-MLST at DialAM-2024 Shared Task: System Description
Arne Binder | Tatiana Anikina | Leonhard Hennig | Simon Ostermann

This paper presents the dfki-mlst submission for the DialAM shared task (Ruiz-Dolz et al., 2024) on identification of argumentative and illocutionary relations in dialogue. Our model achieves best results in the global setting: 48.25 F1 at the focused level when looking only at the related arguments/locutions and 67.05 F1 at the general level when evaluating the complete argument maps. We describe our implementation of the data pre-processing, relation encoding and classification, evaluating 11 different base models and performing experiments with, e.g., node text combination and data augmentation. Our source code is publicly available.

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KnowComp at DialAM-2024: Fine-tuning Pre-trained Language Models for Dialogical Argument Mining with Inference Anchoring Theory
Yuetong Wu | Yukai Zhou | Baixuan Xu | Weiqi Wang | Yangqiu Song

In this paper, we present our framework for DialAM-2024 TaskA: Identification of Propositional Relations and TaskB: Identification of Illocutionary Relations. The goal of task A is to detect argumentative relations between propositions in an argumentative dialogue. i.e., Inference, Conflict, Rephrase while task B aims to detect illocutionary relations between locutions and argumentative propositions in a dialogue. e.g., Asserting, Agreeing, Arguing, Disagreeing. Noticing the definition of the relations are strict and professional under the context of IAT framework, we meticulously curate prompts which not only incorporate formal definition of the relations, but also exhibit the subtle differences between them. The PTLMs are then fine-tuned on the human-designed prompts to enhance its discrimination capability in classifying different theoretical relations by learning from the human instruction and the ground truth samples. After extensive experiments, a fine-tuned DeBERTa-v3-base model exhibits the best performance among all PTLMs with an F1 score of 78.90% on Task B. It is worth noticing that our framework ranks #2 in the ILO - General official leaderboard.

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KNOWCOMP POKEMON Team at DialAM-2024: A Two-Stage Pipeline for Detecting Relations in Dialogue Argument Mining
Zihao Zheng | Zhaowei Wang | Qing Zong | Yangqiu Song

Dialogue Argument Mining(DialAM) is an important branch of Argument Mining(AM). DialAM-2024 is a shared task focusing on dialogue argument mining, which requires us to identify argumentative relations and illocutionary relations among proposition nodes and locution nodes. To accomplish this, we propose a two-stage pipeline, which includes the Two-Step S-Node Prediction Model in Stage 1 and the YA-Node Prediction Model in Stage 2. We also augment the training data in both stages and introduce context in the prediction of Stage 2. We successfully completed the task and achieved good results. Our team KNOWCOMP POKEMON ranked 1st in the ARI Focused score and 4th in the Global Focused score.

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Pungene at DialAM-2024: Identification of Propositional and Illocutionary Relations
Sirawut Chaixanien | Eugene Choi | Shaden Shaar | Claire Cardie

In this paper we tackle the shared task DialAM-2024 aiming to annotate dialogue based on the inference anchoring theory (IAT). The task can be split into two parts, identification of propositional relations and identification of illocutionary relations. We propose a pipelined system made up of three parts: (1) locutionary-propositions relation detection, (2) propositional relations detection, and (3) illocutionary relations identification. We fine-tune models independently for each step, and combine at the end for the final system. Our proposed system ranks second overall compared to other participants in the shared task, scoring an average f1-score on both sub-parts of 63.7.

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Turiya at DialAM-2024: Inference Anchoring Theory Based LLM Parsers
Sougata Saha | Rohini Srihari

Representing discourse as argument graphs facilitates robust analysis. Although computational frameworks for constructing graphs from monologues exist, there is a lack of frameworks for parsing dialogue. Inference Anchoring Theory (IAT) is a theoretical framework for extracting graphical argument structures and relationships from dialogues. Here, we introduce computational models for implementing the IAT framework for parsing dialogues. We experiment with a classification-based biaffine parser and Large Language Model (LLM)-based generative methods and compare them. Our results demonstrate the utility of finetuning LLMs for constructing IAT-based argument graphs from dialogues, which is a nuanced task.

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Overview of PerpectiveArg2024 The First Shared Task on Perspective Argument Retrieval
Neele Falk | Andreas Waldis | Iryna Gurevych

Argument retrieval is the task of finding relevant arguments for a given query. While existing approaches rely solely on the semantic alignment of queries and arguments, this first shared task on perspective argument retrieval incorporates perspectives during retrieval, ac- counting for latent influences in argumenta- tion. We present a novel multilingual dataset covering demographic and socio-cultural (so- cio) variables, such as age, gender, and politi- cal attitude, representing minority and major- ity groups in society. We distinguish between three scenarios to explore how retrieval systems consider explicitly (in both query and corpus) and implicitly (only in query) formulated per- spectives. This paper provides an overview of this shared task and summarizes the results of the six submitted systems. We find substantial challenges in incorporating perspectivism, especially when aiming for personalization based solely on the text of arguments without explicitly providing socio profiles. Moreover, re- trieval systems tend to be biased towards the majority group but partially mitigate bias for the female gender. While we bootstrap per- spective argument retrieval, further research is essential to optimize retrieval systems to facilitate personalization and reduce polarization.

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Sövereign at The Perspective Argument Retrieval Shared Task 2024: Using LLMs with Argument Mining
Robert Günzler | Özge Sevgili | Steffen Remus | Chris Biemann | Irina Nikishina

This paper presents the Sövereign submission for the shared task on perspective argument retrieval for the Argument Mining Workshop 2024. The main challenge is to perform argument retrieval considering socio-cultural aspects such as political interests, occupation, age, and gender. To address the challenge, we apply open-access Large Language Models (Mistral-7b) in a zero-shot fashion for re-ranking and explicit similarity scoring. Additionally, we combine different features in an ensemble setup using logistic regression. Our system ranks second in the competition for all test set rounds on average for the logistic regression approach using LLM similarity scores as a feature. In addition to the description of the approach, we also provide further results of our ablation study. Our code will be open-sourced upon acceptance.

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Turiya at PerpectiveArg2024: A Multilingual Argument Retriever and Reranker
Sougata Saha | Rohini Srihari

While general argument retrieval systems have significantly matured, multilingual argument retrieval in a socio-cultural setting is an overlooked problem. Advancements in such systems are imperative to enhance the inclusivity of society. The Perspective Argument Retrieval (PAR) task addresses these aspects and acknowledges their potential latent influence on argumentation. Here, we present a multilingual retrieval system for PAR that accounts for societal diversity during retrieval. Our approach couples a retriever and a re-ranker and spans multiple languages, thus factoring in diverse socio-cultural settings. The performance of our end-to-end system on three distinct test sets testify to its robustness.

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Twente-BMS-NLP at PerspectiveArg 2024: Combining Bi-Encoder and Cross-Encoder for Argument Retrieval
Leixin Zhang | Daniel Braun

The paper describes our system for the Perspective Argument Retrieval Shared Task. The shared task consists of three scenarios in which relevant political arguments have to be retrieved based on queries (Scenario 1). In Scenario 2 explicit socio-cultural properties are provided and in Scenario 3 implicit socio-cultural properties within the arguments have to be used. We combined a Bi-Encoder and a Cross-Encoder to retrieve relevant arguments for each query. For the third scenario, we extracted linguistic features to predict socio-demographic labels as a separate task. However, the socio-demographic match task proved challenging due to the constraints of argument lengths and genres. The described system won both tracks of the shared task.

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GESIS-DSM at PerpectiveArg2024: A Matter of Style? Socio-Cultural Differences in Argumentation
Maximilian Maurer | Julia Romberg | Myrthe Reuver | Negash Weldekiros | Gabriella Lapesa

This paper describes the contribution of team GESIS-DSM to the Perspective Argument Retrieval Task, a task on retrieving socio-culturally relevant and diverse arguments for different user queries. Our experiments and analyses aim to explore the nature of the socio-cultural specialization in argument retrieval: (how) do the arguments written by different socio-cultural groups differ? We investigate the impact of content and style for the task of identifying arguments relevant to a query and a certain demographic attribute. In its different configurations, our system employs sentence embedding representations, arguments generated with Large Language Model, as well as stylistic features. final method places third overall in the shared task, and, in comparison, does particularly well in the most difficult evaluation scenario, where the socio-cultural background of the argument author is implicit (i.e. has to be inferred from the text). This result indicates that socio-cultural differences in argument production may indeed be a matter of style.

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XFACT Team0331 at PerspectiveArg2024: Sampling from Bounded Clusters for Diverse Relevant Argument Retrieval
Wan Ju Kang | Jiyoung Han | Jaemin Jung | James Thorne

This paper reports on the argument mining system submitted to the ArgMining workshop 2024 for The Perspective Argument Retrieval Shared Task (Falk et al., 2024). We com- bine the strengths of a smaller Sentence BERT model and a Large Language Model: the for- mer is fine-tuned for a contrastive embedding objective and a classification objective whereas the latter is invoked to augment the query and populate the latent space with diverse relevant arguments. We conduct an ablation study on these components to find that each contributes substantially to the diversity and relevance cri- teria for the top-k retrieval of arguments from the given corpus.

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bib (full) Proceedings of the 23rd Workshop on Biomedical Natural Language Processing

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Proceedings of the 23rd Workshop on Biomedical Natural Language Processing
Dina Demner-Fushman | Sophia Ananiadou | Makoto Miwa | Kirk Roberts | Junichi Tsujii

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Improving Self-training with Prototypical Learning for Source-Free Domain Adaptation on Clinical Text
Seiji Shimizu | Shuntaro Yada | Lisa Raithel | Eiji Aramaki

Domain adaptation is crucial in the clinical domain since the performance of a model trained on one domain (source) degrades seriously when applied to another domain (target). However, conventional domain adaptation methods often cannot be applied due to data sharing restrictions on source data. Source-Free Domain Adaptation (SFDA) addresses this issue by only utilizing a source model and unlabeled target data to adapt to the target domain. In SFDA, self-training is the most widely applied method involving retraining models with target data using predictions from the source model as pseudo-labels. Nevertheless, this approach is prone to contain substantial numbers of errors in pseudo-labeling and might limit model performance in the target domain. In this paper, we propose a Source-Free Prototype-based Self-training (SFPS) aiming to improve the performance of self-training. SFPS generates prototypes without accessing source data and utilizes them for prototypical learning, namely prototype-based pseudo-labeling and contrastive learning. Also, we compare entropy-based, centroid-based, and class-weights-based prototype generation methods to identify the most effective formulation of the proposed method. Experimental results across various datasets demonstrate the effectiveness of the proposed method, consistently outperforming vanilla self-training. The comparison of various prototype-generation methods identifies the most reliable generation method that improves the source model persistently. Additionally, our analysis illustrates SFPS can successfully alleviate errors in pseudo-labeling.

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Generation and Evaluation of Synthetic Endoscopy Free-Text Reports with Differential Privacy
Agathe Zecevic | Xinyue Zhang | Sebastian Zeki | Angus Roberts

The development of NLP models in the healthcare sector faces important challenges due to the limited availability of patient data, mainly driven by privacy concerns. This study proposes the generation of synthetic free-text medical reports, specifically focusing on the gastroenterology domain, to address the scarcity of specialised datasets, while preserving patient privacy. We fine-tune BioGPT on over 90 000 endoscopy reports and integrate Differential Privacy (DP) into the training process. 10 000 DP-private synthetic reports are generated by this model. The generated synthetic data is evaluated through multiple dimensions: similarity to real datasets, language quality, and utility in both supervised and semi-supervised NLP tasks. Results suggest that while DP integration impacts text quality, it offers a promising balance between data utility and privacy, improving the performance of a real-world downstream task. Our study underscores the potential of synthetic data to facilitate model development in the healthcare domain without compromising patient privacy.

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Evaluating the Robustness of Adverse Drug Event Classification Models using Templates
Dorothea MacPhail | David Harbecke | Lisa Raithel | Sebastian Möller

An adverse drug effect (ADE) is any harmful event resulting from medical drug treatment. Despite their importance, ADEs are often under-reported in official channels. Some research has therefore turned to detecting discussions of ADEs in social media. Impressive results have been achieved in various attempts to detect ADEs. In a high-stakes domain such as medicine, however, an in-depth evaluation of a model’s abilities is crucial. We address the issue of thorough performance evaluation in detecting ADEs with hand-crafted templates for four capabilities, temporal order, negation, sentiment and beneficial effect. We find that models with similar performance on held-out test sets have varying results on these capabilities.

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Advancing Healthcare Automation: Multi-Agent System for Medical Necessity Justification
Himanshu Gautam Pandey | Akhil Amod | Shivang Kumar

Prior Authorization delivers safe, appropriate, and cost-effective care that is medically justified with evidence-based guidelines. However, the process often requires labor-intensive manual comparisons between patient medical records and clinical guidelines, that is both repetitive and time-consuming. Recent developments in Large Language Models (LLMs) have shown potential in addressing complex medical NLP tasks with minimal supervision. This paper explores the application of Multi-Agent System (MAS) that utilize specialized LLM agents to automate Prior Authorization task by breaking them down into simpler and manageable sub-tasks. Our study systematically investigates the effects of various prompting strategies on these agents and benchmarks the performance of different LLMs. We demonstrate that GPT-4 achieves an accuracy of 86.2% in predicting checklist item-level judgments with evidence, and 95.6% in determining overall checklist judgment. Additionally, we explore how these agents can contribute to explainability of steps taken in the process, thereby enhancing trust and transparency in the system.

Instruction-tuned Large Language Models (LLMs) can perform a wide range of tasks given natural language instructions to do so, but they are sensitive to how such instructions are phrased. This issue is especially concerning in healthcare, as clinicians are unlikely to be experienced prompt engineers and the potential consequences of inaccurate outputs are heightened in this domain. This raises a practical question: How robust are instruction-tuned LLMs to natural variations in the instructions provided for clinical NLP tasks? We collect prompts from medical doctors across a range of tasks and quantify the sensitivity of seven LLMs—some general, others specialized—to natural (i.e., non-adversarial) instruction phrasings. We find that performance varies substantially across all models, and that—perhaps surprisingly—domain-specific models explicitly trained on clinical data are especially brittle, compared to their general domain counterparts. Further, arbitrary phrasing differences can affect fairness, e.g., valid but distinct instructions for mortality prediction yield a range both in overall performance, and in terms of differences between demographic groups.

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Analysing zero-shot temporal relation extraction on clinical notes using temporal consistency
Vasiliki Kougia | Anastasiia Sedova | Andreas Joseph Stephan | Klim Zaporojets | Benjamin Roth

This paper presents the first study for temporal relation extraction in a zero-shot setting focusing on biomedical text. We employ two types of prompts and five Large Language Models (LLMs; GPT-3.5, Mixtral, Llama 2, Gemma, and PMC-LLaMA) to obtain responses about the temporal relations between two events. Our experiments demonstrate that LLMs struggle in the zero-shot setting, performing worse than fine-tuned specialized models in terms of F1 score. This highlights the challenging nature of this task and underscores the need for further research to enhance the performance of LLMs in this context. We further contribute a novel comprehensive temporal analysis by calculating consistency scores for each LLM. Our findings reveal that LLMs face challenges in providing responses consistent with the temporal properties of uniqueness and transitivity. Moreover, we study the relation between the temporal consistency of an LLM and its accuracy, and whether the latter can be improved by solving temporal inconsistencies. Our analysis shows that even when temporal consistency is achieved, the predictions can remain inaccurate.

Recent developments in natural language generation have tremendous implications for healthcare. For instance, state-of-the-art systems could automate the generation of sections in clinical reports to alleviate physician workload and streamline hospital documentation. To explore these applications, we present a shared task consisting of two subtasks: (1) Radiology Report Generation (RRG24) and (2) Discharge Summary Generation (“Discharge Me!”). RRG24 involves generating the ‘Findings’ and ‘Impression’ sections of radiology reports given chest X-rays. “Discharge Me!” involves generating the ‘Brief Hospital Course’ and '‘Discharge Instructions’ sections of discharge summaries for patients admitted through the emergency department. “Discharge Me!” submissions were subsequently reviewed by a team of clinicians. Both tasks emphasize the goal of reducing clinician burnout and repetitive workloads by generating documentation. We received 201 submissions from across 8 teams for RRG24, and 211 submissions from across 16 teams for “Discharge Me!”.

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e-Health CSIRO at RRG24: Entropy-Augmented Self-Critical Sequence Training for Radiology Report Generation
Aaron Nicolson | Jinghui Liu | Jason Dowling | Anthony Nguyen | Bevan Koopman

The core novelty of our approach lies in the addition of entropy regularisation to self-critical sequence training. This helps maintain a higher entropy in the token distribution, preventing overfitting to common phrases and ensuring a broader exploration of the vocabulary during training, which is essential for handling the diversity of the radiology reports in the RRG24 datasets. We apply this to a multimodal language model with RadGraph as the reward. Additionally, our model incorporates several other aspects. We use token type embeddings to differentiate between findings and impression section tokens, as well as image embeddings. To handle missing sections, we employ special tokens. We also utilise an attention mask with non-causal masking for the image embeddings and a causal mask for the report token embeddings.

This study aims to leverage state of the art language models to automate generating the “Brief Hospital Course” and “Discharge Instructions” sections of Discharge Summaries from the MIMIC-IV dataset, reducing clinicians’ administrative workload. We investigate how automation can improve documentation accuracy, alleviate clinician burnout, and enhance operational efficacy in healthcare facilities. This research was conducted within our participation in the Shared Task Discharge Me! at BioNLP @ ACL 2024. Various strategies were employed, including Few-Shot learning, instruction tuning, and Dynamic Expert Selection (DES), to develop models capable of generating the required text sections. Utilizing an additional clinical domain-specific dataset demonstrated substantial potential to enhance clinical language processing. The DES method, which optimizes the selection of text outputs from multiple predictions, proved to be especially effective. It achieved the highest overall score of 0.332 in the competition, surpassing single-model outputs. This finding suggests that advanced deep learning methods in combination with DES can effectively automate parts of electronic health record documentation. These advancements could enhance patient care by freeing clinician time for patient interactions. The integration of text selection strategies represents a promising avenue for further research.

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Overview of the BioLaySumm 2024 Shared Task on the Lay Summarization of Biomedical Research Articles
Tomas Goldsack | Carolina Scarton | Matthew Shardlow | Chenghua Lin

This paper presents the setup and results of the second edition of the BioLaySumm shared task on the Lay Summarisation of Biomedical Research Articles, hosted at the BioNLP Workshop at ACL 2024. In this task edition, we aim to build on the first edition’s success by further increasing research interest in this important task and encouraging participants to explore novel approaches that will help advance the state-of-the-art. Encouragingly, we found research interest in the task to be high, with this edition of the task attracting a total of 53 participating teams, a significant increase in engagement from the previous edition. Overall, our results show that a broad range of innovative approaches were adopted by task participants, with a predictable shift towards the use of Large Language Models (LLMs).

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UIUC_BioNLP at BioLaySumm: An Extract-then-Summarize Approach Augmented with Wikipedia Knowledge for Biomedical Lay Summarization
Zhiwen You | Shruthan Radhakrishna | Shufan Ming | Halil Kilicoglu

As the number of scientific publications is growing at a rapid pace, it is difficult for laypeople to keep track of and understand the latest scientific advances, especially in the biomedical domain. While the summarization of scientific publications has been widely studied, research on summarization targeting laypeople has remained scarce. In this study, considering the lengthy input of biomedical articles, we have developed a lay summarization system through an extract-then-summarize framework with large language models (LLMs) to summarize biomedical articles for laypeople. Using a fine-tuned GPT-3.5 model, our approach achieves the highest overall ranking and demonstrates the best relevance performance in the BioLaySumm 2024 shared task.

The lack of comprehensive and standardised databases containing Pharmacokinetic (PK) parameters presents a challenge in the drug development pipeline. Efficiently managing the increasing volume of published PK Parameters requires automated approaches that centralise information from diverse studies. In this work, we present the Pharmacokinetic Relation Extraction Dataset (PRED), a novel, manually curated corpus developed by pharmacometricians and NLP specialists, covering multiple types of PK parameters and numerical expressions reported in open-access scientific articles. PRED covers annotations for various entities and relations involved in PK parameter measurements from 3,600 sentences. We also introduce an end-to-end relation extraction model based on BioBERT, which is trained with joint named entity recognition (NER) and relation extraction objectives. The optimal pipeline achieved a micro-average F1-score of 94% for NER and over 85% F1-score across all relation types. This work represents the first resource for training and evaluating models for PK end-to-end extraction across multiple parameters and study types. We make our corpus and model openly available to accelerate the construction of large PK databases and to support similar endeavours in other scientific disciplines.

Large Language Models (LLMs) have significantly advanced healthcare innovation on generation capabilities. However, their application in real clinical settings is challenging due to potential deviations from medical facts and inherent biases. In this work, we develop an augmented LLM framework, KG-Rank, which leverages a medical knowledge graph (KG) with ranking and re-ranking techniques, aiming to improve free-text question-answering (QA) in the medical domain. Specifically, upon receiving a question, we initially retrieve triplets from a medical KG to gather factual information. Subsequently, we innovatively apply ranking methods to refine the ordering of these triplets, aiming to yield more precise answers. To the best of our knowledge, KG-Rank is the first application of ranking models combined with KG in medical QA specifically for generating long answers. Evaluation of four selected medical QA datasets shows that KG-Rank achieves an improvement of over 18% in the ROUGE-L score. Moreover, we extend KG-Rank to open domains, where it realizes a 14% improvement in ROUGE-L, showing the effectiveness and potential of KG-Rank.

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MedExQA: Medical Question Answering Benchmark with Multiple Explanations
Yunsoo Kim | Jinge Wu | Yusuf Abdulle | Honghan Wu

This paper introduces MedExQA, a novel benchmark in medical question-answering, to evaluate large language models’ (LLMs) understanding of medical knowledge through explanations. By constructing datasets across five distinct medical specialties that are underrepresented in current datasets and further incorporating multiple explanations for each question-answer pair, we address a major gap in current medical QA benchmarks which is the absence of comprehensive assessments of LLMs’ ability to generate nuanced medical explanations. Our work highlights the importance of explainability in medical LLMs, proposes an effective methodology for evaluating models beyond classification accuracy, and sheds light on one specific domain, speech language pathology, where current LLMs including GPT4 lack good understanding. Our results show generation evaluation with multiple explanations aligns better with human assessment, highlighting an opportunity for a more robust automated comprehension assessment for LLMs. To diversify open-source medical LLMs (currently mostly based on Llama2), this work also proposes a new medical model, MedPhi-2, based on Phi-2 (2.7B). The model outperformed medical LLMs based on Llama2-70B in generating explanations, showing its effectiveness in the resource-constrained medical domain. We will share our benchmark datasets and the trained model.

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Do Clinicians Know How to Prompt? The Need for Automatic Prompt Optimization Help in Clinical Note Generation
Zonghai Yao | Ahmed Jaafar | Beining Wang | Zhichao Yang | Hong Yu

This study examines the effect of prompt engineering on the performance of Large Language Models (LLMs) in clinical note generation. We introduce an Automatic Prompt Optimization (APO) framework to refine initial prompts and compare the outputs of medical experts, non-medical experts, and APO-enhanced GPT3.5 and GPT4. Results highlight GPT4-APO’s superior performance in standardizing prompt quality across clinical note sections. A human-in-the-loop approach shows that experts maintain content quality post-APO, with a preference for their own modifications, suggesting the value of expert customization. We recommend a two-phase optimization process, leveraging APO-GPT4 for consistency and expert input for personalization.

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Domain-specific or Uncertainty-aware models: Does it really make a difference for biomedical text classification?
Aman Sinha | Timothee Mickus | Marianne Clausel | Mathieu Constant | Xavier Coubez

The success of pretrained language models (PLMs) across a spate of use-cases has led to significant investment from the NLP community towards building domain-specific foundational models. On the other hand, in mission critical settings such as biomedical applications, other aspects also factor in—chief of which is a model’s ability to produce reasonable estimates of its own uncertainty. In the present study, we discuss these two desiderata through the lens of how they shape the entropy of a model’s output probability distribution. We find that domain specificity and uncertainty awareness can often be successfully combined, but the exact task at hand weighs in much more strongly.

Developing imaging models capable of detecting pathologies from chest X-rays can be cost and time-prohibitive for large datasets as it requires supervision to attain state-of-the-art performance. Instead, labels extracted from radiology reports may serve as distant supervision since these are routinely generated as part of clinical practice. Despite their widespread use, current rule-based methods for label extraction rely on extensive rule sets that are limited in their robustness to syntactic variability. To alleviate these limitations, we introduce RadPert, a rule-based system that integrates an uncertainty-aware information schema with a streamlined set of rules, enhancing performance. Additionally, we have developed RadPrompt, a multi-turn prompting strategy that leverages RadPert to bolster the zero-shot predictive capabilities of large language models, achieving a statistically significant improvement in weighted average F1 score over GPT-4 Turbo. Most notably, RadPrompt surpasses both its underlying models, showcasing the synergistic potential of LLMs with rule-based models. We have evaluated our methods on two English Corpora: the MIMIC-CXR gold-standard test set and a gold-standard dataset collected from the Cambridge University Hospitals.

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Using Large Language Models to Evaluate Biomedical Query-Focused Summarisation
Hashem Hijazi | Diego Molla | Vincent Nguyen | Sarvnaz Karimi

Biomedical question-answering systems remain popular for biomedical experts interacting with the literature to answer their medical questions. However, these systems are difficult to evaluate in the absence of costly human experts. Therefore, automatic evaluation metrics are often used in this space. Traditional automatic metrics such as ROUGE or BLEU, which rely on token overlap, have shown a low correlation with humans. We present a study that uses large language models (LLMs) to automatically evaluate systems from an international challenge on biomedical semantic indexing and question answering, called BioASQ. We measure the agreement of LLM-produced scores against human judgements. We show that LLMs correlate similarly to lexical methods when using basic prompting techniques. However, by aggregating evaluators with LLMs or by fine-tuning, we find that our methods outperform the baselines by a large margin, achieving a Spearman correlation of 0.501 and 0.511, respectively.

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Continuous Predictive Modeling of Clinical Notes and ICD Codes in Patient Health Records
Mireia Hernandez Caralt | Clarence Boon Liang Ng | Marek Rei

Electronic Health Records (EHR) serve as a valuable source of patient information, offering insights into medical histories, treatments, and outcomes. Previous research has developed systems for detecting applicable ICD codes that should be assigned while writing a given EHR document, mainly focusing on discharge summaries written at the end of a hospital stay. In this work, we investigate the potential of predicting these codes for the whole patient stay at different time points during their stay, even before they are officially assigned by clinicians. The development of methods to predict diagnoses and treatments earlier in advance could open opportunities for predictive medicine, such as identifying disease risks sooner, suggesting treatments, and optimizing resource allocation. Our experiments show that predictions regarding final ICD codes can be made already two days after admission and we propose a custom model that improves performance on this early prediction task.

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Can GPT Redefine Medical Understanding? Evaluating GPT on Biomedical Machine Reading Comprehension
Shubham Vatsal | Ayush Singh

Large language models (LLMs) have shown remarkable performance on many tasks in different domains. However, their performance in contextual biomedical machine reading comprehension (MRC) has not been evaluated in depth. In this work, we evaluate GPT on four contextual biomedical MRC benchmarks. We experiment with different conventional prompting techniques as well as introduce our own novel prompting method. To solve some of the retrieval problems inherent to LLMs, we propose a prompting strategy named Implicit Retrieval Augmented Generation (RAG) that alleviates the need for using vector databases to retrieve important chunks in traditional RAG setups. Moreover, we report qualitative assessments on the natural language generation outputs from our approach. The results show that our new prompting technique is able to get the best performance in two out of four datasets and ranks second in rest of them. Experiments show that modern-day LLMs like GPT even in a zero-shot setting can outperform supervised models, leading to new state-of-the-art (SoTA) results on two of the benchmarks.

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Get the Best out of 1B LLMs: Insights from Information Extraction on Clinical Documents
Saeed Farzi | Soumitra Ghosh | Alberto Lavelli | Bernardo Magnini

While the popularity of large, versatile language models like ChatGPT continues to rise, the landscape shifts when considering open-source models tailored to specific domains. Moreover, many areas, such as clinical documents, suffer from a scarcity of training data, often amounting to only a few hundred instances. Additionally, in certain settings, such as hospitals, cloud-based solutions pose privacy concerns, necessitating the deployment of language models on traditional hardware, such as single GPUs or powerful CPUs. To address these complexities, we conduct extensive experiments on both clinical entity detection and relation extraction in clinical documents using 1B parameter models. Our study delves into traditional fine-tuning, continuous pre-training in the medical domain, and instruction-tuning methods, providing valuable insights into their effectiveness in a multilingual setting. Our results underscore the importance of domain-specific models and pre-training for clinical natural language processing tasks. Furthermore, data augmentation using cross-lingual information improves performance in most cases, highlighting the potential for multilingual enhancements.

Ensuring the accuracy of responses provided by large language models (LLMs) is crucial, particularly in clinical settings where incorrect information may directly impact patient health. To address this challenge, we construct K-QA, a dataset containing 1,212 patient questions originating from real-world conversations held on a popular clinical online platform. We employ a panel of in-house physicians to answer and manually decompose a subset of K-QA into self-contained statements. Additionally, we formulate two NLI-based evaluation metrics approximating recall and precision: (1) comprehensiveness, measuring the percentage of essential clinical information in the generated answer and (2) hallucination rate, measuring the number of statements from the physician-curated response contradicted by the LLM answer. Finally, we use K-QA along with these metrics to evaluate several state-of-the-art models, as well as the effect of in-context learning and medically-oriented augmented retrieval schemes developed by the authors. Our findings indicate that in-context learning improves the comprehensiveness of the models, and augmented retrieval is effective in reducing hallucinations. We will make K-QA available to to the community to spur research into medically accurate NLP applications.

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Large Language Models for Biomedical Knowledge Graph Construction: Information extraction from EMR notes
Vahan Arsenyan | Spartak Bughdaryan | Fadi Shaya | Kent Wilson Small | Davit Shahnazaryan

The automatic construction of knowledge graphs (KGs) is an important research area in medicine, with far-reaching applications spanning drug discovery and clinical trial design. These applications hinge on the accurate identification of interactions among medical and biological entities. In this study, we propose an end-to-end machine learning solution based on large language models (LLMs) that utilize electronic medical record notes to construct KGs. The entities used in the KG construction process are diseases, factors, treatments, as well as manifestations that coexist with the patient while experiencing the disease. Given the critical need for high-quality performance in medical applications, we embark on a comprehensive assessment of 12 LLMs of various architectures, evaluating their performance and safety attributes. To gauge the quantitative efficacy of our approach by assessing both precision and recall, we manually annotate a dataset provided by the Macula and Retina Institute. We also assess the qualitative performance of LLMs, such as the ability to generate structured outputs or the tendency to hallucinate. The results illustrate that in contrast to encoder-only and encoder-decoder, decoder-only LLMs require further investigation. Additionally, we provide guided prompt design to utilize such LLMs. The application of the proposed methodology is demonstrated on age-related macular degeneration.

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Document-level Clinical Entity and Relation extraction via Knowledge Base-Guided Generation
Kriti Bhattarai | Inez Y. Oh | Zachary B. Abrams | Albert M. Lai

Generative pre-trained transformer (GPT) models have shown promise in clinical entity and relation extraction tasks because of their precise extraction and contextual understanding capability. In this work, we further leverage the Unified Medical Language System (UMLS) knowledge base to accurately identify medical concepts and improve clinical entity and relation extraction at the document level. Our framework selects UMLS concepts relevant to the text and combines them with prompts to guide language models in extracting entities. Our experiments demonstrate that this initial concept mapping and the inclusion of these mapped concepts in the prompts improves extraction results compared to few-shot extraction tasks on generic language models that do not leverage UMLS. Further, our results show that this approach is more effective than the standard Retrieval Augmented Generation (RAG) technique, where retrieved data is compared with prompt embeddings to generate results. Overall, we find that integrating UMLS concepts with GPT models significantly improves entity and relation identification, outperforming the baseline and RAG models. By combining the precise concept mapping capability of knowledge-based approaches like UMLS with the contextual understanding capability of GPT, our method highlights the potential of these approaches in specialized domains like healthcare.

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BiCAL: Bi-directional Contrastive Active Learning for Clinical Report Generation
Tianyi Wu | Jingqing Zhang | Wenjia Bai | Kai Sun

State-of-the-art performance by large pre-trained models in computer vision (CV) and natural language processing (NLP) suggests their potential for domain-specific tasks. However, training these models requires vast amounts of labelled data, a challenge in many domains due to the cost and expertise required for data labelling. Active Learning (AL) can mitigate this by selecting minimal yet informative data for model training. While AL has been mainly applied to single-modal tasks in the fields of NLP and CV, its application in multi-modal tasks remains underexplored. In this work, we proposed a novel AL strategy, Bidirectional Contrastive Active Learning strategy (BiCAL), that used both image and text latent spaces to identify contrastive samples to select batches to query for labels. BiCAL was robust to class imbalance data problems by its design, which is a problem that is commonly seen in training domain-specific models. We assessed BiCAL’s performance in domain-specific learning on the clinical report generation tasks from chest X-ray images. Our experiments showed that BiCAL outperforms State-of-the-art methods in clinical efficacy metrics, improving recall by 2.4% and F1 score by 9.5%, showcasing its effectiveness in actively training domain-specific multi-modal models.

The consequences of a healthcare data breach can be devastating for the patients, providers, and payers. The average financial impact of a data breach in recent months has been estimated to be close to USD 10 million. This is especially significant for healthcare organizations in India that are managing rapid digitization while still establishing data governance procedures that align with the letter and spirit of the law. Computer-based systems for de-identification of personal information are vulnerable to data drift, often rendering them ineffective in cross-institution settings. Therefore, a rigorous assessment of existing de-identification against local health datasets is imperative to support the safe adoption of digital health initiatives in India. Using a small set of de-identified patient discharge summaries provided by an Indian healthcare institution, in this paper, we report the nominal performance of de-identification algorithms (based on language models) trained on publicly available non-Indian datasets, pointing towards a lack of cross-institutional generalization. Similarly, experimentation with off-the-shelf de-identification systems reveals potential risks associated with the approach. To overcome data scarcity, we explore generating synthetic clinical reports (using publicly available and Indian summaries) by performing in-context learning over Large Language Models (LLMs). Our experiments demonstrate the use of generated reports as an effective strategy for creating high-performing de-identification systems with good generalization capabilities.

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Pre-training data selection for biomedical domain adaptation using journal impact metrics
Mathieu Lai-king | Patrick Paroubek

Domain adaptation is a widely used method in natural language processing (NLP) to improve the performance of a language model within a specific domain. This method is particularly common in the biomedical domain, which sees regular publication of numerous scientific articles. PubMed, a significant corpus of text, is frequently used in the biomedical domain. The primary objective of this study is to explore whether refining a pre-training dataset using specific quality metrics for scientific papers can enhance the performance of the resulting model. To accomplish this, we employ two straightforward journal impact metrics and conduct experiments by continually pre-training BERT on various subsets of the complete PubMed training set, we then evaluate the resulting models on biomedical language understanding tasks from the BLURB benchmark. Our results show that pruning using journal impact metrics is not efficient. But we also show that pre-training using fewer abstracts (but with the same number of training steps) does not necessarily decrease the resulting model’s performance.

Building genetic tools to engineer microorganisms is at the core of understanding and redesigning natural biological systems for useful purposes. Every project to build such a genetic toolbox for an organism starts with a survey of available tools. Despite a decade-long investment and advancement in the field, it is still challenging to mine information about a genetic tool published in the literature and connect that information to microbial genomics and other microbial databases. This information gap not only limits our ability to identify and adopt available tools to a new chassis but also conceals available opportunities to engineer a new microbial host. Recent advances in natural language processing (NLP), particularly large language models (LLMs), offer solutions by enabling efficient extraction of genetic terms and biological entities from a vast array of publications. This work present a method to automate this process, using text-mining to refine models with data from bioRxiv and other databases. We evaluated various LLMs to investigate their ability to recognize bacterial host organisms and genetic toolboxes for engineering. We demonstrate our methodology with a web application that integrates a conversational LLM and visualization tool, connecting user inquiries to genetic resources and literature findings, thereby saving researchers time, money and effort in their laboratory work.

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REAL: A Retrieval-Augmented Entity Linking Approach for Biomedical Concept Recognition
Darya Shlyk | Tudor Groza | Marco Mesiti | Stefano Montanelli | Emanuele Cavalleri

Large Language Models (LLMs) offer an appealing alternative to training dedicated models for many Natural Language Processing (NLP) tasks. However, outdated knowledge and hallucination issues can be major obstacles in their application in knowledge-intensive biomedical scenarios. In this study, we consider the task of biomedical concept recognition (CR) from unstructured scientific literature and explore the use of Retrieval Augmented Generation (RAG) to improve accuracy and reliability of the LLM-based biomedical CR. Our approach, named REAL (Retrieval Augmented Entity Linking), combines the generative capabilities of LLMs with curated knowledge bases to automatically annotate natural language texts with concepts from bio-ontologies. By applying REAL to benchmark corpora on phenotype concept recognition, we show its effectiveness in improving LLM-based CR performance. This research highlights the potential of combining LLMs with external knowledge sources to advance biomedical text processing.

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Is That the Right Dose? Investigating Generative Language Model Performance on Veterinary Prescription Text Analysis
Brian Hur | Lucy Lu Wang | Laura Hardefeldt | Meliha Yetisgen

Optimizing antibiotic dosing recommendations is a vital aspect of antimicrobial stewardship (AMS) programs aimed at combating antimicrobial resistance (AMR), a significant public health concern, where inappropriate dosing contributes to the selection of AMR pathogens. A key challenge is the extraction of dosing information, which is embedded in free-text clinical records and necessitates numerical transformations. This paper assesses the utility of Large Language Models (LLMs) in extracting essential prescription attributes such as dose, duration, active ingredient, and indication. We evaluate methods to optimize LLMs on this task against a baseline BERT-based ensemble model. Our findings reveal that LLMs can achieve exceptional accuracy by combining probabilistic predictions with deterministic calculations, enforced through functional prompting, to ensure data types and execute necessary arithmetic. This research demonstrates new prospects for automating aspects of AMS when no training data is available.

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MiDRED: An Annotated Corpus for Microbiome Knowledge Base Construction
William Hogan | Andrew Bartko | Jingbo Shang | Chun-Nan Hsu

The interplay between microbiota and diseases has emerged as a significant area of research facilitated by the proliferation of cost-effective and precise sequencing technologies. To keep track of the many findings, domain experts manually review publications to extract reported microbe-disease associations and compile them into knowledge bases. However, manual curation efforts struggle to keep up with the pace of publications. Relation extraction has demonstrated remarkable success in other domains, yet the availability of datasets supporting such methods within the domain of microbiome research remains limited. To bridge this gap, we introduce the Microbe-Disease Relation Extraction Dataset (MiDRED); a human-annotated dataset containing 3,116 annotations of fine-grained relationships between microbes and diseases. We hope this dataset will help address the scarcity of data in this crucial domain and facilitate the development of advanced text-mining solutions to automate the creation and maintenance of microbiome knowledge bases.

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Do Numbers Matter? Types and Prevalence of Numbers in Clinical Texts
Rahmad Mahendra | Damiano Spina | Lawrence Cavedon | Karin Verspoor

In this short position paper, we highlight the importance of numbers in clinical text. We first present a taxonomy of number variants. We then perform corpus analysis to analyze characteristics of number use in several clinical corpora. Based on our findings of extensive use of numbers, and limited understanding of the impact of numbers on clinical NLP tasks, we identify the need for a public benchmark that will support investigation of numerical processing tasks for the clinical domain.

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A Fine-grained citation graph for biomedical academic papers: the finding-citation graph
Yuan Liang | Massimo Poesio | Roonak Rezvani

Citations typically mention findings as well as papers. To model this richer notion of citation, we introduce a richer form of citation graph with nodes for both academic papers and their findings: the finding-citation graph (FCG). We also present a new pipeline to construct such a graph, which includes a finding identification module and a citation sentence extraction module. From each paper, it extracts rich basic information, abstract, and structured full text first. The abstract and vital sections, such as the results and discussion, are input into the finding identification module. This module identifies multiple findings from a paper, achieving an 80% accuracy in multiple findings evaluation. The full text is input into the citation sentence extraction module to identify inline citation sentences and citation markers, achieving 97.7% accuracy. Then, the graph is constructed using the outputs from the two modules mentioned above. We used the Europe PMC to build such a graph using the pipeline, resulting in a graph with 14.25 million nodes and 76 million edges.

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Evaluating Large Language Models for Predicting Protein Behavior under Radiation Exposure and Disease Conditions
Ryan Engel | Gilchan Park

The primary concern with exposure to ionizing radiation is the risk of developing diseases. While high doses of radiation can cause immediate damage leading to cancer, the effects of low-dose radiation (LDR) are less clear and more controversial. To further investigate this, it necessitates focusing on the underlying biological structures affected by radiation. Recent work has shown that Large Language Models (LLMs) can effectively predict protein structures and other biological properties. The aim of this research is to utilize open-source LLMs, such as Mistral, Llama 2, and Llama 3, to predict both radiation-induced alterations in proteins and the dynamics of protein-protein interactions (PPIs) within the presence of specific diseases. We show that fine-tuning these models yields state-of-the-art performance for predicting protein interactions in the context of neurodegenerative diseases, metabolic disorders, and cancer. Our findings contribute to the ongoing efforts to understand the complex relationships between radiation exposure and disease mechanisms, illustrating the nuanced capabilities and limitations of current computational models.

The latest breakthroughs in large language models (LLMs) and vision-language models (VLMs) have showcased promising capabilities toward performing a wide range of tasks. Such models are typically trained on massive datasets comprising billions of image-text pairs with diverse tasks. However, their performance on task-specific domains, such as radiology, is still under-explored. While few works have recently explored LLMs-based conversational medical models, they mainly focus on text-based analysis. In this paper, we introduce XrayGPT, a conversational medical vision-language (VLMs) model that can analyze and answer open-ended questions about chest radiographs. Specifically, we align both medical visual encoder with a fine-tuned LLM to possess visual conversation abilities, grounded in an understanding of radiographs and medical knowledge. For improved alignment of chest radiograph data, we generate ~217k interactive and high-quality summaries from free-text radiology reports. Extensive experiments are conducted to validate the merits of XrayGPT. To conduct an expert evaluation, certified medical doctors evaluated the output of our XrayGPT on a test subset and the results reveal that more than 70% of the responses are scientifically accurate, with an average score of 4/5. We hope our simple and effective method establishes a solid baseline, facilitating future research toward automated analysis and summarization of chest radiographs. Code, models, and instruction sets will be publicly released.

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Multilevel Analysis of Biomedical Domain Adaptation of Llama 2: What Matters the Most? A Case Study
Vicente Ivan Sanchez Carmona | Shanshan Jiang | Takeshi Suzuki | Bin Dong

Domain adaptation of Large Language Models (LLMs) leads to models better suited for a particular domain by capturing patterns from domain text which leads to improvements in downstream tasks. To the naked eye, these improvements are visible; however, the patterns are not so. How can we know which patterns and how much they contribute to changes in downstream scores? Through a Multilevel Analysis we discover and quantify the effect of text patterns on downstream scores of domain-adapted Llama 2 for the task of sentence similarity (BIOSSES dataset). We show that text patterns from PubMed abstracts such as clear writing and simplicity, as well as the amount of biomedical information, are the key for improving downstream scores. Also, we show how another factor not usually quantified contributes equally to downstream scores: choice of hyperparameters for both domain adaptation and fine-tuning.

Biomedical information extraction is crucial for advancing research, enhancing healthcare, and discovering treatments by efficiently analyzing extensive data. Given the extensive amount of biomedical data available, automated information extraction methods are necessary due to manual extraction’s labor-intensive, expertise-dependent, and costly nature. In this paper, we propose a novel two-stage system for information extraction where we annotate biomedical articles based on a specific ontology (HOIP). The major challenge is annotating relation between biomedical processes often not explicitly mentioned in text articles. Here, we first predict the candidate processes and then determine the relationships between these processes. The experimental results show promising outcomes in mention-agnostic process identification using Large Language Models (LLMs). In relation classification, BERT-based supervised models still outperform LLMs significantly. The end-to-end evaluation results suggest the difficulty of this task and room for improvement in both process identification and relation classification.

We present a novel approach to automating the identification of risk factors for diseases from medical literature, leveraging pre-trained models in the bio-medical domain, while tuning them for the specific task. Faced with the challenges of the diverse and unstructured nature of medical articles, our study introduces a multi-step system to first identify relevant articles, then classify them based on the presence of risk factor discussions and, finally, extract specific risk factor information for a disease through a question-answering model. Our contributions include the development of a comprehensive pipeline for the automated extraction of risk factors and the compilation of several datasets, which can serve as valuable resources for further research in this area. These datasets encompass a wide range of diseases, as well as their associated risk factors, meticulously identified and validated through a fine-grained evaluation scheme. We conducted both automatic and thorough manual evaluation, demonstrating encouraging results. We also highlight the importance of improving models and expanding dataset comprehensiveness to keep pace with the rapidly evolving field of medical research.

We explore different information extraction tools for annotation of interventions to support automated systematic reviews of preclinical AD animal studies. We compare two PICO (Population, Intervention, Comparison, and Outcome) extraction tools and two prompting-based learning strategies based on Large Language Models (LLMs). Motivated by the high recall of a dictionary-based approach, we define a two-stage method, removing false positives obtained from regexes with a pre-trained LM. With ChatGPT-based filtering using three-shot prompting, our approach reduces almost two-thirds of False Positives compared to the dictionary approach alone, while outperforming knowledge-free instructional prompting.

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Efficient Biomedical Entity Linking: Clinical Text Standardization with Low-Resource Techniques
Akshit Achara | Sanand Sasidharan | Gagan N

Clinical text is rich in information, with mentions of treatment, medication and anatomy among many other clinical terms. Multiple terms can refer to the same core concepts which can be referred as a clinical entity. Ontologies like the Unified Medical Language System (UMLS) are developed and maintained to store millions of clinical entities including the definitions, relations and other corresponding information. These ontologies are used for standardization of clinical text by normalizing varying surface forms of a clinical term through Biomedical entity linking. With the introduction of transformer-based language models, there has been significant progress in Biomedical entity linking. In this work, we focus on learning through synonym pairs associated with the entities. As compared to the existing approaches, our approach significantly reduces the training data and resource consumption. Moreover, we propose a suite of context-based and context-less reranking techniques for performing the entity disambiguation. Overall, we achieve similar performance to the state-of-the-art zero-shot and distant supervised entity linking techniques on the Medmentions dataset, the largest annotated dataset on UMLS, without any domain-based training. Finally, we show that retrieval performance alone might not be sufficient as an evaluation metric and introduce an article level quantitative and qualitative analysis to reveal further insights on the performance of entity linking methods.

In electronic health records, text data is considered a valuable resource as it complements a medical history and may contain information that cannot be easily included in tables. But why does the inclusion of clinical texts as additional input into multimodal models, not always significantly improve the performance of medical decision-support systems? Explainable AI (XAI) might provide the answer. We examine which information in text and structured data influences the performance of models in the context of multimodal decision support for biomedical tasks. Using data from an intensive care unit and targeting a mortality prediction task, we compare information that has been considered relevant by XAI methods to the opinion of a physician.

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Optimizing Multimodal Large Language Models for Detection of Alcohol Advertisements via Adaptive Prompting
Daniel Cabrera Lozoya | Jiahe Liu | Simon D’Alfonso | Mike Conway

Adolescents exposed to advertisements promoting addictive substances exhibit a higher likelihood of subsequent substance use. The predominant source for youth exposure to such advertisements is through online content accessed via smartphones. Detecting these advertisements is crucial for establishing and maintaining a safer online environment for young people. In our study, we utilized Multimodal Large Language Models (MLLMs) to identify addictive substance advertisements in digital media. The performance of MLLMs depends on the quality of the prompt used to instruct the model. To optimize our prompts, an adaptive prompt engineering approach was implemented, leveraging a genetic algorithm to refine and enhance the prompts. To evaluate the model’s performance, we augmented the RICO dataset, consisting of Android user interface screenshots, by superimposing alcohol ads onto them. Our results indicate that the MLLM can detect advertisements promoting alcohol with a 0.94 accuracy and a 0.94 F1 score.

We fill a gap in scholarship by applying a generative Large Language Model (LLM) to extract information from clinical free text about the frequency of seizures experienced by people with epilepsy. Seizure frequency is difficult to determine across time from unstructured doctors’ and nurses’ reports of outpatients’ visits that are stored in Electronic Health Records (EHRs) in the United Kingdom’s National Health Service (NHS). We employ Meta’s Llama 2 to mine the EHRs of people with epilepsy and determine, where possible, a person’s seizure frequency at a given point in time. The results demonstrate that the new, powerful generative LLMs may improve outcomes for clinical NLP research in epilepsy and other areas.

Large language models (LLMs) have recently become the leading source of answers for users’ questions online. Despite their ability to offer eloquent answers, their accuracy and reliability can pose a significant challenge. This is especially true for sensitive domains such as biomedicine, where there is a higher need for factually correct answers. This paper introduces a biomedical retrieval-augmented generation (RAG) system designed to enhance the reliability of generated responses. The system is based on a fine-tuned LLM for the referenced question-answering, where retrieved relevant abstracts from PubMed are passed to LLM’s context as input through a prompt. Its output is an answer based on PubMed abstracts, where each statement is referenced accordingly, allowing the users to verify the answer. Our retrieval system achieves an absolute improvement of 23% compared to the PubMed search engine. Based on the manual evaluation on a small sample, our fine-tuned LLM component achieves comparable results to GPT-4 Turbo in referencing relevant abstracts. We make the dataset used to fine-tune the models and the fine-tuned models based on Mistral-7B-instruct-v0.1 and v0.2 publicly available.

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Low Resource ICD Coding of Hospital Discharge Summaries
Ashton Williamson | David de Hilster | Amnon Meyers | Nina Hubig | Amy Apon

Medical coding is the process by which standardized medical codes are assigned to patient health records. This is a complex and challenging task that typically requires an expert human coder to review health records and assign codes from a classification system based on a standard set of rules. Since health records typically consist of a large proportion of free-text documents, this problem has traditionally been approached as a natural language processing (NLP) task. While machine learning-based methods have seen recent popularity on this task, they tend to struggle with codes that are assigned less frequently, for which little or no training data exists. In this work we utilize the open-source NLP programming language, NLP++, to design and build an automated system to assign International Classification of Diseases (ICD) codes to discharge summaries that functions in the absence of labeled training data. We evaluate our system using the MIMIC-III dataset and find that for codes with little training data, our approach achieves competitive performance compared to state-of-the-art machine learning approaches.

In emergency wards, patients are prioritized by clinical staff according to the urgency of their medical condition. This can be achieved by categorizing patients into different labels of urgency ranging from immediate to not urgent. However, in order to train machine learning models offering support in this regard, there is more than approaching this as a multi-class problem. This work explores the challenges and obstacles of automatic triage using anonymized real-world multi-modal ambulance data in Germany.

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Creating Ontology-annotated Corpora from Wikipedia for Medical Named-entity Recognition
Johann Frei | Frank Kramer

Acquiring annotated corpora for medical NLP is challenging due to legal and privacy constraints and costly annotation efforts, and using annotated public datasets may do not align well to the desired target application in terms of annotation style or language. We investigate the approach of utilizing Wikipedia and WikiData jointly to acquire an unsupervised annotated corpus for named-entity recognition (NER). By controlling the annotation ruleset through WikiData’s ontology, we extract custom-defined annotations and dynamically impute weak annotations by an adaptive loss scaling. Our validation on German medication detection datasets yields competitive results. The entire pipeline only relies on open models and data resources, enabling reproducibility and open sharing of models and corpora. All relevant assets are shared on GitHub.

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Paragraph Retrieval for Enhanced Question Answering in Clinical Documents
Vojtech Lanz | Pavel Pecina

Healthcare professionals often manually extract information from large clinical documents to address patient-related questions. The use of Natural Language Processing (NLP) techniques, particularly Question Answering (QA) models, is a promising direction for improving the efficiency of this process. However, document-level QA from large documents is often impractical or even infeasible (for model training and inference). In this work, we solve the document-level QA from clinical reports in a two-step approach: first, the entire report is split into segments and for a given question the most relevant segment is predicted by a NLP model; second, a QA model is applied to the question and the retrieved segment as context. We investigate the effectiveness of heading-based and naive paragraph segmentation approaches for various paragraph lengths on two subsets of the emrQA dataset. Our experiments reveal that an average paragraph length used as a parameter for the segmentation has no significant effect on performance during the whole document-level QA process. That means experiments focusing on segmentation into shorter paragraphs perform similarly to those focusing on entire unsegmented reports. Surprisingly, naive uniform segmentation is sufficient even though it is not based on prior knowledge of the clinical document’s characteristics.

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CID at RRG24: Attempting in a Conditionally Initiated Decoding of Radiology Report Generation with Clinical Entities
Yuxiang Liao | Yuanbang Liang | Yipeng Qin | Hantao Liu | Irena Spasic

Radiology Report Generation (RRG) seeks to leverage deep learning techniques to automate the reporting process of radiologists. Current methods are typically modelling RRG as an image-to-text generation task that takes X-ray images as input and generates textual reports describing the corresponding clinical observations. However, the wording of the same clinical observation could have been influenced by the expression preference of radiologists. Nevertheless, such variability can be mitigated by normalizing textual reports into structured representations such as a graph structure. In this study, we attempt a novel paradigm for incorporating graph structural data into the RRG model. Our approach involves predicting graph labels based on visual features and subsequently initiating the decoding process through a template injection conditioned on the predicted labels. We trained and evaluated our model on the BioNLP 2024 Shared Task on Large-Scale Radiology Report Generation and submitted our results to the ViLMedic RRG leaderboard. Although our model showed a moderate ranking on the leaderboard, the results provide preliminary evidence for the feasibility of this new paradigm, warranting further exploration and refinement.

This paper discusses the participation of the MSR MAIRA team in the Large-Scale Radiology Report Generation Shared Task Challenge, as part of the BioNLP workshop at ACL 2024. We present a radiology-specific multimodal model designed to generate radiological reports from chest X-Rays (CXRs). Our proposed model combines a CXR-specific image encoder RAD-DINO with a Large Language Model (LLM) based on Vicuna-7B, via a multi-layer perceptron (MLP) adapter. Both the adapter and the LLM have been fine-tuned in a single-stage training setup to generate radiology reports. Experimental results indicate that a joint training setup with findings and impression sections improves findings prediction. Additionally, incorporating lateral images alongside frontal images when available further enhances all metrics. More information and resources about MAIRA can be found on the project website: http://aka.ms/maira.

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AIRI at RRG24: LLaVa with specialised encoder and decoder
Marina Munkhoeva | Dmitry Umerenkov | Valentin Samokhin

We present a new approach to generating the ‘Findings’ and ‘Impression’ sections in the chest X-rays radiology reports, developed as part of the shared radiology task at BioNLP 2024. By integrating a DINOv2 vision encoder trained on medical data with specialized biomedical large language model using the LLaVA framework, our method addresses complex medical semantics and diverse findings in imaging. We use datasets from PadChest, BIMCV-COVID19, CheXpert, OpenI, and MIMIC-CXR. The evaluation metrics demonstrate our method’s effectiveness and the potential for automating the generation of radiology reports.

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iHealth-Chile-1 at RRG24: In-context Learning and Finetuning of a Large Multimodal Model for Radiology Report Generation
Diego Campanini | Oscar Loch | Pablo Messina | Rafael Elberg | Denis Parra

This paper presents the approach of the iHealth-Chile-1 team for the shared task of Large-Scale Radiology Report Generation at the BioNLP workshop, inspired by progress in large multimodal models for processing images and text. In this work, we leverage LLaVA, a Visual-Language Model (VLM), composed of a vision-encoder, a vision-language connector or adapter, and a large language model able to process text and visual embeddings. We achieve our best result by enriching the input prompt of LLaVA with the text output of a simpler report generation model. With this enriched-prompt technique, we improve our results in 4 of 5 metrics (BLEU-4, Rouge-L, BertScore and F1-RadGraph,), only doing in-context learning. Moreover, we provide details about different architecture settings, fine-tuning strategies, and dataset configurations.

This paper presents the approaches of the iHealth-Chile-3 and iHealth-Chile-2 teams for the shared task of Large-Scale Radiology Report Generation at the BioNLP workshop. Inspired by prior work on template-based report generation, both teams focused on exploring various template-based strategies, using predictions from multi-label image classifiers as input. Our best approach achieved a modest F1-RadGraph score of 19.42 on the findings hidden test set, ranking 7th on the leaderboard. Notably, we consistently observed a discrepancy between our classification metrics and the F1-CheXbert metric reported on the leaderboard, which always showed lower scores. This suggests that the F1-CheXbert metric may be missing some of the labels mentioned by the templates.

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Gla-AI4BioMed at RRG24: Visual Instruction-tuned Adaptation for Radiology Report Generation
Xi Zhang | Zaiqiao Meng | Jake Lever | Edmond S.L. Ho

This paper introduces a radiology-focused visual language model designed to generate radiology reports from chest X-rays. Building on previous findings that large language models can acquire multimodal capabilities when aligned with pretrained vision encoders, we demonstrate similar potential with chest X-ray images. The model combines an image encoder (CLIP) with a fine-tuned large language model (LLM) based on the Vicuna-7B architecture. The training process involves a two-stage approach: initial alignment of chest X-ray features with the LLM, followed by fine-tuning for radiology report generation. The study highlights the importance of generating both FINDINGS and IMPRESSIONS sections in radiology reports and evaluates the model’s performance using various metrics, achieving notable accuracy in generating high-quality medical reports. The research also addresses the need for domain-specific fine-tuning to capture the intricate details necessary for accurate medical interpretations and reports.

Radiology report generation (RRG) aims to create free-text radiology reports from clinical imaging. Our solution employs a lightweight multimodal language model (MLLM) enhanced with a two-stage post-processing strategy, utilizing a Large Language Model (LLM) to boost diagnostic accuracy and ensure patient safety. We introduce the “First, Do No Harm” SafetyNet, which incorporates Xraydar, an advanced X-ray classification model, to cross-verify the model outputs and specifically address false negatives from the MLLM. This comprehensive approach combines the efficiency of lightweight models with the robustness of thorough post-processing techniques, offering a reliable solution for radiology report generation. Our system achieved fourth place on the F1-Radgraph metric for findings generation in the Radiology Report Generation Shared Task (RRG24).

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Shimo Lab at “Discharge Me!”: Discharge Summarization by Prompt-Driven Concatenation of Electronic Health Record Sections
Yunzhen He | Hiroaki Yamagiwa | Hidetoshi Shimodaira

In this paper, we present our approach to the shared task “Discharge Me!” at the BioNLP Workshop 2024. The primary goal of this task is to reduce the time and effort clinicians spend on writing detailed notes in the electronic health record (EHR). Participants develop a pipeline to generate the “Brief Hospital Course” and “Discharge Instructions” sections from the EHR. Our approach involves a first step of extracting the relevant sections from the EHR. We then add explanatory prompts to these sections and concatenate them with separate tokens to create the input text. To train a text generation model, we perform LoRA fine-tuning on the ClinicalT5-large model. On the final test data, our approach achieved a ROUGE-1 of 0.394, which is comparable to the top solutions.

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Ixa-Med at Discharge Me! Retrieval-Assisted Generation for Streamlining Discharge Documentation
Jordan C. Koontz | Maite Oronoz | Alicia Pérez

In this paper we present our system for the BioNLP ACL’24 “Discharge Me!” task on automating discharge summary section generation. Using Retrieval-Augmented Generation, we combine a Large Language Model (LLM) with external knowledge to guide the generation of the target sections. Our approach generates structured patient summaries from discharge notes using an instructed LLM, retrieves relevant “Brief Hospital Course” and “Discharge Instructions” examples via BM25 and SentenceBERT, and provides this context to a frozen LLM for generation. Our top system using SentenceBERT retrieval achieves an overall score of 0.183, outperforming zero-shot baselines. We analyze performance across different aspects, discussing limitations and future research directions.

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QUB-Cirdan at “Discharge Me!”: Zero shot discharge letter generation by open-source LLM
Rui Guo | Greg Farnan | Niall McLaughlin | Barry Devereux

The BioNLP ACL’24 Shared Task on Streamlining Discharge Documentation aims to reduce the administrative burden on clinicians by automating the creation of critical sections of patient discharge letters. This paper presents our approach using the Llama3 8B quantized model to generate the “Brief Hospital Course” and “Discharge Instructions” sections. We employ a zero-shot method combined with Retrieval-Augmented Generation (RAG) to produce concise, contextually accurate summaries. Our contributions include the development of a curated template-based approach to ensure reliability and consistency, as well as the integration of RAG for word count prediction. We also describe several unsuccessful experiments to provide insights into our pathway for the competition. Our results demonstrate the effectiveness and efficiency of our approach, achieving high scores across multiple evaluation metrics.

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e-Health CSIRO at “Discharge Me!” 2024: Generating Discharge Summary Sections with Fine-tuned Language Models
Jinghui Liu | Aaron Nicolson | Jason Dowling | Bevan Koopman | Anthony Nguyen

Clinical documentation is an important aspect of clinicians’ daily work and often demands a significant amount of time. The BioNLP 2024 Shared Task on Streamlining Discharge Documentation (Discharge Me!) aims to alleviate this documentation burden by automatically generating discharge summary sections, including brief hospital course and discharge instruction, which are often time-consuming to synthesize and write manually. We approach the generation task by fine-tuning multiple open-sourced language models (LMs), including both decoder-only and encoder-decoder LMs, with various configurations on input context. We also examine different setups for decoding algorithms, model ensembling or merging, and model specialization. Our results show that conditioning on the content of discharge summary prior to the target sections is effective for the generation task. Furthermore, we find that smaller encoder-decoder LMs can work as well or even slightly better than larger decoder-based LMs fine-tuned through LoRA. The model checkpoints from our team (aehrc) are openly available.

Automatic generation of discharge summaries presents significant challenges due to the length of clinical documentation, the dispersed nature of patient information, and the diverse terminology used in healthcare. This paper presents a hybrid solution for generating discharge summary sections as part of our participation in the “Discharge Me!” Challenge at the BioNLP 2024 Shared Task. We developed a two-stage generation method using both extractive and abstractive techniques, in which we first apply name entity recognition (NER) to extract key clinical concepts, which are then used as input for a prompt-tuning based GatorTronGPT model to generate coherent text for two important sections including “Brief Hospital Course” and “Discharge Instructions”. Our system was ranked 5th in this challenge, achieving an overall score of 0.284. The results demonstrate the effectiveness of our hybrid solution in improving the quality of automated discharge section generation.

This paper presents our contribution to the Streamlining Discharge Documentation shared task organized as part of the ACL’24 workshop. We propose MEDISCHARGE (Meditron-7B Based Medical Summary Generation System for Discharge Me), an LLM-based system to generate Brief Hospital Course and Discharge Instruction summaries based on a patient’s Electronic Health Record. Our system is build on a Meditron-7B with context window extension, ensuring the system can handle cases of variable lengths with high quality. When the length of the input exceeds the system input limitation, we use a dynamic information selection framework to automatically extract important sections from the full discharge text. Then, extracted sections are removed in increasing order of importance until the input length requirement is met. We demonstrate our approach outperforms tripling the size of the context window of the model. Our system obtains a 0.289 overall score in the leaderboard, an improvement of 183% compared to the baseline, and a ROUGE-1 score of 0.444, achieving a second place performance in the shared task.

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UoG Siephers at “Discharge Me!”: Exploring Ways to Generate Synthetic Patient Notes From Multi-Part Electronic Health Records
Erlend Frayling | Jake Lever | Graham McDonald

This paper presents the UoG Siephers team participation at the Discharge Me! Shared Task on Streamlining Discharge Documentation. For our participation, we investigate appropriately selecting and encoding specific sections of Electronic Health Records (EHR) as input data for sequence-to-sequence models, to generate the discharge instructions and brief hospital course sections of a patient’s EHR. We found that, despite the large volume of disparate information that is often available in EHRs, selectively choosing an appropriate EHR section for training and prompting sequence-to-sequence models resulted in improved generative quality. In particular, we found that using only the history of present illness section of an EHR as input often led to better performance than using multiple EHR sections.

Healthcare providers spend a significant amount of time reading and synthesizing electronic health records (EHRs), negatively impacting patient outcomes and causing provider burnout. Traditional supervised machine learning approaches using large language models (LLMs) to summarize clinical text have struggled due to hallucinations and lack of relevant training data. Here, we present a novel, simplified solution for the “Discharge Me!” shared task. Our approach mimics human clinical workflow, using pre-trained LLMs to answer specific questions and summarize the answers obtained from discharge summaries and other EHR sections. This method (i) avoids hallucinations through hybrid-RAG/zero-shot contextualized prompting; (ii) requires no extensive training or fine-tuning; and (iii) is adaptable to various clinical tasks.

In this work, we propose our top-ranking (2nd place) pipeline for the generation of discharge summary subsections as a part of the BioNLP 2024 Shared Task 2: “Discharge Me!”. We evaluate both encoder-decoder and state-of-the-art decoder-only language models on the generation of two key sections of the discharge summary. To evaluate the ability of NLP methods to further alleviate the documentation burden on physicians, we also design a novel pipeline to generate the brief hospital course directly from structured information found in the EHR. Finally, we evaluate a constrained beam search approach to inject external knowledge about relevant patient problems into the text generation process. We find that a BioBART model fine-tuned on a larger fraction of the data without constrained beam search outperforms all other models.

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IgnitionInnovators at “Discharge Me!”: Chain-of-Thought Instruction Finetuning Large Language Models for Discharge Summaries
An Quang Tang | Xiuzhen Zhang | Minh Ngoc Dinh

This paper presents our proposed approach to the Discharge Me! shared task, collocated with the 23th Workshop on Biomedical Natural Language Processing (BioNLP). In this work, we develop an LLM-based framework for solving the Discharge Summary Documentation (DSD) task, i.e., generating the two critical target sections ‘Brief Hospital Course’ and ‘Discharge Instructions’ in the discharge summary. By streamlining the recent instruction-finetuning process on LLMs, we explore several prompting strategies for optimally adapting LLMs to specific generation task of DSD. Experimental results show that providing a clear output structure, complimented by a set of comprehensive Chain-of-Thoughts (CoT) questions, effectively improves the model’s reasoning capability, and thereby, enhancing the structural correctness and faithfulness of clinical information in the generated text. Source code is available at: https://anonymous.4open.science/r/Discharge_LLM-A233

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MLBMIKABR at “Discharge Me!”: Concept Based Clinical Text Description Generation
Abir Naskar | Jane Hocking | Patty Chondros | Douglas Boyle | Mike Conway

This paper presents a method called Concept Based Description Generation, aimed at creating summaries (Brief Hospital Course and Discharge Instructions) using source (Discharge and Radiology) texts. We propose a rule-based approach for segmenting both the source and target texts. In the target text, we not only segment the content but also identify the concept of each segment based on text patterns. Our methodology involves creating a combined summarized version of each text segment, extracting important information, and then fine-tuning a Large Language Model (LLM) to generate aspects. Subsequently, we fine-tune a new LLM using a specific aspect, the combined summary, and a list of all aspects to generate detailed descriptions for each task. This approach integrates segmentation, concept identification, summarization, and language modeling to achieve accurate and informative descriptions for medical documentation tasks. Due to lack to time, We could only train on 10000 training data.

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DeakinNLP at BioLaySumm: Evaluating Fine-tuning Longformer and GPT-4 Prompting for Biomedical Lay Summarization
Huy Quoc To | Ming Liu | Guangyan Huang

This paper presents our approaches for the BioLaySumm 2024 Shared Task. We evaluate two methods for generating lay summaries based on biomedical articles: (1) fine-tuning the Longformer-Encoder-Decoder (LED) model, and (2) zero-shot and few-shot prompting on GPT-4. In the fine-tuning approach, we individually fine-tune the LED model using two datasets: PLOS and eLife. This process is conducted under two different settings: one utilizing 50% of the training dataset, and the other utilizing the entire 100% of the training dataset. We compare the results of both methods with GPT-4 in zero-shot and few-shot prompting. The experiment results demonstrate that fine-tuning with 100% of the training data achieves better performance than prompting with GPT-4. However, under data scarcity circumstances, prompting GPT-4 seems to be a better solution.

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ELiRF-VRAIN at BioLaySumm: Boosting Lay Summarization Systems Performance with Ranking Models
Vicent Ahuir | Diego Torres | Encarna Segarra | Lluís-F. Hurtado

This paper presents our contribution to the BioLaySumm 2024 shared task of the 23rd BioNLP Workshop. The task is to create a lay summary, given a biomedical research article and its technical summary. As the input to the system could be large, a Longformer Encoder-Decoder (LED) has been used. We continuously pre-trained a general domain LED model with biomedical data to adapt it to this specific domain. In the pre-training phase, several pre-training tasks were aggregated to inject linguistic knowledge and increase the abstractivity of the generated summaries. Since the distribution of samples between the two datasets, eLife and PLOS, is unbalanced, we fine-tuned two models: one for eLife and another for PLOS. To increase the quality of the lay summaries of the system, we developed a regression model that helps us rank the summaries generated by the summarization models. This regression model predicts the quality of the summary in three different aspects: Relevance, Readability, and Factuality. We present the results of our models and a study to measure the ranking capabilities of the regression model.

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BioLay_AK_SS at BioLaySumm: Domain Adaptation by Two-Stage Fine-Tuning of Large Language Models used for Biomedical Lay Summary Generation
Akanksha Karotia | Seba Susan

Lay summarization is essential but challenging, as it simplifies scientific information for non-experts and keeps them updated with the latest scientific knowledge. In our participation in the Shared Task: Lay Summarization of Biomedical Research Articles @ BioNLP Workshop (Goldsack et al., 2024), ACL 2024, we conducted a comprehensive evaluation on abstractive summarization of biomedical literature using Large Language Models (LLMs) and assessed the performance using ten metrics across three categories: relevance, readability, and factuality, using eLife and PLOS datasets provided by the organizers. We developed a two-stage framework for lay summarization of biomedical scientific articles. In the first stage, we generated summaries using BART and PEGASUS LLMs by fine-tuning them on the given datasets. In the second stage, we combined the generated summaries and input them to BioBART, and then fine-tuned it on the same datasets. Our findings show that combining general and domain-specific LLMs enhances performance.

This paper details the efforts of the WisPerMed team in the BioLaySumm2024 Shared Task on automatic lay summarization in the biomedical domain, aimed at making scientific publications accessible to non-specialists. Large language models (LLMs), specifically the BioMistral and Llama3 models, were fine-tuned and employed to create lay summaries from complex scientific texts. The summarization performance was enhanced through various approaches, including instruction tuning, few-shot learning, and prompt variations tailored to incorporate specific context information. The experiments demonstrated that fine-tuning generally led to the best performance across most evaluated metrics. Few-shot learning notably improved the models’ ability to generate relevant and factually accurate texts, particularly when using a well-crafted prompt. Additionally, a Dynamic Expert Selection (DES) mechanism to optimize the selection of text outputs based on readability and factuality metrics was developed. Out of 54 participants, the WisPerMed team reached the 4th place, measured by readability, factuality, and relevance. Determined by the overall score, our approach improved upon the baseline by approx. 5.5 percentage points and was only approx. 1.5 percentage points behind the first place.

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HULAT-UC3M at BiolaySumm: Adaptation of BioBART and Longformer models to summarizing biomedical documents
Adrian Gonzalez Sanchez | Paloma Martínez

This article presents our submission to the Bio- LaySumm 2024 shared task: Lay Summarization of Biomedical Research Articles. The objective of this task is to generate summaries that are simplified in a concise and less technical way, in order to facilitate comprehension by non-experts users. A pre-trained BioBART model was employed to fine-tune the articles from the two journals, thereby generating two models, one for each journal. The submission achieved the 12th best ranking in the task, attaining a meritorious first place in the Relevance ROUGE-1 metric.

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Saama Technologies at BioLaySumm: Abstract based fine-tuned models with LoRA
Hwanmun Kim | Kamal raj Kanakarajan | Malaikannan Sankarasubbu

Lay summarization of biomedical research articles is a challenging problem due to their use of technical terms and background knowledge requirements, despite the potential benefits of these research articles to the public. We worked on this problem as participating in BioLaySumm 2024. We experimented with various fine-tuning approaches to generate better lay summaries for biomedical research articles. After several experiments, we built a LoRA model with unsupervised fine-tuning based on the abstracts of the given articles, followed by a post-processing unit to take off repeated sentences. Our model was ranked 3rd overall in the BioLaySumm 2024 leaderboard. We analyzed the different approaches we experimented with and suggested several ideas to improve our model further.

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AUTH at BioLaySumm 2024: Bringing Scientific Content to Kids
Loukritia Stefanou | Tatiana Passali | Grigorios Tsoumakas

The BioLaySumm 2024 shared task at the ACL 2024 BioNLP workshop aims to transform biomedical research articles into lay summaries suitable for a broad audience, including children. We utilize the BioBART model, designed for the biomedical sector, to convert complex scientific data into clear, concise summaries. Our dataset, which includes a range of scientific abstracts, enables us to address the diverse information needs of our audience. This focus ensures that our summaries are accessible to both general and younger lay audience. Additionally, we employ specialized tokens and augmentation techniques to optimize the model’s performance. Our methodology proved effective, earning us the 7th rank on the final leaderboard out of 57 participants.

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SINAI at BioLaySumm: Self-Play Fine-Tuning of Large Language Models for Biomedical Lay Summarisation
Mariia Chizhikova | Manuel Carlos Díaz-Galiano | L. Alfonso Ureña-López | María-Teresa Martín-Valdivia

An effective disclosure of scientific knowledge and advancements to the general public is often hindered by the complexity of the technical language used in research which often results very difficult, if not impossible, for non-experts to understand. In this paper we present the approach developed by the SINAI team as the result of our participation in BioLaySumm shared task hosted by the BioNLP workshop at ACL 2024. Our approach stems from the experimentation we performed in order to test the ability of state-of-the-art pre-trained large language models, namely GPT 3.5, GPT 4 and Llama-3, to tackle this task in a few-shot manner. In order to improve this baseline, we opted for fine-tuning Llama-3 by applying parameter-efficient methodologies. The best performing system which resulted from applying self-play fine tuning method which allows the model to improve while learning to distinguish between its own generations from the previous step from the gold standard summaries. This approach achieved 0.4205 ROUGE-1 score and 0.8583 BERTScore.

This paper introduces the RAG-RLRC-LaySum framework, designed to make complex biomedical research accessible to laymen through advanced Natural Language Processing (NLP) techniques. Our innovative Retrieval Augmentation Generation (RAG) solution, enhanced by a reranking method, utilizes multiple knowledge sources to ensure the precision and pertinence of lay summaries. Additionally, our Reinforcement Learning for Readability Control (RLRC) strategy improves readability, making scientific content comprehensible to non-specialists. Evaluations using the publicly accessible PLOS and eLife datasets show that our methods surpass Plain Gemini model, demonstrating a 20% increase in readability scores, a 15% improvement in ROUGE-2 relevance scores, and a 10% enhancement in factual accuracy. The RAG-RLRC-LaySum framework effectively democratizes scientific knowledge, enhancing public engagement with biomedical discoveries.

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Team YXZ at BioLaySumm: Adapting Large Language Models for Biomedical Lay Summarization
Jieli Zhou | Cheng Ye | Pengcheng Xu | Hongyi Xin

Biomedical literature are crucial for disseminating new scientific findings. However, the complexity of these research articles often leads to misinterpretations by the public. To address this urgent issue, we participated in the BioLaySumm task at the 2024 ACL BioNLP workshop, which focuses on automatically simplifying technical biomedical articles for non-technical audiences. We conduct a systematic evaluation of the SOTA large language models (LLMs) in 2024 and found that LLMs can generally achieve better readability scores than smaller models like Bart. Then we iteratively developed techniques of title infusing, K-shot prompting , LLM rewriting and instruction finetuning to further boost readability while balancing factuality and relevance. Notably, our submission achieved the first place in readability at the workshop, and among the top-3 teams with the highest readability scores, we have the best overall rank. Here, we present our experiments and findings on how to effectively adapt LLMs for automatic lay summarization. Our code is available at https://github.com/zhoujieli/biolaysumm.

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Eulerian at BioLaySumm: Preprocessing Over Abstract is All You Need
Satyam Modi | T Karthikeyan

In this paper, we present our approach to the BioLaySumm 2024 Shared Task on Lay Sum- marization of Biomedical Research Articles at BioNLP workshop 2024. The task aims to generate lay summaries from the abstract and main texts of biomedical research articles, making them understandable to lay audiences. We used some preprocessing techniques and finetuned FLAN-T5 models for the summarization task. Our method achieved an AlignScore of 0.9914 and a SummaC metric score of 0.944.

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HGP-NLP at BioLaySumm: Leveraging LoRA for Lay Summarization of Biomedical Research Articles using Seq2Seq Transformers
Hemang Malik | Gaurav Pradeep | Pratinav Seth

Lay summarization aims to generate summaries of technical articles for non-experts, enabling easy comprehension for a general audience. The technical language used in research often hinders effective communication of scientific knowledge, making it difficult for non-experts to understand. Automatic lay summarization can enhance access to scientific literature, promoting interdisciplinary knowledge sharing and public understanding. This has become especially important for biomedical articles, given the current global need for clear medical information. Large Language Models (LLMs), with their remarkable language understanding capabilities, are ideal for abstractive summarization, helping to make complex information accessible to the public. This paper details our submissions to the BioLaySumm 2024 Shared Task: Lay Summarization of Biomedical Research Articles. We fine-tune and evaluate sequence-to-sequence models like T5 across various training dataset settings and optimization methods such as LoRA for lay summarization. Our submission achieved the 53rd position overall.

Lay summaries play a crucial role in making scientific research accessible to a wider audience. However, generating lay summaries from lengthy articles poses significant challenges. We consider two approaches to address this issue: Hard Truncation, which preserves the most informative initial portion of the article, and Text Chunking, which segments articles into smaller, manageable chunks. Our workflow encompasses data preprocessing, augmentation, prompt engineering, and fine-tuning large language models. We explore the influence of pretrained model selection, inference prompt design, and hyperparameter tuning on summarization performance. Our methods demonstrate effectiveness in generating high-quality, informative lay summaries, achieving the second-best performance in the BioLaySumm shared task at BioNLP 2024.

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bib (full) Proceedings of the 2nd Workshop on Cross-Cultural Considerations in NLP

As the scaling of Large Language Models (LLMs) has dramatically enhanced their capabilities, there has been a growing focus on the alignment problem to ensure their responsible and ethical use. While existing alignment efforts predominantly concentrate on universal values such as the HHH principle, the aspect of culture, which is inherently pluralistic and diverse, has not received adequate attention. This work introduces a new benchmark, CDEval, aimed at evaluating the cultural dimensions of LLMs. CDEval is constructed by incorporating both GPT-4’s automated generation and human verification, covering six cultural dimensions across seven domains. Our comprehensive experiments provide intriguing insights into the culture of mainstream LLMs, highlighting both consistencies and variations across different dimensions and domains. The findings underscore the importance of integrating cultural considerations in LLM development, particularly for applications in diverse cultural settings. This benchmark serves as a valuable resource for cultural studies in LLMs, paving the way for more culturally aware and sensitive models.

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Conformity, Confabulation, and Impersonation: Persona Inconstancy in Multi-Agent LLM Collaboration
Razan Baltaji | Babak Hemmatian | Lav Varshney

This study explores the sources of instability in maintaining cultural personas and opinions within multi-agent LLM systems. Drawing on simulations of inter-cultural collaboration and debate, we analyze agents’ pre- and post-discussion private responses alongside chat transcripts to assess the stability of cultural personas and the impact of opinion diversity on group outcomes. Our findings suggest that multi-agent discussions can encourage collective decisions that reflect diverse perspectives, yet this benefit is tempered by the agents’ susceptibility to conformity due to perceived peer pressure and challenges in maintaining consistent personas and opinions. Counterintuitively, instructions that encourage debate in support of one’s opinions increase the rate of instability. Without addressing the factors we identify, the full potential of multi-agent frameworks for producing more culturally diverse AI outputs will remain untapped.

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Synchronizing Approach in Designing Annotation Guidelines for Multilingual Datasets: A COVID-19 Case Study Using English and Japanese Tweets
Kiki Ferawati | Wan Jou She | Shoko Wakamiya | Eiji Aramaki

The difference in culture between the U.S. and Japan is a popular subject for Western vs. Eastern cultural comparison for researchers. One particular challenge is to obtain and annotate multilingual datasets. In this study, we utilized COVID-19 tweets from the two countries as a case study, focusing particularly on discussions concerning masks. The annotation task was designed to gain insights into societal attitudes toward the mask policies implemented in both countries. The aim of this study is to provide a practical approach for the annotation task by thoroughly documenting how we aligned the multilingual annotation guidelines to obtain a comparable dataset. We proceeded to document the effective practices during our annotation process to synchronize our multilingual guidelines. Furthermore, we discussed difficulties caused by differences in expression style and culture, and potential strategies that helped improve our agreement scores and reduce discrepancies between the annotation results in both languages. These findings offer an alternative method for synchronizing multilingual annotation guidelines and achieving feasible agreement scores for cross-cultural annotation tasks. This study resulted in a multilingual guideline in English and Japanese to annotate topics related to public discourses about COVID-19 masks in the U.S. and Japan.

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CRAFT: Extracting and Tuning Cultural Instructions from the Wild
Bin Wang | Geyu Lin | Zhengyuan Liu | Chengwei Wei | Nancy Chen

Large language models (LLMs) have rapidly evolved as the foundation of various natural language processing (NLP) applications. Despite their wide use cases, their understanding of culturally-related concepts and reasoning remains limited. Meantime, there is a significant need to enhance these models’ cultural reasoning capabilities, especially concerning underrepresented regions. This paper introduces a novel pipeline for extracting high-quality, culturally-related instruction tuning datasets from vast unstructured corpora. We utilize a self-instruction generation pipeline to identify cultural concepts and trigger instruction. By integrating with a general-purpose instruction tuning dataset, our model demonstrates enhanced capabilities in recognizing and understanding regional cultural nuances, thereby enhancing its reasoning capabilities. We conduct experiments across three regions: Singapore, the Philippines, and the United States, achieving performance improvement of up to 6%. Our research opens new avenues for extracting cultural instruction tuning sets directly from unstructured data, setting a precedent for future innovations in the field.

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Does Cross-Cultural Alignment Change the Commonsense Morality of Language Models?
Yuu Jinnai

Alignment of the language model with human preferences is a common approach to making a language model useful to end users.However, most alignment work is done in English, and human preference datasets are dominated by English, reflecting only the preferences of English-speaking annotators.Nevertheless, it is common practice to use the English preference data, either directly or by translating it into the target language, when aligning a multilingual language model.The question is whether such an alignment strategy marginalizes the preference of non-English speaking users.To this end, we investigate the effect of aligning Japanese language models with (mostly) English resources.In particular, we focus on evaluating whether the commonsense morality of the resulting fine-tuned models is aligned with Japanese culture using the JCommonsenseMorality (JCM) and ETHICS datasets.The experimental results show that the fine-tuned model outperforms the SFT model. However, it does not demonstrate the same level of improvement as a model fine-tuned using the JCM, suggesting that while some aspects of commonsense morality are transferable, others may not be.

While preliminary findings indicate that multilingual LLMs exhibit reduced bias compared to monolingual ones, a comprehensive understanding of the effect of multilingual training on bias mitigation, is lacking. This study addresses this gap by systematically training six LLMs of identical size (2.6B parameters) and architecture: five monolingual models (English, German, French, Italian, and Spanish) and one multilingual model trained on an equal distribution of data across these languages, all using publicly available data. To ensure robust evaluation, standard bias benchmarks were automatically translated into the five target languages and verified for both translation quality and bias preservation by human annotators. Our results consistently demonstrate that multilingual training effectively mitigates bias. Moreover, we observe that multilingual models achieve not only lower bias but also superior prediction accuracy when compared to monolingual models with the same amount of training data, model architecture, and size.

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Sociocultural Considerations in Monitoring Anti-LGBTQ+ Content on Social Media
Sidney Wong

The purpose of this paper is to ascertain the influence of sociocultural factors (i.e., social, cultural, and political) in the development of hate speech detection systems. We set out to investigate the suitability of using open-source training data to monitor levels of anti-LGBTQ+ content on social media across different national-varieties of English. Our findings suggests the social and cultural alignment of open-source hate speech data sets influences the predicted outputs. Furthermore, the keyword-search approach of anti-LGBTQ+ slurs in the development of open-source training data encourages detection models to overfit on slurs; therefore, anti-LGBTQ+ content may go undetected. We recommend combining empirical outputs with qualitative insights to ensure these systems are fit for purpose.

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Are Generative Language Models Multicultural? A Study on Hausa Culture and Emotions using ChatGPT
Ibrahim Ahmad | Shiran Dudy | Resmi Ramachandranpillai | Kenneth Church

Large Language Models (LLMs), such as ChatGPT, are widely used to generate content for various purposes and audiences. However, these models may not reflect the cultural and emotional diversity of their users, especially for low-resource languages. In this paper, we investigate how ChatGPT represents Hausa’s culture and emotions. We compare responses generated by ChatGPT with those provided by native Hausa speakers on 37 culturally relevant questions. We conducted experiments using emotion analysis. We also used two similarity metrics to measure the alignment between human and ChatGPT responses. We also collect human participants ratings and feedback on ChatGPT responses. Our results show that ChatGPT has some level of similarity to human responses, but also exhibits some gaps and biases in its knowledge and awareness of Hausa culture and emotions. We discuss the implications and limitations of our methodology and analysis and suggest ways to improve the performance and evaluation of LLMs for low-resource languages.

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Computational Language Documentation: Designing a Modular Annotation and Data Management Tool for Cross-cultural Applicability
Alexandra O’Neil | Daniel Swanson | Shobhana Chelliah

While developing computational language documentation tools, researchers must center the role of language communities in the process by carefully reflecting on and designing tools to support the varying needs and priorities of different language communities. This paper provides an example of how cross-cultural considerations discussed in literature about language documentation, data sovereignty, and community-led documentation projects can motivate the design of a computational language documentation tool by reflecting on our design process as we work towards developing an annotation and data management tool. We identify three recurring themes for cross-cultural consideration in the literature - Linguistic Sovereignty, Cultural Specificity, and Reciprocity - and present eight essential features for an annotation and data management tool that reflect these themes.

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bib (full) Proceedings of the 1st Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2024)

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Climate Policy Transformer: Utilizing NLP to track the coherence of Climate Policy Documents in the Context of the Paris Agreement
Prashant Singh | Erik Lehmann | Mark Tyrrell

Climate policy implementation is pivotal inglobal efforts to mitigate and adapt to climatechange. In this context, this paper explores theuse of Natural Language Processing (NLP) as atool for policy advisors to efficiently track andassess climate policy and strategies, such asNationally Determined Contributions (NDCs).These documents are essential for monitoringcoherence with the Paris Agreement, yet theiranalysis traditionally demands significant la-bor and time. We demonstrate how to leverageNLP on existing climate policy databases totransform this process by structuring informa-tion extracted from these otherwise unstruc-tured policy documents and opening avenuesfor a more in-depth analysis of national and re-gional policies. Central to our approach is thecreation of a machine-learning (ML) dataset’CPo-CD’, based on data provided by the Inter-national Climate Initiative (IKI) and ClimateWatch (CW). The CPo-CD dataset is utilizedto fine-tune Transformer Models on classify-ing climate targets, actions, policies, and plans,along with their sector, mitigation-adaptation,and greenhouse gas (GHG) components. Wepublish our model and dataset on a HuggingFace repository.

We present a research agenda focused on efficiently extracting, assuring quality, and consolidating textual company sustainability information to address urgent climate change decision-making needs. Starting from the goal to create integrated FAIR (Findable, Accessible, Interoperable, Reusable) climate-related data, we identify research needs pertaining to the technical aspects of information extraction as well as to the design of the integrated sustainability datasets that we seek to compile. Regarding extraction, we leverage technological advancements, particularly in large language models (LLMs) and Retrieval-Augmented Generation (RAG) pipelines, to unlock the underutilized potential of unstructured textual information contained in corporate sustainability reports. In applying these techniques, we review key challenges, which include the retrieval and extraction of CO₂ emission values from PDF documents, especially from unstructured tables and graphs therein, and the validation of automatically extracted data through comparisons with human-annotated values. We also review how existing use cases and practices in climate risk analytics relate to choices of what textual information should be extracted and how it could be linked to existing structured data.

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My Climate Advisor: An Application of NLP in Climate Adaptation for Agriculture
Vincent Nguyen | Sarvnaz Karimi | Willow Hallgren | Ashley Harkin | Mahesh Prakash

Climate adaptation in the agricultural sector necessitates tools that equip farmers and farm advisors with relevant and trustworthy information to help increase their resiliency to climate change. We introduce My Climate Advisor, a question-answering (QA) prototype that synthesises information from different data sources, such as peer-reviewed scientific literature and high-quality, industry-relevant grey literature to generate answers, with references, to a given user’s question. Our prototype uses open-source generative models for data privacy and intellectual property protection, and retrieval augmented generation for answer generation, grounding and provenance. While there are standard evaluation metrics for QA systems, no existing evaluation framework suits our LLM-based QA application in the climate adaptation domain. We design an evaluation framework with seven metrics based on the requirements of the domain experts to judge the generated answers from 12 different LLM-based models. Our initial evaluations through a user study via domain experts show promising usability results.

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Generative Debunking of Climate Misinformation
Francisco Zanartu | Yulia Otmakhova | John Cook | Lea Frermann

Misinformation about climate change causes numerous negative impacts, necessitating corrective responses. Psychological research has offered various strategies for reducing the influence of climate misinformation, such as the fact-myth-fallacy-fact-structure. However, practically implementing corrective interventions at scale represents a challenge. Automatic detection and correction of misinformation offers a solution to the misinformation problem. This study documents the development of large language models that accept as input a climate myth and produce a debunking that adheres to the fact-myth-fallacy-fact (“truth sandwich”) structure, by incorporating contrarian claim classification and fallacy detection into an LLM prompting framework. We combine open (Mixtral, Palm2) and proprietary (GPT-4) LLMs with prompting strategies of varying complexity. Experiments reveal promising performance of GPT-4 and Mixtral if combined with structured prompts. We identify specific challenges of debunking generation and human evaluation, and map out avenues for future work. We release a dataset of high-quality truth-sandwich debunkings, source code and a demo of the debunking system.

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Decoding Climate Disagreement: A Graph Neural Network-Based Approach to Understanding Social Media Dynamics
Ruiran Su | Janet Pierrehumbert

This paper presents the ClimateSent-GAT Model, a novel approach that combines Graph Attention Networks (GATs) with natural language processing techniques to accurately identify and predict disagreements within Reddit comment-reply pairs. Our model classifies disagreements into three categories: agree, disagree, and neutral. Leveraging the inherent graph structure of Reddit comment-reply pairs, the model significantly outperforms existing benchmarks by capturing complex interaction patterns and sentiment dynamics. This research advances graph-based NLP methodologies and provides actionable insights for policymakers and educators in climate science communication.

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Evaluating ChatNetZero, an LLM-Chatbot to Demystify Climate Pledges
Angel Hsu | Mason Laney | Ji Zhang | Diego Manya | Linda Farczadi

This paper introduces and evaluates ChatNetZero, a large-language model (LLM) chatbot developed through Retrieval-Augmented Generation (RAG), which uses generative AI to produce answers grounded in verified, climate-domain specific information. We describe ChatNetZero’s design, particularly the innovation of anti-hallucination and reference modules designed to enhance the accuracy and credibility of generated responses. To evaluate ChatNetZero’s performance against other LLMs, including GPT-4, Gemini, Coral, and ChatClimate, we conduct two types of validation: comparing LLMs’ generated responses to original source documents to verify their factual accuracy, and employing an expert survey to evaluate the overall quality, accuracy and relevance of each response. We find that while ChatNetZero responses show higher factual accuracy when compared to original source data, experts surveyed prefer lengthier responses that provide more context. Our results highlight the importance of prioritizing information presentation in the design of domain-specific LLMs to ensure that scientific information is effectively communicated, especially as even expert audiences find it challenging to assess the credibility of AI-generated content.

To better understand how extreme climate events impact society, we need to increase the availability of accurate and comprehensive information about these impacts. We propose a method for building large-scale databases of climate extreme impacts from online textual sources, using LLMs for information extraction in combination with more traditional NLP techniques to improve accuracy and consistency. We evaluate the method against a small benchmark database created by human experts and find that extraction accuracy varies for different types of information. We compare three different LLMs and find that, while the commercial GPT-4 model gives the best performance overall, the open-source models Mistral and Mixtral are competitive for some types of information.

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Envisioning NLP for intercultural climate communication
Steven Bird | Angelina Aquino | Ian Gumbula

Climate communication is often seen by the NLP community as an opportunity for machine translation, applied to ever smaller languages. However, over 90% the world’s linguistic diversity comes from languages with ‘primary orality’ and mostly spoken in non-Western oral societies. A case in point is the Aboriginal communities of Northern Australia, where we have been conducting workshops on climate communication, revealing shortcomings in existing communication practices along with new opportunities for improving intercultural communication. We present a case study of climate communication in an oral society, including the voices of many local people, and draw several lessons for the research program of NLP in the climate space.

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EnClaim: A Style Augmented Transformer Architecture for Environmental Claim Detection
Diya Saha | Manjira Sinha | Tirthankar Dasgupta

Across countries, a noteworthy paradigm shift towards a more sustainable and environmentally responsible economy is underway. However, this positive transition is accompanied by an upsurge in greenwashing, where companies make exaggerated claims about their environmental commitments. To address this challenge and protect consumers, initiatives have emerged to substantiate green claims. With the proliferation of environmental and scientific assertions, a critical need arises for automated methods to detect and validate these claims at scale. In this paper, we introduce EnClaim, a transformer network architecture augmented with stylistic features for automatically detecting claims from open web documents or social media posts. The proposed model considers various linguistic stylistic features in conjunction with language models to predict whether a given statement constitutes a claim. We have rigorously evaluated the model using multiple open datasets. Our initial findings indicate that incorporating stylistic vectors alongside the BERT-based language model enhances the overall effectiveness of environmental claim detection.

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LEAF: Predicting the Environmental Impact of Food Products based on their Name
Bas Krahmer

Although food consumption represents a sub- stantial global source of greenhouse gas emis- sions, assessing the environmental impact of off-the-shelf products remains challenging. Currently, this information is often unavailable, hindering informed consumer decisions when grocery shopping. The present work introduces a new set of models called LEAF, which stands for Linguistic Environmental Analysis of Food Products. LEAF models predict the life-cycle environmental impact of food products based on their name. It is shown that LEAF models can accurately predict the environmental im- pact based on just the product name in a multi- lingual setting, greatly outperforming zero-shot classification methods. Models of varying sizes and capabilities are released, along with the code and dataset to fully reproduce the study.

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Large Scale Narrative Messaging around Climate Change: A Cross-Cultural Comparison
Haiqi Zhou | David Hobson | Derek Ruths | Andrew Piper

In this study, we explore the use of Large Language Models (LLMs) such as GPT-4 to extract and analyze the latent narrative messaging in climate change-related news articles from North American and Chinese media. By defining “narrative messaging” as the intrinsic moral or lesson of a story, we apply our model to a dataset of approximately 15,000 news articles in English and Mandarin, categorized by climate-related topics and ideological groupings. Our findings reveal distinct differences in the narrative values emphasized by different cultural and ideological contexts, with North American sources often focusing on individualistic and crisis-driven themes, while Chinese sources emphasize developmental and cooperative narratives. This work demonstrates the potential of LLMs in understanding and influencing climate communication, offering new insights into the collective belief systems that shape public discourse on climate change across different cultures.

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Challenges in End-to-End Policy Extraction from Climate Action Plans
Nupoor Gandhi | Tom Corringham | Emma Strubell

Gray policy literature such as climate action plans (CAPs) provide an information-rich resource with potential to inform analysis and decision-making. However, these corpora are currently underutilized due to the substantial manual effort and expertise required to sift through long and detailed documents. Automatically structuring relevant information using information extraction (IE) would be useful for assisting policy scientists in synthesizing vast gray policy corpora to identify relevant entities, concepts and themes. LLMs have demonstrated strong performance on IE tasks in the few-shot setting, but it is unclear whether these gains transfer to gray policy literature which differs significantly to traditional benchmark datasets in several aspects, such as format of information content, length of documents, and inconsistency of document structure. We perform a case study on end-to-end IE with California CAPs, inspecting the performance of state-of-the-art tools for: (1) extracting content from CAPs into structured markup segments; (2) few-shot IE with LLMs; and (3) the utility of extracted entities for downstream analyses. We identify challenges at several points of the end-to-end IE pipeline for CAPs, and we provide recommendations for open problems centered around representing rich non-textual elements, document structure, flexible annotation schemes, and global information. Tackling these challenges would make it possible to realize the potential of LLMs for IE with gray policy literature.

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Structuring Sustainability Reports for Environmental Standards with LLMs guided by Ontology
Aida Usmanova | Ricardo Usbeck

Following the introduction of the European Sustainability Reporting Standard (ESRS), companies will have to adapt to a new policy and provide mandatory sustainability reports. However, implementing such reports entails a challenge, such as the comprehension of a large number of textual information from various sources. This task can be accelerated by employing Large Language Models (LLMs) and ontologies to effectively model the domain knowledge. In this study, we extended an existing ontology to model ESRS Topical Standard for disclosure. The developed ontology would enable automated reasoning over the data and assist in constructing Knowledge Graphs (KGs). Moreover, the proposed ontology extension would also help to identify gaps in companies’ sustainability reports with regard to the ESRS requirements.Additionally, we extracted knowledge from corporate sustainability reports via LLMs guided with a proposed ontology and developed their KG representation.

Misinformation regarding climate change is a key roadblock in addressing one of the most serious threats to humanity. This paper investigates factual accuracy in large language models (LLMs) regarding climate information. Using true/false labeled Q&A data for fine-tuning and evaluating LLMs on climate-related claims, we compare open-source models, assessing their ability to generate truthful responses to climate change questions. We investigate the detectability of models intentionally poisoned with false climate information, finding that such poisoning may not affect the accuracy of a model’s responses in other domains. Furthermore, we compare the effectiveness of unlearning algorithms, fine-tuning, and Retrieval-Augmented Generation (RAG) for factually grounding LLMs on climate change topics. Our evaluation reveals that unlearning algorithms can be effective for nuanced conceptual claims, despite previous findings suggesting their inefficacy in privacy contexts. These insights aim to guide the development of more factually reliable LLMs and highlight the need for additional work to secure LLMs against misinformation attacks.

Environment, Social, and Governance (ESG) KPIs assess an organization’s performance on issues such as climate change, greenhouse gas emissions, water consumption, waste management, human rights, diversity, and policies. ESG reports convey this valuable quantitative information through tables. Unfortunately, extracting this information is difficult due to high variability in the table structure as well as content. We propose Statements, a novel domain agnostic data structure for extracting quantitative facts and related information. We propose translating tables to statements as a new supervised deep-learning universal information extraction task. We introduce SemTabNet - a dataset of over 100K annotated tables. Investigating a family of T5-based Statement Extraction Models, our best model generates statements which are 82% similar to the ground-truth (compared to baseline of 21%). We demonstrate the advantages of statements by applying our model to over 2700 tables from ESG reports. The homogeneous nature of statements permits exploratory data analysis on expansive information found in large collections of ESG reports.

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CLIMATELI: Evaluating Entity Linking on Climate Change Data
Shijia Zhou | Siyao Peng | Barbara Plank

Climate Change (CC) is a pressing topic of global importance, attracting increasing attention across research fields, from social sciences to Natural Language Processing (NLP). CC is also discussed in various settings and communication platforms, from academic publications to social media forums. Understanding who and what is mentioned in such data is a first critical step to gaining new insights into CC. We present CLIMATELI (CLIMATe Entity LInking), the first manually annotated CC dataset that links 3,087 entity spans to Wikipedia. Using CLIMATELI (CLIMATe Entity LInking), we evaluate existing entity linking (EL) systems on the CC topic across various genres and propose automated filtering methods for CC entities. We find that the performance of EL models notably lags behind humans at both token and entity levels. Testing within the scope of retaining or excluding non-nominal and/or non-CC entities particularly impacts the models’ performances.

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Aligning Unstructured Paris Agreement Climate Plans with Sustainable Development Goals
Daniel Spokoyny | Janelle Cai | Tom Corringham | Taylor Berg-Kirkpatrick

Aligning unstructured climate policy documents according to a particular classification taxonomy with little to no labeled examples is challenging and requires manual effort of climate policy researchers. In this work we examine whether large language models (LLMs) can act as an effective substitute or assist in the annotation process. Utilizing a large set of text spans from Paris Agreement Nationally Determined Contributions (NDCs) linked to United Nations Sustainable Development Goals (SDGs) and targets contained in the Climate Watch dataset from the World Resources Institute in combination with our own annotated data, we validate our approaches and establish a benchmark for model performance evaluation on this task. With our evaluation benchmarking we quantify the effectiveness of using zero-shot or few-shot prompted LLMs to align these documents.

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Granular Analysis of Social Media Users’ Truthfulness Stances Toward Climate Change Factual Claims
Haiqi Zhang | Zhengyuan Zhu | Zeyu Zhang | Jacob Devasier | Chengkai Li

Climate change poses an urgent global problem that requires efficient data analysis mechanisms to provide insights into climate-related discussions on social media platforms. This paper presents a framework aimed at understanding social media users’ perceptions of various climate change topics and uncovering the insights behind these perceptions. Our framework employs large language model to develop a taxonomy of factual claims related to climate change and build a classification model that detects the truthfulness stance of tweets toward the factual claims. The findings reveal two key conclusions: (1) The public tends to believe the claims are true, regardless of the actual claim veracity; (2) The public shows a lack of discernment between facts and misinformation across different topics, particularly in areas related to politics, economy, and environment.

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SDG target detection in environmental reports using Retrieval-augmented Generation with LLMs
Dario Garigliotti

With the consolidation of Large Language Models (LLM) as a dominant component in approaches for multiple linguistic tasks, the interest in these technologies has greatly increased within a variety of areas and domains. A particular scenario of information needs where to exploit these approaches is climate-aware NLP. Paradigmatically, the vast manual labour of inspecting long, heterogeneous documents to find environment-relevant expressions and claims suits well within a recently established Retrieval-augmented Generation (RAG) framework. In this paper, we tackle two dual problems within environment analysis dealing with the common goal of detecting a Sustainable Developmental Goal (SDG) target being addressed in a textual passage of an environmental assessment report.We develop relevant test collections, and propose and evaluate a series of methods within the general RAG pipeline, in order to assess the current capabilities of LLMs for the tasks of SDG target evidence identification and SDG target detection.

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Assessing the Effectiveness of GPT-4o in Climate Change Evidence Synthesis and Systematic Assessments: Preliminary Insights
Elphin Joe | Sai Koneru | Christine Kirchhoff

In this research short, we examine the potential of using GPT-4o, a state-of-the-art large language model (LLM) to undertake evidence synthesis and systematic assessment tasks. Traditional workflows for such tasks involve large groups of domain experts who manually review and synthesize vast amounts of literature. The exponential growth of scientific literature and recent advances in LLMs provide an opportunity to complementing these traditional workflows with new age tools. We assess the efficacy of GPT-4o to do these tasks on a sample from the dataset created by the Global Adaptation Mapping Initiative (GAMI) where we check the accuracy of climate change adaptation related feature extraction from the scientific literature across three levels of expertise. Our results indicate that while GPT-4o can achieve high accuracy in low-expertise tasks like geographic location identification, their performance in intermediate and high-expertise tasks, such as stakeholder identification and assessment of depth of the adaptation response, is less reliable. The findings motivate the need for designing assessment workflows that utilize the strengths of models like GPT-4o while also providing refinements to improve their performance on these tasks.

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bib (full) Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics

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Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics
Tatsuki Kuribayashi | Giulia Rambelli | Ece Takmaz | Philipp Wicke | Yohei Oseki

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BAMBINO-LM: (Bilingual-)Human-Inspired Continual Pre-training of BabyLM
Zhewen Shen | Aditya Joshi | Ruey-Cheng Chen

Children from bilingual backgrounds benefit from interactions with parents and teachers to re-acquire their heritage language. In this paper, we investigate how this insight from behavioral study can be incorporated into the learning of small-scale language models. We introduce BAMBINO-LM, a continual pre-training strategy for BabyLM that uses a novel combination of alternation and PPO-based perplexity reward induced from a parent Italian model. Upon evaluation on zero-shot classification tasks for English and Italian, BAMBINO-LM improves the Italian language capability of a BabyLM baseline. Our ablation analysis demonstrates that employing both the alternation strategy and PPO-based modeling is key to this effectiveness gain. We also show that, as a side effect, the proposed method leads to a similar degradation in L1 effectiveness as human children would have had in an equivalent learning scenario. Through its modeling and findings, BAMBINO-LM makes a focused contribution to the pre-training of small-scale language models by first developing a human-inspired strategy for pre-training and then showing that it results in behaviours similar to that of humans.

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Evaluating Vision-Language Models on Bistable Images
Artemis Panagopoulou | Coby Melkin | Chris Callison-Burch

Bistable images, also known as ambiguous or reversible images, present visual stimuli that can be seen in two distinct interpretations, though not simultaneously, by the observer. In this study, we conduct the most extensive examination of vision-language models using bistable images to date. We manually gathered a dataset of 29 bistable images, along with their associated labels, and subjected them to 121 different manipulations in brightness, resolution, tint, and rotation. We evaluated twelve different models in both classification and generative tasks across six model architectures. Our findings reveal that, with the exception of models from the Idefics family and LLaVA1.5-13b, there is a pronounced preference for one interpretation over another among the models, and minimal variance under image manipulations, with few exceptions on image rotations. Additionally, we compared the models’ preferences with humans, noting that the models do not exhibit the same continuity biases as humans and often diverge from human initial interpretations. We also investigated the influence of variations in prompts and the use of synonymous labels, discovering that these factors significantly affect model interpretations more than image manipulations showing a higher influence of the language priors on bistable image interpretations compared to image-text training data. All code and data is open sourced.

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Locally Biased Transformers Better Align with Human Reading Times
Andrea De Varda | Marco Marelli

Recent psycholinguistic theories emphasize the interdependence between linguistic expectations and memory limitations in human language processing. We modify the self-attention mechanism of a transformer model to simulate a lossy context representation, biasing the model’s predictions to give additional weight to the local linguistic context. We show that surprisal estimates from our locally-biased model generally provide a better fit to human psychometric data, underscoring the sensitivity of the human parser to local linguistic information.

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Do large language models resemble humans in language use?
Zhenguang Cai | Xufeng Duan | David Haslett | Shuqi Wang | Martin Pickering

It is unclear whether large language models (LLMs) develop humanlike characteristics in language use. We subjected ChatGPT and Vicuna to 12 pre-registered psycholinguistic experiments ranging from sounds to dialogue. ChatGPT and Vicuna replicated the human pattern of language use in 10 and 7 out of the 12 experiments, respectively. The models associated unfamiliar words with different meanings depending on their forms, continued to access recently encountered meanings of ambiguous words, reused recent sentence structures, attributed causality as a function of verb semantics, and accessed different meanings and retrieved different words depending on an interlocutor’s identity. In addition, ChatGPT, but not Vicuna, nonliterally interpreted implausible sentences that were likely to have been corrupted by noise, drew reasonable inferences, and overlooked semantic fallacies in a sentence. Finally, unlike humans, neither model preferred using shorter words to convey less informative content, nor did they use context to resolve syntactic ambiguities. We discuss how these convergences and divergences may result from the transformer architecture. Overall, these experiments demonstrate that LLMs such as ChatGPT (and Vicuna to a lesser extent) are humanlike in many aspects of human language processing.

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The Curious Case of Representational Alignment: Unravelling Visio-Linguistic Tasks in Emergent Communication
Tom Kouwenhoven | Max Peeperkorn | Bram Van Dijk | Tessa Verhoef

Natural language has the universal properties of being compositional and grounded in reality. The emergence of linguistic properties is often investigated through simulations of emergent communication in referential games. However, these experiments have yielded mixed results compared to similar experiments addressing linguistic properties of human language. Here we address representational alignment as a potential contributing factor to these results. Specifically, we assess the representational alignment between agent image representations and between agent representations and input images. Doing so, we confirm that the emergent language does not appear to encode human-like conceptual visual features, since agent image representations drift away from inputs whilst inter-agent alignment increases. We moreover identify a strong relationship between inter-agent alignment and topographic similarity, a common metric for compositionality, and address its consequences. To address these issues, we introduce an alignment penalty that prevents representational drift but interestingly does not improve performance on a compositional discrimination task. Together, our findings emphasise the key role representational alignment plays in simulations of language emergence.

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Hierarchical syntactic structure in human-like language models
Michael Wolfman | Donald Dunagan | Jonathan Brennan | John Hale

Language models (LMs) are a meeting point for cognitive modeling and computational linguistics. How should they be designed to serve as adequate cognitive models? To address this question, this study contrasts two Transformer-based LMs that share the same architecture. Only one of them analyzes sentences in terms of explicit hierarchical structure. Evaluating the two LMs against fMRI time series via the surprisal complexity metric, the results implicate the superior temporal gyrus. These findings underline the need for hierarchical sentence structures in word-by-word models of human language comprehension.

Understanding human perception of nonsense words is helpful to devise product and character names that match their characteristics. Previous studies have suggested the usefulness of Large Language Models (LLMs) for estimating such human perception, but they did not focus on its emotional aspects. Hence, this study aims to elucidate the relationship of emotions evoked by nonsense words between humans and LLMs. Using a representative LLM, GPT-4, we reproduce the procedure of an existing study to analyze evoked emotions of humans for nonsense words. A positive correlation of 0.40 was found between the emotion intensity scores reproduced by GPT-4 and those manually annotated by humans. Although the correlation is not very high, this demonstrates that GPT-4 may agree with humans on emotional associations to nonsense words. Considering that the previous study reported that the correlation among human annotators was about 0.68 on average and that between a regression model trained on the annotations for real words and humans was 0.17, GPT-4’s agreement with humans is notably strong.

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Large language models fail to derive atypicality inferences in a human-like manner
Charlotte Kurch | Margarita Ryzhova | Vera Demberg

Recent studies have claimed that large language models (LLMs) are capable of drawing pragmatic inferences (Qiu et al., 2023; Hu et al., 2022; Barattieri di San Pietro et al., 2023). The present paper sets out to test LLM’s abilities on atypicality inferences, a type of pragmatic inference that is triggered through informational redundancy. We test several state-of-the-art LLMs in a zero-shot setting and find that LLMs fail to systematically fail to derive atypicality inferences. Our robustness analysis indicates that when inferences are seemingly derived in a few-shot settings, these results can be attributed to shallow pattern matching and not pragmatic inferencing. We also analyse the performance of the LLMs at the different derivation steps required for drawing atypicality inferences – our results show that models have access to script knowledge and can use it to identify redundancies and accommodate the atypicality inference. The failure instead seems to stem from not reacting to the subtle maxim of quantity violations introduced by the informationally redundant utterances.

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Predict but Also Integrate: an Analysis of Sentence Processing Models for English and Hindi
Nina Delcaro | Luca Onnis | Raquel Alhama

Fluent speakers make implicit predictions about forthcoming linguistic items while processing sentences, possibly to increase efficiency in real-time comprehension. However, the extent to which prediction is the primary mode of processing human language is widely debated. The human language processor may also gain efficiency by integrating new linguistic information with prior knowledge and the preceding context, without actively predicting. At present, the role of probabilistic integration, as well as its computational foundation, remains relatively understudied. Here, we explored whether a Delayed Recurrent Neural Network (d-RNN, Turek et al., 2020), as an implementation of both prediction and integration, can explain patterns of human language processing over and above the contribution of a purely predictive RNN model. We found that incorporating integration contributes to explaining variability in eye-tracking data for English and Hindi.

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Transformer Attention vs Human Attention in Anaphora Resolution
Anastasia Kozlova | Albina Akhmetgareeva | Aigul Khanova | Semen Kudriavtsev | Alena Fenogenova

Motivated by human cognitive processes, attention mechanism within transformer architecture has been developed to assist neural networks in allocating focus to specific aspects within input data. Despite claims regarding the interpretability achieved by attention mechanisms, the extent of correlation and similarity between machine and human attention remains a subject requiring further investigation.In this paper, we conduct a quantitative analysis of human attention compared to neural attention mechanisms in the context of the anaphora resolution task. We collect an eye-tracking dataset based on the Winograd schema challenge task for the Russian language. Leveraging this dataset, we conduct an extensive analysis of the correlations between human and machine attention maps across various transformer architectures, network layers of pre-trained and fine-tuned models. Our aim is to investigate whether insights from human attention mechanisms can be used to enhance the performance of neural networks in tasks such as anaphora resolution. The results reveal distinctions in anaphora resolution processing, offering promising prospects for improving the performance of neural networks and understanding the cognitive nuances of human perception.

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Evaluating Lexical Aspect with Large Language Models
Bolei Ma

In this study, we explore the proficiency of large language models (LLMs) in understanding two key lexical aspects: duration (durative/stative) and telicity (telic/atelic). Through experiments on datasets featuring sentences, verbs, and verb positions, we prompt the LLMs to identify aspectual features of verbs in sentences. Our findings reveal that certain LLMs, particularly those closed-source ones, are able to capture information on duration and telicity, albeit with some performance variations and weaker results compared to the baseline. By employing prompts at three levels (sentence-only, sentence with verb, and sentence with verb and its position), we demonstrate that integrating verb information generally enhances performance in aspectual feature recognition, though it introduces instability. We call for future research to look deeper into methods aimed at optimizing LLMs for aspectual feature comprehension.

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Daily auditory environments in French-speaking infants: A longitudinal dataset
Estelle Hervé | Clément François | Laurent Prevot

Babies’ daily auditory environment plays a crucial role in language development. Most previous research estimating the quantitative and qualitative aspects of early speech inputs has predominantly focused on English- and Spanish-speaking families. In addition, validation studies for daylong recordings’ analysis tools are scarce on French data sets.In this paper, we present a French corpus of daylong audio recordings longitudinally collected with the LENA (Language ENvironment Analysis) system from infants aged 3 to 24 months. We conduct a thorough exploration of this data set, which serves as a quality check for both the data and the analysis tools.We evaluate the reliability of LENA metrics by systematically comparing them with those obtained from the ChildProject set of tools and by checking the known dynamics of the metrics with age. These metrics are also used to replicate, on our data set, findings from (Warlaumont et al, 2014) about the increase of infants’ speech vocalizations and temporal contingencies between infants and caregivers with age.

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Analysing and Validating Language Complexity Metrics Across South American Indigenous Languages
Felipe Serras | Miguel Carpi | Matheus Branco | Marcelo Finger

Language complexity is an emerging concept critical for NLP and for quantitative and cognitive approaches to linguistics. In this work, we evaluate the behavior of a set of compression-based language complexity metrics when applied to a large set of native South American languages. Our goal is to validate the desirable properties of such metrics against a more diverse set of languages, guaranteeing the universality of the techniques developed on the basis of this type of theoretical artifact. Our analysis confirmed with statistical confidence most propositions about the metrics studied, affirming their robustness, despite showing less stability than when the same metrics were applied to Indo-European languages. We also observed that the trade-off between morphological and syntactic complexities is strongly related to language phylogeny.

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How can large language models become more human?
Daphne Wang | Mehrnoosh Sadrzadeh | Miloš Stanojević | Wing-Yee Chow | Richard Breheny

Psycholinguistic experiments reveal that efficiency of human language use is founded on predictions at both syntactic and lexical levels. Previous models of human prediction exploiting LLMs have used an information theoretic measure called surprisal, with success on naturalistic text in a wide variety of languages, but under-performance on challenging text such as garden path sentences. This paper introduces a novel framework that combines the lexical predictions of an LLM with the syntactic structures provided by a dependency parser. The framework gives rise to an Incompatibility Fraction. When tested on two garden path datasets, it correlated well with human reading times, distinguished between easy and hard garden path, and outperformed surprisal.

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Morphology Matters: Probing the Cross-linguistic Morphological Generalization Abilities of Large Language Models through a Wug Test
Dang Anh | Limor Raviv | Lukas Galke

We develop a multilingual version of the Wug Test, an artificial word completion experiment that is typically used to test the morphological knowledge of children, and apply it to the GPT family of large language models (LLMs). LLMs’ performance on this test was evaluated by native speakers of six different languages, who judged whether the inflected and derived forms generated by the models conform to the morphological rules of their language. Our results show that LLMs can generalize their morphological knowledge to new, unfamiliar words, but that their success in generating the “correct” generalization (as judged by native human speakers) is predicted by a language’s morphological complexity (specifically, integrative complexity). We further find that the amount of training data has surprisingly little on LLMs’ morphological generalization abilities within the scope of the analyzed languages. These findings highlight that “morphology matters”, and have important implications for improving low-resource language modeling.

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Evaluating Grammatical Well-Formedness in Large Language Models: A Comparative Study with Human Judgments
Zhuang Qiu | Xufeng Duan | Zhenguang Cai

Research in artificial intelligence has witnessed the surge of large language models (LLMs) demonstrating improved performance in various natural language processing tasks. This has sparked significant discussions about the extent to which large language models emulate human linguistic cognition and usage. This study delves into the representation of grammatical well-formedness in LLMs, which is a critical aspect of linguistic knowledge. In three preregistered experiments, we collected grammaticality judgment data for over 2400 English sentences with varying structures from ChatGPT and Vicuna, comparing them with human judgment data. The results reveal substantial alignment in the assessment of grammatical correctness between LLMs and human judgments, albeit with LLMs often showing more conservative judgments for grammatical correctness or incorrectness.

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What does Kiki look like? Cross-modal associations between speech sounds and visual shapes in vision-and-language models
Tessa Verhoef | Kiana Shahrasbi | Tom Kouwenhoven

Humans have clear cross-modal preferences when matching certain novel words to visual shapes. Evidence suggests that these preferences play a prominent role in our linguistic processing, language learning, and the origins of signal-meaning mappings. With the rise of multimodal models in AI, such as vision-and-language (VLM) models, it becomes increasingly important to uncover the kinds of visio-linguistic associations these models encode and whether they align with human representations. Informed by experiments with humans, we probe and compare four VLMs for a well-known human cross-modal preference, the bouba-kiki effect. We do not find conclusive evidence for this effect but suggest that results may depend on features of the models, such as architecture design, model size, and training details. Our findings inform discussions on the origins of the bouba-kiki effect in human cognition and future developments of VLMs that align well with human cross-modal associations.

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Evaluating Semantic Relations in Predicting Textual Labels for Images of Abstract and Concrete Concepts
Tarun Tater | Sabine Schulte Im Walde | Diego Frassinelli

This study investigates the performance of SigLIP, a state-of-the-art Vision-Language Model (VLM), in predicting labels for images depicting 1,278 concepts. Our analysis across 300 images per concept shows that the model frequently predicts the exact user-tagged labels, but similarly, it often predicts labels that are semantically related to the exact labels in various ways: synonyms, hypernyms, co-hyponyms, and associated words, particularly for abstract concepts. We then zoom into the diversity of the user tags of images and word associations for abstract versus concrete concepts. Surprisingly, not only abstract but also concrete concepts exhibit significant variability, thus challenging the traditional view that representations of concrete concepts are less diverse.

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Diachronic change in verb usage statistics predicts differences in sentence processing across the lifespan
Ellis Cain | Rachel Ryskin

Diachronic corpus analyses reveal that syntactic usage patterns change over time. Are these changes reflected in differences in language processing across the human lifespan? We use the attachment of with-prepositional phrases (PPs) as a case study for investigating this question: a with-PP can attach to a verb, describing an instrument with which to perform the action (e.g., Slice the cake [with a knife]), or to a direct object (DO), modifying the noun (e.g., Slice the cake [with the pink frosting]). The relative frequencies of the instrument and modifier constructions differ depending on the verb in the sentence — the ‘verb bias’. Using two diachronic corpora, Syntgram and CCOHA, we analyzed the co-occurrence statistics of 27 verbs and instrument vs. modifier with-PPs. Between the 1940s and the 2000s, some verbs were more instrument-biased (i.e., more likely to co-occur with with-PPs that attach to the verb than the DO) than others and co-occurrence patterns were more similar for temporally close decades, suggesting subtle diachronic changes in usage patterns. We collected sentence interpretation data probing with-PP attachment preferences in participants ranging in age from 25 to 75. Interpretations of globally ambiguous sentences (e.g., Pet the rabbit with the towel) differed depending on the verb (i.e., some verbs elicit more instrument than modifier interpretations of the PP than others and vice versa) and on the age of the participant. In particular, verbs which became less instrument-biased over time elicited more instrument interpretations among older adults than young adults, suggesting that variation in language comprehension can be in part predicted from the corpus statistics of the time periods that an individual experienced.

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How Useful is Context, Actually? Comparing LLMs and Humans on Discourse Marker Prediction
Emily Sadlier-Brown | Millie Lou | Miikka Silfverberg | Carla Kam

This paper investigates the adverbial discourse particle actually. We compare LLM and human performance on cloze tests involving actually on examples sourced from the Providence Corpus of speech around children. We explore the impact of utterance context on cloze test performance. We find that context is always helpful, though the extent to which additional context is helpful, and what relative placement of context (i.e. before or after the masked word) is most helpful differs for individual models and humans. The best-performing LLM, GPT-4, narrowly outperforms humans. In an additional experiment, we explore cloze performance on synthetic LLM-generated examples, and find that several models vastly outperform humans.

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LLMs’ morphological analyses of complex FST-generated Finnish words
Anssi Moisio | Mathias Creutz | Mikko Kurimo

Rule-based language processing systems have been overshadowed by neural systems in terms of utility, but it remains unclear whether neural NLP systems, in practice, learn the grammar rules that humans use. This work aims to shed light on the issue by evaluating state-of-the-art LLMs in a task of morphological analysis of complex Finnish noun forms. We generate the forms using an FST tool, and they are unlikely to have occurred in the training sets of the LLMs, therefore requiring morphological generalisation capacity. We find that GPT-4-turbohas some difficulties in the task while GPT-3.5-turbo struggles and smaller models Llama2-70B and Poro-34B fail nearly completely.

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An Eye Opener Regarding Task-Based Text Gradient Saliency
Guojun Wu | Lena Bolliger | David Reich | Lena Jäger

Eye movements in reading reveal humans’ cognitive processes involved in language understanding. The duration a reader’s eyes fixate on a word has been used as a measure of the visual attention given to that word or its significance to the reader. This study investigates the correlation between the importance attributed to input tokens by language models (LMs) on the one hand and humans, in the form of fixation durations, on the other hand. While previous research on the internal processes of LMs have employed the models’ attention weights, recent studies have argued in favor of gradient-based methods. Moreover, previous approaches to interpret LMs’ internals with human gaze have neglected the tasks readers performed during reading, even though psycholinguistic research underlines that reading patterns are task-dependent. We therefore employ a gradient-based saliency method to measure the importance of input tokens when LMs are targeted on specific tasks, and we find that task specificity plays a crucial role in the correlation between human- and model-assigned importance. Our implementation is available at https://github.com/gjwubyron/Scan.

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Improving Language Models for Emotion Analysis: Insights from Cognitive Science
Constant Bonard | Gustave Cortal

We propose leveraging cognitive science research on emotions and communication to improve language models for emotion analysis. First, we present the main emotion theories in psychology and cognitive science. Then, we introduce the main methods of emotion annotation in natural language processing and their connections to psychological theories. We also present the two main types of analyses of emotional communication in cognitive pragmatics. Finally, based on the cognitive science research presented, we propose directions for improving language models for emotion analysis. We suggest that these research efforts pave the way for constructing new annotation schemes, methods, and a possible benchmark for emotional understanding, considering different facets of human emotion and communication.

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bib (full) Proceedings of the 1st Workshop on Data Contamination (CONDA)

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Evaluating Chinese Large Language Models on Discipline Knowledge Acquisition via Memorization and Robustness Assessment
Chuang Liu | Renren Jin | Mark Steedman | Deyi Xiong

Chinese LLMs demonstrate impressive performance on NLP tasks, particularly on discipline knowledge benchmarks, with some results approaching those of GPT-4. Previous research has viewed these advancements as potential outcomes of data contamination or leakage, prompting efforts to create new detection methods and address evaluation issues in LLM benchmarks. However, there has been a lack of comprehensive assessment of the evolution of Chinese LLMs. To address this gap, this paper offers a thorough investigation of Chinese LLMs on discipline knowledge evaluation, delving into the advancements of various LLMs, including a group of related models and others. Specifically, we have conducted six assessments ranging from knowledge memorization to comprehension for robustness, encompassing tasks like predicting incomplete questions and options, identifying behaviors by the contaminational fine-tuning, and answering rephrased questions. Experimental findings indicate a positive correlation between the release time of LLMs and their memorization capabilities, but they struggle with variations in original question-options pairs. Additionally, our findings suggest that question descriptions have a more significant impact on LLMs’ performance.

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Confounders in Instance Variation for the Analysis of Data Contamination
Behzad Mehrbakhsh | Dario Garigliotti | Fernando Martínez-Plumed | Jose Hernandez-Orallo

Test contamination is a serious problem for the evaluation of large language models (LLMs) because it leads to the overestimation of their performance and a quick saturation of benchmarks, even before the actual capability is achieved. One strategy to address this issue is the (adversarial) generation of variations, by including different exemplars and different rephrasings of the questions. However, these two interventions can lead to instances that can be more difficult (accumulating on the expected loss of performance by partly removing the contamination) but also to instances that can be less difficult (cancelling the expected loss of performance), which would make contamination undetectable. Understanding these two phenomena in terms of instance difficulty is critical to determine and measure contamination. In this paper we conduct a comprehensive analysis of these two interventions on an addition task with fine-tuned LLAMA-2 models.

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A Taxonomy for Data Contamination in Large Language Models
Medha Palavalli | Amanda Bertsch | Matthew Gormley

Large language models pretrained on extensive web corpora demonstrate remarkable performance across a wide range of downstream tasks. However, a growing concern is data contamination, where evaluation datasets may unintentionally be contained in the pretraining corpus, inflating model performance. Decontamination, the process of detecting and removing such data, is a potential solution; yet these contaminants may originate from altered versions of the test set, evading detection during decontamination. How different types of contamination impact the performance of language models on downstream tasks is not fully understood. We present a taxonomy that categorizes the various types of contamination encountered by LLMs during the pretraining phase and identify which types pose the highest risk. We analyze the impact of contamination on two key NLP tasks—summarization and question answering—revealing how different types of contamination influence task performance during evaluation.

The 1st Workshop on Data Contamination (CONDA 2024) focuses on all relevant aspects of data contamination in natural language processing, where data contamination is understood as situations where evaluation data is included in pre-training corpora used to train large scale models, compromising evaluation results. The workshop fostered a shared task to collect evidence on data contamination in current available datasets and models. The goal of the shared task and associated database is to assist the community in understanding the extent of the problem and to assist researchers in avoiding reporting evaluation results on known contaminated resources. The shared task provides a structured, centralized public database for the collection of contamination evidence, open to contributions from the community via GitHub pool requests. This first compilation paper is based on 566 reported entries over 91 contaminated sources from a total of 23 contributors. The details of the individual contamination events are available in the platform. The platform continues to be online, open to contributions from the community.

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bib (full) Proceedings of the 3rd Workshop on NLP Applications to Field Linguistics (Field Matters 2024)

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The Parallel Corpus of Russian and Ruska Romani Languages
Kirill Koncha | Abina Kukanova | Kazakova Tatiana | Gloria Rozovskaya

The paper presents a parallel corpus for the Ruska Romani dialect and Russian language. Ruska Romani is the dialect of Romani language attributed to Ruska Roma, the largest subgroup of Romani people in Russia. The corpus contains the translations of Russian literature into Ruska Romani dialect. The corpus creation involved manual alignment of a small part of translations with original works, fine-tuning a language model on the aligned pairs, and using the fine-tuned model to align the remaining data. Ruska Romani sentences were annotated using a morphological analyzer, with rules crafted for proper nouns and borrowings. The corpus, available in JSON and Russian National Corpus XML formats. It includes 88,742 Russian tokens and 84,635 Ruska Romani tokens, 74,291 of which were grammatically annotated. The corpus could be used for linguistic research, including comparative and diachronic studies, bilingual dictionary creation, stylometry research, and NLP/MT tool development for Ruska Romani.

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ManWav: The First Manchu ASR Model
Jean Seo | Minha Kang | SungJoo Byun | Sangah Lee

This study addresses the widening gap in Automatic Speech Recognition (ASR) research between high resource and extremely low resource languages, with a particular focus on Manchu, a severely endangered language. Manchu exemplifies the challenges faced by marginalized linguistic communities in accessing state-of-the-art technologies. In a pioneering effort, we introduce the first-ever Manchu ASR model ManWav, leveraging Wav2Vec2-XLSR-53. The results of the first Manchu ASR is promising, especially when trained with our augmented data. Wav2Vec2-XLSR-53 fine-tuned with augmented data demonstrates a 0.02 drop in CER and 0.13 drop in WER compared to the same base model fine-tuned with original data.

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User-Centered Design of Digital Tools for Sociolinguistic Studies in Under-Resourced Languages
Jonas Adler | Carsten Scholle | Daniel Buschek | Nicolo’ Brandizzi | Muhadj Adnan

Investigating language variation is a core aspect of sociolinguistics, especially through the use of linguistic corpora. Collecting and analyzing spoken language in text-based corpora can be time-consuming and error-prone, especially for under-resourced languages with limited software assistance. This paper explores the language variation research process using a User-Centered Design (UCD) approach from the field of Human-Computer Interaction (HCI), offering guidelines for the development of digital tools for sociolinguists. We interviewed four researchers, observed their workflows and software usage, and analyzed the data using Grounded Theory. This revealed key challenges in manual tasks, software assistance, and data management. Based on these insights, we identified a set of requirements that future tools should meet to be valuable for researchers in this domain. The paper concludes by proposing design concepts with sketches and prototypes based on the identified requirements. These concepts aim to guide the implementation of a fully functional, open-source tool. This work presents an interdisciplinary approach between sociolinguistics and HCI by emphasizing the practical aspects of research that are often overlooked.

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Documenting Endangered Languages with LangDoc: A Wordlist-Based System and A Case Study on Moklen
Piyapath Spencer

Language documentation, especially languages lacking standardised writing systems, is a laborious and time-consuming process. This paper introduces LangDoc, a comprehensive system designed to address challenges and improve the efficiency and accuracy of language documentation projects. LangDoc offers several features, including tools for managing, recording, and reviewing the collected data. It operates both online and offline, crucial for fieldwork in remote locations. The paper also presents a comparative analysis demonstrating LangDoc’s efficiency compared to other methods. A case study of the Moklen language documentation project demonstrates how the features address the specific challenges of working with endangered languages and remote communities. Future development areas include integrating with NLP tools for advanced linguistic analysis and emphasising its potential to support the preservation of language diversity.

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Leveraging Deep Learning to Shed Light on Tones of an Endangered Language: A Case Study of Moklen
Sireemas Maspong | Francesco Burroni | Teerawee Sukanchanon | Warunsiri Pornpottanamas | Pittayawat Pittayaporn

Moklen, a tonal Austronesian language spoken in Thailand, exhibits two tones with unbalanced distributions. We employed machine learning techniques for time-series classification to investigate its acoustic properties. Our analysis reveals that a synergy between pitch and vowel quality is crucial for tone distinction, as the model trained with these features achieved the highest accuracy.

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A Comparative Analysis of Speaker Diarization Models: Creating a Dataset for German Dialectal Speech
Lea Fischbach

Speaker diarization is a critical task in the field of computer science, aiming to assign timestamps and speaker labels to audio segments. The aim of these tests in this Publication is to find a pretrained speaker diarization pipeline capable of distinguishing dialectal speakers from each other and an explorer. To achieve this, three pipelines, namely Pyannote, CLEAVER and NeMo, are tested and compared, across various segmentation and parameterization strategies. The study considers multiple scenarios, such as the impact of threshold values, overlap handling, and minimum duration parameters, on classification accuracy. Additionally, this study aims to create a dataset for German dialect identification (DID) based on the findings from this research.

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Noise Be Gone: Does Speech Enhancement Distort Linguistic Nuances?
Iñigo Parra

This study evaluates the impact of speech enhancement (SE) techniques on linguistic research, focusing on their ability to maintain essential acoustic characteristics in enhanced audio without introducing significant artifacts. Through a sociophonetic analysis of Peninsular and Peruvian Spanish speakers, using both original and enhanced recordings, we demonstrate that SE effectively preserves critical speech nuances such as voicing and vowel quality. This supports the use of SE in improving the quality of speech samples. This study marks an initial effort to assess SE’s reliability in language studies and proposes a methodology for enhancing low-quality audio corpora of under-resourced languages.

Automatic speech recognition (ASR) has the potential to accelerate the documentation of endangered languages, but the dearth of resources poses a major obstacle. Čakavian, an endangered variety spoken primarily in Croatia, is a case in point, lacking transcription tools that could aid documentation efforts. We compare training a new ASR model on a limited dataset using the Kaldi-based ASR pipeline Elpis to using the same dataset to adapt the transformer-based pretrained multilingual model Whisper, to determine which is more practical in the documentation context. Results show that Whisper outperformed Elpis, achieving the lowest average Word Error Rate (WER) of 57.3% and median WER of 35.48%. While Elpis offers a less computationally expensive model and friendlier user experience, Whisper appears better at adapting to our collected Čakavian data.

Supervised learning approaches in NLP, exemplified by POS tagging, rely heavily on the presence of large amounts of annotated data. However, acquiring such data often requires significant amount of resources and incurs high costs. In this work, we explore zero-shot cross-lingual transfer learning to address data scarcity issues in Filipino POS tagging, particularly focusing on optimizing source language selection. Our zero-shot approach demonstrates superior performance compared to previous studies, with top-performing fine-tuned PLMs achieving F1 scores as high as 79.10%. The analysis reveals moderate correlations between cross-lingual transfer performance and specific linguistic distances–featural, inventory, and syntactic–suggesting that source languages with these features closer to Filipino provide better results. We identify tokenizer optimization as a key challenge, as PLM tokenization sometimes fails to align with meaningful representations, thus hindering POS tagging performance.

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bib (full) Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP)

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Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP)
Agnieszka Faleńska | Christine Basta | Marta Costa-jussà | Seraphina Goldfarb-Tarrant | Debora Nozza

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A Parameter-Efficient Multi-Objective Approach to Mitigate Stereotypical Bias in Language Models
Yifan Wang | Vera Demberg

Pre-trained language models have shown impressive abilities of understanding and generating natural languages. However, they typically inherit undesired human-like bias and stereotypes from training data, which raises concerns about putting these models into use in real-world scenarios. Although prior research has proposed to reduce bias using different fairness objectives, they usually fail to capture different representations of bias and, therefore, struggle with fully debiasing models. In this work, we introduce a multi-objective probability alignment approach to overcome current challenges by incorporating multiple debiasing losses to locate and penalize bias in different forms. Compared to existing methods, our proposed method can more effectively and comprehensively reduce stereotypical bias, and maintains the language ability of pre-trained models at the same time. Besides, we adopt prefix-tuning to optimize fairness objectives, and results show that it can achieve better bias removal than full fine-tuning while requiring much fewer computational resources. Our code and data are available at https://github.com/Ewanwong/debias_NLG.

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Do PLMs and Annotators Share the Same Gender Bias? Definition, Dataset, and Framework of Contextualized Gender Bias
Shucheng Zhu | Bingjie Du | Jishun Zhao | Ying Liu | Pengyuan Liu

Pre-trained language models (PLMs) have achieved success in various of natural language processing (NLP) tasks. However, PLMs also introduce some disquieting safety problems, such as gender bias. Gender bias is an extremely complex issue, because different individuals may hold disparate opinions on whether the same sentence expresses harmful bias, especially those seemingly neutral or positive. This paper first defines the concept of contextualized gender bias (CGB), which makes it easy to measure implicit gender bias in both PLMs and annotators. We then construct CGBDataset, which contains 20k natural sentences with gendered words, from Chinese news. Similar to the task of masked language models, gendered words are masked for PLMs and annotators to judge whether a male word or a female word is more suitable. Then, we introduce CGBFrame to measure the gender bias of annotators. By comparing the results measured by PLMs and annotators, we find that though there are differences on the choices made by PLMs and annotators, they show significant consistency in general.

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We Don’t Talk About That: Case Studies on Intersectional Analysis of Social Bias in Large Language Models
Hannah Devinney | Jenny Björklund | Henrik Björklund

Despite concerns that Large Language Models (LLMs) are vectors for reproducing and amplifying social biases such as sexism, transphobia, islamophobia, and racism, there is a lack of work qualitatively analyzing how such patterns of bias are generated by LLMs. We use mixed-methods approaches and apply a feminist, intersectional lens to the problem across two language domains, Swedish and English, by generating narrative texts using LLMs. We find that hegemonic norms are consistently reproduced; dominant identities are often treated as ‘default’; and discussion of identity itself may be considered ‘inappropriate’ by the safety features applied to some LLMs. Due to the differing behaviors of models, depending both on their design and the language they are trained on, we observe that strategies of identifying “bias” must be adapted to individual models and their socio-cultural contexts._Content warning: This research concerns the identification of harms, including stereotyping, denigration, and erasure of minoritized groups. Examples, including transphobic and racist content, are included and discussed._

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An Explainable Approach to Understanding Gender Stereotype Text
Manuela Jeyaraj | Sarah Delany

Gender Stereotypes refer to the widely held beliefs and assumptions about the typical traits, behaviours, and roles associated with a collective group of individuals of a particular gender in society. These typical beliefs about how people of a particular gender are described in text can cause harmful effects to individuals leading to unfair treatment. In this research, the aim is to identify the words and language constructs that can influence a text to be considered a gender stereotype. To do so, a transformer model with attention is fine-tuned for gender stereotype detection. Thereafter, words/language constructs used for the model’s decision are identified using a combined use of attention- and SHAP (SHapley Additive exPlanations)-based explainable approaches. Results show that adjectives and verbs were highly influential in predicting gender stereotypes. Furthermore, applying sentiment analysis showed that words describing male gender stereotypes were more positive than those used for female gender stereotypes.

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A Fairness Analysis of Human and AI-Generated Student Reflection Summaries
Bhiman Baghel | Arun Balajiee Lekshmi Narayanan | Michael Miller Yoder

This study examines the fairness of human- and AI-generated summaries of student reflections in university STEM classes, focusing on potential gender biases. Using topic modeling, we first identify topics that are more prevalent in reflections from female students and others that are more common among male students. We then analyze whether human and AI-generated summaries reflect the concerns of students of any particular gender over others. Our analysis reveals that though human-generated and extractive AI summarization techniques do not show a clear bias, abstractive AI-generated summaries exhibit a bias towards male students. Pedagogical themes are over-represented from male reflections in these summaries, while concept-specific topics are under-represented from female reflections. This research contributes to a deeper understanding of AI-generated bias in educational contexts, highlighting the need for future work on mitigating these biases.

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On Shortcuts and Biases: How Finetuned Language Models Distinguish Audience-Specific Instructions in Italian and English
Nicola Fanton | Michael Roth

Instructional texts for different audience groups can help to address specific needs, but at the same time run the risk of perpetrating biases. In this paper, we extend previous findings on disparate social norms and subtle stereotypes in wikiHow in two directions: We explore the use of fine-tuned language models to determine how audience-specific instructional texts can be distinguished and we transfer the methodology to another language, Italian, to identify cross-linguistic patterns. We find that language models mostly rely on group terms, gender markings, and attributes reinforcing stereotypes.

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The power of Prompts: Evaluating and Mitigating Gender Bias in MT with LLMs
Aleix Sant | Carlos Escolano | Audrey Mash | Francesca De Luca Fornaciari | Maite Melero

This paper studies gender bias in machine translation through the lens of Large Language Models (LLMs). Four widely-used test sets are employed to benchmark various base LLMs, comparing their translation quality and gender bias against state-of-the-art Neural Machine Translation (NMT) models for English to Catalan (En → Ca) and English to Spanish (En → Es) translation directions. Our findings reveal pervasive gender bias across all models, with base LLMs exhibiting a higher degree of bias compared to NMT models.To combat this bias, we explore prompting engineering techniques applied to an instruction-tuned LLM. We identify a prompt structure that significantly reduces gender bias by up to 12% on the WinoMT evaluation dataset compared to more straightforward prompts. These results significantly reduce the gender bias accuracy gap between LLMs and traditional NMT systems.

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Detecting Gender Discrimination on Actor Level Using Linguistic Discourse Analysis
Stefanie Urchs | Veronika Thurner | Matthias Aßenmacher | Christian Heumann | Stephanie Thiemichen

With the usage of tremendous amounts of text data for training powerful large language models such as ChatGPT, the issue of analysing and securing data quality has become more pressing than ever. Any biases, stereotypes and discriminatory patterns that exist in the training data can be reproduced, reinforced or broadly disseminated by the models in production. Therefore, it is crucial to carefully select and monitor the text data that is used as input to train the model. Due to the vast amount of training data, this process needs to be (at least partially) automated. In this work, we introduce a novel approach for automatically detecting gender discrimination in text data on the actor level based on linguistic discourse analysis. Specifically, we combine existing information extraction (IE) techniques to partly automate the qualitative research done in linguistic discourse analysis. We focus on two important steps: Identifying the respectiveperson-named-entity (an actor) and all forms it is referred to (Nomination), and detecting the characteristics it is ascribed (Predication). Asa proof of concept, we integrate these two steps into a pipeline for automated text analysis. The separate building blocks of the pipeline could be flexibly adapted, extended, and scaled for bigger datasets to accommodate a wide range of usage scenarios and specific ML tasks or help social scientists with analysis tasks. We showcase and evaluate our approach on several real and simulated exemplary texts.

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What Can Go Wrong in Authorship Profiling: Cross-Domain Analysis of Gender and Age Prediction
Hongyu Chen | Michael Roth | Agnieszka Falenska

Authorship Profiling (AP) aims to predict the demographic attributes (such as gender and age) of authors based on their writing styles. Ever-improving models mean that this task is gaining interest and application possibilities. However, with greater use also comes the risk that authors are misclassified more frequently, and it remains unclear to what extent the better models can capture the bias and who is affected by the models’ mistakes. In this paper, we investigate three established datasets for AP as well as classical and neural classifiers for this task. Our analyses show that it is often possible to predict the demographic information of the authors based on textual features. However, some features learned by the models are specific to datasets. Moreover, models are prone to errors based on stereotypes associated with topical bias.

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Towards Fairer NLP Models: Handling Gender Bias In Classification Tasks
Nasim Sobhani | Sarah Delany

Measuring and mitigating gender bias in natural language processing (NLP) systems is crucial to ensure fair and ethical AI. However, a key challenge is the lack of explicit gender information in many textual datasets. This paper proposes two techniques, Identity Term Sampling (ITS) and Identity Term Pattern Extraction (ITPE), as alternatives to template-based approaches for measuring gender bias in text data. These approaches identify test data for measuring gender bias in the dataset itself and can be used to measure gender bias on any NLP classifier. We demonstrate the use of these approaches for measuring gender bias across various NLP classification tasks, including hate speech detection, fake news identification, and sentiment analysis. Additionally, we show how these techniques can benefit gender bias mitigation, proposing a variant of Counterfactual Data Augmentation (CDA), called Gender-Selective CDA (GS-CDA), which reduces the amount of data augmentation required in training data while effectively mitigating gender bias and maintaining overall classification performance.

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Investigating Gender Bias in STEM Job Advertisements
Malika Dikshit | Houda Bouamor | Nizar Habash

Gender inequality has been historically prevalent in academia, especially within the fields of Science, Technology, Engineering, and Mathematics (STEM). In this study, we propose to examine gender bias in academic job descriptions in the STEM fields. We go a step further than previous studies that merely identify individual words as masculine-coded and feminine-coded and delve into the contextual language used in academic job advertisements. We design a novel approach to detect gender biases in job descriptions using Natural Language Processing techniques. Going beyond binary masculine-feminine stereotypes, we propose three big group types to understand gender bias in the language of job descriptions, namely agentic, balanced, and communal. We cluster similar information in job descriptions into these three groups using contrastive learning and various clustering techniques. This research contributes to the field of gender bias detection by providing a novel approach and methodology for categorizing gender bias in job descriptions, which can aid more effective and targeted job advertisements that will be equally appealing across all genders.

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Dissecting Biases in Relation Extraction: A Cross-Dataset Analysis on People’s Gender and Origin
Marco Stranisci | Pere-Lluís Huguet Cabot | Elisa Bassignana | Roberto Navigli

Relation Extraction (RE) is at the core of many Natural Language Understanding tasks, including knowledge-base population and Question Answering. However, any Natural Language Processing system is exposed to biases, and the analysis of these has not received much attention in RE. We propose a new method for inspecting bias in the RE pipeline, which is completely transparent in terms of interpretability. Specifically, in this work we analyze biases related to gender and place of birth. Our methodology includes (i) obtaining semantic triplets (subject, object, semantic relation) involving ‘person’ entities from RE resources, (ii) collecting meta-information (‘gender’ and ‘place of birth’) using Entity Linking technologies, and then (iii) analyze the distribution of triplets across different groups (e.g., men versus women). We investigate bias at two levels: In the training data of three commonly used RE datasets (SREDFM, CrossRE, NYT), and in the predictions of a state-of-the-art RE approach (ReLiK). To enable cross-dataset analysis, we introduce a taxonomy of relation types mapping the label sets of different RE datasets to a unified label space. Our findings reveal that bias is a compounded issue affecting underrepresented groups within data and predictions for RE.

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Gender Bias in Turkish Word Embeddings: A Comprehensive Study of Syntax, Semantics and Morphology Across Domains
Duygu Altinok

Gender bias in word representations has emerged as a prominent research area in recent years. While numerous studies have focused on measuring and addressing bias in English word embeddings, research on the Turkish language remains limited. This work aims to bridge this gap by conducting a comprehensive evaluation of gender bias in Turkish word embeddings, considering the dimensions of syntax, semantics, and morphology. We employ subword-based static word vectors trained on three distinct domains: web crawl, academical text, and medical text. Through the analysis of gender-associated words in each domain, we not only uncover gender bias but also gain insights into the unique characteristics of these domains. Additionally, we explore the influence of Turkish suffixes on word gender, providing a novel perspective on gender bias. Our findings reveal the pervasive nature of gender biases across various aspects of the Turkish language, including word frequency, semantics, parts-of-speech, and even the smallest linguistic unit - suffixes. Notably, we demonstrate that the majority of noun and verb lemmas, as well as adverbs and adjectives, exhibit masculine gendering in the general-purpose written language. This study is the first of its kind to offer a comprehensive examination of gender bias in the Turkish language.

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Disagreeable, Slovenly, Honest and Un-named Women? Investigating Gender Bias in English Educational Resources by Extending Existing Gender Bias Taxonomies
Haotian Zhu | Kexin Gao | Fei Xia | Mari Ostendorf

Gender bias has been extensively studied in both the educational field and the Natural Language Processing (NLP) field, the former using human coding to identify patterns associated with and causes of gender bias in text and the latter to detect, measure and mitigate gender bias in NLP output and models. This work aims to use NLP to facilitate automatic, quantitative analysis of educational text within the framework of a gender bias taxonomy. Analyses of both educational texts and a lexical resource (WordNet) reveal patterns of bias that can inform and aid educators in updating textbooks and lexical resources and in designing assessment items.

Machine translation (MT) systems often translate terms with ambiguous gender (e.g., English term “the nurse”) into the gendered form that is most prevalent in the systems’ training data (e.g., “enfermera”, the Spanish term for a female nurse). This often reflects and perpetuates harmful stereotypes present in society. With MT user interfaces in mind that allow for resolving gender ambiguity in a frictionless manner, we study the problem of generating all grammatically correct gendered translation alternatives. We open source train and test datasets for five language pairs and establish benchmarks for this task. Our key technical contribution is a novel semi-supervised solution for generating alternatives that integrates seamlessly with standard MT models and maintains high performance without requiring additional components or increasing inference overhead.

Name-based gender prediction has traditionally categorized individuals as either female or male based on their names, using a binary classification system. That binary approach can be problematic in the cases of gender-neutral names that do not align with any one gender, among other reasons. Relying solely on binary gender categories without recognizing gender-neutral names can reduce the inclusiveness of gender prediction tasks. We introduce an additional gender category, i.e., “neutral”, to study and address potential gender biases in Large Language Models (LLMs). We evaluate the performance of several foundational and large language models in predicting gender based on first names only. Additionally, we investigate the impact of adding birth years to enhance the accuracy of gender prediction, accounting for shifting associations between names and genders over time. Our findings indicate that most LLMs identify male and female names with high accuracy (over 80%) but struggle with gender-neutral names (under 40%), and the accuracy of gender prediction is higher for English-based first names than non-English names. The experimental results show that incorporating the birth year does not improve the overall accuracy of gender prediction, especially for names with evolving gender associations. We recommend using caution when applying LLMs for gender identification in downstream tasks, particularly when dealing with non-binary gender labels.

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Is there Gender Bias in Dependency Parsing? Revisiting “Women’s Syntactic Resilience”
Paul Go | Agnieszka Falenska

In this paper, we revisit the seminal work of Garimella et al. 2019, who reported that dependency parsers learn demographically-related signals from their training data and perform differently on sentences authored by people of different genders. We re-run all the parsing experiments from Garimella et al. 2019 and find that their results are not reproducible. Additionally, the original patterns suggesting the presence of gender biases fail to generalize to other treebank and parsing architecture. Instead, our data analysis uncovers methodological shortcomings in the initial study that artificially introduced differences into female and male datasets during preprocessing. These disparities potentially compromised the validity of the original conclusions.

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From ‘Showgirls’ to ‘Performers’: Fine-tuning with Gender-inclusive Language for Bias Reduction in LLMs
Marion Bartl | Susan Leavy

Gender bias is not only prevalent in Large Language Models (LLMs) and their training data, but also firmly ingrained into the structural aspects of language itself. Therefore, adapting linguistic structures within LLM training data to promote gender-inclusivity can make gender representations within the model more inclusive.The focus of our work are gender-exclusive affixes in English, such as in ‘show-girl’ or ‘man-cave’, which can perpetuate gender stereotypes and binary conceptions of gender.We use an LLM training dataset to compile a catalogue of 692 gender-exclusive terms along with gender-neutral variants and from this, develop a gender-inclusive fine-tuning dataset, the ‘Tiny Heap’. Fine-tuning three different LLMs with this dataset, we observe an overall reduction in gender-stereotyping tendencies across the models. Our approach provides a practical method for enhancing gender inclusivity in LLM training data and contributes to incorporating queer-feminist linguistic activism in bias mitigation research in NLP.

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Sociodemographic Bias in Language Models: A Survey and Forward Path
Vipul Gupta | Pranav Narayanan Venkit | Shomir Wilson | Rebecca Passonneau

Sociodemographic bias in language models (LMs) has the potential for harm when deployed in real-world settings. This paper presents a comprehensive survey of the past decade of research on sociodemographic bias in LMs, organized into a typology that facilitates examining the different aims: types of bias, quantifying bias, and debiasing techniques. We track the evolution of the latter two questions, then identify current trends and their limitations, as well as emerging techniques. To guide future research towards more effective and reliable solutions, and to help authors situate their work within this broad landscape, we conclude with a checklist of open questions.

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Stop! In the Name of Flaws: Disentangling Personal Names and Sociodemographic Attributes in NLP
Vagrant Gautam | Arjun Subramonian | Anne Lauscher | Os Keyes

Personal names simultaneously differentiate individuals and categorize them in ways that are important in a given society. While the natural language processing community has thus associated personal names with sociodemographic characteristics in a variety of tasks, researchers have engaged to varying degrees with the established methodological problems in doing so. To guide future work that uses names and sociodemographic characteristics, we provide an overview of relevant research: first, we present an interdisciplinary background on names and naming. We then survey the issues inherent to associating names with sociodemographic attributes, covering problems of validity (e.g., systematic error, construct validity), as well as ethical concerns (e.g., harms, differential impact, cultural insensitivity). Finally, we provide guiding questions along with normative recommendations to avoid validity and ethical pitfalls when dealing with names and sociodemographic characteristics in natural language processing.

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Evaluating Gender Bias in Multilingual Multimodal AI Models: Insights from an Indian Context
Kshitish Ghate | Arjun Choudhry | Vanya Bannihatti Kumar

We evaluate gender biases in multilingual multimodal image and text models in two settings: text-to-image retrieval and text-to-image generation, to show that even seemingly gender-neutral traits generate biased results. We evaluate our framework in the context of people from India, working with two languages: English and Hindi. We work with frameworks built around mCLIP-based models to ensure a thorough evaluation of recent state-of-the-art models in the multilingual setting due to their potential for widespread applications. We analyze the results across 50 traits for retrieval and 8 traits for generation, showing that current multilingual multimodal models are biased towards men for most traits, and this problem is further exacerbated for lower-resource languages like Hindi. We further discuss potential reasons behind this observation, particularly stemming from the bias introduced by the pretraining datasets.

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Detecting and Mitigating LGBTQIA+ Bias in Large Norwegian Language Models
Selma Bergstrand | Björn Gambäck

The paper aims to detect and mitigate LGBTQIA+ bias in large language models (LLMs). As the usage of LLMs quickly increases, so does the significance of the harms they may cause due to bias. The research field of bias in LLMs has seen massive growth, but few attempts have been made to detect or mitigate other biases than gender bias, and most focus has been on English LLMs. This work shows experimentally that LLMs may cause representational harms towards LGBTQIA+ individuals when evaluated on sentence completion tasks and on a benchmark dataset constructed from stereotypes reported by the queer community of Norway, collected through a survey in order to directly involve the affected community. Furthermore, Norwegian training corpora are probed for queer bias, revealing strong associations between queer terms and anti-queer slurs, as well as words related to pedophilia. Finally, a fine-tuning-based debiasing method is applied to two Norwegian LLMs. This method does not consistently reduce bias, but shows that queer bias can be altered, laying the foundation for future debiasing approaches. By shedding light on the severe discrimination that can occur through the usage of LLMs, this paper contributes to the ongoing fight for equal rights for the LGBTQIA+ community.

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Whose wife is it anyway? Assessing bias against same-gender relationships in machine translation
Ian Stewart | Rada Mihalcea

Machine translation often suffers from biased data and algorithms that can lead to unacceptable errors in system output. While bias in gender norms has been investigated, less is known about whether MT systems encode bias about social relationships, e.g., “the lawyer kissed her wife.” We investigate the degree of bias against same-gender relationships in MT systems, using generated template sentences drawn from several noun-gender languages (e.g., Spanish) and comprised of popular occupation nouns. We find that three popular MT services consistently fail to accurately translate sentences concerning relationships between entities of the same gender. The error rate varies considerably based on the context, and same-gender sentences referencing high female-representation occupations are translated with lower accuracy. We provide this work as a case study in the evaluation of intrinsic bias in NLP systems with respect to social relationships.

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Analysis of Annotator Demographics in Sexism Detection
Narjes Tahaei | Sabine Bergler

This study explores the effect of annotators’ demographic features on labeling sexist content in social media datasets, specifically focusing on the EXIST dataset, which includes direct sexist messages, reports and descriptions of sexist experiences and stereotypes. We investigate how various demographic backgrounds influence annotation outcomes and examine methods to incorporate these features into BERT-based model training. Our experiments demonstrate that adding demographic information improves performance in detecting sexism and assessing intention of the author.

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An Empirical Study of Gendered Stereotypes in Emotional Attributes for Bangla in Multilingual Large Language Models
Jayanta Sadhu | Maneesha Saha | Rifat Shahriyar

The influence of Large Language Models (LLMs) is rapidly growing, automating more jobs over time. Assessing the fairness of LLMs is crucial due to their expanding impact. Studies reveal the reflection of societal norms and biases in LLMs, which creates a risk of propagating societal stereotypes in downstream tasks. Many studies on bias in LLMs focus on gender bias in various NLP applications. However, there’s a gap in research on bias in emotional attributes, despite the close societal link between emotion and gender. This gap is even larger for low-resource languages like Bangla. Historically, women are associated with emotions like empathy, fear, and guilt, while men are linked to anger, bravado, and authority. This pattern reflects societal norms in Bangla-speaking regions. We offer the first thorough investigation of gendered emotion attribution in Bangla for both closed and open source LLMs in this work. Our aim is to elucidate the intricate societal relationship between gender and emotion specifically within the context of Bangla. We have been successful in showing the existence of gender bias in the context of emotions in Bangla through analytical methods and also show how emotion attribution changes on the basis of gendered role selection in LLMs. All of our resources including code and data are made publicly available to support future research on Bangla NLP. Warning: This paper contains explicit stereotypical statements that many may find offensive.

We describe the details of the Shared Task of the 5th ACL Workshop on Gender Bias in Natural Language Processing (GeBNLP 2024). The task uses dataset to investigate the quality of Machine Translation systems on a particular case of gender robustness. We report baseline results as well as the results of the first participants. The shared task will be permanently available in the Dynabench platform.

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bib (full) Proceedings of the 1st Human-Centered Large Language Modeling Workshop

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Human Speech Perception in Noise: Can Large Language Models Paraphrase to Improve It?
Anupama Chingacham | Miaoran Zhang | Vera Demberg | Dietrich Klakow

Large Language Models (LLMs) can generate text by transferring style attributes like formality resulting in formal or informal text.However, instructing LLMs to generate text that when spoken, is more intelligible in an acoustically difficult environment, is an under-explored topic.We conduct the first study to evaluate LLMs on a novel task of generating acoustically intelligible paraphrases for better human speech perception in noise.Our experiments in English demonstrated that with standard prompting, LLMs struggle to control the non-textual attribute, i.e., acoustic intelligibility, while efficiently capturing the desired textual attributes like semantic equivalence. To remedy this issue, we propose a simple prompting approach, prompt-and-select, which generates paraphrases by decoupling the desired textual and non-textual attributes in the text generation pipeline.Our approach resulted in a 40% relative improvement in human speech perception, by paraphrasing utterances that are highly distorted in a listening condition with babble noise at signal-to-noise ratio (SNR) -5 dB. This study reveals the limitation of LLMs in capturing non-textual attributes, and our proposed method showcases the potential of using LLMs for better human speech perception in noise.

Traditional reference-based metrics, such as BLEU and ROUGE, are less effective for assessing outputs from Large Language Models (LLMs) that produce highly creative or superior-quality text, or in situations where reference outputs are unavailable. While human evaluation remains an option, it is costly and difficult to scale. Recent work using LLMs as evaluators (LLM-as-a-judge) is promising, but trust and reliability remain a significant concern. Integrating human input is crucial to ensure criteria used to evaluate are aligned with the human’s intent, and evaluations are robust and consistent. This paper presents a user study of a design exploration called EvaluLLM, that enables users to leverage LLMs as customizable judges, promoting human involvement to balance trust and cost-saving potential with caution. Through interviews with eight domain experts, we identified the need for assistance in developing effective evaluation criteria aligning the LLM-as-a-judge with practitioners’ preferences and expectations. We offer findings and design recommendations to optimize human-assisted LLM-as-judge systems.

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Parameter-Efficient Detoxification with Contrastive Decoding
Tong Niu | Caiming Xiong | Yingbo Zhou | Semih Yavuz

The field of natural language generation has witnessed significant advancements in recent years, including the development of controllable text generation techniques. However, controlling the attributes of the generated text remains a challenge, especially when aiming to avoid undesirable behavior such as toxicity. In this work, we introduce Detoxification Generator (DETOXIGEN), an inference-time algorithm that steers the generation away from unwanted styles. DETOXIGEN is an ensemble of a pre-trained language model (generator) and a detoxifier. The detoxifier is trained intentionally on the toxic data representative of the undesirable attribute, encouraging it to generate text in that style exclusively. During the actual generation, we use the trained detoxifier to produce undesirable tokens for the generator to contrast against at each decoding step. This approach directly informs the generator to avoid generating tokens that the detoxifier considers highly likely. We evaluate DETOXIGEN on the commonly used REALTOXICITYPROMPTS benchmark (Gehman et al., 2020) with various language models as generators. We find that it significantly outperforms previous approaches in detoxification metrics while not compromising on the generation quality. Moreover, the detoxifier is obtained by soft prompt-tuning using the same backbone language model as the generator. Hence, DETOXIGEN requires only a tiny amount of extra weights from the virtual tokens of the detoxifier to be loaded into GPU memory while decoding, making it a promising lightweight, practical, and parameter-efficient detoxification strategy.

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To What Extent Are Large Language Models Capable of Generating Substantial Reflections for Motivational Interviewing Counseling Chatbots? A Human Evaluation
Erkan Basar | Iris Hendrickx | Emiel Krahmer | Gert-Jan Bruijn | Tibor Bosse

Motivational Interviewing is a counselling style that requires skillful usage of reflective listening and engaging in conversations about sensitive and personal subjects. In this paper, we investigate to what extent we can use generative large language models in motivational interviewing chatbots to generate precise and variable reflections on user responses. We conduct a two-step human evaluation where we first independently assess the generated reflections based on four criteria essential to health counseling; appropriateness, specificity, naturalness, and engagement. In the second step, we compare the overall quality of generated and human-authored reflections via a ranking evaluation. We use GPT-4, BLOOM, and FLAN-T5 models to generate motivational interviewing reflections, based on real conversational data collected via chatbots designed to provide support for smoking cessation and sexual health. We discover that GPT-4 can produce reflections of a quality comparable to human-authored reflections. Finally, we conclude that large language models have the potential to enhance and expand reflections in predetermined health counseling chatbots, but a comprehensive manual review is advised.

Large Vision Language Models can be used to assist visually impaired individuals by describing images they capture in their daily lives. Current evaluation datasets may not reflect the diverse cultural user backgrounds nor the situational context of this use case. To address this problem, we create a survey to determine caption preferences and propose a culture-centric evaluation benchmark by filtering VizWiz, an existing dataset with images taken by people who are blind. We then evaluate different models and prompts, investigating their reliability as visual assistants. While the evaluation results for state-of-the-art models seem promising, we identified some weak spots such as hallucinations and problems with conventional evaluation metrics. Our survey, data, code, and model outputs will be publicly available.

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Evaluating Large Language Models on Social Signal Sensitivity: An Appraisal Theory Approach
Zhen Wu | Ritam Dutt | Carolyn Rose

We present a framework to assess the sensitivity of Large Language Models (LLMs) to textually embedded social signals using an Appraisal Theory perspective. We report on an experiment that uses prompts encoding three dimensions of social signals: Affect, Judgment, and Appreciation. In response to the prompt, an LLM generates both an analysis (Insight) and a conversational Response, which are analyzed in terms of sensitivity to the signals. We quantitatively evaluate the output text through topical analysis of the Insight and predicted social intelligence scores of the Response in terms of empathy and emotional polarity. Key findings show that LLMs are more sensitive to positive signals. The personas impact Responses but not the Insight. We discuss how our framework can be extended to a broader set of social signals, personas, and scenarios to evaluate LLM behaviors under various conditions.

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Aligning to Adults Is Easy, Aligning to Children Is Hard: A Study of Linguistic Alignment in Dialogue Systems
Dorothea French | Sidney D’Mello | Katharina von der Wense

During conversations, people align to one another over time, by using similar words, concepts, and syntax. This helps form a shared understanding of the conversational content and is associated with increased engagement and satisfaction. It also affects conversation outcomes: e.g., when talking to language learners, an above normal level of linguistic alignment of parents or language teachers is correlated with faster language acquisition. These benefits make human-like alignment an important property of dialogue systems, which has often been overlooked by the NLP community. In order to fill this gap, we ask: (RQ1) Due to the importance for engagement and satisfaction, to what degree do state-of-the-art dialogue systems align to adult users? (RQ2) With a potential application to child language acquisition in mind, do systems, similar to parents, show high levels of alignment during conversations with children? Our experiments show that ChatGPT aligns to adults at roughly human levels, while Llama2 shows elevated alignment. However, when responding to a child, both systems’ alignment is below human levels.

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bib (full) Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)

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Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)
Elizabeth Salesky | Marcello Federico | Marine Carpuat

This paper reports on the shared tasks organized by the 21st IWSLT Conference. The shared tasks address 7 scientific challenges in spoken language translation: simultaneous and offline translation, automatic subtitling and dubbing, speech-to-speech translation, dialect and low-resource speech translation, and Indic languages. The shared tasks attracted 17 teams whose submissions are documented in 27 system papers. The growing interest towards spoken language translation is also witnessed by the constantly increasing number of shared task organizers and contributors to the overview paper, almost evenly distributed across industry and academia.

Automatic dubbing aims to translate the speech of a video into another language, ensuring the new speech naturally fits the original video. This paper details Huawei Translation Services Center’s (HW-TSC) submission for IWSLT 2024’s automatic dubbing task, under an unconstrained setting. Our system’s machine translation (MT) component utilizes a Transformer-based MT model and an LLM-based post-editor to produce translations of varying lengths. The text-to-speech (TTS) component employs a VITS-based TTS model and a voice cloning module to emulate the original speaker’s vocal timbre. For enhanced dubbing synchrony, we introduce a parsing-informed pause selector. Finally, we rerank multiple results based on lip-sync error distance (LSE-D) and character error rate (CER). Our system achieves LSE-D of 10.75 and 12.19 on subset1 and subset2 of DE-EN test sets respectively, superior to last year’s best system.

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NICT’s Cascaded and End-To-End Speech Translation Systems using Whisper and IndicTrans2 for the Indic Task
Raj Dabre | Haiyue Song

This paper presents the NICT’s submission for the IWSLT 2024 Indic track, focusing on three speech-to-text (ST) translation directions: English to Hindi, Bengali, and Tamil. We aim to enhance translation quality in this low-resource scenario by integrating state-of-the-art pre-trained automated speech recognition (ASR) and text-to-text machine translation (MT) models. Our cascade system incorporates a Whisper model fine-tuned for ASR and an IndicTrans2 model fine-tuned for MT. Additionally, we propose an end-to-end system that combines a Whisper model for speech-to-text conversion with knowledge distilled from an IndicTrans2 MT model. We first fine-tune the IndicTrans2 model to generate pseudo data in Indic languages. This pseudo data, along with the original English speech data, is then used to fine-tune the Whisper model. Experimental results show that the cascaded system achieved a BLEU score of 51.0, outperforming the end-to-end model, which scored 19.1 BLEU. Moreover, the analysis indicates that applying knowledge distillation from the IndicTrans2 model to the end-to-end ST model improves the translation quality by about 0.7 BLEU.

Large language models (LLMs) are trained on text-only data that go far beyond the languages with paired speech and text data. At the same time, Dual Encoder (DE) based retrieval systems project queries and documents into the same embedding space and have demonstrated their success in retrieval and bi-text mining. To match speech and text in many languages, we propose using LLMs to initialize multi-modal DE retrieval systems. Unlike traditional methods, our system doesn’t require speech data during LLM pre-training and can exploit LLM’s multilingual text understanding capabilities to match speech and text in languages unseen during retrieval training. Our multi-modal LLM-based retrieval system is capable of matching speech and text in 102 languages despite only training on 21 languages. Our system outperforms previous systems trained explicitly on all 102 languages. We achieve a 10% absolute improvement in Recall@1 averaged across these languages. Additionally, our model demonstrates cross-lingual speech and text matching, which is further enhanced by readily available machine translation data.

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Conditioning LLMs with Emotion in Neural Machine Translation
Charles Brazier | Jean-Luc Rouas

Large Language Models (LLMs) have shown remarkable performance in Natural Language Processing tasks, including Machine Translation (MT). In this work, we propose a novel MT pipeline that integrates emotion information extracted from a Speech Emotion Recognition (SER) model into LLMs to enhance translation quality. We first fine-tune five existing LLMs on the Libri-trans dataset and select the most performant model. Subsequently, we augment LLM prompts with different dimensional emotions and train the selected LLM under these different configurations. Our experiments reveal that integrating emotion information, especially arousal, into LLM prompts leads to notable improvements in translation quality.

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The NYA’s Offline Speech Translation System for IWSLT 2024
Yingxin Zhang | Guodong Ma | Binbin Du

This paper reports the NYA’s submissions to IWSLT 2024 Offline Speech Translation (ST) task on the sub-tasks including English to Chinese, Japanese, and German. In detail, we participate in the unconstrained training track using the cascaded ST structure. For the automatic speech recognition (ASR) model, we use the Whisper large-v3 model. For the neural machine translation (NMT) model, the wider and deeper Transformer is adapted as the backbone model. Furthermore, we use data augmentation technologies to augment training data and data filtering strategies to improve the quality of training data. In addition, we explore many MT technologies such as Back Translation, Forward Translation, R-Drop, and Domain Adaptation.

This paper presents HW-TSC’s submission to the IWSLT 2024 Offline Speech Translation Task and Speech-to-Speech Translation Task. The former includes three translation directions: English to German, English to Chinese, and English to Japanese, while the latter only includes the translation direction of English to Chinese. We attend all three tracks (Constraint training, Constrained with Large Language Models training, and Unconstrained training) of offline speech translation task, using the cascade model architecture. Under the constrained training track, we train an ASR model from scratch, and then employ R-Drop and domain data selection to train the NMT model. In the constrained with Large Language Models training track, we use Wav2vec 2.0 and mBART50 for ASR model training initialization, and then train the LLama2-7B-based MT model using continuous training with sentence-aligned parallel data, supervised fine-tuning, and contrastive preference optimization. In the unconstrained training track, we fine-tune the whisper model for speech recognition, and then ensemble the translation results of NMT models and LLMs to produce superior translation output. For the speech-to-speech translation Task, we initially employ the offline speech translation system described above to generate the translated text. Then, we utilize the VITS model to generate the corresponding speech and employ the OpenVoice model for timbre cloning.

This article introduces the process of HW-TSC and the results of IWSLT 2024 Indic Track Speech to Text Translation. We designed a cascade system consisting of an ASR model and a machine translation model to translate speech from one language to another. For the ASR part, we directly use whisper large v3 as our ASR model. Our main task is to optimize the machine translation model (en2ta, en2hi, en2bn). In the process of optimizing the translation model, we first use bilingual corpus to train the baseline model. Then we use monolingual data to construct pseudo-corpus data to further enhance the baseline model. Finally, we filter the parallel corpus data through the labse filtering method and finetune the model again, which can further improve the bleu value. We also selected domain data from bilingual corpus to finetune previous model to achieve the best results.

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Multi-Model System for Effective Subtitling Compression
Carol-Luca Gasan | Vasile Păiș

This paper presents RACAI’s system used for the shared task of ‘Subtitling track: Subtitle Compression’ (the English to Spanish language direction), organized as part of ‘the 21st edition of The International Conference on Spoken Language Translation (IWSLT 2024)’. The proposed system consists of multiple models whose outputs are then ensembled using an algorithm, which has the purpose of maximizing the similarity of the initial and resulting text. We present the introduced datasets and the models’ training strategy, along with the reported results on the proposed test set.

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FBK@IWSLT Test Suites Task: Gender Bias evaluation with MuST-SHE
Beatrice Savoldi | Marco Gaido | Matteo Negri | Luisa Bentivogli

This paper presents the FBK contribution to the IWSLT-2024 ‘Test suites’ shared subtask, part of the Offline Speech Translation Task. Our contribution consists of the MuST-SHE-IWSLT24 benchmark evaluation, designed to assess gender bias in speech translation. By focusing on the en-de language pair, we rely on a newly created test suite to investigate systems’ ability to correctly translate feminine and masculine gender. Our results indicate that – under realistic conditions – current ST systems achieve reasonable and comparable performance in correctly translating both feminine and masculine forms when contextual gender information is available. For ambiguous references to the speaker, however, we attest a consistent preference towards masculine gender, thus calling for future endeavours on the topic. Towards this goal we make MuST-SHE-IWSLT24 freely available at: https://mt.fbk.eu/must-she/

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SimulSeamless: FBK at IWSLT 2024 Simultaneous Speech Translation
Sara Papi | Marco Gaido | Matteo Negri | Luisa Bentivogli

This paper describes the FBK’s participation in the Simultaneous Translation Evaluation Campaign at IWSLT 2024. For this year’s submission in the speech-to-text translation (ST) sub-track, we propose SimulSeamless, which is realized by combining AlignAtt and SeamlessM4T in its medium configuration. The SeamlessM4T model is used ‘off-the-shelf’ and its simultaneous inference is enabled through the adoption of AlignAtt, a SimulST policy based on cross-attention that can be applied without any retraining or adaptation of the underlying model for the simultaneous task. We participated in all the Shared Task languages (English->German, Japanese, Chinese, and Czech->English), achieving acceptable or even better results compared to last year’s submissions. SimulSeamless, covering more than 143 source languages and 200 target languages, is released at: https://github.com/hlt-mt/FBK-fairseq/.

Natural Language Processing (NLP) research and development has experienced rapid progression in the recent times due to advances in deep learning. The introduction of pre-trained large language models (LLMs) is at the core of this transformation, significantly enhancing the performance of machine translation (MT) and speech technologies. This development has also led to fundamental changes in modern translation and speech tools and their methodologies. However, there remain challenges when extending this progress to underrepresented dialects and low-resource languages, primarily due to the need for more data. This paper details our submissions to the IWSLT speech translation (ST) tasks. We used the Whisper model for the automatic speech recognition (ASR) component. We then used mBART and NLLB as cascaded systems for utilising their MT capabilities. Our research primarily focused on exploring various dialects of low-resource languages and harnessing existing resources from linguistically related languages. We conducted our experiments for two morphologically diverse language pairs: Irish-to-English and Maltese-to-English. We used BLEU, chrF and COMET for evaluating our MT models.

The paper describes the FBK submissions to the Subtitling track of the 2024 IWSLT Evaluation Campaign, which covers both the Automatic Subtitling and the Subtitle Compression task for two language pairs: English to German (en-de) and English to Spanish (en-es). For the Automatic Subtitling task, we submitted two systems: i) a direct model, trained in constrained conditions, that produces the SRT files from the audio without intermediate outputs (e.g., transcripts), and ii) a cascade solution that integrates only free-to-use components, either taken off-the-shelf or developed in-house. Results show that, on both language pairs, our direct model outperforms both cascade and direct systems trained in constrained conditions in last year’s edition of the campaign, while our cascade solution is competitive with the best 2023 runs. For the Subtitle Compression task, our primary submission involved prompting a Large Language Model (LLM) in zero-shot mode to shorten subtitles that exceed the reading speed limit of 21 characters per second. Our results highlight the challenges inherent in shrinking out-of-context sentence fragments that are automatically generated and potentially error-prone, underscoring the need for future studies to develop targeted solutions.

The IWSLT low-resource track encourages innovation in the field of speech translation, particularly in data-scarce conditions. This paper details our submission for the IWSLT 2024 low-resource track shared task for Maltese-English and North Levantine Arabic-English spoken language translation using an unconstrained pipeline approach. Using language models, we improve ASR performance by correcting the produced output. We present a 2 step approach for MT using data from external sources showing improvements over baseline systems. We also explore transliteration as a means to further augment MT data and exploit the cross-lingual similarities between Maltese and Arabic.

This paper presents our IWSLT-2024 shared task submission on the low-resource track. This submission forms part of the constrained setup; implying limited data for training. Following the introduction, this paper consists of a literature review defining previous approaches to speech translation, as well as their application to Maltese, followed by the defined methodology, evaluation and results, and the conclusion. A cascaded submission on the Maltese to English language pair is presented; consisting of a pipeline containing: a DeepSpeech 1 Automatic Speech Recognition (ASR) system, a KenLM model to optimise the transcriptions, and finally an LSTM machine translation model. The submission achieves a 0.5 BLEU score on the overall test set, and the ASR system achieves a word error rate of 97.15%. Our code is made publicly available.

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Compact Speech Translation Models via Discrete Speech Units Pretraining
Tsz Kin Lam | Alexandra Birch | Barry Haddow

We propose a pretraining method to use Self-Supervised Speech (SSS) model to creating more compact Speech-to-text Translation. In contrast to using the SSS model for initialization, our method is more suitable to memory constrained scenario such as on-device deployment. Our method is based on Discrete Speech Units (DSU) extracted from the SSS model. In the first step, our method pretrains two smaller encoder-decoder models on 1) Filterbank-to-DSU (Fbk-to-DSU) and 2) DSU-to-Translation (DSU-to-Trl) data respectively. The DSU thus become the distillation inputs of the smaller models. Subsequently, the encoder from the Fbk-to-DSU model and the decoder from the DSU-to-Trl model are taken to initialise the compact model. Finally, the compact model is finetuned on the paired Fbk-Trl data. In addition to being compact, our method requires no transcripts, making it applicable to low-resource settings. It also avoids speech discretization in inference and is more robust to the DSU tokenization. Evaluation on CoVoST-2 (X-En) shows that our method has consistent improvement over the baseline in three metrics while being compact i.e., only half the SSS model size.

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QUESPA Submission for the IWSLT 2024 Dialectal and Low-resource Speech Translation Task
John E. Ortega | Rodolfo Joel Zevallos | Ibrahim Said Ahmad | William Chen

This article describes the QUESPA team speech translation (ST) submissions for the Quechua to Spanish (QUE–SPA) track featured in the Evaluation Campaign of IWSLT 2024: dialectal and low-resource speech translation. Two main submission types were supported in the campaign: constrained and unconstrained. This is our second year submitting our ST systems to the IWSLT shared task and we feel that we have achieved novel performance, surpassing last year’s submissions. Again, we were able to submit six total systems of which our best (primary) constrained system consisted of an ST model based on the Fairseq S2T framework where the audio representations were created using log mel-scale filter banks as features and the translations were performed using a transformer. The system was similar to last year’s submission with slight configuration changes, allowing us to achieve slightly higher performance (2 BLEU). Contrastingly, we were able to achieve much better performance than last year on the unconstrained task using a larger pre-trained language (PLM) model for ST (without cascading) and the inclusion of parallel QUE–SPA data found on the internet. The fine-tuning of Microsoft’s SpeechT5 model in a ST setting along with the addition of new data and a data augmentation technique allowed us to achieve 19.7 BLEU. Additionally, we present the other four submissions (2 constrained and 2 unconstrained) which are part of additional efforts of hyper-parameter and configuration tuning on existent models and the inclusion of Whisper for speech recognition

We created a collection of speech data for 48 low resource languages. The corpus is extracted from radio broadcasts and processed with novel speech detection and language identification models based on a manually vetted subset of the audio for 10 languages. The data is made publicly available.

Johns Hopkins University (JHU) submitted systems for all eight language pairs in the 2024 Low-Resource Language Track. The main effort of this work revolves around fine-tuning large and publicly available models in three proposed systems: i) end-to-end speech translation (ST) fine-tuning of Seamless4MT v2; ii) ST fine-tuning of Whisper; iii) a cascaded system involving automatic speech recognition with fine-tuned Whisper and machine translation with NLLB. On top of systems above, we conduct a comparative analysis on different training paradigms, such as intra-distillation for NLLB as well as joint training and curriculum learning for SeamlessM4T v2. Our results show that the best-performing approach differs by language pairs, but that i) fine-tuned SeamlessM4T v2 tends to perform best for source languages on which it was pre-trained, ii) multi-task training helps Whisper fine-tuning, iii) cascaded systems with Whisper and NLLB tend to outperform Whisper alone, and iv) intra-distillation helps NLLB fine-tuning.

This paper describes CMU’s submission to the IWSLT 2024 Simultaneous Speech Translation (SST) task for translating English speech to German text in a streaming manner. Our end-to-end speech-to-text (ST) system integrates the WavLM speech encoder, a modality adapter, and the Llama2-7B-Base model as the decoder. We employ a two-stage training approach: initially, we align the representations of speech and text, followed by full fine-tuning. Both stages are trained on MuST-c v2 data with cross-entropy loss. We adapt our offline ST model for SST using a simple fixed hold-n policy. Experiments show that our model obtains an offline BLEU score of 31.1 and a BLEU score of 29.5 under 2 seconds latency on the MuST-C-v2 tst-COMMON.

In this work, we submitted our systems to the low-resource track of the IWSLT 2024 Speech Translation Campaign. Our systems tackled the unconstrained condition of the Dialectal Arabic North Levantine (ISO-3 code: apc) to English language pair. We proposed a cascaded solution consisting of an automatic speech recognition (ASR) model and a machine translation (MT) model. It was noted that the ASR model employed the pre-trained Whisper-large-v3 model to process the speech data, while the MT model adopted the Transformer architecture. To improve the quality of the MT model, it was stated that our system utilized not only the data provided by the competition but also an additional 54 million parallel sentences. Ultimately, we reported that our final system achieved a BLEU score of 24.7 for apc-to-English translation.

This work describes CMU’s submission to the IWSLT 2024 Offline Speech Translation (ST) Shared Task for translating English speech to German, Chinese, and Japanese text. We are the first participants to employ a long-form strategy which directly processes unsegmented recordings without the need for a separate voice-activity detection stage (VAD). We show that the Whisper automatic speech recognition (ASR) model has a hallucination problem when applied out-of-the-box to recordings containing non-speech noises, but a simple noisy fine-tuning approach can greatly enhance Whisper’s long-form robustness across multiple domains. Then, we feed English ASR outputs into fine-tuned NLLB machine translation (MT) models which are decoded using COMET-based Minimum Bayes Risk. Our VAD-free ASR+MT cascade is tested on TED talks, TV series, and workout videos and shown to outperform prior winning IWSLT submissions and large open-source models.

This paper describes NAIST’s submission to the simultaneous track of the IWSLT 2024 Evaluation Campaign: English-to-German, Japanese, Chinese speech-to-text translation and English-to-Japanese speech-to-speech translation. We develop a multilingual end-to-end speech-to-text translation model combining two pre-trained language models, HuBERT and mBART. We trained this model with two decoding policies, Local Agreement (LA) and AlignAtt. The submitted models employ the LA policy because it outperformed the AlignAtt policy in previous models. Our speech-to-speech translation method is a cascade of the above speech-to-text model and an incremental text-to-speech (TTS) module that incorporates a phoneme estimation model, a parallel acoustic model, and a parallel WaveGAN vocoder. We improved our incremental TTS by applying the Transformer architecture with the AlignAtt policy for the estimation model. The results show that our upgraded TTS module contributed to improving the system performance.

Large Language Models (LLMs) are currently under exploration for various tasks, including Automatic Speech Recognition (ASR), Machine Translation (MT), and even End-to-End Speech Translation (ST). In this paper, we present KIT’s offline submission in the constrained + LLM track by incorporating recently proposed techniques that can be added to any cascaded speech translation. Specifically, we integrate Mistral-7B into our system to enhance it in two ways. Firstly, we refine the ASR outputs by utilizing the N-best lists generated by our system and fine-tuning the LLM to predict the transcript accurately. Secondly, we refine the MT outputs at the document level by fine-tuning the LLM, leveraging both ASR and MT predictions to improve translation quality. We find that integrating the LLM into the ASR and MT systems results in an absolute improvement of 0.3% in Word Error Rate and 0.65% in COMET for tst2019 test set. In challenging test sets with overlapping speakers and background noise, we find that integrating LLM is not beneficial due to poor ASR performance. Here, we use ASR with chunked long-form decoding to improve context usage that may be unavailable when transcribing with Voice Activity Detection segmentation alone.

This paper presents ALADAN’s approach to the IWSLT 2024 Dialectal and Low-resource shared task, focusing on Levantine Arabic (apc) and Tunisian Arabic (aeb) to English speech translation (ST). Addressing challenges such as the lack of standardized orthography and limited training data, we propose a solution for data normalization in Dialectal Arabic, employing a modified Levenshtein distance and Word2vec models to find orthographic variants of the same word. Our system consists of a cascade ST system integrating two ASR systems (TDNN-F and Zipformer) and two NMT modules derived from pre-trained models (NLLB-200 1.3B distilled model and CohereAI’s Command-R). Additionally, we explore the integration of unsupervised textual and audio data, highlighting the importance of multi-dialectal datasets for both ASR and NMT tasks. Our system achieves BLEU score of 31.5 for Levantine Arabic on the official validation set.

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Enhancing Translation Accuracy of Large Language Models through Continual Pre-Training on Parallel Data
Minato Kondo | Takehito Utsuro | Masaaki Nagata

In this paper, we propose a two-phase training approach where pre-trained large language models are continually pre-trained on parallel data and then supervised fine-tuned with a small amount of high-quality parallel data. To investigate the effectiveness of our proposed approach, we conducted continual pre-training with a 3.8B-parameter model and parallel data across eight different formats. We evaluate these methods on thirteen test sets for Japanese-to-English and English-to-Japanese translation. The results demonstrate that when utilizing parallel data in continual pre-training, it is essential to alternate between source and target sentences. Additionally, we demonstrated that the translation accuracy improves only for translation directions where the order of source and target sentences aligns between continual pre-training data and inference. In addition, we demonstrate that the LLM-based translation model is more robust in translating spoken language and achieves higher accuracy with less training data compared to supervised encoder-decoder models. We also show that the highest accuracy is achieved when the data for continual pre-training consists of interleaved source and target sentences and when tags are added to the source sentences.

This paper presents KIT’s submissions to the IWSLT 2024 dialectal and low-resource track. In this work, we build systems for translating into English from speech in Maltese, Bemba, and two Arabic dialects Tunisian and North Levantine. Under the unconstrained condition, we leverage the pre-trained multilingual models by fine-tuning them for the target language pairs to address data scarcity problems in this track. We build cascaded and end-to-end speech translation systems for different language pairs and show the cascaded system brings slightly better overall performance. Besides, we find utilizing additional data resources boosts speech recognition performance but slightly harms machine translation performance in cascaded systems. Lastly, we show that Minimum Bayes Risk is effective in improving speech translation performance by combining the cascaded and end-to-end systems, bringing a consistent improvement of around 1 BLUE point.

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Empowering Low-Resource Language Translation: Methodologies for Bhojpuri-Hindi and Marathi-Hindi ASR and MT
Harpreet Singh Anand | Amulya Ratna Dash | Yashvardhan Sharma

The paper describes our submission for the unconstrained track of ‘Dialectal and Low-Resource Task’ proposed in IWSLT-2024. We designed cascaded Speech Translation systems for the language pairs Marathi-Hindi and Bhojpuri-Hindi utilising and fine-tuning different pre-trained models for carrying out Automatic Speech Recognition (ASR) and Machine Translation (MT).

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Recent Highlights in Multilingual and Multimodal Speech Translation
Danni Liu | Jan Niehues

Speech translation has witnessed significant progress driven by advancements in modeling techniques and the growing availability of training data. In this paper, we highlight recent advances in two ongoing research directions in ST: scaling the models to 1) many translation directions (multilingual ST) and 2) beyond the text output modality (multimodal ST). We structure this review by examining the sequential stages of a model’s development lifecycle: determining training resources, selecting model architecture, training procedures, evaluation metrics, and deployment considerations. We aim to highlight recent developments in each stage, with a particular focus on model architectures (dedicated speech translation models and LLM-based general-purpose model) and training procedures (task-specific vs. task-invariant approaches). Based on the reviewed advancements, we identify and discuss ongoing challenges within the field of speech translation.

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Word Order in English-Japanese Simultaneous Interpretation: Analyses and Evaluation using Chunk-wise Monotonic Translation
Kosuke Doi | Yuka Ko | Mana Makinae | Katsuhito Sudoh | Satoshi Nakamura

This paper analyzes the features of monotonic translations, which follow the word order of the source language, in simultaneous interpreting (SI). Word order differences are one of the biggest challenges in SI, especially for language pairs with significant structural differences like English and Japanese. We analyzed the characteristics of chunk-wise monotonic translation (CMT) sentences using the NAIST English-to-Japanese Chunk-wise Monotonic Translation Evaluation Dataset and identified some grammatical structures that make monotonic translation difficult in English-Japanese SI. We further investigated the features of CMT sentences by evaluating the output from the existing speech translation (ST) and simultaneous speech translation (simulST) models on the NAIST English-to-Japanese Chunk-wise Monotonic Translation Evaluation Dataset as well as on existing test sets. The results indicate the possibility that the existing SI-based test set underestimates the model performance. The results also suggest that using CMT sentences as references gives higher scores to simulST models than ST models, and that using an offline-based test set to evaluate the simulST models underestimates the model performance.

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Leveraging Synthetic Audio Data for End-to-End Low-Resource Speech Translation
Yasmin Moslem

This paper describes our system submission to the International Conference on Spoken Language Translation (IWSLT 2024) for Irish-to-English speech translation. We built end-to-end systems based on Whisper, and employed a number of data augmentation techniques, such as speech back-translation and noise augmentation. We investigate the effect of using synthetic audio data and discuss several methods for enriching signal diversity.

This paper outlines our submission for the IWSLT 2024 Simultaneous Speech-to-Text (SimulS2T) and Speech-to-Speech (SimulS2S) Translation competition. We have engaged in all four language directions and both the SimulS2T and SimulS2S tracks: English-German (EN-DE), English-Chinese (EN-ZH), English-Japanese (EN-JA), and Czech-English (CS-EN). For the S2T track, we have built upon our previous year’s system and further honed the cascade system composed of ASR model and MT model. Concurrently, we have introduced an end-to-end system specifically for the CS-EN direction. This end-to-end (E2E) system primarily employs the pre-trained seamlessM4T model. In relation to the SimulS2S track, we have integrated a novel TTS model into our SimulS2T system. The final submission for the S2T directions of EN-DE, EN-ZH, and EN-JA has been refined over our championship system from last year. Building upon this foundation, the incorporation of the new TTS into our SimulS2S system has resulted in the ASR-BLEU surpassing last year’s best score.

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UoM-DFKI submission to the low resource shared task
Kumar Rishu | Aiden Williams | Claudia Borg | Simon Ostermann

This system description paper presents the details of our primary and contrastive approaches to translating Maltese into English for IWSLT 24. The Maltese language shares a large vocabulary with Arabic and Italian languages, thus making it an ideal candidate to test the cross-lingual capabilities of recent state-of-the-art models. We experiment with two end-to-end approaches for our submissions: the Whisper and wav2vec 2.0 models. Our primary system gets a BLEU score of 35.1 on the combined data, whereas our contrastive approach gets 18.5. We also provide a manual analysis of our contrastive approach to identify some pitfalls that may have caused this difference.

This paper introduces HW-TSC’s submission to the IWSLT 2024 Subtitling track. For the automatic subtitling track, we use an unconstrained cascaded strategy, with the main steps being: ASR with word-level timestamps, sentence segmentation based on punctuation restoration, further alignment using CTC or using machine translation with length penalty. For the subtitle compression track, we employ a subtitle compression strategy that integrates machine translation models and extensive rewriting models. We acquire the subtitle text requiring revision through the CPS index, then utilize a translation model to obtain the English version of this text. Following this, we extract the compressed-length subtitle text through controlled decoding. If this method fails to compress the text successfully, we resort to the Llama2 few-shot model for further compression.

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Charles Locock, Lowcock or Lockhart? Offline Speech Translation: Test Suite for Named Entities
Maximilian Awiszus | Jan Niehues | Marco Turchi | Sebastian Stüker | Alex Waibel

Generating rare words is a challenging task for natural language processing in general and in speech translation (ST) specifically. This paper introduces a test suite prepared for the Offline ST shared task at IWSLT. In the test suite, corresponding rare words (i.e. named entities) were annotated on TED-Talks for English and German and the English side was made available to the participants together with some distractors (irrelevant named entities). Our evaluation checks the capabilities of ST systems to leverage the information in the contextual list of named entities and improve translation quality. Systems are ranked based on the recall and precision of named entities (separately on person, location, and organization names) in the translated texts. Our evaluation shows that using contextual information improves translation quality as well as the recall and precision of NEs. The recall of organization names in all submissions is the lowest of all categories with a maximum of 87.5 % confirming the difficulties of ST systems in dealing with names.

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Fixed and Adaptive Simultaneous Machine Translation Strategies Using Adapters
Abderrahmane Issam | Yusuf Can Semerci | Jan Scholtes | Gerasimos Spanakis

Simultaneous machine translation aims at solving the task of real-time translation by starting to translate before consuming the full input, which poses challenges in terms of balancing quality and latency of the translation. The wait-k policy offers a solution by starting to translate after consuming words, where the choice of the number k directly affects the latency and quality. In applications where we seek to keep the choice over latency and quality at inference, the wait-k policy obliges us to train more than one model. In this paper, we address the challenge of building one model that can fulfil multiple latency levels and we achieve this by introducing lightweight adapter modules into the decoder. The adapters are trained to be specialized for different wait-k values and compared to other techniques they offer more flexibility to allow for reaping the benefits of parameter sharing and minimizing interference. Additionally, we show that by combining with an adaptive strategy, we can further improve the results. Experiments on two language directions show that our method outperforms or competes with other strong baselines on most latency values.

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IWSLT 2024 Indic Track system description paper: Speech-to-Text Translation from English to multiple Low-Resource Indian Languages
Deepanjali Singh | Ayush Anand | Abhyuday Chaturvedi | Niyati Baliyan

Our Speech-to-Text (ST) translation system addresses low-resource Indian languages (Hindi, Bengali, Tamil) by combining advanced transcription and translation models for accurate and efficient translations. The key components of the system are: The Audio Processor and Transcription Module which utilizes ResembleAI for noise reduction and OpenAI’s Whisper model for transcription. The Input Module validates and preprocesses audio files. The Translation Modules integrate the Helsinki-NLP model for English to Hindi translation and Facebook’s MBart model for English to Tamil and Bengali translations, fine-tuned for better quality. The Output Module corrects syntax and removes hallucinations, delivering the final translated text. For performance evaluation purpose, SacreBLEU scores were used and attained the following values: English-to-Hindi: 24.21 (baseline: 5.23); English-to-Bengali: 16.18 (baseline: 5.86); English-to-Tamil: 10.79 (baseline: 1.9). The solution streamlines workflow from input validation to output delivery, significantly enhancing communication across different linguistic contexts and achieving substantial improvements in SacreBLEU scores. Through the creation of dedicated datasets and the development of robust models, our aim is to facilitate seamless communication and accessibility across diverse linguistic communities, ultimately promoting inclusivity and empowerment.

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bib (full) Proceedings of the 1st Workshop on Knowledge Graphs and Large Language Models (KaLLM 2024)

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Multi-hop Database Reasoning with Virtual Knowledge Graph
Juhee Son | Yeon Seonwoo | Seunghyun Yoon | James Thorne | Alice Oh

Application of LLM to database queries on natural language sentences has demonstrated impressive results in both single and multi-hop scenarios.In the existing methodologies, the requirement to re-encode query vectors at each stage for processing multi-hop queries presents a significant bottleneck to the inference speed.This paper proposes VKGFR (Virtual Knowledge Graph based Fact Retriever) that leverages large language models to extract representations corresponding to a sentence’s knowledge graph, significantly enhancing inference speed for multi-hop reasoning without performance loss.Given that both the queries and natural language database sentences can be structured as a knowledge graph, we suggest extracting a Virtual Knowledge Graph (VKG) representation from sentences with LLM.Over the pre-constructed VKG, our VKGFR conducts retrieval with a tiny model structure, showing performance improvements with higher computational efficiency. We evaluate VKGFR on the WikiNLDB and MetaQA dataset, designed for multi-hop database reasoning over text. The results indicate 13x faster inference speed on the WikiNLDB dataset without performance loss.

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Zero- and Few-Shots Knowledge Graph Triplet Extraction with Large Language Models
Andrea Papaluca | Daniel Krefl | Sergio Rodríguez Méndez | Artem Lensky | Hanna Suominen

In this work, we tested the Triplet Extraction (TE) capabilities of a variety of Large Language Models (LLMs) of different sizes in the Zero- and Few-Shots settings. In detail, we proposed a pipeline that dynamically gathers contextual information from a Knowledge Base (KB), both in the form of context triplets and of (sentence, triplets) pairs as examples, and provides it to the LLM through a prompt. The additional context allowed the LLMs to be competitive with all the older fully trained baselines based on the Bidirectional Long Short-Term Memory (BiLSTM) Network architecture. We further conducted a detailed analysis of the quality of the gathered KB context, finding it to be strongly correlated with the final TE performance of the model. In contrast, the size of the model appeared to only logarithmically improve the TE capabilities of the LLMs. We release the code on GitHub for reproducibility.

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Analysis of LLM’s “Spurious” Correct Answers Using Evidence Information of Multi-hop QA Datasets
Ai Ishii | Naoya Inoue | Hisami Suzuki | Satoshi Sekine

Recent LLMs show an impressive accuracy on one of the hallmark tasks of language understanding, namely Question Answering (QA). However, it is not clear if the correct answers provided by LLMs are actually grounded on the correct knowledge related to the question. In this paper, we use multi-hop QA datasets to evaluate the accuracy of the knowledge LLMs use to answer questions, and show that as much as 31% of the correct answers by the LLMs are in fact spurious, i.e., the knowledge LLMs used to ground the answer is wrong while the answer is correct. We present an analysis of these spurious correct answers by GPT-4 using three datasets in two languages, while suggesting future pathways to correct the grounding information using existing external knowledge bases.

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Application of Generative AI as an Enterprise Wikibase Knowledge Graph Q&A System
Renê Mendes | Dimas Oliveira | Victor Garcia

Generative AI and Large Language Models are increasingly used in business contexts. One application involves natural language conversations contextualized by company data, which can be accomplished by Enterprise Knowledge Graphs, standardized representations of data. This paper outlines an architecture for implementation of an Enterprise Knowledge Graph using open-source Wikibase software. Additionally, it is presented a Knowledge Graph Q&A System powered by Generative AI.

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KGAST: From Knowledge Graphs to Annotated Synthetic Texts
Nakanyseth Vuth | Gilles Sérasset | Didier Schwab

In recent years, the use of synthetic data, either as a complement or a substitute for original data, has emerged as a solution to challenges such as data scarcity and security risks. This paper is an initial attempt to automatically generate such data for Information Extraction tasks. We accomplished this by developing a novel synthetic data generation framework called KGAST, which leverages Knowledge Graphs and Large Language Models. In our preliminary study, we conducted simple experiments to generate synthetic versions of two datasets—a French security defense dataset and an English general domain dataset, after which we evaluated them both intrinsically and extrinsically. The results indicated that synthetic data can effectively complement original data, improving the performance of models on classes with limited training samples. This highlights KGAST’s potential as a tool for generating synthetic data for Information Extraction tasks.

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HRGraph: Leveraging LLMs for HR Data Knowledge Graphs with Information Propagation-based Job Recommendation
Azmine Toushik Wasi

Knowledge Graphs (KGs) serving as semantic networks, prove highly effective in managing complex interconnected data in different domains, by offering a unified, contextualized, and structured representation with flexibility that allows for easy adaptation to evolving knowledge. Processing complex Human Resources (HR) data, KGs can help in different HR functions like recruitment, job matching, identifying learning gaps, and enhancing employee retention. Despite their potential, limited efforts have been made to implement practical HR knowledge graphs. This study addresses this gap by presenting a framework for effectively developing HR knowledge graphs from documents using Large Language Models. The resulting KG can be used for a variety of downstream tasks, including job matching, identifying employee skill gaps, and many more. In this work, we showcase instances where HR KGs prove instrumental in precise job matching, yielding advantages for both employers and employees. Empirical evidence from experiments with information propagation in KGs and Graph Neural Nets, along with case studies underscores the effectiveness of KGs in tasks such as job and employee recommendations and job area classification. Code and data are available at : https://github.com/azminewasi/HRGraph

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Adapting Multilingual LLMs to Low-Resource Languages with Knowledge Graphs via Adapters
Daniil Gurgurov | Mareike Hartmann | Simon Ostermann

This paper explores the integration of graph knowledge from linguistic ontologies into multilingual Large Language Models (LLMs) using adapters to improve performance for low-resource languages (LRLs) in sentiment analysis (SA) and named entity recognition (NER). Building upon successful parameter-efficient fine-tuning techniques, such as K-ADAPTER and MAD-X, we propose a similar approach for incorporating knowledge from multilingual graphs, connecting concepts in various languages with each other through linguistic relationships, into multilingual LLMs for LRLs. Specifically, we focus on eight LRLs — Maltese, Bulgarian, Indonesian, Nepali, Javanese, Uyghur, Tibetan, and Sinhala — and employ language-specific adapters fine-tuned on data extracted from the language-specific section of ConceptNet, aiming to enable knowledge transfer across the languages covered by the knowledge graph. We compare various fine-tuning objectives, including standard Masked Language Modeling (MLM), MLM with full-word masking, and MLM with targeted masking, to analyze their effectiveness in learning and integrating the extracted graph data. Through empirical evaluation on language-specific tasks, we assess how structured graph knowledge affects the performance of multilingual LLMs for LRLs in SA and NER, providing insights into the potential benefits of adapting language models for low-resource scenarios.

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Ontology-guided Knowledge Graph Construction from Maintenance Short Texts
Zeno Cauter | Nikolay Yakovets

Large-scale knowledge graph construction remains infeasible since it requires significant human-expert involvement. Further complications arise when building graphs from domain-specific data due to their unique vocabularies and associated contexts. In this work, we demonstrate the ability of open-source large language models (LLMs), such as Llama-2 and Llama-3, to extract facts from domain-specific Maintenance Short Texts (MSTs). We employ an approach which combines ontology-guided triplet extraction and in-context learning. By using only 20 semantically similar examples with the Llama-3-70B-Instruct model, we achieve performance comparable to previous methods that relied on fine-tuning techniques like SpERT and REBEL. This indicates that domain-specific fact extraction can be accomplished through inference alone, requiring minimal labeled data. This opens up possibilities for effective and efficient semi-automated knowledge graph construction for domain-specific data.

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Educational Material to Knowledge Graph Conversion: A Methodology to Enhance Digital Education
Miquel Canal-Esteve | Yoan Gutierrez

This article argues that digital educational content should be structured as knowledge graphs (KGs). Unlike traditional repositories such as Moodle, a KG offers a more flexible representation of the relationships between concepts, facilitating intuitive navigation and discovery of connections. In addition, it integrates effectively with Large Language Models, enhancing personalized explanations, answers, and recommendations. This article studies different proposals based on semantics and knowledge modelling to determine the most appropriate ways to strengthen intelligent educational technologies.

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STAGE: Simplified Text-Attributed Graph Embeddings using Pre-trained LLMs
Aaron Zolnai-Lucas | Jack Boylan | Chris Hokamp | Parsa Ghaffari

We present STAGE, a straightforward yet effective method for enhancing node features in Graph Neural Network (GNN) models that encode Text-Attributed Graphs (TAGs). Our approach leverages Large-Language Models (LLMs) to generate embeddings for textual attributes. STAGE achieves competitive results on various node classification benchmarks while also maintaining a simplicity in implementation relative to current state-of-the-art (SoTA) techniques. We show that utilizing pre-trained LLMs as embedding generators provides robust features for ensemble GNN training, enabling pipelines that are simpler than current SoTA approaches which require multiple expensive training and prompting stages. We also implement diffusion-pattern GNNs in an effort to make this pipeline scalable to graphs beyond academic benchmarks.

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Zero-Shot Fact-Checking with Semantic Triples and Knowledge Graphs
Moy Yuan | Andreas Vlachos

Despite progress in automated fact-checking, most systems require a significant amount of labeled training data, which is expensive. In this paper, we propose a novel zero-shot method, which instead of operating directly on the claim and evidence sentences, decomposes them into semantic triples augmented using external knowledge graphs, and uses large language models trained for natural language inference. This allows it to generalize to adversarial datasets and domains that supervised models require specific training data for. Our empirical results show that our approach outperforms previous zero-shot approaches on FEVER, FEVER-Symmetric, FEVER 2.0, and Climate-FEVER, while being comparable or better than supervised models on the adversarial and the out-of-domain datasets.

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Fine-tuning Language Models for Triple Extraction with Data Augmentation
Yujia Zhang | Tyler Sadler | Mohammad Reza Taesiri | Wenjie Xu | Marek Reformat

Advanced language models with impressive capabilities to process textual information can more effectively extract high-quality triples, which are the building blocks of knowledge graphs. Our work examines language models’ abilities to extract entities and the relationships between them. We use a diverse data augmentation process to fine-tune large language models to extract triples from the text. Fine-tuning is performed using a mix of trainers from HuggingFace and five public datasets, such as different variations of the WebNLG, SKE, DocRed, FewRel, and KELM. Evaluation involves comparing model outputs with test-set triples based on several criteria, such as type, partial, exact, and strict accuracy.The obtained results outperform ChatGPT and even match or exceed the performance of GPT-4.

Large language models (LLMs) have shown remarkable capabilities in generating natural language texts for various tasks. However, using LLMs for question answering on knowledge graphs still remains a challenge, especially for questions requiring multi-hop reasoning. In this paper, we present a novel planned query guidance approach that improves large language model (LLM) performance in multi-hop question answering on knowledge graphs (KGQA). We do this by designing few-shot examples that implicitly demonstrate a systematic reasoning methodology to answer multi-hop questions. We evaluate our approach for two graph query languages, Cypher and SPARQL, and show that the queries generated using our strategy outperform the queries generated using a baseline LLM and typical few-shot examples by up to 24.66% and 7.7% in execution match accuracy for the MetaQA and the Spider benchmarks respectively. We also conduct an ablation study to analyze the incremental effects of the different techniques of designing few-shot examples. Our results suggest that our approach enables the LLM to effectively leverage the few-shot examples to generate queries for multi-hop KGQA.

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bib (full) Proceedings of the 3rd Workshop on Knowledge Augmented Methods for NLP

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GADePo: Graph-Assisted Declarative Pooling Transformers for Document-Level Relation Extraction
Andrei Coman | Christos Theodoropoulos | Marie-Francine Moens | James Henderson

Document-level relation extraction typically relies on text-based encoders and hand-coded pooling heuristics to aggregate information learned by the encoder. In this paper, we leverage the intrinsic graph processing capabilities of the Transformer model and propose replacing hand-coded pooling methods with new tokens in the input, which are designed to aggregate information via explicit graph relations in the computation of attention weights. We introduce a joint text-graph Transformer model and a graph-assisted declarative pooling (GADePo) specification of the input, which provides explicit and high-level instructions for information aggregation. GADePo allows the pooling process to be guided by domain-specific knowledge or desired outcomes but still learned by the Transformer, leading to more flexible and customisable pooling strategies. We evaluate our method across diverse datasets and models and show that our approach yields promising results that are consistently better than those achieved by the hand-coded pooling functions.

Question-answering for domain-specific applications has recently attracted much interest due to the latest advancements in large language models (LLMs). However, accurately assessing the performance of these applications remains a challenge, mainly due to the lack of suitable benchmarks that effectively simulate real-world scenarios. To address this challenge, we introduce two product question-answering (QA) datasets focused on Adobe Acrobat and Photoshop products to help evaluate the performance of existing models on domain-specific product QA tasks. Additionally, we propose a novel knowledge-driven RAG-QA framework to enhance the performance of the models in the product QA task. Our experiments demonstrated that inducing domain knowledge through query reformulation allowed for increased retrieval and generative performance when compared to standard RAG-QA methods. This improvement, however, is slight, and thus illustrates the challenge posed by the datasets introduced.

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Collecting High-quality Multi-modal Conversational Search Data for E-Commerce
Marcus Collins | Oleg Rokhlenko | Eugene Agichtein | Shervin Malmasi

Continued improvement of conversational assistants in knowledge-rich domains like E-Commerce requires large volumes of realistic high-quality conversation data to power increasingly sophisticated large language model chatbots, dialogue managers, response rankers, and recommenders. The problem is exacerbated for multi-modal interactions in realistic conversational product search and recommendation. Here, an artificial sales agent must interact intelligently with a customer using both textual and visual information and incorporate results from external search systems, such as a product catalog. Yet, it remains an open question how to best crowd-source large-scale, naturalistic multi-modal dialogue and action data, required to train such an artificial agent. We describe our crowd-sourced task where one worker (the Buyer) plays the role of the customer, and another (the Seller) plays the role of the sales agent. We identify subtle interactions between one worker’s environment and their partner’s behavior mediated by workers’ word choice. We find that limiting information presented to the Buyer, both in their backstory and by the Seller, improves conversation quality. We also show how conversations are improved through minimal automated Seller “coaching”. While typed and spoken messages are slightly different, the differences are not as large as frequently assumed. We plan to release our platform code and the resulting dialogues to advance research on conversational search agents.

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Learning to Trust Your Feelings: Leveraging Self-awareness in LLMs for Hallucination Mitigation
Yuxin Liang | Zhuoyang Song | Hao Wang | Jiaxing Zhang

We evaluate the ability of Large Language Models (LLMs) to discern and express their internal knowledge state, a key factor in countering factual hallucination and ensuring reliable application of LLMs. We observe a robust self-awareness of internal knowledge state in LLMs, evidenced by over 85% accuracy in knowledge state probing. However, LLMs often fail to faithfully express their internal knowledge during generation, leading to factual hallucinations. We develop an automated hallucination annotation tool, DreamCatcher, which merges knowledge probing and consistency checking methods to rank factual preference data. Using knowledge preference as reward, We propose a Reinforcement Learning from Knowledge Feedback (RLKF) training framework, leveraging reinforcement learning to enhance the factuality and honesty of LLMs. Our experiments across multiple models show that RLKF training effectively enhances the ability of models to utilize their internal knowledge state, boosting performance in a variety of knowledge-based and honesty-related tasks.

This paper aims to augment fans’ ability to critique and exploreinformation related to celebrities of interest. First, we collect postsfrom X (formerly Twitter) that discuss matters related to specificcelebrities. For the collection of major impressions from these posts,we employ ChatGPT as a large language model (LLM) to analyze andsummarize key sentiments. Next, based on collected impressions, wesearch for Web pages and collect the content of the top 30 ranked pagesas the source for exploring the reasons behind those impressions. Oncethe Web page content collection is complete, we collect and aggregatedetailed reasons for the impressions on the celebrities from the contentof each page. For this part, we continue to use ChatGPT, enhanced bythe retrieval augmented generation (RAG) framework, to ensure thereliability of the collected results compared to relying solely on theprior knowledge of the LLM. Evaluation results by comparing a referencethat is manually collected and aggregated reasons with those predictedby ChatGPT revealed that ChatGPT achieves high accuracy in reasoncollection and aggregation. Furthermore, we compared the performance ofChatGPT with an existing model of mT5 in reason collection and confirmedthat ChatGPT exhibits superior performance.

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DSLR: Document Refinement with Sentence-Level Re-ranking and Reconstruction to Enhance Retrieval-Augmented Generation
Taeho Hwang | Soyeong Jeong | Sukmin Cho | SeungYoon Han | Jong Park

Recent advancements in Large Language Models (LLMs) have significantly improved their performance across various Natural Language Processing (NLP) tasks.However, LLMs still struggle with generating non-factual responses due to limitations in their parametric memory.Retrieval-Augmented Generation (RAG) systems address this issue by incorporating external knowledge with a retrieval module.Despite their successes, however, current RAG systems face challenges with retrieval failures and the limited ability of LLMs to filter out irrelevant information.Therefore, in this work, we propose DSLR (Document Refinement with Sentence-Level Re-ranking and Reconstruction), an unsupervised framework that decomposes retrieved documents into sentences, filters out irrelevant sentences, and reconstructs them again into coherent passages.We experimentally validate DSLR on multiple open-domain QA datasets and the results demonstrate that DSLR significantly enhances the RAG performance over conventional fixed-size passage.Furthermore, our DSLR enhances performance in specific, yet realistic scenarios without the need for additional training, providing an effective and efficient solution for refining retrieved documents in RAG systems.

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Enhancing Robustness of Retrieval-Augmented Language Models with In-Context Learning
SeongIl Park | Seungwoo Choi | Nahyun Kim | Jay-Yoon Lee

Retrieval-Augmented Language Models (RALMs) have significantly improved performance in open-domain question answering (QA) by leveraging external knowledge. However, RALMs still struggle with unanswerable queries, where the retrieved contexts do not contain the correct answer, and with conflicting information, where different sources provide contradictory answers due to imperfect retrieval. This study introduces an in-context learning-based approach to enhance the reasoning capabilities of RALMs, making them more robust in imperfect retrieval scenarios. Our method incorporates Machine Reading Comprehension (MRC) demonstrations, referred to as cases, to boost the model’s capabilities to identify unanswerabilities and conflicts among the retrieved contexts. Experiments on two open-domain QA datasets show that our approach increases accuracy in identifying unanswerable and conflicting scenarios without requiring additional fine-tuning. This work demonstrates that in-context learning can effectively enhance the robustness of RALMs in open-domain QA tasks.

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bib (full) Proceedings of the 1st Workshop on Towards Knowledgeable Language Models (KnowLLM 2024)

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PhonologyBench: Evaluating Phonological Skills of Large Language Models
Ashima Suvarna | Harshita Khandelwal | Nanyun Peng

Phonology, the study of speech’s structure and pronunciation rules, is a critical yet often overlooked component in Large Language Model (LLM) research. LLMs are widely used in various downstream applications that leverage phonology such as educational tools and poetry generation. Moreover, LLMs can potentially learn imperfect associations between orthographic and phonological forms from the training data. Thus, it is imperative to benchmark the phonological skills of LLMs. To this end, we present PhonologyBench, a novel benchmark consisting of three diagnostic tasks designed to explicitly test the phonological skills of LLMs in English: grapheme-to-phoneme conversion, syllable counting, and rhyme word generation. Despite having no access to speech data, LLMs showcased notable performance on the PhonologyBench tasks. However, we observe a significant gap of 17% and 45% on Rhyme Word Generation and Syllable counting, respectively, when compared to humans. Our findings underscore the importance of studying LLM performance on phonological tasks that inadvertently impact real-world applications. Furthermore, we encourage researchers to choose LLMs that perform well on the phonological task that is closely related to the downstream application since we find that no single model consistently outperforms the others on all the tasks.

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Is Your Large Language Model Knowledgeable or a Choices-Only Cheater?
Nishant Balepur | Rachel Rudinger

Recent work shows that large language models (LLMs) can answer multiple-choice questions using only the choices, but does this mean that MCQA leaderboard rankings of LLMs are largely influenced by abilities in choices-only settings? To answer this, we use a contrast set that probes if LLMs over-rely on choices-only shortcuts in MCQA. While previous works build contrast sets via expensive human annotations or model-generated data which can be biased, we employ graph mining to extract contrast sets from existing MCQA datasets. We use our method on UnifiedQA, a group of six commonsense reasoning datasets with high choices-only accuracy, to build an 820-question contrast set. After validating our contrast set, we test 12 LLMs, finding that these models do not exhibit reliance on choice-only shortcuts when given both the question and choices. Thus, despite the susceptibility of MCQA to high choices-only accuracy, we argue that LLMs are not obtaining high ranks on MCQA leaderboards solely due to their ability to exploit choices-only shortcuts.

Ensuring factual consistency between the summary and the original document is paramount in summarization tasks. Consequently, considerable effort has been dedicated to detecting inconsistencies. With the advent of Large Language Models (LLMs), recent studies have begun to leverage their advanced language understanding capabilities for inconsistency detection. However, early attempts have shown that LLMs underperform traditional models due to their limited ability to follow instructions and the absence of an effective detection methodology. In this study, we reassess summary inconsistency detection with LLMs, comparing the performances of GPT-3.5 and GPT-4. To advance research in LLM-based inconsistency detection, we propose SIFiD (Summary Inconsistency Detection with Filtered Document) that identify key sentences within documents by either employing natural language inference or measuring semantic similarity between summaries and documents.

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Beyond Text: Unveiling Multimodal Proficiency of Large Language Models with MultiAPI Benchmark
Xiao Liu | Jianfeng Lin | Jiawei Zhang

The proliferation of Large Language Models like ChatGPT has significantly advanced language understanding and generation, impacting a broad spectrum of applications. However, these models predominantly excel in text-based tasks, overlooking the complexity of real-world multimodal information. This study introduces MultiAPI, a pioneering comprehensive large-scale API benchmark dataset aimed at expanding LLMs’ proficiency in multimodal contexts. Developed collaboratively through ChatGPT, MultiAPI consists of 187 diverse API calls and 1,799 contextual prompts, offering a unique platform evaluation of tool-augmented LLMs handling multimodal tasks. Through comprehensive experiments, our findings reveal that while LLMs demonstrate proficiency in API call decision-making, they face challenges in domain identification, function selection, and argument generation. What’s more, we surprisingly notice that auxiliary context can actually impair the performance. An in-depth error analysis paves the way for a new paradigm to address these challenges, suggesting a potential direction for future LLM research.

This survey analyses how external knowledge can be integrated into language models in the context of retrieval-augmentation.The main goal of this work is to give an overview of: (1) Which external knowledge can be augmented? (2) Given a knowledge source, how to retrieve from it and then integrate the retrieved knowledge? To achieve this, we define and give a mathematical formulation of retrieval-augmented knowledge integration (RAKI). We discuss retrieval and integration techniques separately in detail, for each of the following knowledge formats: knowledge graph, tabular and natural language.

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ClinicalRAG: Enhancing Clinical Decision Support through Heterogeneous Knowledge Retrieval
Yuxing Lu | Xukai Zhao | Jinzhuo Wang

Large Language Models (LLMs) have revolutionized text generation across diverse domains, showcasing an ability to mimic human-like text with remarkable accuracy. Yet, these models frequently encounter a significant hurdle: producing hallucinations, a flaw particularly detrimental in the healthcare domain where precision is crucial. In this paper, we introduce ClinicalRAG, a novel multi-agent pipeline to rectify this issue by incorporating heterogeneous medical knowledge—both structured and unstructured—into LLMs to bolster diagnosis accuracy. ClinicalRAG can extract related medical entities from user inputs and dynamically integrate relevant medical knowledge during the text generation process. Comparative analyses reveal that ClinicalRAG significantly outperforms knowledge-deficient methods, offering enhanced reliability in clinical decision support. This advancement marks a pivotal proof-of-concept step towards mitigating misinformation risks in healthcare applications of LLMs.

The integration of retrieved passages and large language models (LLMs), such as ChatGPTs, has significantly contributed to improving open-domain question answering. However, there is still a lack of exploration regarding the optimal approach for incorporating retrieved passages into the answer generation process. This paper aims to fill this gap by investigating different methods of combining retrieved passages with LLMs to enhance answer generation. We begin by examining the limitations of a commonly-used concatenation approach. Surprisingly, this approach often results in generating “unknown” outputs, even when the correct document is among the top-k retrieved passages. To address this issue, we explore four alternative strategies for integrating the retrieved passages with the LLMs. These strategies include two single-round methods that utilize chain-of-thought reasoning and two multi-round strategies that incorporate feedback loops. Through comprehensive analyses and experiments, we provide insightful observations on how to effectively leverage retrieved passages to enhance the answer generation capability of LLMs. On three open-domain question answering datesets, NQ, TriviaQA and SQuAD, our multi-round approaches outperform traditional concatenation approach, achieving over a 10% improvement in answer EM.

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AcKnowledge: Acquired Knowledge Representation by Small Language Model Without Pre-training
Sourav Das | Sanjay Chatterji | Imon Mukherjee

Large language models (LLMs) are pre-trained on enormous amounts of text data and show acclaimed success in knowledge representation. However, there are two bottlenecks with this approach. (1) Pre-training data cannot be regularly updated once the models are deployed, and it is not very fruitful if the model cannot represent updated knowledge. (2) The consistently increasing size and computational resources make it difficult for non-commercial and individual researchers to fine-tune and scale these language models. Major LLMs with external knowledge are also proprietary. In this paper, we propose AcKnowledge, a framework wrapped around a small, non-pre-trained language model for an open-domain question-answering (QA) experiment. AcKnowledge learns relevant knowledge from the internet via meta-learning based on user questions, and re-learns from user feedback if knowledge is misrepresented. Our efficient knowledge representation framework avoids pre-training overhead while enabling updated information. Benchmarking shows competitive performance against similarly sized state-of-the-art (SoTA) LLMs on gold standard QA datasets, demonstrating the potential of integrating internet search and user feedback for improved performance and generalizability.

Powerful LLMs like ChatGPT are adopted rapidly for a wide array of tasks, but their limitations in domain-specific areas become apparent, particularly when prompted to recite facts. This is critical especially for knowledge workers, who are adopting LLM-based tools rapidly.While there are various techniques that can help ingest knowledge into LLMs such as instruction tuning and alignment, most have disadvantages. We examine the impact of prominent training techniques on LLMs’ knowledge accuracy using a knowledge-dense dataset that we curate from r/AskHistorians, a rich source of historical knowledge. We evaluate the impact of different models sizes from 1.3B to 7B parameters and other factors such as LoRA adapters, quantization, overfitting, and the inclusion of Reddit data in pretraining.In addition, we measure linguistic metrics and human and LLM-based preference. Our results suggest that pretraining and model size have a much stronger effect on knowledge accuracy than continued pretraining – unless the model is overfit to the tested knowledge.Fine-tuning on our Reddit dataset introduces less complex, but slightly more toxic language. Our study explores the challenges of injecting domain-specific datasets into LLMs and has implications for practitioners, e.g., when LLMs are to be fine-tuned with a company’s datasets.

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Beyond Probabilities: Unveiling the Misalignment in Evaluating Large Language Models
Chenyang Lyu | Minghao Wu | Alham Aji

Large Language Models (LLMs) have demonstrated remarkable capabilities across various applications, fundamentally reshaping the landscape of natural language processing (NLP) research. However, recent evaluation frameworks often rely on the output probabilities of LLMs for predictions, primarily due to computational constraints, diverging from real-world LLM usage scenarios. While widely employed, the efficacy of these probability-based evaluation strategies remains an open research question. This study aims to scrutinize the validity of such probability-based evaluation methods within the context of using LLMs for Multiple Choice Questions (MCQs), highlighting their inherent limitations. Our empirical investigation reveals that the prevalent probability-based evaluation method inadequately aligns with generation-based prediction. Furthermore, current evaluation frameworks typically assess LLMs through predictive tasks based on output probabilities rather than directly generating responses, owing to computational limitations. We illustrate that these probability-based approaches do not effectively correspond with generative predictions. The outcomes of our study can enhance the understanding of LLM evaluation methodologies and provide insights for future research in this domain.

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PromptRE: Weakly-Supervised Document-Level Relation Extraction via Prompting-Based Data Programming
Chufan Gao | Xulin Fan | Jimeng Sun | Xuan Wang

Relation extraction aims to classify the relationships between two entities into pre-defined categories. While previous research has mainly focused on sentence-level relation extraction, recent studies have expanded the scope to document-level relation extraction. Traditional relation extraction methods heavily rely on human-annotated training data, which is time-consuming and labor-intensive. To mitigate the need for manual annotation, recent weakly-supervised approaches have been developed for sentence-level relation extraction while limited work has been done on document-level relation extraction. Weakly-supervised document-level relation extraction faces significant challenges due to an imbalanced number “no relation” instances and the failure of directly probing pretrained large language models for document relation extraction. To address these challenges, we propose PromptRE, a novel weakly-supervised document-level relation extraction method that combines prompting-based techniques with data programming. Furthermore, PromptRE incorporates the label distribution and entity types as prior knowledge to improve the performance. By leveraging the strengths of both prompting and data programming, PromptRE achieves improved performance in relation classification and effectively handles the “no relation” problem. Experimental results on ReDocRED, a benchmark dataset for document-level relation extraction, demonstrate the superiority of PromptRE over baseline approaches.

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Patent Response System Optimised for Faithfulness: Procedural Knowledge Embodiment with Knowledge Graph and Retrieval Augmented Generation
Jung-Mei Chu | Hao-Cheng Lo | Jieh Hsiang | Chun-Chieh Cho

A successful response to Office Action is crucial for an invention to obtain a patent. While previous attempts have applied generalised LLMs, such as GPT-4, in the response process, there remains significant room for improvement in generating faithful, unbiased, and practically valuable responses. To address this issue, we propose the Patent Response System Optimised for Faithfulness (PRO). PRO explicitly incorporates procedural knowledge used by patent agents during drafting arguments in response. This framework comprises several key components: (1) Our proposed PRLLM is a LLM tailored for patent responses, designed to have comprehensive patent domain-specific knowledge. (2) Our proposed PPNet encodes legal interpretations and relationships between technical components from judicial sources through a knowledge graph. (3) The augmented generation processes retrieve relevant information from both the patent text and PPNet to augment the PRLLM’s input and generate faithful responses. Results show that PRO significantly reduces unfaithfulness across six error types compared to several settings. For instance, PRO outperforms GPT-4 by an average of 39% in terms of faithfulness. This demonstrates the effectiveness of our domain-specific approach in improving the quality of automated patent responses.

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Safe-Embed: Unveiling the Safety-Critical Knowledge of Sentence Encoders
Jinseok Kim | Jaewon Jung | Sangyeop Kim | Sohhyung Park | Sungzoon Cho

Despite the impressive capabilities of Large Language Models (LLMs) in various tasks, their vulnerability to unsafe prompts remains a critical issue. These prompts can lead LLMs to generate responses on illegal or sensitive topics, posing a significant threat to their safe and ethical use. Existing approaches address this issue using classification models, divided into LLM-based and API-based methods. LLM based models demand substantial resources and large datasets, whereas API-based models are cost-effective but might overlook linguistic nuances. With the increasing complexity of unsafe prompts, similarity search-based techniques that identify specific features of unsafe content provide a more robust and effective solution to this evolving problem. This paper investigates the potential of sentence encoders to distinguish safe from unsafe content. We introduce new pairwise datasets and the Cate021 gorical Purity (CP) metric to measure this capability. Our findings reveal both the effectiveness and limitations of existing sentence encoders, proposing directions to improve sentence encoders to operate as robust safety detectors.

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Measuring the Inconsistency of Large Language Models in Preferential Ranking
Xiutian Zhao | Ke Wang | Wei Peng

Despite large language models’ (LLMs’) recent advancements, their bias and hallucination issues persist, and their ability to offer consistent and preferential rankings remains underexplored. This study investigates the capacity of LLMs to provide consistent ordinal preferences, a crucial aspect in scenarios lacking absolute answers. We introduce a formalization of consistency based on order theory, outlining criteria such as transitivity, asymmetry, reversibility, and independence from irrelevant alternatives. Our diagnostic experiments on selected state-of-the-art LLMs reveal their inability to meet these criteria, indicating a strong positional bias and poor transitivity, with preferences easily swayed by irrelevant alternatives. These findings highlight a significant inconsistency in LLM-generated preferential rankings, underscoring the need for further research to address these limitations.

Retrieval-augmented generation (RAG) has recently emerged as a promising solution for incorporating up-to-date or domain-specific knowledge into large language models (LLMs) and improving LLM factuality, but is predominantly studied in English-only settings. In this work, we consider RAG in the multilingual setting (mRAG), i.e. with user queries and the datastore in 13 languages, and investigate which components and with which adjustments are needed to build a well-performing mRAG pipeline, that can be used as a strong baseline in future works. Our findings highlight that despite the availability of high-quality off-the-shelf multilingual retrievers and generators, task-specific prompt engineering is needed to enable generation in user languages. Moreover, current evaluation metrics need adjustments for multilingual setting, to account for variations in spelling named entities. The main limitations to be addressed in future works include frequent code-switching in non-Latin alphabet languages, occasional fluency errors, wrong reading of the provided documents, or irrelevant retrieval. We release the code for the resulting mRAG baseline pipeline at https://github.com/naver/bergen, Documentation: https://github.com/naver/bergen/blob/main/documentations/multilingual.md.

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Retrieve, Generate, Evaluate: A Case Study for Medical Paraphrases Generation with Small Language Models
Ioana Buhnila | Aman Sinha | Mathieu Constant

Recent surge in the accessibility of large language models (LLMs) to the general population can lead to untrackable use of such models for medical-related recommendations. Language generation via LLMs models has two key problems: firstly, they are prone to hallucination and therefore, for any medical purpose they require scientific and factual grounding; secondly, LLMs pose tremendous challenge to computational resources due to their gigantic model size. In this work, we introduce pRAGe, a Pipeline for Retrieval Augmented Generation and Evaluation of medical paraphrases generation using Small Language Models (SLM). We study the effectiveness of SLMs and the impact of external knowledge base for medical paraphrase generation in French.

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bib (full) Proceedings of the 1st Workshop on Language + Molecules (L+M 2024)

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L+M-24: Building a Dataset for Language+Molecules @ ACL 2024
Carl Edwards | Qingyun Wang | Lawrence Zhao | Heng Ji

Language-molecule models have emerged as an exciting direction for molecular discovery and understanding. However, training these models is challenging due to the scarcity of molecule-language pair datasets. At this point, datasets have been released which are 1) small and scraped from existing databases, 2) large but noisy and constructed by performing entity linking on the scientific literature, and 3) built by converting property prediction datasets to natural language using templates. In this document, we detail the L+M-24 dataset, which has been created for the Language + Molecules Workshop shared task at ACL 2024. In particular, L+M-24 is designed to focus on three key benefits of natural language in molecule design: compositionality, functionality, and abstraction

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Could Chemical Language Models benefit from Message Passing
Jiaqing Xie | Ziheng Chi

Pretrained language models (LMs) showcase significant capabilities in processing molecular text, while concurrently, message passing neural networks (MPNNs) demonstrate resilience and versatility in the domain of molecular science. Despite these advancements, we find there are limited studies investigating the bidirectional interactions between molecular structures and their corresponding textual representations. Therefore, in this paper, we propose two strategies to evaluate whether an information integration can enhance the performance: contrast learning, which involves utilizing an MPNN to supervise the training of the LM, and fusion, which exploits information from both models. Our empirical analysis reveals that the integration approaches exhibit superior performance compared to baselines when applied to smaller molecular graphs, while these integration approaches do not yield performance enhancements on large scale graphs.

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ALMol: Aligned Language-Molecule Translation LLMs through Offline Preference Contrastive Optimisation
Dimitris Gkoumas

The field of chemistry and Artificial Intelligence (AI) intersection is an area of active research that aims to accelerate scientific discovery. The integration of large language models (LLMs) with scientific modalities has shown significant promise in this endeavour. However, challenges persist in effectively addressing training efficacy and the out-of-distribution problem, particularly as existing approaches rely on larger models and datasets. In this context, we focus on machine language-molecule translation and deploy a novel training approach called contrastive preference optimisation, which avoids generating translations that are merely adequate but not perfect. To ensure generalisability and mitigate memorisation effects, we conduct experiments using only 10% of the data. Our results demonstrate that our models achieve up to a 32% improvement compared to counterpart models. Finally, we introduce a fine-grained, domain-agnostic evaluation method to assess hallucination in LLMs and promote responsible use.

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Evaluating Extrapolation Ability of Large Language Model in Chemical Domain
Taehun Cha | Donghun Lee

Solving a problem outside the training space, i.e. extrapolation, has been a long problem in the machine learning community. The current success of large language models demonstrates the LLM’s extrapolation ability to several unseen tasks. In line with these works, we evaluate the LLM”s extrapolation ability in the chemical domain. We construct a data set measuring the material properties of epoxy polymers depending on various raw materials and curing processes. LLM should predict the material property when novel raw material is introduced utilizing its chemical knowledge. Through experiments, LLM tends to choose the right direction of adjustment but fails to determine the exact degree, resulting in poor MAE on some properties. But LLM can successfully adjust the degree with only a one-shot example. The results show that LLM can extrapolate to new unseen material utilizing its chemical knowledge learned through massive pre-training.

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Design Proteins Using Large Language Models: Enhancements and Comparative Analyses
Kamyar Zeinalipour | Neda Jamshidi | Monica Bianchini | Marco Maggini | Marco Gori

Pre-trained LLMs have demonstrated substantial capabilities across a range of conventional natural language processing (NLP) tasks, such as summarization and entity recognition. In this paper, we explore the application of LLMs in the generation of high-quality protein sequences. Specifically, we adopt a suite of pre-trained LLMs, including Mistral-7B, Llama-2-7B, Llama-3-8B, and gemma-7B, to produce valid protein sequences. All of these models are publicly available (https://github.com/KamyarZeinalipour/protein-design-LLMs).Unlike previous work in this field, our approach utilizes a relatively small dataset comprising 42,000 distinct human protein sequences. We retrain these models to process protein-related data, ensuring the generation of biologically feasible protein structures. Our findings demonstrate that even with limited data, the adapted models exhibit efficiency comparable to established protein-focused models such as ProGen varieties, ProtGPT2, and ProLLaMA, which were trained on millions of protein sequences. To validate and quantify the performance of our models, we conduct comparative analyses employing standard metrics such as pLDDT, RMSD, TM-score, and REU. Furthermore, we commit to making the trained versions of all four models publicly available, fostering greater transparency and collaboration in the field of computational biology.

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Enhanced BioT5+ for Molecule-Text Translation: A Three-Stage Approach with Data Distillation, Diverse Training, and Voting Ensemble
Qizhi Pei | Lijun Wu | Kaiyuan Gao | Jinhua Zhu | Rui Yan

This paper presents our enhanced BioT5+ method for the Language + Molecules shared task at the ACL 2024 Workshop. The task involves “translating” between molecules and natural language, including molecule captioning and text-based molecule generation using the L+M-24 dataset. Our method consists of three stages. In the first stage, we distill data from various models. In the second stage, combined with extra version of the provided dataset, we train diverse models for subsequent voting ensemble.We also adopt Transductive Ensemble Learning (TEL) to enhance these base models. Lastly, all models are integrated using a voting ensemble method. Experimental results demonstrate that BioT5+ achieves superior performance on L+M-24 dataset. On the final leaderboard, our method (team name: qizhipei) ranks first in the text-based molecule generation task and second in the molecule captioning task, highlighting its efficacy and robustness in translating between molecules and natural language. The pre-trained BioT5+ models are available at https://github.com/QizhiPei/BioT5.

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ChatMol Copilot: An Agent for Molecular Modeling and Computation Powered by LLMs
Jinyuan Sun | Auston Li | Yifan Deng | Jiabo Li

Large Language Models (LLMs) like ChatGPT excel at diverse tasks when given explicit instructions, yet they often struggle with specialized domains such as molecular science, lacking in-depth reasoning and sophisticated planning capabilities. To address these limitations, we introduce ChatMol Copilot, a chatbot-like agent specifically engineered for protein design and small molecule computations. ChatMol Copilot employs a multi-level abstraction framework to expand the LLM‘s capability. At the basic level, it integrates external computational tools through function calls, thus offloading complex tasks and enabling a focus on strategic decision-making. The second level is data abstraction. Large data sets (such as a large number of molecules created by a generative model) are stored in Redis cache, and the redis keys are referenced by LLMs for data sources involved in computation. The third level of abstraction allows the LLM to orchestrate these tools, either directly or via dynamically generated Python executables. Our evaluations demonstrate that ChatMol Copilot can adeptly manage molecular modeling tasks, effectively utilizing a variety of tools as directed. By simplifying access to sophisticated molecular modeling resources, ChatMol Copilot stands to significantly accelerate drug discovery and biotechnological innovation, empowering biochemists with advanced, user-friendly AI capabilities. The open-sourced code is available at https://github.com/ChatMol/ChatMol

Large language models (LLMs) have made substantial strides, but their use in reliably tackling issues within specialized domains, particularly in interdisciplinary areas like pharmaceutical sciences, is hindered by data heterogeneity, knowledge complexity, unique objectives, and a spectrum of constraint conditions. In this area, diverse modalities such as nucleic acids, proteins, molecular structures, and natural language are often involved. We designed a specialized token set and introduced a new Mixture-of-Experts (MoEs) pre-training and fine-tuning strategy to unify these modalities in one model. With this strategy, we’ve created a multi-modal mixture-of-experts foundational model for pharmaceutical sciences, named SciMind. This model has undergone extensive pre-training on publicly accessible datasets including nucleic acid sequences, protein sequences, molecular structure strings, and biomedical texts, and delivers good performance on biomedical text comprehension, promoter prediction, protein function prediction, molecular description, and molecular generation.

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Knowledge Graph Extraction from Total Synthesis Documents
Andres M Bran | Zlatko Jončev | Philippe Schwaller

Knowledge graphs (KGs) have emerged as a powerful tool for organizing and integrating complex information, making it a suitable format for scientific knowledge. However, translating scientific knowledge into KGs is challenging as a wide variety of styles and elements to present data and ideas is used. Although efforts for KG extraction (KGE) from scientific documents exist, evaluation remains challenging and field-dependent; and existing benchmarks do not focuse on scientific information. Furthermore, establishing a general benchmark for this task is challenging as not all scientific knowledge has a ground-truth KG representation, making any benchmark prone to ambiguity. Here we propose Graph of Organic Synthesis Benchmark (GOSyBench), a benchmark for KG extraction from scientific documents in chemistry, that leverages the native KG-like structure of synthetic routes in organic chemistry. We develop KG-extraction algorithms based on LLMs (GPT-4, Claude, Mistral) and VLMs (GPT-4o), the best of which reaches 73% recovery accuracy and 59% precision, leaving a lot of room for improvement. We expect GOSyBench can serve as a valuable resource for evaluating and advancing KGE methods in the scientific domain, ultimately facilitating better organization, integration, and discovery of scientific knowledge.

This paper presents our approach submitted to the Language + Molecules 2024 (L+M-24) Shared Task in the Molecular Captioning track. The task involves generating captions that describe the properties of molecules that are provided in SMILES format.We propose a method for the task that decomposes the challenge of generating captions from SMILES into a classification problem,where we first predict the molecule’s properties. The molecules whose properties can be predicted with high accuracy show high translation metric scores in the caption generation by LLMs, while others produce low scores. Then we use the predicted properties to select the captions generated by different types of LLMs, and use that prediction as the final output. Our submission achieved an overall increase score of 15.21 on the dev set and 12.30 on the evaluation set, based on translation metrics and property metrics from the baseline.

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Knowlab’s Submission to L+M Shared Task: All you need is continued pretraining of chemistry texts even for molecule captioning
Yunsoo Kim | Honghan Wu

This paper presents our submission to the L+M-24 shared task, focused on translating molecular structures into natural language descriptions, known as the molecule captioning task. We selected a small language model (SLM), Phi-3-mini-4k, to evaluate the impact of continued pretraining and instruction tuning for domain-specific chemical knowledge. The Phi-3 model was continued pretrained with 90M chemistry textbooks and abstracts, followed by instruction tuning on 150K question answering sets of SMILES and general chemistry knowledge. Despite the continued pretraining phase not including direct exposure to SMILES representations, it significantly enhanced the Phi-3 model’s performance, a 300% increase for the BLEU scores, in the molecule captioning task. The code and model are released at https://github.com/bluesky333/Phi3KnowChem to facilitate research in chemical small language modeling.

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Mol2Lang-VLM: Vision- and Text-Guided Generative Pre-trained Language Models for Advancing Molecule Captioning through Multimodal Fusion
Duong Tran | Nhat Truong Pham | Nguyen Nguyen | Balachandran Manavalan

This paper introduces Mol2Lang-VLM, an enhanced method for refining generative pre-trained language models for molecule captioning using multimodal features to achieve more accurate caption generation. Our approach leverages the encoder and decoder blocks of the Transformer-based architecture by introducing third sub-layers into both. Specifically, we insert sub-layers in the encoder to fuse features from SELFIES strings and molecular images, while the decoder fuses features from SMILES strings and their corresponding descriptions. Moreover, cross multi-head attention is employed instead of common multi-head attention to enable the decoder to attend to the encoder’s output, thereby integrating the encoded contextual information for better and more accurate caption generation. Performance evaluation on the CheBI-20 and L+M-24 benchmark datasets demonstrates Mol2Lang-VLM’s superiority, achieving higher accuracy and quality in caption generation compared to existing methods. Our code and pre-processed data are available at https://github.com/nhattruongpham/mol-lang-bridge/tree/mol2lang/.

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DNA Language Model and Interpretable Graph Neural Network Identify Genes and Pathways Involved in Rare Diseases
Ali Saadat | Jacques Fellay

Identification of causal genes and pathways is a critical step for understanding the genetic underpinnings of rare diseases. We propose novel approaches to gene prioritization and pathway identification using DNA language model, graph neural networks, and genetic algorithm. Using HyenaDNA, a long-range genomic foundation model, we generated dynamic gene embeddings that reflect changes caused by deleterious variants. These gene embeddings were then utilized to identify candidate genes and pathways. We validated our method on a cohort of rare disease patients with partially known genetic diagnosis, demonstrating the re-identification of known causal genes and pathways and the detection of novel candidates. These findings have implications for the prevention and treatment of rare diseases by enabling targeted identification of new drug targets and therapeutic pathways.

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Repurformer: Transformers for Repurposing-Aware Molecule Generation
Changhun Lee | Gyumin Lee

Generating as diverse molecules as possible with desired properties is crucial for drug discovery research, which invokes many approaches based on deep generative models today. Despite recent advancements in these models, particularly in variational autoencoders (VAEs), generative adversarial networks (GANs), Transformers, and diffusion models, a significant challenge known as the sample bias problem remains. This problem occurs when generated molecules targeting the same protein tend to be structurally similar, reducing the diversity of generation. To address this, we propose leveraging multi-hop relationships among proteins and compounds. Our model, Repurformer, integrates bi-directional pretraining with Fast Fourier Transform (FFT) and low-pass filtering (LPF) to capture complex interactions and generate diverse molecules. A series of experiments on BindingDB dataset confirm that Repurformer successfully creates substitutes for anchor compounds that resemble positive compounds, increasing diversity between the anchor and generated compounds.

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Lang2Mol-Diff: A Diffusion-Based Generative Model for Language-to-Molecule Translation Leveraging SELFIES Representation
Nguyen Nguyen | Nhat Truong Pham | Duong Tran | Balachandran Manavalan

Generating de novo molecules from textual descriptions is challenging due to potential issues with molecule validity in SMILES representation and limitations of autoregressive models. This work introduces Lang2Mol-Diff, a diffusion-based language-to-molecule generative model using the SELFIES representation. Specifically, Lang2Mol-Diff leverages the strengths of two state-of-the-art molecular generative models: BioT5 and TGM-DLM. By employing BioT5 to tokenize the SELFIES representation, Lang2Mol-Diff addresses the validity issues associated with SMILES strings. Additionally, it incorporates a text diffusion mechanism from TGM-DLM to overcome the limitations of autoregressive models in this domain. To the best of our knowledge, this is the first study to leverage the diffusion mechanism for text-based de novo molecule generation using the SELFIES molecular string representation. Performance evaluation on the L+M-24 benchmark dataset shows that Lang2Mol-Diff outperforms all existing methods for molecule generation in terms of validity. Our code and pre-processed data are available at https://github.com/nhattruongpham/mol-lang-bridge/tree/lang2mol/.

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bib (full) Proceedings of the 5th Workshop on Computational Approaches to Historical Language Change

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Invited paper: Computer-Assisted Language Comparison with EDICTOR 3
Johann-Mattis List | Kellen van Dam

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Exploring Diachronic and Diatopic Changes in Dialect Continua: Tasks, Datasets and Challenges
Melis Çelikkol | Lydia Körber | Wei Zhao

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Historical Ink: Semantic Shift Detection for 19th Century Spanish
Tony Montes | Laura Manrique-Gómez | Rubén Manrique

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Presence or Absence: Are Unknown Word Usages in Dictionaries?
Xianghe Ma | Dominik Schlechtweg | Wei Zhao

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Towards a GoldenHymns Dataset for Studying Diachronic Trends in 19th Century Danish Religious Hymns
Ea Lindhardt Overgaard | Pascale Feldkamp | Yuri Bizzoni

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A Feature-Based Approach to Annotate the Syntax of Ancient Chinese
Chenrong Zhao

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Improving Word Usage Graphs with Edge Induction
Bill Noble | Francesco Periti | Nina Tahmasebi

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Towards a Complete Solution to Lexical Semantic Change: an Extension to Multiple Time Periods and Diachronic Word Sense Induction
Francesco Periti | Nina Tahmasebi

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TartuNLP @ AXOLOTL-24: Leveraging Classifier Output for New Sense Detection in Lexical Semantics
Aleksei Dorkin | Kairit Sirts

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EtymoLink: A Structured English Etymology Dataset
Yuan Gao | Weiwei Sun

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Complexity and Indecision: A Proof-of-Concept Exploration of Lexical Complexity and Lexical Semantic Change
David Alfter

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Towards an Onomasiological Study of Lexical Semantic Change Through the Induction of Concepts
Bastien Liétard | Mikaela Keller | Pascal Denis

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Deep-change at AXOLOTL-24: Orchestrating WSD and WSI Models for Semantic Change Modeling
Denis Kokosinskii | Mikhail Kuklin | Nikolay Arefyev

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Exploring Sound Change Over Time: A Review of Computational and Human Perception
Siqi He | Wei Zhao

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A Few-shot Learning Approach for Lexical Semantic Change Detection Using GPT-4
Zhengfei Ren | Annalina Caputo | Gareth Jones

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bib (full) Proceedings of the Seventh Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2024)

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Tuning LLMs with Contrastive Alignment Instructions for Machine Translation in Unseen, Low-resource Languages
Zhuoyuan Mao | Yen Yu

This article introduces contrastive alignment instructions (AlignInstruct) to address two challenges in machine translation (MT) on large language models (LLMs). One is the expansion of supported languages to previously unseen ones. The second relates to the lack of data in low-resource languages. Model fine-tuning through MT instructions (MTInstruct) is a straightforward approach to the first challenge. However, MTInstruct is limited by weak cross-lingual signals inherent in the second challenge. AlignInstruct emphasizes cross-lingual supervision via a cross-lingual discriminator built using statistical word alignments. Our results based on fine-tuning the BLOOMZ models (1b1, 3b, and 7b1) in up to 24 unseen languages showed that: (1) LLMs can effectively translate unseen languages using MTInstruct; (2) AlignInstruct led to consistent improvements in translation quality across 48 translation directions involving English; (3) Discriminator-based instructions outperformed their generative counterparts as cross-lingual instructions; (4) AlignInstruct improved performance in 30 zero-shot directions.

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HeSum: a Novel Dataset for Abstractive Text Summarization in Hebrew
Itai Mondshine | Tzuf Paz-Argaman | Asaf Achi Mordechai | Reut Tsarfaty

While large language models (LLMs) excel in various natural language tasks in English, their performance in low-resource languages like Hebrew, especially for generative tasks such as abstractive summarization, remains unclear. The high morphological richness in Hebrew adds further challenges due to the ambiguity in sentence comprehension and the complexities in meaning construction. In this paper, we address this evaluation and resource gap by introducing HeSum, a novel benchmark dataset specifically designed for Hebrew abstractive text summarization. HeSum consists of 10,000 article-summary pairs sourced from Hebrew news websites written by professionals. Linguistic analysis confirms HeSum’s high abstractness and unique morphological challenges. We show that HeSum presents distinct difficulties even for state-of-the-art LLMs, establishing it as a valuable testbed for advancing generative language technology in Hebrew, and MRLs generative challenges in general.

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KpopMT: Translation Dataset with Terminology for Kpop Fandom
JiWoo Kim | Yunsu Kim | JinYeong Bak

While machines learn from existing corpora, humans have the unique capability to establish and accept new language systems. This makes human form unique language systems within social groups. Aligning with this, we focus on a gap remaining in addressing translation challenges within social groups, where in-group members utilize unique terminologies. We propose KpopMT dataset, which aims to fill this gap by enabling precise terminology translation, choosing Kpop fandom as an initiative for social groups given its global popularity. Expert translators provide 1k English translations for Korean posts and comments, each annotated with specific terminology within social groups’ language systems. We evaluate existing translation systems including GPT models on KpopMT to identify their failure cases. Results show overall low scores, underscoring the challenges of reflecting group-specific terminologies and styles in translation. We make KpopMT publicly available.

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Challenges in Urdu Machine Translation
Abdul Basit | Abdul Hameed Azeemi | Agha Ali Raza

Recent advancements in Neural Machine Translation (NMT) systems have significantly improved model performance on various translation benchmarks. However, these systems still face numerous challenges when translating low-resource languages such as Urdu. In this work, we highlight the specific issues faced by machine translation systems when translating Urdu language. We first conduct a comprehensive evaluation of English to Urdu Machine Translation with four diverse models: GPT-3.5 (a large language model), opus-mt-en-ur (a bilingual translation model), NLLB (a model trained for translating 200 languages), and IndicTrans2 (a specialized model for translating low-resource Indic languages). The results demonstrate that IndicTrans2 significantly outperforms other models in Urdu Machine Translation. To understand the differences in the performance of these models, we analyze the Urdu word distribution in different training datasets and compare the training methodologies. Finally, we uncover the specific translation issues and provide suggestions for improvements in Urdu machine translation systems.

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Linguistically Informed Transformers for Text to American Sign Language Translation
Abhishek Varanasi | Manjira Sinha | Tirthankar Dasgupta

In this paper we propose a framework for automatic translation of English text to American Sign Language (ASL) which leverages a linguistically informed transformer model to translate English sentences into ASL gloss sequences. These glosses are then associated with respective ASL videos, effectively representing English text in ASL. To facilitate experimentation, we create an English-ASL parallel dataset on banking domain.Our preliminary results demonstrated that the linguistically informed transformer model achieves a 97.83% ROUGE-L score for text-to-gloss translation on the ASLG-PC12 dataset. Furthermore, fine-tuning the transformer model on the banking domain dataset yields an 89.47% ROUGE-L score when fine-tuned on ASLG-PC12 + banking domain dataset. These results demonstrate the effectiveness of the linguistically informed model for both general and domain-specific translations. To facilitate parallel dataset generation in banking-domain, we choose ASL despite having limited benchmarks and data corpus compared to some of the other sign languages.

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Low-Resource Cross-Lingual Summarization through Few-Shot Learning with Large Language Models
Gyutae Park | Seojin Hwang | Hwanhee Lee

Cross-lingual summarization (XLS) aims to generate a summary in a target language different from the source language document. While large language models (LLMs) have shown promising zero-shot XLS performance, their few-shot capabilities on this task remain unexplored, especially for low-resource languages with limited parallel data. In this paper, we investigate the few-shot XLS performance of various models, including Mistral-7B-Instruct-v0.2, GPT-3.5, and GPT-4.Our experiments demonstrate that few-shot learning significantly improves the XLS performance of LLMs, particularly GPT-3.5 and GPT-4, in low-resource settings. However, the open-source model Mistral-7B-Instruct-v0.2 struggles to adapt effectively to the XLS task with limited examples. Our findings highlight the potential of few-shot learning for improving XLS performance and the need for further research in designing LLM architectures and pre-training objectives tailored for this task. We provide a future work direction to explore more effective few-shot learning strategies and to investigate the transfer learning capabilities of LLMs for cross-lingual summarization.

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Enhancing Low-Resource NMT with a Multilingual Encoder and Knowledge Distillation: A Case Study
Aniruddha Roy | Pretam Ray | Ayush Maheshwari | Sudeshna Sarkar | Pawan Goyal

Neural Machine Translation (NMT) remains a formidable challenge, especially when dealing with low-resource languages. Pre-trained sequence-to-sequence (seq2seq) multi-lingual models, such as mBART-50, have demonstrated impressive performance in various low-resource NMT tasks. However, their pre-training has been confined to 50 languages, leaving out support for numerous low-resource languages, particularly those spoken in the Indian subcontinent. Expanding mBART-50’s language support requires complex pre-training, risking performance decline due to catastrophic forgetting. Considering these expanding challenges, this paper explores a framework that leverages the benefits of a pre-trained language model along with knowledge distillation in a seq2seq architecture to facilitate translation for low-resource languages, including those not covered by mBART-50. The proposed framework employs a multilingual encoder-based seq2seq model as the foundational architecture and subsequently uses complementary knowledge distillation techniques to mitigate the impact of imbalanced training. Our framework is evaluated on three low-resource Indic languages in four Indic-to-Indic directions, yielding significant BLEU-4 and chrF improvements over baselines. Further, we conduct human evaluation to confirm effectiveness of our approach. Our code is publicly available at https://github.com/raypretam/Two-step-low-res-NMT.

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Leveraging Mandarin as a Pivot Language for Low-Resource Machine Translation between Cantonese and English
King Yiu Suen | Rudolf Chow | Albert Y.S. Lam

Cantonese, the second most prevalent Chinese dialect after Mandarin, has been relatively overlooked in machine translation (MT) due to a scarcity of bilingual resources. In this paper, we propose to leverage Mandarin, a high-resource language, as a pivot language for translating between Cantonese and English. Our method utilizes transfer learning from pre-trained Bidirectional and Auto-Regressive Transformer (BART) models to initialize auxiliary source-pivot and pivot-target MT models. The parameters of the trained auxiliary models are then used to initialize the source-target model. Based on our experiments, our proposed method outperforms several baseline initialization strategies, naive pivot translation, and two commercial translation systems in both translation directions.

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Enhancing Turkish Word Segmentation: A Focus on Borrowed Words and Invalid Morpheme
Soheila Behrooznia | Ebrahim Ansari | Zdenek Zabokrtsky

This study addresses a challenge in morphological segmentation: accurately segmenting words in languages with rich morphology. Current probabilistic methods, such as Morfessor, often produce results that lack consistency with human-segmented words. Our study adds some steps to the Morfessor segmentation process to consider invalid morphemes and borrowed words from other languages to improve morphological segmentation significantly. Comparing our idea to the results obtained from Morfessor demonstrates its efficiency, leading to more accurate morphology segmentation. This is particularly evident in the case of Turkish, highlighting the potential for further advancements in morpheme segmentation for morphologically rich languages.

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Super donors and super recipients: Studying cross-lingual transfer between high-resource and low-resource languages
Vitaly Protasov | Elisei Stakovskii | Ekaterina Voloshina | Tatiana Shavrina | Alexander Panchenko

Despite the increasing popularity of multilingualism within the NLP community, numerous languages continue to be underrepresented due to the lack of available resources.Our work addresses this gap by introducing experiments on cross-lingual transfer between 158 high-resource (HR) and 31 low-resource (LR) languages.We mainly focus on extremely LR languages, some of which are first presented in research works.Across 158*31 HR–LR language pairs, we investigate how continued pretraining on different HR languages affects the mT5 model’s performance in representing LR languages in the LM setup.Our findings surprisingly reveal that the optimal language pairs with improved performance do not necessarily align with direct linguistic motivations, with subtoken overlap playing a more crucial role. Our investigation indicates that specific languages tend to be almost universally beneficial for pretraining (super donors), while others benefit from pretraining with almost any language (super recipients). This pattern recurs in various setups and is unrelated to the linguistic similarity of HR-LR pairs.Furthermore, we perform evaluation on two downstream tasks, part-of-speech (POS) tagging and machine translation (MT), showing how HR pretraining affects LR language performance.

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Tokenisation in Machine Translation Does Matter: The impact of different tokenisation approaches for Maltese
Kurt Abela | Kurt Micallef | Marc Tanti | Claudia Borg

In Machine Translation, various tokenisers are used to segment inputs before training a model. Despite tokenisation being mostly considered a solved problem for languages such as English, it is still unclear as to how effective different tokenisers are for morphologically rich languages. This study aims to explore how different approaches to tokenising Maltese impact machine translation results on the English-Maltese language pair.We observed that the OPUS-100 dataset has tokenisation inconsistencies in Maltese. We empirically found that training models on the original OPUS-100 dataset led to the generation of sentences with these issues.We therefore release an updated version of the OPUS-100 parallel English-Maltese dataset, referred to as OPUS-100-Fix, fixing these inconsistencies in Maltese by using the MLRS tokeniser. We show that after fixing the inconsistencies in the dataset, results on the fixed test set increase by 2.49 BLEU points over models trained on the original OPUS-100. We also experiment with different tokenisers, including BPE and SentencePiece to find the ideal tokeniser and vocabulary size for our setup, which was shown to be BPE with a vocabulary size of 8,000. Finally, we train different models in both directions for the ENG-MLT language pair using OPUS-100-Fix by training models from scratch as well as fine-tuning other pre-trained models, namely mBART-50 and NLLB, where a finetuned NLLB model performed the best.

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Machine Translation Through Cultural Texts: Can Verses and Prose Help Low-Resource Indigenous Models?
Antoine Cadotte | Nathalie André | Fatiha Sadat

We propose the first MT models for Innu-Aimun, an Indigenous language in Eastern Canada, in an effort to provide assistance tools for translation and language learning. This project is carried out in collaboration with an Innu community school and involves the participation of students in Innu-Aimun translation, within the framework of a meaningful consideration of Indigenous perspectives.Our contributions in this paper result from the three initial stages of this project. First, we aim to align bilingual Innu-Aimun/French texts with collaboration and participation of Innu-Aimun locutors. Second, we present the training and evaluation results of the MT models (both statistical and neural) based on these aligned corpora. And third, we collaboratively analyze some of the translations resulting from the MT models.We also see these developments for Innu-Aimun as a useful case study for answering a larger question: in a context where few aligned bilingual sentences are available for an Indigenous language, can cultural texts such as literature and poetry be used in the development of MT models?

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Rule-Based, Neural and LLM Back-Translation: Comparative Insights from a Variant of Ladin
Samuel Frontull | Georg Moser

This paper explores the impact of different back-translation approaches on machine translation for Ladin, specifically the Val Badia variant. Given the limited amount of parallel data available for this language (only 18k Ladin-Italian sentence pairs), we investigate the performance of a multilingual neural machine translation model fine-tuned for Ladin-Italian. In addition to the available authentic data, we synthesise further translations by using three different models: a fine-tuned neural model, a rule-based system developed specifically for this language pair, and a large language model. Our experiments show that all approaches achieve comparable translation quality in this low-resource scenario, yet round-trip translations highlight differences in model performance.

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AGE: Amharic, Ge’ez and English Parallel Dataset
Henok Ademtew | Mikiyas Birbo

African languages are not well-represented in Natural Language Processing (NLP). The main reason is a lack of resources for training models. Low-resource languages, such as Amharic and Ge’ez, cannot benefit from modern NLP methods because of the lack of high-quality datasets. This paper presents AGE, an open-source tripartite alignment of Amharic, Ge’ez, and English parallel dataset. Additionally, we introduced a novel, 1,000 Ge’ez-centered sentences sourced from areas such as news and novels. Furthermore, we developed a model from a multilingual pre-trained language model, which brings 12.29 and 30.66 for English-Ge’ez and Ge’ez to English, respectively, and 9.39 and 12.29 for Amharic-Ge’ez and Ge’ez-Amharic respectively.

Using large language models, this paper presents techniques to improve extremely low-resourced indigenous language translations. Our approaches are grounded in the use of (1) the presence of a datastore consisting of a limited number of parallel translation examples, (2) the inherent capabilities of LLMs like GPT-3.5, and (3) a word-level translation dictionary. We harness the potential of LLMs and in-context learning techniques in such a setting for using LLM as universal translators for extremely low-resourced languages. Our methodology hinges on utilizing LLMs as language compilers for selected language pairs, hypothesizing that they could internalize syntactic structures to facilitate accurate translation. We introduce three techniques: KNN-Prompting with Retrieved Prompting Context, Chain-of-Thought Prompting, and Learning-from-Mistakes Prompting, with the last method addressing past errors. The evaluation results suggest that, even with limited corpora, LLMs, when paired with proper prompting, can effectively translate extremely low-resource languages.

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Adopting Ensemble Learning for Cross-lingual Classification of Crisis-related Text On Social Media
Shareefa Al Amer | Mark Lee | Phillip Smith

Cross-lingual classification poses a significant challenge in Natural Language Processing (NLP), especially when dealing with languages with scarce training data. This paper delves into the adaptation of ensemble learning to address this challenge, specifically for disaster-related social media texts. Initially, we employ Machine Translation to generate a parallel corpus in the target language to mitigate the issue of data scarcity and foster a robust training environment. Following this, we implement the bagging ensemble technique, integrating multiple classifiers into a cohesive model that demonstrates enhanced performance over individual classifiers. Our experimental results reveal significant improvements in adapting models for Arabic, utilising only English training data and markedly outperforming models intended for linguistically similar languages to English, with our ensemble model achieving an accuracy and F1 score of 0.78 when tested on original Arabic data. This research makes a substantial contribution to the field of cross-lingual classification, establishing a new benchmark for enhancing the effectiveness of language transfer in linguistically challenging scenarios.

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Finetuning End-to-End Models for Estonian Conversational Spoken Language Translation
Tiia Sildam | Andra Velve | Tanel Alumäe

This paper investigates the finetuning of end-to-end models for bidirectional Estonian-English and Estonian-Russian conversational speech-to-text translation. Due to the limited availability of speech translation data for Estonian, we created additional training data by web scraping and synthesizing data from speech recognition datasets using machine translation. We evaluated three publicly available end-to-end models: Whisper, OWSM 3.1, and SeamlessM4T. Our results indicate that fine-tuning with synthetic data enhances translation accuracy by a large margin, with SeamlessM4T matching or surpassing cascaded speech translation systems that use state-of-the-art speech recognition and machine translation models.

Assessing the performance of machine translation systems is of critical value, especially to languages with lower resource availability.Due to the large evaluation effort required by the translation task, studies often compare new systems against single systems or commercial solutions. Consequently, determining the best-performing system for specific languages is often unclear. This work benchmarks publicly available translation systems across 4 datasets and 26 languages, including low-resource languages. We consider both effectiveness and efficiency in our evaluation.Our results are made public through BENG—a FAIR benchmarking platform for Natural Language Generation tasks.

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Rosetta Balcanica: Deriving a “Gold Standard” Neural Machine Translation (NMT) Parallel Dataset from High-Fidelity Resources for Western Balkan Languages
Edmon Begoli | Maria Mahbub | Sudarshan Srinivasan

The Rosetta Balcanica is an ongoing effort in resource expansion for low-resource Western Balkans languages. This effort focuses on discovering and using accurately translated, officially mapped, and curated parallel language resources and their preparation and use as neural machine translation (NMT) datasets. Some of the guiding principles, practices, and methods employed by Rosetta Balcanica are generalizable and could apply to other low-resource language resource expansion efforts. With this goal in mind, we present our rationale and approach to discovering and using meticulously translated and officially curated low-resource language resources and our use of these resources to develop a parallel “gold standard” translation training resource. Secondly, we describe our specific methodology for NMT dataset development from these resources and its publication to a widely-used and accessible repository for natural language processing (Hugging Face Hub). Finally, we discuss the trade-offs and limitations of our current approach, and the roadmap for future development and the expansion of the current Rosetta Balcanica language resource.

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Irish-based Large Language Model with Extreme Low-Resource Settings in Machine Translation
Khanh-Tung Tran | Barry O’Sullivan | Hoang Nguyen

Large Language Models (LLMs) have demonstrated exceptional performances in a wide range of natural language processing tasks. However, their success does not always extend to machine translation, particularly in challenging scenarios such as translating low-resource languages. This study investigates the multilingual capability of LLMs, with a case study on Irish, an extremely low-resource language, focusing on translation tasks between English and Irish. We propose a dynamic, efficient language adaptation framework for English-centric LLMs, which involves layer-specific adjustments and subsequent fine-tuning for machine translation. Our findings highlight several key insights: (1) different layers in the LLM serve distinct functions such as language understanding and task reasoning, (2) effective translation requires extensive pre-training on both source and target languages, and (3) targeted fine-tuning for machine translation leads to significant improvements of 36.7% for English to Irish and 133.4% for Irish to English compared to the previous state-of-the-art.

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bib (full) Proceedings of the 1st Workshop on Machine Learning for Ancient Languages (ML4AL 2024)

Automatic correction of errors in Handwritten Text Recognition (HTR) output poses persistent challenges yet to be fully resolved. In this study, we introduce a shared task aimed at addressing this challenge, which attracted 271 submissions, yielding only a handful of promising approaches. This paper presents the datasets, the most effective methods, and an experimental analysis in error-correcting HTRed manuscripts and papyri in Byzantine Greek, the language that followed Classical and preceded Modern Greek. By using recognised and transcribed data from seven centuries, the two best-performing methods are compared, one based on a neural encoder-decoder architecture and the other based on engineered linguistic rules. We show that the recognition error rate can be reduced by both, up to 2.5 points at the level of characters and up to 15 at the level of words, while also elucidating their respective strengths and weaknesses.

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MsBERT: A New Model for the Reconstruction of Lacunae in Hebrew Manuscripts
Avi Shmidman | Ometz Shmidman | Hillel Gershuni | Moshe Koppel

Hebrew manuscripts preserve thousands of textual transmissions of post-Biblical Hebrew texts from the first millennium. In many cases, the text in the manuscripts is not fully decipherable, whether due to deterioration, perforation, burns, or otherwise. Existing BERT models for Hebrew struggle to fill these gaps, due to the many orthographical deviations found in Hebrew manuscripts. We have pretrained a new dedicated BERT model, dubbed MsBERT (short for: Manuscript BERT), designed from the ground up to handle Hebrew manuscript text. MsBERT substantially outperforms all existing Hebrew BERT models regarding the prediction of missing words in fragmentary Hebrew manuscript transcriptions in multiple genres, as well as regarding the task of differentiating between quoted passages and exegetical elaborations. We provide MsBERT for free download and unrestricted use, and we also provide an interactive and user-friendly website to allow manuscripts scholars to leverage the power of MsBERT in their scholarly work of reconstructing fragmentary Hebrew manuscripts.

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Predicate Sense Disambiguation for UMR Annotation of Latin: Challenges and Insights
Federica Gamba

This paper explores the possibility to exploit different Pretrained Language Models (PLMs) to assist in a manual annotation task consisting in assigning the appropriate sense to verbal predicates in a Latin text. Indeed, this represents a crucial step when annotating data according to the Uniform Meaning Representation (UMR) framework, designed to annotate the semantic content of a text in a cross-linguistic perspective. We approach the study as a Word Sense Disambiguation task, with the primary goal of assessing the feasibility of leveraging available resources for Latin to streamline the labor-intensive annotation process. Our methodology revolves around the exploitation of contextual embeddings to compute token similarity, under the assumption that predicates sharing a similar sense would also share their context of occurrence. We discuss our findings, emphasizing applicability and limitations of this approach in the context of Latin, for which the limited amount of available resources poses additional challenges.

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Classification of Paleographic Artifacts at Scale: Mitigating Confounds and Distribution Shift in Cuneiform Tablet Dating
Danlu Chen | Jiahe Tian | Yufei Weng | Taylor Berg-Kirkpatrick | Jacobo Myerston

Cuneiform is the oldest writing system used for more than 3,000 years in ancient Mesopotamia. Cuneiform is written on clay tablets, which are hard to date because they often lack explicit references to time periods and their paleographic traits are not always reliable as a dating criterion. In this paper, we systematically analyse cuneiform dating problems using machine learning. We build baseline models for both visual and textual features and identify two major issues: confounds and distribution shift. We apply adversarial regularization and deep domain adaptation to mitigate these issues. On tablets from the same museum collections represented in the training set, we achieve accuracies as high as 84.42%. However, when test tablets are taken from held-out collections, models generalize more poorly. This is only partially mitigated by robust learning techniques, highlighting important challenges for future work.

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Classifier identification in Ancient Egyptian as a low-resource sequence-labelling task
Dmitry Nikolaev | Jorke Grotenhuis | Haleli Harel | Orly Goldwasser

The complex Ancient Egyptian (AE) writing system was characterised by widespread use of graphemic classifiers (determinatives): silent (unpronounced) hieroglyphic signs clarifying the meaning or indicating the pronunciation of the host word. The study of classifiers has intensified in recent years with the launch and quick growth of the iClassifier project, a web-based platform for annotation and analysis of classifiers in ancient and modern languages. Thanks to the data contributed by the project participants, it is now possible to formulate the identification of classifiers in AE texts as an NLP task. In this paper, we make first steps towards solving this task by implementing a series of sequence-labelling neural models, which achieve promising performance despite the modest amount of training data. We discuss tokenisation and operationalisation issues arising from tackling AE texts and contrast our approach with frequency-based baselines.

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Long Unit Word Tokenization and Bunsetsu Segmentation of Historical Japanese
Hiroaki Ozaki | Kanako Komiya | Masayuki Asahara | Toshinobu Ogiso

In Japanese, the natural minimal phrase of a sentence is the “bunsetsu” and it serves as a natural boundary of a sentence for native speakers rather than words, and thus grammatical analysis in Japanese linguistics commonly operates on the basis of bunsetsu units.In contrast, because Japanese does not have delimiters between words, there are two major categories of word definition, namely, Short Unit Words (SUWs) and Long Unit Words (LUWs).Though a SUW dictionary is available, LUW is not.Hence, this study focuses on providing deep learning-based (or LLM-based) bunsetsu and Long Unit Words analyzer for the Heian period (AD 794-1185) and evaluating its performances.We model the parser as transformer-based joint sequential labels model, which combine bunsetsu BI tag, LUW BI tag, and LUW Part-of-Speech (POS) tag for each SUW token.We train our models on corpora of each period including contemporary and historical Japanese.The results range from 0.976 to 0.996 in f1 value for both bunsetsu and LUW reconstruction indicating that our models achieve comparable performance with models for a contemporary Japanese corpus.Through the statistical analysis and diachronic case study, the estimation of bunsetsu could be influenced by the grammaticalization of morphemes.

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A new machine-actionable corpus for ancient text restoration
Will Fitzgerald | Justin Barney

The Machine-Actionable Ancient Text (MAAT) Corpus is a new resource providing training and evaluation data for restoring lacunae in ancient Greek, Latin, and Coptic texts. Current text restoration systems require large amounts of data for training and task-relevant means for evaluation. The MAAT Corpus addresses this need by converting texts available in EpiDoc XML format into a machine-actionable format that preserves the most textually salient aspects needed for machine learning: the text itself, lacunae, and textual restorations. Structured test cases are generated from the corpus that align with the actual text restoration task performed by papyrologists and epigraphist, enabling more realistic evaluation than the synthetic tasks used previously. The initial 1.0 beta release contains approximately 134,000 text editions, 178,000 text blocks, and 750,000 individual restorations, with Greek and Latin predominating. This corpus aims to facilitate the development of computational methods to assist scholars in accurately restoring ancient texts.

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Lacuna Language Learning: Leveraging RNNs for Ranked Text Completion in Digitized Coptic Manuscripts
Lauren Levine | Cindy Li | Lydia BremerMcCollum | Nicholas Wagner | Amir Zeldes

Ancient manuscripts are frequently damaged, containing gaps in the text known as lacunae. In this paper, we present a bidirectional RNN model for character prediction of Coptic characters in manuscript lacunae. Our best model performs with 72% accuracy on single character reconstruction, but falls to 37% when reconstructing lacunae of various lengths. While not suitable for definitive manuscript reconstruction, we argue that our RNN model can help scholars rank the likelihood of textual reconstructions. As evidence, we use our RNN model to rank reconstructions in two early Coptic manuscripts. Our investigation shows that neural models can augment traditional methods of textual restoration, providing scholars with an additional tool to assess lacunae in Coptic manuscripts.

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Deep Learning Meets Egyptology: a Hieroglyphic Transformer for Translating Ancient Egyptian
Mattia De Cao | Nicola De Cao | Angelo Colonna | Alessandro Lenci

This work explores the potential of Transformer models focusing on the translation of ancient Egyptian hieroglyphs. We present a novel Hieroglyphic Transformer model, built upon the powerful M2M-100 multilingual translation framework and trained on a dataset we customised from the Thesaurus Linguae Aegyptiae database. Our experiments demonstrate promising results, with the model achieving significant accuracy in translating hieroglyphics into both German and English. This work holds significant implications for Egyptology, potentially accelerating the translation process and unlocking new research approaches.

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Neural Lemmatization and POS-tagging models for Coptic, Demotic and Earlier Egyptian
Aleksi Sahala | Eliese-Sophia Lincke

We present models for lemmatizing and POS-tagging Earlier Egyptian, Coptic and Demotic to test the performance of our pipeline for the ancient languages of Egypt. Of these languages, Demotic and Egyptian are known to be difficult to annotate due to their high extent of ambiguity. We report lemmatization accuracy of 86%, 91% and 99%, and XPOS-tagging accuracy of 89%, 95% and 98% for Earlier Egyptian, Demotic and Coptic, respectively.

Oracle bone script (OBS) is the earliest writing system in China, which is of great value in the improvement of archaeology and Chinese cultural history. However, there are some problems such as the lack of labels and the difficulty to distinguish the glyphs from the background of OBS, which makes the automatic recognition of OBS in the real world not achieve the satisfactory effect. In this paper, we propose a character recognition method based on an unsupervised domain adaptive network (UFCNet). Firstly, a convolutional attention fusion module (CAFM) is designed in the encoder to obtain more global features through multi-layer feature fusion. Second, we construct a Fourier transform (FT) module that focuses on the differences between glyphs and backgrounds. Finally, to further improve the network’s ability to recognize character edges, we introduce a kernel norm-constrained loss function. Extensive experiments perform on the Oracle-241 dataset show that the proposed method is superior to other adaptive methods. The code will be available at https://github.com/zhouynan/UFCNet.

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Coarse-to-Fine Generative Model for Oracle Bone Inscriptions Inpainting
Shibin Wang | Wenjie Guo | Yubo Xu | Dong Liu | Xueshan Li

Due to ancient origin, there are many incomplete characters in the unearthed Oracle Bone Inscriptions(OBI), which brings the great challenges to recognition and research. In recent years, image inpainting techniques have made remarkable progress. However, these models are unable to adapt to the unique font shape and complex text background of OBI. To meet these aforementioned challenges, we propose a two-stage method for restoring damaged OBI using Generative Adversarial Networks (GAN), which incorporates a dual discriminator structure to capture both global and local image information. In order to accurately restore the image structure and details, the spatial attention mechanism and a novel loss function are proposed. By feeding clear copies of existing OBI and various types of masks into the network, it learns to generate content for the missing regions. Experimental results demonstrate the effectiveness of our proposed method in completing OBI compared to several state-of-the-art techniques.

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Restoring Mycenaean Linear B ‘A&B’ series tablets using supervised and transfer learning
Katerina Papavassileiou | Dimitrios Kosmopoulos

We investigate the problem of restoring Mycenaean linear B clay tablets, dating from about 1400 B.C. to roughly 1200 B.C., by using text infilling methods based on machine learning models. Our goals here are: first to try to improve the results of the methods used in the related literature by focusing on the characteristics of the Mycenaean Linear B writing system (series D), second to examine the same problem for the first time on series A&B and finally to investigate transfer learning using series D as source and the smaller series A&B as target. Our results show promising results in the supervised learning tasks, while further investigation is needed to better exploit the merits of transfer learning.

Cuneiform documents, the earliest known form of writing, are prolific textual sources of the ancient past. Experts publish editions of these texts in transliteration using specialized typesetting, but most remain inaccessible for computational analysis in traditional printed books or legacy materials. Off-the-shelf OCR systems are insufficient for digitization without adaptation. We present CuReD (Cuneiform Recognition-Documents), a deep learning-based human-in-the-loop OCR pipeline for digitizing scanned transliterations of cuneiform texts. CuReD has a character error rate of 9% on clean data and 11% on representative scans. We digitized a challenging sample of transliterated cuneiform documents, as well as lexical index cards from the University of Pennsylvania Museum, demonstrating the feasibility of our platform for enabling computational analysis and bolstering machine-readable cuneiform text datasets. Our result provide the first human-in-the-loop pipeline and interface for digitizing transliterated cuneiform sources and legacy materials, enabling the enrichment of digital sources of these low-resource languages.

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Towards Context-aware Normalization of Variant Characters in Classical Chinese Using Parallel Editions and BERT
Florian Kessler

For the automatic processing of Classical Chinese texts it is highly desirable to normalize variant characters, i.e. characters with different visual forms that are being used to represent the same morpheme, into a single form. However, there are some variant characters that are used interchangeably by some writers but deliberately employed to distinguish between different meanings by others. Hence, in order to avoid losing information in the normalization processes by conflating meaningful distinctions between variants, an intelligent normalization system that takes context into account is needed. Towards the goal of developing such a system, in this study, we describe how a dataset with usage samples of variant characters can be extracted from a corpus of paired editions of multiple texts. Using the dataset, we conduct two experiments, testing whether models can be trained with contextual word embeddings to predict variant characters. The results of the experiments show that while this is often possible for single texts, most conventions learned do not transfer well between documents.

In this paper, we present a study of transformer-based Named Entity Recognition (NER) as applied to Ancient Greek texts, with an emphasis on retrieving personal names. Recent research shows that, while the task remains difficult, the use of transformer models results in significant improvements. We, therefore, compare the performance of four transformer models on the task of NER for the categories of people, locations and groups, and add an out-of-domain test set to the existing datasets. Results on this set highlight the shortcomings of the models when confronted with a random sample of sentences. To be able to more straightforwardly integrate domain and linguistic knowledge to improve performance, we narrow down our approach to the category of people. The task is simplified to a binary PERS/MISC classification on the token level, starting from capitalised words. Next, we test the use of domain and linguistic knowledge to improve the results. We find that including simple gazetteer information as a binary mask has a marginally positive effect on newly annotated data and that treebanks can be used to help identify multi-word individuals if they are scarcely or inconsistently annotated in the available training data. The qualitative error analysis identifies the potential for improvement in both manual annotation and the inclusion of domain and linguistic knowledge in the transformer models.

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Adapting transformer models to morphological tagging of two highly inflectional languages: a case study on Ancient Greek and Latin
Alek Keersmaekers | Wouter Mercelis

Natural language processing for Greek and Latin, inflectional languages with small corpora, requires special techniques. For morphological tagging, transformer models show promising potential, but the best approach to use these models is unclear. For both languages, this paper examines the impact of using morphological lexica, training different model types (a single model with a combined feature tag, multiple models for separate features, and a multi-task model for all features), and adding linguistic constraints. We find that, although simply fine-tuning transformers to predict a monolithic tag may already yield decent results, each of these adaptations can further improve tagging accuracy.

In this paper we present a deep learning pipeline for automatically dating ancient Greek papyrus fragments based solely on fragment images. The overall pipeline consists of several stages, including handwritten text recognition (HTR) to detect and classify characters, filtering and grouping of detected characters, 24 character-level date prediction models, and a fragment-level date prediction model that utilizes the per-character predictions. A new dataset (containing approximately 7,000 fragment images and 778,000 character images) was created by scraping papyrus databases, extracting fragment images with known dates, and running them through our HTR models to obtain labeled character images. Transfer learning was then used to fine-tune separate ResNets to predict dates for individual characters which are then used, in aggregate, to train the fragment-level date prediction model. Experiments show that even though the average accuracies of character-level dating models is low, between 35%-45%, the fragment-level model can achieve up to 79% accuracy in predicting a broad, two-century date range for fragments with many characters. We then discuss the limitations of this approach and outline future work to improve temporal resolution and further testing on additional papyri. This image-based deep learning approach has great potential to assist scholars in the palaeographical analysis and dating of ancient Greek manuscripts.

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UD-ETCSUX: Toward a Better Understanding of Sumerian Syntax
Kenan Jiang | Adam Anderson

Beginning with the discovery of the cuneiform writing system in 1835, there have been numerous grammars published illustrating the complexities of the Sumerian language. However, the one thing they have in common is their omission of dependency rules for syntax in Sumerian linguistics. For this reason we are working toward a better understanding of Sumerian syntax, by means of dependency-grammar in the Universal Dependencies (UD) framework. Therefore, in this study we articulate the methods and engineering techniques that can address the hardships in annotating dependency relationships in the Sumerian texts in transliteration from the Electronic Text Corpora of Sumerian (ETCSUX). Our code can be found at https://github.com/ancient-world-citation-analysis/UD-ETCSUX.

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SumTablets: A Transliteration Dataset of Sumerian Tablets
Cole Simmons | Richard Diehl Martinez | Dan Jurafsky

Transliterating Sumerian is a key step in understanding Sumerian texts, but remains a difficult and time-consuming task. With more than 100,000 known texts and comparatively few specialists, manually maintaining up-to-date transliterations for the entire corpus is impractical. While many transliterations have been published online thanks to the dedicated effort of previous projects, the lack of a comprehensive, easily accessible dataset that pairs digital representations of source glyphs with their transliterations has hindered the application of natural language processing (NLP) methods to this task.To address this gap, we present SumTablets, the largest collection of Sumerian cuneiform tablets structured as Unicode glyph–transliteration pairs. Our dataset comprises 91,606 tablets (totaling 6,970,407 glyphs) with associated period and genre metadata. We release SumTablets as a Hugging Face Dataset.To construct SumTablets, we first preprocess and standardize publicly available transliterations. We then map them back to a Unicode representation of their source glyphs, retaining parallel structural information (e.g., surfaces, newlines, broken segments) through the use of special tokens.We leverage SumTablets to implement and evaluate two transliteration approaches: 1) weighted sampling from a glyph’s possible readings, 2) fine-tuning an autoregressive language model. Our fine-tuned language model achieves an average transliteration character-level F-score (chrF) of 97.55, demonstrating the potential use of deep learning methods in Assyriological research.

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Latin Treebanks in Review: An Evaluation of Morphological Tagging Across Time
Marisa Hudspeth | Brendan O’Connor | Laure Thompson

Existing Latin treebanks draw from Latin’s long written tradition, spanning 17 centuries and a variety of cultures. Recent efforts have begun to harmonize these treebanks’ annotations to better train and evaluate morphological taggers. However, the heterogeneity of these treebanks must be carefully considered to build effective and reliable data. In this work, we review existing Latin treebanks to identify the texts they draw from, identify their overlap, and document their coverage across time and genre. We additionally design automated conversions of their morphological feature annotations into the conventions of standard Latin grammar. From this, we build new time-period data splits that draw from the existing treebanks which we use to perform a broad cross-time analysis for POS and morphological feature tagging. We find that BERT-based taggers outperform existing taggers while also being more robust to cross-domain shifts.

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The Metronome Approach to Sanskrit Meter: Analysis for the Rigveda
Yuzuki Tsukagoshi | Ikki Ohmukai

This study analyzes the verses of the Rigveda, the oldest Sanskrit text, from a metrical perspective. Based on metrical structures, the verses are represented by four elements: light syllables, heavy syllables, word boundaries, and line boundaries. As a result, it became evident that among verses traditionally categorized under the same metrical name, there are those forming distinct clusters. Furthermore, the study reveals commonalities in metrical structures, such as similar metrical patterns grouping together despite differences in the number of lines. Going forward, it is anticipated that this methodology will enable comparisons across multiple languages within the Indo-European language family.

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Ancient Wisdom, Modern Tools: Exploring Retrieval-Augmented LLMs for Ancient Indian Philosophy
Priyanka Mandikal

LLMs have revolutionized the landscape of information retrieval and knowledge dissemination. However, their application in specialized areas is often hindered by limitations such as factual inaccuracies and hallucinations, especially in long-tail knowledge distributions. In this work, we explore the potential of retrieval-augmented generation (RAG) models in performing long-form question answering (LFQA) on a specially curated niche and custom knowledge domain. We present VedantaNY-10M, a dataset curated from extensive public discourses on the ancient Indian philosophy of Advaita Vedanta. We develop and benchmark a RAG model against a standard, non-RAG LLM, focusing on transcription, retrieval, and generation performance. A human evaluation involving computational linguists and domain experts, shows that the RAG model significantly outperforms the standard model in producing factual, comprehensive responses having fewer hallucinations. In addition, we find that a keyword-based hybrid retriever that focuses on unique low-frequency words further improves results. Our study provides insights into the future development of real-world RAG models for custom and niche areas of knowledge.

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Leveraging Part-of-Speech Tagging for Enhanced Stylometry of Latin Literature
Sarah Chen | Patrick Burns | Thomas Bolt | Pramit Chaudhuri | Joseph Dexter

In literary critical applications, stylometry can benefit from hand-curated feature sets capturing various syntactic and rhetorical functions. For premodern languages, calculation of such features is hampered by a lack of adequate computational resources for accurate part-of-speech tagging and semantic disambiguation. This paper reports an evaluation of POS-taggers for Latin and their use in augmenting a hand-curated stylometric feature set. Our experiments show that POS-augmented features not only provide more accurate counts than POS-blind features but also perform better on tasks such as genre classification. In the course of this work we introduce POS n-grams as a feature for Latin stylometry.

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Exploring intertextuality across the Homeric poems through language models
Maria Konstantinidou | John Pavlopoulos | Elton Barker

Past research has modelled statistically the language of the Homeric poems, assessing the degree of surprisal for each verse through diverse metrics and resulting to the HoLM resource. In this study we utilise the HoLM resource to explore cross poem affinity at the verse level, looking at Iliadic verses and passages that are less surprising to the Odyssean model than to the Iliadic one and vice-versa. Using the same tool, we investigate verses that evoke greater surprise when assessed by a local model trained solely on their source book, compared to a global model trained on the entire source poem. Investigating deeper on the distribution of such verses across the Homeric poems we employ machine learning text classification to further analyse quantitatively cross-poem affinity in selected books.

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Detecting Narrative Patterns in Biblical Hebrew and Greek
Hope McGovern | Hale Sirin | Tom Lippincott | Andrew Caines

We present a novel approach to extracting recurring narrative patterns, or type-scenes, in Biblical Hebrew and Biblical Greek with an information retrieval network. We use cross-references to train an encoder model to create similar representations for verses linked by a cross-reference. We then query our trained model with phrases informed by humanities scholarship and designed to elicit particular kinds of narrative scenes. Our models can surface relevant instances in the top-10 ranked candidates in many cases.Through manual error analysis and discussion, we address the limitations and challenges inherent in our approach. Our findings contribute to the field of Biblical scholarship by offering a new perspective on narrative analysis within ancient texts, and to computational modeling of narrative with a genre-agnostic approach for pattern-finding in long, literary texts.

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bib (full) Proceedings of the 6th Workshop on NLP for Conversational AI (NLP4ConvAI 2024)

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On the Benchmarking of LLMs for Open-Domain Dialogue Evaluation
John Mendonça | Alon Lavie | Isabel Trancoso

Large Language Models (LLMs) have showcased remarkable capabilities in various Natural Language Processing tasks. For automatic open-domain dialogue evaluation in particular, LLMs have been seamlessly integrated into evaluation frameworks, and together with human evaluation, compose the backbone of most evaluations. However, existing evaluation benchmarks often rely on outdated datasets and evaluate aspects like Fluency and Relevance, which fail to adequately capture the capabilities and limitations of state-of-the-art chatbot models. This paper critically examines current evaluation benchmarks, highlighting that the use of older response generators and quality aspects fail to accurately reflect modern chatbot capabilities. A small annotation experiment on a recent LLM-generated dataset (SODA) reveals that LLM evaluators such as GPT-4 struggle to detect actual deficiencies in dialogues generated by current LLM chatbots.

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Exploring Description-Augmented Dataless Intent Classification
Ruoyu Hu | Foaad Khosmood | Abbas Edalat

In this work, we introduce several schemes to leverage description-augmented embedding similarity for dataless intent classification using current state-of-the-art (SOTA) text embedding models. We report results of our methods on four commonly used intent classification datasets and compare against previous works of a similar nature. Our work shows promising results for dataless classification scaling to a large number of unseen intents. We show competitive results and significant improvements (+6.12% Avg.) over strong zero-shot baselines, all without training on labelled or task-specific data. Furthermore, we provide qualitative error analysis of the shortfalls of this methodology to help guide future research in this area.

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Revealing User Familiarity Bias in Task-Oriented Dialogue via Interactive Evaluation
Takyoung Kim | Jamin Shin | Young-Ho Kim | Sanghwan Bae | Sungdong Kim

Most task-oriented dialogue (TOD) benchmarks assume users that know exactly how to use the system by constraining the user behaviors within the system’s capabilities via strict user goals, namely “user familiarity” bias. This data bias deepens when it combines with data-driven TOD systems, as it is impossible to fathom the effect of it with existing static evaluations. Hence, we conduct an interactive user study to unveil how vulnerable TOD systems are against realistic scenarios. In particular, we compare users with 1) detailed goal instructions that conform to the system boundaries (closed-goal) and 2) vague goal instructions that are often unsupported but realistic (open-goal). Our study reveals that conversations in open-goal settings lead to catastrophic failures of the system, in which 92% of the dialogues had significant issues. Moreover, we conduct a thorough analysis to identify distinctive features between the two settings through error annotation. From this, we discover a novel “pretending” behavior, in which the system pretends to handle the user requests even though they are beyond the system’s capabilities. We discuss its characteristics and toxicity while showing recent large language models can also suffer from this behavior.

Dialogue summarization involves summarizing long conversations while preserving the most salient information. Real-life dialogues often involve naturally occurring variations (e.g., repetitions, hesitations). In this study, we systematically investigate the impact of such variations on state-of-the-art open dialogue summarization models whose details are publicly known (e.g., architectures, weights, and training corpora). To simulate real-life variations, we introduce two types of perturbations: utterance-level perturbations that modify individual utterances with errors and language variations, and dialogue-level perturbations that add non-informative exchanges (e.g., repetitions, greetings). We perform our analysis along three dimensions of robustness: consistency, saliency, and faithfulness, which aim to capture different aspects of performance of a summarization model. We find that both fine-tuned and instruction-tuned models are affected by input variations, with the latter being more susceptible, particularly to dialogue-level perturbations. We also validate our findings via human evaluation. Finally, we investigate whether the robustness of fine-tuned models can be improved by training them with a fraction of perturbed data. We find that this approach does not yield consistent performance gains, warranting further research. Overall, our work highlights robustness challenges in current open encoder-decoder summarization models and provides insights for future research.

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Engineering Conversational Search Systems: A Review of Applications, Architectures, and Functional Components
Phillip Schneider | Wessel Poelman | Michael Rovatsos | Florian Matthes

Conversational search systems enable information retrieval via natural language interactions, with the goal of maximizing users’ information gain over multiple dialogue turns. The increasing prevalence of conversational interfaces adopting this search paradigm challenges traditional information retrieval approaches, stressing the importance of better understanding the engineering process of developing these systems. We undertook a systematic literature review to investigate the links between theoretical studies and technical implementations of conversational search systems. Our review identifies real-world application scenarios, system architectures, and functional components. We consolidate our results by presenting a layered architecture framework and explaining the core functions of conversational search systems. Furthermore, we reflect on our findings in light of the rapid progress in large language models, discussing their capabilities, limitations, and directions for future research.

Recent studies have demonstrated significant improvements in selection tasks, and a considerable portion of this success is attributed to incorporating informative negative samples during training. While traditional methods for constructing hard negatives provide meaningful supervision, they depend on static samples that do not evolve during training, leading to sub-optimal performance. Dynamic hard negative sampling addresses this limitation by continuously adapting to the model’s changing state throughout training. However, the high computational demands of this method restrict its applicability to certain model architectures. To overcome these challenges, we introduce an efficient dynamic hard negative sampling (EDHNS). EDHNS enhances efficiency by pre-filtering easily discriminable negatives, thereby reducing the number of candidates the model needs to compute during training. Additionally, it excludes question-candidate pairs where the model already exhibits high confidence from loss computations, further reducing training time. These approaches maintain learning quality while minimizing computation and streamlining the training process. Extensive experiments on DSTC9, DSTC10, Ubuntu, and E-commerce benchmarks demonstrate that EDHNS significantly outperforms baseline models, proving its effectiveness in dialogue selection tasks.

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Chamain: Harmonizing Character Persona Integrity with Domain-Adaptive Knowledge in Dialogue Generation
Seung-Moo Yang | Jeehyun Lee | Won Ik Cho

Recent advances in large language models (LLMs) have shown their capacity for generating natural dialogues, leveraging extensive pre-trained knowledge. However, the seamless integration of domain-specific knowledge into dialogue agents, without undermining their personas or unique textual style, remains a challenging task. Traditional approaches, such as constructing knowledge-aware character dialogue datasets or training LLMs from the ground up, require considerable resources. Sequentially fine-tuning character chatbots across multiple datasets or applying existing merging techniques often leads to catastrophic forgetting, resulting in the loss of both knowledge and the character’s distinct persona. This compromises the model’s ability to consistently generate character-driven dialogues within a user-centric framework. In this context, we introduce a novel model merging method, Chamain, which effortlessly enhances the performance of character models, much like finding a “free lunch”. Chamain merges domain-specific knowledge into a character model by parameter-wise weight combination of instruction-tuned models and learns to reflect persona’s unique characteristics and style through Layer-wise merging. Our experiments demonstrate that Chamain effectively maintains style while also solving domain-specific problems to a certain extent compared to the baselines, even showing a higher style probability compared to the character model in legal QA.

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Faithful Persona-based Conversational Dataset Generation with Large Language Models
Pegah Jandaghi | Xianghai Sheng | Xinyi Bai | Jay Pujara | Hakim Sidahmed

High-quality conversational datasets are essential for developing AI models that can communicate with users. One way to foster deeper interactions between a chatbot and its user is through personas, aspects of the user’s character that provide insights into their personality, motivations, and behaviors. Training Natural Language Processing (NLP) models on a diverse and comprehensive persona-based dataset can lead to conversational models that create a deeper connection with the user, and maintain their engagement. In this paper, we leverage the power of Large Language Models (LLMs) to create a large, high-quality conversational dataset from a seed dataset. We propose a Generator-Critic architecture framework to expand the initial dataset, while improving the quality of its conversations. The Generator is an LLM prompted to output conversations. The Critic consists of a mixture of expert LLMs that control the quality of the generated conversations. These experts select the best generated conversations, which we then use to improve the Generator. We release Synthetic-Persona-Chat, consisting of 20k conversations seeded from Persona-Chat. We evaluate the quality of Synthetic-Persona-Chat and our generation framework on different dimensions through extensive experiments, and observe that the losing rate of Synthetic-Persona-Chat against Persona-Chat during an AI detection test decreases from 17.2% to 8.8% over three iterations.

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bib (full) Proceedings of the 2nd Workshop on Natural Language Reasoning and Structured Explanations (@ACL 2024)

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GraphReason: Enhancing Reasoning Capabilities of Large Language Models through A Graph-Based Verification Approach
Lang Cao

Large Language Models (LLMs) have showcased impressive reasoning capabilities, particularly when guided by specifically designed prompts in complex reasoning tasks such as math word problems. These models typically solve tasks using a chain-of-thought approach, which not only bolsters their reasoning abilities but also provides valuable insights into their problem-solving process. However, there is still significant room for enhancing the reasoning abilities of LLMs. Some studies suggest that the integration of an LLM output verifier can boost reasoning accuracy without necessitating additional model training. In this paper, we follow these studies and introduce a novel graph-based method to further augment the reasoning capabilities of LLMs. We posit that multiple solutions to a reasoning task, generated by an LLM, can be represented as a reasoning graph due to the logical connections between intermediate steps from different reasoning paths. Therefore, we propose the Reasoning Graph Verifier (GraphReason) to analyze and verify the solutions generated by LLMs. By evaluating these graphs, models can yield more accurate and reliable results.Our experimental results show that our graph-based verification method not only significantly enhances the reasoning abilities of LLMs but also outperforms existing verifier methods in terms of improving these models’ reasoning performance.

Planning in a text-based environment continues to be a significant challenge for AI systems. Recent approaches have utilized language models to predict planning domain definitions (e.g., PDDL) but have only been evaluated in closed-domain simulated environments. To address this, we present Proc2PDDL, the first dataset containing open-domain procedural texts paired with expert-annotated PDDL representations. Using this dataset, we evaluate the task of predicting domain actions (parameters, preconditions, and effects). We experiment with various large language models (LLMs) and prompting mechanisms, including a novel instruction inspired by the zone of proximal development (ZPD), which reconstructs the task as incremental basic skills. Our results demonstrate that Proc2PDDL is highly challenging for end-to-end LLMs, with GPT-3.5’s success rate close to 0% and GPT-4o’s 38%. With ZPD instructions, GPT-4o’s success rate increases to 45%, outperforming regular chain-of-thought prompting’s 34%. Our analysis systematically examines both syntactic and semantic errors, providing insights into the strengths and weaknesses of language models in generating domain-specific programs.

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Towards A Unified View of Answer Calibration for Multi-Step Reasoning
Shumin Deng | Ningyu Zhang | Nay Oo | Bryan Hooi

Large Language Models (LLMs) employing Chain-of-Thought (CoT) prompting have broadened the scope for improving multi-step reasoning capabilities. We generally divide multi-step reasoning into two phases: *path generation* to generate the reasoning path(s); and *answer calibration* post-processing the reasoning path(s) to obtain a final answer. However, the existing literature lacks systematic analysis on different answer calibration approaches. In this paper, we summarize the taxonomy of recent answer calibration techniques and break them down into step-level and path-level strategies. We then conduct a thorough evaluation on these strategies from a unified view, systematically scrutinizing step-level and path-level answer calibration across multiple paths. Experimental results reveal that integrating the dominance of both strategies tends to derive optimal outcomes. Our study holds the potential to illuminate key insights for optimizing multi-step reasoning with answer calibration.

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Applying RLAIF for Code Generation with API-usage in Lightweight LLMs
Sujan Dutta | Sayantan Mahinder | Raviteja Anantha | Bortik Bandyopadhyay

Reinforcement Learning from AI Feedback (RLAIF) has demonstrated significant potential across various domains, including mitigating harm in LLM outputs, enhancing text summarization, and mathematical reasoning. This paper introduces an RLAIF framework for improving the code generation abilities of lightweight (<1B parameters) LLMs. We specifically focus on code generation tasks that require writing appropriate API calls, which is challenging due to the well-known issue of hallucination in LLMs. Our framework extracts AI feedback from a larger LLM (e.g., GPT-3.5) through a specialized prompting strategy and uses this data to train a reward model towards better alignment from smaller LLMs. We run our experiments on the Gorilla dataset and meticulously assess the quality of the model-generated code across various metrics, including AST, ROUGE, and Code-BLEU, and develop a pipeline to compute its executability rate accurately. Our approach significantly enhances the fine-tuned LLM baseline’s performance, achieving a 4.5% improvement in executability rate. Notably, a smaller LLM model (780M parameters) trained with RLAIF surpasses a much larger fine-tuned baseline with 7B parameters, achieving a 1.0% higher code executability rate.

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SummEQuAL: Summarization Evaluation via Question Answering using Large Language Models
Junyuan Liu | Zhengyan Shi | Aldo Lipani

Summarization is hard to evaluate due to its diverse and abstract nature. Although N-gram-based metrics like BLEU and ROUGE are prevalent, they often do not align well with human evaluations. While model-based alternatives such as BERTScore improve, they typically require extensive labelled data. The advent of Large Language Models (LLMs) presents a promising avenue for evaluation. To this end, we introduce SummEQuAL, a novel content-based framework using LLMs for unified, reproducible summarization evaluation. SummEQuAL evaluates summaries by comparing their content with the source document, employing a question-answering approach to gauge both recall and precision. To validate SummEQuAL’s effectiveness, we develop a dataset based on MultiWOZ. We conduct experiments on SummEval and our MultiWOZ-based dataset, showing that SummEQuAL largely improves the quality of summarization evaluation. Notably, SummEQuAL demonstrates a 19.7% improvement over QuestEval in terms of sample-level Pearson correlation with human assessments of consistency on the SummEval dataset. Furthermore, it exceeds the performance of the BERTScore baseline by achieving a 17.3% increase in Spearman correlation on our MultiWOZ-based dataset. Our study illuminates the potential of LLMs for a unified evaluation framework, setting a new paradigm for future summarization evaluation.

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LOGIC-LM++: Multi-Step Refinement for Symbolic Formulations
Shashank Kirtania | Priyanshu Gupta | Arjun Radhakrishna

In this paper we examine the limitations of Large Language Models (LLMs) for complex reasoning tasks. While current approaches leverage formal languages as intermediate representation for these reasoning problems, they still struggle with generating intermediate for-mal specifications with great correctness and in refining these representations. To address these issues, this paper proposes Logic-LM++, an improvement on Logic-LM (Pan et al., 2023). It uses the ability of LLMs to do pairwise comparisons, allowing the evaluation of the refinements suggested by the LLM. The paper demonstrates that Logic-LM++ outperforms Logic-LM and LLM based techniques on natural language reasoning tasks on two datasets, FOLIO, ProofWriter and AR-LSAT. Logic-LM++ show an average improvement of 18.5% on standard prompting, 12.3% on chain of thought prompting and 5% on Logic-LM.

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From Good to Great: Improving Math Reasoning with Tool-Augmented Interleaf Prompting
Nuo Chen | Hongguang Li | Baoyuan Wang | Jia Li

This paper investigates the performance of Large Language Models (LLMs) and Tool-augmented LLMs in tackling complex mathematical reasoning tasks. We introduce IMR-TIP: Improving Math Reasoning with Tool-augmented Interleaf Prompting, a framework that combines the strengths of both LLMs and Tool-augmented LLMs. IMR-TIP follows the “From Good to Great” concept, collecting multiple potential solutions from both LLMs and their Tool-Augmented counterparts for the same math problem, and then selecting or re-generating the most accurate answer after cross-checking these solutions via tool-augmented interleaf prompting. The framework incorporates two key aspects: self-prompt and tool-augmented interleaf prompting (TIP). The former allows LLMs to autonomously refine and improve an initial prompt related to tool usage, while the latter enables LLMs to derive the final answer by dynamically analyzing the problem, cross-checking potential solutions, and revising previous reasoning hints in an interleaved manner. Experimental analysis shows that IMR-TIP achieves enhanced mathematical capabilities and outperforms traditional LLMs and tool-augmented LLMs in accuracy and reasoning diversity on math reasoning tasks. For instance, IMR-TIP can improve Tool-augmented ChatGPT on GSM8K-Hard from 56.0% to 65.2 %.

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bib (full) Proceedings of the Fifth Workshop on Privacy in Natural Language Processing

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Noisy Neighbors: Efficient membership inference attacks against LLMs
Filippo Galli | Luca Melis | Tommaso Cucinotta

The potential of transformer-based LLMs risks being hindered by privacy concerns due to their reliance on extensive datasets, possibly including sensitive information. Regulatory measures like GDPR and CCPA call for using robust auditing tools to address potential privacy issues, with Membership Inference Attacks (MIA) being the primary method for assessing LLMs’ privacy risks. Differently from traditional MIA approaches, often requiring computationally intensive training of additional models, this paper introduces an efficient methodology that generates noisy neighbors for a target sample by adding stochastic noise in the embedding space, requiring operating the target model in inference mode only. Our findings demonstrate that this approach closely matches the effectiveness of employing shadow models, showing its usability in practical privacy auditing scenarios.

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Don’t forget private retrieval: distributed private similarity search for large language models
Guy Zyskind | Tobin South | Alex Pentland

While the flexible capabilities of large language models (LLMs) allow them to answer a range of queries based on existing learned knowledge, information retrieval to augment generation is an important tool to allow LLMs to answer questions on information not included in pre-training data. Such private information is increasingly being generated in a wide array of distributed contexts by organizations and individuals. Performing such information retrieval using neural embeddings of queries and documents always leaked information about queries and database content unless both were stored locally. We present Private Retrieval Augmented Generation (PRAG), an approach that uses multi-party computation (MPC) to securely transmit queries to a distributed set of servers containing a privately constructed database to return top-k and approximate top-k documents. This is a first-of-its-kind approach to dense information retrieval that ensures no server observes a client’s query or can see the database content. The approach introduces a novel MPC friendly protocol for inverted file approximate search (IVF) that allows for fast document search over distributed and private data in sublinear communication complexity. This work presents new avenues through which data for use in LLMs can be accessed and used without needing to centralize or forgo privacy.

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Characterizing Stereotypical Bias from Privacy-preserving Pre-Training
Stefan Arnold | Rene Gröbner | Annika Schreiner

Differential Privacy (DP) can be applied to raw text by exploiting the spatial arrangement of words in an embedding space. We investigate the implications of such text privatization on Language Models (LMs) and their tendency towards stereotypical associations. Since previous studies documented that linguistic proficiency correlates with stereotypical bias, one could assume that techniques for text privatization, which are known to degrade language modeling capabilities, would cancel out undesirable biases. By testing BERT models trained on texts containing biased statements primed with varying degrees of privacy, our study reveals that while stereotypical bias generally diminishes when privacy is tightened, text privatization does not uniformly equate to diminishing bias across all social domains. This highlights the need for careful diagnosis of bias in LMs that undergo text privatization.

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Protecting Privacy in Classifiers by Token Manipulation
Re’em Harel | Yair Elboher | Yuval Pinter

Using language models as a remote service entails sending private information to an untrusted provider. In addition, potential eavesdroppers can intercept the messages, thereby exposing the information. In this work, we explore the prospects of avoiding such data exposure at the level of text manipulation. We focus on text classification models, examining various token mapping and contextualized manipulation functions in order to see whether classifier accuracy may be maintained while keeping the original text unrecoverable. We find that although some token mapping functions are easy and straightforward to implement, they heavily influence performance on the downstream task, and via a sophisticated attacker can be reconstructed. In comparison, the contextualized manipulation provides an improvement in performance.

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A Collocation-based Method for Addressing Challenges in Word-level Metric Differential Privacy
Stephen Meisenbacher | Maulik Chevli | Florian Matthes

Applications of Differential Privacy (DP) in NLP must distinguish between the syntactic level on which a proposed mechanism operates, often taking the form of *word-level* or *document-level* privatization. Recently, several word-level *Metric* Differential Privacy approaches have been proposed, which rely on this generalized DP notion for operating in word embedding spaces. These approaches, however, often fail to produce semantically coherent textual outputs, and their application at the sentence- or document-level is only possible by a basic composition of word perturbations. In this work, we strive to address these challenges by operating *between* the word and sentence levels, namely with *collocations*. By perturbing n-grams rather than single words, we devise a method where composed privatized outputs have higher semantic coherence and variable length. This is accomplished by constructing an embedding model based on frequently occurring word groups, in which unigram words co-exist with bi- and trigram collocations. We evaluate our method in utility and privacy tests, which make a clear case for tokenization strategies beyond the word level.

In recent years, there has been increased demand for speech-to-speech translation (S2ST) systems in industry settings. Although successfully commercialized, cloning-based S2ST systems expose their distributors to liabilities when misused by individuals and can infringe on personality rights when exploited by media organizations. This work proposes a regulated S2ST framework called Preset-Voice Matching (PVM). PVM removes cross-lingual voice cloning in S2ST by first matching the input voice to a similar prior consenting speaker voice in the target-language. With this separation, PVM avoids cloning the input speaker, ensuring PVM systems comply with regulations and reduce risk of misuse. Our results demonstrate PVM can significantly improve S2ST system run-time in multi-speaker settings and the naturalness of S2ST synthesized speech. To our knowledge, PVM is the first explicitly regulated S2ST framework leveraging similarly-matched preset-voices for dynamic S2ST tasks.

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PII-Compass: Guiding LLM training data extraction prompts towards the target PII via grounding
Krishna Nakka | Ahmed Frikha | Ricardo Mendes | Xue Jiang | Xuebing Zhou

The latest and most impactful advances in large models stem from their increased size. Unfortunately, this translates into an improved memorization capacity, raising data privacy concerns. Specifically, it has been shown that models can output personal identifiable information (PII) contained in their training data. However, reported PII extraction performance varies widely, and there is no consensus on the optimal methodology to evaluate this risk, resulting in underestimating realistic adversaries. In this work, we empirically demonstrate that it is possible to improve the extractability of PII by over ten-fold by grounding the prefix of the manually constructed extraction prompt with in-domain data. This approach achieves phone number extraction rates of 0.92%, 3.9%, and 6.86% with 1, 128, and 2308 queries, respectively, i.e., the phone number of 1 person in 15 is extractable.

Automated clinical text anonymization has the potential to unlock the widespread sharing of textual health data for secondary usage while assuring patient privacy. Despite the proposal of many complex and theoretically successful anonymization solutions in literature, these techniques remain flawed. As such, clinical institutions are still reluctant to apply them for open access to their data. Recent advances in developing Large Language Models (LLMs) pose a promising opportunity to further the field, given their capability to perform various tasks. This paper proposes six new evaluation metrics tailored to the challenges of generative anonymization with LLMs. Moreover, we present a comparative study of LLM-based methods, testing them against two baseline techniques. Our results establish LLM-based models as a reliable alternative to common approaches, paving the way toward trustworthy anonymization of clinical text.

Anonymization of clinical text is crucial to allow the sharing and disclosure of health records while safeguarding patient privacy. However, automated anonymization processes are still highly limited in healthcare practice, as these systems cannot assure the anonymization of all private information. This paper explores the application of a novel technique that guarantees the removal of all sensitive information through the usage of text embeddings obtained from a de-identified dataset, replacing every word or sentence of a clinical note. We analyze the performance of different embedding techniques and models by evaluating them using recently proposed evaluation metrics. The results demonstrate that sentence replacement is better at keeping relevant medical information untouched, while the word replacement strategy performs better in terms of anonymization sensitivity.

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PocketLLM: Enabling On-Device Fine-Tuning for Personalized LLMs
Dan Peng | Zhihui Fu | Jun Wang

Recent advancements in large language models (LLMs) have indeed showcased their impressive capabilities. On mobile devices, the wealth of valuable, non-public data generated daily holds great promise for locally fine-tuning personalized LLMs, while maintaining privacy through on-device processing. However, the constraints of mobile device resources pose challenges to direct on-device LLM fine-tuning, mainly due to the memory-intensive nature of derivative-based optimization required for saving gradients and optimizer states. To tackle this, we propose employing derivative-free optimization techniques to enable on-device fine-tuning of LLM, even on memory-limited mobile devices. Empirical results demonstrate that the RoBERTa-large model and OPT-1.3B can be fine-tuned locally on the OPPO Reno 6 smartphone using around 4GB and 6.5GB of memory respectively, using derivative-free optimization techniques. This highlights the feasibility of on-device LLM fine-tuning on mobile devices, paving the way for personalized LLMs on resource-constrained devices while safeguarding data privacy.

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Smart Lexical Search for Label Flipping Adversial Attack
Alberto Gutiérrez-Megías | Salud María Jiménez-Zafra | L. Alfonso Ureña | Eugenio Martínez-Cámara

Language models are susceptible to vulnerability through adversarial attacks, using manipulations of the input data to disrupt their performance. Accordingly, it represents a cibersecurity leak. Data manipulations are intended to be unidentifiable by the learning model and by humans, small changes can disturb the final label of a classification task. Hence, we propose a novel attack built upon explainability methods to identify the salient lexical units to alter in order to flip the classification label. We asses our proposal on a disinformation dataset, and we show that our attack reaches high balance among stealthiness and efficiency.

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Can LLMs get help from other LLMs without revealing private information?
Florian Hartmann | Duc-Hieu Tran | Peter Kairouz | Victor Cărbune | Blaise Aguera Y Arcas

Cascades are a common type of machine learning systems in which a large, remote model can be queried if a local model is not able to accurately label a user’s data by itself. Serving stacks for large language models (LLMs) increasingly use cascades due to their ability to preserve task performance while dramatically reducing inference costs. However, applying cascade systems in situations where the local model has access to sensitive data constitutes a significant privacy risk for users since such data could be forwarded to the remote model. In this work, we show the feasibility of applying cascade systems in such setups by equipping the local model with privacy-preserving techniques that reduce the risk of leaking private information when querying the remote model. To quantify information leakage in such setups, we introduce two privacy measures. We then propose a system that leverages the recently introduced social learning paradigm in which LLMs collaboratively learn from each other by exchanging natural language. Using this paradigm, we demonstrate on several datasets that our methods minimize the privacy loss while at the same time improving task performance compared to a non-cascade baseline.

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Cloaked Classifiers: Pseudonymization Strategies on Sensitive Classification Tasks
Arij Riabi | Menel Mahamdi | Virginie Mouilleron | Djamé Seddah

Protecting privacy is essential when sharing data, particularly in the case of an online radicalization dataset that may contain personal information. In this paper, we explore the balance between preserving data usefulness and ensuring robust privacy safeguards, since regulations like the European GDPR shape how personal information must be handled. We share our method for manually pseudonymizing a multilingual radicalization dataset, ensuring performance comparable to the original data. Furthermore, we highlight the importance of establishing comprehensive guidelines for processing sensitive NLP data by sharing our complete pseudonymization process, our guidelines, the challenges we encountered as well as the resulting dataset.

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Improving Authorship Privacy: Adaptive Obfuscation with the Dynamic Selection of Techniques
Hemanth Kandula | Damianos Karakos | Haoling Qiu | Brian Ulicny

Authorship obfuscation, the task of rewriting text to protect the original author’s identity, is becoming increasingly important due to the rise of advanced NLP tools for authorship attribution techniques. Traditional methods for authorship obfuscation face significant challenges in balancing content preservation, fluency, and style concealment. This paper introduces a novel approach, the Obfuscation Strategy Optimizer (OSO), which dynamically selects the optimal obfuscation technique based on a combination of metrics including embedding distance, meaning similarity, and fluency. By leveraging an ensemble of language models OSO achieves superior performance in preserving the original content’s meaning and grammatical fluency while effectively concealing the author’s unique writing style. Experimental results demonstrate that the OSO outperforms existing methods and approaches the performance of larger language models. Our evaluation framework incorporates adversarial testing against state-of-the-art attribution systems to validate the robustness of the obfuscation techniques. We release our code publicly at https://github.com/BBN-E/ObfuscationStrategyOptimizer

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Deconstructing Classifiers: Towards A Data Reconstruction Attack Against Text Classification Models
Adel Elmahdy | Ahmed Salem

Natural language processing (NLP) models have become increasingly popular in real-world applications, such as text classification. However, they are vulnerable to privacy attacks, including data reconstruction attacks that aim to extract the data used to train the model. Most previous studies on data reconstruction attacks have focused on LLM, while classification models were assumed to be more secure. In this work, we propose a new targeted data reconstruction attack called the Mix And Match attack, which takes advantage of the fact that most classification models are based on LLM. The Mix And Match attack uses the base model of the target model to generate candidate tokens and then prunes them using the classification head. We extensively demonstrate the effectiveness of the attack using both random and organic canaries. This work highlights the importance of considering the privacy risks associated with data reconstruction attacks in classification models and offers insights into possible leakages.

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PrivaT5: A Generative Language Model for Privacy Policies
Mohammad Zoubi | Santosh T.y.s.s | Edgar Rosas | Matthias Grabmair

In the era of of digital privacy, users often neglect to read privacy policies due to their complexity. To bridge this gap, NLP models have emerged to assist in understanding privacy policies. While recent generative language models like BART and T5 have shown prowess in text generation and discriminative tasks being framed as generative ones, their application to privacy policy domain tasks remains unexplored. To address that, we introduce PrivaT5, a T5-based model that is further pre-trained on privacy policy text. We evaluate PrivaT5 over a diverse privacy policy related tasks and notice its superior performance over T5, showing the utility of continued domain-specific pre-training. Our results also highlight challenges faced by these generative models in complex structured output label space, especially in sequence tagging tasks, where they fall short compared to lighter encoder-only models.

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Reinforcement Learning-Driven LLM Agent for Automated Attacks on LLMs
Xiangwen Wang | Jie Peng | Kaidi Xu | Huaxiu Yao | Tianlong Chen

Recently, there has been a growing focus on conducting attacks on large language models (LLMs) to assess LLMs’ safety. Yet, existing attack methods face challenges, including the need to access model weights or merely ensuring LLMs output harmful information without controlling the specific content of their output. Exactly control of the LLM output can produce more inconspicuous attacks which could reveal a new page for LLM security. To achieve this, we propose RLTA: the Reinforcement Learning Targeted Attack, a framework that is designed for attacking language models (LLMs) and is adaptable to both white box (weight accessible) and black box (weight inaccessible) scenarios. It is capable of automatically generating malicious prompts that trigger target LLMs to produce specific outputs. We demonstrate RLTA in two different scenarios: LLM trojan detection and jailbreaking. The comprehensive experimental results show the potential of RLTA in enhancing the security measures surrounding contemporary LLMs.

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A Privacy-preserving Approach to Ingest Knowledge from Proprietary Web-based to Locally Run Models for Medical Progress Note Generation
Sarvesh Soni | Dina Demner-Fushman

Clinical documentation is correlated with increasing clinician burden, leading to the rise of automated methods to generate medical notes. Due to the sensitive nature of patient electronic health records (EHRs), locally run models are preferred for a variety of reasons including privacy, bias, and cost. However, most open-source locally run models (including medical-specific) are much smaller with limited input context size compared to the more powerful closed-source large language models (LLMs) generally available through web APIs (Application Programming Interfaces). In this paper, we propose a framework to harness superior reasoning capabilities and medical knowledge from closed-source online LLMs in a privacy-preserving manner and seamlessly incorporate it into locally run models. Specifically, we leverage a web-based model to distill the vast patient information available in EHRs into a clinically relevant subset without sending sensitive patient health information online and use this distilled knowledge to generate progress notes by a locally run model. Our ablation results indicate that the proposed framework improves the performance of the Mixtral model on progress note generation by 4.6 points on ROUGE (a text-matching based metric) and 7.56 points on MEDCON F1 (a metric that measures the clinical concepts overlap).

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bib (full) Proceedings of the 9th Workshop on Representation Learning for NLP (RepL4NLP-2024)

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Learning Contextualized Box Embeddings with Prototypical Networks
Kohei Oda | Kiyoaki Shirai | Natthawut Kertkeidkachorn

This paper proposes ProtoBox, a novel method to learn contextualized box embeddings. Unlike an ordinary word embedding, which represents a word as a single vector, a box embedding represents the meaning of a word as a box in a high-dimensional space: that is suitable for representing semantic relations between words. In addition, our method aims to obtain a “contextualized” box embedding, which is an abstract representation of a word in a specific context. ProtoBox is based on Prototypical Networks, which is a robust method for classification problems, especially focusing on learning the hypernym–hyponym relation between senses. ProtoBox is evaluated on three tasks: Word Sense Disambiguation (WSD), New Sense Classification (NSC), and Hypernym Identification (HI). Experimental results show that ProtoBox outperforms baselines for the HI task and is comparable for the WSD and NSC tasks.

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DomainInv: Domain Invariant Fine Tuning and Adversarial Label Correction For Unsupervised QA Domain Adaptation
Anant Khandelwal

Existing Question Answering (QA) systems are limited in their ability to answer questions from unseen domains or any out-of-domain distributions, making them less reliable for deployment in real scenarios. Importantly, all existing QA domain adaptation methods are either based on generating synthetic data or pseudo-labeling the target domain data. Domain adaptation methods relying on synthetic data and pseudo-labeling suffer from either the need for extensive computational resources or an additional overhead of carefully selecting the confidence threshold to distinguish noisy examples from the training dataset. In this paper, we propose unsupervised domain adaptation for an unlabeled target domain by transferring the target representation close to the source domain without using supervision from the target domain. To achieve this, we introduce the idea of domain-invariant fine-tuning along with adversarial label correction (DomainInv) to identify target instances that are distant from the source domain. This involves learning the domain invariant feature encoder to minimize the distance between such target instances and source instances class-wisely. This eliminates the possibility of learning features of the target domain that are still close to the source support but are ambiguous. The evaluation of our QA domain adaptation method, namely DomainInv, on multiple target QA datasets reveals a performance improvement over the strongest baseline.

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Relevance-aware Diverse Query Generation for Out-of-domain Text Ranking
Jia-Huei Ju | Chao-Han Yang | Szu-Wei Fu | Ming-Feng Tsai | Chuan-Ju Wang

Domain adaptation presents significant challenges for out-of-domain text ranking, especially when supervised data is limited. In this paper, we present ReadQG (Relevance-Aware Diverse Query Generation), a method to generate informative synthetic queries to facilitate the adaptation process of text ranking models. Unlike previous approaches focusing solely on relevant query generation, our ReadQG generates diverse queries with continuous relevance scores. Specifically, we propose leveraging soft-prompt tuning and diverse generation objectives to control query generation according to the given relevance. Our experiments show that integrating negative queries into the learning process enhances the effectiveness of text ranking models in out-of-domain information retrieval (IR) benchmarks. Furthermore, we measure the quality of query generation, highlighting the underlying beneficial characteristics of negative queries. Our empirical results and analysis also shed light on potential directions for more advanced data augmentation in IR. The data and code have been released.

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Learning from Others: Similarity-based Regularization for Mitigating Dataset Bias.
Reda Igbaria | Yonatan Belinkov

Common methods for mitigating spurious correlations in natural language understanding (NLU) usually operate in the output space, encouraging a main model to behave differently from a bias model by down-weighing examples where the bias model is confident.While improving out of distribution (OOD) performance, it was recently observed that the internal representations of the presumably debiased models are actually more, rather than less biased. We propose SimgReg, a new method for debiasing internal model components via similarity-based regularization, in representation space: We encourage the model to learn representations that are either similar to an unbiased model or different from a biased model. We experiment with three NLU tasks and different kinds of biases.We find that SimReg improves OOD performance, with little in-distribution degradation. Moreover, the representations learned by SimReg are less biased than in other methods.

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Prior Knowledge-Guided Adversarial Training
Lis Pereira | Fei Cheng | Wan Jou She | Masayuki Asahara | Ichiro Kobayashi

We introduce a simple yet effective Prior Knowledge-Guided ADVersarial Training (PKG-ADV) algorithm to improve adversarial training for natural language understanding. Our method simply utilizes task-specific label distribution to guide the training process. By prioritizing the use of prior knowledge of labels, we aim to generate more informative adversarial perturbations. We apply our model to several challenging temporal reasoning tasks. Our method enables a more reliable and controllable data training process than relying on randomized adversarial perturbation. Albeit simple, our method achieved significant improvements in these tasks. To facilitate further research, we will release the code and models.

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IT-Tuning : Parameter Efficient Information Token Tuning for Language Model
Jungu Kim | Hyeoncheol Kim

Recently, language models have demonstrated exceptional performance compared to their predecessors. In this context, attention mechanisms and pre-training significantly contribute to the enhanced performance of modern language models. Additionally, a continuously increasing number of parameters plays a crucial role in these advancements . However, an increase in the number of parameters significantly increases the GPU memory and training time required during fine-tuning of language models, this makes fine-tuning infeasible in environments with limited computing resources. Furthermore, after fine-tuning, the storage space required for deployment increases proportionally with the number of tasks, making it challenging to deploy devices with limited storage capacities. In this study, we propose IT-Tuning, a Parameter Efficient Fine-Tuning method that introduces a new concept called information tokens to address these issues.

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Bridging the Gap: Transfer Learning from English PLMs to Malaysian English
MohanRaj Chanthran | Lay-Ki Soon | Huey Fang Ong | Bhawani Selvaretnam

Malaysian English is a low resource creole languages, where it carries the elements of Malay, Chinese, and Tamil languages, in addition to Standard English. Named Entity Recognition (NER) models underperforms when capturing entities from Malaysian English text due to its distinctive morphosyntactic adaptations, semantic features and code-switching (mixing English and Malay). Considering these gaps, we introduce MENmBERT and MENBERT, a pre-trained language model with contextual understanding, specifically tailored for Malaysian English. We have fine-tuned MENmBERT and MENBERT using manually annotated entities and relations from the Malaysian English News Article (MEN) Dataset. This fine-tuning process allows the PLM to learn representations that capture the nuances of Malaysian English relevant for NER and RE tasks. MENmBERT achieved a 1.52% and 26.27% improvement on NER and RE tasks respectively compared to the bert-base-multilingual-cased model. While the overall performance for NER does not have significant improvement, our further analysis shows that there is a significant improvement when evaluated by the 12 entity labels. These findings suggest that pre-training language models on language-specific and geographically-focused corpora can be a promising approach for improving NER performance in low-resource settings. The dataset and code published through this paper provide valuable resources for NLP research work focusing on Malaysian English.

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Unified Interpretation of Smoothing Methods for Negative Sampling Loss Functions in Knowledge Graph Embedding
Xincan Feng | Hidetaka Kamigaito | Katsuhiko Hayashi | Taro Watanabe

Knowledge Graphs (KGs) are fundamental resources in knowledge-intensive tasks in NLP. Due to the limitation of manually creating KGs, KG Completion (KGC) has an important role in automatically completing KGs by scoring their links with KG Embedding (KGE). To handle many entities in training, KGE relies on Negative Sampling (NS) loss that can reduce the computational cost by sampling. Since the appearance frequencies for each link are at most one in KGs, sparsity is an essential and inevitable problem. The NS loss is no exception. As a solution, the NS loss in KGE relies on smoothing methods like Self-Adversarial Negative Sampling (SANS) and subsampling. However, it is uncertain what kind of smoothing method is suitable for this purpose due to the lack of theoretical understanding. This paper provides theoretical interpretations of the smoothing methods for the NS loss in KGE and induces a new NS loss, Triplet Adaptive Negative Sampling (TANS), that can cover the characteristics of the conventional smoothing methods. Experimental results of TransE, DistMult, ComplEx, RotatE, HAKE, and HousE on FB15k-237, WN18RR, and YAGO3-10 datasets and their sparser subsets show the soundness of our interpretation and performance improvement by our TANS.

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How Useful is Continued Pre-Training for Generative Unsupervised Domain Adaptation?
Rheeya Uppaal | Yixuan Li | Junjie Hu

Recent breakthroughs in scale have enabled the emergence of powerful generative language models, and the ability to fine-tune these models on various tasks by casting them into prompts or instructions. In this landscape, the problem of Unsupervised Domain Adaptation (UDA), or the problem of leveraging knowledge from a labeled source domain to an unlabeled target domain, has been left behind, with recent UDA methods still addressing discriminative classification. In particular, two popular UDA approaches, involving Continued Pre-Training (CPT) and learning domain invariant representations, have been under-explored in the generative setting, signaling a gap. In this work, we evaluate the utility of CPT for generative UDA. We first perform an empirical evaluation to measure the trade-offs between CPT and strong methods promoting domain invariance. We further evaluate how well the benefits of CPT extend to different architectures, tuning methods and data regimes. We then motivate the use of CPT by studying to what degree it benefits classification performance on the target domain. Finally, we attempt to understand the mechanism behind which CPT improves classification performance on the unlabeled target domain. Our findings suggest that a implicitly learns the downstream task while predicting masked words informative to that task. Our work connects the body of UDA research with that of instruction tuning, enabling an initial step towards a wider applicability of modern language models.

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Word Boundary Information Isn’t Useful for Encoder Language Models
Edward Gow-Smith | Dylan Phelps | Harish Tayyar Madabushi | Carolina Scarton | Aline Villavicencio

All existing transformer-based approaches to NLP using subword tokenisation algorithms encode whitespace (word boundary information) through the use of special space symbols (such as ## or _) forming part of tokens. These symbols have been shown to a) lead to reduced morphological validity of tokenisations, and b) give substantial vocabulary redundancy. As such, removing these symbols has been shown to have a beneficial effect on the processing of morphologically complex words for transformer encoders in the pretrain-finetune paradigm. In this work, we explore whether word boundary information is at all useful to such models. In particular, we train transformer encoders across four different training scales, and investigate several alternative approaches to including word boundary information, evaluating on two languages (English and Finnish) with a range of tasks across different domains and problem set-ups: sentence classification datasets, NER (for token-level classification), and two classification datasets involving complex words (Superbizarre and FLOTA). Overall, through an extensive experimental setup that includes the pre-training of 35 models, we find no substantial improvements from our alternative approaches, suggesting that modifying tokenisers to remove word boundary information isn’t leading to a loss of useful information.

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Beyond Link Prediction: On Pre-Training Knowledge Graph Embeddings
Daniel Ruffinelli | Rainer Gemulla

Knowledge graph embeddings (KGEs) provide low-dimensional representations of the entities and relations in a knowledge graph (KG) in order to reason about the KG and to inject structured knowledge into various downstream applications. Most prior work, however, focuses almost exclusively on training and evaluating KGE models for the task of link prediction. In this work, we explore KGE models as general-purpose representations of KGs and study their suitability (i) for more generally capturing properties of the KG and (ii) for downstream tasks such as entity classification and regression. For (i), we designed a new set of graph-structure prediction tasks to assess whether models capture different structures in the graph. For (ii), we investigate whether models provide useful features for a variety of downstream tasks. We found that strong link prediction performance was neither an indication that models generally capture patterns in the graph, nor that they were more useful in downstream tasks. As a result, we included our proposed graph-structure prediction tasks as additional training objectives and found that models trained with this multi-task approach generally, but not always, performed better at both graph-structure prediction and downstream tasks. However, the most suitable choice of pre-training tasks varies across KGE models and types of downstream tasks, suggesting opportunities for more research into the relation between pre-training KGE models and their usability on downstream applications.

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Learn it or Leave it: Module Composition and Pruning for Continual Learning
Mingyang Wang | Heike Adel | Lukas Lange | Jannik Strötgen | Hinrich Schuetze

In real-world environments, continual learning is essential for machine learning models, as they need to acquire new knowledge incrementally without forgetting what they have already learned. While pretrained language models have shown impressive capabilities on various static tasks, applying them to continual learning poses significant challenges, including avoiding catastrophic forgetting, facilitating knowledge transfer, and maintaining parameter efficiency. In this paper, we introduce MoCL-P, a novel lightweight continual learning method that addresses these challenges simultaneously. Unlike traditional approaches that continuously expand parameters for newly arriving tasks, MoCL-P integrates task representation-guided module composition with adaptive pruning, effectively balancing knowledge integration and computational overhead. Our evaluation across three continual learning benchmarks with up to 176 tasks shows that MoCL-P achieves state-of-the-art performance and improves parameter efficiency by up to three times, demonstrating its potential for practical applications where resource requirements are constrained.

Traditional image clustering techniques only find a single grouping within visual data. In particular, they do not provide a possibility to explicitly define multiple types of clustering. This work explores the potential of large vision-language models to facilitate alternative image clustering. We propose Text-Guided Alternative Image Consensus Clustering (TGAICC), a novel approach that leverages user-specified interests via prompts to guide the discovery of diverse clusterings. To achieve this, it generates a clustering for each prompt, groups them using hierarchical clustering, and then aggregates them using consensus clustering. TGAICC outperforms image- and text-based baselines on four alternative image clustering benchmark datasets. Furthermore, using count-based word statistics, we are able to obtain text-based explanations of the alternative clusterings. In conclusion, our research illustrates how contemporary large vision-language models can transform explanatory data analysis, enabling the generation of insightful, customizable, and diverse image clusterings.

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QAVSA: Question Answering using Vector Symbolic Algebras
Ryan Laube | Chris Eliasmith

With the advancement of large pretrained language models (PLMs), many question answering (QA) benchmarks have been developed in order to evaluate the reasoning capabilities of these models. Augmenting PLMs with external knowledge in the form of Knowledge Graphs (KGs) has been a popular method to improve their reasoning capabilities, and a common method to reason over KGs is to use Graph Neural Networks (GNNs). As an alternative to GNNs to augment PLMs, we propose a novel graph reasoning module using Vector Symbolic Algebra (VSA) graph representations and a k-layer MLP. We demonstrate that our VSA-based model performs as well as QA-GNN, a model combining a PLM and a GNN-module, on 3 multiple-choice question answering (MCQA) datasets. Our model has a simpler architecture than QA-GNN and also converges 39% faster during training.

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Tracking linguistic information in transformer-based sentence embeddings through targeted sparsification
Vivi Nastase | Paola Merlo

Analyses of transformer-based models have shown that they encode a variety of linguistic information from their textual input. While these analyses have shed a light on the relation between linguistic information on one side, and internal architecture and parameters on the other, a question remains unanswered: how is this linguistic information reflected in sentence embeddings? Using datasets consisting of sentences with known structure, we test to what degree information about chunks (in particular noun, verb or prepositional phrases), such as grammatical number, or semantic role, can be localized in sentence embeddings. Our results show that such information is not distributed over the entire sentence embedding, but rather it is encoded in specific regions. Understanding how the information from an input text is compressed into sentence embeddings helps understand current transformer models and help build future explainable neural models.

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Learning New Tasks from a Few Examples with Soft-Label Prototypes
Avyav Singh | Ekaterina Shutova | Helen Yannakoudakis

Existing approaches to few-shot learning in NLP rely on large language models (LLMs) and/or fine-tuning of these to generalise on out-of-distribution data. In this work, we propose a novel few-shot learning approach based on soft-label prototypes (SLPs) designed to collectively capture the distribution of different classes across the input domain space. We focus on learning previously unseen NLP tasks from very few examples (4, 8, 16) per class and experimentally demonstrate that our approach achieves superior performance on the majority of tested tasks in this data-lean setting while being highly parameter efficient. We also show that our few-shot adaptation method can be integrated into more generalised learning settings, primarily meta-learning, to yield superior performance against strong baselines.

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Learned Transformer Position Embeddings Have a Low-Dimensional Structure
Ulme Wennberg | Gustav Henter

Position embeddings have long been essential for sequence-order encoding in transformer models, yet their structure is underexplored. This study uses principal component analysis (PCA) to quantitatively compare the dimensionality of absolute position and word embeddings in BERT and ALBERT. We find that, unlike word embeddings, position embeddings occupy a low-dimensional subspace, typically utilizing under 10% of the dimensions available. Additionally, the principal vectors are dominated by a few low-frequency rotational components, a structure arising independently across models.

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Multi-label Learning with Random Circular Vectors
Ken Nishida | Kojiro Machi | Kazuma Onishi | Katsuhiko Hayashi | Hidetaka Kamigaito

The extreme multi-label classification (XMC) task involves learning a classifier that can predict from a large label set the most relevant subset of labels for a data instance. While deep neural networks (DNNs) have demonstrated remarkable success in XMC problems, the task is still challenging because it must deal with a large number of output labels, which make the DNN training computationally expensive. This paper addresses the issue by exploring the use of random circular vectors, where each vector component is represented as a complex amplitude. In our framework, we can develop an output layer and loss function of DNNs for XMC by representing the final output layer as a fully connected layer that directly predicts a low-dimensional circular vector encoding a set of labels for a data instance. We conducted experiments on synthetic datasets to verify that circular vectors have better label encoding capacity and retrieval ability than normal real-valued vectors. Then, we conducted experiments on actual XMC datasets and found that these appealing properties of circular vectors contribute to significant improvements in task performance compared with a previous model using random real-valued vectors, while reducing the size of the output layers by up to 99%.

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Mitigating Semantic Leakage in Cross-lingual Embeddings via Orthogonality Constraint
Dayeon Ki | Cheonbok Park | Hyunjoong Kim

Accurately aligning contextual representations in cross-lingual sentence embeddings is key for effective parallel data mining. A common strategy for achieving this alignment involves disentangling semantics and language in sentence embeddings derived from multilingual pre-trained models. However, we discover that current disentangled representation learning methods suffer from semantic leakage—a term we introduce to describe when a substantial amount of language-specific information is unintentionally leaked into semantic representations. This hinders the effective disentanglement of semantic and language representations, making it difficult to retrieve embeddings that distinctively represent the meaning of the sentence. To address this challenge, we propose a novel training objective, ORthogonAlity Constraint LEarning (ORACLE), tailored to enforce orthogonality between semantic and language embeddings. ORACLE builds upon two components: intra-class clustering and inter-class separation. Through experiments on cross-lingual retrieval and semantic textual similarity tasks, we demonstrate that training with the ORACLE objective effectively reduces semantic leakage and enhances semantic alignment within the embedding space.

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Whitening Not Recommended for Classification Tasks in LLMs
Ali Forooghi | Shaghayegh Sadeghi | Jianguo Lu

Sentence embedding is a cornerstone in NLP. Whitening has been claimed to be an effective method to improve embeddings obtained from Large Language Models (LLMs) for sentence embedding. However, we find that the effectiveness of whitening is model-dependent and task-dependent. In particular, whitening degenerates embeddings for classification tasks. The conclusion is supported by extensive experiments. A by-product of our research is embedding evaluation platform for LLMs called SentEval+

bib (full) Proceedings of the Fourth Workshop on Scholarly Document Processing (SDP 2024)

The workshop on Scholarly Document Processing (SDP) started in 2020 to accelerate research, inform policy and educate the public on natural language processing for scientific text. The fourth iteration of the workshop, SDP24 was held at the 62nd Annual Meeting of the Association for Computational Linguistics (ACL24) as a hybrid event. The SDP workshop saw a great increase in interest, with 57 submissions, of which 28 were accepted. The program consisted of a research track, four invited talks and two shared tasks: 1) DAGPap24: Detecting automatically generated scientific papers and 2) Context24: Multimodal Evidence and Grounding Context Identification for Scientific Claims. The program was geared towards NLP, information extraction, information retrieval, and data mining for scholarly documents, with an emphasis on identifying and providing solutions to open challenges.

This paper provides an overview of the 2024 ACL Scholarly Document Processing workshop shared task on the detection of automatically generated scientific papers. Unlike our previous task, which focused on the binary classification of whether scientific passages were machine-generated or not, one likely use case for text generation technology in scientific writing is to intersperse human-written text with passages of machine-generated text. We frame the detection problem as a multiclass span classification task: given an expert of text, label token spans in the text as human-written or machine-generated We shared a dataset containing excerpts from human-written papers as well as artificially generated content collected by Elsevier publishing and editorial teams. As a test set, the participants were provided with a corpus of openly accessible human-written as well as generated papers from the same scientific domains of documents. The shared task saw 457 submissions across 28 participating teams and resulted in three published technical reports. We discuss our findings from the shared task in this overview paper.

To appropriately interpret and use scientific claims for sensemaking and decision-making, it is critical to contextualize them, not just with textual evidence that the claim was in fact asserted, but also with key supporting empirical evidence, such as a figure that describes a key result, and methodological details, such as the methods of data collection. Retrieving this contextual information when encountering claims in isolation, away from their source papers, is difficult and time-consuming for humans. Scholarly document processing models could help to contextualize scientific claims, but there is a lack of datasets designed for this task. Thus, we contribute a dataset of 585 scientific claims with gold annotations for supporting figures and tables, and gold text snippets of methodological details, that ground the key results behind each claim and run the Context24 shared task to encourage model development for this task. This report describes details of our dataset construction process, summarizes results from the shared task conducted at the 4th Workshop on Scholarly Document Processing (SDP), and discusses future research directions in this space. To support further research, we also publicly release the dataset on HuggingFace.

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Controllable Citation Sentence Generation with Language Models
Nianlong Gu | Richard Hahnloser

Citation generation aims to generate a citation sentence that refers to a chosen paper in the context of a manuscript. However, a rigid citation generation process is at odds with an author’s desire to control specific attributes, such as 1) the citation intent, e.g., either introducing background information or comparing results, and 2) keywords that should appear in the citation text. To provide these degrees of controllability during citation generation, we propose to integrate the manuscript context, the context of the referenced paper, and the desired control attributes into a structured template and use it to fine-tune a language model (LM) via next-token prediction. We then utilize Proximal Policy Optimization to directly optimize the LM in favor of a high score of our proposed controllability metric. The proposed workflow harmoniously combines citation attribute suggestion and conditional citation generation into one LM, allowing for better user control.

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Toward Structured Related Work Generation with Novelty Statements
Kazuya Nishimura | Kuniaki Saito | Tosho Hirasawa | Yoshitaka Ushiku

To help readers understand the novelty and the research context, an excellent related work section is structured (i.e., the section consists of paragraphs determined by categorizing papers into several topics) and includes descriptions of novelty. However, previous studies viewed related work generation as multi-document summarization, and the structure and novelty statement are ignored in such studies. In this paper, we redefine the related work generation task as summarization with structure (i.e., multiple paragraphs with citation) and novelty statement. For this task, we propose a quality-oriented dataset and evaluation metrics. Experiments evaluated the state-of-the-art language models on our tasks, and we confirmed the issues with the current models and the validity of the evaluation indicators.

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Understanding Survey Paper Taxonomy about Large Language Models via Graph Representation Learning
Jun Zhuang | Casey Kennington

As new research on Large Language Models (LLMs) continues, it is difficult to keep up with new research and models. To help researchers synthesize the new research many have written survey papers, but even those have become numerous. In this paper, we develop a method to automatically assign survey papers to a taxonomy. We collect the metadata of 144 LLM survey papers and explore three paradigms to classify papers within the taxonomy. Our work indicates that leveraging graph structure information on co-category graphs can significantly outperform the language models in two paradigms; pre-trained language models’ fine-tuning and zero-shot/few-shot classifications using LLMs. We find that our model surpasses an average human recognition level and that fine-tuning LLMs using weak labels generated by a smaller model, such as the GCN in this study, can be more effective than using ground-truth labels, revealing the potential of weak-to-strong generalization in the taxonomy classification task.

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Beyond Retrieval: Topic-based Alignment of Scientific Papers to Research Proposal
Rudra Palit | Manasi Patwardhan | Lovekesh Vig | Gautam Shroff

The inception of a research agenda typically commences with the creation of a comprehensive research proposal. The efficacy of the proposal often hinges on its ability to connect with the existing scientific literature that supports its ideas. To effectively assess the relevance of existing articles to a research proposal, it is imperative to categorize these articles into high-level thematic groups, referred to as topics, that align with the proposal. This paper introduces a novel task of aligning scientific articles, relevant to a proposal, with researcher-provided proposal topics. Additionally, we construct a dataset to serve as a benchmark for this task. We establish human and Large Language Model (LLM) baselines and propose a novel three-stage approach to address this challenge. We synthesize and use pseudo-labels that map proposal topics to text spans from cited articles to train Language Models (LMs) for two purposes: (i) as a retriever, to extract relevant text spans from cited articles for each topic, and (ii) as a classifier, to categorize the articles into the proposal topics. Our retriever-classifier pipeline, which employs very small open-source LMs fine-tuned with our constructed dataset, achieves results comparable to a vanilla paid LLM-based classifier, demonstrating its efficacy. However, a notable gap of 23.57 F1 score between our approach and the human baseline highlights the complexity of this task and emphasizes the need for further research.

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Evaluating the Effectiveness of Retrieval-Augmented Large Language Models in Scientific Document Reasoning
Sai Munikoti | Anurag Acharya | Sridevi Wagle | Sameera Horawalavithana

Despite the dramatic progress in Large Language Model (LLM) development, LLMs often provide seemingly plausible but not factual information, often referred to as hallucinations. Retrieval-augmented LLMs provide a non-parametric approach to solve these issues by retrieving relevant information from external data sources and augment the training process. These models help to trace evidence from an externally provided knowledge base allowing the model predictions to be better interpreted and verified. In this work, we critically evaluate these models in their ability to perform in scientific document reasoning tasks. To this end, we tuned multiple such model variants with science-focused instructions and evaluated them on a scientific document reasoning benchmark for the usefulness of the retrieved document passages. Our findings suggest that models justify predictions in science tasks with fabricated evidence and leveraging scientific corpus as pretraining data does not alleviate the risk of evidence fabrication.

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Cited Text Spans for Scientific Citation Text Generation
Xiangci Li | Yi-Hui Lee | Jessica Ouyang

An automatic citation generation system aims to concisely and accurately describe the relationship between two scientific articles. To do so, such a system must ground its outputs to the content of the cited paper to avoid non-factual hallucinations. Due to the length of scientific documents, existing abstractive approaches have conditioned only on cited paper abstracts. We demonstrate empirically that the abstract is not always the most appropriate input for citation generation and that models trained in this way learn to hallucinate. We propose to condition instead on the cited text span (CTS) as an alternative to the abstract. Because manual CTS annotation is extremely time- and labor-intensive, we experiment with distant labeling of candidate CTS sentences, achieving sufficiently strong performance to substitute for expensive human annotations in model training, and we propose a human-in-the-loop, keyword-based CTS retrieval approach that makes generating citation texts grounded in the full text of cited papers both promising and practical.

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CiteAssist: A System for Automated Preprint Citation and BibTeX Generation
Lars Kaesberg | Terry Ruas | Jan Philip Wahle | Bela Gipp

We present CiteAssist, a system to automate the generation of BibTeX entries for preprints, streamlining the process of bibliographic annotation. Our system extracts metadata, such as author names, titles, publication dates, and keywords, to create standardized annotations within the document. CiteAssist automatically attaches the BibTeX citation to the end of a PDF and links it on the first page of the document so other researchers gain immediate access to the correct citation of the article. This method promotes platform flexibility by ensuring that annotations remain accessible regardless of the repository used to publish or access the preprint. The annotations remain available even if the preprint is viewed externally to CiteAssist. Additionally, the system adds relevant related papers based on extracted keywords to the preprint, providing researchers with additional publications besides those in related work for further reading. Researchers can enhance their preprints organization and reference management workflows through a free and publicly available web interface.

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An end-to-end entity recognition and disambiguation framework for identifying Author Affiliation from literature publications
Lianghong Lin | Wenxixie-c@my.cityu.edu.hk Wenxixie-c@my.cityu.edu.hk | Spczili@speed-polyu.edu.hk Spczili@speed-polyu.edu.hk | Tianyong Hao

Author affiliation information plays a key role in bibliometric analyses and is essential for evaluating studies. However, as author affiliation information has not been standardized, which leads to difficulties such as synonym ambiguity and incomplete data during automated processing. To address the challenge, this paper proposes an end-to-end entity recognition and disambiguation framework for identifying author affiliation from literature publications. For entity disambiguation, an algorithm combining word embedding and spatial embedding is presented considering that author affiliation texts often contain rich geographic information. The disambiguation algorithm utilizes the semantic information and geographic information, which effectively enhances entity recognition and disambiguation effect. In addition, the proposed framework facilitates the effective utilization of the extensive literature in the PubMed database for comprehensive bibliometric analysis. The experimental results verify the robustness and effectiveness of the algorithm.

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Utilizing an Ensemble Model with Anomalous Label Smoothing to Detect Generated Scientific Papers
Yuan Zhao | Junruo Gao | Junlin Wang | Gang Luo | Liang Tang

Generative AI, as it becomes increasingly integrated into our lives, has brought convenience, though some concerns have arisen regarding its potential impact on the rigor and authenticity of scientific research. To encourage the development of robust and reliable automatically-generated scientific text detection systems, the “DAGPap24: Detecting Automatically Generated Scientific Papers” competition was held and shared the same task with the 4th Workshop on Scholarly Document Processing (SDP 2024) to be held at ACL 2024. In the DAGPap24 competition, participants were tasked with constructing a generative text detection model that could accurately distinguish between the human written fragment, the synonym replacement fragment, the ChatGPT rewrite fragment, and the generated summary fragment of a paper. In this competition, we first conducted a comprehensive analysis of the training set to build a generative paper detection model. Then we tried various language models, including SciBERT, ALBERT, DeBERTa, RoBERTa, etc. After that, we introduced an Anomalous Label Smoothing (ALS) method and a majority voting method to improve the final results. Finally, we achieved 0.9948 and 0.9944 F1 scores during the development and testing phases respectively, and we achieved second place in the competition.

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AffilGood: Building reliable institution name disambiguation tools to improve scientific literature analysis
Nicolau Duran-Silva | Pablo Accuosto | Piotr Przybyła | Horacio Saggion

The accurate attribution of scientific works to research organizations is hindered by the lack of openly available manually annotated data–in particular when multilingual and complex affiliation strings are considered. The AffilGood framework introduced in this paper addresses this gap. We identify three sub-tasks relevant for institution name disambiguation and make available annotated datasets and tools aimed at each of them, including i) a dataset annotated with affiliation spans in noisy automatically-extracted strings; ii) a dataset annotated with named entities for the identification of organizations and their locations; iii) seven datasets annotated with the Research Organization Registry (ROR) identifiers for the evaluation of entity-linking systems. In addition, we describe, evaluate and make available newly developed tools that use these datasets to provide solutions for each of the identified sub-tasks. Our results confirm the value of the developed resources and methods in addressing key challenges in institution name disambiguation.

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Metadata Enhancement Using Large Language Models
Hyunju Song | Steven Bethard | Andrea Thomer

In the natural sciences, a common form of scholarly document is a physical sample record, which provides categorical and textual metadata for specimens collected and analyzed for scientific research. Physical sample archives like museums and repositories publish these records in data repositories to support reproducible science and enable the discovery of physical samples. However, the success of resource discovery in such interfaces depends on the completeness of the sample records. We investigate approaches for automatically completing the scientific metadata fields of sample records. We apply large language models in zero and few-shot settings and incorporate the hierarchical structure of the taxonomy. We show that a combination of record summarization, bottom-up taxonomy traversal, and few-shot prompting yield F1 as high as 0.928 on metadata completion in the Earth science domain.

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MISTI: Metadata-Informed Scientific Text and Image Representation through Contrastive Learning
Pawin Taechoyotin | Daniel Acuna

In scientific publications, automatic representations of figures and their captions can be used in NLP, computer vision, and information retrieval tasks. Contrastive learning has proven effective for creating such joint representations for natural scenes, but its application to scientific imagery and descriptions remains under-explored. Recent open-access publication datasets provide an opportunity to understand the effectiveness of this technique as well as evaluate the usefulness of additional metadata, which are available only in the scientific context. Here, we introduce MISTI, a novel model that uses contrastive learning to simultaneously learn the representation of figures, captions, and metadata, such as a paper’s title, sections, and curated concepts from the PubMed Open Access Subset. We evaluate our model on multiple information retrieval tasks, showing substantial improvements over baseline models. Notably, incorporating metadata doubled retrieval performance, achieving a Recall@1 of 30% on a 70K-item caption retrieval task. We qualitatively explore how metadata can be used to strategically retrieve distinctive representations of the same concept but for different sections, such as introduction and results. Additionally, we show that our model seamlessly handles out-of-domain tasks related to image segmentation. We share our dataset and methods (https://github.com/Khempawin/scientific-image-caption-pair/tree/section-attr) and outline future research directions.

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First Steps in Building a Knowledge Base of Mathematical Results
Shrey Mishra | Yacine Brihmouche | Théo Delemazure | Antoine Gauquier | Pierre Senellart

This paper explores the initial steps towards extracting information about theorems and proofs from scholarly documents to build a knowledge base of interlinked results. Specifically, we consider two main tasks: extracting results and their proofs from the PDFs of scientific articles and establishing which results are used in the proofs of others across the scientific literature. We discuss the problem statement, methodologies, and preliminary findings employed in both phases of our approach, highlighting the challenges faced.

When examining reviews of research papers, we can distinguish between two hypothetical referees: the maximally lenient referee who accepts any paper with a vacuous review and the maximally strict one who rejects any paper with an overly pedantic review. Clearly, both are of no practical value. Our interest is in a referee who makes a balanced judgement and provides a review abiding by the guidelines. In this paper, we present a case study of automatic correction of an existing machine-generated or human review. The \tt{AutoRef}\ system implements an iterative approach that progressively “refines” a review by attempting to make it more compliant with pre-defined requirements of a “good” review. It implements the following steps: (1) Translate the review requirements into a specification in natural language, of “yes/no” questions; (2) Given a (paper,review) pair, extract answers to the questions; (3) Use the results in (2) to generate a new review; and (4) Return to Step (2) with the paper and the new review. Here, (2) and (3) are implemented by large language model (LLM) based agents. We present a case study using papers and reviews made available for the International Conference on Learning Representations (ICLR). Our initial empirical results suggest that \tt{AutoRef}\ progressively improves the compliance of the generated reviews to the specification. Currently designed specification makes \tt{AutoRef}\ progressively generate reviews which are stricter, making the decisions more inclined towards “rejections”. This demonstrates the applicability of $AutoRef $ for: (1) The progressive correction of overly lenient reviews, being useful for referees and meta-reviewers; and (2) The generation of progressively stricter reviews for a paper, starting from a vacuous review (“Great paper. Accept.”), facilitating authors when trying to assess weaknesses in their papers.

It is desirable to coarsely classify short scientific texts, such as grant or publication abstracts, for strategic insight or research portfolio management. These texts efficiently transmit dense information to experts possessing a rich body of knowledge to aid interpretation. Yet this task is remarkably difficult to automate because of brevity and the absence of context. To address this gap, we have developed a novel approach to generate and appropriately assign coarse domain-specific labels. We show that a Large Language Model (LLM) can provide metadata essential to the task, in a process akin to the augmentation of supplemental knowledge representing human intuition, and propose a workflow. As a pilot study, we use a corpus of award abstracts from the National Aeronautics and Space Administration (NASA). We develop new assessment tools in concert with established performance metrics.

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Synthetic Context with LLM for Entity Linking from Scientific Tables
Yuji Oshima | Hiroyuki Shindo | Hiroki Teranishi | Hiroki Ouchi | Taro Watanabe

Tables in scientific papers contain crucial information, such as experimental results.Entity Linking (EL) is a promising technology that analyses tables and associates them with a knowledge base.EL for table cells requires identifying the referent concept of each cell while understanding the context relevant to each cell in the paper. However, extracting the relevant context from the paper is challenging because the relevant parts are scattered in the main text and captions.This study defines a rule-based method for extracting broad context from the main text, including table captions and sentences that mention the table.Furthermore, we propose synthetic context as a more refined context generated by large language models (LLMs).In a synthetic context, contexts from the entire paper are refined by summarizing, injecting supplemental knowledge, and clarifying the referent concept.We observe this approach improves accuracy for EL by more than 10 points on the S2abEL dataset, and our qualitative analysis suggests potential future works.

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Papilusion at DAGPap24: Paper or Illusion? Detecting AI-generated Scientific Papers
Nikita Andreev | Alexander Shirnin | Vladislav Mikhailov | Ekaterina Artemova

This paper presents Papilusion, an AI-generated scientific text detector developed within the DAGPap24 shared task on detecting automatically generated scientific papers. We propose an ensemble-based approach and conduct ablation studies to analyze the effect of the detector configurations on the performance. Papilusion is ranked 6th on the leaderboard, and we improve our performance after the competition ended, achieving 99.46 (+9.63) of the F1-score on the official test set.

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Multi-head Span-based Detector for AI-generated Fragments in Scientific Papers
German Gritsai | Ildar Khabutdinov | Andrey Grabovoy

This paper describes a system designed to distinguish between AI-generated and human-written scientific excerpts in the DAGPap24 competition hosted within the Fourth Workshop on Scientific Document Processing. In this competition the task is to find artificially generated token-level text fragments in documents of a scientific domain. Our work focuses on the use of a multi-task learning architecture with two heads. The application of this approach is justified by the specificity of the task, where class spans are continuous over several hundred characters. We considered different encoder variations to obtain a state vector for each token in the sequence, as well as a variation in splitting fragments into tokens to further feed into the input of a transform-based encoder. This approach allows us to achieve a 9% quality improvement relative to the baseline solution score on the development set (from 0.86 to 0.95) using the average macro F₁-score, as well as a score of 0.96 on a closed test part of the dataset from the competition.

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Guiding Large Language Models via External Attention Prompting for Scientific Extreme Summarization
Yuan Chang | Ziyue Li | Xiaoqiu Le

Scientific extreme summarization, the task of generating concise one-sentence summaries (TLDRs) for scientific papers, presents significant challenges due to the need for deep domain-specific understanding and the ability to distill salient information. This study identifies the critical role of titles and keywords in enhancing TLDR generation through quantitative analysis. We propose a novel method, External Attention Prompting (EAP), which leverages LLMs by guiding them to focus on the most critical parts of the source text through varying degrees of attention signals. Our method employs Markdown emphasis syntax to annotate attention levels, enabling LLMs to prioritize salient information effectively. Extensive experiments demonstrate that EAP significantly outperforms baseline methods across various LLMs and metrics in both zero-shot and few-shot settings. Further evaluations by GPT-4 demonstrate that EAP can enable LLMs to generate TLDRs of higher human-aligned quality.

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Simulating Expert Discussions with Multi-agent for Enhanced Scientific Problem Solving
Ziyue Li | Yuan Chang | Xiaoqiu Le

Large Language Models (LLMs) have shown remarkable potential across various domains, yet their application in addressing complex scientific problems remains a formidable challenge. This paper presents a novel methodology to augment the problem-solving capabilities of LLMs by assigning them roles as domain-specific experts. By simulating a panel of experts, each LLM is tasked with delivering professional and cautious responses to scientific inquiries. Our approach involves querying multiple LLMs and assessing the consistency of their responses. High agreement among the LLMs suggests greater confidence in the proposed solution, whereas discrepancies prompt a collaborative discussion among the LLMs to reach a consensus. This method emulates real-world scientific problem-solving processes, fostering a more reliable and robust mechanism for LLMs to tackle scientific questions. Our experimental results show that assigning roles to multiple LLMs as domain-specific experts significantly improves their accuracy and reliability in solving scientific problems. This framework has the potential to advance the application of AI in scientific research, enhancing its effectiveness and trustworthiness.

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An Analysis of Tasks and Datasets in Peer Reviewing
Moritz Staudinger | Wojciech Kusa | Florina Piroi | Allan Hanbury

Taking note of the current challenges of the peer review system, this paper inventories the research tasks for analysing and possibly automating parts of the reviewing process, like matching submissions with a reviewer’s domain of expertise. For each of these tasks we list their associated datasets, analysing their quality in terms of available documentation of creation and use. Building up on this, we give a set of recommendations to take into account when collecting and releasing data.

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Zero-shot Scientific Claim Verification Using LLMs and Citation Text
Carlos Alvarez | Maxwell Bennett | Lucy Wang

Due to rapidly changing and advancing science, it is important to check the veracity of scientific claims and whether they are supported by research evidence. Previous versions of this task depended on supervised training, where labeled datasets were constructed through manual claim writing and evidence identification, sometimes coupled with mining citation relationships in papers. In this work, we investigate whether zero-shot scientific claim verification could be enabled using large language models (LLMs) and distant supervision examples taken directly from citation texts. We derive an in-context learning (ICL) dataset, SCitance, consisting of citation sentences (“citances”), LLM-generated negations, evidence documents, and veracity labels, and find that prompting GPT-4 with ICL examples from this dataset yields comparable performance (within 1 point F1) to previous finetuned models trained on manually curated claim-evidence pairs. Our results suggest that prompting LLMs with citance-evidence pairs directly poses a viable alternative to finetuning scientific claim verification models with manually-curated data.

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Researcher Representations Based on Aggregating Embeddings of Publication Titles: A Case Study in a Japanese Academic Database
Hiroyoshi Nagao | Marie Katsurai

Constructing researcher representations is crucial for search and recommendation in academic databases. While recent studies presented methods based on knowledge graph embeddings, obtaining a complete graph of academic entities might be sometimes challenging due to the lack of linked data.By contrast, the textual list of publications of each researcher, which represents their research interests and expertise, is usually easy to obtain.Therefore, this study focuses on creating researcher representations based on textual embeddings of their publication titles and assesses their practicality. We aggregate embeddings of each researcher’s multiple publications into a single vector and apply it to research field classification and similar researcher search tasks. We experimented with multiple language models and embedding aggregation methods to compare their performance.From the model perspective, we confirmed the effectiveness of using sentence embedding models and a simple averaging approach.

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CoSAEmb: Contrastive Section-aware Aspect Embeddings for Scientific Articles
Shruti Singh | Mayank Singh

Research papers are long documents that contain information about various aspects such as background, prior work, methodology, and results. Existing works on scientific document representation learning only leverage the title and abstract of the paper. We present CoSAEmb, a model that learns representations from the full text of 97402 scientific papers from the S2ORC dataset. We present a novel supervised contrastive training framework for long documents using triplet loss and margin gradation. Our framework can be used to learn representations of long documents with any existing encoder-only transformer model without retraining it from scratch. CoSAEmb shows improved performance on information retrieval from the paper’s full text in comparison to models trained only on paper titles and abstracts. We also evaluate CoSAEmb on SciRepEval and CSFCube benchmarks, showing comparable performance with existing state-of-the-art models.

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Integrating Table Representations into Large Language Models for Improved Scholarly Document Comprehension
Buse Sibel Korkmaz | Antonio Del Rio Chanona

We address the challenge of interpreting and reasoning over scientific tables with Large Language Models (LLMs), a crucial aspect of scholarly documents. Despite significant progress in natural language processing, the integration of tabular data into scientific LLMs remains limited. We propose an innovative approach leveraging intermediate task pre-training on table question-answering datasets, followed by model adaptation to comprehend tables in computer science literature. Our findings reveal that incorporating table understanding substantially improves the performance of LLMs on scientific literature understanding tasks, which we showcase in peer-review score prediction. This improvement underscores the importance of utilizing tabular data in the training of scientific language models. The code and models are publicly available at [this link](https://github.com/buseskorkmaz/Integrating-Table-Representations-into-LLMs).

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Harnessing CLIP for Evidence Identification in Scientific Literature: A Multimodal Approach to Context24 Shared Task
Anukriti Kumar | Lucy Wang

Knowing whether scientific claims are supported by evidence is fundamental to scholarly communication and evidence-based decision-making. We present our approach to Task 1 of the Context24 Shared Task—Contextualizing Scientific Figures and Tables (SDP@ACL2024), which focuses on identifying multimodal evidence from scientific publications that support claims. We finetune CLIP, a state-of-the-art model for image-text similarity tasks, to identify and rank figures and tables in papers that substantiate specific claims. Our methods focus on text and image preprocessing techniques and augmenting the organizer-provided training data with labeled examples from the SciMMIR and MedICaT datasets. Our best-performing model achieved NDCG@5 and NDCG@10 values of 0.26 and 0.30, respectively, on the Context24 test split. Our findings underscore the effectiveness of data augmentation and preprocessing in improving the model’s ability in evidence matching.

Finding evidence for claims from content presented in experimental results of scientific articles is difficult. The evidence is often presented in the form of tables and figures, and correctly matching it to scientific claims presents automation challenges. The Context24 shared task is launched to support the development of systems able to verify claims by extracting supporting evidence from articles. We explore different facets of this shared task modelled as a search problem and as an information extraction task. We experiment with a range of methods in each of these categories for the two sub-tasks of evidence identification and grounding context identification in the Context24 shared task.

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OSX at Context24: How Well Can GPT Tackle Contexualizing Scientific Figures and Tables
Tosho Hirasawa

Identifying the alignment between different parts of a scientific paper is fundamental to scholarly document processing.In the Context24 shared task, participants are given a scientific claim and asked to identify (1) key figures or tables that support the claim and (2) methodological details.While employing a supervised approach to train models on task-specific data is a prevailing strategy for both subtasks, such an approach is not feasible for low-resource domains.Therefore, this paper introduces data-free systems supported by Large Language Models.We propose systems based on GPT-4o and GPT-4-turbo for each task.The experimental results reveal the zero-shot capabilities of GPT-4* in both tasks.

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bib (full) Proceedings of the 10th SIGHAN Workshop on Chinese Language Processing (SIGHAN-10)

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Automatic Quote Attribution in Chinese Literary Works
Xingxing Yang | Yu Wang

Quote attribution in fiction refers to the extraction of dialogues and speaker identification of dialogues, which can be divided into 2 steps: quotation annotation and speaker annotation. We use a pipeline for quote attribution, which involves classification, extractive QA, multi-choice QA, and coreference resolution. We also had an evaluation of our model performance by predicting explicit and implicit speakers using a combination of different models.

In this paper, we present TeleChat, a collection of large language models (LLMs) with parameters of 7 billion and 12 billion. TeleChat is initially pretrained on an extensive corpus containing a diverse collection of texts from both English and Chinese languages, encompassing trillions of tokens. Subsequently, the model undergoes fine-tuning to align with human preferences, following a detailed methodology that we describe. We evaluate the performance of TeleChat on various tasks, including general dialogue generation, language understanding, mathematics, reasoning, code generation, and knowledge-based question answering. Our findings indicate that TeleChat achieves state-of-the-art performance to other open-source models of similar size across a wide range of public benchmarks. To support future research and applications utilizing LLMs, we release the fine-tuned model checkpoints of TeleChat-7B and TeleChat-12B, along with code and a portion of our filtered high-quality pretraining data, to the public community.

According to the internationally recognized PIRLS (Progress in International Reading Literacy Study) assessment standards, reading comprehension questions should require not only information retrieval, but also higher-order processes such as inferencing, interpreting and evaluation. However, these kinds of questions are often not available in large quantities for training question generation models. This paper investigates whether pre-trained Large Language Models (LLMs) can produce higher-order questions. Human assessment on a Chinese dataset shows that few-shot LLM prompting generates more usable and higher-order questions than two competitive neural baselines.

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Adversarial Learning for Multi-Lingual Entity Linking
Bingbing Wang | Bin Liang | Zhixin Bai | Yongzhuo Ma

Entity linking aims to identify mentions from the text and link them to a knowledge base. Further, Multi-lingual Entity Linking (MEL) is a more challenging task, where the language-specific mentions need to be linked to a multi-lingual knowledge base. To tackle the MEL task, we propose a novel model that employs the merit of adversarial learning and few-shot learning to generalize the learning ability across languages. Specifically, we first randomly select a fraction of language-agnostic unlabeled data as the language signal to construct the language discriminator. Based on it, we devise a simple and effective adversarial learning framework with two characteristic branches, including an entity classifier and a language discriminator with adversarial training. Experimental results on two benchmark datasets indicate the excellent performance in few-shot learning and the effectiveness of the proposed adversarial learning framework.

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Incremental pre-training from smaller language models
Han Zhang | Hui Wang | Ruifeng Xu

Large language models have recently become a new learning paradigm and led to state-of-the-art performance across a range of tasks. As explosive open-source pre-trained models are available, it is worth investigating how to better utilize existing models. We propose a simple yet effective method, Incr-Pretrain, for incrementally pre-training language models from smaller well-trained source models. Different layer-wise transfer strategies were introduced for model augmentation including parameter copying, initial value padding, and model distillation. Experiments on multiple zero-shot learning tasks demonstrate satisfying inference performance upon transferring and promising training efficiency during continuing pre-training. Compared to training from scratch, Incr-Pretrain can save up to half the training time to get a similar testing loss.

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Holistic Exploration on Universal Decompositional Semantic Parsing: Architecture, Data Augmentation, and LLM Paradigm
Hexuan Deng | Xin Zhang | Meishan Zhang | Xuebo Liu | Min Zhang

In this paper, we conduct a holistic exploration of Universal Decompositional Semantic (UDS) parsing, aiming to provide a more efficient and effective solution for semantic parsing and to envision the development prospects after the emergence of large language models (LLMs). To achieve this, we first introduce a cascade model for UDS parsing that decomposes the complex task into semantically appropriate subtasks. Our approach outperforms prior models while significantly reducing inference time. Furthermore, to further exploit the hierarchical and automated annotation process of UDS, we explore the use of syntactic information and pseudo-labels, both of which enhance UDS parsing. Lastly, we investigate ChatGPT’s efficacy in handling the UDS task, highlighting its proficiency in attribute parsing but struggles in relation parsing, revealing that small parsing models still hold research significance. Our code is available at https://github.com/hexuandeng/HExp4UDS.

Vast amount of online conversations are produced on a daily basis, resulting in a pressing need to automatic conversation understanding. As a basis to structure a discussion, we identify the responding relations in the conversation discourse, which link response utterances to their initiations. To figure out who responded to whom, here we explore how the consistency of topic contents and dependency of discourse roles indicate such interactions, whereas most prior work ignore the effects of latent factors underlying word occurrences. We propose a neural model to learn latent topics and discourse in word distributions, and predict pairwise initiation-response links via exploiting topic consistency and discourse dependency. Experimental results on both English and Chinese conversations show that our model significantly outperforms the previous state of the arts.

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Cantonese Natural Language Processing in the Transformers Era
Rong Xiang | Ming Liao | Jing Li

Despite being spoken by a large population of speakers worldwide, Cantonese is under-resourced in terms of the data scale and diversity compared to other major languages. This limitation has excluded it from the current “pre-training and fine-tuning” paradigm that is dominated by Transformer architectures.In this paper, we provide a comprehensive review on the existing resources and methodologies for Cantonese Natural Language Processing, covering the recent progress in language understanding, text generation and development of language models.We finally discuss two aspects of the Cantonese language that could make it potentially challenging even for state-of-the-art architectures: colloquialism and multilinguality.

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Auto-ACE: An Automatic Answer Correctness Evaluation Method for Conversational Question Answering
Zhixin Bai | Bingbing Wang | Bin Liang | Ruifeng Xu

Conversational question answering aims to respond to questions based on relevant contexts and previous question-answer history. Existing studies typically use ground-truth answers in history, leading to the inconsistency between the training and inference phases. However, in real-world scenarios, progress in question answering can only be made using predicted answers. Since not all predicted answers are correct, indiscriminately using all predicted answers for training introduces noise into the model. To tackle these challenges, we propose an automatic answer correctness evaluation method named **Auto-ACE**. Specifically, we first construct an Att-BERT model which employs attention weight to the BERT model, so as to bridge the relation between the current question and the question-answer pair in history. Furthermore, to reduce the interference of the irrelevant information in the predicted answer, A-Scorer, an answer scorer is designed to evaluate the confidence of the predicted answer. We conduct a series of experiments on QuAC and CoQA datasets, and the results demonstrate the effectiveness and practicality of our proposed Auto-ACE framework.

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TMAK-Plus at SIGHAN-2024 dimABSA Task: Multi-Agent Collaboration for Transparent and Rational Sentiment Analysis
Xin Kang | Zhifei Zhang | 周嘉政周嘉政 | Raino.wu@dataarobotics.com Raino.wu@dataarobotics.com | 2020010107@mail.hfut.edu.cn 2020010107@mail.hfut.edu.cn | Kazuyuki Matsumoto

The TMAK-Plus team proposes a Multi-Agent Collaboration (MAC) model for the dimensional Aspect-Based Sentiment Analysis (dimABSA) task at SIGHAN-2024. The MAC model leverages Neuro-Symbolic AI to solve dimABSA transparently and rationally through symbolic message exchanges among generative AI agents. These agents collaborate on aspect detection, opinion detection, aspect classification, and intensity estimation. We created 8 sentiment intensity agents with distinct character traits to mimic diverse sentiment perceptions and average their outputs. The AI agents received clear instructions and 20 training examples to ensure task understanding. Our results suggest that the MAC model is effective in solving the dimABSA task and offers a transparent and rational approach to understanding the solution process.

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YNU-HPCC at SIGHAN-2024 dimABSA Task: Using PLMs with a Joint Learning Strategy for Dimensional Intensity Prediction
Wangzehui@stu.ynu.edu.cn Wangzehui@stu.ynu.edu.cn | You Zhang | Jin Wang | Dan Xu | Xuejie Zhang

The dimensional approach can represent more fine-grained emotional information than discrete affective states. In this paper, a pretrained language model (PLM) with a joint learning strategy is proposed for the SIGHAN-2024 shared task on Chinese dimensional aspect-based sentiment analysis (dimABSA), which requires submitted models to provide fine-grained multi-dimensional (Valance and Arousal) intensity predictions for given aspects of a review. The proposed model consists of three parts: an input layer that concatenates both given aspect terms and input sentences; a Chinese PLM encoder that generates aspect-specific review representation; and separate linear predictors that jointly predict Valence and Arousal sentiment intensities. Moreover, we merge simplified and traditional Chinese training data for data augmentation. Our systems ranked 2nd place out of 5 participants in subtask 1-intensity prediction. The code is publicly available at https://github.com/WZH5127/2024_subtask1_intensity_prediction.

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CCIIPLab at SIGHAN-2024 dimABSA Task: Contrastive Learning-Enhanced Span-based Framework for Chinese Dimensional Aspect-Based Sentiment Analysis
Zeliang Tong | Wei Wei

This paper describes our system and findings for SIGHAN-2024 Shared Task Chinese Dimensional Aspect-Based Sentiment Analysis (dimABSA). Our team CCIIPLab proposes an Contrastive Learning-Enhanced Span-based (CL-Span) framework to boost the performance of extracting triplets/quadruples and predicting sentiment intensity. We first employ a span-based framework that integrates contextual representations and incorporates rotary position embedding. This approach fully considers the relational information of entire aspect and opinion terms, and enhancing the model’s understanding of the associations between tokens. Additionally, we utilize contrastive learning to predict sentiment intensities in the valence-arousal dimensions with greater precision. To improve the generalization ability of the model, additional datasets are used to assist training. Experiments have validated the effectiveness of our approach. In the official test results, our system ranked 2nd among the three subtasks.

The DimABSA task requires fine-grained sentiment intensity prediction for restaurant reviews, including scores for Valence and Arousal dimensions for each Aspect Term. In this study, we propose a Coarse-to-Fine In-context Learning(CFICL) method based on the Baichuan2-7B model for the DimABSA task in the SIGHAN 2024 workshop. Our method improves prediction accuracy through a two-stage optimization process. In the first stage, we use fixed in-context examples and prompt templates to enhance the model’s sentiment recognition capability and provide initial predictions for the test data. In the second stage, we encode the Opinion field using BERT and select the most similar training data as new in-context examples based on similarity. These examples include the Opinion field and its scores, as well as related opinion words and their average scores. By filtering for sentiment polarity, we ensure that the examples are consistent with the test data. Our method significantly improves prediction accuracy and consistency by effectively utilizing training data and optimizing in-context examples, as validated by experimental results.

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JN-NLP at SIGHAN-2024 dimABSA Task: Extraction of Sentiment Intensity Quadruples Based on Paraphrase Generation
Yunfan Jiang | Liutianci@stu.jiangnan.edu.cn Liutianci@stu.jiangnan.edu.cn | Heng-yang Lu

Aspect-based sentiment analysis(ABSA) is a fine-grained sentiment analysis task, which aims to extract multiple specific sentiment elements from text. The current aspect-based sentiment analysis task mainly involves four basic elements: aspect term, aspect category, opinion term, and sentiment polarity. With the development of ABSA, methods for predicting the four sentiment elements are gradually increasing. However, traditional ABSA usually only distinguishes between “positive”, “negative”, or “neutral”attitudes when judging sentiment polarity, and this simplified classification method makes it difficult to highlight the sentimentintensity of different reviews. SIGHAN 2024 provides a more challenging evaluation task, the Chinese dimensional ABSA shared task (dimABSA), which replaces the traditional sentiment polarity judgment task with a dataset in a multidimensional space with continuous sentiment intensity scores, including valence and arousal. Continuous sentiment intensity scores can obtain more detailed emotional information. In this task, we propose a new paraphrase generation paradigm that uses generative questioning in an end-to-end manner to predict sentiment intensity quadruples, which can fully utilize semantic information and reduce propagation errors in the pipeline approach.

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DS-Group at SIGHAN-2024 dimABSA Task: Constructing In-context Learning Structure for Dimensional Aspect-Based Sentiment Analysis
Ling-ang Meng | Tianyu Zhao | Dawei Song

Aspect-Based Sentiment Analysis (ABSA) is an important subtask in Natural Language Processing (NLP). More recent research within ABSA have consistently focused on conducting more precise sentiment analysis on aspects, i.e., dimensional Aspect-Based Sentiment Analysis (dimABSA). However, previous approaches have not systematically explored the use of Large Language Models (LLMs) in dimABSA. To fill the gap, we propose a novel In-Context Learning (ICL) structure with a novel aspect-aware ICL example selection method, to enhance the performance of LLMs in dimABSA. Experiments show that our proposed ICL structure significantly improves the fine-grained sentiment analysis abilities of LLMs.

Prompting is an alternative approach for utilizing pre-trained language models (PLMs) in classification tasks. In contrast to fine-tuning, prompting is more understandable for humans because it utilizes natural language to interact with the PLM, but it often falls short in terms of accuracy. While current research primarily focuses on enhancing the performance of prompting methods to compete with fine-tuning, we believe that these two approaches are not mutually exclusive, each having its strengths and weaknesses. In our study, we depart from the competitive view of prompting versus fine-tuning and instead combine them, introducing a novel method called F&P. This approach enables us to harness the advantages of Fine-tuning for accuracy and the explainability of Prompting simultaneously. Specifically, we reformulate the sample into a prompt and subsequently fine-tune a linear classifier on top of the PLM. Following this, we extract verbalizers according to the weight of this classifier. During the inference phase, we reformulate the sample in the same way and query the PLM. The PLM generates a word, which is then subject to a dictionary lookup by the verbalizer to obtain the prediction. Experiments show that keeping only 30 keywords for each class can achieve comparable performance as fine-tuning. On the other hand, both the prompt and verbalizers are constructed in natural language, making them fully understandable to humans. Hence, the F&P method offers an effective and transparent way to employ a PLM for classification tasks.

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CausalBench: A Comprehensive Benchmark for Evaluating Causal Reasoning Capabilities of Large Language Models
Zeyu Wang

Causal reasoning, a core aspect of human cognition, is essential for advancing large language models (LLMs) towards artificial general intelligence (AGI) and reducing their propensity for generating hallucinations. However, existing datasets for evaluating causal reasoning in LLMs are limited by narrow domain coverage and a focus on cause-to-effect reasoning through textual problems, which does not comprehensively assess whether LLMs truly grasp causal relationships or merely guess correct answers. To address these shortcomings, we introduce a novel benchmark that spans textual, mathematical, and coding problem domains. Each problem is crafted to probe causal understanding from four perspectives: cause-to-effect, effect-to-cause, cause-to-effect with intervention, and effect-to-cause with intervention. This multi-dimensional evaluation method ensures that LLMs must exhibit a genuine understanding of causal structures by correctly answering questions across all four dimensions, mitigating the possibility of correct responses by chance. Furthermore, our benchmark explores the relationship between an LLM’s causal reasoning performance and its tendency to produce hallucinations. We present evaluations of state-of-the-art LLMs using our benchmark, providing valuable insights into their current causal reasoning capabilities across diverse domains. The dataset is publicly available for download at https://huggingface.co/datasets/CCLV/CausalBench

In conversational AI, effectively employing long-term memory improves personalized and consistent response generation. Existing work only concentrated on a single type of long-term memory, such as preferences, dialogue history, or social relationships, overlooking their interaction in real-world contexts. To this end, inspired by the concept of semantic memory and episodic memory from cognitive psychology, we create a new and more comprehensive Chinese dataset, coined as PerLTQA, in which world knowledge, profiles, social relationships, events, and dialogues are considered to leverage the interaction between different types of long-term memory for question answering (QA) in conversation. Further, based on PerLTQA, we propose a novel framework for memory integration in QA, consisting of three subtasks: Memory Classification, Memory Retrieval, and Memory Fusion, which provides a comprehensive paradigm for memory modeling, enabling consistent and personalized memory utilization. This essentially allows the exploitation of more accurate memory information for better responses in QA. We evaluate this framework using five LLMs and three retrievers. Experimental results demonstrate the importance of personal long-term memory in the QA task

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Overview of the SIGHAN 2024 shared task for Chinese dimensional aspect-based sentiment analysis
Lung-Hao Lee | Liang-Chih Yu | Suge Wang | Jian Liao

This paper describes the SIGHAN-2024 shared task for Chinese dimensional aspect-based sentiment analysis (ABSA), including task description, data preparation, performance metrics, and evaluation results. Compared to representing affective states as several discrete classes (i.e., sentiment polarity), the dimensional approach represents affective states as continuous numerical values (called sentiment intensity) in the valence-arousal space, providing more fine-grained affective states. Therefore, we organized a dimensional ABSA (shorted dimABSA) shared task, comprising three subtasks: 1) intensity prediction, 2) triplet extraction, and 3) quadruple extraction, receiving a total of 214 submissions from 61 registered participants during evaluation phase. A total of eleven teams provided selected submissions for each subtask and seven teams submitted technical reports for the subtasks. This shared task demonstrates current NLP techniques for dealing with Chinese dimensional ABSA. All data sets with gold standards and evaluation scripts used in this shared task are publicly available for future research.

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HITSZ-HLT at SIGHAN-2024 dimABSA Task: Integrating BERT and LLM for Chinese Dimensional Aspect-Based Sentiment Analysis
Hongling Xu | Delong Zhang | Yice Zhang | Ruifeng Xu

This paper presents the winning system participating in the ACL 2024 workshop SIGHAN-10 shared task: Chinese dimensional aspect-based sentiment analysis (dimABSA). This task aims to identify four sentiment elements in restaurant reviews: aspect, category, opinion, and sentiment intensity evaluated in valence-arousal dimensions, providing a concise yet fine-grained sentiment description for user opinions. To tackle this task, we introduce a system that integrates BERT and large language models (LLM) to leverage their strengths. First, we explore their performance in entity extraction, relation classification, and intensity prediction. Based on preliminary experiments, we develop an integrated approach to fully utilize their advantages in different scenarios. Our system achieves first place in all subtasks and obtains a 41.7% F1-score in quadruple extraction.

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bib (full) Proceedings of the First Workshop on Natural Language Processing for Turkic Languages (SIGTURK 2024)

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Unsupervised Learning of Turkish Morphology with Multiple Codebook VQ-VAE
Müge Kural | Deniz Yuret

This paper presents an interpretable unsupervised morphological learning model, showing comparable performance to supervised models in learning complex morphological rules of Turkish as evidenced by its application to the problem of morphological inflection within the SIGMORPHON Shared Tasks. The significance of our unsupervised approach lies in its alignment with how humans naturally acquire rules from raw data without supervision. To achieve this, we construct a model with multiple codebooks of VQ-VAE employing continuous and discrete latent variables during word generation. We evaluate the model’s performance under high and low-resource scenarios, and use probing techniques to examine encoded information in latent representations. We also evaluate its generalization capabilities by testing unseen suffixation scenarios within the SIGMORPHON-UniMorph 2022 Shared Task 0. Our results demonstrate our model’s ability to distinguish word structures into lemmas and suffixes, with each codebook specialized for different morphological features, contributing to the interpretability of our model and effectively performing morphological inflection on both seen and unseen morphological features.

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Open foundation models for Azerbaijani language
Jafar Isbarov | Kavsar Huseynova | Elvin Mammadov | Mammad Hajili | Duygu Ataman

The emergence of multilingual large language models has enabled the development of language understanding and generation systems in Azerbaijani. However, most of the production-grade systems rely on cloud solutions, such as GPT-4. While there have been several attempts to develop open foundation models for Azerbaijani, these works have not found their way into common use due to a lack of systemic benchmarking. This paper encompasses several lines of work that promote open-source foundation models for Azerbaijani. We introduce (1) a large text corpus for Azerbaijani, (2) a family of encoder-only language models trained on this dataset, (3) labeled datasets for evaluating these models, and (4) extensive evaluation that covers all major open-source models with Azerbaijani support.

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ImplicaTR: A Granular Dataset for Natural Language Inference and Pragmatic Reasoning in Turkish
Mustafa Halat | Ümit Atlamaz

We introduce ImplicaTR, a linguistically informed diagnostic dataset designed to evaluate semantic and pragmatic reasoning capabilities of Natural Language Inference (NLI) models in Turkish. Existing Turkish NLI datasets treat NLI as determining whether a sentence pair represents entailment, contradiction, or a neutral relation. Such datasets do not distinguish between semantic entailment and pragmatic implicature, which linguists have long recognized as separate inferences types. ImplicaTR addresses this by testing NLI models’ ability to differentiate between entailment and implicature, thus assessing their pragmatic reasoning skills. The dataset consists of 19,350 semi-automatically generated sentence pairs covering implicature, entailment, contradiction, and neutral relations. We evaluated various models (BERT, Gemma, Llama-2, and Mistral) on ImplicaTR and found out that these models can reach up to 98% accuracy on semantic and pragmatic reasoning. We also fine tuned various models on subsets of ImplicaTR to test the abilities of NLI models to generalize across unseen implicature contexts. Our results indicate that model performance is highly dependent on the diversity of linguistic expressions within each subset, highlighting a weakness in the abstract generalization capabilities of large language models regarding pragmatic reasoning. We share all the code, models, and the dataset.

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A coreference corpus of Turkish situated dialogs
Faruk Büyüktekin | Umut Özge

The paper introduces a publicly available corpus of Turkish situated dialogs annotated for coreference. We developed an annotation scheme for coreference annotation in Turkish, a language with pro-drop and rich agglutinating morphology. The annotation scheme is tailored for these aspects of the language, making it potentially applicable to similar languages. The corpus comprises 60 dialogs containing in total 3900 sentences, 18360 words, and 6120 mentions.

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Do LLMs Recognize me, When I is not me: Assessment of LLMs Understanding of Turkish Indexical Pronouns in Indexical Shift Contexts
Metehan Oğuz | Yusuf Ciftci | Yavuz Faruk Bakman

Large language models (LLMs) have shown impressive capabilities in tasks such as machine translation, text summarization, question answering, and solving complex mathematical problems. However, their primary training on data-rich languages like English limits their performance in low-resource languages. This study addresses this gap by focusing on the Indexical Shift problem in Turkish. The Indexical Shift problem involves resolving pronouns in indexical shift contexts, a grammatical challenge not present in high-resource languages like English. We present the first study examining indexical shift in any language, releasing a Turkish dataset specifically designed for this purpose. Our Indexical Shift Dataset consists of 156 multiple-choice questions, each annotated with necessary linguistic details, to evaluate LLMs in a few-shot setting. We evaluate recent multilingual LLMs, including GPT-4, GPT-3.5, Cohere-AYA, Trendyol-LLM, and Turkcell-LLM, using this dataset. Our analysis reveals that even advanced models like GPT-4 struggle with the grammatical nuances of indexical shift in Turkish, achieving only moderate performance. These findings underscore the need for focused research on the grammatical challenges posed by low-resource languages. We released the dataset and code here.

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Towards a Clean Text Corpus for Ottoman Turkish
Fatih Karagöz | Berat Doğan | Şaziye Betül Özateş

Ottoman Turkish, as a historical variant of modern Turkish, suffers from a scarcity of available corpora and NLP models. This paper outlines our pioneering endeavors to address this gap by constructing a clean text corpus of Ottoman Turkish materials. We detail the challenges encountered in this process and offer potential solutions. Additionally, we present a case study wherein the created corpus is employed in continual pre-training of BERTurk, followed by evaluation of the model’s performance on the named entity recognition task for Ottoman Turkish. Preliminary experimental results suggest the effectiveness of our corpus in adapting existing models developed for modern Turkish to historical Turkish.

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Turkish Delights: a Dataset on Turkish Euphemisms
Hasan Biyik | Patrick Lee | Anna Feldman

Euphemisms are a form of figurative language relatively understudied in natural language processing. This research extends the current computational work on potentially euphemistic terms (PETs) to Turkish. We introduce the Turkish PET dataset, the first available of its kind in the field. By creating a list of euphemisms in Turkish, collecting example contexts, and annotating them, we provide both euphemistic and non-euphemistic examples of PETs in Turkish. We describe the dataset and methodologies, and also experiment with transformer-based models on Turkish euphemism detection by using our dataset for binary classification. We compare performances across models using F1, accuracy, and precision as evaluation metrics.

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Do LLMs Speak Kazakh? A Pilot Evaluation of Seven Models
Akylbek Maxutov | Ayan Myrzakhmet | Pavel Braslavski

We conducted a systematic evaluation of seven large language models (LLMs) on tasks in Kazakh, a Turkic language spoken by approximately 13 million native speakers in Kazakhstan and abroad. We used six datasets corresponding to different tasks – questions answering, causal reasoning, middle school math problems, machine translation, and spelling correction. Three of the datasets were prepared for this study. As expected, the quality of the LLMs on the Kazakh tasks is lower than on the parallel English tasks. GPT-4 shows the best results, followed by Gemini and . In general, LLMs perform better on classification tasks and struggle with generative tasks. Our results provide valuable insights into the applicability of currently available LLMs for Kazakh. We made the data collected for this study publicly available: https://github.com/akylbekmaxutov/LLM-eval-using-Kazakh.

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Intelligent Tutor to Support Teaching and Learning of Tatar
Alsu Zakirova | Jue Hou | Anisia Katinskaia | Anh-Duc Vu | Roman Yangarber

This paper presents our work on tools to support the Tatar language, using Revita, a web-based Intelligent Tutoring System for language teaching and learning. The system allows the users — teachers and learners — to upload arbitrary authentic texts, and automatically creates exercises based on these texts that engage the learners in active production of language. It provides graduated feedback when they make mistakes, and performs continuous assessment, based on which the system selects exercises for the learners at the appropriate level. The assessment also helps the students maintain their learning pace, and helps the teachers to monitor their progress.The paper describes the functionality currently implemented for Tatar, which enables learners — who possess basic proficiency beyond the beginner level — to improve their competency, using texts of their choice as learning content. Support for Tatar is being developed to increase public interest in learning the language of this important regional minority, as well as to to provide tools for improving fluency to “heritage speakers” — those who have substantial passive competency, but lack active fluency and need support for regular practice.

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bib (full) Proceedings of The 9th Social Media Mining for Health Research and Applications (SMM4H 2024) Workshop and Shared Tasks

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Proceedings of The 9th Social Media Mining for Health Research and Applications (SMM4H 2024) Workshop and Shared Tasks
Dongfang Xu | Graciela Gonzalez-Hernandez

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ThangDLU at #SMM4H 2024: Encoder-decoder models for classifying text data on social disorders in children and adolescents
Thang Ta | Abu Rahman | Lotfollah Najjar | Alexander Gelbukh

This paper describes our participation in Task 3 and Task 5 of the #SMM4H (Social Media Mining for Health) 2024 Workshop, explicitly targeting the classification challenges within tweet data. Task 3 is a multi-class classification task centered on tweets discussing the impact of outdoor environments on symptoms of social anxiety. Task 5 involves a binary classification task focusing on tweets reporting medical disorders in children. We applied transfer learning from pre-trained encoder-decoder models such as BART-base and T5-small to identify the labels of a set of given tweets. We also presented some data augmentation methods to see their impact on the model performance. Finally, the systems obtained the best F1 score of 0.627 in Task 3 and the best F1 score of 0.841 in Task 5

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CTYUN-AI@SMM4H-2024: Knowledge Extension Makes Expert Models
Yuming Fan | Dongming Yang | Lina Cao

This paper explores the potential of social media as a rich source of data for understanding public health trends and behaviors, particularly focusing on emotional well-being and the impact of environmental factors. We employed large language models (LLMs) and developed a suite of knowledge extension techniques to analyze social media content related to mental health issues, specifically examining 1) effects of outdoor spaces on social anxiety symptoms in Reddit,2) tweets reporting children’s medical disorders, and 3) self-reported ages in posts of Twitter and Reddit. Our knowledge extension approach encompasses both supervised data (i.e., sample augmentation and cross-task fine-tuning) and unsupervised data (i.e., knowledge distillation and cross-task pre-training), tackling the inherent challenges of sample imbalance and informality of social media language. The effectiveness of our approach is demonstrated by the superior performance across multiple tasks (i.e., Task 3, 5 and 6) at the SMM4H-2024. Notably, we achieved the best performance in all three tasks, underscoring the utility of our models in real-world applications.

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DILAB at #SMM4H 2024: RoBERTa Ensemble for Identifying Children’s Medical Disorders in English Tweets
Azmine Toushik Wasi | Sheikh Rahman

This paper details our system developed for the 9th Social Media Mining for Health Research and Applications Workshop (SMM4H 2024), addressing Task 5 focused on binary classification of English tweets reporting children’s medical disorders. Our objective was to enhance the detection of tweets related to children’s medical issues. To do this, we use various pre-trained language models, like RoBERTa and BERT. We fine-tuned these models on the task-specific dataset, adjusting model layers and hyperparameters in an attempt to optimize performance. As we observe unstable fluctuations in performance metrics during training, we implement an ensemble approach that combines predictions from different learning epochs. Our model achieves promising results, with the best-performing configuration achieving F1 score of 93.8% on the validation set and 89.8% on the test set.

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DILAB at #SMM4H 2024: Analyzing Social Anxiety Effects through Context-Aware Transfer Learning on Reddit Data
Sheikh Rahman | Azmine Toushik Wasi

This paper illustrates the system we design for Task 3 of the 9th Social Media Mining for Health (SMM4H 2024) shared tasks. The task presents posts made on the Reddit social media platform, specifically the *r/SocialAnxiety* subreddit, along with one or more outdoor activities as pre-determined keywords for each post. The task then requires each post to be categorized as either one of *positive*, *negative*, *no effect*, or *not outdoor activity* based on what effect the keyword(s) have on social anxiety. Our approach focuses on fine-tuning pre-trained language models to classify the posts. Additionally, we use fuzzy string matching to select only the text around the given keywords so that the model only has to focus on the contextual sentiment associated with the keywords. Using this system, our peak score is 0.65 macro-F1 on the validation set and 0.654 on test set.

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Dolomites@#SMM4H 2024: Helping LLMs “Know The Drill” in Low-Resource Settings - A Study on Social Media Posts
Giuliano Tortoreto | Seyed Mahed Mousavi

The amount of data to fine-tune LLMs plays a crucial role in the performance of these models in downstream tasks. Consequently, it is not straightforward to deploy these models in low-resource settings. In this work, we investigate two new multi-task learning data augmentation approaches for fine-tuning LLMs when little data is available: “In-domain Augmentation” of the training data and extracting “Drills” as smaller tasks from the target dataset. We evaluate the proposed approaches in three natural language processing settings in the context of SMM4H 2024 competition tasks: multi-class classification, entity recognition, and information extraction. The results show that both techniques improve the performance of the models in all three settings, suggesting a positive impact from the knowledge learned in multi-task training to perform the target task.

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RIGA at SMM4H-2024 Task 1: Enhancing ADE discovery with GPT-4
Eduards Mukans | Guntis Barzdins

The following is a description of the RIGA team’s submissions for the SMM4H-2024 Task 1: Extraction and normalization of adverse drug events (ADEs) in English tweets. Our approach focuses on utilizing Large Language Models (LLMs) to generate data that enhances the fine-tuning of classification and Named Entity Recognition (NER) models. Our solution significantly outperforms mean and median submissions of other teams. The efficacy of our ADE extraction from tweets is comparable to the current state-of-the-art solution, established as the task baseline. The code for our method is available on GitHub (https://github.com/emukans/smm4h2024-riga)

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Golden_Duck at #SMM4H 2024: A Transformer-based Approach to Social Media Text Classification
Md Ayon Mia | Mahshar Yahan | Hasan Murad | Muhammad Khan

In this paper, we have addressed Task 3 on social anxiety disorder identification and Task 5 on mental illness recognition organized by the SMM4H 2024 workshop. In Task 3, a multi-classification problem has been presented to classify Reddit posts about outdoor spaces into four categories: Positive, Neutral, Negative, or Unrelated. Using the pre-trained RoBERTa-base model along with techniques like Mean pooling, CLS, and Attention Head, we have scored an F1-Score of 0.596 on the test dataset for Task 3. Task 5 aims to classify tweets into two categories: those describing a child with conditions like ADHD, ASD, delayed speech, or asthma (class 1), and those merely mentioning a disorder (class 0). Using the pre-trained RoBERTa-large model, incorporating a weighted ensemble of the last 4 hidden layers through concatenation and mean pooling, we achieved an F1 Score of 0.928 on the test data for Task 5.

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SRCB at #SMM4H 2024: Making Full Use of LLM-based Data Augmentation in Adverse Drug Event Extraction and Normalization
Hongyu Li | Yuming Zhang | Yongwei Zhang | Shanshan Jiang | Bin Dong

This paper reports on the performance of SRCB’s system in the Social Media Mining for Health (#SMM4H) 2024 Shared Task 1: extraction and normalization of adverse drug events (ADEs) in English tweets. We develop a system composed of an ADE extraction module and an ADE normalization module which furtherly includes a retrieval module and a filtering module. To alleviate the data imbalance and other issues introduced by the dataset, we employ 4 data augmentation techniques based on Large Language Models (LLMs) across both modules. Our best submission achieves an F1 score of 53.6 (49.4 on the unseen subset) on the ADE normalization task and an F1 score of 52.1 on ADE extraction task.

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LT4SG@SMM4H’24: Tweets Classification for Digital Epidemiology of Childhood Health Outcomes Using Pre-Trained Language Models
Dasun Athukoralage | Thushari Atapattu | Menasha Thilakaratne | Katrina Falkner

This paper presents our approaches for the SMM4H’24 Shared Task 5 on the binary classification of English tweets reporting children’s medical disorders. Our first approach involves fine-tuning a single RoBERTa-large model, while the second approach entails ensembling the results of three fine-tuned BERTweet-large models. We demonstrate that although both approaches exhibit identical performance on validation data, the BERTweet-large ensemble excels on test data. Our best-performing system achieves an F1-score of 0.938 on test data, outperforming the benchmark classifier by 1.18%.

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UTRad-NLP at #SMM4H 2024: Why LLM-Generated Texts Fail to Improve Text Classification Models
Yosuke Yamagishi | Yuta Nakamura

In this paper, we present our approach to addressing the binary classification tasks, Tasks 5 and 6, as part of the Social Media Mining for Health (SMM4H) text classification challenge. Both tasks involved working with imbalanced datasets that featured a scarcity of positive examples. To mitigate this imbalance, we employed a Large Language Model to generate synthetic texts with positive labels, aiming to augment the training data for our text classification models. Unfortunately, this method did not significantly improve model performance. Through clustering analysis using text embeddings, we discovered that the generated texts significantly lacked diversity compared to the raw data. This finding highlights the challenges of using synthetic text generation for enhancing model efficacy in real-world applications, specifically in the context of health-related social media data.

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HBUT at #SMM4H 2024 Task1: Extraction and Normalization of Adverse Drug Events with a Large Language Model
Yuanzhi Ke | Hanbo Jin | Xinyun Wu | Caiquan Xiong

In this paper, we describe our proposed systems for the Social Media Mining for Health 2024 shared task 1. We built our system on the basis of GLM, a pre-trained large language model with few-shot Learning capabilities, using a two-step prompting strategy to extract adverse drug event (ADE) and an ensemble method for normalization. In first step of extraction phase, we extract all the potential ADEs with in-context few-shot learning. In the second step for extraction, we let GLM to filer out false positive outputs in the first step by a tailored prompt. Then we normalize each ADE to its MedDRA preferred term ID (ptID) by an ensemble method using Reciprocal Rank Fusion (RRF). Our method achieved excellent recall rate. It obtained 41.1%, 42.8%, and 40.6% recall rate for ADE normalization, ADE recognition, and normalization for unseen ADEs, respectively. Compared to the performance of the average and median among all the participants in terms of recall rate, our recall rate scores are generally 10%-20% higher than the other participants’ systems.

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SMM4H 2024: 5 Fold Cross Validation for Classification of tweets reporting children’s disorders
Lipika Dey | B Naik | Oppangi Poojita | Kovidh Pothireddi

This paper presents our system developed for the Social Media Mining for Health (SMM4H) 2024 Task 05. The task objective was binary classification of tweets provided in the dataset, distinguishing between those reporting medical disorders and those merely mentioning diseases. We address this challenge through the utilization of a 5-fold cross-validation approach, employing the RoBERTa-Large model. Evaluation results demonstrate an F1-score of 0.886 on the validation dataset and 0.823 on the test dataset.

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HBUT at #SMM4H 2024 Task2: Cross-lingual Few-shot Medical Entity Extraction using a Large Language Model
Yuanzhi Ke | Zhangju Yin | Xinyun Wu | Caiquan Xiong

Named entity recognition (NER) of drug and disorder/body function mentions in web text is challenging in the face of multilingualism, limited data, and poor data quality. Traditional small-scale models struggle to cope with the task. Large language models with conventional prompts also yield poor results. In this paper, we introduce our system, which employs a large language model (LLM) with a novel two-step prompting strategy. Instead of directly extracting the target medical entities, our system firstly extract all entities and then prompt the LLM to extract drug and disorder entities given the all-entity list and original input text as the context. The experimental and test results indicate that this strategy successfully enhanced our system performance, especially for German language.

We present our approach to solving the task of identifying the effect of outdoor activities on social anxiety based on reddit posts. We employed state-of-the-art transformer models enhanced with a combination of advanced loss functions. Data augmentation techniques were also used to address class imbalance within the training set. Our method achieved a macro-averaged F1-score of 0.655 on the test data, surpassing the workshop’s mean F1-Score of 0.519. These findings suggest that integrating weighted loss functions improves the performance of transformer models in classifying unbalanced text data, while data augmentation can improve the model’s ability to generalize.

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Transformers at #SMM4H 2024: Identification of Tweets Reporting Children’s Medical Disorders And Effects of Outdoor Spaces on Social Anxiety Symptoms on Reddit Using RoBERTa
Kriti Singhal | Jatin Bedi

With the widespread increase in the use of social media platforms such as Twitter, Instagram, and Reddit, people are sharing their views on various topics. They have become more vocal on these platforms about their views and opinions on the medical challenges they are facing. This data is a valuable asset of medical insights in the study and research of healthcare. This paper describes our adoption of transformer-based approaches for tasks 3 and 5. For both tasks, we fine-tuned large RoBERTa, a BERT-based architecture, and achieved a highest F1 score of 0.413 and 0.900 in tasks 3 and 5, respectively.

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Enhancing Social Media Health Prediction Certainty by Integrating Large Language Models with Transformer Classifiers
Sedigh Khademi | Christopher Palmer | Muhammad Javed | Jim Buttery | Gerardo Dimaguila

This paper presents our approach for SMM4H 2024 Task 5, focusing on identifying tweets where users discuss their child’s health conditions of ADHD, ASD, delayed speech, or asthma. Our approach uses a pipeline that combines transformer-based classifiers and GPT-4 large language models (LLMs). We first address data imbalance in the training set using topic modelling and under-sampling. Next, we train RoBERTa-based classifiers on the adjusted data. Finally, GPT-4 refines the classifier’s predictions for uncertain cases (confidence below 0.9). This strategy achieved significant improvement over the baseline RoBERTa models. Our work demonstrates the effectiveness of combining transformer classifiers and LLMs for extracting health insights from social media conversations.

This is the demonstration of systems and results of our team’s participation in the Social Medical Mining for Health (SMM4H) 2024 Shared Task. Our team participated in two tasks: Task 1 and Task 5. Task 5 requires the detection of tweet sentences that claim children’s medical disorders from certain users. Task 1 needs teams to extract and normalize Adverse Drug Event terms in the tweet sentence. The team selected several Pre-trained Language Models and generative Large Language Models to meet the requirements. Strategies to improve the performance include cloze test, prompt engineering, Low Rank Adaptation etc. The test result of our system has an F1 score of 0.935, Precision of 0.954 and Recall of 0.917 in Task 5 and an overall F1 score of 0.08 in Task 1.

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Deloitte at #SMM4H 2024: Can GPT-4 Detect COVID-19 Tweets Annotated by Itself?
Harika Abburi | Nirmala Pudota | Balaji Veeramani | Edward Bowen | Sanmitra Bhattacharya

The advent of Large Language Models (LLMs) such as Generative Pre-trained Transformers (GPT-4) mark a transformative era in Natural Language Generation (NLG). These models demonstrate the ability to generate coherent text that closely resembles human-authored content. They are easily accessible and have become invaluable tools in handling various text-based tasks, such as data annotation, report generation, and question answering. In this paper, we investigate GPT-4’s ability to discern between data it has annotated and data annotated by humans, specifically within the context of tweets in the medical domain. Through experimental analysis, we observe GPT-4 outperform other state-of-the-art models. The dataset used in this study was provided by the SMM4H (Social Media Mining for Health Research and Applications) shared task. Our model achieved an accuracy of 0.51, securing a second rank in the shared task.

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IMS_medicALY at #SMM4H 2024: Detecting Impacts of Outdoor Spaces on Social Anxiety with Data Augmented Ensembling
Amelie Wuehrl | Lynn Greschner | Yarik Menchaca Resendiz | Roman Klinger

Many individuals affected by Social Anxiety Disorder turn to social media platforms to share their experiences and seek advice. This includes discussing the potential benefits of engaging with outdoor environments. As part of #SMM4H 2024, Shared Task 3 focuses on classifying the effects of outdoor spaces on social anxiety symptoms in Reddit posts. In our contribution to the task, we explore the effectiveness of domain-specific models (trained on social media data – SocBERT) against general domain models (trained on diverse datasets – BERT, RoBERTa, GPT-3.5) in predicting the sentiment related to outdoor spaces. Further, we assess the benefits of augmenting sparse human-labeled data with synthetic training instances and evaluate the complementary strengths of domain-specific and general classifiers using an ensemble model. Our results show that (1) fine-tuning small, domain-specific models generally outperforms large general language models in most cases. Only one large language model (GPT-4) exhibits performance comparable to the fine-tuned models (52% F1). Further, we find that (2) synthetic data does improve the performance of fine-tuned models in some cases, and (3) models do not appear to complement each other in our ensemble setup.

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1024m at SMM4H 2024: Tasks 3, 5 & 6 - Self Reported Health Text Classification through Ensembles
Ram Kadiyala | M.v.p. Rao

Social media is a great source of data for users reporting information and regarding their health and how various things have had an effect on them. This paper presents various approaches using Transformers and Large Language Models and their ensembles, their performance along with advantages and drawbacks for various tasks of SMM4H’24 - Classifying texts on impact of nature and outdoor spaces on the author’s mental health (Task 3), Binary classification of tweets reporting their children’s health disorders like Asthma, Autism, ADHD and Speech disorder (task 5), Binary classification of users self-reporting their age (task 6).

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Experimenting with Transformer-based and Large Language Models for Classifying Effects of Outdoor Spaces on Social Anxiety in Social Media Data
Falwah Alhamed | Julia Ive | Lucia Specia

Social Anxiety Disorder (SAD) is a common condition, affecting a significant portion of the population. While research suggests spending time in nature can alleviate anxiety, the specific impact on SAD remains unclear. This study explores the relationship between discussions of outdoor spaces and social anxiety on social media. We leverage transformer-based and large language models (LLMs) to analyze a social media dataset focused on SAD. We developed three methods for the task of predicting the effects of outdoor spaces on SAD in social media. A two-stage pipeline classifier achieved the best performance of our submissions with results exceeding baseline performance.

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interrupt-driven@SMM4H’24: Relevance-weighted Sentiment Analysis of Reddit Posts
Jessica Elliott | Roland Elliott

This paper describes our approach to Task 3 of the Social Media Mining for Health 2024 (SMM4H’24) shared tasks. The objective of the task was to classify the sentiment of social media posts, taken from the social anxiety subreddit, with reference to the outdoors, as positive, negative, neutral, or unrelated. We classified posts using a relevance-weighted sentiment analysis, which scored poorly, at 0.45 accuracy on the test set and 0.396 accuracy on the evaluation set. We consider what factors contributed to these low scores, and what alternatives could yield improvements, namely: improved data cleaning, a sentiment analyzer trained on a more suitable data set, improved sentiment heuristics, and a more involved relevance-weighting.

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IITRoorkee@SMM4H 2024 Cross-Platform Age Detection in Twitter and Reddit Using Transformer-Based Model
Thadavarthi Sankar | Dudekula Suraj | Mallamgari Reddy | Durga Toshniwal | Amit Agarwal

This paper outlines the methodology for the automatic extraction of self-reported ages from social media posts as part of the Social Media Mining for Health (SMM4H) 2024 Workshop Shared Tasks. The focus was on Task 6: “Self-reported exact age classification with cross-platform evaluation in English.” The goal was to accurately identify age-related information from user-generated content, which is crucial for applications in public health monitoring, targeted advertising, and demographic research. A number of transformer-based models were employed, including RoBERTa-Base, BERT-Base, BiLSTM, and Flan T5 Base, leveraging their advanced capabilities in natural language understanding. The training strategies included fine-tuning foundational pre-trained language models and evaluating model performance using standard metrics: F1-score, Precision, and Recall. The experimental results demonstrated that the RoBERTa-Base model significantly outperformed the other models in this classification task. The best results achieved with the RoBERTa-Base model were an F1-score of 0.878, a Precision of 0.899, and a Recall of 0.858.

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SMM4H’24 Task6 : Extracting Self-Reported Age with LLM and BERTweet: Fine-Grained Approaches for Social Media Text
Jaskaran Singh | Jatin Bedi | Maninder Kaur

The paper presents two distinct approaches to Task 6 of the SMM4H’24 workshop: extracting self-reported exact age information from social media posts across platforms. This research task focuses on developing methods for automatically extracting self-reported ages from posts on two prominent social media platforms: Twitter (now X) and Reddit. The work leverages two ways, one Mistral-7B-Instruct-v0.2 Large Language Model (LLM) and another pre-trained language model BERTweet, to achieve robust and generalizable age classification, surpassing limitations of existing methods that rely on predefined age groups. The proposed models aim to advance the automatic extraction of self-reported exact ages from social media posts, enabling more nuanced analyses and insights into user demographics across different platforms.

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AAST-NLP@#SMM4H’24: Finetuning Language Models for Exact Age Classification and Effect of Outdoor Spaces on Social Anxiety
Ahmed El-Sayed | Omar Nasr | Noha Tawfik

This paper evaluates the performance of “AAST-NLP” in the Social Media Mining for Health (SMM4H) Shared Tasks 3 and 6, where more than 20 teams participated in each. We leveraged state-of-the-art transformer-based models, including Mistral, to achieve our results. Our models consistently outperformed both the mean and median scores across the tasks. Specifically, an F1-score of 0.636 was achieved in classifying the impact of outdoor spaces on social anxiety symptoms, while an F1-score of 0.946 was recorded for the classification of self-reported exact ages.

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CogAI@SMM4H 2024: Leveraging BERT-based Ensemble Models for Classifying Tweets on Developmental Disorders
Liza Dahiya | Rachit Bagga

This paper presents our work for the Task 5 of the Social Media Mining for Health Applications 2024 Shared Task - Binary classification of English tweets reporting children’s medical disorders. In this paper, we present and compare multiple approaches for automatically classifying tweets from parents based on whether they mention having a child with attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorders (ASD), delayed speech, or asthma. We use ensemble of various BERT-based models trained on provided dataset that yields an F1 score of 0.901 on the test data.

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ADE Oracle at #SMM4H 2024: A Two-Stage NLP System for Extracting and Normalizing Adverse Drug Events from Tweets
Andrew Davis | Billy Dickson | Sandra Kübler

This study describes the approach of Team ADE Oracle for Task 1 of the Social Media Mining for Health Applications (#SMM4H) 2024 shared task. Task 1 challenges participants to detect adverse drug events (ADEs) within English tweets and normalize these mentions against the Medical Dictionary for Regulatory Activities standards. Our approach utilized a two-stage NLP pipeline consisting of a named entity recognition model, retrained to recognize ADEs, followed by vector similarity assessment with a RoBERTa-based model. Despite achieving a relatively high recall of 37.4% in the extraction of ADEs, indicative of effective identification of potential ADEs, our model encountered challenges with precision. We found marked discrepancies between recall and precision between the test set and our validation set, which underscores the need for further efforts to prevent overfitting and enhance the model’s generalization capabilities for practical applications.

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BrainStorm @ iREL at #SMM4H 2024: Leveraging Translation and Topical Embeddings for Annotation Detection in Tweets
Manav Chaudhary | Harshit Gupta | Vasudeva Varma

The proliferation of LLMs in various NLP tasks has sparked debates regarding their reliability, particularly in annotation tasks where biases and hallucinations may arise. In this shared task, we address the challenge of distinguishing annotations made by LLMs from those made by human domain experts in the context of COVID-19 symptom detection from tweets in Latin American Spanish. This paper presents BrainStorm @ iREL’s approach to the #SMM4H 2024 Shared Task, leveraging the inherent topical information in tweets, we propose a novel approach to identify and classify annotations, aiming to enhance the trustworthiness of annotated data.

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UKYNLP@SMM4H2024: Language Model Methods for Health Entity Tagging and Classification on Social Media (Tasks 4 & 5)
Motasem Obeidat | Vinu Ekanayake | Md Sultan Al Nahian | Ramakanth Kavuluru

We describe the methods and results of our submission to the 9th Social Media Mining for Health Research and Applications (SMM4H) 2024 shared tasks 4 and 5. Task 4 involved extracting the clinical and social impacts of non-medical substance use and task 5 focused on the binary classification of tweets reporting children’s medical disorders. We employed encoder language models and their ensembles, achieving the top score on task 4 and a high score for task 5.

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LHS712_ADENotGood at #SMM4H 2024 Task 1: Deep-LLMADEminer: A deep learning and LLM pharmacovigilance pipeline for extraction and normalization of adverse drug event mentions on Twitter
Yifan Zheng | Jun Gong | Shushun Ren | Dalton Simancek | V.G.Vinod Vydiswaran

Adverse drug events (ADEs) pose major public health risks, with traditional reporting systems often failing to capture them. Our proposed pipeline, called Deep-LLMADEminer, used natural language processing approaches to tackle this issue for #SMM4H 2024 shared task 1. Using annotated tweets, we built a three part pipeline: RoBERTa for classification, GPT-4-turbo for span extraction, and BioBERT for normalization. Our models achieved F1-scores of 0.838, 0.306, and 0.354, respectively, offering a novel system for Task 1 and similar pharmacovigilance tasks.

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HaleLab_NITK@SMM4H’24: Binary classification of English tweets reporting children’s medical disorders
Ritik Mahajan | Sowmya S.

This paper describes the work undertaken as part of the SMM4H-2024 shared task, specifically Task 5, which involves the binary classification of English tweets reporting children’s medical disorders. The primary objective is to develop a system capable of automatically identifying tweets from users who report their pregnancy and mention children with specific medical conditions, such as attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorders (ASD), delayed speech, or asthma, while distinguishing them from tweets that merely reference a disorder without much context. Our approach leverages advanced natural language processing techniques and machine learning algorithms to accurately classify the tweets. The system achieved an overall F1-score of 0.87, highlighting its robustness and effectiveness in addressing the classification challenge posed by this task.

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Team Yseop at #SMM4H 2024: Multilingual Pharmacovigilance Named Entity Recognition and Relation Extraction
Anubhav Gupta

This paper describes three RoBERTa based systems. The first one recognizes adverse drug events (ADEs) in English tweets and links themwith MedDRA concepts. It scored F1-norm of 40 for the Task 1. The next one extracts pharmacovigilance related named entities inFrench and scored a F1 of 0.4132 for the Task 2a. The third system extracts pharmacovigilance related named entities and their relationsin Japanese. It obtained a F1 of 0.5827 for the Task 2a and 0.0301 for the Task 2b. The French and Japanese systems are the best performing system for the Task 2

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KUL@SMM4H2024: Optimizing Text Classification with Quality-Assured Augmentation Strategies
Sumam Francis | Marie-Francine Moens

This paper presents our models for the Social Media Mining for Health 2024 shared task, specifically Task 5, which involves classifying tweets reporting a child with childhood disorders (annotated as “1”) versus those merely mentioning a disorder (annotated as “0”). We utilized a classification model enhanced with diverse textual and language model-based augmentations. To ensure quality, we used semantic similarity, perplexity, and lexical diversity as evaluation metrics. Combining supervised contrastive learning and cross-entropy-based learning, our best model, incorporating R-drop and various LM generation-based augmentations, achieved an impressive F1 score of 0.9230 on the test set, surpassing the task mean and median scores.

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LHS712NV at #SMM4H 2024 Task 4: Using BERT to classify Reddit posts on non-medical substance use
Valeria Fraga | Neha Nair | Dalton Simancek | V.G.Vinod Vydiswaran

This paper summarizes our participation in the Shared Task 4 of #SMM4H 2024. Task 4 was a named entity recognition (NER) task identifying clinical and social impacts of non-medical substance use in English Reddit posts. We employed the Bidirectional Encoder Representations from Transformers (BERT) model to complete this task. Our team achieved an F1-score of 0.892 on a validation set and a relaxed F1-score of 0.191 on the test set.

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712forTask7 at #SMM4H 2024 Task 7: Classifying Spanish Tweets Annotated by Humans versus Machines with BETO Models
Hafizh Yusuf | David Belmonte | Dalton Simancek | V.G.Vinod Vydiswaran

The goal of Social Media Mining for Health (#SMM4H) 2024 Task 7 was to train a machine learning model that is able to distinguish between annotations made by humans and those made by a Large Language Model (LLM). The dataset consisted of tweets originating from #SMM4H 2023 Task 3, wherein the objective was to extract COVID-19 symptoms in Latin-American Spanish tweets. Due to the lack of additional annotated tweets for classification, we reframed the task using the available tweets and their corresponding human or machine annotator labels to explore differences between the two subsets of tweets. We conducted an exploratory data analysis and trained a BERT-based classifier to identify sampling biases between the two subsets. The exploratory data analysis found no significant differences between the samples and our best classifier achieved a precision of 0.52 and a recall of 0.51, indicating near-random performance. This confirms the lack of sampling biases between the two sets of tweets and is thus a valid dataset for a task designed to assess the authorship of annotations by humans versus machines.

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TLab at #SMM4H 2024: Retrieval-Augmented Generation for ADE Extraction and Normalization
Jacob Berkowitz | Apoorva Srinivasan | Jose Cortina | Nicholas Tatonetti1

SMM4H 2024 Task 1 is focused on the identification of standardized Adverse Drug Events (ADEs) in tweets. We introduce a novel Retrieval-Augmented Generation (RAG) method, leveraging the capabilities of Llama 3, GPT-4, and the SFR-embedding-mistral model, along with few-shot prompting techniques, to map colloquial tweet language to MedDRA Preferred Terms (PTs) without relying on extensive training datasets. Our method achieved competitive performance, with an F1 score of 0.359 in the normalization task and 0.392 in the named entity recognition (NER) task. Notably, our model demonstrated robustness in identifying previously unseen MedDRA PTs (F1=0.363) greatly surpassing the median task score of 0.141 for such terms.

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BIT@UA at #SMM4H 2024 Tasks 1 and 5: finding adverse drug events and children’s medical disorders in English tweets
Luis Afonso | João Almeida | Rui Antunes | José Oliveira

In this paper we present our proposed systems, for Tasks 1 and 5 of the #SMM4H-2024 shared task (Social Media Mining for Health), responsible for identifying health-related aspects in English social media text. Task 1 consisted of identifying text spans mentioning adverse drug events and linking them to unique identifiers from the medical terminology MedDRA, whereas in Task 5 the aim was to distinguish tweets that report a user having a child with a medical disorder from tweets that merely mention a disorder.For Task 1, our system, composed of a pre-trained RoBERTa model and a random forest classifier, achieved 0.397 and 0.295 entity recognition and normalization F1-scores respectively. In Task 5, we obtained a 0.840 F1-score using a pre-trained BERT model.

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FORCE: A Benchmark Dataset for Foodborne Disease Outbreak and Recall Event Extraction from News
Sudeshna Jana | Manjira Sinha | Tirthankar Dasgupta

The escalating prevalence of food safety incidents within the food supply chain necessitates immediate action to protect consumers. These incidents encompass a spectrum of issues, including food product contamination and deliberate food and feed adulteration for economic gain leading to outbreaks and recalls. Understanding the origins and pathways of contamination is imperative for prevention and mitigation. In this paper, we introduce FORCE Foodborne disease Outbreak and ReCall Event extraction from openweb). Our proposed model leverages a multi-tasking sequence labeling architecture in conjunction with transformer-based document embeddings. We have compiled a substantial annotated corpus comprising relevant articles published between 2011 and 2023 to train and evaluate the model. The dataset will be publicly released with the paper. The event detection model demonstrates fair accuracy in identifying food-related incidents and outbreaks associated with organizations, as assessed through cross-validation techniques.

This paper provides an overview of Task 2 from the Social Media Mining for Health 2024 shared task (#SMM4H 2024), which focused on Named Entity Recognition (NER, Subtask 2a) and the joint task of NER and Relation Extraction (RE, Subtask 2b) for detecting adverse drug reactions (ADRs) in German, Japanese, and French texts written by patients. Participants were challenged with a few-shot learning scenario, necessitating models that can effectively generalize from limited annotated examples. Despite the diverse strategies employed by the participants, the overall performance across submissions from three teams highlighted significant challenges. The results underscored the complexity of extracting entities and relations in multi-lingual contexts, especially from the noisy and informal nature of user-generated content. Further research is required to develop robust systems capable of accurately identifying and associating ADR-related information in low-resource and multilingual settings.

For the past nine years, the Social Media Mining for Health Applications (#SMM4H) shared tasks have promoted community-driven development and evaluation of advanced natural language processing systems to detect, extract, and normalize health-related information in publicly available user-generated content. This year, #SMM4H included seven shared tasks in English, Japanese, German, French, and Spanish from Twitter, Reddit, and health forums. A total of 84 teams from 22 countries registered for #SMM4H, and 45 teams participated in at least one task. This represents a growth of 180% and 160% in registration and participation, respectively, compared to the last iteration. This paper provides an overview of the tasks and participating systems. The data sets remain available upon request, and new systems can be evaluated through the post-evaluation phase on CodaLab.

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bib (full) Proceedings of the 4th Workshop on Spatial Language Understanding and Grounded Communication for Robotics (SpLU-RoboNLP 2024)

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Proceedings of the 4th Workshop on Spatial Language Understanding and Grounded Communication for Robotics (SpLU-RoboNLP 2024)
Parisa Kordjamshidi | Xin Eric Wang | Yue Zhang | Ziqiao Ma | Mert Inan

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Language-guided World Models: A Model-based Approach to AI Control
Alex Zhang | Khanh Nguyen | Jens Tuyls | Albert Lin | Karthik Narasimhan

Developing internal world models for artificial agents opens an efficient channel for humans to communicate with and control them. In addition to updating policies, humans can modify the world models of these agents in order to influence their decisions.The challenge, however, is that currently existing world models are difficult for humans to adapt because they lack a natural communication interface. Aimed at addressing this shortcoming, we develop *Language-Guided World Models* (LWMs), which can capture environment dynamics by reading language descriptions. These models enhance agent communication efficiency, allowing humans to simultaneously alter their behavior on multiple tasks with concise language feedback. They also enable agents to self-learn from texts originally written to instruct humans. To facilitate the development of LWMs, we design a challenging benchmark based on the game of MESSENGER (Hanjie et al., 2021), requiring compositional generalization to new language descriptions and environment dynamics. Our experiments reveal that the current state-of-the-art Transformer architecture performs poorly on this benchmark, motivating us to design a more robust architecture. To showcase the practicality of our proposed LWMs, we simulate a scenario where these models augment the interpretability and safety of an agent by enabling it to generate and discuss plans with a human before execution. By effectively incorporating language feedback on the plan, the models boost the agent performance in the real environment by up to three times without collecting any interactive experiences in this environment.

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Learning Communication Policies for Different Follower Behaviors in a Collaborative Reference Game
Philipp Sadler | Sherzod Hakimov | David Schlangen

In this work, we evaluate the adaptability of neural agents towards assumed partner behaviors in a collaborative reference game. In this game, success is achieved when a knowledgeable guide can verbally lead a follower to the selection of a specific puzzle piece among several distractors. We frame this language grounding and coordination task as a reinforcement learning problem and measure to which extent a common reinforcement training algorithm (PPO) is able to produce neural agents (the guides) that perform well with various heuristic follower behaviors that vary along the dimensions of confidence and autonomy. We experiment with a learning signal that in addition to the goal condition also respects an assumed communicative effort. Our results indicate that this novel ingredient leads to communicative strategies that are less verbose (staying silent in some of the steps) and that with respect to that the guide’s strategies indeed adapt to the partner’s level of confidence and autonomy.

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Collection of Japanese Route Information Reference Expressions Using Maps as Stimuli
Yoshiko Kawabata | Mai Omura | Hikari Konishi | Masayuki Asahara | Johane Takeuchi

We constructed a database of Japanese expressions based on route information. Using 20 maps as stimuli, we requested descriptions of routes between two points on each map from 40 individuals per route, collecting 1600 route information reference expressions. We determined whether the expressions were based solely on relative reference expressions by using landmarks on the maps. In cases in which only relative reference expressions were used, we labeled the presence or absence of information regarding the starting point, waypoints, and destination. Additionally, we collected clarity ratings for each expression using a survey.

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bib (full) Proceedings of the Sixth Workshop on Teaching NLP

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Documenting the Unwritten Curriculum of Student Research
Shomir Wilson

Graduate and undergraduate student researchers in natural language processing (NLP) often need mentoring to learn the norms of research. While methodological and technical knowledge are essential, there is also a “hidden curriculum” of experiential knowledge about topics like work strategies, common obstacles, collaboration, conferences, and scholarly writing. As a professor, I have written a set of guides that cover typically unwritten customs and procedures for academic research. I share them with advisees to help them understand research norms and to help us focus on their specific questions and interests. This paper describes these guides, which are freely accessible on the web (https://shomir.net/advice), and I provide recommendations to faculty who are interested in creating similar materials for their advisees.

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Example-Driven Course Slides on Natural Language Processing Concepts
Natalie Parde

Natural language processing (NLP) is a fast-paced field and a popular course topic in many undergraduate and graduate programs. This paper presents a comprehensive suite of example-driven course slides covering NLP concepts, ranging from fundamental building blocks to modern state-of-the-art approaches. In contributing these slides, I hope to alleviate burden for those starting out as faculty or in need of course material updates. The slides are publicly available for external use and are updated regularly to incorporate new advancements.

This paper presents a course on neural networks based on the Transformer architecture targeted at diverse groups of people from academia and industry with experience in Python, Machine Learning, and Deep Learning but little or no experience with Transformers. The course covers a comprehensive overview of the Transformers NLP applications and their use for other data types. The course features 15 sessions, each consisting of a lecture and a practical part, and two homework assignments organized as CodaLab competitions. The first six sessions of the course are devoted to the Transformer and the variations of this architecture (e.g., encoders, decoders, encoder-decoders) as well as different techniques of model tuning. Subsequent sessions are devoted to multilingualism, multimodality (e.g., texts and images), efficiency, event sequences, and tabular data.We ran the course for different audiences: academic students and people from industry. The first run was held in 2022. During the subsequent iterations until 2024, it was constantly updated and extended with recently emerged findings on GPT-4, LLMs, RLHF, etc. Overall, it has been ran six times (four times in industry and twice in academia) and received positive feedback from academic and industry students.

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Striking a Balance between Classical and Deep Learning Approaches in Natural Language Processing Pedagogy
Aditya Joshi | Jake Renzella | Pushpak Bhattacharyya | Saurav Jha | Xiangyu Zhang

While deep learning approaches represent the state-of-the-art of natural language processing (NLP) today, classical algorithms and approaches still find a place in NLP textbooks and courses of recent years. This paper discusses the perspectives of conveners of two introductory NLP courses taught in Australia and India, and examines how classical and deep learning approaches can be balanced within the lecture plan and assessments of the courses. We also draw parallels with the objects-first and objects-later debate in CS1 education. We observe that teaching classical approaches adds value to student learning by building an intuitive understanding of NLP problems, potential solutions, and even deep learning models themselves. Despite classical approaches not being state-of-the-art, the paper makes a case for their inclusion in NLP courses today.

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Co-Creational Teaching of Natural Language Processing
John McCrae

Traditional lectures have poorer outcomes compared to active learning methodologies, yet many natural language processing classes in higher education still follow this outdated methodology. In this paper, we present, co-creational teaching, a methodology that encourages partnership between staff and lecturers and show how this can be applied to teach natural language processing. As a fast-moving and dynamic area of study with high interest from students, natural language processing is an ideal subject for innovative teaching methodologies to improve student outcomes. We detail our experience with teaching natural language processing through partnership with students and provide detailed descriptions of methodologies that can be used by others in their teaching, including considerations of diverse student populations.

In this work, we present a collaboratively and continuously developed open-source educational resource (OSER) for teaching natural language processing at two different universities. We shed light on the principles we followed for the initial design of the course and the rationale for ongoing developments, followed by a reflection on the inter-university collaboration for designing and maintaining teaching material. When reflecting on the latter, we explicitly emphasize the considerations that need to be made when facing heterogeneous groups and when having to accommodate multiple examination regulations within one single course framework. Relying on the fundamental principles of OSER developments as defined by Bothmann et al. (2023) proved to be an important guideline during this process. The final part pertains to open-sourcing our teaching material, coping with the increasing speed of developments in the field, and integrating the course digitally, also addressing conflicting priorities and challenges we are currently facing.

The teaching laboratory we have created integrates methodologies to address the topic of hate speech on social media among students while fostering computational thinking and AI education for societal impact. We provide a foundational understanding of hate speech and introduce computational concepts using matrices, bag of words, and practical exercises in platforms like Colaboratory. Additionally, we emphasize the application of AI, particularly in NLP, to address real-world challenges. Through retrospective evaluation, we assess the efficacy of our approach, aiming to empower students as proactive contributors to societal betterment. With this paper we present an overview of the laboratory’s structure, the primary materials used, and insights gleaned from six editions conducted to the present date.

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Tightly Coupled Worksheets and Homework Assignments for NLP
Laura Biester | Winston Wu

In natural language processing courses, students often struggle to debug their code. In this paper, we present three homework assignments that are tightly coupled with in-class worksheets. The worksheets allow students to confirm their understanding of the algorithms on paper before trying to write code. Then, as students complete the coding portion of the assignments, the worksheets aid students in the debugging process as test cases for the code, allowing students to seamlessly compare their results to those from the correct execution of the algorithm.

This paper presents teaching materials, particularly assignments and ideas for classroom activities, from a new course on large language modelsThe assignments include experiments with LLM inference for weather report generation and machine translation.The classroom activities include class quizzes, focused research on downstream tasks and datasets, and an interactive “best paper” session aimed at reading and comprehension of research papers.

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Empowering the Future with Multilinguality and Language Diversity
En-Shiun Lee | Kosei Uemura | Syed Wasti | Mason Shipton

The rapid advancements and the widespread transformation of Large Language Models, have made it necessary to incorporate these cutting-edge techniques into the educational curricula of Natural Language Processing (NLP) with limited computing resources. This paper presents an applied NLP course designed for upper-year computer science undergraduate students on state-of-the-art techniques with an emphasis on multilinguality and language diversity. We hope to empower learners to advance their language community while preparing for industry.

This paper presents a shared task that we organized at the Foundations of Language Technology (FoLT) course in 2023/2024 at the Technical University of Darmstadt, which focuses on evaluating the output of Large Language Models (LLMs) in generating harmful answers to health-related clinical questions. We describe the task design considerations and report the feedback we received from the students. We expect the task and the findings reported in this paper to be relevant for instructors teaching natural language processing (NLP).

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A Prompting Assignment for Exploring Pretrained LLMs
Carolyn Anderson

As the scale of publicly-available large language models (LLMs) has increased, so has interest in few-shot prompting methods. This paper presents an assignment that asks students to explore three aspects of large language model capabilities (commonsense reasoning, factuality, and wordplay) with a prompt engineering focus. The assignment consists of three tasks designed to share a common programming framework, so that students can reuse and adapt code from earlier tasks. Two of the tasks also involve dataset construction: students are asked to construct a simple dataset for the wordplay task, and a more challenging dataset for the factuality task. In addition, the assignment includes reflection questions that ask students to think critically about what they observe.

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Teaching Natural Language Processing in Law School
Daniel Braun

Fuelled by technical advances, the interest in Natural Language Processing in the legal domain has rapidly increased over the last months and years. The design, usage, and testing of domain-specific systems, but also assessing these systems from a legal perspective, needs competencies at the intersection of law and Natural Language Processing. While the demand for such competencies is high among students, only a few law schools, particularly in Europe, teach such competencies. In this paper, we present the design for a Natural Language Processing course for postgraduate law students that is based on the principle of constructive alignment and has proven to be successful over the last three years.

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Exploring Language Representation through a Resource Inventory Project
Carolyn Anderson

The increasing scale of large language models has led some students to wonder what contributions can be made in academia. However, students are often unaware that LLM-based approaches are not feasible for the majority of the world’s languages due to lack of data availability. This paper presents a research project in which students explore the issue of language representation by creating an inventory of the data, preprocessing, and model resources available for a less-resourced language. Students are put into small groups and assigned a language to research. Within the group, students take on one of three roles: dataset investigator, preprocessing investigator, or downstream task investigator. Students then work together to create a 7-page research report about their language.

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BELT: Building Endangered Language Technology
Michael Ginn | David Saavedra-Beltrán | Camilo Robayo | Alexis Palmer

The development of language technology (LT) for an endangered language is often identified as a goal in language revitalization efforts, but developing such technologies is typically subject to additional methodological challenges as well as social and ethical concerns. In particular, LT development has too often taken on colonialist qualities, extracting language data, relying on outside experts, and denying the speakers of a language sovereignty over the technologies produced.We seek to avoid such an approach through the development of the Building Endangered Language Technology (BELT) website, an educational resource designed for speakers and community members with limited technological experience to develop LTs for their own language. Specifically, BELT provides interactive lessons on basic Python programming, coupled with projects to develop specific language technologies, such as spellcheckers or word games. In this paper, we describe BELT’s design, the motivation underlying many key decisions, and preliminary responses from learners.

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Training an NLP Scholar at a Small Liberal Arts College: A Backwards Designed Course Proposal
Grusha Prasad | Forrest Davis

The rapid growth in natural language processing (NLP) over the last couple yearshas generated student interest and excitement in learning more about the field. In this paper, we present two types of students that NLP courses might want to train. First, an “NLP engineer” who is able to flexibly design, build and apply new technologies in NLP for a wide range of tasks. Second, an “NLP scholar” who is able to pose, refine and answer questions in NLP and how it relates to the society, while also learning to effectively communicate these answers to a broader audience. While these two types of skills are not mutually exclusive — NLP engineers should be able to think critically, and NLP scholars should be able to build systems — we think that courses can differ in the balance of these skills. As educators at Small Liberal Arts Colleges, the strengths of our students and our institution favors an approach that is better suited to train NLP scholars. In this paper we articulate what kinds of skills an NLP scholar should have, and then adopt a backwards design to propose course components that can aid the acquisition of these skills.

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An Interactive Toolkit for Approachable NLP
AriaRay Brown | Julius Steuer | Marius Mosbach | Dietrich Klakow

We present a novel tool designed for teaching and interfacing the information-theoretic modeling abilities of large language models. The Surprisal Toolkit allows students from diverse linguistic and programming backgrounds to learn about measures of information theory and natural language processing (NLP) through an online interactive tool. In addition, the interface provides a valuable research mechanism for obtaining measures of surprisal. We implement the toolkit as part of a classroom tutorial in three different learning scenarios and discuss the overall receptive student feedback. We suggest this toolkit and similar applications as resourceful supplements to instruction in NLP topics, especially for the purpose of balancing conceptual understanding with technical instruction, grounding abstract topics, and engaging students with varying coding abilities.

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Occam’s Razor and Bender and Koller’s Octopus
Michael Guerzhoy

We discuss the teaching of the controversy surrounding Bender and Koller’s prominent 2020 paper, “Climbing toward NLU: On Meaning, Form, and Understanding in the Age of Data” (ACL 2020)We present what we understand to be the main contentions of the paper, and then recommend that the students engage with the natural counter-arguments to the claims in the paper.We attach teaching materials that we use to facilitate teaching this topic to undergraduate students.

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bib (full) Proceedings of TextGraphs-17: Graph-based Methods for Natural Language Processing

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Learning Human Action Representations from Temporal Context in Lifestyle Vlogs
Oana Ignat | Santiago Castro | Weiji Li | Rada Mihalcea

We address the task of human action representation and show how the approach to generating word representations based on co-occurrence can be adapted to generate human action representations by analyzing their co-occurrence in videos. To this end, we formalize the new task of human action co-occurrence identification in online videos, i.e., determine whether two human actions are likely to co-occur in the same interval of time.We create and make publicly available the Co-Act (Action Co-occurrence) dataset, consisting of a large graph of ~12k co-occurring pairs of visual actions and their corresponding video clips. We describe graph link prediction models that leverage visual and textual information to automatically infer if two actions are co-occurring.We show that graphs are particularly well suited to capture relations between human actions, and the learned graph representations are effective for our task and capture novel and relevant information across different data domains.

Learning on text-attributed graphs (TAGs), in which nodes are associated with one or more texts, has been the subject of much recent work. However, most approaches tend to make strong assumptions about the downstream task of interest, are reliant on hand-labeled data, or fail to equally balance the importance of both text and graph representations. In this work, we propose Contrastive Graph-Text pretraining (ConGraT), a general, self-supervised approach for jointly learning separate representations of texts and nodes in a TAG. Our method trains a language model (LM) and a graph neural network (GNN) to align their representations in a common latent space using a batch-wise contrastive learning objective inspired by CLIP. We further propose an extension to the CLIP objective that leverages graph structure to incorporate information about inter-node similarity. Extensive experiments demonstrate that ConGraT outperforms baselines on various downstream tasks, including node and text category classification, link prediction, and language modeling. Finally, we present an application of our method to community detection in social graphs, which enables finding more textually grounded communities, rather than purely graph-based ones.

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Uniform Meaning Representation Parsing as a Pipelined Approach
Jayeol Chun | Nianwen Xue

Uniform Meaning Representation (UMR) is the next phase of semantic formalism following Abstract Meaning Representation (AMR), with added focus on inter-sentential relations allowing the representational scope of UMR to cover a full document. This, in turn, greatly increases the complexity of its parsing task with the additional requirement of capturing document-level linguistic phenomena such as coreference, modal and temporal dependencies. In order to establish a strong baseline despite the small size of recently released UMR v1.0 corpus, we introduce a pipeline model that does not require any training. At the core of our method is a two-track strategy of obtaining UMR’s sentence and document graphs separately, with the document-level triples being compiled at the token level and the sentence graph being converted from AMR graphs. By leveraging alignment between AMR and its sentence, we are able to generate the first automatic English UMR parses.

Ontology population, which aims to extract structured data to enrich domain-specific ontologies from unstructured text, typically faces challenges in terms of data scarcity and linguistic complexity, particularly in specialized fields such as retail banking. In this study, we investigate the application of large language models (LLMs) to populate domain-specific ontologies of retail banking products from Thai corporate documents. We compare traditional span-based approaches to LLMs-based generative methods, with different prompting techniques. Our findings reveal that while span-based methods struggle with data scarcity and the complex linguistic structure, LLMs-based generative approaches substantially outperform, achieving a 61.05% F1 score, with the most improvement coming from providing examples in the prompts. This improvement highlights the potential of LLMs for ontology population tasks, offering a scalable and efficient solution for structured information extraction in especially in low-resource language settings.

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Prompt Me One More Time: A Two-Step Knowledge Extraction Pipeline with Ontology-Based Verification
Alla Chepurova | Yuri Kuratov | Aydar Bulatov | Mikhail Burtsev

This study explores a method for extending real-world knowledge graphs (specifically, Wikidata) by extracting triplets from texts with the aid of Large Language Models (LLMs). We propose a two-step pipeline that includes the initial extraction of entity candidates, followed by their refinement and linkage to the canonical entities and relations of the knowledge graph. Finally, we utilize Wikidata relation constraints to select only verified triplets. We compare our approach to a model that was fine-tuned on a machine-generated dataset and demonstrate that it performs better on natural data. Our results suggest that LLM-based triplet extraction from texts, with subsequent verification, is a viable method for real-world applications.

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Towards Understanding Attention-based Reasoning through Graph Structures in Medical Codes Classification
Noon Goldstein | Saadullah Amin | Günter Neumann

A common approach to automatically assigning diagnostic and procedural clinical codes to health records is to solve the task as a multi-label classification problem. Difficulties associated with this task stem from domain knowledge requirements, long document texts, large and imbalanced label space, reflecting the breadth and dependencies between medical diagnoses and procedures. Decisions in the healthcare domain also need to demonstrate sound reasoning, both when they are correct and when they are erroneous. Existing works address some of these challenges by incorporating external knowledge, which can be encoded into a graph-structured format. Incorporating graph structures on the output label space or between the input document and output label spaces have shown promising results in medical codes classification. Limited focus has been put on utilizing graph-based representation on the input document space. To partially bridge this gap, we represent clinical texts as graph-structured data through the UMLS Metathesaurus; we explore implicit graph representation through pre-trained knowledge graph embeddings and explicit domain-knowledge guided encoding of document concepts and relational information through graph neural networks. Our findings highlight the benefits of pre-trained knowledge graph embeddings in understanding model’s attention-based reasoning. In contrast, transparent domain knowledge guidance in graph encoder approaches is overshadowed by performance loss. Our qualitative analysis identifies limitations that contribute to prediction errors.

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Leveraging Graph Structures to Detect Hallucinations in Large Language Models
Noa Nonkes | Sergei Agaronian | Evangelos Kanoulas | Roxana Petcu

Large language models are extensively applied across a wide range of tasks, such as customer support, content creation, educational tutoring, and providing financial guidance. However, a well-known drawback is their predisposition to generate hallucinations. This damages the trustworthiness of the information these models provide, impacting decision-making and user confidence. We propose a method to detect hallucinations by looking at the structure of the latent space and finding associations within hallucinated and non-hallucinated generations. We create a graph structure that connects generations that lie closely in the embedding space. Moreover, we employ a Graph Attention Network which utilizes message passing to aggregate information from neighboring nodes and assigns varying degrees of importance to each neighbor based on their relevance. Our findings show that 1) there exists a structure in the latent space that differentiates between hallucinated and non-hallucinated generations, 2) Graph Attention Networks can learn this structure and generalize it to unseen generations, and 3) the robustness of our method is enhanced when incorporating contrastive learning. When evaluated against evidence-based benchmarks, our model performs similarly without access to search-based methods.

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Semantic Graphs for Syntactic Simplification: A Revisit from the Age of LLM
Peiran Yao | Kostyantyn Guzhva | Denilson Barbosa

Symbolic sentence meaning representations, such as AMR (Abstract Meaning Representation) provide expressive and structured semantic graphs that act as intermediates that simplify downstream NLP tasks. However, the instruction-following capability of large language models (LLMs) offers a shortcut to effectively solve NLP tasks, questioning the utility of semantic graphs. Meanwhile, recent work has also shown the difficulty of using meaning representations merely as a helpful auxiliary for LLMs. We revisit the position of semantic graphs in syntactic simplification, the task of simplifying sentence structures while preserving their meaning, which requires semantic understanding, and evaluate it on a new complex and natural dataset. The AMR-based method that we propose, AMRS³, demonstrates that state-of-the-art meaning representations can lead to easy-to-implement simplification methods with competitive performance and unique advantages in cost, interpretability, and generalization. With AMRS³ as an anchor, we discover that syntactic simplification is a task where semantic graphs are helpful in LLM prompting. We propose AMRCoC prompting that guides LLMs to emulate graph algorithms for explicit symbolic reasoning on AMR graphs, and show its potential for improving LLM on semantic-centered tasks like syntactic simplification.

This paper describes the results of the Knowledge Graph Question Answering (KGQA) shared task that was co-located with the TextGraphs 2024 workshop. In this task, given a textual question and a list of entities with the corresponding KG subgraphs, the participating system should choose the entity that correctly answers the question. Our competition attracted thirty teams, four of which outperformed our strong ChatGPT-based zero-shot baseline. In this paper, we overview the participating systems and analyze their performance according to a large-scale automatic evaluation. To the best of our knowledge, this is the first competition aimed at the KGQA problem using the interaction between large language models (LLMs) and knowledge graphs.

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nlp_enjoyers at TextGraphs-17 Shared Task: Text-Graph Representations for Knowledge Graph Question Answering using all-MPNet
Nikita Kurdiukov | Viktoriia Zinkovich | Sergey Karpukhin | Pavel Tikhomirov

This paper presents a model for solving the Multiple Choice Question Answering (MCQA) problem, focusing on the impact of subgraph extraction from a Knowledge Graph on model performance. The proposed method combines textual and graph information by adding linearized subgraphs directly into the main question prompt with separate tokens, enhancing the performance of models working with each modality separately. The study also includes an examination of Large Language Model (LLM) backbones and the benefits of linearized subgraphs and sequence length, with efficient training achieved through fine-tuning with LoRA. The top benchmark, using subgraphs and MPNet, achieved an F1 score of 0.3887. The main limitation of the experiments is the reliance on pre-generated subgraphs/triplets from the graph, and the lack of exploration of in-context learning and prompting strategies with decoder-based architectures.

In this paper, we present an effective method for TextGraphs-17 Shared Task. This task requires selecting an entity from the candidate entities that is relevant to the given question and answer. The selection process is aided by utilizing the shortest path graph in the knowledge graph, connecting entities in the query to the candidate entity. This task aims to explore how to enhance LLMs output with KGs, although current LLMs have certain logical reasoning capabilities, they may not be certain about their own outputs, and the answers they produce may be correct by chance through incorrect paths. In this case, we have introduced a LLM prompt design strategy based on self-ranking and emotion. Specifically, we let the large model score its own answer choices to reflect its confidence in the answer. Additionally, we add emotional incentives to the prompts to encourage the model to carefully examine the questions. Our submissions was conducted under zero-resource setting, and we achieved the second place in the task with an F1-score of 0.8321.

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TIGFORMER at TextGraphs-17 Shared Task: A Late Interaction Method for text and Graph Representations in KBQA Classification Task
Mayank Rakesh | Parikshit Saikia | Saket Shrivastava

This paper introduces a novel late interaction mechanism for knowledge base question answering (KBQA) systems, combining Graphormer and transformer representations. We conducted extensive experiments, comparing various pooling mechanisms and configurations. Our results demonstrate significant improvements in F1-score compared to traditional baselines. Specifically, we found that attention pooling, in conjunction with linearized graph and question features alongside sub-graph representations, yields the best performance. Our study highlights the importance of advanced interaction mechanisms and the integration of diverse modalities in KBQA systems.

This paper presents the approach of the NLPeople team for the Text-Graph Representations for KGQA Shared Task at TextGraphs-17. The task involved selecting an answer for a given question from a list of candidate entities. We show that prompting Large Language models (LLMs) to break down a natural language question into a series of sub-questions, allows models to understand complex questions. The LLMs arrive at the final answer by answering the intermediate questions using their internal knowledge and without needing additional context. Our approach to the task uses an ensemble of prompting strategies to guide how LLMs interpret various types of questions. Our submission achieves an F1 score of 85.90, ranking 1st among the other participants in the task.

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Skoltech at TextGraphs-17 Shared Task: Finding GPT-4 Prompting Strategies for Multiple Choice Questions
Maria Lysyuk | Pavel Braslavski

In this paper, we present our solution to the TextGraphs-17 Shared Task on Text-Graph Representations for KGQA. GPT-4 alone, with chain-of-thought reasoning and a given set of answers, achieves an F1 score of 0.78. By employing subgraph size as a feature, Wikidata answer description as an additional context, and question rephrasing technique, we further strengthen this result. These tricks help to answer questions that were not initially answered and to eliminate irrelevant, identical answers. We have managed to achieve an F1 score of 0.83 and took 2nd place, improving the score by 0.05 over the baseline. An open implementation of our method is available on GitHub.

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JellyBell at TextGraphs-17 Shared Task: Fusing Large Language Models with External Knowledge for Enhanced Question Answering
Julia Belikova | Evegeniy Beliakin | Vasily Konovalov

This work describes an approach to develop Knowledge Graph Question Answering (KGQA) system for TextGraphs-17 shared task. The task focuses on the fusion of Large Language Models (LLMs) with Knowledge Graphs (KGs). The goal is to select a KG entity (out of several candidates) which corresponds to an answer given a textual question. Our approach applies LLM to identify the correct answer among the list of possible candidates. We confirm that integrating external information is particularly beneficial when the subject entities are not well-known, and using RAG can negatively impact the performance of LLM on questions related to popular entities, as the retrieved context might be misleading. With our result, we achieved 2nd place in the post-evaluation phase.

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bib (full) Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis

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Enhanced Financial Sentiment Analysis and Trading Strategy Development Using Large Language Models
Kemal Kirtac | Guido Germano

This study examines a novel methodology for enhanced financial sentiment analysis and trading strategy development using large language models (LLMs) such as OPT, BERT, FinBERT, LLAMA 3, and RoBERTa. Utilizing a dataset of 965,375 U.S. financial news articles from 2010 to 2023, our research demonstrates that the GPT-3-based OPT significantly outperforms other models, achieving a prediction accuracy of 74.4% for stock market returns. Our findings reveal that the advanced capabilities of LLMs, particularly OPT, surpass traditional sentiment analysis methods such as the Loughran-McDonald dictionary model in predicting and explaining stock returns. For instance, a self-financing strategy based on OPT scores achieves a Sharpe ratio of 3.05 over our sample period, compared to a Sharpe ratio of 1.23 for the strategy based on the dictionary model. This study highlights the superior performance of LLMs in financial sentiment analysis, encouraging further research into integrating artificial intelligence and LLMs in financial markets.

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SEC: Context-Aware Metric Learning for Efficient Emotion Recognition in Conversation
Barbara Gendron | Gaël Guibon

The advent of deep learning models has made a considerable contribution to the achievement of Emotion Recognition in Conversation (ERC). However, this task still remains an important challenge due to the plurality and subjectivity of human emotions. Previous work on ERC provides predictive models using mostly graph-based conversation representations. In this work, we propose a way to model the conversational context that we incorporate into a metric learning training strategy, with a two-step process. This allows us to perform ERC in a flexible classification scenario and end up with a lightweight yet efficient model. Using metric learning through a Siamese Network architecture, we achieve 57.71 in macro F1 score for emotion classification in conversation on DailyDialog dataset, which outperforms the related work. This state-of-the-art result is promising in terms of the use of metric learning for emotion recognition, yet perfectible compared to the micro F1 score obtained.

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Modeling Complex Interactions in Long Documents for Aspect-Based Sentiment Analysis
Zehong Yan | Wynne Hsu | Mong-Li Lee | David Bartram-Shaw

The growing number of online articles and reviews necessitates innovative techniques for document-level aspect-based sentiment analysis. Capturing the context in which an aspect is mentioned is crucial. Existing models have focused on relatively short reviews and may fail to consider distant contextual information. This is especially so in longer documents where an aspect may be referred to in multiple ways across dispersed sentences. This work introduces a hierarchical Transformer-based architecture that encodes information at different level of granularities with attention aggregation mechanisms to learn the local and global aspect-specific document representations. For empirical validation, we curate two datasets of long documents: one on social issues, and another covering various topics involving trust-related issues. Experimental results show that the proposed architecture outperforms state-of-the-art methods for document-level aspect-based sentiment classification. We also demonstrate the potential applicability of our approach for long document trust prediction.

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Hierarchical Adversarial Correction to Mitigate Identity Term Bias in Toxicity Detection
Johannes Schäfer | Ulrich Heid | Roman Klinger

Corpora that are the fundament for toxicity detection contain such expressions typically directed against a target individual or group, e.g., people of a specific gender or ethnicity. Prior work has shown that the target identity mention can constitute a confounding variable. As an example, a model might learn that Christians are always mentioned in the context of hate speech. This misguided focus can lead to a limited generalization to newly emerging targets that are not found in the training data. In this paper, we hypothesize and subsequently show that this issue can be mitigated by considering targets on different levels of specificity. We distinguish levels of (1) the existence of a target, (2) a class (e.g., that the target is a religious group), or (3) a specific target group (e.g., Christians or Muslims). We define a target label hierarchy based on these three levels and then exploit this hierarchy in an adversarial correction for the lowest level (i.e. (3)) while maintaining some basic target features. This approach does not lower the toxicity detection performance but increases the generalization to targets not being available at training time.

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A Systematic Analysis on the Temporal Generalization of Language Models in Social Media
Asahi Ushio | Jose Camacho-Collados

In machine learning, temporal shifts occur when there are differences between training and test splits in terms of time. For streaming data such as news or social media, models are commonly trained on a fixed corpus from a certain period of time, and they can become obsolete due to the dynamism and evolving nature of online content. This paper focuses on temporal shifts in social media and, in particular, Twitter. We propose a unified evaluation scheme to assess the performance of language models (LMs) under temporal shift on standard social media tasks. LMs are tested on five diverse social media NLP tasks under different temporal settings, which revealed two important findings: (i) the decrease in performance under temporal shift is consistent across different models for entity-focused tasks such as named entity recognition or disambiguation, and hate speech detection, but not significant in the other tasks analysed (i.e., topic and sentiment classification); and (ii) continuous pre-training on the test period does not improve the temporal adaptability of LMs.

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LLaMA-Based Models for Aspect-Based Sentiment Analysis
Jakub Šmíd | Pavel Priban | Pavel Kral

While large language models (LLMs) show promise for various tasks, their performance in compound aspect-based sentiment analysis (ABSA) tasks lags behind fine-tuned models. However, the potential of LLMs fine-tuned for ABSA remains unexplored. This paper examines the capabilities of open-source LLMs fine-tuned for ABSA, focusing on LLaMA-based models. We evaluate the performance across four tasks and eight English datasets, finding that the fine-tuned Orca 2 model surpasses state-of-the-art results in all tasks. However, all models struggle in zero-shot and few-shot scenarios compared to fully fine-tuned ones. Additionally, we conduct error analysis to identify challenges faced by fine-tuned models.

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A Multi-Faceted NLP Analysis of Misinformation Spreaders in Twitter
Dimosthenis Antypas | Alun Preece | Jose Camacho-Collados

Social media is an integral part of the daily life of an increasingly large number of people worldwide. Used for entertainment, communication and news updates, it constitutes a source of information that has been extensively used to study human behaviour. Unfortunately, the open nature of social media platforms along with the difficult task of supervising their content has led to a proliferation of misinformation posts. In this paper, we aim to identify the textual differences between the profiles of user that share misinformation from questionable sources and those that do not. Our goal is to better understand user behaviour in order to be better equipped to combat this issue. To this end, we identify Twitter (X) accounts of potential misinformation spreaders and apply transformer models specialised in social media to extract characteristics such as sentiment, emotion, topic and presence of hate speech. Our results indicate that, while there may be some differences between the behaviour of users that share misinformation and those that do not, there are no large differences when it comes to the type of content shared.

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Entity-Level Sentiment: More than the Sum of Its Parts
Egil Rønningstad | Roman Klinger | Lilja Øvrelid | Erik Velldal

In sentiment analysis of longer texts, there may be a variety of topics discussed, of entities mentioned, and of sentiments expressed regarding each entity. We find a lack of studies exploring how such texts express their sentiment towards each entity of interest, and how these sentiments can be modelled. In order to better understand how sentiment regarding persons and organizations (each entity in our scope) is expressed in longer texts, we have collected a dataset of expert annotations where the overall sentiment regarding each entity is identified, together with the sentence-level sentiment for these entities separately. We show that the reader’s perceived sentiment regarding an entity often differs from an arithmetic aggregation of sentiments at the sentence level. Only 70% of the positive and 55% of the negative entities receive a correct overall sentiment label when we aggregate the (human-annotated) sentiment labels for the sentences where the entity is mentioned. Our dataset reveals the complexity of entity-specific sentiment in longer texts, and allows for more precise modelling and evaluation of such sentiment expressions.

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MBIAS: Mitigating Bias in Large Language Models While Retaining Context
Shaina Raza | Ananya Raval | Veronica Chatrath

The deployment of Large Language Models (LLMs) in diverse applications necessitates an assurance of safety without compromising the contextual integrity of the generated content. Traditional approaches, including safety-specific fine-tuning or adversarial testing, often yield safe outputs at the expense of contextual meaning. This can result in a diminished capacity to handle nuanced aspects of bias and toxicity, such as underrepresentation or negative portrayals across various demographics. To address these challenges, we introduce MBIAS, an LLM framework carefully instruction fine-tuned on a custom dataset designed specifically for safety interventions. MBIAS is designed to significantly reduce biases and toxic elements in LLM outputs while preserving the main information. This work also details our further use of LLMs: as annotator under human supervision and as evaluator of generated content. Empirical analysis reveals that MBIAS achieves a reduction in bias and toxicity by over 30% in standard evaluations, and by more than 90% in diverse demographic tests, highlighting the robustness of our approach. We make the dataset and the fine-tuned MBIAS model available to the research community for further investigation and to ensure reproducibility. The code for this project can be accessed here https://github.com/shainarazavi/MBIAS.

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Polarization of Autonomous Generative AI Agents Under Echo Chambers
Masaya Ohagi

Online social networks often create echo chambers where people only hear opinions reinforcing their beliefs.An echo chamber often generates polarization, leading to conflicts between people with radical opinions.The echo chamber has been viewed as a human-specific problem, but this implicit assumption is becoming less reasonable as large language models, such as ChatGPT, acquire social abilities. In response to this situation, we investigated the potential for polarization to occur among a group of autonomous AI agents based on generative language models in an echo chamber environment. We had AI agents discuss specific topics and analyzed how the group’s opinions changed as the discussion progressed. As a result, we found that the group of agents based on ChatGPT tended to become polarized in echo chamber environments. The analysis of opinion transitions shows that this result is caused by ChatGPT’s high prompt understanding ability to update its opinion by considering its own and surrounding agents’ opinions. We conducted additional experiments to investigate under what specific conditions AI agents tended to polarize. As a result, we identified factors that influence polarization, such as the agent’s persona.

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Know Thine Enemy: Adaptive Attacks on Misinformation Detection Using Reinforcement Learning
Piotr Przybyła | Euan McGill | Horacio Saggion

We present XARELLO: a generator of adversarial examples for testing the robustness of text classifiers based on reinforcement learning. Our solution is adaptive, it learns from previous successes and failures in order to better adjust to the vulnerabilities of the attacked model. This reflects the behaviour of a persistent and experienced attacker, which are common in the misinformation-spreading environment. We evaluate our approach using several victim classifiers and credibility-assessment tasks, showing it generates better-quality examples with less queries, and is especially effective against the modern LLMs. We also perform a qualitative analysis to understand the language patterns in the misinformation text that play a role in the attacks.

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The Model Arena for Cross-lingual Sentiment Analysis: A Comparative Study in the Era of Large Language Models
Xiliang Zhu | Shayna Gardiner | Tere Roldán | David Rossouw

Sentiment analysis serves as a pivotal component in Natural Language Processing (NLP). Advancements in multilingual pre-trained models such as XLM-R and mT5 have contributed to the increasing interest in cross-lingual sentiment analysis. The recent emergence in Large Language Models (LLM) has significantly advanced general NLP tasks, however, the capability of such LLMs in cross-lingual sentiment analysis has not been fully studied. This work undertakes an empirical analysis to compare the cross-lingual transfer capability of public Small Multilingual Language Models (SMLM) like XLM-R, against English-centric LLMs such as Llama-3, in the context of sentiment analysis across English, Spanish, French and Chinese. Our findings reveal that among public models, SMLMs exhibit superior zero-shot cross-lingual performance relative to LLMs. However, in few-shot cross-lingual settings, public LLMs demonstrate an enhanced adaptive potential. In addition, we observe that proprietary GPT-3.5 and GPT-4 lead in zero-shot cross-lingual capability, but are outpaced by public models in few-shot scenarios.

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Guiding Sentiment Analysis with Hierarchical Text Clustering: Analyzing the German X/Twitter Discourse on Face Masks in the 2020 COVID-19 Pandemic
Silvan Wehrli | Chisom Ezekannagha | Georges Hattab | Tamara Boender | Bert Arnrich | Christopher Irrgang

Social media are a critical component of the information ecosystem during public health crises. Understanding the public discourse is essential for effective communication and misinformation mitigation. Computational methods can aid these efforts through online social listening. We combined hierarchical text clustering and sentiment analysis to examine the face mask-wearing discourse in Germany during the COVID-19 pandemic using a dataset of 353,420 German X (formerly Twitter) posts from 2020. For sentiment analysis, we annotated a subsample of the data to train a neural network for classifying the sentiments of posts (neutral, negative, or positive). In combination with clustering, this approach uncovered sentiment patterns of different topics and their subtopics, reflecting the online public response to mask mandates in Germany. We show that our approach can be used to examine long-term narratives and sentiment dynamics and to identify specific topics that explain peaks of interest in the social media discourse.

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Emotion Identification for French in Written Texts: Considering Modes of Emotion Expression as a Step Towards Text Complexity Analysis
Aline Étienne | Delphine Battistelli | Gwénolé Lecorvé

The objective of this paper is to predict (A) whether a sentence in a written text expresses an emotion, (B) the mode(s) in which the emotion is expressed, (C) whether it is basic or complex, and (D) its emotional category.One of our major contributions, in addition to a dataset and a model, is to integrate the fact that an emotion can be expressed in different modes: from a direct mode, essentially lexicalized, to a more indirect mode, where emotions will only be suggested, a mode that NLP approaches generally don’t take into account. The scope is on written texts, i.e. it does not focus on conversational or multi-modal data. In this context, modes of expression are seen as a factor towards the automatic analysis of complexity in texts.Experiments on French texts show acceptable results compared to the human annotators’ agreement to predict the mode and category, and outperforming results compared to using a large language model with in-context learning (i.e. no fine-tuning) on all tasks.Dataset and model can be downloaded on HuggingFace: https://huggingface.co/TextToKids .

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Comparing Tools for Sentiment Analysis of Danish Literature from Hymns to Fairy Tales: Low-Resource Language and Domain Challenges
Pascale Feldkamp | Jan Kostkan | Ea Overgaard | Mia Jacobsen | Yuri Bizzoni

While Sentiment Analysis has become increasingly central in computational approaches to literary texts, the literary domain still poses important challenges for the detection of textual sentiment due to its highly complex use of language and devices - from subtle humor to poetic imagery. Furthermore these challenges are only further amplified in low-resource language and domain settings. In this paper we investigate the application and efficacy of different Sentiment Analysis tools on Danish literary texts, using historical fairy tales and religious hymns as our datasets. The scarcity of linguistic resources for Danish and the historical context of the data further compounds the challenges for the tools. We compare human annotations to the continuous valence scores of both transformer- and dictionary-based Sentiment Analysis methods to assess their performance, seeking to understand how distinct methods handle the language of Danish prose and poetry.

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Multi-Target User Stance Discovery on Reddit
Benjamin Steel | Derek Ruths

We consider how to credibly and reliably assess the opinions of individuals using their social media posts. To this end, this paper makes three contributions. First, we assemble a workflow and approach to applying modern natural language processing (NLP) methods to multi-target user stance detection in the wild. Second, we establish why the multi-target modeling of user stance is qualitatively more complicated than uni-target user-stance detection. Finally, we validate our method by showing how multi-dimensional measurement of user opinions not only reproduces known opinion polling results, but also enables the study of opinion dynamics at high levels of temporal and semantic resolution.

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Subjectivity Detection in English News using Large Language Models
Mohammad Shokri | Vivek Sharma | Elena Filatova | Shweta Jain | Sarah Levitan

Trust in media has reached a historical low as consumers increasingly doubt the credibility of the news they encounter. This growing skepticism is exacerbated by the prevalence of opinion-driven articles, which can influence readers’ beliefs to align with the authors’ viewpoints. In response to this trend, this study examines the expression of opinions in news by detecting subjective and objective language. We conduct an analysis of the subjectivity present in various news datasets and evaluate how different language models detect subjectivity and generalize to out-of-distribution data. We also investigate the use of in-context learning (ICL) within large language models (LLMs) and propose a straightforward prompting method that outperforms standard ICL and chain-of-thought (CoT) prompts.

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Monitoring Depression Severity and Symptoms in User-Generated Content: An Annotation Scheme and Guidelines
Falwah Alhamed | Rebecca Bendayan | Julia Ive | Lucia Specia

Depression is a highly prevalent condition recognized by the World Health Organization as a leading contributor to global disability. Many people suffering from depression express their thoughts and feelings using social media, which thus becomes a source of data for research in this domain. However, existing annotation schemes tailored to studying depression symptoms in social media data remain limited. Reliable and valid annotation guidelines are crucial for accurately measuring mental health conditions for those studies. This paper addresses this gap by presenting a novel depression annotation scheme and guidelines for detecting depression symptoms and their severity in social media text. Our approach leverages validated depression questionnaires and incorporates the expertise of psychologists and psychiatrists during scheme refinement. The resulting annotation scheme achieves high inter-rater agreement, demonstrating its potential for suitable depression assessment in social media contexts.

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RideKE: Leveraging Low-resource Twitter User-generated Content for Sentiment and Emotion Detection on Code-switched RHS Dataset.
Naome Etori | Maria Gini

Social media has become a crucial open-access platform enabling individuals to freely express opinions and share experiences. These platforms contain user-generated content facilitating instantaneous communication and feedback. However, leveraging low-resource language data from Twitter can be challenging due to the scarcity and poor quality of content with significant variations in language use, such as slang and code-switching. Automatically identifying tweets in low-resource languages can also be challenging because Twitter primarily supports high-resource languages; low-resource languages often lack robust linguistic and contextual support. This paper analyzes Kenyan code-switched data from Twitter using four transformer-based pretrained models for sentiment and emotion classification tasks using supervised and semi-supervised methods. We detail the methodology behind data collection, the annotation procedure, and the challenges encountered during the data curation phase. Our results show that XLM-R outperforms other models; for sentiment analysis, XLM-R supervised model achieves the highest accuracy (69.2%) and F1 score (66.1%), XLM-R semi-supervised (67.2% accuracy, 64.1% F1 score). In emotion analysis, DistilBERT supervised leads in accuracy (59.8%) and F1 score (31%), mBERT semi-supervised (accuracy (59% and F1 score 26.5%). AfriBERTa models show the lowest accuracy and F1 scores. This indicates that the semi-supervised method’s performance is constrained by the small labeled dataset.

This paper presents the POLygraph dataset, a unique resource for fake news detection in Polish. The dataset, created by an interdisciplinary team, is composed of two parts: the “fake-or-not” dataset with 11,360 pairs of news articles (identified by their URLs) and corresponding labels, and the “fake-they-say” dataset with 5,082 news articles (identified by their URLs) and tweets commenting on them. Unlike existing datasets, POLygraph encompasses a variety of approaches from source literature, providing a comprehensive resource for fake news detection. The data was collected through manual annotation by expert and non-expert annotators. The project also developed a software tool that uses advanced machine learning techniques to analyze the data and determine content authenticity. The tool and dataset are expected to benefit various entities, from public sector institutions to publishers and fact-checking organizations. Further dataset exploration will foster fake news detection and potentially stimulate the implementation of similar models in other languages. The paper focuses on the creation and composition of the dataset, so it does not include a detailed evaluation of the software tool for content authenticity analysis, which is planned at a later stage of the project.

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Exploring Language Models to Analyze Market Demand Sentiments from News
Tirthankar Dasgupta | Manjira Sinha

Obtaining demand trends for products is an essential aspect of supply chain planning. It helps in generating scenarios for simulation before actual demands start pouring in. Presently, experts obtain this number manually from different News sources. In this paper, we have presented methods that can automate the information acquisition process. We have presented a joint framework that performs information extraction and sentiment analysis to acquire demand related information from business text documents. The proposed system leverages a TwinBERT-based deep neural network model to first extract product information for which demand is associated and then identify the respective sentiment polarity. The articles are also subjected to causal analytics, that, together yield rich contextual information about reasons for rise or fall of demand of various products. The enriched information is targeted for the decision-makers, analysts and knowledge workers. We have exhaustively evaluated our proposed models with datasets curated and annotated for two different domains namely, automobile sector and housing. The proposed model outperforms the existing baseline systems.

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Impact of Decoding Methods on Human Alignment of Conversational LLMs
Shaz Furniturewala | Kokil Jaidka | Yashvardhan Sharma

To be included into chatbot systems, Large language models (LLMs) must be aligned with human conversational conventions. However, being trained mainly on web-scraped data gives existing LLMs a voice closer to informational text than actual human speech. In this paper, we examine the effect of decoding methods on the alignment between LLM-generated and human conversations, including Beam Search, Top K Sampling, and Nucleus Sampling. We present new measures of alignment in substance, style, and psychometric orientation, and experiment with two conversation datasets. Our results provide subtle insights: better alignment is attributed to fewer beams in Beam Search and lower values of P in Nucleus Sampling. We also find that task-oriented and open-ended datasets perform differently in terms of alignment, indicating the significance of taking into account the context of the interaction.

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Loneliness Episodes: A Japanese Dataset for Loneliness Detection and Analysis
Naoya Fujikawa | Nguyen Toan | Kazuhiro Ito | Shoko Wakamiya | Eiji Aramaki

Loneliness, a significant public health concern, is closely connected to both physical and mental well-being. Hence, detection and intervention for individuals experiencing loneliness are crucial. Identifying loneliness in text is straightforward when it is explicitly stated but challenging when it is implicit. Detecting implicit loneliness requires a manually annotated dataset because whereas explicit loneliness can be detected using keywords, implicit loneliness cannot be. However, there are no freely available datasets with clear annotation guidelines for implicit loneliness. In this study, we construct a freely accessible Japanese loneliness dataset with annotation guidelines grounded in the psychological definition of loneliness. This dataset covers loneliness intensity and the contributing factors of loneliness. We train two models to classify whether loneliness is expressed and the intensity of loneliness. The model classifying loneliness versus non-loneliness achieves an F1-score of 0.833, but the model for identifying the intensity of loneliness has a low F1-score of 0.400, which is likely due to label imbalance and a shortage of a certain label in the dataset. We validate performance in another domain, specifically X (formerly Twitter), and observe a decrease. In addition, we propose improvement suggestions for domain adaptation.

Measuring happiness as a determinant of well-being is increasingly recognized as crucial. While previous studies have utilized free-text descriptions to estimate happiness on a broad scale, limited research has focused on tracking individual fluctuations in happiness over time owing to the challenges associated with longitudinal data collection. This study addresses this issue by obtaining longitudinal data from two workplaces over two and six months respectively.Subsequently, the data is used to construct a happiness estimation model and assess individual happiness levels.Evaluation of the model performance using correlation coefficients shows variability in the correlation values among individuals.Notably, the model performs satisfactorily in estimating 9 of the 11 users’ happiness scores, with a correlation coefficient of 0.4 or higher. To investigate the factors affecting the model performance, we examine the relationship between the model performance and variables such as sentence length, lexical diversity, and personality traits. Correlations are observed between these features and model performance.

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Subjectivity Theory vs. Speaker Intuitions: Explaining the Results of a Subjectivity Regressor Trained on Native Speaker Judgements
Elena Savinova | Jet Hoek

In this paper, we address the issue of explainability in a transformer-based subjectivity regressor trained on native English speakers’ judgements. The main goal of this work is to test how the regressor’s predictions, and therefore native speakers’ intuitions, relate to theoretical accounts of subjectivity. We approach this goal using two methods: a top-down manual selection of theoretically defined subjectivity features and a bottom-up extraction of top subjective and objective features using the LIME explanation method. The explainability of the subjectivity regressor is evaluated on a British news dataset containing sentences taken from social media news posts and from articles on the websites of the same news outlets. Both methods provide converging evidence that theoretically defined subjectivity features, such as emoji, evaluative adjectives, exclamations, questions, intensifiers, and first person pronouns, are prominent predictors of subjectivity scores. Thus, our findings show that the predictions of the regressor, and therefore native speakers’ perceptions of subjectivity, align with subjectivity theory. However, an additional comparison of the effects of different subjectivity features in author text and the text of cited sources reveals that the distinction between author and source subjectivity might not be as salient for naïve speakers as it is in the theory.

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Comparing Pre-trained Human Language Models: Is it Better with Human Context as Groups, Individual Traits, or Both?
Nikita Soni | Niranjan Balasubramanian | H. Andrew Schwartz | Dirk Hovy

Pre-trained language models consider the context of neighboring words and documents but lack any author context of the human generating the text. However, language depends on the author’s states, traits, social, situational, and environmental attributes, collectively referred to as human context (Soni et al., 2024). Human-centered natural language processing requires incorporating human context into language models. Currently, two methods exist: pre-training with 1) group-wise attributes (e.g., over-45-year-olds) or 2) individual traits. Group attributes are simple but coarse — not all 45-year-olds write the same way — while individual traits allow for more personalized representations, but require more complex modeling and data. It is unclear which approach benefits what tasks. We compare pre-training models with human context via 1) group attributes, 2) individual users, and 3) a combined approach on five user- and document-level tasks. Our results show that there is no best approach, but that human-centered language modeling holds avenues for different methods.

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LLMs for Targeted Sentiment in News Headlines: Exploring the Descriptive-Prescriptive Dilemma
Jana Juroš | Laura Majer | Jan Snajder

News headlines often evoke sentiment by intentionally portraying entities in particular ways, making targeted sentiment analysis (TSA) of headlines a worthwhile but difficult task. Due to its subjectivity, creating TSA datasets can involve various annotation paradigms, from descriptive to prescriptive, either encouraging or limiting subjectivity. LLMs are a good fit for TSA due to their broad linguistic and world knowledge and in-context learning abilities, yet their performance depends on prompt design. In this paper, we compare the accuracy of state-of-the-art LLMs and fine-tuned encoder models for TSA of news headlines using descriptive and prescriptive datasets across several languages. Exploring the descriptive–prescriptive continuum, we analyze how performance is affected by prompt prescriptiveness, ranging from plain zero-shot to elaborate few-shot prompts. Finally, we evaluate the ability of LLMs to quantify uncertainty via calibration error and comparison to human label variation. We find that LLMs outperform fine-tuned encoders on descriptive datasets, while calibration and F1-score generally improve with increased prescriptiveness, yet the optimal level varies.

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Context is Important in Depressive Language: A Study of the Interaction Between the Sentiments and Linguistic Markers in Reddit Discussions
Neha Sharma | Kairit Sirts

Research exploring linguistic markers in individuals with depression has demonstrated that language usage can serve as an indicator of mental health. This study investigates the impact of discussion topic as context on linguistic markers and emotional expression in depression, using a Reddit dataset to explore interaction effects. Contrary to common findings, our sentiment analysis revealed a broader range of emotional intensity in depressed individuals, with both higher negative and positive sentiments than controls. This pattern was driven by posts containing no emotion words, revealing the limitations of the lexicon based approaches in capturing the full emotional context. We observed several interesting results demonstrating the importance of contextual analyses. For instance, the use of 1st person singular pronouns and words related to anger and sadness correlated with increased positive sentiments, whereas a higher rate of present-focused words was associated with more negative sentiments. Our findings highlight the importance of discussion contexts while interpreting the language used in depression, revealing that the emotional intensity and meaning of linguistic markers can vary based on the topic of discussion.

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To Aggregate or Not to Aggregate. That is the Question: A Case Study on Annotation Subjectivity in Span Prediction
Kemal Kurniawan | Meladel Mistica | Timothy Baldwin | Jey Han Lau

This paper explores the task of automatic prediction of text spans in a legal problem description that support a legal area label. We use a corpus of problem descriptions written by laypeople in English that is annotated by practising lawyers. Inherent subjectivity exists in our task because legal area categorisation is a complex task, and lawyers often have different views on a problem. Experiments show that training on majority-voted spans outperforms training on disaggregated ones.

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Findings of WASSA 2024 Shared Task on Empathy and Personality Detection in Interactions
Salvatore Giorgi | João Sedoc | Valentin Barriere | Shabnam Tafreshi

This paper presents the results of the WASSA 2024 shared task on predicting empathy, emotion, and personality in conversations and reactions to news articles. Participating teams were given access to a new, unpublished extension of the WASSA 2023 shared task dataset. This task is both multi-level and multi-modal: data is available at the person, essay, dialog, and dialog-turn levels and includes formal (news articles) and informal text (essays and dialogs), self-report data (personality and distress), and third-party annotations (empathy and emotion). The shared task included a new focus on conversations between humans and LLM-based virtual agents which occur immediately after reading and reacting to the news articles. Participants were encouraged to explore the multi-level and multi-modal nature of this data. Participation was encouraged in four tracks: (i) predicting the perceived empathy at the dialog level, (ii) predicting turn-level empathy, emotion polarity, and emotion intensity in conversations, (iii) predicting state empathy and distress scores, and (iv) predicting personality. In total, 14 teams participated in the shared task. We summarize the methods and resources used by the participating teams.

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RU at WASSA 2024 Shared Task: Task-Aligned Prompt for Predicting Empathy and Distress
Haein Kong | Seonghyeon Moon

This paper describes our approach for the WASSA 2024 Shared Task on Empathy Detection and Emotion Classification and Personality Detection in Interactions at ACL 2024. We focused on Track 3: Empathy Prediction (EMP) which aims to predict the empathy and distress of writers based on their essays. Recently, LLMs have been used to detect the psychological status of the writers based on the texts. Previous studies showed that the performance of LLMs can be improved by designing prompts properly. While diverse approaches have been made, we focus on the fact that LLMs can have different nuances for psychological constructs such as empathy or distress to the specific task. In addition, people can express their empathy or distress differently according to the context. Thus, we tried to enhance the prediction performance of LLMs by proposing a new prompting strategy: Task-Aligned Prompt (TAP). This prompt consists of aligned definitions for empathy and distress to the original paper and the contextual information about the dataset. Our proposed prompt was tested using ChatGPT and GPT4o with zero-shot and few-shot settings and the performance was compared to the plain prompts. The results showed that the TAP-ChatGPT-zero-shot achieved the highest average Pearson correlation of empathy and distress on the EMP track.

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Chinchunmei at WASSA 2024 Empathy and Personality Shared Task: Boosting LLM’s Prediction with Role-play Augmentation and Contrastive Reasoning Calibration
Tian Li | Nicolay Rusnachenko | Huizhi Liang

This paper presents the Chinchunmei team’s contributions to the WASSA2024 Shared-Task 1: Empathy Detection and Emotion Classification. We participated in Tracks 1, 2, and 3 to predict empathetic scores based on dialogue, article, and essay content. We choose Llama3-8b-instruct as our base model. We developed three supervised fine-tuning schemes: standard prediction, role-play, and contrastive prediction, along with an innovative scoring calibration method called Contrastive Reasoning Calibration during inference. Pearson Correlation was used as the evaluation metric across all tracks. For Track 1, we achieved 0.43 on the devset and 0.17 on the testset. For Track 2 emotion, empathy, and polarity labels, we obtained 0.64, 0.66, and 0.79 on the devset and 0.61, 0.68, and 0.58 on the testset. For Track 3 empathy and distress labels, we got 0.64 and 0.56 on the devset and 0.33 and 0.35 on the testset.

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Empathify at WASSA 2024 Empathy and Personality Shared Task: Contextualizing Empathy with a BERT-Based Context-Aware Approach for Empathy Detection
Arda Numanoğlu | Süleyman Ateş | Nihan Cicekli | Dilek Küçük

Empathy detection from textual data is a complex task that requires an understanding of both the content and context of the text. This study presents a BERT-based context-aware approach to enhance empathy detection in conversations and essays. We participated in the WASSA 2024 Shared Task, focusing on two tracks: empathy and emotion prediction in conversations (CONV-turn) and empathy and distress prediction in essays (EMP). Our approach leverages contextual information by incorporating related articles and emotional characteristics as additional inputs, using BERT-based Siamese (parallel) architecture. Our experiments demonstrated that using article summaries as context significantly improves performance, with the parallel BERT approach outperforming the traditional method of concatenating inputs with the ‘[SEP]‘ token. These findings highlight the importance of context-awareness in empathy detection and pave the way for future improvements in the sensitivity and accuracy of such systems.

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Zhenmei at WASSA-2024 Empathy and Personality Shared Track 2 Incorporating Pearson Correlation Coefficient as a Regularization Term for Enhanced Empathy and Emotion Prediction in Conversational Turns
Liting Huang | Huizhi Liang

In the realm of conversational empathy and emotion prediction, emotions are frequently categorized into multiple levels. This study seeks to enhance the performance of emotion prediction models by incorporating the Pearson correlation coefficient as a regularization term within the loss function. This regularization approach ensures closer alignment between predicted and actual emotion levels, mitigating extreme predictions and resulting in smoother and more consistent outputs. Such outputs are essential for capturing the subtle transitions between continuous emotion levels. Through experimental comparisons between models with and without Pearson regularization, our findings demonstrate that integrating the Pearson correlation coefficient significantly boosts model performance, yielding higher correlation scores and more accurate predictions. Our system officially ranked 9th at the Track 2: CONV-turn. The code for our model can be found at Link .

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Empaths at WASSA 2024 Empathy and Personality Shared Task: Turn-Level Empathy Prediction Using Psychological Indicators
Shaz Furniturewala | Kokil Jaidka

For the WASSA 2024 Empathy and Personality Prediction Shared Task, we propose a novel turn-level empathy detection method that decomposes empathy into six psychological indicators: Emotional Language, Perspective-Taking, Sympathy and Compassion, Extroversion, Openness, and Agreeableness. A pipeline of text enrichment using a Large Language Model (LLM) followed by DeBERTA fine-tuning demonstrates a significant improvement in the Pearson Correlation Coefficient and F1 scores for empathy detection, highlighting the effectiveness of our approach. Our system officially ranked 7th at the CONV-turn track.

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NU at WASSA 2024 Empathy and Personality Shared Task: Enhancing Personality Predictions with Knowledge Graphs; A Graphical Neural Network and LightGBM Ensemble Approach
Emmanuel Osei-Brefo | Huizhi Liang

This paper proposes a novel ensemble approach that combines Graph Neural Networks (GNNs) and LightGBM to enhance personality prediction based on the personality Big 5 model. By integrating BERT embeddings from user essays with knowledge graph-derived embeddings, our method accurately captures rich semantic and relational information. Additionally, a special loss function that combines Mean Squared Error (MSE), Pearson correlation loss, and contrastive loss to improve model performance is introduced. The proposed ensemble model, made of Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), and LightGBM, demonstrates superior performance over other models, with significant improvements in prediction accuracy for the Big Five personality traits achieved. Our system officially ranked 2^nd at the Track 4: PER track.

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Daisy at WASSA 2024 Empathy and Personality Shared Task: A Quick Exploration on Emotional Pattern of Empathy and Distress
Rendi Chevi | Alham Aji

When we encountered upsetting or tragic situations involving other people, we might feel certain emotions that are congruent, though not necessarily identical, to what that person might went through. These kind of vicarious emotions are what defined empathy and distress, they can be seen as a form of emotional response to other people in need. In this paper, we describe our participation in WASSA 2024 Shared Task 3 in predicting writer’s level of empathy and distress from their personal essays. We approach this task by assuming one’s level of empathy and distress can be revealed from the emotional patterns within their essay. By extracting the emotional patterns from essays via an emotion classifier, we regress the empathy and distress levels from these patterns. Through correlation and model explainability analysis, we found that there are similar set of emotions, such as sadness or disappointment, and distinct set of emotions, such as anger or approval, that might describe the writer’s level of empathy and distress. We hope that our approach and findings could serve as a basis for future work that try to model and explain empathy and distress from emotional patterns.

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WASSA 2024 Shared Task: Enhancing Emotional Intelligence with Prompts
Svetlana Churina | Preetika Verma | Suchismita Tripathy

This paper describes the system for the last-min-submittion team in WASSA-2024 Shared Task 1:Empathy Detection and Emotion Classification. This task aims at developing models which can predict the empathy, emotion, and emotional polarity. This system achieved relatively goodresults on the competition’s official leaderboard.The code of this system is available here.

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hyy33 at WASSA 2024 Empathy and Personality Shared Task: Using the CombinedLoss and FGM for Enhancing BERT-based Models in Emotion and Empathy Prediction from Conversation Turns
Huiyu Yang | Liting Huang | Tian Li | Nicolay Rusnachenko | Huizhi Liang

This paper presents our participation to the WASSA 2024 Shared Task on Empathy Detection and Emotion Classification and Personality Detection in Interactions. We focus on Track 2: Empathy and Emotion Prediction in Conversations Turns (CONV-turn), which consists of predicting the perceived empathy, emotion polarity and emotion intensity at turn level in a conversation. In the method, we conduct BERT and DeBERTa based finetuning, implement the CombinedLoss which consists of a structured contrastive loss and Pearson loss, adopt adversarial training using Fast Gradient Method (FGM). This method achieved Pearson correlation of 0.581 for Emotion,0.644 for Emotional Polarity and 0.544 for Empathy on the test set, with the average value of 0.590 which ranked 4th among all teams. After submission to WASSA 2024 competition, we further introduced the segmented mix-up for data augmentation, boosting for ensemble and regression experiments, which yield even better results: 0.6521 for Emotion, 0.7376 for EmotionalPolarity, 0.6326 for Empathy in Pearson correlation on the development set. The implementation and fine-tuned models are publicly-available at https://github.com/hyy-33/hyy33-WASSA-2024-Track-2.

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Fraunhofer SIT at WASSA 2024 Empathy and Personality Shared Task: Use of Sentiment Transformers and Data Augmentation With Fuzzy Labels to Predict Emotional Reactions in Conversations and Essays
Raphael Frick | Martin Steinebach

Predicting emotions and emotional reactions during conversations and within texts poses challenges, even for advanced AI systems. The second iteration of the WASSA Empathy and Personality Shared Task focuses on creating innovative models that can anticipate emotional responses to news articles containing harmful content across four tasks.In this paper, we introduce our Fraunhofer SIT team’s solutions for the three tasks: Task 1 (CONVD), Task 2 (CONVT), and Task 3 (EMP).It involves combining LLM-driven data augmentation with fuzzy labels and fine-tuning RoBERTa models pre-trained on sentiment classification tasks to solve the regression problems. In the competition, our solutions achieved first place in Task 1, X in Task 2, and third place in Task 3.

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EmpatheticFIG at WASSA 2024 Empathy and Personality Shared Task: Predicting Empathy and Emotion in Conversations with Figurative Language
Gyeongeun Lee | Zhu Wang | Sathya N. Ravi | Natalie Parde

Recent research highlights the importance of figurative language as a tool for amplifying emotional impact. In this paper, we dive deeper into this phenomenon and outline our methods for Track 1, Empathy Prediction in Conversations (CONV-dialog) and Track 2, Empathy and Emotion Prediction in Conversation Turns (CONV-turn) of the WASSA 2024 shared task. We leveraged transformer-based large language models augmented with figurative language prompts, specifically idioms, metaphors and hyperbole, that were selected and trained for each track to optimize system performance. For Track 1, we observed that a fine-tuned BERT with metaphor and hyperbole features outperformed other models on the development set. For Track 2, DeBERTa, with different combinations of figurative language prompts, performed well for different prediction tasks. Our method provides a novel framework for understanding how figurative language influences emotional perception in conversational contexts. Our system officially ranked 4th in the 1st track and 3rd in the 2nd track.

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ConText at WASSA 2024 Empathy and Personality Shared Task: History-Dependent Embedding Utterance Representations for Empathy and Emotion Prediction in Conversations
Patrícia Pereira | Helena Moniz | Joao Paulo Carvalho

Empathy and emotion prediction are key components in the development of effective and empathetic agents, amongst several other applications. The WASSA shared task on empathy empathy and emotion prediction in interactions presents an opportunity to benchmark approaches to these tasks.Appropriately selecting and representing the historical context is crucial in the modelling of empathy and emotion in conversations. In our submissions, we model empathy, emotion polarity and emotion intensity of each utterance in a conversation by feeding the utterance to be classified together with its conversational context, i.e., a certain number of previous conversational turns, as input to an encoder Pre-trained Language Model (PLM), to which we append a regression head for prediction. We also model perceived counterparty empathy of each interlocutor by feeding all utterances from the conversation and a token identifying the interlocutor for which we are predicting the empathy. Our system officially ranked 1st at the CONV-turn track and 2nd at the CONV-dialog track.

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Findings of the WASSA 2024 EXALT shared task on Explainability for Cross-Lingual Emotion in Tweets
Aaron Maladry | Pranaydeep Singh | Els Lefever

This paper presents a detailed description and results of the first shared task on explainability for cross-lingual emotion in tweets. Given a tweet in one of the five target languages (Dutch, Russian, Spanish, English, and French), systems should predict the correct emotion label (Task 1), as well as the words triggering the predicted emotion label (Task 2). The tweets were collected based on a list of stop words to prevent topical or emotional bias and were subsequently manually annotated. For both tasks, only a training corpus for English was provided, obliging participating systems to design cross-lingual approaches. Our shared task received submissions from 14 teams for the emotion detection task and from 6 teams for the trigger word detection task. The highest macro F1-scores obtained for both tasks are respectively 0.629 and 0.616, demonstrating that cross-lingual emotion detection is still a challenging task.

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Cross-lingual Emotion Detection through Large Language Models
Ram Mohan Rao Kadiyala

This paper presents a detailed system description of our entry which finished 1st with a large lead at WASSA 2024 Task 2, focused on cross-lingual emotion detection. We utilized a combination of large language models (LLMs) and their ensembles to effectively understand and categorize emotions across different languages. Our approach not only outperformed other submissions with a large margin, but also demonstrated the strength of integrating multiple models to enhance performance. Additionally, We conducted a thorough comparison of the benefits and limitations of each model used. An error analysis is included along with suggested areas for future improvement. This paper aims to offer a clear and comprehensive understanding of advanced techniques in emotion detection, making it accessible even to those new to the field.

Emotion detection from text is a crucial task in understanding natural language with wide-ranging applications. Existing approaches for multilingual emotion detection from text face challenges with data scarcity across many languages and a lack of interpretability. We propose a novel method that leverages both monolingual and multilingual pre-trained language models to improve performance and interpretability. Our approach involves 1) training a high-performing English monolingual model in parallel with a multilingual model and 2) using knowledge distillation to transfer the emotion detection capabilities from the monolingual teacher to the multilingual student model. Experiments on a multilingual dataset demonstrate significant performance gains for refined multilingual models like XLM-RoBERTa and E5 after distillation. Furthermore, our approach enhances interpretability by enabling better identification of emotion-trigger words. Our work presents a promising direction for building accurate, robust and explainable multilingual emotion detection systems.

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HITSZ-HLT at WASSA-2024 Shared Task 2: Language-agnostic Multi-task Learning for Explainability of Cross-lingual Emotion Detection
Feng Xiong | Jun Wang | Geng Tu | Ruifeng Xu

This paper describes the system developed by the HITSZ-HLT team for WASSA-2024 Shared Task 2, which addresses two closely linked sub-tasks: Cross-lingual Emotion Detection and Binary Trigger Word Detection in tweets. The main goal of Shared Task 2 is to simultaneously identify the emotions expressed and detect the trigger words across multiple languages. To achieve this, we introduce a Language-agnostic Multi Task Learning (LaMTL) framework that integrates emotion prediction and emotion trigger word detection tasks. By fostering synergistic interactions between task-specific and task-agnostic representations, the LaMTL aims to mutually enhance emotional cues, ultimately improving the performance of both tasks. Additionally, we leverage large-scale language models to translate the training dataset into multiple languages, thereby fostering the formation of language-agnostic representations within the model, significantly enhancing the model’s ability to transfer and perform well across multilingual data. Experimental results demonstrate the effectiveness of our framework across both tasks, with a particular highlight on its success in achieving second place in sub-task 2.

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UWB at WASSA-2024 Shared Task 2: Cross-lingual Emotion Detection
Jakub Šmíd | Pavel Přibáň | Pavel Král

This paper presents our system built for the WASSA-2024 Cross-lingual Emotion Detection Shared Task. The task consists of two subtasks: first, to assess an emotion label from six possible classes for a given tweet in one of five languages, and second, to predict words triggering the detected emotions in binary and numerical formats. Our proposed approach revolves around fine-tuning quantized large language models, specifically Orca 2, with low-rank adapters (LoRA) and multilingual Transformer-based models, such as XLM-R and mT5. We enhance performance through machine translation for both subtasks and trigger word switching for the second subtask. The system achieves excellent performance, ranking 1st in numerical trigger words detection, 3rd in binary trigger words detection, and 7th in emotion detection.

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PCICUNAM at WASSA 2024: Cross-lingual Emotion Detection Task with Hierarchical Classification and Weighted Loss Functions
Jesús Vázquez-Osorio | Gerardo Sierra | Helena Gómez-Adorno | Gemma Bel-Enguix

This paper addresses the shared task of multi-lingual emotion detection in tweets, presented at the Workshop on Computational Approaches to Subjectivity, Sentiment, and Social Media Analysis (WASSA) co-located with the ACL 2024 conference. The task involves predicting emotions from six classes in tweets from five different languages using only English for model training. Our approach focuses on addressing class imbalance through data augmentation, hierarchical classification, and the application of focal loss and weighted cross-entropy loss functions. These methods enhance our transformer-based model’s ability to transfer emotion detection capabilities across languages, resulting in improved performance despite the constraints of limited computational resources.

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TEII: Think, Explain, Interact and Iterate with Large Language Models to Solve Cross-lingual Emotion Detection
Long Cheng | Qihao Shao | Christine Zhao | Sheng Bi | Gina-Anne Levow

Cross-lingual emotion detection allows us to analyze global trends, public opinion, and social phenomena at scale. We participated in the Explainability of Cross-lingual Emotion Detection (EXALT) shared task, achieving an F1-score of 0.6046 on the evaluation set for the emotion detection sub-task. Our system outperformed the baseline by more than 0.16 F1-score absolute, and ranked second amongst competing systems. We conducted experiments using fine-tuning, zero-shot learning, and few-shot learning for Large Language Model (LLM)-based models as well as embedding-based BiLSTM and KNN for non-LLM-based techniques. Additionally, we introduced two novel methods: the Multi-Iteration Agentic Workflow and the Multi-Binary-Classifier Agentic Workflow. We found that LLM-based approaches provided good performance on multilingual emotion detection. Furthermore, ensembles combining all our experimented models yielded higher F1-scores than any single approach alone.

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NYCU-NLP at EXALT 2024: Assembling Large Language Models for Cross-Lingual Emotion and Trigger Detection
Tzu-Mi Lin | Zhe-Yu Xu | Jian-Yu Zhou | Lung-Hao Lee

This study describes the model design of the NYCU-NLP system for the EXALT shared task at the WASSA 2024 workshop. We instruction-tune several large language models and then assemble various model combinations as our main system architecture for cross-lingual emotion and trigger detection in tweets. Experimental results showed that our best performing submission is an assembly of the Starling (7B) and Llama 3 (8B) models. Our submission was ranked sixth of 17 participating systems for the emotion detection subtask, and fifth of 7 systems for the binary trigger detection subtask.

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Effectiveness of Scalable Monolingual Data and Trigger Words Prompting on Cross-Lingual Emotion Detection Task
Yao-Fei Cheng | Jeongyeob Hong | Andrew Wang | Anita Silva | Gina-Anne Levow

This paper introduces our submitted systems for WASSA 2024 Shared Task 2: Cross-Lingual Emotion Detection. We implemented a BERT-based classifier and an in-context learning-based system. Our best-performing model, using English Chain of Thought prompts with trigger words, reached 3rd overall with an F1 score of 0.6015. Following the motivation of the shared task, we further analyzed the scalability and transferability of the monolingual English dataset on cross-lingual tasks. Our analysis demonstrates the importance of data quality over quantity. We also found that augmented multilingual data does not necessarily perform better than English monolingual data in cross-lingual tasks. We open-sourced the augmented data and source code of our system for future research.

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WU_TLAXE at WASSA 2024 Explainability for Cross-Lingual Emotion in Tweets Shared Task 1: Emotion through Translation using TwHIN-BERT and GPT
Jon Davenport | Keren Ruditsky | Anna Batra | Yulha Lhawa | Gina-Anne Levow

This paper describes our task 1 submission for the WASSA 2024 shared task on Explainability for Cross-lingual Emotion in Tweets. Our task is to predict the correct emotion label (Anger, Sadness, Fear, Joy, Love, and Neutral) for a dataset of English, Dutch, French, Spanish, and Russian tweets, while training exclusively on English emotion labeled data, to reveal what kind of emotion detection information is transferable cross-language (Maladry et al., 2024). To that end, we used an ensemble of models with a GPT-4 decider. Our ensemble consisted of a few-shot GPT-4 prompt system and a TwHIN-BERT system fine-tuned on the EXALT and additional English data. We ranked 8th place under the name WU_TLAXE with an F1 Macro score of 0.573 on the test set. We also experimented with an English-only TwHIN-BERT model by translating the other languages into English for inference, which proved to be worse than the other models.

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Enhancing Cross-Lingual Emotion Detection with Data Augmentation and Token-Label Mapping
Jinghui Zhang | Yuan Zhao | Siqin Zhang | Ruijing Zhao | Siyu Bao

Cross-lingual emotion detection faces challenges such as imbalanced label distribution, data scarcity, cultural and linguistic differences, figurative language, and the opaqueness of pre-trained language models. This paper presents our approach to the EXALT shared task at WASSA 2024, focusing on emotion transferability across languages and trigger word identification. We employ data augmentation techniques, including back-translation and synonym replacement, to address data scarcity and imbalance issues in the emotion detection sub-task. For the emotion trigger identification sub-task, we utilize token and label mapping to capture emotional information at the subword level. Our system achieves competitive performance, ranking 13th, 1st, and 2nd in the Emotion Detection, Binary Trigger Word Detection, and Numerical Trigger Word Detection tasks.