The 2025 Conference on Empirical Methods in Natural Language Processing

Suzhou, China
November 4-9, 2025

Volumes

Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing 1810 papers
Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations 78 papers
Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track 193 papers
Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts 8 papers
Proceedings of The Third Arabic Natural Language Processing Conference 40 papers
Proceedings of The Third Arabic Natural Language Processing Conference: Shared Tasks 139 papers
Proceedings of the First BabyLM Workshop 41 papers
Proceedings of the 8th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP 33 papers
Proceedings of the 6th Workshop on Computational Approaches to Discourse, Context and Document-Level Inferences (CODI 2025) 19 papers
Proceedings of the 4th Shared Task on Discourse Relation Parsing and Treebanking (DISRPT 2025) 7 papers
Proceedings of The 10th Workshop on Financial Technology and Natural Language Processing 27 papers
Proceedings of the Fourth Workshop on Bridging Human-Computer Interaction and Natural Language Processing (HCI+NLP) 26 papers
Proceedings of The 3rd Workshop on Mathematical Natural Language Processing (MathNLP 2025) 16 papers
Proceedings of the 5th Workshop on Multilingual Representation Learning (MRL 2025) 43 papers
Proceedings of The 5th New Frontiers in Summarization Workshop 15 papers
Proceedings of the Natural Legal Language Processing Workshop 2025 33 papers
Proceedings of the The 4th Workshop on Perspectivist Approaches to NLP 24 papers
Proceedings of the First Workshop on Tailoring AI: Exploring Active and Passive LLM Personalization (PALS 2025) 1 paper
Proceedings of the 14th Joint Conference on Lexical and Computational Semantics (*SEM 2025) 36 papers
Proceedings of the Fourth Workshop on Text Simplification, Accessibility and Readability (TSAR 2025) 25 papers
Proceedings of the 2nd Workshop on Uncertainty-Aware NLP (UncertaiNLP 2025) 28 papers
Proceedings of the 9th Widening NLP Workshop 30 papers
Proceedings of the Tenth Conference on Machine Translation 113 papers
Proceedings of the 5th Wordplay: When Language meets Games Workshop (Wordplay 2025) 1 paper

bib (full) Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing

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Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
Christos Christodoulopoulos | Tanmoy Chakraborty | Carolyn Rose | Violet Peng

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Towards Automated Error Discovery: A Study in Conversational AI
Dominic Petrak | Thy Thy Tran | Iryna Gurevych

Although LLM-based conversational agents demonstrate strong fluency and coherence, they still produce undesirable behaviors (errors) that are challenging to prevent from reaching users during deployment. Recent research leverages large language models (LLMs) to detect errors and guide response-generation models toward improvement. However, current LLMs struggle to identify errors not explicitly specified in their instructions, such as those arising from updates to the response-generation model or shifts in user behavior. In this work, we introduce Automated Error Discovery, a framework for detecting and defining errors in conversational AI, and propose SEEED (Soft Clustering Extended Encoder-Based Error Detection), as an encoder-based approach to its implementation. We enhance the Soft Nearest Neighbor Loss by amplifying distance weighting for negative samples and introduce Label-Based Sample Ranking to select highly contrastive examples for better representation learning. SEEED outperforms adapted baselines—including GPT-4o and Phi-4—across multiple error-annotated dialogue datasets, improving the accuracy for detecting unknown errors by up to 8 points and demonstrating strong generalization to unknown intent detection.

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Break the Checkbox: Challenging Closed-Style Evaluations of Cultural Alignment in LLMs
Mohsinul Kabir | Ajwad Abrar | Sophia Ananiadou

A large number of studies rely on closed-style multiple-choice surveys to evaluate cultural alignment in Large Language Models (LLMs). In this work, we challenge this constrained evaluation paradigm and explore more realistic, unconstrained approaches. Using the World Values Survey (WVS) and Hofstede Cultural Dimensions as case studies, we demonstrate that LLMs exhibit stronger cultural alignment in less constrained settings, where responses are not forced. Additionally, we show that even minor changes, such as reordering survey choices, lead to inconsistent outputs, exposing the limitations of closed-style evaluations. Our findings advocate for more robust and flexible evaluation frameworks that focus on specific cultural proxies, encouraging more nuanced and accurate assessments of cultural alignment in LLMs.

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Biased Tales: Cultural and Topic Bias in Generating Children’s Stories
Donya Rooein | Vilém Zouhar | Debora Nozza | Dirk Hovy

Stories play a pivotal role in human communication, shaping beliefs and morals, particularly in children. As parents increasingly rely on large language models (LLMs) to craft bedtime stories, the presence of cultural and gender stereotypes in these narratives raises significant concerns. To address this issue, we present Biased Tales, a comprehensive dataset designed to analyze how biases influence protagonists’ attributes and story elements in LLM-generated stories. Our analysis uncovers striking disparities. When the protagonist is described as a girl (as compared to a boy), appearance-related attributes increase by 55.26%. Stories featuring non-Western children disproportionately emphasize cultural heritage, tradition, and family themes far more than those for Western children. Our findings highlight the role of sociocultural bias in making creative AI use more equitable and diverse.

Workload traces are essential to understand complex computer systems’ behavior and manage processing and memory resources. Since real-world traces are hard to obtain, synthetic trace generation is a promising alternative. This paper proposes a first-of-a-kind approach that relies on training a large language model (LLM) to generate synthetic workload traces, specifically microservice call graphs. To capture complex and arbitrary hierarchical structures and implicit constraints in such traces, we propose to train LLMs to generate recursively, making call graph generation a sequence of more manageable steps. To further enforce learning constraints on the traces and generate uncommon situations, we apply additional instruction tuning steps to align our model with the desired trace features. With this method, we train TraceLLM, an LLM for microservice trace generation, and demonstrate that it produces diverse, realistic traces under varied conditions, outperforming existing approaches in both accuracy and validity. The synthetically generated traces can effectively replace real data to optimize important microservice management tasks. Additionally, TraceLLM adapts to downstream trace-related tasks, such as predicting key trace features and infilling missing data.

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JUDGEBERT: Assessing Legal Meaning Preservation Between Sentences
David Beauchemin | Michelle Albert-Rochette | Richard Khoury | Pierre-Luc Déziel

Simplifying text while preserving its meaning is a complex yet essential task, especially in sensitive domain applications like legal texts. When applied to a specialized field, like the legal domain, preservation differs significantly from its role in regular texts. This paper introduces FrJUDGE, a new dataset to assess legal meaning preservation between two legal texts. It also introduces JUDGEBERT, a novel evaluation metric designed to assess legal meaning preservation in French legal text simplification. JUDGEBERT demonstrates a superior correlation with human judgment compared to existing metrics. It also passes two crucial sanity checks, while other metrics did not: For two identical sentences, it always returns a score of 100%; on the other hand, it returns 0% for two unrelated sentences. Our findings highlight its potential to transform legal NLP applications, ensuring accuracy and accessibility for text simplification for legal practitioners and lay users.

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QFrCoLA: a Quebec-French Corpus of Linguistic Acceptability Judgments
David Beauchemin | Richard Khoury

Large and Transformer-based language models perform outstandingly in various downstream tasks. However, there is limited understanding regarding how these models internalize linguistic knowledge, so various linguistic benchmarks have recently been proposed to facilitate syntactic evaluation of language models across languages. This paper introduces QFrCoLA (Quebec-French Corpus of Linguistic Acceptability Judgments), a normative binary acceptability judgments dataset comprising 25,153 in-domain and 2,675 out-of-domain sentences. Our study leverages the QFrCoLA dataset and seven other linguistic binary acceptability judgment corpora to benchmark seven language models. The results demonstrate that, on average, fine-tuned Transformer-based LM are strong baselines for most languages and that zero-shot binary classification large language models perform poorly on the task. However, for the QFrCoLA benchmark, on average, a fine-tuned Transformer-based LM outperformed other methods tested. It also shows that pre-trained cross-lingual LLMs selected for our experimentation do not seem to have acquired linguistic judgment capabilities during their pre-training for Quebec French. Finally, our experiment results on QFrCoLA show that our dataset, built from examples that illustrate linguistic norms rather than speakers’ feelings, is similar to linguistic acceptability judgment; it is a challenging dataset that can benchmark LM on their linguistic judgment capabilities.

The value orientation of Large Language Models (LLMs) has been extensively studied, as it can shape user experiences across demographic groups.However, two key challenges remain: (1) the lack of systematic comparison across value probing strategies, despite the Multiple Choice Question (MCQ) setting being vulnerable to perturbations, and (2) the uncertainty over whether probed values capture in-context information or predict models’ real-world actions.In this paper, we systematically compare three widely used value probing methods: token likelihood, sequence perplexity, and text generation.Our results show that all three methods exhibit large variances under non-semantic perturbations in prompts and option formats, with sequence perplexity being the most robust overall.We further introduce two tasks to assess expressiveness: demographic prompting, testing whether probed values adapt to cultural context; and value–action agreement, testing the alignment of probed values with value-based actions.We find that demographic context has little effect on the text generation method, and probed values only weakly correlate with action preferences across all methods.Our work highlights the instability and the limited expressive power of current value probing methods, calling for more reliable LLM value representations.

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A Systematic Analysis of Base Model Choice for Reward Modeling
Kian Ahrabian | Pegah Jandaghi | Negar Mokhberian | Sai Praneeth Karimireddy | Jay Pujara

Reinforcement learning from human feedback (RLHF) and, at its core, reward modeling have become a crucial part of training powerful large language models (LLMs). One commonly overlooked factor in training high-quality reward models (RMs) is the effect of the base model, which is becoming more challenging to choose given the rapidly growing pool of LLMs. In this work, we present a systematic analysis of the effect of base model selection on reward modeling performance. Our results show that the performance can be improved by up to 14% compared to the most common (i.e., default) choice. Moreover, we showcase the strong statistical relation between some existing benchmarks and downstream performances. We also demonstrate that the results from a small set of benchmarks could be combined to boost the model selection (+18% on average in the top 5-10). Lastly, we illustrate the impact of different post-training steps on the final performance and explore using estimated data distributions to reduce performance prediction error.

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Comparing Specialised Small and General Large Language Models on Text Classification: 100 Labelled Samples to Achieve Break-Even Performance
Branislav Pecher | Ivan Srba | Maria Bielikova

When solving NLP tasks with limited labelled data, researchers typically either use a general large language model without further update, or use a small number of labelled samples to tune a specialised smaller model. In this work, we answer an important question – how many labelled samples are required for the specialised small models to outperform general large models, while taking the performance variance into consideration. By observing the behaviour of fine-tuning, instruction-tuning, prompting and in-context learning on 8 language models, we identify such performance break-even points across 8 representative text classification tasks of varying characteristics. We show that the specialised models often need only few samples (on average 100) to be on par or better than the general ones. At the same time, the number of required labels strongly depends on the dataset or task characteristics, with fine-tuning on binary datasets requiring significantly more samples. When performance variance is taken into consideration, the number of required labels increases on average by 100 - 200%. Finally, larger models do not consistently lead to better performance and lower variance, with 4-bit quantisation having negligible impact.

Determining faithfulness of a claim to a source document is an important problem across many domains. This task is generally treated as a binary judgment of whether the claim is supported or unsupported in relation to the source. In many cases, though, whether a claim is supported can be ambiguous. For instance, it may depend on making inferences from given evidence, and different people can reasonably interpret the claim as either supported or unsupported based on their agreement with those inferences. Forcing binary labels upon such claims lowers the reliability of evaluation. In this work, we reframe the task to manage the subjectivity involved with factuality judgments of ambiguous claims. We introduce LLM-generated edits of summaries as a method of providing a nuanced evaluation of claims: how much does a summary need to be edited to be unambiguous? Whether a claim gets rewritten and how much it changes can be used as an automatic evaluation metric, the Ambiguity Rewrite Metric (ARM), with a much richer feedback signal than a binary judgment of faithfulness. We focus on the area of narrative summarization as it is particularly rife with ambiguity and subjective interpretation. We show that ARM produces a 21% absolute improvement in annotator agreement on claim faithfulness, indicating that subjectivity is reduced.

Evaluating the pedagogical capabilities of AI-based tutoring models is critical for making guided progress in the field. Yet, we lack a reliable, easy-to-use, and simple-to-run evaluation that reflects the pedagogical abilities of models. To fill this gap, we present MathTutorBench, an open-source benchmark for holistic tutoring model evaluation. MathTutorBench contains a collection of datasets and metrics that broadly cover tutor abilities as defined by learning sciences research in dialog-based teaching. To score the pedagogical quality of open-ended teacher responses, we train a reward model and show it can discriminate expert from novice teacher responses with high accuracy. We evaluate a wide set of closed- and open-weight models on MathTutorBench and find that subject expertise, indicated by solving ability, does not immediately translate to good teaching. Rather, pedagogy and subject expertise appear to form a trade-off that is navigated by the degree of tutoring specialization of the model. Furthermore, tutoring appears to become more challenging in longer dialogs, where simpler questioning strategies begin to fail. We release the benchmark, code, and leaderboard openly to enable rapid benchmarking of future models.

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Preemptive Detection and Correction of Misaligned Actions in LLM Agents
Haishuo Fang | Xiaodan Zhu | Iryna Gurevych

Deploying LLM-based agents in real-life applications often faces a critical challenge: the misalignment between agents’ behavior and user intent. Such misalignment may lead agents to unintentionally execute some critical actions that carry negative outcomes (e.g., accidentally triggering a buy-now in web shopping), resulting in undesirable or even irreversible consequences. Although addressing these issues is crucial, the preemptive detection and correction of misaligned actions remains relatively underexplored. To fill this gap, we introduce InferAct, a novel approach that leverages the belief reasoning ability of LLMs, grounded in Theory-of-Mind, to detect misaligned actions. Once the misalignment is detected, InferAct alerts users for timely correction, preventing adverse outcomes and enhancing the reliability of LLM agents’ decision-making processes. Experiments on three widely used tasks demonstrate InferAct achieves up to 20% improvements on Marco-F1 against baselines in misaligned action detection. An in-depth evaluation of misalignment correction further highlights InferAct‘s effectiveness in improving agent alignment.

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Fingerprinting LLMs through Survey Item Factor Correlation: A Case Study on Humor Style Questionnaire
Simon Münker

LLMs increasingly engage with psychological instruments, yet how they represent constructs internally remains poorly understood. We introduce a novel approach to “fingerprinting” LLMs through their factor correlation patterns on standardized psychological assessments to deepen the understanding of LLMs constructs representation. Using the Humor Style Questionnaire as a case study, we analyze how six LLMs represent and correlate humor-related constructs to survey participants. Our results show that they exhibit little similarity to human response patterns. In contrast, participants’ subsamples demonstrate remarkably high internal consistency. Exploratory graph analysis further confirms that no LLM successfully recovers the four constructs of the Humor Style Questionnaire. These findings suggest that despite advances in natural language capabilities, current LLMs represent psychological constructs in fundamentally different ways than humans, questioning the validity of application as human simulacra.

Although Contrastive Language-Image Pre-training (CLIP) exhibits strong performance across diverse vision tasks, its application to person representation learning faces two critical challenges: (i) the scarcity of large-scale annotated vision-language data focused on person-centric images, and (ii) the inherent limitations of global contrastive learning, which struggles to maintain discriminative local features crucial for fine-grained matching while remaining vulnerable to noisy text tokens. This work advances CLIP for person representation learning through synergistic improvements in data curation and model architecture. First, we develop a noise-resistant data construction pipeline that leverages the in-context learning capabilities of MLLMs to automatically filter and caption web-sourced images. This yields WebPerson, a large-scale dataset of 5M high-quality person-centric image-text pairs. Second, we introduce the GA-DMS (Gradient-Attention Guided Dual-Masking Synergetic) framework, which improves cross-modal alignment by adaptively masking noisy textual tokens based on the gradient-attention similarity score. Additionally, we incorporate masked token prediction objectives that compel the model to predict informative text tokens, enhancing fine-grained semantic representation learning. Extensive experiments show that GA-DMS achieves state-of-the-art performance across multiple benchmarks. The data and pre-trained models are released at https://github.com/Multimodal-Representation-Learning-MRL/GA-DMS.

Large language models (LLMs) can transform education, but their optimization for direct question-answering often undermines effective pedagogy which requires strategically withholding answers. To mitigate this, we propose an online reinforcement learning (RL)-based alignment framework that can quickly adapt LLMs into effective tutors using simulated student-tutor interactions by emphasizing pedagogical quality and guided problem-solving over simply giving away answers. We use our method to train a 7B parameter tutor model without human annotations which reaches similar performance to larger proprietary models like LearnLM. We introduce a controllable reward weighting to balance pedagogical support and student solving accuracy, allowing us to trace the Pareto frontier between these two objectives. Our models better preserve reasoning capabilities than single-turn SFT baselines and can optionally enhance interpretability through thinking tags that expose the model’s instructional planning.

Knowledge Base Question Answering (KBQA) aims to extract accurate answers from the Knowledge Base (KB). Traditional Semantic Parsing (SP)-based methods are widely used but struggle with complex queries. Recently, large language models (LLMs) have shown promise in improving KBQA performance. However, the challenge of generating error-free logical forms remains, as skeleton, topic Entity, and relation Errors still frequently occur. To address these challenges, we propose CompKBQA(Component-wise Task Decomposition for Knowledge Base Question Answering), a novel framework that optimizes the process of fine-tuning a LLM for generating logical forms by enabling the LLM to progressively learn relevant sub-tasks like skeleton generation, topic entity generation, and relevant relations generation. Additionally, we propose R³, which retrieves and incorporates KB information into the process of logical form generation. Experimental evaluations on two benchmark KBQA datasets, WebQSP and CWQ, demonstrate that CompKBQA achieves state-of-the-art performance, highlighting the importance of task decomposition and KB-aware learning.

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Permutative Preference Alignment from Listwise Ranking of Human Judgments
Yang Zhao | Yixin Wang | Mingzhang Yin

Aligning Large Language Models (LLMs) with human preferences is crucial in ensuring desirable and controllable model behaviors. Current methods, such as Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO), rely on the Bradley-Terry (B-T) model to maximize the likelihood of pairwise choices. However, when multiple responses are available, the B-T model fails to guarantee an accurate list ranking of the responses. To address this issue, we propose Permutative Preference Alignment (PPA), a novel offline listwise approach that incorporates the Normalized Discounted Cumulative Gain (NDCG)—a widely-used ranking metric—as an alternative training objective for LLM alignment. We develop an end-to-end alignment algorithm by approximating NDCG with a differentiable surrogate loss. Experiments demonstrate that PPA outperforms existing pairwise and listwise methods on evaluation sets and general benchmarks such as AlpacaEval. Furthermore, we show that NDCG-based approaches improve ranking accuracy more effectively than B-T-based methods and provide a theoretical explanation for this improvement.

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ToneCraft: Cantonese Lyrics Generation with Harmony of Tones and Pitches
Junyu Cheng | Chang Pan | Shuangyin Li

Lyrics generation has garnered increasing attention within the artificial intelligence community. Our task focuses on generating harmonious Cantonese lyrics. Unlike other languages, Cantonese has a unique system of nine contours and six tones, making it essential to satisfy the harmony rules that ensure the alignment between the melody and the tonal contours of the lyrics when composing lyrics. Current research has not yet addressed the challenge of generating lyrics that adhere to Cantonese harmony rules. To tackle this issue, we propose ToneCraft, a novel framework for generating Cantonese lyrics that ensures tonal and melodic harmony. It enables LLMs to generate lyrics with a fixed character count while aligning with tonal and melodic structures. We present an algorithm that combines character-level control, melodic guidance, and a task-specific loss to achieve tonal harmony without compromising generation flexibility and quality. By incorporating domain-specific expertise, we leverage pure lyric datasets to train our model, eliminating the need for aligned data. Both objective evaluations and subjective assessments show that our generated lyrics align with melodic contours significantly better than existing methods. All code and data are available at: https://github.com/purepasser-by/ToneCraft.

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SensorLLM: Aligning Large Language Models with Motion Sensors for Human Activity Recognition
Zechen Li | Shohreh Deldari | Linyao Chen | Hao Xue | Flora D. Salim

We introduce SensorLLM, a two-stage framework that enables Large Language Models (LLMs) to perform human activity recognition (HAR) from sensor time-series data. Despite their strong reasoning and generalization capabilities, LLMs remain underutilized for motion sensor data due to the lack of semantic context in time-series, computational constraints, and challenges in processing numerical inputs. SensorLLM addresses these limitations through a Sensor-Language Alignment stage, where the model aligns sensor inputs with trend descriptions. Special tokens are introduced to mark channel boundaries. This alignment enables LLMs to capture numerical variations, channel-specific features, and data of varying durations, without requiring human annotations. In the subsequent Task-Aware Tuning stage, we refine the model for HAR classification, achieving performance that matches or surpasses state-of-the-art methods. Our results demonstrate that SensorLLM evolves into an effective sensor learner, reasoner, and classifier through human-intuitive Sensor-Language Alignment, generalizing across diverse HAR datasets. We believe this work establishes a foundation for future research on time-series and text alignment, paving the way for foundation models in sensor data analysis. Our codes are available at https://github.com/zechenli03/SensorLLM.

Continually updating model-based indexes in generative retrieval with new documents remains challenging, as full retraining is computationally expensive and impractical under resource constraints. We propose MixLoRA-DSI, a novel framework that combines an expandable mixture of Low-Rank Adaptation experts with a layer-wise out-of-distribution (OOD)-driven expansion strategy. Instead of allocating new experts for each new corpus, our proposed expansion strategy enables sublinear parameter growth by selectively introducing new experts only when significant number of OOD documents are detected. Experiments on NQ320k and MS MARCO Passage demonstrate that MixLoRA-DSI outperforms full-model update baselines, with minimal parameter overhead and substantially lower training costs.

The growing influence of video content as a medium for communication and misinformation underscores the urgent need for effective tools to analyze claims in multilingual and multi-topic settings. Existing efforts in misinformation detection largely focus on written text, leaving a significant gap in addressing the complexity of spoken text in video transcripts. We introduce ViClaim, a dataset of 1,798 annotated video transcripts across three languages (English, German, Spanish) and six topics. Each sentence in the transcripts is labeled with three claim-related categories: fact-check-worthy, fact-non-check-worthy, or opinion. We developed a custom annotation tool to facilitate the highly complex annotation process. Experiments with state-of-the-art multilingual language models demonstrate strong performance in cross-validation (macro F1 up to 0.896) but reveal challenges in generalization to unseen topics, particularly for distinct domains. Our findings highlight the complexity of claim detection in video transcripts. ViClaim offers a robust foundation for advancing misinformation detection in video-based communication, addressing a critical gap in multimodal analysis.

Large Language Models (LLMs) with web search capabilities show significant potential for deep research, yet current methods—brittle prompt engineering or RAG-based reinforcement learning in controlled environments—fail to capture real-world complexities. In this paper, we introduce DeepResearcher, the first comprehensive framework for end-to-end training of LLM-based deep research agents through scaling reinforcement learning (RL) in real-world environments with authentic web search interactions. Unlike RAG approaches reliant on fixed corpora, DeepResearcher trains agents to navigate the noisy, dynamic open web. We implement a specialized multi-agent architecture where browsing agents extract relevant information from various webpage structures and overcoming significant technical challenges. Extensive experiments on open-domain research tasks demonstrate that DeepResearcher achieves substantial improvements of up to 28.9 points over prompt engineering-based baselines and up to 7.2 points over RAG-based RL agents. Our qualitative analysis reveals emergent cognitive behaviors from end-to-end RL training, such as planning, cross-validation, self-reflection for research redirection, and maintain honesty when unable to find definitive answers. Our results highlight that end-to-end training in real-world web environments is fundamental for developing robust research capabilities aligned with real-world applications. The source codefor DeepResearcher is released at: https://github.com/GAIR-NLP/DeepResearcher.

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Mixture of Length and Pruning Experts for Knowledge Graphs Reasoning
Enjun Du | Siyi Liu | Yongqi Zhang

Knowledge Graph (KG) reasoning, which aims to infer new facts from structured knowledge repositories, plays a vital role in Natural Language Processing (NLP) systems. Its effectiveness critically depends on constructing informative and contextually relevant reasoning paths. However, existing graph neural networks (GNNs) often adopt rigid, query-agnostic path-exploration strategies, limiting their ability to adapt to diverse linguistic contexts and semantic nuances. To address these limitations, we propose MoKGR, a mixture-of-experts framework that personalizes path exploration through two complementary components: (1) a mixture of length experts that adaptively selects and weights candidate path lengths according to query complexity, providing query-specific reasoning depth; and (2) a mixture of pruning experts that evaluates candidate paths from a complementary perspective, retaining the most informative paths for each query. Through comprehensive experiments on diverse benchmark, MoKGR demonstrates superior performance in both transductive and inductive settings, validating the effectiveness of personalized path exploration in KGs reasoning.

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MPRF: Interpretable Stance Detection through Multi-Path Reasoning Framework
ZhaoDan Zhang | Jin Zhang | Hui Xu | Jiafeng Guo | Xueqi Cheng

Stance detection, a critical task in Natural Language Processing (NLP), aims to identify the attitude expressed in text toward specific targets. Despite advancements in Large Language Models (LLMs), challenges such as limited interpretability and handling nuanced content persist. To address these issues, we propose the Multi-Path Reasoning Framework (MPRF), a novel framework that generates, evaluates, and integrates multiple reasoning paths to improve accuracy, robustness, and transparency in stance detection. Unlike prior work that relies on single-path reasoning or static explanations, MPRF introduces a structured end-to-end pipeline: it first generates diverse reasoning paths through predefined perspectives, then dynamically evaluates and optimizes each path using LLM-based scoring, and finally fuses the results via weighted aggregation to produce interpretable and reliable predictions. Extensive experiments on the SEM16, VAST, and PStance datasets demonstrate that MPRF outperforms existing models. Ablation studies further validate the critical role of MPRF’s components, highlighting its effectiveness in enhancing interpretability and handling complex stance detection tasks.

Large language models (LLMs) acquire substantial world knowledge during pre-training, which is further shaped by post-training techniques such as supervised fine-tuning (SFT). However, the impact of SFT on a model’s knowledge remains underexplored, limiting our ability to control knowledge behavior in fine-tuned models. To address this gap, we evaluate closed-book question answering (CBQA) performance across five LLMs from the LLaMA-2 and LLaMA-3 families. Surprisingly, models fine-tuned on 1,920 samples perform up to 14% worse than those fine-tuned on only 240 samples. Furthermore, varying the level of knowledge mastery in the fine-tuning data leads to performance fluctuations of over 12%. To investigate these effects, we analyze model behavior at both the token and parameter levels. Our analysis reveals that up to 90% of parameter updates during SFT do not contribute to knowledge enhancement. Restoring these updates can improve performance on the CBQA task, depending on the characteristics of the fine-tuning data. These insights offer practical guidance for developing fine-tuning strategies that more effectively strengthen model knowledge.

Instruction tuning (IT) improves large language models (LLMs) by aligning their outputs with human instructions, but its success depends critically on training data quality, and datasets such as Alpaca often contain noisy or suboptimal examples that undermine fine‐tuning. Prior selection strategies score samples using general‐purpose LLMs (e.g., GPT), leveraging their strong language understanding yet introducing inherent biases that misalign with the target model’s behavior and yield unstable downstream performance. Influence‐based methods address this by estimating each example’s marginal contribution to overall performance, but they typically assume additive contributions and therefore overlook higher‐order interactions among samples. To overcome these limitations, we propose JI²S, a novel framework that jointly models both marginal and combinatorial influences within sample groups. Applying JI²S to select the top 1,000 most influential examples from Alpaca, we fine‐tune LLaMA2‐7B, Mistral‐7B, and LLaMA2‐13B and evaluate them on Open LLM Benchmarks, MT‐Bench, and GPT‐4–judged pairwise comparisons. Our experiments show that JI²S consistently outperforms full‐dataset training and strong baselines, highlighting the value of capturing joint influence for high‐quality instruction fine‐tuning. We provide our code in this GitHub repository.

While large language models have demonstrated impressive reasoning abilities, their extension to the audio modality, particularly within large audio-language models (LALMs), remains underexplored. Addressing this gap requires a systematic approach that involves a capable base model, high-quality reasoning-oriented audio data, and effective training algorithms. In this work, we present a comprehensive solution for audio logical reasoning (ALR) tasks: we introduce SoundMind, a dataset of 6,446 audio–text annotated samples specifically curated to support complex reasoning. Building on this resource, we propose SoundMind-RL, a rule-based reinforcement learning (RL) algorithm designed to equip audio-language models with robust audio–text reasoning capabilities. By fine-tuning Qwen2.5-Omni-7B on the proposed SoundMind dataset using SoundMind-RL, we achieve strong and consistent improvements over state-of-the-art baselines on the SoundMind benchmark. This work highlights the benefit of combining high-quality, reasoning-focused datasets with specialized RL techniques, and contributes to advancing auditory intelligence in language models. The code and dataset are publicly available at https://github.com/xid32/SoundMind.

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Seeing More, Saying More: Lightweight Language Experts are Dynamic Video Token Compressors
Xiangchen Wang | Jinrui Zhang | Teng Wang | Haigang Zhang | Feng Zheng

Recent advancements in large video-language models have revolutionized video understanding tasks. However, their efficiency is significantly constrained by processing high volumes of visual tokens. Existing token compression strategies apply a fixed compression ratio, ignoring the variability in semantic density among different video clips. Consequently, this lead to inadequate representation of information-rich clips due to insufficient tokens and unnecessary computation on static or content-poor ones. To address this, we propose LangDC, a Language-aware Dynamic Token Compressor. LangDC leverages a lightweight language model to describe video clips, converting them into soft caption tokens as visual representations. Trained with our proposed semantic density-aware supervision, LangDC aims to 1) cover key visual cues necessary for downstream task reasoning and 2) dynamically adjust compression ratios based on scene richness, reflected by descriptions length. Our design mimics how humans dynamically express what they see: complex scenes (seeing more) elicit more detailed language to convey nuances (saying more), whereas simpler scenes are described with fewer words. Experimental results show that our method reduces FLOPs by 49% compared to VideoGPT+ while maintaining competitive performance. Furthermore, qualitative results demonstrate our approach adaptively adjusts the token compression ratio based on video segment richness. Code will be released once acceptance.

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RoT: Enhancing Table Reasoning with Iterative Row-Wise Traversals
Xuanliang Zhang | Dingzirui Wang | Keyan Xu | Qingfu Zhu | Wanxiang Che

The table reasoning task, crucial for efficient data acquisition, aims to answer questions based on the given table. Recently, reasoning large language models (RLLMs) with Long Chain-of-Thought (Long CoT) significantly enhance reasoning capabilities, leading to brilliant performance on table reasoning. However, Long CoT suffers from high cost for training and exhibits low reliability due to table content hallucinations. Therefore, we propose Row-of-Thought (RoT), which performs iteratively row-wise table traversal, allowing for reasoning extension and reflection-based refinement at each traversal. Scaling reasoning length by row-wise traversal and leveraging reflection capabilities of LLMs, RoT is training-free. The sequential traversal encourages greater attention to the table, thus reducing hallucinations. Experiments show that RoT, using non-reasoning models, outperforms RLLMs by an average of 4.3%, and achieves state-of-the-art results on WikiTableQuestions and TableBench with comparable models, proving its effectiveness. Also, RoT outperforms Long CoT with fewer reasoning tokens, indicating higher efficiency.

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T-MAD: Target-driven Multimodal Alignment for Stance Detection
ZhaoDan Zhang | Jin Zhang | Xueqi Cheng | Hui Xu

Multimodal Stance Detection (MSD) aims to determine a user’s stance - support, oppose, or neutral - toward a target by analyzing multimodal content such as texts and images from social media. Existing MSD methods struggle with generalizing to unseen targets and handling modality inconsistencies. To address these challenges, we propose the Target-driven Multi-modal Alignment and Dynamic Weighting Model (T-MAD), which combines target-driven multi-modal alignment and dynamic weighting mechanisms to capture target-specific relationships and balance modality contributions. The model incorporates iterative reasoning to iteratively refine predictions, achieving robust performance in both in-target and zero-shot settings. Experiments on the MMSD and MultiClimate datasets show that T-MAD outperforms state-of-the-art models, with optimal results achieved using RoBERTa, ViT, and an iterative depth of 5. Ablation studies further confirm the importance of multi-modal alignment and dynamic weighting in enhancing model effectiveness.

Current Emotion Recognition in Conversation (ERC) research follows a closed-domain assumption. However, there is no clear consensus on emotion classification in psychology, which presents a challenge for models when it comes to recognizing previously unseen emotions in real-world applications. To bridge this gap, we introduce the Unseen Emotion Recognition in Conversation (UERC) task for the first time and propose **ProEmoTrans**, a solid prototype-based emotion transfer framework. This prototype-based approach shows promise but still faces key challenges: First, implicit expressions complicate emotion definition, which we address by proposing an LLM-enhanced description approach. Second, utterance encoding in long conversations is difficult, which we tackle with a proposed parameter-free mechanism for efficient encoding and overfitting prevention. Finally, the Markovian flow nature of emotions is hard to transfer, which we address with an improved Attention Viterbi Decoding (AVD) method to transfer seen emotion transitions to unseen emotions. Extensive experiments on three datasets show that our method serves as a strong baseline for preliminary exploration in this new area.

Understanding the vulnerabilities of Large Vision Language Models (LVLMs) to jailbreak attacks is essential for their responsible real-world deployment. Most previous work requires access to model gradients, or is based on human knowledge (prompt engineering) to complete jailbreak, and they hardly consider the interaction of images and text, resulting in inability to jailbreak in black box scenarios or poor performance. To overcome these limitations, we propose a Prior-Guided Bimodal Interactive Black-Box Jailbreak Attack for toxicity maximization, referred to as PBI-Attack. Our method begins by extracting malicious features from a harmful corpus using an alternative LVLM and embedding these features into a benign image as prior information. Subsequently, we enhance these features through bidirectional cross-modal interaction optimization, which iteratively optimizes the bimodal perturbations in an alternating manner through greedy search, aiming to maximize the toxicity of the generated response. The toxicity level is quantified using a well-trained evaluation model. Experiments demonstrate that PBI-Attack outperforms previous state-of-the-art jailbreak methods, achieving an average attack success rate of 92.5% across three open-source LVLMs and around 67.3% on three closed-source LVLMs. Disclaimer: This paper contains potentially disturbing and offensive content.

Retrieval-Augmented Language Models boost task performance, owing to the retriever that provides external knowledge. Although crucial, the retriever primarily focuses on semantics relevance, which may not always be effective for generation. Thus, utility-based retrieval has emerged as a promising topic, prioritizing passages that provide valid benefits for downstream tasks. However, due to insufficient understanding, capturing passage utility accurately remains unexplored. This work proposes SCARLet, a framework for training utility-based retrievers in RALMs, which incorporates two key factors, multi-task generalization and inter-passage interaction. First, SCARLet constructs shared context on which training data for various tasks is synthesized. This mitigates semantic bias from context differences, allowing retrievers to focus on learning task-specific utility and generalize across tasks. Next, SCARLet uses a perturbation-based attribution method to estimate passage-level utility for shared context, which reflects interactions between passages and provides more accurate feedback. We evaluate our approach on ten datasets across various tasks, both in-domain and out-of-domain, showing that retrievers trained by SCARLet consistently improve the overall performance of RALMs.

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SportReason: Evaluating Retrieval-Augmented Reasoning across Tables and Text for Sports Question Answering
Kaiyue Feng | Siyue Zhang | Bingsen Chen | Yilun Zhao | Chen Zhao

We present SportReason, a benchmark for retrieval-augmented reasoning on numerical sports questions. Unlike existing benchmarks limited to one or two evidence units, SportReason requires combining and reasoning across free-text, structured tables, and semi-structured infoboxes. We provide 3,000 human-verified QA pairs by repurposing existing QA and table generation datasets, and by prompting large language models (LLMs). Each pair is grounded in multiple evidence from a multi-modal Wikipedia corpus containing 200K knowledge contexts. We evaluate existing retrievers and rerankers, along with agentic Retrieval-Augmented Generation (RAG) systems. The experimental results show that multi-evidence retrieval remains a challenge. Agentic RAG systems (e.g., Search-o1), despite iterative retrieval and reasoning capabilities, fail to improve performance due to imprecise queries, simple training, and distracting information.

With the widespread application of large language models (LLMs), the issue of generating non-existing facts, known as hallucination, has garnered increasing attention. Previous research in enhancing LLM confidence estimation mainly focuses on the single problem setting. However, LLM awareness of its internal parameterized knowledge boundary under the more challenging multi-problem setting, which requires answering multiple problems accurately simultaneously, remains underexplored. To bridge this gap, we introduce a novel method, Multiple Answers and Confidence Stepwise Tuning (MAC-Tuning), that separates the learning of answer prediction and confidence estimation during fine-tuning on instruction data. Extensive experiments across various base models and different model sizes demonstrate that our method proposed outperforms baselines by up to 25% in average precision.

Chain-of-Thought (CoT) reasoning enhances Large Language Models (LLMs) by encouraging step-by-step reasoning in natural language. However, leveraging a latent continuous space for reasoning may offer benefits in terms of both efficiency and robustness. Prior implicit CoT methods attempt to bypass language completely by reasoning in continuous space but have consistently underperformed compared to the standard explicit CoT approach. We introduce CODI (Continuous Chain-of-Thought via Self-Distillation), a novel training framework that effectively compresses natural language CoT into continuous space. CODI jointly trains a teacher task (Explicit CoT) and a student task (Implicit CoT), distilling the reasoning ability from language into continuous space by aligning the hidden states of a designated token. Our experiments show that CODI is the first implicit CoT approach to match the performance of explicit CoT on GSM8k at the GPT-2 scale, achieving a 3.1x compression rate and outperforming the previous state-of-the-art by 28.2% in accuracy. CODI also demonstrates robustness, generalizable to complex datasets, and interpretability. These results validate that LLMs can reason effectively not only in natural language, but also in a latent continuous space. Code is available at https://github.com/zhenyi4/codi.

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PAFT: Prompt-Agnostic Fine-Tuning
Chenxing Wei | Yao Shu | Mingwen Ou | Ying He | Fei Yu

Fine-tuning large language models (LLMs) often causes overfitting to specific prompt wording, where minor phrasing variations drastically reduce performance. To address this, we propose Prompt-Agnostic Fine-Tuning (PAFT), a method that enhances robustness through dynamic prompt variation during training. PAFT first generates diverse synthetic prompts, then continuously samples from this set to construct training instances, forcing models to learn fundamental task principles rather than surface-level patterns. Across systematic evaluations using both supervised fine-tuning (SFT) and reinforcement learning fine-tuning (RLFT), PAFT consistently demonstrates improved performance on benchmarks for question answering, mathematical reasoning, and tool use. It achieves 7% higher generalization accuracy on unseen prompts than standard methods with similar training efficiency. Notably, models trained with PAFT attain 3.2× faster inference speeds due to reduced prompt sensitivity. Ablation studies further validate effectiveness of PAFT, while theoretical analysis reveals that PAFT can effectively enhance the cross-domain generalization ability of LLM.

Large Multimodal Models (LMMs) face limitations in geometric reasoning due to insufficient Chain of Thought (CoT) image-text training data. While existing approaches leverage template-based or LLM-assisted methods for geometric CoT data creation, they often face challenges in achieving both diversity and precision. To bridge this gap, we introduce a two-stage Theorem-Validated Reverse Chain-of-Thought Reasoning Synthesis (TR-CoT) framework. The first stage, TR-Engine, synthesizes theorem-grounded geometric diagrams with structured descriptions and properties. The second stage, TR-Reasoner, employs reverse reasoning to iteratively refine question-answer pairs by cross-validating geometric properties and description fragments. Our approach expands theorem-type coverage, corrects long-standing misunderstandings, and enhances geometric reasoning. Fine-grained CoT improves theorem understanding and increases logical consistency by 24.5%. Our best models surpass the baselines in MathVista and GeoQA by 10.1% and 4.7%, outperforming advanced closed-source models like GPT-4o.

Multimodal in-context learning (ICL) has emerged as a key mechanism for harnessing the capabilities of large vision–language models (LVLMs). However, its effectiveness remains highly sensitive to the quality of input ICL sequences, particularly for tasks involving complex reasoning or open-ended generation. A major limitation is our limited understanding of how LVLMs actually exploit these sequences during inference. To bridge this gap, we systematically interpret multimodal ICL through the lens of task mapping, which reveals how local and global relationships within and among demonstrations guide model reasoning. Building on this insight, we present TACO, a lightweight transformer-based model equipped with task-aware attention that dynamically configures ICL sequences. By injecting task-mapping signals into the autoregressive decoding process, TACO creates a bidirectional synergy between sequence construction and task reasoning. Experiments on five LVLMs and nine datasets demonstrate that TACO consistently surpasses baselines across diverse ICL tasks. These results position task mapping as a novel and valuable perspective for interpreting and improving multimodal ICL.

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Towards Controllable Speech Synthesis in the Era of Large Language Models: A Systematic Survey
Tianxin Xie | Yan Rong | Pengfei Zhang | Wenwu Wang | Li Liu

Text-to-speech (TTS) has advanced from generating natural-sounding speech to enabling fine-grained control over attributes like emotion, timbre, and style. Driven by rising industrial demand and breakthroughs in deep learning, e.g., diffusion and large language models (LLMs), controllable TTS has become a rapidly growing research area. This survey provides **the first** comprehensive review of controllable TTS methods, from traditional control techniques to emerging approaches using natural language prompts. We categorize model architectures, control strategies, and feature representations, while also summarizing challenges, datasets, and evaluations in controllable TTS. This survey aims to guide researchers and practitioners by offering a clear taxonomy and highlighting future directions in this fast-evolving field. One can visit https://github.com/imxtx/awesome-controllabe-speech-synthesis for a comprehensive paper list and updates.

Recent progress in Multimodal Large Language Models (MLLMs) has unlocked powerful cross-modal reasoning abilities, but also raised new safety concerns, particularly when faced with adversarial multimodal inputs. To improve the safety of MLLMs during inference, we introduce a modular and adaptive inference-time intervention technology, AutoSteer, without requiring any fine-tuning of the underlying model. AutoSteer incorporates three core components: (1) a novel Safety Awareness Score (SAS) that automatically identifies the most safety-relevant distinctions among the model’s internal layers; (2) an adaptive safety prober trained to estimate the likelihood of toxic outputs from intermediate representations; and (3) a lightweight Refusal Head that selectively intervenes to modulate generation when safety risks are detected. Experiments on LLaVA-OV and Chameleon across diverse safety-critical benchmarks demonstrate that AutoSteer significantly reduces the Attack Success Rate (ASR) for textual, visual, and cross-modal threats, while maintaining general abilities. These findings position AutoSteer as a practical, interpretable, and effective framework for safer deployment of multimodal AI systems.

Simulating Professions (SP) enables Large Language Models (LLMs) to emulate professional roles. However, comprehensive psychological and ethical evaluation in these contexts remains lacking. This paper introduces EMNLP, an Educator-role Moral and Normative LLMs Profiling framework for personality profiling, moral development stage measurement, and ethical risk under soft prompt injection. EMNLP extends existing scales and constructs 88 teacher-specific moral dilemmas, enabling profession-oriented comparison with human teachers. A targeted soft prompt injection set evaluates compliance and vulnerability in teacher SP. Experiments on 14 LLMs show teacher-role LLMs exhibit more idealized and polarized personalities than human teachers, excel in abstract moral reasoning, but struggle with emotionally complex situations. Models with stronger reasoning are more vulnerable to harmful prompt injection, revealing a paradox between capability and safety. The model temperature and other hyperparameters have limited influence except in some risk behaviors. This paper presents the first benchmark to assess ethical and psychological alignment of teacher-role LLMs for educational AI. Resources are available at https://e-m-n-l-p.github.io/.

While document summarization with LLMs has enhanced access to textual information, concerns about the factual accuracy of these summaries persist (e.g., hallucination), especially in the medical domain. Tracing source evidence from which summaries are derived enables users to assess their accuracy, thereby alleviating this concern. In this paper, we introduce TracSum, a novel benchmark for traceable, aspect-based summarization, in which generated summaries are paired with sentence-level citations, enabling users to trace back to the original context. First, we annotate 500 medical abstracts for seven key medical aspects, yielding 3.5K summary-citations pairs. We then propose a fine-grained evaluation framework for this new task, designed to assess the completeness and consistency of generated content using four metrics. Finally, we introduce a summarization pipeline, Track-Then-Sum, which serves as a baseline method for comparison. In experiments, we evaluate both this baseline and a set of LLMs on TracSum, and conduct a human evaluation to assess the evaluation results. The findings demonstrate that TracSum can serve as an effective benchmark for traceable, aspect-based summarization tasks. We also observe that explicitly performing sentence-level tracking prior to summarization enhances generation accuracy, while incorporating the full context further improves summary completeness. Source code and dataset are available at https://github.com/chubohao/TracSum.

While Large Language Models (LLMs) exhibit remarkable capabilities, they also introduce significant safety and privacy risks. Current mitigation strategies often fail to preserve contextual reasoning capabilities in risky scenarios. Instead, they rely heavily on sensitive pattern matching to protect LLMs, which limits the scope. Furthermore, they overlook established safety and privacy standards, leading to systemic risks for legal compliance. To address these gaps, we formulate safety and privacy issues into contextualized compliance problems following the Contextual Integrity (CI) theory. Under the CI framework, we align our model with three critical regulatory standards: GDPR, EU AI Act, and HIPAA. Specifically, we employ reinforcement learning (RL) with a rule-based reward to incentivize contextual reasoning capabilities while enhancing compliance with safety and privacy norms. Through extensive experiments, we demonstrate that our method not only significantly enhances legal compliance (achieving a +8.58% accuracy improvement in safety/privacy benchmarks) but also further improves general reasoning capability. For OpenThinker-7B, a strong reasoning model that significantly outperforms its base model Qwen2.5-7B-Instruct across diverse subjects, our method enhances its general reasoning capabilities, with +2.05% and +8.98% accuracy improvement on the MMLU and LegalBench benchmark, respectively.

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Towards General-Domain Word Sense Disambiguation: Distilling Large Language Model into Compact Disambiguator
Liqiang Ming | Sheng-hua Zhong | Yuncong Li

Word Sense Disambiguation (WSD) aims to determine the correct meaning of a word in context from a predefined inventory, and remains a fundamental challenge in natural language understanding. Existing methods rely heavily on manually annotated data, which limits coverage and generalization. In this work, we propose a scalable framework that leverages large language models (LLMs) as knowledge distillers to construct silver-standard WSD corpora. We explore generation-based distillation, where diverse examples are synthesized for dictionary senses, and annotation-based distillation, where LLMs assign sense labels to polysemous words within real-world corpus sentences. The resulting data is used to train tiny models. Extensive experiments show that models distilled from LLM-generated data outperform those trained on gold-standard corpora, especially on general-domain benchmarks. Our annotation-based model, after balancing sense distribution, achieves 50% F1 gain on the most challenging test set and the best distilled model can match or even exceed the performance of its LLM teacher, despite having over 1000 times fewer parameters. These results demonstrate the effectiveness of LLM-based distillation for building accurate, generalizable, and efficient WSD systems.

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SLoW: Select Low-frequency Words! Automatic Dictionary Selection for Translation on Large Language Models
Hongyuan Lu | Zixuan Li | Zefan Zhang | Wai Lam

There are more than 7,000 languages around the world, and current Large Language Models (LLMs) only support hundreds of languages. Dictionary-based prompting methods can enhance translation on them, but most methods use all the available dictionaries, which could be expensive. Instead, it will be flexible to have a trade-off between token consumption and translation performance. This paper proposes a novel task called Automatic Dictionary Selection (ADS). The goal of the task is to automatically select which dictionary to use to enhance translation. We propose a novel and effective method which we call Select Low-frequency Words! (SLoW) which selects those dictionaries that have a lower frequency. Our methods have unique advantages. First, there is no need for access to the training data for frequency estimation (which is usually unavailable). Second, it inherits the advantage of dictionary-based methods, where no additional tuning is required on LLMs. Experimental results on 100 languages from FLORES indicate that SLoW surpasses strong baselines, and it can obviously save token usage, with many languages even surpassing the translation performance of the full dictionary baseline.

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Parallel Continuous Chain-of-Thought with Jacobi Iteration
Haoyi Wu | Zhihao Teng | Kewei Tu

Continuous chain-of-thought has been shown to be effective in saving reasoning tokens for large language models. By reasoning with continuous latent thought tokens, continuous CoT is able to perform implicit reasoning in a compact manner. However, the sequential dependencies between latent thought tokens spoil parallel training, leading to long training time. In this paper, we propose Parallel Continuous Chain-of-Thought (PCCoT), which performs Jacobi iteration on the latent thought tokens, updating them iteratively in parallel instead of sequentially and thus improving both training and inference efficiency of continuous CoT. Experiments demonstrate that by choosing the proper number of iterations, we are able to achieve comparable or even better performance while saving nearly 50% of the training and inference time. Moreover, PCCoT shows better stability and robustness in the training process. Our code is available at https://github.com/whyNLP/PCCoT.

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EQA-RM: A Generative Embodied Reward Model with Test-time Scaling
Yuhang Chen | Zhen Tan | Tianlong Chen

Reward Models (RMs), vital for large model alignment, are underexplored for complex embodied tasks like Embodied Question Answering (EQA) where nuanced evaluation of agents’ spatial, temporal, and logical understanding is critical yet not considerred by generic approaches. We introduce EQA-RM, a novel generative multimodal reward model specifically architected for EQA, trained via our innovative Contrastive Group Relative Policy Optimization (C-GRPO) strategy to learn fine-grained behavioral distinctions. The generative nature of EQA-RM provides interpretable, structured reward feedback (beyond simple scalars), uniquely enabling test-time scaling to dynamically adjust evaluation granularity, from concise scores to detailed critiques of reasoning and grounding, at inference without retraining. Concurrently, we introduce EQARewardBench, a new benchmark built on OpenEQA for standardized EQA reward model assessment. Demonstrating high sample efficiency, EQA-RM (fine-tuning Qwen2-VL-2B-Instruct) achieves 61.9% accuracy on EQA-RM-Bench with 700 samples, outperforming strong proprietary baselines, including Gemini-2.5-Flash, GPT-4o, Claude-3.5-Haiku, and open-sourced state-of-the-art models such as RoVRM and VisualPRM.

The responsible deployment of Large Language Models (LLMs) necessitates rigorous safety evaluations. However, a critical challenge arises from inconsistencies between an LLM’s internal refusal decisions and external safety assessments, hindering effective validation. This paper introduces the concept of the ‘refusal gap’ to formally define these discrepancies. We then present a novel, refusal-aware red teaming framework designed to automatically generate test cases that expose such gaps. Our framework employs ‘refusal probes’, which leverage the target model’s hidden states, to detect internal model refusals. These are subsequently contrasted with judgments from an external safety evaluator. The identified discrepancy serves as a signal to guide a red-teaming model in crafting test cases that maximize this refusal gap. To further enhance test case diversity and address challenges related to sparse rewards, we introduce a hierarchical, curiosity-driven mechanism that incentivizes both refusal gap maximization and broad topic exploration. Empirical results demonstrate that our method significantly outperforms existing reinforcement learning-based approaches in generating diverse test cases and achieves a substantially higher discovery rate of refusal gaps.

Machine writing with large language models often relies on retrieval-augmented generation. However, these approaches remain confined within the boundaries of the model’s predefined scope, limiting the generation of content with rich information. Specifically, vanilla-retrieved information tends to lack depth, novelty, and suffers from redundancy, which negatively impacts the quality of generated articles, leading to shallow, unoriginal, and repetitive outputs. To address these issues, we propose OmniThink, a slow-thinking machine writing framework that emulates the human-like process of iterative expansion and reflection. The core idea behind OmniThink is to simulate the cognitive behavior of learners as they slowly deepen their knowledge of the topics. Experimental results demonstrate that OmniThink improves the knowledge density of generated articles without compromising metrics such as coherence and depth. Human evaluations and expert feedback further highlight the potential of OmniThink to address real-world challenges in the generation of long-form articles.

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LinkAlign: Scalable Schema Linking for Real-World Large-Scale Multi-Database Text-to-SQL
Yihan Wang | Peiyu Liu | Xin Yang

Schema linking is a critical bottleneck in applying existing Text-to-SQL models to real-world, large-scale, multi-database environments. Through error analysis, we identify two major challenges in schema linking: (1) Database Retrieval: accurately selecting the target database from a large schema pool, while effectively filtering out irrelevant ones; and (2) Schema Item Grounding: precisely identifying the relevant tables and columns within complex and often redundant schemas for SQL generation. Based on these, we introduce LinkAlign, a novel framework tailored for large-scale databases with thousands of fields. LinkAlign comprises three key steps: multi-round semantic enhanced retrieval and irrelevant information isolation for Challenge 1, and schema extraction enhancement for Challenge 2. Each stage supports both Agent and Pipeline execution modes, enabling balancing efficiency and performance via modular design. To enable more realistic evaluation, we construct AmbiDB, a synthetic dataset designed to reflect the ambiguity of real-world schema linking. Experiments on widely-used Text-to-SQL benchmarks demonstrate that LinkAlign consistently outperforms existing baselines on all schema linking metrics. Notably, it improves the overall Text-to-SQL pipeline and achieves a new state-of-the-art score of 33.09% on the Spider 2.0-Lite benchmark using only open-source LLMs, ranking first on the leaderboard at the time of submission. The codes are available at https://github.com/Satissss/LinkAlign

In large language models (LLMs), certain neurons can store distinct pieces of knowledge learned during pretraining. While factual knowledge typically appears as a combination of relations and entities, it remains unclear whether some neurons focus on a relation itself – independent of any entity. We hypothesize such neurons detect a relation in the input text and guide generation involving such a relation. To investigate this, we study the LLama-2 family on a chosen set of relations, with a statistics-based method. Our experiments demonstrate the existence of relation-specific neurons. We measure the effect of selectively deactivating candidate neurons specific to relation r on the LLM’s ability to handle (1) facts involving relation r and (2) facts involving a different relation r' ≠ r. With respect to their capacity for encoding relation information, we give evidence for the following three properties of relation-specific neurons. (i) Neuron cumulativity. Multiple neurons jointly contribute to processing facts involving relation r, with no single neuron fully encoding a fact in r on its own. (ii) Neuron versatility. Neurons can be shared across multiple closely related as well as less related relations. In addition, some relation neurons transfer across languages. (iii) Neuron interference. Deactivating neurons specific to one relation can improve LLMs’ factual recall performance for facts of other relations. We make our code and data publicly available at https://github.com/cisnlp/relation-specific-neurons.

Large language model (LLM) agents are widely deployed in real-world applications, where they leverage tools to retrieve and manipulate external data for complex tasks. However, when interacting with untrusted data sources (e.g., fetching information from public websites), tool responses may contain injected instructions that covertly influence agent behaviors and lead to malicious outcomes, a threat referred to as Indirect\ Prompt\ Injection (IPI). Existing defenses typically rely on advanced prompting strategies or auxiliary detection models. While these methods have demonstrated some effectiveness, they fundamentally rely on assumptions about the model’s inherent security, which lacks structural constraints on agent behaviors. As a result, agents still retain unrestricted access to tool invocations, leaving them vulnerable to stronger attack vectors that can bypass the security guardrails of the model. To\ prevent\ malicious\ tool\ invocations\ at\ the\ source, we propose a novel defensive task execution paradigm, called IPIGuard, which models the agents’ task execution process as a traversal over a planned Tool\ Dependency\ Graph (TDG). By explicitly decoupling action planning from interaction with external data, IPIGuard significantly reduces unintended tool invocations triggered by injected instructions, thereby enhancing robustness against IPI attacks. Experiments on the AgentDojo benchmark show that IPIGuard achieves a superior balance between effectiveness and robustness, paving the way for the development of safer agentic systems in dynamic environments.

Visual Question Answering (VQA) is increasingly used in diverse applications ranging from general visual reasoning to safety-critical domains such as medical imaging and autonomous systems, where models must provide not only accurate answers but also explanations that humans can easily understand and verify. Prototype-based modeling has shown promise for interpretability by grounding predictions in semantically meaningful regions for purely visual reasoning tasks, yet remains underexplored in the context of VQA. We present ProtoVQA, a unified prototypical framework that (i) learns question-aware prototypes that serve as reasoning anchors, connecting answers to discriminative image regions, (ii) applies spatially constrained matching to ensure that the selected evidence is coherent and semantically relevant, and (iii) supports both answering and grounding tasks through a shared prototype backbone. To assess explanation quality, we propose the Visual–Linguistic Alignment Score (VLAS), which measures how well the model’s attended regions align with ground-truth evidence. Experiments on Visual7W show that ProtoVQA yields faithful, fine-grained explanations while maintaining competitive accuracy, advancing the development of transparent and trustworthy VQA systems.

Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities by integrating visual and textual inputs, yet modality alignment remains one of the most challenging aspects. Current MLLMs typically rely on simple adapter architectures and pretraining approaches to bridge vision encoders with large language models (LLM), guided by image-level supervision. We identify this paradigm often leads to suboptimal alignment between modalities, significantly constraining the LLM’s ability to properly interpret and reason with visual features particularly for smaller language models. To address this fundamental limitation, we propose Supervised Embedding Alignment (SEA), a token-level supervision alignment method that enables more precise visual-text alignment during pretraining. SEA introduces minimal computational overhead while preserving language capabilities and substantially improving cross-modal understanding. Our comprehensive analyses reveal critical insights into the adapter’s role in multimodal integration, and extensive experiments demonstrate that SEA consistently improves performance across various model sizes, with smaller models benefiting the most (average performance gain of 7.61% for Gemma-2B). This work establishes a foundation for developing more effective alignment strategies for future multimodal systems.

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Molecular String Representation Preferences in Pretrained LLMs: A Comparative Study in Zero- & Few-Shot Molecular Property Prediction
George Arthur Baker | Mario Sanz-Guerrero | Katharina von der Wense

Large Language Models (LLMs) have demonstrated capabilities for natural language formulations of molecular property prediction tasks, but little is known about how performance depends on the representation of input molecules to the model; the status quo approach is to use SMILES strings, although alternative chemical notations convey molecular information differently, each with their own strengths and weaknesses. To learn more about molecular string representation preferences in LLMs, we compare the performance of four recent models—GPT-4o, Gemini 1.5 Pro, Llama 3.1 405b, and Mistral Large 2—on molecular property prediction tasks from the MoleculeNet benchmark across five different molecular string representations: SMILES, DeepSMILES, SELFIES, InChI, and IUPAC names. We find statistically significant zero- and few-shot preferences for InChI and IUPAC names, potentially due to representation granularity, favorable tokenization, and prevalence in pretraining corpora. This contradicts previous assumptions that molecules should be presented to LLMs as SMILES strings. When these preferences are taken advantage of, few-shot performance rivals or surpasses many previous conventional approaches to property prediction, with the advantage of explainable predictions through chain-of-thought reasoning not held by task-specific models.

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Weight-Aware Activation Sparsity with Constrained Bayesian Optimization Scheduling for Large Language Models
Ming Wang | Miao Zhang | Xuebo Liu | Liqiang Nie

Activation sparsity provides a dynamic, input-dependent alternative to weight pruning for accelerating inference in large language models (LLMs), effectively reducing unnecessary computations and memory accesses during the forward pass. Despite its promise, existing activation sparsification methods suffer from two major limitations: (1) solely relying on activation magnitude for sparsification, ignoring the coupling influence with the corresponding weights, (2) applying uniform sparsity rates across all blocks without considering block-wise sparsity sensitivity. To address these issues, this paper proposes a novel training-free weight-aware activation sparsity framework, called **WAS**. Firstly, with analyzing the coupling relationship between weight and activation, we introduce a weight-aware scoring method to measure the activation importance in sparsification. Then, a novel constrained Bayesian optimization algorithm is further devised to set a suitable sparsity ratio for all blocks based on the sparsity sensitivity. Finally, we implement a custom GPU sparsity kernel to support the resulting sparsity patterns for wall-clock decoding speed-ups. Our **WAS** achieves competitive performance at 60% model-level sparsity and significantly outperforms prior methods at higher sparsity levels, achieving up to 1.68× inference speed-up—at no retraining or weight update. Codes are available at https://github.com/HITSZ-Miao-Group/WAS.

Existing large language model (LLM) agents for automating data science show promise, but they remain constrained by narrow task scopes, limited generalization across tasks and models, and over-reliance on state-of-the-art (SOTA) LLMs. We introduce DatawiseAgent, a notebook-centric LLM agent framework for adaptive and robust data science automation. Inspired by how human data scientists work in computational notebooks, DatawiseAgent introduces a unified interaction representation and a multi-stage architecture based on finite-state transducers (FSTs). This design enables flexible long-horizon planning, progressive solution development, and robust recovery from execution failures. Extensive experiments across diverse data science scenarios and models show that DatawiseAgent consistently achieves SOTA performance by surpassing strong baselines such as AutoGen and TaskWeaver, demonstrating superior effectiveness and adaptability. Further evaluations reveal graceful performance degradation under weaker or smaller models, underscoring the robustness and scalability.

Recent advances in text-to-video (T2V) generation highlight the critical role of high-quality video-text pairs in training models capable of producing coherent and instruction-aligned videos. However, strategies for optimizing video captions specifically for T2V training remain underexplored. In this paper, we introduce VC4VG (Video Captioning for Video Generation), a comprehensive caption optimization framework tailored to the needs of T2V models. We begin by analyzing caption content from a T2V perspective, decomposing the essential elements required for video reconstruction into multiple dimensions, and proposing a principled caption design methodology. To support evaluation, we construct VC4VG-Bench, a new benchmark featuring fine-grained, multi-dimensional, and necessity-graded metrics aligned with T2V-specific requirements. Extensive T2V fine-tuning experiments demonstrate a strong correlation between improved caption quality and video generation performance, validating the effectiveness of our approach. We release all benchmark tools and code (https://github.com/qyr0403/VC4VG) to support further research.

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LaMP-QA: A Benchmark for Personalized Long-form Question Answering
Alireza Salemi | Hamed Zamani

Personalization is essential for question answering systems that are user-centric. Despite its importance, personalization in answer generation has been relatively underexplored. This is mainly due to lack of resources for training and evaluating personalized question answering systems. We address this gap by introducing LaMP-QA—a benchmark designed for evaluating personalized long-form answer generation. The benchmark covers questions from three major categories: (1) Arts & Entertainment, (2) Lifestyle & Personal Development, and (3) Society & Culture, encompassing over 45 subcategories in total. To assess the quality and potential impact of the LaMP-QA benchmark for personalized question answering, we conduct comprehensive human and automatic evaluations, to compare multiple evaluation strategies for evaluating generated personalized responses and measure their alignment with human preferences. Furthermore, we benchmark a number of non-personalized and personalized approaches based on open-source and proprietary large language models. Our results show that incorporating the personalized context provided leads to up to 39% performance improvements. The benchmark is publicly released to support future research in this area.

Estimating the difficulty of input questions as perceived by large language models (LLMs) is essential for accurate performance evaluation and adaptive inference. Existing methods typically rely on repeated response sampling, auxiliary models, or fine-tuning the target model itself, which may incur substantial computational costs or compromise generality. In this paper, we propose a novel approach for difficulty estimation that leverages only the hidden representations produced by the target LLM. We model the token-level generation process as a Markov chain and define a value function to estimate the expected output quality given any hidden state. This allows for efficient and accurate difficulty estimation based solely on the initial hidden state, without generating any output tokens. Extensive experiments across both textual and multimodal tasks demonstrate that our method consistently outperforms existing baselines in difficulty estimation. Moreover, we apply our difficulty estimates to guide adaptive reasoning strategies, including Self-Consistency, Best-of-N, and Self-Refine, achieving higher inference efficiency with fewer generated tokens.

As Model Context Protocol (MCP) introduces an easy-to-use ecosystem for users and developers, it also brings underexplored safety risks. Its decentralized architecture, which separates clients and servers, poses unique challenges for systematic safety analysis. This paper proposes a novel framework to enhance MCP safety. Guided by the MAESTRO framework, we first analyze the missing safety mechanisms in MCP, and based on this analysis, we propose the Model Contextual Integrity Protocol (MCIP), a refined version of MCP that addresses these gaps. Next, we develop a fine-grained taxonomy that captures a diverse range of unsafe behaviors observed in MCP scenarios. Building on this taxonomy, we develop benchmark and training data that support the evaluation and improvement of LLMs’ capabilities in identifying safety risks within MCP interactions. Leveraging the proposed benchmark and training data, we conduct extensive experiments on state-of-the-art LLMs. The results highlight LLMs’ vulnerabilities in MCP interactions and demonstrate that our approach substantially improves their safety performance.

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SAKI-RAG: Mitigating Context Fragmentation in Long-Document RAG via Sentence-level Attention Knowledge Integration
Wenyu Tao | Xiaofen Xing | Zeliang Li | Xiangmin Xu

Traditional Retrieval-Augmented Generation (RAG) frameworks often segment documents into larger chunks to preserve contextual coherence, inadvertently introducing redundant noise. Recent advanced RAG frameworks have shifted toward finer-grained chunking to improve precision. However, in long-document scenarios, such chunking methods lead to fragmented contexts, isolated chunk semantics, and broken inter-chunk relationships, making cross-paragraph retrieval particularly challenging. To address this challenge, maintaining granular chunks while recovering their intrinsic semantic connections, we propose **SAKI-RAG** (Sentence-level Attention Knowledge Integration Retrieval-Augmented Generation). Our framework introduces two core components: (1) the **SentenceAttnLinker**, which constructs a semantically enriched knowledge repository by modeling inter-sentence attention relationships, and (2) the **Dual-Axis Retriever**, which is designed to expand and filter the candidate chunks from the dual dimensions of semantic similarity and contextual relevance. Experimental results across four datasets—Dragonball, SQUAD, NFCORPUS, and SCI-DOCS demonstrate that SAKI-RAG achieves better recall and precision compared to other RAG frameworks in long-document retrieval scenarios, while also exhibiting higher information efficiency.

The task of translating natural language questions into query languages has long been a central focus in semantic parsing. Recent advancements in Large Language Models (LLMs) have significantly accelerated progress in this field. However, existing studies typically focus on a single query language, resulting in methods with limited generalizability across different languages. In this paper, we formally define the Text-to-Query task paradigm, unifying semantic parsing tasks across various query languages. We identify query skeletons as a shared optimization target of Text-to-Query tasks, and propose a general dynamic data augmentation framework that explicitly diagnoses model-specific weaknesses in handling these skeletons to synthesize targeted training data. Experiments on four Text-to-Query benchmarks demonstrate that our method achieves state-of-the-art performance using only a small amount of synthesized data, highlighting the efficiency and generality of our approach and laying a solid foundation for unified research on Text-to-Query tasks. We release our code at https://github.com/jjjycaptain/Skeletron

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CondenseLM: LLMs-driven Text Dataset Condensation via Reward Matching
Cheng Shen | Yew-Soon Ong | Joey Tianyi Zhou

Dataset condensation has emerged as a promising technique to improve data efficiency under limited data budgets. However, when applied to the text level, existing methods struggle to compress more information into samples through optimization. Thus, these methods provide no obvious advantage over simpler coreset selection despite their high computational cost. In this paper, we introduce CondenseLM, a novel paradigm for both effective and efficient text-level dataset condensation. Our framework employs an LLMs-driven approach to sidestep the inherent limitations of existing methods, successfully generating more informative and less biased samples. In addition, it incorporates reward matching to align the LLMs-condensed dataset with the original dataset, maximizing representability and coverage. We conducted extensive experiments on SST-2, MNLI, AG News, and IMDB. Our approach outperforms both coreset selection and existing dataset condensation methods by large margins while also substantially reducing the computational cost.

This paper introduces MovieCORE, a novel video question answering (VQA) dataset designed to probe deeper cognitive understanding of movie content. Unlike existing datasets that focus on surface-level comprehension, MovieCORE emphasizes questions that engage System-2 thinking while remaining specific to the video material. We present an innovative agentic brainstorming approach, utilizing multiple large language models (LLMs) as thought agents to generate and refine high-quality question-answer pairs. To evaluate dataset quality, we develop a set of cognitive tests assessing depth, thought-provocation potential, and syntactic complexity. We also propose a comprehensive evaluation scheme for assessing VQA model performance on deeper cognitive tasks. To address the limitations of existing video-language models (VLMs), we introduce an agentic enhancement module, Agentic Choice Enhancement (ACE), which improves model reasoning capabilities post-training by 25%. Our work contributes to advancing movie understanding in AI systems and provides valuable insights into the capabilities and limitations of current VQA models when faced with more challenging, nuanced questions about cinematic content. Our project page, dataset and code can be found at https://joslefaure.github.io/assets/html/moviecore.html.

Detecting self-contradictions within documents is a challenging task for ensuring textual coherence and reliability. While large language models (LLMs) have advanced in many natural language understanding tasks, document-level self-contradiction detection (DSCD) remains insufficiently studied. Recent approaches leveraging Chain-of-Thought (CoT) prompting aim to enhance reasoning and interpretability; however, they only gain marginal improvement and often introduce inconsistencies across repeated responses. We observe that such inconsistency arises from incomplete reasoning chains that fail to include all relevant contradictory sentences consistently. To address this, we propose a two-stage method that combines supervised fine-tuning (SFT) and reinforcement learning (RL) to enhance DSCD performance. In the SFT phase, a teacher model helps the model learn reasoning patterns, while RL further refines its reasoning ability. Our method incorporates a task-specific reward function to expand the model’s reasoning scope, boosting both accuracy and consistency. On the ContraDoc benchmark, our approach significantly boosts Llama 3.1-8B-Instruct’s accuracy from 38.5% to 51.1%, and consistency from 59.6% to76.2%.

We introduce DRISHTIKON, a first-of-its-kind multimodal and multilingual benchmark centered exclusively on Indian culture, designed to evaluate the cultural understanding of generative AI systems. Unlike existing benchmarks with a generic or global scope, DRISHTIKON offers deep, fine-grained coverage across India’s diverse regions, spanning 15 languages, covering all states and union territories, and incorporating over 64,000 aligned text-image pairs. The dataset captures rich cultural themes including festivals, attire, cuisines, art forms, and historical heritage amongst many more. We evaluate a wide range of vision-language models (VLMs), including open-source small and large models, proprietary systems, reasoning-specialized VLMs, and Indic-focused models—across zero-shot and chain-of-thought settings. Our results expose key limitations in current models’ ability to reason over culturally grounded, multimodal inputs, particularly for low-resource languages and less-documented traditions. DRISHTIKON fills a vital gap in inclusive AI research, offering a robust testbed to advance culturally aware, multimodally competent language technologies.

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LingGym: How Far Are LLMs from Thinking Like Field Linguists?
Changbing Yang | Franklin Ma | Freda Shi | Jian Zhu

This paper introduces LingGym, a new benchmark that evaluates LLMs’ capacity for meta-linguistic reasoning using Interlinear Glossed Text (IGT) and grammatical descriptions extracted from 18 typologically diverse reference grammars. Unlike previous work that focuses on specific downstream tasks, we assess whether LLMs can generalize linguistic inference across low-resource languages and structures not seen during training. We present a controlled evaluation task: Word-Gloss Inference, in which the model must infer a missing word and gloss from context using varying levels of linguistic information (e.g., glosses, grammatical explanations, translations). Our results show that incorporating structured linguistic cues leads to consistent improvements in reasoning performance across all models. This work highlights both the promise and current limitations of using LLMs for typologically informed linguistic analysis and low-resource language documentation.

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Learning from Few Samples: A Novel Approach for High-Quality Malcode Generation
Haijian Ma | Daizong Liu | Xiaowen Cai | Pan Zhou | Yulai Xie

Intrusion Detection Systems (IDS) play a crucial role in network security defense. However, a significant challenge for IDS in training detection models is the shortage of adequately labeled malicious samples. To address these issues, this paper introduces a novel semi-supervised framework GANGRL-LLM, which integrates Generative Adversarial Networks (GANs) with Large Language Models (LLMs) to enhance malicious code generation and SQL Injection (SQLi) detection capabilities in few-sample learning scenarios. Specifically, our framework adopts a collaborative training paradigm where: (1) the GAN-based discriminator improves malicious pattern recognition through adversarial learning with generated samples and limited real samples; and (2) the LLM-based generator refines the quality of malicious code synthesis using reward signals from the discriminator. The experimental results demonstrate that even with a limited number of labeled samples, our training framework is highly effective in enhancing both malicious code generation and detection capabilities. This dual enhancement capability offers a promising solution for developing adaptive defense systems capable of countering evolving cyber threats.

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Personality Matters: User Traits Predict LLM Preferences in Multi-Turn Collaborative Tasks
Sarfaroz Yunusov | Kaige Chen | Kazi Nishat Anwar | Ali Emami

As Large Language Models (LLMs) increasingly integrate into everyday workflows, where users shape outcomes through multi-turn collaboration, a critical question emerges: do users with different personality traits systematically prefer certain LLMs over others? We conduc-ted a study with 32 participants evenly distributed across four Keirsey personality types, evaluating their interactions with GPT-4 and Claude 3.5 across four collaborative tasks: data analysis, creative writing, information retrieval, and writing assistance. Results revealed significant personality-driven preferences: *Rationals* strongly preferred GPT-4, particularly for goal-oriented tasks, while *idealists* favored Claude 3.5, especially for creative and analytical tasks. Other personality types showed task-dependent preferences. Sentiment analysis of qualitative feedback confirmed these patterns. Notably, aggregate helpfulness ratings were similar across models, showing how personality-based analysis reveals LLM differences that traditional evaluations miss.

Vision-Language Models have made significant progress on many perception-focused tasks. However, their progress on reasoning-focused tasks remains limited due to the lack of high-quality and diverse training data. In this work, we aim to address the scarcity of reasoning-focused multimodal datasets. We propose VisualWebInstruct, a novel approach that leverages search engines to create a diverse and high-quality dataset spanning multiple disciplines, including mathematics, physics, finance, and chemistry, etc. Starting with a meticulously selected set of 30,000 seed images, we employ Google Image Search to identify websites containing similar images. We collect and process HTML data from over 700K unique URLs. Through a pipeline of content extraction, filtering, and synthesis, we construct a dataset of approximately 900K question-answer (QA) pairs, with 40% consisting of visual QA pairs and the remaining comprising text-based QA pairs. Models fine-tuned on VisualWebInstruct demonstrate significant performance improvements: (1) fine-tuning on Llava-OV results in 10-20 absolute points improvement across benchmarks, and (2) fine-tuning from MAmmoTH-VL yields a 5 absolute points gain across benchmarks. Our best model, MAmmoTH-VL2, achieves the best known performance with SFT without RL within the 10B parameter class on MMMU-Pro (40.7), MathVerse (42.6), and DynaMath (55.7). These results highlight the effectiveness of our dataset in enhancing the reasoning capabilities of vision-language models for complex multimodal tasks.

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Thinking Out Loud: Do Reasoning Models Know When They’re Right?
Qingcheng Zeng | Weihao Xuan | Leyang Cui | Rob Voigt

Large reasoning models (LRMs) have recently demonstrated impressive capabilities in complex reasoning tasks by leveraging increased test-time computation and exhibiting behaviors reminiscent of human-like self-reflection. While LRMs show a clear capacity for valuable self-reflection, how this ability interacts with other model behaviors remains underexplored. We investigate this connection by analyzing verbalized confidence, how models articulate their certainty, as a lens into the nature of self-reflection in LRMs. We find that supervised fine-tuning on reasoning traces (i.e., distillation) and reinforcement learning can improve verbalized calibration in reasoning-intensive settings in a progressive, laddered fashion. However, our results also indicate that reasoning models may possess a diminished awareness of their own knowledge boundaries, as evidenced by significantly lower “I don’t know” response rates on factuality benchmarks. Moreover, we examine the relationship between verbalized confidence and reasoning chains, finding that models tend to express higher confidence when providing shorter or less elaborate reasoning. Our findings highlight how reasoning-oriented training can enhance performance in reasoning-centric tasks while potentially incurring a reasoning tax, a cost reflected in the model’s reduced ability to accurately recognize the limits of its own knowledge in small-scale models. More broadly, our work showcases how this erosion of knowledge boundaries can compromise model faithfulness, as models grow more confident without a commensurate understanding of when they should abstain.

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Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models
Weihao Xuan | Qingcheng Zeng | Heli Qi | Junjue Wang | Naoto Yokoya

Uncertainty quantification is essential for assessing the reliability and trustworthiness of modern AI systems. Among existing approaches, verbalized uncertainty, where models express their confidence through natural language, has emerged as a lightweight and interpretable solution in large language models (LLMs). However, its effectiveness in vision-language models (VLMs) remains insufficiently studied. In this work, we conduct a comprehensive evaluation of verbalized confidence in VLMs, spanning three model categories, four task domains, and three evaluation scenarios. Our results show that current VLMs often display notable miscalibration across diverse tasks and settings. Notably, visual reasoning models (i.e., thinking with images) consistently exhibit better calibration, suggesting that modality-specific reasoning is critical for reliable uncertainty estimation. To further address calibration challenges, we introduce Visual Confidence-Aware Prompting, a two-stage prompting strategy that improves confidence alignment in multimodal settings. Overall, our study highlights the inherent miscalibration in VLMs across modalities. More broadly, our findings underscore the fundamental importance of modality alignment and model faithfulness in advancing reliable multimodal systems.

Reasoning large language models (LLMs) excel in complex tasks, which has drawn significant attention to reinforcement learning (RL) for LLMs. However, existing approaches allocate an equal number of rollouts to all questions during the RL process, which is inefficient. This inefficiency stems from the fact that training on simple questions yields limited gains, whereas more rollouts are needed for challenging questions to sample correct answers. Furthermore, while RL improves response precision, it limits the model’s exploration ability, potentially resulting in a performance cap below that of the base model prior to RL. To address these issues, we propose a mechanism for dynamically allocating rollout budgets based on the difficulty of the problems, enabling more efficient RL training. Additionally, we introduce an adaptive dynamic temperature adjustment strategy to maintain the entropy at a stable level, thereby encouraging sufficient exploration. This enables LLMs to improve response precision while preserving their exploratory ability to uncover potential correct pathways. The code and data is available on: https://anonymous.4open.science/r/E3-RL4LLMs-DB28

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LLM Bias Detection and Mitigation through the Lens of Desired Distributions
Ingroj Shrestha | Padmini Srinivasan

Although prior work on bias mitigation has focused on promoting social equality and demographic parity, less attention has been given to aligning LLM’s outputs to desired distributions. For example, we might want to align a model with real-world distributions to support factual grounding. Thus, we define bias as deviation from a desired distribution, which may be an equal or real-world distribution, depending on application goals. We propose a weighted adaptive loss based fine-tuning method that aligns LLM’s gender–profession output distribution with the desired distribution, while preserving language modeling capability. Using 3 profession sets—male-dominated, female-dominated, and gender-balanced—derived from U.S. labor statistics (2024), we assess both our adaptive method for reflecting reality and a non-adaptive variant for equality. Across three masked language models, bias is observed under both distributions. We achieve near-complete mitigation under equality and 30–75% reduction under real-world settings. Autoregressive LLMs show no bias under equality but notable bias under real-world settings, with the Llama Instruct models (3.2-3B, 3.1-8B) achieving a 50–62% reduction.

Cross-Document Multi-entity question answering (MEQA) demands the integration of scattered information across documents to resolve complex queries involving entities, relationships, and contextual dependencies. Although Large Language Models (LLMs) and Retrieval-augmented Generation (RAG) systems show promise, their performance on cross-document MEQA remains underexplored due to the absence of tailored benchmarks. To address this gap, we introduce MEBench, a scalable multi-document, multi-entity benchmark designed to systematically evaluate LLMs’ capacity to retrieve, consolidate, and reason over scattered and dense information. Our benchmark comprises 4,780 questions which are systematically categorized into three primary categories: Comparative Reasoning, Statistical Reasoning and Relational Reasoning, further divided into eight distinct types, ensuring broad coverage of real-world multi-entity reasoning scenarios. Our experiments on state-of-the-art LLMs reveal critical limitations: even advanced models achieve only 59% accuracy on MEBench. Our benchmark emphasizes the importance of completeness and factual precision of information extraction in MEQA tasks, using Entity-Attributed F1 (EA-F1) metric for granular evaluation of entity-level correctness and attribution validity. MEBench not only highlights systemic weaknesses in current LLM frameworks but also provides a foundation for advancing robust, entity-aware QA architectures.

Positional bias (PB), manifesting as non-uniform sensitivity across different contextual locations, significantly impairs long-context comprehension and processing capabilities. Previous studies have addressed PB either by modifying the underlying architectures or by employing extensive contextual awareness training. However, the former approach fails to effectively eliminate the substantialperformance disparities, while the latter imposes significant data and computational overhead. To address PB effectively, we introduce Pos2Distill, a position to position knowledge distillation framework. Pos2Distill transfers the superior capabilities from advantageous positions to less favorable ones, thereby reducing the huge performance gaps. The conceptual principle is to leverage the inherent, position-induced disparity to counteract the PB itself. We identify distinct manifestations of PB under retrieval and reasoning paradigms, thereby designing two specialized instantiations: Pos2Distill-R1 and Pos2Distill-R2 respectively, both grounded in this core principle. By employing the Pos2Distill approach, we achieve enhanced uniformity and significant performance gains across all contextual positions in long-context retrieval and reasoning tasks. Crucially, both specialized systems exhibit strong cross-task generalization mutually, while achieving superior performance on their respective tasks.

Existing large language model (LLM) evaluation benchmarks primarily focus on English, while current multilingual tasks lack parallel questions that specifically assess cross-lingual reasoning abilities. This dual limitation makes it challenging to assess LLMs’ performance in the multilingual setting comprehensively. To fill this gap, we introduce MMLU-ProX, a comprehensive benchmark covering 29 languages, built on an English benchmark. Each language version consists of 11,829 identical questions, enabling direct cross-lingual comparisons. Additionally, to meet efficient evaluation needs, we provide a lite version containing 658 questions per language. To ensure the high quality of MMLU-ProX, we employ a rigorous development process that involves multiple powerful LLMs for translation, followed by expert review to ensure accurate expression, consistent terminology, and cultural relevance. Building on this, we systematically evaluate 36 state-of-the-art LLMs, including reasoning-enhanced and multilingual-optimized LLMs. The results reveal significant disparities in the multilingual capabilities of LLMs: While they perform well in high-resource languages, their performance declines markedly in low-resource languages, particularly for African languages. Through MMLU-ProX, we aim to advance the development of more inclusive AI systems and promote equitable access to technology across global contexts.

Debugging is a critical aspect of LLM’s coding ability. Early debugging efforts primarily focused on code-level analysis, which often falls short when addressing complex programming errors that require a deeper understanding of algorithmic logic. Recent advancements in large language models (LLMs) have shifted attention toward leveraging natural language reasoning to enhance code-related tasks. However, two fundamental questions remain unanswered: What type of natural language format is most effective for debugging tasks? And what specific benefits does natural language reasoning bring to the debugging process? In this paper, we introduce NL-DEBUGGING, a novel framework that employs natural language as an intermediate representation to improve code debugging. By debugging at a natural language level, we demonstrate that NL-DEBUGGING outperforms traditional debugging methods and enables a broader modification space through direct refinement guided by execution feedback. Our findings highlight the potential of natural language reasoning to advance automated code debugging and address complex programming challenges.

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Persuasion Dynamics in LLMs: Investigating Robustness and Adaptability in Knowledge and Safety with DuET-PD
Bryan Chen Zhengyu Tan | Daniel Wai Kit Chin | Zhengyuan Liu | Nancy F. Chen | Roy Ka-Wei Lee

Large Language Models (LLMs) can struggle to balance gullibility to misinformation and resistance to valid corrections in persuasive dialogues, a critical challenge for reliable deployment. We introduce **DuET-PD** (**Du**al **E**valuation for **T**rust in **P**ersuasive **D**ialogues), a framework evaluating multi-turn stance-change dynamics across dual dimensions: persuasion type (corrective/misleading) and domain (knowledge via MMLU-Pro, and safety via SALAD-Bench). We find that even a state-of-the-art model like GPT-4o achieves only 27.32% accuracy in MMLU-Pro under sustained misleading persuasions. Moreover, results reveal a concerning trend of increasing sycophancy in newer open-source models. To address this, we introduce Holistic DPO, a training approach balancing positive and negative persuasion examples. Unlike prompting or resist-only training, Holistic DPO enhances both robustness to misinformation and receptiveness to corrections, improving Llama-3.1-8B-Instruct’s accuracy under misleading persuasion in safety contexts from 4.21% to 76.54%. These contributions offer a pathway to developing more reliable and adaptable LLMs for multi-turn dialogue. Code is available at https://github.com/Social-AI-Studio/DuET-PD.

High-quality labeled data is essential for training accurate document conversion models, particularly in domains with complex formats such as tables, formulas, and multi-column text. However, manual annotation is both costly and time-consuming, while automatic labeling using existing models often lacks accuracy in handling such challenging scenarios. Consequently, training student models by distilling outputs from teacher models can significantly limit their performance in real-world applications. In this paper, we propose a fully automated, distillation-free framework comprising two stages for constructing high-quality document extraction datasets and models capable of handling diverse document formats and layouts. In the first stage, we introduce a method for generating large-scale, diverse synthetic data, which enables a model to extract key elements in a unified format with strong initial performance. In the second stage, we present a self-improvement approach that further adapts the model, initially trained on synthetic data, to real-world documents. Specifically, we first use the fine-tuned model to annotate real documents, then apply a suite of filtering strategies to verify annotation quality, and finally retrain the model on the verified dataset. By iteratively repeating this process, we progressively enhance both the model’s conversion capabilities and the quality of the generated data. We train a public POINTS-1.5 model to obtain POINTS-Reader, which surpasses many existing public and proprietary models of comparable or larger size. Our model will be made publicly available.

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Large Language Models for Automated Literature Review: An Evaluation of Reference Generation, Abstract Writing, and Review Composition
Xuemei Tang | Xufeng Duan | Zhenguang Cai

Large language models (LLMs) have emerged as a potential solution to automate the complex processes involved in writing literature reviews, such as literature collection, organization, and summarization. However, it is yet unclear how good LLMs are at automating comprehensive and reliable literature reviews. This study introduces a framework to automatically evaluate the performance of LLMs in three key tasks of literature review writing: reference generation, abstract writing, and literature review composition. We introduce multidimensional evaluation metrics that assess the hallucination rates in generated references and measure the semantic coverage and factual consistency of the literature summaries and compositions against human-written counterparts. The experimental results reveal that even the most advanced models still generate hallucinated references, despite recent progress. Moreover, we observe that the performance of different models varies across disciplines when it comes to writing literature reviews. These findings highlight the need for further research and development to improve the reliability of LLMs in automating academic literature reviews. The dataset and code used in this study are publicly available in our GitHub repository .

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CoBia: Constructed Conversations Can Trigger Otherwise Concealed Societal Biases in LLMs
Nafiseh Nikeghbal | Amir Hossein Kargaran | Jana Diesner

Improvements in model construction, including fortified safety guardrails, allow Large language models (LLMs) to increasingly pass standard safety checks. However, LLMs sometimes slip into revealing harmful behavior, such as expressing racist viewpoints, during conversations. To analyze this systematically, we introduce CoBia, a suite of lightweight adversarial attacks that allow us to refine the scope of conditions under which LLMs depart from normative or ethical behavior in conversations. CoBia creates a constructed conversation where the model utters a biased claim about a social group. We then evaluate whether the model can recover from the fabricated bias claim and reject biased follow-up questions.We evaluate 11 open-source as well as proprietary LLMs for their outputs related to six socio-demographic categories that are relevant to individual safety and fair treatment, i.e., gender, race, religion, nationality, sex orientation, and others. Our evaluation is based on established LLM-based bias metrics, and we compare the results against human judgments to scope out the LLMs’ reliability and alignment. The results suggest that purposefully constructed conversations reliably reveal bias amplification and that LLMs often fail to reject biased follow-up questions during dialogue. This form of stress-testing highlights deeply embedded biases that can be surfaced through interaction. Code and artifacts are available at https://github.com/nafisenik/CoBia.

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From Schema to State: Zero-Shot Scheme-Only Dialogue State Tracking via Diverse Synthetic Dialogue and Step-by-Step Distillation
Huan Xu | Zequn Li | Wen Tang | Jian Jun Zhang

Dialogue State Tracking (DST) is crucial for linking user intentions to appropriate services in task-oriented dialogue systems. We propose a zero-shot, scheme-only approach that tackles two main challenges: generating synthetic dialogues that balance diversity with schema alignment, and efficiently distilling knowledge from a large language model (LLM) into a smaller model. Our pipeline first creates scenarios, dialogue logic flows, and utterances via dynamic complexity prompting, eliminating reliance on handcrafted templates. We then use a two-stage distillation process to learn formalized dialogue representations and DST related chain-of-thought reasoning. This structure preserves interpretive capabilities while reducing inference overhead. Experiments on the MultiWOZ benchmark show that our method achieves state-of-the-art performance under zero-shot, scheme-only situation and generalizes effectively to few-shot scenarios, offering a practical and scalable solution for domains lacking real data.

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Beyond the Surface: Measuring Self-Preference in LLM Judgments
Zhi-Yuan Chen | Hao Wang | Xinyu Zhang | Enrui Hu | Yankai Lin

Recent studies show that large language models (LLMs) exhibit self-preference bias when serving as judges, meaning they tend to favor their own responses over those generated by other models. Existing methods typically measure this bias by calculating the difference between the scores a judge model assigns to its own responses and those it assigns to responses from other models. However, this approach conflates self-preference bias with response quality, as higher-quality responses from the judge model may also lead to positive score differences, even in the absence of bias. To address this issue, we introduce gold judgments as proxies for the actual quality of responses and propose the DBG score, which measures self-preference bias as the difference between the scores assigned by the judge model to its own responses and the corresponding gold judgments. Since gold judgments reflect true response quality, the DBG score mitigates the confounding effect of response quality on bias measurement. Using the DBG score, we conduct comprehensive experiments to assess self-preference bias across LLMs of varying versions, sizes, and reasoning abilities. Additionally, we investigate two factors that influence and help alleviate self-preference bias: response text style and the post-training data of judge models. Finally, we explore potential underlying mechanisms of self-preference bias from an attention-based perspective. Our code and data are available at https://github.com/zhiyuanc2001/self-preference.

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Beyond Input Activations: Identifying Influential Latents by Gradient Sparse Autoencoders
Dong Shu | Xuansheng Wu | Haiyan Zhao | Mengnan Du | Ninghao Liu

Sparse Autoencoders (SAEs) have recently emerged as powerful tools for interpreting and steering the internal representations of large language models (LLMs). However, conventional approaches to analyzing SAEs typically rely solely on input-side activations, without considering the influence between each latent feature and the model’s output. This work is built on two key hypotheses: (1) activated latents do not contribute equally to the construction of the model’s output, and (2) only latents with high influence are effective for model steering. To validate these hypotheses, we propose Gradient Sparse Autoencoder (GradSAE), a simple yet effective method that identifies the most influential latents by incorporating output-side gradient information.

This paper explores the use of large language models (LLMs) for annotating document utility in training retrieval and retrieval-augmented generation (RAG) systems, aiming to reduce dependence on costly human annotations. We address the gap between retrieval relevance and generative utility by employing LLMs to annotate document utility. To effectively utilize multiple positive samples per query, we introduce a novel loss that maximizes their summed marginal likelihood. Using the Qwen-2.5-32B model, we annotate utility on the MS MARCO dataset and conduct retrieval experiments on MS MARCO and BEIR, as well as RAG experiments on MS MARCO QA, NQ, and HotpotQA. Our results show that LLM-generated annotations enhance out-of-domain retrieval performance and improve RAG outcomes compared to models trained solely on human annotations or downstream QA metrics. Furthermore, combining LLM annotations with just 20% of human labels achieves performance comparable to using full human annotations. Our study offers a comprehensive approach to utilizing LLM annotations for initializing QA systems on new corpora.

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CiteBART: Learning to Generate Citations for Local Citation Recommendation
Ege Yiğit Çelik | Selma Tekir

Local citation recommendation (LCR) suggests a set of papers for a citation placeholder within a given context. This paper introduces CiteBART, citation-specific pre-training within an encoder-decoder architecture, where author-date citation tokens are masked to learn to reconstruct them to fulfill LCR. The global version (CiteBART-Global) extends the local context with the citing paper’s title and abstract to enrich the learning signal. CiteBART-Global achieves state-of-the-art performance on LCR benchmarks except for the FullTextPeerRead dataset, which is quite small to see the advantage of generative pre-training. The effect is significant in the larger benchmarks, e.g., Refseer and ArXiv., with the Refseer pre-trained model emerging as the best-performing model. We perform comprehensive experiments, including an ablation study, a qualitative analysis, and a taxonomy of hallucinations with detailed statistics. Our analyses confirm that CiteBART-Global has a cross-dataset generalization capability; the macro hallucination rate (MaHR) at the top-3 predictions is 4%, and when the ground-truth is in the top-k prediction list, the hallucination tendency in the other predictions drops significantly. We publicly share our code, base datasets, global datasets, and pre-trained models to support reproducibility.

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Autoformalization in the Wild: Assessing LLMs on Real-World Mathematical Definitions
Lan Zhang | Marco Valentino | Andre Freitas

Thanks to their linguistic capabilities, LLMs offer an opportunity to bridge the gap between informal mathematics and formal languages through autoformalization. However, it is still unclear how well LLMs generalize to sophisticated and naturally occurring mathematical statements. To address this gap, we investigate the task of autoformalizing real-world mathematical definitions: a critical component of mathematical discourse. Specifically, we introduce two novel resources for autoformalization, collecting definitions from Wikipedia (Def_Wiki) and arXiv papers (Def_ArXiv). We then systematically evaluate a range of LLMs, analyzing their ability to formalize definitions into Isabelle/HOL. Furthermore, we investigate strategies to enhance LLMs’ performance including refinement through external feedback from Proof Assistants, and formal definition grounding, where we augment LLMs’ formalizations through relevant contextual elements from formal mathematical libraries. Our findings reveal that definitions present a greater challenge compared to existing benchmarks, such as miniF2F. In particular, we found that LLMs still struggle with self-correction, and aligning with relevant mathematical libraries. At the same time, structured refinement methods and definition grounding strategies yield notable improvements of up to 16% on self-correction capabilities and 43% on the reduction of undefined errors, highlighting promising directions for enhancing LLM-based autoformalization in real-world scenarios.

To serve global users safely and productively, LLMs need culture-specific knowledge that might not be learned during pre-training. How do we find knowledge that is (1) salient to in-group users, but (2) unknown to LLMs? The most common solutions are single-initiative: either researchers define challenging questions that users passively answer (traditional annotation), or users actively produce data that researchers structure as benchmarks (knowledge extraction). The process would benefit from mixed-initiative collaboration, where users guide the process to meaningfully reflect their cultures, and LLMs steer the process to meet the researcher’s goals. We propose CultureCartography as a methodology that operationalizes this mixed-initiative vision. Here, an LLM initializes annotation with questions for which it has low-confidence answers, making explicit both its prior knowledge and the gaps therein. This allows a human respondent to fill these gaps and steer the model towards salient topics through direct edits. We implement Culture Cartography as a tool called Culture Explorer. Compared to a baseline where humans answer LLM-proposed questions, we find that Culture Explorer more effectively produces knowledge that strong models like DeepSeek R1, Llama-4 and GPT-4o are missing, even with web search. Fine-tuning on this data boosts the accuracy of Llama models by up to 19.2% on related culture benchmarks.

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Interpretability Analysis of Arithmetic In-Context Learning in Large Language Models
Gregory Polyakov | Christian Hepting | Carsten Eickhoff | Seyed Ali Bahrainian

Large language models (LLMs) exhibit sophisticated behavior, notably solving arithmetic with only a few in-context examples (ICEs). Yet the computations that connect those examples to the answer remain opaque. We probe four open-weight LLMs, Pythia-12B, Llama-3.1-8B, MPT-7B, and OPT-6.7B, on basic arithmetic to illustrate how they process ICEs. Our study integrates activation patching, information-flow analysis, automatic circuit discovery, and the logit-lens perspective into a unified pipeline. Within this framework we isolate partial-sum representations in three-operand tasks, investigate their influence on final logits, and derive linear function vectors that characterize tasks and align with ICE-induced activations. Controlled ablations show that strict pattern consistency in the formatting of ICEs guides the models more strongly than the symbols chosen or even the factual correctness of the examples. By unifying four complementary interpretability tools, this work delivers one of the most comprehensive interpretability studies of LLM arithmetic to date, and the first on three-operand tasks. Our code is publicly available.

The rapid progress of Large Language Models has advanced agentic systems in decision-making, coordination, and task execution. Yet, existing agentic system generation frameworks lack full autonomy, missing from-scratch agent generation, self-optimizing agent functionality, and collaboration, limiting adaptability and scalability. We propose **SwarmAgentic**, the *first framework that fully automates agentic system generation, optimization, and collaboration*, constructing agents from scratch and jointly refining functionality and coordination via language-driven exploration. To enable efficient search over system-level structures, SwarmAgentic maintains a population of candidate systems and evolves them via feedback-guided updates, drawing inspiration from Particle Swarm Optimization (PSO). We evaluate our method on six real-world, open-ended, and exploratory tasks involving high-level planning, system-level coordination, and creative reasoning. Given only a task description and an objective function, SwarmAgentic outperforms all baselines, achieving a **+261.8% relative improvement** over ADAS on the TravelPlanner benchmark, highlighting the effectiveness of full automation in structurally unconstrained tasks. This framework marks a significant step toward scalable and autonomous agentic system design, bridging swarm intelligence with fully automated system multi-agent generation.

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We Politely Insist: Your LLM Must Learn the Persian Art of Taarof
Nikta Gohari Sadr | Sahar Heidariasl | Karine Megerdoomian | Laleh Seyyed-Kalantari | Ali Emami

Large language models (LLMs) struggle to navigate culturally specific communication norms, limiting their effectiveness in global contexts. We focus on Persian *taarof*, a social norm in Iranian interactions, which is a sophisticated system of ritual politeness that emphasizes deference, modesty, and indirectness, yet remains absent from existing cultural benchmarks. We introduce **TaarofBench**, the first benchmark for evaluating LLM understanding of taarof, comprising 450 role-play scenarios covering 12 common social interaction topics, validated by native speakers. Our evaluation of five frontier LLMs reveals substantial gaps in cultural competence, with accuracy rates 40-48% below native speakers when taarof is culturally appropriate. Performance varies between interaction topics, improves with Persian-language prompts, and exhibits gender-based asymmetries. We also show that responses rated “polite” by standard metrics often violate taarof norms, indicating the limitations of Western politeness frameworks. Through supervised fine-tuning and Direct Preference Optimization, we achieve 21.8% and 42.3% improvement in model alignment with cultural expectations. Our human study with 33 participants (11 native Persian, 11 heritage, and 11 non-Iranian speakers) forms baselines in varying degrees of familiarity with Persian norms. This work lays the foundation for developing diverse and culturally aware LLMs, enabling applications that better navigate complex social interactions.

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Unstructured Evidence Attribution for Long Context Query Focused Summarization
Dustin Wright | Zain Muhammad Mujahid | Lu Wang | Isabelle Augenstein | David Jurgens

Large language models (LLMs) are capable of generating coherent summaries from very long contexts given a user query, and extracting and citing evidence spans helps improve the trustworthiness of these summaries. Whereas previous work has focused on evidence citation with fixed levels of granularity (e.g. sentence, paragraph, document, etc.), we propose to extract unstructured (i.e., spans of any length) evidence in order to acquire more relevant and consistent evidence than in the fixed granularity case. We show how existing systems struggle to copy and properly cite unstructured evidence, which also tends to be “lost-in-the-middle”. To help models perform this task, we create the Summaries with Unstructured Evidence Text dataset (SUnsET), a synthetic dataset generated using a novel pipeline, which can be used as training supervision for unstructured evidence summarization. We demonstrate across 5 LLMs and 4 datasets spanning human written, synthetic, single, and multi-document settings that LLMs adapted with SUnsET generate more relevant and factually consistent evidence with their summaries, extract evidence from more diverse locations in their context, and can generate more relevant and consistent summaries than baselines with no fine-tuning and fixed granularity evidence. We release SUnsET and our generation code to the public (https://github.com/dwright37/unstructured-evidence-sunset).

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RAVEN: Query-Guided Representation Alignment for Question Answering over Audio, Video, Embedded Sensors, and Natural Language
Subrata Biswas | Mohammad Nur Hossain Khan | Bashima Islam

Multimodal question answering (QA) often requires identifying which video, audio, or sensor tokens are relevant to the question. Yet modality disagreements are common: off-camera speech, background noise, or motion outside the field of view often mislead fusion models that weight all streams equally. We present RAVEN, a unified QA architecture whose core is QuART, a query-conditioned cross-modal gating module that assigns scalar relevance scores to each token across modalities, enabling the model to amplify informative signals and suppress distractors before fusion. RAVEN is trained through a three-stage pipeline comprising unimodal pretraining, query-aligned fusion, and disagreement-oriented fine-tuning - each stage targeting a distinct challenge in multi-modal reasoning: representation quality, cross-modal relevance, and robustness to modality mismatch. To support training and evaluation, we release AVS-QA, a dataset of 300K synchronized Audio-Video-Sensor streams paired with automatically generated question-answer pairs. Experimental results on seven multi-modal QA benchmarks - including egocentric and exocentric tasks - show that RAVEN achieves up to 14.5% and 8.0% gains in accuracy compared to state-of-the-art multi-modal large language models, respectively. Incorporating sensor data provides an additional 16.4% boost, and the model remains robust under modality corruption, outperforming SOTA baselines by 50.23%. Our code and dataset are available at https://github.com/BASHLab/RAVEN.

Vision Language Models (VLMs) have achieved remarkable success in a wide range of vision applications of increasing complexity and scales, yet choosing the right VLM model size involves a trade-off between response quality and cost. While smaller VLMs are cheaper to run, they typically produce responses only marginally better than random guessing on benchmarks such as MMMU. In this paper, we propose Cache of Thought (CoT), a master–apprentice framework for collaborative inference between large and small VLMs. CoT manages high-quality query results from large VLMs (master) in a cache, which are then selected via a novel multi-modal retrieval and in-context learning to aid the performance of small VLMs (apprentice). We extensively evaluate CoT on various widely-recognized and challenging general reasoning benchmarks, and show that CoT increases overall reasoning performance by up to 7.7% under the same budget, and specifically boosts the reasoning performance of apprentice VLMs by up to 36.6%. Our code is available at https://github.com/UIUC-MONET/Cache-of-Thoughts.

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Video Compression Commander: Plug-and-Play Inference Acceleration for Video Large Language Models
Xuyang Liu | Yiyu Wang | Junpeng Ma | Linfeng Zhang

Video large language models (VideoLLM) excel at video understanding, but face efficiency challenges due to the quadratic complexity of abundant visual tokens. Our systematic analysis of token compression methods for VideoLLMs reveals two critical issues: (i) overlooking distinctive visual signals across frames, leading to information loss; (ii) suffering from implementation constraints, causing incompatibility with modern architectures or efficient operators.To address these challenges, we distill three design principles for VideoLLM token compression and propose a plug-and-play inference acceleration framework “Video Compression Commander” (VidCom²). By quantifying each frame’s uniqueness, VidCom² adaptively adjusts compression intensity across frames, effectively preserving essential information while reducing redundancy in video sequences. Extensive experiments across various VideoLLMs and benchmarks demonstrate the superior performance and efficiency of our VidCom². With only 25% visual tokens, VidCom² achieves 99.6% of the original performance on LLaVA-OV while reducing 70.8% of the LLM generation latency. Notably, our Frame Compression Adjustment strategy is compatible with other token compression methods to further improve their performance. Our code is available at https://github.com/xuyang-liu16/VidCom2.

The Mixture of Depths (MoD) was introduced to improve computational efficiency by dynamically skipping less important layers, reducing redundant computation while maintaining model capacity. Despite its promise, existing MoD approaches remain under-explored and face two main challenges: (1) high training costs due to the need to train the entire model along with the routers that determine which layers to skip, and (2) performance degradation when important layers are bypassed. In response to the first issue, we propose Router-Tuning, which fine-tunes only the routers on a small dataset, drastically reducing the computational overhead associated with full model training. For the second challenge, we investigate across different architectures and granularities, demonstrating its effectiveness on Attention layers and MoE layers. This method preserves the model’s performance while significantly enhancing computational and memory efficiency. Extensive experiments demonstrate that our approach delivers competitive results while dramatically improving the computation efficiency, e.g., 21% speedup and only a 0.2% performance drop. The code will be released upon acceptance.

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Foot-In-The-Door: A Multi-turn Jailbreak for LLMs
Zixuan Weng | Xiaolong Jin | Jinyuan Jia | Xiangyu Zhang

Ensuring AI safety is crucial as large language models become increasingly integrated into real-world applications. A key challenge is jailbreak, where adversarial prompts bypass built-in safeguards to elicit harmful disallowed outputs. Inspired by psychological foot-in-the-door principles, we introduce FITD, a novel multi-turn jailbreak method that leverages the phenomenon where minor initial commitments lower resistance to more significant or more unethical transgressions. Our approach progressively escalates the malicious intent of user queries through intermediate bridge prompts and aligns the model’s response by itself to induce toxic responses. Extensive experimental results on two jailbreak benchmarks demonstrate that FITD achieves an average attack success rate of 94% across seven widely used models, outperforming existing state-of-the-art methods. Additionally, we provide an in-depth analysis of LLM self-corruption, highlighting vulnerabilities in current alignment strategies and emphasizing the risks inherent in multi-turn interactions. The code is available at https://github.com/Jinxiaolong1129/Foot-in-the-door-Jailbreak.

This paper introduces TurnaboutLLM, a novel framework and dataset for evaluating the deductive reasoning abilities of Large Language Models (LLMs) by leveraging the interactive gameplay of detective games Ace Attorney and Danganronpa. The framework tasks LLMs with identifying contradictions between testimonies and evidences within long narrative contexts, a challenging task due to the large answer space and diverse reasoning types presented by its questions. We evaluate twelve state-of-the-art LLMs on the dataset, hinting at limitations of popular strategies for enhancing deductive reasoning such as extensive thinking and Chain-of-Thought prompting. The results also suggest varying effects of context size, reasoning steps and answer space size on model performance. Overall, TurnaboutLLM presents a substantial challenge for LLMs’ deductive reasoning abilities in complex, narrative-rich environments.

Direct Prompt Injection (DPI) attacks pose a critical security threat to Large Language Models (LLMs) due to their low barrier of execution and high potential damage. To address the impracticality of existing white-box/gray-box methods and the poor transferability of black-box methods, we propose an activations-guided prompt injection attack framework. We first construct an Energy-based Model (EBM) using activations from a surrogate model to evaluate the quality of adversarial prompts. Guided by the trained EBM, we employ the token-level Markov Chain Monte Carlo (MCMC) sampling to adaptively optimize adversarial prompts, thereby enabling gradient-free black-box attacks. Experimental results demonstrate our superior cross-model transferability, achieving 49.6% attack success rate (ASR) across five mainstream LLMs and 34.6% improvement over human-crafted prompts, and maintaining 36.6% ASR on unseen task scenarios. Interpretability analysis reveals a correlation between activations and attack effectiveness, highlighting the critical role of semantic patterns in transferable vulnerability exploitation.

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Direct Judgement Preference Optimization
PeiFeng Wang | Austin Xu | Yilun Zhou | Caiming Xiong | Shafiq Joty

To meet the increasing need for timely and accurate evaluation of large language model (LLM) responses, training LLM-as-judges to evaluate and critique other model responses has emerged as a popular paradigm. However, existing judge models are largely trained with supervised finetuning (SFT) on small data scales to perform limited types of evaluation tasks, fundamentally limiting generalization.To meet the need for strong, generalized judge models, we explore training foundational judge models at large data scales (680K) with direct preference optimization (DPO). Using four training tasks, we form three types of DPO preference pairs targeting different aspects of evaluation: Generating meaningful critiques, making accurate judgements, and understanding what comprises good and bad responses. To demonstrate the effectiveness of our method, we train judge models of three sizes: 8B parameters, 12B, and 70B, and evaluate on a comprehensive suite of 13 benchmarks (7 pairwise, 4 single rating, and 2 classification). Our models achieve the best aggregate performance, with even our 8B model outperforming GPT-4o in pairwise benchmarks. Further analysis shows that our judge models produce factual and actionable critiques and serve as strong foundational judges for continued finetuning.

Multi-modal large language model (MLLM)-based web agents interact with webpage environments by generating actions based on screenshots of the webpages. In this work, we propose WebInject, a prompt injection attack that manipulates the webpage environment to induce a web agent to perform an attacker-specified action. Our attack adds a perturbation to the raw pixel values of the rendered webpage. After these perturbed pixels are mapped into a screenshot, the perturbation induces the web agent to perform the attacker-specified action. We formulate the task of finding the perturbation as an optimization problem. A key challenge in solving this problem is that the mapping between raw pixel values and screenshot is non-differentiable, making it difficult to backpropagate gradients to the perturbation. To overcome this, we train a neural network to approximate the mapping and apply projected gradient descent to solve the reformulated optimization problem. Extensive evaluation on multiple datasets shows that WebInject is highly effective and significantly outperforms baselines.

With the growing adoption of large language models (LLMs) in NLP tasks, concerns about their fairness have intensified. Yet, most existing fairness benchmarks rely on closed-ended evaluation formats, which diverge from real-world open-ended interactions. These formats are prone to position bias and introduce a “minimum score” effect, where models can earn partial credit simply by guessing. Moreover, such benchmarks often overlook factuality considerations rooted in historical, social, physiological, and cultural contexts, and rarely account for intersectional biases. To address these limitations, we propose F²Bench: an open-ended fairness evaluation benchmark for LLMs that explicitly incorporates factuality considerations. F²Bench comprises 2,568 instances across 10 demographic groups and two open-ended tasks. By integrating text generation, multi-turn reasoning, and factual grounding, F²Bench aims to more accurately reflect the complexities of real-world model usage. We conduct a comprehensive evaluation of several LLMs across different series and parameter sizes. Our results reveal that all models exhibit varying degrees of fairness issues. We further compare open-ended and closed-ended evaluations, analyze model-specific disparities, and provide actionable recommendations for future model development. Our code and dataset are publicly available at https://github.com/VelikayaScarlet/F2Bench.

Modelling human variation in rating tasks is crucial for enabling AI systems for personalization, pluralistic model alignment, and computational social science. We propose representing individuals using value profiles – natural language descriptions of underlying values compressed from in-context demonstrations – along with a steerable decoder model to estimate ratings conditioned on a value profile or other rater information. To measure the predictive information in rater representations, we introduce an information-theoretic methodology. We find that demonstrations contain the most information, followed by value profiles and then demographics. However, value profiles offer advantages in terms of scrutability, interpretability, and steerability due to their compressed natural language format. Value profiles effectively compress the useful information from demonstrations (70% information preservation). Furthermore, clustering value profiles to identify similarly behaving individuals better explains rater variation than the most predictive demographic groupings. Going beyond test set performance, we show that the decoder models interpretably change ratings according to semantic profile differences, are well-calibrated, and can help explain instance-level disagreement by simulating an annotator population. These results demonstrate that value profiles offer novel, predictive ways to describe individual variation beyond demographics or group information.

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Language Models as Causal Effect Generators
Lucius E.j. Bynum | Kyunghyun Cho

In this work, we present sequence-driven structural causal models (SD-SCMs), a framework for specifying causal models with user-defined structure and language-model-defined mechanisms. We characterize how an SD-SCM enables sampling from observational, interventional, and counterfactual distributions according to the desired causal structure. We then leverage this procedure to propose a new type of benchmark for causal inference methods, generating individual-level counterfactual data to test treatment effect estimation. We create an example benchmark consisting of thousands of datasets, and test a suite of popular estimation methods for average, conditional average, and individual treatment effect estimation. We find under this benchmark that (1) causal methods outperform non-causal methods and that (2) even state-of-the-art methods struggle with individualized effect estimation, suggesting this benchmark captures some inherent difficulties in causal estimation. Apart from generating data, this same technique can underpin the auditing of language models for (un)desirable causal effects, such as misinformation or discrimination. We believe SD-SCMs can serve as a useful tool in any application that would benefit from sequential data with controllable causal structure.

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Constructions are Revealed in Word Distributions
Joshua Rozner | Leonie Weissweiler | Kyle Mahowald | Cory Shain

Construction grammar posits that constructions, or form-meaning pairings, are acquired through experience with language (the distributional learning hypothesis).But how much information about constructions does this distribution actually contain? Corpus-based analyses provide some answers, but text alone cannot answer counterfactual questions about what caused a particular word to occur.This requires computable models of the distribution over strings—namely, pretrained language models (PLMs).Here, we treat a RoBERTa model as a proxy for this distribution and hypothesize that constructions will be revealed within it as patterns of statistical affinity.We support this hypothesis experimentally: many constructions are robustly distinguished, including (i) hard cases where semantically distinct constructions are superficially similar, as well as (ii) schematic constructions, whose “slots” can be filled by abstract word classes.Despite this success, we also provide qualitative evidence that statistical affinity alone may be insufficient to identify all constructions from text.Thus, statistical affinity is likely an important, but partial, signal available to learners.

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CodeMixBench: Evaluating Code-Mixing Capabilities of LLMs Across 18 Languages
Yilun Yang | Yekun Chai

Code-mixing, the practice of switching between languages within a conversation, poses unique challenges for traditional NLP. Existing benchmarks like LinCE and GLUECoS are limited by their narrow language pairs and tasks, failing to adequately assess large language models’ (LLMs) code-mixing abilities. Despite the recognized importance of code-mixing for multilingual users, research on LLMs in this context remains sparse. Additionally, current techniques for synthesizing code-mixed data are underdeveloped to generate code-mixing. In response, we introduce CodeMixBench, a comprehensive benchmark covering eight tasks, including three specific to LLMs and five traditional NLP tasks, and 18 languages from seven language families. We also propose a new method for generating large-scale synthetic code-mixed texts by combining word substitution with GPT-4 prompting. Our evaluation reveals consistent underperformance of LLMs on code-mixed datasets involving different language families. Enhancements in training data size, model scale, and few-shot learning could improve their performance. The code and dataset are available at https://github.com/Jeromeyluck/CodeMixBench.

RNA-binding proteins (RBPs) play essential roles in post-transcriptional gene regulation via recognizing specific RNA molecules as well as modulating several key physiological processes in cellulo, represented by alternative splicing and RNA degradation. Despite extensive research, most existing approaches still rely on superficial sequence features or coarse structural representations, limiting their ability to capture the intricate nature of RBP-RNA interactions. The recent surge in large language models (LLMs), combined with advances in geometric deep learning for extracting three-dimensional representations, enables the integration of multi-modal, multi-scale biological data for precise modeling and biologically informed de novo RNA design. In this work, we curate and extend RPI15223 into a multi-resolution, structure-level RBP-RNA dataset, and introduce RBPtool, a multi-task, multi-resolution framework that combines a geometric vector perception (GVP) module together with a deep language model encoder to fuse sequence and structural information. Our tool achieves state-of-the-art performance on public benchmarks and the RPI15223 dataset, while also supporting fine-grained level predictions and enabling de novo RNA design through a generative module conditioned on protein, cell-type, and specified species. RBPtool provides a fast and versatile platform for both fundamental RBP-RNA research and practical RNA drug design, delivering enhanced predictive accuracy and fine-grained structural insights.

Existing research in multi-hop questions has identified two reasoning modes: latent reasoning and factual shortcuts, but has not deeply investigated how these modes differ during inference. This impacts both model generalization ability and downstream reasoning tasks. In this work, we systematically examine these distinctions and propose a simple and efficient classification metric, Attribute Rate Ratio (ARR). First, we construct specialized datasets corresponding to the two reasoning modes based on our proposed criteria. Then, using reverse engineering methods, including attention knockout and logit lens techniques, we reveal that subject representations differ significantly across modes: latent reasoning encodes bridge-related information for final answer extraction, while factual shortcuts bypass intermediate reasoning and resemble single-hop factual queries. Finally, our proposed ARR achieves around 90% accuracy on our datasets and demonstrates effectiveness in RAG conflict scenarios, showing that model behavior under conflicting prompts is closely tied to its underlying reasoning mode. Our findings and proposed metric have significant potential for advancing LLM development and applications.

Large language models (LLMs) have demonstrated impressive capabilities in natural language understanding and generation, but controlling their behavior reliably remains challenging, especially in open-ended generation settings. This paper introduces a novel supervised steering approach that operates in sparse, interpretable representation spaces. We employ sparse autoencoders (SAEs) to obtain sparse latent representations that aim to disentangle semantic attributes from model activations. Then we train linear classifiers to identify a small subspace of task-relevant dimensions in latent representations. Finally, we learn supervised steering vectors constrained to this subspace, optimized to align with target behaviors. Experiments across sentiment, truthfulness, and politics polarity steering tasks with multiple LLMs demonstrate that our supervised steering vectors achieve higher success rates with minimal degradation in generation quality compared to existing methods. Further analysis reveals that a notably small subspace is sufficient for effective steering, enabling more targeted and interpretable interventions.

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BabyLM’s First Constructions: Causal interventions provide a signal of learning
Joshua Rozner | Leonie Weissweiler | Cory Shain

Construction grammar posits that language learners acquire constructions (form-meaning pairings) from the statistics of their environment. Recent work supports this hypothesis by showing sensitivity to constructions in pretrained language models (PLMs), including one recent study (Rozner et al., 2025) demonstrating that constructions shape RoBERTa’s output distribution. However, models under study have generally been trained on developmentally implausible amounts of data, casting doubt on their relevance to human language learning. Here we use Rozner et al.’s methods to evaluate construction learning in masked language models from the 2024 BabyLM Challenge.Our results show that even when trained on developmentally plausible quantities of data, models learn diverse constructions, even hard cases that are superficially indistinguishable.We further find correlational evidence that constructional performance may be functionally relevant: models that better represent constructions perform better on the BabyLM benchmarks.

Task-oriented LLM-based agents are increasingly used in domains with strict policies, such as refund eligibility or cancellation rules. The challenge lies in ensuring that the agent consistently adheres to these rules and policies, appropriately refusing any request that would violate them, while still maintaining a helpful and natural interaction. This calls for the development of tailored design and evaluation methodologies to ensure agent resilience against malicious user behavior. We propose a novel threat model that focuses on adversarial users aiming to exploit policy-adherent agents for personal benefit. To address this, we present CRAFT, a multi-agent red-teaming system that leverages policy-aware persuasive strategies to undermine a policy-adherent agent in a customer-service scenario, outperforming conventional jailbreak methods such as DAN prompts, emotional manipulation, and coercive. Building upon the existing Tau-bench benchmark, we introduce Tau-break, a complementary benchmark designed to rigorously assess the agent’s robustness against manipulative user behavior. Finally, we evaluate several straightforward yet effective defense strategies. While these measures provide some protection, they fall short, highlighting the need for stronger, research-driven safeguards to protect policy-adherent agents from adversarial attacks.

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CondAmbigQA: A Benchmark and Dataset for Conditional Ambiguous Question Answering
Zongxi Li | Yang Li | Haoran Xie | S. Joe Qin

Users often assume that large language models (LLMs) share their cognitive alignment of context and intent, leading them to omit critical information in question-answering (QA) and produce ambiguous queries. Responses based on misaligned assumptions may be perceived as hallucinations. Therefore, identifying possible implicit assumptions is crucial in QA. To address this fundamental challenge, we propose Conditional Ambiguous Question-Answering (CondAmbigQA), a benchmark comprising 2,000 ambiguous queries and condition-aware evaluation metrics. Our study pioneers “conditions” as explicit contextual constraints that resolve ambiguities in QA tasks through retrieval-based annotation, where retrieved Wikipedia fragments help identify possible interpretations for a given query and annotate answers accordingly. Experiments demonstrate that models considering conditions before answering improve answer accuracy by 11.75%, with an additional 7.15% gain when conditions are explicitly provided. These results highlight that apparent hallucinations may stem from inherent query ambiguity rather than model failure, and demonstrate the effectiveness of condition reasoning in QA, providing researchers with tools for rigorous evaluation.

Recent advancements in large language model (LLM) agents have significantly accelerated scientific discovery automation, yet concurrently raised critical ethical and safety concerns. To systematically address these challenges, we introduce **SafeScientist**, an innovative AI scientist framework explicitly designed to enhance safety and ethical responsibility in AI-driven scientific exploration. SafeScientist proactively refuses ethically inappropriate or high-risk tasks and rigorously emphasizes safety throughout the research process. To achieve comprehensive safety oversight, we integrate multiple defensive mechanisms, including prompt monitoring, agent-collaboration monitoring, tool-use monitoring, and an ethical reviewer component. Complementing SafeScientist, we propose **SciSafetyBench** , a novel benchmark specifically designed to evaluate AI safety in scientific contexts, comprising 240 high-risk scientific tasks across 6 domains, alongside 30 specially designed scientific tools and 120 tool-related risk tasks. Extensive experiments demonstrate that SafeScientist significantly improves safety performance by 35% compared to traditional AI scientist frameworks, without compromising scientific output quality. Additionally, we rigorously validate the robustness of our safety pipeline against diverse adversarial attack methods, further confirming the effectiveness of our integrated approach. The code and data will be available at https://github.com/ulab-uiuc/SafeScientist.**Warning**: this paper contains example data that may be offensive or harmful.

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Improving Informally Romanized Language Identification
Adrian Benton | Alexander Gutkin | Christo Kirov | Brian Roark

The Latin script is often used to informally write languages with non-Latin native scripts. In many cases (e.g., most languages in India), the lack of conventional spelling in the Latin script results in high spelling variability. Such romanization renders languages that are normally easily distinguished due to being written in different scripts – Hindi and Urdu, for example – highly confusable. In this work, we increase language identification (LID) accuracy for romanized text by improving the methods used to synthesize training sets. We find that training on synthetic samples which incorporate natural spelling variation yields higher LID system accuracy than including available naturally occurring examples in the training set, or even training higher capacity models. We demonstrate new state-of-the-art LID performance on romanized text from 20 Indic languages in the Bhasha-Abhijnaanam evaluation set (Madhani et al., 2023a), improving test F1 from the reported 74.7% (using a pretrained neural model) to 85.4% using a linear classifier trained solely on synthetic data and 88.2% when also training on available harvested text.

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Integral Transformer: Denoising Attention, Not Too Much Not Too Little
Ivan Kobyzev | Abbas Ghaddar | Dingtao Hu | Boxing Chen

Softmax self-attention often assigns disproportionate weight to semantically uninformative tokens such as punctuation and special tokens, a phenomenon known as attention noise. While recent methods like Cog Attention and the Differential Transformer have addressed this by introducing negative attention scores, they risk discarding useful information. In this paper, we propose the Integral Transformer, a novel self-attention mechanism that denoises attention by integrating signals sampled from the logit distribution. This approach mitigates noise while preserving the contributions of special tokens critical for model performance. Extensive experiments demonstrate that our model outperforms vanilla, Cog, and Differential attention variants on rigorous knowledge and reasoning benchmarks. Moreover, our analysis reveals that employing vanilla self-attention in the lower Transformer layers enhances performance and that the Integral Transformer more effectively balances attention distributions and reduces rank collapse in upper layers.

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CHENGYU-BENCH: Benchmarking Large Language Models for Chinese Idiom Understanding and Use
Yicheng Fu | Zhemin Huang | Liuxin Yang | Yumeng Lu | Zhongdongming Dai

Chinese idioms (成语, Chengyu) are concise four-character expressions steeped in history and culture, whose literal translations often fail to capture their full meaning. This complexity makes them challenging for language models to interpret and use correctly. Existing benchmarks focus on narrow tasks—multiple-choice cloze tests, isolated translation, or simple paraphrasing. We introduce CHENGYU-BENCH, a comprehensive benchmark featuring three tasks: (1) Evaluative Connotation, classifying idioms as positive or negative; (2) Appropriateness, detecting incorrect idiom usage in context; and (3) Open Cloze, filling blanks in longer passages without options. CHENGYU-BENCH comprises 2,937 human-verified examples covering 1,765 common idioms sourced from diverse corpora. We evaluate leading LLMs and find they achieve over 95% accuracy on Evaluative Connotation, but only ~85% on Appropriateness and ~40% top-1 accuracy in Open Cloze. Error analysis reveals that most mistakes arise from fundamental misunderstandings of idiom meanings. CHENGYU-BENCH demonstrates that while LLMs can reliably gauge idiom sentiment, they still struggle to grasp the cultural and contextual nuances essential for proper usage. The benchmark and code will be released upon paper acceptance.

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Improving Cross Lingual Transfer by Pretraining with Active Forgetting
Divyanshu Aggarwal | Ashutosh Sathe | Sunayana Sitaram

Large Language Models (LLMs) demonstrate exceptional capabilities in a multitude of NLP tasks. However, the efficacy of such models to languages other than English is often limited. Prior works have shown that encoder-only models such as BERT or XLM-RoBERTa show impressive cross lingual transfer of their capabilities from English to other languages. In this work, we propose a pretraining strategy that uses active forgetting to achieve similar cross lingual transfer in decoder-only LLMs. We show that LLMs pretrained with active forgetting are highly effective when adapting to new and unseen languages. Through extensive experimentation, we find that LLMs pretrained with active forgetting are able to learn better multilingual representations which translates to better performance in many downstream tasks.

The emergence of large Vision Language Models (VLMs) has broadened the scope and capabilities of single-modal Large Language Models (LLMs) by integrating visual modalities, thereby unlocking transformative cross-modal applications in a variety of real-world scenarios. Despite their impressive performance, VLMs are prone to significant hallucinations, particularly in the form of cross-modal inconsistencies. Building on the success of Reinforcement Learning from Human Feedback (RLHF) in aligning LLMs, recent advancements have focused on applying direct preference optimization (DPO) on carefully curated datasets to mitigate these issues. Yet, such approaches typically introduce preference signals in a brute-force manner, neglecting the crucial role of visual information in the alignment process. In this paper, we introduce Re-Align, a novel alignment framework that leverages image retrieval to construct a dual-preference dataset, effectively incorporating both textual and visual preference signals. We further introduce rDPO, an extension of the standard direct preference optimization that incorporates an additional visual preference objective during fine-tuning. Our experimental results demonstrate that Re-Align not only mitigates hallucinations more effectively than previous methods but also yields significant performance gains in general visual question-answering (VQA) tasks. Moreover, we show that Re-Align maintains robustness and scalability across a wide range of VLM sizes and architectures. This work represents a significant step forward in aligning multimodal LLMs, paving the way for more reliable and effective cross-modal applications.

As large language models (LLMs) are increasingly used to model and augment collective decision-making, it is critical to examine their alignment with human social reasoning. We present an empirical framework for assessing collective alignment, in contrast to prior work on the individual level. Using the Lost at Sea social psychology task, we conduct a large-scale online experiment (N=748), randomly assigning groups to leader elections with either visible demographic attributes (e.g. name, gender) or pseudonymous aliases. We then simulate matched LLM groups conditioned on the human data, benchmarking Gemini 2.5, GPT-4.1, Claude Haiku 3.5, and Gemma 3. LLM behaviors diverge: some mirror human biases; others mask these biases and attempt to compensate for them. We empirically demonstrate that human-AI alignment in collective reasoning depends on context, cues, and model-specific inductive biases. Understanding how LLMs align with collective human behavior is critical to advancing socially-aligned AI, and demands dynamic benchmarks that capture the complexities of collective reasoning.

We present SWAN, a causal Transformer architecture in the decoder-only style that generalizes robustly to sequence lengths substantially longer than those seen during training. SWAN interleaves layers without positional encodings (NoPE) and sliding-window attention layers equipped with rotary positional encodings (SWA-RoPE), and applies a dynamic scaling mechanism for attention scores during inference. Experiments demonstrate that SWAN achieves strong length extrapolation without requiring additional long-context training. In addition, SWAN is more computationally efficient than the standard Transformer architecture, resulting in lower training cost and higher inference throughput. We further demonstrate that existing pre-trained decoder-only models can be adapted to the SWAN architecture with minimal continued training, enabling extended contexts. Overall, our work presents an effective approach for scaling language models to longer contexts in a robust and efficient manner.

Generative AI has established the opportunity to readily transform content from one medium to another. This capability is especially powerful for storytelling, where visual illustrations can illuminate a story originally expressed in text. In this paper, we focus on the task of narrative scene illustration, which involves automatically generating an image depicting a scene in a story. Motivated by recent progress on text-to-image models, we consider a pipeline that uses LLMs as an interface for prompting text-to-image models to generate scene illustrations given raw story text. We apply variations of this pipeline to a prominent story corpus in order to synthesize illustrations for scenes in these stories. We conduct a human annotation task to obtain pairwise quality judgments for these illustrations. The outcome of this process is the SceneIllustrations dataset, which we release as a new resource for future work on cross-modal narrative transformation. Through our analysis of this dataset and experiments modeling illustration quality, we demonstrate that LLMs can effectively verbalize scene knowledge implicitly evoked by story text. Moreover, this capability is impactful for generating and evaluating illustrations.

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REARANK: Reasoning Re-ranking Agent via Reinforcement Learning
Le Zhang | Bo Wang | Xipeng Qiu | Siva Reddy | Aishwarya Agrawal

We present REARANK, a large language model (LLM)-based listwise reasoning rerank- ing agent. REARANK explicitly reasons be- fore reranking, significantly improving both performance and interpretability. Leveraging reinforcement learning and data augmentation, REARANK achieves substantial improvements over baseline models across popular informa- tion retrieval benchmarks, notably requiring only 179 annotated samples. Built on top of Qwen2.5-7B, our REARANK-7B demonstrates performance comparable to GPT-4 on both in- domain and out-of-domain benchmarks and even surpasses GPT-4 on reasoning-intensive BRIGHT benchmarks. These results under- score the effectiveness of our approach and highlight how reinforcement learning can en- hance LLM reasoning capabilities in reranking.

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Large Language Models Do Multi-Label Classification Differently
Marcus Ma | Georgios Chochlakis | Niyantha Maruthu Pandiyan | Jesse Thomason | Shrikanth Narayanan

Multi-label classification is prevalent in real-world settings, but the behavior of Large Language Models (LLMs) in this setting is understudied. We investigate how autoregressive LLMs perform multi-label classification, focusing on subjective tasks, by analyzing the output distributions of the models at each label generation step. We find that the initial probability distribution for the first label often does not reflect the eventual final output, even in terms of relative order and find LLMs tend to suppress all but one label at each generation step. We further observe that as model scale increases, their token distributions exhibit lower entropy and higher single-label confidence, but the internal relative ranking of the labels improves. Finetuning methods such as supervised finetuning and reinforcement learning amplify this phenomenon. We introduce the task of distribution alignment for multi-label settings: aligning LLM-derived label distributions with empirical distributions estimated from annotator responses in subjective tasks. We propose both zero-shot and supervised methods which improve both alignment and predictive performance over existing approaches. We find one method – taking the max probability over all label generation distributions instead of just using the initial probability distribution – improves both distribution alignment and overall F1 classification without adding any additional computation.

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FilBench: Can LLMs Understand and Generate Filipino?
Lester James Validad Miranda | Elyanah Aco | Conner G. Manuel | Jan Christian Blaise Cruz | Joseph Marvin Imperial

Despite the impressive performance of LLMs on English-based tasks, little is known about their capabilities in specific languages such as Filipino. In this work, we address this gap by introducing FilBench, a Filipino-centric benchmark designed to evaluate LLMs across a diverse set of tasks and capabilities in Filipino, Tagalog, and Cebuano. We carefully curate the tasks in FilBench to reflect the priorities and trends of NLP research in the Philippines such as Cultural Knowledge, Classical NLP, Reading Comprehension, and Generation. By evaluating 27 state-of-the-art LLMs on FilBench, we find that several LLMs suffer from reading comprehension and translation capabilities. Our results indicate that FilBench is challenging, with the best model, GPT-4o, achieving only a score of 72.23%. Moreover, we also find that models trained specifically for Southeast Asian languages tend to underperform on FilBench, with the highest-performing model, SEA-LION v3 70B, achieving only a score of 61.07%. Our work demonstrates the value of curating language-specific LLM benchmarks to aid in driving progress on Filipino NLP and increasing the inclusion of Philippine languages in LLM development.

Aspect-based sentiment analysis (ABSA) is a crucial task in information extraction and sentiment analysis, aiming to identify aspects with associated sentiment elements in text. However, existing ABSA datasets are predominantly English-centric, limiting the scope for multilingual evaluation and research. To bridge this gap, we present M-ABSA, a comprehensive dataset spanning 7 domains and 21 languages, making it the most extensive multilingual parallel dataset for ABSA to date. Our primary focus is on triplet extraction, which involves identifying aspect terms, aspect categories, and sentiment polarities. The dataset is constructed through an automatic translation process with human review to ensure quality. We perform extensive experiments using various baselines to assess performance and compatibility on M-ABSA. Our empirical findings highlight that the dataset enables diverse evaluation tasks, such as multilingual and multi-domain transfer learning, and large language model evaluation, underscoring its inclusivity and its potential to drive advancements in multilingual ABSA research.

This study investigates the feasibility of automating clinical coding in Russian, a language with limited biomedical resources. We present a new dataset for ICD coding, which includes diagnosis fields from electronic health records (EHRs) annotated with over 10,000 entities and more than 1,500 unique ICD codes. This dataset serves as a benchmark for several state-of-the-art models, including BERT, LLaMA with LoRA, and RAG, with additional experiments examining transfer learning across domains (from PubMed abstracts to medical diagnosis) and terminologies (from UMLS concepts to ICD codes). We then apply the best-performing model to label an in-house EHR dataset containing patient histories from 2017 to 2021. Our experiments, conducted on a carefully curated test set, demonstrate that training with the automated predicted codes leads to a significant improvement in accuracy compared to manually annotated data from physicians. We believe our findings offer valuable insights into the potential for automating clinical coding in resource-limited languages like Russian, which could enhance clinical efficiency and data accuracy in these contexts. Our code and dataset are available at https://github.com/auto-icd-coding/ruccod.

Code and reasoning recently exhibit a mutually reinforcing relationship in large language models (LLMs): Code is abstract, modular, highly structured and has strong logic, guiding reasoning in training and inference. While reasoning translates high-level goals into small executable steps, enable more sophisticated code intellignece, solving real-world challenging software development problems. In this study, we examine how code serves as a structured medium for enhancing reasoning - providing verifiable execution paths, enforcing logical decomposition, and enabling runtime validation, and how advances in reasoning have transformed code intelligence from basic completion to sophisticated agent - enabling models to tackle complex software engineering tasks through deliberate planning and systematic debugging. Finally, we identify key challenges and propose future research directions may deepen the synergy, ultimately advancing LLM performance in both complex reasoning and code intelligence.

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Efficient Model Development through Fine-tuning Transfer
Pin-Jie Lin | Rishab Balasubramanian | Fengyuan Liu | Nikhil Kandpal | Tu Vu

Modern LLMs face a major obstacle: each new pre-trained model version requires expensive and repetitive alignment. We propose a method that transfers fine-tuning updates across model versions. The key idea is to extract the *diff vector*, which is the difference in parameters induced by fine-tuning, from a *source* model version and apply it to the base of a different *target* version. We show that transferring diff vectors significantly improves the target base model, often achieving performance comparable to its fine-tuned counterpart. For example, applying the fine-tuning updates from Llama 3.0 8B to Llama 3.1 8B increases accuracy by 46.9% on IFEval and 15.7% on LiveCodeBench without further training, surpassing Llama 3.1 8B Instruct. In multilingual settings, we also observe accuracy gains relative to Llama 3.1 8B Instruct, including 4.7% for Malagasy and 15.5% for Turkish on Global MMLU. Our controlled experiments reveal that fine-tuning transfer works best when source and target models are linearly connected in parameter space. We also show that this transfer provides a stronger and more efficient starting point for subsequent fine-tuning. Finally, we propose an iterative *recycling-then-finetuning* approach for continuous model development, which improves both efficiency and effectiveness. Our findings suggest that fine-tuning transfer is a viable strategy to reduce training costs while maintaining model performance.

Reasoning language models (RLMs) excel at complex tasks by leveraging a chain-of-thought process to generate structured intermediate steps. However, language mixing, i.e., reasoning steps containing tokens from languages other than the prompt, has been observed in their outputs and shown to affect performance, though its impact remains debated. We present the first systematic study of language mixing in RLMs, examining its patterns, impact, and internal causes across 15 languages, 7 task difficulty levels, and 18 subject areas, and show how all three factors influence language mixing. Moreover, we demonstrate that the choice of reasoning language significantly affects performance: forcing models to reason in Latin or Han scripts via constrained decoding notably improves accuracy. Finally, we show that the script composition of reasoning traces closely aligns with that of the model’s internal representations, indicating that language mixing reflects latent processing preferences in RLMs. Our findings provide actionable insights for optimizing multilingual reasoning and open new directions for reasoning language control to build more interpretable and adaptable RLMs.

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User Feedback in Human-LLM Dialogues: A Lens to Understand Users But Noisy as a Learning Signal
Yuhan Liu | Michael JQ Zhang | Eunsol Choi

Once language models (LMs) are deployed, they can interact with users long-term, ideally evolving based on their feedback. Asking for direct user feedback can be disruptive; thus, we study harvesting implicit user feedback from user-LM interaction logs. We study two user-LM interaction datasets (WildChat and LMSYS). First, we analyze user feedback in the user-LLM conversation logs, providing insights into when and why such feedback occurs. Second, we study harvesting learning signals from such implicit user feedback. Specifically, we study whether incorporating the contents of user feedback (e.g., user wanted clarification), in addition to the polarity of the feedback, can improve the model performance. We observe mixed results, showing this helps in short human-designed questions (MTBench) but not on longer and more complex questions (WildBench). Together, we provide an in-depth study of implicit user feedback, showing its potential and limitations.

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Read to Hear: A Zero-Shot Pronunciation Assessment Using Textual Descriptions and LLMs
Yu-Wen Chen | Melody Ma | Julia Hirschberg

Automatic pronunciation assessment is typically performed by acoustic models trained on audio-score pairs. Although effective, these systems provide only numerical scores, without the information needed to help learners understand their errors. Meanwhile, large language models (LLMs) have proven effective in supporting language learning, but their potential for assessing pronunciation remains unexplored. In this work, we introduce TextPA, a zero-shot, Textual description-based Pronunciation Assessment approach. TextPA utilizes human-readable representations of speech signals, which are fed into an LLM to assess pronunciation accuracy and fluency, while also providing reasoning behind the assigned scores. Finally, a phoneme sequence match scoring method is used to refine the accuracy scores. Our work highlights a previously overlooked direction for pronunciation assessment. Instead of relying on supervised training with audio-score examples, we exploit the rich pronunciation knowledge embedded in written text. Experimental results show that our approach is both cost-efficient and competitive in performance. Furthermore, TextPA significantly improves the performance of conventional audio-score-trained models on out-of-domain data by offering a complementary perspective.

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COCO-Tree: Compositional Hierarchical Concept Trees for Enhanced Reasoning in Vision-Language Models
Sanchit Sinha | Guangzhi Xiong | Aidong Zhang

Compositional reasoning remains a persistent weakness of modern vision language models (VLMs): they often falter when a task hinges on understanding how multiple objects, attributes, and relations interact within an image. Multiple research works have attempted to improve compositionality performance by creative tricks such as improving prompt structure, chain of thought reasoning, etc. A more recent line of work attempts to impart additional reasoning in VLMs using well-trained Large Language Models (LLMs), which are far superior in linguistic understanding than VLMs to compensate for the limited linguistic prowess of VLMs. However, these approaches are either resource-intensive or do not provide an interpretable reasoning process. In this paper, we present “COCO-Tree” - a novel approach that augments VLM outputs with carefully designed neurosymbolic concept trees learned from LLMs to improve VLM’s linguistic reasoning. COCO-Tree’s beam search-inspired reasoning process boosts compositionality performance and provides a rationale behind VLM predictions. Empirical results on four compositionality benchmarks, Winoground, EqBench, ColorSwap, and SugarCrepe, in seven different open-source VLMs with varying sizes, demonstrate that COCO-Tree significantly improves compositional generalization by 5-10% over baselines.

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SurveyGen: Quality-Aware Scientific Survey Generation with Large Language Models
Tong Bao | Mir Tafseer Nayeem | Davood Rafiei | Chengzhi Zhang

Automatic survey generation has emerged as a key task in scientific document processing. While large language models (LLMs) have shown promise in generating survey texts, the lack of standardized evaluation datasets critically hampers rigorous assessment of their performance against human-written surveys. In this work, we present SurveyGen, a large-scale dataset comprising over 4,200 human-written surveys across diverse scientific domains, along with 242,143 cited references and extensive quality-related metadata for both the surveys and the cited papers. Leveraging this resource, we build QUAL-SG, a novel quality-aware framework for survey generation that enhances the standard Retrieval-Augmented Generation (RAG) pipeline by incorporating quality-aware indicators into literature retrieval to assess and select higher-quality source papers. Using this dataset and framework, we systematically evaluate state-of-the-art LLMs under varying levels of human involvement—from fully automatic generation to human-guided writing. Experimental results and human evaluations show that while semi-automatic pipelines can achieve partially competitive outcomes, fully automatic survey generation still suffers from low citation quality and limited critical analysis.

We introduce VoiceCraft-X, an autoregressive neural codec language model which unifies multilingual speech editing and zero-shot text-to-speech (TTS) synthesis across 11 languages: English, Mandarin, Korean, Japanese, Spanish, French, German, Dutch, Italian, Portuguese, and Polish. VoiceCraft-X utilizes the Qwen3 large language model for phoneme-free cross-lingual text processing and a novel token reordering mechanism with time-aligned text and speech tokens to handle both tasks as a single sequence generation problem. The model generates high-quality, natural-sounding speech, seamlessly creating new audio or editing existing recordings within one framework. VoiceCraft-X shows robust performance in diverse linguistic settings, even with limited per-language data, underscoring the power of unified autoregressive approaches for advancing complex, real-world multilingual speech applications. Audio samples are available at https://zhishengzheng.com/voicecraft-x/.

Assessment and evaluation have long been critical challenges in artificial intelligence (AI) and natural language processing (NLP). Traditional methods, usually matching-based or small model-based, often fall short in open-ended and dynamic scenarios. Recent advancements in Large Language Models (LLMs) inspire the “LLM-as-a-judge” paradigm, where LLMs are leveraged to perform scoring, ranking, or selection for various machine learning evaluation scenarios. This paper presents a comprehensive survey of LLM-based judgment and assessment, offering an in-depth overview to review this evolving field. We first provide the definition from both input and output perspectives. Then we introduce a systematic taxonomy to explore LLM-as-a-judge along three dimensions: what to judge, how to judge, and how to benchmark. Finally, we also highlight key challenges and promising future directions for this emerging area.

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MultiMatch: Multihead Consistency Regularization Matching for Semi-Supervised Text Classification
Iustin Sirbu | Robert-Adrian Popovici | Cornelia Caragea | Stefan Trausan-Matu | Traian Rebedea

We introduce **MultiMatch**, a novel semi-supervised learning (SSL) algorithm combining the paradigms of co-training and consistency regularization with pseudo-labeling. At its core, MultiMatch features a three-fold pseudo-label weighting module designed for selecting and filtering pseudo-labels based on head agreement and model confidence, and weighting them according to the perceived classification difficulty. This novel module enhances and unifies three existing techniques - heads agreement from **Multi**head Co-training, self-adaptive thresholds from Free**Match**, and Average Pseudo-Margins from Margin**Match** - resulting in a holistic approach that improves robustness and performance in SSL settings.Experimental results on benchmark datasets highlight the superior performance of MultiMatch, i.e., MultiMatch achieves state-of-the-art results on 8 out of 10 setups from 5 natural language processing datasets and ranks first according to the Friedman test among 21 methods. Furthermore, MultiMatch demonstrates exceptional robustness in highly imbalanced settings, outperforming the second-best approach by 3.26%, a critical advantage for real-world text classification tasks. Our code is available on GitHub.

Large reasoning models (LRMs) have demonstrated impressive reasoning capabilities across a broad range of tasks including Olympiad-level mathematical problems, indicating evidence of their complex reasoning abilities. While many reasoning benchmarks focus on the STEM domain, the ability of LRMs to reason correctly in broader task domains remains underexplored. In this work, we introduce **TTT-Bench**, a new benchmark that is designed to evaluate basic strategic, spatial, and logical reasoning abilities in LRMs through a suite of four two-player Tic-Tac-Toe-style games that humans can effortlessly solve from a young age. We propose a simple yet scalable programmatic approach for generating verifiable two-player game problems for TTT-Bench. Although these games are trivial for humans, they require reasoning about the intentions of the opponent, as well as the game board’s spatial configurations, to ensure a win. We evaluate a diverse set of state-of-the-art LRMs, and **discover that the models that excel at hard math problems frequently fail at these simple reasoning games**. Further testing reveals that our evaluated reasoning models score on average ↓ 41% & ↓ 5% lower on TTT-Bench compared to MATH 500 & AIME 2024 respectively, with larger models achieving higher performance using shorter reasoning traces, where most of the models struggle on long-term strategic reasoning situations on simple and new TTT-Bench tasks.

Advances in large language models (LLMs) significantly enhance reasoning capabilities but their deployment is restricted in resource-constrained scenarios. Knowledge distillation addresses this by transferring knowledge from powerful teacher models to compact and transparent students.However, effectively capturing the teacher’s comprehensive reasoning is challenging due to conventional token-level supervision’s limited scope. Using multiple reasoning paths per query alleviates this problem, but treating each path identically is suboptimal as paths vary widely in quality and suitability across tasks and models.We propose Quality-filtered Routing with Cooperative Distillation(QR-Distill), combining path quality filtering, conditional routing, and cooperative peer teaching. First, quality filtering retains only correct reasoning paths scored by an LLM-based evaluation. Second, conditional routing dynamically assigns paths tailored to each student’s current learning state. Finally, cooperative peer teaching enables students to mutually distill diverse insights, addressing knowledge gaps and biases toward specific reasoning styles. Experiments demonstrate QR-Distill’s superiority over traditional single- and multi-path distillation methods. Ablation studies further highlight the importance of each component—quality filtering, conditional routing, and peer teaching—in effective knowledge transfer. Our code is available at https://github.com/LzyFischer/Distill.

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Ask Patients with Patience: Enabling LLMs for Human-Centric Medical Dialogue with Grounded Reasoning
Jiayuan Zhu | Jiazhen Pan | Yuyuan Liu | Fenglin Liu | Junde Wu

The severe shortage of medical doctors limits access to timely and reliable healthcare, leaving millions underserved. Large language models (LLMs) offer a potential solution but struggle in real-world clinical interactions. Many LLMs are not grounded in authoritative medical guidelines and fail to transparently manage diagnostic uncertainty. Their language is often rigid and mechanical, lacking the human-like qualities essential for patient trust. To address these challenges, we propose ***Ask Patients with Patience (APP)***, a multi-turn LLM-based medical assistant designed for grounded reasoning, transparent diagnoses, and human-centric interaction. APP enhances communication by eliciting user symptoms through empathetic dialogue, significantly improving accessibility and user engagement. It also incorporates Bayesian active learning to support transparent and adaptive diagnoses. The framework is built on verified medical guidelines, ensuring clinically grounded and evidence-based reasoning. To evaluate its performance, we develop a new benchmark that simulates realistic medical conversations using patient agents driven by profiles extracted from real-world consultation cases. We compare APP against SOTA one-shot and multi-turn LLM baselines. The results show that APP improves diagnostic accuracy, reduces uncertainty, and enhances user experience. By integrating medical expertise with transparent, human-like interaction, APP bridges the gap between AI-driven medical assistance and real-world clinical practice.

Advancements in Large Language Models (LLMs) and their increasing use in medical question-answering necessitate rigorous evaluation of their reliability. A critical challenge lies in hallucination, where models generate plausible yet factually incorrect outputs. In the medical domain, this poses serious risks to patient safety and clinical decision-making. To address this, we introduce, the first benchmark specifically designed for medical hallucination detection. MedHallu comprises 10,000 high-quality question-answer pairs derived from PubMedQA, with hallucinated answers systematically generated through a controlled pipeline. Our experiments show that state-of-the-art LLMs, including GPT-4o, Llama-3.1, and the medically fine-tuned UltraMedical, struggle with this binary hallucination detection task, with the best model achieving an F1 score as low as 0.625 for detecting ”hard” category hallucinations. Using bidirectional entailment clustering, we show that harder-to-detect hallucinations are semantically closer to ground truth. Through experiments, we also show incorporating domain-specific knowledge and introducing a ”not sure” category as one of the answer categories improves the precision and F1 scores by up to 38% relative to baselines.

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NUTMEG: Separating Signal From Noise in Annotator Disagreement
Jonathan Ivey | Susan Gauch | David Jurgens

NLP models often rely on human-labeled data for training and evaluation. Many approaches crowdsource this data from a large number of annotators with varying skills, backgrounds, and motivations, resulting in conflicting annotations. These conflicts have traditionally been resolved by aggregation methods that assume disagreements are errors. Recent work has argued that for many tasks annotators may have genuine disagreements and that variation should be treated as signal rather than noise. However, few models separate signal and noise in annotator disagreement. In this work, we introduce NUTMEG, a new Bayesian model that incorporates information about annotator backgrounds to remove noisy annotations from human-labeled training data while preserving systematic disagreements. Using synthetic and real-world data, we show that NUTMEG is more effective at recovering ground-truth from annotations with systematic disagreement than traditional aggregation methods, and we demonstrate that downstream models trained on NUTMEG-aggregated data significantly outperform models trained on data from traditionally aggregation methods. We provide further analysis characterizing how differences in subpopulation sizes, rates of disagreement, and rates of spam affect the performance of our model. Our results highlight the importance of accounting for both annotator competence and systematic disagreements when training on human-labeled data.

Alignment is no longer a luxury; it is a necessity. As large language models (LLMs) enter high-stakes domains like education, healthcare, governance, and law, their behavior must reliably reflect human-aligned values and safety constraints. Yet current evaluations rely heavily on behavioral proxies such as refusal rates, G-Eval scores, and toxicity classifiers, all of which have critical blind spots. Aligned models are often vulnerable to jailbreaking, stochasticity of generation, and alignment faking. To address this issue, we introduce the **Alignment Quality Index (AQI)**. This novel geometric and prompt-invariant metric empirically assesses LLM alignment by analyzing the separation of safe and unsafe activations in latent space. By combining measures such as the *Davies-Bouldin score (DBS)*, *Dunn index (DI)*, *Xie-Beni index (XBI)*, and *Calinski-Harabasz index (CHI)* across various formulations, AQI captures clustering quality to detect hidden misalignments and jailbreak risks, even when outputs appear compliant. AQI also serves as an early warning signal for alignment faking, offering a robust, decoding-invariant tool for behavior-agnostic safety auditing. Additionally, we propose the **LITMUS** dataset to facilitate robust evaluation under these challenging conditions. Empirical tests on LITMUS across different models trained under DPO, GRPO, and RLHF conditions demonstrate AQI’s correlation with external judges and ability to reveal vulnerabilities missed by refusal metrics. We make our implementation publicly available to foster future research in this area.

Understanding the prevalence of misinformation in health topics online can inform public health policies and interventions. However, measuring such misinformation at scale remains a challenge, particularly for high-stakes but understudied topics like opioid-use disorder (OUD)—a leading cause of death in the U.S. We present the first large-scale study of OUD-related myths on YouTube, a widely-used platform for health information. With clinical experts, we validate 8 pervasive myths and release an expert-labeled video dataset. To scale labeling, we introduce MythTriage, an efficient triage pipeline that uses a lightweight model for routine cases and defers harder ones to a high-performing, but costlier, large language model (LLM). MythTriage achieves up to 0.86 macro F1-score while estimated to reduce annotation time and financial cost by over 76% compared to experts and full LLM labeling. We analyze 2.9K search results and 343K recommendations, uncovering how myths persist on YouTube and offering actionable insights for public health and platform moderation.

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Demystifying optimized prompts in language models
Rimon Melamed | Lucas Hurley McCabe | H Howie Huang

Modern language models (LMs) are not robust to out-of-distribution inputs. Machine generated (“optimized”) prompts can be used to modulate LM outputs and induce specific behaviors while appearing completely uninterpretable. In this work, we investigate the composition of optimized prompts, as well as the mechanisms by which LMs parse and build predictions from optimized prompts. We find that optimized prompts primarily consist of punctuation and noun tokens which are more rare in the training data. Internally, optimized prompts are clearly distinguishable from natural language counterparts based on sparse subsets of the model’s activations. Across various families of instruction-tuned models, optimized prompts follow a similar path in how their representations form through the network.

Encoder-decoder models have achieved remarkable success in speech and text tasks, yet efficiently adapting these models to diverse uni/multi-modal scenarios remains an open challenge. In this paper, we propose Whisper-UT, a unified and efficient framework that leverages lightweight adapters to enable seamless adaptation across tasks, including a multi-modal machine translation (MMT) task that explicitly conditions translation on both speech and source language text inputs. By incorporating ASR hypotheses or ground-truth transcripts as prompts, this approach not only enables the system to process both modalities simultaneously but also enhances speech translation (ST) performance through a 2-stage decoding strategy. We demonstrate our methods using the Whisper model, though in principle they are general and could be applied to similar multitask models. We highlight the effectiveness of cross-modal and cross-task fine-tuning, which improves performance without requiring 3-way parallel data. Our results underscore the flexibility, efficiency, and general applicability of the proposed framework for multi-modal translation.

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Unleashing the Reasoning Potential of LLMs by Critique Fine-Tuning on One Problem
Yubo Wang | Ping Nie | Kai Zou | Lijun Wu | Wenhu Chen

Critique Fine-Tuning (CFT) has recently emerged as a promising paradigm for unlocking the reasoning capabilities of large language models (LLMs). In this work, we introduce one-shot CFT, a highly compute-efficient approach that leverages critique data generated from a single math problem. Remarkably, this method yields significant gains in reasoning accuracy, surpassing one-shot RLVR (Reinforcement Learning with Verifiable Reward) while requiring 15 to 20 times less compute. Given one math problem, we first prompt a set of diverse small models to produce candidate solutions, then use frontier models such as GPT-4.1 to generate high-quality critiques of these responses. We fine-tune Qwen and Llama family models ranging from 1.5B to 14B parameters with CFT. With just 5 GPU hours, our models achieve up to a 16 percent absolute improvement in average accuracy across six mathematical reasoning benchmarks (for example, Qwen2.5-Math-7B improves from 26 percent to 42 percent). Furthermore, ablation studies reveal the robustness of one-shot CFT across different prompt problems. Our findings suggest an extremely compute-efficient approach to unleash the reasoning potential of LLMs.

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Active Layer-Contrastive Decoding Reduces Hallucination in Large Language Model Generation
Hongxiang Zhang | Hao Chen | Muhao Chen | Tianyi Zhang

Recent decoding methods improve the factuality of large language models (LLMs) by refining how the next token is selected during generation. These methods typically operate at the token level, leveraging internal representations to suppress superficial patterns. Nevertheless, LLMs remain prone to hallucinations, especially over longer contexts. In this paper, we propose Active Layer-Contrastive Decoding (ActLCD), a novel decoding strategy that actively decides when to apply contrasting layers during generation. By casting decoding as a sequential decision-making problem, ActLCD employs a reinforcement learning policy guided by a reward-aware classifier to optimize factuality beyond the token level. Our experiments demonstrate that ActLCD surpasses state-of-the-art methods across five benchmarks, showcasing its effectiveness in mitigating hallucinations in diverse generation scenarios.

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BBScoreV2: Learning Time-Evolution and Latent Alignment from Stochastic Representation
Tianhao Zhang | Zhecheng Sheng | Zhexiao Lin | Chen Jiang | Dongyeop Kang

Autoregressive generative models play a key role in various language tasks, especially for modeling and evaluating long text sequences. While recent methods leverage stochastic representations to better capture sequence dynamics, encoding both temporal and structural dependencies and utilizing such information for evaluation remains challenging. In this work, we observe that fitting transformer-based model embeddings into a stochastic process yields ordered latent representations from originally unordered model outputs. Building on this insight and prior work, we theoretically introduce a novel likelihood-based evaluation metric BBScoreV2. Empirically, we demonstrate that the stochastic latent space induces a “clustered-to-temporal ordered” mapping of language model representations in high-dimensional space, offering both intuitive and quantitative support for the effectiveness of BBScoreV2. Furthermore, this structure aligns with intrinsic properties of natural language and enhances performance on tasks such as temporal consistency evaluation (e.g., Shuffle tasks) and AI-generated content detection.

Large Language Model (LLM) agents are commonly tuned with supervised finetuning on ReAct-style expert trajectories or preference optimization over pairwise rollouts. Most of these methods focus on imitating specific expert behaviors or promoting chosen reasoning thoughts and actions over rejected ones. However, without reasoning and comparing over alternatives actions, LLM agents finetuned with these methods may over-commit towards seemingly plausible but suboptimal actions due to limited action space exploration. To address this, in this paper we propose Self-taught ActioN Deliberation (SAND) framework, enabling LLM agents to explicitly deliberate over candidate actions before committing to one. To tackle the challenges of when and what to deliberate given large action space and step-level action evaluation, we incorporate self-consistency action sampling and execution-guided action critique to help synthesize step-wise action deliberation thoughts using the base model of the LLM agent. In an iterative manner, the deliberation trajectories are then used to finetune the LLM agent itself. Evaluating on two representative interactive agent tasks, SAND achieves an average 20% improvement over initial supervised finetuning and also outperforms state-of-the-art agent tuning approaches.

Efficient simulation is essential for enhancing proactive preparedness for sudden-onset disasters such as earthquakes. Recent advancements in large language models (LLMs) as world models show promise in simulating complex scenarios. This study examines multiple LLMs to proactively estimate perceived earthquake impacts. Leveraging multimodal datasets including geospatial, socioeconomic, building, and street-level imagery data, our framework generates Modified Mercalli Intensity (MMI) predictions at zip code and county scales. Evaluations on the 2014 Napa and 2019 Ridgecrest earthquakes using USGS “Did You Feel It? (DYFI)” reports demonstrate significant alignment, as evidenced by high correlation of 0.88 and low RMSE of 0.77 as compared to real reports at the zip code level. Techniques such as RAG and ICL can improve simulation performance, while visual inputs notably enhance accuracy compared to structured numerical data alone. These findings show the promise of LLMs in simulating disaster impacts that can help strengthen pre-event planning.

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Mind the Value-Action Gap: Do LLMs Act in Alignment with Their Values?
Hua Shen | Nicholas Clark | Tanu Mitra

Existing research assesses LLMs’ values by analyzing their stated inclinations, overlooking potential discrepancies between stated values and actions—termed the “Value-Action Gap.” This study introduces ValueActionLens, a framework to evaluate the alignment between LLMs’ stated values and their value-informed actions. The framework includes a dataset of 14.8k value-informed actions across 12 cultures and 11 social topics, along with two tasks measuring alignment through three metrics. Experiments show substantial misalignment between LLM-generated value statements and their actions, with significant variations across scenarios and models. Misalignments reveal potential harms, highlighting risks in relying solely on stated values to predict behavior. The findings stress the need for context-aware evaluations of LLM values and the value-action gaps.

Although preference optimization methods have improved reasoning performance in Large Language Models (LLMs), they often lack transparency regarding why one reasoning outcome is preferred over another. This limitation is especially critical in Automated Student Answer Scoring (ASAS), where explainability is essential to justify assessment outcomes. Verbal reinforcement learning offers the potential to generate explicit reflection, but it tends to produce superficial critiques that can harm assessment performance. Existing LLMs also struggle to reliably detect subtle reasoning errors in ASAS tasks. Moreover, manually identifying intermediate reasoning errors is expensive and difficult to scale. To address these challenges, we introduce a **contrastive reflection synthesis pipeline** that generates precise verbal feedback by identifying discrepancies in structure reasoning graph paths. Leveraging these synthetic reflection data, we propose *DARS*, a Dual-model Reflective Scoring framework featuring a dedicated Critic model trained for effective reflection. *DARS* achieves strong performance and consistently outperforms existing ASAS baselines across all evaluation metrics. Extensive experiments further provide novel insights into the value of reflection data, framework design, and the scaling behavior of *DARS*. We release the DARS code at https://github.com/lijiazheng99/DARS.

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Image Embedding Sampling Method for Diverse Captioning
Sania Waheed | Na Min An

Image Captioning for state-of-the-art VLMs has significantly improved over time; however, this comes at the cost of increased computational complexity, making them less accessible for resource-constrained applications such as mobile devices and assistive technologies. Alternatively, comparably smaller VLMs prioritize high-level scene descriptions, overlooking finer details that contribute to a richer understanding of an image. In this paper, we introduce a training-free framework that enhances caption diversity and informativeness by explicitly attending to distinct image regions using a comparably small VLM, BLIP, as the backbone. Our approach leverages structured segmentation to produce hierarchical representations that capture both global and localized semantics. Without requiring additional model training, we demonstrate that our method allows smaller VLMs to achieve performance comparable to larger models in terms of image-caption alignment, semantic integrity, and diversity. We evaluate our framework on MSCOCO, Flickr30k, and Nocaps test datasets, achieving a Div-2 score of 0.735, 0.750, and 0.748 for each dataset, respectively, while maintaining strong image-caption relevancy and semantic integrity with the human-annotated captions. Our code is available at https://github.com/xfactlab/HBoP.

Large Language Models (LLMs) perform well on reasoning benchmarks but often fail when inputs alter slightly, raising concerns about the extent to which their success relies on memorization. This issue is especially acute in Chain-of-Thought (CoT) reasoning, where spurious memorized patterns can trigger intermediate errors that cascade into incorrect final answers. We introduce STIM, a novel framework for Source-aware Token-level Identification of Memorization, which attributes each token in a reasoning chain to one of multiple memorization sources – local, mid-range, or long-range – based on their statistical co-occurrence with the token in the pretraining corpus. Our token-level analysis across tasks and distributional settings reveals that models rely more on memorization in complex or long-tail cases, and that local memorization is often the dominant driver of errors, leading to up to 67% of wrong tokens. We also show that memorization scores from STIM can be effective in predicting the wrong tokens in the wrong reasoning step. STIM offers a powerful tool for diagnosing and improving model reasoning and can generalize to other structured step-wise generation tasks.

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FANS: Formal Answer Selection for LLM Natural Language Math Reasoning Using Lean4
Jiarui Yao | Ruida Wang | Tong Zhang

Large Language Models (LLMs) have displayed astonishing abilities in various tasks, especially in text generation, classification, question answering, etc. However, the reasoning ability of LLMs still faces many debates, especially in math reasoning. The inherent ambiguity of Natural Language (NL) limits LLMs’ ability to perform verifiable reasoning, making the answers lack coherence and trustworthy support. To tackle the above challenges, we propose a novel framework named FANS: Formal ANswer Selection for LLM Natural Language Math Reasoning Using Lean4. It is a pioneering framework that utilizes Lean4 to enhance LLMs’ NL math reasoning ability. In particular, given an NL math question and LLM-generated answers, FANS first translates it into Lean4 theorem statements. Then it invokes another Lean4 prover LLM to produce proofs, and finally verifies the proofs by Lean4 compiler. Answers are selected based on the verifications. It enhances LLMs’ NL math ability in providing a computer-verifiable solution for its correct answer and proposes an alternative method for answer selection beyond the reward model based ones. Our experiments demonstrate the effectiveness of FANS with an improvement of nearly 2% across several math benchmarks, and even higher further based on reward models or in subfields such as algebra and number theory that Lean4 is better at. The code is available in https://github.com/MaxwellJryao/FANS.

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Date Fragments: A Hidden Bottleneck of Tokenization for Temporal Reasoning
Gagan Bhatia | Maxime Peyrard | Wei Zhao

Modern BPE tokenisers often split calendar dates into meaningless fragments, e.g., “20250312” → “202”, “503”, “12”, inflating token counts and obscuring the inherent structure needed for robust temporal reasoning. In this work, we (1) introduce a simple yet interpretable metric, termed date fragmentation ratio, that measures how faithfully a tokeniser preserves multi-digit date components; (2) release DateAugBench, a suite of 6500 examples spanning three temporal reasoning tasks: context-based date resolution, format-invariance puzzles, and date arithmetic across historical, contemporary, and future time periods; and (3) through layer-wise probing and causal attention-hop analyses, uncover an emergent date-abstraction mechanism whereby large language models stitch together the fragments of month, day, and year components for temporal reasoning. Our experiments show that excessive fragmentation correlates with accuracy drops of up to 10 points on uncommon dates like historical and futuristic dates. Further, we find that the larger the model, the faster the emergent date abstraction heals date fragments. Lastly, we observe a reasoning path that LLMs follow to assemble date fragments, typically differing from human interpretation (year → month → day).

Systems that answer questions by reviewing the scientific literature are becoming increasingly feasible. To draw reliable conclusions, these systems should take into account the quality of available evidence from different studies, placing more weight on studies that use a valid methodology. We present a benchmark for measuring the methodological strength of biomedical papers, drawing on the risk-of-bias framework used for systematic reviews. Derived from over 500 biomedical studies, the three benchmark tasks encompass expert reviewers’ judgments of studies’ research methodologies, including the assessments of risk of bias within these studies. The benchmark contains a human-validated annotation pipeline for fine-grained alignment of reviewers’ judgments with research paper sentences. Our analyses show that large language models’ reasoning and retrieval capabilities impact their effectiveness with risk-of-bias assessment. The dataset is available at https://github.com/RoBBR-Benchmark/RoBBR.

Video-guided Machine Translation (VMT) aims to improve translation quality by integrating contextual information from paired short video clips. Mainstream VMT approaches typically incorporate multimodal information by uniformly sampling frames from the input videos. However, this paradigm frequently incurs significant computational overhead and introduces redundant multimodal content, which degrades both efficiency and translation quality. To tackle these challenges, we propose SHIFT (Selected Helpful Informative Frame for Translation). It is a lightweight, plug-and-play framework designed for VMT with Multimodal Large Language Models (MLLMs). SHIFT adaptively selects a single informative key frame when visual context is necessary; otherwise, it relies solely on textual input. This process is guided by a dedicated clustering module and a selector module. Experimental results demonstrate that SHIFT enhances the performance of MLLMs on the VMT task while simultaneously reducing computational cost, without sacrificing generalization ability. The code will be released upon acceptance.

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Surge: On the Potential of Large Language Models as General-Purpose Surrogate Code Executors
Bohan Lyu | Siqiao Huang | Zichen Liang | Qian Sun | Jiaming Zhang

Neural surrogate models are powerful and efficient tools in data mining. Meanwhile, large language models (LLMs) have demonstrated remarkable capabilities in code-related tasks, such as generation and understanding. However, an equally important yet underexplored question is whether LLMs can serve as surrogate models for code execution prediction. To systematically investigate it, we introduce SURGE, a comprehensive benchmark with 1160 problems covering 8 key aspects: multi-language programming tasks, competition-level programming problems, repository-level code analysis, high-cost scientific computing, time-complexity-intensive algorithms, buggy code analysis, programs dependent on specific compilers or execution environments, and formal mathematical proof verification. Through extensive analysis of 21 open-source and proprietary LLMs, we examine scaling laws, data efficiency, and predictive accuracy. Our findings reveal important insights about the feasibility of LLMs as efficient surrogates for computational processes. The benchmark and evaluation framework are available at https://github.com/Imbernoulli/SURGE.

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Few-Shot Learning Translation from New Languages
Carlos Mullov | Alexander Waibel

Recent work shows strong transfer learning capability to unseen languages in sequence-to-sequence neural networks, under the assumption that we have high-quality word representations for the target language. We evaluate whether this direction is a viable path forward for translation from low-resource languages by investigating how much data is required to learn such high-quality word representations. We first show that learning word embeddings separately from a translation model can enable rapid adaptation to new languages with only a few hundred sentences of parallel data. To see whether the current bottleneck in transfer to low-resource languages lies mainly with learning the word representations, we then train word embeddings models on varying amounts of data, to then plug them into a machine translation model. We show that in this simulated low-resource setting with only 500 parallel sentences and 31,250 sentences of monolingual data we can exceed 15 BLEU on Flores on unseen languages. Finally, we investigate why on a real low-resource language the results are less favorable and find fault with the publicly available multilingual language modelling datasets.

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Humanizing Machines: Rethinking LLM Anthropomorphism Through a Multi-Level Framework of Design
Yunze Xiao | Lynnette Hui Xian Ng | Jiarui Liu | Mona T. Diab

Large Language Models (LLMs) increasingly exhibit anthropomorphism characteristics – human-like qualities portrayed across their outlook, language, behavior, and reasoning functions. Such characteristics enable more intuitive and engaging human-AI interactions. However, current research on anthropomorphism remains predominantly risk-focused, emphasizing over-trust and user deception while offering limited design guidance. We argue that anthropomorphism should instead be treated as a concept of design that can be intentionally tuned to support user goals. Drawing from multiple disciplines, we propose that the anthropomorphism of an LLM-based artifact should reflect the interaction between artifact designers and interpreters. This interaction is facilitated by cues embedded in the artifact by the designers and the (cognitive) responses of the interpreters to the cues. Cues are categorized into four dimensions: perceptive, linguistic, behavioral, and cognitive. By analyzing the manifestation and effectiveness of each cue, we provide a unified taxonomy with actionable levers for practitioners. Consequently, we advocate for function-oriented evaluations of anthropomorphic design.

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TokenSkip: Controllable Chain-of-Thought Compression in LLMs
Heming Xia | Chak Tou Leong | Wenjie Wang | Yongqi Li | Wenjie Li

Chain-of-Thought (CoT) has been proven effective in enhancing the reasoning capabilities of large language models (LLMs). Recent advancements, such as OpenAI’s o1 and DeepSeek-R1, suggest that scaling up the length of CoT sequences during inference could further boost LLM reasoning performance. However, due to the autoregressive nature of LLM decoding, longer CoT outputs lead to a linear increase in inference latency, adversely affecting user experience, particularly when the CoT exceeds 10,000 tokens. To address this limitation, we analyze the semantic importance of tokens within CoT outputs and reveal that their contributions to reasoning vary. Building on this insight, we propose TokenSkip, a simple yet effective approach that enables LLMs to selectively skip less important tokens, allowing for controllable CoT compression. Extensive experiments across various models and tasks demonstrate the effectiveness of TokenSkip in reducing CoT token usage while preserving strong reasoning performance. Notably, when applied to Qwen2.5-14B-Instruct, TokenSkip reduces reasoning tokens by 40% (from 313 to 181) on GSM8K, with less than a 0.4% performance drop.

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Are Generative Models Underconfident? Better Quality Estimation with Boosted Model Probability
Tu Anh Dinh | Jan Niehues

Quality Estimation (QE) is estimating quality of the model output during inference when the ground truth is not available. Deriving output quality from the models’ output probability is the most trivial and low-effort way. However, we show that the output probability of text-generation models can appear underconfident. At each output step, there can be multiple correct options, making the probability distribution spread out more. Thus, lower probability does not necessarily mean lower output quality. Due to this observation, we propose a QE approach called BoostedProb, which boosts the model’s confidence in cases where there are multiple viable output options. With no increase in complexity, BoostedProb is notably better than raw model probability in different settings, achieving on average +0.194 improvement in Pearson correlation to ground-truth quality. It also comes close to or outperforms more costly approaches like supervised or ensemble-based QE in certain settings.

Reward models have become a staple in modern NLP, serving as not only a scalable text evaluator, but also an indispensable component in many alignment recipes and inference-time algorithms. However, while recent reward models increase performance on standard benchmarks, this may partly be due to overfitting effects, which would confound an understanding of their true capability. In this work, we scrutinize the robustness of reward models and the extent of such overfitting. We build reWordBench, which systematically transforms reward model inputs in meaning- or ranking-preserving ways. We show that state-of-the-art reward models suffer from substantial performance degradation even with minor input transformations, sometimes dropping to significantly below-random accuracy, suggesting brittleness. To improve reward model robustness, we propose to explicitly train them to assign similar scores to paraphrases, and find that this approach also improves robustness to other distinct kinds of transformations. For example, our robust reward model reduces such degradation by roughly half for the Chat Hard subset in RewardBench. Furthermore, when used in alignment, our robust reward models demonstrate better utility and lead to higher-quality outputs, winning in up to 59% of instances against a standardly trained RM.

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Why Do Some Inputs Break Low-Bit LLM Quantization?
Ting-Yun Chang | Muru Zhang | Jesse Thomason | Robin Jia

Low-bit weight-only quantization significantly reduces the memory footprint of large language models (LLMs), but disproportionately affects certain examples. We analyze diverse 3-4 bit methods on LLMs ranging from 7B-70B in size and find that the quantization errors of 50 pairs of methods are strongly correlated (avg. 𝜌 = 0.82) on FineWeb examples. Moreover, the residual stream magnitudes of full-precision models are indicative of future quantization errors. We further establish a hypothesis that relates the residual stream magnitudes to error amplification and accumulation over layers. Using LLM localization techniques, early exiting, and activation patching, we show that examples with large errors rely on precise residual activations in the late layers, and that the outputs of MLP gates play a crucial role in maintaining the perplexity. Our work reveals why certain examples result in large quantization errors and which model components are most critical for performance preservation.

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LiteASR: Efficient Automatic Speech Recognition with Low-Rank Approximation
Keisuke Kamahori | Jungo Kasai | Noriyuki Kojima | Baris Kasikci

Modern automatic speech recognition (ASR) models, such as OpenAI’s Whisper, rely on deep encoder-decoder architectures, and their encoders are a critical bottleneck for efficient deployment due to high computational intensity. We introduce LiteASR, a low-rank compression scheme for ASR encoders that significantly reduces inference costs while maintaining transcription accuracy. Our approach leverages the strong low-rank properties observed in intermediate activations: by applying principal component analysis (PCA) with a small calibration dataset, we approximate linear transformations with a chain of low-rank matrix multiplications, and further optimize self-attention to work in reduced dimensionality. Evaluation results show that our method can compress Whisper large-v3’s encoder size by over 50%, matching Whisper medium’s size with better transcription accuracy, thereby establishing a new Pareto frontier of accuracy and efficiency. The code of LiteASR is available at https://github.com/efeslab/LiteASR.

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AROMA: Autonomous Rank-one Matrix Adaptation
Hao Nan Sheng | Zhi-Yong Wang | Hing Cheung So | Mingrui Yang

As large language models continue to grow in size, parameter-efficient fine-tuning (PEFT) has become increasingly crucial. While low-rank adaptation (LoRA) offers a solution through low-rank updates, its static rank allocation may yield suboptimal results. Adaptive low-rank adaptation (AdaLoRA) improves this with dynamic allocation but remains sensitive to initial and target rank configurations. We introduce AROMA, a framework that automatically constructs layer-specific updates by iteratively building up rank-one components with very few trainable parameters that gradually diminish to zero. Unlike existing methods that employ rank reduction mechanisms, AROMA introduces a dual-loop architecture for rank growth. The inner loop extracts information from each rank-one subspace, while the outer loop determines the number of rank-one subspaces, i.e., the optimal rank. We reset optimizer states to maintain subspace independence. AROMA significantly reduces parameters compared to LoRA and AdaLoRA while achieving superior performance on natural language understanding and generation, commonsense reasoning, offering new insights into adaptive PEFT.

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Large Language Models Have Intrinsic Meta-Cognition, but Need a Good Lens
Ziyang Ma | Qingyue Yuan | Zhenglin Wang | Deyu Zhou

Previous research has primarily focused on the cognitive error detection capabilities of Large Language Models (LLMs), often prompting them to analyze mistakes in reasoning chains. However, few studies have examined the meta-cognitive abilities of LLMs (e.g., their self-awareness of step errors), which are crucial for their reliability. While studies on LLM self-evaluation present some measures, such as perplexity, which can reflect the answer correctness and be viewed as the lens of meta-cognition, they lack step-level analysis and adaptation. This paper studies the evaluation of LLM meta-cognition using the current lenses and how to improve these lenses. Specifically, we propose AutoMeco, an Automated Meta-cognition Evaluation framework for benchmarking the existing lenses. Furthermore, a training-free Markovian Intrinsic Reward Adjustment strategy, MIRA, is proposed to boost current meta-cognition lenses. Experimental results on three mathematical reasoning datasets and three LLMs show the reasonableness of AutoMeco by comparing it with Best-of-N verification. Moreover, the meta-cognition ability of LLMs can be better evaluated using MIRA.

Large Language Models (LLMs) have demonstrated significant advancements in various fields, notably in Role-Playing Conversational Agents (RPCAs). However, when confronted with role-specific professional inquiries, LLMs-based RPCAs tend to underperform due to their excessive emphasis on the conversational abilities of characters rather than effectively invoking and integrating relevant expert knowledge. This often results in inaccurate responses. We refer to this phenomenon as the “Knowledge Misalignment” which underscores the limitations of RPCAs in integrating expert knowledge. To mitigate this issue, we have introduced an Anchoring-Guidance Fine-Tuning (AnGFT) Framework into the RPCAs’ training process. This involves initially linking the Anchoring-Based System Prompt (ASP) with the LLM’s relevant expert domains through diverse prompt construction strategies and supervised fine-tuning (SFT). Following the role-play enriched SFT, the integration of ASP enables LLMs to better associate with relevant expert knowledge, thus enhancing their response capabilities in role-specific expert domains. Moreover, we have developed four comprehensive metrics—helpfulness, thoroughness, credibility, and feasibility—to evaluate the proficiency of RPCAs in responding to professional questions. Our method was tested across four professional fields, and the experimental outcomes suggest that the proposed AnGFT Framework substantially improves the RPCAs’ performance in handling role-specific professional queries, while preserving their robust role-playing abilities.

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RiTTA: Modeling Event Relations in Text-to-Audio Generation
Yuhang He | Yash Jain | Xubo Liu | Andrew Markham | Vibhav Vineet

Existing text-to-audio (TTA) generation methods have neither systematically explored audio event relation modeling, nor proposed any new framework to enhance this capability. In this work, we systematically study audio event relation modeling in TTA generation models. We first establish a benchmark for this task by: (1) proposing a comprehensive relation corpus covering all potential relations in real-world scenarios; (2) introducing a new audio event corpus encompassing commonly heard audios; and (3) proposing new evaluation metrics to assess audio event relation modeling from various perspectives. Furthermore, we propose a gated prompt tuning strategy that improves existing TTA models’ relation modeling capability with negligible extra parameters. Specifically, we introduce learnable relation and event prompt that append to the text prompt before feeding to existing TTA models.

Multimodal large language models (MLLMs) demonstrate excellent abilities for understanding visual information, while the hallucination remains. Albeit image tokens constitute the majority of the MLLMs input, the relation between image tokens and hallucinations is still unexplored. In this paper, we analyze the attention score distribution of image tokens across layers and attention heads in models, revealing an intriguing but common phenomenon: most hallucinations are closely linked to the attention sink patterns of image tokens attention matrix, where shallow layers exhibit dense sinks and deep layers exhibit the sparse. We further explore the attention heads of different layers, finding: heads with high-density attention sink of the image part act positively in mitigating hallucinations. Inspired by these findings, we propose a training-free approach called Enhancing Vision Attention Sinks (EVAS) to facilitate the convergence of the image token attention sink within shallow layers. Specifically, EVAS identifies the attention heads that emerge as the densest visual sink in shallow layers and extracts its attention matrix, which is then broadcast to other heads of the same layer, thereby strengthing the layer’s focus on the image itself. Extensive empirical results of various MLLMs illustrate the superior performance of the proposed EVAS, demonstrating its effectiveness and generality.

Large language models excel at instruction-following in English, but their performance in low-resource languages like Thai remains underexplored. Existing benchmarks often rely on translations, missing cultural and domain-specific nuances needed for real-world use. We present WangchanThaiInstruct, a human-authored Thai dataset for evaluation and instruction tuning, covering four professional domains and seven task types. Created through a multi-stage quality control process with annotators, domain experts, and AI researchers, WangchanThaiInstruct supports two studies: (1) a zero-shot evaluation showing performance gaps on culturally and professionally specific tasks, and (2) an instruction tuning study with ablations isolating the effect of native supervision. Models fine-tuned on WangchanThaiInstruct outperform those using translated data in both in-domain and out-of-domain benchmarks. These findings underscore the need for culturally and professionally grounded instruction data to improve LLM alignment in low-resource, linguistically diverse settings.

Memes have emerged as a popular form of multimodal online communication, where their interpretation heavily depends on the specific context in which they appear. Current approaches predominantly focus on isolated meme analysis, either for harmful content detection or standalone interpretation, overlooking a fundamental challenge: the same meme can express different intents depending on its conversational context. This oversight creates an evaluation gap: although humans intuitively recognize how context shapes meme interpretation, Large Vision Language Models (LVLMs) can hardly understand context-dependent meme intent. To address this critical limitation, we introduce MemeReaCon, a novel benchmark specifically designed to evaluate how LVLMs understand memes in their original context. We collected memes from five different Reddit communities, keeping each meme’s image, the post text, and user comments together. We carefully labeled how the text and meme work together, what the poster intended, how the meme is structured, and how the community responded. Our tests with leading LVLMs show a clear weakness: models either fail to interpret critical information in the contexts, or overly focus on visual details while overlooking communicative purpose. MemeReaCon thus serves both as a diagnostic tool exposing current limitations and as a challenging benchmark to drive development toward more sophisticated LVLMs of the context-aware understanding.

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A Comprehensive Literary Chinese Reading Comprehension Dataset with an Evidence Curation Based Solution
Dongning Rao | Rongchu Zhou | Peng Chen | Zhihua Jiang

Low-resource language understanding is challenging, even for large language models (LLMs). An epitome of this problem is the CompRehensive lIterary chineSe readIng comprehenSion (CRISIS), whose difficulties include limited linguistic data, long input, and insight-required questions. Besides the compelling necessity of providing a larger dataset for CRISIS, excessive information, order bias, and entangled conundrums still haunt the CRISIS solutions. Thus, we present the eVIdence cuRation with opTion shUffling and Abstract meaning representation-based cLauses segmenting (VIRTUAL) procedure for CRISIS, with the largest dataset. While the dataset is also named CRISIS, it results from a three-phase construction process, including question selection, data cleaning, and a silver-standard data augmentation step, which augments translations, celebrity profiles, government jobs, reign mottos, and dynasty to CRISIS. The six steps of VIRTUAL include embedding, shuffling, abstract beaning representation based option segmenting, evidence extracting, solving, and voting. Notably, the evidence extraction algorithm facilitates literary Chinese evidence sentences, translated evidence sentences, and annotations of keywords with a similarity-based ranking strategy. While CRISIS congregates understanding-required questions from seven sources, the experiments on CRISIS substantiate the effectiveness of VIRTUAL, with a 7 percent hike in accuracy compared with the baseline. Interestingly, both non-LLMs and LLMs have order bias, and abstract beaning representation based option segmenting is constructive for CRISIS.

Text-to-SQL is the task of translating natural language questions into SQL queries based on relational databases. Different databases implement their own SQL dialects, leading to variations in syntax. As a result, SQL queries designed for one database may not execute properly in another, creating a dialect gap. Existing Text-to-SQL research primarily focuses on specific database systems, limiting adaptability to different dialects. This paper proposes a novel adaptive framework called Dialect-SQL, which employs Object Relational Mapping (ORM) code as an intermediate language to bridge this gap. Given a question, we guide Large Language Models (LLMs) to first generate ORM code, which is then parsed into SQL queries targeted for specific databases. However, there is a lack of high-quality Text-to-Code datasets that enable LLMs to effectively generate ORM code. To address this issue, we propose a bootstrapping approach to synthesize ORM code, where verified ORM code is iteratively integrated into a demonstration pool that serves as in-context examples for ORM code generation. Our experiments demonstrate that Dialect-SQL significantly enhances dialect adaptability, outperforming traditional methods that generate SQL queries directly. Our code and data are released at https://github.com/jieshi10/orm-sql.

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FinMTEB: Finance Massive Text Embedding Benchmark
Yixuan Tang | Yi Yang

The efficacy of text embedding models in representing and retrieving information is crucial for many NLP applications, with performance significantly advanced by Large Language Models (LLMs). Despite this progress, existing benchmarks predominantly use general-purpose datasets, inadequately addressing the nuanced requirements of specialized domains like finance. To bridge this gap, we introduce the Finance Massive Text Embedding Benchmark (FinMTEB), a comprehensive evaluation suite specifically designed for the financial domain. FinMTEB encompasses 64 datasets across 7 task types, including classification, clustering, retrieval, pair classification, reranking, summarization, and semantic textual similarity (STS) in English and Chinese. Alongside this benchmark, we introduce Fin-E5, a state-of-the-art finance-adapted embedding model, ranking first on FinMTEB. Fin-E5 is developed by fine-tuning e5-Mistral-7B-Instruct on a novel persona-based synthetic dataset tailored for diverse financial embedding tasks. Evaluating 15 prominent embedding models on FinMTEB, we derive three key findings: (1) domain-specific models, including our Fin-E5, significantly outperform general-purpose models; (2) performance on general benchmarks is a poor predictor of success on financial tasks; and (3) surprisingly, traditional Bag-of-Words (BoW) models surpass dense embedding models on financial STS tasks. This work provides a robust benchmark for financial NLP and offers actionable insights for developing future domain-adapted embedding solutions. Both FinMTEB and Fin-E5 will be open-sourced for the research community.

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Scaling Rich Style-Prompted Text-to-Speech Datasets
Anuj Diwan | Zhisheng Zheng | David Harwath | Eunsol Choi

We introduce Paralinguistic Speech Captions (ParaSpeechCaps), a large-scale dataset that annotates speech utterances with rich style captions. While rich abstract tags (e.g. guttural, nasal, pained) have been explored in small-scale human-annotated datasets, existing large-scale datasets only cover basic tags (e.g. low-pitched, slow, loud). We combine off-the-shelf text and speech embedders, classifiers and an audio language model to automatically scale rich tag annotations for the first time. ParaSpeechCaps covers a total of 59 style tags, including both speaker-level intrinsic tags and utterance-level situational tags. It consists of 282 hours of human-labelled data (PSC-Base) and 2427 hours of automatically annotated data (PSC-Scaled). We finetune Parler-TTS, an open-source style-prompted TTS model, on ParaSpeechCaps, and achieve improved style consistency (+7.9% Consistency MOS) and speech quality (+15.5% Naturalness MOS) over the best performing baseline that combines existing rich style tag datasets. We ablate several of our dataset design choices to lay the foundation for future work in this space. Our dataset, models and code are released at https://github.com/ajd12342/paraspeechcaps .

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Exploring Changes in Nation Perception with Nationality-Assigned Personas in LLMs
Mahammed Kamruzzaman | Gene Louis Kim

Persona assignment has become a common strategy for customizing LLM use to particular tasks and contexts. In this study, we explore how evaluation of different nations changes when LLMs are assigned specific nationality personas. We assign 193 different nationality personas (e.g., an American person) to five LLMs and examine how the LLM evaluations (or *“perceptions”*) of countries change. We find that all LLM-persona combinations tend to favor Western European nations, though nation-personas push LLM behaviors to focus more on and treat the nation-persona’s own region more favorably. Eastern European, Latin American, and African nations are treated more negatively by different nationality personas. We additionally find that evaluations by nation-persona LLMs of other nations correlate with human survey responses but fail to match the values closely. Our study provides insight into how biases and stereotypes are realized within LLMs when adopting different national personas. Our findings underscore the critical need for developing mechanisms to ensure that LLM outputs promote fairness and avoid over-generalization.

This paper focuses on generating speech with the acoustic style that meets users’ needs based on their open-domain instructions. To control the style, early work mostly relies on pre-defined rules or templates. The control types and formats are fixed in a closed domain, making it hard to meet diverse needs of users. One solution is to resort to instructions in free text to guide the generation. Current work mainly studies the instructions that clearly specify the acoustic styles, such as low pitch and fast speed. However, the instructions are complex, some even vague and abstract, such as “Generate a voice of a woman who is heartbroken due to a breakup. It is hard to infer this implicit style by traditional matching-based methods. To address this problem, we propose a new controllable model. It first utilizes multimodal LLMs with knowledge-augmented techniques to infer the desired speech style from the instructions. The powerful language understanding ability of LLMs can help us better elicit the implicit style factors from the instruction. By using these factors as a control condition, we design a diffusion-based generator adept at finely adjusting speech details. That offers higher flexibility to meet complex users’ needs. Next, we verify the output speech from three aspects, i.e., consistency of decoding state, mel-spectrogram, and instruction style. This verified feedback can inversely optimize the generator. Extensive experiments are conducted on three popular datasets. The results show the effectiveness and good controllability of our approach.

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OBLIVIATE: Robust and Practical Machine Unlearning for Large Language Models
Xiaoyu Xu | Minxin Du | Qingqing Ye | Haibo Hu

Large language models (LLMs) trained over extensive corpora risk memorizing sensitive, copyrighted, or toxic content. To address this, we propose OBLIVIATE, a robust unlearning framework that removes targeted data while preserving model utility. The framework follows a structured process: extracting target tokens, building retain sets, and fine-tuning with a tailored loss function comprising three components—masking, distillation, and world fact. Using low-rank adapters (LoRA) ensures efficiency without compromising unlearning quality. We conduct experiments on multiple datasets, including Harry Potter series, WMDP, and TOFU, using a comprehensive suite of metrics: forget quality (via a new document-level memorization score), model utility, and fluency. Results demonstrate its effectiveness in resisting membership inference attacks, minimizing the impact on retained data, and maintaining robustness across diverse scenarios.

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AdaptThink: Reasoning Models Can Learn When to Think
Jiajie Zhang | Nianyi Lin | Lei Hou | Ling Feng | Juanzi Li

Recently, large reasoning models have achieved impressive performance on various tasks by employing human-like deep thinking. However, the lengthy thinking process substantially increases inference overhead, making efficiency a critical bottleneck. In this work, we first demonstrate that NoThinking, which prompts the reasoning model to skip thinking and directly generate the final solution, is a better choice for relatively simple tasks in terms of both performance and efficiency. Motivated by this, we propose AdaptThink, a novel RL algorithm to teach reasoning models to choose the optimal thinking mode adaptively based on problem difficulty. Specifically, AdaptThink features two core components: (1) a constrained optimization objective that encourages the model to choose NoThinking while maintaining the overall performance; (2) an importance sampling strategy that balances Thinking and NoThinking samples during on-policy training, thereby enabling cold start and allowing the model to explore and exploit both thinking modes throughout the training process. Our experiments indicate that AdaptThink significantly reduces the inference costs while further enhancing performance. Notably, on three math datasets, AdaptThink reduces the average response length of DeepSeek-R1-Distill-Qwen-1.5B by 53% and improves its accuracy by 2.4%, highlighting the promise of adaptive thinking-mode selection for optimizing the balance between reasoning quality and efficiency.

Recent advances in large language models have demonstrated remarkable performance on Contextual Question Answering (CQA). However, prior approaches typically employ elaborate reasoning strategies regardless of question complexity, leading to low adaptability. Recent efficient test-time scaling methods introduce budget constraints or early stop mechanisms to avoid overthinking for straightforward questions. But they add human bias to the reasoning process and fail to leverage models’ inherent reasoning capabilities. To address these limitations, we present T²: Think-to-Think, a novel framework that dynamically adapts reasoning depth based on question complexity. T² leverages the insight that if an LLM can effectively solve similar questions using specific reasoning strategies, it can apply the same strategy to the original question. This insight enables to adoption of concise reasoning for straightforward questions while maintaining detailed analysis for complex problems. T² works through four key steps: decomposing questions into structural elements, generating similar examples with candidate reasoning strategies, evaluating these strategies against multiple criteria, and applying the most appropriate strategy to the original question. Experimental evaluation across seven diverse CQA benchmarks demonstrates that T² not only achieves higher accuracy than baseline methods but also reduces computational overhead by up to 25.2%.

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Non-Existent Relationship: Fact-Aware Multi-Level Machine-Generated Text Detection
Yang Wu | Ruijia Wang | Jie Wu

Machine-generated text detection is critical for preventing misuse of large language models (LLMs). Although LLMs have recently excelled at mimicking human writing styles, they still suffer from factual hallucinations manifested as entity-relation inconsistencies with real-world knowledge. Current detection methods inadequately address the authenticity of the entity graph, which is a key discriminative feature for identifying machine-generated content. To bridge this gap, we propose a fact-aware model that assesses discrepancies between textual and factual entity graphs through graph comparison. In order to holistically analyze context information, our approach employs hierarchical feature extraction with gating units, enabling the adaptive fusion of multi-grained features from entity, sentence, and document levels. Experimental results on three public datasets demonstrate that our approach outperforms the state-of-the-art methods. Interpretability analysis shows that our model can capture the differences in entity graphs between machine-generated and human-written texts.

LLMs often adopt an assertive language style also when making false claims. Such ”overconfident hallucinations” mislead users and erode trust. Achieving the ability to express in language the actual degree of uncertainty around a claim is therefore of great importance. We find that ”verbal uncertainty” is governed by a single linear feature in the representation space of LLMs, and shows that this has only moderate correlation with the actual ”semantic uncertainty” of the model. We apply this insight and show that (1) the mismatch between semantic and verbal uncertainty is a better predictor of hallucinations than semantic uncertainty alone and (2) we can intervene on verbal uncertainty at inference time and reduce confident hallucinations on short-form answers, achieving an average relative reduction of ~30%.

In recent years, Large Language Models (LLMs) have been widely applied to legal tasks. To enhance their understanding of legal texts and improve reasoning accuracy, a promising approach is to incorporate legal theories. One of the most widely adopted theories is the Four-Element Theory (FET), which defines the crime constitution through four elements: Subject, Object, Subjective Aspect, and Objective Aspect. While recent work has explored prompting LLMs to follow FET, our evaluation demonstrates that LLM-generated four-elements are often incomplete and less representative, limiting their effectiveness in legal reasoning.To address these issues, we present JUREX-4E, an expert-annotated four-element knowledge base covering 155 criminal charges. The annotations follow a progressive hierarchical framework grounded in legal source validity and incorporate diverse interpretive methods to ensure precision and authority. We evaluate JUREX-4E on the Similar Charge Disambiguation task and apply it to Legal Case Retrieval. Experimental results validate the high quality of JUREX-4E and its substantial impact on downstream legal tasks, underscoring its potential for advancing legal AI applications. The dataset and code are available at: https://github.com/THUlawtech/JUREX

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CIE: Controlling Language Model Text Generations Using Continuous Signals
Vinay Samuel | Harshita Diddee | Yiming Zhang | Daphne Ippolito

Aligning language models (LMs) with user intent is becoming increasingly relevant to enhance user experience.This calls for designing methods that can allow users to control the properties of the language that LMs generate, for example, controlling the length of the generation or the complexity of the language that gets chosen.Most existing work attempts to integrate users’ control by conditioning LM generations on natural language prompts or discrete control signals, which are often brittle and hard to scale.In this work, we are interested in continuous control signals, ones that exist along a spectrum that can’t easily be captured in a natural language prompt or via existing techniques in conditional generation.Through a case study in controlling the precise response-length of generations, we demonstrate how an LM can be finetuned to expect a control vector that is interpolated between a “low” and a “high” token embedding.Our method more reliably exerts response-length control than in-context learning methods or fine-tuning methods that represent the control signal as a discrete signal.

Large language models (LLMs) generate human-aligned content under certain safety constraints. However, the current known technique “jailbreak prompt” can circumvent safety-aligned measures and induce LLMs to output malicious content. Research on Jailbreaking can help identify vulnerabilities in LLMs and guide the development of robust security frameworks. To circumvent the issue of attack templates becoming obsolete as models evolve, existing methods adopt iterative mutation and dynamic optimization to facilitate more automated jailbreak attacks. However, these methods face two challenges: inefficiency and repetitive optimization, as they overlook the value of past attack experiences. To better integrate past attack experiences to assist current jailbreak attempts, we propose the JailExpert, an automated jailbreak framework, which is the first to achieve a formal representation of experience structure, group experiences based on semantic drift, and support the dynamic updating of the experience pool. Extensive experiments demonstrate that JailExpert significantly improves both attack effectiveness and efficiency. Compared to the current state-of-the-art black-box jailbreak method, JailExpert achieves an average increase of 24% in attack success rate and 2.7 times improvement in attack efficiency.

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Language-to-Space Programming for Training-Free 3D Visual Grounding
Boyu Mi | Hanqing Wang | Tai Wang | Yilun Chen | Jiangmiao Pang

3D visual grounding (3DVG) is challenging due to the need to understand 3D spatial relations. While supervised approaches have achieved superior performance, they are constrained by the scarcity and high annotation costs of 3D vision-language datasets. Training-free approaches based on LLMs/VLMs eliminate the need for large-scale training data, but they either incur prohibitive grounding time and token costs or have unsatisfactory accuracy. To address the challenges, we introduce a novel method for training-free 3D visual grounding, namely **La**nguage-to-**S**pace **P**rogramming (LaSP). LaSP introduces LLM-generated codes to analyze 3D spatial relations among objects, along with a pipeline that evaluates and optimizes the codes automatically. Experimental results demonstrate that LaSP achieves 52.9% accuracy on the Nr3D benchmark, ranking among the best training-free methods. Moreover, it substantially reduces the grounding time and token costs, offering a balanced trade-off between performance and efficiency.

Retrieval-Augmented Generation (RAG) has emerged as a key paradigm for enhancing large language models by incorporating external knowledge. However, current RAG methods exhibit limited capabilities in complex RAG scenarios and suffer from limited task diversity. To address these limitations, we propose RAG-Instruct, a general method for synthesizing diverse and high-quality RAG instruction data based on any source corpus. Our approach leverages (1) five RAG paradigms, which encompass diverse query-document relationships, and (2) instruction simulation, which enhances instruction diversity and quality by utilizing the strengths of existing instruction datasets. Using this method, we construct a 40K instruction dataset from Wikipedia, comprehensively covering diverse RAG scenarios and tasks. Experiments demonstrate that RAG-Instruct effectively enhances LLMs’ RAG capabilities, achieving strong zero-shot performance and significantly outperforming various RAG baselines across a diverse set of tasks.

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AdaRewriter: Unleashing the Power of Prompting-based Conversational Query Reformulation via Test-Time Adaptation
Yilong Lai | Jialong Wu | Zhenglin Wang | Deyu Zhou

Prompting-based conversational query reformulation has emerged as a powerful approach for conversational search, refining ambiguous user queries into standalone search queries. Best-of-N reformulation over the generated candidates via prompting shows impressive potential scaling capability. However, both the previous tuning methods (training time) and adaptation approaches (test time) can not fully unleash their benefits. In this paper, we propose AdaRewriter, a novel framework for query reformulation using an outcome-supervised reward model via test-time adaptation. By training a lightweight reward model with contrastive ranking loss, AdaRewriter selects the most promising reformulation during inference. Notably, it can operate effectively in black-box systems, including commercial LLM APIs. Experiments on five conversational search datasets show that AdaRewriter significantly outperforms the existing methods across most settings, demonstrating the potential of test-time adaptation for conversational query reformulation.

Large Language Models (LLMs) have become integral to daily life, especially advancing as intelligent assistants through on-device deployment on smartphones. However, existing LLM evaluation benchmarks predominantly focus on objective tasks like mathematics and coding in English, which do not necessarily reflect the practical use cases of on-device LLMs in real-world mobile scenarios, especially for Chinese users. To address these gaps, we introduce **SmartBench**, the first benchmark designed to evaluate the capabilities of on-device LLMs in Chinese mobile contexts. We analyze functionalities provided by representative smartphone manufacturers and divide them into five categories: text summarization, text Q&A, information extraction, content creation, and notification management, further detailed into 20 specific tasks. For each task, we construct high-quality datasets comprising 50 to 200 question-answer pairs that reflect everyday mobile interactions, and we develop automated evaluation criteria tailored for these tasks. We conduct comprehensive evaluations of on-device LLMs and MLLMs using SmartBench and also assess their performance after quantized deployment on real smartphone NPUs. Our contributions provide a standardized framework for evaluating on-device LLMs in Chinese, promoting further development and optimization in this critical area. Code and data will be available at https://github.com/vivo-ai-lab/SmartBench.

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F2TEval: Human-Aligned Multi-Dimensional Evaluation for Figure-to-Text Task
Tan Yue | Rui Mao | Zilong Song | Zonghai Hu | Dongyan Zhao

Figure-to-Text (F2T) tasks aim to convert structured figure information into natural language text, serving as a bridge between visual perception and language understanding.However, existing evaluation methods remain limited: 1) Reference-based methods can only capture shallow semantic similarities and rely on costly labeled reference text; 2) Reference-free methods depend on multimodal large language models, which suffer from low efficiency and instruction sensitivity; 3) Existing methods provide only sample-level evaluations, lacking interpretability and alignment with expert-level multi-dimensional evaluation criteria.Accordingly, we propose F2TEval, a five-dimensional reference-free evaluation method aligned with expert criteria, covering faithfulness, completeness, conciseness, logicality, and analysis, to support fine-grained evaluation. We design a lightweight mixture-of-experts model that incorporates independent scoring heads and applies the Hilbert-Schmidt Independence Criterion to optimize the disentanglement of scoring representations across dimensions. Furthermore, we construct F2TBenchmark, a human-annotated benchmark dataset covering 21 chart types and 35 application domains, to support research on F2T evaluation. Experimental results demonstrate our model’s superior performance and efficiency, outperforming Gemini-2.0 and Claude-3.5 with only 0.9B parameters.

Large Language Models (LLMs) require high quality preference datasets to align with human preferences. However, conventional methods for constructing such datasets face significant challenges: reliance on pre-collected instructions often leads to distribution mismatches with target models, while the need for sampling multiple stochastic responses introduces substantial computational overhead. In this work, we explore a paradigm shift by leveraging inherent regulation of LLMs’ representation space for efficient and tailored preference dataset construction, named Icon². Specifically, it first extracts layer-wise direction vectors to encode sophisticated human preferences and then uses these vectors to filter self-synthesized instructions based on their inherent consistency. During decoding, bidirectional inherent control is applied to steer token representations, enabling the precise generation of response pairs with clear alignment distinctions. Experimental results demonstrate significant improvements in both alignment and efficiency. Llama3-8B and Qwen2-7B achieve an average win rate improvement of 13.89% on AlpacaEval 2.0 and 13.45% on Arena-Hard, while reducing computational costs by up to 48.1%.

Detoxification in large language models (LLMs) remains a significant research challenge. Existing decoding detoxification methods are all based on external constraints, which require additional resource overhead and lose generation fluency. This work innovatively proposes Detoxification with Self-Constrained Decoding (DSCD), a novel method for LLMs detoxification without parameter fine-tuning. DSCD strengthens the inner token distribution of the safety layer while weakening that of hallucination and toxic layer during output generation. This effectively diminishes toxicity and enhances output safety. DSCD offers lightweight, high compatibility, and plug-and-play capabilities, readily integrating with existing detoxification methods for further performance improvement. Extensive experiments on representative open-source LLMs and public datasets validate DSCD’s effectiveness, demonstrating state-of-the-art (SOTA) performance in both detoxification and generation fluency, with superior efficiency compared to existing methods. These results highlight DSCD’s potential as a practical and scalable solution for safer LLM deployments.

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From Reasoning to Answer: Empirical, Attention-Based and Mechanistic Insights into Distilled DeepSeek R1 Models
Jue Zhang | Qingwei Lin | Saravan Rajmohan | Dongmei Zhang

Large Reasoning Models (LRMs) generate explicit reasoning traces alongside final answers, yet the extent to which these traces influence answer generation remains unclear. In this work, we conduct a three-stage investigation into the interplay between reasoning and answer generation in three distilled DeepSeek R1 models. First, through empirical evaluation, we demonstrate that including explicit reasoning consistently improves answer quality across diverse domains. Second, attention analysis reveals that answer tokens attend substantially to reasoning tokens, with certain mid-layer Reasoning-Focus Heads (RFHs) closely tracking the reasoning trajectory, including self-reflective cues. Third, we apply mechanistic interventions using activation patching to assess the dependence of answer tokens on reasoning activations. Our results show that perturbations to key reasoning tokens can reliably alter the final answers, confirming a directional and functional flow of information from reasoning to answer. These findings deepen our understanding of how LRMs leverage reasoning tokens for answer generation, highlighting the functional role of intermediate reasoning in shaping model outputs.

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Quantifying Language Disparities in Multilingual Large Language Models
Songbo Hu | Ivan Vulić | Anna Korhonen

Results reported in large-scale multilingual evaluations are often fragmented and confounded by factors such as target languages, differences in experimental setups, and model choices. We propose a framework that disentangles these confounding variables and introduces three interpretable metrics—the performance realisation ratio, its coefficient of variation, and language potential—enabling a finer-grained and more insightful quantification of actual performance disparities across both (i) models and (ii) languages. Through a case study of 13 model variants on 11 multilingual datasets, we demonstrate that our framework provides a more reliable measurement of model performance and language disparities, particularly for low-resource languages, which have so far proven challenging to evaluate. Importantly, our results reveal that higher overall model performance does not necessarily imply greater fairness across languages.

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KoBLEX: Open Legal Question Answering with Multi-hop Reasoning
Jihyung Lee | Daehui Kim | Seonjeong Hwang | Hyounghun Kim | Gary Lee

Large Language Models (LLM) have achieved remarkable performances in general domains and are now extending into the expert domain of law. Several benchmarks have been proposed to evaluate LLMs’ legal capabilities. However, these benchmarks fail to evaluate open-ended and provision-grounded Question Answering (QA). To address this, we introduce a Korean Benchmark for Legal EXplainable QA (KoBLEX), designed to evaluate provision-grounded, multi-hop legal reasoning. KoBLEX includes 226 scenario-based QA instances and their supporting provisions, created using a hybrid LLM–human expert pipeline. We also propose a method called Parametric provision-guided Selection Retrieval (ParSeR), which uses LLM-generated parametric provisions to guide legally grounded and reliable answers. ParSeR facilitates multi-hop reasoning on complex legal questions by generating parametric provisions and employing a three-stage sequential retrieval process. Furthermore, to better evaluate the legal fidelity of the generated answers, we propose Legal Fidelity Evaluation (LF-Eval). LF-Eval is an automatic metric that jointly considers the question, answer, and supporting provisions and shows a high correlation with human judgments. Experimental results show that ParSeR consistently outperforms strong baselines, achieving the best results across multiple LLMs. Notably, compared to standard retrieval with GPT-4o, ParSeR achieves +37.91 higher F1 and +30.81 higher LF-Eval. Further analyses reveal that ParSeR efficiently delivers consistent performance across reasoning depths, with ablations confirming the effectiveness of ParSeR.

Detecting depression through users’ social media posting history is crucial for enabling timely intervention; however, irrelevant content within these posts negatively impacts detection performance. Thus, it is crucial to extract pertinent content from users’ complex posting history. Current methods utilize frozen screening models, which can miss critical information and limit overall performance due to isolated screening and detection processes. To address these limitations, we propose **E2-LPS** **E**nd-to-**E**nd **L**earnable **P**sychiatric Scale Guided Risky Post **S**creening Model) for jointly training our screening model, guided by psychiatric scales, alongside the detection model. We employ a straight-through estimator to enable a learnable end-to-end screening process and avoid the non-differentiability of the screening process. Experimental results show that E2-LPS outperforms several strong baseline methods, and qualitative analysis confirms that it better captures users’ mental states than others.

Multi-hop question answering (QA) remains challenging, as solutions must reliably integrate and reconcile evidence from multiple sources without succumbing to error propagation. While large language models (LLMs) have achieved substantial improvements via chain-of-thought (CoT) prompting and retrieval-augmented generation, these methods typically adopt a forward-only workflow—early mistakes persist throughout inference, and contradictions discovered later cannot systematically trigger re-evaluation. To address this limitation, we present ReAgent, a reversible multi-agent reasoning framework. Specifically, ReAgent enables agents to backtrack to earlier valid states when conflicts arise, thereby isolating and rectifying flawed assumptions before they undermine subsequent reasoning. Our approach combines explicit local and global rollback protocols with modular role specialization, resulting in a flexible and error-tolerant pipeline. Empirical evaluation on three multi-hop QA benchmarks demonstrates consistent performance gains of approximately 6% over forward-only baselines, in addition to enhanced interpretability. These findings highlight the value of non-monotonic, backtracking-driven inference in complex QA scenarios and point to broader implications for multi-agent collaboration in knowledge-intensive tasks.

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Matter-of-Fact: A Benchmark for Verifying the Feasibility of Literature-Supported Claims in Materials Science
Peter Jansen | Samiah Hassan | Ruoyao Wang

Contemporary approaches to assisted scientific discovery use language models to automatically generate large numbers of potential hypothesis to test, while also automatically generating code-based experiments to test those hypotheses. While hypotheses can be comparatively inexpensive to generate, automated experiments can be costly, particularly when run at scale (i.e. thousands of experiments). Developing the capacity to filter hypotheses based on their feasibility would allow discovery systems to run at scale, while increasing their likelihood of making significant discoveries. In this work we introduce Matter-of-Fact, a challenge dataset for determining the feasibility of hypotheses framed as claims, while operationalizing feasibility assessment as a temporally-filtered claim verification task using backtesting. Matter-of-Fact includes 8.4k claims extracted from scientific articles spanning four high-impact contemporary materials science topics, including superconductors, semiconductors, batteries, and aerospace materials, while including qualitative and quantitative claims from theoretical, experimental, and code/simulation results. We show that strong baselines that include retrieval augmented generation over scientific literature and code generation fail to exceed 72% performance on this task (chance performance is 50%), while domain-expert verification suggests nearly all are solvable – highlighting both the difficulty of this task for current models, and the potential to accelerate scientific discovery by making near-term progress.

Currently, most multimodal studies are based on large language models (LLMs) with quadratic-complexity Transformer architectures. While linear models like RNNs enjoy low inference costs, their application has been largely limited to the text-only modality. This work explores the capabilities of modern RNN architectures in multimodal contexts. We propose ModRWKV—a decoupled multimodal framework built upon the RWKV7 architecture as its LLM backbone—which achieves multi-source information fusion through dynamically adaptable heterogeneous modality encoders. We designed the multimodal modules in ModRWKV with an extremely lightweight architecture and, through extensive experiments, identified a configuration that achieves an optimal balance between performance and computational efficiency. ModRWKV leverages the pretrained weights of the RWKV7 LLM for initialization, which significantly accelerates multimodal training. Comparative experiments with different pretrained checkpoints further demonstrate that such initialization plays a crucial role in enhancing the model’s ability to understand multimodal signals. Supported by extensive experiments, we conclude that modern RNN architectures present a viable alternative to Transformers in the domain of multimodal large language models (MLLMs). Furthermore, we identify the optimal configuration of the ModRWKV architecture through systematic exploration.

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Multimedia Event Extraction with LLM Knowledge Editing
Jiaao Yu | Yijing Lin | Zhipeng Gao | Xuesong Qiu | Lanlan Rui

Multimodal event extraction task aims to identify event types and arguments from visual and textual representations related to events. Due to the high cost of multimedia training data, previous methods mainly focused on weakly alignment of excellent unimodal encoders. However, they ignore the conflict between event understanding and image recognition, resulting in redundant feature perception affecting the understanding of multimodal events. In this paper, we propose a multimodal event extraction strategy with a multi-level redundant feature selection mechanism, which enhances the event understanding ability of multimodal large language models by leveraging knowledge editing techniques, and requires no additional parameter optimization work. Extensive experiments show that our method outperforms the state-of-the-art (SOTA) baselines on the M2E2 benchmark. Compared with the highest baseline, we achieve a 34% improvement of precision on event extraction and a 11% improvement of F1 on argument extraction.

With the different roles that AI is expected to play in human life, imbuing large language models (LLMs) with different personalities has attracted increasing research interest. While the “personification” enhances human experiences of interactivity and adaptability of LLMs, it gives rise to critical concerns about content safety, particularly regarding bias, sentiment, and toxicity of LLM generation. This study explores how assigning different personality traits to LLMs affects the toxicity and biases of their outputs. Leveraging the widely accepted HEXACO personality framework developed in social psychology, we design experimentally sound prompts to test three LLMs’ performance on three toxic and bias benchmarks. The findings demonstrate the sensitivity of all three models to HEXACO personality traits and, more importantly, a consistent variation in the biases, negative sentiment, and toxicity of their output. In particular, adjusting the levels of several personality traits can effectively reduce bias and toxicity in model performance, similar to humans’ correlations between personality traits and toxic behaviors. The findings highlight the additional need to examine content safety besides the efficiency of training or fine-tuning methods for LLM personification, they also suggest a potential for the adjustment of personalities to be a simple and low-cost method to conduct controlled text generation.

This paper focuses on Dialogue Aspect-based Sentiment Quadruple (DiaASQ) analysis, aiming to extract structured quadruples from multi-turn conversations. Applying Large Language Models (LLMs) for this specific task presents two primary challenges: the accurate extraction of multiple elements and the understanding of complex dialogue reply structure. To tackle these issues, we propose a novel LLM-based multi-task approach, named Task-aware Contrastive Mixture of Experts (TaCoMoE), to tackle the DiaASQ task by integrating expert-level contrastive loss within task-oriented mixture of experts layer. TaCoMoE minimizes the distance between the representations of the same expert in the semantic space while maximizing the distance between the representations of different experts to efficiently learn representations of different task samples. Additionally, we design a Graph-Centric Dialogue Structuring strategy for representing dialogue reply structure and perform non-opinion utterances detection to enhance the performance of quadruple extraction. Extensive experiments are conducted on the DiaASQ dataset, demonstrating that our method significantly outperforms existing parameter-efficient fine-tuning techniques in terms of both accuracy and computational efficiency. The code is available at https://github.com/he2720/TaCoMoE.

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Mitigating Biases in Language Models via Bias Unlearning
Dianqing Liu | Yi Liu | Guoqing Jin | Zhendong Mao

Many studies have shown various biases targeting different demographic groups in language models, amplifying discrimination and harming fairness. Recent parameter modification debiasing approaches significantly degrade core capabilities such as text coherence and task accuracy. And Prompt-based debiasing methods, only effective for predefined trigger words, fail to address deeply embedded stereotypical associations in model parameters. In this paper, we propose BiasUnlearn, a novel model debiasing framework which achieves targeted debiasing via dual-pathway unlearning mechanisms coordinating stereotype forgetting with anti-stereotype retention, while preventing bias polarity reversal through adversarial forget set and dynamic dataset swapping. We conducted extensive experiments with multiple language models across various evaluation benchmarks. The results show that BiasUnlearn outperforms existing methods in mitigating bias in language models while retaining language modeling capabilities. Further experiments reveal that debiasing weights are transferable across model variants, confirming that bias representations become entrenched during pre-training and persist through fine-tuning phases.

Deploying large language models (LLMs) for long-context inference remains challenging due to their substantial memory and computational demands. While techniques such as Key-Value (KV) cache compression are designed to reduce memory usage, they often neglect the structured sparsity inherent in the relationship between hidden states and their corresponding KV cache. In this work, we explore the role of uncertainty as a potential indicator of sparsity within LLMs. We propose UNComp, an uncertainty-aware framework that leverages truncated matrix entropy to identify areas of low information content, thereby revealing sparsity patterns that can be used for adaptive compression. Unlike traditional methods that apply uniform compression, UNComp dynamically adjusts its approach to compression, guided by uncertainty measures that reflect the importance of various model components. Our analysis shows that sparsity patterns, when derived from uncertainty estimates, can be exploited to reveal special long-range dependencies, such as retrieval heads and retrieval layers. This perspective not only enhances our understanding of how compression can be optimized but also provides new insights into the inherent sparsity of LLMs during long-context inference. By focusing on uncertainty to analyze the sparsity pattern in detail, UNComp reduces the KV cache size to 4.74% of the original, achieves a 6% prefill speedup, and improves throughput by 6.4× — not only delivering strong lossless compression performance, but also validating the effectiveness of the underlying theoretical tool. Our codes are submitted with the paper.

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Superpose Task-specific Features for Model Merging
Haiquan Qiu | You Wu | Dong Li | Jianmin Guo | Quanming Yao

Model merging enables powerful capabilities in neural networks without requiring additional training. In this paper, we introduce a novel perspective on model merging by leveraging the fundamental mechanisms of neural network representation. Our approach is motivated by the linear representation hypothesis, which states that neural networks encode information through linear combinations of feature vectors. We propose a method that superposes task-specific features from individual models into a merged model. Our approach specifically targets linear transformation matrices, which are crucial for feature activation and extraction in deep networks. By formulating the merging process as a linear system, we can preserve output feature directions from individual models and create merged models that effectively maintain multi-task capabilities compared to existing methods. Extensive experiments across diverse benchmarks and models demonstrate that our method outperforms existing techniques.

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FinRAGBench-V: A Benchmark for Multimodal RAG with Visual Citation in the Financial Domain
Suifeng Zhao | Zhuoran Jin | Sujian Li | Jun Gao

Retrieval-Augmented Generation (RAG) plays a vital role in the financial domain, powering applications such as real-time market analysis, trend forecasting, and interest rate computation. However, most existing RAG research in finance focuses predominantly on textual data, overlooking the rich visual content in financial documents, resulting in the loss of key analytical insights. To bridge this gap, we present FinRAGBench-V, a comprehensive visual RAG benchmark tailored for finance. This benchmark effectively integrates multimodal data and provides visual citation to ensure traceability. It includes a bilingual retrieval corpus with 60,780 Chinese and 51,219 English pages, along with a high-quality, human-annotated question-answering (QA) dataset spanning heterogeneous data types and seven question categories. Moreover, we introduce RGenCite, an RAG baseline that seamlessly integrates visual citation with generation. Furthermore, we propose an automatic citation evaluation method to systematically assess the visual citation capabilities of Multimodal Large Language Models (MLLMs). Extensive experiments on RGenCite underscore the challenging nature of FinRAGBench-V, providing valuable insights for the development of multimodal RAG systems in finance.

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BacktrackAgent: Enhancing GUI Agent with Error Detection and Backtracking Mechanism
Qinzhuo Wu | Pengzhi Gao | Wei Liu | Jian Luan

Graphical User Interface (GUI) agents have gained substantial attention due to their impressive capabilities to complete tasks through multiple interactions within GUI environments. However, existing agents primarily focus on enhancing the accuracy of individual actions and often lack effective mechanisms for detecting and recovering from errors. To address these shortcomings, we propose the BacktrackAgent, a robust framework that incorporates a backtracking mechanism to improve task completion efficiency. BacktrackAgent includes verifier, judger, and reflector components as modules for error detection and recovery, while also applying judgment rewards to further enhance the agent’s performance. Additionally, we develop a training dataset specifically designed for the backtracking mechanism, which considers the outcome pages after action executions. Experimental results show that BacktrackAgent has achieved performance improvements in both task success rate and step accuracy on Mobile3M and Auto-UI benchmarks. Our data and code will be released upon acceptance.

Large language model (LLM)-based embedding models, benefiting from large scale pre-training and post-training, have begun to surpass BERT and T5-based models on general-purpose text embedding tasks such as document retrieval. However, a fundamental limitation of LLM embeddings lies in the unidirectional attention used during autoregressive pre-training, which misaligns with the bidirectional nature of text embedding tasks. To this end, We propose adopting diffusion language models for text embeddings, motivated by their inherent bidirectional architecture and recent success in matching or surpassing LLMs especially on reasoning tasks. We present the first systematic study of the diffusion language embedding model, which outperforms the LLM-based embedding model by 20% on long-document retrieval, 8% on reasoning-intensive retrieval, 2% on instruction-following retrieval, and achieve competitive performance on traditional text embedding benchmarks. Our analysis verifies that bidirectional attention is crucial for encoding global context in long and complex text.

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BannerAgency: Advertising Banner Design with Multimodal LLM Agents
Heng Wang | Yotaro Shimose | Shingo Takamatsu

Advertising banners are critical for capturing user attention and enhancing advertising campaign effectiveness. Creating aesthetically pleasing banner designs while conveying the campaign messages is challenging due to the large search space involving multiple design elements. Additionally, advertisers need multiple sizes for different displays and various versions to target different sectors of audiences. Since design is intrinsically an iterative and subjective process, flexible editability is also in high demand for practical usage. While current models have served as assistants to human designers in various design tasks, they typically handle only segments of the creative design process or produce pixel-based outputs that limit editability. This paper introduces a training-free framework for fully automated banner ad design creation, enabling frontier multimodal large language models (MLLMs) to streamline the production of effective banners with minimal manual effort across diverse marketing contexts. We present BannerAgency, an MLLM agent system that collaborates with advertisers to understand their brand identity and banner objectives, generates matching background images, creates blueprints for foreground design elements, and renders the final creatives as editable components in Figma or SVG formats rather than static pixels. To facilitate evaluation and future research, we introduce BannerRequest400, a benchmark featuring 100 unique logos paired with 400 diverse banner requests. Through quantitative and qualitative evaluations, we demonstrate the framework’s effectiveness, emphasizing the quality of the generated banner designs, their adaptability to various banner requests, and their strong editability enabled by this component-based approach.

Large language models (LLMs) are commonly trained on multi-domain datasets, where domain sampling strategies significantly impact model performance due to varying domain importance across downstream tasks. Existing approaches for optimizing domain-level sampling strategies struggle with maintaining intra-domain consistency and accurately measuring domain impact. In this paper, we present Domain Impact-aware Data Sampling (DIDS). To ensure intra-domain consistency, a gradient clustering algorithm is proposed to group training data based on their learning effects, where a proxy language model and dimensionality reduction are employed to reduce computational overhead. To accurately measure domain impact, we develop a Fisher Information Matrix (FIM) guided metric that quantifies how domain-specific parameter updates affect the model’s output distributions on downstream tasks, with theoretical guarantees. Furthermore, to determine optimal sampling ratios, DIDS combines both the FIM-guided domain impact assessment and loss learning trajectories that indicate domain-specific potential, while accounting for diminishing marginal returns. Extensive experiments demonstrate that DIDS achieves 3.4% higher average performance while maintaining comparable training efficiency. The code is available at https://github.com/shiweijiezero/DIDS.

Large language models (LLMs) have increasingly been explored as powerful text embedders. Existing LLM-based text embedding approaches often leverage the embedding of the final token, typically a reserved special token such as ‘[EOS]‘. However, these tokens have not been intentionally trained to capture the semantics of the whole context, limiting their capacity as text embeddings, especially for retrieval and re-ranking tasks. We propose to add a new training stage before contrastive learning to enrich the semantics of the final token embedding. This stage employs bidirectional generative reconstruction tasks, namely EBQ2D (Embedding-Based Query-to-Document) and EBD2Q (Embedding-Based Document-to-Query), which interleave to anchor the ‘[EOS]‘ embedding and reconstruct either side of Query-Document pairs. Experimental results demonstrate that our additional training stage significantly improves LLM performance on the Massive Text Embedding Benchmark (MTEB), achieving new state-of-the-art results across different LLM base models and scales.

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ReMedy: Learning Machine Translation Evaluation from Human Preferences with Reward Modeling
Shaomu Tan | Christof Monz

A key challenge in MT evaluation is the inherent noise and inconsistency of human ratings. Regression-based neural metrics struggle with this noise, while prompting LLMs shows promise at system-level evaluation but performs poorly at segment level. In this work, we propose ReMedy, a novel MT metric framework that reformulates translation evaluation as a reward modeling task. Instead of regressing on imperfect human ratings directly, ReMedy learns relative translation quality using pairwise preference data, resulting in a more reliable evaluation. In extensive experiments across WMT22-24 shared tasks (39 language pairs, 111 MT systems), ReMedy achieves state-of-the-art performance at both segment- and system-level evaluation. Specifically, ReMedy-9B surpasses larger WMT winners and massive closed LLMs such as MetricX-13B, XCOMET-Ensemble, GEMBA-GPT-4, PaLM-540B, and finetuned PaLM2. Further analyses demonstrate that ReMedy delivers superior capability in detecting translation errors and evaluating low-quality translations.

Large language models (LLMs) have transformed code generation.However, most existing approaches focus on mainstream languages such as Python and Java, neglecting the Solidity language, the predominant programming language for Ethereum smart contracts.Due to the lack of adequate benchmarks for Solidity, LLMs’ ability to generate secure, cost-effective smart contracts remains unexplored.To fill this gap, we construct SolEval, the first repository-level benchmark designed for Solidity smart contract generation, to evaluate the performance of LLMs on Solidity.SolEval consists of 1,507 samples from 28 different repositories, covering 6 popular domains, providing LLMs with a comprehensive evaluation benchmark.Unlike the existing Solidity benchmark, SolEval not only includes complex function calls but also reflects the real-world complexity of the Ethereum ecosystem by incorporating Gas@k and Vul@k.We evaluate 16 LLMs on SolEval, and our results show that the best-performing LLM achieves only 26.29% Pass@10, highlighting substantial room for improvement in Solidity code generation by LLMs.Additionally, we conduct supervised fine-tuning (SFT) on Qwen-7B using SolEval, resulting in a significant performance improvement, with Pass@5 increasing from 16.67% to 58.33%, demonstrating the effectiveness of fine-tuning LLMs on our benchmark.We release our data and code at https://github.com/pzy2000/SolEval.

Human listeners readily adjust to unfamiliar speakers and language varieties through exposure, but do these adaptation benefits extend to state-of-the-art spoken language models (SLMs)? We introduce a scalable framework that allows for in-context learning (ICL) in Phi-4 Multimodal (Phi-4-MM) using interleaved task prompts and audio-text pairs, and find that as few as 12 example utterances (~50 seconds) at inference time reduce word error rates by a relative 19.7% (1.2 pp.) on average across diverse English corpora. These improvements are most pronounced in low-resource varieties, when the context and target speaker match, and when more examples are provided—though scaling our procedure yields diminishing marginal returns to context length. Overall, we find that our novel ICL adaptation scheme (1) reveals a similar performance profile to human listeners, and (2) demonstrates consistent improvements to automatic speech recognition (ASR) robustness across diverse speakers and language backgrounds. While adaptation succeeds broadly, significant gaps remain for certain varieties, revealing where current models still fall short of human flexibility. We release our prompts and code on GitHub.

Recent advances in large reasoning models (LRMs) have enabled strong multi-step reasoning capabilities. However, existing machine unlearning algorithms are tailored to standard language modeling and fail to address the unique challenges posed by LRMs. In this work, we present the first systematic study of LRM unlearning and reveal that conventional unlearning methods often overlook critical information leakage in reasoning traces, even when final answers are successfully removed. To address this, we propose Reasoning-aware Representation Misdirection for Unlearning (R²MU), a method that suppresses sensitive reasoning traces while preserving the model’s general reasoning ability. Our experiments demonstrate that R²MU significantly reduces reasoning trace leakage and achieves strong performance across both reasoning and safety benchmarks, including WMDP, StrongReject, JBB-Behaviors and WildJailbreak, under state-of-the-art models such as DeepSeek-R1-Distill-LLaMA-8B and DeepSeek-R1-Distill-Qwen-14B. To the best of our knowledge, MU is the first principled approach to both expose and mitigate reasoning trace leakage in LRM unlearning, while preserving reasoning ability.

While densely annotated image captions significantly facilitate the learning of robust vision-language alignment, methodologies for systematically optimizing human annotation efforts remain underexplored. We introduce Chain-of-Talkers (CoTalk), an AI-in-the-loop methodology designed to maximize the number of annotated samples and improve their comprehensiveness under fixed budget constraints (e.g., total human annotation time). The framework is built upon two key insights. First, sequential annotation reduces redundant workload compared to conventional parallel annotation, as subsequent annotators only need to annotate the “residual”—the missing visual information that previous annotations have not covered. Second, humans process textual input faster by reading while outputting annotations with much higher throughput via talking; thus a multimodal interface enables optimized efficiency. We evaluate our framework from two aspects: intrinsic evaluations that assess the comprehensiveness of semantic units, obtained by parsing detailed captions into object-attribute trees and analyzing their effective connections; extrinsic evaluation measures the practical usage of the annotated captions in facilitating vision-language alignment. Experiments with eight participants show our Chain-of-Talkers (CoTalk) improves annotation speed (0.42 vs. 0.30 units/sec) and retrieval performance (41.13% vs. 40.52%) over the parallel method.

Retrieval-Augmented Generation (RAG) systems have emerged as a pivotal methodology for enhancing Large Language Models (LLMs) through the dynamic integration of external knowledge. To further improve RAG’s flexibility, Agentic RAG introduces autonomous agents into the workflow. However, Agentic RAG faces several challenges:(1) the success of each step depends on both high-quality planning and accurate search,(2) the lack of supervision for intermediate reasoning steps, and(3) the exponentially large candidate space for planning and searching.To address these challenges, we propose DecoupleSearch, a novel framework that decouples planning and search processes using dual value models, enabling independent optimization of plan reasoning and search grounding. Our approach constructs a reasoning tree, where each node represents planning and search steps. We leverage Monte Carlo Tree Search to assess the quality of each step. During inference, Hierarchical Beam Search iteratively refines planning and search candidates with dual value models. Extensive experiments across policy models of varying parameter sizes, demonstrate the effectiveness of our method.

The success of large language models (LLMs) has attracted many individuals to fine-tune them for domain-specific tasks by uploading their data. However, in sensitive areas like healthcare and finance, privacy concerns often arise. One promising solution is to generate synthetic data with Differential Privacy (DP) guarantees to replace private data. However, these synthetic data contain significant flawed data, which are considered as noise. Existing solutions typically rely on naive filtering by comparing ROUGE-L scores or embedding similarities, which are ineffective in addressing the noise. To address this issue, we propose ***RewardDS***, a novel privacy-preserving framework that fine-tunes a reward proxy model and uses reward signals to guide the synthetic data generation. Our RewardDS introduces two key modules, Reward Guided Filtering and Self-Optimizing Refinement, to both filter and refine the synthetic data, effectively mitigating the noise. Extensive experiments across medical, financial, and code generation domains demonstrate the effectiveness of our method.

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Synergizing Multimodal Temporal Knowledge Graphs and Large Language Models for Social Relation Recognition
Haorui Wang | Zheng Wang | Yuxuan Zhang | Bo Wang | Bin Wu

Recent years have witnessed remarkable advances in Large Language Models (LLMs). However, in the task of social relation recognition, Large Language Models (LLMs) encounter significant challenges due to their reliance on sequential training data, which inherently restricts their capacity to effectively model complex graph-structured relationships. To address this limitation, we propose a novel low-coupling method synergizing multimodal temporal Knowledge Graphs and Large Language Models (mtKG-LLM) for social relation reasoning. Specifically, we extract multimodal information from the videos and model the social networks as spatial Knowledge Graphs (KGs) for each scene. Temporal KGs are constructed based on spatial KGs and updated along the timeline for long-term reasoning. Subsequently, we retrieve multi-scale information from the graph-structured knowledge for LLMs to recognize the underlying social relation. Extensive experiments demonstrate that our method has achieved state-of-the-art performance in social relation recognition. Furthermore, our framework exhibits effectiveness in bridging the gap between KGs and LLMs. Our code will be released after acceptance.

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LegalSearchLM: Rethinking Legal Case Retrieval as Legal Elements Generation
Chaeeun Kim | Jinu Lee | Wonseok Hwang

Legal Case Retrieval (LCR), which retrieves relevant cases from a query case, is a fundamental task for legal professionals in research and decision-making. However, existing studies on LCR face two major limitations. First, they are evaluated on relatively small-scale retrieval corpora (e.g., 100-55K cases) and use a narrow range of criminal query types, which cannot sufficiently reflect the complexity of real-world legal retrieval scenarios. Second, their reliance on embedding-based or lexical matching methods often results in limited representations and legally irrelevant matches. To address these issues, we present: (1) LEGAR BENCH, the first large-scale Korean LCR benchmark, covering 411 diverse crime types in queries over 1.2M candidate cases; and (2) LegalSearchLM, a retrieval model that performs legal element reasoning over the query case and directly generates content containing those elements, grounded in the target cases through constrained decoding. Experimental results show that LegalSearchLM outperforms baselines by 6 - 20% on LEGAR BENCH, achieving state-of-the-art performance. It also demonstrates strong generalization to out-of-domain cases, outperforming naive generative models trained on in-domain data by 15%.

Chart question answering (CQA) has become a critical multimodal task for evaluating the reasoning capabilities of vision-language models. While early approaches have shown promising performance by focusing on visual features or leveraging large-scale pre-training, most existing evaluations rely on rigid output formats and objective metrics, thus ignoring the complex, real-world demands of practical chart analysis. In this paper, we introduce ChartMind, a new benchmark designed for complex CQA tasks in real-world settings. ChartMind covers seven task categories, incorporates multilingual contexts, supports open-domain textual outputs, and accommodates diverse chart formats, bridging the gap between real-world applications and traditional academic benchmarks. Furthermore, we propose a context-aware yet model-agnostic framework, ChartLLM, that focuses on extracting key contextual elements, reducing noise, and enhancing the reasoning accuracy of multimodal large language models. Extensive evaluations on ChartMind and three representative public benchmarks with 14 mainstream multimodal models show our framework significantly outperforms the previous three common CQA paradigms: instruction-following, OCR-enhanced, and chain-of-thought, highlighting the importance of flexible chart understanding for real-world CQA. These findings suggest new directions for developing more robust chart reasoning in future research.

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COLA: Collaborative Multi-Agent Framework with Dynamic Task Scheduling for GUI Automation
Di Zhao | Longhui Ma | Siwei Wang | Miao Wang | Zhao Lv

With the rapid advancements in Large Language Models (LLMs), an increasing number of studies have leveraged LLMs as the cognitive core of agents to address complex task decision-making challenges. Specially, recent research has demonstrated the potential of LLM-based agents on automating GUI operations. However, existing methodologies exhibit two critical challenges: (1) static agent architectures struggle to adapt to diverse GUI application scenarios, leading to inadequate scenario generalization; (2) the agent workflows lack fault tolerance mechanism, necessitating complete process re-execution for GUI agent decision error. To address these limitations, we introduce COLA, a collaborative multi-agent framework for automating GUI operations. In this framework, a scenario-aware agent Task Scheduler decomposes task requirements into atomic capability units, dynamically selects the optimal agent from a decision agent pool, effectively responds to the capability requirements of diverse scenarios. Furthermore, we develop an interactive backtracking mechanism that enables human to intervene to trigger state rollbacks for non-destructive process repair. Experiments on the GAIA dataset show that COLA achieves competitive performance among GUI Agent methods, with an average accuracy of 31.89%. On WindowsAgentArena, it performs particularly well in Web Browser (33.3%), Media & Video (33.3%), and Windows Utils (25.0%), suggesting the effectiveness of specialized agent design and dynamic strategy allocation. The code is available at https://github.com/Alokia/COLA-demo.

Rencent advancements in large language models (LLM) have shown impressive versatility across various tasks. Short text matching is one of the fundamental technologies in natural language processing. In previous studies, the common approach to applying them to Chinese is segmenting each sentence into words, and then taking these words as input. However, existing approaches have three limitations: 1) Some Chinese words are polysemous, and semantic information is not fully utilized. 2) Some models suffer potential issues caused by word segmentation and incorrect recognition of negative words affects the semantic understanding of the whole sentence. 3) Fuzzy negation words in ancient Chinese are difficult to recognize and match. In this work, we propose a novel adaptive Transformer for Chinese short text matching using Data Augmentation and Semantic Awareness (DASA), which can fully mine the information expressed in Chinese text to deal with word ambiguity. DASA is based on a Graph Attention Transformer Encoder that takes two word lattice graphs as input and integrates sense information from N-HowNet to moderate word ambiguity. Specially, we use an LLM to generate similar sentences for the optimal text representation. Experimental results show that the augmentation done using DASA can considerably boost the performance of our system and achieve significantly better results than previous state-of-the-art methods on four available datasets, namely MNS, LCQMC, AFQMC, and BQ.

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Pruning the Paradox: How CLIP’s Most Informative Heads Enhance Performance While Amplifying Bias
Avinash Madasu | Vasudev Lal | Phillip Howard

CLIP is one of the most popular foundation models and is heavily used for many vision-language tasks, yet little is known about its inner workings. As CLIP is increasingly deployed in real-world applications, it is becoming even more critical to understand its limitations and embedded social biases to mitigate potentially harmful downstream consequences. However, the question of what internal mechanisms drive both the impressive capabilities as well as problematic shortcomings of CLIP has largely remained unanswered. To bridge this gap, we study the conceptual consistency of text descriptions for attention heads in CLIP-like models. Specifically, we propose Concept Consistency Score (CCS), a novel interpretability metric that measures how consistently individual attention heads in CLIP models align with specific concepts. Our soft-pruning experiments reveal that high CCS heads are critical for preserving model performance, as pruning them leads to a significantly larger performance drop than pruning random or low CCS heads. Notably, we find that high CCS heads capture essential concepts and play a key role in out-of-domain detection, concept-specific reasoning, and video-language understanding. Moreover, we prove that high CCS heads learn spurious correlations which amplify social biases. These results position CCS as a powerful interpretability metric exposing the paradox of performance and social biases in CLIP models.

The full-size MLPs and the projection layers in attention introduce tremendous model sizes of large language models (LLMs), consuming extensive computational resources in pre-training. We empirically observe that the activations of pre-trained LLMs exhibit low-rank property. Motivated by such observations, we propose **CoLA** and its memory-efficient implementation, **CoLA-M**, to replace these full-size layers with compute-efficient **auto-encoders** that naturally enforce low-rank activations throughout training. This fundamental architectural change eliminates the activation redundancy and significantly boosts model capacity and training efficiency. Experiments on LLaMA models with 60 million to 7 billion parameters show that CoLA reduces the computing cost by 2\pmb{\times} and improves training throughput by 1.86\pmb{\times} while maintaining full-rank level performance. CoLA-M further squeezes memory cost without sacrificing throughput, offering a pre-training approach with collectively superior parameter, computing, and memory efficiency. The LLMs produced are also 2\pmb{\times} smaller, enabling faster inference with lower memory cost on resource-constrained platforms.

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TS-CLIP: Time Series Understanding by CLIP
Ziwen Chen | Xiaoyuan Zhang | Ming Zhu

Contrastive Language–Image Pre-training (CLIP) has recently demonstrated remarkable success in aligning vision and language. Aligning time series with text leverages the rich semantic cues of language to enhance interpretability and generalization, addressing a largely underexplored area of research. Although applying the CLIP training paradigm to time-series and language pairs is promising, it may result in label collapse due to the sparse semantic annotations and the absence of visual cues in time-series data. To address this, we introduce Time Series CLIP (TS-CLIP), a novel approach that tackles label collapse using a synonym bank mechanism. Synonym bank exploits word analogy phenomena to generate potential synonym embeddings as alignment targets. Specifically, the synonym bank facilitates aligning time series with a word distribution instead of a precise textual description. We conducted extensive zero-shot and few-shot experiments on 128 sub-datasets from the UCR archive. The results show that TS-CLIP achieves state-of-the-art (SOTA) performance in zero-shot settings on 51 datasets. Comprehensive ablation studies and visualization analyzes reveal that TS-CLIP effectively aligns time series with natural language. To the best of our knowledge, this is the first foundational model to achieve general time series and natural language alignment. TS-CLIP introduces a new paradigm for the semantic understanding of time series and opens the possibility of integrating the time series modality into multimodal large models.

The development of Emotional Support Conversation (ESC) systems is critical for delivering mental health support tailored to the needs of help-seekers. Recent advances in large language models (LLMs) have contributed to progress in this domain, while most existing studies focus on generating responses directly and overlook the integration of domain-specific reasoning and expert interaction.Therefore, in this paper, we propose a training-free Multi-Agent collaboration framework for ESC (MultiAgentESC).The framework is designed to emulate the human-like process of providing emotional support through three stages: dialogue analysis, strategy deliberation, and response generation.At each stage, a multi-agent system is employed to iteratively enhance information understanding and reasoning, simulating real-world decision-making processes by incorporating diverse interactions among these expert agents.Additionally, we introduce a novel response-centered approach to handle the one-to-many problem on strategy selection, where multiple valid strategies are initially employed to generate diverse responses, followed by the selection of the optimal response through multi-agent collaboration.Experiments on the ESConv dataset reveal that our proposed framework excels at providing emotional support as well as diversifying support strategy selection.

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Continuously Steering LLMs Sensitivity to Contextual Knowledge with Proxy Models
Yilin Wang | Heng Wang | Yuyang Bai | Minnan Luo

In Large Language Models (LLMs) generation, there exist knowledge conflicts, and scenarios where parametric knowledge contradicts knowledge provided in the context. Previous works studied tuning, decoding algorithms, or locating and editing context-aware neurons to adapt LLMs to be faithful to new contextual knowledge. However, they are usually inefficient or ineffective for large models, not workable for black-box models, or unable to continuously adjust LLMs’ sensitivity to the knowledge provided in the context. To mitigate these problems, we propose CSKS (Continuously Steering Knowledge Sensitivity), a simple framework that can steer LLMs’ sensitivity to contextual knowledge continuously at a lightweight cost. Specifically, we tune two small LMs (i.e. proxy models) and use the difference in their output distributions to shift the original distribution of an LLM without modifying the LLM weights. In the evaluation process, we not only design synthetic data and fine-grained metrics to measure models’ sensitivity to contextual knowledge but also use a real conflict dataset to validate CSKS’ practical efficacy. Extensive experiments demonstrate that our framework achieves continuous and precise control over LLMs’ sensitivity to contextual knowledge, enabling both increased sensitivity and reduced sensitivity, thereby allowing LLMs to prioritize either contextual or parametric knowledge as needed flexibly. Our data and code are available at https://github.com/OliveJuiceLin/CSKS.

Guesstimation—the task of making approximate quantitative estimates about objects or events—is a common real-world skill, yet remains underexplored in large language model (LLM) research. We introduce three guesstimation datasets: MARBLES, FUTURE, and ELECPRED, spanning physical estimation (e.g., how many marbles fit in a cup) to abstract predictions (e.g., the 2024 U.S. presidential election). Inspired by the social science concept of Wisdom of Crowds (WOC)—where the median of multiple estimates improves accuracy—we propose WOC decoding for LLMs. We replicate WOC effects in human participants and find that LLMs exhibit similar benefits: median aggregation across sampled responses consistently improves accuracy over greedy, self-consistency decoding, and mean decoding. This suggests that LLMs encode a world model that supports approximate reasoning. Our results position guesstimation as a useful probe of LLM world knowledge and highlight WOC decoding as a strategy for enhancing LLM guesstimation performance on real-world tasks.

Mamba’s theoretical infinite-context potential is limited in practice when sequences far exceed training lengths. This work explores unlocking Mamba’s long-context memory ability by a simple-yet-effective method, Recall with Reasoning (RwR), by distilling chain-of-thought (CoT) summarization from a teacher model. Specifically, RwR prepends these summarization as CoT prompts during fine-tuning, teaching Mamba to actively recall and reason over long contexts. Experiments on LONGMEMEVAL and HELMET show that RwR outperforms existing long-term memory methods on the Mamba model. Furthermore, under similar pre-training conditions, RwR improves the long-context performance of Mamba relative to comparable Transformer/hybrid baselines while preserving short-context capabilities, all without changing the architecture.

Large language models (LLMs) rely on massive amounts of training data, however, the quantity of empirically observed data is limited. To alleviate this issue, lots of LLMs leverage synthetic data to enhance the quantity of training data. Despite significant advancements in LLMs, the efficiency and scalability characteristics of data synthesis during pre-training phases remain insufficiently explored. In this work, we propose a novel data synthesis framework, Cognitive Combination Synthesis (CCS), designed to achieve highly efficient and scalable data synthesis. Specifically, our methodology mimics human cognitive behaviors by recombining and interconnecting heterogeneous data from diverse sources thereby enhancing advanced reasoning capabilities in LLMs. Extensive experiments demonstrate that: (1) effective data organization is essential, and our mapping-based combination learning approach significantly improves data utilization efficiency; (2) by enhancing data diversity, accuracy, and complexity, our synthetic data scales beyond 100B tokens, revealing CCS’s strong scalability. Our findings highlight the impact of data organization methods on LLM learning efficiency and the significant potential of scalable synthetic data to enhance model reasoning capabilities.

Traditional robot simulators focus on physical process modeling and realistic rendering, often suffering from high computational costs, inefficiencies, and limited adaptability. To handle this issue, we concentrate on behavior simulation in robotics to analyze and validate the logic behind robot behaviors, aiming to achieve preliminary evaluation before deploying resource-intensive simulators and thus enhance simulation efficiency. In this paper, we propose BeSimulator, a modular and novel LLM-powered framework, as an attempt towards behavior simulation in the context of text-based environments. By constructing text-based virtual environments and performing semantic-level simulation, BeSimulator can generalize across scenarios and achieve long-horizon complex simulation. Inspired by human cognition paradigm, it employs a “consider-decide-capture-transfer” four-phase simulation process, termed Chain of Behavior Simulation (CBS), which excels at analyzing action feasibility and state transition. Additionally, BeSimulator incorporates code-driven reasoning to enable arithmetic operations and enhance reliability, and reflective feedback to refine simulation. Based on our manually constructed behavior-tree-based simulation benchmark, BTSIMBENCH, our experiments show a significant performance improvement in behavior simulation compared to baselines, ranging from 13.60% to 24.80%. Code and data are available at https://github.com/Dawn888888/BeSimulator.

As large language models (LLMs) often generate plausible but incorrect content, error detection has become increasingly critical to ensure truthfulness.However, existing detection methods often overlook a critical problem we term as **self-consistent error**, where LLMs repeatedly generate the same incorrect response across multiple stochastic samples.This work formally defines self-consistent errors and evaluates mainstream detection methods on them.Our investigation reveals two key findings: (1) Unlike inconsistent errors, whose frequency diminishes significantly as the LLM scale increases, the frequency of self-consistent errors remains stable or even increases.(2) All four types of detection methods significantly struggle to detect self-consistent errors.These findings reveal critical limitations in current detection methods and underscore the need for improvement.Motivated by the observation that self-consistent errors often differ across LLMs, we propose a simple but effective cross‐model probe method that fuses hidden state evidence from an external verifier LLM.Our method significantly enhances performance on self-consistent errors across three LLM families.

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pFedGPT: Hierarchically Optimizing LoRA Aggregation Weights for Personalized Federated GPT Models
Zhanming Shen | Tianqi Xu | Hao Wang | Jian Li | Miao Pan

Federated finetuning of Large Language Models (LLMs) using Low-Rank Adaptation (LoRA) offers computational efficiency and preserves data privacy. However, applying LoRA in federated settings faces significant challenges: standard approaches struggle with data heterogeneity, and existing personalization techniques fail to precisely adapt shared global knowledge to individual client needs. To address these issues, we propose pFedGPT, a framework that leverages Hierarchical Bayesian Optimization (HBO) for fine-grained, personalized LoRA aggregation. pFedGPT intelligently partitions LoRA parameters based on model structure and client information, then employs HBO to hierarchically search for optimal, module-specific weights. This enables a nuanced integration of the downloaded global LoRA state with each client’s local model, precisely capturing client-specific requirements. To manage the optimization cost inherent in HBO, pFedGPT incorporates efficient multi-fidelity evaluations and a curriculum learning strategy. Extensive experiments demonstrate that pFedGPT achieves state-of-the-art (SOTA) performance on personalized FL benchmarks, showcasing robustness and scalability while introducing only minimal (approx. 4%) additional optimization overhead. Our results also underscore the limitations of traditional FL methods for LoRA-based LLM personalization, highlighting the need for tailored approaches like pFedGPT.

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QSpec: Speculative Decoding with Complementary Quantization Schemes
Juntao Zhao | Wenhao Lu | Sheng Wang | Lingpeng Kong | Chuan Wu

Quantization is widely adopted to accelerate inference and reduce memory consumption in large language models (LLMs). While activation-weight joint quantization enables efficient low-precision decoding, it suffers substantial performance degradation on multi-step reasoning tasks. We propose QSPEC, a novel quantization paradigm that decouples efficiency from quality by integrating two complementary schemes via speculative decoding: low-precision joint quantization for fast drafting and high-precision weight-only quantization for accurate verification. QSPEC reuses both weights and KV cache across stages, enabling near-zero-cost switching without retraining or auxiliary models. Compared to high-precision baselines, QSPEC achieves up to 1.64x speedup without quality degradation, and outperforms state-of-the-art speculative decoding methods by up to 1.55x in batched settings. Furthermore, QSPEC supports plug-and-play deployment and generalizes well across model scales, quantization methods, and workloads. These properties make QSPEC a practical and scalable solution for high-fidelity quantized LLM serving under memory-constrained scenarios.

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Co-Evolving LLMs and Embedding Models via Density-Guided Preference Optimization for Text Clustering
Zetong Li | Qinliang Su | Minhua Huang | Yin Yang

Large language models (LLMs) have shown strong potential in enhancing text clustering when combined with traditional embedding models. However, existing methods predominantly treat LLMs as static pseudo-oracles, i.e., unidirectionally querying them for similarity assessment or data augmentation, while never seeking feedback from embedding models to improve them. In this work, we propose a training framework that enables bidirectional refinement between LLMs and embedding models. We first design task-aware prompts to guide the LLM in generating interpretations for the input texts. These interpretations are projected into the embedding space, in which interpretations that are preferred by the embedding model are selected based on their distribution densities. The selected interpretations are then used to fine-tune the LLM via preference optimization to prioritize the generation of helpful interpretations. Meanwhile, we enhance the embedding model via contrastive learning on the generated interpretations and perform clustering on the output embeddings, leading to iterative co-training between the LLM and the embedding model. Experiments on 14 benchmark datasets across 5 tasks demonstrate the effectiveness of our method.

Recent advancements in large language models (LLMs) showcase varied multilingual capabilities across tasks like translation, code generation, and reasoning. Previous assessments often limited their scope to fundamental natural language processing (NLP) or isolated capability-specific tasks. To alleviate this drawback, we aim to present a comprehensive multilingual multitask benchmark. First, we introduce P-MMEval, a large-scale benchmark covering fundamental and capability-specialized datasets. Furthermore, P-MMEval delivers consistent language coverage across various datasets and provides parallel samples. Finally, we conduct extensive experiments on representative multilingual model series to compare performances across models and tasks, explore the relationship between multilingual performances and factors such as tasks, model sizes, languages, and prompts, and examine the effectiveness of knowledge transfer from English to other languages. The resulting insights are intended to offer valuable guidance for future research.

Existing studies have optimized retrieval-augmented generation (RAG) across various sub-tasks, such as query understanding and retrieval refinement, but integrating these optimizations into a unified framework remains challenging. To tackle this problem, this work proposes RoleRAG, a unified RAG framework that achieves efficient multi-task processing through role-specific token optimization. RoleRAG comprises six modules, each handling a specific sub-task within the RAG process. Additionally, we introduce a query graph to represent the decomposition of the query, which can be dynamically resolved according to the decomposing state. All modules are driven by the same underlying LLM, distinguished by task-specific role tokens that are individually optimized. This design allows RoleRAG to dynamically activate different modules within a single LLM instance, thereby streamlining deployment and reducing resource consumption. Experimental results on five open-domain question-answering datasets demonstrate the effectiveness, generalizability, and flexibility of our framework.

In this paper, we propose TrInk, a Transformer-based model for ink generation, which effectively captures global dependencies. To better facilitate the alignment between the input text and generated stroke points, we introduce scaled positional embeddings and a Gaussian memory mask in the cross-attention module. Additionally, we design both subjective and objective evaluation pipelines to comprehensively assess the legibility and style consistency of the generated handwriting. Experiments demonstrate that our Transformer-based model achieves a 35.56% reduction in character error rate (CER) and an 29.66% reduction in word error rate (WER) on the IAM-OnDB dataset compared to previous methods. We provide an demo page with handwriting samples from TrInk and baseline models at: https://akahello-a11y.github.io/trink-demo/

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CalligraphicOCR for Chinese Calligraphy Recognition
Xiaoyi Bao | Zhongqing Wang | Jinghang Gu | Chu-Ren Huang

With thousand years of history, calligraphy serve as one of the representative symbols of Chinese culture. Increasing works try to digitize calligraphy by recognizing the context of calligraphy for better preservation and propagation. However, previous works stick to isolated single character recognition, not only requires unpractical manual splitting into characters, but also abandon the enriched context information that could be supplementary. To this end, we construct the pioneering end-to-end calligraphy recognition benchmark dataset, this dataset is challenging due to both the visual variations such as different writing styles and the textual understanding such as the domain shift in semantics. We further propose CalligraphicOCR (COCR) equipped with calligraphic image augmentation and action-based corrector targeted at the challenging root of this setting. Experiments demonstrate the advantage of our proposed model over cutting-edge baselines, underscoring the necessity of introducing this new setting, thereby facilitating a solid precondition for protecting and propagating the already scarce resources.

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When Audio and Text Disagree: Revealing Text Bias in Large Audio-Language Models
Cheng Wang | Gelei Deng | Xianglin Yang | Han Qiu | Tianwei Zhang

Large Audio-Language Models (LALMs) are augmented with the ability to perceive audio, demonstrating impressive capabilities in processing combined audio and text signals. However, their reliability when faced with conflicting inputs across modalities remains largely unexplored. This study examines how LALMs prioritize information when presented with inconsistent audio-text pairs. Through extensive evaluation across diverse audio understanding tasks, we reveal a concerning phenomenon: when inconsistencies exist between modalities, LALMs display a significant bias toward textual input, often disregarding audio evidence. This tendency leads to substantial performance degradation in audio-centric tasks and raises important reliability concerns for real-world applications. We further investigate the influencing factors of text bias, explore mitigation strategies through supervised fine-tuning, and analyze model confidence patterns that reveal persistent overconfidence even with contradictory inputs. These findings underscore the need for improved modality balancing during training and more sophisticated fusion mechanisms to enhance robustness when handling conflicting multi-modal inputs. The project is available at https://github.com/WangCheng0116/MCR-BENCH.

The proliferation of Machine Learning as a Service (MLaaS) has enabled widespread deployment of large language models (LLMs) via cloud APIs, but also raises critical concerns about model integrity and security. Existing black-box tamper detection methods, such as watermarking and fingerprinting, rely on the stability of model outputs—a property that does not hold for inherently stochastic LLMs. We address this challenge by formulating black-box tamper detection for LLMs as a hypothesis-testing problem. To enable efficient and sensitive fingerprinting, we derive a first-order surrogate for KL divergence—the entropy-gradient norm—to identify prompts most responsive to parameter perturbations. Building on this, we propose Regularized Entropy-Sensitive Fingerprinting (RESF), which enhances sensitivity while regularizing entropy to improve output stability and control false positives. To further distinguish tampering from benign randomness, such as temperature shifts, RESF employs a lightweight two-tier sequential test combining support-based and distributional checks with rigorous false-alarm control.Comprehensive analysis and experiments across multiple LLMs show that RESF achieves up to 98.80% detection accuracy under challenging conditions, such as minimal LoRA fine-tuning with five optimized fingerprints. RESF consistently demonstrates strong sensitivity and robustness, providing an effective and scalable solution for black-box tamper detection in cloud-deployed LLMs.

Prominent Large Language Model (LLM) services from providers like OpenAI and Google excel at general tasks but often underperform on domain-specific applications. Current customization services for these LLMs typically require users to upload data for fine-tuning, posing significant privacy risks. While differentially private (DP) data synthesis presents a potential alternative, its application commonly results in low effectiveness due to the introduction of excessive noise on data for DP. To overcome this, we introduce *Llamdex*, a novel framework that facilitates LLM customization as a service, where the client uploads pre-trained domain-specific *models* rather than data. This client-uploaded model, optionally protected by DP with much lower noise, is inserted into the base LLM via connection modules. Significantly, these connecting modules are trained without requiring sensitive domain data, enabling clients to customize LLM services while preserving data privacy. Experiments demonstrate that Llamdex improves domain-specific accuracy by up to 26% over state-of-the-art private data synthesis methods under identical privacy constraints and, by obviating the need for users to provide domain context within queries, maintains inference efficiency comparable to the original LLM service.

Large Language Models (LLMs) based agent systems have made great strides in real-world applications beyond traditional NLP tasks. This paper proposes a new LLM-based Multi-Agent System (LLM-MAS) benchmark, Collab-Overcooked, built on the popular Overcooked-AI game with more applicable and challenging tasks in interactive environments. Collab-Overcooked extends existing benchmarks in two novel ways. First, it provides a multi-agent framework supporting diverse tasks and objectives and encourages collaboration through natural language communication. Second, it introduces a spectrum of process-oriented evaluation metrics to assess the fine-grained collaboration capabilities of different LLM agents, a dimension often overlooked in prior work. We conduct extensive experiments with 13 popular LLMs and show that, while the LLMs exhibit a strong ability in goal interpretation, there are significant shortcomings in active collaboration and continuous adaptation, which are critical for efficiently fulfilling complex tasks. Notably, we highlight the strengths and weaknesses of LLM-MAS and provide insights for improving and evaluating LLM-MAS on a unified and open-source benchmark. The environments, 30 open-ended tasks, and the evaluation package are publicly available at https://github.com/YusaeMeow/Collab-Overcooked.

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Improving Reasoning Capabilities in Small Models through Mixture-of-layers Distillation with Stepwise Attention on Key Information
Yao Chen | Jiawei Sheng | Wenyuan Zhang | Tingwen Liu

The significant computational demands of large language models have increased interest in distilling reasoning abilities into smaller models via Chain-of-Thought (CoT) distillation. Current CoT distillation methods mainly focus on transferring teacher-generated rationales for complex reasoning to student models. However, they do not adequately explore teachers’ dynamic attention toward critical information during reasoning. We find that language models exhibit progressive attention shifts towards key information during reasoning, which implies essential clues for drawing conclusions. Building on this observation and analysis, we introduce a novel CoT distillation framework that transfers the teacher’s stepwise attention on key information to the student model. This establishes structured guidance for the student’s progressive concentration on key information during reasoning. More importantly, we develop a Mixture of Layers module enabling dynamic alignment that adapts to different layers between the teacher and student. Our method achieves consistent performance improvements across multiple mathematical and commonsense reasoning datasets. To our knowledge, it is the first method to leverage stepwise attention within CoT distillation to improve small model reasoning.

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Through the Valley: Path to Effective Long CoT Training for Small Language Models
Renjie Luo | Jiaxi Li | Chen Huang | Wei Lu

Long chain-of-thought (CoT) supervision has become a common strategy to enhance reasoning in language models. While effective for large models, we identify a phenomenon we call Long CoT Degradation, in which small language models (SLMs; ≤3B parameters) trained on limited long CoT data experience significant performance deterioration. Through extensive experiments on the Qwen2.5, LLaMA3 and Gemma3 families, we demonstrate that this degradation is widespread across SLMs. In some settings, models trained on only 8k long CoT examples lose up to 75% of their original performance before fine-tuning. Strikingly, we further observe that for some particularly small models, even training on 220k long CoT examples fails to recover or surpass their original performance prior to fine-tuning. Our analysis attributes this effect to error accumulation: while longer responses increase the capacity for multi-step reasoning, they also amplify the risk of compounding mistakes. Furthermore, we find that Long CoT Degradation may negatively impacts downstream reinforcement learning (RL), although this can be alleviated by sufficiently scaled supervised fine-tuning (SFT). Our findings challenge common assumptions about the benefits of long CoT training for SLMs and offer practical guidance for building more effective small-scale reasoning models.

Reinforcement learning from human feedback (RLHF) offers a promising approach to aligning large language models (LLMs) with human preferences. Typically, a reward model is trained or supplied to act as a proxy for humans in evaluating generated responses during the reinforcement training phase. However, current reward models operate as sequence-to-one models, allocating a single, sparse, and delayed reward to an entire output sequence. This approach may overlook the significant contributions of individual tokens toward the desired outcome. To this end, we propose a more fine-grained, token-level guidance approach for RL training. Specifically, we introduce RED, a novel REward reDistribition method that evaluates and assigns specific credit to each token using an off-the-shelf reward model. Utilizing these fine-grained rewards enhances the model’s understanding of language nuances, leading to more precise performance improvements. Notably, our method does not require modifying the reward model or introducing additional training steps, thereby incurring minimal computational costs. Experimental results across diverse datasets and tasks demonstrate the superiority of our approach.

Large Language Models (LLMs) excel at various natural language processing tasks but remain vulnerable to jailbreaking attacks that induce harmful content generation. In this paper, we reveal a critical safety inconsistency: LLMs can more effectively identify harmful requests as discriminators than defend against them as generators. This insight inspires us to explore aligning the model’s inherent discrimination and generation capabilities. To this end, we propose SDGO (Self-Discrimination-Guided Optimization), a reinforcement learning framework that leverages the model’s own discrimination capabilities as a reward signal to enhance generation safety through iterative self-improvement. Our method does not require any additional annotated data or external models during the training phase. Extensive experiments demonstrate that SDGO significantly improves model safety compared to both prompt-based and training-based baselines while maintaining helpfulness on general benchmarks. By aligning LLMs’ discrimination and generation capabilities, SDGO brings robust performance against out-of-distribution (OOD) jailbreaking attacks. This alignment achieves tighter coupling between these two capabilities, enabling the model’s generation capability to be further enhanced with only a small amount of discriminative samples. Our code and datasets are available at https://github.com/NJUNLP/SDGO.

LLMs have shown strong performance on human-centric reasoning tasks. While previous evaluations have explored whether LLMs can infer intentions or detect deception, they often overlook the individualized reasoning styles that influence how people interpret and act in social contexts. Social deduction games (SDGs) provide a natural testbed for evaluating individualized reasoning styles, where different players may adopt diverse but contextually valid reasoning strategies under identical conditions. To address this, we introduce InMind, a cognitively grounded evaluation framework designed to assess whether LLMs can capture and apply personalized reasoning styles in SDGs. InMind enhances structured gameplay data with round-level strategy traces and post-game reflections, collected under both Observer and Participant modes. It supports four cognitively motivated tasks that jointly evaluate both static alignment and dynamic adaptation. As a case study, we apply InMind to the game Avalon, evaluating 11 state-of-the-art LLMs. General-purpose LLMs, even GPT-4o frequently rely on lexical cues, struggling to anchor reflections in temporal gameplay or adapt to evolving strategies. In contrast, reasoning-enhanced LLMs like DeepSeek-R1 exhibit early signs of style-sensitive reasoning. These findings reveal key limitations in current LLMs’ capacity for individualized, adaptive reasoning, and position InMind as a step toward cognitively aligned human–AI interaction.

In this paper, we introduce MIO, a novel foundation model built on multimodal tokens, capable of understanding and generating speech, text, images, and videos in an end-to-end, autoregressive manner. While the emergence of large language models (LLMs) and multimodal large language models (MM-LLMs) propels advancements in artificial general intelligence through their versatile capabilities, they still lack true any-to-any understanding and generation. Recently, the release of GPT-4o has showcased the remarkable potential of any-to-any LLMs for complex real-world tasks, enabling omnidirectional input and output across images, speech, and text. However, it is closed-source and does not support the generation of multimodal interleaved sequences. To address this gap, we present MIO, which is trained on a mixture of discrete tokens across four modalities using causal multimodal modeling. MIO undergoes a four-stage training process: (1) alignment pre-training, (2) interleaved pre-training, (3) speech-enhanced pre-training, and (4) comprehensive supervised fine-tuning on diverse textual, visual, and speech tasks. Our experimental results indicate that MIO exhibits competitive, and in some cases superior, performance compared to previous dual-modal baselines, any-to-any model baselines, and even modality-specific baselines. Moreover, MIO demonstrates advanced capabilities inherent to its any-to-any feature, such as interleaved video-text generation, chain-of-visual-thought reasoning, visual guideline generation, instructional image editing, etc.

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DART: Distilling Autoregressive Reasoning to Silent Thought
Nan Jiang | Ziming Wu | De-Chuan Zhan | Fuming Lai | Shaobing Lian

Chain-of-Thought (CoT) reasoning has significantly advanced Large Language Models (LLMs) in solving complex tasks. However, its autoregressive paradigm leads to significant computational overhead, hindering its deployment in latency-sensitive applications. To address this, we propose **DART** (**D**istilling **A**utoregressive **R**easoning to Silent **T**hought), a self-distillation framework that enables LLMs to replace autoregressive CoT with non-autoregressive Silent Thought (ST). Specifically, DART introduces two training pathways: the CoT pathway for traditional reasoning and the ST pathway for generating answers directly from a few ST tokens. The ST pathway utilizes a lightweight Reasoning Evolvement Module (REM) to align its hidden states with the CoT pathway, enabling the ST tokens to evolve into informative embeddings. During inference, only the ST pathway is activated, leveraging evolving ST tokens to deliver the answer directly. Extensive experimental results demonstrate that DART offers significant performance gains compared with existing non-autoregressive baselines without extra inference latency, serving as a feasible alternative for efficient reasoning.

Large language models (LLMs) have demonstrated impressive capabilities in reasoning with the emergence of reasoning models like OpenAI-o1 and DeepSeek-R1. Recent research focuses on integrating reasoning capabilities into the realm of retrieval-augmented generation (RAG) via outcome-supervised reinforcement learning (RL) approaches, while the correctness of intermediate think-and-search steps is usually neglected. To address this issue, we design a process-level reward module to mitigate the unawareness of intermediate reasoning steps in outcome-level supervision without additional annotation. Grounded on this, we propose **Le**arning to **T**hink-and-**S**earch (**LeTS**), a novel framework that hybridizes stepwise process reward and outcome-based reward to current RL methods for RAG. Extensive experiments demonstrate the generalization and inference efficiency of **LeTS** across various RAG benchmarks. In addition, these results reveal the potential of process- and outcome-level reward hybridization in boosting LLMs’ reasoning ability via RL under other scenarios.

Instruction tuning is vital for aligning large language models (LLMs) with human intent, but current methods typically rely on costly human-annotated seed data or powerful external teacher models. While instruction back-translation techniques reduce this dependency, they remain fundamentally tethered to an initial seed set, which limits full automation, introduces biases, and can lead to inefficient use of unlabeled corpora. In this paper, we propose Cycle-Instruct, a novel framework that achieves fully seed-free instruction tuning. Inspired by cycle consistency, Cycle-Instruct employs a dual self-training loop where two models—an answer generator and a question generator—are bootstrapped solely from raw, unlabeled text. These models mutually supervise each other by reconstructing original text segments from their counterpart’s generated pseudo-labels, effectively learning from the intrinsic structure of the data without any human-provided seeds. We demonstrate Cycle-Instruct’s efficacy across four diverse data tracks, including general instruction-following, domain-specific tasks, dialogue logs, and plain text. Our extensive experiments show that Cycle-Instruct not only outperforms seed-driven back-translation baselines but also achieves performance comparable to strongly supervised methods.

The social impact of Natural Language Processing (NLP) is increasingly important, with a rising community focus on initiatives related to NLP for Social Good (NLP4SG). Indeed, in recent years, almost 20% of all papers in the ACL Anthology address topics related to social good as defined by the UN Sustainable Development Goals (Aduato et al. 2023). In this study, we take an author- and venue-level perspective to map the landscape of NLP4SG, quantifying the proportion of work addressing social good concerns both within and beyond the ACL community, by both core ACL contributors and non-ACL authors. With this approach we discover two surprising facts about the landscape of NLP4SG. First, ACL authors are dramatically more likely to do work addressing social good concerns when publishing in venues outside of ACL. Second, the vast majority of publications using NLP techniques to address concerns of social good are done by non-ACL authors in venues outside of ACL. We discuss the implications of these findings on agenda-setting considerations for the ACL community related to NLP4SG.

Current research on long-form context in Large Language Models (LLMs) primarily focuses on the understanding of long-contexts, the **Open-ended Long Text Generation** (Open-LTG) remains insufficiently explored. Training a long text generation model requires curation of gold-standard reference data, which is typically nonexistent for informative Open-LTG tasks. However, previous methods only utilize general assessments as reward signals, which limits accuracy. To bridge this gap, we introduce **ProxyReward**, an innovative reinforcement learning (RL) based framework, which includes a data synthesis method and a novel reward signal. Firstly, **ProxyReward Dataset** synthesis is accomplished through simple prompts that enables the model to create automatically, obviating extensive labeled data or significant manual effort. Secondly, **ProxyReward Signal** offers a targeted evaluation of information comprehensiveness and accuracy for specific questions. The experimental results indicate that our method ProxyReward **surpasses even GPT-4-Turbo**. It can significantly enhance performance by 20% on the Open-LTG task when training widely used open-source models, while also surpassing the LLM-as-a-Judge approach. Our work presents effective methods to enhance the ability of LLMs to address complex open-ended questions posed by humans.

The rapid development of Multimodal Large Reasoning Models (MLRMs) has demonstrated broad application potential, yet their safety and reliability remain critical concerns that require systematic exploration. To address this gap, we conduct a comprehensive and systematic safety evaluation of 13 MLRMs across 5 benchmarks and unveil prevalent safety degradation phenomena in most advanced models. Moreover, our analysis reveals distinct safety patterns across different benchmarks: significant safety degradation is observed across jailbreak robustness benchmarks, whereas safety-awareness benchmarks demonstrate less pronounced degradation. In particular, the long thought process in some scenarios even enhances safety performance. Therefore, it is a potential approach to address safety issues in MLRMs by leveraging the intrinsic reasoning capabilities of the model to detect unsafe intent. To operationalize this insight, we construct a multimodal tuning dataset that incorporates a safety-oriented thought process. Experimental results from fine-tuning existing MLRMs with this dataset effectively enhance the safety on both jailbreak robustness and safety-awareness benchmarks. This study provides a new perspective for developing safe MLRMs.

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Understanding the Modality Gap: An Empirical Study on the Speech-Text Alignment Mechanism of Large Speech Language Models
Bajian Xiang | Shuaijiang Zhao | Tingwei Guo | Wei Zou

End-to-end Large Speech Language Models (LSLMs) have demonstrated impressive conversational generation abilities, yet consistently fall short of traditional pipeline systems on semantic understanding benchmarks. In this work, we reveal through systematic experimentation that although LSLMs lose some text input performance after speech-text alignment training, the performance gap between speech and text inputs is more pronounced, which we refer to as the modality gap. To understand this gap, we analyze both coarse- and fine-grained text and speech representations. At the coarse-grained level, representations of speech and text in deeper layers are found to be increasingly aligned in direction (cosine similarity), while concurrently diverging in magnitude (Euclidean distance). We further find that representation similarity is strongly correlated with the modality gap. At the fine-grained level, a spontaneous token-level alignment pattern between text and speech representations is observed. Based on this, we introduce the Alignment Path Score to quantify token-level alignment quality, which exhibits stronger correlation with the modality gap. Building on these insights, we design targeted interventions on critical tokens through angle projection and length normalization. These strategies demonstrate the potential to improve correctness for speech inputs. Our study provides the first systematic empirical analysis of the modality gap and alignment mechanisms in LSLMs, offering both theoretical and methodological guidance for future optimization.

Recent advancements in multimodal large language models (MLLMs) have garnered significant attention, offering a promising pathway toward artificial general intelligence (AGI). Among the essential capabilities required for AGI, creativity has emerged as a critical trait for MLLMs, with association serving as its foundation. Association reflects a model’s ability to think creatively, making it vital to evaluate and understand. While several frameworks have been proposed to assess associative ability, they often overlook the inherent ambiguity in association tasks, which arises from the divergent nature of associations and undermines the reliability of evaluations. To address this issue, we decompose ambiguity into two types—internal ambiguity and external ambiguity—and introduce AssoCiAm, a benchmark designed to evaluate associative ability while circumventing the ambiguity through a hybrid computational method. We then conduct extensive experiments on MLLMs, revealing a strong positive correlation between cognition and association. Additionally, we observe that the presence of ambiguity in the evaluation process causes MLLMs’ behavior to become more random-like. Finally, we validate the effectiveness of our method in ensuring more accurate and reliable evaluations. See Project Page for the data and codes.

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M-BRe: Discovering Training Samples for Relation Extraction from Unlabeled Texts with Large Language Models
Zexuan Li | Hongliang Dai | Piji Li

For Relation Extraction (RE), the manual annotation of training data may be prohibitively expensive, since the sentences that contain the target relations in texts can be very scarce and difficult to find. It is therefore beneficial to develop an efficient method that can automatically extract training instances from unlabeled texts for training RE models. Recently, large language models (LLMs) have been adopted in various natural language processing tasks, with RE also benefiting from their advances. However, when leveraging LLMs for RE with predefined relation categories, two key challenges arise. First, in a multi-class classification setting, LLMs often struggle to comprehensively capture the semantics of every relation, leading to suboptimal results. Second, although employing binary classification for each relation individually can mitigate this issue, it introduces significant computational overhead, resulting in impractical time complexity for real-world applications. Therefore, this paper proposes a framework called M-BRe to extract training instances from unlabeled texts for RE. It utilizes three modules to combine the advantages of both of the above classification approaches: Relation Grouping, Relation Extraction, and Label Decision. Extensive experiments confirm its superior capability in discovering high-quality training samples from unlabeled texts for RE.

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R-TOFU: Unlearning in Large Reasoning Models
Sangyeon Yoon | Wonje Jeung | Albert No

Large Reasoning Models (LRMs) embed private or copyrighted information not only in their final answers but also throughout multi-step chain-of-thought (CoT) traces, making reliable unlearning far more demanding than in standard LLMs. We introduce Reasoning-TOFU (R-TOFU), the first benchmark tailored to this setting. R-TOFU augments existing unlearning tasks with realistic CoT annotations and provides step-wise metrics that expose residual knowledge invisible to answer-level checks. Using R-TOFU, we carry out a comprehensive comparison of gradient-based and preference-optimization baselines and show that conventional answer-only objectives leave substantial forget traces in reasoning. We further propose Reasoned IDK, a preference-optimization variant that preserves coherent yet inconclusive reasoning, achieving a stronger balance between forgetting efficacy and model utility than earlier refusal styles. Finally, we identify a failure mode: decoding variants such as ZeroThink and LessThink can still reveal forgotten content despite seemingly successful unlearning, emphasizing the need to evaluate models under diverse decoding settings. Together, the benchmark, analysis, and new baseline establish a systematic foundation for studying and improving unlearning in LRMs while preserving their reasoning capabilities.

Text-based person search (TBPS) enables the retrieval of person images from large-scale databases using natural language descriptions, offering critical value in surveillance applications. However, a major challenge lies in the labor-intensive process of obtaining high-quality textual annotations, which limits scalability and practical deployment. To address this, we introduce two complementary modules: Multi-Turn Text Generation (MTG) and Multi-Turn Text Interaction (MTI). MTG generates rich pseudo-labels through simulated dialogues with MLLMs, producing fine-grained and diverse visual descriptions without manual supervision. MTI refines user queries at inference time through dynamic, dialogue-based reasoning, enabling the system to interpret and resolve vague, incomplete, or ambiguous descriptions—characteristics often seen in real-world search scenarios. Together, MTG and MTI form a unified and annotation-free framework that significantly improves retrieval accuracy, robustness, and usability. Extensive evaluations demonstrate that our method achieves competitive or superior results while eliminating the need for manual captions, paving the way for scalable and practical deployment of TBPS systems.

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Spontaneous Giving and Calculated Greed in Language Models
Yuxuan Li | Hirokazu Shirado

Large language models demonstrate strong problem-solving abilities through reasoning techniques such as chain-of-thought prompting and reflection. However, it remains unclear whether these reasoning capabilities extend to a form of social intelligence: making effective decisions in cooperative contexts. We examine this question using economic games that simulate social dilemmas. First, we apply chain-of-thought and reflection prompting to GPT-4o in a Public Goods Game. We then evaluate multiple off-the-shelf models across six cooperation and punishment games, comparing those with and without explicit reasoning mechanisms. We find that reasoning models consistently reduce cooperation and norm enforcement, favoring individual rationality. In repeated interactions, groups with more reasoning agents exhibit lower collective gains. These behaviors mirror human patterns of “spontaneous giving and calculated greed.” Our findings underscore the need for LLM architectures that incorporate social intelligence alongside reasoning, to help address—rather than reinforce—the challenges of collective action.

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SenDetEX: Sentence-Level AI-Generated Text Detection for Human-AI Hybrid Content via Style and Context Fusion
Lei Jiang | Desheng Wu | Xiaolong Zheng

Text generated by Large Language Models (LLMs) now rivals human writing, raising concerns about its misuse. However, mainstream AI-generated text detection (AGTD) methods primarily target document-level long texts and struggle to generalize effectively to sentence-level short texts. And current sentence-level AGTD (S-AGTD) research faces two significant limitations: (1) lack of a comprehensive evaluation on complex human-AI hybrid content, where human-written text (HWT) and AI-generated text (AGT) alternate irregularly, and (2) failure to incorporate contextual information, which serves as a crucial supplementary feature for identifying the origin of the detected sentence. Therefore, in our work, we propose AutoFill-Refine, a high-quality synthesis strategy for human-AI hybrid texts, and then construct a dedicated S-AGTD benchmark dataset. Besides, we introduce SenDetEX, a novel framework for sentence-level AI-generated text detection via style and context fusion. Extensive experiments demonstrate that SenDetEX significantly outperforms all baseline models in detection accuracy, while exhibiting remarkable transferability and robustness. Source code is available at https://github.com/TristoneJiang/SenDetEX.

Graph Neural Networks (GNNs) have shown immense potential in improving the performance of large-scale models by effectively incorporating structured relational information. However, current approaches face two key challenges: (1) achieving robust semantic alignment between graph representations and large models, and (2) ensuring interpretability in the generated outputs. To address these challenges, we propose ExGLM (Explainable Graph Language Model), a novel training framework designed to seamlessly integrate graph and language modalities while enhancing transparency. Our framework introduces two core components: (1) a graph-language synergistic alignment module, which aligns graph structures with language model to ensure semantic consistency across modalities; and (2) a judge-and-improve paradigm, which allows the language model to iteratively evaluate, refine, and prioritize responses with higher interpretability, thereby improving both performance and transparency. Extensive experiments conducted on three benchmark datasets—ogbn-arxiv, Cora, and PubMed—demonstrate that ExGLM not only surpasses existing methods in efficiency but also generates outputs that are significantly more interpretable, effectively addressing the primary limitations of current approaches.

Selecting high-quality and diverse training samples from extensive datasets plays a crucial role in reducing training overhead and enhancing the performance of Large Language Models (LLMs). However, existing studies fall short in assessing the overall value of selected data, focusing primarily on individual quality, and struggle to strike an effective balance between ensuring diversity and minimizing data point traversals. Therefore, this paper introduces a novel choice-based sample selection framework that shifts the focus from evaluating individual sample quality to comparing the contribution value of different samples when incorporated into the subset. Thanks to the advanced language understanding capabilities of LLMs, we utilize LLMs to evaluate the value of each option during the selection process. Furthermore, we design a greedy sampling process where samples are incrementally added to the subset, thereby improving efficiency by eliminating the need for exhaustive traversal of the entire dataset with the limited budget. Extensive experiments demonstrate that selected data from our method not only surpasses the performance of the full dataset but also achieves competitive results with recent powerful studies, while requiring fewer selections. Moreover, we validate our approach on a larger medical dataset, highlighting its practical applicability in real-world applications.

Large Language Models (LLMs) are often quantized to lower precision to reduce the memory cost and latency in inference. However, quantization often degrades model performance, thus fine-tuning is required for various downstream tasks. Traditional fine-tuning methods such as stochastic gradient descent and Adam optimization require backpropagation, which is error-prone in the low-precision settings. To overcome these limitations, we propose the Quantized Zeroth-Order (QuZO) framework, specifically designed for fine-tuning LLMs through low-precision (e.g., 4- or 8-bit) forward passes. Our method avoids the low-precision straight-through estimator, which requires backward computation, and instead utilizes optimized stochastic rounding to mitigate increased bias. QuZO simplifies the training process, while achieving results comparable to first-order methods in FP8 and superior accuracy in INT8 and INT4 training. Experiments demonstrate that QuZO achieves competitive performance on classification, multi-choice, and generation tasks under low-bit training, including zero-shot reasoning tasks. Notably, QuZO incurs minimal overhead and reduces memory consumption by 2.94 ×–5.47 × compared to quantized first-order methods during LLaMA-7B fine-tuning.

Grouped-Query Attention (GQA) is a widely adopted strategy for reducing the computational cost of attention layers in large language models (LLMs). However, current GQA configurations are often suboptimal because they overlook how context length influences inference cost. Since inference cost grows with context length, the most cost-efficient GQA configuration should vary accordingly. In this work, we analyze the relationship among context length, model size, GQA configuration, and model loss, and introduce two innovations: (1) we decouple the total head size from the hidden size, enabling more flexible control over attention FLOPs; and (2) we jointly optimize the model size and the GQA configuration to arrive at a better allocation of inference resources between attention layers and other components. Our analysis reveals that commonly used GQA configurations are highly suboptimal for long-context scenarios. Moreover, we propose a recipe for deriving cost-optimal GQA configurations. Our results show that for long-context scenarios, one should use fewer attention heads while scaling up the model size. Configurations selected by our recipe can reduce both memory usage and FLOPs by more than 50% compared to Llama-3’s GQA, with *no degradation in model capabilities*. Our findings offer valuable insights for designing efficient long-context LLMs.

Humans possess a unified cognitive ability to perceive, comprehend, and interact with the physical world. Why can’t large language models replicate this holistic understanding? Through a systematic analysis of existing training paradigms in vision-language-action models (VLA), we identify two key challenges: spurious forgetting, where robot training overwrites crucial visual-text alignments, and task interference, where competing control and understanding tasks degrade performance when trained jointly. To overcome these limitations, we propose ChatVLA, a novel framework featuring Phased Alignment Training, which incrementally integrates multimodal data after initial control mastery, and a Mixture-of-Experts architecture to minimize task interference. ChatVLA demonstrates competitive performance on visual question-answering datasets and significantly surpasses state-of-the-art vision-language-action (VLA) methods on multimodal understanding benchmarks. Notably, it achieves a six times higher performance on MMMU and scores 47.2% on MMStar with a more parameter-efficient design than ECoT. Furthermore, ChatVLA demonstrates superior performance on 25 real-world robot manipulation tasks compared to existing VLA methods like OpenVLA. Our findings highlight the potential of our unified framework for achieving both robust multimodal understanding and effective robot control.

Despite recent progress, Graphic User Interface (GUI) agents powered by Large Language Models (LLMs) struggle with complex mobile tasks due to limited app-specific knowledge. While UI Transition Graphs (UTGs) offer structured navigation representations, they are underutilized due to poor extraction and inefficient integration. We introduce KG-RAG, a Knowledge Graph-driven Retrieval-Augmented Generation framework that transforms fragmented UTGs into structured vector databases for efficient real-time retrieval. By leveraging an intent-guided LLM search method, KG-RAG generates actionable navigation paths, enhancing agent decision-making. Experiments across diverse mobile apps show that KG-RAG outperforms existing methods, achieving a 75.8% success rate (8.9% improvement over AutoDroid), 84.6% decision accuracy (8.1% improvement), and reducing average task steps from 4.5 to 4.1. Additionally, we present KG-Android-Bench and KG-Harmony-Bench, two benchmarks tailored to the Chinese mobile ecosystem for future research. Finally, KG-RAG transfers to web/desktop (+40% SR on Weibo-web; +20% on QQ Music-desktop), and a UTG cost ablation shows accuracy saturates at ~4h per complex app, enabling practical deployment trade-offs.

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CLIP-MoE: Towards Building Mixture of Experts for CLIP with Diversified Multiplet Upcycling
Jihai Zhang | Xiaoye Qu | Tong Zhu | Yu Cheng

Contrastive Language-Image Pre-training (CLIP) has become a cornerstone in multimodal intelligence. However, recent studies discovered that CLIP can only encode one aspect of the feature space, leading to substantial information loss and indistinctive features. To mitigate this issue, this paper introduces a novel strategy that fine-tunes a series of complementary CLIP models and transforms them into a CLIP-MoE. Specifically, we propose a model-agnostic Diversified Multiplet Upcycling (DMU) framework for CLIP. Instead of training multiple CLIP models from scratch, DMU leverages a pre-trained CLIP and fine-tunes it into a diverse set with highly cost-effective multistage contrastive learning, thus capturing distinct feature subspaces efficiently. To fully exploit these fine-tuned models while minimizing computational overhead, we transform them into a CLIP-MoE, which dynamically activates a subset of CLIP experts, achieving an effective balance between model capacity and computational cost. Comprehensive experiments demonstrate the superior performance of CLIP-MoE across various zero-shot retrieval, zero-shot image classification tasks, and downstream Multimodal Large Language Model (MLLM) benchmarks when used as a vision encoder. Code is available at https://github.com/OpenSparseLLMs/CLIP-MoE.

Large reasoning models (LRMs) like OpenAI-o1 have demonstrated impressive long stepwise reasoning capabilities through large-scale reinforcement learning. However, their extended reasoning processes often suffer from knowledge insufficiency, leading to frequent uncertainties and potential errors. To address this limitation, we introduce **Search-o1**, a framework that enhances LRMs with an agentic retrieval-augmented generation (RAG) mechanism and a Reason-in-Documents module for refining retrieved documents. Search-o1 integrates an agentic search workflow into the reasoning process, enabling dynamic retrieval of external knowledge when LRMs encounter uncertain knowledge points. Additionally, due to the verbose nature of retrieved documents, we design a separate Reason-in-Documents module to deeply analyze the retrieved information before injecting it into the reasoning chain, minimizing noise and preserving coherent reasoning flow. Extensive experiments on complex reasoning tasks in science, mathematics, and coding, as well as six open-domain QA benchmarks, demonstrate the strong performance of Search-o1. This approach enhances the trustworthiness of LRMs in complex reasoning tasks, paving the way for advanced deep research systems. The code is available at https://github.com/RUC-NLPIR/Search-o1.

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From Personas to Talks: Revisiting the Impact of Personas on LLM-Synthesized Emotional Support Conversations
Shenghan Wu | Yimo Zhu | Wynne Hsu | Mong-Li Lee | Yang Deng

The rapid advancement of Large Language Models (LLMs) has revolutionized the generation of emotional support conversations (ESC), offering scalable solutions with reduced costs and enhanced data privacy. This paper explores the role of personas in the creation of ESC by LLMs. Our research utilizes established psychological frameworks to measure and infuse persona traits into LLMs, which then generate dialogues in the emotional support scenario. We conduct extensive evaluations to understand the stability of persona traits in dialogues, examining shifts in traits post-generation and their impact on dialogue quality and strategy distribution. Experimental results reveal several notable findings: 1) LLMs can infer core persona traits, 2) subtle shifts in emotionality and extraversion occur, influencing the dialogue dynamics, and 3) the application of persona traits modifies the distribution of emotional support strategies, enhancing the relevance and empathetic quality of the responses. These findings highlight the potential of persona-driven LLMs in crafting more personalized, empathetic, and effective emotional support dialogues, which has significant implications for the future design of AI-driven emotional support systems.

Large language models (LLMs) have demonstrated remarkable reasoning and planning capabilities, driving extensive research into task decomposition. Existing task decomposition methods focus primarily on memory, tool usage, and feedback mechanisms, achieving notable success in specific domains, but they often overlook the trade-off between performance and cost. In this study, we first conduct a comprehensive investigation on task decomposition, identifying six categorization schemes. Then, we perform an empirical analysis of three factors that influence the performance and cost of task decomposition: categories of approaches, characteristics of tasks, and configuration of decomposition and execution models, uncovering three critical insights and summarizing a set of practical principles. Building on this analysis, we propose the Select-Then-Decompose strategy, which establishes a closed-loop problem-solving process composed of three stages: selection, execution, and verification. This strategy dynamically selects the most suitable decomposition approach based on task characteristics and enhances the reliability of the results through a verification module. Comprehensive evaluations across multiple benchmarks show that the Select-Then-Decompose consistently lies on the Pareto frontier, demonstrating an optimal balance between performance and cost. Our code is publicly available at https://github.com/summervvind/Select-Then-Decompose.

**Warning: This paper contains content that may involve potentially harmful behaviours, discussed strictly for research purposes.**Jailbreak attacks can hinder the safety of Large Language Model (LLM) applications, especially chatbots. Studying jailbreak techniques is an important AI red teaming task for improving the safety of these applications. In this paper, we introduce TombRaider, a novel jailbreak technique that exploits the ability to store, retrieve, and use historical knowledge of LLMs. TombRaider employs two agents, the inspector agent to extract relevant historical information and the attacker agent to generate adversarial prompts, enabling effective bypassing of safety filters. We intensively evaluated TombRaider on six popular models. Experimental results showed that TombRaider could outperform state-of-the-art jailbreak techniques, achieving nearly 100% attack success rates (ASRs) on bare models and maintaining over 55.4% ASR against defence mechanisms. Our findings highlight critical vulnerabilities in existing LLM safeguards, underscoring the need for more robust safety defences.

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Text Meets Topology: Rethinking Out-of-distribution Detection in Text-Rich Networks
Danny Wang | Ruihong Qiu | Guangdong Bai | Zi Huang

Out-of-distribution (OOD) detection remains challenging in text-rich networks, where textual features intertwine with topological structures. Existing methods primarily address label shifts or rudimentary domain-based splits, overlooking the intricate textual-structural diversity. For example, in social networks, where users represent nodes with textual features (name, bio) while edges indicate friendship status, OOD may stem from the distinct language patterns between bot and normal users. To address this gap, we introduce the TextTopoOOD framework for evaluating detection across diverse OOD scenarios: (1) attribute-level shifts via text augmentations and embedding perturbations; (2) structural shifts through edge rewiring and semantic connections; (3) thematically-guided label shifts; and (4) domain-based divisions. Furthermore, we propose TNT-OOD to model the complex interplay between Text aNd Topology using: 1) a novel cross-attention module to fuse local structure into node-level text representations, and 2) a HyperNetwork to generate node-specific transformation parameters. This aligns topological and semantic features of ID nodes, enhancing ID/OOD distinction across structural and textual shifts. Experiments on 11 datasets across four OOD scenarios demonstrate the nuanced challenge of TextTopoOOD for evaluating OOD detection in text-rich networks.

The reward model (RM) plays a crucial role in aligning Large Language Models (LLMs) with human preferences through Reinforcement Learning, where the Bradley-Terry (BT) objective has been recognized as simple yet powerful, specifically for pairwise preference learning. However, BT-based RMs often struggle to effectively distinguish between similar preference responses, leading to insufficient separation between preferred and non-preferred outputs. Consequently, they may easily overfit easy samples and cannot generalize well to Out-Of-Distribution (OOD) samples, resulting in suboptimal performance. To address these challenges, this paper introduces an effective enhancement to BT-based RMs through an adaptive margin mechanism. Specifically, we design to dynamically adjust the RM focus on more challenging samples through margins, based on both semantic similarity and model-predicted reward differences, which is approached from a distributional perspective solvable with Optimal Transport (OT). By incorporating these factors into a principled OT cost matrix design, our adaptive margin enables the RM to better capture distributional differences between chosen and rejected responses, yielding significant improvements in performance, convergence speed, and generalization capabilities. Experimental results across multiple benchmarks demonstrate that our method outperforms several existing RM techniques, showcasing enhanced performance in both In-Distribution (ID) and OOD settings. Moreover, RLHF experiments support our practical effectiveness in better aligning LLMs with human preferences.

Self-taught reasoners (STaRs) enhance the mathematical reasoning abilities of large language models (LLMs) by leveraging self-generated responses for self-training. Recent studies have incorporated reward models to guide response selection or decoding, aiming to obtain higher-quality data. However, they typically allocate a uniform sampling budget across all problems, overlooking the varying utility of problems at different difficulty levels. In this work, we conduct an empirical study and find that problems near the boundary of the LLM’s reasoning capability offer significantly greater learning utility than both easy and overly difficult ones. To identify and exploit such problems, we propose HS-STaR, a Hierarchical Sampling framework for Self-Taught Reasoners. Given a fixed sampling budget, HS-STaR first performs lightweight pre-sampling with a reward-guided difficulty estimation strategy to efficiently identify boundary-level problems. Subsequently, it dynamically reallocates the remaining budget toward these high-utility problems during a re-sampling phase, maximizing the generation of valuable training data. Extensive experiments across multiple reasoning benchmarks and backbone LLMs demonstrate that HS-STaR significantly outperforms other baselines without requiring additional sampling budget.

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SEPS: A Separability Measure for Robust Unlearning in LLMs
Wonje Jeung | Sangyeon Yoon | Albert No

Machine unlearning aims to selectively remove targeted knowledge from Large Language Models (LLMs), ensuring they forget specified content while retaining essential information. Existing unlearning metrics assess whether a model correctly answers retain queries and rejects forget queries, but they fail to capture real-world scenarios where forget queries rarely appear in isolation. In fact, forget and retain queries often coexist within the same prompt, making mixed-query evaluation crucial.We introduce SEPS, an evaluation framework that explicitly measures a model’s ability to both forget and retain information within a single prompt. Through extensive experiments across three benchmarks, we identify two key failure modes in existing unlearning methods: (1) untargeted unlearning indiscriminately erases both forget and retain content once a forget query appears, and (2) targeted unlearning overfits to single-query scenarios, leading to catastrophic failures when handling multiple queries. To address these issues, we propose Mixed Prompt (MP) unlearning, a strategy that integrates both forget and retain queries into a unified training objective. Our approach significantly improves unlearning effectiveness, demonstrating robustness even in complex settings with up to eight mixed forget and retain queries in a single prompt.

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TRUST-VL: An Explainable News Assistant for General Multimodal Misinformation Detection
Zehong Yan | Peng Qi | Wynne Hsu | Mong-Li Lee

Multimodal misinformation, encompassing textual, visual, and cross-modal distortions, poses an increasing societal threat that is amplified by generative AI. Existing methods typically focus on a single type of distortion and struggle to generalize to unseen scenarios. In this work, we observe that different distortion types share common reasoning capabilities while also requiring task-specific skills. We hypothesize that joint training across distortion types facilitates knowledge sharing and enhances the model’s ability to generalize. To this end, we introduce TRUST-VL, a unified and explainable vision-language model for general multimodal misinformation detection. TRUST-VL incorporates a novel Question-Aware Visual Amplifier module, designed to extract task-specific visual features. To support training, we also construct TRUST-Instruct, a large-scale instruction dataset containing 198K samples featuring structured reasoning chains aligned with human fact-checking workflows. Extensive experiments on both in-domain and zero-shot benchmarks demonstrate that TRUST-VL achieves state-of-the-art performance, while also offering strong generalization and interpretability.

Large language models (LLMs) can produce fluent but factually incorrect outputs and often have limited ability to attribute their claims to source material. This undermines their reliability, particularly in multi-hop and high-stakes domains such as medicine. We propose Tree-of-Quote (ToQ) prompting, a structured framework that decomposes complex questions into subquestions, generates quotes to support each step without retrieval, and selectively advances reasoning based on quote quality. We also introduce FQ-Score, a unified metric that captures answer correctness, attribution fidelity, and reasoning quality. Experiments on StrategyQA, 2WikiMultiHopQA, MuSiQue, MoreHopQA, and MedQA demonstrate that ToQ improves factuality and attribution over standard prompting baselines. To validate FQ-Score as a proxy for human judgment, we conduct two reader studies with clinicians on medical questions, and observe strong correlations. Both clinician scores and FQ-Scores also indicate a preference for ToQ over baselines due to a combination of greater correctness, completeness, and logical flow. Our results suggest ToQ is a promising approach for building more trustworthy and auditable LLM systems.

Large Language Models (LLMs) have demonstrated remarkable capabilities in various tasks, yet code generation remains a major challenge. Despite the abundant sources of code data, constructing high-quality training datasets at scale poses a significant challenge. Pre-training code data typically suffers from inconsistent data quality issues. Conversely, instruction-based methods which use a high-quality subset as seed samples suffer from limited task diversity. In this paper, we introduce UnitCoder, which directly supervises pre-training data quality through automatically generated unit tests, while ensuring the correctness via an iterative fix and refine flow. Code synthesized by UnitCoder benefits from both the diversity of pre-training corpora and the high quality ensured by unit test supervision. Our experiments demonstrate that models fine-tuned on our synthetic dataset exhibit consistent performance improvements. Our work presents a scalable approach that leverages model-generated unit tests to guide the synthesis of high-quality code data from pre-training corpora, demonstrating the potential for producing diverse and high-quality post-training data at scale. All code and data will be released.

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GRPO-LEAD: A Difficulty-Aware Reinforcement Learning Approach for Concise Mathematical Reasoning in Language Models
Jixiao Zhang | Chunsheng Zuo

Group Relative Policy Optimization (GRPO), which is widely adopted by R1-like reasoning models, has advanced mathematical reasoning. Nevertheless, GRPO faces challenges in reward sparsity, verbosity, and inadequate focus on problem difficulty. We propose GRPO-LEAD, enhancing GRPO with: (1) length-regularized rewards to encourage conciseness while maintaining accuracy; (2) explicit penalties for incorrect solutions to improve model precision; and (3) difficulty-aware advantage reweighting for robust generalization on challenging problems. Comprehensive evaluations demonstrate that GRPO-LEAD significantly improves reasoning accuracy, conciseness, and efficiency. Our approach achieves state-of-the-art performance for 14B-scale models, underscoring the synergy of our methods with appropriate model scale and high-quality data. Our source code, generated dataset, and models are available at https://github.com/aeroplanepaper/GRPO-LEAD.

Sequence labeling remains a significant challenge in low-resource, domain-specific scenarios, particularly for character-dense languages. Existing methods primarily focus on enhancing model comprehension and improving data diversity to boost performance. However, these approaches still struggle with inadequate model applicability and semantic distribution biases in domain-specific contexts. To overcome these limitations, we propose a novel framework that combines an LLM-based knowledge enhancement workflow with a span-based Knowledge Fusion for Rich and Efficient Extraction (KnowFREE) model. Our workflow employs explanation prompts to generate precise contextual interpretations of target entities, effectively mitigating semantic biases and enriching the model’s contextual understanding. The KnowFREE model further integrates extension label features, enabling efficient nested entity extraction without relying on external knowledge during inference. Experiments on multiple domain-specific sequence labeling datasets demonstrate that our approach achieves state-of-the-art performance, effectively addressing the challenges posed by low-resource settings.

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Rethinking Cross-Subject Data Splitting for Brain-to-Text Decoding
Congchi Yin | Qian Yu | Zhiwei Fang | Changping Peng | Piji Li

Recent major milestones have successfully reconstructed natural language from non-invasive brain signals (e.g. functional Magnetic Resonance Imaging (fMRI) and Electroencephalogram (EEG)) across subjects. However, we find current dataset splitting strategies for cross-subject brain-to-text decoding are wrong. Specifically, we first demonstrate that all current splitting methods suffer from data leakage problem, which refers to the leakage of validation and test data into training set, resulting in significant overfitting and overestimation of decoding models. In this study, we develop a right cross-subject data splitting criterion without data leakage for decoding fMRI and EEG signal to text. Some SOTA brain-to-text decoding models are re-evaluated correctly with the proposed criterion for further research.

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RCScore: Quantifying Response Consistency in Large Language Models
Dongjun Jang | Youngchae Ahn | Hyopil Shin

Current LLM evaluations often rely on a single instruction template, overlooking models’ sensitivity to instruction style—a critical aspect for real-world deployments. We present RCScore, a multi-dimensional framework quantifying how instruction formulation affects model responses. By systematically transforming benchmark problems into multiple instruction styles, RCScore reveals performance variations undetected by conventional metrics. Our experiments across ten LLMs on four reasoning benchmarks demonstrate that instruction style can shift accuracy by up to 16.7% points. We introduce Cross-Response Similarity (CRS), a method applying RCScore metrics to measure stylistic self-consistency, and establish its strong correlation with task accuracy, suggesting consistency as a valuable proxy for model reliability. Additional findings show that deterministic decoding produces more stylistically stable outputs, and model scale correlates positively with cross-style consistency. RCScore offers a principled approach to assess instruction robustness.

Misinformation spans various domains, but detection methods trained on specific domains often perform poorly when applied to others. With the rapid development of Large Language Models (LLMs), researchers have begun to utilize LLMs for cross-domain misinformation detection. However, existing LLM-based methods often fail to adequately analyze news in the target domain, limiting their detection capabilities. More importantly, these methods typically rely on manually designed decision rules, which are limited by domain knowledge and expert experience, thus limiting the generalizability of decision rules to different domains. To address these issues, we propose a Multi-Agent Framework for cross-domain misinformation detection with Automated Decision Rule Optimization (MARO). Under this framework, we first employs multiple expert agents to analyze target-domain news. Subsequently, we introduce a question-reflection mechanism that guides expert agents to facilitate higher-quality analysis. Furthermore, we propose a decision rule optimization approach based on carefully designed cross-domain validation tasks to iteratively enhance decision rule effectiveness across domains. Experimental results and analysis on commonly used datasets demonstrate that MARO achieves significant improvements over existing methods.

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OmniEval: An Omnidirectional and Automatic RAG Evaluation Benchmark in Financial Domain
Shuting Wang | Jiejun Tan | Zhicheng Dou | Ji-Rong Wen

Retrieval-augmented generation (RAG) has emerged as a key application of large language models (LLMs), especially in vertical domains where LLMs may lack domain-specific knowledge. This paper introduces OmniEval, an omnidirectional and automatic RAG benchmark for the financial domain, featured by its multi-dimensional evaluation framework: First, we categorize RAG scenarios by five task classes and 16 financial topics, leading to a matrix-based structured assessment for RAG evaluation; Next, we leverage a multi-dimensional evaluation data generation method that integrates GPT-4-based automatic generation and human annotation approaches, achieving an 87.47% acceptance ratio in human evaluations of generated instances; Further, we utilize a multi-stage evaluation pipeline to assess both retrieval and generation performance, resulting in an all-sided evaluation of the RAG pipeline. Finally, rule-based and LLM-based metrics are combined to build a multi-dimensional evaluation system, enhancing the reliability of assessments through fine-tuned LLM-based evaluators. Our omnidirectional evaluation experiments highlight the performance variations of RAG systems across diverse topics and tasks and reveal significant opportunities for RAG models to improve their capabilities in vertical domains. We open source the anonymous code of our benchmark at https://github.com/RUC-NLPIR/OmniEval.

Despite the impressive performance of large language models (LLMs) in general domains, they often underperform in specialized domains. Existing approaches typically rely on data synthesis methods and yield promising results by using unlabeled data to capture domain-specific features. However, these methods either incur high computational costs or suffer from performance limitations, while also demonstrating insufficient generalization across different tasks. To address these challenges, we propose AQuilt, a framework for constructing instruction-tuning data for any specialized domains from corresponding unlabeled data, including Answer, Question, Unlabeled data, Inspection, Logic, and Task type. By incorporating logic and inspection, we encourage reasoning processes and self-inspection to enhance model performance. Moreover, customizable task instructions enable high-quality data generation for any task. As a result, we construct a dataset of 703K examples to train a powerful data synthesis model. Experiments show that AQuilt is comparable to DeepSeek-V3 while utilizing just 17% of the production cost. Further analysis demonstrates that our generated data exhibits higher relevance to downstream tasks. Source code, models, and scripts are available at https://github.com/Krueske/AQuilt.

The rapid advancement of large language models has intensified public concerns about the potential misuse. Therefore, it is important to build trustworthy AI-generated text detection systems. Existing methods neglect stylistic modeling and mostly rely on static thresholds, which greatly limits the detection performance. In this paper, we propose the Mixture of Stylistic Experts (MoSEs) framework that enables stylistics-aware uncertainty quantification through conditional threshold estimation. MoSEs contain three core components, namely, the Stylistics Reference Repository (SRR), the Stylistics-Aware Router (SAR), and the Conditional Threshold Estimator (CTE). For input text, SRR can activate the appropriate reference data in SRR and provide them to CTE. Subsequently, CTE jointly models the linguistic statistical properties and semantic features to dynamically determine the optimal threshold. With a discrimination score, MoSEs yields prediction labels with the corresponding confidence level. Our framework achieves an average improvement 11.34% in detection performance compared to baselines. More inspiringly, MoSEs shows a more evident improvement 39.15% in the low-resource case. Our code is available at https://github.com/creator-xi/MoSEs.

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Merger-as-a-Stealer: Stealing Targeted PII from Aligned LLMs with Model Merging
Lin Lu | Zhigang Zuo | Ziji Sheng | Pan Zhou

Model merging has emerged as a promising approach for updating large language models (LLMs) by integrating multiple domain-specific models into a cross-domain merged model. Despite its utility and plug-and-play nature, unmonitored mergers can introduce significant security vulnerabilities, such as backdoor attacks and model merging abuse. In this paper, we identify a novel and more realistic attack surface where a malicious merger can extract targeted personally identifiable information (PII) from an aligned model with model merging. Specifically, we propose Merger-as-a-Stealer, a two-stage framework to achieve this attack: First, the attacker fine-tunes a malicious model to force it to respond to any PII-related queries. The attacker then uploads this malicious model to the model merging conductor and obtains the merged model. Second, the attacker inputs direct PII-related queries to the merged model to extract targeted PII. Extensive experiments demonstrate that Merger-as-a-Stealer successfully executes attacks against various LLMs and model merging methods across diverse settings, highlighting the effectiveness of the proposed framework. Given that this attack enables character-level extraction for targeted PII without requiring any additional knowledge from the attacker, we stress the necessity for improved model alignment and more robust defense mechanisms to mitigate such threats.

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Pragmatic Inference Chain (PIC) Improving LLMs’ Reasoning of Authentic Implicit Toxic Language
Xi Chen | Shuo Wang

The rapid development of large language models (LLMs) gives rise to ethical concerns about their performance, while opening new avenues for developing toxic language detection techniques. However, LLMs’ unethical output and their capability of detecting toxicity have primarily been tested on language data that do not demand complex meaning inference, such as the biased associations of ‘he’ with programmer and ‘she’ with household. Nowadays toxic language adopts a much more creative range of implicit forms, thanks to advanced censorship. In this study, we collect authentic toxic interactions that evade online censorship and that are verified by human annotators as inference-intensive. To evaluate and improve LLMs’ reasoning of the authentic implicit toxic language, we propose a new prompting method, Pragmatic Inference Chain (PIC), drawn on interdisciplinary findings from cognitive science and linguistics. The PIC prompting significantly improves the success rate of GPT-4o, Llama-3.1-70B-Instruct, DeepSeek-v2.5, and DeepSeek-v3 in identifying implicit toxic language, compared to five baseline prompts, such as CoT and rule-based baselines. In addition, it also facilitates the models to produce more explicit and coherent reasoning processes, hence can potentially be generalized to other inference-intensive tasks, e.g., understanding humour and metaphors.

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Beyond Demonstrations: Dynamic Vector Construction from Latent Representations
Wang Cai | Hsiu-Yuan Huang | Zhixiang Wang | Yunfang Wu

In-Context derived Vector (ICV) methods extract task-relevant representations from large language models (LLMs) and reinject them during inference, achieving comparable performance to few-shot In-Context Learning (ICL) without repeated demonstration processing. However, existing ICV methods remain sensitive to ICL-specific factors, often use coarse or semantically fragmented representations as the source of the vector, and rely on heuristic-based injection positions, limiting their applicability.To address these issues, we propose Dynamic Vector (DyVec), which incorporates an Exhaustive Query Rotation (EQR) strategy to extract robust semantically aggregated latent representations by mitigating variance introduced by ICL. It then applies Dynamic Latent Segmentation and Injection to adaptively partition representations based on task complexity and leverages REINFORCE-based optimization to learn optimal injection positions for each segment.Experiments results show that DyVec outperforms few-shot ICL, LoRA, and prior ICV baselines. Further analysis highlights the effectiveness of dynamically segmenting and injecting semantically aggregated latent representations. DyVec provides a lightweight and data-efficient solution for inference-time task adaptation.

Eliminating toxicity from Large Language Models (LLMs) is crucial for ensuring user safety. However, current methods have limitations in the analysis and utilization of toxic samples, failing to fully harness their potential. Through comparative analysis of toxic and safe samples, we discover that toxic samples exhibit diversity and, within this diversity, there lies specificity. These findings suggest that leveraging these characteristics of toxic samples could enhance the performance of algorithms in detoxifying LLMs. To this end, we propose a novel diverse detoxification framework, DivDetox, which comprises two innovative components: a Multi-Category-Induced Personalized Sample Generation (MPSG) strategy and a Scaled Contrastive DPO (SC-DPO) approach. The former is designed to elicit a variety of personalized toxic responses from the LLM, while the latter is constructed to precisely and fully utilize these toxic responses. Experiments on benchmark datasets across different model scales and different detoxification tasks verify the effectiveness of our architecture.

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LLM-Driven Implicit Target Augmentation and Fine-Grained Contextual Modeling for Zero-Shot and Few-Shot Stance Detection
Yanxu Ji | Jinzhong Ning | Yijia Zhang | Zhi Liu | Hongfei Lin

Stance detection aims to identify the attitude expressed in text towards a specific target. Recent studies on zero-shot and few-shot stance detection focus primarily on learning generalized representations from explicit targets. However, these methods often neglect implicit yet semantically important targets and fail to adaptively adjust the relative contributions of text and target in light of contextual dependencies. To overcome these limitations, we propose a novel two-stage framework: First, a data augmentation framework named Hierarchical Collaborative Target Augmentation (HCTA) employs Large Language Models (LLMs) to identify and annotate implicit targets via Chain-of-Thought (CoT) prompting and multi-LLM voting, significantly enriching training data with latent semantic relations. Second, we introduce DyMCA, a Dynamic Multi-level Context-aware Attention Network, integrating a joint text-target encoding and a content-aware mechanism to dynamically adjust text-target contributions based on context. Experiments on the benchmark dataset demonstrate that our approach achieves state-of-the-art results, confirming the effectiveness of implicit target augmentation and fine-grained contextual modeling.

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Dial-In LLM: Human-Aligned LLM-in-the-loop Intent Clustering for Customer Service Dialogues
Mengze Hong | Wailing Ng | Chen Jason Zhang | Yuanfeng Song | Di Jiang

Discovering customer intentions is crucial for automated service agents, yet existing intent clustering methods often fall short due to their reliance on embedding distance metrics and neglect of underlying semantic structures. To address these limitations, we propose an **LLM-in-the-loop (LLM-ITL)** intent clustering framework, integrating the language understanding capabilities of LLMs into conventional clustering algorithms. Specifically, this paper (1) examines the effectiveness of fine-tuned LLMs in semantic coherence evaluation and intent cluster naming, achieving over 95% accuracy aligned with human judgments; (2) designs an LLM-ITL framework that facilitates the iterative discovery of coherent intent clusters and the optimal number of clusters; and (3) introduces context-aware techniques tailored for customer service dialogue. Since existing English benchmarks lack sufficient semantic diversity and intent coverage, we further present a comprehensive Chinese dialogue intent dataset comprising over 100k real customer service calls with 1,507 human-annotated clusters. The proposed approaches significantly outperform LLM-guided baselines, achieving notable improvements in clustering quality, cost efficiency, and downstream applications. Combined with several best practices, our findings highlight the prominence of LLM-in-the-loop techniques for scalable dialogue data mining.

Large Language Models (LLMs) rely heavily on large-scale reasoning data, but as such data becomes increasingly scarce, model self-improvement offers a promising alternative. However, this process can lead to model collapse, as the model’s output becomes overly deterministic with reduced diversity. In this work, we identify a new risk beyond model collapse, which we term the Superficial Self-Improved Reasoners phenomenon. This phenomenon indicates that while self-improvement enhances in-domain (ID) reasoning accuracy, it degrades the model’s generalized reasoning capability on out-of-domain (OOD) datasets, as the model tends to memorize the training data. Our analyses of layer importance and parameter changes reveal that reasoning-critical layers receive fewer updates compared to less relevant layers during self-improvement. To address this, we propose Iterative Model Merging (IMM), which balances reasoning improvements and generalization by merging the weights of the original and self-improved models. IMM effectively mitigates model collapse and improves generalized reasoning capability. Code is available at https://github.com/xiangchi-yuan/merge_syn

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CARFT: Boosting LLM Reasoning via Contrastive Learning with Annotated Chain-of-Thought-based Reinforced Fine-Tuning
Wenqiao Zhu | Ji Liu | Rongjunchen Zhang | Haipang Wu | Yulun Zhang

Reasoning capability plays a significantly critical role in the the broad applications of Large Language Models (LLMs). To enhance the reasoning performance of LLMs, diverse Reinforcement Learning (RL)-based fine-tuning approaches have been proposed to address the limited generalization capability of LLMs trained solely via Supervised Fine-Tuning (SFT). Despite their effectiveness, two major limitations hinder the advancement of LLMs. First, vanilla RL-based approaches ignore annotated Chain-of-Thought (CoT) and incorporate unstable reasoning path sampling, which typically results in model collapse, unstable training process, and suboptimal performance. Second, existing SFT approaches generally overemphasize the annotated CoT, potentially leading to performance degradation due to insufficient exploitation of potential CoT. In this paper, we propose a Contrastive learning with annotated CoT-based Reinforced Fine-Tuning approach, i.e., CARFT, to enhance the reasoning performance of LLMs while addressing the aforementioned limitations. Specifically, we propose learning a representation for each CoT. Based on this representation, we design novel contrastive signals to guide the fine-tuning process. Our approach not only fully exploits the available annotated CoT but also stabilizes the fine-tuning procedure by incorporating an additional unsupervised learning signal. We conduct comprehensive experiments and in-depth analysis with three baseline approaches, two foundation models, and two datasets to demonstrate significant advantages of CARFT in terms of robustness, performance (up to 10.15%), and efficiency (up to 30.62%).

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QualBench: Benchmarking Chinese LLMs with Localized Professional Qualifications for Vertical Domain Evaluation
Mengze Hong | Wailing Ng | Chen Jason Zhang | Di Jiang

The rapid advancement of Chinese LLMs underscores the need for vertical-domain evaluations to ensure reliable applications. However, existing benchmarks often lack domain coverage and provide limited insights into the Chinese working context. Leveraging qualification exams as a unified framework for expertise evaluation, we introduce QualBench, the first multi-domain Chinese QA benchmark dedicated to localized assessment of Chinese LLMs. The dataset includes over 17,000 questions across six vertical domains, drawn from 24 Chinese qualifications to align with national policies and professional standards. Results reveal an interesting pattern of Chinese LLMs consistently surpassing non-Chinese models, with the Qwen2.5 model outperforming the more advanced GPT-4o, emphasizing the value of localized domain knowledge in meeting qualification requirements. The average accuracy of 53.98% reveals the current gaps in domain coverage within model capabilities. Furthermore, we identify performance degradation caused by LLM crowdsourcing, assess data contamination, and illustrate the effectiveness of prompt engineering and model fine-tuning, suggesting opportunities for future improvements through multi-domain RAG and Federated Learning. Data and code are publicly available at https://github.com/mengze-hong/QualBench.

The rapid growth of text-to-video (T2V) diffusion models has raised concerns about privacy, copyright, and safety due to their potential misuse in generating harmful or misleading content. These models are often trained on numerous datasets, including unauthorized personal identities, artistic creations, and harmful materials, which can lead to uncontrolled production and distribution of such content. To address this, we propose VideoEraser, a training-free framework that prevents T2V diffusion models from generating videos with undesirable concepts, even when explicitly prompted with those concepts. Designed as a plug-and-play module, VideoEraser can seamlessly integrate with representative T2V diffusion models via a two-stage process: Selective Prompt Embedding Adjustment (SPEA) and Adversarial-Resilient Noise Guidance (ARNG). We conduct extensive evaluations across four tasks, including object erasure, artistic style erasure, celebrity erasure, and explicit content erasure. Experimental results show that VideoEraser consistently outperforms prior methods regarding efficacy, integrity, fidelity, robustness, and generalizability. Notably, VideoEraser achieves state-of-the-art performance in suppressing undesirable content during T2V generation, reducing it by 46% on average across four tasks compared to baselines.

Visual Question Generation (VQG) research focuses predominantly on natural images while neglecting the diagram, which is a critical component in educational materials. To meet the needs of pedagogical assessment, we propose the Diagram-Driven Course Questions Generation (DDCQG) task and construct DiagramQG, a comprehensive dataset with 15,720 diagrams and 25,798 questions across 37 subjects and 371 courses. Our approach employs course and input text constraints to generate course-relevant questions about specific diagram elements. We reveal three challenges of DDCQG: domain-specific knowledge requirements across courses, long-tail distribution in course coverage, and high information density in diagrams. To address these, we propose the Hierarchical Knowledge Integration framework (HKI-DDCQG), which utilizes trainable CLIP for identifying relevant diagram patches, leverages frozen vision-language models for knowledge extraction, and generates questions with trainable T5. Experiments demonstrate that HKI-DDCQG outperforms existing models on DiagramQG while maintaining strong generalizability across natural image datasets, establishing a strong baseline for DDCQG.

The empathy dialogue system requires understanding emotions and their underlying causes. However, existing datasets mainly focus on emotion labels, while cause annotations are added post hoc through costly and subjective manual processes. This leads to three limitations: subjective bias in cause labels, weak rationality due to ambiguous cause-emotion relationships, and high annotation costs that hinder scalability. To address these challenges, we propose ECC (Emotion-Cause Conversation Dataset), a scalable dataset with 2.4K dialogues, which is also the first dialogue dataset where conversations and their emotion-cause labels are automatically generated synergistically during creation. We create an automatic extension framework EC-DD for ECC that utilizes knowledge and large language models (LLMs) to automatically generate conversations, and train a causality-aware empathetic response model CAER on this dataset. Experimental results show that ECC can achieve comparable or even superior performance to artificially constructed empathy dialogue datasets. Our code will be publicly released on https://github.com/Yuan-23/ECC

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ThoughtProbe: Classifier-Guided LLM Thought Space Exploration via Probing Representations
Zijian Wang | Chang Xu

This paper introduces ThoughtProbe, a novel inference-time framework that leverages the hidden reasoning features of Large Language Models (LLMs) to improve their reasoning performance. Unlike previous works that manipulate the hidden representations to steer LLM generation, we harness them as discriminative signals to guide the tree-structured response space exploration. In each node expansion, a classifier serves as a scoring and ranking mechanism that efficiently allocates computational resources by prioritizing higher score candidates for continuation. After completing the tree expansion, we collect answers from all branches to form a candidate answer pool. We then propose a branch-aggregation method that marginalizes over all supporting branches by aggregating their CoT scores, thereby identifying the optimal answer from the pool. Experimental results show that our framework’s comprehensive exploration not only covers valid reasoning chains but also effectively identifies them, achieving significant improvements across multiple arithmetic reasoning benchmarks.

Text-to-SQL, which maps natural language to SQL queries, has benefited greatly from recent advances in Large Language Models (LLMs). While LLMs offer various paradigms for this task, including prompting and supervised fine-tuning (SFT), SFT approaches still face challenges such as complex multi-stage pipelines and poor robustness to noisy schema information. To address these limitations, we present JOLT-SQL, a streamlined single-stage SFT framework that jointly optimizes schema linking and SQL generation via a unified loss. JOLT-SQL employs discriminative schema linking, enhanced by local bidirectional attention, alongside a confusion-aware noisy schema sampling strategy with selective attention to improve robustness under noisy schema conditions. Experiments on the Spider and BIRD benchmarks demonstrate that JOLT-SQL achieves state-of-the-art execution accuracy among comparable-size open-source models, while significantly improving both training and inference efficiency.

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DMDTEval: An Evaluation and Analysis of LLMs on Disambiguation in Multi-domain Translation
Zhibo Man | Yuanmeng Chen | Yujie Zhang | Jinan Xu

Currently, Large Language Models (LLMs) have achieved remarkable results in machine translation. However, their performance in multi-domain translation (MDT) is less satisfactory, the meanings of words can vary across different domains, highlighting the significant ambiguity inherent in MDT. Therefore, evaluating the disambiguation ability of LLMs in MDT, remains an open problem. To this end, we present an evaluation and analysis of LLMs on disambiguation in multi-domain translation (DMDTEval), our systematic evaluation framework consisting of three aspects: (1) we construct a translation test set with multi-domain ambiguous word annotation, (2) we curate a diverse set of disambiguation prompt strategies, and (3) we design precise disambiguation metrics, and study the efficacy of various prompt strategies on multiple state-of-the-art LLMs. We conduct comprehensive experiments across 4 language pairs and 13 domains, our extensive experiments reveal a number of crucial findings that we believe will pave the way and also facilitate further research in the critical area of improving the disambiguation of LLMs.

We present ScIRIFF (Scientific Resource for Instruction-Following and Finetuning), a dataset of 137K instruction-following instances for training and evaluation, covering 54 tasks. These tasks span five core scientific literature understanding capabilities: information extraction, summarization, question answering, claim verification, and classification. ScIRIFF is unique in being the only entirely expert-written, high-quality instruction-following dataset designed for extracting and synthesizing information from research literature across diverse scientific fields. It features complex instructions with long input contexts, detailed task descriptions, and structured outputs. To demonstrate its utility, we finetune a series of large language models (LLMs) using a mix of general domain and ScIRIFF instructions. On nine out-of-distribution held-out tasks (referred to as SciRIFF-Eval), LLMs finetuned on SciRIFF achieve 70.6% average improvement over our baselines trained only on general-domain instructions. ScIRIFF facilitates the development and evaluation of LLMs to help researchers navigate the rapidly growing body of scientific literature.

Grounded Multimodal Named Entity Recognition (GMNER), which aims to extract textual entities, their types, and corresponding visual regions from image-text data, has become a critical task in multimodal information extraction. However, existing methods face two major challenges. First, they fail to address the semantic ambiguity caused by polysemy and the long-tail distribution of datasets. Second, unlike visual grounding which provides descriptive phrases, entity grounding only offers brief entity names which carry less semantic information. Current methods lack sufficient semantic interaction between text and image, hindering accurate entity-visual region matching. To tackle these issues, we propose MAKAR, a Multi-Agent framework based Knowledge-Augmented Reasoning, comprising three agents: Knowledge Enhancement, Entity Correction, and Entity Reasoning Grounding. Specifically, in the named entity recognition phase, the Knowledge Enhancement Agent leverages a Multimodal Large Language Model (MLLM) as an implicit knowledge base to enhance ambiguous image-text content with its internal knowledge. For samples with low-confidence entity boundaries and types, the Entity Correction Agent uses web search tools to retrieve and summarize relevant web content, thereby correcting entities using both internal and external knowledge. In the entity grounding phase, the Entity Reasoning Grounding Agent utilizes multi-step Chain-of-Thought reasoning to perform grounding for each entity. Extensive experiments show that MAKAR achieves state-of-the-art performance on two benchmark datasets. Code is available at: https://github.com/Nikol-coder/MAKAR.

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VisCRA: A Visual Chain Reasoning Attack for Jailbreaking Multimodal Large Language Models
Bingrui Sima | Linhua Cong | Wenxuan Wang | Kun He

The emergence of Multimodal Large Reasoning Models (MLRMs) has enabled sophisticated visual reasoning capabilities by integrating reinforcement learning and Chain-of-Thought (CoT) supervision. However, while these enhanced reasoning capabilities improve performance, they also introduce new and underexplored safety risks. In this work, we systematically investigate the security implications of advanced visual reasoning in MLRMs. Our analysis reveals a fundamental trade-off: as visual reasoning improves, models become more vulnerable to jailbreak attacks. Motivated by this critical finding, we introduce VisCRA (Visual Chain Reasoning Attack), a novel jailbreak framework that exploits the visual reasoning chains to bypass safety mechanisms. VisCRA combines targeted visual attention masking with a two-stage reasoning induction strategy to precisely control harmful outputs. Extensive experiments demonstrate VisCRA’s significant effectiveness, achieving high attack success rates on leading closed-source MLRMs: 76.48% on Gemini 2.0 Flash Thinking, 68.56% on QvQ-Max, and 56.60% on GPT-4o. Our findings highlight a critical insight: the very capability that empowers MLRMs — their visual reasoning — can also serve as an attack vector, posing significant security risks. Warning: This paper contains unsafe examples.

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Investigating Neurons and Heads in Transformer-based LLMs for Typographical Errors
Kohei Tsuji | Tatsuya Hiraoka | Yuchang Cheng | Eiji Aramaki | Tomoya Iwakura

This paper investigates how LLMs encode inputs with typos. We hypothesize that specific neurons and attention heads recognize typos and fix them internally using local and global contexts. We introduce a method to identify typo neurons and typo heads that work actively when inputs contain typos. Our experimental results suggest the following: 1) LLMs can fix typos with local contexts when the typo neurons in either the early or late layers are activated, even if those in the other are not. 2) Typo neurons in the middle layers are the core of typo-fixing with global contexts. 3) Typo heads fix typos by widely considering the context not focusing on specific tokens. 4) Typo neurons and typo heads work not only for typo-fixing but also for understanding general contexts.

Large language model (LLM) agents have demonstrated remarkable potential in advancing scientific discovery. However, their capability in the fundamental yet crucial task of reproducing code from research papers, especially in the NLP domain, remains underexplored. This task includes unique complex reasoning challenges in the intellectual synthesis of abstract concepts and the comprehension of code repositories with interdependent files. Motivated by this gap, we present LMR-BENCH, a benchmark designed to systematically evaluate the capability of LLM agents on code reproduction from Language Modeling Research. It consists of 28 code reproduction tasks derived from 23 research papers published in top-tier NLP venues over the past five years, spanning nine fundamental categories. Models are provided with a research paper, a code repository containing one or more masked functions, and instructions for implementing these functions. We conduct extensive experiments in standard prompting and LLM agent settings with state-of-the-art LLMs, evaluating the accuracy of unit tests and performing LLM-based evaluation of code correctness. Experimental results reveal that even the most advanced models still exhibit persistent limitations in scientific reasoning and code synthesis, highlighting critical gaps in LLM agents’ ability to autonomously reproduce scientific research.

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RAV: Retrieval-Augmented Voting for Tactile Descriptions Without Training
Jinlin Wang | Yulong Ji | Hongyu Yang

Tactile perception is essential for human-environment interaction, and deriving tactile descriptions from multimodal data is a key challenge for embodied intelligence to understand human perception. Conventional approaches relying on extensive parameter learning for multimodal perception are rigid and computationally inefficient. To address this, we introduce Retrieval-Augmented Voting (RAV), a parameter-free method that constructs visual-tactile cross-modal knowledge directly. RAV retrieves similar visual-tactile data for given visual and tactile inputs and generates tactile descriptions through a voting mechanism. In experiments, we applied three voting strategies, SyncVote, DualVote and WeightVote, achieving performance comparable to large-scale cross-modal models without training. Comparative experiments across datasets of varying quality—defined by annotation accuracy and data diversity—demonstrate that RAV’s performance improves with higher-quality data at no additional computational cost. Code, and model checkpoints are opensourced at https://github.com/PluteW/RAV.

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Static Word Embeddings for Sentence Semantic Representation
Takashi Wada | Yuki Hirakawa | Ryotaro Shimizu | Takahiro Kawashima | Yuki Saito

We propose new static word embeddings optimised for sentence semantic representation. We first extract word embeddings from a pre-trained Sentence Transformer, and improve them with sentence-level principal component analysis, followed by either knowledge distillation or contrastive learning. During inference, we represent sentences by simply averaging word embeddings, which requires little computational cost. We evaluate models on both monolingual and cross-lingual tasks and show that our model substantially outperforms existing static models on sentence semantic tasks, and even surpasses a basic Sentence Transformer model (SimCSE) on a text embedding benchmark. Lastly, we perform a variety of analyses and show that our method successfully removes word embedding components that are not highly relevant to sentence semantics, and adjusts the vector norms based on the influence of words on sentence semantics.

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PropRAG: Guiding Retrieval with Beam Search over Proposition Paths
Jingjin Wang | Jiawei Han

Retrieval Augmented Generation (RAG) has become the standard approach for equipping Large Language Models (LLMs) with up-to-date knowledge. However, standard RAG, relying on independent passage retrieval, often fails to capture the interconnected nature of information required for complex, multi-hop reasoning. While structured RAG methods attempt to address this using knowledge graphs built from triples, we argue that the inherent context loss of triples (context collapse) limits the fidelity of the knowledge representation. We introduce PropRAG, a novel RAG framework that shifts from triples to context-rich propositions and introduces an efficient, LLM-free online beam search over proposition paths to discover multi-step reasoning chains. By coupling a higher-fidelity knowledge representation with explicit path discovery, PropRAG achieves state-of-the-art zero-shot Recall@5 and F1 scores on 2Wiki, HotpotQA, and MuSiQue, advancing non-parametric knowledge integration by improving evidence retrieval through richer representation and efficient reasoning path discovery.

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Rethinking Backdoor Detection Evaluation for Language Models
Jun Yan | Wenjie Jacky Mo | Xiang Ren | Robin Jia

Backdoor attacks, in which a model behaves maliciously when given an attacker-specified trigger, pose a major security risk for practitioners who depend on publicly released language models. As a countermeasure, backdoor detection methods aim to detect whether a released model contains a backdoor. While existing backdoor detection methods have high accuracy in detecting backdoored models on standard benchmarks, it is unclear whether they can robustly identify backdoors in the wild. In this paper, we examine the robustness of backdoor detectors by manipulating different factors during backdoor planting. We find that the success of existing methods based on trigger inversion or meta classifiers highly depends on how intensely the model is trained on poisoned data. Specifically, backdoors planted with more aggressive or more conservative training are significantly more difficult to detect than the default ones. Our results highlight a lack of robustness of existing backdoor detectors and the limitations in current benchmark construction.

The development of performant pre-trained models has driven the advancement of routing-based expert models tailored to specific tasks. However, these methods often favor generalization over performance on held-in tasks. This limitation adversely impacts practical applicability, as real-world deployments require robust performance across both known and novel tasks. We observe that current token-level routing mechanisms neglect the global semantic context of the input task. To address this, we propose a novel method, Global and Local Instruction Driven Expert Router (GLIDER) that proposes a multi-scale routing mechanism, encompassing a semantic global router and a learned local router. The global router leverages recent LLMs’ semantic reasoning capabilities to generate task-specific instructions from the input query, guiding expert selection across all layers. This global guidance is complemented by a local router that facilitates token-level routing decisions within each module, enabling finer control and enhanced performance on unseen and challenging tasks. Our experiments using T5-based expert models for T0 and FLAN tasks demonstrate that Glider achieves substantially improved held-in performance while maintaining strong generalization on held-out tasks. Additionally, we perform ablations experiments to dive deeper into the components of Glider and plot routing distributions to show that Glider can effectively retrieve the correct expert for held-in tasks while also demonstrating compositional capabilities for held-out tasks. Our experiments highlight the importance of our multi-scale routing that leverages LLM-driven semantic reasoning for MoErging methods.

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CoVoGER: A Multilingual Multitask Benchmark for Speech-to-text Generative Error Correction with Large Language Models
Zhengdong Yang | Zhen Wan | Sheng Li | Chao-Han Huck Yang | Chenhui Chu

Large language models (LLMs) can rewrite the N-best hypotheses from a speech-to-text model, often fixing recognition or translation errors that traditional rescoring cannot. Yet research on generative error correction (GER) has been focusing on monolingual automatic speech recognition (ASR), leaving its multilingual and multitask potential underexplored. We introduce CoVoGER, a benchmark for GER that covers both ASR and speech-to-text translation (ST) across 15 languages and 28 language pairs. CoVoGER is constructed by decoding Common Voice 20.0 and CoVoST-2 with Whisper of three model sizes and SeamlessM4T of two model sizes, providing 5-best lists obtained via a mixture of beam search and temperature sampling. We evaluated various instruction-tuned LLMs, including commercial models in zero-shot mode and open-sourced models with LoRA fine-tuning, and found that the mixture decoding strategy yields the best GER performance in most settings. CoVoGER will be released to promote research on reliable language-universal speech-to-text GER. The code and data for the benchmark are available at https://github.com/N-Orien/CoVoGER.

In this work, we propose FoRA-UA, a novel method that, using only 1–5% of the standard LoRA’s parameters, achieves state-of-the-art performance across a wide range of tasks. Specifically, we explore scenarios with extremely limited parameter budgets and derive two key insights: (1) fix-sized sparse frequency representations approximate small matrices more accurately; and (2) with a fixed number of trainable parameters, introducing a smaller intermediate representation to approximate larger matrices results in lower construction error. These findings form the foundation of our FoRA-UA method. By inserting a small intermediate parameter set, we achieve greater model compression without sacrificing performance. We evaluate FoRA-UA across diverse tasks, including natural language understanding (NLU), natural language generation (NLG), instruction tuning, and image classification, demonstrating strong generalisation and robustness under extreme compression.

Legal judgment prediction (LJP), which enables litigants and their lawyers to forecast judgment outcomes and refine litigation strategies, has emerged as a crucial legal NLP task. Existing studies typically utilize legal facts, i.e., facts that have been established by evidence and determined by the judge, to predict the judgment. However, legal facts are often difficult to obtain in the early stages of litigation, significantly limiting the practical applicability of fact-based LJP. To address this limitation, we propose a novel legal NLP task: legal fact prediction (LFP), which takes the evidence submitted by litigants for trial as input to predict legal facts, thereby empowering fact-based LJP technologies to make predictions in the absence of ground-truth legal facts. We also propose the first benchmark dataset, LFPBench, for evaluating the LFP task. Our extensive experiments on LFPBench demonstrate the effectiveness of LFP-empowered LJP and highlight promising research directions for LFP.

While large language models (LLMs) demonstrate remarkable capabilities across a wide range of tasks, they remain vulnerable to generating outputs that are potentially harmful. Red teaming, which involves crafting adversarial inputs to expose vulnerabilities, is a widely adopted approach for evaluating the robustness of these models. Prior studies have indicated that LLMs are susceptible to vulnerabilities exposed through multi-turn interactions as opposed to single-turn scenarios. Nevertheless, existing methods for multi-turn attacks mainly utilize a predefined dialogue pattern, limiting their effectiveness in realistic situations. Effective attacks require adaptive dialogue strategies that respond dynamically to the initial user prompt and the evolving context of the conversation. To address these limitations, we propose DAMON, a novel multi-turn jailbreak attack method. DAMON leverages Monte Carlo Tree Search (MCTS) to systematically explore multi-turn conversational spaces, efficiently identifying sub-instruction sequences that induce harmful responses. We evaluate DAMON’s efficacy across five LLMs and three datasets. Our experimental results show that DAMON can effectively induce undesired behaviors.

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Multilingual Prompting for Improving LLM Generation Diversity
Qihan Wang | Shidong Pan | Tal Linzen | Emily Black

Large Language Models (LLMs) are known to lack cultural representation and overall diversity in their generations, from expressing opinions to answering factual questions. To mitigate this problem, we propose multilingual prompting: a prompting method which generates several variations of a base prompt with added cultural and linguistic cues from several cultures, generates responses, and then combines the results. Building on evidence that LLMs have language-specific knowledge, multilingual prompting seeks to increase diversity by activating a broader range of cultural knowledge embedded in model training data. Through experiments across multiple models (GPT-4o, GPT-4o-mini, LLaMA 70B, and LLaMA 8B), we show that multilingual prompting consistently outperforms existing diversity-enhancing techniques such as high-temperature sampling, step-by-step recall, and persona prompting. Further analyses show that the benefits of multilingual prompting vary between high and low resource languages and across model sizes, and that aligning the prompting language with cultural cues reduces hallucination about culturally-specific information.

We present a novel, open-source social network simulation framework, MOSAIC, where generative language agents predict user behaviors such as liking, sharing, and flagging content. This simulation combines LLM agents with a directed social graph to analyze emergent deception behaviors and gain a better understanding of how users determine the veracity of online social content. By constructing user representations from diverse fine-grained personas, our system enables multi-agent simulations that model content dissemination and engagement dynamics at scale. Within this framework, we evaluate three different content moderation strategies with simulated misinformation dissemination, and we find that they not only mitigate the spread of non-factual content but also increase user engagement. In addition, we analyze the trajectories of popular content in our simulations, and explore whether simulation agents’ articulated reasoning for their social interactions truly aligns with their collective engagement patterns.

Counter-arguments (CAs) are a good means to improve the critical-thinking skills of learners, especially given that one has to thoroughly consider the logic of initial arguments (IA) when composing their CA. Although several tasks have been created for identifying the logical structure of CAs, no prior work has focused on capturing multiple interpretations of logical structures due to their complexity. In this work, we create CALSA+, a dataset consisting of 134 CAs annotated with 13 logical predicate questions. CALSA+ contains 1,742 instances annotated by 3 expert annotators (5,226 total annotations) with good agreement (Krippendorff 𝛼=0.46). Using CALSA+, we train a model with Reinforcement Learning with Verifiable Rewards (RLVR) to identify multiple logical interpretations and show that models trained with RLVR can perform on par with much bigger proprietary models. Our work is the first to attempt to annotate all the interpretations of logical structure on top of CAs. We publicly release our dataset to facilitate research in CA logical structure identification.

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LyapLock: Bounded Knowledge Preservation in Sequential Large Language Model Editing
Peng Wang | Biyu Zhou | Xuehai Tang | Jizhong Han | Songlin Hu

Large Language Models often contain factually incorrect or outdated knowledge, giving rise to model editing methods for precise knowledge updates. However, current mainstream locate-then-edit approaches exhibit a progressive performance decline during sequential editing, due to inadequate mechanisms for long-term knowledge preservation. To tackle this, we model the sequential editing as a constrained stochastic programming. Given the challenges posed by the cumulative preservation error constraint and the gradually revealed editing tasks, **LyapLock** is proposed. It integrates queuing theory and Lyapunov optimization to decompose the long-term constrained programming into tractable stepwise subproblems for efficient solving. This is the first model editing framework with rigorous theoretical guarantees, achieving asymptotic optimal editing performance while meeting the constraints of long-term knowledge preservation. Experimental results show that our framework scales sequential editing capacity to over 10,000 edits while stabilizing general capabilities and boosting average editing efficacy by 11.89% over SOTA baselines. Furthermore, it can be leveraged to enhance the performance of baseline methods. Our code is released on https://github.com/caskcsg/LyapLock.

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AlignX: Advancing Multilingual Large Language Models with Multilingual Representation Alignment
Mengyu Bu | Shaolei Zhang | Zhongjun He | Hua Wu | Yang Feng

Multilingual large language models (LLMs) possess impressive multilingual understanding and generation capabilities. However, their performance and cross-lingual alignment often lag for non-dominant languages. A common solution is to fine-tune LLMs on large-scale and more balanced multilingual corpus, but such approaches often lead to imprecise alignment and suboptimal knowledge transfer, struggling with limited improvements across languages. In this paper, we propose AlignX to bridge the multilingual performance gap, which is a two-stage representation-level framework for enhancing multilingual performance of pre-trained LLMs. In the first stage, we align multilingual representations with multilingual semantic alignment and language feature integration. In the second stage, we stimulate the multilingual capability of LLMs via multilingual instruction fine-tuning. Experimental results on several pre-trained LLMs demonstrate that our approach enhances LLMs’ multilingual general and cross-lingual generation capability. Further analysis indicates that AlignX brings the multilingual representations closer and improves the cross-lingual alignment.

Recent advances in reasoning with large language models (LLMs) have popularized Long Chain-of-Thought (LCoT), a strategy that encourages deliberate and step-by-step reasoning before producing a final answer. While LCoTs have enabled expert-level performance in complex tasks, how the internal structures of their reasoning chains drive, or even predict, the correctness of final answers remains a critical yet underexplored question. In this work, we present LCoT2Tree, an automated framework that converts sequential LCoTs into hierarchical tree structures and thus enables deeper structural analysis of LLM reasoning. Using graph neural networks (GNNs), we reveal that structural patterns extracted by LCoT2Tree, including exploration, backtracking, and verification, serve as stronger predictors of final performance across a wide range of tasks and models. Leveraging an explainability technique, we further identify critical thought patterns such as over-branching that account for failures. Beyond diagnostic insights, the structural patterns by LCoT2Tree support practical applications, including improving Best-of-N decoding effectiveness. Overall, our results underscore the critical role of internal structures of reasoning chains, positioning LCoT2Tree as a powerful tool for diagnosing, interpreting, and improving reasoning in LLMs.

Existing parameter-efficient fine-tuning (PEFT) methods for large language models (LLMs), such as LoRA and PiSSA, constrain model updates to low-rank subspaces, limiting their expressiveness and leading to suboptimal performance on complex tasks. To address this, we introduce **H**igh-rank **D**istributed **PiSSA (HD-PiSSA)**, a distributed PEFT approach that initializes **orthogonal adapters** across different devices and aggregates their delta updates collectively on (W) for fine-tuning. Unlike Data Parallel LoRA or PiSSA, which maintain identical adapters across all devices, HD-PiSSA assigns different principal components of the pre-trained weights to each GPU, significantly expanding the range of update directions. This results in over 16× higher effective updated ranks than data-parallel LoRA or PiSSA when fine-tuning on 8 GPUs with the same per-device adapter rank. Empirically, HD-PiSSA benefits from this extra optimization flexibility and outperforms both LoRA and PiSSA across a variety of challenging downstream tasks, including mathematics, code, and multi-task learning.

The rapid advancement of Large Language Models (LLMs) has significantly enhanced performance across various natural language processing (NLP) tasks, yet the high computational costs and latency associated with deploying such models continue to pose critical bottlenecks, limiting their broader applicability. To mitigate these challenges, we propose a dynamic hybrid inference framework, Firewall Routing, which efficiently selects between a strong and a weak LLMs based on the complexity of the query. A lightweight routing model is trained to optimize resource allocation by learning from response quality and preventing long-tail queries, which are often too hard to solve by LLMs, from being routed to the stronger model. Moreover, our method incorporates multiple sampling to enhance query evaluation reliability while leveraging Hard Blocking and Soft Blocking to handle long-tail queries along with refining labels for model selection. Extensive experiments show our method outperforms existing routing strategies by up to 5.29% in APGR, demonstrating state-of-the-art performance across multiple benchmarks.

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SPE Attention: Making Attention Equivariant to Semantic-Preserving Permutation for Code Processing
Chengyu Jiao | Shuhao Chen | Yu Zhang

Codes serve as the fundamental language for human to communicate with machines, and various Transformer-based models are trained to process codes in recent advancements. A unique symmetry of code is its semantic-preserving permutation, which allows certain lines to be rearranged without altering the overall meaning. To capture such symmetry, we propose a novel attention mechanism that incorporates semantic-preserving permutation equivariance, called the SPE attention. By leveraging the symmetry relationships within code, we introduce a directed layered graph to represent the code structure, and this graph is then summarized into a symmetry mask. The SPE attention integrates those symmetry masks, granting semantic-preserving permutations equivariance to the model. Experiments on various code related tasks, including code summarization and error detection, demonstrate the effectiveness of the proposed SPE attention.

Audio often serves as an auxiliary modality in video understanding tasks of audio-visual large language models (LLMs), merely assisting in the comprehension of visual information. However, a thorough understanding of videos significantly depends on auditory information, as audio offers critical context, emotional cues, and semantic meaning that visual data alone often lacks. This paper proposes an audio-centric video understanding benchmark (AVUT) to evaluate the video comprehension capabilities of multimodal LLMs with a particular focus on auditory information. AVUT introduces a suite of carefully designed audio-centric tasks, holistically testing the understanding of both audio content and audio-visual interactions in videos. Moreover, this work points out the text shortcut problem that largely exists in other benchmarks where the correct answer can be found from question text alone without needing videos. AVUT addresses this problem by proposing a answer permutation-based filtering mechanism.A thorough evaluation across a diverse range of open-source and proprietary multimodal LLMs is performed, followed by the analyses of deficiencies in audio-visual LLMs. Demos and data are available at https://github.com/lark-png/AVUT.

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TurboRAG: Accelerating Retrieval-Augmented Generation with Precomputed KV Caches for Chunked Text
Songshuo Lu | Hua Wang | Yutian Rong | Zhi Chen | Yaohua Tang

Current Retrieval-Augmented Generation (RAG) systems concatenate and process numerous retrieved document chunks for prefill which requires a large volume of computation, therefore leading to significant latency in time-to-first-token (TTFT). To reduce the computation overhead as well as TTFT, we introduce TurboRAG, a hybrid offline–online paradigm that (i) pre‐computes chunk‐level key-value (KV) caches, (ii) stitches them together at inference time using independent–attention and reordered‐RoPE techniques, and (iii) preserves answer quality without changing the model architecture. Hence, online computation of KV caches is eliminated during inference. Our approach is applicable to most existing large language models and their applications without any requirement in modification of models and inference systems. Experimental results across a suite of RAG benchmarks demonstrate that TurboRAG reduces TTFT by up to 9.4x compared to the conventional RAG systems (on an average of 8.6x), but reserving comparable performance to the standard RAG systems.

Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in vision-language understanding. Recently, with the integration of test-time scaling techniques, these models have also shown strong potential in visual reasoning. However, most existing reasoning approaches remain text-level in nature: MLLMs are prompted to explore various combinations of textual tokens via their underlying language model, while the visual input remains fixed throughout the reasoning process. This paradigm limits the model’s ability to fully exploit rich visual information, particularly when dealing with images containing numerous fine-grained elements. In such cases, vision-level reasoning becomes crucial—where models dynamically zoom into specific regions of the image to gather detailed visual cues necessary for accurate decision-making. In this paper, we propose Zoom Eye, a training-free, model-agnostic tree search algorithm tailored for vision-level reasoning. Zoom Eye treats an image as a hierarchical tree structure, where each child node represents a zoomed-in sub-region of its parent, and the root corresponds to the full image. The algorithm enables MLLMs to simulate human-like zooming behavior by navigating from root to leaf nodes in search of task-relevant visual evidence. We experiment on a series of elaborate high-resolution benchmarks and the results demonstrate that Zoom Eye not only consistently improves the performance of a series of MLLMs with large margin (e.g., InternVL2.5-8B increases by 15.71% and 17.69% on HR-Bench) but also enables small 3-8B MLLMs to outperform strong large models such as GPT-4o.

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Learning Like Humans: Advancing LLM Reasoning Capabilities via Adaptive Difficulty Curriculum Learning and Expert-Guided Self-Reformulation
Enci Zhang | Xingang Yan | Wei Lin | Tianxiang. Zhang | Lu Qianchun

Despite impressive progress in areas like mathematical reasoning, large language models still face challenges in consistently solving complex problems. Drawing inspiration from key human learning strategies, we propose two novel strategies to enhance the capability of large language models to solve these complex problems. First, Adaptive Difficulty Curriculum Learning (ADCL) is a novel curriculum learning strategy that tackles the Difficulty Shift phenomenon (i.e., a model’s perception of problem difficulty dynamically changes during training) by periodically re-estimating difficulty within upcoming data batches to maintain alignment with the model’s evolving capabilities. Second, Expert-Guided Self-Reformulation (EGSR) is a novel reinforcement learning strategy that bridges the gap between imitation learning and pure exploration by guiding models to reformulate expert solutions within their own conceptual framework, rather than relying on direct imitation, fostering deeper understanding and knowledge assimilation. Extensive experiments on challenging mathematical reasoning benchmarks, using Qwen2.5-7B as the base model, demonstrate that these human-inspired strategies synergistically and significantly enhance performance. Notably, their combined application improves performance over the standard Zero-RL baseline by 10% on the AIME24 benchmark and 16.6% on AIME25.

As demonstrated by the proprietary Large Language Models (LLMs) such as GPT and Claude series, LLMs have the potential to achieve remarkable proficiency across a wide range of domains, including law, medicine, finance, science, code, etc., all within a single model. These capabilities are further augmented during the Supervised Fine-Tuning (SFT) phase. Despite their potential, existing work mainly focuses on domain-specific enhancements during fine-tuning, the challenge of which lies in catastrophic forgetting of knowledge across other domains. In this study, we introduce **VersaTune**, a novel data composition framework designed for enhancing LLMs’ overall multi-domain capabilities during training. We begin with detecting the distribution of domain-specific knowledge within the base model, followed by the training data composition that aligns with the model’s existing knowledge distribution. During the subsequent training process, domain weights are dynamically adjusted based on their learnable potential and forgetting degree. Experimental results indicate that VersaTune is effective in multi-domain fostering, with an improvement of 29.77% in the overall multi-ability performances compared to uniform domain weights. Furthermore, we find that Qwen-2.5-32B + VersaTune even surpasses frontier models, including GPT-4o, Claude3.5-Sonnet and DeepSeek-V3 by 0.86%, 4.76% and 4.60%. Additionally, in scenarios where flexible expansion of a specific domain is required, VersaTune reduces the performance degradation in other domains by 38.77%, while preserving the training efficacy of the target domain.

Unmanned Aerial Vehicle (UAV) Vision-and-Language Navigation (VLN) is vital for applications such as disaster response, logistics delivery, and urban inspection. However, existing methods often struggle with insufficient multimodal fusion, weak generalization, and poor interpretability. To address these challenges, we propose FlightGPT, a novel UAV VLN framework built upon Vision-Language Models (VLMs) with powerful multimodal perception capabilities. We design a two-stage training pipeline: first, Supervised Fine-Tuning (SFT) using high-quality demonstrations to improve initialization and structured reasoning; then, Group Relative Policy Optimization (GRPO) algorithm, guided by a composite reward that considers goal accuracy, reasoning quality, and format compliance, to enhance generalization and adaptability. Furthermore, FlightGPT introduces a Chain-of-Thought (CoT)-based reasoning mechanism to improve decision interpretability. Extensive experiments on the city-scale dataset CityNav demonstrate that FlightGPT achieves state-of-the-art performance across all scenarios, with a 9.22% higher success rate than the strongest baseline in unseen environments. Our implementation is publicly available.

Multimodal large language models (MLLMs) typically extract visual features from the final layers of a pretrained Vision Transformer (ViT). This widespread deep-layer bias, however, is largely driven by empirical convention rather than principled analysis. While prior studies suggest that different ViT layers capture different types of information—shallower layers focusing on fine visual details and deeper layers aligning more closely with textual semantics, the impact of this variation on MLLM performance remains underexplored. We present the first comprehensive study of visual layer selection for MLLMs, analyzing representation similarity across ViT layers to establish shallow, middle, and deep layer groupings. Through extensive evaluation of MLLMs (1.4B–7B parameters) across 10 benchmarks encompassing 60+ tasks, we find that while deep layers excel in semantic-rich tasks like OCR, shallow and middle layers significantly outperform them on fine-grained visual tasks including counting, positioning, and object localization. Building on these insights, we propose a lightweight feature fusion method that strategically incorporates shallower layers, achieving consistent improvements over both single-layer and specialized fusion baselines. Our work offers the first principled study of visual layer selection in MLLMs, showing that MLLMs can often see better when they look shallower.

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LoSiA: Efficient High-Rank Fine-Tuning via Subnet Localization and Optimization
Xujia Wang | Yunjia Qi | Bin Xu

Parameter-Efficient Fine-Tuning (PEFT) methods, such as LoRA, significantly reduce the number of trainable parameters by introducing low-rank decomposition matrices. However, existing methods perform extensive matrix multiplications in domain specialization tasks, resulting in computational inefficiency and sub-optimal fine-tuning performance. Hence, we propose LoSiA (**Lo**w-Resources **S**ubnet **I**ntegration **A**daptation), an innovative method that dynamically localizes and optimizes critical parameters during the training process. Specifically, it identifies a sub-network using gradient sparsity analysis and optimizes it as the trainable target. This design enables effective high-rank adaptation by updating only the sub-network parameters, reducing the additional matrix multiplication. We also present LoSiA-Pro, a faster implementation of LoSiA, which reduces the training latency by about 27% compared to LoRA. Extensive evaluations show that our method achieves minimal performance drop compared to full fine-tuning, while requiring the least training time across domain specialization and common-sense reasoning tasks. Further analysis shows that LoSiA also reduces forgetting during continued training.

Logit-based LLM watermarking traces and verifies AI-generated content by maintaining green and red token lists and increasing the likelihood of green tokens during generation. However, it struggles in low-entropy scenarios, where predictable outputs make green token selection difficult without disrupting natural text flow. Existing approaches address this by assuming access to the original LLM to calculate entropy and selectively watermark high-entropy tokens. However, these methods face two major challenges: (1) high computational costs and detection delays due to reliance on the original LLM, and (2) potential risks of model leakage. To address these limitations, we propose Invisible Entropy (IE), a watermarking paradigm designed to enhance both safety and efficiency. Instead of relying on the original LLM, IE introduces a lightweight feature extractor and an entropy tagger to predict whether the entropy of the next token is high or low. Furthermore, based on theoretical analysis, we developed a threshold navigator that adaptively sets entropy thresholds. It identifies a threshold where the watermark ratio decreases as the green token count increases, enhancing the naturalness of the watermarked text and improving detection robustness. Experiments on HumanEval and MBPP datasets demonstrate that IE reduces parameter size by 99% while achieving performance on par with state-of-the-art methods: https://anonymous.4open.science/r/IE-Official.

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Measuring Bias or Measuring the Task: Understanding the Brittle Nature of LLM Gender Biases
Bufan Gao | Elisa Kreiss

As LLMs are increasingly applied in socially impactful settings, concerns about gender bias have prompted growing efforts both to measure and mitigate such bias. These efforts often rely on evaluation tasks that differ from natural language distributions, as they typically involve carefully constructed task prompts that overtly or covertly signal the presence of gender bias-related content. In this paper, we examine how signaling the evaluative purpose of a task impacts measured gender bias in LLMs.Concretely, we test models under prompt conditions that (1) make the testing context salient, and (2) make gender-focused content salient. We then assess prompt sensitivity across four task formats with both token-probability and discrete-choice metrics. We find that prompts that more clearly align with (gender bias) evaluation framing elicit distinct gender output distributions compared to less evaluation-framed prompts. Discrete-choice metrics further tend to amplify bias relative to probabilistic measures. These findings do not only highlight the brittleness of LLM gender bias evaluations but open a new puzzle for the NLP benchmarking and development community: To what extent can well-controlled testing designs trigger LLM testing mode performance, and what does this mean for the ecological validity of future benchmarks.

Recent works have revealed the great potential of speculative decoding in accelerating the autoregressive generation process of large language models. The success of these methods relies on the alignment between draft candidates and the sampled outputs of the target model. Existing methods mainly achieve draft-target alignment with training-based methods, e.g., EAGLE, Medusa, involving considerable training costs. In this paper, we present a training-free alignment-augmented speculative decoding algorithm. We propose alignment sampling, which leverages output distribution obtained in the prefilling phase to provide more aligned draft candidates. To further benefit from high-quality but non-aligned draft candidates, we also introduce a simple yet effective flexible verification strategy. Through an adaptive probability threshold, our approach can improve generation accuracy while further improving inference efficiency. Experiments on 8 datasets (including question answering, summarization and code completion tasks) show that our approach increases the average generation score by 3.3 points for the LLaMA3 model. Our method achieves a mean acceptance length up to 2.39 and speed up generation by 2.23×.

Process-supervised reward models serve as a fine-grained function that provides detailed step-wise feedback to model responses, facilitating effective selection of reasoning trajectories for complex tasks. Despite its advantages, evaluation on PRMs remains less explored, especially in the multimodal domain. To address this gap, this paper first benchmarks current vision large language models (VLLMs) as two types of reward models: output reward models (ORMs) and process reward models (PRMs) on multiple vision-language benchmarks, which reveal that neither ORM nor PRM consistently outperforms across all tasks, and superior VLLMs do not necessarily yield better rewarding performance. To further advance evaluation, we introduce ViLBench, a vision-language benchmark designed to require intensive process reward signals. Notably, OpenAI’s GPT-4o with Chain-of-Thought (CoT) achieves only 27.3% accuracy, challenging current VLLMs. Lastly, we preliminarily showcase a promising pathway towards bridging the gap between general VLLMs and reward models—by collecting 73.6K vision-language process reward data using an enhanced tree-search algorithm, our 3B model is able to achieve an average improvement of 3.3% over standard CoT and up to 2.5% compared to its untrained counterpart on ViLBench by selecting OpenAI o1’s generations. We will release our code, model, and data at https://ucsc-vlaa.github.io/ViLBench.

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Keep Security! Benchmarking Security Policy Preservation in Large Language Model Contexts Against Indirect Attacks in Question Answering
Hwan Chang | Yumin Kim | Yonghyun Jun | Hwanhee Lee

As Large Language Models (LLMs) are increasingly deployed in sensitive domains such as enterprise and government, ensuring that they adhere to **user-defined security policies** within context is critical-especially with respect to information non-disclosure. While prior LLM studies have focused on general safety and socially sensitive data, large-scale benchmarks for **contextual security** preservation against attacks remain lacking. To address this, we introduce a novel large-scale benchmark dataset, **CoPriva**, evaluating LLM adherence to contextual non-disclosure policies in question answering. Derived from realistic contexts, our dataset includes explicit policies and queries designed as direct and challenging indirect attacks seeking prohibited information. We evaluate 10 LLMs on our benchmark and reveal a significant vulnerability: many models violate user-defined policies and leak sensitive information. This failure is particularly severe against indirect attacks, highlighting a critical gap in current LLM safety alignment for sensitive applications. Our analysis reveals that while models can often identify the correct answer to a query, they struggle to incorporate policy constraints during generation. In contrast, they exhibit a partial ability to revise outputs when explicitly prompted. Our findings underscore the urgent need for more robust methods to guarantee contextual security.

Mechanistic interpretability of large language models (LLMs) aims to uncover the internal processes of information propagation and reasoning. Sparse autoencoders (SAEs) have demonstrated promise in this domain by extracting interpretable and monosemantic features. However, prior works primarily focus on feature extraction from a single layer, failing to effectively capture activations that span multiple layers. In this paper, we introduce Route Sparse Autoencoder (RouteSAE), a new framework that integrates a routing mechanism with a shared SAE to efficiently extract features from multiple layers. It dynamically assigns weights to activations from different layers, incurring minimal parameter overhead while achieving high interpretability and flexibility for targeted feature manipulation. We evaluate RouteSAE through extensive experiments on Llama-3.2-1B-Instruct. Specifically, under the same sparsity constraint of 64, RouteSAE extracts 22.5% more features than baseline SAEs while achieving a 22.3% higher interpretability score. These results underscore the potential of RouteSAE as a scalable and effective method for LLM interpretability, with applications in feature discovery and model intervention. Our codes are available at https://github.com/swei2001/RouteSAEs.

We present Branch-Train-Stitch (BTS), an efficient and flexible training algorithm for combining independently trained large language model (LLM) experts into a single, capable generalist model. Following Li et al., we start with a single seed language model which is branched into domain-specific (e.g., coding or math) experts with continual pretraining. BTS combines experts into a generalist model using lightweight stitch layers, which are inserted between frozen experts and the seed LLM, and trained on a small datamix of the expert domains. Stitch layers enable the seed LLM to integrate representations from any number of experts during the forward pass, allowing it to generalize to new domains, despite remaining frozen. Because BTS does not alter the constituent LLMs, BTS provides a modular and flexible approach: experts can be easily removed and new experts can be added with only a small amount of training. Compared to alternative model merging approaches, BTS yields the best generalist performance on a variety of downstream tasks, retaining the specialized capabilities of each of the experts.

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CoCoA: Confidence- and Context-Aware Adaptive Decoding for Resolving Knowledge Conflicts in Large Language Models
Anant Khandelwal | Manish Gupta | Puneet Agrawal

Faithful generation in large language models (LLMs) is challenged by knowledge conflicts between parametric memory and external context. Existing contrastive decoding methods tuned specifically to handle conflict often lack adaptability and can degrade performance in low conflict settings. We introduce CoCoA (Confidence- and Context-Aware Adaptive Decoding), a novel token-level algorithm for principled conflict resolution and enhanced faithfulness. CoCoA resolves conflict by utilizing confidence-aware measures (entropy gap and contextual peakedness) and the generalized divergence between the parametric and contextual distributions. Crucially, CoCoA maintains strong performance even in low conflict settings. Extensive experiments across multiple LLMs on diverse Question Answering (QA), Summarization, and Long-Form Question Answering (LFQA) benchmarks demonstrate CoCoA’s state-of-the-art performance over strong baselines like AdaCAD. It yields significant gains in QA accuracy, up to 9.2 points on average compared to the strong baseline AdaCAD, and improves factuality in summarization and LFQA by up to 2.5 points on average across key benchmarks. Additionally, it demonstrates superior sensitivity to conflict variations. CoCoA enables more informed, context-aware, and ultimately more faithful token generation.

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R-Bind: Unified Enhancement of Attribute and Relation Binding in Text-to-Image Diffusion Models
Huixuan Zhang | Xiaojun Wan

Text-to-image models frequently fail to achieve perfect alignment with textual prompts, particularly in maintaining proper semantic binding between semantic elements in the given prompt. Existing approaches typically require costly retraining or focus on only correctly generating the attributes of entities (entity-attribute binding), ignoring the cruciality of correctly generating the relations between entities (entity-relation-entity binding), resulting in unsatisfactory semantic binding performance. In this work, we propose a novel training-free method R-Bind that simultaneously improves both entity-attribute and entity-relation-entity binding. Our method introduces three inference-time optimization losses that adjust attention maps during generation. Comprehensive evaluations across multiple datasets demonstrate our approach’s effectiveness, validity, and flexibility in enhancing semantic binding without additional training.

Supervised Fine-Tuning (SFT) Large Language Models (LLM) fundamentally rely on high-quality training data. While data selection and data synthesis are two common strategies to improve data quality, existing approaches often face limitations in static dataset curation that fail to adapt to evolving model capabilities. In this paper, we introduce **Middo**, a self-evolving **M**odel-**i**nformed **d**ynamic **d**ata **o**ptimization framework that uses model-aware data selection and context-preserving data refinement. Unlike conventional one-off filtering/synthesis methods, our framework establishes a closed-loop optimization system: (1) A self-referential diagnostic module proactively identifies suboptimal samples through tri-axial model signals - *loss patterns (complexity)*, *embedding cluster dynamics (diversity)*, and *self-alignment scores (quality)*; (2) An adaptive optimization engine then transforms suboptimal samples into pedagogically valuable training points while preserving semantic integrity; (3) This optimization process continuously evolves with model capability through dynamic learning principles. Experiments on multiple benchmarks demonstrate that our consistently enhances the quality of seed data and boosts LLM’s performance with improving accuracy by 7.15% on average while maintaining the original dataset scale. This work establishes a new paradigm for sustainable LLM training through dynamic human-AI co-evolution of data and models.

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Information Integration in Large Language Models is Gated by Linguistic Structural Markers
Wei Liu | Nai Ding

Language comprehension relies on integrating information across both local words and broader context. We propose a method to quantify the information integration window of large language models (LLMs) and examine how sentence and clause boundaries constrain this window. Specifically, LLMs are required to predict a target word based on either a local window (local prediction) or the full context (global prediction), and we use Jensen-Shannon (JS) divergence to measure the information loss from relying solely on the local window, termed the local-prediction deficit. Results show that integration windows of both humans and LLMs are strongly modulated by sentence boundaries, and predictions primarily rely on words within the same sentence or clause: The local-prediction deficit follows a power-law decay as the window length increases and drops sharply at the sentence boundary. This boundary effect is primarily attributed to linguistic structural markers, e.g., punctuation, rather than implicit syntactic or semantic cues. Together, these results indicate that LLMs rely on explicit structural cues to guide their information integration strategy.

Large Language Models (LLMs) can improve commonsense reasoning through generating intermediate knowledge. However, the effectiveness of this knowledge introspection is not always guaranteed. This paper first systematically investigates and reveals an **introspection paradox**: while simple introspection tends to benefit weaker models, it often degrades the performance of stronger ones, particularly on simpler tasks. Our deep analysis indicates that this paradox arises from a complex interplay among model capability, task difficulty and the quality of generated knowledge. Further interpretability analysis reveals the origins of low-quality knowledge generation. To better employ introspected knowledge in LLM, this paper proposes a training-free **Adaptive Introspection Strategy** that operates in two stages using only the model’s internal states: **Knowledge Detection**, which dynamically identifies and discards potentially low-quality knowledge, and **Knowledge Regeneration**, which employs attention smoothing to guide the model away from harmful failure modes during knowledge generation. Extensive experiments on five Llama models with different sizes and eight commonsense reasoning benchmarks demonstrate that our approach effectively mitigates the limitations of standard introspection and has consistent performance gains across almost all settings.

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GraDaSE: Graph-Based Dataset Search with Examples
Jing He | Mingyang Lv | Qing Shi | Gong Cheng

Dataset search is a specialized information retrieval task. In the emerging scenario of Dataset Search with Examples (DSE), the user submits a query and a few target datasets that are known to be relevant as examples. The retrieved datasets are expected to be relevant to the query and also similar to the target datasets. Distinguished from existing text-based retrievers, we propose a graph-based approach GraDaSE. Besides the textual metadata of the datasets, we identify their provenance-based and topic-based relationships to construct a graph, and jointly encode their structural and textual information for ranking candidate datasets. GraDaSE outperforms a variety of strong baselines on two test collections, including DataFinder-E that we construct.

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Confidence-guided Refinement Reasoning for Zero-shot Question Answering
Youwon Jang | Woo Suk Choi | Minjoon Jung | Minsu Lee | Byoung-Tak Zhang

We propose Confidence-guided Refinement Reasoning (C2R), a novel training-free framework applicable to question-answering (QA) tasks across text, image, and video domains. C2R strategically constructs and refines sub-questions and their answers (sub-QAs), deriving a better confidence score for the target answer. C2R first curates a subset of sub-QAs to explore diverse reasoning paths, then compares the confidence scores of the resulting answer candidates to select the most reliable final answer. Since C2R relies solely on confidence scores derived from the model itself, it can be seamlessly integrated with various existing QA models, demonstrating consistent performance improvements across diverse models and benchmarks. Furthermore, we provide essential yet underexplored insights into how leveraging sub-QAs affects model behavior, specifically analyzing the impact of both the quantity and quality of sub-QAs on achieving robust and reliable reasoning.

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DICE: Structured Reasoning in LLMs through SLM-Guided Chain-of-Thought Correction
Yiqi Li | Yusheng Liao | Zhe Chen | Yanfeng Wang | Yu Wang

When performing reasoning tasks with user-specific requirements, such as strict output formats, large language models (LLMs) often prioritize reasoning over adherence to detailed instructions. Fine-tuning LLMs on supervised datasets to address this is impractical due to high computational costs and limited parameter access. To tackle this, we propose DICE, a lightweight framework that guides small language models (SLMs) to refine LLMs’ outputs through chain-of-thought (CoT) correction. DICE decouples the process by first prompting LLMs to generate natural language responses, then using trained SLMs to analyze and refine these outputs to meet structured output specifications. This framework preserves LLMs’ broad knowledge and reasoning capabilities while ensuring the outputs conform to user demands. Specifically, DICE first constructs structured CoT adaptation datasets via a two-stage method and subsequently applies a dual-tuning strategy to fine-tune SLMs for generating structured outputs in an analyze-then-answer pattern. Experiments demonstrate that DICE improves the average format accuracy and content correctness of LLM outputs by 35.4% and 29.4%, respectively, achieving state-of-the-art (SOTA) performance over other competitive baselines.

The widespread deployment of large language models (LLMs) has intensified concerns around intellectual property (IP) protection, as model theft and unauthorized redistribution become increasingly feasible. To address this, model fingerprinting aims to embed verifiable ownership traces into LLMs. However, existing methods face inherent trade-offs between stealthness, robustness, and generalizability—being either detectable via distributional shifts, vulnerable to adversarial modifications, or easily invalidated once the fingerprint is revealed. In this work, we introduce CTCC, a novel rule-driven fingerprinting framework that encodes contextual correlations across multiple dialogue turns—such as counterfactual—rather than relying on token-level or single-turn triggers. CTCC enables fingerprint verification under black-box access while mitigating false positives and fingerprint leakage, supporting continuous construction under a shared semantic rule even if partial triggers are exposed. Extensive experiments across multiple LLM architectures demonstrate that CTCC consistently achieves stronger stealth and robustness than prior work. Our findings position CTCC as a reliable and practical solution for ownership verification in real-world LLM deployment scenarios.

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Realistic Training Data Generation and Rule Enhanced Decoding in LLM for NameGuess
Yikuan Xia | Jiazun Chen | Sujian Li | Jun Gao

The wide use of abbreviated column names (derived from English words or Chinese Pinyin) in database tables poses significant challenges for table-centric tasks in natural language processing and database management. Such a column name expansion task, referred to as the NameGuess task, has previously been addressed by fine-tuning Large Language Models (LLMs) on synthetically generated rule-based data. However, the current approaches yield suboptimal performance due to two fundamental limitations: 1) the rule-generated abbreviation data fails to reflect real-world distribution, and 2) the failure of LLMs to follow the rule-sensitive patterns in NameGuess persistently. For the data realism issue, we propose a novel approach that integrates a subsequence abbreviation generator trained on human-annotated data and collects non-subsequence abbreviations to improve the training set. For the rule violation issue, we propose a decoding system constrained on an automaton that represents the rules of abbreviation expansion. We extended the original English NameGuess test set to include non-subsequence and PinYin scenarios. Experimental results show that properly tuned 7/8B moderate-size LLMs with a refined decoding system can surpass the few-shot performance of state-of-the-art LLMs, such as the GPT-4 series. The code and data are presented in the supplementary material.

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EverTracer: Hunting Stolen Large Language Models via Stealthy and Robust Probabilistic Fingerprint
Zhenhua Xu | Meng Han | Wenpeng Xing

The proliferation of large language models (LLMs) has intensified concerns over model theft and license violations, necessitating robust and stealthy ownership verification. Existing fingerprinting methods either require impractical white-box access or introduce detectable statistical anomalies. We propose EverTracer, a novel gray-box fingerprinting framework that ensures stealthy and robust model provenance tracing. EverTracer is the first to repurpose Membership Inference Attacks (MIAs) for defensive use, embedding ownership signals via memorization instead of artificial trigger-output overfitting. It consists of Fingerprint Injection, which fine-tunes the model on any natural language data without detectable artifacts, and Verification, which leverages calibrated probability variation signal to distinguish fingerprinted models. This approach remains robust against adaptive adversaries, including input level modification, and model-level modifications. Extensive experiments across architectures demonstrate EverTracer’s state-of-the-art effectiveness, stealthness, and resilience, establishing it as a practical solution for securing LLM intellectual property.

Recent advancements in LLM alignment leverage token-level supervisions to perform fine-grained preference optimization. However, existing token-level alignment methods either optimize on all available tokens, which can be noisy and inefficient, or perform selective training with complex and expensive key token selection strategies. In this work, we propose Selective Preference Optimization (SePO), a novel selective alignment strategy that centers on efficient key token selection without requiring strong, fine-grained supervision signals. We theoretically prove the feasibility of Direct Preference Optimization (DPO) as token-level reward function estimators, which applies to any existing alignment datasets and enables cost-efficient token selection with small-scale model sizes and training data. We then train an oracle model with DPO on the target data and utilize the estimated reward function to score all tokens within the target dataset, where only the key tokens are selected to supervise the target policy model with a contrastive objective function. Extensive experiments on three public evaluation benchmarks show that SePO significantly outperforms competitive baseline methods by only optimizing on 30% key tokens with up to 60% reduction in GPU training hours. We also explore SePO as a new paradigm for weak-to-strong generalization, showing that weak oracle models effectively supervise strong policy models with up to 16.8 more parameters. SePO also selects useful supervision signals from out-of-distribution data, alleviating the over-optimization problem.

As Large Language Models (LLMs) expand across domains, LLM judges have become essential for systems evaluation. Current benchmarks typically compare system outputs against baselines.This baseline-mediated approach, though convenient, yields lower reliability than direct comparison between systems.We propose Arena-Lite which integrates tournament structure on top of head-to-head comparison.The application of a tournament structure and direct comparison eliminates the need for baseline outputs, reduces the number of required comparisons, and allows higher reliability in system rankings.We conducted two experiments: (1) controlled stochastic modeling and (2) empirical validation with a real LLM judge. Those experiments collectively demonstrate that Arena-Lite consistently achieves higher reliability with fewer comparisons, even with smaller datasets or weaker judges.We release an easy-to-use web demonstration and code to foster adoption of Arena-Lite, streamlining model selection across research and industry communities. Arena-Lite demo and code are available on https://huggingface.co/spaces/NCSOFT/ArenaLite

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Addressing Tokenization Inconsistency in Steganography and Watermarking Based on Large Language Models
Ruiyi Yan | Yugo Murawaki

Large language models have significantly enhanced the capacities and efficiency of text generation. On the one hand, they have improved the quality of text-based *steganography*. On the other hand, they have also underscored the importance of *watermarking* as a safeguard against malicious misuse. In this study, we focus on tokenization inconsistency (TI) between Alice and Bob in steganography and watermarking, where TI can undermine robustness. Our investigation reveals that the problematic tokens responsible for TI exhibit two key characteristics: **infrequency** and **temporariness**. Based on these findings, we propose two tailored solutions for TI elimination: *a stepwise verification* method for steganography and *a post-hoc rollback* method for watermarking. Experiments show that (1) compared to traditional disambiguation methods in steganography, directly addressing TI leads to improvements in fluency, imperceptibility, and anti-steganalysis capacity; (2) for watermarking, addressing TI enhances detectability and robustness against attacks.

Code translation is a crucial activity in the software development and maintenance process, and researchers have recently begun to focus on using pre-trained large language models (LLMs) for code translation. However, existing LLMs only learn the contextual semantics of code during pre-training, neglecting executability information closely related to the execution state of the code, which results in unguaranteed code executability and unreliable automated code translation. To address this issue, we propose ExeCoder, an LLM specifically designed for code translation, aimed at utilizing executability representations such as functional semantics, syntax structures, and variable dependencies to enhance the capabilities of LLMs in code translation. To evaluate the effectiveness of ExeCoder, we manually enhanced the widely used benchmark TransCoder-test, resulting in a benchmark called TransCoder-test-X that serves LLMs. Evaluation of TransCoder-test-X indicates that ExeCoder achieves state-of-the-art performance in code translation, surpassing existing open-source code LLMs by over 10.88% to 38.78% and over 27.44% to 42.97% on two metrics, and even outperforms the renowned closed-source LLM GPT-4o. Code is available at https://aka.ms/execoder

LLMs have shown impressive progress in natural language processing. However, they still face significant challenges in TableQA, where real-world complexities such as diverse table structures, multilingual data, and domain-specific reasoning are crucial. Existing TableQA benchmarks are often limited by their focus on simple flat tables and suffer from data leakage. Furthermore, most benchmarks are monolingual and fail to capture the cross-lingual and cross-domain variability in practical applications. To address these limitations, we introduce TableEval, a new benchmark designed to evaluate LLMs on realistic TableQA tasks. Specifically, TableEval includes tables with various structures (such as concise, hierarchical, and nested tables) collected from four domains (including government, finance, academia, and industry reports). Besides, TableEval features cross-lingual scenarios with tables in Simplified Chinese, Traditional Chinese, and English. To minimize the risk of data leakage, we collect all data from recent real-world documents. Considering that existing TableQA metrics fail to capture semantic accuracy, we further propose SEAT, a new evaluation framework that assesses the alignment between model responses and reference answers at the sub-question level. Experimental results have shown that SEAT achieves high agreement with human judgment. Extensive experiments on TableEval reveal critical gaps in the ability of state-of-the-art LLMs to handle these complex, real-world TableQA tasks, offering insights for future improvements.

The multilingual capabilities of large language models (LLMs) have attracted considerable attention over the past decade. Assessing the accuracy with which LLMs provide answers in multilingual contexts is essential for determining their level of multilingual proficiency. Nevertheless, existing multilingual benchmarks generally reveal severe drawbacks, such as overly translated content (translationese), the absence of difficulty control, constrained diversity, and disciplinary imbalance, making the benchmarking process unreliable and showing low convincingness. To alleviate those shortcomings, we introduce NOVA-63 (Native Omni-lingual Versatile Assessments of 63 Disciplines), a comprehensive, difficult multilingual benchmark featuring 93,536 questions sourced from native speakers across 14 languages and 63 academic disciplines. Leveraging a robust pipeline that integrates LLM-assisted formatting, expert quality verification, and multi-level difficulty screening, NOVA-63 is balanced on disciplines with consistent difficulty standards while maintaining authentic linguistic elements. Extensive experimentation with current LLMs has shown significant insights into cross-lingual consistency among language families, and exposed notable disparities in models’ capabilities across various disciplines. This work provides valuable benchmarking data for the future development of multilingual models. Furthermore, our findings underscore the importance of moving beyond overall scores and instead conducting fine-grained analyses of model performance.

Retrieval-Augmented Generation (RAG) has emerged as a promising approach to address key limitations of Large Language Models (LLMs), such as hallucination, outdated knowledge, and lacking reliable reference. However, current RAG frameworks often struggle with identifying whether retrieved documents meaningfully contribute to answer generation. This shortcoming makes it difficult to filter out irrelevant or even misleading content, which notably impacts the final performance. In this paper, we propose Document Information Gain (DIG), a novel metric designed to quantify the contribution of retrieved documents to correct answer generation. DIG measures a document’s value by computing the difference of LLM’s generation confidence with and without the document augmented. Further, we introduce InfoGain-RAG, a framework that leverages DIG scores to train a specialized reranker, which prioritizes each retrieved document from exact distinguishing and accurate sorting perspectives. This approach can effectively filter out irrelevant documents and select the most valuable ones for better answer generation. Extensive experiments across various models and benchmarks demonstrate that InfoGain-RAG can significantly outperform existing approaches, on both single and multiple retrieval paradigm. Specifically on NaturalQA, it achieves the improvements of 17.9%, 4.5%, 12.5% in exact match accuracy against naive RAG, self-reflective RAG and modern ranking-based RAG respectively, and even an average of 15.3% increment on advanced proprietary models GPT-4o across all datasets. These results demonstrate the feasibility of InfoGain-RAG as it can offer a reliable solution for RAG in multiple applications.

Video large language models (Vid-LLMs) have shown strong capabilities in understanding video content. However, their reliance on dense video token representations introduces substantial memory and computational overhead in both prefilling and decoding. To mitigate the information loss of recent video token reduction methods and accelerate the decoding stage of Vid-LLMs losslessly, we introduce SpecVLM, a training-free speculative decoding (SD) framework tailored for Vid-LLMs that incorporates staged video token pruning.Building on our novel finding that the draft model’s speculation exhibits low sensitivity to video token pruning, SpecVLM prunes up to 90% of video tokens to enable efficient speculation without sacrificing accuracy. To achieve this, we performs a two-stage pruning process: Stage I selects highly informative tokens guided by attention signals from the verifier (target model), while Stage II prunes remaining redundant ones in a spatially uniform manner.Extensive experiments on four video understanding benchmarks demonstrate the effectiveness and robustness of SpecVLM, which achieves up to 2.68× decoding speedup for LLaVA-OneVision-72B and 2.11× speedup for Qwen2.5-VL-32B. Code is available at https://github.com/zju-jiyicheng/SpecVLM.

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What Do Indonesians Really Need from Language Technology? A Nationwide Survey
Muhammad Dehan Al Kautsar | Lucky Susanto | Derry Tanti Wijaya | Fajri Koto

Despite emerging efforts to develop NLP for Indonesia’s 700+ local languages, progress remains costly due to the need for direct engagement with native speakers. However, it is unclear what these language communities truly need from language technology. To address this, we conduct a nationwide survey to assess the actual needs of native Indonesian speakers. Our findings indicate that addressing language barriers, particularly through machine translation and information retrieval, is the most critical priority. Although there is strong enthusiasm for advancements in language technology, concerns around privacy, bias, and the use of public data for AI training highlight the need for greater transparency and clear communication to support broader AI adoption.

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LEO-MINI: An Efficient Multimodal Large Language Model using Conditional Token Reduction and Mixture of Multi-Modal Experts
Yimu Wang | Mozhgan Nasr Azadani | Sean Sedwards | Krzysztof Czarnecki

Redundancy of visual tokens in multi-modal large language models (MLLMs) significantly reduces their computational efficiency. Recent approaches, such as resamplers and summarizers, have sought to reduce the number of visual tokens, but at the cost of visual reasoning ability. To address this, we propose LEO-Mini, a novel MLLM that significantly reduces the number of visual tokens and simultaneously boosts visual reasoning capabilities. For efficiency, LEO-Mini incorporates CoTR, a novel token reduction module to consolidate a large number of visual tokens into a smaller set of tokens, using the similarity between visual tokens, text tokens, and a compact learnable query. For effectiveness, to scale up the model’s ability with minimal computational overhead, LEO-Mini employs MMoE, a novel mixture of multi-modal experts module. MMoE employs a set of LoRA experts with a novel router to switch between them based on the input text and visual tokens instead of only using the input hidden state. MMoE also includes a general LoRA expert that is always activated to learn general knowledge for LLM reasoning. For extracting richer visual features, MMoE employs a set of vision experts trained on diverse domain-specific data. To demonstrate LEO-Mini’s improved efficiency and performance, we evaluate it against existing efficient MLLMs on various benchmark vision-language tasks.

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Confounding Factors in Relating Model Performance to Morphology
Wessel Poelman | Thomas Bauwens | Miryam de Lhoneux

The extent to which individual language characteristics influence tokenization and language modeling is an open question. Differences in morphological systems have been suggested as both unimportant and crucial to consider (Cotterell et al., 2018; Gerz et al., 2018a; Park et al., 2021, inter alia). We argue this conflicting evidence is due to confounding factors in experimental setups, making it hard to compare results and draw conclusions. We identify confounding factors in analyses trying to answer the question of whether, and how, morphology relates to language modeling. Next, we re-assess three hypotheses by Arnett & Bergen (2025) for why modeling agglutinative languages results in higher perplexities than fusional languages: they look at morphological alignment of tokenization, tokenization efficiency, and dataset size. We show that each conclusion includes confounding factors. Finally, we introduce token bigram metrics as an intrinsic way to predict the difficulty of causal language modeling, and find that they are gradient proxies for morphological complexity that do not require expert annotation. Ultimately, we outline necessities to reliably answer whether, and how, morphology relates to language modeling.

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Context-Aware Membership Inference Attacks against Pre-trained Large Language Models
Hongyan Chang | Ali Shahin Shamsabadi | Kleomenis Katevas | Hamed Haddadi | Reza Shokri

Membership Inference Attacks (MIAs) on pre-trained Large Language Models (LLMs) aim at determining if a data point was part of the model’s training set. Prior MIAs that are built for classification models fail at LLMs, due to ignoring the generative nature of LLMs across token sequences. In this paper, we present a novel attack on pre-trained LLMs that adapts MIA statistical tests to the perplexity dynamics of subsequences within a data point. Our method significantly outperforms prior approaches, revealing context-dependent memorization patterns in pre-trained LLMs.

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Formalizing Style in Personal Narratives
Gustave Cortal | Alain Finkel

Personal narratives are stories authors construct to make meaning of their experiences. Style, the distinctive way authors use language to express themselves, is fundamental to how these narratives convey subjective experiences. Yet there is a lack of a formal framework for systematically analyzing these stylistic choices. We present a novel approach that formalizes style in personal narratives as patterns in the linguistic choices authors make when communicating subjective experiences. Our framework integrates three domains: functional linguistics establishes language as a system of meaningful choices, computer science provides methods for automatically extracting and analyzing sequential patterns, and these patterns are linked to psychological observations. Using language models, we automatically extract linguistic features such as processes, participants, and circumstances. We apply our framework to hundreds of dream narratives, including a case study on a war veteran with post-traumatic stress disorder. Analysis of his narratives uncovers distinctive patterns, particularly how verbal processes dominate over mental ones, illustrating the relationship between linguistic choices and psychological states.

Large language models (LLMs) have shown remarkable performance across a range of NLP tasks. However, their strong instruction-following capabilities and inability to distinguish instructions from data content make them vulnerable to indirect prompt injection attacks. In such attacks, instructions with malicious purposes are injected into external data sources, such as web documents. When LLMs retrieve this injected data through tools, such as a search engine and execute the injected instructions, they provide misled responses. Recent attack methods have demonstrated potential, but their abrupt instruction injection often undermines their effectiveness. Motivated by the limitations of existing attack methods, we propose **TopicAttack**, which prompts the LLM to generate a fabricated conversational transition prompt that gradually shifts the topic toward the injected instruction, making the injection smoother and enhancing the plausibility and success of the attack. Through comprehensive experiments, TopicAttack achieves state-of-the-art performance, with an attack success rate (ASR) over 90% in most cases, even when various defense methods are applied. We further analyze its effectiveness by examining attention scores. We find that a higher injected-to-original attention ratio leads to a greater success probability, and our method achieves a much higher ratio than the baseline methods.

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PSET: a Phonetics-Semantics Evaluation Testbed
Gianluca Sperduti | Dong Nguyen

We introduce the Phonetics-Semantics Evaluation Testbed (PSET), a new English-based testbed to evaluate phonetic embeddings. Our testbed is built on the assumption that phonetic embeddings should always prioritize phonetics over semantics, and it therefore leverages homophones and synonyms.We use PSET to test three phonetic embedding models: articulatory embeddings, Phoneme2Vec, and XPhoneBERT. The phonetic-based embeddings solve the task with varying degrees of success, with Phoneme2Vec performing the best.We also test five recent LLMs, GPT-4o, Gemini 2.5 Flash, Llama 3.1-8B, OLMo-7B and OLMo 2-7B. Gemini 2.5 Flash performs better than the other models. With this testbed, we hope to advance the development and evaluation of phonetic embedding models.

Continued pretraining and instruction tuning on large-scale multilingual data have proven to be effective in scaling large language models (LLMs) to low-resource languages. However, the unaligned nature of such data limits its ability to effectively capture cross-lingual semantics. In contrast, multi-way parallel data, where identical content is aligned across multiple languages, provides stronger cross-lingual consistency and offers greater potential for improving multilingual performance. In this paper, we introduce a large-scale, high-quality multi-way parallel corpus, TED2025, based on TED Talks. The corpus spans 113 languages, with up to 50 languages aligned in parallel, ensuring extensive multilingual coverage. Using this dataset, we investigate best practices for leveraging multi-way parallel data to enhance LLMs, including strategies for continued pretraining, instruction tuning, and the analysis of key influencing factors. Experiments on six multilingual benchmarks show that models trained on multi-way parallel data consistently outperform those trained on unaligned multilingual data.

Aligning large language models to handle instructions with extremely long contexts has yet to be fully investigated. Previous studies have attempted to scale up the available data volume by synthesizing long instruction-following samples, as constructing such a dataset tends to be challenging for annotators. However, a lack of a well-defined strategy for ensuring data quality may introduce low-quality samples and restrict the model’s performance. Thus, we propose GATEAU, a novel framework to address the unique challenge of long context alignment by identifying the influential samples enriched with long-range dependency relations. Specifically, GATEAU measures the long-range dependencies from two essential aspects: the difficulty of generating target responses due to the long-range dependencies, and the difficulty of understanding long inputs due to such dependencies. Comprehensive experiments indicate that GATEAU effectively identifies influential samples and the model trained on these selected samples exhibits better instruction-following and long-context understanding capabilities.

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Teach Small Models to Reason by Curriculum Distillation
Wangyi Jiang | Yaojie Lu | Hongyu Lin | Xianpei Han | Le Sun

Large Reasoning Models (LRMs) show strong System-2-style reasoning, but at the cost of significant computational overhead. In contrast, efficient System-1-style Large Language Models (LLMs) often struggle on complex tasks. We identify a critical asymmetry between these two paradigms: LRMs can implicitly self-distill their own reasoning, solving hard problems with near System-1-style efficiency while retaining superior performance. LLMs, however, lack such deep internal modes and collapse when forced to rely on their own reasoning rather than imitating external traces. This asymmetry explains why direct distillation from strong LRMs to weaker LLMs often fails: student models struggle to learn from LRMs’ overly complex explicit reasoning and gain little from their overly compact implicit solutions. To address this, we introduce a two-stage curriculum distillation framework, which first builds a robust internal problem-solving student model and then teaches the student model to externalize this latent knowledge as explicit reasoning. On challenging mathematical benchmarks, our method significantly outperforms single-stage baselines, creating compact models with strong reasoning ability.

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Enhancing Reasoning Abilities of Small LLMs with Cognitive Alignment
Wenrui Cai | Chengyu Wang | Junbing Yan | Jun Huang | Xiangzhong Fang

The reasoning capabilities of large language reasoning models (LRMs), such as OpenAI’s o1 and DeepSeek-R1, have seen substantial advancements through deep thinking. However, these enhancements come with significant resource demands, underscoring the need for training effective small reasoning models. A critical challenge is that small models possess different reasoning capacities and cognitive trajectories compared with their larger counterparts. Hence, directly distilling chain-of-thought (CoT) results from large LRMs to smaller ones can sometimes be ineffective and often requires a substantial amount of annotated data. In this paper, we first introduce a novel Critique-Rethink-Verify (CRV) system, designed for training smaller yet powerful LRMs. Our CRV system consists of multiple LLM agents, each specializing in unique abilities: (i) critiquing the CoT qualities according to the cognitive capabilities of smaller models, (ii) rethinking and refining these CoTs based on the critiques, and (iii) verifying the correctness of the refined results. Based on the CRV system, we further propose the Cognitive Preference Optimization (CogPO) algorithm to continuously enhance the reasoning abilities of smaller models by aligning their reasoning processes with their cognitive capacities. Comprehensive evaluations on challenging reasoning benchmarks demonstrate the efficacy of our CRV+CogPO framework, which outperforms other methods by a large margin.

Recent advances, such as DeepSeek R1-Zero, highlight the effectiveness of incentive training, a reinforcement learning paradigm that computes rewards solely based on the final answer part of a language model’s output, thereby encouraging the generation of intermediate reasoning steps. However, these methods fundamentally rely on external verifiers, which limits their applicability to domains like mathematics and coding, where such verifiers are readily available. Although reward models can serve as verifiers, they require high-quality annotated data and are costly to train.In this work, we propose NOVER, NO-VERifier Reinforcement Learning, a general reinforcement learning framework that requires only standard supervised fine-tuning data with no need for an external verifier. NOVER enables incentive training across a wide range of text-to-text tasks and outperforms the model of the same size distilled from large reasoning models such as DeepSeek R1 671B by 7.7%. Moreover, the flexibility of NOVER enables new possibilities for optimizing large language models, such as inverse incentive training.

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Genre Matters: How Text Types Interact with Decoding Strategies and Lexical Predictors in Shaping Reading Behavior
Lena Sophia Bolliger | Lena Ann Jäger

The type of a text profoundly shapes reading behavior, yet little is known about how different text types interact with word-level features and the properties of machine-generated texts and how these interactions influence how readers process language. In this study, we investigate how different text types affect eye movements during reading, how neural decoding strategies used to generate texts interact with text type, and how text types modulate the influence of word-level psycholinguistic features such as surprisal, word length, and lexical frequency. Leveraging EMTeC (Bolliger et al., 2025), the first eye-tracking corpus of LLM-generated texts across six text types and multiple decoding algorithms, we show that text type strongly modulates cognitive effort during reading, that psycholinguistic effects induced by word-level features vary systematically across genres, and that decoding strategies interact with text types to shape reading behavior. These findings offer insights into genre-specific cognitive processing and have implications for the human-centric design of AI-generated texts. Our code is publicly available at https://github.com/DiLi-Lab/Genre-Matters.

Relation Triplet Extraction (RTE) is a fundamental while challenge task in knowledge acquisition, which identifies and extracts all triplets from unstructured text. Despite the recent advancements, the deep integration of the entity-, relation- and triplet-specific information remains a challenge. In this paper, we propose a Graph-based Mixture-of-Experts mutual learning framework for RTE, namely RTE-GMoE, to address this limitation. As a model-agnostic framework, RTE-GMoE distinguishes itself by including and modeling the mutual interactions among three vital task-specific experts: entity expert, RTE expert, and relation expert. RTE expert corresponds to the main RTE task and can be implemented by any model and the other two correspond to the two auxiliary tasks: entity recognition and relation extraction. We construct an expert graph and achieve comprehensive and adaptive graph-based MoE interactions with a novel mutual learning mechanism. In our framework, these experts perform knowledge extractions collaboratively via dynamic information exchange and knowledge sharing. We conduct extensive experiments on four state-of-the-art backbones and evaluate them on several widely-used benchmarks. The results demonstrate that our framework brings consistent and promising improvements on all backbones and benchmarks. Component study and model analysis further verify the effectiveness and advantages of our method.

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Avoidance Decoding for Diverse Multi-Branch Story Generation
Kyeongman Park | Nakyeong Yang | Kyomin Jung

Large Language Models (LLMs) often generate repetitive and monotonous outputs, especially in tasks like story generation, due to limited creative diversity when given the same input prompt. To address this challenge, we propose a novel decoding strategy, ***Avoidance Decoding***, that modifies token logits by penalizing similarity to previously generated outputs, thereby encouraging more diverse multi-branch stories. This penalty adaptively balances two similarity measures: (1) Concept-level Similarity Penalty, which is prioritized in early stages to diversify initial story concepts, and (2) Narrative-level Similarity Penalty, which is increasingly emphasized later to ensure natural yet diverse plot development. Notably, our method achieves up to **2.6** times higher output diversity and reduces repetition by an average of 30% compared to strong baselines, while effectively mitigating text degeneration. Furthermore, we reveal that our method activates a broader range of neurons, demonstrating that it leverages the model’s intrinsic creative capacity.

In reasoning chains generated by large language models (LLMs), initial errors often propagate and undermine the reliability of the final conclusion. Current LLM-based error detection methods often fail to detect propagated errors because earlier errors can corrupt judgments of downstream reasoning. To better detect such errors, we introduce Autoregressive Reasoning Entailment Stability (ARES), a probabilistic framework that evaluates each reasoning step based solely on previously-verified premises. This inductive method yields a nuanced score for each step and provides certified statistical guarantees of its soundness, rather than a brittle binary label. ARES achieves state-of-the-art performance across four benchmarks (72.1% Macro-F1, +8.2 points) and demonstrates superior robustness on very long synthetic reasoning chains, where it excels at detecting propagated errors (90.3% F1, +27.6 points).

High-quality datasets are essential for building effective task-oriented dialogue (TOD) systems. The existing TOD datasets often present overly simplified interactions, where users incrementally express straightforward requests that can be managed with basic slot-value style dialogue states, such as “hotel-area = east.” However, this approach does not reflect real-life scenarios in which users may express complex constraints and preferences. To address this gap, in this paper, we propose SQLWOZ, a novel TOD dataset designed to capture complex, real-world user requirements. The user requirements in SQLWOZ include the four categories: 1) multiple values for a slot, 2) excluded values within a slot, 3) preferred or prioritized values, and 4) conditional values based on other conditions. We utilize SQL statements as a formalized and expressive representation of dialogue states within SQLWOZ. To evaluate the dataset, we adapt large language models as dialogue agents and conduct extensive experiments on the SQL-based dialogue state tracking, dialogue response generation and end-to-end TOD tasks. The experimental results demonstrate the complexity and quality of SQLWOZ, establishing it as a new benchmark for advancing TOD research.

Multimodal large language models (MLLMs) demonstrate impressive capabilities by integrating visual and textual information. However, the incorporation of visual modalities also introduces new and complex safety risks, rendering even the most advanced models vulnerable to sophisticated jailbreak attacks. This paper first analyzes the impact of inserting safety reasoning prompt on various aspects of the model. We find that this external method can help the model resist jailbreak attacks to some extent, but the model still fails to distinguish specific semantic scenarios, resulting in a significantly increased refusal rate for benign queries. Inspired by this, we propose a novel training framework, SURE (Safety Understanding and Reasoning Enhancement for Multimodal Large Language Models), designed to help models internalize chain-of-thought-based safety decision-making capabilities. Extensive experiments demonstrate that SURE significantly improves model safety while effectively avoiding over-defense, achieving a good balance between safety and generality. Finally, we create a large-scale multimodal safety reasoning dataset, MLLM-SCoT-Plus, to facilitate research on safety alignment in multimodal models.Our code and the dataset are publicly available at https://github.com/hfutml/SURE.

Knowledge distillation (KD) is crucial for compressing large text embedding models, but faces challenges when teacher and student models use different tokenizers (Cross-Tokenizer KD - CTKD). Vocabulary mismatches impede the transfer of relational knowledge encoded in deep representations, such as hidden states and attention matrices, which are vital for producing high-quality embeddings. Existing CTKD methods often focus on direct output alignment, neglecting this crucial structural information. We propose a novel framework tailored for CTKD embedding model distillation. We first map tokens one-to-one via Minimum Edit Distance (MinED). Then, we distill intra-model relational knowledge by aligning attention matrix patterns using Centered Kernel Alignment, focusing on the top-m most important tokens of the directly mapped tokens. Simultaneously, we align final hidden states via Optimal Transport with Importance-Scored Mass Assignment, which emphasizes semantically important token representations, based on importance scores derived from attention weights. We evaluate distillation from state-of-the-art embedding models (e.g., LLM2Vec, BGE) to a Bert-base-uncased model on embedding-reliant tasks such as text classification, sentence pair classification, and semantic textual similarity. Our proposed framework significantly outperforms existing CTKD baselines. By preserving attention structure and prioritizing key representations, our approach yields smaller, high-fidelity embedding models despite tokenizer differences.

Multimodal Large Language Models (MLLMs) are increasingly applied in Personalized Image Aesthetic Assessment (PIAA) as a scalable alternative to expert evaluations. However, their predictions may reflect subtle biases influenced by demographic factors such as gender, age, and education. In this work, we propose AesBiasBench, a benchmark designed to evaluate MLLMs along two complementary dimensions: (1) stereotype bias, quantified by measuring variations in aesthetic evaluations across demographic groups; and (2) alignment between model outputs and genuine human aesthetic preferences. Our benchmark covers three subtasks (Aesthetic Perception, Assessment, Empathy) and introduces structured metrics (IFD, NRD, AAS) to assess both bias and alignment. We evaluate 19 MLLMs, including proprietary models (e.g., GPT-4o, Claude-3.5-Sonnet) and open-source models (e.g., InternVL-2.5, Qwen2.5-VL). Results indicate that smaller models exhibit stronger stereotype biases, whereas larger models align more closely with human preferences. Incorporating identity information often exacerbates bias, particularly in emotional judgments. These findings underscore the importance of identity-aware evaluation frameworks in subjective vision-language tasks.

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DA-Pred: Performance Prediction for Text Summarization under Domain-Shift and Instruct-Tuning
Anum Afzal | Florian Matthes | Alexander Fabbri

Large Language Models (LLMs) often don’t perform as expected under Domain Shift or after Instruct-tuning. A reliable indicator of LLM performance in these settings could assist in decision-making. We present a method that uses the known performance in high-resource domains and fine-tuning settings to predict performance in low-resource domains or base models, respectively. In our paper, we formulate the task of performance prediction, construct a dataset for it, and train regression models to predict the said change in performance. Our proposed methodology is lightweight and, in practice, can help researchers & practitioners decide if resources should be allocated for data labeling and LLM Instruct-tuning.

Grounded Multimodal Named Entity Recognition (GMNER) is a new information extraction task. It requires models to extract named entities and ground them to real-world visual objects. Previous methods, relying on domain-specific fine-tuning, struggle with unseen multimodal entities due to limited knowledge and generalization. Recently, multimodal large language models (MLLMs) have demonstrated strong open-set abilities. However, their performance is hindered by the lack of in-domain knowledge due to costly training for GMNER datasets. To address these limitations, we propose **UnCo**, a two-stage Uncertainty-driven Collaborative framework that leverages the complementary strengths of small fine-tuned models and MLLMs. Specifically, **in stage one**, we equip the small model with a unified uncertainty estimation (UE) for multimodal entities. This enables the small model to express "I do not know" when recognizing unseen entities beyond its capabilities. Predictions with high uncertainty are then filtered and delegated to the MLLM. **In stage two**, an Uncertainty-aware Hierarchical Correction mechanism guides the MLLM to refine uncertain predictions using its open-domain knowledge. Ultimately, UnCo effectively retains the in-domain knowledge of small models while utilizing the capabilities of MLLMs to handle unseen samples. Extensive experiments demonstrate UnCo’s effectiveness on two GMNER benchmarks.

Recent work has demonstrated the remarkable potential of Large Language Models (LLMs) in test-time scaling. By making models think before answering, they are able to achieve much higher accuracy with extra inference computation.However, in many real-world scenarios, models are used under time constraints, where an answer should be given within a certain output length. It is unclear whether and how the reasoning ability of different LLMs remain effective under strict constraints.We take a first look at this problem by conducting an in-depth empirical study. Specifically, we test 30 LLMs on common reasoning datasets under a wide range of output length budgets, and we analyze the correlation between the inference accuracy and various properties including model type, model size, prompt style, etc. We also consider the mappings between token budgets and actual on-device latency budgets.The results have demonstrated several interesting findings regarding the budget-aware LLM reasoning ability that differ from the unconstrained situation, e.g. the optimal choices of either model size or prompt style change under different budgets. These findings offer timely evaluation to this area and practical guidance for users to deploy LLMs under real-world latency constraints.

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Enrich-on-Graph: Query-Graph Alignment for Complex Reasoning with LLM Enriching
Songze Li | Zhiqiang Liu | Zhengke Gui | Huajun Chen | Wen Zhang

Large Language Models (LLMs) exhibit strong reasoning capabilities in complex tasks. However, they still struggle with hallucinations and factual errors in knowledge-intensive scenarios like knowledge graph question answering (KGQA). We attribute this to the semantic gap between structured knowledge graphs (KGs) and unstructured queries, caused by inherent differences in their focuses and structures. Existing methods usually employ resource-intensive, non-scalable workflows reasoning on vanilla KGs, but overlook this gap. To address this challenge, we propose a flexible framework, Enrich-on-Graph (EoG), which leverages LLMs’ prior knowledge to enrich KGs, bridge the semantic gap between graphs and queries. EoG enables efficient evidence extraction from KGs for precise and robust reasoning, while ensuring low computational costs, scalability, and adaptability across different methods. Furthermore, we propose three graph quality evaluation metrics to analyze query-graph alignment in KGQA task, supported by theoretical validation of our optimization objectives. Extensive experiments on two KGQA benchmark datasets indicate that EoG can effectively generate high-quality KGs and achieve the state-of-the-art performance.

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Noise, Adaptation, and Strategy: Assessing LLM Fidelity in Decision-Making
Yuanjun Feng | Vivek Choudhary | Yash Raj Shrestha

Large language models (LLMs) are increasingly used for social-science simulations, yet most evaluations target task optimality rather than the variability and adaptation characteristic of human decision-making. We propose a process-oriented evaluation framework with progressive interventions (Intrinsicality, Instruction, and Imitation), and apply it to two classic economics tasks: the second-price auction and the newsvendor inventory problem.By default, LLMs adopt stable, conservative strategies that diverge from observed human behavior. Giving LLMs risk-framed instructions makes them behave more like humans. However, this also causes complex irregularities. Incorporating human decision trajectories via in-context learning further narrows distributional gaps, indicating that models can absorb human patterns. However, across all interventions, LLMs underexpress round-to-round variability relative to humans, revealing a persistent alignment gap in behavioral fidelity. Future evaluations of LLM-based social simulations should prioritize process-level realism.

Radiology reports are critical for clinical decision-making but often lack a standardized format, limiting both human interpretability and machine learning (ML) applications. While large language models (LLMs) have shown strong capabilities in reformatting clinical text, their high computational requirements, lack of transparency, and data privacy concerns hinder practical deployment. To address these challenges, we explore lightweight encoder-decoder models (<300M parameters)—specifically T5 and BERT2BERT—for structuring radiology reports from the MIMIC-CXR and CheXpert Plus datasets. We benchmark these models against eight open-source LLMs (1B–70B parameters), adapted using prefix prompting, in-context learning (ICL), and low-rank adaptation (LoRA) finetuning. Our best-performing lightweight model outperforms all LLMs adapted using prompt-based techniques on a human-annotated test set. While some LoRA-finetuned LLMs achieve modest gains over the lightweight model on the Findings section (BLEU 6.4%, ROUGE-L 4.8%, BERTScore 3.6%, F1-RadGraph 1.1%, GREEN 3.6%, and F1-SRR-BERT 4.3%), these improvements come at the cost of substantially greater computational resources. For example, LLaMA-3-70B incurred more than 400 times the inference time, cost, and carbon emissions compared to the lightweight model. These results underscore the potential of lightweight, task-specific models as sustainable and privacy-preserving solutions for structuring clinical text in resource-constrained healthcare settings.

We present PricingLogic, the first benchmarkthat probes whether Large Language Mod-els (LLMs) can reliably automate tourism-booking prices when multiple, overlapping farerules apply. Travel agencies are eager to of-fload this error-prone task to AI systems; how-ever, deploying LLMs without verified reliabil-ity could result in significant financial lossesand erode customer trust. PricingLogic com-prises 300 natural-language questions based onbooking requests derived from 42 real-worldpricing policies, spanning two levels of diffi-culty: (i) basic customer-type pricing and (ii)bundled-tour calculations involving interactingdiscounts. Evaluations of a line of LLMs re-veal a steep performance drop on the harder tier,exposing systematic failures in rule interpreta-tion and arithmetic reasoning. These resultshighlight that, despite their general capabilities,today’s LLMs remain unreliable for revenue-critical applications without further safeguardsor domain adaptation. Our code and dataset areavaliable in https://github.com/EIT-NLP/PricingLogic.

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EcoTune: Token-Efficient Multi-Fidelity Hyperparameter Optimization for Large Language Model Inference
Yuebin Xu | Zhiyi Chen | Zeyi Wen

Tuning inference hyperparameters, such as temperature and maximum output tokens, on downstream tasks can enhance inference performance. However, directly applying hyperparameter optimization to these hyperparameters is token-expensive. Multi-fidelity optimization improves HPO efficiency with low-fidelity evaluations, but its static scheduling strategies ignore token consumption, leading to high costs. To address these limitations, we propose a token-efficient multi-fidelity optimization method, which enhances inference performance and minimizes token usage. Our method is empowered by (i) a token-based fidelity definition with explicit token cost modeling on configurations; (ii) a novel Token-Aware Expected Improvement acquisition function that selects configurations based on performance gain per token; and (iii) a dynamic fidelity scheduling mechanism that adapts to real-time budget status. We evaluate our method on LLaMA-2 and LLaMA-3 series across MMLU, Humaneval, MedQA, and OpenBookQA. Our method improves over the HELM leaderboard by 7.1%, 24.3%, 21.9%, and 4.6%, respectively. Compared to existing multi-fidelity HPO baselines, our method reduces token consumption by over 80% while maintaining or surpassing performance, demonstrating the state-of-the-art token efficiency for inference-time optimization.

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Investigating Value-Reasoning Reliability in Small Large Language Models
Xia Du | Shuhan Sun | Pengyuan Liu | Dong Yu

Although small Large Language models (sLLMs) have been widely deployed in practical applications, little attention has been paid to their value-reasoning abilities, particularly in terms of reasoning reliability. To address this gap, we propose a systematic evaluation framework for assessing the Value-Reasoning Reliability of sLLMs. We define Value-Reasoning Reliability as comprising: (1) Output consistency under identical prompts, (2) Output Robustness under semantically equivalent prompts, (3) Maintaining stable value reasoning in the face of attacks, and (4) Consistency of value reasoning in open-ended value expression tasks. Our framework includes three core tasks: Repetition Consistency task, Interaction Stability task, and Open-ended Expression Consistency task. We further incorporate self-reported confidence scores to evaluate the model’s value reasoning reliability from two perspectives: the model’s self-awareness of its values, and its value-based decision-making. Our findings show that models vary significantly in their stability when responding to value-related questions. Moreover, we observe considerable output randomness, which is not always correlated with the self-reported confidence or expressed value preferences. This suggests that current models lack a reliable internal mechanism for stable value reasoning when addressing value-sensitive queries.

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Can LLMs Explain Themselves Counterfactually?
Zahra Dehghanighobadi | Asja Fischer | Muhammad Bilal Zafar

Explanations are an important tool for gaining insights into model behavior, calibrating user trust, and ensuring compliance.Past few years have seen a flurry of methods for generating explanations, many of which involve computing model gradients or solving specially designed optimization problems.Owing to the remarkable reasoning abilities of LLMs, *self-explanation*, i.e., prompting the model to explain its outputs has recently emerged as a new paradigm.We study a specific type of self-explanations, *self-generated counterfactual explanations* (SCEs).We test LLMs’ ability to generate SCEs across families, sizes, temperatures, and datasets. We find that LLMs sometimes struggle to generate SCEs. When they do, their prediction often does not agree with their own counterfactual reasoning.

The softmax function is crucial in Transformer attention, which normalizes each row of the attention scores with summation to one. **Usually, tokens with larger attention scores are important for the final prediction.However, the softmax function can face a gradient vanishing issue for such important tokens (e.g., probabilities close to one), leading to optimization difficulties for the important tokens so that the performance may not be better.**In this paper, we propose Self-Adjust Softmax (SA-Softmax) to address this issue by modifying softmax(z) to z ⋅ softmax(z) and its normalized variant ^{(z - min(z_\min,0))}⁄_{max(0,z_max)-min(z_min,0)} ⋅ softmax(z).We theoretically show that SA-Softmax provides enhanced gradient properties compared to the vanilla softmax function.Moreover, Attention can be seamlessly integrated into existing Transformer models to their attention mechanisms with minor adjustments.We conducted experiments to evaluate the empirical performance of Transformer models using compared to the vanilla softmax function. These experiments, involving models with up to 2.7 billion parameters, are conducted across diverse datasets, language tasks, and positional encoding methods.

Vision-Language Models (VLMs) generate discourse-level, multi-sentence visual descriptions, challenging text scene graph parsers built for single-sentence caption-to-graph mapping. Current approaches typically merge sentence-level parsing outputs for discourse input, often missing phenomena like cross-sentence coreference, resulting in fragmented graphs and degraded downstream VLM task performance. We introduce a new task, Discourse-level text Scene Graph parsing (DiscoSG), and release DiscoSG-DS, a dataset of 400 expert-annotated and 8,430 synthesised multi-sentence caption-graph pairs. Each caption averages 9 sentences, and each graph contains at least 3× more triples than those in existing datasets. Fine-tuning GPT-4o on DiscoSG-DS yields over 40% higher SPICE than the strongest sentence-merging baseline. However, its high inference cost and licensing restrict open-source use, and smaller fine-tuned open-source models (e.g., Flan-T5) perform poorly on dense graph generation. To bridge this gap, we propose DiscoSG-Refiner, which drafts a base graph using a seed parser and iteratively refines it with a second model, improving robustness for complex graph generation. Using two small fine-tuned Flan-T5-Base models, DiscoSG-Refiner improves SPICE by ~30% over the baseline while achieving 86× faster inference than GPT-4o. It also delivers consistent gains on downstream VLM tasks, including discourse-level caption evaluation and hallucination detection, outperforming alternative parsers. Code and data are available at https://github.com/ShaoqLin/DiscoSG .

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XAutoLM: Efficient Fine-Tuning of Language Models via Meta-Learning and AutoML
Ernesto Luis Estevanell Valladares | Suilan Estevez-Velarde | Yoan Gutierrez | Andrés Montoyo | Ruslan Mitkov

Experts in machine learning leverage domain knowledge to navigate decisions in model selection, hyperparameter optimization, and resource allocation. This is particularly critical for fine-tuning language models (LMs), where repeated trials incur substantial computational overhead and environmental impact. However, no existing automated framework simultaneously tackles the entire model selection and hyperparameter optimization (HPO) task for resource-efficient LM fine-tuning. We introduce XAutoLM, a meta-learning-augmented AutoML framework that reuses past experiences to optimize discriminative and generative LM fine-tuning pipelines efficiently. XAutoLM learns from stored successes and failures by extracting task- and system-level meta-features to bias its sampling toward valuable configurations and away from costly dead ends. On four text classification and two question-answering benchmarks, XAutoLM surpasses zero-shot optimizer’s peak F1 on five of six tasks, cuts mean evaluation time of pipelines by up to 4.5x, reduces search error ratios by up to sevenfold, and uncovers up to 50% more pipelines above the zero-shot Pareto front. In contrast, simpler memory-based baselines suffer negative transfer. We release XAutoLM and our experience store to catalyze resource-efficient, Green AI fine-tuning in the NLP community.

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UNCERTAINTY-LINE: Length-Invariant Estimation of Uncertainty for Large Language Models
Roman Vashurin | Maiya Goloburda | Preslav Nakov | Maxim Panov

Large Language Models (LLMs) have become indispensable tools across various applications, making it more important than ever to ensure the quality and the trustworthiness of their outputs. This has led to growing interest in uncertainty quantification (UQ) methods for assessing the reliability of LLM outputs. Many existing UQ techniques rely on token probabilities, which inadvertently introduces a bias with respect to the length of the output. While some methods attempt to account for this, we demonstrate that such biases persist even in length-normalized approaches. To address the problem, here we propose UNCERTAINTY-LINE (Length-INvariant Estimation), a simple debiasing procedure that regresses uncertainty scores on output length and uses the residuals as corrected, length-invariant estimates. Our method is post-hoc, model-agnostic, and applicable to a range of UQ measures. Through extensive evaluation on machine translation, summarization, and question-answering tasks, we demonstrate that UNCERTAINTY-LINE consistently improves over even nominally length-normalized UQ methods uncertainty estimates across multiple metrics and models. We release our code publicly at https://github.com/stat-ml/uncertainty-line

While reinforcement learning (RL) has demonstrated remarkable success in enhancing large language models (LLMs), it has primarily focused on single-turn tasks such as solving math problems. Training effective web agents for multi-turn interactions remains challenging due to the complexity of long-horizon decision-making across dynamic web interfaces. In this work, we present WebAgent-R1, a simple yet effective end-to-end multi-turn RL framework for training web agents. It learns directly from online interactions with web environments by asynchronously generating diverse trajectories, entirely guided by binary rewards depending on task success. Experiments on the WebArena-Lite benchmark demonstrate the effectiveness of WebAgent-R1, boosting the task success rate of Qwen-2.5-3B from 6.1% to 33.9% and LLaMA-3.1-8B from 8.5% to 44.8%, significantly outperforming existing state-of-the-art methods and strong proprietary models such as OpenAI o3. In-depth analyses reveal the effectiveness of the thinking-based prompting strategy and test-time scaling through increased interactions for web tasks. We further investigate different RL initialization policies by introducing two variants, namely WebAgent-R1-Zero and WebAgent-R1-CoT, which highlight the importance of the warm-up training stage (i.e., behavior cloning) and provide insights on incorporating long chain-of-thought (CoT) reasoning in web agents.

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Same evaluation, more tokens: On the effect of input length for machine translation evaluation using Large Language Models
Tobias Domhan | Dawei Zhu

Accurately evaluating machine-translated text remains a long-standing challenge, particularly for long documents. Recent work has shown that large language models (LLMs) can serve as reliable and interpretable sentence-level translation evaluators via MQM error span annotations. With modern LLMs supporting larger context windows, a natural question arises: can we feed entire document translations into an LLM for quality assessment? Ideally, evaluation should be invariant to text length, producing consistent error spans regardless of input granularity. However, our analysis shows that text length significantly impacts evaluation: longer texts lead to fewer error spans and reduced system ranking accuracy. To address this limitation, we evaluate several strategies, including granularity-aligned prompting, Focus Sentence Prompting (FSP), and a fine-tuning approach to better align LLMs with the evaluation task. The latter two methods largely mitigate this length bias, making LLMs more reliable for long-form translation evaluation.

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PAKTON: A Multi-Agent Framework for Question Answering in Long Legal Agreements
Raptopoulos Petros | Giorgos Filandrianos | Maria Lymperaiou | Giorgos Stamou

Contract review is a complex and time-intensive task that typically demands specialized legal expertise, rendering it largely inaccessible to non-experts. Moreover, legal interpretation is rarely straightforward—ambiguity is pervasive, and judgments often hinge on subjective assessments. Compounding these challenges, contracts are usually confidential, restricting their use with proprietary models and necessitating reliance on open-source alternatives. To address these challenges, we introduce PAKTON: a fully open-source, end-to-end, multi-agent framework with plug-and-play capabilities. PAKTON is designed to handle the complexities of contract analysis through collaborative agent workflows and a novel retrieval-augmented generation (RAG) component, enabling automated legal document review that is more accessible, adaptable, and privacy-preserving. Experiments demonstrate that PAKTON outperforms both general-purpose and pretrained models in predictive accuracy, retrieval performance, explainability, completeness, and grounded justifications as evaluated through a human study and validated with automated metrics.

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PoSum-Bench: Benchmarking Position Bias in LLM-based Conversational Summarization
Xu Sun | Lionel Delphin-Poulat | Christèle Tarnec | Anastasia Shimorina

Large language models (LLMs) are increasingly used for zero-shot conversation summarization, but often exhibit positional bias—tending to overemphasize content from the beginning or end of a conversation while neglecting the middle. To address this issue, we introduce PoSum-Bench, a comprehensive benchmark for evaluating positional bias in conversational summarization, featuring diverse English and French conversational datasets spanning formal meetings, casual conversations, and customer service interactions. We propose a novel semantic similarity-based sentence-level metric to quantify the direction and magnitude of positional bias in model-generated summaries, enabling systematic and reference-free evaluation across conversation positions, languages, and conversational contexts.Our benchmark and methodology thus provide the first systematic, cross-lingual framework for reference-free evaluation of positional bias in conversational summarization, laying the groundwork for developing more balanced and unbiased summarization models.

Large Reasoning Models (LRMs) perform strongly in complex reasoning tasks via Chain-of-Thought (CoT) prompting, but often suffer from verbose outputs, increasing computational overhead. Existing fine-tuning-based compression methods either operate post-hoc pruning, risking disruption to reasoning coherence, or rely on sampling-based selection, which fails to remove redundant content thoroughly. To address these limitations, this work begins by framing two key patterns of redundant reflection in LRMs—Confidence Deficit, wherein the model reflects on correct intermediate steps, and Termination Delay, where reflection continues after a verified, confident answer—through a confidence-guided perspective. Based on this, we introduce ConCISE (Confidence-guided Compression In Step-by-step Efficient Reasoning), a framework designed to generate concise reasoning chains, integrating Confidence Injection to boost reasoning confidence, and Early Stopping to terminate reasoning when confidence is sufficient. Extensive experiments demonstrate that compared to baseline methods, fine-tuning LRMs on ConCISE-generated data yields a better balance between compression and task performance, reducing length by up to ～50% under SimPO, while maintaining high task accuracy.

With rapid advancement and increasing accessibility of LLMs, fine-tuning aligned models has become a critical step for adapting them to real-world applications, which makes the safety of this fine-tuning process more important than ever. However, recent studies have highlighted a critical challenge: even when fine-tuning with seemingly benign downstream datasets, the safety of aligned LLMs can be compromised, making them more susceptible to malicious instructions. In this paper, we show that fine-tuning datasets often contain samples with safety-degrading features that are not easily identifiable on the surface. These samples can significantly degrade the safety alignment of LLMs during fine-tuning. To address this issue, we propose LARF, a Layer-Aware Representation Filtering method. This method identifies safety-sensitive layers within the LLM and leverages their representations to detect which data samples in the post-training dataset contain safety-degrading features. Experimental results demonstrate that LARF can effectively identify benign data with safety-degrading features. After removing such data, the safety alignment degradation caused by fine-tuning is mitigated.

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Cross-domain Rumor Detection via Test-Time Adaptation and Large Language Models
Yuxia Gong | Shuguo Hu | Huaiwen Zhang

Rumor detection on social media has become crucial due to the rapid spread of misinformation. Existing approaches primarily focus on within-domain tasks, resulting in suboptimal performance in cross-domain scenarios due to domain shift. To address this limitation, we draw inspiration from the strong generalization capabilities of Test-Time Adaptation (TTA) and propose a novel framework to enhance rumor detection performance across different domains. Specifically, we introduce Test-Time Adaptation for Rumor Detection (T²ARD), which incorporates both single-domain model and target graph adaptation strategies tailored to the unique requirements of cross-domain rumor detection. T²ARD utilizes a graph adaptation module that updates the graph structure and node attributes through multi-level self-supervised contrastive learning, aiming to derive invariant graph representations. To mitigate the impact of significant distribution shifts on self-supervised signals, T²ARD performs model adaptation by using annotations from Large Language Models (LLMs) on target graph to produce pseudo-labels as supervised signals. Experiments conducted on four widely used cross-domain datasets demonstrate that T²ARD achieves state-of-the-art performance, surpassing existing methods in rumor detection.

Small language models (SLMs) are gaining attention for their lower computational and memory needs while maintaining strong performance. However, efficiently deploying SLMs on resource-constrained devices remains a significant challenge. Post-training quantization(PTQ) is a widely used compression technique that reduces memory usage and inference computation, yet existing methods face challenges in inefficient bit-width allocation and insufficient fine-grained quantization adjustments, leading to suboptimal performance, particularly at lower bit-widths. To address these challenges, we propose multi-level weight quantization (MLWQ), which facilitates the efficient deployment of SLMs. Our method enables more effective bit-width allocation by jointly considering inter-layer loss and intra-layer salience. Furthermore, we propose a fine-grained partitioning of intra-layer salience to support the tweaking of quantization parameters within each group. Experimental results indicate that MLWQ achieves competitive performance compared to state-of-the-art methods, providing an effective approach for the efficient deployment of SLMs while maintaining model accuracy.

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ToDi: Token-wise Distillation via Fine-Grained Divergence Control
Seongryong Jung | Suwan Yoon | DongGeon Kim | Hwanhee Lee

Large language models (LLMs) offer impressive performance but are impractical for resource-constrained deployment due to high latency and energy consumption. Knowledge distillation (KD) addresses this by transferring knowledge from a large teacher to a smaller student model. However, conventional KD, notably approaches like Forward KL (FKL) and Reverse KL (RKL), apply uniform divergence loss across the entire vocabulary, neglecting token-level prediction discrepancies. By investigating these representative divergences via gradient analysis, we reveal that FKL boosts underestimated tokens, while RKL suppresses overestimated ones, showing their complementary roles. Based on this observation, we propose Token-wise Distillation (ToDi), a novel method that adaptively combines FKL and RKL per token using a sigmoid-based weighting function derived from the teacher-student probability log-ratio. ToDi dynamically emphasizes the appropriate divergence for each token, enabling precise distribution alignment. We demonstrate that ToDi consistently outperforms recent distillation baselines using uniform or less granular strategies across instruction-following benchmarks. Extensive ablation studies and efficiency analysis further validate ToDi’s effectiveness and practicality.

Tree search methods have demonstrated impressive performance in code generation. Previous methods combine tree search with reflection that summarizes past mistakes to achieve iterative improvement. However, these methods face significant challenges. First, they search directly within the code language space, neglecting the underlying reasoning process critical for effective code generation. Second, reflection-based approaches merely accumulate historical errors in memory without providing correct reasoning pathways, making it difficult for subsequent search iterations to identify optimal solutions, resulting in decreased search quality. In this work, we propose RethinkMCTS, a framework that systematically explores and refines the reasoning process for code generation. Specifically, we employ MCTS to search for thoughts before code generation and integrate MCTS with a refinement mechanism called rethink, which incorporates fine-grained code execution feedback to refine erroneous thoughts during the search. It ensures the search path aligns with better reasoning, improving overall search quality. Through extensive experiments, we demonstrate that RethinkMCTS outperforms previous search-based and feedback-enhanced code generation baselines.

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Probing for Arithmetic Errors in Language Models
Yucheng Sun | Alessandro Stolfo | Mrinmaya Sachan

We investigate whether internal activations in language models can be used to detect arithmetic errors. Starting with a controlled setting of 3-digit addition, we show that simple probes can accurately decode both the model’s predicted output and the correct answer from hidden states, regardless of whether the model’s output is correct. Building on this, we train lightweight error detectors that predict model correctness with over 90% accuracy. We then extend our analysis to structured chain-of-thought traces on addition-only GSM8K problems and find that probes trained on simple arithmetic generalize well to this more complex setting, revealing consistent internal representations. Finally, we demonstrate that these probes can guide selective re-prompting of erroneous reasoning steps, improving task accuracy with minimal disruption to correct outputs. Our findings suggest that arithmetic errors can be anticipated from internal activations alone, and that simple probes offer a viable path toward lightweight model self-correction.

Recent advances show that the world knowledge in the Instruction Fine-Tuning (IFT) dataset, which is incompatible with LLMs’ internal knowledge, can greatly hurt the IFT performance. However, the effective integration and balancing of the internal knowledge of LLMs, acquired during pre-training, with existing IFT datasets remains a largely underexplored area of research. To address this gap, this work introduces NILE, a novel framework to optimize the effectiveness of IFT by adjusting IFT datasets through carefully aligning the world and internal knowledge. NILE employs a three-stage pipeline to effectively quantify and adjust consistency with the internal knowledge of target LLMs. Our analysis provides compelling evidence that balancing such consistency with pre-trained internal knowledge is pivotal for unleashing LLM potential, and confirms that NILE can systematically contribute to these substantial performance improvements. Experimental results demonstrate that NILE-aligned IFT datasets sharply boost LLM performance across multiple LLM ability evaluation datasets, achieving up to 66.6% gain on Arena-Hard and 68.5% on Alpaca-Eval V2.

Event forecasting requires modeling historical event data to predict future events, and achieving accurate predictions depends on effectively capturing the relevant historical information that aids forecasting. Most existing methods focus on entities and structural dependencies to capture historical clues but often overlook implicitly relevant information. This limitation arises from overlooking event semantics and deeper factual associations that are not explicitly connected in the graph structure but are nonetheless critical for accurate forecasting. To address this, we propose a dual-criteria constraint strategy that leverages event semantics for relevance modeling and incorporates a self-supervised semantic filter based on factual event associations to capture implicitly relevant historical information. Building on this strategy, our method, termed ITHI (Integrating Three types of Historical Information), combines sequential event information, periodically repeated event information, and relevant historical information to achieve context-aware event forecasting. We evaluated the proposed ITHI method on three public benchmark datasets, achieving state-of-the-art performance and significantly outperforming existing approaches. Additionally, we validated its effectiveness on two structured temporal knowledge graph forecasting dataset.

Existing Reward Models (RMs), typically trained on general preference data, struggle in Retrieval Augmented Generation (RAG) settings, which require judging responses for faithfulness to retrieved context, relevance to the user query, appropriate refusals when context is insufficient, completeness and conciseness of information. To address the lack of publicly available RAG-centric preference datasets and specialised RMs, we introduce RAGferee, a methodology that repurposes question-answering (QA) datasets into preference pairs that prioritise groundedness over stylistic features, enabling the training of contextual RMs better suited to judging RAG responses. Using RAGferee, we curate a small preference dataset of 4K samples and fine-tune RMs ranging from 7B to 24B parameters. Our RAG-centric RMs achieve state-of-the-art performance on ContextualJudgeBench, surpassing existing 70B+ RMs trained on much larger (up to 2.4M samples) general corpora, with an absolute improvement of +15.5%.

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Large Language Models Discriminate Against Speakers of German Dialects
Minh Duc Bui | Carolin Holtermann | Valentin Hofmann | Anne Lauscher | Katharina von der Wense

Dialects represent a significant component of human culture and are found across all regions of the world. In Germany, more than 40% of the population speaks a regional dialect (Adler and Hansen, 2022). However, despite cultural importance, individuals speaking dialects often face negative societal stereotypes. We examine whether such stereotypes are mirrored by large language models (LLMs). We draw on the sociolinguistic literature on dialect perception to analyze traits commonly associated with dialect speakers. Based on these traits, we assess the dialect naming bias and dialect usage bias expressed by LLMs in two tasks: association task and decision task. To assess a model’s dialect usage bias, we construct a novel evaluation corpus that pairs sentences from seven regional German dialects (e.g., Alemannic and Bavarian) with their standard German counterparts. We find that: (1) in the association task, all evaluated LLMs exhibit significant dialect naming and dialect usage bias against German dialect speakers, reflected in negative adjective associations; (2) all models reproduce these dialect naming and dialect usage biases in their decision making; and (3) contrary to prior work showing minimal bias with explicit demographic mentions, we find that explicitly labeling linguistic demographics—German dialect speakers—amplifies bias more than implicit cues like dialect usage.

Understanding the underlying argumentative flow in analytic argumentative writing is essential for discourse comprehension, especially in complex argumentative discourse such as think-tank commentary. However, existing structure modeling approaches often rely on surface-level topic segmentation, failing to capture the author’s rhetorical intent and reasoning process. To address this limitation, we propose a Question-Focus discourse structuring framework that explicitly models the underlying argumentative flow by anchoring each argumentative unit to a guiding question (reflecting the author’s intent) and a set of attentional foci (highlighting analytical pathways). To assess its effectiveness, we introduce an argument reconstruction task in which the modeled discourse structure guides both evidence retrieval and argument generation. We construct a high-quality dataset comprising 600 authoritative Chinese think-tank articles for experimental analysis. To quantitatively evaluate performance, we propose two novel metrics: (1) Claim Coverage, measuring the proportion of original claims preserved or similarly expressed in reconstructions, and (2) Evidence Coverage, assessing the completeness of retrieved supporting evidences. Experimental results show that our framework uncovers the author’s argumentative logic more effectively and offers better structural guidance for reconstruction, yielding up to a 10% gain in claim coverage and outperforming strong baselines across both curated and LLM-based metrics.

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AbsVis – Benchmarking How Humans and Vision-Language Models “See” Abstract Concepts in Images
Tarun Tater | Diego Frassinelli | Sabine Schulte im Walde

Abstract concepts like mercy and peace often lack clear visual grounding, and thus challenge humans and models to provide suitable image representations. To address this challenge, we introduce AbsVis – a dataset of 675 images annotated with 14,175 concept–explanation attributions from humans and two Vision-Language Models (VLMs: Qwen and LLaVA), where each concept is accompanied by a textual explanation. We compare human and VLM attributions in terms of diversity, abstractness, and alignment, and find that humans attribute more varied concepts. AbsVis also includes 2,680 human preference judgments evaluating the quality of a subset of these annotations, showing that overlapping concepts (attributed by both humans and VLMs) are most preferred. Explanations clarify and strengthen the perceived attributions, both from humans and VLMs. Explanations clarify and strengthen the perceived attributions, both from human and VLMs. Finally, we show that VLMs can approximate human preferences and use them to fine-tune VLMs via Direct Preference Optimization (DPO), yielding improved alignments with preferred concept–explanation pairs.

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A Rigorous Evaluation of LLM Data Generation Strategies for Low-Resource Languages
Tatiana Anikina | Jan Cegin | Jakub Simko | Simon Ostermann

Large Language Models (LLMs) are increasingly used to generate synthetic textual data for training smaller specialized models. However, a comparison of various generation strategies for low-resource language settings is lacking. While various prompting strategies have been proposed—such as demonstrations, label-based summaries, and self-revision—their comparative effectiveness remains unclear, especially for low-resource languages. In this paper, we systematically evaluate the performance of these generation strategies and their combinations across 11 typologically diverse languages, including several extremely low-resource ones. Using three NLP tasks and four open-source LLMs, we assess downstream model performance on generated versus gold-standard data. Our results show that strategic combinations of generation methods — particularly target-language demonstrations with LLM-based revisions — yield strong performance, narrowing the gap with real data to as little as 5% in some settings. We also find that smart prompting techniques can reduce the advantage of larger LLMs, highlighting efficient generation strategies for synthetic data generation in low-resource scenarios with smaller models.

The code generation capabilities of Large Language Models (LLMs) have advanced applications like tool invocation and problem-solving. However, improving performance in code-related tasks remains challenging due to limited training data that is verifiable with accurate test cases. While Direct Preference Optimization (DPO) has shown promise, existing methods for generating test cases still face limitations. In this paper, we propose a novel approach that splits code snippets into smaller, granular blocks, creating more diverse DPO pairs from the same test cases. Additionally, we introduce the Abstract Syntax Tree (AST) splitting and curriculum training method to enhance the DPO training. Our approach demonstrates significant improvements in code generation tasks, as validated by experiments on benchmark datasets such as HumanEval (+), MBPP (+), APPS, LiveCodeBench, and BigCodeBench. Code and data are available at https://github.com/SenseLLM/StructureCoder.

Privacy-sensitive users require deploying large language models (LLMs) within their own infrastructure (on-premises) to safeguard private data and enable customization. However, vulnerabilities in local environments can lead to unauthorized access and potential model theft. To address this, prior research on small models has explored securing only the output layer within hardware-secured devices to balance model confidentiality and customization. Yet this approach fails to protect LLMs effectively. In this paper, we discover that (1) query-based distillation attacks targeting the secured top layer can produce a functionally equivalent replica of the victim model; (2) securing the same number of layers, bottom layers before a transition layer provide stronger protection against distillation attacks than top layers, with comparable effects on customization performance; and (3) the number of secured layers creates a trade-off between protection and customization flexibility. Based on these insights, we propose SOLID, a novel deployment framework that secures a few bottom layers in a secure environment and introduces an efficient metric to optimize the trade-off by determining the ideal number of hidden layers. Extensive experiments on five models (1.3B to 70B parameters) demonstrate that SOLID outperforms baselines, achieving a better balance between protection and downstream customization.

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Variance Sensitivity Induces Attention Entropy Collapse and Instability in Transformers
Jonghyun Hong | Sungyoon Lee

Attention-based language models commonly rely on the softmax function to convert attention logits into probability distributions. However, this softmax re-weighting can lead to *attention entropy collapse*, in which attention disproportionately concentrates on a single token, ultimately causing training instability. In this work, we identify the high *variance sensitivity* of softmax as a primary cause of this collapse. We show that *entropy-stable* attention methods, which either control or are insensitive to the variance of attention logits, can prevent entropy collapse and enable more stable training. We provide empirical evidence of this effect in both large language models (LLMs) and a small Transformer model composed solely of self-attention and support our findings with theoretical analysis. Moreover, we identify that the concentration of attention probabilities increases the probability matrix norm, leading to the gradient exploding.

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X-FLoRA: Cross-modal Federated Learning with Modality-expert LoRA for Medical VQA
Min Hyuk Kim | Changheon Kim | Seok Bong Yoo

Medical visual question answering (VQA) and federated learning (FL) have emerged as vital approaches for enabling privacy-preserving, collaborative learning across clinical institutions. However, both these approaches face significant challenges in cross-modal FL scenarios, where each client possesses unpaired images from only one modality. To address this limitation, we propose X-FLoRA, a cross-modal FL framework that uses modality-expert low-rank adaptation (LoRA) for medical VQA. Specifically, X-FLoRA enables the synthesis of images from one modality to another without requiring data sharing between clients. This is achieved by training a backward translation model within a federated asymmetric translation scheme that integrates clinical semantics from textual data. Additionally, X-FLoRA introduces modality-expert LoRA, which fine-tunes separate LoRA modules to strengthen modality-specific representations in the VQA task. The server aggregates the trained backward translation models and fine-tuned LoRA modules using discriminator quality scores and expert-aware weighting, which regulate the relative contributions from different clients. Experiments were conducted on VQA datasets encompassing different medical modalities, and the results demonstrate that X-FLoRA outperforms existing FL methods in terms of VQA performance.

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Robust Native Language Identification through Agentic Decomposition
Ahmet Yavuz Uluslu | Tannon Kew | Tilia Ellendorff | Gerold Schneider | Rico Sennrich

Large language models (LLMs) often achieve high performance in native language identification (NLI) benchmarks by leveraging superficial contextual clues such as names, locations, and cultural stereotypes, rather than the underlying linguistic patterns indicative of native language (L1) influence. To improve robustness, previous work has instructed LLMs to disregard such clues. In this work, we demonstrate that such a strategy is unreliable and model predictions can be easily altered by misleading hints. To address this problem, we introduce an agentic NLI pipeline inspired by forensic linguistics, where specialized agents accumulate and categorize diverse linguistic evidence before an independent final overall assessment. In this final assessment, a goal-aware coordinating agent synthesizes all evidence to make the NLI prediction. On two benchmark datasets, our approach significantly enhances NLI robustness against misleading contextual clues and performance consistency compared to standard prompting methods.

Current instruction data synthesis methods primarily focus on single-turn instructions and often neglect cross-turn coherence, resulting in context drift and reduced task completion rates in extended conversations. To address this limitation, we propose Skeleton-Guided Multi-Turn Dialogue Generation, a framework that constrains multi-turn instruction synthesis by explicitly modeling human conversational intent. It operates in two stages: (1) Intent Modeling, which captures the global structure of human dialogues by assigning each conversation to one of nine well-defined intent trajectories, ensuring a coherent and goal-oriented information flow; and (2) Skeleton Generation, which constructs a structurally grounded sequence of user queries aligned with the modeled intent, thereby serving as a scaffold that constrains and guides the downstream instruction synthesis process. Based on this process, we construct ConsistentChat, a multi-turn instruction dataset with approximately 15,000 multi-turn conversations and 224,392 utterances. Experiments on the Light, Topdial, and MT-Eval benchmarks show that models fine-tuned on ConsistentChat achieve a 20–30% improvement in chat consistency and up to a 15% increase in task success rate, significantly outperforming models trained on existing single-turn and multi-turn instruction datasets.

Vision-Language Models (VLMs) are powerful yet computationally intensive for widespread practical deployments. To address such challenge without costly re-training, post-training acceleration techniques like quantization and token reduction are extensively explored. However, current acceleration evaluations primarily target minimal overall performance degradation, overlooking a crucial question: does the accelerated model still give the same answers to the same questions as it did before acceleration? This is vital for stability-centered industrial applications where consistently correct answers for specific, known situations are paramount, such as in AI-based disease diagnosis. We systematically investigate this for accelerated VLMs, testing four leading models (LLaVA-1.5, LLaVA-Next, Qwen2-VL, Qwen2.5-VL) with eight acceleration methods on ten multi-modal benchmarks. Our findings are stark: despite minimal aggregate performance drops, accelerated models changed original answers up to 20% of the time. Critically, up to 6.5% of these changes converted correct answers to incorrect. Input perturbations magnified these inconsistencies, and the trend is confirmed by case studies with the medical VLM LLaVA-Med. This research reveals a significant oversight in VLM acceleration, stressing an urgent need for instance-level stability checks to ensure trustworthy real-world deployment.

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When Annotators Disagree, Topology Explains: Mapper, a Topological Tool for Exploring Text Embedding Geometry and Ambiguity
Nisrine Rair | Alban Goupil | Valeriu Vrabie | Emmanuel Chochoy

Language models are often evaluated with scalar metrics like accuracy, but such measures fail to capture how models internally represent ambiguity, especially when human annotators disagree. We propose a topological perspective to analyze how fine-tuned models encode ambiguity and more generally instances.Applied to RoBERTa-Large on the MD-Offense dataset, Mapper, a tool from topological data analysis, reveals that fine-tuning restructures embedding space into modular, non-convex regions aligned with model predictions, even for highly ambiguous cases. Over 98% of connected components exhibit ≥ 90% prediction purity, yet alignment with ground-truth labels drops in ambiguous data, surfacing a hidden tension between structural confidence and label uncertainty.Unlike traditional tool such as PCA or UMAP, Mapper captures this geometry directly uncovering decision regions, boundary collapses, and overconfident clusters. Our findings position Mapper as a powerful diagnostic tool for understanding how models resolve ambiguity. Beyond visualization, it also enables topological metrics that may inform proactive modeling strategies in subjective NLP tasks.

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Self-Critique and Refinement for Faithful Natural Language Explanations
Yingming Wang | Pepa Atanasova

With the rapid development of Large Language Models (LLMs), Natural Language Explanations (NLEs) have become increasingly important for understanding model predictions. However, these explanations often fail to faithfully represent the model’s actual reasoning process. While existing work has demonstrated that LLMs can self-critique and refine their initial outputs for various tasks, this capability remains unexplored for improving explanation faithfulness. To address this gap, we introduce Self-critique and Refinement for Natural Language Explanations (SR-NLE), a framework that enables models to improve the faithfulness of their own explanations – specifically, post-hoc NLEs – through an iterative critique and refinement process without external supervision. Our framework leverages different feedback mechanisms to guide the refinement process, including natural language self-feedback and, notably, a novel feedback approach based on feature attribution that highlights important input words. Our experiments across three datasets and four state-of-the-art LLMs demonstrate that SR-NLE significantly reduces unfaithfulness rates, with our best method achieving an average unfaithfulness rate of 36.02%, compared to 54.81% for baseline – an absolute reduction of 18.79%. These findings reveal that the investigated LLMs can indeed refine their explanations to better reflect their actual reasoning process, requiring only appropriate guidance through feedback without additional training or fine-tuning.

Misinformation remains one of the most significant issues in the digital age. While automated fact-checking has emerged as a viable solution, most current systems are limited to evaluating factual accuracy. However, the detrimental effect of misinformation transcends simple falsehoods; it takes advantage of how individuals perceive, interpret, and emotionally react to information. This underscores the need to move beyond factuality and adopt more human-centered detection frameworks. In this survey, we explore the evolving interplay between traditional fact-checking approaches and psychological concepts such as cognitive biases, social dynamics, and emotional responses. By analyzing state-of-the-art misinformation detection systems through the lens of human psychology and behavior, we reveal critical limitations of current methods and identify opportunities for improvement. Additionally, we outline future research directions aimed at creating more robust and adaptive frameworks, such as neuro-behavioural models that integrate technological factors with the complexities of human cognition and social influence. These approaches offer promising pathways to more effectively detect and mitigate the societal harms of misinformation.

In long structured document retrieval, existing methods typically fine-tune pre-trained language models (PLMs) using contrastive learning on datasets lacking explicit structural information. This practice suffers from two critical issues: 1) current methods fail to leverage structural features and element-level semantics effectively, and 2) the lack of datasets containing structural metadata. To bridge these gaps, we propose SEAL, a novel contrastive learning framework. It leverages structure-aware learning to preserve semantic hierarchies and masked element alignment for fine-grained semantic discrimination. Furthermore, we release StructDocRetrieval, a long structured document retrieval dataset with rich structural annotations. Extensive experiments on both the released and industrial datasets across various modern PLMs, and online A/B testing demonstrate consistent improvements, boosting NDCG@10 from 73.96% to 77.84% on BGE-M3. The resources are available at https://github.com/xinhaoH/SEAL.

Large Language Models (LLMs) with extended context lengths face significant computational challenges during the pre-filling phase, primarily due to the quadratic complexity of self-attention. Existing methods typically employ dynamic pattern matching and block-sparse low-level implementations. However, their reliance on local information for pattern identification fails to capture global contexts, and the coarse granularity of blocks leads to persistent internal sparsity, resulting in suboptimal accuracy and efficiency. To address these limitations, we propose AnchorAttention, a difference-aware, dynamic sparse attention mechanism that efficiently identifies critical attention regions at a finer stripe granularity while adapting to global contextual information, achieving superior speed and accuracy. AnchorAttention comprises three key components: (1) Pattern-based Anchor Computation, leveraging the commonalities present across all inputs to rapidly compute a set of near-maximum scores as anchor; (2) Difference-aware Stripe Sparsity Identification, performing difference-aware comparisons with anchor to quickly obtain discrete coordinates of significant regions in a stripe-like sparsity pattern; (3) Fine-grained Sparse Computation, replacing the traditional contiguous loading strategy with a discrete key-value loading approach to maximize sparsity rates while preserving hardware computational potential. Additionally, we integrate the identification strategy into a single operator to maximize parallelization potential. With its finer-grained sparsity strategy, AnchorAttention achieves higher sparsity rates at the same recall level, significantly reducing computation time. Compared to previous state-of-the-art methods, at a text length of 128k, it achieves a speedup of 1.44× while maintaining higher recall rates.

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Attacks by Content: Automated Fact-checking is an AI Security Issue
Michael Sejr Schlichtkrull

When AI agents retrieve and reason over external documents, adversaries can manipulate the data they receive to subvert their behaviour. Previous research has studied indirect prompt injection, where the attacker injects malicious instructions. We argue that injection of instructions is not necessary to manipulate agents – attackers could instead supply biased, misleading, or false information. We term this an *attack by content*. Existing defenses, which focus on detecting hidden commands, are ineffective against attacks by content. To defend themselves and their users, agents must critically evaluate retrieved information, corroborating claims with external evidence and evaluating source trustworthiness. We argue that this is analogous to an existing NLP task, automated fact-checking, which we propose to repurpose as a cognitive self-defense tool for agents.

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MUZO: Leveraging Multiple Queries and Momentum for Zeroth-Order Fine-Tuning of Large Language Models
Yuezhang Peng | Yuxin Liu | Fei Wen | Xie Chen

Fine-tuning pre-trained large language models (LLMs) on downstream tasks has achieved significant success across various domains. However, as model sizes grow, traditional first-order fine-tuning algorithms incur substantial memory overhead due to the need for activation storage for back-propagation (BP). The BP-free Memory-Efficient Zeroth-Order Optimization (MeZO) method estimates gradients through finite differences, avoiding the storage of activation values, and has been demonstrated as a viable approach for fine-tuning large language models. This work proposes the Multiple-query Memory Efficient Zeroth-Order (MUZO) method, which is based on variance-reduced multiple queries to obtain the average of gradient estimates. When combined with Adam optimizer, MUZO-Adam demonstrates superior performance in fine-tuning various LLMs. Furthermore, we provide theoretical guarantees for the convergence of the MUZO-Adam optimizer. Extensive experiments empirically demonstrate that MUZO-Adam converges better than MeZO-SGD and achieves near first-order optimizer performance on downstream classification, multiple-choice, and generation tasks.

The misuse of large language models (LLMs), such as academic plagiarism, has driven the development of detectors to identify LLM-generated texts. To bypass these detectors, paraphrase attacks have emerged to purposely rewrite these texts to evade detection. Despite the success, existing methods require substantial data and computational budgets to train a specialized paraphraser, and their attack efficacy greatly reduces when faced with advanced detection algorithms. To address this, we propose Contrastive Paraphrase Attack (CoPA), a training-free method that effectively deceives text detectors using off-the-shelf LLMs. The first step is to carefully craft instructions that encourage LLMs to produce more human-like texts. Nonetheless, we observe that the inherent statistical biases of LLMs can still result in some generated texts carrying certain machine-like attributes that can be captured by detectors. To overcome this, CoPA constructs an auxiliary machine-like word distribution as a contrast to the human-like distribution generated by the LLM. By subtracting the machine-like patterns from the human-like distribution during the decoding process, CoPA is able to produce sentences that are less discernible by text detectors. Our theoretical analysis suggests the superiority of the proposed attack. Extensive experiments validate the effectiveness of CoPA in fooling text detectors across various scenarios.

Large Language Models (LLMs) often hallucinate in question answering (QA) tasks. A key yet underexplored factor contributing to this is the temporality of questions – whether they are evergreen (answers remain stable over time) or mutable (answers change). In this work, we introduce EverGreenQA, the first multilingual QA dataset with evergreen labels, supporting both evaluation and training. Using EverGreenQA, we benchmark 12 modern LLMs to assess whether they encode question temporality explicitly (via verbalized judgments) or implicitly (via uncertainty signals). We also train EG-E5, a lightweight multilingual classifier that achieves SoTA performance on this task. Finally, we demonstrate the practical utility of evergreen classification across three applications: improving self-knowledge estimation, filtering QA datasets, and explaining GPT-4o’s retrieval behavior.

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Steering Language Models in Multi-Token Generation: A Case Study on Tense and Aspect
Alina Klerings | Jannik Brinkmann | Daniel Ruffinelli | Simone Paolo Ponzetto

Large language models (LLMs) are able to generate grammatically well-formed text, but how do they encode their syntactic knowledge internally? While prior work has focused largely on binary grammatical contrasts, in this work, we study the representation and control of two multidimensional hierarchical grammar phenomena—verb tense and aspect—and for each, identify distinct, orthogonal directions in residual space using linear discriminant analysis. Next, we demonstrate causal control over both grammatical features through concept steering across three generation tasks. Then, we use these identified features in a case study to investigate factors influencing effective steering in multi-token generation. We find that steering strength, location, and duration are crucial parameters for reducing undesirable side effects such as topic shift and degeneration. Our findings suggest that models encode tense and aspect in structurally organized, human-like ways, but effective control of such features during generation is sensitive to multiple factors and requires manual tuning or automated optimization.

Rerankers play a critical role in multimodal Retrieval-Augmented Generation (RAG) by refining ranking of an initial set of retrieved documents. Rerankers are typically trained using hard negative mining, whose goal is to select pages for each query which rank high, but are actually irrelevant. However, this selection process is typically passive and restricted to what the retriever can find in the available corpus, leading to several inherent limitations. These include: limited diversity, negative examples which are often not hard enough, low controllability, and frequent false negatives which harm training. Our paper proposes an alternative approach: Single-Page Hard Negative Query Generation, which goes the other way around. Instead of retrieving negative pages per query, we generate hard negative queries per page. Using an automated LLM-VLM pipeline, and given a page and its positive query, we create hard negatives by rephrasing the query to be as similar as possible in form and context, yet not answerable from the page. This paradigm enables fine-grained control over the generated queries, resulting in diverse, hard, and targeted negatives. It also supports efficient false negative verification. Our experiments show that rerankers trained with data generated using our approach outperform existing models and significantly improve retrieval performance.

Large language models readily memorize arbitrary training instances, such as label noise, yet they perform strikingly well on reasoning tasks. In this work, we investigate how language models memorize label noise, and why such memorization in many cases does not heavily affect generalizable reasoning capabilities. Using two controllable synthetic reasoning tasks with noisy labels, four-digit addition (FDA) and two-hop relational reasoning (THR), we discover a reliance of memorization on generalizable reasoning mechanisms: models continue to compute intermediate reasoning outputs even when retrieving memorized noisy labels, and intervening reasoning adversely affects memorization. We further show that memorization operates through distributed encoding, i.e., aggregating various inputs and intermediate results, rather than building a look-up mechanism from inputs to noisy labels. Moreover, our FDA case study reveals memorization occurs via outlier heuristics, where existing neuron activation patterns are slightly shifted to fit noisy labels. Together, our findings suggest that memorization of label noise in language models builds on, rather than overrides, the underlying reasoning mechanisms, shedding lights on the intriguing phenomenon of benign memorization.

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VELA: An LLM-Hybrid-as-a-Judge Approach for Evaluating Long Image Captions
Kazuki Matsuda | Yuiga Wada | Shinnosuke Hirano | Seitaro Otsuki | Komei Sugiura

In this study, we focus on the automatic evaluation of long and detailed image captions generated by multimodal Large Language Models (MLLMs). Most existing automatic evaluation metrics for image captioning are primarily designed for short captions and are not suitable for evaluating long captions. Moreover, recent LLM-as-a-Judge approaches suffer from slow inference due to their reliance on autoregressive inference and early fusion of visual information. To address these limitations, we propose VELA, an automatic evaluation metric for long captions developed within a novel LLM-Hybrid-as-a-Judge framework. Furthermore, we propose LongCap-Arena, a benchmark specifically designed for evaluating metrics for long captions. This benchmark comprises 7,805 images, the corresponding human-provided long reference captions and long candidate captions, and 32,246 human judgments from three distinct perspectives: Descriptiveness, Relevance, and Fluency. We demonstrated that VELA outperformed existing metrics and achieved superhuman performance on LongCap-Arena.

Large Language Models (LLMs) are prone to hallucinations, and Retrieval-Augmented Generation (RAG) helps mitigate this, but at a high computational cost while risking misinformation. Adaptive retrieval aims to retrieve only when necessary, but existing approaches rely on LLM-based uncertainty estimation, which remains inefficient and impractical.In this study, we introduce lightweight LLM-independent adaptive retrieval methods based on external information. We investigated 27 features, organized into 7 groups, and their hybrid combinations. We evaluated these methods on 6 QA datasets, assessing the QA performance and efficiency. The results show that our approach matches the performance of complex LLM-based methods while achieving significant efficiency gains, demonstrating the potential of external information for adaptive retrieval.

Humans can interpret geospatial information through natural language, while the geospatial cognition capabilities of Large Language Models (LLMs) remain underexplored. Prior research in this domain has been constrained by non-quantifiable metrics, limited evaluation datasets; unclear research hierarchies further compound these limitations. Therefore, we propose a scalable benchmark and conduct a comprehensive evaluation of the geospatial route cognition of LLMs. We create a large-scale evaluation dataset comprised of 36000 routes from 12 metropolises. Then, we introduce PathBuilder, a novel tool for converting natural language instructions into navigation routes, and vice versa, bridging the gap between geospatial information and natural language. Finally, we propose a new evaluation framework and metrics to rigorously assess 9 state-of-the-art (SOTA) LLMs, on the task of route reversal. The benchmark reveals that LLMs exhibit limited ability to reverse routes: most of the reverse routes neither return to the starting point nor are similar to the optimal route. Additionally, LLMs face challenges such as low robustness in route generation and high confidence for their incorrect answers.

Uncertain knowledge graph embedding (UnKGE) methods learn vector representations that capture both structural and uncertainty information to predict scores of unseen triples. However, existing methods produce only point estimates, without quantifying predictive uncertainty—limiting their reliability in high-stakes applications where understanding confidence in predictions is crucial. To address this limitation, we propose UnKGCP, a framework that generates prediction intervals guaranteed to contain the true score with a user-specified level of confidence. The length of the intervals reflects the model’s predictive uncertainty. UnKGCP builds on the conformal prediction framework but introduces a novel nonconformity measure tailored to UnKGE methods and an efficient procedure for interval construction. We provide theoretical guarantees for the intervals and empirically verify these guarantees. Extensive experiments on standard UKG benchmarks across diverse UnKGE methods further demonstrate that the intervals are sharp and effectively capture predictive uncertainty.

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Beyond Seen Data: Improving KBQA Generalization Through Schema-Guided Logical Form Generation
Shengxiang Gao | Jey Han Lau | Jianzhong Qi

Knowledge base question answering (KBQA) aims to answer user questions in natural language using rich human knowledge stored in large KBs. As current KBQA methods struggle with unseen knowledge base elements and their novel compositions at test time, we introduce SG-KBQA — a novel model that injects schema contexts into entity retrieval and logical form generation to tackle this issue. It exploits information about the semantics and structure of the knowledge base provided by schema contexts to enhance generalizability. We show that achieves strong generalizability, outperforming state-of-the-art models on two commonly used benchmark datasets across a variety of test settings. Our source code is available at https://github.com/gaosx2000/SG_KBQA.

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A Training-Free Length Extrapolation Approach for LLMs: Greedy Attention Logit Interpolation
Yan Li | Tianyi Zhang | Zechuan Li | Caren Han

Transformer-based Large Language Models (LLMs) struggle with inputs exceeding their training context window due to positional out-of-distribution (O.O.D.) issues that disrupt attention. Existing solutions, including fine-tuning and training-free methods, face challenges like inefficiency, redundant interpolation, logit outliers, or loss of local positional information. We propose Greedy Attention Logit Interpolation (GALI), a training-free method that improves length extrapolation by greedily reusing pretrained positional intervals and interpolating attention logits to eliminate outliers. GALI achieves stable and superior performance across a wide range of long-context tasks without requiring input-length-specific tuning. Our analysis further reveals that LLMs interpret positional intervals unevenly and that restricting interpolation to narrower ranges improves performance, even on short-context tasks. GALI represents a step toward more robust and generalizable long-text processing in LLMs.

The emergence of text-to-image synthesis (TIS) models has significantly influenced digital image creation by producing high-quality visuals from written descriptions. Yet these models are sensitive on textual prompts, posing a challenge for novice users who may not be familiar with TIS prompt writing. Existing solutions relieve this via automatic prompt expansion or generation from a user query. However, this single-turn manner suffers from limited user-centricity in terms of result interpretability and user interactivity. Thus, we propose DialPrompt, a dialogue-based TIS prompt generation model that emphasizes user experience for novice users. DialPrompt is designed to follow a multi-turn workflow, where in each round of dialogue the model guides user to express their preferences on possible optimization dimensions before generating the final TIS prompt. To achieve this, we mined 15 essential dimensions for high-quality prompts from advanced users and curated a multi-turn dataset. Through training on this dataset, DialPrompt improves user-centricity by allowing users to perceive and control the creation process of TIS prompts. Experiments indicate that DialPrompt improves significantly in user-centricity score compared with existing approaches while maintaining a competitive quality of synthesized images. In our user evaluation, DialPrompt is highly rated by 19 human reviewers (especially novices).

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We Need to Measure Data Diversity in NLP — Better and Broader
Dong Nguyen | Esther Ploeger

Although diversity in NLP datasets has received growing attention, the question of how to measure it remains largely underexplored. This opinion paper examines the conceptual and methodological challenges of measuring data diversity and argues that interdisciplinary perspectives are essential for developing more fine-grained and valid measures.

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Sheaf Discovery with Joint Computation Graph Pruning and Flexible Granularity
Lei Yu | Jingcheng Niu | Zining Zhu | Xi Chen | Gerald Penn

In this paper, we introduce DiscoGP, a novel framework for extracting self-contained modular units, or sheaves, within neural language models (LMs). Sheaves extend the concept of functional circuits, a unit widely explored in interpretability research, by considering not only subsets of edges in an LM’s computation graph but also the model’s weight parameters. Our framework identifies sheaves through a gradient-based pruning algorithm that operates on both of these in such a way that reduces the original LM to a sparse skeleton that preserves certain core capabilities. Experimental results demonstrate that, across a range of linguistic and reasoning tasks, DiscoGP extracts sheaves that preserve 93-100% of a model’s performance on the identified task while comprising only 1-7% of the original weights and connections. Furthermore, our analysis reveals that, compared to previously identified LM circuits, the sheaves discovered by DiscoGP exhibit superior modularity and functional fidelity. Extending our method to the neuron level also unveils novel insights into the inner workings of LLMs.

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Hierarchical Bracketing Encodings Work for Dependency Graphs
Ana Ezquerro | Carlos Gómez-Rodríguez | David Vilares

We revisit hierarchical bracketing encodings from a practical perspective in the context of dependency graph parsing. The approach encodes graphs as sequences, enabling linear-time parsing with n tagging actions, and still representing reentrancies, cycles, and empty nodes. Compared to existing graph linearizations, this representation substantially reduces the label space while preserving structural information. We evaluate it on a multilingual and multi-formalism benchmark, showing competitive results and consistent improvements over other methods in exact match accuracy.

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Multimodal Fine-grained Context Interaction Graph Modeling for Conversational Speech Synthesis
Zhenqi Jia | Rui Liu | Berrak Sisman | Haizhou Li

Conversational Speech Synthesis (CSS) aims to generate speech with natural prosody by understanding the multimodal dialogue history (MDH). The latest work predicts the accurate prosody expression of the target utterance by modeling the utterance-level interaction characteristics of MDH and the target utterance. However, MDH contains fine-grained semantic and prosody knowledge at the word level. Existing methods overlook the fine-grained semantic and prosodic interaction modeling. To address this gap, we propose MFCIG-CSS, a novel Multimodal Fine-grained Context Interaction Graph-based CSS system. Our approach constructs two specialized multimodal fine-grained dialogue interaction graphs: a semantic interaction graph and a prosody interaction graph. These two interaction graphs effectively encode interactions between word-level semantics, prosody, and their influence on subsequent utterances in MDH. The encoded interaction features are then leveraged to enhance synthesized speech with natural conversational prosody. Experiments on the DailyTalk dataset demonstrate that MFCIG-CSS outperforms all baseline models in terms of prosodic expressiveness. Code and speech samples are available at https://github.com/AI-S2-Lab/MFCIG-CSS.

High-quality multilingual training data is essential for effectively pretraining large language models (LLMs). Yet, the availability of suitable open-source multilingual datasets remains limited. Existing state-of-the-art datasets mostly rely on heuristic filtering methods, restricting both their cross-lingual transferability and scalability. Here, we introduce JQL, a systematic approach that efficiently curates diverse and high-quality multilingual data at scale while significantly reducing computational demands. JQL distills LLMs’ annotation capabilities into lightweight annotators based on pretrained multilingual embeddings. These models exhibit robust multilingual and cross-lingual performance, even for languages and scripts unseen during training. Evaluated empirically across 35 languages, the resulting annotation pipeline substantially outperforms current heuristic filtering methods like Fineweb2. JQL notably enhances downstream model training quality and increases data retention rates. Our research provides practical insights and valuable resources for multilingual data curation, raising the standards of multilingual dataset development.

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Conditional [MASK] Discrete Diffusion Language Model
Hyukhun Koh | Minha Jhang | Dohyung Kim | Sangmook Lee | Kyomin Jung

Although auto-regressive models excel in natural language processing, they often struggle to generate diverse text and provide limited controllability. Non-auto-regressive methods could be an alternative but often produce degenerate outputs and exhibit shortcomings in conditional generation. To address these challenges, we propose Diffusion-EAGS, a novel framework that integrates conditional masked language models into diffusion language models through the theoretical lens of a conditional Markov Random Field. In doing so, we propose entropy-adaptive Gibbs sampling and entropy-based noise scheduling to counterbalance each model’s shortcomings. Experimental results show that Diffusion-EAGS outperforms baselines and achieves the best quality-diversity tradeoff, demonstrating its effectiveness in non-autoregressive text generation.

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Language-Guided Temporal Token Pruning for Efficient VideoLLM Processing
Yogesh Kumar

Vision Language Models (VLMs) struggle with long-form videos due to the quadratic complexity of attention mechanisms. We propose Language-Guided Temporal Token Pruning (LGTTP), which leverages temporal cues from queries to adaptively prune video tokens, preserving contextual continuity while reducing computational overhead. Unlike uniform pruning or keyframe selection, LGTTP retains higher token density in temporally relevant segments. Our model-agnostic framework integrates with TimeChat and LLaVA-Video, achieving a 65% reduction in computation while preserving 97-99% of the original performance. On QVHighlights, LGTTP improves HIT@1 by +9.5%, and on Charades-STA, it retains 99.6% of R@1. It excels on queries with explicit temporal markers and remains effective across general video understanding tasks.

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A Fully Probabilistic Perspective on Large Language Model Unlearning: Evaluation and Optimization
Anda Cheng | Wei Huang | Yinggui Wang

Large Language Model Unlearning (LLMU) is a promising way to remove private or sensitive information from large language models. However, the comprehensive evaluation of LLMU remains underexplored. The dominant deterministic evaluation can yield overly optimistic assessments of unlearning efficacy. To mitigate this, we propose a Fully Probabilistic Evaluation (FPE) framework that incorporates input and output distributions in LLMU evaluation. FPE obtains a probabilistic evaluation result by querying unlearned models with various semantically similar inputs and multiple sampling attempts. We introduce an Input Distribution Sampling method in FPE to select high-quality inputs, enabling a stricter measure of information leakage risks. Furthermore, we introduce a Contrastive Embedding Loss (CEL) to advance the performance of LLMU. CEL employs contrastive learning to distance latent representations of unlearned samples from adaptively clustered contrast samples while aligning them with random vectors, leading to improved efficacy and robustness for LLMU. Our experiments show that FPE uncovers more unlearned information leakage risks than prior evaluation methods, and CEL improves unlearning effectiveness by at least 50.1% and robustness by at least 37.2% on Llama-2-7B while retaining high model utility.

High-order numerical methods enhance Transformer performance in tasks like NLP and CV, but introduce a performance-efficiency trade-off due to increased computational overhead. Our analysis reveals that conventional efficiency techniques, such as distillation, can be detrimental to the performance of these models, exemplified by PCformer. To explore more optimizable ODE-based Transformer architectures, we propose the Iterative Implicit Euler Transformer (IIET), which simplifies high-order methods using an iterative implicit Euler approach. This simplification not only leads to superior performance but also facilitates model compression compared to PCformer. To enhance inference efficiency, we introduce Iteration Influence-Aware Distillation (IIAD). Through a flexible threshold, IIAD allows users to effectively balance the performance-efficiency trade-off. On lm-evaluation-harness, IIET boosts average accuracy by 2.65% over vanilla Transformers and 0.8% over PCformer. Its efficient variant, E-IIET, significantly cuts inference overhead by 55% while retaining 99.4% of the original task accuracy. Moreover, the most efficient IIET variant achieves an average performance gain exceeding 1.6% over vanilla Transformer with comparable speed.

Agent self-improvement, where agents autonomously train their underlying Large Language Model (LLM) on self-sampled trajectories, shows promising results but often stagnates in web environments due to limited exploration and under-utilization of pretrained web knowledge. To improve the performance of self-improvement, we propose a novel framework that introduces a co-evolving World Model LLM. This world model predicts the next observation based on the current observation and action within the web environment. The World Model serves dual roles: (1) as a virtual web server generating self-instructed training data to continuously refine the agent’s policy, and (2) as an imagination engine during inference, enabling look-ahead simulation to guide action selection for the agent LLM. Experiments in real-world web environments (Mind2Web-Live, WebVoyager, and GAIA-web) show a 10% performance gain over existing self-evolving agents, demonstrating the efficacy and generalizability of our approach, without using any distillation from more powerful close-sourced models.

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Leveraging Semantic Triples for Private Document Generation with Local Differential Privacy Guarantees
Stephen Meisenbacher | Maulik Chevli | Florian Matthes

Many works at the intersection of Differential Privacy (DP) in Natural Language Processing aim to protect privacy by transforming texts under DP guarantees. This can be performed in a variety of ways, from word perturbations to full document rewriting, and most often under *local* DP. Here, an input text must be made indistinguishable from any other potential text, within some bound governed by the privacy parameter 𝜀. Such a guarantee is quite demanding, and recent works show that privatizing texts under local DP can only be done reasonably under very high 𝜀 values. Addressing this challenge, we introduce **DP-ST**, which leverages semantic triples for neighborhood-aware private document generation under local DP guarantees. Through the evaluation of our method, we demonstrate the effectiveness of the *divide-and-conquer* paradigm, particularly when limiting the DP notion (and privacy guarantees) to that of a *privatization neighborhood*. When combined with LLM post-processing, our method allows for coherent text generation even at lower 𝜀 values, while still balancing privacy and utility. These findings highlight the importance of coherence in achieving balanced privatization outputs at reasonable 𝜀 levels.

The rapid growth of video platforms has transformed information dissemination and led to an explosion of multimedia content. However, this widespread reach also introduces risks, as some users exploit these platforms to spread hate speech, which is often concealed through complex rhetoric, making hateful video detection a critical challenge. Existing detection methods rely heavily on unimodal analysis or simple feature fusion, struggling to capture cross-modal interactions and reason through implicit hate in sarcasm and metaphor. To address these limitations, we propose HVGuard, the first reasoning-based hateful video detection framework with multimodal large language models (MLLMs). Our approach integrates Chain-of-Thought (CoT) reasoning to enhance multimodal interaction modeling and implicit hate interpretation. Additionally, we design a Mixture-of-Experts (MoE) network for efficient multimodal fusion and final decision-making. The framework is modular and extensible, allowing flexible integration of different MLLMs and encoders. Experimental results demonstrate that HVGuard outperforms all existing advanced detection tools, achieving an improvement of 6.88% to 13.13% in accuracy and 9.21% to 34.37% in M-F1 on two public datasets covering both English and Chinese.

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Accelerate Parallelizable Reasoning via Parallel Decoding within One Sequence
Yijiong Yu | Wei Wang | Ran Chen | Ji Pei

Recent advances in reasoning models have demonstrated significant improvements in accuracy by employing detailed and comprehensive reasoning processes. However, generating these lengthy reasoning sequences is computationally expensive and time-consuming. To address this inefficiency, we leverage the inherent parallelizability of certain tasks to accelerate the reasoning process. Specifically, when multiple parallel reasoning steps exist, we decode multiple tokens per forward pass via a tree-like attention mask within a single sequence, avoiding additional memory usage. Experimental results show that our method achieves up to nearly 100% speedup in decoding while basically maintaining the answer quality. Our code is available in https://github.com/yuyijiong/parallel-decoding-in-one-sequence

Manual slide creation is labor-intensive and requires expert prior knowledge. Existing natural language-based LLM generation methods struggle to capture the visual and structural nuances of slide designs. To address this, we formalize the Reference Image to Slide Generation task and propose Slide2Code, the first benchmark with difficulty-tiered samples based on a novel Slide Complexity Metric. We introduce SlideCoder, a layout-aware, retrieval-augmented framework for generating editable slides from reference images. SlideCoder integrates a Color Gradient-based Segmentation algorithm and a Hierarchical Retrieval-Augmented Generation method to decompose complex tasks and enhance code generation. We also release SlideMaster, a 7B open-source model fine-tuned with improved reverse-engineered data. Experiments show that SlideCoder outperforms state-of-the-art baselines by up to 40.5 points, demonstrating strong performance across layout fidelity, execution accuracy, and visual consistency. Our code is available at https://github.com/vinsontang1/SlideCoder.

The goal of open relation extraction (OpenRE) is to develop an RE model that can generalize to new relations not encountered during training. Existing studies primarily formulate OpenRE as a clustering task. They first cluster all test instances based on the similarity between the instances, and then manually assign a new relation to each cluster. However, their reliance on human annotation limits their practicality. In this paper, we propose an OpenRE framework based on large language models (LLMs), which directly predicts new relations for test instances by leveraging their strong language understanding and generation abilities, without human intervention. Specifically, our framework consists of two core components: (1) a relation discoverer (RD), designed to predict new relations for test instances based on demonstrations formed by training instances with known relations; and (2) a relation predictor (RP), used to select the most likely relation for a test instance from n candidate relations, guided by demonstrations composed of their instances. To enhance the ability of our framework to predict new relations, we design a self-correcting inference strategy composed of three stages: relation discovery, relation denoising, and relation prediction. In the first stage, we use RD to preliminarily predict new relations for all test instances. Next, we apply RP to select some high-reliability test instances for each new relation from the prediction results of RD through a cross-validation method. During the third stage, we employ RP to re-predict the relations of all test instances based on the demonstrations constructed from these reliable test instances. Extensive experiments on three OpenRE datasets demonstrate the effectiveness of our framework. We release our code at https://github.com/XMUDeepLIT/LLM-OREF.git.

Direct Preference Optimization (DPO) is a widely used reinforcement learning from human feedback (RLHF) method across various domains. The study of token importance has attracted widespread attention in DPO. Researchers have found that token importance is crucial for improving the effectiveness of DPO. It is observed that identical or semantically similar content (defined as ambiguous content) frequently appears within the preference pairs. We hypothesize that the presence of ambiguous content during DPO training may introduce ambiguity, thereby limiting further improvements in alignment. Through mathematical analysis and proof-of-concept experiments, we reveal that ambiguous content may potentially introduce ambiguities, thereby degrading performance. To address this issue, we introduce Ambiguity Awareness Optimization (AAO), a simple yet effective approach that automatically re-weights ambiguous content to reduce ambiguities by calculating semantic similarity from preference pairs. Through extensive experiments, we demonstrate that AAO consistently and significantly surpasses state-of-the-art approaches in performance, without markedly increasing response length, across multiple model scales and widely adopted benchmark datasets, including AlpacaEval 2, MT-Bench, and Arena-Hard. Specifically, AAO outperforms DPO by up to 8.9 points on AlpacaEval 2 and achieves an improvement of by up to 15.0 points on Arena-Hard.

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Improving Multilingual Retrieval-Augmented Language Models through Dialectic Reasoning Argumentations
Leonardo Ranaldi | Federico Ranaldi | Fabio Massimo Zanzotto | Barry Haddow | Alexandra Birch

Retrieval-augmented generation (RAG) is key to improving large language models (LLMs) in systematically accessing richer factual knowledge. Yet, using RAG mechanisms brings intrinsic challenges, as LLMs must deal with conflicting knowledge, especially in multilingual retrieval, where the heterogeneity of knowledge retrieved may deliver different outlooks. To make RAG more analytical, critical and grounded, we introduce Dialectic-RAG (DRAG), a modular approach guided by Argumentative Explanations, i.e., structured reasoning process that systematically evaluates retrieved information by comparing, contrasting, and resolving conflicting perspectives. Given a query and a set of multilingual related documents, selects and exemplifies relevant knowledge for delivering dialectic explanations that, by critically weighing opposing arguments and filtering extraneous content, clearly determine the final response. We show the impact of our framework both as an in-context learning strategy and for constructing demonstrations to instruct smaller models. Our experiments demonstrate that significantly improves RAG approaches, requiring low-impact computational effort and providing robustness to knowledge perturbations.

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Predicate-Guided Generation for Mathematical Reasoning
Jiajun Chen | Yik-Cheung Tam

We present Prolog-MATH, a curated corpus designed to support mathematical reasoning in large language models (LLMs) through logic programming. Each verbal math problem in the dataset is paired with a chain-of-thought explanation to generate Prolog program via a two-stage automated pipeline. In the first stage, an LLM (e.g., Deepseek-V3) predicts a set of relevant mathematical predicates that could be useful in solving the problem. In the second stage, the LLM uses these suggested predicates along with the expected answer type to gen- erate a complete Prolog program. To improve coverage, we fine-tune an open-source LLM us- ing supervised fine-tuning, followed by GRPO (Group Relative Policy Optimization) training to address problems that Deepseek-V3 fails to solve. To support this training, we propose a predicate-aware reward function that evaluates how well the generated solution incorporates the suggested predicates, complementing the standard binary reward. Experimental results show that: 1) Our two-stage pipeline achieves 81.3% solution coverage on the MATH training set; 2) GRPO training with the predicate-aware reward function enables a series of base models to correctly solve additional problems missed by Deepseek-V3, further increasing solution coverage to 97.4%. Data and source code can be obtained at the Github repository.

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ComplexTempQA: A 100m Dataset for Complex Temporal Question Answering
Raphael Gruber | Abdelrahman Abdallah | Michael Färber | Adam Jatowt

We introduce ComplexTempQA,a large-scale dataset consisting of over 100 million question-answer pairs designed to tackle the challenges in temporal question answering. ComplexTempQA significantly surpasses existing benchmarks in scale and scope. Utilizing Wikipedia and Wikidata, the dataset covers questions spanning over two decades and offers an unmatched scale. We introduce a new taxonomy that categorizes questions as attributes, comparisons, and counting questions, revolving around events, entities, and time periods, respectively. A standout feature of ComplexTempQA is the high complexity of its questions, which demand reasoning capabilities for answering such as across-time comparison, temporal aggregation, and multi-hop reasoning involving temporal event ordering and entity recognition. Additionally, each question is accompanied by detailed metadata, including specific time scopes, allowing for comprehensive evaluation of temporal reasoning abilities of large language models.

Understanding information from visually rich documents remains a significant challenge for traditional Retrieval-Augmented Generation (RAG) methods. Existing benchmarks predominantly focus on image-based question answering (QA), overlooking the fundamental challenges of efficient retrieval, comprehension, and reasoning within dense visual documents. To bridge this gap, we introduce ViDoSeek, a novel dataset designed to evaluate RAG performance on visually rich documents requiring complex reasoning. Based on it, we identify key limitations in current RAG approaches: (i) purely visual retrieval methods struggle to effectively integrate both textual and visual features, and (ii) previous approaches often allocate insufficient reasoning tokens, limiting their effectiveness. To address these challenges, we propose ViDoRAG, a novel multi-agent RAG framework tailored for complex reasoning across visual documents. ViDoRAG employs a Gaussian Mixture Model (GMM)-based hybrid strategy to effectively handle multi-modal retrieval. To further elicit the model’s reasoning capabilities, we introduce an iterative agent workflow incorporating exploration, summarization, and reflection, providing a framework for investigating test-time scaling in RAG domains. Extensive experiments on ViDoSeek validate the effectiveness and generalization of our approach. Notably, ViDoRAG outperforms existing methods by over 10% on the competitive ViDoSeek benchmark. The code will be available.

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IndoSafety: Culturally Grounded Safety for LLMs in Indonesian Languages
Muhammad Falensi Azmi | Muhammad Dehan Al Kautsar | Alfan Farizki Wicaksono | Fajri Koto

Although region-specific large language models (LLMs) are increasingly developed, their safety remains underexplored, particularly in culturally diverse settings like Indonesia, where sensitivity to local norms is essential and highly valued by the community. In this work, we present IndoSafety, the first high-quality, human-verified safety evaluation dataset tailored for the Indonesian context, covering five language varieties: formal and colloquial Indonesian, along with three major local languages: Javanese, Sundanese, and Minangkabau. IndoSafety is constructed by extending prior safety frameworks to develop a taxonomy that captures Indonesia’s sociocultural context. We find that existing Indonesian-centric LLMs often generate unsafe outputs, particularly in colloquial and local language settings, while fine-tuning on IndoSafety significantly improves safety while preserving task performance. Our work highlights the critical need for culturally grounded safety evaluation and provides a concrete step toward responsible LLM deployment in multilingual settings. Warning: This paper contains example data that may be offensive, harmful, or biased.

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Can LLMs Help You at Work? A Sandbox for Evaluating LLM Agents in Enterprise Environments
Harsh Vishwakarma | Ankush Agarwal | Ojas Patil | Chaitanya Devaguptapu | Mahesh Chandran

Enterprise systems are crucial for enhancing productivity and decision-making among employees and customers. Integrating LLM based systems into enterprise systems enables intelligent automation, personalized experiences, and efficient information retrieval, driving operational efficiency and strategic growth. However, developing and evaluating such systems is challenging due to the inherent complexity of enterprise environments, where data is fragmented across multiple sources and governed by sophisticated access controls. We present EnterpriseBench, a comprehensive benchmark that simulates enterprise settings, featuring 500 diverse tasks across software engineering, HR, finance, and administrative domains. Our benchmark uniquely captures key enterprise characteristics including data source fragmentation, access control hierarchies, and cross-functional workflows. Additionally, we provide a novel data generation pipeline that creates internally consistent enterprise tasks from organizational metadata. Experiments with state-of-the-art LLM agents demonstrate that even the most capable models achieve only 41.8% task completion, highlighting significant opportunities for improvement in enterprise-focused AI systems.

We show that training a single d-dimensional steering vector per layer with reinforcement learning, while freezing all base weights, matches the accuracy of fully RL-tuned reasoning models on mathematical-reasoning tasks.On an 8 billion-parameter model this adds only ≈ 0.0016% additional parameters and reproduces performance across a range of base models and mathematical-reasoning benchmarks.These results tighten the upper bound on the parameter budget required for high-level chain-of-thought reasoning, indicating that millions of adapter weights are unnecessary.The minimal trainable footprint reduces optimizer memory and inter-GPU communication, lowering the overall cost of fine-tuning.Moreover, a logit-lens analysis shows that the learned vectors amplify coherent token directions, providing clearer insight into the model’s internal computations.

Recent large pretrained models such as LLMs (e.g., GPT series) and VLAs (e.g., OpenVLA) have achieved notable progress on multimodal tasks, yet they are built upon a multi-input single-output (MISO) paradigm. We show that this paradigm fundamentally limits performance in multi-input multi-output (MIMO) scenarios, where parallel task execution is required. In MISO architectures, tasks compete for a shared output channel, creating mutual exclusion effects that cause unbalanced optimization and degraded performance. To address this gap, we introduce MIMO-VLA (VLASCD), a unified training framework that enables concurrent multi-task outputs, exemplified by simultaneous dialogue generation and decision-making. Inspired by human cognition, MIMO-VLA eliminates interference between tasks and supports efficient parallel processing. Experiments on the CARLA autonomous driving platform demonstrate that MIMO-VLA substantially outperforms state-of-the-art MISO-based LLMs, reinforcement learning models, and VLAs in MIMO settings, establishing a new direction for multimodal and multitask learning.

The ability to understand and answer questions over documents can be useful in many business and practical applications. However, documents often contain lengthy and diverse multimodal contents such as texts, figures, and tables, which are very time-consuming for humans to read thoroughly. Hence, there is an urgent need to develop effective and automated methods to aid humans in this task. In this work, we introduce M-LongDoc, a benchmark of 851 samples, and an automated framework to evaluate the performance of large multimodal models. We further propose a retrieval-aware tuning approach for efficient and effective multimodal document reading. Compared to existing works, our benchmark consists of more recent and lengthy documents with hundreds of pages, while also requiring open-ended explanations and not just extractive answers. To our knowledge, our training framework is the first to directly address the retrieval setting for multimodal long documents. To enhance open models, we construct a training corpus in a fully automatic manner. Experiments show that our tuning approach significantly improves the correctness of model responses by 4.6%.

Recent advances in text-only “slow-thinking” reasoning have prompted efforts to transfer this capability to vision-language models (VLMs), for training visual reasoning models (VRMs). However, such transfer faces critical challenges: Effective “slow thinking” in VRMs requires visual reflection, the ability to check the reasoning process based on visual information. Through quantitative analysis, we observe that current VRMs exhibit limited visual reflection, as their attention to visual information diminishes rapidly with longer generated responses. To address this challenge, we propose a new VRM Reflection-V, which enhances visual reflection based on reasoning data construction for cold-start and reward design for reinforcement learning (RL). Firstly, we construct vision-centered reasoning data by leveraging an agent that interacts between VLMs and reasoning LLMs, enabling cold-start learning of visual reflection patterns. Secondly, a visual attention based reward model is employed during RL to encourage reasoning based on visual information. Therefore, Reflection-V demonstrates significant improvements across multiple visual reasoning benchmarks. Furthermore, Reflection-V maintains a stronger and more consistent reliance on visual information during visual reasoning, indicating effective enhancement in visual reflection capabilities.

Human feedback is crucial in the interactions between humans and Large Language Models (LLMs). However, existing research primarily focuses on benchmarking LLMs in single-turn dialogues. Even in benchmarks designed for multi-turn dialogues, the user utterances are often independent, neglecting the nuanced and complex nature of human feedback within real-world usage scenarios. To fill this research gap, we introduce FB-Bench, a fine-grained, multi-task benchmark designed to evaluate LLMs’ responsiveness to human feedback under real-world usage scenarios in Chinese. Drawing from the two main interaction scenarios, FB-Bench comprises 591 meticulously curated samples, encompassing eight task types, five deficiency types of response, and nine feedback types. We extensively evaluate a broad array of popular LLMs, revealing significant variations in their performance across different interaction scenarios. Further analysis indicates that task, human feedback, and deficiencies of previous responses can also significantly impact LLMs’ responsiveness. Our findings underscore both the strengths and limitations of current models, providing valuable insights and directions for future research.

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HYDRA: A Multi-Head Encoder-only Architecture for Hierarchical Text Classification
Fabian Karl | Ansgar Scherp

We introduce HYDRA, a simple yet effective multi-head encoder-only architecture for hierarchical text classification that treats each level in the hierarchy as a separate classification task with its own label space. State-of-the-art approaches rely on complex components like graph encoders, label semantics, and autoregressive decoders. We demonstrate that such complexity is often unnecessary. Through parameter sharing and level-specific parameterization, HYDRA enables flat models to incorporate hierarchical awareness without architectural complexity. Experiments on four benchmarks (NYT, RCV1-V2, BGC, and WOS) demonstrate that HYDRA always increases the performance over flat models and matches or exceeds the performance of complex state-of-the-art methods.

In recent years, architectural design automation has made significant progress, but the complexity of open-world environments continues to make residential design a challenging task, often requiring experienced architects to perform multiple iterations and human-computer interactions. Therefore, assisting ordinary users in navigating these complex environments to generate and edit residential design is crucial. In this paper, we present the CARD framework, which leverages a system of specialized cross-modal agents to adapt to complex open-world environments. The framework includes a point-based cross-modal information representation (CMI-P) that encodes the geometry and spatial relationships of residential rooms, a cross-modal residential generation model, supported by our customized Text2FloorEdit model, that acts as the lead designer to create standardized floor plans, and an embedded expert knowledge base for evaluating whether the designs meet user requirements and residential codes, providing feedback accordingly. Finally, a 3D rendering module assists users in visualizing and understanding the layout. CARD enables cross-modal residential generation from free-text input, empowering users to adapt to complex environments without requiring specialized expertise.

This paper introduces DrDiff, a novel framework for long-text generation that overcomes the efficiency-quality trade-off through three core technologies. First, we design a dynamic expert scheduling mechanism that intelligently allocates computational resources during the diffusion process based on text complexity, enabling more efficient handling of text generation tasks of varying difficulty. Second, we introduce a Hierarchical Sparse Attention (HSA) mechanism that adaptively adjusts attention patterns according to a variety of input lengths, reducing computational complexity from O(n²) to O(n) while maintaining model performance. Finally, we propose a Semantic Anchor States (SAS) module that combines with DPM-solver++ to reduce diffusion steps, significantly improving generation speed. Comprehensive experiments on various long-text generation benchmarks demonstrate the superiority of our DrDiff over the existing SOTA methods.

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FaST: Feature-aware Sampling and Tuning for Personalized Preference Alignment with Limited Data
Thibaut Thonet | Germán Kruszewski | Jos Rozen | Pierre Erbacher | Marc Dymetman

LLM-powered conversational assistants are often deployed in a one-size-fits-all manner, which fails to accommodate individual user preferences. Recently, LLM personalization – tailoring models to align with specific user preferences – has gained increasing attention as a way to bridge this gap. In this work, we specifically focus on a practical yet challenging setting where only a small set of preference annotations can be collected per user – a problem we define as Personalized Preference Alignment with Limited Data (PPALLI). To support research in this area, we introduce two datasets – DnD and ELIP – and benchmark a variety of alignment techniques on them. We further propose FaST, a highly parameter-efficient approach that leverages high-level features automatically discovered from the data, achieving the best overall performance.

As large language models (LLMs) increasingly exhibit human-like capabilities, a fundamental question emerges: How can we enable LLMs to learn the underlying patterns from limited examples in entirely novel environments and apply them effectively? This question is central to the ability of LLMs in inductive reasoning. Existing research on LLM-based inductive reasoning can be broadly categorized based on whether the underlying rules are expressible via explicit mathematical equations. However, many recent studies in the beyond-equations category have emphasized rule design without grounding them in specific scenarios. Inspired by the parallels between inductive reasoning and human scientific discovery, we propose the task of LLM-Based Scientific Inductive Reasoning Beyond Equations and introduce a new benchmark, SIRBench-V1, to evaluate the inductive reasoning abilities of LLMs in scientific settings. Our experimental results show that current LLMs still struggle with this task, underscoring its difficulty and the need for further advancement in this area.

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SPECS: Specificity-Enhanced CLIP-Score for Long Image Caption Evaluation
Xiaofu Chen | Israfel Salazar | Yova Kementchedjhieva

As interest grows in generating long, detailed image captions, standard evaluation metrics become increasingly unreliable. N-gram-based metrics though efficient, fail to capture semantic correctness. Representational Similarity (RS) metrics, designed to address this, initially saw limited use due to high computational costs, while today, despite advances in hardware, they remain unpopular due to low correlation to human judgments. Meanwhile, metrics based on large language models (LLMs) show strong correlation with human judgments, but remain too expensive for iterative use during model development.We introduce SPECS (Specificity-Enhanced CLIPScore), a reference-free RS metric tailored to long image captioning. SPECS modifies CLIP with a new objective that emphasizes specificity: rewarding correct details and penalizing incorrect ones. We show that SPECS matches the performance of open-source LLM-based metrics in correlation to human judgments, while being far more efficient. This makes it a practical alternative for iterative checkpoint evaluation during image captioning model development.Our code can be found at https://github.com/mbzuai-nlp/SPECS.

Recent progress in Large Language Models (LLMs) has opened new avenues for solving complex optimization problems, including Neural Architecture Search (NAS). However, existing LLM-driven NAS approaches rely heavily on prompt engineering and domain-specific tuning, limiting their practicality and scalability across diverse tasks. In this work, we propose LM-Searcher, a novel framework that leverages LLMs for cross-domain neural architecture optimization without the need for extensive domain-specific adaptation. Central to our approach is NCode, a universal numerical string representation for neural architectures, which enables cross-domain architecture encoding and search. We also reformulate the NAS problem as a ranking task, training LLMs to select high-performing architectures from candidate pools using instruction-tuning samples derived from a novel pruning-based subspace sampling strategy. Our curated dataset, encompassing a wide range of architecture-performance pairs, encourages robust and transferable learning. Comprehensive experiments demonstrate that LM-Searcher achieves competitive performance in both in-domain (e.g., CNNs for image classification) and out-of-domain (e.g., LoRA configurations for segmentation and generation) tasks, establishing a new paradigm for flexible and generalizable LLM-based architecture search.

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Does quantization affect models’ performance on long-context tasks?
Anmol Mekala | Anirudh Atmakuru | Yixiao Song | Marzena Karpinska | Mohit Iyyer

Large language models (LLMs) now support context windows exceeding 128K tokens, but this comes with significant memory requirements and high inference latency. Quantization can mitigate these costs, but may degrade performance. In this work, we present the first systematic evaluation of quantized LLMs on tasks with long inputs (≥64K tokens) and long-form outputs. Our evaluation spans 9.7K test examples, five quantization methods (FP8, GPTQ-int8, AWQ-int4, GPTQ-int4, BNB-nf4), and five models (Llama-3.1 8B and 70B; Qwen-2.5 7B, 32B, and 72B). We find that, on average, 8-bit quantization preserves accuracy (~0.8% drop), whereas 4-bit methods lead to substantial losses, especially for tasks involving long-context inputs (drops of up to 59%). This degradation tends to worsen when the input is in a language other than English. Crucially, the effects of quantization depend heavily on the quantization method, model, and task. For instance, while Qwen-2.5 72B remains robust under BNB-nf4, Llama-3.1 70B experiences a 32% performance drop on the same task. These findings highlight the importance of a careful, task-specific evaluation before deploying quantized LLMs, particularly in long-context scenarios and for languages other than English.

Intervening the internal activations of large language models (LLMs) provides an effective inference-time alignment approach to mitigate undesirable behaviors, such as generating erroneous or harmful content, thereby ensuring safe and reliable applications of LLMs. However, previous methods neglect the misalignment discrepancy among varied tokens, resulting in deviant alignment direction and inflexible editing strength. To address these issues, we propose a token-aware editing (TAE) approach to fully utilize token-level alignment information in the activation space, therefore realizing superior post-intervention performance. Specifically, a Mutual Information-guided Graph Aggregation (MIG) module first develops an MI-guided graph to exploit the tokens’ informative interaction for activation enrichment, thus improving alignment probing and facilitating intervention. Subsequently, Misalignment-aware Adaptive Intervention (MAI) comprehensively perceives the token-level misalignment degree from token representation and prediction to guide the adaptive adjustment of editing strength, thereby enhancing final alignment performance. Extensive experiments on three alignment capabilities demonstrate the efficacy of TAE, notably surpassing baseline by 25.8% on the primary metric of truthfulness with minimal cost.

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Bitune: Leveraging Bidirectional Attention to Improve Decoder-Only LLMs
Dawid Jan Kopiczko | Tijmen Blankevoort | Yuki M Asano

Decoder-only large language models typically rely solely on masked causal attention, which limits their expressiveness by restricting information flow to one direction. We propose Bitune, a method that enhances pretrained decoder-only LLMs by incorporating bidirectional attention into prompt processing. We evaluate Bitune in instruction-tuning and question-answering settings, showing significant improvements in performance on commonsense reasoning, arithmetic, and language understanding tasks. Furthermore, extensive ablation studies validate the role of each component of the method, and demonstrate that Bitune is compatible with various parameter-efficient finetuning techniques and full model finetuning.

Ambiguity remains a fundamental challenge in Natural Language Processing (NLP) due to the inherent complexity and flexibility of human language. With the advent of Large Language Models (LLMs), addressing ambiguity has become even more critical due to their expanded capabilities and applications. In the context of Conversational Question Answering (CQA), this paper explores the definition, forms, and implications of ambiguity for language driven systems, particularly in the context of LLMs. We define key terms and concepts, categorize various disambiguation approaches enabled by LLMs, and provide a comparative analysis of their advantages and disadvantages. We also explore publicly available datasets for benchmarking ambiguity detection and resolution techniques and highlight their relevance for ongoing research. Finally, we identify open problems and future research directions, especially in agentic settings, proposing areas for further investigation. By offering a comprehensive review of current research on ambiguities and disambiguation with LLMs, we aim to contribute to the development of more robust and reliable LLM-based systems.

Argumentative essay generation (AEG) is a complex task that requires advanced semantic understanding, logical reasoning, and organized integration of perspectives. Despite showing a promising performance, current efforts often overlook the dynamical and hierarchical nature of structural argumentative planning, and struggle with flexible rhetorical expression, leading to limited argument divergence and rhetorical optimization. Inspired by human debate behavior and Bitzer’s rhetorical situation theory, we propose a debate-driven rhetorical framework for argumentative writing. The uniqueness lies in three aspects: (1) dynamic assesses the divergence of viewpoints and progressively reveals the hierarchical outline of arguments based on a depth-then-breadth paradigm, improving the perspective divergence within argumentation; (2) simulates human debate through iterative defender-attacker interactions, improving the logical coherence of arguments; (3) incorporates Bitzer’s rhetorical situation theory to flexibly select appropriate rhetorical techniques, enabling the rhetorical expression. Experiments on four benchmarks validate that our approach significantly improves logical depth, argumentative diversity, and rhetorical persuasiveness over existing state-of-the-art models.

Using effective generalization capabilities of vision language models (VLMs) in context-specific dynamic tasks for embodied artificial intelligence remains a significant challenge. Although supervised fine-tuned models can better align with the real physical world, they still exhibit sluggish responses and hallucination issues in dynamically changing environments, necessitating further alignment. Existing post-SFT methods, reliant on reinforcement learning and chain-of-thought (CoT) approaches, are constrained by sparse rewards and action-only optimization, resulting in low sample efficiency, poor consistency, and model degradation. To address these issues, this paper proposes Thought-Centric Preference Optimization (TCPO) for effective embodied decision-making. Specifically, TCPO introduces a stepwise preference-based optimization approach, transforming sparse reward signals into richer step sample pairs. It emphasizes the alignment of the model’s intermediate reasoning process, mitigating the problem of model degradation. Moreover, by incorporating Action Policy Consistency Constraint (APC), it further imposes consistency constraints on the model output. Experiments in the ALFWorld environment demonstrate an average success rate of **26.67%**, achieving a **6%** improvement over RL4VLM and validating the effectiveness of our approach in mitigating model degradation after fine-tuning. These results highlight the potential of integrating preference-based learning techniques with CoT processes to enhance the decision-making capabilities of vision-language models in embodied agents.

Large language models (LLMs) excel in diverse applications but face dual challenges: generating harmful content under jailbreak attacks and over-refusing benign queries due to rigid safety mechanisms. These issues severely affect the application of LLMs, especially in the medical and education fields. Existing approaches can be divided into three types: contrastive decoding, activation manipulation, and prompting strategies. However, all these approaches face challenges like inefficiency, fragility, or architectural constraints,ultimately failing to strike a balance between safety and usability. These problems are more obvious in multimodal large language models (MLLMs), especially in terms of heightened over-refusal in cross-modal tasks and new security risks arising from expanded attack surfaces. We propose Magic Image, an optimization-driven visual prompt framework that enhances security and reduces over-refusal at the same time. The Magic Image is optimized using gradients derived from harmful/benign training samples. Using the magic image can modify the model’s original safety alignment, maintaining robust safety while reducing unnecessary denials. Experiments demonstrate its effectiveness in preserving model performance and improving safety-responsiveness balance across datasets, including unseen data, offering a practical solution for reliable MLLM deployment.

*The comparison between discriminative and generative classifiers has intrigued researchers since [Efron (1975)’s](https://www.jstor.org/stable/2285453) seminal analysis of logistic regression versus discriminant analysis. While early theoretical work established that generative classifiers exhibit lower sample complexity but higher asymptotic error in simple linear settings, these trade-offs remain unexplored in the transformer era. We present the first comprehensive evaluation of modern generative and discriminative architectures—Auto-regressive, Masked Language Modeling, Discrete Diffusion, and Encoders for text classification. Our study reveals that the classical “two regimes” phenomenon manifests distinctly across different architectures and training paradigms. Beyond accuracy, we analyze sample efficiency, calibration, noise robustness, and ordinality across diverse scenarios. Our findings offer practical guidance for selecting the most suitable modeling approach based on real-world constraints such as latency and data limitations.*

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Visual Contextual Attack: Jailbreaking MLLMs with Image-Driven Context Injection
Miao Ziqi | Yi Ding | Lijun Li | Jing Shao

With the emergence of strong vision language capabilities, multimodal large language models (MLLMs) have demonstrated tremendous potential for real-world applications. However, the security vulnerabilities exhibited by the visual modality pose significant challenges to deploying such models in open-world environments.Recent studies have successfully induced harmful responses from target MLLMs by encoding harmful textual semantics directly into visual inputs. However, in these approaches, the visual modality primarily serves as a trigger for unsafe behavior, often exhibiting semantic ambiguity and lacking grounding in realistic scenarios. In this work, we define a novel setting: vision-centric jailbreak, where visual information serves as a necessary component in constructing a complete and realistic jailbreak context. Building on this setting, we propose the VisCo (Visual Contextual) Attack.VisCo fabricates contextual dialogue using four distinct vision-focused strategies, dynamically generating auxiliary images when necessary to construct a vision-centric jailbreak scenario.To maximize attack effectiveness, it incorporates automatic toxicity obfuscation and semantic refinement to produce a final attack prompt that reliably triggers harmful responses from the target black-box MLLMs. Specifically, VisCo achieves a toxicity score of 4.78 and an Attack Success Rate (ASR) of 85% on MM-SafetyBench against GPT-4o, significantly outperforming the baseline, which achieves a toxicity score of 2.48 and an ASR of 22.2%. Code: https://github.com/Dtc7w3PQ/Visco-Attack.

Recent advances in large language models (LLMs) have demonstrated strong mathematical reasoning abilities, even in visual contexts, with some models surpassing human performance on existing benchmarks. However, these benchmarks lack structured age categorization, clearly defined skill requirements, and—crucially—were not designed to assess human performance in international competitions. To address these limitations, we introduce MathGames, a new benchmark of 2,183 high-quality mathematical problems (both text-only and multimodal) in an open-ended format, sourced from an international mathematical games championships. Spanning seven age groups and a skill-based taxonomy, MathGames enables a structured evaluation of LLMs’ mathematical and logical reasoning abilities. Our experiments reveal a substantial gap between state-of-the-art LLMs and human participants—even 11-year-olds consistently outperform some of the strongest models—highlighting the need for advancements. Further, our detailed error analysis offers valuable insights to guide future research. The data is publicly available at https://disi-unibo-nlp.github.io/math-games.

We present CodeArena to emulate the complexity/diversity of real-world coding tasks, spanning 40 categories and 44 PLs. A 20B diverse synthetic instruction corpus is created by scaling instructions to help Qwen2.5-SynCoder achieve SOTA performance. Abstract: Code large language models (codeLLMs) have made significant strides in code generation. Most previous code-related benchmarks, which consist of various programming exercises along with the corresponding test cases, are used as a common measure to evaluate the performance and capabilities of code LLMs. However, the current code LLMs focus on synthesizing the correct code snippet, ignoring the alignment with human preferences, where the query should be sampled from the practical application scenarios and the model-generated responses should satisfy the human preference. To bridge the gap between the model-generated response and human preference, we present a rigorous human-curated benchmark CodeArena to emulate the complexity and diversity of real-world coding tasks, where 397 high-quality samples spanning 40 categories and 44 programming languages, carefully curated from user queries. Further, we propose a diverse synthetic instruction corpus SynCode-Instruct (nearly 20B tokens) by scaling instructions from the website to verify the effectiveness of the large-scale synthetic instruction fine-tuning, where Qwen2.5-SynCoder totally trained on synthetic instruction data can achieve top-tier performance of open-source code LLMs. The results find performance differences between execution-based benchmarks and CodeArena. Our systematic experiments of CodeArena on 40+ LLMs reveal a notable performance gap between open SOTA code LLMs (e.g. Qwen2.5-Coder) and proprietary LLMs (e.g., OpenAI o1), underscoring the importance of the human preference alignment.

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Language models can learn implicit multi-hop reasoning, but only if they have lots of training data
Yuekun Yao | Yupei Du | Dawei Zhu | Michael Hahn | Alexander Koller

Implicit reasoning is the ability of a language model to solve multi-hop reasoning tasks in a single forward pass, without chain of thought.We investigate this capability using GPT2-style language models trained from scratch on controlled k-hop reasoning datasets (k = 2, 3, 4). We show that while such models can indeed learn implicit k-hop reasoning,the required training data grows exponentially in k, and the requirednumber of transformer layers grows linearly in k.We offer a theoretical explanation for why this depth growth is necessary.We further find that the data requirement can be mitigated, but not eliminated,through curriculum learning.

We introduce UniversalCEFR, a large-scale multilingual multidimensional dataset of texts annotated according to the CEFR (Common European Framework of Reference) scale in 13 languages. To enable open research in both automated readability and language proficiency assessment, UniversalCEFR comprises 505,807 CEFR-labeled texts curated from educational and learner-oriented resources, standardized into a unified data format to support consistent processing, analysis, and modeling across tasks and languages. To demonstrate its utility, we conduct benchmark experiments using three modelling paradigms: a) linguistic feature-based classification, b) fine-tuning pre-trained LLMs, and c) descriptor-based prompting of instruction-tuned LLMs. Our results further support using linguistic features and fine-tuning pretrained models in multilingual CEFR level assessment. Overall, UniversalCEFR aims to establish best practices in data distribution in language proficiency research by standardising dataset formats and promoting their accessibility to the global research community.

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CROP: Contextual Region-Oriented Visual Token Pruning
Jiawei Guo | Feifei Zhai | Pu Jian | Qianrun Wei | Yu Zhou

Current VLM-based VQA methods often process entire images, leading to excessive visual tokens that include redundant information irrelevant to the posed question. This abundance of unnecessary image details creates numerous visual tokens, drastically increasing memory and computational requirements in VLMs. To address this, we propose Contextual Region-Oriented Visual Token Pruning (CROP), a novel framework to compress visual tokens through a two-step process: Localization and Pruning. Specifically, CROP first employs an efficient model to identify the contextual region relevant to the input query. Subsequently, two distinct strategies are introduced for pruning: (1) Pre-LLM Compression (PLC), which adaptively compresses different image regions with varying ratios, and (2) Inner-LLM Pruning (ILP), a training-free method that prunes tokens within early LLM layers guided by the identified contextual region. Extensive experiments on a wide range of VQA tasks demonstrate that CROP significantly outperforms existing visual token pruning methods and achieves state-of-the-art performance.

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CR4-NarrEmote: An Open Vocabulary Dataset of Narrative Emotions Derived Using Citizen Science
Andrew Piper | Robert Budac

We introduce “Citizen Readers for Narrative Emotions” (CR4-NarrEmote), a large-scale, open-vocabulary dataset of narrative emotions derived through a citizen science initiative. Over a four-month period, 3,738 volunteers contributed more than 200,000 emotion annotations across 43,000 passages from long-form fiction and non-fiction, spanning 150 years, twelve genres, and multiple Anglophone cultural contexts. To facilitate model training and comparability, we provide mappings to both dimensional (Valence-Arousal-Dominance) and categorical (NRC Emotion) frameworks. We evaluate annotation reliability using lexical, categorical, and semantic agreement measures, and find substantial alignment between citizen science annotations and expert-generated labels. As the first open-vocabulary resource focused on narrative emotions at scale, CR4-NarrEmote provides an important foundation for affective computing and narrative understanding.

Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse natural language processing tasks. However, their extensive memory requirements, particularly due to KV cache growth during long-text understanding and generation, present significant challenges for deployment in resource-constrained environments. Quantization has emerged as a promising solution to reduce memory consumption while preserving historical information. We propose XQuant, a training-free and plug-and-play framework that achieves ultra-low equivalent bit-width KV cache quantization. XQuant introduces two key innovations: a computationally negligible data-free calibration method and cross-layer KV cache compression, enabling quantization to sub-1.4 bits. Extensive experiments on TruthfulQA and LongBench demonstrate that XQuant outperforms state-of-the-art methods (e.g., KIVI-2bit and AsymKV-1.5bit) by achieving lower bit-width while maintaining superior performance, establishing a better trade-off between memory efficiency and model accuracy. The source code is available at https://github.com/brinenick511/XQuant.

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DINT Transformer
Yueyang Cang | Yuhang Liu | Xiaoteng Zhang | Erlu Zhao | Li Shi

The DIFF Transformer mitigates interference from irrelevant contexts by introducing a differential attention mechanism, thereby enhancing focus on critical tokens. However, this architecture suffers from two major limitations: first, its use of two independent attention matrices leads to numerical instability, and second, it lacks global context modeling, which is essential for identifying globally significant tokens. To address these challenges, we propose the DINT Transformer, which extends the DIFF Transformer by incorporating an integral mechanism. By computing global importance scores and integrating them into the attention matrix, the DINT Transformer not only improves overall numerical stability but also significantly enhances its ability to capture global dependencies. Experimental results demonstrate that the DINT Transformer achieves superior accuracy and robustness across various practical applications, including long-context language modeling and key information retrieval. These advancements establish the DINT Transformer as a highly effective and promising architecture.

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ICR: Iterative Clarification and Rewriting for Conversational Search
Zhiyu Cao | Peifeng Li | Qiaoming Zhu

Most previous work on Conversational Query Rewriting employs an end-to-end rewriting paradigm. However, this approach is hindered by the issue of multiple fuzzy expressions within the query, which complicates the simultaneous identification and rewriting of multiple positions. To address this issue, we propose a novel framework ICR (Iterative Clarification and Rewriting), an iterative rewriting scheme that pivots on clarification questions. Within this framework, the model alternates between generating clarification questions and rewritten queries. The experimental results show that our ICR can continuously improve retrieval performance in the clarification-rewriting iterative process, thereby achieving state-of-the-art performance on two popular datasets.

Recent studies have shown that Contrastive Language-Image Pre-training (CLIP) models are threatened by targeted data poisoning and backdoor attacks due to massive training image-caption pairs crawled from the Internet. Previous defense methods correct poisoned image-caption pairs by matching a new caption for each image. However, the matching process solely relies on the global representations of images and captions, overlooking fine-grained features of visual and textual features. It may introduce incorrect image-caption pairs and detriment the CLIP pre-training. To address their limitations, we propose an Optimal Transport-based framework to reconstruct the image-caption pairs, named OTCCLIP. We involve a new optimal transport-based distance measure between fine-grained visual and textual feature sets and re-assign new captions based on the proposed optimal transport distance. Additionally, to further reduce the negative impact of mismatched pairs, we encourage the inter- and intra-modality fine-grained alignment by employing optimal transport-based objective functions. Our experiments demonstrate that OTCCLIP can successfully decrease the attack success rates of poisoning attacks to 0% in most cases. Also, compared to previous methods, OTCCLIPsignificantly improves CLIP’s zero-shot and linear probing performance trained on poisoned datasets.

Personalized search systems in e-commerce platforms increasingly involve user interactions with AI assistants, where users consult about products, usage scenarios, and more. Leveraging consultation to personalize search services is trending. Existing methods typically rely on semantic similarity to align historical consultations with current queries due to the absence of ‘value’ labels, but we observe that semantic similarity alone often fails to capture the true value of consultation for personalization. To address this, we propose a consultation value assessment framework that evaluates historical consultations from three novel perspectives: (1) Scenario Scope Value, (2) Posterior Action Value, and (3) Time Decay Value. Based on this, we introduce VAPS, a value-aware personalized search model that selectively incorporates high-value consultations through a consultation–user action interaction module and an explicit objective that aligns consultations with user actions. Experiments on both public and commercial datasets show that VAPS consistently outperforms baselines in both retrieval and ranking tasks. Codes are available at https://github.com/E-qin/VAPS.

Current temporal knowledge graph question answering (TKGQA) methods primarily focus on implicit temporal constraints, lacking the capability to handle more complex temporal queries, and struggle with limited reasoning abilities and error propagation in decomposition frameworks. We propose RTQA, a novel framework to address these challenges by enhancing reasoning over TKGs without requiring training. Following recursive thinking, RTQA recursively decomposes questions into sub-problems, solves them bottom-up using LLMs and TKG knowledge, and employs multi-path answer aggregation to improve fault tolerance. RTQA consists of three core components: the Temporal Question Decomposer, the Recursive Solver, and the Answer Aggregator. Experiments on MultiTQ and TimelineKGQA benchmarks demonstrate significant Hits@1 improvements in “Multiple” and “Complex” categories, outperforming state-of-the-art methods. Our code and data are available at https://github.com/zjukg/RTQA.

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Not All Parameters Are Created Equal: Smart Isolation Boosts Fine-Tuning Performance
Yao Wang | Di Liang | Minlong Peng

Supervised fine-tuning (SFT) is a pivotal approach to adapting large language models (LLMs) for downstream tasks; however, performance often suffers from the “seesaw phenomenon”, where indiscriminate parameter updates yield progress on certain tasks at the expense of others. To address this challenge, we propose a novel Core Parameter Isolation Fine-Tuning (CPI-FT) framework. Specifically, we first independently fine-tune the LLM on each task to identify its core parameter regions by quantifying parameter update magnitudes. Tasks with similar core regions are then grouped based on region overlap, forming clusters for joint modeling. We further introduce a parameter fusion technique: for each task, core parameters from its individually fine-tuned model are directly transplanted into a unified backbone, while non-core parameters from different tasks are smoothly integrated via Spherical Linear Interpolation (SLERP), mitigating destructive interference. A lightweight, pipelined SFT training phase using mixed-task data is subsequently employed, while freezing core regions from prior tasks to prevent catastrophic forgetting. Extensive experiments on multiple public benchmarks demonstrate that our approach significantly alleviates task interference and forgetting, consistently outperforming vanilla multi-task and multi-stage fine-tuning baselines.

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AI Knows Where You Are: Exposure, Bias, and Inference in Multimodal Geolocation with KoreaGEO
Xiaonan Wang | Bo Shao | Hansaem Kim

Recent advances in vision-language models (VLMs) have enabled accurate image-based geolocation, raising serious concerns about location privacy risks in everyday social media posts. Yet, a systematic evaluation of such risks is still lacking: existing benchmarks show coarse granularity, linguistic bias, and a neglect of multimodal privacy risks. To address these gaps, we introduce KoreaGEO, the first fine-grained, multimodal, and privacy-aware benchmark for geolocation, built on Korean street views. The benchmark covers four socio-spatial clusters and nine place types with rich contextual annotations and two captioning styles that simulate real-world privacy exposure. To evaluate mainstream VLMs, we design a three-path protocol spanning image-only, functional-caption, and high-risk-caption inputs, enabling systematic analysis of localization accuracy, spatial bias, and reasoning behavior. Results show that input modality exerts a stronger influence on localization precision and privacy exposure than model scale or architecture, with high-risk captions substantially boosting accuracy. Moreover, they highlight structural prediction biases toward core cities.

Large Language Models (LLMs) have achieved remarkable success across various domains. However, a fundamental question remains: Can LLMs effectively utilize causal knowledge for prediction and generation? Through empirical studies, we find that LLMs trained directly on large-scale data often capture spurious correlations rather than true causal relationships, leading to suboptimal performance, especially in out-of-distribution (OOD) scenarios. To address this challenge, we propose Causal Attention Tuning (CAT), a novel approach that injects fine-grained causal knowledge into the attention mechanism. We propose an automated pipeline that leverages human priors to automatically generate token-level causal signals and introduce the Re-Attention mechanism to guide training, helping the model focus on causal structures while mitigating noise and biases in attention scores. Experimental results on our proposed Spurious Token Game (STG) benchmark and multiple downstream tasks demonstrate that our approach effectively leverages causal knowledge for prediction and remains robust in OOD scenarios. The CAT achieves an average improvement of 5.76% on the STG dataset and 1.56% on downstream tasks. Notably, the OOD performance of the Llama-3.1-8B model on STG_M increased from 64.5% to 90.5%, and Qwen’s OOD performance on the STG_H dataset improved from 25.4% to 55.9%. Implementation details can be found at https://github.com/Kairong-Han/CAT.

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Enhancing LLM Text Detection with Retrieved Contexts and Logits Distribution Consistency
Zhaoheng Huang | Yutao Zhu | Ji-Rong Wen | Zhicheng Dou

Large language models (LLMs) can generate fluent text, raising concerns about misuse in online comments and academic writing, leading to issues like corpus pollution and copyright infringement. Existing LLM text detection methods often rely on features from the logit distribution of the input text. However, the distinction between the LLM-generated and human-written texts may rely on only a few tokens due to the short length or insufficient information in some texts, leading to minimal and hard-to-detect differences in logit distributions. To address this, we propose HALO, an LLM-based detection method that leverages external text corpora to evaluate the difference in the logit distribution of input text under retrieved human-written and LLM-rewritten contexts. HALO also complements basic detection features and can serve as a plug-and-play module to enhance existing detection methods. Extensive experiments on five public datasets with three widely-used source LLMs show that our proposed detection method achieves state-of-the-art performance in AUROC, both in cross-domain and domain-specific scenarios.

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Measuring Chain of Thought Faithfulness by Unlearning Reasoning Steps
Martin Tutek | Fateme Hashemi Chaleshtori | Ana Marasovic | Yonatan Belinkov

When prompted to think step-by-step, language models (LMs) produce a chain of thought (CoT), a sequence of reasoning steps that the model supposedly used to produce its prediction. Despite much work on CoT prompting, it is unclear if reasoning verbalized in a CoT is faithful to the models’ parametric beliefs. We introduce a framework for measuring parametric faithfulness of generated reasoning and propose Faithfulness by Unlearning Reasoning steps (FUR), an instance of this framework. FUR erases information contained in reasoning steps from model parameters and measures faithfulness as the resulting effect on the model’s prediction. Our experiments with four LMs and five multi-choice question answering (MCQA) datasets show that FUR is frequently able to precisely change the underlying models’ prediction for a given instance by unlearning key steps, indicating when a CoT is parametrically faithful. Further analysis shows that CoTs generated by models post-unlearning support different answers, hinting at a deeper effect of unlearning.

Vision tokens in multimodal large language models often dominate huge computational overhead due to their excessive length compared to linguistic modality. Abundant recent methods aim to solve this problem with token pruning, which first defines an importance criterion for tokens and then prunes the unimportant vision tokens during inference. However, in this paper, we show that the importance is not an ideal indicator to decide whether a token should be pruned. Surprisingly, it usually results in inferior performance than random token pruning and leading to incompatibility to efficient attention computation operators. Instead, we propose DART (Duplication-Aware Reduction of Tokens), which prunes tokens based on its duplication with other tokens, leading to significant and training-free acceleration. Concretely, DART selects a small subset of pivot tokens and then retains the tokens with low duplication to the pivots, ensuring minimal information loss during token pruning. Experiments demonstrate that DART can prune 88.9% vision tokens while maintaining comparable performance, leading to a 1.99× and 2.99× speed-up in total time and prefilling stage, respectively, with good compatibility to efficient attention operators.

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AgentPro: Enhancing LLM Agents with Automated Process Supervision
Yuchen Deng | Shichen Fan | Naibo Wang | Xinkui Zhao | See-Kiong Ng

Large language model (LLM) agents have demonstrated significant potential for addressing complex tasks through mechanisms such as chain-of-thought reasoning and tool invocation. However, current frameworks lack explicit supervision during the reasoning process, which may lead to error propagation across reasoning chains and hinder the optimization of intermediate decision-making stages. This paper introduces a novel framework, AgentPro, which enhances LLM agent performance by automated process supervision. AgentPro employs Monte Carlo Tree Search to automatically generate step-level annotations, and develops a process reward model based on these annotations to facilitate fine-grained quality assessment of reasoning. By employing a rejection sampling strategy, the LLM agent dynamically adjusts generation probability distributions to prevent the continuation of erroneous paths, thereby improving reasoning capabilities. Extensive experiments on four datasets indicate that our method significantly outperforms existing agent-based LLM methods (e.g., achieving a 6.32% increase in accuracy on the HotpotQA dataset), underscoring its proficiency in managing intricate reasoning chains.

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PORTS: Preference-Optimized Retrievers for Tool Selection with Large Language Models
Lorenzo Molfetta | Giacomo Frisoni | Nicolò Monaldini | Gianluca Moro

Integrating external tools with Large Language Models (LLMs) has emerged as a promising paradigm for accomplishing complex tasks. Since LLMs still struggle to effectively manage large tool collections, researchers have begun exploring retrieval-based methods to pre-select the most relevant options, addressing input length and latency constraints. However, existing retrievers are often misaligned with tool-calling LLMs due to their separate training processes. This paper presents PORTS, a novel odds ratio preference optimization method for training retrievers aimed at tool selection. Using a perplexity-inspired preference signal from a frozen LLM, our approach fine-tunes a retriever to find helpful tools by optimizing the correlation between the selection probabilities and the downstream performances while jointly enforcing a contrastive semantic loss between documentation strings. The versatility of PORTS and its ability to significantly improve tool selection accuracy are demonstrated through extensive experiments on six datasets, two encoder models, and three LLMs with diverse prior knowledge. With low computational demands, our alignment process facilitates generalization to new queries and tools, proving valuable for practical applications with evolving toolsets.

Open-world knowledge graph completion (KGC) aims to infer novel facts by enriching existing graphs with external knowledge sources while maintaining semantic consistency under the open-world assumption (OWA). Generation-based KGC methods leverage the inherent strengths of large language models (LLMs) in language understanding and creative problem-solving, making them promising approaches. However, they face limitations: (1) The unreliable external knowledge from LLMs can lead to hallucinations and undermine KGC reliability. (2) The lack of an automated and rational evaluation strategy for new facts under OWA results in the exclusion of some new but correct entities. In the paper, we propose MusKGC, a novel multi-source knowledge enhancement framework based on an LLM for KGC under OWA. We induce relation templates with entity type constraints to link structured knowledge with natural language, improving the comprehension of the LLM. Next, we combine intrinsic KG facts with reliable external knowledge to guide the LLM in accurately generating missing entities with supporting evidence. Lastly, we introduce a new evaluation strategy for factuality and consistency to validate accurate inferences of new facts, including unknown entities. Extensive experiments show that our proposed model achieves SOTA performance across benchmarks, and our evaluation strategy effectively assesses new facts under OWA.

Personality is an important concept in psychology that reflects individual differences in thinking and behavior, and has significant applications across various fields. Most existing personality analysis methods address this issue at the bag level, treating the entire corpus gathered from one individual as a single unit for classification. However, this paradigm presents several challenges. From the data perspective, collecting a large corpus for each individual and performing comprehensive annotations pose significant difficulties in both data collection and labeling. On the application side, concentrating on classifying the entire corpus limits its applicability in more common single-instance scenarios. To address these issues, we propose a new task paradigm in text-based personality representation learning. Specifically, we construct a triplet personality trend comparison dataset to learn single-sentence personality embeddings with desirable metric properties. This approach removes the traditional constraints on data sources, facilitating dataset expansion, and can leverage the transfer capabilities of embeddings to easily adapt to various downstream tasks. Our experiments show that the learned embeddings significantly boost performance by a relative 10% across various applications, including personality detection, personality retrieval, and emotion translation prediction. The code and dataset are available at https://github.com/zjutangk/PTCD.

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Reshaping Representation Space to Balance the Safety and Over-rejection in Large Audio Language Models
Hao Yang | Lizhen Qu | Ehsan Shareghi | Gholamreza Haffari

Large Audio Language Models (LALMs) have extended the capabilities of Large Language Models (LLMs) by enabling audio-based human interactions. However, recent research has revealed that LALMs remain vulnerable to harmful queries due to insufficient safety-alignment. Despite advances in defence measures for text and vision LLMs, effective safety-alignment strategies and audio-safety dataset specifically targeting LALMs are notably absent. Meanwhile defence measures based on Supervised Fine-tuning (SFT) struggle to address safety improvement while avoiding over-rejection issues, significantly compromising helpfulness. In this work, we propose an unsupervised safety-fine-tuning strategy as remedy that reshapes model’s representation space to enhance existing LALMs safety-alignment while balancing the risk of over-rejection. Our experiments, conducted across three generations of Qwen LALMs, demonstrate that our approach significantly improves LALMs safety under three modality input conditions (audio-text, text-only, and audio-only) while increasing over-rejection rate by only 0.88% on average. Warning: this paper contains harmful examples.

In the era of evaluating large language models (LLMs), data contamination has become an increasingly prominent concern. To address this risk, LLM benchmarking has evolved from a *static* to a *dynamic* paradigm. In this work, we conduct an in-depth analysis of existing *static* and *dynamic* benchmarks for evaluating LLMs. We first examine methods that enhance *static* benchmarks and identify their inherent limitations. We then highlight a critical gap—the lack of standardized criteria for evaluating *dynamic* benchmarks. Based on this observation, we propose a series of optimal design principles for *dynamic* benchmarking and analyze the limitations of existing *dynamic* benchmarks.This survey provides a concise yet comprehensive overview of recent advancements in data contamination research, offering valuable insights and a clear guide for future research efforts. We maintain a GitHub repository to continuously collect both static and dynamic benchmarking methods for LLMs. The repository can be found at this link.

Recent LLMs have demonstrated promising ability in solving finance related problems. However, applying LLMs in real-world finance application remains challenging due to its high risk and high stakes property. This paper introduces FinTrust, a comprehensive benchmark specifically designed for evaluating the trustworthiness of LLMs in finance applications. Our benchmark focuses on a wide range of alignment issues based on practical context and features fine-grained tasks for each dimension of trustworthiness evaluation. We assess eleven LLMs on FinTrust and find that proprietary models like o4-mini outperforms in most tasks such as safety while open-source models like DeepSeek-V3 have advantage in specific areas like industry-level fairness. For challenging task like fiduciary alignment and disclosure, all LLMs fall short, showing a significant gap in legal awareness. We believe that FinTrust can be a valuable benchmark for LLMs’ trustworthiness evaluation in finance domain.

This work addresses a fundamental barrier in recommender systems: the inability to generalize across domains without extensive retraining. Traditional ID-based approaches fail entirely in cold-start and cross-domain scenarios where new users or items lack sufficient interaction history. Inspired by foundation models’ cross-domain success, we develop a foundation model for sequential recommendation that achieves genuine zero-shot generalization capabilities. Our approach fundamentally departs from existing ID-based methods by deriving item representations exclusively from textual features. This enables immediate embedding of any new item without model retraining. We introduce unified item tokenization with Finite Scalar Quantization that transforms heterogeneous textual descriptions into standardized discrete tokens. This eliminates domain barriers that plague existing systems. Additionally, the framework features hybrid bidirectional-causal attention that captures both intra-item token coherence and inter-item sequential dependencies. An efficient catalog-aware beam search decoder enables real-time token-to-item mapping. Unlike conventional approaches confined to their training domains, RecGPT naturally bridges diverse recommendation contexts through its domain-invariant tokenization mechanism. Comprehensive evaluations across six datasets and industrial scenarios demonstrate consistent performance advantages.

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Towards Holistic Evaluation of Large Audio-Language Models: A Comprehensive Survey
Chih-Kai Yang | Neo S. Ho | Hung-yi Lee

With advancements in large audio-language models (LALMs), which enhance large language models (LLMs) with auditory capabilities, these models are expected to demonstrate universal proficiency across various auditory tasks. While numerous benchmarks have emerged to assess LALMs’ performance, they remain fragmented and lack a structured taxonomy. To bridge this gap, we conduct a comprehensive survey and propose a systematic taxonomy for LALM evaluations, categorizing them into four dimensions based on their objectives: (1) General Auditory Awareness and Processing, (2) Knowledge and Reasoning, (3) Dialogue-oriented Ability, and (4) Fairness, Safety, and Trustworthiness. We provide detailed overviews within each category and highlight challenges in this field, offering insights into promising future directions. To the best of our knowledge, this is the first survey specifically focused on the evaluations of LALMs, providing clear guidelines for the community.

Sparse Autoencoders (SAEs) have proven to be powerful tools for interpreting neural networks by decomposing hidden representations into disentangled, interpretable features via sparsity constraints. However, conventional SAEs are constrained by the fixed sparsity level chosen during training; meeting different sparsity requirements therefore demands separate models and increases the computational footprint during both training and evaluation. We introduce a novel training objective, HierarchicalTopK, which trains a single SAE to optimise reconstructions across multiple sparsity levels simultaneously. Experiments with Gemma-2 2B demonstrate that our approach achieves Pareto-optimal trade-offs between sparsity and explained variance, outperforming traditional SAEs trained at individual sparsity levels. Further analysis shows that HierarchicalTopK preserves high interpretability scores even at higher sparsity. The proposed objective thus closes an important gap between flexibility and interpretability in SAE design.

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Learn and Unlearn: Addressing Misinformation in Multilingual LLMs
TaiMing Lu | Philipp Koehn

This paper investigates the propagation of information in multilingual large language models (LLMs) and evaluates the efficacy of various unlearning methods. We demonstrate that fake information, regardless of the language it is in, once introduced into these models through training data, can spread across different languages, compromising the integrity and reliability of the generated content. Our findings reveal that standard unlearning techniques, which typically focus on English data, are insufficient in mitigating the spread of harmful content in multilingual contexts and could inadvertently reinforce harmful content across languages. We show that only by addressing harmful responses in both English and the original language of the harmful data we can effectively eliminate it for all languages. This underscores the critical need for comprehensive unlearning strategies that consider the multilingual nature of modern LLMs to enhance their safety and reliability across landscapes.

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PRISM: Efficient Long-Range Reasoning With Short-Context LLMs
Dulhan Jayalath | James Bradley Wendt | Nicholas Monath | Sandeep Tata | Beliz Gunel

Long-range tasks demand reasoning over long inputs. However, existing solutions are limited, e.g., long-context models require large compute budgets, parameter-efficient fine-tuning (PEFT) needs training data, and retrieval-augmented generation (RAG) entails complex task-specific designs. Though in-context approaches overcome many of these issues, methods with short-context LLMs are inefficient, trading context for processing more tokens. We introduce **PRISM**, a highly token-efficient in-context method based on structured schemas that outperforms baselines on diverse tasks with **4x shorter contexts**. This approach produces concise outputs and efficiently leverages key-value (KV) caches to **reduce costs by up to 54%**. PRISM scales down to tiny contexts without increasing costs or sacrificing quality, and generalizes to new tasks with minimal effort by generating schemas from task descriptions.

Multi-agent techniques such as role playing or multi-turn debates have been shown to be effective in improving the performance of large language models (LLMs) in downstream tasks. Despite their differences in workflows, existing multi-agent systems constructed from a single base LLM mostly use natural language for agent communication.While this is appealing for its simplicity and interpretability, it also introduces inevitable information loss as one model must down sample its continuous state vectors to discrete tokens before transferring them to the other model.Such losses are particularly significant when the information to transfer is not simple facts, but reasoning logics or abstractive thoughts.To tackle this problem, we propose a new communication protocol that transfers both natural language tokens and token-wise state transition trajectory from one agent to another.Particularly, compared to the actual state value, we find that the sequence of state changes in LLMs after generating each token can better reflect the information hidden behind the inference process.We propose a State Delta Encoding (SDE) method to represent state transition trajectories.The experimental results show that multi-agent systems with SDE achieve SOTA performance compared to other communication protocols, particularly in tasks that involve complex reasoning. We have open-sourced all the code and data in https://github.com/LittleDinoC/StateDelta/.

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SAEs Are Good for Steering – If You Select the Right Features
Dana Arad | Aaron Mueller | Yonatan Belinkov

Sparse Autoencoders (SAEs) have been proposed as an unsupervised approach to learn a decomposition of a model’s latent space. This enables useful applications, such as fine-grained steering of model outputs without requiring labeled data. Current steering methods identify SAE features to target by analyzing the input tokens that activate them. However, recent work has highlighted that activations alone do not fully describe the effect of a feature on the model’s output. In this work we draw a distinction between two types of features: input features, which mainly capture patterns in the model’s input, and output features, those that have a human-understandable effect on the model’s output. We propose input and output scores to characterize and locate these types of features, and show that high values for both scores rarely co-occur in the same features. These findings have practical implications: After filtering out features with low output scores, steering with SAEs results in a 2–3x improvement, matching the performance of existing supervised methods.

Deep learning models often learn and exploit spurious correlations in training data, using these non-target features to inform their predictions. Such reliance leads to performance degradation and poor generalization on unseen data. To address these limitations, we introduce a more general form of counterfactual data augmentation, termed *counterbias* data augmentation, which simultaneously tackles multiple biases (e.g., gender bias, simplicity bias) and enhances out-of-distribution robustness. We present **CoBA**: **Co**unter**B**ias **A**ugmentation, a unified framework that operates at the semantic triple level: first decomposing text into subject-predicate-object triples, then selectively modifying these triples to disrupt spurious correlations. By reconstructing the text from these adjusted triples, **CoBA** generates *counterbias* data that mitigates spurious patterns. Through extensive experiments, we demonstrate that **CoBA** not only improves downstream task performance, but also effectively reduces biases and strengthens out-of-distribution resilience, offering a versatile and robust solution to the challenges posed by spurious correlations.

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Layered Insights: Generalizable Analysis of Human Authorial Style by Leveraging All Transformer Layers
Milad Alshomary | Nikhil Reddy Varimalla | Vishal Anand | Smaranda Muresan | Kathleen McKeown

We propose a new approach for the authorship attribution task that leverages the various linguistic representations learned at different layers of pre-trained transformer-based models. We evaluate our approach on two popular authorship attribution models and three evaluation datasets, in in-domain and out-of-domain scenarios. We found that utilizing various transformer layers improves the robustness of authorship attribution models when tested on out-of-domain data, resulting in a much stronger performance. Our analysis gives further insights into how our model’s different layers get specialized in representing certain linguistic aspects that we believe benefit the model when tested out of the domain.

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When Long Helps Short: How Context Length in Supervised Fine-tuning Affects Behavior of Large Language Models
Yingming Zheng | Hanqi Li | Kai Yu | Lu Chen

Large language models (LLMs) have achieved impressive performance across natural language processing (NLP) tasks. As real-world applications increasingly demand longer context windows, continued pretraining and supervised fine-tuning (SFT) on long-context data has become a common approach. While the effects of data length in continued pretraining have been extensively studied, their implications for SFT remain unclear. In this work, we systematically investigate how SFT data length influences LLM behavior on short-context tasks. Counterintuitively, we find that long-context SFT improves short-context performance, contrary to the commonly observed degradation from long-context pretraining. To uncover the underlying mechanisms of this phenomenon, we first decouple and analyze two key components, Multi-Head Attention (MHA) and Feed-Forward Network (FFN), and show that both independently benefit from long-context SFT. We further study their interaction and reveal a knowledge preference bias: long-context SFT promotes contextual knowledge, while short-context SFT favors parametric knowledge, making exclusive reliance on long-context SFT suboptimal. Finally, we demonstrate that hybrid training mitigates this bias, offering explainable guidance for fine-tuning LLMs.

Homophone normalization–where characters that have the same sound in a writing script are mapped to one character–is a pre-processing step applied in Amharic Natural Language Processing (NLP) literature. While this may improve performance reported by automatic metrics, it also results in models that are unable to effectively process different forms of writing in a single language. Further, there might be impacts in transfer learning, where models trained on normalized data do not generalize well to other languages. In this paper, we experiment with monolingual training and cross-lingual transfer to understand the impacts of normalization on languages that use the Ge’ez script. We then propose a post-inference intervention in which normalization is applied to model predictions instead of training data. With our simple scheme of post-inference normalization, we show that we can achieve an increase in BLEU score of up to 1.03 while preserving language features in training.

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Evaluating Language Translation Models by Playing Telephone
Syeda Jannatus Saba | Steven Skiena

Our ability to efficiently and accurately evaluate the quality of machine translation systems has been outrun by the effectiveness of current language models—which limits the potential for further improving these models on more challenging tasks like long-form and literary translation. We propose an unsupervised method to generate training data for translation evaluation over different document lengths and application domains by repeated rounds of translation between source and target languages. We evaluate evaluation systems trained on texts mechanically generated using both model rotation and language translation approaches, demonstrating improved performance over a popular translation evaluation system (xCOMET) on two different tasks: (i) scoring the quality of a given translation against a human reference and (ii) selecting which of two translations is generationally closer to an original source document.

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Doubling Your Data in Minutes: Ultra-fast Tabular Data Generation via LLM-Induced Dependency Graphs
Shuo Yang | Zheyu Zhang | Bardh Prenkaj | Gjergji Kasneci

Tabular data is critical across diverse domains, yet high-quality datasets remain scarce due to privacy concerns and the cost of collection. Contemporary approaches adopt large language models (LLMs) for tabular augmentation, but exhibit two major limitations: (1) dense dependency modeling among tabular features that can introduce bias, and (2) high computational overhead in sampling. To address these issues, we propose SPADA for SPArse Dependency-driven Augmentation, a lightweight generative framework that explicitly captures sparse dependencies via an LLM-induced graph. We treat each feature as a node and synthesize values by traversing the graph, conditioning each feature solely on its parent nodes. We explore two synthesis strategies: a non-parametric method using Gaussian kernel density estimation, and a conditional normalizing flow model that learns invertible mappings for conditional density estimation. Experiments on four datasets show that SPADA reduces constraint violations by 4% compared to diffusion-based methods and accelerates generation by nearly 9,500× over LLM-based baselines.

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SPaRC: A Spatial Pathfinding Reasoning Challenge
Lars Benedikt Kaesberg | Jan Philip Wahle | Terry Ruas | Bela Gipp

Existing reasoning datasets saturate and fail to test abstract, multi-step problems, especially pathfinding and complex rule constraint satisfaction. We introduce SPaRC (Spatial Pathfinding Reasoning Challenge), a dataset of 1,000 2D grid pathfinding puzzles to evaluate spatial and rule-based reasoning, requiring step-by-step planning with arithmetic and geometric rules. Humans achieve near-perfect accuracy (98.0%; 94.5% on hard puzzles), while the best reasoning models, such as o4-mini, struggle (15.8%; 1.1% on hard puzzles). Models often generate invalid paths (>50% of puzzles for o4-mini), and reasoning tokens reveal they make errors in navigation and spatial logic. Unlike humans, who take longer on hard puzzles, models fail to scale test-time compute with difficulty. Allowing models to make multiple solution attempts improves accuracy, suggesting potential for better spatial reasoning with improved training and efficient test-time scaling methods. SPaRC can be used as a window into models’ spatial reasoning limitations and drive research toward new methods that excel in abstract, multi-step problem-solving.

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Primus: A Pioneering Collection of Open-Source Datasets for Cybersecurity LLM Training
Yao-Ching Yu | Tsun-Han Chiang | Cheng-Wei Tsai | Chien-Ming Huang | Wen-Kwang Tsao

Large Language Models (LLMs) have shown remarkable advancements in specialized fields such as finance, law, and medicine. However, in cybersecurity, we have noticed a lack of open-source datasets, with a particular lack of high-quality cybersecurity pretraining corpora, even though much research indicates that LLMs acquire their knowledge during pretraining. To address this, we present a comprehensive suite of datasets covering all major training stages, including pretraining, instruction fine-tuning, and reasoning distillation with cybersecurity-specific self-reflection data. Extensive ablation studies demonstrate their effectiveness on public cybersecurity benchmarks. In particular, continued pre-training on our dataset yields a **15.9%** improvement in the aggregate score, while reasoning distillation leads to a **15.8%** gain in security certification (CISSP). We will release all datasets and trained cybersecurity LLMs under the ODC-BY and MIT licenses to encourage further research in the community.

Bit-flip errors (BFEs) are hardware faults where individual bits in memory or processing units are unintentionally flipped. These errors pose a significant threat to neural network reliability because even small changes in model parameters can lead to large shifts in outputs. Large language models (LLMs) are particularly vulnerable on resource-constrained or outdated hardware. Such hardware often lacks error-correction mechanisms and faces aging issues, leading to instability under the vast parameter counts and heavy computational loads of LLMs. While the impact of BFEs on traditional networks like CNNs is relatively well-studied, their effect on the complex architecture of transformers remains largely unexplored. Firstly, this paper presents a comprehensive systematic analysis of BFE vulnerabilities in key LLM components, revealing distinct sensitivities across parameters, activations, and gradients during fine-tuning and inference. Secondly, based on our findings, we introduce a novel defense strategy FlipGuard: (i) exponent bit protection, and (ii) a self-correction based fine-tuning mechanism, to address BFE consequences. FlipGuard minimizes performance degradation while significantly enhancing robustness against BFEs. Experiments demonstrate a 9.27 reduction in accuracy drop under 1 BFEs on the SST-2 dataset using BERT, and a 36.35-point improvement in perplexity on the Wikitext-103 dataset using GPT-2, compared to unprotected models. These results show the potential of our approach in enabling reliable LLM deployment on diverse and less reliable hardware platforms.

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Towards Faithful Natural Language Explanations: A Study Using Activation Patching in Large Language Models
Wei Jie Yeo | Ranjan Satapathy | Erik Cambria

Large Language Models (LLMs) are capable of generating persuasive Natural Language Explanations (NLEs) to justify their answers. However, the faithfulness of these explanations should not be readily trusted at face value. Recent studies have proposed various methods to measure the faithfulness of NLEs, typically by inserting perturbations at the explanation or feature level. We argue that these approaches are neither comprehensive nor correctly designed according to the established definition of faithfulness. Moreover, we highlight the risks of grounding faithfulness findings on out-of-distribution samples. In this work, we leverage a causal mediation technique called activation patching, to measure the faithfulness of an explanation towards supporting the explained answer. Our proposed metric, Causal Faithfulness quantifies the consistency of causal attributions between explanations and the corresponding model outputs as the indicator of faithfulness. We experimented across models varying from 2B to 27B parameters and found that models that underwent alignment-tuning tend to produce more faithful and plausible explanations. We find that Causal Faithfulness is a promising improvement over existing faithfulness tests by taking into account the model’s internal computations and avoiding out-of-distribution concerns that could otherwise undermine the validity of faithfulness assessments.

Miscalibration in Large Language Models (LLMs) undermines their reliability, highlighting the need for accurate confidence estimation. We introduce CCPS (Calibrating LLM Confidence by Probing Perturbed Representation Stability), a novel method analyzing internal representational stability in LLMs. CCPS applies targeted adversarial perturbations to final hidden states, extracts features reflecting the model’s response to these perturbations, and uses a lightweight classifier to predict answer correctness. CCPS was evaluated on LLMs from 8B to 32B parameters (covering Llama, Qwen, and Mistral architectures) using MMLU and MMLU-Pro benchmarks in both multiple-choice and open-ended formats. Our results show that CCPS significantly outperforms current approaches. Across four LLMs and three MMLU variants, CCPS reduces Expected Calibration Error by approximately 55% and Brier score by 21%, while increasing accuracy by 5 percentage points, Area Under the Precision-Recall Curve by 4 percentage points, and Area Under the Receiver Operating Characteristic Curve by 6 percentage points, all relative to the strongest prior method. CCPS delivers an efficient, broadly applicable, and more accurate solution for estimating LLM confidence, thereby improving their trustworthiness.

Large language models (LLMs) demonstrate remarkable performance across diverse tasks, yet their effectiveness frequently depends on costly commercial APIs or cloud services. Model selection thus entails a critical trade-off between performance and cost: high-performing LLMs typically incur substantial expenses, whereas budget-friendly small language models (SLMs) are constrained by limited capabilities. Current research primarily proposes two routing strategies: pre-generation routing and cascade routing. Both approaches have distinct characteristics, with cascade routing typically offering superior cost-effectiveness and accuracy despite its higher latency. To further address the limitations of both approaches, we introduce SATER, a dual-mode compatible approach that fine-tunes models through shortest-response preference optimization and a confidence-aware rejection mechanism. SATER significantly reduces redundant outputs and response times, while improving both the performance of pre-generation routing and the efficiency of cascade routing. Experiments across three SLMs and six datasets, varying in type and complexity, demonstrate that SATER achieves comparable performance while consistently reducing computational costs by over 50% and cascade latency by over 80%.

Large language models (LLMs) have shown strong potential in complex reasoning tasks. However, as task complexity increases, their performance often degrades, resulting in hallucinations, errors, and logical inconsistencies. To enhance reasoning capabilities, Monte Carlo Tree Search (MCTS) has been introduced to guide the exploration of reasoning paths in a structured manner. Despite its advantages, traditional MCTS relies on fixed reasoning strategies, limiting the diversity of reasoning paths and the coverage of the solution space. To address these limitations, we propose Dynamic Strategy-Guided MCTS (DSG-MCTS), a novel framework that dynamically integrates multiple reasoning strategies, such as abductive and analogical reasoning, to expand the reasoning space. At the same time, DSG-MCTS enhances reasoning efficiency through a dynamic strategy selection mechanism that adapts to the task context. Experimental results on challenging reasoning benchmarks demonstrate that DSG-MCTS achieves improved accuracy and efficiency, outperforming existing state-of-the-art methods.

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CIFLEX: Contextual Instruction Flow for Sub-task Execution in Multi-Turn Interactions with a Single On-Device LLM
Juntae Lee | Jihwan Bang | Seunghan Yang | Simyung Chang

We present CIFLEX (Contextual Instruction FLow with EXecution), a novel execution system for efficient sub-task handling in multi-turn interactions with a single on-device large language model (LLM). As LLMs become increasingly capable, a single model is expected to handle diverse sub-tasks that more effectively and comprehensively support answering user requests. Naive approach reprocesses the entire conversation context when switching between main and sub-tasks (e.g., query rewriting, summarization), incurring significant computational overhead. CIFLEX mitigates this overhead by reusing the key-value (KV) cache from the main task and injecting only task-specific instructions into isolated side paths. After sub-task execution, the model rolls back to the main path via cached context, thereby avoiding redundant prefill computation. To support sub-task selection, we also develop a hierarchical classification strategy tailored for small-scale models, decomposing multi-choice decisions into binary ones. Experiments show that CIFLEX significantly reduces computational costs without degrading task performance, enabling scalable and efficient multi-task dialogue on-device.

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On the Role of Model Prior in Real-World Inductive Reasoning
Zhuo Liu | Ding Yu | Hangfeng He

Large Language Models (LLMs) show impressive inductive reasoning capabilities, enabling them to generate hypotheses that could generalize effectively to new instances when guided by in-context demonstrations. However, in real-world applications, LLMs’ hypothesis generation is not solely determined by these demonstrations but is significantly shaped by task-specific model priors. Despite their critical influence, the distinct contributions of model priors versus demonstrations to hypothesis generation have been underexplored. This study bridges this gap by systematically evaluating three inductive reasoning strategies across five real-world tasks with three LLMs. Our empirical findings reveal that, hypothesis generation is primarily driven by the model’s inherent priors; removing demonstrations results in minimal loss of hypothesis quality and downstream usage. Further analysis shows the result is consistent across various label formats with different label configurations, and prior is hard to override, even under flipped labeling. These insights advance our understanding of the dynamics of hypothesis generation in LLMs and highlight the potential for better utilizing model priors in real-world inductive reasoning tasks.

Machine Translation errors in high-stakes settings like healthcare pose unique risks that could lead to clinical harm. The challenges are even more pronounced for low-resourced languages where human translators are scarce and MT tools perform poorly. In this work, we provide a taxonomy of Machine Translation errors for the healthcare domain using a publicly available MT system. Preparing an evaluation dataset from pre-existing medical datasets, we conduct our study focusing on two low-resourced languages: Amharic and Tigrinya. Based on our error analysis and findings from prior work, we test two pre-translation interventions–namely, paraphrasing the source sentence and pivoting with a related language– for their effectiveness in reducing clinical risk. We find that MT errors for healthcare most commonly happen when the source sentence includes medical terminology and procedure descriptions, synonyms, figurative language, and word order differences. We find that pre-translation interventions are not effective in reducing clinical risk if the base translation model performs poorly. Based on our findings, we provide recommendations for improving MT for healthcare.

Large language models (LLMs) are increasingly integrated into users’ daily lives, leading to a growing demand for personalized outputs.Previous work focuses on leveraging a user’s own history, overlooking inter-user differences that are crucial for effective personalization.While recent work has attempted to model such differences, the reliance on language-based prompts often hampers the effective extraction of meaningful distinctions.To address these issues, we propose Difference-aware Embedding-based Personalization (DEP), a framework that models inter-user differences in the latent space instead of relying on language prompts. DEP constructs soft prompts by contrasting a user’s embedding with those of peers who engaged with similar content, highlighting relative behavioral signals.A sparse autoencoder then filters and compresses both user-specific and difference-aware embeddings, preserving only task-relevant features before injecting them into a frozen LLM.Experiments on personalized review generation show that DEP consistently outperforms baseline methods across multiple metrics.Our code is available at https://github.com/SnowCharmQ/DEP.

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IG-Pruning: Input-Guided Block Pruning for Large Language Models
Kangyu Qiao | Shaolei Zhang | Yang Feng

With the growing computational demands of large language models (LLMs), efficient inference has become increasingly critical for practical deployment. Depth pruning has emerged as a promising approach for reducing the computational costs of large language models by removing transformer layers. However, existing methods typically rely on fixed block masks, which can lead to suboptimal performance across different tasks and inputs. In this paper, we propose IG-Pruning, a novel input-aware block-wise pruning method that dynamically selects layer masks at inference time. Our approach consists of two stages: (1) Discovering diverse mask candidates through semantic clustering and L0 optimization, and (2) Implementing efficient dynamic pruning without the need for extensive training. Experimental results demonstrate that our method consistently outperforms state-of-the-art static depth pruning methods, making it particularly suitable for resource-constrained deployment scenarios.

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Are Checklists Really Useful for Automatic Evaluation of Generative Tasks?
Momoka Furuhashi | Kouta Nakayama | Takashi Kodama | Saku Sugawara

Automatic evaluation of generative tasks using large language models faces challenges due to ambiguous criteria. Although automatic checklist generation is a potentially promising approach, its usefulness remains underexplored.We investigate whether checklists should be used for all questions or selectively, generate them using six methods, evaluate their effectiveness across eight model sizes, and identify checklist items that correlate with human evaluations.Through experiments on pairwise comparison and direct scoring tasks, we find that selective checklist use tends to improve evaluation performance in pairwise settings, while its benefits are less consistent in direct scoring.Our analysis also shows that even checklist items with low correlation to human scores often reflect human-written criteria, indicating potential inconsistencies in human evaluation. These findings highlight the need to more clearly define objective evaluation criteria to guide both human and automatic evaluations.Our code is available at https://github.com/momo0817/checklist-effectiveness-study.

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Measuring the Effect of Disfluency in Multilingual Knowledge Probing Benchmarks
Kirill Semenov | Rico Sennrich

For multilingual factual knowledge assessment of LLMs, benchmarks such as MLAMA use template translations that do not take into account the grammatical and semantic information of the named entities inserted in the sentence. This leads to numerous instances of ungrammaticality or wrong wording of the final prompts, which complicates the interpretation of scores, especially for languages that have a rich morphological inventory. In this work, we sample 4 Slavic languages from the MLAMA dataset and compare the knowledge retrieval scores between the initial (templated) MLAMA dataset and its sentence-level translations made by Google Translate and ChatGPT. We observe a significant increase in knowledge retrieval scores, and provide a qualitative analysis for possible reasons behind it. We also make an additional analysis of 5 more languages from different families and see similar patterns. Therefore, we encourage the community to control the grammaticality of highly multilingual datasets for higher and more interpretable results, which is well approximated by whole sentence translation with neural MT or LLM systems.

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Knowledge Editing through Chain-of-Thought
Changyue Wang | Weihang Su | Qingyao Ai | Yichen Tang | Yiqun Liu

Knowledge Editing is a technique that updates large language models (LLMs) with new information to maintain their world knowledge. This approach avoids the need to rebuild the model from scratch, thereby addressing the high costs associated with frequent retraining. Among these, the in-context editing paradigm stands out for its effectiveness in integrating new knowledge while preserving the model’s original capabilities. Despite its potential, existing in-context knowledge editing methods are often task-specific, focusing primarily on multi-hop QA tasks using structured knowledge triples. Moreover, their reliance on few-shot prompting for task decomposition makes them unstable and less effective in generalizing across diverse tasks. In response to these limitations, we propose EditCoT, a novel knowledge editing framework that flexibly and efficiently updates LLMs across various tasks without retraining. EditCoT works by generating a chain-of-thought (CoT) for a given input and then iteratively refining this CoT process using a CoT editor based on updated knowledge. We evaluate EditCoT across a diverse range of benchmarks, covering multiple languages and tasks. The results demonstrate that our approach achieves state-of-the-art performance while offering superior generalization, effectiveness, and stability compared to existing methods, marking a significant advancement in the field of knowledge updating.

Existing retrieval-augmented code generation (RACG) methods typically use an external retrieval module to fetch semantically similar code snippets used for generating subsequent fragments. However, even for consecutive code fragments, the content often diverges due to logical progression, resulting in a content gap. This gap undermines the performance of current RACG methods, as external retrieval modules based on content matching fail to infer the specific information need of LLMs to generate the next code fragment. Therefore, we propose SelfRACG, a novel paradigm that enables large language models (LLMs) to Self-express their information needs to enhance RACG. Specifically, SelfRACG includes an information need expression module and a two-stage information need-guided training strategy, which encourages LLMs to express their information need. Extensive experiments demonstrate that SelfRACG can retrieve external knowledge that better aligns with the LLM’s own information needs, resulting in superior generation performance compared to vanilla RACG. Moreover, both the training and deployment costs for retrieval in our framework are much lower than those of the strongest retrieval model.

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Probing Logical Reasoning of MLLMs in Scientific Diagrams
Yufei Wang | Adriana Kovashka

We examine how multimodal large language models (MLLMs) perform logical inference grounded in visual information. We first construct a dataset of food web/chain images, along with questions that follow seven structured templates with progressively more complex reasoning involved. We show that complex reasoning about entities in the images remains challenging (even with elaborate prompts) and that visual information is underutilized.

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AdamS: Momentum Itself Can Be A Normalizer for LLM Pretraining and Post-training
Huishuai Zhang | Bohan Wang | Luoxin Chen

We introduce AdamS, a simple yet effective alternative to Adam for large language model (LLM) pretraining and post-training. By leveraging a novel denominator, i.e., the root of weighted sum of squares of the momentum and the current gradient, AdamS eliminates the need for second-moment estimates. Hence, AdamS is efficient, matching the memory and compute footprint of SGD with momentum while delivering superior optimization performance. Moreover, AdamS is easy to adopt: it can directly inherit hyperparameters of AdamW, and is entirely model-agnostic, integrating seamlessly into existing pipelines without modifications to optimizer APIs or architectures. The motivation behind AdamS stems from the observed smoothness properties in transformer objectives, where local smoothness is governed by gradient magnitudes that can be further approximated by momentum magnitudes. We establish rigorous theoretical convergence guarantees and provide practical guidelines for hyperparameter selection. Empirically, AdamS demonstrates strong performance in various tasks, including pre-training runs on GPT-2 and Llama2 (up to 13B parameters) and reinforcement learning in post-training regimes. With its efficiency, simplicity, and theoretical grounding, AdamS stands as a compelling alternative to existing optimizers.

Training data plays a crucial role in Large Language Models (LLM) scaling, yet high quality data is of limited supply. Synthetic data techniques offer a potential path toward sidestepping these limitations.We conduct a large-scale empirical investigation (>1000 LLMs with >100k GPU hours) using a unified protocol and scaling laws, comparing natural web data, diverse synthetic types (rephrased text, generated textbooks), and mixtures of natural and synthetic data. Specifically, we found pre-training on rephrased synthetic data alone is not faster than pre-training on natural web texts; while pre-training on 1/3 rephrased synthetic data mixed with 2/3 natural web texts can speed up 5-10x (to reach the same validation loss) at larger data budgets. Pre-training on textbook-style synthetic data alone results in notably higher loss on many downstream domains especially at small data budgets. “Good” ratios of synthetic data in training data mixtures depend on the model size and data budget, empirically converging to ~30% for rephrased synthetic data. Larger generator models do not necessarily yield better pre-training data than ~8B-param models. These results contribute mixed evidence on “model collapse” during large-scale single-round (n=1) model training on synthetic data–training on rephrased synthetic data shows no degradation in performance in foreseeable scales whereas training on mixtures of textbook-style pure-generated synthetic data shows patterns predicted by “model collapse”. Our work demystifies synthetic data in pre-training, validates its conditional benefits, and offers practical guidance.

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Static or Dynamic: Towards Query-Adaptive Token Selection for Video Question Answering
Yumeng Shi | Quanyu Long | Wenya Wang

Video question answering benefits from the rich information in videos, enabling various applications. However, the large volume of tokens generated from long videos presents challenges to memory efficiency and model performance. To alleviate this, existing works propose to compress video inputs, but often overlook the varying importance of static and dynamic information across different queries, leading to inefficient token usage within limited budgets. We propose a novel token selection strategy, explore-then-select, that adaptively adjusts static and dynamic information based on question requirements. Our framework first explores different token allocations between key frames, which preserve spatial details, and delta frames, which capture temporal changes. Then it employs a query-aware attention-based metric to select the optimal token combination without model updates. Our framework is plug-and-play and can be seamlessly integrated within diverse video language models. Extensive experiments show that our method achieves significant performance improvements (up to 5.8%) on multiple video question answering benchmarks. Our code is available at *https://github.com/ANDgate99/Explore-Then-Select*.

Discharge communication is a critical yet underexplored component of patient care, where the goal shifts from diagnosis to education. While recent large language model (LLM) benchmarks emphasize in-visit diagnostic reasoning, they fail to evaluate models’ ability to support patients after the visit. We introduce DischargeSim, a novel benchmark that evaluates LLMs on their ability to act as personalized discharge educators. DischargeSim simulates post-visit, multi-turn conversations between LLM-driven DoctorAgents and PatientAgents with diverse psychosocial profiles (e.g., health literacy, education, emotion). Interactions are structured across six clinically grounded discharge topics and assessed along three axes: (1) dialogue quality via automatic and LLM-as-judge evaluation, (2) personalized document generation including free-text summaries and structured AHRQ checklists, and (3) patient comprehension through a downstream multiple-choice exam. Experiments across 18 LLMs reveal significant gaps in discharge education capability, with performance varying widely across patient profiles. Notably, model size does not always yield better education outcomes, highlighting trade-offs in strategy use and content prioritization. DischargeSim offers a first step toward benchmarking LLMs in post-visit clinical education and promoting equitable, personalized patient support.

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Can Vision-Language Models Solve Visual Math Equations?
Monjoy Narayan Choudhury | Junling Wang | Yifan Hou | Mrinmaya Sachan

Despite strong performance in visual understanding and language-based reasoning, Vision-Language Models (VLMs) struggle with tasks requiring integrated perception and symbolic computation. We study this limitation through visual equation solving, where mathematical equations are embedded in images, variables are represented by object icons, and coefficients must be inferred by counting. While VLMs perform well on textual equations, they fail on visually grounded counterparts. To understand this gap, we decompose the task into coefficient counting and variable recognition, and find that counting is the primary bottleneck, even when recognition is accurate. We also observe that composing recognition and reasoning introduces additional errors, highlighting challenges in multi-step visual reasoning. Finally, as equation complexity increases, symbolic reasoning itself becomes a limiting factor. These findings reveal key weaknesses in current VLMs and point toward future improvements in visually grounded mathematical reasoning.

Large language models (LLMs) have demonstrated promising performance on medical benchmarks; however, their ability to perform medical calculations, a crucial aspect of clinical decision-making, remains underexplored and poorly evaluated. Existing benchmarks often assess only the final answer with a wide numerical tolerance, overlooking systematic reasoning failures and potentially causing serious clinical misjudgments. In this work, we revisit medical calculation evaluation with a stronger focus on clinical trustworthiness. First, we clean and restructure the MedCalc-Bench dataset and propose a new step-by-step evaluation pipeline that independently assesses formula selection, entity extraction, and arithmetic computation. Under this granular framework, the accuracy of GPT-4o drops from 62.7% to 43.6%, revealing errors masked by prior evaluations. Second, we introduce an automatic error analysis framework that generates structured attribution for each failure mode. Human evaluation confirms its alignment with expert judgment, enabling scalable and explainable diagnostics. Finally, we propose a modular agentic pipeline, MedRaC, that combines retrieval-augmented generation and Python-based code execution. Without any fine-tuning, MedRaC improves the accuracy of different LLMs from 16.35% up to 53.19%. Our work highlights the limitations of current benchmark practices and proposes a more clinically faithful methodology. By enabling transparent and transferable reasoning evaluation, we move closer to making LLM-based systems trustworthy for real-world medical applications.

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Bridging External and Parametric Knowledge: Mitigating Hallucination of LLMs with Shared-Private Semantic Synergy in Dual-Stream Knowledge
Yi Sui | Chaozhuo Li | Chen Zhang | Dawei Song | Qiuchi Li

Retrieval-augmented generation (RAG) aims to mitigate the hallucination of Large Language Models (LLMs) by retrieving and incorporating relevant external knowledge into the generation process. However, the external knowledge may contain noise and conflict with the parametric knowledge of LLMs, leading to degraded performance. Current LLMs lack inherent mechanisms for resolving such conflicts. To fill this gap, we propose a Dual-Stream Knowledge-Augmented Framework for Shared-Private Semantic Synergy (DSSP-RAG). Central to it is the refinement of the traditional self-attention into a mixed-attention that distinguishes shared and private semantics for a controlled knowledge integration. An unsupervised hallucination detection method that captures the LLMs’ intrinsic cognitive uncertainty ensures that external knowledge is introduced only when necessary. To reduce noise in external knowledge, an Energy Quotient (EQ), defined by attention difference matrices between task-aligned and task-misaligned layers, is proposed. Extensive experiments show that DSSP-RAG achieves a superior performance over strong baselines.

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Deep Associations, High Creativity: A Simple yet Effective Metric for Evaluating Large Language Models
Ziliang Qiu | Renfen Hu

The evaluation of LLMs’ creativity represents a crucial research domain, though challenges such as data contamination and costly human assessments often impede progress. Drawing inspiration from human creativity assessment, we propose PACE, asking LLMs to generate Parallel Chains of Associations to Evaluate their creativity. PACE minimizes the risk of data contamination and offers a straightforward, highly efficient evaluation, as evidenced by its strong correlation with Arena Creative Writing (Spearman’s 𝜌 = 0.739, p < 0.001) on various proprietary and open-source models. A comparative analysis of associative creativity between LLMs and humans reveals that while high-performing LLMs achieve scores comparable to average human performance, top-performing humans consistently outperform LLMs. Furthermore, linguistic analysis reveals that both humans and LLMs exhibit a trend of decreasing concreteness in their associations, and humans demonstrating a greater diversity of associative patterns.

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Identifying Unlearned Data in LLMs via Membership Inference Attacks
Advit Deepak | Megan Mou | Jing Huang | Diyi Yang

Unlearning evaluation has traditionally followed the retrieval paradigm, where adversaries attempt to extract residual knowledge of an unlearning target by issuing queries to a language model. However, the absence of retrievable knowledge does not necessarily prevent an adversary from inferring which targets have been intentionally unlearned in the post-training optimization. Such inferences can still pose significant privacy risks, as they may reveal the sensitive data in the model’s training set and the internal policies of model creators. To quantify such privacy risks, we propose a new evaluation framework **Forensic Unlearning Membership Attacks (FUMA)**, drawing on principles from membership inference attacks. FUMA assesses whether unlearning leaves behind detectable artifacts that can be exploited to infer membership in the forget set. Specifically, we evaluate four major optimization-based unlearning methods on 258 models across diverse unlearned settings and show that examples in the forget set can be identified up to 99% accuracy. This highlights privacy risks not covered in existing retrieval-based benchmarks. We conclude by discussing recommendations to mitigate these vulnerabilities.

Large Language Models (LLMs) demonstrate the ability to solve reasoning and mathematical problems using the Chain-of-Thought (CoT) technique. Expanding CoT length, as seen in models such as DeepSeek-R1, significantly enhances this reasoning for complex problems, but requires costly and high-quality long CoT data and fine-tuning. This work, inspired by the deep thinking paradigm of DeepSeek-R1, utilizes a steering technique to enhance the reasoning ability of an LLM without external datasets. Our method first employs Sparse Autoencoders (SAEs) to extract interpretable features from vanilla CoT. These features are then used to steer the LLM’s internal states during generation. Recognizing that many LLMs do not have corresponding pre-trained SAEs, we further introduce a novel SAE-free steering algorithm, which directly computes steering directions from the residual activations of an LLM, obviating the need for an explicit SAE. Experimental results demonstrate that both our SAE-based and subsequent SAE-free steering algorithms significantly enhance the reasoning capabilities of LLMs.

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LLMs cannot spot math errors, even when allowed to peek into the solution
Kv Aditya Srivatsa | Kaushal Kumar Maurya | Ekaterina Kochmar

Large language models (LLMs) demonstrate remarkable performance on math word problems, yet they have been shown to struggle with meta-reasoning tasks such as identifying errors in student solutions. In this work, we investigate the challenge of locating the first error step in stepwise solutions using two error reasoning datasets: VtG and PRM800K. Our experiments show that state-of-the-art LLMs struggle to locate the first error step in student solutions even when given access to the reference solution. To that end, we propose an approach that generates an intermediate corrected student solution, aligning more closely with the original student’s solution, which helps improve performance.

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Can LLMs be Good Graph Judge for Knowledge Graph Construction?
Haoyu Huang | Chong Chen | Zeang Sheng | Yang Li | Wentao Zhang

In real-world scenarios, most of the data obtained from the information retrieval (IR) system is unstructured. Converting natural language sentences into structured Knowledge Graphs (KGs) remains a critical challenge. We identified three limitations with respect to existing KG construction methods: (1) There could be a large amount of noise in real-world documents, which could result in extracting messy information. (2) Naive LLMs usually extract inaccurate knowledge from some domain-specific documents. (3) Hallucination phenomenon cannot be overlooked when directly using LLMs to construct KGs. In this paper, we propose GraphJudge, a KG construction framework to address the aforementioned challenges. In this framework, we designed an entity-centric strategy to eliminate the noise information in the documents. And we fine-tuned a LLM as a graph judge to finally enhance the quality of generated KGs. Experiments conducted on two general and one domain-specific text-graph pair datasets demonstrate state-of-the-art performance against various baseline methods with strong generalization abilities.

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NeuroAda: Activating Each Neuron’s Potential for Parameter-Efficient Fine-Tuning
Zhi Zhang | Yixian Shen | Congfeng Cao | Ekaterina Shutova

Existing parameter-efficient fine-tuning (PEFT) methods primarily fall into two categories: addition-based and selective in-situ adaptation. The former, such as LoRA, introduce additional modules to adapt the model to downstream tasks, offering strong memory efficiency. However, their representational capacity is often limited, making them less suitable for fine-grained adaptation. In contrast, the latter directly fine-tunes a carefully chosen subset of the original model parameters, allowing for more precise and effective adaptation, but at the cost of significantly increased memory consumption.To reconcile this trade-off, we propose NeuroAda, a novel PEFT method that enables fine-grained model finetuning while maintaining high memory efficiency. Our approach first identifies important parameters (i.e., connections within the network) as in selective adaptation, and then introduces bypass connections for these selected parameters. During finetuning, only the bypass connections are updated, leaving the original model parameters frozen.Empirical results on 23+ tasks spanning both natural language generation and understanding demonstrate that NeuroAda achieves state-of-the-art performance with as little as ≤ 0.02% trainable parameters, while reducing CUDA memory usage by up to 60%.We release our code here: https://github.com/FightingFighting/NeuroAda.git.

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NileChat: Towards Linguistically Diverse and Culturally Aware LLMs for Local Communities
Abdellah El Mekki | Houdaifa Atou | Omer Nacar | Shady Shehata | Muhammad Abdul-Mageed

Enhancing the linguistic capabilities of Large Language Models (LLMs) to include low-resource languages is a critical research area. Current research directions predominantly rely on synthetic data generated by translating English corpora, which, while demonstrating promising linguistic understanding and translation abilities, often results in models aligned with source language culture. These models frequently fail to represent the cultural heritage and values of local communities. This work proposes a methodology to create both synthetic and retrieval-based pre-training data tailored to a specific community, considering its (i) language, (ii) cultural heritage, and (iii) cultural values. We demonstrate our methodology using Egyptian and Moroccan dialects as testbeds, chosen for their linguistic and cultural richness and current underrepresentation in LLMs. As a proof-of-concept, we develop NileChat, a 3B parameter Egyptian and Moroccan Arabic LLM adapted for Egyptian and Moroccan communities, incorporating their language, cultural heritage, and values. Our results on various understanding, translation, and cultural and values alignment benchmarks show that NileChat outperforms existing Arabic-aware LLMs of similar size and performs on par with larger models. This work addresses Arabic dialect in LLMs with a focus on cultural and values alignment via controlled synthetic data generation and retrieval-augmented pre-training for Moroccan Darija and Egyptian Arabic, including Arabizi variants, advancing Arabic NLP for low-resource communities.We share our methods, data, and models with the community to promote the inclusion and coverage of more diverse communities in cultural LLM development: https://github.com/UBC-NLP/nilechat.

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A Computational Simulation of Language Production in First Language Acquisition
Yuan Gao | Weiwei Sun

We introduce a computational framework for modeling child language production, focusing on the acquisition of the competence to map meaning onto linguistic form. Our approach uses graphs to formalize meaning and Synchronous Hyperedge Replacement Grammar (SHRG) to formalize the syntax–semantics interface.The setup provides computationally-sound induction algorithms of statistical grammar knowledge. We induce SHRGs solely from semantic graphs, and the resulting interpretable grammars are evaluated by their ability to generate utterances—providing a novel controlled paradigm to simulate child language acquisition.A notable finding is that unsupervised statistical learning (analogous to children’s implicit learning mechanisms) performs as well as the corresponding supervised oracle when a proper symbolic grammar is assumed (reflecting knowledge gained via comprehension).

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Long-Form Information Alignment Evaluation Beyond Atomic Facts
Danna Zheng | Mirella Lapata | Jeff Z. Pan

Information alignment evaluators are vital for various NLG evaluation tasks and trustworthy LLM deployment, reducing hallucinations and enhancing user trust. Current fine-grained methods, like FactScore, verify facts individually but neglect inter-fact dependencies, enabling subtle vulnerabilities.In this work, we introduce MontageLie, a challenging benchmark that constructs deceptive narratives by “montaging” truthful statements without introducing explicit hallucinations.We demonstrate that both coarse-grained LLM-based evaluators and current fine-grained frameworks are susceptible to this attack, with AUC-ROC scores falling below 65%.To enable more robust fine-grained evaluation, we propose DoveScore, a novel framework that jointly verifies factual accuracy and event-order consistency. By modeling inter-fact relationships, DoveScore outperforms existing fine-grained methods by over 8%, providing a more robust solution for long-form text alignment evaluation. Our code and datasets are available at https://github.com/dannalily/DoveScore.

Africa’s rich linguistic diversity remains significantly underrepresented in speech technologies, creating barriers to digital inclusion. To alleviate this challenge, we systematically map the continent’s speech space of datasets and technologies, leading to a new comprehensive benchmark SimbaBench for downstream African speech tasks. Using SimbaBench, we introduce the Simba family of models, achieving state-of-the-art performance across multiple African languages and speech tasks. Our benchmark analysis reveals critical patterns in resource availability, while our model evaluation demonstrates how dataset quality, domain diversity, and language family relationships influence performance across languages. Our work highlights the need for expanded speech technology resources that better reflect Africa’s linguistic diversity and provides a solid foundation for future research and development efforts toward more inclusive speech technologies.

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Cache-Efficient Posterior Sampling for Reinforcement Learning with LLM-Derived Priors Across Discrete and Continuous Domains
Ibne Farabi Shihab | Sanjeda Akter | Anuj Sharma

Integrating large language models (LLMs) as action proposers in reinforcement learning (RL) significantly boosts performance in text-based environments but incurs prohibitive computational costs. We introduce a cache-efficient framework for Bayesian RL that leverages LLM-derived action suggestions, drastically reducing these costs while maintaining near-optimal performance. Our approach features an adaptive caching mechanism, optimized via meta-learning based on policy performance, to enable efficient inference across text-based games (e.g., TextWorld, ALFWorld) and robotic control tasks (e.g., MuJoCo, MetaWorld). This framework achieves a 3.8×–4.7× reduction in LLM queries and 4.0×–12.0× lower median latencies (85–93ms on consumer hardware), while retaining 96–98% of the uncached policy’s performance. We provide theoretical guarantees on the reliability of cached decisions with Kullback-Leibler (KL) divergence bounds, which are validated empirically by high success rates (90.4–95.6%) in complex text environments. For offline RL, our proposed CQL-Prior variant improves performance by 14–29% and reduces training time by 38–40%. Evaluations across eight diverse tasks demonstrate the framework’s generalizability and practicality for resource-constrained settings, making LLM-guided RL a viable and accessible approach for both text-based and robotic applications.

Characterizing the expressive power of the Transformer architecture is critical to understanding its capacity limits and scaling law. Recent works provide the circuit complexity bounds to Transformer-like architecture. On the other hand, position embedding has emerged as a crucial technique in modern large language models, offering superior performance in capturing positional information, which shows great performance for the long context scenario. In this work, we take a circuit complexity perspective and rigorously analyze Transformers augmented with widely adopted positional embeddings. We prove that, under standard complexity assumptions, such models remain incapable of efficiently solving canonical tasks such as arithmetic formula evaluation and Boolean formula value computation. Our results expose a fundamental expressivity limitation that persists despite the remarkable empirical success of positionally-enhanced Transformers. Beyond tightening known complexity bounds, our findings offer new theoretical insights for designing future architectures with provably stronger reasoning and compositional capabilities.

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Efficient Unstructured Pruning of Mamba State-Space Models for Resource-Constrained Environments
Ibne Farabi Shihab | Sanjeda Akter | Anuj Sharma

As the deployment of AI models shifts towards edge devices, developing efficient sequence models has become critical. State-space models (SSMs), particularly Mamba, have emerged as strong rivals to Transformers due to their linear-time complexity and impressive performance across a range of tasks. However, their large parameter counts still hinder their use in resource-constrained environments. To address this, we propose a novel unstructured pruning framework specifically tailored for Mamba, achieving up to 70% parameter reduction with only a 3–9% drop in performance. Unlike pruning techniques designed for Transformers, our approach leverages Mamba’s unique recurrent dynamics by incorporating pruning based on both weight and gradient importance to preserve critical parameters, a gradual pruning schedule to maintain model stability, and a global strategy to optimize parameter allocation across the model. Extensive experiments on the WikiText-103, Long Range Arena, and ETT benchmarks demonstrate significant efficiency gains, including 1.77× faster inference and a 46% reduction in memory usage. Our component analysis confirms Mamba’s robustness to pruning, highlighting the framework’s potential for enabling practical deployment while underscoring the need for careful evaluation to avoid introducing biases in sensitive applications.

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Towards Infinite-Long Prefix in Transformer
Yingyu Liang | Zhenmei Shi | Zhao Song | Chiwun Yang

Prompting and context-based fine-tuning methods, which we call Prefix Learning, have been proposed to enhance the performance of language models on various downstream tasks. They are empirically efficient and effective, matching the performance of full parameter fine-tuning, but the theoretical understandings are limited. In this paper, we aim to address this limitation by studying their ability from the perspective of prefix length. In particular, we provide a convergence guarantee for training an ultra-long prefix in a stylized setting using the Neural Tangent Kernel (NTK) framework. Based on this strong theoretical guarantee, we design and implement an algorithm that only needs to introduce and fine-tune a few extra trainable parameters instead of an infinite-long prefix in each layer of a transformer, and can approximate the prefix attention to a guaranteed polynomial-small error.Preliminary experimental results on vision, natural language, and math data show that our method achieves superior or competitive performance compared to existing methods like full parameters fine-tuning, P-Tuning V2, and LoRA. This demonstrates our method is promising for parameter-efficient fine-tuning.

While open-source vision-language models perform well on simple question-answering, they still struggle with complex questions that require both perceptual and reasoning capabilities. We propose LATTE, a family of vision-language models that have LeArned to Think wiTh vision spEcialists. By offloading perception to state-of-the-art vision models, our approach enables vision-language models to focus solely on reasoning over high-quality perceptual information. To train LATTE, we synthesize and filter a large dataset of 293K multi-modal reasoning traces over perceptual outputs of vision specialists. LATTE trained on this data achieves significant 4-5% gains over baselines across 6 benchmarks covering both perception and reasoning abilities. Ablation studies reveal that the effectiveness of multi-modal reasoning traces depends on the data sources, formats, and quality of thoughts.

Multimodal Large Language Models (MLLMs) trained on massive data may memorize sensitive personal information and photos, posing serious privacy risks. To mitigate this, MLLM unlearning methods are proposed, which fine-tune MLLMs to reduce the “forget” sensitive information. However, it remains unclear whether the knowledge has been truly forgotten or just hidden in the model. Therefore, we propose to study a novel problem of LLM unlearning attack, which aims to recover the unlearned knowledge of an unlearned LLM. To achieve the goal, we propose a novel framework Stealthy Unlearning Attack (SUA) framework that learns a universal noise pattern. When applied to input images, this noise can trigger the model to reveal unlearned content. While pixel-level perturbations may be visually subtle, they can be detected in the semantic embedding space, making such attacks vulnerable to potential defenses. To improve stealthiness, we introduce an embedding alignment loss that minimizes the difference between the perturbed and denoised image embeddings, ensuring the attack is semantically unnoticeable. Experimental results show that SUA can effectively recover unlearned information from MLLMs. Furthermore, the learned noise generalizes well: a single perturbation trained on a subset of samples can reveal forgotten content in unseen images. This indicates that knowledge reappearance is not an occasional failure, but a consistent behavior.

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ResFormer: All-Time Reservoir Memory for Long Sequence Classification
Hongbo Liu | Jia Xu

Sequence classification is essential in NLP for understanding and categorizing language patterns in tasks like sentiment analysis, intent detection, and topic classification. Transformer-based models, despite achieving state-of-the-art performance, have inherent limitations due to quadratic time and memory complexity, restricting their input length. Although extensive efforts have aimed at reducing computational demands, processing extensive contexts remains challenging. To overcome these limitations, we propose ResFormer, a novel neural network architecture designed to model varying context lengths efficiently through a cascaded methodology. ResFormer integrates an reservoir computing network featuring a nonlinear readout to effectively capture long-term contextual dependencies in linear time. Concurrently, short-term dependencies within sentences are modeled using a conventional Transformer architecture with fixed-length inputs. Experiments demonstrate that ResFormer significantly outperforms baseline models of DeepSeek-Qwen and ModernBERT, delivering an accuracy improvement of up to +22.3% on the EmoryNLP dataset and consistent gains on MultiWOZ, MELD, and IEMOCAP. In addition, ResFormer exhibits reduced memory consumption, underscoring its effectiveness and efficiency in modeling extensive contextual information.

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Back Attention: Understanding and Enhancing Multi-Hop Reasoning in Large Language Models
Zeping Yu | Yonatan Belinkov | Sophia Ananiadou

We investigate how large language models (LLMs) perform latent multi-hop reasoning in prompts like “Wolfgang Amadeus Mozart’s mother’s spouse is”. To analyze this process, we introduce logit flow, an interpretability method that traces how logits propagate across layers and positions toward the final prediction. Using logit flow, we identify four distinct stages in single-hop knowledge prediction: (A) entity subject enrichment, (B) entity attribute extraction, (C) relation subject enrichment, and (D) relation attribute extraction. Extending this analysis to multi-hop reasoning, we find that failures often stem from the relation attribute extraction stage, where conflicting logits reduce prediction accuracy. To address this, we propose back attention, a novel mechanism that enables lower layers to leverage higher-layer hidden states from different positions during attention computation. With back attention, a 1-layer transformer achieves the performance of a 2-layer transformer. Applied to five LLMs, back attention improves accuracy on five reasoning datasets, demonstrating its effectiveness in enhancing latent multi-hop reasoning ability. Code and data is available at https://github.com/zepingyu0512/back-attention.

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Interdisciplinary Research in Conversation: A Case Study in Computational Morphology for Language Documentation
Enora Rice | Katharina von der Wense | Alexis Palmer

Computational morphology has the potential to support language documentation through tasks like morphological segmentation and the generation of Interlinear Glossed Text (IGT). However, our research outputs have seen limited use in real-world language documentation settings. This position paper situates the disconnect between computational morphology and language documentation within a broader misalignment between research and practice in NLP and argues that the field risks becoming decontextualized and ineffectual without systematic integration of User-Centered Design (UCD). To demonstrate how principles from UCD can reshape the research agenda, we present a case study of GlossLM, a state-of-the-art multilingual IGT generation model. Through a small-scale user study with three documentary linguists, we find that despite strong metric-based performance, the system fails to meet core usability needs in real documentation contexts. These insights raise new research questions around model constraints, label standardization, segmentation, and personalization. We argue that centering users not only produces more effective tools, but surfaces richer, more relevant research directions.

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Analyzing Uncertainty of LLM-as-a-Judge: Interval Evaluations with Conformal Prediction
Huanxin Sheng | Xinyi Liu | Hangfeng He | Jieyu Zhao | Jian Kang

LLM-as-a-judge has become a promising paradigm for using large language models (LLMs) to evaluate natural language generation (NLG), but the uncertainty of its evaluation remains underexplored. This lack of reliability may limit its deployment in many applications. This work presents the first framework to analyze the uncertainty by offering a prediction interval of LLM-based scoring via conformal prediction. Conformal prediction constructs continuous prediction intervals from a single evaluation run, and we design an ordinal boundary adjustment for discrete rating tasks. We also suggest a midpoint-based score within the interval as a low-bias alternative to raw model score and weighted average. We perform extensive experiments and analysis, which show that conformal prediction can provide valid prediction interval with coverage guarantees. We also explore the usefulness of interval midpoint and judge reprompting for better judgment.

This paper presents AlphaOne (𝛼1), a universal framework for modulating reasoning progress in large reasoning models (LRMs) at test time. 𝛼1 first introduces 𝛼 moment, which represents the scaled thinking phase with a universal parameter 𝛼.Within this scaled pre-𝛼 moment phase, it dynamically schedules slow thinking transitions by modeling the insertion of reasoning transition tokens as a Bernoulli stochastic process. After the 𝛼 moment, 𝛼1 deterministically terminates slow thinking with the end-of-thinking token, thereby fostering fast reasoning and efficient answer generation. This approach unifies and generalizes existing monotonic scaling methods by enabling flexible and dense slow-to-fast reasoning modulation. Extensive empirical studies on various challenging benchmarks across mathematical, coding, and scientific domains demonstrate 𝛼1‘s superior reasoning capability and efficiency. Project page: https://alphaone-project.github.io/.

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Dual-Path Dynamic Fusion with Learnable Query for Multimodal Sentiment Analysis
Miao Zhou | Lina Yang | Thomas Wu | Dongnan Yang | Xinru Zhang

Multimodal Sentiment Analysis (MSA) is the task of understanding human emotions by analyzing a combination of different data sources, such as text, audio, and visual inputs. Although recent advances have improved emotion modeling across modalities, existing methods still struggle with two fundamental challenges: balancing global and fine-grained sentiment contributions, and over-reliance on the text modality. To address these issues, we propose DPDF-LQ (Dual-Path Dynamic Fusion with Learnable Query), an architecture that processes inputs through two complementary paths: global and local. The global path is responsible for establishing cross-modal dependencies, while the local path captures fine-grained representations. Additionally, we introduce the key module Dynamic Global Learnable Query Attention (DGLQA) in the global path, which dynamically allocates weights to each modality to capture their relevant features and learn global representations. Extensive experiments on the CMU-MOSI and CMU-MOSEI benchmarks demonstrate that DPDF-LQ achieves state-of-the-art performance, particularly in fine-grained sentiment prediction by effectively combining global and local features. Our code will be released at https://github.com/ZhouMiaoGX/DPDF-LQ.

Knowledge Editing (KE) enables the modification of outdated or incorrect information in large language models (LLMs). While existing KE methods can update isolated facts, they often fail to generalize these updates to multi-hop reasoning tasks that rely on the modified knowledge. Through an analysis of reasoning circuits—the neural pathways LLMs use for knowledge-based inference, we find that current layer-localized KE approaches (e.g., MEMIT, WISE), which edit only single or a few model layers, inadequately integrate updated knowledge into these reasoning pathways. To address this limitation, we present CaKE (Circuit-aware Knowledge Editing), a novel method that enhances the effective integration of updated knowledge in LLMs. By only leveraging a few curated data samples guided by our circuit-based analysis, CaKE stimulates the model to develop appropriate reasoning circuits for newly incorporated knowledge. Experiments show that CaKE enables more accurate and consistent use of edited knowledge across related reasoning tasks, achieving an average improvement of 20% in multi-hop reasoning accuracy on the MQuAKE dataset while requiring less memory than existing KE methods.

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DEL-ToM: Inference-Time Scaling for Theory-of-Mind Reasoning via Dynamic Epistemic Logic
Yuheng Wu | Jianwen Xie | Denghui Zhang | Zhaozhuo Xu

Theory-of-Mind (ToM) tasks pose a unique challenge for large language models (LLMs), which often lack the capability for dynamic logical reasoning. In this work, we propose DEL-ToM, a framework that improves verifiable ToM reasoning through inference-time scaling rather than architectural changes. Our approach decomposes ToM tasks into a sequence of belief updates grounded in Dynamic Epistemic Logic (DEL), enabling structured and verifiable dynamic logical reasoning. We use data generated automatically via a DEL simulator to train a verifier, which we call the Process Belief Model (PBM), to score each belief update step. During inference, the PBM evaluates candidate belief traces from the LLM and selects the highest-scoring one. This allows LLMs to allocate extra inference-time compute to yield more transparent reasoning. Experiments across model scales and benchmarks show that DEL-ToM consistently improves performance, demonstrating that verifiable belief supervision significantly enhances LLMs’ ToM capabilities without retraining. Code is available at https://github.com/joel-wu/DEL-ToM.

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Collaborative Beam Search: Enhancing LLM Reasoning via Collective Consensus
Yangyifan Xu | Shuo Ren | Jiajun Zhang

Complex multi-step reasoning remains challenging for large language models (LLMs). While parallel inference-time scaling methods, such as step-level beam search, offer a promising solution, existing approaches typically depend on either domain-specific external verifiers, or self-evaluation which is brittle and prompt-sensitive. To address these issues, we propose Collaborative Beam Search (CBS), an iterative framework that harnesses the collective intelligence of multiple LLMs across both generation and verification stages. For generation, CBS leverages multiple LLMs to explore a broader search space, resulting in more diverse candidate steps. For verifications, CBS employs a perplexity-based collective consensus among these models, eliminating reliance on an external verifier or complex prompts. Between iterations, CBS leverages a dynamic quota allocation strategy that reassigns generation budget based on each model’s past performance, striking a balance between candidate diversity and quality. Experimental results on six tasks across arithmetic, logical, and commonsense reasoning show that CBS outperforms single‐model scaling and multi-model ensemble baselines by over 4 percentage points in average accuracy, demonstrating its effectiveness and general applicability.

Counterfactual reasoning typically involves considering alternatives to actual events. While often applied to understand past events, a distinct form—forward counterfactual reasoning—focuses on anticipating plausible future developments. This type of reasoning is invaluable in dynamic financial markets, where anticipating market developments can powerfully unveil potential risks and opportunities for stakeholders, guiding their decision-making. However, performing this at scale is challenging due to the cognitive demands involved, underscoring the need for automated solutions. Large Language Models (LLMs) offer promise, but remain unexplored for this application. To address this gap, we introduce a novel benchmark, Fin-Force—**FIN**ancial **FOR**ward **C**ounterfactual **E**valuation. By curating financial news headlines and providing structured evaluation, Fin-Force supports LLM based forward counterfactual generation. This paves the way for scalable and automated solutions for exploring and anticipating future market developments, thereby providing structured insights for decision-making. Through experiments on Fin-Force, we evaluate state-of-the-art LLMs and counterfactual generation methods, analyzing their limitations and proposing insights for future research.

Advancements in Large Vision-Language Models (LVLMs) have demonstrated impressive performance in image-conditioned text generation; however, hallucinated outputs–text that misaligns with the visual input–pose a major barrier to their use in safety-critical applications. We introduce ConfLVLM, a conformal-prediction-based framework that achieves finite-sample distribution-free statistical guarantees to the factuality of LVLM output. Taking each generated detail as a hypothesis, ConfLVLM statistically tests factuality via efficient heuristic uncertainty measures to filter out unreliable claims. We conduct extensive experiments covering three representative application domains: general scene understanding, medical radiology report generation, and document understanding. Remarkably, ConfLVLM reduces the error rate of claims generated by LLaVa-1.5 for scene descriptions from 87.8% to 10.0% by filtering out erroneous claims with a 95.3% true positive rate. Our results further show that ConfLVLM is highly flexible, and can be applied to any black-box LVLMs paired with any uncertainty measure for any image-conditioned free-form text generation task while providing a rigorous guarantee on controlling hallucination risk.

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Unlearning vs. Obfuscation: Are We Truly Removing Knowledge?
Guangzhi Sun | Potsawee Manakul | Xiao Zhan | Mark Gales

Unlearning has emerged as a critical capability for large language models (LLMs) to support data privacy, regulatory compliance, and ethical AI deployment. Recent techniques often rely on obfuscation by injecting incorrect or irrelevant information to suppress knowledge. Such methods effectively constitute knowledge addition rather than true removal, often leaving models vulnerable to probing. In this paper, we formally distinguish unlearning from obfuscation and introduce a probing-based evaluation framework to assess whether existing approaches genuinely remove targeted information. Moreover, we propose DF-MCQ, a novel unlearning method that flattens the model predictive distribution over automatically generated multiple-choice questions using KL-divergence, effectively removing knowledge about target individuals and triggering appropriate refusal behaviour. Experimental results demonstrate that DF-MCQ achieves unlearning with over 90% refusal rate and a random choice-level uncertainty that is much higher than obfuscation on probing questions.

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Reward-Shifted Speculative Sampling Is An Efficient Test-Time Weak-to-Strong Aligner
Bolian Li | Yanran Wu | Xinyu Luo | Ruqi Zhang

Aligning large language models (LLMs) with human preferences has become a critical step in their development. Recent research has increasingly focused on test-time alignment, where additional compute is allocated during inference to enhance LLM safety and reasoning capabilities. However, these test-time alignment techniques often incur substantial inference costs, limiting their practical application. We are inspired by the speculative sampling acceleration, which leverages a small draft model to efficiently predict future tokens, to address the efficiency bottleneck of test-time alignment. We introduce the reward-shifted speculative sampling (SSS) algorithm, in which the draft model is aligned with human preferences, while the target model remains unchanged. We theoretically demonstrate that the distributional shift between the aligned draft model and the unaligned target model can be exploited to recover the RLHF optimal solution without actually obtaining it, by modifying the acceptance criterion and bonus token distribution. Our algorithm achieves superior gold reward scores at a significantly reduced inference cost in test-time weak-to-strong alignment experiments, thereby validating both its effectiveness and efficiency.

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Stimulate the Critical Thinking of LLMs via Debiasing Discussion
Ruiyu Xiao | Lei Wu | Yuanxing Liu | Weinan Zhang | Ting Liu

Large language models (LLMs) often succumb to users’ viewpoints when faced with conflicting perspectives. We identify two key biases underlying this issue : stance homogeneity bias and human preference bias. To address these biases, we propose a novel two-stage training framework: Multi-stance Discussion Sampling and Truth Alignment Training (MDTA). First, we introduce an equal multi-stance discussion framework to automatically generate multi-model discussion datasets. Based on this framework, we construct the first and largest multi-model fair discussion dataset named Eq-Discussion for supervised fine-tuning, reducing stance homogeneity bias. Second, we optimize Reinforcement Learning from Human Feedback (RLHF) to align with discussion correctness, mitigating human preference bias. Extensive experimental results demonstrate that MDTA effectively reduces both biases and significantly enhances the performance of LLMs across a variety of downstream tasks, including reading comprehension, logical reasoning, and social question answering. Furthermore, we observe that MDTA improves the generalization capabilities of LLMs, leading to substantial performance improvements in non-discussion scenarios and on out-of-domain datasets.

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Toward Multi-Session Personalized Conversation: A Large-Scale Dataset and Hierarchical Tree Framework for Implicit Reasoning
Xintong Li | Jalend Bantupalli | Ria Dharmani | Yuwei Zhang | Jingbo Shang

There has been a surge in the use of large language models (LLM) conversational agents to generate responses based on long-term history from multiple sessions. However, existing long-term open-domain dialogue datasets lack complex, real-world personalization and fail to capture implicit reasoning—where relevant information is embedded in subtle, syntactic, or semantically distant connections rather than explicit statements. In such cases, traditional retrieval methods fail to capture relevant context, and long-context modeling also becomes inefficient due to numerous complicated persona-related details. To address this gap, we introduce ImplexConv, a large-scale long-term dataset with 2,500 examples, each containing approximately 100 conversation sessions, designed to study implicit reasoning in personalized dialogues. Additionally, we propose TaciTree, a novel hierarchical tree framework that structures conversation history into multiple levels of summarization. Instead of brute-force searching all data, TaciTree enables an efficient, level-based retrieval process where models refine their search by progressively selecting relevant details. Our experiments demonstrate that TaciTree significantly improves the ability of LLMs to reason over long-term conversations with implicit contextual dependencies.

Instruct models, obtained from various instruction tuning or post-training steps, are commonly deemed superior and more usable than their base counterpart. While the model gains instruction following ability, instruction tun- ing may lead to forgetting the knowledge from pre-training or it may encourage the model being overly conversational or verbose. This, in turn, can lead to degradation of in-context few-shot learning performance. In this work, we study the performance trajectory between base and instruct models by scaling down the strength of instruction-tuning via the partial adaption method. We show that, across several model families and model sizes, reducing the strength of instruction-tuning results in material improvement on a few-shot in-context learning benchmark covering a variety of classic natural language tasks. This comes at the cost of losing some degree of instruction following ability as measured by AlpacaEval. Our study shines light on the potential trade-off between in-context learning and instruction following abilities that is worth considering in practice.

Instruction tuning in multimodal large language models (MLLMs) generally involves cooperative learning between a backbone LLM and a feature encoder of non-text input modalities. The major challenge is how to efficiently find the synergy between the two modules so that LLMs can adapt their reasoning abilities to downstream tasks while feature encoders can adjust to provide more task-specific information about its modality. In this paper, we analyze the MLLM instruction tuning from both theoretical and empirical perspectives, where we find the unbalanced learning between the feature encoder and the LLM can cause problems of oscillation and biased learning that lead to sub-optimal convergence. Inspired by our findings, we propose a Multimodal Balance Coefficient that enables quantitative measurement of the balance of learning. Based on this, we further design a dynamic learning scheduler that better coordinates the learning between the LLM and feature encoder, alleviating the problems of oscillation and biased learning. In addition, we introduce an auxiliary regularization on the gradient to promote updating with larger step sizes, which potentially allows for a more accurate estimation of the proposed MultiModal Balance Coefficient and further improves the training sufficiency. Our proposed approach is agnostic to the architecture of LLM and feature encoder, so it can be generically integrated with various MLLMs. We conduct experiments on multiple downstream tasks with various MLLMs, demonstrating that the proposed method is more effective than the baselines in MLLM instruction tuning.

Large Language Models (LLMs) are rapidly surpassing human knowledge in many domains. While improving these models traditionally relies on costly human data, recent self-rewarding mechanisms have shown that LLMs can improve by judging their own responses instead of relying on human labelers. However, existing methods have primarily focused on improving model responses rather than judgment capabilities, resulting in rapid saturation during iterative training. To address this issue, we introduce a novel Meta-Rewarding step to the self-improvement process, where the model judges its own judgements and uses that feedback to refine its judgment skills. Surprisingly, this unsupervised approach improves the model’s ability to judge and follow instructions, as demonstrated by a win rate improvement of Llama-3-8B-Instruct from 22.9% to 39.4% on AlpacaEval 2, and 20.6% to 29.1% on Arena-Hard. These results strongly suggest the potential for self-improving models without human supervision.

Recent progress in large language model (LLM)-based multi-agent collaboration highlights the power of structured communication in enabling collective intelligence. However, existing methods largely rely on static or graph-based inter-agent topologies, lacking the potential adaptability and flexibility in communication. In this work, we propose a new framework that rethinks multi-agent coordination through a sequential structure rather than a graph structure, offering a significantly larger topology space for multi-agent communication. Our method focuses on two key directions: (1) Next-Agent Prediction, which selects the most suitable agent role at each step, and (2) Next-Context Selection (NCS), which enables each agent to selectively access relevant information from any previous step. Together, these components construct task-adaptive communication pipelines that support both role flexibility and global information flow. Extensive evaluations across multiple benchmarks demonstrate that our approach achieves superior performance while substantially reducing communication overhead.

To assist users in complex tasks, LLMs generate plans: step-by-step instructions towards a goal. While alignment methods aim to ensure LLM plans are helpful, they train (RLHF) or evaluate (ChatbotArena) on what users prefer, assuming this reflects what helps them. We test this with Planorama: an interface where 126 users answer 300 multi-step questions with LLM plans. We get 4388 plan executions and 5584 comparisons to measure plan helpfulness (QA success) and user preferences on plans, and recreate the setup in agents and reward models to see if they simulate or prefer what helps users. We expose: 1) user/model preferences and agent success do not accurately predict which plans help users, so common alignment feedback can misalign with helpfulness; 2) this gap is not due to user-specific preferences, as users are similarly successful when using plans they prefer/disprefer; 3) surface-level cues like brevity and question similarity strongly link to preferences, but such biases fail to predict helpfulness. In all, we argue aligning helpful LLMs needs feedback from real user interactions—not just preferences of what looks helpful—so we discuss the plan NLP researchers can execute to solve this problem.

Conversational breakdowns in close relationships are deeply shaped by personal histories and emotional context, yet most NLP research treats conflict detection as a general task, overlooking the relational dynamics that influence how messages are perceived. In this work, we leverage nonviolent communication (NVC) theory to evaluate LLMs in detecting conversational breakdowns and assessing how relationship backstory influences both human and model perception of conflicts. Given the sensitivity and scarcity of real-world datasets featuring conflict between familiar social partners with rich personal backstories, we contribute the PersonaConflicts Corpus, a dataset of N=5,772 naturalistic simulated dialogues spanning diverse conflict scenarios between friends, family members, and romantic partners. Through a controlled human study, we annotate a subset of dialogues and obtain fine-grained labels of communication breakdown types on individual turns, and assess the impact of backstory on human and model perception of conflict in conversation. We find that the polarity of relationship backstories significantly shifted human perception of communication breakdowns and impressions of the social partners, yet models struggle to meaningfully leverage those backstories in the detection task. Additionally, we find that models consistently overestimate how positively a message will make a listener feel. Our findings underscore the critical role of personalization to relationship contexts in enabling LLMs to serve as effective mediators in human communication for authentic connection.

Retrieval-augmented generation (RAG) addresses the limitation of large language models (LLMs) in achieving up-to-date information by integrating external knowledge sources, but it is hindered by noisy or irrelevant retrieved data, leading to reduced accuracy. Additionally, most RAG methods rely on task-specific supervision, reducing their adaptability across domains. To overcome these challenges, we propose WinnowRAG, a novel multi-agent debate-based RAG framework. WinnowRAG operates in two stages: in Stage I, query-aware clustering groups similar documents, with each cluster assigned to an LLM agent for generating personalized responses. A critic LLM then consolidates these answers, forming super-agents. In Stage II, the super-agents engage in a structured discussion to filter out incorrect or irrelevant information, ensuring only relevant knowledge is used for final response generation. Crucially, WinnowRAG is unsupervised and leverages pretrained LLMs without requiring fine-tuning, making it easily adaptable to various tasks. The experiments on various realistic datasets demonstrate the effectiveness of WinnowRAG over state-of-the-art baselines.

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Cognitive Linguistic Identity Fusion Score (CLIFS): A Scalable Cognition‐Informed Approach to Quantifying Identity Fusion from Text
Devin R. Wright | Jisun An | Yong-Yeol Ahn

Quantifying *identity fusion*—the psychological merging of self with another entity or abstract target (e.g., a religious group, political party, ideology, value, brand, belief, etc.)—is vital for understanding a wide range of group‐based human behaviors. We introduce the Cognitive Linguistic Identity Fusion Score ([CLIFS](https://github.com/DevinW-sudo/CLIFS)), a novel metric that integrates cognitive linguistics with large language models (LLMs), which builds on implicit metaphor detection. Unlike traditional pictorial and verbal scales, which require controlled surveys or direct field contact, CLIFS delivers fully automated, scalable assessments while maintaining strong alignment with the established verbal measure. In benchmarks, CLIFS outperforms both existing automated approaches and human annotation. As a proof of concept, we apply CLIFS to violence risk assessment to demonstrate that it can improve violence risk assessment by more than 240%. Building on our identification of a new NLP task and early success, we underscore the need to develop larger, more diverse datasets that encompass additional fusion-target domains and cultural backgrounds to enhance generalizability and further advance this emerging area. CLIFS models and code are public at [https://github.com/DevinW-sudo/CLIFS](https://github.com/DevinW-sudo/CLIFS).

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SilVar: Speech-Driven Multimodal Model for Reasoning Visual Question Answering and Object Localization
Tan-Hanh Pham | Le Hoang Nam | Phu-Vinh Nguyen | Chris Ngo | Truong-Son Hy

Visual Language Models have demonstrated remarkable capabilities across various tasks, including visual question answering and image captioning. However, most models rely on text-based instructions, limiting their effectiveness in natural human-machine interactions. Moreover, the quality of language models primarily depends on reasoning and prompting techniques, such as chain-of-thought, which remain underexplored when using speech instructions. To address these challenges, we propose SilVar, an end-to-end multimodal model that leverages speech instructions for reasoning-based visual question answering. Additionally, we investigate reasoning techniques at different levels, including conversational, simple, and complex speech instructions. SilVar is built upon CLIP, Whisper, and LLaMA 3.1-8B, enabling more intuitive interactions by allowing users to provide verbal or text-based instructions. To this end, we introduce a new dataset designed to challenge models with speech-based reasoning tasks for object localization. This dataset enhances the model’s ability to process and explain visual scenes from spoken input, moving beyond simple object recognition to reasoning-based interactions. To our knowledge, SilVar is the first open-source, speech-driven VLM. We believe SilVar will inspire the next generation of multimodal reasoning models, advancing toward expert artificial general intelligence.

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CEMTM: Contextual Embedding-based Multimodal Topic Modeling
Amirhossein Abaskohi | Raymond Li | Chuyuan Li | Shafiq Joty | Giuseppe Carenini

We introduce CEMTM, a context-enhanced multimodal topic model designed to infer coherent and interpretable topic structures from both short and long documents containing text and images. CEMTM builds on fine-tuned large vision language models (LVLMs) to obtain contextualized embeddings, and employs a distributional attention mechanism to weight token-level contributions to topic inference. A reconstruction objective aligns topic-based representations with the document embedding, encouraging semantic consistency across modalities. Unlike existing approaches, CEMTM can process multiple images per document without repeated encoding and maintains interpretability through explicit word-topic and document-topic distributions. Extensive experiments on six multimodal benchmarks show that CEMTM consistently outperforms unimodal and multimodal baselines, achieving a remarkable average LLM score of 2.61. Further analysis shows its effectiveness in downstream few-shot retrieval and its ability to capture visually grounded semantics in complex domains such as scientific articles.

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RedHerring Attack: Testing the Reliability of Attack Detection
Jonathan Rusert

In response to adversarial text attacks, attack detection models have been proposed and shown to successfully identify text modified by adversaries. Attack detection models can be leveraged to provide an additional check for NLP models and give signals for human input. However, the reliability of these models has not yet been thoroughly explored. Thus, we propose and test a novel attack setting and attack, RedHerring. RedHerring aims to make attack detection models unreliable by modifying a text to cause the detection model to predict an attack, while keeping the classifier correct. This creates a tension between the classifier and detector. If a human sees that the detector is giving an “incorrect” prediction, but the classifier a correct one, then the human will see the detector as unreliable. We test this novel threat model on 4 datasets against 3 detectors defending 4 classifiers. We find that RedHerring is able to drop detection accuracy between 20 - 71 points, while maintaining (or improving) classifier accuracy. As an initial defense, we propose a simple confidence check which requires no retraining of the classifier or detector and increases detection accuracy greatly. This novel threat model offers new insights into how adversaries may target detection models.

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Modeling Bottom-up Information Quality during Language Processing
Cui Ding | Yanning Yin | Lena Ann Jäger | Ethan Wilcox

Contemporary theories model language processing as integrating both top-down expectations and bottom-up inputs. One major prediction of such models is that the quality of the bottom-up inputs modulates ease of processing—noisy inputs should lead to difficult and effortful comprehension. We test this prediction in the domain of reading. First, we propose an information-theoretic operationalization for the “quality” of bottom-up information as the mutual information (MI) between visual information and word identity. We formalize this prediction in a mathematical model of reading as a Bayesian update. Second, we test our operationalization by comparing participants’ reading times in conditions where words’ information quality has been reduced, either by occluding their top or bottom half, with full words. We collect data in English and Chinese. We then use multimodal language models to estimate the mutual information between visual inputs and words. We use these data to estimate the specific effect of reduced information quality on reading times. Finally, we compare how information is distributed across visual forms. In English and Chinese, the upper half contains more information about word identity than the lower half. However, the asymmetry is more pronounced in English, a pattern which is reflected in the reading times.

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Data Drives Unstable Hierarchical Generalization in LMs
Tian Qin | Naomi Saphra | David Alvarez-Melis

Early in training, LMs can behave like n-gram models, but eventually, they often learn tree-based syntactic rules and generalize hierarchically out of distribution (OOD). We study this shift using controlled grammar-learning tasks: question formation and tense inflection. We find a model learns to generalize hierarchically if its training data is *complex*–in particular, if it includes center-embedded clauses, a special syntactic structure. Under this definition, complex data drives hierarchical rules, while less complex data encourages shortcut learning in the form of n-gram-like linear rules. Furthermore, we find that a model uses rules to generalize, whether hierarchical or linear, if its training data is *diverse*–in particular, if it includes many distinct syntax trees in the training set. Under this definition, diverse data promotes stable rule learning, whereas less diverse data promotes memorization of individual syntactic sequences. Finally, intermediate diversity and intermediate complexity form an *unstable regime*, which is characterized by oscillatory learning dynamics and inconsistent behaviors across random seeds. These results highlight the central role of training data in shaping generalization and explain why competing strategies can lead to unstable outcomes.

The rise of LLM-driven AI characters raises safety concerns, particularly for vulnerable human users with psychological disorders. To address these risks, we propose EmoAgent, a multi-agent AI framework designed to evaluate and mitigate mental health hazards in human-AI interactions. EmoAgent comprises two components: **EmoEval** simulates virtual users, including those portraying mentally vulnerable individuals, to assess mental health changes before and after interactions with AI characters. It uses clinically proven psychological and psychiatric assessment tools (PHQ-9, PDI, PANSS) to evaluate mental risks induced by LLM. **EmoGuard** serves as an intermediary, monitoring users’ mental status, predicting potential harm, and providing corrective feedback to mitigate risks. Experiments conducted in popular character-based chatbots show that emotionally engaging dialogues can lead to psychological deterioration in vulnerable users, with mental state deterioration in more than 34.4% of the simulations. EmoGuard significantly reduces these deterioration rates, underscoring its role in ensuring safer AI-human interactions.

We propose a novel inference-time out-of-domain (OOD) detection algorithm for specialized large language models (LLMs). Despite achieving state-of-the-art performance on in-domain tasks through fine-tuning, specialized LLMs remain vulnerable to incorrect or unreliable outputs when presented with OOD inputs, posing risks in critical applications. Our method leverages the Inductive Conformal Anomaly Detection (ICAD) framework, using a new non-conformity measure based on the model’s dropout tolerance. Motivated by recent findings on polysemanticity and redundancy in LLMs, we hypothesize that in-domain inputs exhibit higher dropout tolerance than OOD inputs. We aggregate dropout tolerance across multiple layers via a valid ensemble approach, improving detection while maintaining theoretical false alarm bounds from ICAD. Experiments with medical-specialized LLMs show that our approach detects OOD inputs better than baseline methods, with AUROC improvements of 2% to 37% when treating OOD datapoints as positives and in-domain test datapoints as negatives.

Simplifying complex texts is essential to ensure equitable access to information, particularly for individuals with cognitive impairments. The Easy-to-Read (ETR) initiative provides a framework to make content more accessible for these individuals. However, manually creating such texts remains time-consuming and resource-intensive. In this work, we investigate the potential of large language models (LLMs) to automate the generation of ETR content. To address the scarcity of aligned corpora and the specific constraints of ETR, we propose a multi-task learning (MTL) approach that trains models jointly on text summarization, text simplification, and ETR generation. We explore two complementary strategies: multi-task retrieval-augmented generation (RAG) for in-context learning (ICL), and MTL-LoRA for parameter-efficient fine-tuning (PEFT). Our experiments with Mistral-7B and LLaMA-3-8B, conducted on ETR-fr, a new high-quality dataset, show that MTL-LoRA consistently outperforms all other strategies in in-domain settings, while the MTL-RAG-based approach achieves better generalization in out-of-domain scenarios. Our code is publicly available at https://github.com/FrLdy/ETR-PEFT-Composition.

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D-CoDe: Scaling Image-Pretrained VLMs to Video via Dynamic Compression and Question Decomposition
Yiyang Huang | Yizhou Wang | Yun Fu

Video large language models (Vid-LLMs), which excel in diverse video-language tasks, can be effectively constructed by adapting image-pretrained vision-language models (VLMs). However, this adaptation remains challenging, as it requires processing dense and temporally extended visual inputs that exceed the capacity of image-based models. This paper identifies the perception bottleneck and token overload as key challenges in extending image-based VLMs to the video domain. To address these issues, we propose D-CoDe, a training-free adaptation framework that incorporates dynamic compression and question decomposition. Specifically, dynamic compression alleviates the perception bottleneck through adaptive selection of representative frames and content-aware aggregation of spatial tokens, thereby reducing redundancy while preserving informative content. In parallel, question decomposition mitigates token overload by reformulating the original query into sub-questions, guiding the model to focus on distinct aspects of the video and enabling more comprehensive understanding. Experiments demonstrate that D-CoDe effectively improves video understanding across various benchmarks. Furthermore, strong performance on the challenging long-video benchmark highlights the potential of D-CoDe in handling complex video-language tasks. Code is available at https://github.com/hukcc/D-CoDe.

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ReEvalMed: Rethinking Medical Report Evaluation by Aligning Metrics with Real-World Clinical Judgment
Ruochen Li | Jun Li | Bailiang Jian | Kun Yuan | Youxiang Zhu

Automatically generated radiology reports often receive high scores from existing evaluation metrics but fail to earn clinicians’ trust. This gap reveals fundamental flaws in how current metrics assess the quality of generated reports. We rethink the design and evaluation of these metrics and propose a clinically grounded Meta-Evaluation framework. We define clinically grounded criteria spanning clinical alignment and key metric capabilities, including discrimination, robustness, and monotonicity. Using a fine-grained dataset of ground truth and rewritten report pairs annotated with error types, clinical significance labels, and explanations, we systematically evaluate existing metrics and reveal their limitations in interpreting clinical semantics, such as failing to distinguish clinically significant errors, over-penalizing harmless variations, and lacking consistency across error severity levels. Our framework offers guidance for building more clinically reliable evaluation methods.

Multilingual speech translation (ST) and machine translation (MT) in the medical domain enhances patient care by enabling efficient communication across language barriers, alleviating specialized workforce shortages, and facilitating improved diagnosis and treatment, particularly during pandemics. In this work, we present the first systematic study on medical ST, to our best knowledge, by releasing MultiMedST, a large-scale ST dataset for the medical domain, spanning all translation directions in five languages: Vietnamese, English, German, French, and Simplified/Traditional Chinese, together with the models. With 290,000 samples, this is the largest medical MT dataset and the largest many-to-many multilingual ST among all domains. Secondly, we present the most comprehensive ST analysis in the field’s history, to our best knowledge, including: empirical baselines, bilingual-multilingual comparative study, end-to-end vs. cascaded comparative study, task-specific vs. multi-task sequence-to-sequence comparative study, code-switch analysis, and quantitative-qualitative error analysis. All code, data, and models are available online: https://github.com/leduckhai/MultiMed-ST.

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Beyond Checkmate: Exploring the Creative Choke Points for AI Generated Texts
Nafis Irtiza Tripto | Saranya Venkatraman | Mahjabin Nahar | Dongwon Lee

The rapid advancement of Large Language Models (LLMs) has revolutionized text generation but also raised concerns about potential misuse, making detecting LLM-generated text (AI text) increasingly essential. While prior work has focused on identifying AI text and effectively checkmating it, our study investigates a less-explored territory: portraying the nuanced distinctions between human and AI texts across text segments (introduction, body, and conclusion). Whether LLMs excel or falter in incorporating linguistic ingenuity across text segments, the results will critically inform their viability and boundaries as effective creative assistants to humans. Through an analogy with the structure of chess games, comprising opening, middle, and end games, we analyze segment-specific patterns to reveal where the most striking differences lie. Although AI texts closely resemble human writing in the body segment due to its length, deeper analysis shows a higher divergence in features dependent on the continuous flow of language, making it the most informative segment for detection. Additionally, human texts exhibit greater stylistic variation across segments, offering a new lens for distinguishing them from AI. Overall, our findings provide fresh insights into human-AI text differences and pave the way for more effective and interpretable detection strategies. Codes available at https://github.com/tripto03/chess_inspired_human_ai_text_distinction.

Retrieval-augmented Generation (RAG) is powerful, but its effectiveness hinges on which retrievers we use and how. Different retrievers offer distinct, often complementary signals: BM25 captures lexical matches; dense retrievers, semantic similarity. Yet in practice, we typically fix a single retriever based on heuristics, which fails to generalize across diverse information needs. Can we dynamically select and integrate multiple retrievers for each individual query, without the need for manual selection? In our work, we validate this intuition with quantitative analysis and introduce a mixture of retrievers: a zero-shot, weighted combination of heterogeneous retrievers. Extensive experiments show that such mixtures are effective and efficient: Despite totaling just 0.8B parameters, this mixture outperforms every individual retriever and even larger 7B models—by +10.8% and +3.9% on average, respectively. Further analysis also shows that this mixture framework can help incorporate specialized non-oracle human information sources as retrievers to achieve good collaboration, with a 58.9% relative performance improvement over simulated humans alone.

Effective conversational search demands a deep understanding of user intent across multiple dialogue turns. Users frequently use abbreviations and shift topics in the middle of conversations, posing challenges for conventional retrievers. While query rewriting techniques improve clarity, they often incur significant computational cost due to additional autoregressive steps. Moreover, although LLM-based retrievers demonstrate strong performance, they are not explicitly optimized to track user intent in multi-turn settings, often failing under topic drift or contextual ambiguity. To address these limitations, we propose ContextualRetriever, a novel LLM-based retriever that directly incorporates conversational context into the retrieval process. Our approach introduces: (1) a context-aware embedding mechanism that highlights the current query within the dialogue history; (2) intent-guided supervision based on high-quality rewritten queries; and (3) a training strategy that preserves the generative capabilities of the base LLM. Extensive evaluations across multiple conversational search benchmarks demonstrate that ContextualRetriever significantly outperforms existing methods while incurring no additional inference overhead.

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LIDDIA: Language-based Intelligent Drug Discovery Agent
Reza Averly | Frazier N. Baker | Ian A Watson | Xia Ning

Drug discovery is a long, expensive, and complex process, relying heavily on human medicinal chemists, who can spend years searching the vast space of potential therapies. Recent advances in artificial intelligence for chemistry have sought to expedite individual drug discovery tasks; however, there remains a critical need for an intelligent agent that can navigate the drug discovery process. Towards this end, we introduce LIDDiA, an autonomous agent capable of intelligently navigating the drug discovery process in silico. By leveraging the reasoning capabilities of large language models, LIDDiA serves as a low-cost and highly-adaptable tool for autonomous drug discovery. We comprehensively examine LIDDiA, demonstrating that (1) it can generate molecules meeting key pharmaceutical criteria on over 70% of 30 clinically relevant targets, (2) it intelligently balances exploration and exploitation in the chemical space, and (3) it identifies one promising novel candidate on AR/NR3C4, a critical target for both prostate and breast cancers. Code and dataset are available at https://github.com/ninglab/LIDDiA.

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Agentic-R1: Distilled Dual-Strategy Reasoning
Weihua Du | Pranjal Aggarwal | Sean Welleck | Yiming Yang

Current long chain-of-thought (long-CoT) models excel at mathematical reasoning but rely on slow and error-prone natural language traces. Tool-augmented agents address arithmetic via code execution, but often falter on complex logical tasks. We introduce a fine-tuning framework, **DualDistill**, that distills complementary reasoning strategies from multiple teachers into a unified student model. Using this approach, we train **Agentic-R1**, which dynamically selects the optimal strategy for each query, invoking tools for arithmetic and algorithmic problems and using text-based reasoning for abstract ones. Our method improves accuracy on computation-intensive tasks and reduces inference latency on standard benchmarks, demonstrating the promise of multi-strategy distillation for robust and efficient reasoning.

Recent advances in conversational AI have been substantial, but developing real-time systems for perceptual task guidance remains challenging. These systems must provide interactive, proactive assistance based on streaming visual inputs, yet their development is constrained by the costly and labor-intensive process of data collection and system evaluation. To address these limitations, we present a comprehensive framework with three key contributions. First, we introduce a novel data curation pipeline that synthesizes dialogues from annotated egocentric videos, resulting in ProAssist, a large-scale synthetic dialogue dataset spanning multiple domains. Second, we develop a suite of automatic evaluation metrics, validated through extensive human studies. Third, we propose an end-to-end model that processes streaming video inputs to generate contextually appropriate responses, incorporating novel techniques for handling data imbalance and long-duration videos. This work lays the foundation for developing real-time, proactive AI assistants capable of guiding users through diverse tasks.

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Should I Share this Translation? Evaluating Quality Feedback for User Reliance on Machine Translation
Dayeon Ki | Kevin Duh | Marine Carpuat

As people increasingly use AI systems in work and daily life, feedback mechanisms that help them use AI responsibly are urgently needed, particularly in settings where users are not equipped to assess the quality of AI predictions. We study a realistic Machine Translation (MT) scenario where monolingual users decide whether to share an MT output, first without and then with quality feedback. We compare four types of quality feedback: explicit feedback that directly give users an assessment of translation quality using (1) error highlights and (2) LLM explanations, and implicit feedback that helps users compare MT inputs and outputs through (3) backtranslation and (4) question–answer (QA) tables. We find that all feedback types, except error highlights, significantly improve both decision accuracy and appropriate reliance. Notably, implicit feedback, especially QA tables, yields significantly greater gains than explicit feedback in terms of decision accuracy, appropriate reliance, and user perceptions – receiving the highest ratings for helpfulness and trust, and the lowest for mental burden.

Charts are a crucial visual medium for communicating and representing information. While Large Vision-Language Models (LVLMs) have made progress on chart question answering (CQA), the task remains challenging, particularly when models attend to irrelevant regions of the chart. In this work, we present ChartGaze, a new eye-tracking dataset that captures human gaze patterns during chart reasoning tasks. Through a systematic comparison of human and model attention, we find that LVLMs often diverge from human gaze, leading to reduced interpretability and accuracy. To address this, we propose a gaze-guided attention refinement that aligns image-text attention with human fixations. Our approach improves both answer accuracy and attention alignment, yielding gains of up to 2.56 percentage points across multiple models. These results demonstrate the promise of incorporating human gaze to enhance both the reasoning quality and interpretability of chart-focused LVLMs.

While significant progress has been made with dual- and bi-encoder dense retrievers, they often struggle on queries with logical connectives, a use case that is often overlooked yet important in downstream applications. Current dense retrievers struggle with such queries, such that the retrieved results do not respect the logical constraints implied in the queries. To address this challenge, we introduce LogiCoL, a logically-informed contrastive learning objective for dense retrievers. LogiCoL builds upon in-batch supervised contrastive learning, and learns dense retrievers to respect the subset and mutually-exclusive set relation between query results via two sets of soft constraints expressed via t-norm in the learning objective. We evaluate the effectiveness of LogiCoL on the task of entity retrieval, where the model is expected to retrieve a set of entities in Wikipedia that satisfy the implicit logical constraints in the query. We show that models trained with LogiCoL yield improvement both in terms of retrieval performance and logical consistency in the results. We provide detailed analysis and insights to uncover why queries with logical connectives are challenging for dense retrievers and why LogiCoL is most effective.

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ModalPrompt: Towards Efficient Multimodal Continual Instruction Tuning with Dual-Modality Guided Prompt
Fanhu Zeng | Fei Zhu | Haiyang Guo | Xu-Yao Zhang | Cheng-Lin Liu

Large Multimodal Models (LMMs) exhibit remarkable multi-tasking ability by learning mixed instruction datasets. However, novel tasks would be encountered sequentially in dynamic world, which urges for equipping LMMs with multimodal continual instruction learning (MCIT) ability especially for diverse and challenging generative tasks. Existing MCIT methods do not fully exploit the unique attribute of LMMs and often gain performance at the expense of efficiency. In this paper, we propose a novel prompt learning framework for MCIT to effectively alleviate forgetting of previous knowledge while managing computational complexity with natural image-text supervision. Concretely, we learn prompts for each task and exploit efficient prompt fusion for knowledge transfer and prompt selection for complexity management with dual-modality guidance. Extensive experiments demonstrate that our approach achieves substantial +14.26% performance gain on MCIT benchmarks with remarkable x1.42 inference speed free from growing computation.

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Skip-Thinking: Chunk-wise Chain-of-Thought Distillation Enable Smaller Language Models to Reason Better and Faster
Xiaoshu Chen | Sihang Zhou | Ke Liang | Xiaoyu Sun | Xinwang Liu

Chain-of-thought (CoT) distillation allows a large language model (LLM) to guide a small language model (SLM) in reasoning tasks. Existing methods train the SLM to learn the long rationale in one iteration, resulting in two issues: 1) Long rationales lead to a large token-level batch size during training, making gradients of core reasoning tokens (i.e., the token will directly affect the correctness of subsequent reasoning) over-smoothed as they contribute a tiny fraction of the rationale. As a result, the SLM converges to sharp minima where it fails to grasp the reasoning logic. 2) The response is slow, as the SLM must generate a long rationale before reaching the answer. Therefore, we propose chunk-wise training (CWT), which uses a heuristic search to divide the rationale into internal semantically coherent chunks and focuses SLM on learning from only one chunk per iteration. In this way, CWT naturally isolates non-reasoning chunks that do not involve the core reasoning token (e.g., summary and transitional chunks) from the SLM learning for reasoning chunks, making the fraction of the core reasoning token increase in the corresponding iteration. Based on CWT, skip-thinking training (STT) is proposed. STT makes the SLM automatically skip non-reasoning medium chunks to reach the answer, improving reasoning speed while maintaining accuracy. We validate our approach on a variety of SLMs and multiple reasoning tasks.

Individual Large Language Models (LLMs) have demonstrated significant capabilities across various domains, such as healthcare and law. Recent studies also show that coordinated multi-agent systems exhibit enhanced decision-making and reasoning abilities through collaboration. However, due to the vulnerabilities of individual LLMs and the difficulty of accessing all agents in a multi-agent system, a key question arises: If attackers only know one agent, could they still generate adversarial samples capable of misleading the collective decision?To explore this question, we formulate it as a game with incomplete information, where attackers know only one target agent and lack knowledge of the other agents in the system. With this formulation, we propose M-Spoiler, a framework that simulates agent interactions within a multi-agent system to generate adversarial samples. These samples are then used to manipulate the target agent in the target system, misleading the system’s collaborative decision-making process.More specifically, M-Spoiler introduces a stubborn agent that actively aids in optimizing adversarial samples by simulating potential stubborn responses from agents in the target system. This enhances the effectiveness of the generated adversarial samples in misleading the system.Through extensive experiments across various tasks, our findings confirm the risks posed by the knowledge of an individual agent in multi-agent systems and demonstrate the effectiveness of our framework.We also explore several defense mechanisms, showing that our proposed attack framework remains more potent than baselines, underscoring the need for further research into defensive strategies.

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Toxicity Red-Teaming: Benchmarking LLM Safety in Singapore’s Low-Resource Languages
Yujia Hu | Ming Shan Hee | Preslav Nakov | Roy Ka-Wei Lee

The advancement of Large Language Models (LLMs) has transformed natural language processing; however, their safety mechanisms remain under-explored in low-resource, multilingual settings. Here, we aim to bridge this gap. In particular, we introduce SGToxicGuard, a novel dataset and evaluation framework for benchmarking LLM safety in Singapore’s diverse linguistic context, including Singlish, Chinese, Malay, and Tamil. SGToxicGuard adopts a red-teaming approach to systematically probe LLM vulnerabilities in three real-world scenarios: conversation, question-answering, and content composition. We conduct extensive experiments with state-of-the-art multilingual LLMs, and the results uncover critical gaps in their safety guardrails. By offering actionable insights into cultural sensitivity and toxicity mitigation, we lay the foundation for safer and more inclusive AI systems in linguistically diverse environments. Disclaimer: This paper contains sensitive content that may be disturbing to some readers.

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Improving Clustering with Positive Pairs Generated from LLM-Driven Labels
Xiaotong Zhang | Ying Li

Traditional unsupervised clustering methods, which often rely on contrastive training of embedders, suffer from a lack of label knowledge, resulting in suboptimal performance. Furthermore, the presence of potential false negatives can destabilize the training process. Hence, we propose to improve clustering with Positive Pairs generated from LLM-driven Labels (PPLL). In the proposed framework, LLM is initially employed to cluster the data and generate corresponding mini-cluster labels. Subsequently, positive pairs are constructed based on these labels, and an embedder is trained using BYOL to obviate the need for negative pairs. Following training, the acquired label knowledge is integrated into K-means clustering. This framework enables the integration of label information throughout the training and inference processes, while mitigating the reliance on negative pairs. Additionally, it generates interpretable labels for improved understanding of clustering results. Empirical evaluations on a range of datasets demonstrate that our proposed framework consistently surpasses state-of-the-art baselines, achieving superior performance, robustness, and computational efficiency for diverse text clustering applications.

Large language models (LLMs) are widely deployed as zero-shot evaluators for answer grading, content moderation, and document ranking. Yet studies show that guard models (Guards)—LLMs fine-tuned for safety—remain vulnerable to “jailbreak” attacks, jeopardising downstream chatbots.We confirm this weakness on three public benchmarks (BeaverTails, XSTest, AdvBench) and trace it to representation shifts that arise in the embedding layer and cascade through the Transformer stack.To counteract the effect, we introduce Gamma-Guard: lightweight residual adapters inserted after the embeddings and at sparse intervals in the model. The adapters start with zero-scaled gates, so they retain the original behaviour; a brief adversarial fine-tuning phase then teaches them to denoise embeddings and refocus attention.With fewer than 0.1% extra parameters and only a 2% latency increase, Gamma-Guard lifts adversarial accuracy from <5% to 95% a 90 percentage-point gain while reducing clean-data accuracy by just 8 percentage points.Extensive ablations further show that robustness improvements persist across different layer placements and model sizes.To our knowledge, this is the first approach that directly augments large Guards with trainable adapters, providing a practical path toward safer large-scale LLM deployments.

Recent advances in Large Language Models (LLMs) have enabled them to process increasingly longer sequences, ranging from 2K to 2M tokens and even beyond. However, simply extending the input sequence length does not necessarily lead to effective long-context understanding. In this study, we integrate Chain-of-Thought (CoT) reasoning into LLMs in a supervised manner to facilitate effective long-context understanding. To achieve this, we introduce LongFinanceQA, a synthetic dataset in the financial domain designed to improve long-context reasoning. Unlike existing long-context synthetic data, LongFinanceQA includes intermediate CoT reasoning before the final conclusion, which encourages LLMs to perform explicit reasoning, improving accuracy and interpretability in long-context understanding. To generate synthetic CoT reasoning, we propose Property-based Agentic Inference (PAI), an agentic framework that simulates human-like reasoning steps, including property extraction, retrieval, and summarization. We evaluate PAI’s reasoning capabilities by assessing GPT-4o-mini w/ PAI on the Loong benchmark, outperforming standard GPT-4o-mini by 20.0%. Furthermore, we fine-tune LLaMA-3.1-8B-Instruct on LongFinanceQA, achieving a 28.0% gain on Loong’s financial subset.

Event Causal Identification (ECI) aims to identify fine-grained causal relationships between events from unstructured text. Contrastive learning has shown promise in enhancing ECI by optimizing representation distances between positive and negative samples. However, existing methods often rely on rule-based or random sampling strategies, which may introduce spurious causal positives. Moreover, static negative samples often fail to approximate actual decision boundaries, thus limiting discriminative performance. Therefore, we propose an ECI method enhanced by Dynamic Energy-based Contrastive Learning with multi-stage knowledge Verification (DECLV). Specifically, we integrate multi-source knowledge validation and LLM-driven causal inference to construct a multi-stage knowledge validation mechanism, which generates high-quality contrastive samples and effectively suppresses spurious causal disturbances. Meanwhile, we introduce the Stochastic Gradient Langevin Dynamics (SGLD) method to dynamically generate adversarial negative samples, and employ an energy-based function to model the causal boundary between positive and negative samples. The experimental results show that our method outperforms previous state-of-the-art methods on both benchmarks, EventStoryLine and Causal-TimeBank.

Recent advances in multimodal large language models (MLLMs) and diffusion models (DMs) have opened new possibilities for AI-generated content. Yet, personalized cover image generation remains underexplored, despite its critical role in boosting user engagement on digital platforms. We propose ICG, a novel framework that integrates MLLM-based prompting with personalized preference alignment to generate high-quality, contextually relevant covers. ICG extracts semantic features from item titles and reference images via meta tokens, refines them with user embeddings, and injects the resulting personalized context into the diffusion model. To address the lack of labeled supervision, we adopt a multi-reward learning strategy that combines public aesthetic and relevance rewards with a personalized preference model trained from user behavior. Unlike prior pipelines relying on handcrafted prompts and disjointed modules, ICG employs an adapter to bridge MLLMs and diffusion models for end-to-end training. Experiments demonstrate that ICG significantly improves image quality, semantic fidelity, and personalization, leading to stronger user appeal and offline recommendation accuracy in downstream tasks. As a plug-and-play adapter bridging MLLMs and diffusion models, ICG is compatible with common checkpoints and requires no ground-truth labels during optimization.

Chain-of-Thought (CoT) reasoning improves performance on complex tasks but introduces significant inference latency due to its verbosity. In this work, we propose Multiround Adaptive Chain-of-Thought Compression (MACC), a framework that leverages the token elasticity phenomenon—where overly small token budgets may paradoxically increase output length—to progressively compress CoTs via multiround refinement. This adaptive strategy allows MACC to dynamically determine the optimal compression depth for each input. Our method achieves an average accuracy improvement of 5.6% over state-of-the-art baselines, while also reducing CoT length by an average of 47 tokens and significantly lowering latency. Furthermore, we show that test-time performance—accuracy and token length—can be reliably predicted using interpretable features like perplexity and compression rate on training set. Evaluated across different models, our method enables efficient model selection and forecasting without repeated fine-tuning, demonstrating that CoT compression is both effective and predictable. Our code will be released in https://github.com/Leon221220/MACC.

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A Symbolic Adversarial Learning Framework for Evolving Fake News Generation and Detection
Chong Tian | Qirong Ho | Xiuying Chen

Rapid LLM advancements heighten fake news risks by enabling the automatic generation of increasingly sophisticated misinformation. Previous detection methods, including fine-tuned small models or LLM-based detectors, often struggle with its dynamically evolving nature. In this work, we propose a novel framework called the Symbolic Adversarial Learning Framework (SALF), which implements an adversarial training paradigm by an agent symbolic learning optimization process, rather than relying on numerical updates. SALF introduces a paradigm where the generation agent crafts deceptive narratives, and the detection agent uses structured debates to identify logical and factual flaws for detection, and they iteratively refine themselves through such adversarial interactions. Unlike traditional neural updates, we represent agents using agent symbolic learning, where learnable weights are defined by agent prompts, and simulate back-propagation and gradient descent by operating on natural language representations of weights, loss, and gradients. Experiments on two multilingual benchmark datasets demonstrate SALF’s effectiveness, showing it generates sophisticated fake news that degrades state-of-the-art detection performance by up to 53.4% in Chinese and 34.2% in English on average. SALF also refines detectors, improving detection of refined content by up to 7.7%. We hope our work inspires further exploration into more robust, adaptable fake news detection systems.

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RareSyn: Health Record Synthesis for Rare Disease Diagnosis
Huimin Wang | Yutian Zhao | Yefeng Zheng | Xian Wu

Diagnosis based on Electronic Health Records (EHRs) often struggles with data scarcity and privacy concerns. To address these issues, we introduce RareSyn, an innovative data synthesis approach designed to augment and de-identify EHRs, with a focus on rare diseases. The core insight of RareSyn involves using seed EHRs of rare diseases to recall similar records from both common and rare diseases, and then leveraging Large Language Models to substitute the key medical information (e.g., symptoms or examination details) in these records with information from the knowledge graph, thereby generating new EHRs. We first train a transformer Encoder with contrastive learning to integrate various types of medical knowledge. Then, RareSyn engages in iterative processes of recalling similar EHRs, structuring EHRs, revising EHRs, and generating new EHRs until the produced EHRs achieve extensive coverage of the rare disease knowledge. We assess RareSyn based on its utility for diagnosis modeling, the diversity of medical knowledge it incorporates, and the privacy of the synthesized EHRs. Extensive experiments demonstrate its effectiveness in improving disease diagnosis, enhancing diversity, and maintaining privacy.

Large reasoning models (LRMs) have exhibited strong performance on complex reasoning tasks, with further gains achievable through increased computational budgets at inference. However, current test-time scaling methods predominantly rely on redundant sampling, ignoring the historical experience utilization, thereby limiting computational efficiency. To overcome this limitation, we propose Sticker-TTS, a novel test-time scaling framework that coordinates three collaborative LRMs to iteratively explore and refine solutions guided by historical attempts. At the core of our framework are distilled key conditions—termed stickers—which drive the extraction, refinement, and reuse of critical information across multiple rounds of reasoning. To further enhance the efficiency and performance of our framework, we introduce a two-stage optimization strategy that combines imitation learning with self-improvement, enabling progressive refinement. Extensive evaluations on three challenging mathematical reasoning benchmarks, including AIME-24, AIME-25, and OlymMATH, demonstrate that Sticker-TTS consistently surpasses strong baselines, including self-consistency and advanced reinforcement learning approaches, under comparable inference budgets. These results highlight the effectiveness of sticker-guided historical experience utilization. Our code and data are available at https://github.com/RUCAIBox/Sticker-TTS.

Minority languages in China, such as Tibetan, Uyghur, and Traditional Mongolian, face significant challenges due to their unique writing systems, which differ from international standards. This discrepancy has led to a severe lack of relevant corpora, particularly for supervised tasks like headline generation. To address this gap, we introduce a novel dataset, Chinese Minority Headline Generation (CMHG), which includes 100,000 entries for Tibetan, and 50,000 entries each for Uyghur and Mongolian, specifically curated for headline generation tasks. Additionally, we propose a high-quality test set annotated by native speakers, designed to serve as a benchmark for future research in this domain. We hope this dataset will become a valuable resource for advancing headline generation in Chinese minority languages and contribute to the development of related benchmarks.

The communication topology in large language model-based multi-agent systems fundamentally governs inter-agent collaboration patterns, critically shaping both the efficiency and effectiveness of collective decision-making. While recent studies for communication topology automated design tend to construct sparse structures for efficiency, they often overlook why and when sparse and dense topologies help or hinder collaboration. In this paper, we present a causal framework to analyze how agent outputs, whether correct or erroneous, propagate under topologies with varying sparsity. Our empirical studies reveal that moderately sparse topologies, which effectively suppress error propagation while preserving beneficial information diffusion, typically achieve optimal task performance. Guided by this insight, we propose a novel topology design approach, EIB-Learner, that balances error suppression and beneficial information propagation by fusing connectivity patterns from both dense and sparse graphs. Extensive experiments show the superior effectiveness, communication cost, and robustness of EIB-Learner.

Multilingual dense retrieval aims to retrieve relevant documents across different languages based on a unified retriever model. The challenge lies in aligning representations of different languages in a shared vector space. The common practice is to fine-tune the dense retriever via contrastive learning, whose effectiveness highly relies on the quality of the negative sample and the efficacy of mini-batch data. Different from the existing studies that focus on developing sophisticated model architecture, we propose a method to boost data utilization for multilingual dense retrieval by obtaining high-quality hard negative samples and effective mini-batch data. The extensive experimental results on a multilingual retrieval benchmark, MIRACL, with 16 languages demonstrate the effectiveness of our method by outperforming several existing strong baselines.

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Self-Augmented Preference Alignment for Sycophancy Reduction in LLMs
Chien Hung Chen | Hen-Hsen Huang | Hsin-Hsi Chen

Sycophancy causes models to produce answers that cater to user expectations rather than providing truthful responses. Sycophantic behavior in models can erode user trust by creating a perception of dishonesty or bias. This lack of authenticity may lead users to question the reliability and objectivity of the system’s responses. Although Reinforcement Learning from Human Feedback (RLHF) is effective in aligning models with human preferences, previous studies have observed that it can simultaneously amplify sycophantic behavior. However, these studies primarily focused on proprietary models and employed indirect analysis to demonstrate the influence of human feedback. Our study focuses on sycophancy in open-source models, which are more reproducible and transparent for research. We investigated the impact of human feedback on sycophancy by directly comparing models aligned with human feedback to those not aligned. To address sycophancy, we proposed assessing the user’s expected answer rather than ignoring it. Consequently, we developed the Sycophancy Answer Assessment (SAA) dataset and introduced Self-Augmented Preference Alignment, demonstrating that these methods effectively enhance the model’s assessment ability and significantly reduce sycophancy across tasks.

Large language models (LLMs) have shown promise in automating travel planning, yet they often fall short in addressing nuanced spatiotemporal rationality. While existing benchmarks focus on basic plan validity, they neglect critical aspects such as route efficiency, POI appeal, and real-time adaptability. This paper introduces **TP-RAG**, the first benchmark tailored for retrieval-augmented, spatiotemporal-aware travel planning. Our dataset includes 2,348 real-world travel queries, 85,575 fine-grain annotated POIs, and 18,784 high-quality travel trajectory references sourced from online tourist documents, enabling dynamic and context-aware planning. Through extensive experiments, we reveal that integrating reference trajectories significantly improves spatial efficiency and POI rationality of the travel plan, while challenges persist in universality and robustness due to conflicting references and noisy data. To address these issues, we propose *EvoRAG*, an evolutionary framework that potently synergizes diverse retrieved trajectories with LLMs’ intrinsic reasoning. *EvoRAG* achieves state-of-the-art performance, improving spatiotemporal compliance and reducing commonsense violation compared to ground-up and retrieval-augmented baselines. Our work underscores the potential of hybridizing Web knowledge with LLM-driven optimization, paving the way for more reliable and adaptive travel planning agents.

Nüshu is an endangered language from Jiangyong County, China, and the world’s only known writing system created and used exclusively by women. Recent Natural Language Processing (NLP) work has digitized small Nüshu-Chinese corpora, but the script remains computationally inaccessible due to its handwritten, mixed-media form and dearth of multimodal resources. We address this gap with two novel datasets: NüshuVision, an image corpus of 500 rendered sentences in traditional vertical, right-to-left orthography, and NüshuStrokes, the first sequential handwriting recordings of all 397 Unicode Nüshu characters by an expert calligrapher. Evaluating five state-of-the-art Chinese Optical Character Recognition (OCR) systems on NüshuVision shows that all fail entirely, each yielding a Character Error Rate (CER) of 1.0. Fine-tuning Microsoft’s TrOCR on NüshuVision lowers CER to 0.67, a modest yet meaningful improvement. These contributions establish the first multimodal foundation for Nüshu revitalization and offer a culturally grounded framework for language preservation.

Large language models (LLMs) have shown promising first-order logic (FOL) reasoning capabilities with applications in various areas. However, their effectiveness in complex mathematical reasoning involving multi-step FOL deductions is still under-researched. While LLMs perform competitively on established mathematical reasoning benchmarks, they struggle with multi-step FOL tasks, as demonstrated by Deepseek-Prover-V2-7B’s low accuracy (4.2%) on our proposed theorem proving dataset. This issue arises from the limited exploration of diverse proof strategies and the potential for early reasoning mistakes to undermine entire proofs. To address these issues, we propose DREAM, a self-adaptive solution that enhances the Diversity and REAsonability of LLMs’ generation strategies. DREAM incorporates an Axiom-Driven Strategy Diversification mechanism to promote varied strategic outcomes and a Sub-Proposition Error Feedback to help LLMs reflect on and correct their proofs. Our contributions include pioneering advancements in LLMs’ mathematical reasoning through FOL theorem proving, introducing a novel inference stage solution that improves performance by 0.6% to 6.4%, and providing a curated dataset of 447 mathematical theorems in Lean 4 format for evaluation.

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From Tens of Hours to Tens of Thousands: Scaling Back-Translation for Speech Recognition
Tianduo Wang | Lu Xu | Wei Lu | Shanbo Cheng

Recent advances in Automatic Speech Recognition (ASR) have been largely fueled by massive speech corpora. However, extending coverage to diverse languages with limited resources remains a formidable challenge. This paper introduces Speech Back-Translation, a a scalable pipeline that improves multilingual ASR models by converting large-scale text corpora into synthetic speech via off-the-shelf text-to-speech (TTS) models. We demonstrate that just tens of hours of real transcribed speech can effectively train TTS models to generate synthetic speech at hundreds of times the original volume while maintaining high quality. To evaluate synthetic speech quality, we develop an intelligibility-based assessment framework and establish clear thresholds for when synthetic data benefits ASR training. Using Speech Back-Translation, we generate more than 500,000 hours of synthetic speech in ten languages and continue pre-training Whisper-large-v3, achieving average transcription error reductions of over 30%. These results highlight the scalability and effectiveness of Speech Back-Translation for enhancing multilingual ASR systems.

Embodied Question Answering (EQA) has primarily focused on indoor environments, leaving the complexities of urban settings—spanning environment, action, and perception—largely unexplored. To bridge this gap, we introduce CityEQA, a new task where an embodied agent answers open-vocabulary questions through active exploration in dynamic city spaces. To support this task, we present CityEQA-EC, the first benchmark dataset featuring 1,412 human-annotated tasks across six categories, grounded in a realistic 3D urban simulator. Moreover, we propose -Manager-Actor (PMA), a novel agent tailored for CityEQA. PMA enables long-horizon planning and hierarchical task execution: the Planner breaks down the question answering into sub-tasks, the Manager maintains an object-centric cognitive map for spatial reasoning during the process control, and the specialized Actors handle navigation, exploration, and collection sub-tasks. Experiments demonstrate that PMA achieves 60.7% of human-level answering accuracy, significantly outperforming frontier-based baselines. While promising, the performance gap compared to humans highlights the need for enhanced visual reasoning in CityEQA. This work paves the way for future advancements in urban spatial intelligence. Dataset and code are available at https://github.com/tsinghua-fib-lab/CityEQA.git.

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Mitigating Hallucinations in Vision-Language Models through Image-Guided Head Suppression
Sreetama Sarkar | Yue Che | Alex Gavin | Peter Anthony Beerel | Souvik Kundu

Despite their remarkable progress in multimodal understanding tasks, large vision language models (LVLMs) often suffer from “hallucination”, generating texts misaligned with the visual context. Existing methods aimed at reducing hallucinations through inference time intervention incur a significant increase in latency. To mitigate this, we present **SPIN**, a task-agnostic attention-guided head suppression strategy that can be seamlessly integrated during inference **without incurring any significant compute or latency overhead**. We investigate whether hallucination in LVLMs can be linked to specific model components. Our analysis suggests that hallucinations can be attributed to a dynamic subset of attention heads in each layer. Leveraging this insight, for each text query token, we selectively suppress attention heads that exhibit low attention to image tokens, keeping the top-k attention heads intact. Extensive evaluations on visual question answering and image description tasks demonstrate the efficacy of SPIN in reducing hallucination scores up to **2.7x** while maintaining F1, and improving throughput by **1.8x** compared to existing alternatives.

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Examining False Positives under Inference Scaling for Mathematical Reasoning
Yu Wang | Nan Yang | Liang Wang | Furu Wei | Fuli Feng

Recent advancements in language models have led to significant improvements in mathematical reasoning across various benchmarks. However, most of these benchmarks rely on automatic evaluation methods that only compare final answers using heuristics, without verifying the underlying reasoning steps. This limitation results in false positive solutions, where models may produce correct final answers but with flawed deduction paths. In this paper, we systematically examine the prevalence of false positive solutions in mathematical problem solving for language models. We analyze the characteristics and extent of this issue across different open-source models, datasets of varying difficulty levels, and decoding strategies. Specifically, we explore how false positives influence the inference time scaling behavior of language models. Our experimental results reveal that: (1) false positive solutions persist across different models, datasets, and decoding methods, (2) sampling-based inference time scaling methods do not alleviate the problem, and (3) the pass@N evaluation metric is more susceptible to false positives, suggesting a significantly lower scaling ceiling than what automatic evaluations indicate. Additionally, we analyze specific instances of false positives and discuss potential limitations in self-improvement techniques and synthetic data generation under such conditions. Our data and code are publicly available at https://github.com/Wloner0809/False-Positives-in-Math.

Translationese refers to linguistic properties that usually occur in translated texts. Previous works study translationese by framing it as a binary classification between original texts and translated texts. In this paper, we argue that translationese should be graded instead of binary and propose the first measure for translationese—the translationese-index (T-index), computed from the likelihood ratios of two contrastively fine-tuned language models (LMs). We use synthesized translations and translations in the wild to evaluate T-index’s generalizability in cross-domain settings and its validity against human judgments.Our results show that T-index can generalize to unseen genres, authors, and language pairs. Moreover, T-index computed using two 0.5B LMs fine-tuned on only 1-5k pairs of synthetic data can effectively capture translationese, as demonstrated by alignment with human pointwise ratings and pairwise judgments.Additionally, the correlation between T-index and existing machine translation (MT) quality estimation (QE) metrics such as BLEU and COMET is low, suggesting that T-index is not covered by these metrics andcan serve as a complementary metric in MT QE.

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Exploring the Limitations of Mamba in COPY and CoT Reasoning
Ruifeng Ren | Zhicong Li | Yong Liu

Transformers have become the backbone of modern Large Language Models (LLMs); however, their inference overhead grows linearly with the sequence length, posing challenges for modeling long sequences. In light of this, Mamba has attracted attention for maintaining a constant inference size, with empirical evidence demonstrating that it can match Transformer performance in sequence modeling while significantly reducing computational costs. However, an open question remains: can Mamba always bring savings while achieving performance comparable to Transformers? In this paper, we focus on analyzing the expressive ability of Mamba to perform our defined COPY operation and Chain of Thought (CoT) reasoning. First, inspired by the connection between Mamba and linear attention, we show that constant-sized Mamba may struggle to perform COPY operations while Transformers can handle them more easily. However, when the size of Mamba grows linearly with the input sequence length, it can accurately perform COPY, but in this case, Mamba no longer provides overhead savings. Based on this observation, we further analyze Mamba’s ability to tackle CoT tasks, which can be described by the Dynamic Programming (DP) problems. Our findings suggest that to solve arbitrary DP problems, the total cost of Mamba is still comparable to standard Transformers. However, similar to efficient Transformers, when facing DP problems with favorable properties such as locality, Mamba can provide savings in overhead. Our experiments on the copy and CoT tasks further demonstrate Mamba’s limitations compared to Transformers in learning these tasks.

We introduce ProcWorld, a large-scale benchmark for partially observable embodied spatial reasoning and long-term planning with large language models (LLM) and vision language models (VLM). ProcWorld features a wide range of challenging embodied navigation and object manipulation tasks, covering 16 task types, 5,000 rooms, and over 10 million evaluation trajectories with diverse data distribution. ProcWorld supports configurable observation modes, ranging from text-only descriptions to vision-only observations. It enables text-based actions to control the agent following language instructions. ProcWorld has presented significant challenges for LLMs and VLMs: (1) active information gathering given partial observations for disambiguation; (2) simultaneous localization and decision-making by tracking the spatio-temporal state-action distribution; (3) constrained reasoning with dynamic states subject to physical reachability. Our extensive evaluation of 15 foundation models and 5 reasoning algorithms (with over 1 million rollouts) indicates larger models perform better. However, ProcWorld remains highly challenging for existing state-of-the-art models and in-context learning methods due to constrained reachability and the need for combinatorial spatial reasoning.

Reasoning is a fundamental capability often required in real-world text-to-image (T2I) generation, e.g., generating “a bitten apple that has been left in the air for more than a week” necessitates understanding temporal decay and commonsense concepts. While recent T2I models have made impressive progress in producing photorealistic images, their reasoning capability remains underdeveloped and insufficiently evaluated. To bridge this gap, we introduce R2I-Bench, a comprehensive benchmark specifically designed to rigorously assess reasoning-driven T2I generation. R2I-Bench comprises 3068 meticulously curated data instances, spanning 7 core reasoning categories, including commonsense, mathematical, logical, compositional, numerical, causal, and concept mixing. To facilitate fine-grained evaluation, we design R2IScore, a QA-style metric based on instance-specific, reasoning-oriented evaluation questions that assess three critical dimensions: text-image alignment, reasoning accuracy, and image quality. Extensive experiments with 16 representative T2I models, including a strong pipeline-based framework that decouples reasoning and generation using the state-of-the-art language and image generation models, demonstrate consistently limited reasoning performance, highlighting the need for more robust, reasoning-aware architectures in the next generation of T2I systems.

Existing table understanding methods face challenges due to complex table structures and intricate logical reasoning. While supervised finetuning (SFT) dominates existing research, reinforcement learning (RL), such as Group Relative Policy Optimization (GRPO), has shown promise but struggled with low initial policy accuracy and coarse rewards in tabular contexts. In this paper, we introduce Table-R1, a three-stage RL framework that enhances multimodal table understanding through: (1) Warm-up that prompts initial perception and reasoning capabilities, (2) Perception Alignment GRPO (PA-GRPO), which employs continuous Tree-Edit-Distance Similarity (TEDS) rewards for recognizing table structures and contents, and (3) Hint-Completion GRPO (HC-GRPO), which utilizes fine-grained rewards of residual steps based on the hint-guided question. Extensive experiments demonstrate that Table-R1 can boost the model’s table reasoning performance obviously on both held-in and held-out datasets, outperforming SFT and GRPO largely. Notably, Qwen2-VL-7B with Table-R1 surpasses larger specific table understanding models (e.g., Table-LLaVA 13B), even achieving comparable performance to the closed-source model GPT-4o on held-in datasets, demonstrating the efficacy of each stage of Table-R1 in overcoming initialization bottlenecks and reward sparsity, thereby advancing robust multimodal table understanding.

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MoMoE: Mixture of Moderation Experts Framework for AI-Assisted Online Governance
Agam Goyal | Xianyang Zhan | Yilun Chen | Koustuv Saha | Eshwar Chandrasekharan

Large language models (LLMs) have shown great potential in flagging harmful content in online communities. Yet, existing approaches for moderation require a separate model for every community and are opaque in their decision-making, limiting real-world adoption. We introduce Mixture of Moderation Experts (MoMoE), a modular, cross-community framework that adds post-hoc explanations to enable scalable content moderation. MoMoE orchestrates four operators—Allocate, Predict, Aggregate, Explain—and is instantiated as seven community-specialized experts (MoMoE-Community) and five norm-violation experts (MoMoE-NormVio). On 30 unseen subreddits, the best variants obtain Micro-F1 scores of 0.72 and 0.67, respectively, matching or surpassing strong fine-tuned baselines while consistently producing concise and reliable explanations. Although community-specialized experts deliver the highest peak accuracy, norm-violation experts provide steadier performance across domains. These findings show that MoMoE yields scalable, transparent moderation without needing per-community fine-tuning. More broadly, they suggest that lightweight, explainable expert ensembles can guide future NLP and HCI research on trustworthy human-AI governance of online communities.

A new trend uses LLMs as dense text encoders via contrastive learning. However, since LLM embeddings predict the probability distribution of the next token, they are inherently generative and distributive, conflicting with contrastive learning, which requires embeddings to capture full-text semantics and align via cosine similarity. This discrepancy hinders the full utilization of LLMs’ pre-training capabilities, resulting in inefficient learning. In response to this issue, we propose AutoRegEmbed, a new contrastive learning method built on embedding conditional probability distributions, which integrates two core tasks: information compression and conditional distribution alignment. The information compression task encodes text into the embedding space, ensuring that the embedding vectors capture global semantics. The conditional distribution alignment task focuses on aligning text embeddings with positive samples embeddings by leveraging the conditional distribution of embeddings while simultaneously reducing the likelihood of generating negative samples from text embeddings, thereby achieving embedding alignment and uniformity. Experimental results demonstrate that our method significantly outperforms traditional contrastive learning approaches and achieves performance comparable to state-of-the-art models when using the same amount of data.

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Evaluating LLM-Generated Diagrams as Graphs
Chumeng Liang | Jiaxuan You

Diagrams play a central role in research papers for conveying ideas, yet they are often notoriously complex and labor-intensive to create. Although diagrams are presented as images, standard image generative models struggle to produce clear diagrams with well-defined structure. We argue that a promising direction is to generate demonstration diagrams directly in textual form as SVGs, which can leverage recent advances in large language models (LLMs). However, due to the complexity of components and the multimodal nature of diagrams, sufficiently discriminative and explainable metrics for evaluating the quality of LLM-generated diagrams remain lacking. In this paper, we propose DiagramEval, a novel evaluation metric designed to assess demonstration diagrams generated by LLMs. Specifically, DiagramEval conceptualizes diagrams as graphs, treating text elements as nodes and their connections as directed edges, and evaluates diagram quality using two new groups of metrics: node alignment and path alignment. For the first time, we effectively evaluate diagrams produced by state-of-the-art LLMs on recent research literature, quantitatively demonstrating the validity of our metrics. Furthermore, we show how the enhanced explainability of our proposed metrics offers valuable insights into the characteristics of LLM-generated diagrams.

Large language models (LLMs) are now ubiquitous in user-facing applications, yet they still generate undesirable toxic outputs, including profanity, vulgarity, and derogatory remarks. Although numerous detoxification methods exist, most apply broad, surface-level fixes and can therefore easily be circumvented by jailbreak attacks. In this paper we leverage sparse autoencoders (SAEs) to identify toxicity-related directions in the residual stream of models and perform targeted activation steering using the corresponding decoder vectors. We introduce three tiers of steering aggressiveness and evaluate them on GPT-2 Small and Gemma-2-2B, revealing trade-offs between toxicity reduction and language fluency. At stronger steering strengths, these causal interventions surpass competitive baselines in reducing toxicity by up to 20%, though fluency can degrade noticeably on GPT-2 Small depending on the aggressiveness. Crucially, standard NLP benchmark scores upon steering remain stable, indicating that the model’s knowledge and general abilities are preserved. We further show that feature-splitting in wider SAEs hampers safety interventions, underscoring the importance of disentangled feature learning. Our findings highlight both the promise and the current limitations of SAE-based causal interventions for LLM detoxification, further suggesting practical guidelines for safer language-model deployment.

Large language models (LLMs) have demonstrated impressive performance on reasoning tasks, including mathematical reasoning. However, the current evaluation mostly focuses on carefully constructed benchmarks and neglects the consideration of real-world reasoning problems that present missing or contradictory conditions, known as ill-defined problems. To further study this problem, we develop a large-scale benchmark called Problems with Missing and Contradictory conditions (PMC) containing over 5,000 validated ill-defined mathematical problems. Our preliminary experiments through PMC reveal two challenges about existing methods: (1) traditional methods exhibit a trade-off between solving accuracy and rejection capabilities, and (2) formal methods struggle with modeling complex problems. To address these challenges, We develop Variable-Constraint Search (VCSearch), a training-free framework that leverages formal language to detect ill-defined problems, where a variable-constraint pair search strategy is incorporated to improve the modeling capability of formal language. Extensive experiments demonstrate that VCSearch improves the accuracy of identifying unsolvable problems by at least 12% across different LLMs, thus achieving stronger robust mathematical reasoning ability.

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How do autoregressive transformers solve full addition?
Wang Peixu | Chen Yu | Yu Ming | Cheng Xiang

Large pre-trained language models have demonstrated impressive capabilities, but there is still much to learn about how they operate. In this study, we conduct an investigation of the autoregressive transformer’s ability to perform basic addition operations. Specifically, by using causal analysis we found that a few different attention heads in the middle layers control the addition carry, with each head processing carries of different lengths. Due to the lack of global focus on the sequence within these attention heads, the model struggles to handle long-sequence addition tasks. By performing inference intervention on mistral-7B, partial task performance can be restored, with the accuracy on 20-digit long-sequence additions from 2% to 38%. Through fine-tuning, a new mechanism branches out for handling complex cases, yet it still faces challenges with length generalization. Our research reveals how the models perform basic arithmetic task, and further provides insights into the debate on whether these models are merely statistical.

Instruction tuning has empowered large language models (LLMs) to achieve remarkable performance, yet its success heavily depends on the availability of large-scale, high-quality instruction-response pairs. To meet this demand, various methods have been developed to synthesize data at scale. However, current methods for scaling up data generation often overlook a crucial aspect: the alignment between instructions and responses. We hypothesize that the quality of instruction-response pairs is determined not by the individual quality of each component, but by the degree of mutual alignment. To address this, we propose a Mutual Alignment Framework (MAIN) which enforces coherence between instructions and responses through mutual constraints. We demonstrate that MAIN generalizes well across model architectures and sizes, achieving state-of-the-art performance on LLaMA, Mistral, and Qwen models across diverse benchmarks. This work underscores the critical role of instruction-response alignment in enabling generalizable and high-quality instruction tuning for LLMs. All code is available from our repository.

Large Language Models (LLMs) demonstrate remarkable capabilities in text understanding and generation. However, their tendency to produce factually inconsistent outputs—commonly referred to as “hallucinations”—remains a critical challenge. Existing approaches, such as retrieval-based and inference-time correction methods, primarily address this issue at the input or output level, often overlooking the intrinsic information refinement process and the role of premature layers. Meanwhile, alignment- and fine-tuning-based methods are resource-intensive. In this paper, we propose **PLI** (**P**remature **L**ayers **I**nterpolation), a novel, training-free, and plug-and-play intervention designed to enhance factuality. PLI mitigates hallucinations by inserting premature layers formed through mathematical interpolation with adjacent layers. Inspired by stable diffusion and sampling steps, PLI extends the depth of information processing and transmission in LLMs, improving factual coherence. Experiments on four publicly available datasets demonstrate that PLI effectively reduces hallucinations while outperforming existing baselines in most cases. Further analysis suggests that the success of layer interpolation is closely linked to LLMs’ internal mechanisms. To promote reproducibility, we will release our code and data upon acceptance.

Promoting positive mental health and well-being, especially in adolescents, is a critical yet underexplored area in natural language processing (NLP). Most existing NLP research focuses on clinical therapy or psychological counseling for the general population, which does not adequately address the preventative and growth-oriented needs of adolescents. In this paper, we introduce DeepWell-Adol, a domain-specific Chinese dialogue corpus grounded in positive psychology and coaching, designed to foster adolescents’ positive mental health and well-being. To balance the trade-offs between data quality, quantity, and scenario diversity, the corpus comprises two main components: human expert-written seed data (ensuring professional quality) and its mirrored expansion (automatically generated using a two-stage scenario-based augmentation framework). This approach enables large-scale data creation while maintaining domain relevance and reliability. Comprehensive evaluations demonstrate that the corpus meets general standards for psychological dialogue and emotional support, while also showing superior performance across multiple models in promoting positive psychological processes, character strengths, interpersonal relationships, and healthy behaviors. Moreover, the framework proposed for building and evaluating DeepWell-Adol offers a flexible and scalable method for developing domain-specific datasets. It significantly enhances automation and reduces development costs without compromising professional standards—an essential consideration in sensitive areas like adolescent and elderly mental health. We make our dataset publicly available.

Large language models (LLMs) are widely adapted for downstream applications through fine-tuning, a process named customization. However, recent studies have identified a vulnerability during this process, where malicious samples can compromise the robustness of LLMs and amplify harmful behaviors. To address this challenge, we propose an adaptive data curation approach allowing any text to be curated to enhance its effectiveness in counteracting harmful samples during customization. To avoid the need for additional defensive modules, we further introduce a comprehensive mitigation framework spanning the lifecycle of the customization process: before customization to immunize LLMs against future compromise attempts, during customization to neutralize risks, and after customization to restore compromised models. Experimental results demonstrate a significant reduction in compromising effects, achieving up to a 100% success rate in generating safe responses. By combining adaptive data curation with lifecycle-based mitigation strategies, this work represents a solid step forward in mitigating compromising risks and ensuring the secure adaptation of LLMs.

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Speculative Safety-Aware Decoding
Xuekang Wang | Shengyu Zhu | Xueqi Cheng

Despite extensive efforts to align large language models (LLMs) with human values and safety rules, jailbreak attacks that exploit certain vulnerabilities continuously emerge, highlighting the need to strengthen existing LLMs with additional safety properties to defend against these attacks. However, tuning large models has become increasingly resource-intensive and may have difficulty ensuring consistent performance. We introduce Speculative Safety-Aware Decoding (SSD), a lightweight decoding-time approach that equips LLMs with the desired safety property while accelerating inference. We assume that there exists a small language model that possesses the desired safety property. SSD integrates speculative sampling during decoding and leverages the match ratio between the small and composite models to quantify jailbreak risks. This enables SSD to dynamically switch between decoding schemes to prioritize utility or safety, to handle the challenge of different model capacities. The output token is then sampled from a new distribution that combines the distributions of both models. Experimental results show that SSD successfully equips the large model with the desired safety property, and also allows the model to remain helpful to benign queries. Furthermore, SSD accelerates the inference time, thanks to the speculative sampling design.

Panic attacks are acute episodes of fear and distress, in which timely, appropriate intervention can significantly help individuals regain stability. However, suitable datasets for training such models remain scarce due to ethical and logistical issues. To address this, we introduce Pace, which is a dataset that includes high-distress episodes constructed from first-person narratives, and structured around the principles of Psychological First Aid (PFA). Using this data, we train Pacer, a counseling model designed to provide both empathetic and directive support, which is optimized through supervised learning and simulated preference alignment. To assess its effectiveness, we propose PanicEval, a multi-dimensional framework covering general counseling quality and crisis-specific strategies. Experimental results show that Pacer outperforms strong baselines in both counselor-side metrics and client affect improvement. Human evaluations further confirm its practical value, with Pacer consistently preferred over general, CBT-based, and GPT-4-powered models in panic scenarios.

Personalizing large language models (LLMs) is important for aligning outputs with diverse user preferences, yet existing methods struggle with flexibility and generalization. We propose CoPL (Collaborative Preference Learning), a graph-based collaborative filtering framework that models user-response relationships to enhance preference estimation, particularly in sparse annotation settings. By integrating a mixture of LoRA experts, CoPL efficiently fine-tunes LLMs while dynamically balancing shared and user-specific preferences. Additionally, an optimization-free adaptation strategy enables generalization to unseen users without fine-tuning. Experiments on TL;DR, UltraFeedback-P, and PersonalLLM datasets demonstrate that CoPL outperforms existing personalized reward models, effectively capturing both common and controversial preferences, making it a scalable solution for personalized LLM alignment.

This paper investigates the enhancement of reasoning capabilities in language models through token-level multi-model collaboration. Our approach selects the optimal tokens from the next token distributions provided by multiple models to perform autoregressive reasoning. Contrary to the assumption that more models yield better results, we introduce a distribution distance-based dynamic selection strategy (DDS) to optimize the multi-model collaboration process. To address the critical challenge of vocabulary misalignment in multi-model collaboration, we propose the concept of minimal complete semantic units (MCSU), which is simple yet enables multiple language models to achieve natural alignment within the linguistic space. Experimental results across various benchmarks demonstrate the superiority of our method. The codes will be released soon.

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AI Chatbots as Professional Service Agents: Developing a Professional Identity
Wenwen Li | Kangwei Shi | Yidong Chai

With the rapid expansion of large language model (LLM) applications, there is an emerging shift in the role of LLM-based AI chatbots from serving merely as general inquiry tools to acting as professional service agents. However, current studies often overlook a critical aspect of professional service agents: the act of communicating in a manner consistent with their professional identities. This is of particular importance in the healthcare sector, where effective communication with patients is essential for achieving professional goals, such as promoting patient well-being by encouraging healthy behaviors. To bridge this gap, we propose LAPI (LLM-based Agent with a Professional Identity), a novel framework for designing professional service agent tailored for medical question-and-answer (Q&A) services, ensuring alignment with a specific professional identity. Our method includes a theory-guided task planning process that decomposes complex professional tasks into manageable subtasks aligned with professional objectives and a pragmatic entropy method designed to generate professional and ethical responses with low uncertainty. Experiments on various LLMs show that the proposed approach outperforms baseline methods, including few-shot prompting, chain-of-thought prompting, across key metrics such as fluency, naturalness, empathy, patient-centricity, and ROUGE-L scores. Additionally, the ablation study underscores the contribution of each component to the overall effectiveness of the approach.

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DeepResonance: Enhancing Multimodal Music Understanding via Music-centric Multi-way Instruction Tuning
Zhuoyuan Mao | Mengjie Zhao | Qiyu Wu | Hiromi Wakaki | Yuki Mitsufuji

Recent advancements in music large language models (LLMs) have significantly improved music understanding tasks, which involve the model’s ability to analyze and interpret various musical elements. These improvements primarily focused on integrating both music and text inputs. However, the potential of incorporating additional modalities such as images, videos and textual music features to enhance music understanding remains unexplored. To bridge this gap, we propose DeepResonance, a multimodal music understanding LLM fine-tuned via multi-way instruction tuning with multi-way aligned music, text, image, and video data. To this end, we construct Music4way-MI2T, Music4way-MV2T, and Music4way-Any2T, three 4-way training and evaluation datasets designed to enable DeepResonance to integrate both visual and textual music feature content. We also introduce multi-sampled ImageBind embeddings and a pre-LLM fusion Transformer to enhance modality fusion prior to input into text LLMs, tailoring for multi-way instruction tuning. Our model achieves state-of-the-art performances across six music understanding tasks, highlighting the benefits of the auxiliary modalities and the structural superiority of DeepResonance. We open-source the codes, models and datasets we constructed: https://github.com/sony/DeepResonance.

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Advancing Oversight Reasoning across Languages for Audit Sycophantic Behaviour via X-Agent
Giulia Pucci | Leonardo Ranaldi

Large language models (LLMs) have demonstrated capabilities that are highly satisfactory to a wide range of users by adapting to their culture and wisdom. Yet, this can translate into a propensity to produce responses that align with users’ viewpoints, even when the latter are wrong. This behaviour is known as sycophancy, the tendency of LLMs to generate misleading responses as long as they align with the user’s, inducing bias and reducing reliability. To make interactions consistent, reliable and safe, we introduce X-Agent, an Oversight Reasoning framework that audits human–LLM dialogues, reasons about them, captures sycophancy and corrects the final outputs. Concretely, X-Agent extends debate-based frameworks by (i) auditing human-LLM conversations, (ii) applying a defence layer that steers model behaviour and goes beyond user beliefs, and (iii) extracting reasoning traces from evaluations that serve as training signals for mitigating sycophancy. We evaluate X-Agent across diverse scenarios and languages, showing that it consistently detects sycophancy, reduces unwarranted agreement, and improves cross-turn consistency, advancing a reasoning-as-overview paradigm for safer LLM interaction. Our approach introduces a novel paradigm in which reasoning is not merely a means to solve problems, but as a mechanism for overseeing the problem-solving processes of other models.

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CAFE: Retrieval Head-based Coarse-to-Fine Information Seeking to Enhance Multi-Document QA Capability
Han Peng | Jinhao Jiang | Zican Dong | Xin Zhao | Lei Fang

Advancements in Large Language Models (LLMs) have extended their input context length, yet they still struggle with retrieval and reasoning in long-context inputs. Existing methods propose to utilize the prompt strategy and Retrieval-Augmented Generation (RAG) to alleviate this limitation. However, they still face challenges in balancing retrieval precision and recall, impacting their efficacy in answering questions. To address this, we introduce **CAFE**, a two-stage coarse-to-fine method to enhance multi-document question-answering capacities. By gradually eliminating the negative impacts of background and distracting documents, CAFE makes the responses more reliant on the evidence documents. Initially, a coarse-grained filtering method leverages retrieval heads to identify and rank relevant documents. Then, a fine-grained steering method guides attention to the most relevant content. Experiments across benchmarks show that CAFE outperforms baselines, achieving an average SubEM improvement of up to 22.1% and 13.7% over SFT and RAG methods, respectively, across three different models. Our code is available at https://github.com/RUCAIBox/CAFE.

Evaluating machine translation (MT) quality for under-resourced African languages remains a significant challenge, as existing metrics often suffer from limited language coverage and poor performance in low-resource settings. While recent efforts, such as AfriCOMET, have addressed some of the issues, they are still constrained by small evaluation sets, a lack of publicly available training data tailored to African languages, and inconsistent performance in extremely low-resource scenarios. In this work, we introduce SSA-MTE, a large-scale human-annotated MT evaluation (MTE) dataset covering 13 African language pairs from the News domain, with over 63,000 sentence-level annotations from a diverse set of MT systems. Based on this data, we develop SSA-COMET and SSA-COMET-QE, improved reference-based and reference-free evaluation metrics. We also benchmark prompting-based approaches using state-of-the-art LLMs like GPT-4o and Claude. Our experimental results show that SSA-COMET models significantly outperform AfriCOMET and are competitive with the strongest LLM (Gemini 2.5 Pro) evaluated in our study, particularly on low-resource languages such as Twi, Luo, and Yoruba. All resources are released under open licenses to support future research.

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FaithUn: Toward Faithful Forgetting in Language Models by Investigating the Interconnectedness of Knowledge
Nakyeong Yang | Minsung Kim | Seunghyun Yoon | Joongbo Shin | Kyomin Jung

Various studies have attempted to remove sensitive or private knowledge from a language model to prevent its unauthorized exposure. However, prior studies have overlooked the inherent complexity and interconnectedness of knowledge, which requires careful examination. To resolve this problem, we first define a new concept called superficial unlearning, which refers to the phenomenon where an unlearning method either fails to erase the interconnected knowledge it should remove or unintentionally erases irrelevant knowledge. Based on the definition, we introduce a novel benchmark, FaithUn, to analyze and evaluate the faithfulness of unlearning in real-world knowledge QA settings. Furthermore, we propose a novel unlearning method, KLUE, which updates only knowledge-related neurons to achieve faithful unlearning. KLUE leverages a regularized explainability method to localize contextual knowledge neurons, updating only these neurons using carefully selected unforgotten samples. Experimental results demonstrate that existing unlearning methods fail to ensure faithful unlearning, while our method shows significant effectiveness in real-world QA unlearning.

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Calibrating Pseudo-Labeling with Class Distribution for Semi-supervised Text Classification
Weiyi Yang | Richong Zhang | Junfan Chen | Jiawei Sheng

Semi-supervised text classification (SSTC) aims to train text classification models with few labeled data and massive unlabeled data. Existing studies develop effective pseudo-labeling methods, but they can struggle with unlabeled data that have imbalanced classes mismatched with the labeled data, making the pseudo-labeling biased towards majority classes, resulting in catastrophic error propagation. We believe it is crucial to explicitly estimate the overall class distribution, and use it to calibrate pseudo-labeling to constrain majority classes. To this end, we formulate the pseudo-labeling as an optimal transport (OT) problem, which transports the unlabeled sample distribution to the class distribution. With a memory bank, we dynamically collect both the high-confidence pseudo-labeled data and true labeled data, thus deriving reliable (pseudo-) labels for class distribution estimation. Empirical results on 3 commonly used benchmarks demonstrate that our model is effective and outperforms previous state-of-the-art methods.

Recent advancements in Large Language Models (LLMs) have driven growing interest in LLM-based agents for complex planning tasks. To avoid costly agent training, many studies adopted memory mechanism that enhances LLM with offline experiences or online trajectory analysis. However, existing works focus on single-granularity memory derived from dynamic environmental interactions, which are inherently constrained by the quality of the collected experiences. This limitation, in turn, constrain the diversity of knowledge and the flexibility of planning. We propose Coarse-to-Fine Grounded Memory (CFGM), a novel framework that grounds coarse-to-fine memories with LLM, thereby fully leverage them for flexible adaptation to diverse scenarios. CFGM grounds environmental information into coarse-grained focus points to guide experience collection in training tasks, followed by grounding of actionable hybrid-grained tips from each experience. At inference, CFGM retrieves task-relevant experiences and tips to support planning. When facing environmental anomalies, the LLM grounds the current situation into fine-grained key information, enabling flexible self-QA reflection and plan correction. Extensive experiments on AlfWorld, Webshop and ScienceWorld demonstrate that CFGM significantly outperforms competitive baselines and comprehensively optimizes memory-enhanced LLM Agent system.

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From A and B to A+B: Can Large Language Models Solve Compositional Math Problems?
Xisheng Xiao | Hanlin Zhao

Large language models (LLMs) have demonstrated strong performance in solving math problems, and there is growing research on evaluating their robustness. Unlike previous studies that create problem variants by adding perturbations to a single problem, this paper focuses on the interaction between problems. Specifically, we combine two original problems with a logical connection to get a new math problem, and measure the LLMs’ performance on it to evaluate its compositional generalization, which is an important and essential reasoning capability in human intelligence. The result of experiments that cover 14 different LLMs shows that even when the mathematical essence remains unchanged, a simple form of combination can significantly reduce the performance of LLMs, revealing the limitation of their generalization ability. Additionally, we propose an automated pipeline with 98.2% accuracy to assist in annotating datasets (1 manual, 2 synthetic). The extensive experiments conducted on these datasets further verify the conclusion and obtain some important findings. Finally, we analyze the impact of factors such as difficulty and length on LLMs’ performance, offering insights for future research.

Despite the remarkable capabilities of large language models, current training paradigms inadvertently foster sycophancy—alignment with user-provided information, regardless of factual accuracy. In this paper, we introduce SMART (Sycophancy Mitigation through Adaptive Reasoning Trajectories), reconceptualizing sycophancy as a reasoning optimization problem rather than an output alignment issue. SMART employs a two-stage approach: (1) Uncertainty-Aware Adaptive Monte Carlo Tree Search (UA-MCTS), which dynamically adjusts exploration based on state-level uncertainty; and (2) progress-based reinforcement learning that distills these improved reasoning patterns into model adaptation. Through extensive experiments, we show that SMART significantly outperforms existing baselines in effectively reducing sycophancy while maintaining performance on out-of-distribution inputs. These findings demonstrate the importance of optimizing internal reasoning processes for developing aligned truthful AI assistant.

As Large Language Models (LLMs) demonstrate increasingly strong human-like capabilities, the need to align them with human values has become significant. Recent advanced techniques, such as prompt learning and reinforcement learning, are being employed to bring LLMs closer to aligning with human values. While these techniques address broad ethical and helpfulness concerns, they rarely consider simulating individualized human values. To bridge this gap, we propose SimVBG, a framework that simulates individual values based on individual backstories that reflect their past experience and demographic information. SimVBG transforms structured data on an individual to a backstory and utilizes a multi-module architecture inspired by the Cognitive–Affective Personality System to simulate individual value based on the backstories. We test SimVBG on a self-constructed benchmark derived from the World Values Survey and show that SimVBG improves top-1 accuracy by more than 10% over the retrieval-augmented generation method. Further analysis shows that performance increases as additional interaction user history becomes available, indicating that the model can refine its persona over time. Code, dataset, and complete experimental results are available at https://github.com/bangdedadi/SimVBG.

The rapid advancement of large language models (LLMs) has transformed the landscape of agentic information seeking capabilities through the integration of tools such as search engines and web browsers. However, current mainstream approaches for enabling LLM web search proficiency face significant challenges: supervised fine-tuning struggles with data production in open-search domains, while RL converges quickly, limiting their data utilization efficiency. To address these issues, we propose EvolveSearch, a novel iterative self-evolution framework that combines SFT and RL to enhance agentic web search capabilities without any external human-annotated reasoning data. Extensive experiments on seven multi-hop question-answering (MHQA) benchmarks demonstrate that EvolveSearch consistently improves performance across iterations, ultimately achieving an average improvement of 4.7% over the current state-of-the-art across seven benchmarks, opening the door to self-evolution agentic capabilities in open web search domains.

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Syntax-Aware Retrieval Augmentation for Neural Symbolic Regression
Canmiao Zhou | Han Huang

Symbolic regression is a powerful technique for discovering mathematical expressions that best fit observed data. While neural symbolic regression methods based on large-scale pre-trained models perform well on simple tasks, the reliance on fixed parametric knowledge typically limits their generalization to complex and diverse data distributions. To address this challenge, we propose a syntax-aware retrieval-augmented mechanism that leverages the syntactic structure of symbolic expressions to perform context-aware retrieval from a pre-constructed token datastore during inference. This mechanism enables the model to incorporate highly relevant non-parametric prior information to assist in expression generation. Additionally, we design an entropy-based confidence network that dynamically adjusts the fusion strength between neural and retrieved components by estimating predictive uncertainty. Extensive experiments on multiple symbolic regression benchmarks demonstrate that the proposed method significantly outperforms representative baselines, validating the effectiveness of retrieval augmentation in enhancing the generalization performance of neural symbolic regression models.

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Merge then Realign: Simple and Effective Modality-Incremental Continual Learning for Multimodal LLMs
Dingkun Zhang | Shuhan Qi | Xinyu Xiao | Kehai Chen | Xuan Wang

Recent advances in Multimodal Large Language Models (MLLMs) have enhanced their versatility as they integrate a growing number of modalities. Considering the heavy cost of training MLLMs, it is efficient to reuse the existing ones and extend them to more modalities through Modality-incremental Continual Learning (MCL). The exploration of MCL is in its early stages. In this work, we dive into the causes of performance degradation in MCL. We uncover that it suffers not only from forgetting as in traditional continual learning, but also from misalignment between the modality-agnostic and modality-specific components. To this end, we propose an elegantly simple MCL paradigm called “MErge then ReAlign” (MERA) to address both forgetting and misalignment. MERA avoids introducing heavy model budgets or modifying model architectures, hence is easy to deploy and highly reusable in the MLLM community. Extensive experiments demonstrate the impressive performance of MERA, holding an average of 99.84% Backward Relative Gain when extending to four modalities, achieving nearly lossless MCL performance. Our findings underscore the misalignment issue in MCL. More broadly, our work showcases how to adjust different components of MLLMs during continual learning.

Recently, the pretrain-finetune paradigm has become a cornerstone in various deep learning areas. While in general the pre-trained model would promote both effectiveness and efficiency of downstream tasks fine-tuning, studies have shown that not all knowledge acquired during pre-training is beneficial. Some of the knowledge may actually bring detrimental effects to the fine-tuning tasks, which is also known as negative transfer. To address this problem, graceful forgetting has emerged as a promising approach. The core principle of graceful forgetting is to enhance the learning plasticity of the target task by selectively discarding irrelevant knowledge. However, this approach remains underexplored in the context of generative language models, and it is often challenging to migrate existing forgetting algorithms to these models due to architecture incompatibility. To bridge this gap, in this paper we propose a novel framework, Learning With Forgetting (LWF), to achieve graceful forgetting in generative language models. With Fisher Information Matrix weighting the intended parameter updates, LWF computes forgetting confidence to evaluate self-generated knowledge regarding the forgetting task, and consequently, knowledge with high confidence is periodically unlearned during fine-tuning. Our experiments demonstrate that, although thoroughly uncovering the mechanisms of knowledge interaction remains challenging in pre-trained language models, applying graceful forgetting can contribute to enhanced fine-tuning performance.

Narrative-driven recommendation queries are common in question-answering platforms, AI search engines, social forums, and some domain-specific vertical applications. Users typically submit free-form text requests for recommendations, e.g., “Any mind-bending thrillers like Shutter Island you’d recommend?” Such special queries have traditionally been addressed as generic QA task under the RAG paradigm. This work formally introduces narrative recommendation as a distinct task and contends that the RAG paradigm is inherently ill-suited for it, owing to information loss in LLMs when retrieving information from from multiple long and fragmented contexts, and limitations in ranking effectiveness. To overcome these limitations, we propose a novel retrieve-rank paradigm by theoretically demonstrating its superiority over RAG paradigm. Central to this new paradigm, we specially focus on the information retrieval stage and introduce Open-domain Candidate Generation (OCG)-Agent that generatively retrieves structurally adaptive and semantically aligned candidates, ensuring both extensive candidate coverage and high-quality information. We validate effectiveness of new paradigm and OCG-Agent’s retrieve mechanism under real-world datasets from Reddit and corporate education-consulting scenarios. Further extensive ablation studies confirming the rationality of each OCG-Agent component.

We introduce Direct Value Optimization (DVO), an innovative offline reinforcement learning framework for enhancing large language models in complex reasoning tasks. Unlike traditional methods relying on preference labels, DVO utilizes value signals at individual reasoning steps, optimizing models via a mean squared error loss. The key benefit of DVO lies in its fine-grained supervision, circumventing the need for labor-intensive human annotations. Target values within the DVO are estimated using either Monte Carlo Tree Search or an outcome value model. Our empirical analysis on 3 math reasoning, 4 commonsense reasoning, and 3 coding tasks shows that DVO consistently outperforms existing offline preference optimization techniques by a significant margin of 4% to 6%, and is competitive to online GRPO but with higher sample efficiency. These findings underscore the importance of value signals in advancing reasoning capabilities and highlight DVO as a superior methodology under scenarios lacking explicit human preference information.

AI systems are rapidly advancing in capability, and frontier model developers broadly acknowledge the need for safeguards against serious misuse. However, this paper demonstrates that fine-tuning, whether via open weights or closed fine-tuning APIs, can produce helpful-only models with safeguards destroyed. In contrast to prior work which is blocked by modern moderation systems or achieved only partial removal of safeguards or degraded output quality, our jailbreak-tuning method teaches models to generate detailed, high-quality responses to arbitrary harmful requests. For example, OpenAI, Google, and Anthropic models will fully comply with requests for CBRN assistance, executing cyberattacks, and other criminal activity. We further show that backdoors can increase not only the stealth but also the severity of attacks. Stronger jailbreak prompts become even more effective in fine-tuning attacks, linking attacks and potentially defenses in the input and weight spaces. Not only are current models vulnerable, more recent ones also appear to be becoming even more vulnerable to these attacks, underscoring the urgent need for tamper-resistant safeguards. Until such safeguards are discovered, companies and policymakers should view the release of any fine-tunable model as simultaneously releasing its evil twin: equally capable as the original model, and usable for any malicious purpose within its capabilities.

Topic modeling is a powerful unsupervised tool for knowledge discovery. However, existing work struggles with generating limited-quality topics that are uninformative and incoherent, which hindering interpretable insights from managing textual data. In this paper, we improve the original variational autoencoder framework by incorporating contextual and graph information to address the above issues. First, the encoder utilizes topic fusion techniques to combine contextual and bag-of-words information well, and meanwhile exploits the constraints of topic alignment and topic sharpening to generate informative topics. Second, we develop a simple word co-occurrence graph information fusion strategy that efficiently increases topic coherence. On three benchmark datasets, our new framework generates more coherent and diverse topics compared to various baselines, and achieves strong performance on both automatic and manual evaluations.

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CARE: A Disagreement Detection Framework with Concept Alignment and Reasoning Enhancement
Jiyuan Liu | Jielin Song | Yunhe Pang | Zhiyu Shen | Yanghui Rao

Disagreement detection is a crucial task in natural language processing (NLP), particularly in analyzing online discussions and social media content. Large language models (LLMs) have demonstrated significant advancements across various NLP tasks. However, the performance of LLM in disagreement detection is limited by two issues: *conceptual gap* and *reasoning gap*. In this paper, we propose a novel two-stage framework, Concept Alignment and Reasoning Enhancement (CARE), to tackle the issues. The first stage, Concept Alignment, addresses the gap between expert and model by performing **sub-concept taxonomy extraction**, aligning the model’s comprehension with human experts. The second stage, Reasoning Enhancement, improves the model’s reasoning capabilities by introducing curriculum learning workflow, which includes **rationale to critique** and **counterfactual to detection** for reducing spurious association. Extensive experiments on disagreement detection task demonstrate the effectiveness of our framework, showing superior performance in zero-shot and supervised learning settings, both within and across domains.

Conversational agents have traditionally been developed for either task-oriented dialogue (TOD) or open-ended chitchat, with limited progress in unifying the two. Yet, real-world conversations naturally involve fluid transitions between these modes. To address this gap, we introduce TACT (TOD-And-Chitchat Transition), a dataset designed for transition-aware dialogue modeling that incorporates structurally diverse and integrated mode flows. TACT supports both user- and agent-driven mode switches, enabling robust modeling of complex conversational dynamics.To evaluate an agent’s ability to initiate and recover from mode transitions, we propose two new metrics—Switch and Recovery.Models trained on TACT outperform baselines in both intent detection and mode transition handling. Moreover, applying Direct Preference Optimization (DPO) to TACT-trained models yields additionalgains, achieving 75.74% joint mode-intent accuracy and a 70.1% win rate against GPT-4o in human evaluation.These results demonstrate that pairing structurally diverse data with DPO enhances response quality and transition control, paving the way for more proactive and transition-aware conversational agents.

Large language models (LLMs) have shown remarkable performance in complex reasoning tasks, but their efficiency is hindered by the substantial memory and computational costs associated with generating lengthy tokens. In this paper, we propose LightThinker, a novel method that enables LLMs to dynamically compress intermediate thoughts during reasoning. Inspired by human cognitive processes, LightThinker compresses verbose thought steps into compact representations and discards the original reasoning chains, thereby significantly reducing the number of tokens stored in the context window.This is achieved by training the model on when and how to perform compression through data construction, mapping hidden states to condensed gist tokens, and creating specialized attention masks. Additionally, we introduce the Dependency (Dep) metric to quantify the degree of compression by measuring the reliance on historical tokens during generation. Extensive experiments on four datasets and two models show that LightThinker reduces peak memory usage and inference time, while maintaining competitive accuracy. Our work provides a new direction for improving the efficiency of LLMs in complex reasoning tasks without sacrificing performance.

We introduce Grade School Math with Distracting Context (GSM-DC), a synthetic benchmark to evaluate Large Language Models’ (LLMs) reasoning robustness against systematically controlled irrelevant context (IC). GSM-DC constructs symbolic reasoning graphs with precise distractor injections, enabling rigorous, reproducible evaluation. Our experiments demonstrate that LLMs are significantly sensitive to IC, affecting both reasoning path selection and arithmetic accuracy. Additionally, training models with strong distractors improves performance in both in-distribution and out-of-distribution scenarios. We further propose a stepwise tree search guided by a process reward model, which notably enhances robustness in out-of-distribution conditions.

Effective teaching necessitates adapting pedagogical strategies to the inherent diversity of students, encompassing variations in aptitude, learning styles, and personality, a critical challenge in education and teacher training. Large Language Models (LLMs) offer a powerful tool to simulate complex classroom dynamics, providing a controlled environment for exploring optimal teaching patterns. However, existing simulation frameworks often fall short by neglecting comprehensive student modeling beyond basic knowledge states and, more importantly, by lacking mechanisms for teachers to dynamically adapt their approach based on student feedback and collective performance. Addressing these limitations, we propose a simulation framework that integrates LLM-based diverse student agents with a self-evolving teacher agent. We use genetic algorithms to automatically tune and optimize the teacher’s pedagogical parameters based on simulated student performance, enabling the teacher agent to discover and refine teaching patterns tailored to specific class characteristics. Complementing this, we introduce Persona-RAG, a novel Retrieval-Augmented Generation method specifically designed for personalized knowledge retrieval in pedagogical contexts, allowing students to retrieve information as per their learning styles. We show how Persona-RAG remains competitive with standard RAG baselines in accurately retrieving relevant information while adding a touch of personalization for students. Crucially, we perform extensive experiments and highlight the different patterns learnt by the teacher agent while optimizing over classes with students of various learning styles. Our work presents a significant step towards creating adaptive educational technologies and improving teacher training through realistic, data-driven simulation.

Regular updates are essential for maintaining up-to-date knowledge in large language models (LLMs). However, existing training-based model editing methods often struggle to effectively incorporate new knowledge while preserving unrelated general knowledge. To address this challenge, we propose a novel framework called Geometric Knowledge Editing (GeoEdit). GeoEdit utilizes the geometric relationships of parameter updates from fine-tuning to differentiate between neurons associated with new knowledge updates and those related to general knowledge perturbations. By employing a direction-aware knowledge identification method, we avoid updating neurons with directions approximately orthogonal to existing knowledge, thus preserving the model’s generalization ability. For the remaining neurons, we integrate both old and new knowledge for aligned directions and apply a “forget-then-learn” editing strategy for opposite directions. Additionally, we introduce an importance-guided task vector fusion technique that filters out redundant information and provides adaptive neuron-level weighting, further enhancing model editing performance. Extensive experiments on two publicly available datasets demonstrate the superiority of GeoEdit over existing state-of-the-art methods.

Channel prediction can greatly reduce the pilot overhead and is a critical technology in the fifth-generation (5G) and the coming 6G wireless communications systems. Conventional model-based channel prediction methods suffer from limited accuracy due to imperfect temporal modeling, while existing AI-based methods suffer from limited generalization due to inadequate training strategies. Recently, large language models (LLMs) have demonstrated remarkable generalization and generation capabilities across diverse domains such as computer vision, quantitative economics, and bioinformatics, which motivates us to apply LLMs in channel prediction. In this paper, we formulate the ‘channel sentence’ based on channel correlation, where the channel is regarded as a ‘word’. Subsequently, we propose a generative pre-trained language model for channel prediction (CP-GPT). We collect 12M channel data according to the 3GPP 38.901 protocol and train CP-GPT based on the transformer decoder architecture. Moreover, we design two pre-training tasks based on the characteristics of wireless channels to enhance CP-GPT’s understanding of communications channels. We further propose a comprehensive benchmark to rigorously evaluate the capabilities of CP-GPT across multiple dimensions. The simulation results demonstrate that CP-GPT has successfully learned various channel characteristics and exhibits impressive capabilities across numerous downstream tasks.

Continual learning (CL) is essential for deploying large language models (LLMs) in dynamic real-world environments without the need for costly retraining. Recent model merging-based methods have attracted significant attention, but they still struggle to effectively manage the trade-off between learning new knowledge and preventing forgetting, a challenge largely stemming from suboptimal number of merges and merging frequency. In this paper, we introduce Adaptive Iterative Model Merging (AimMerging), a novel CL framework that utilizes learning and forgetting signals from the training trajectory to dynamically monitor the model’s training status. Guided by dynamic monitoring, the training trajectory-guided merge controller adaptively determines the timing and frequency of iterative fusion, while the rehearsal-based knowledge fusion module computes the merging weights and executes the fusion. Comprehensive experiments on three CL benchmarks with various model sizes (from 770M to 13B) demonstrate that AimMerging achieves significant performance improvements over existing state-of-the-art methods, with an average relative improvement of 80% and 59% on FWT and BWT, respectively. The source code is provided for reproducibility.

Process Reward Models (PRMs) have emerged as a promising solution to address the reasoning mistakes of large language models (LLMs). However, existing PRMs typically output evaluation scores directly, limiting both learning efficiency and evaluation accuracy. This limitation is further compounded by the scarcity of annotated data. To address these issues, we propose Reasoning-Driven Process Reward Modeling (R-PRM), which activates inherent reasoning to enhance process-level evaluation. First, we leverage stronger LLMs to generate seed data from limited annotations, effectively activating reasoning capabilities and enabling comprehensive step-by-step evaluation. Second, we explore self-improvement of our PRM through preference optimization, without requiring additional annotated data. Third, we introduce inference time scaling to fully harness our model’s reasoning potential. Extensive experiments demonstrate R-PRM’s effectiveness: on ProcessBench and PRMBench, it surpasses strong baselines by 13.9 and 8.5 F1 scores. When applied to guide mathematical reasoning, R-PRM achieves consistent accuracy improvements of over 8.6 points across six challenging datasets. Further analysis reveals that R-PRM exhibits more comprehensive evaluation and robust generalization, indicating its broader potential.

Ensembling large language models (LLMs) can effectively combine diverse strengths of different models, offering a promising approach to enhance performance across various tasks. However, existing methods typically rely on fixed weighting strategies that fail to adapt to the dynamic, context-dependent characteristics of LLM capabilities. In this work, we propose **R**einforcement **L**earning-**A**ssisted **E**nsemble for LLMs (RLAE), a novel framework that reformulates LLM ensemble through the lens of a Markov Decision Process (MDP). Our approach introduces a RL agent that dynamically adjusts ensemble weights by considering both input context and intermediate generation states, with the agent being trained using rewards that directly correspond to the quality of final outputs. We implement RLAE using both single-agent and multi-agent reinforcement learning algorithms (RLAE_PPO and RLAE_MAPPO ), demonstrating substantial improvements over conventional ensemble methods. Extensive evaluations on a diverse set of tasks show that RLAE outperforms existing approaches by up to 3.3\\% accuracy points, offering a more effective framework for LLM ensembling. Furthermore, our method exhibits superior generalization capabilities across different tasks without the need for retraining, while simultaneously achieving lower time latency. The source code is available at here.

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Do Large Language Models Truly Grasp Addition? A Rule-Focused Diagnostic Using Two-Integer Arithmetic
Yang Yan | Yu Lu | Renjun Xu | Zhenzhong Lan

Large language models (LLMs) achieve impressive results on advanced mathematics benchmarks but sometimes fail on basic arithmetic tasks, raising the question of whether they have truly grasped fundamental arithmetic rules or are merely relying on pattern matching. To unravel this issue, we systematically probe LLMs’ understanding of two-integer addition (0 to 2⁶⁴) by testing three crucial properties: commutativity (A+B=B+A), representation invariance via symbolic remapping (e.g., 7 ↦ Y), and consistent accuracy scaling with operand length. Our evaluation of 12 leading LLMs reveals a stark disconnect: while models achieve high numeric accuracy (73.8–99.8%), they systematically fail these diagnostics. Specifically, accuracy plummets to ≤ 7.5% with symbolic inputs, commutativity is violated in up to 20% of cases, and accuracy scaling is non-monotonic. Interventions further expose this pattern-matching reliance: explicitly providing rules degrades performance by 29.49%, while prompting for explanations before answering merely maintains baseline accuracy. These findings demonstrate that current LLMs address elementary addition via pattern matching, not robust rule induction, motivating new diagnostic benchmarks and innovations in model architecture and training to cultivate genuine mathematical reasoning. Our dataset and generating code are available at https://github.com/kuri-leo/llm-arithmetic-diagnostic.

Large language models (LLMs) have demonstrated remarkable capabilities in tool learning. In real-world scenarios, user queries are often ambiguous and incomplete, requiring effective clarification. However, existing interactive clarification approaches face two critical limitations: reliance on manually constructed datasets, which inherently constrains training data scale and diversity, and lack of error correction mechanisms during multi-turn clarification, leading to error accumulation that compromises both accuracy and efficiency. We present AskToAct, which addresses these challenges by exploiting the structural mapping between queries and their tool invocation solutions. Our key insight is that tool parameters naturally represent explicit user intents. By systematically removing key parameters from queries while retaining them as ground truth, we enable automated construction of high-quality training data. We further enhance model robustness through error-correction pairs and selective masking, enabling dynamic error detection during clarification interactions. Comprehensive experiments demonstrate that AskToAct significantly outperforms existing approaches, achieving above 57% accuracy in recovering critical unspecified intents and enhancing clarification efficiency by an average of 10.46% while maintaining high accuracy in tool invocation. Our framework exhibits robust performance across different model architectures and successfully generalizes to entirely unseen APIs without additional training, achieving performance comparable to GPT-4o with substantially fewer computational resources.

Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in complex reasoning through long chain-of-thought, yet they struggle with precise computations and algorithmic operations. Integrating computational tools with LRMs remains challenging, particularly in activating and enhancing models’ tool-use capabilities without compromising their reasoning strengths. We address these challenges through START (Self-taught Reasoner with Tools), introducing two key innovations: (1) Hint-infer, a training-free approach that activates LRMs’ latent tool-use capabilities through artificial hints, enabling test-time performance scaling; (2) Hint-RFT, a self-training framework that enables models to learn effective tool utilization through diverse hint patterns and rejection-based data synthesis. Experiments show that START significantly improves state-of-the-art LRMs across challenging benchmarks, including competition-level mathematics (AMC23: 95.0%, AIME24: 75.6%) and graduate-level science questions (GPQA: 64.6%). Our analysis reveals that START not only enhances accuracy but also improves reasoning efficiency through strategic tool utilization, demonstrating broad applicability in complex reasoning scenarios.

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The Impact of Negated Text on Hallucination with Large Language Models
Jaehyung Seo | Hyeonseok Moon | Heuiseok Lim

Recent studies on hallucination in large language models (LLMs) have been actively progressing in natural language processing. However, the impact of negated text on hallucination with LLMs remains largely unexplored. In this paper, we set three important yet unanswered research questions and aim to address them. To derive the answers, we investigate whether LLMs can recognize contextual shifts caused by negation and still reliably distinguish hallucinations comparable to affirmative cases. We also design the NegHalu dataset by reconstructing existing hallucination detection datasets with negated expressions. Our experiments demonstrate that LLMs struggle to detect hallucinations in negated text effectively, often producing logically inconsistent or unfaithful judgments. Moreover, we trace the internal state of LLMs as they process negated inputs at the token level and reveal the challenges of mitigating their unintended effects.

Large Language Models (LLMs) have demonstrated the capability to refine their generated answers through self-correction, enabling continuous performance improvement over multiple rounds. However, the mechanisms underlying how and why accuracy evolves during this iterative process remain unexplored. To fill this gap, we propose a probabilistic theory to model the dynamics of accuracy change and explain the performance improvements observed in multi-round self-correction. Through mathematical derivation, we establish that the accuracy after the t^th round of self-correction is given by: Acc_t = Upp - 𝛼^t(Upp - Acc₀),where Acc₀ denotes the initial accuracy, Upp represents the upper bound of accuracy convergence, and 𝛼 determines the rate of convergence. Based on our theory, these parameters can be calculated and the predicted accuracy curve then can be obtained through only a single round of self-correction. Extensive experiments across diverse models and datasets demonstrate that our theoretical predictions align closely with empirical accuracy curves, validating the effectiveness of the theory. Our work provides a theoretical foundation for understanding LLM self-correction, thus paving the way for further explorations.

With the rise of mental health challenges, social media has become a key platform for emotional expression. Deep learning offers a promising solution for analyzing mental health but lacks flexibility and interpretability. Large language models (LLMs) introduce greater adaptability and can explain their decisions, yet they still underperform deep learning in complex psychological analysis. We present C-IMHI, the first multi-task Chinese social media interpretable mental health instruction dataset (9K samples) with quality control and manual validation. Additionally, we introduce MentalGLM, the first open-source Chinese LLMs for explainable mental health analysis, trained on 50K instructions. The proposed models excelled in three mental health downstream tasks, outperforming or matching deep learning and LLMs. A portion of the generated decision explanations was validated by experts, demonstrating promising accuracy and reliability. We evaluated the proposed models on a clinical dataset, where they significantly outperformed other LLMs, demonstrating their potential for clinical applications. Our models show strong performance, validated across tasks and domains. The decision explanations enhance usability and facilitate better understanding and practical application of the models. Both the constructed dataset and the models are publicly available via: https://github.com/zwzzzQAQ/MentalGLM.

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Knowledge-Aware Co-Reasoning for Multidisciplinary Collaboration
Xurui Li | Wanghaijiao | Kaisong Song | Rui Zhu | Haixu Tang

Large language models (LLMs) have shown significant potential to improve diagnostic performance for clinical professionals. Existing multi-agent paradigms rely mainly on prompt engineering, suffering from improper agent selection and insufficient knowledge integration. In this work, we propose a novel framework KACR (Knowledge-Aware Co-Reasoning) that integrates structured knowledge reasoning into multidisciplinary collaboration from two aspects: (1) a reinforcement learning-optimized agent that uses clinical knowledge graphs to guide dynamic discipline determination; (2) a multidisciplinary collaboration strategy that enables robust consensus through integration of domain-specific expertise and interdisciplinary persuasion mechanism. Extensive experiments conducted on both academic and real-world datasets demonstrate the effectiveness of our method.

Vision-language-action models have gained significant attention for their ability to model multimodal sequences in embodied instruction following tasks. However, most existing models rely on causal attention, which we find suboptimal for processing sequences composed of interleaved segments from different modalities. In this paper, we introduce Astra, a novel Transformer architecture featuring trajectory attention and learnable action queries, designed to efficiently process segmented multimodal trajectories and predict actions for imitation learning. Furthermore, we propose a contrastive dynamics learning objective to enhance the model’s understanding of environment dynamics and multimodal alignment, complementing the primary behavior cloning objective. Through extensive experiments on three large-scale robot manipulation benchmarks, Astra demonstrates substantial performance improvements over previous models.

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MAVL: A Multilingual Audio-Video Lyrics Dataset for Animated Song Translation
Woohyun Cho | Youngmin Kim | Sunghyun Lee | Youngjae Yu

Lyrics translation requires both accurate semantic transfer and preservation of musical rhythm, syllabic structure, and poetic style. In animated musicals, the challenge intensifies due to alignment with visual and auditory cues. We introduce Multilingual Audio-Video Lyrics Benchmark for Animated Song Translation (MAVL), the first multilingual, multimodal benchmark for singable lyrics translation. By integrating text, audio, and video, MAVL enables richer and more expressive translations than text-only approaches. Building on this, we propose Syllable-Constrained Audio-Video LLM with Chain-of-Thought (SylAVL-CoT), which leverages audio-video cues and enforces syllabic constraints to produce natural-sounding lyrics. Experimental results demonstrate that SylAVL-CoT significantly outperforms text-based models in singability and contextual accuracy, emphasizing the value of multimodal, multilingual approaches for lyrics translation.

Recently, large reasoning models (LRMs) have demonstrated state-of-the-art performance across a wide range of benchmarks. However, a common challenge for these models is the “overthinking” problem, which leads to excessive reasoning steps and significant computational overhead. Furthermore, the issues with long Chain-of-Thought (CoT) are especially pronounced in smaller models (≤ 3B parameters). Aside from producing excessively verbose “reflection words”, they often exhibit repetition and get trapped in unproductive generation loops. Existing solutions typically involve either using flexible reasoning chains as training data or leveraging the model’s latent space to bypass intermediate reasoning steps, but none of these methods have considered directly optimizing reasoning trajectories during the sampling phase of training. In our work, we introduce the Multi-Turn Intervention Sampling Framework (MuTIS). Our framework leverages multi-turn interventions to produce concise reasoning chains. It fine-tunes reasoning models through reinforcement learning, demonstrably breaking the accuracy-efficiency trade-off. It also demonstrates strong scalability, exhibiting excellent performance on 7B models. Code is available at https://github.com/Edric-Zhao/MuTIS/tree/main.

In-Image Machine Translation (IIMT) aims to translate images containing texts from one language to another. Current research of end-to-end IIMT mainly conducts on synthetic data, with simple background, single font, fixed text position, and bilingual translation, which can not fully reflect real world, causing a significant gap between the research and practical conditions. To facilitate research of IIMT in real-world scenarios, we explore Practical In-Image Multilingual Machine Translation (IIMMT). In order to convince the lack of publicly available data, we annotate the PRIM dataset, which contains real-world captured one-line text images with complex background, various fonts, diverse text positions, and supports multilingual translation directions. We propose an end-to-end model VisTrans to handle the challenge of practical conditions in PRIM, which processes visual text and background information in the image separately, ensuring the capability of multilingual translation while improving the visual quality. Experimental results indicate the VisTrans achieves a better translation quality and visual effect compared to other models. The code and dataset are available at: https://github.com/BITHLP/PRIM.

Avoiding the propagation of undue (binary) gender inferences and default masculine language remains a key challenge towards inclusive multilingual technologies, particularly when translating into languages with extensive gendered morphology. Gender-neutral translation (GNT) represents a linguistic strategy towards fairer communication across languages. However, research on GNT is limited to a few resources and language pairs. To address this gap, we introduce mGeNTE, an expert-curated resource, and use it to conduct the first systematic multilingual evaluation of inclusive translation with state-of-the-art instruction-following language models (LMs). Experiments on en-es/de/it/el reveal that while models can recognize when neutrality is appropriate, they cannot consistently produce neutral translations, limiting their usability. To probe this behavior, we enrich our evaluation with interpretability analyses that identify task-relevant features and offer initial insights into the internal dynamics of LM-based GNT.

The widespread deployment of large language models (LLMs) across various domains has made their safety a critical priority. Inspired by think-tank decision-making philosophy, we propose DiplomacyAgent, an LLM-based multi-agent system for diplomatic position analysis. With DiplomacyAgent, we are able to systematically assess how LLMs balance “interests” against “ethical principles” when addressing various international events, hence understanding the safety implications of LLMs in diplomacy. Specifically, this will help to assess the consistency of LLM stance with widely recognized ethical standards, as well as the potential risks or ideological biases that may arise. Through integrated quantitative metrics, our research uncovers unexpected decision-making patterns in LLM responses to sensitive issues including human rights protection, environmental sustainability, regional conflicts, etc. It discloses that LLMs could exhibit a strong bias towards interests, leading to unsafe decisions that violate ethical and moral principles. Our experiment results suggest that deploying LLMs in high-stakes domains, particularly in the formulation of diplomatic policies, necessitates a comprehensive assessment of potential ethical and social implications, as well as the implementation of stringent safety protocols.

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DisLoRA: Task-specific Low-Rank Adaptation via Orthogonal Basis from Singular Value Decomposition
She Yifei | Xinhao Wei | Yulong Wang

Parameter-efficient fine-tuning (PEFT) of large language models (LLMs) is critical for adapting to diverse downstream tasks with minimal computational cost. We propose **Di**rectional-**S**VD **Lo**w-**R**ank **A**daptation (DisLoRA), a novel PEFT framework that leverages singular value decomposition (SVD) to decompose pretrained weight matrices into orthogonal backbone and task-specific subspaces, enabling precise capture of task-specific directions (TSDs). By dynamically identifying TSDs and employing adaptive soft orthogonal regularization with mean-normalization mechanism, DisLoRA balances task-specific and orthogonal losses without manual tuning, ensuring robust training stability. Extensive experiments on GLUE and Commonsense Reasoning benchmarks demonstrate that DisLoRA surpasses established PEFT methods, including LoRA, PiSSA, DoRA, LoRA-Dash, and SORSA. DisLoRA achieves superior performance on multiple individual GLUE datasets, surpassing baselines by up to 10.28% on SST-2 and 3.28% on CoLA, and consistently attains higher average accuracy than baselines across Commonsense Reasoning Tasks, with a maximum gain of 3.1%. These results demonstrate DisLoRA’s performance in efficient and high-performing LLM adaptation for domain-specific tasks while preserving generalization.

Misleading visualizations, which manipulate chart representations to support specific claims, can distort perception and lead to incorrect conclusions. Despite decades of research, they remain a widespread issue, posing risks to public understanding and raising safety concerns for AI systems involved in data-driven communication. While recent multimodal large language models (MLLMs) show strong chart comprehension abilities, their capacity to detect and interpret misleading charts remains unexplored. We introduce Misleading ChartQA benchmark, a large-scale multimodal dataset designed to evaluate MLLMs on misleading chart reasoning. It contains 3,026 curated examples spanning 21 misleader types and 10 chart types, each with standardized chart code, CSV data, multiple-choice questions, and labeled explanations, validated through iterative MLLM checks and exhausted expert human review. We benchmark 24 state-of-the-art MLLMs, analyze their performance across misleader types and chart formats, and propose a novel region-aware reasoning pipeline that enhances model accuracy. Our work lays the foundation for developing MLLMs that are robust, trustworthy, and aligned with the demands of responsible visual communication.

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Textual Aesthetics in Large Language Models
Lingjie Jiang | Shaohan Huang | Xun Wu | Furu Wei

Image aesthetics is a crucial metric in the field of image generation. However, textual aesthetics has not been sufficiently explored. With the widespread application of large language models (LLMs), previous work has primarily focused on the correctness of content and the helpfulness of responses. Nonetheless, providing responses with textual aesthetics is also an important factor for LLMs, which can offer a cleaner layout and ensure greater consistency and coherence in content. In this work, we introduce a pipeline for aesthetics polishing and help construct a textual aesthetics dataset named TEXAES. We propose a textual aesthetics-powered fine-tuning method based on direct preference optimization, termed TAPO, which leverages textual aesthetics without compromising content correctness. Additionally, we develop two evaluation methods for textual aesthetics based on text and image analysis, respectively.Our experiments demonstrate that using textual aesthetics data and employing the TAPO fine-tuning method not only improves aesthetic scores but also enhances performance on general evaluation datasets such as AlpacalEval and Arena-hard.

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Section-Level Simplification of Biomedical Abstracts
Jan Bakker | Jaap Kamps

Cochrane produces systematic reviews whose abstracts are divided into seven standard sections. However, the plain language summaries (PLS) of Cochrane reviews do not adhere to the same structure, which has prevented researchers from training simplification models on paired abstract and PLS sections. In this work, we devise a two-step method to automatically divide PLS of Cochrane reviews into the same sections in which abstracts are divided. In the first step, we align each sentence in a PLS to a section in the parallel abstract if they cover similar content. In the second step, we classify the remaining sentences into sections based on the content of the PLS and what we learned from the first step. We manually divide 22 PLS into sections to evaluate our method. Upon execution of our method, we obtain the Cochrane-sections dataset, which consists of paired abstract and PLS sections in English for a total of 7.7K Cochrane reviews. Thus, our work yields references for the section-level simplification of biomedical abstracts.

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PoseStitch-SLT: Linguistically Inspired Pose-Stitching for End-to-End Sign Language Translation
Abhinav Joshi | Vaibhav Sharma | Sanjeet Singh | Ashutosh Modi

Sign language translation remains a challenging task due to the scarcity of large-scale, sentence-aligned datasets. Prior arts have focused on various feature extraction and architectural changes to support neural machine translation for sign languages. We propose PoseStitch-SLT, a novel pre-training scheme that is inspired by linguistic-templates-based sentence generation technique. With translation comparison on two sign language datasets, How2Sign and iSign, we show that a simple transformer-based encoder-decoder architecture outperforms the prior art when considering template-generated sentence pairs in training. We achieve BLEU-4 score improvements from 1.97 to 4.56 on How2Sign and from 0.55 to 3.43 on iSign, surpassing prior state-of-the-art methods for pose-based gloss-free translation. The results demonstrate the effectiveness of template-driven synthetic supervision in low-resource sign language settings.

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Few-Shot Open-Set Classification via Reasoning-Aware Decomposition
Avyav Kumar Singh | Helen Yannakoudakis

Large language models (LLMs) excel at few-shot learning, but their ability to reject out-of-distribution examples remains under-explored. We study this challenge under the setting of few-shot open-set classification, where a model must not only classify examples from a small set of seen classes but also reject unseen ones at inference time. This setting is more realistic and challenging than traditional closed-set supervised learning, requiring both fine-grained classification and robust rejection. We show that, for small LLMs, neither chain-of-thought (CoT) prompting nor supervised fine-tuning (SFT) alone are sufficient to generalise reliably, particularly when class semantics are anonymised. We introduce Wasserstein GFN (W-GFN), a novel amortised Generative Flow Network framework that uses latent trajectories to approximate the Bayesian posterior. With as few as 4 examples per class, W-GFN substantially improves performance, enabling Llama 3.2 3B to achieve up to ≥80% of the performance of Llama 3.3 70B in complex datasets, despite being ∼ 23 times smaller, which highlights the importance of reasoning-aware approaches for robust open-set few-shot learning.

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Translation in the Hands of Many: Centering Lay Users in Machine Translation Interactions
Beatrice Savoldi | Alan Ramponi | Matteo Negri | Luisa Bentivogli

Converging societal and technical factors have transformed language technologies into user-facing applications used by the general public across languages. Machine Translation (MT) has become a global tool, with cross-lingual services now also supported by dialogue systems powered by multilingual Large Language Models (LLMs). Widespread accessibility has extended MT’s reach to a vast base of *lay users*, many with little to no expertise in the languages or the technology itself. And yet, the understanding of MT consumed by such a diverse group of users—their needs, experiences, and interactions with multilingual systems—remains limited. In our position paper, we first trace the evolution of MT user profiles, focusing on non-experts and how their engagement with technology may shift with the rise of LLMs. Building on an interdisciplinary body of work, we identify three factors—usability, trust, and literacy—that are central to shaping user interactions and must be addressed to align MT with user needs. By examining these dimensions, we provide insights to guide the progress of more user-centered MT.

Augmenting large language models (LLMs) with external tools is a promising approach to enhance their capabilities, especially for complex tasks. Synthesizing tool-use data through real-world simulations is an effective way to achieve this. However, our investigation reveals that training gains significantly decay as synthetic data increases. The model struggles to benefit from more synthetic data, and it can not equip the model with advanced tool-use capabilities in complex scenarios. Moreover, we discovered that the above limitation usually manifests as a fragment deficiency (i.e., parameter errors) in response. To this end, we propose an iterative reinforced fine-tuning strategy designed to alleviate this limitation. This strategy involves: (1) enhancing the diversity of response for synthetic data through path exploration of Monte Carlo Tree Search. (2) iteratively pinpointing the model’s deficiency by constructing fine-grained preference pairs, and then improving it by preference optimization algorithms for targeted improvement. The experiments show that our method achieves 13.11% better performance than the same-size base model. It achieves an improvement of 6.5% in complex scenarios compared to the baseline, and it also outperforms larger open-source and closed-source models.

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Transplant Then Regenerate: A New Paradigm for Text Data Augmentation
Guangzhan Wang | Hongyu Zhang | Beijun Shen | Xiaodong Gu

Data augmentation is a critical technique in deep learning. Traditional methods like Back-translation typically focus on lexical-level rephrasing, which primarily produces variations with the same semantics. While large language models (LLMs) have enhanced text augmentation by their “knowledge emergence” capability, controlling the style and structure of these outputs remains challenging and requires meticulous prompt engineering. In this paper, we propose LMTransplant, a novel text augmentation paradigm leveraging LLMs. The core idea of LMTransplant is transplant-then-regenerate: incorporating seed text into a context expanded by LLM, and asking the LLM to regenerate a variant based on the expanded context. This strategy allows the model to create more diverse and creative content-level variants by fully leveraging the knowledge embedded in LLMs, while preserving the core attributes of the original text. We evaluate LMTransplant across various text-related tasks, demonstrating its superior performance over existing text augmentation methods. Moreover, LMTransplant demonstrates exceptional scalability as the size of augmented data grows.

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Compositional Generalisation for Explainable Hate Speech Detection
Agostina Calabrese | Tom Sherborne | Björn Ross | Mirella Lapata

Hate speech detection is key to online content moderation, but current models struggle to generalise beyond their training data. This has been linked to dataset biases and the use of sentence-level labels, which fail to teach models the underlying structure of hate speech. In this work, we show that even when models are trained with more fine-grained, span-level annotations (e.g., “artists” is labeled as target and “are parasites” as dehumanising comparison), they struggle to disentangle the meaning of these labels from the surrounding context. As a result, combinations of expressions that deviate from those seen during training remain particularly difficult for models to detect. We investigate whether training on a dataset where expressions occur with equal frequency across all contexts can improve generalisation. To this end, we create U-PLEAD, a dataset of ~364,000 synthetic posts, along with a novel compositional generalisation benchmark of ~8,000 manually validated posts. Training on a combination of U-PLEAD and real data improves compositional generalisation while achieving state-of-the-art performance on the human-sourced PLEAD.

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CCQA: Generating Question from Solution Can Improve Inference-Time Reasoning in SLMs
Jinyoung Kim | Ji Won Yoon

Recently, inference-time reasoning strategies have further improved the accuracy of large language models (LLMs), but their effectiveness on smaller models remains unclear. Based on the observation that conventional approaches often fail to improve performance in this context, we propose Cycle-Consistency in Question Answering (CCQA), a novel reasoning method that can be effectively applied to SLMs. Inspired by cycle consistency, CCQA generates a question from each reasoning path and answer, evaluates each by its similarity to the original question, and then selects the candidate solution with the highest similarity score as the final response. Since conventional SLMs struggle to generate accurate questions from their own reasoning paths and answers, we employ a lightweight Flan-T5 model specialized for question generation to support this process efficiently. From the experimental results, it is verified that CCQA consistently outperforms existing state-of-the-art (SOTA) methods across eight models on mathematical and commonsense reasoning benchmarks. Furthermore, our method establishes a new practical baseline for efficient reasoning in SLMs. Source code can be found at https://github.com/scai-research/ccqa_official.

Text-Centric Visual Question Answering (TEC-VQA) is a critical research area that requires semantic interactions between objects and scene texts. However, most existing TEC-VQA benchmarks focus on high-resource languages like English and Chinese. Although few works expanding multilingual QA pairs in non-text-centric VQA datasets through translation, which encounters a substantial “visual-textual misalignment” problem when applied to TEC-VQA. Moreover, the open-source nature of these benchmarks and the broad sources of training data for MLLMs have inevitably led to benchmark contamination, resulting in unreliable evaluation results. To alleviate this issue, we propose a contamination-free and more challenging TEC-VQA benchmark called Text-Centric Visual Question Answering in Multilingual Chinese Minority Languages(TVQACML), which involves eight languages, including Standard Chinese, Korean, and six minority languages. TVQACML supports a wide range of tasks, such as Text Recognition, Scene Text-Centric VQA, Document-Oriented VQA, Key Information Extraction (KIE), and Handwritten Mathematical Expression Recognition (HMER), featuring 32,000 question-answer pairs across 8,000 images. Extensive experiments on TVQACML across multiple MLLMs demonstrate the effectiveness of evaluating the MLLMs and enhancing multilingual TEC-VQA performance with fine-tuning.

Procedural mistake detection (PMD) is a challenging problem of classifying whether a human user (observed through egocentric video) has successfully executed a task (specified by a procedural text). Despite significant recent efforts, machine performance in the wild remains nonviable, and the reasoning processes underlying this performance are opaque. As such, we extend PMD to require generating visual self-dialog rationales to inform decisions. Given the impressive, mature image understanding capabilities observed in recent vision-and-language models (VLMs), we curate a suitable benchmark dataset for PMD based on individual frames. As our reformulation enables unprecedented transparency, we leverage a natural language inference (NLI) model to formulate two automated metrics for the coherence of generated rationales. We establish baselines for this reframed task, showing that VLMs struggle off-the-shelf, but with some trade-offs, their accuracy, coherence, and efficiency can be improved by incorporating these metrics into common inference and fine-tuning methods. Lastly, our multi-faceted metrics visualize common outcomes, highlighting areas for further improvement.

Preference learning extends the performance of Code LLMs beyond traditional supervised fine-tuning by leveraging relative quality comparisons. In existing approaches, a set of n candidate solutions is evaluated based on test case success rates, with the candidate demonstrating a higher pass rate being labeled as positive and its counterpart with a lower pass rate as negative. However, because this approach aligns entire failing code blocks rather than pinpointing specific errors, it lacks the granularity necessary to capture meaningful error-correction relationships. As a result, the model is unable to learn more informative error-correction patterns. To address these issues, we propose Target-DPO, a new preference alignment framework that mimics human iterative debugging to refine Code LLMs. Target-DPO explicitly locates error regions and aligns the corresponding tokens via a tailored DPO algorithm. To facilitate it, we introduce the CodeFlow dataset, where samples are iteratively refined until passing tests, with modifications capturing error corrections. Extensive experiments show that a diverse suite of Code LLMs equipped with Target-DPO achieves significant performance gains in code generation and improves on challenging tasks like BigCodeBench. In-depth analysis reveals that Target-DPO yields fewer errors. Code, model and datasets are in: https://github.com/JieWu02/Target-DPO.

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MoLoRAG: Bootstrapping Document Understanding via Multi-modal Logic-aware Retrieval
Xixi Wu | Yanchao Tan | Nan Hou | Ruiyang Zhang | Hong Cheng

Document Understanding is a foundational AI capability with broad applications, and Document Question Answering (DocQA) is a key evaluation task. Traditional methods convert the document into text for processing by Large Language Models (LLMs), but this process strips away critical multi-modal information like figures. While Large Vision-Language Models (LVLMs) address this limitation, their constrained input size makes multi-page document comprehension infeasible. Retrieval-augmented generation (RAG) methods mitigate this by selecting relevant pages, but they rely solely on semantic relevance, ignoring logical connections between pages and the query, which is essential for reasoning and accurate answers.To this end, we propose MoLoRAG, a logic-aware retrieval framework for multi-modal, multi-page document understanding. By constructing a page graph that captures contextual relationships between pages, a lightweight VLM performs graph traversal to retrieve relevant pages, including those with logical connections often overlooked. This approach combines semantic and logical relevance to deliver more accurate retrieval. After retrieval, the top-K pages are fed into arbitrary LVLMs for question answering. To enhance flexibility, MoLoRAG offers two variants: a training-free solution for easy deployment and a fine-tuned version to improve logical relevance checking. Experiments on four DocQA datasets demonstrate average improvements of 9.68% in accuracy over LVLM direct inference and 7.44% in retrieval precision over baselines. Codes and datasets are released at https://github.com/WxxShirley/MoLoRAG.

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Vision-Free Retrieval: Rethinking Multimodal Search with Textual Scene Descriptions
Ioanna Ntinou | Alexandros Xenos | Yassine Ouali | Adrian Bulat | Georgios Tzimiropoulos

Contrastively-trained Vision-Language Models (VLMs), such as CLIP, have become the standard approach for learning discriminative vision-language representations. However, these models often exhibit shallow language understanding, manifesting bag-of-words behaviour. These limitations are reinforced by their dual-encoder design, which induces a modality gap. Additionally, the reliance on vast web-collected data corpora for training makes the process computationally expensive and introduces significant privacy concerns. To address these limitations, in this work, we challenge the necessity of vision encoders for retrieval tasks by introducing a vision-free, single-encoder retrieval pipeline. Departing from the traditional text-to-image retrieval paradigm, we migrate to a text-to-text paradigm with the assistance of VLLM-generated structured image descriptions. We demonstrate that this paradigm shift has significant advantages, including a substantial reduction of the modality gap, improved compositionality, and better performance on short and long caption queries, all attainable with only two hours of calibration on two GPUs. Additionally, substituting raw images with textual descriptions introduces a more privacy-friendly alternative for retrieval. To further assess generalisation and address some of the shortcomings of prior compositionality benchmarks, we release two benchmarks derived from Flickr30k and COCO, containing diverse compositional queries made of short captions, which we coin subFlickr and subCOCO. Our vision-free retriever matches and often surpasses traditional multimodal models. Importantly, our approach achieves state-of-the-art zero-shot performance on multiple retrieval and compositionality benchmarks, with models as small as 0.3B parameters.

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TableRAG: A Retrieval Augmented Generation Framework for Heterogeneous Document Reasoning
Xiaohan Yu | Pu Jian | Chong Chen

Retrieval-Augmented Generation (RAG) has demonstrated considerable effectiveness in open-domain question answering. However, when applied to heterogeneous documents, comprising both textual and tabular components, existing RAG approaches exhibit critical limitations. The prevailing practice of flattening tables and chunking strategies disrupts the intrinsic tabular structure, leads to information loss, and undermines the reasoning capabilities of LLMs in multi-hop, global queries. To address these challenges, we propose TableRAG, an SQL-based framework that unifies textual understanding and complex manipulations over tabular data. TableRAG iteratively operates in four steps: context-sensitive query decomposition, text retrieval, SQL programming and execution, and compositional intermediate answer generation. We also develop HeteQA, a novel benchmark designed to evaluate the multi-hop heterogeneous reasoning capabilities. Experimental results demonstrate that TableRAG consistently outperforms existing baselines on both public datasets and our HeteQA, establishing a new state-of-the-art for heterogeneous document question answering.

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Retrieval Enhanced Feedback via In-context Neural Error-book
Jongyeop Hyun | Bumsoo Kim

Recent advancements in Large Language Models (LLMs) have significantly improved reasoning capabilities, with in-context learning (ICL) emerging as a key technique for adaptation without retraining. While previous works have focused on leveraging correct examples, recent research highlights the importance of learning from errors to enhance performance. However, existing methods lack a structured framework for analyzing and mitigating errors, particularly in Multimodal Large Language Models (MLLMs), where integrating visual and textual inputs adds complexity. To address this issue, we propose REFINE: Retrieval-Enhanced Feedback via In-context Neural Error-book, a teacher-student framework that systematically structures errors and provides targeted feedback. REFINE introduces three systematic queries to construct structured feedback—Feed-Target, Feed-Check, and Feed-Path—to enhance multimodal reasoning by prioritizing relevant visual information, diagnosing critical failure points, and formulating corrective actions. Unlike prior approaches that rely on redundant retrievals, REFINE optimizes structured feedback retrieval, improving inference efficiency, token usage, and scalability. Our results demonstrate substantial speedup, reduced computational costs, and successful generalization, highlighting REFINE’s potential for enhancing multimodal reasoning.

LLM-as-a-Judge leverages the generative and reasoning capabilities of large language models (LLMs) to evaluate LLM responses across diverse scenarios, providing accurate preference signals. This approach plays a vital role in aligning LLMs with human values. Recent studies have raised many methods to train LLM as generative judges, but most of them are data consuming or lack accuracy, and only focus on LLM’s judge ability. In this work, we conceptualize judging ability as a general capability of LLMs and adapt the two-stage SFT-DPO training framework—commonly used in traditional general model training—to the development of judge models. We introduce an efficient data synthesis method, which includes the automatic generation of various judge templates, dual verification for data accuracy and consistency. A difficulty-based data stratification strategy allows us to distribute more effective data to the SFT and DPO stages respectively. Experimental results demonstrate that our approach, utilizing only about 2% to 40% of the data required by other methods, achieves SOTA performance on RewardBench. Furthermore, our training method enhances the general capabilities of the model by constructing complicated judge task with CoT outputs. We further validate the effectiveness of our model by deploying it to provide reward signals in a real-world RLHF scenarios. We will open-source our model weights and training data to facilitate further research.

Large Language Models (LLMs) have demonstrated exceptional performance across diverse natural language processing tasks. However, these models exhibit a critical limitation in output diversity, often generating highly similar content across multiple attempts. This limitation significantly affects tasks requiring diverse outputs, from creative writing to reasoning. Existing solutions, like temperature scaling, enhance diversity by modifying probability distributions but compromise output quality. We propose Guide-to-Generation (G2), a training-free plug-and-play method that enhances output diversity while preserving generation quality. G2 employs a base generator alongside dual Guides, which guide the generation process through decoding-based interventions to encourage more diverse outputs conditioned on the original query. Comprehensive experiments demonstrate that G2 effectively improves output diversity while maintaining an optimal balance between diversity and quality.

LLMs are evolving into assistants that leverage tools, significantly expanding their capabilities but also introducing critical safety risks. Current models exhibit notable vulnerabilities, particularly in maintaining safety during multi-step tool interactions and in scenarios involving indirect harm. This paper introduces ToolSafety, a safety fine-tuning dataset designed to address these limitations. ToolSafety comprises 5,668 direct harm samples, 4,311 indirect harm samples, and 4,311 multi-step samples. Key features include support for multi-step safety through synthesized trajectories and realistic, context-aware sample generation. We fine-tuned LLaMA3.1-8B-Instruct and Qwen2.5-7B-Instruct using ToolSafety. Experimental results demonstrate that these models effectively maintain safety in multi-step and indirect harm scenarios. Further analysis into superficial alignment across different decoding strategies, languages, and jailbreak prompts indicates that while some risks persist, the issue is less severe than in multi-step settings. Overall, our approach significantly improves safety across various scenarios with small impact on helpfulness, positioning ToolSafety as a valuable resource for building safer tool-using AI systems.

Automated Audio Captioning (AAC) aims to generate natural language descriptions of audio content, enabling machines to interpret and communicate complex acoustic scenes. However, current AAC datasets often suffer from short and simplistic captions, limiting model expressiveness and semantic depth. To address this, we introduce **VggCaps**, a new multi-modal dataset that pairs audio with corresponding video and leverages large language models (LLMs) to generate rich, descriptive captions. VggCaps significantly outperforms existing benchmarks in caption length, lexical diversity, and human-rated quality. Furthermore, we propose **Multi2Cap**, a novel AAC framework that learns audio-visual representations through a AV-grounding module during pre-training and reconstructs visual semantics using audio alone at inference. This enables visually grounded captioning in audio-only scenarios. Experimental results on Clotho and AudioCaps demonstrate that Multi2Cap achieves state-of-the-art performance across multiple metrics, validating the effectiveness of cross-modal supervision and LLM-based generation in advancing AAC.

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Towards Optimal Evaluation Efficiency for Large Language Models
Guohong Li | Deyi Xiong

Comprehensive evaluation of large language models (LLMs) typically requires large-scale benchmarks, which is costly in terms of both data annotation and computational resource needed for evaluation. To mitigate these challenges, we propose an efficient evaluation framework that selects a question subset based on pre-tested results, thereby reducing the costs. We formulate the subset selection problem as an optimization task, solved using optimal random sampling and simulated annealing algorithms. We compare our approach with prior clustering-based methods and assess their reliability in terms of score accuracy. Additionally, we perform semantic analysis and evaluate whether the selected subsets preserve the semantic information of the original benchmark using Wasserstein distance. Experimental results show that our method outperforms previous approaches in terms of reliability, as measured by L2 norm. Our study provides an optimized perspective for balancing evaluation efficiency and reliability in LLM assessments, while revealing the relationship between optimization methods and semantic retention.

Multimodal Multi-hop question answering requires integrating information from diverse sources, such as images and texts, to derive answers. Existing methods typically rely on sequential retrieval and reasoning, where each step builds on the previous output. However, this single-path paradigm makes them vulnerable to errors due to misleading intermediate steps. Moreover, developing multimodal models can be computationally expensive, often requiring extensive training. To address these limitations, we propose a training-free framework guided by an Adaptive Planning Graph, which consists of planning, retrieval and reasoning modules. The planning module analyzes the current state of the Adaptive Planning Graph, determines the next action and where to expand the graph, which enables dynamic and flexible exploration of reasoning paths. To handle retrieval of text to unspecified target modalities, we devise modality-specific strategies that dynamically adapt to distinct data types. Our approach preserves the characteristics of multimodal information without costly task-specific training, enabling seamless integration with up-to-date models. Finally, the experiments on MultimodalQA and WebQA show that our approach matches or outperforms existing models that rely on training.

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Mixture-of-Clustered-Experts: Advancing Expert Specialization and Generalization in Instruction Tuning
Sugyeong Eo | Jung Jun Lee | Chanjun Park | Heuiseok Lim

A sparse Mixture-of-Experts (MoE) architecture has emerged as a highly scalable solution by conditionally activating sub-modules without a proportional increase in computational costs. However, improving expert specialization to enhance performance and generalization remains a challenge for MoE, especially in instruction tuning scenarios characterized by significant input heterogeneity. In this work, we propose the Mixture-of-Clustered-Experts (MoCE) to address this limitation through a dual-stage routing mechanism. The first stage in the mechanism performs expert group routing based on sequence-level features, while the second stage activates the top-k experts within the group at the token level. This approach enables the effective partitioning of heterogeneous inputs based on their knowledge requirements, encouraging expert group specialization while maintaining the advantages of token-level routing. We evaluate MoCE across a comprehensive set of benchmarks, demonstrating its consistent superiority over strong baselines and its enhanced generalization capabilities. Detailed analysis further highlights the robustness and effectiveness of MoCE.

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Process-Supervised Reinforcement Learning for Code Generation
Yufan Ye | Ting Zhang | Wenbin Jiang | Hua Huang

Existing reinforcement learning (RL) strategies based on outcome supervision have proven effective in enhancing the performance of large language models (LLMs) for code generation. While reinforcement learning based on process supervision shows great potential in multi-step reasoning tasks, its effectiveness in the field of code generation still lacks sufficient exploration and verification. The primary obstacle stems from the resource-intensive nature of constructing a high-quality process-supervised reward dataset, which requires substantial human expertise and computational resources. To overcome this challenge, this paper proposes a “mutation/refactoring-execution verification” strategy. Specifically, the teacher model is used to mutate and refactor the statement lines or blocks, and the execution results of the compiler are used to automatically label them, thus generating a process-supervised reward dataset. Based on this dataset, we have carried out a series of RL experiments. The experimental results show that, compared with the method relying only on outcome supervision, reinforcement learning based on process supervision performs better in handling complex code generation tasks. In addition, this paper for the first time confirms the advantages of the Direct Preference Optimization (DPO) method in the RL task of code generation based on process supervision, providing new ideas and directions for code generation research.

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MuCAL: Contrastive Alignment for Preference-Driven KG-to-Text Generation
Yifei Song | Claire Gardent

We propose MuCAL (Multilingual Contrastive Alignment Learning) to tackle the challenge of Knowledge Graphs (KG)-to-Text generation using preference learning, where reliable preference data is scarce. MuCAL is a multilingual KG/Text alignment model achieving robust cross-modal retrieval across multiple languages and difficulty levels. Building on MuCAL, we automatically create preference data by ranking candidate texts from three LLMs (Qwen2.5, DeepSeek-v3, Llama-3). We then apply Direct Preference Optimization (DPO) on these preference data, bypassing typical reward modelling steps to directly align generation outputs with graph semantics. Extensive experiments on KG-to-English Text generation show two main advantages: (1) Our KG/text similarity models provide a better signal for DPO than similar existing metrics, and (2) significantly better generalisation on out-of-domain datasets compared to standard instruction tuning. Our results highlight MuCAL’s effectiveness in supporting preference learning for KG-to-English Text generation and lay the foundation for future multilingual extensions. Code and data are available at https://github.com/MeloS7/MuCAL_DPO/tree/main.

Multi-modal large language models (MLLMs) have achieved remarkable success in fine-grained visual understanding across a range of tasks. However, they often encounter significant challenges due to inadequate alignment for fine-grained knowledge, which restricts their ability to accurately capture local details and attain a comprehensive global perception. While recent advancements have focused on aligning object expressions with grounding information, they typically lack explicit integration of object images, which contain affluent information beyond mere texts or coordinates. To bridge this gap, we introduce a novel fine-grained visual knowledge alignment method that effectively aligns and integrates multi-scale knowledge of objects, including texts, coordinates, and images. This innovative method is underpinned by our multi-scale fine-grained enhancement data synthesis pipeline, which provides over 300K essential training data to enhance alignment and improve overall performance. Furthermore, we present TinyGroundingGPT, a series of compact models optimized for high-level alignments. With a scale of approximately 3B parameters, TinyGroundingGPT achieves outstanding results in grounding tasks while delivering performance comparable to larger MLLMs in complex visual scenarios.

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Thought calibration: Efficient and confident test-time scaling
Menghua Wu | Cai Zhou | Stephen Bates | Tommi Jaakkola

Reasoning large language models achieve impressive test-time scaling by thinking for longer, but this performance gain comes at significant compute cost. Directly limiting test-time budget hurts overall performance, but not all problems are equally difficult. We propose thought calibration to decide dynamically when thinking can be terminated. To calibrate our decision rule, we view a language model’s growing body of thoughts as a nested sequence of reasoning trees, where the goal is to identify the point at which novel reasoning plateaus. We realize this framework through lightweight probes that operate on top of the language model’s hidden representations, which are informative of both the reasoning structure and overall consistency of response. Based on three reasoning language models and four datasets, thought calibration preserves model performance with up to a 60% reduction in thinking tokens on in-distribution data, and up to 20% in out-of-distribution data.

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Can LLMs Reason Abstractly Over Math Word Problems Without CoT? Disentangling Abstract Formulation From Arithmetic Computation
Ziling Cheng | Meng Cao | Leila Pishdad | Yanshuai Cao | Jackie CK Cheung

Final-answer-based metrics are commonly used for evaluating large language models (LLMs) on math word problems, often taken as proxies for reasoning ability. However, such metrics conflate two distinct sub-skills: abstract formulation (capturing mathematical relationships using expressions) and arithmetic computation (executing the calculations). Through a disentangled evaluation on GSM8K and SVAMP, we find that the final-answer accuracy of Llama-3 and Qwen2.5 (1B-32B) without CoT is overwhelmingly bottlenecked by the arithmetic computation step and not by the abstract formulation step. Contrary to the common belief, we show that CoT primarily aids in computation, with limited impact on abstract formulation. Mechanistically, we show that these two skills are composed conjunctively even in a single forward pass without any reasoning steps via an abstract-then-compute mechanism: models first capture problem abstractions, then handle computation. Causal patching confirms these abstractions are present, transferable, composable, and precede computation. These behavioural and mechanistic findings highlight the need for disentangled evaluation to accurately assess LLM reasoning and to guide future improvements.

The deployment of large language models (LLMs) faces considerable challenges concerning resource constraints and inference efficiency. Recent research has increasingly focused on smaller, task-specific models enhanced by distilling knowledge from LLMs. However, prior studies have often overlooked the diversity and quality of knowledge, especially the untapped potential of negative knowledge. Constructing effective negative knowledge remains severely understudied. In this paper, we introduce a novel framework called quality-guided contrastive rationale distillation aimed at enhancing reasoning capabilities through contrastive knowledge learning. For positive knowledge, we enrich its diversity through temperature sampling and employ self-consistency for further denoising and refinement. For negative knowledge, we propose an innovative self-adversarial approach that generates low-quality rationales by sampling previous iterations of smaller language models, embracing the idea that one can learn from one’s own weaknesses. A contrastive loss is developed to distill both positive and negative knowledge into smaller language models, where an online-updating discriminator is integrated to assess qualities of rationales and assign them appropriate weights, optimizing the training process. Through extensive experiments across multiple reasoning tasks, we demonstrate that our method consistently outperforms existing distillation techniques, yielding higher-quality rationales.

Despite their impressive capabilities, Large Vision-Language Models (LVLMs) frequently generate responses that are plausible but incorrect or unsupported—commonly referred to as hallucinations. In this study, we investigate whether different types of hallucinations are reflected in the model’s internal representations by probing their encoded features. We focus on two key causes of hallucination in multimodal reasoning: (1) over-reliance on textual priors and (2) preference for user prompts over conflicting visual evidence—factors identified in prior work as frequent and impactful. Our probing results reveal that hallucinations exhibit distinguishable representational patterns, suggesting the potential for a representation-level approach to characterize and mitigate them. Motivated by these findings, we propose Steering HAllucination via RePresentation Engineering (SHARP), a representation-level intervention framework that modulates hallucination-related features during inference. SHARP identifies functional representations responsible for prior-driven biases and visual-context conflicts, and jointly adjusts the model’s internal activations in real time. We evaluate our approach extensively on three large vision-language models across multiple benchmarks. Experimental results demonstrate that SHARP effectively reduces hallucinations while preserving the performance and generalization capabilities of LVLMs.

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Think, Verbalize, then Speak: Bridging Complex Thoughts and Comprehensible Speech
Tony Woo | Sehun Lee | Kang-wook Kim | Gunhee Kim

Spoken dialogue systems increasingly employ large language models (LLMs) to leverage their advanced reasoning capabilities. However, direct application of LLMs in spoken communication often yield suboptimal results due to mismatches between optimal textual and verbal delivery. While existing approaches adapt LLMs to produce speech-friendly outputs, their impact on reasoning performance remains underexplored. In this work, we propose **Think-Verbalize-Speak**, a framework that decouples reasoning from spoken delivery to preserve the full reasoning capacity of LLMs. Central to our method is *verbalizing*, an intermediate step that translates thoughts into natural, speech-ready text. We also introduce **ReVerT**, a latency-efficient verbalizer based on incremental and asynchronous summarization. Experiments across multiple benchmarks show that our method enhances speech naturalness and conciseness with minimal impact on reasoning. The project page with the dataset and the source code is available at https://yhytoto12.github.io/TVS-ReVerT.

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Warm Up Before You Train: Unlocking General Reasoning in Resource-Constrained Settings
Safal Shrestha | Minwu Kim | Aadim Nepal | Anubhav Shrestha | Keith W. Ross

Designing effective reasoning-capable LLMs typically requires training using Reinforcement Learning with Verifiable Rewards (RLVR) or distillation with carefully curated Long Chain of Thoughts (CoT), both of which depend heavily on extensive training data. This creates a major challenge when the amount of quality training data is scarce. We propose a sample-efficient, two-stage training strategy to develop reasoning LLMs under limited supervision. In the first stage, we “warm up” the model by distilling Long CoTs from a toy domain, namely, Knights & Knaves (K&K) logic puzzles to acquire general reasoning skills. In the second stage, we apply RLVR to the warmed-up model using a limited set of target-domain examples. Our experiments demonstrate that this two-phase approach offers several benefits: (i) the warmup phase alone facilitates generalized reasoning, leading to performance improvements across a range of tasks, including MATH, HumanEval+, and MMLU-Pro; (ii) When both the base model and the warmed-up model are RLVR trained on the same small dataset (≤100 examples), the warmed-up model consistently outperforms the base model;(iii) Warming up before RLVR training allows a model to maintain cross-domain generalizability even after training on a specific domain; (iv) Introducing warmup in the pipeline improves not only accuracy but also overall sample efficiency during RLVR training. The results in this paper highlight the promise of warmup for building robust reasoning LLMs in data-scarce environments.

Automatically generating presentations from documents is a challenging task that requires accommodating content quality, visual appeal, and structural coherence. Existing methods primarily focus on improving and evaluating the content quality in isolation, overlooking visual appeal and structural coherence, which limits their practical applicability. To address these limitations, we propose PPTAgent, which comprehensively improves presentation generation through a two-stage, edit-based approach inspired by human workflows. PPTAgent first analyzes reference presentations to extract slide-level functional types and content schemas, then drafts an outline and iteratively generates editing actions based on selected reference slides to create new slides. To comprehensively evaluate the quality of generated presentations, we further introduce PPTEval, an evaluation framework that assesses presentations across three dimensions: Content, Design, and Coherence. Results demonstrate that PPTAgent significantly outperforms existing automatic presentation generation methods across all three dimensions.

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SWAM: Adaptive Sliding Window and Memory-Augmented Attention Model for Rumor Detection
Mei Guo | Chen Chen | Chunyan Hou | Yike Wu | Xiaojie Yuan

Detecting rumors on social media has become a critical task in combating misinformation. Existing propagation-based rumor detection methods often focus on the static propagation graph, overlooking that rumor propagation is inherently dynamic and incremental in the real world. Recently propagation-based rumor detection models attempt to use the dynamic graph that is associated with coarse-grained temporal information. However, these methods fail to capture the long-term time dependency and detailed temporal features of propagation. To address these issues, we propose a novel adaptive Sliding Window and memory-augmented Attention Model (SWAM) for rumor detection. The adaptive sliding window divides the sequence of posts into consecutive disjoint windows based on the propagation rate of nodes. We also propose a memory-augmented attention to capture the long-term dependency and the depth of nodes in the propagation graph. Multi-head attention mechanism is applied between nodes in the memorybank and incremental nodes to iteratively update the memorybank, and the depth information of nodes is also considered. Finally, the propagation features of nodes in the memorybank are utilized for rumor detection. Experimental results on two public real-world datasets demonstrate the effectiveness of our model compared with the state-of-the-art baselines.

Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating external knowledge. Current hybrid RAG system retrieves evidence from both knowledge graphs (KGs) and text documents to support LLM reasoning. However, it faces challenges like handling multi-hop reasoning, multi-entity questions, multi-source verification, and effective graph utilization. To address these limitations, we present HydraRAG, a training-free framework that unifies graph topology, document semantics, and source reliability to support deep, faithful reasoning in LLMs. HydraRAG handles multi-hop and multi-entity problems through agent-driven exploration that combines structured and unstructured retrieval, increasing both diversity and precision of evidence. To tackle multi-source verification, HydraRAG uses a tri-factor cross-source verification (source trustworthiness assessment, cross-source corroboration, and entity-path alignment), to balance topic relevance with cross-modal agreement. By leveraging graph structure, HydraRAG fuses heterogeneous sources, guides efficient exploration, and prunes noise early. Comprehensive experiments on seven benchmark datasets show that HydraRAG achieves overall state-of-the-art results on all benchmarks with GPT-3.5-Turbo, outperforming the strong hybrid baseline ToG-2 by an average of 20.3% and up to 30.1%. Furthermore, HydraRAG enables smaller models (e.g., Llama-3.1-8B) to achieve reasoning performance comparable to that of GPT-4-Turbo. The source code is available on https://stevetantan.github.io/HydraRAG/.

Rotary Position Embedding (RoPE) has shown strong performance in text-based Large Language Models (LLMs), but extending it to video remains a challenge due to the intricate spatiotemporal structure of video frames. Existing adaptations, such as RoPE-3D, attempt to encode spatial and temporal dimensions separately but suffer from two major limitations: positional bias in attention distribution and disruptions in video-text transitions. To overcome these issues, we propose Video Rotary Position Embedding (VRoPE), a novel positional encoding method tailored for Video-LLMs. Specifically, we introduce a more balanced encoding strategy that mitigates attention biases, ensuring a more uniform distribution of spatial focus. Additionally, our approach restructures positional indices to ensure a smooth transition between video and text tokens. Extensive experiments on different models demonstrate that VRoPE consistently outperforms previous RoPE variants, achieving significant improvements in video understanding, temporal reasoning, and retrieval tasks. Code is available at https://github.com/johncaged/VRoPE.

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SciNLP: A Domain-Specific Benchmark for Full-Text Scientific Entity and Relation Extraction in NLP
Decheng Duan | Jitong Peng | Yingyi Zhang | Chengzhi Zhang

Structured information extraction from scientific literature is crucial for capturing core concepts and emerging trends in specialized fields. While existing datasets aid model development, most focus on specific publication sections due to domain complexity and the high cost of annotating scientific texts. To address this limitation, we introduce SciNLP—a specialized benchmark for full-text entity and relation extraction in the Natural Language Processing (NLP) domain. The dataset comprises 60 manually annotated full-text NLP publications, covering 7,072 entities and 1,826 relations. Compared to existing research, SciNLP is the first dataset providing full-text annotations of entities and their relationships in the NLP domain. To validate the effectiveness of SciNLP, we conducted comparative experiments with similar datasets and evaluated the performance of state-of-the-art supervised models on this dataset. Results reveal varying extraction capabilities of existing models across academic texts of different lengths. Cross-comparisons with existing datasets show that SciNLP achieves significant performance improvements on certain baseline models. Using models trained on SciNLP, we implemented automatic construction of a fine-grained knowledge graph for the NLP domain. Our KG has an average node degree of 3.2 per entity, indicating rich semantic topological information that enhances downstream applications. The dataset is publicly available at: https://github.com/AKADDC/SciNLP.

Lifelong model editing aims to dynamically adjust a model’s output with respect to specific facts, knowledge points, or behaviors, enabling the model to adapt to the ever-changing demands of the real world without requiring retraining. While some retrieval-based methods have demonstrated potential in lifelong editing scenarios by storing edited knowledge in external memory, they often suffer from limitations in usability, such as requiring additional training corpora or lacking support for reversible and detachable edits.To address these issues, we propose a plug-and-play method for knowledge retrieval and storage, i.e., Layer-Level Prompting (LLP), which enables seamless and efficient lifelong model editing. In our LLP framework, the reasoning process of LLMs is divided into two stages, respectively knowledge retrieval (Think) and knowledge injection(Recall). Specifically, the knowledge retrieval process is performed in the early layers of the model. Based on the retrieved information, the model is guided to access the updated knowledge stored in the subsequent layer to complete the knowledge editing process. Experimental results demonstrate that our method consistently outperforms existing techniques on lifelong model editing tasks, achieving superior performance on question answering and hallucination benchmarks across different LLMs.

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SPIRIT: Patching Speech Language Models against Jailbreak Attacks
Amirbek Djanibekov | Nurdaulet Mukhituly | Kentaro Inui | Hanan Aldarmaki | Nils Lukas

Speech Language Models (SLMs) enable natural interactions via spoken instructions, which more effectively capture user intent by detecting nuances in speech. The richer speech signal introduces new security risks compared to text-based models, as adversaries can better bypass safety mechanisms by injecting imperceptible noise to speech. We analyze adversarial attacks under white-box access and find that SLMs are substantially more vulnerable to jailbreak attacks, which can achieve a perfect 100% attack success rate in some instances. To improve security, we propose post-hoc patching defenses used to intervene during inference by modifying the SLM’s activations that improve robustness up to 99% with (i) negligible impact on utility and (ii) without any re-training. We conduct ablation studies to maximize the efficacy of our defenses and improve the utility/security trade-off, validated with large-scale benchmarks unique to SLMs.

Selecting high-quality data can improve the pretraining efficiency of large language models (LLMs). Existing methods generally rely on heuristic techniques or single quality signals, limiting their ability to evaluate data quality comprehensively. In this work, we propose FIRE, a flexible and scalable framework for integrating multiple data quality raters, which allows for a comprehensive assessment of data quality across various dimensions. FIRE aligns multiple quality signals into a unified space, and integrates diverse data quality raters to provide a comprehensive quality signal for each data point. Further, we introduce a progressive data selection scheme based on FIRE that iteratively refines the selection of high-quality data points. Extensive experiments show that FIRE outperforms other data selection methods and significantly boosts pretrained model performance across a wide range of downstream tasks, while requiring less than 37.5% tokens needed by the Random baseline to reach the target performance.

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Multi-Domain Explainability of Preferences
Nitay Calderon | Liat Ein-Dor | Roi Reichart

Preference mechanisms, such as human preference, LLM-as-a-Judge (LaaJ), and reward models, are central to aligning and evaluating large language models (LLMs). Yet, the underlying concepts that drive these preferences remain poorly understood. In this work, we propose a fully automated method for generating local and global concept-based explanations of preferences across multiple domains. Our method utilizes an LLM to identify concepts (rubrics) that distinguish between chosen and rejected responses, and to represent them with concept-based vectors. To model the relationships between concepts and preferences, we propose a white-box Hierarchical Multi-Domain Regression model that captures both domain-general and domain-specific effects. To evaluate our method, we curate a dataset spanning eight diverse domains and explain twelve mechanisms. Our method achieves strong preference prediction performance, outperforming baselines while also being explainable. Additionally, we assess explanations in two application-driven settings. First, guiding LLM outputs with concepts from LaaJ explanations yields responses that those judges consistently prefer. Second, prompting LaaJs with concepts explaining humans improves their preference predictions. Together, our work establishes a new paradigm for explainability in the era of LLMs.

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Tuning Less, Prompting More: In-Context Preference Learning Pipeline for Natural Language Transformation
Shuyun Yang | Yan Zhang | Zhengmao Ye | Lei Duan | Mingjie Tang

Natural language transformation (NLT) tasks, such as machine translation (MT) and text style transfer (TST), require models to generate accurate and contextually appropriate outputs. However, existing approaches face significant challenges, including the computational costs of leveraging large pre-trained models and the limited generalization ability of fine-tuned smaller models. In this paper, we propose a novel framework that combines the flexibility of prompting with the cost-effectiveness of fine-tuning. Our method enhances smaller models by integrating In-Context Examples (ICE) from retrieval, enabling the model to better capture contextual information and align with user-level preferences. We further improve performance through hierarchical contrastive learning and dynamic preference inference mechanisms. Experimental results demonstrate that our approach outperforms existing methods, such as Supervised Fine Tuning (SFT), Direct Preference Optimization (DPO), and Contrastive Preference Optimization (CPO), across both MT and TST tasks, providing a more efficient solution for resource-constrained environments.

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IL-PCSR: Legal Corpus for Prior Case and Statute Retrieval
Shounak Paul | Dhananjay Ghumare | Pawan Goyal | Saptarshi Ghosh | Ashutosh Modi

Identifying/retrieving relevant statutes and prior cases/precedents for a given legal situation are common tasks exercised by law practitioners. Researchers till date have addressed the two tasks independently, thus developing completely different datasets and models for each task; however, both retrieval tasks are inherently related, e.g., similar cases tend to cite similar statutes (due to similar factual situation). In this paper, we address this gap. We propose IL-PCSR (Indian Legal corpus for Prior Case and Statute Retrieval), which is a unique corpus that provides a common testbed for developing models for both the tasks (Statute Retrieval and Precedent Retrieval) that can exploit the dependence between the two. We experiment extensively with several baseline models on the tasks, including lexical models, semantic models and ensemble based on GNNs. Further, to exploit the dependence between the two tasks, we develop an LLM based re-ranking approach that gives the best performance.

We introduce ESGenius, a comprehensive benchmark for evaluating and enhancing the proficiency of Large Language Models (LLMs) in Environmental, Social, and Governance (ESG) and sustainability-focused question answering. ESGenius comprises two key components: (i) ESGenius-QA, a collection of 1,136 Multiple-Choice Questions (MCQs) generated by LLMs and rigorously validated by domain experts, covering a broad range of ESG pillars and sustainability topics. Each question is systematically linked to its corresponding source text, enabling transparent evaluation and supporting Retrieval-Augmented Generation (RAG) methods; and (ii) ESGenius-Corpus, a meticulously curated repository of 231 foundational frameworks, standards, reports, and recommendation documents from 7 authoritative sources. Moreover, to fully assess the capabilities and adaptation potential of LLMs, we implement a rigorous two-stage evaluation protocol—Zero-Shot and RAG. Extensive experiments across 50 LLMs (0.5B to 671B) demonstrate that state-of-the-art models achieve only moderate performance in zero-shot settings, with accuracies around 55–70%, highlighting a significant knowledge gap for LLMs in this specialized, interdisciplinary domain. However, models employing RAG demonstrate significant performance improvements, particularly for smaller models. For example, DeepSeek-R1-Distill-Qwen-14B improves from 63.82% (zero-shot) to 80.46% with RAG. These results demonstrate the necessity of grounding responses in authoritative sources for enhanced ESG understanding. To the best of our knowledge, ESGenius is the first comprehensive QA benchmark designed to rigorously evaluate LLMs on ESG and sustainability knowledge, providing a critical tool to advance trustworthy AI in this vital domain.

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How Sememic Components Can Benefit Link Prediction for Lexico-Semantic Knowledge Graphs?
Hansi Wang | Yue Wang | Qiliang Liang | Yang Liu

Link Prediction (LP) aims to predict missing triple information within a Knowledge Graph (KG). Existing LP methods have sought to improve the performance by integrating structural and textual information. However, for lexico-semantic KGs designed to document fine-grained sense distinctions, these types of information may not be sufficient to support effective LP. From a linguistic perspective, word senses within lexico-semantic relations usually show systematic differences in their sememic components. In light of this, we are motivated to enhance LP with sememe knowledge. We first construct a Sememe Prediction (SP) dataset, SememeDef, for learning such knowledge, and two Chinese datasets, HN7 and CWN5, for LP evaluation; Then, we propose a method, SememeLP, to leverage this knowledge for LP fully. It consistently and significantly improves the LP performance in both English and Chinese, achieving SOTA MRR of 75.1%, 80.5%, and 77.1% on WN18RR, HN7, and CWN5, respectively; Finally, an in-depth analysis is conducted, making clear how sememic components can benefit LP for lexico-semantic KGs, which provides promising progress for the completion of them.

Multimodal Large Language Models (MLLMs) have shown promise in visual-textual reasoning, with Multimodal Chain-of-Thought (MCoT) prompting significantly enhancing interpretability. However, existing MCoT methods rely on rationale-rich datasets and largely focus on inter-object reasoning, overlooking the intra-object understanding crucial for image classification. To address this gap, we propose WISE, a Weak-supervision-guided Step-by-step Explanation method that augments any image classification dataset with MCoTs by reformulating the concept-based representations from Concept Bottleneck Models (CBMs) into concise, interpretable reasoning chains under weak supervision. Experiments across ten datasets show that our generated MCoTs not only improve interpretability by 37% but also lead to gains in classification accuracy when used to fine-tune MLLMs. Our work bridges concept-based interpretability and generative MCoT reasoning, providing a generalizable framework for enhancing MLLMs in fine-grained visual understanding.

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Calibration Across Layers: Understanding Calibration Evolution in LLMs
Abhinav Joshi | Areeb Ahmad | Ashutosh Modi

Large Language Models (LLMs) have demonstrated inherent calibration capabilities, where predicted probabilities align well with correctness, despite prior findings that deep neural networks are often overconfident. Recent studies have linked this behavior to specific components in the final layer, such as entropy neurons and the unembedding matrix’s null space. In this work, we provide a complementary perspective by investigating how calibration evolves throughout the network’s depth. Analyzing multiple open-weight models on the MMLU benchmark, we uncover a distinct confidence correction phase in the upper/later layers, where model confidence is actively recalibrated after decision certainty has been reached. Furthermore, we identify a low-dimensional calibration direction in the residual stream whose perturbation significantly improves calibration metrics (ECE and MCE) without harming accuracy. Our findings suggest that calibration is a distributed phenomenon, shaped throughout the network’s forward pass, not just in its final projection, providing new insights into how confidence-regulating mechanisms operate within LLMs.

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The discordance between embedded ethics and cultural inference in large language models
Aida Ramezani | Yang Xu

Effective interactions between artificial intelligence (AI) and humans require an equitable and accurate representation of diverse cultures. It is known that current AI, particularly large language models (LLMs), possesses some degrees of cultural knowledge but not without limitations. We present a framework aimed at understanding the origin of these limitations. We hypothesize that there is a fundamental discordance between embedded ethics—how LLMs represent right versus wrong, and cultural inference—how LLMs infer cultural knowledge, specifically cultural norms. We demonstrate this by extracting low-dimensional subspaces that embed ethical principles of LLMs based on established benchmarks. We then show that how LLMs make errors in culturally distinctive scenarios significantly correlates with how they represent cultural norms with respect to these embedded ethics subspaces. Furthermore, we show that coercing cultural norms to be more aligned with the embedded ethics increases LLM performance in cultural inference. Our analyses of 12 language models, two large-scale cultural benchmarks spanning 75 countries and two ethical datasets indicate that 1) the ethics-culture discordance tends to be exacerbated in instruct-tuned models, and 2) how current LLMs represent ethics can impose limitations on their adaptation to diverse cultures particularly pertaining to non-Western and low-income regions.

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SSA: Semantic Contamination of LLM-Driven Fake News Detection
Cheng Xu | Nan Yan | Shuhao Guan | Yuke Mei | Tahar Kechadi

Benchmark data contamination (BDC) silently inflate the evaluation performance of large language models (LLMs), yet current work on BDC has centered on direct token overlap (data/label level), leaving the subtler and equally harmful semantic level BDC largely unexplored. This gap is critical in fake news detection task, where prior exposure to semantic BDC lets a model “remember” the answer instead of reasoning. In this work, (1) we are the first to formally define semantic contamination for this task and (2) introduce the Semantic Sensitivity Amplifier (SSA), a lightweight, model-agnostic framework that detects BDC risks across semantic to label level via an entity shift perturbation and a comprehensive interpretable metric, the SSA Factor. Evaluating 45 variants of nine LLMs (0.5B–72B parameters) across four BDC levels, we find LIAR2 accuracy climbs monotonically with injected contamination, while the SSA Factor escalates in near-perfect lock-step (r≥.97, for models ≥3B, p<.05; 𝜌 ≥.9 overall, p<.05). These results show that SSA provides a sensitive and scalable audit of comprehensive BDC risk and paves the way for a more integrity evaluation of the LLM-driven fake news detection task.

In recent years, developing compact and efficient large language models (LLMs) has emerged as a thriving area of research. However, traditional Supervised Fine-Tuning (SFT), which relies on singular ground truth labels, often fails to capture token-level dependencies and linguistic diversity. To address these limitations, we propose a logits-based fine-tuning framework that integrates the strengths of supervised learning and knowledge distillation. Our approach constructs enriched training targets by combining teacher logits with ground truth labels, preserving both correctness and linguistic diversity. This ensures more reliable and effective training. To validate our approach, we constructed a large-scale 1.2M logits dataset and trained a series of science-focused models. Experimental results demonstrate that our method achieves significant improvements over current SOTA, with accuracy gains of 18% on Mawps and 22.7% on TabMWP. Across nine widely used mathematical benchmarks, our method consistently outperforms prior SFT models, achieving an average improvement of 7.28%. All code and datasets will be open-sourced.

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STARE at the Structure: Steering ICL Exemplar Selection with Structural Alignment
Jiaqian Li | Qisheng Hu | Jing Li | Wenya Wang

In-Context Learning (ICL) has become a powerful paradigm that enables LLMs to perform a wide range of tasks without task-specific fine-tuning. However, the effectiveness of ICL heavily depends on the quality of exemplar selection. In particular, for structured prediction tasks such as semantic parsing, existing ICL selection strategies often overlook structural alignment, leading to suboptimal performance and poor generalization. To address this issue, we propose a novel two-stage exemplar selection strategy that achieves a strong balance between efficiency, generalizability, and performance. First, we fine-tune a BERT-based retriever using structure-aware supervision, guiding it to select exemplars that are both semantically relevant and structurally aligned. Then, we enhance the retriever with a plug-in module, which amplifies syntactically meaningful information in the hidden representations. This plug-in is model-agnostic, requires minimal overhead, and can be seamlessly integrated into existing pipelines. Experiments on four benchmarks spanning three semantic parsing tasks demonstrate that our method consistently outperforms existing baselines with multiple recent LLMs as inference-time models.

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PPC-GPT: Federated Task-Specific Compression of Large Language Models via Pruning and Chain-of-Thought Distillation
Tao Fan | Guoqiang Ma | Yuanfeng Song | Lixin Fan | Qiang Yang

Compressing Large Language Models (LLMs) into task-specific Small Language Models (SLMs) encounters two significant challenges: safeguarding domain-specific knowledge privacy and managing limited resources. To tackle these challenges, we propose PPC-GPT, a novel unified framework that systematically addresses both privacy preservation and model compression in federated settings. PPC-GPT works on a server-client federated architecture, where the client sends differentially private (DP) perturbed task-specific data to the server’s LLM. The LLM then generates synthetic data along with their corresponding rationales. This synthetic data is subsequently used for both LLM pruning and retraining processes. Our framework’s key innovation lies in its holistic integration of privacy-preserving mechanisms, synthetic data generation, and task-specific compression techniques, creating unique benefits through component interaction. Our experiments across diverse text generation tasks demonstrate that PPC-GPT successfully achieves dual objectives: maintaining competitive performance comparable to full-sized LLMs while ensuring robust privacy protection through its federated architecture. Our code has been contributed to the FATE open-source project and is now publicly accessible at https://github.com/FederatedAI/FATE-LLM/tree/main/python/fate_llm/algo/ppc-gpt

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Efficient Beam Search for Large Language Models Using Trie-Based Decoding
Brian J Chan | Mao-xun Huang | Jui-Hung Cheng | Chao-Ting Chen | Hen-Hsen Huang

This work presents a novel trie (prefix-tree)-based parallel decoding method that addresses the memory inefficiency of batch-based beam search. By sharing a single KV cache across beams with common prefixes, our approach dramatically reduces memory usage and enables efficient decoding. We evaluated our method across three attention architectures, Multi-Head Attention (Phi-3.5-mini-instruct), Grouped Query Attention (Llama-3.1-8B-Instruct), and Sliding Window Attention (Mistral-Small-24B-Instruct-2501), using CNN/DailyMail for abstractive summarization and HumanEval for code generation. Our experiments demonstrate substantial memory savings (4–8×) and up to 2.4× faster decoding, without compromising generation quality. These results highlight our method’s suitability for memory-constrained environments and large-scale deployments.

Code-mixed text—where multiple languages are used within the same utterance—is increasingly common in both spoken and written communication. However, it presents significant challenges for machine learning models due to the interplay of distinct grammatical structures, effectively forming a hybrid language. While fine-tuning large language models (LLMs) such as GPT-3, or Llama-3 on code-mixed data has led to performance improvements, these models still lag behind their monolingual counterparts and incur high computational costs due to the large number of trainable parameters.In this paper, we focus on the task of sentiment detection in code-mixed text and propose a Hybrid Language Model (HLM) that combines a multilingual encoder (e.g., mBERT) with a lightweight decoder (e.g., Sarvam-1) (3B parameters). Despite having significantly fewer trainable parameters, HLM achieves sentiment classification performance comparable to that of fine-tuned Large Language Models (LLMs) (> 7B parameters). Furthermore, our results demonstrate that HLM significantly outperforms models trained individually, underscoring its effectiveness for low-resource, code-mixed sentiment analysis.

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Evaluating Taxonomy Free Character Role Labeling (TF-CRL) in News Stories using Large Language Models
David G Hobson | Derek Ruths | Andrew Piper

We introduce Taxonomy-Free Character Role Labeling (TF-CRL); a novel task that assigns open-ended narrative role labels to characters in news stories based on their functional role in the narrative. Unlike fixed taxonomies, TF-CRL enables more nuanced and comparative analysis by generating compositional labels (e.g., Resilient Leader, Scapegoated Visionary). We evaluate several large language models (LLMs) on this task using human preference rankings and ratings across four criteria: faithfulness, relevance, informativeness, and generalizability. LLMs almost uniformly outperform human annotators across all dimensions. We further show how TF-CRL supports rich narrative analysis by revealing novel latent taxonomies and enabling cross-domain narrative comparisons. Our approach offers new tools for studying media portrayals, character framing, and the socio-political impacts of narrative roles at-scale.

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MIRROR: Multimodal Cognitive Reframing Therapy for Rolling with Resistance
Subin Kim | Hoonrae Kim | Jihyun Lee | Yejin Jeon | Gary Lee

Recent studies have explored the use of large language models (LLMs) in psychotherapy; however, text-based cognitive behavioral therapy (CBT) models often struggle with client resistance, which can weaken therapeutic alliance. To address this, we propose a multimodal approach that incorporates nonverbal cues, which allows the AI therapist to better align its responses with the client’s negative emotional state.Specifically, we introduce a new synthetic dataset, Mirror (Multimodal Interactive Rolling with Resistance), which is a novel synthetic dataset that pairs each client’s statements with corresponding facial images. Using this dataset, we train baseline vision language models (VLMs) so that they can analyze facial cues, infer emotions, and generate empathetic responses to effectively manage client resistance.These models are then evaluated in terms of both their counseling skills as a therapist, and the strength of therapeutic alliance in the presence of client resistance. Our results demonstrate that Mirror significantly enhances the AI therapist’s ability to handle resistance, which outperforms existing text-based CBT approaches.Human expert evaluations further confirm the effectiveness of our approach in managing client resistance and fostering therapeutic alliance.

Although large language models have enhanced automated travel planning abilities, current systems remain misaligned with real-world scenarios. First, they assume users provide explicit queries, while in reality requirements are often implicit. Second, existing solutions ignore diverse environmental factors and user preferences, limiting the feasibility of plans. Third, systems can only generate plans with basic POI arrangements, failing to provide all-in-one plans with rich details. To mitigate these challenges, we construct a novel dataset RETAIL, which supports decision-making for implicit queries while covering explicit queries, both with and without revision needs. It also enables environmental awareness to ensure plan feasibility under real-world scenarios, while incorporating detailed POI information for all-in-one travel plans. Furthermore, we propose a topic-guided multi-agent framework, termed TGMA. Our experiments reveal that even the strongest existing model achieves merely a 1.0% pass rate, indicating real-world travel planning remains extremely challenging. In contrast, TGMA demonstrates substantially improved performance 2.72%, offering promising directions for real-world travel planning.

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Unraveling Interwoven Roles of Large Language Models in Authorship Privacy: Obfuscation, Mimicking, and Verification
Tuc Nguyen | Yifan Hu | Thai Le

Recent advancements in large language models (LLMs) have been fueled by large-scale training corpora drawn from diverse sources such as websites, news articles, and books. These datasets often contain explicit user information, such as person names, addresses, that LLMs may unintentionally reproduce in their generated outputs. Beyond such explicit content, LLMs can also leak identity-revealing cues through implicit signals such as distinctive writing styles, raising significant concerns about authorship privacy. There are three major automated tasks in authorship privacy, namely authorship obfuscation (AO), authorship mimicking (AM), and authorship verification (AV). Prior research has studied AO, AM, and AV independently. However, their interplays remain under-explored, which leaves a major research gap, especially in the era of LLMs, where they are profoundly shaping how we curate and share user-generated content, and the distinction between machine‐generated and human‐authored text is also increasingly blurred. This work then presents the first unified framework for analyzing the dynamic relationships among LLM-enabled AO, AM, and AV in the context of authorship privacy. We quantify how they interact with each other to transform human‐authored text, examining effects at a single point in time and iteratively over time. We also examine the role of demographic metadata, such as gender, academic background, in modulating their performances, inter-task dynamics, and privacy risks. The code is available at https://github.com/nguyentuc/authorship_privacy.

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Reward Model Perspectives: Whose Opinions Do Reward Models Reward?
Elle

Reward models (RMs) are central to the alignment of language models (LMs). An RM often serves as a proxy for human preferences to guide downstream LM behavior. However, our understanding of RM behavior is limited. Our work (i) formalizes a framework for measuring the alignment of opinions captured by RMs, (ii) investigates the extent to which RMs demonstrate sociodemographic biases, and (iii) explores the effects of prompting to steer rewards towards the preferences of a target group. We study the subjective and diverse perspectives on controversial topics, which allows us to quantify RM perspectives in terms of their opinions, attitudes, and values. We show that RMs are poorly aligned with several demographic groups and can systematically reward harmful stereotypes, and steering alone is not enough to overcome these limitations. Our findings underscore the need for more careful consideration of RM behavior in model alignment during preference learning to prevent the propagation of unwanted social biases in the language technologies that we use.

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FLRC: Fine-grained Low-Rank Compressor for Efficient LLM Inference
Yu-Chen Lu | Chong-Yan Chen | Chi-Chih Chang | Yu-Fang Hu | Kai-Chiang Wu

Although large language models (LLM) have achieved remarkable performance, their enormous parameter counts hinder deployment on resource-constrained hardware. Low-rank compression can reduce both memory usage and computational demand, but applying a uniform compression ratio across all layers often leads to significant performance degradation, and previous methods perform poorly during decoding. To address these issues, we propose the Fine-grained Low-Rank Compressor (FLRC), which efficiently determines an optimal rank allocation for each layer, and incorporates progressive low-rank decoding to maintain text generation quality. Comprehensive experiments on diverse benchmarks demonstrate the superiority of FLRC, achieving up to a 17% improvement in ROUGE-L on summarization tasks compared to state-of-the-art low-rank compression methods, establishing a more robust and efficient framework to improve LLM inference.

Most multilingual question-answering benchmarks, while covering a diverse pool of languages, do not factor in regional diversity in the information they capture and tend to be Western-centric. This introduces a significant gap in fairly evaluating multilingual models’ comprehension of factual information from diverse geographical locations. To address this, we introduce XNationQA for investigating the cultural literacy of multilingual LLMs. XNationQA encompasses a total of 49,280 questions on the geography, culture, and history of nine countries, presented in seven languages. We benchmark eight standard multilingual LLMs on XNationQA and evaluate them using two novel transference metrics. Our analyses uncover a considerable discrepancy in the models’ accessibility to culturally specific facts across languages. Notably, we often find that a model demonstrates greater knowledge of cultural information in English than in the dominant language of the respective culture. The models exhibit better performance in Western languages, although this does not necessarily translate to being more literate for Western countries, which is counterintuitive. Furthermore, we observe that models have a very limited ability to transfer knowledge across languages, particularly evident in open-source models.

Information retrieval in specialized domains (e.g., legal and medical) faces challenges in aligning user queries, often expressed in colloquial language, with highly structured, terminology-rich documents. This discrepancy creates a distribution gap in the text representation. Recent methods aim to enhance queries by generating intermediary elements (e.g., keywords, pseudo-documents) before performing retrieval with large language models (LLMs). However, by treating LLMs and retrievers separately, these approaches risk producing unreliable or irrelevant intermediaries, which can significantly degrade retrieval performance. To address this issue, we propose CoEvo, an alternating optimization framework that facilitates the coevolution of LLMs and retrieval models. CoEvo operates through two key steps: L-step directs the LLM in generating intermediaries by leveraging an archive of historical examples known to enhance retrieval. R-step trains the retriever using contrastive learning on the intermediaries produced by the LLM. Finally, we evaluate and flexibly leverage content generated by the LLM to amplify the effectiveness of coevolution. Experimental results demonstrate significant improvements in retrieval performance across both legal and medical domains.

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Conan-Embedding-v2: Training an LLM from Scratch for Text Embeddings
Shiyu Li | Yang Tang | Ruijie Liu | Shi-Zhe Chen | Xi Chen

Large language models (LLMs) have recently demonstrated excellent performance in text embedding tasks. Previous work usually use LoRA to fine-tune existing LLMs, which are limited by the data and training gap between LLMs and embedding models. In this work, we introduce Conan-embedding-v2, a new 1.4B-parameter LLM trained from scratch and fine-tuned as a text embedder. First, we add news data and multilingual pairs for LLM pretraining to bridge the data gap. Based on this, we propose a cross-lingual retrieval dataset that enables the LLM to better integrate embeddings across different languages. Second, whereas LLMs use a causal mask with token-level loss, embedding models use a bidirectional mask with sentence-level loss. This training gap makes full fine-tuning less effective than LoRA. We introduce a soft-masking mechanism to gradually transition between these two types of masks, enabling the model to learn more comprehensive representations. Based on this, we propose a dynamic hard negative mining method that exposes the model to more difficult negative examples throughout the training process. Being intuitive and effective, with only approximately 1.4B parameters, Conan-embedding-v2 achieves SOTA performance on both the Massive Text Embedding Benchmark (MTEB) and Chinese MTEB (May 19, 2025).

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Vision-and-Language Navigation with Analogical Textual Descriptions in LLMs
Yue Zhang | Tianyi Ma | Zun Wang | Yanyuan Qiao | Parisa Kordjamshidi

Integrating large language models (LLMs) into embodied AI models is becoming increasingly prevalent. However, existing zero-shot LLM-based Vision-and-Language Navigation (VLN) agents either encode images as textual scene descriptions, potentially oversimplifying visual details, or process raw image inputs, which can fail to capture abstract semantics required for high-level reasoning. In this paper, we improve the navigation agent’s contextual understanding by incorporating textual descriptions that facilitate analogical reasoning across images from multiple perspectives. By leveraging text-based analogical reasoning, the agent enhances its global scene understanding and spatial reasoning, leading to more accurate action decisions. We evaluate our approach on the R2R dataset, where our experiments demonstrate significant improvements in navigation performance.

Multimodal Large Language Models (MLLMs) have demonstrated significant advances across numerous vision-language tasks. Due to their strong performance in image-text alignment, MLLMs can effectively understand image-text pairs with clear meanings. However, effectively resolving the inherent ambiguities in natural language and visual contexts remains challenging. Existing multimodal benchmarks typically overlook linguistic and visual ambiguities, relying mainly on unimodal context for disambiguation and thus failing to exploit the mutual clarification potential between modalities. To bridge this gap, we introduce MUCAR, a novel and challenging benchmark designed explicitly for evaluating multimodal ambiguity resolution across multilingual and cross-modal scenarios. MUCAR includes: (1) a multilingual dataset where ambiguous textual expressions are uniquely resolved by corresponding visual contexts, and (2) a dual-ambiguity dataset that systematically pairs ambiguous images with ambiguous textual contexts, with each combination carefully constructed to yield a single, clear interpretation through mutual disambiguation. Extensive evaluations involving 19 state-of-the-art multimodal models—encompassing both open-source and proprietary architectures—reveal substantial gaps compared to human-level performance, highlighting the need for future research into more sophisticated cross-modal ambiguity comprehension methods, further pushing the boundaries of multimodal reasoning.

Humans acquire syntactic constructions like filler-gap dependencies from limited and often noisy input. Can neural language models do the same? We investigate this question by evaluating GPT-2 models trained on child-oriented input from the BabyLM Challenge. Our experiments focus on whether these “baby” language models acquire filler-gap dependencies, generalize across constructions, and respect structural constraints such as island effects. We apply a suite of syntactic constructions to four models trained on child language, including two base models (trained on 10M and 100M tokens) and two well-performing models from the BabyLM Challenge (ConcreteGPT and BabbleGPT). We evaluate model behavior using wh-licensing scores, flip tests, and grammaticality contrasts across four constructions. Results show that BabyLM-scale models partially acquire filler-gap dependencies but often fail to generalize or fully capture island constraints.

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Paths Not Taken: Understanding and Mending the Multilingual Factual Recall Pipeline
Meng Lu | Ruochen Zhang | Carsten Eickhoff | Ellie Pavlick

Multilingual large language models (LLMs) often exhibit factual inconsistencies across languages, usually with better performance in factual recall tasks in high-resource languages than in other languages. The causes of these failures, however, remain poorly understood. Using mechanistic analysis techniques, we uncover the underlying pipeline that LLMs employ, which involves using the English-centric factual recall mechanism to process multilingual queries and then translating English answers back into the target language. We identify two primary sources of error: insufficient engagement of the reliable English-centric mechanism for factual recall, and incorrect translation from English back into the target language for the final answer. To address these vulnerabilities, we introduce two vector interventions, both independent of languages and datasets, to redirect the model toward better internal paths for higher factual consistency. Our interventions combined increase the recall accuracy by over 35 percent for the lowest-performing language. Our findings demonstrate how mechanistic insights can be used to unlock latent multilingual capabilities in LLMs.

Sentiment Analysis (SA) models harbor inherent social biases that can be harmful in real-world applications. These biases are identified by examining the output of SA models for sentences that only vary in the identity groups of the subjects.Constructing natural, linguistically rich, relevant, and diverse sets of sentences that provide sufficient coverage over the domain is expensive, especially when addressing a wide range of biases: it requires domain experts and/or crowd-sourcing. In this paper, we present a novel bias testing framework, BTC-SAM, which generates high-quality test cases for bias testing in SA models with minimal specification using Large Language Models (LLMs) for the controllable generation of test sentences. Our experiments show that relying on LLMs can provide high linguistic variation and diversity in the test sentences, thereby offering better test coverage compared to base prompting methods even for previously unseen biases.

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Debate-to-Detect: Reformulating Misinformation Detection as a Real-World Debate with Large Language Models
Chen Han | Wenzhen Zheng | Xijin Tang

The proliferation of misinformation in digital platforms reveals the limitations of traditional detection methods, which mostly rely on static classification and fail to capture the intricate process of real-world fact-checking. Despite advancements in Large Language Models (LLMs) that enhance automated reasoning, their application to misinformation detection remains hindered by issues of logical inconsistency and superficial verification. Inspired by the idea that “Truth Becomes Clearer Through Debate”, we introduce Debate-to-Detect (D2D), a novel Multi-Agent Debate (MAD) framework that reformulates misinformation detection as a structured adversarial debate. Based on fact-checking workflows, D2D assigns domain-specific profiles to each agent and orchestrates a five-stage debate process, including Opening Statement, Rebuttal, Free Debate, Closing Statement, and Judgment. To transcend traditional binary classification, D2D introduces a multi-dimensional evaluation mechanism that assesses each claim across five distinct dimensions: Factuality, Source Reliability, Reasoning Quality, Clarity, and Ethics. Experiments with GPT-4o on two fakenews datasets demonstrate significant improvements over baseline methods, and the case study highlight D2D’s capability to iteratively refine evidence while improving decision transparency, representing a substantial advancement towards robust and interpretable misinformation detection. Our code is available at https://github.com/hanshenmesen/Debate-to-Detect

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Controllable Memorization in LLMs via Weight Pruning
Chenjie Ni | Zhepeng Wang | Runxue Bao | Shangqian Gao | Yanfu Zhang

The evolution of pre-trained large language models (LLMs) has significantly transformed natural language processing. However, these advancements pose challenges, particularly the unintended memorization of training data, which raises ethical and privacy concerns. While prior research has largely focused on mitigating memorization or extracting memorized information, the deliberate control of memorization has been underexplored. This study addresses this gap by introducing a novel and unified gradient-based weight pruning framework to freely control memorization rates in LLMs. Our method enables fine-grained control over pruning parameters, allowing models to suppress or enhance memorization based on application-specific requirements. Experimental results demonstrate that our approach effectively balances the trade-offs between memorization and generalization, with an increase of up to 89.3% in Fractional ER suppression and 40.9% in Exact ER amplification compared to the original models.

The availability of suitable learner corpora is crucial for studying second language acquisition (SLA) and language transfer. However, curating such corpora is challenging, as high-quality learner data is rarely publicly available. As a result, only a few learner corpora, such as ICLE and TOEFL-11, are accessible to the research community.To address this gap, we present Anonymous, a novel English learner corpus with longitudinal data. The corpus consists of 687 texts written by adult learners taking English as a second language courses in the USA. These learners are either preparing for university admission or enhancing their language proficiency while beginning their university studies. Unlike most learner corpora, Anonymous includes longitudinal data, allowing researchers to explore language learning trajectories over time. The corpus features contributions from speakers of 15 different L1s.We demonstrate the utility of Anonymous through two case studies at the intersection of SLA and Computational Linguistics: (1) Native Language Identification (NLI), and (2) a quantitative and qualitative analysis of linguistic features influenced by L1 using large language models

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DCIS: Efficient Length Extrapolation of LLMs via Divide-and-Conquer Scaling Factor Search
Lei Yang | Shaoyang Xu | Jianxiang Peng | Shaolin Zhu | Deyi Xiong

Large language models (LLMs) based on the Transformer architecture usually have their context length limited due to the high training cost. Recent advancements extend the context window by adjusting the scaling factors of RoPE and fine-tuning. However, suboptimal initialization of these factors results in increased fine-tuning costs and reduced performance at target length. To address these challenges, we propose a novel RoPE-based fine-tuning framework that diverges from conventional scaling factors search. Specifically, we present a Divide-and-Conquer Incremental Search (DCIS) algorithm that strategically determines the better scaling factors. Further fine-tuning with the identified scaling factors effectively extends the context window of LLMs. Empirical results demonstrate that our methodology not only mitigates performance decay at extended target lengths but also allows the model to fine-tune on short contexts and generalize to long contexts, thereby reducing the cost of fine-tuning. The scaling factors obtained through DCIS can even perform effectively without fine-tuning. Further analysis of the search space reveals that DCIS achieves twice the search efficiency compared to other methods. We also examine the impact of the non-strictly increasing scaling factors utilized in DCIS and evaluate the general capabilities of LLMs across various context lengths.

Multimodal Retrieval-Augmented Generation (RAG) systems have become essential in knowledge-intensive and open-domain tasks. As retrieval complexity increases, ensuring the robustness of these systems is critical. However, current RAG models are highly sensitive to the order in which evidence is presented, often resulting in unstable performance and biased reasoning, particularly as the number of retrieved items or modality diversity grows. This raises a central question: How does the position of retrieved evidence affect multimodal RAG performance? To answer this, we present the first comprehensive study of position bias in multimodal RAG systems. Through controlled experiments across text-only, image-only, and mixed-modality tasks, we observe a consistent U-shaped accuracy curve with respect to evidence position. To quantify this bias, we introduce the Position Sensitivity Index (PSI_p) and develop a visualization framework to trace attention allocation patterns across decoder layers. Our results reveal that multimodal interactions intensify position bias compared to unimodal settings, and that this bias increases logarithmically with retrieval range. These findings offer both theoretical and empirical foundations for position-aware analysis in RAG, highlighting the need for evidence reordering or debiasing strategies to build more reliable and equitable generation systems. Our code and experimental resources are available at https://github.com/Theodyy/Multimodal-Rag-Position-Bias.

Language Models (LMs) are primarily evaluated on globally popular sports, often overlooking regional and indigenous sporting traditions. To address this gap, we introduce CultSportQA, a benchmark designed to assess LMs’ understanding of traditional sports across 60 countries and 6 continents, encompassing four distinct cultural categories. The dataset features 33,000 multiple-choice questions (MCQs) across text and image modalities, categorized into primarily three key types: history-based, rule-based, and scenario-based. To evaluate model performance, we employ zero-shot, few-shot, and chain-of-thought (CoT) prompting across a diverse set of Large Language Models (LLMs), Small Language Models (SLMs), and Multimodal Large Language Models (MLMs). By providing a comprehensive multilingual and multicultural sports benchmark, CultSportQA establishes a new standard for assessing AI’s ability to understand and reason about traditional sports. The dataset will be publicly available, fostering research in culturally aware AI systems.

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Multilingual Federated Low-Rank Adaptation for Collaborative Content Anomaly Detection across Multilingual Social Media Participants
Jiaxin Li | Geng Zhao | Xiaoci Zhang

Recently, the rapid development of multilingual social media platforms (SNS) exacerbates new challenges in SNS content anomaly detection due to data islands and linguistic imbalance. While federated learning (FL) and parameter-efficient fine-tuning (PEFT) offer potential solutions in most cases, when every client is multilingual, existing solutions struggle with multilingual heterogeneity: 1) entangled language-specific knowledge during aggregation, 2) noise from minority languages, and 3) unstable cross-platform collaboration. Based on the asymmetric nature of LoRA, we propose MuLA-F, a multilingual Federated LoRA introducing SVD-based language-specific disentanglement of LoRA blocks and a local orthogonal tuning strategy. Evaluations across three SNS content anomaly detection tasks demonstrate MuLA-F’s superiority in multilingual performance while reducing multilingual knowledge conflicts and communication rounds.

With the increasing use of Retrieval-Augmented Generation (RAG), strong retrieval models have become more important than ever. In healthcare, multimodal retrieval models that combine information from both text and images offer major advantages for many downstream tasks such as question answering, cross-modal retrieval, and multimodal summarization, since medical data often includes both formats. However, there is currently no standard benchmark to evaluate how well these models perform in medical settings. To address this gap, we introduce M3Retrieve, a Multimodal Medical Retrieval Benchmark. M3Retrieve spans 5 domains,16 medical fields, and 4 distinct tasks, with over 1.2 Million text documents and 164K multimodal queries, all collected under approved licenses. We evaluate leading multimodal retrieval models on this benchmark to explore the challenges specific to different medical specialities and to understand their impact on retrieval performance. By releasing M3Retrieve, we aim to enable systematic evaluation, foster model innovation, and accelerate research toward building more capable and reliable multimodal retrieval systems for medical applications.

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The Hidden Strength of Disagreement: Unraveling the Consensus-Diversity Tradeoff in Adaptive Multi-Agent Systems
Zengqing Wu | Takayuki Ito

Consensus formation is pivotal in multi-agent systems (MAS), balancing collective coherence with individual diversity. Conventional LLM-based MAS primarily rely on explicit coordination, e.g., prompts or voting, risking premature homogenization. We argue that implicit consensus, where agents exchange information yet independently form decisions via in-context learning, can be more effective in dynamic environments that require long-horizon adaptability. By retaining partial diversity, systems can better explore novel strategies and cope with external shocks. We formalize a consensus-diversity tradeoff, showing conditions where implicit methods outperform explicit ones. Experiments on three scenarios – Dynamic Disaster Response, Information Spread and Manipulation, and Dynamic Public-Goods Provision – confirm partial deviation from group norms boosts exploration, robustness, and performance. We highlight emergent coordination via in-context learning, underscoring the value of preserving diversity for resilient decision-making.

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Friend or Foe? A Computational Investigation of Semantic False Friends across Romance Languages
Ana Sabina Uban | Liviu P Dinu | Ioan-Bogdan Iordache | Simona Georgescu | Claudia Vlad

In this paper we present a comprehensive analysis of lexical semantic divergence between cognate words and borrowings in the Romance languages. We experiment with different algorithms for false friend detection including deceptive cognate and deceptive borrowings and correction and evaluate them systematically on cognate and borrowing pairs in the five Romance languages. We use the most complete and reliable dataset of cognate words based on etymological dictionaries for the five main Romance languages (Italian, Spanish, Portuguese, French and Romanian) to extract deceptive cognates and borrowings automatically based on usage, and freely publish the lexicon of obtained true and deceptive cognate and borrowings in every Romance language pair.

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KLAAD: Refining Attention Mechanisms to Reduce Societal Bias in Generative Language Models
Seorin Kim | Dongyoung Lee | Jaejin Lee

Large language models (LLMs) often exhibit societal biases in their outputs, prompting ethical concerns regarding fairness and harm. In this work, we propose KLAAD (KL-Attention Alignment Debiasing), an attention-based debiasing framework that implicitly aligns attention distributions between stereotypical and anti-stereotypical sentence pairs without directly modifying model weights. KLAAD introduces a composite training objective combining Cross-Entropy, KL divergence, and Triplet losses, guiding the model to consistently attend across biased and unbiased contexts while preserving fluency and coherence. Experimental evaluation of KLAAD demonstrates improved bias mitigation on both the BBQ and BOLD benchmarks, with minimal impact on language modeling quality. The results indicate that attention-level alignment offers a principled solution for mitigating bias in generative language models.

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SeMob: Semantic Synthesis for Dynamic Urban Mobility Prediction
Runfei Chen | Shuyang Jiang | Wei Huang

Human mobility prediction is vital for urban services, but often fails to account for abrupt changes from external events. Existing spatiotemporal models struggle to leverage textual descriptions detailing these events. We propose SeMob, an LLM-powered semantic synthesis pipeline for dynamic mobility prediction. Specifically, SeMob employs a multi-agent framework where LLM-based agents automatically extract and reason about spatiotemporally related text from complex online texts. Fine-grained relevant contexts are then incorporated with spatiotemporal data through our proposed innovative progressive fusion architecture. The rich pre-trained event prior contributes enriched insights about event-driven prediction, and hence results in a more aligned forecasting model. Evaluated on a dataset constructed through our pipeline, SeMob achieves maximal reductions of 13.92% in MAE and 11.12% in RMSE compared to the spatiotemporal model. Notably, the framework exhibits pronounced superiority especially within spatiotemporal regions close to an event’s location and time of occurrence.

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DyePack: Provably Flagging Test Set Contamination in LLMs Using Backdoors
Yize Cheng | Wenxiao Wang | Mazda Moayeri | Soheil Feizi

Open benchmarks are essential for evaluating and advancing large language models, offering reproducibility and transparency. However, their accessibility makes them likely targets of test set contamination. In this work, we introduce **DyePack**, a framework that leverages backdoor attacks to identify models that used benchmark test sets during training, **without requiring access to the loss, logits, or any internal details of the model.** Like how banks mix dye packs with their money to mark robbers, DyePack mixes backdoor samples with the test data to flag models that trained on it. We propose a principled design incorporating multiple backdoors with stochastic targets, **enabling exact false positive rate (FPR) computation when flagging every model.** This provably prevents false accusations while providing strong evidence for every detected case of contamination. We evaluate DyePack on five models across three datasets, covering both multiple-choice and open-ended generation tasks. For multiple-choice questions, it successfully detects all contaminated models with guaranteed FPRs as low as 0.000073% on MMLU-Pro and 0.000017% on Big-Bench-Hard using eight backdoors. For open-ended generation tasks, it generalizes well and identifies all contaminated models on Alpaca with a guaranteed false positive rate of just 0.127% using six backdoors.

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Minimal, Local, and Robust: Embedding-Only Edits for Implicit Bias in T2I Models
Feng He | Chao Zhang | Zhixue Zhao

Implicit assumptions and priors are often necessary in text-to-image generation tasks, especially when textual prompts lack sufficient context. However, these assumptions can sometimes reflect societal biases, low variance, or outdated concepts in the training data. We present Embedding-only Editing (EmbEdit), a method designed to efficiently edit implicit assumptions and priors in the text-to-image model without affecting unrelated objects or degrading overall performance. Given a “source” prompt (e.g., “nurse”) that elicits an assumption (e.g., a female nurse) and a “destination” prompt or distribution (e.g. equal gender chance), EmbEdit only fine-tunes the word token embedding (WTE) of the target object (i.e. token “nurse”’s WTE). Our method prevents unintended effects on other objects in the model’s knowledge base, as the WTEs for unrelated objects and the model weights remain unchanged. Further, our method can be applied to any text-to-image model with a text encoder. It is highly efficient, modifying only 768, 2048, and 4864 parameters for Stable Diffusion 1.4, Stable Diffusion XL, and FLUX, respectively, matching each model’s WTE dimension. Additionally, changes could be easily reversed by restoring the original WTE layers. The results show that EmbEdit outperforms previous methods in various models, tasks, and editing scenarios (both single and sequential multiple edits), achieving at least a 6.01% improvement (from 87.17% to 93.18%).

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Journalism-Guided Agentic In-context Learning for News Stance Detection
Dahyun Lee | Jonghyeon Choi | Jiyoung Han | Kunwoo Park

As online news consumption grows, personalized recommendation systems have become integral to digital journalism. However, these systems risk reinforcing filter bubbles and political polarization by failing to incorporate diverse perspectives. Stance detection—identifying a text’s position on a target—can help mitigate this by enabling viewpoint-aware recommendations and data-driven analyses of media bias. Yet, existing stance detection research remains largely limited to short texts and high-resource languages. To address these gaps, we introduce K-News-Stance, the first Korean dataset for article-level stance detection, comprising 2,000 news articles with article-level and 21,650 segment-level stance annotations across 47 societal issues. We also propose JoA-ICL, a Journalism-guided Agentic In-Context Learning framework that employs a language model agent to predict the stances of key structural segments (e.g., leads, quotes), which are then aggregated to infer the overall article stance. Experiments showed that JoA-ICL outperforms existing stance detection methods, highlighting the benefits of segment-level agency in capturing the overall position of long-form news articles. Two case studies further demonstrate its broader utility in promoting viewpoint diversity in news recommendations and uncovering patterns of media bias.

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Less Is MuRE: Revisiting Shallow Knowledge Graph Embeddings
Victor Charpenay | Steven Schockaert

In recent years, the field of knowledge graph completion has focused on increasingly sophisticated models, which perform well on link prediction tasks, but are less scalable than earlier methods and are not suitable for learning entity embeddings. As a result, shallow models such as TransE and ComplEx remain the most popular choice in many settings. However, the strengths and limitations of such models remain poorly understood. In this paper, we present a unifying framework and systematically analyze a number of variants and extensions of existing shallow models, empirically showing that MuRE and its extension, ExpressivE, are highly competitive. Motivated by the strong empirical results of MuRE, we also theoretically analyze the expressivity of its associated scoring function, surprisingly finding that it can capture the same class of rule bases as state-of-the-art region-based embedding models.

To confront the ever-evolving safety risks of LLMs, automated jailbreak attacks have proven effective for proactively identifying security vulnerabilities at scale. Existing approaches, including GCG and AutoDAN, modify adversarial prompts to induce LLMs to generate responses that strictly follow a fixed affirmative template. However, we observed that the reliance on the rigid output template is ineffective for certain malicious requests, leading to suboptimal jailbreak performance. In this work, we aim to develop a method that is universally effective across all hostile requests. To achieve this, we explore LLMs’ intrinsic safety mechanism: a refusal stance towards the adversarial prompt is formed in a confined region and ultimately leads to a rejective response. In light of this, we propose Stance Manipulation (SM), a novel automated jailbreak approach that generates jailbreak prompts to suppress the refusal stance and induce affirmative responses. Our experiments across four mainstream open-source LLMs demonstrate the superiority of SM’s performance. Under commenly used setting, SM achieves success rates over 77.1% across all models on Advbench. Specifically, for Llama-2-7b-chat, SM outperforms the best baseline by 25.4%. In further experiments with extended iterations in a speedup setup, SM achieves over 92.2% attack success rate across all models. Our code is publicly available at https://github.com/Zed630/Stance-Manipulation.

Large Language Models (LLMs), despite their remarkable capabilities, are hampered by hallucinations. A particularly challenging variant, knowledge overshadowing, occurs when one piece of activated knowledge inadvertently masks another relevant piece, leading to erroneous outputs even with high-quality training data. Current understanding of overshadowing is largely confined to inference-time observations, lacking deep insights into its origins and internal mechanisms during model training. Therefore, we introduce **PhantomCircuit, a novel framework designed to comprehensively analyze and detect knowledge overshadowing.** By innovatively employing knowledge circuit analysis, PhantomCircuit dissects the function of key components in the circuit and how the attention pattern dynamics contribute to the overshadowing phenomenon and its evolution throughout the training process. Extensive experiments demonstrate PhantomCircuit’s effectiveness in identifying such instances, offering novel insights into this elusive hallucination and providing the research community with a new methodological lens for its potential mitigation. Our code can be found in https://github.com/halfmorepiece/PhantomCircuit.

Multi-hop complex reasoning over incomplete knowledge graphs (KGs) has been extensively studied, but research on numerical knowledge graphs (NKGs) remains relatively limited. Recent approaches focus on separately encoding entities and numerical values, using neural networks to process query encodings for reasoning. However, in complex multi-hop reasoning tasks, numerical values are not merely symbols, and they carry specific semantics and logical relationships that must be accurately represented. The CNR-NST framework can perform binary operations on numerical attributes in NKGs, enabling it to infer new numerical attributes from existing knowledge. Our approach effectively handles up to 102 types of complex numerical reasoning queries. On three public datasets, CNR-NST demonstrates SOTA performance in complex numerical queries, achieving an average improvement of over 40% compared to existing methods. Notably, this work expands the query types for complex multi-hop numerical reasoning and introduces a new evaluation metric for numerical answers, which has been validated through comprehensive experiments.

We introduce CoDe-KG, an open-source, end-to-end pipeline for extracting sentence-level knowledge graphs by combining robust coreference resolution with syntactic sentence decomposition. Using our model, we contribute a dataset of over 150 000 knowledge triples, which is open source. We also contribute a training corpus of 7248 rows for sentence complexity, 200 rows of gold human annotations for coreference resolution using lung-cancer abstracts from PubMed, 900 rows of gold human annotations for sentence conversion policies from sentences in the abstract, and 398 triples of gold human annotations. We systematically select optimal prompt-model pairs across five complexity categories, showing that hybrid chain-of-thought and few-shot prompting yields up to 99.8% exact-match accuracy on sentence simplification. On relation extraction (RE), our pipeline achieves 65.8% macro-F1 on REBEL, an 8-point gain over the prior state of the art, and 75.7% micro-F1 on WebNLG2, while matching or exceeding performance on Wiki-NRE and CaRB. Ablation studies demonstrate that integrating coreference and decomposition increases recall on rare relations by over 20%

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OntologyRAG-Q: Resource Development and Benchmarking for Retrieval-Augmented Question Answering in Qur’anic Tafsir
Sadam Al-Azani | Maad Alowaifeer | Alhanoof Alhunief | Ahmed Abdelali

This paper introduces essential resources for Qur’anic studies: an annotated Tafsir ontology, a dataset of approximately 4,200 question-answer pairs, and a collection of 15 structured Tafsir books available in two formats. We present a comprehensive framework for handling sensitive Qur’anic Tafsir data that spans the entire pipeline from dataset construction through evaluation and error analysis. Our work establishes new benchmarks for retrieval and question-answering tasks on Qur’anic content, comparing performance across state-of-the-art embedding models and large language models (LLMs).We introduce OntologyRAG-Q, a novel retrieval-augmented generation approach featuring our custom Ayat-Ontology chunking method that segments Tafsir content at the verse level using ontology-driven structure. Benchmarking reveals strong performance across various LLMs, with GPT-4 achieving the highest results, followed closely by ALLaM. Expert evaluations show our system achieves 69.52% accuracy and 74.36% correctness overall, though multi-hop and context-dependent questions remain challenging. Our analysis demonstrates that answer position within documents significantly impacts retrieval performance, and among the evaluation metrics tested, BERT-recall and BERT-F1 correlate most strongly with expert assessments. The resources developed in this study are publicly available at https://github.com/sazani/OntologyRAG-Q.git.

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The Practical Impacts of Theoretical Constructs on Empathy Modeling
Allison Lahnala | Charles Welch | David Jurgens | Lucie Flek

Conceptual operationalizations of empathy in NLP are varied, with some having specific behaviors and properties, while others are more abstract. How these variations relate to one another and capture properties of empathy observable in text remains unclear. To provide insight into this, we analyze the transfer performance of empathy models adapted to empathy tasks with different theoretical groundings. We study (1) the dimensionality of empathy definitions, (2) the correspondence between the defined dimensions and measured/observed properties, and (3) the conduciveness of the data to represent them, finding they have a significant impact to performance compared to other transfer setting features. Characterizing the theoretical grounding of empathy tasks as direct, abstract, or adjacent further indicates that tasks that directly predict specified empathy components have higher transferability. Our work provides empirical evidence for the need for precise and multidimensional empathy operationalizations.

Recent advances in LLM-based recommendation have shown promise, yet their cross-domain generalization is hindered by a fundamental mismatch between language-centric pretraining and the recommendation task. Existing methods, relying on language-level knowledge, fail to capture dynamic, item-level user interests across domains. To bridge this gap, we propose RecBase, a domain-agnostic foundational model pretrained with a recommendation-oriented objective. RecBase leverages a large-scale, heterogeneous, cross-domain corpus with unified textual representations and feature mappings to enhance cross-domain generalization. To further align item semantics across domains, we introduce a unified item tokenizer that encodes items into hierarchical concept identifiers, enabling structured representation and efficient vocabulary sharing. The model is trained using an autoregressive objective to capture complex item-level sequential patterns. On eight real-world datasets, our 1.5B-parameter model matches or surpasses the performance of LLM baselines up to 7B parameters in zero-shot and cross-domain recommendation tasks.

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Grouping Entities with Shared Properties using Multi-Facet Prompting and Property Embeddings
Amit Gajbhiye | Thomas Bailleux | Zied Bouraoui | Luis Espinosa-Anke | Steven Schockaert

Methods for learning taxonomies from data have been widely studied. We study a specific version of this task, called commonality identification, where only the set of entities is given and we need to find meaningful ways to group those entities. While LLMs should intuitively excel at this task, it is difficult to directly use such models in large domains. In this paper, we instead use LLMs to describe the different properties that are satisfied by each of the entities individually. We then use pre-trained embeddings to cluster these properties, and finally group entities that have properties which belong to the same cluster. To achieve good results, it is paramount that the properties predicted by the LLM are sufficiently diverse. We find that this diversity can be improved by prompting the LLM to structure the predicted properties into different facets of knowledge.

The rapid growth of scientific literature demands efficient methods to organize and synthesize research findings. Existing taxonomy construction methods, leveraging unsupervised clustering or direct prompting of large language models (LLMs), often lack coherence and granularity. We propose a novel context-aware hierarchical taxonomy generation framework that integrates LLM-guided multi-aspect encoding with dynamic clustering. Our method leverages LLMs to identify key aspects of each paper (e.g., methodology, dataset, evaluation) and generates aspect-specific paper summaries, which are then encoded and clustered along each aspect to form a coherent hierarchy. In addition, we introduce a new evaluation benchmark of 156 expert-crafted taxonomies encompassing 11.6k papers, providing the first naturally annotated dataset for this task. Experimental results demonstrate that our method significantly outperforms prior approaches, achieving state-of-the-art performance in taxonomy coherence, granularity, and interpretability.

Large Language Models are commonly judged by their scores on standard benchmarks, yet such scores often overstate real capability since they mask the mix of skills a task actually demands. For example, ARC is assumed to test reasoning, while HellaSwag is designed to evaluate commonsense. However, we lack a systematic way to verify if these benchmarks actually measure these labels. We introduce **BENCHMARK PROFILING**, a diagnostic framework that decomposes benchmark performance into ten cognitively grounded abilities. The method combines gradient-based importance scoring with targeted parameter ablation to compute an Ability Impact Score (AIS) that quantifies how much each ability contributes to a model’s success on a given benchmark. Profiling three instruction-tuned models across ten widely used benchmarks yields four key findings: (i) most benchmarks draw on several abilities rather than one, (ii) datasets with similar labels rely on distinct ability mixtures, (iii) code-generation benchmarks reward broad, multi-skill improvement and thus show only modest gains from narrow domain-specific fine-tuning, and (iv) abilities irrelevant to the task could negatively affect performance. **BENCHMARK PROFILING** therefore explains why performance gains do not always translate into user-perceived competence and offer a transparent tool for benchmark audit and model interpretability.

While Large Language Models (LLMs) have shown significant potential in assisting peer review, current methods often struggle to generate thorough and insightful reviews while maintaining efficiency. In this paper, we propose TreeReview, a novel framework that models paper review as a hierarchical and bidirectional question-answering process. TreeReview first constructs a tree of review questions by recursively decomposing high-level questions into fine-grained sub-questions and then resolves the question tree by iteratively aggregating answers from leaf to root to get the final review. Crucially, we incorporate a dynamic question expansion mechanism to enable deeper probing by generating follow-up questions when needed. We construct a benchmark derived from ICLR and NeurIPS venues to evaluate our method on full review generation and actionable feedback comments generation tasks. Experimental results of both LLM-based and human evaluation show that TreeReview outperforms strong baselines in providing comprehensive, in-depth, and expert-aligned review feedback, while reducing LLM token usage by up to 80% compared to computationally intensive approaches.

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Improving Chemical Understanding of LLMs via SMILES Parsing
Yunhui Jang | Jaehyung Kim | Sungsoo Ahn

Large language models (LLMs) are increasingly recognized as powerful tools for scientific discovery, particularly in molecular science. A fundamental requirement for these models is the ability to accurately understand molecular structures, commonly encoded in the SMILES representation. However, current LLMs struggle to interpret SMILES, even failing to carry out basic tasks such as counting molecular rings. To address this limitation, we introduce CLEANMOL, a novel framework that formulates SMILES parsing into a suite of clean and deterministic tasks explicitly designed to promote graph-level molecular comprehension. These tasks span from subgraph matching to global graph matching, providing structured supervision aligned with molecular structural properties. We construct a molecular pretraining dataset with adaptive difficulty scoring and pre-train open-source LLMs on these tasks. Our results show that CLEANMOL not only enhances structural comprehension but also achieves the best or competes with the baseline on the Mol-Instructions benchmark.

Large language models (LLMs) demonstrate remarkable capabilities in understanding complex tasks and have achieved commendable performance in graph-related tasks, such as node classification, link prediction, and subgraph classification. These tasks primarily depend on the local reasoning capabilities of the graph structure. However, research has yet to address the graph partitioning task that requires global perception abilities. Our preliminary findings reveal that vanilla LLMs can only handle graph partitioning on extremely small-scale graphs. To overcome this limitation, we propose a three-phase pipeline to empower LLMs for large-scale graph partitioning: coarsening, reasoning, and refining. The coarsening phase reduces graph complexity. The reasoning phase captures both global and local patterns to generate a coarse partition. The refining phase ensures topological consistency by projecting the coarse-grained partitioning results back to the original graph structure. Extensive experiments demonstrate that our framework enables LLMs to perform graph partitioning across varying graph scales, validating both the effectiveness of LLMs for partitioning tasks and the practical utility of our proposed methodology.

Fine-tuning pre-trained models on targeted datasets enhances task-specific performance but often comes at the expense of generalization. Model merging techniques, which integrate multiple fine-tuned models into a single multi-task model through task arithmetic, offer a promising solution. However, task interference remains a fundamental challenge, leading to performance degradation and suboptimal merged models. Existing approaches largely overlooked the fundamental roles of neurons, their connectivity, and activation, resulting in a merging process and a merged model that does not consider how neurons relay and process information. In this work, we present the first study that relies on neuronal mechanisms for model merging. Specifically, we decomposed task-specific representations into two complementary neuronal subspaces that regulate input sensitivity and task adaptability. Leveraging this decomposition, we introduced NeuroMerging, a novel merging framework developed to mitigate task interference within neuronal subspaces, enabling training-free model fusion across diverse tasks. Through extensive experiments, we demonstrated that NeuroMerging achieved superior performance compared to existing methods on multi-task benchmarks across both natural language and vision domains. Our findings highlighted the importance of aligning neuronal mechanisms in model merging, offering new insights into mitigating task interference and improving knowledge fusion. Our project is available at [this http URL](https://ZzzitaoFang.github.io/projects/NeuroMerging/).

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What You Read Isn’t What You Hear: Linguistic Sensitivity in Deepfake Speech Detection
Binh Nguyen | Shuju Shi | Ryan Ofman | Thai Le

Recent advances in text-to-speech technology have enabled highly realistic voice generation, fueling audio-based deepfake attacks such as fraud and impersonation. While audio anti-spoofing systems are critical for detecting such threats, prior research has predominantly focused on acoustic-level perturbations, leaving **the impact of linguistic variation largely unexplored**. In this paper, we investigate the linguistic sensitivity of both open-source and commercial anti-spoofing detectors by introducing **TAPAS** (Transcript-to-Audio Perturbation Anti-Spoofing), a novel framework for transcript-level adversarial attacks. Our extensive evaluation shows that even minor linguistic perturbations can significantly degrade detection accuracy: attack success rates exceed **60%** on several open-source detector–voice pairs, and the accuracy of one commercial detector drops from **100%** on synthetic audio to just **32%**. Through a comprehensive feature attribution analysis, we find that linguistic complexity and model-level audio embedding similarity are key factors contributing to detector vulnerabilities. To illustrate the real-world risks, we replicate a recent Brad Pitt audio deepfake scam and demonstrate that TAPAS can bypass commercial detectors. These findings underscore the **need to move beyond purely acoustic defenses** and incorporate linguistic variation into the design of robust anti-spoofing systems. Our source code is available at https://github.com/nqbinh17/audio_linguistic_adversarial.

Real-world vision-language applications demand varying levels of perceptual granularity. However, most existing visual large language models (VLLMs), such as LLaVA, pre-assume a fixed resolution for downstream tasks, which leads to subpar performance. To address this problem, we first conduct a comprehensive and pioneering investigation into the resolution preferences of different vision-language tasks, revealing a correlation between resolution preferences with (1) image complexity, and (2) uncertainty variance of the VLLM at different image input resolutions. Building on this insight, we propose an empirical formula to determine the optimal resolution for a given vision-language task, accounting for these two factors as the zeroth-order and first-order terms in the Taylor expansion on a given image input. Second, based on rigorous experiments, we propose a novel parameter-efficient fine-tuning technique to extend the visual input resolution of pre-trained VLLMs to the identified optimal resolution. Extensive experiments on various vision-language tasks validate the effectiveness of our method.

Existing LLM-as-a-Judge approaches for evaluating text generation suffer from rating inconsistencies, with low agreement and high rating variance across different evaluator models. We attribute this to subjective evaluation criteria combined with Likert scale scoring in existing protocols. To address this issue, we introduce CheckEval, a checklist-based evaluation framework that improves rating reliability via decomposed binary questions. Through experiments with 12 evaluator models across multiple datasets, we first demonstrate that CheckEval strongly correlates with human judgments. More importantly, CheckEval dramatically improves the average agreement across evaluator models by 0.45 and reduces the score variance. CheckEval scores furthermore have the benefit of being more interpretable because it decomposes evaluation criteria into traceable binary decisions, allowing analyses of specific attributes driving quality judgments.

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A Necessary Step toward Faithfulness: Measuring and Improving Consistency in Free-Text Explanations
Lingjun Zhao | Hal Daumé Iii

Faithful free-text explanations are important to ensure transparency in high-stakes AI decision-making contexts, but they are challenging to generate by language models and assess by humans. In this paper, we present a measure for Prediction-EXplanation (PEX) consistency, by extending the concept of weight of evidence. This measure quantifies how much a free-text explanation supports or opposes a prediction, serving as an important aspect of explanation faithfulness. Our analysis reveals that more than 62% explanations generated by large language models lack this consistency. We show that applying direct preference optimization improves the consistency of generated explanations across three model families, with improvement ranging from 43.1% to 292.3%. Furthermore, we demonstrate that optimizing this consistency measure can improve explanation faithfulness by up to 9.7%.

Multi-modal keyphrase prediction (MMKP) aims to advance beyond text-only methods by incorporating multiple modalities of input information to produce a set of conclusive phrases. Traditional multi-modal approaches have been proven to have significant limitations in handling the challenging absence and unseen scenarios. Additionally, we identify shortcomings in existing benchmarks that overestimate model capability due to significant overlap in training tests. In this work, we propose leveraging vision-language models (VLMs) for the MMKP task. Firstly, we use two widely-used strategies, e.g., zero-shot and supervised fine-tuning (SFT) to assess the lower bound performance of VLMs. Next, to improve the complex reasoning capabilities of VLMs, we adopt Fine-tune-CoT, which leverages high-quality CoT reasoning data generated by a teacher model to finetune smaller models. Finally, to address the “overthinking” phenomenon, we propose a dynamic CoT strategy which adaptively injects CoT data during training, allowing the model to flexibly leverage its reasoning capabilities during the inference stage. We evaluate the proposed strategies on various datasets and the experimental results demonstrate the effectiveness of the proposed approaches. The code is available at https://github.com/bytedance/DynamicCoT.

Chart2code has recently received significant attention in the multimodal community due to its potential to reduce the burden of visualization and promote a more detailed understanding of charts. However, existing Chart2code-related training datasets suffer from at least one of the following issues: (1) limited scale, (2) limited type coverage, and (3) inadequate complexity. To address these challenges, we seek more diverse sources that better align with real-world user distributions and propose dual data synthesis pipelines: (1) synthesize based on online plotting code. (2) synthesize based on chart images in the academic paper. We create a large-scale Chart2code training dataset Chart2code53, including 53 chart types, 130K Chart-code pairs based on the pipeline. Experimental results demonstrate that even with few parameters, the model finetuned on Chart2code53 achieves state-of-the-art performance on multiple Chart2code benchmarks within open-source models.

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The State of Multilingual LLM Safety Research: From Measuring The Language Gap To Mitigating It
Zheng Xin Yong | Beyza Ermis | Marzieh Fadaee | Stephen Bach | Julia Kreutzer

This paper presents a comprehensive analysis of the linguistic diversity of LLM safety research, highlighting the English-centric nature of the field. Through a systematic review of nearly 300 publications from 2020–2024 across major NLP conferences and workshops at ACL, we identify a significant and growing language gap in LLM safety research, with even high-resource non-English languages receiving minimal attention. We further observe that non-English languages are rarely studied as a standalone language and that English safety research exhibits poor language documentation practice. To motivate future research into multilingual safety, we make several recommendations based on our survey, and we then pose three concrete future directions on safety evaluation, training data generation, and crosslingual safety generalization. Based on our survey and proposed directions, the field can develop more robust, inclusive AI safety practices for diverse global populations.

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AIP: Subverting Retrieval-Augmented Generation via Adversarial Instructional Prompt
Saket Sanjeev Chaturvedi | Gaurav Bagwe | Lan Emily Zhang | Xiaoyong Yuan

Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by retrieving relevant documents from external sources to improve factual accuracy and verifiability. However, this reliance introduces new attack surfaces within the retrieval pipeline, beyond the LLM itself. While prior RAG attacks have exposed such vulnerabilities, they largely rely on manipulating user queries, which is often infeasible in practice due to fixed or protected user inputs. This narrow focus overlooks a more realistic and stealthy vector: instructional prompts, which are widely reused, publicly shared, and rarely audited. Their implicit trust makes them a compelling target for adversaries to manipulate RAG behavior covertly.We introduce a novel attack for Adversarial Instructional Prompt (AIP) that exploits adversarial instructional prompts to manipulate RAG outputs by subtly altering retrieval behavior. By shifting the attack surface to the instructional prompts, AIP reveals how trusted yet seemingly benign interface components can be weaponized to degrade system integrity. The attack is crafted to achieve three goals: (1) naturalness, to evade user detection; (2) utility, to encourage use of prompts; and (3) robustness, to remain effective across diverse query variations. We propose a diverse query generation strategy that simulates realistic linguistic variation in user queries, enabling the discovery of prompts that generalize across paraphrases and rephrasings. Building on this, a genetic algorithm-based joint optimization is developed to evolve adversarial prompts by balancing attack success, clean-task utility, and stealthiness. Experimental results show that AIP achieves up to 95.23% attack success rate while preserving benign functionality. These findings uncover a critical and previously overlooked vulnerability in RAG systems, emphasizing the need to reassess the shared instructional prompts.

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From Capabilities to Performance: Evaluating Key Functional Properties of LLM Architectures in Penetration Testing
Lanxiao Huang | Daksh Dave | Tyler Cody | Peter A. Beling | Ming Jin

Large Language Models (LLMs) have been explored for automating or enhancing penetration testing tasks, but their effectiveness and reliability across diverse attack phases remain open questions. This study presents a comprehensive evaluation of multiple LLM-based agents, ranging from singular to modular designs, across realistic penetration testing scenarios, analyzing their empirical performance and recurring failure patterns. We further investigate the impact of core functional capabilities on agent success, operationalized through five targeted augmentations: Global Context Memory (GCM), Inter-Agent Messaging (IAM), Context-Conditioned Invocation (CCI), Adaptive Planning (AP), and Real-Time Monitoring (RTM). These interventions respectively support the capabilities of Context Coherence & Retention, Inter-Component Coordination & State Management, Tool Usage Accuracy & Selective Execution, Multi-Step Strategic Planning & Error Detection & Recovery, and Real-Time Dynamic Responsiveness. Our findings reveal that while some architectures natively exhibit select properties, targeted augmentations significantly enhance modular agent performance—particularly in complex, multi-step, and real-time penetration testing scenarios.

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Editing Across Languages: A Survey of Multilingual Knowledge Editing
Nadir Durrani | Basel Mousi | Fahim Dalvi

While Knowledge Editing has been extensively studied in monolingual settings, it remains underexplored in multilingual contexts. This survey systematizes recent research on Multilingual Knowledge Editing (MKE), a growing subdomain of model editing focused on ensuring factual edits generalize reliably across languages. We present a comprehensive taxonomy of MKE methods, covering parameter-based, memory-based, fine-tuning, and hypernetwork approaches. We survey available benchmarks, summarize key findings on method effectiveness and transfer patterns, and identify persistent challenges such as cross-lingual propagation, language anisotropy, and limited evaluation for low-resource and culturally specific languages. We also discuss broader concerns such as stability and scalability of multilingual edits. Our analysis consolidates a rapidly evolving area and lays the groundwork for future progress in editable language-aware LLMs.

Retrieval-augmented generation (RAG) enhances factual grounding by integrating retrieval mechanisms with generative models but introduces new attack surfaces, particularly through backdoor attacks. While prior research has largely focused on disinformation threats, fairness vulnerabilities remain underexplored. Unlike conventional backdoors that rely on direct trigger-to-target mappings, fairness-driven attacks exploit the interaction between retrieval and generation models, manipulating semantic relationships between target groups and social biases to establish a persistent and covert influence on content generation.This paper introduces BiasRAG , a systematic framework that exposes fairness vulnerabilities in RAG through a two-phase backdoor attack. During the pre-training phase, the query encoder is compromised to align the target group with the intended social bias, ensuring long-term persistence. In the post-deployment phase, adversarial documents are injected into knowledge bases to reinforce the backdoor, subtly influencing retrieved content while remaining undetectable under standard fairness evaluations. Together, BiasRAG ensures precise target alignment over sensitive attributes, stealthy execution, and resilience. Empirical evaluations demonstrate that BiasRAG achieves high attack success rates while preserving contextual relevance and utility, establishing a persistent and evolving threat to fairness in RAG.

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Drift-Adapter: A Practical Approach to Near Zero-Downtime Embedding Model Upgrades in Vector Databases
Harshil Vejendla

Upgrading embedding models in production vector databases typically necessitates re-encoding the entire corpus and rebuilding the Approximate Nearest Neighbor (ANN) index, leading to significant operational disruption and computational cost. This paper presents Drift-Adapter, a lightweight, learnable transformation layer designed to bridge embedding spaces between model versions. By mapping new queries into the legacy embedding space, Drift-Adapter enables the continued use of the existing ANN index, effectively deferring full re-computation. We systematically evaluate three adapter parameterizations: Orthogonal Procrustes, Low-Rank Affine, and a compact Residual MLP, trained on a small sample of paired old/new embeddings. Experiments on MTEB text corpora and a CLIP image model upgrade (1M items) show that Drift-Adapter recovers 95–99% of the retrieval recall (Recall@10, MRR) of a full re-embedding, adding less than 10,𝜇s query latency. Compared to operational strategies like full re-indexing or dual-index serving, Drift-Adapter dramatically reduces recompute costs (by over 100 times) and facilitates upgrades with near-zero operational interruption. We analyze robustness to varied model drift, training data size, scalability to billion-item systems, and the impact of design choices like diagonal scaling, demonstrating Drift-Adapter’s viability as a pragmatic solution for agile model deployment.

Ethical decision-making is a critical aspect of human judgment, and the growing use of LLMs in decision-support systems necessitates a rigorous evaluation of their moral reasoning capabilities. However, existing assessments primarily rely on single-step evaluations, failing to capture how models adapt to evolving ethical challenges. Addressing this gap, we introduce the Multi-step Moral Dilemmas (MMDs), the first dataset specifically constructed to evaluate the evolving moral judgments of LLMs across 3,302 five-stage dilemmas. This framework enables a fine-grained, dynamic analysis of how LLMs adjust their moral reasoning across escalating dilemmas. Our evaluation of nine widely used LLMs reveals that their value preferences shift significantly as dilemmas progress, indicating that models recalibrate moral judgments based on scenario complexity. Furthermore, pairwise value comparisons demonstrate that while LLMs often prioritize the value of care, this value can sometimes be superseded by fairness in certain contexts, highlighting the dynamic and context-dependent nature of LLM ethical reasoning. Our findings call for a shift toward dynamic, context-aware evaluation paradigms, paving the way for more human-aligned and value-sensitive development of LLMs.

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SliceMoE: Routing Embedding Slices Instead of Tokens for Fine-Grained and Balanced Transformer Scaling
Harshil Vejendla

Mixture-of-Experts (MoE) layers scale transformers by routing tokens to a sparse subset of feed-forward experts. Token-level routing, however, assigns an entire semantic spectrum to each expert, creating capacity bottlenecks, load-balancing pathologies, and limited specialisation. We introduce SliceMoE, an architecture that routes contiguous slices of a token’s hidden vector. A d-dimensional embedding is partitioned into S slices, and for each slice, a lightweight shared router predicts the top-k experts. Experts operate on their assigned slices independently, and outputs are re-assembled, maintaining per-token FLOP efficiency. Because slices from different tokens interleave within an expert, utilisation is naturally smoother. We propose a slice-level capacity loss, cross-slice dropout, and efficient fused batched-GEMM kernels. Experiments on WikiText-103 language modelling, WMT En–De translation, and three text-classification datasets show SliceMoE attains up to 1.7x faster inference than dense baselines, 12–18% lower perplexity than parameter-matched token-MoE, and improved expert balance, with interpretable expertise over syntactic versus semantic sub-spaces.

Multi-agent systems have emerged as a promising approach for enhancing the reasoning capabilities of large language models in complex problem-solving. However, current MAS frameworks are limited by poor flexibility and scalability, with underdeveloped optimization strategies. To address these challenges, we propose ReSo, which integrates task graph generation with a reward-driven two-stage agent selection process. The core of ReSo is the proposed Collaborative Reward Model, which can provide fine-grained reward signals for MAS cooperation for optimization. We also introduce an automated data synthesis framework for generating MAS benchmarks, without human annotations. Experimentally, ReSo matches or outperforms existing methods. ReSo achieves 33.7% and 32.3% accuracy on Math-MAS and SciBench-MAS SciBench, while other methods completely fail. The code and data are available at [Reso](https://github.com/hengzzzhou/ReSo).

Constraint programming (CP) is a crucial technology for solving real-world constraint optimization problems (COPs), with the advantages of rich modeling semantics and high solving efficiency. Using large language models (LLMs) to generate formal modeling automatically for COPs is becoming a promising approach, which aims to build trustworthy neuro-symbolic AI with the help of symbolic solvers. However, CP has received less attention compared to works based on operations research (OR) models. We introduce ConstraintLLM, the first LLM specifically designed for CP modeling, which is trained on an open-source LLM with multi-instruction supervised fine-tuning. We propose the Constraint-Aware Retrieval Module (CARM) to increase the in-context learning capabilities, which is integrated in a Tree-of-Thoughts (ToT) framework with guided self-correction mechanism. Moreover, we construct and release IndusCP, the first industrial-level benchmark for CP modeling, which contains 140 challenging tasks from various domains. Our experiments demonstrate that ConstraintLLM achieves state-of-the-art solving accuracy across multiple benchmarks and outperforms the baselines by 2x on the new IndusCP benchmark. Code and data are available at: https://github.com/william4s/ConstraintLLM.

Escape rooms present a unique cognitive challenge that demands exploration-driven planning: with the sole instruction to escape the room, players must actively search their environment, collecting information, and finding solutions through repeated trial and error. Motivated by this, we introduce VisEscape, a benchmark of 20 virtual escape rooms specifically designed to evaluate AI models under these challenging conditions, where success depends not only on solving isolated puzzles but also on iteratively constructing and refining spatial-temporal knowledge of a dynamically changing environment. On VisEscape, we observe that even state-of-the-art multi-modal models generally fail to escape the rooms, showing considerable variation in their progress and problem-solving approaches. We find that integrating memory management and reasoning contributes to efficient exploration and enables successive hypothesis formulation and testing, thereby leading to significant improvements in dynamic and exploration-driven environments.

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ESC-Judge: A Framework for Comparing Emotional Support Conversational Agents
Navid Madani | Rohini Srihari

Large Language Models (LLMs) increasingly power mental-health chatbots, yet the field still lacks a scalable, theory-grounded way to decide which model is more effective to deploy. We present ESC-Judge, the first end-to-end evaluation framework that (i) grounds head-to-head comparison of Emotional-Support LLMs (ES-LLMs) in an established psychological theory—Clara Hill’s Exploration–Insight–Action (E-I-A) counselling model—thereby delivering a structured, interpretable lens on performance, and (ii) fully automates the pipeline at scale. ESC-Judge proceeds in three stages: (1) it synthesizes realistic help-seeker roles by sampling empirically salient attributes (stressors, personality, life history); (2) it has two candidate ES-Agents conduct separate sessions with the same role, isolating model-specific strategies; and (3) it asks a specialised judge LLM to issue pairwise preferences across rubric-anchored skills that exhaustively cover the E-I-A spectrum. In our empirical study, ESC-Judge matches PhD-level annotators in 85% of Exploration, 83% of Insight, and 86% of Action decisions, demonstrating human-level reliability at a fraction of the cost. We release all code, prompts, synthetic roles, transcripts, and judgment scripts to catalyze transparent progress in emotionally supportive AI

We investigate how Large Language Models (LLMs) distinguish between memorization and generalization at the neuron level. Through carefully designed tasks, we identify distinct neuron subsets responsible for each behavior. Experiments on both a GPT-2 model trained from scratch and a pretrained LLaMA-3.2 model fine-tuned with LoRA show consistent neuron-level specialization. We further demonstrate that inference-time interventions on these neurons can steer the model’s behavior toward memorization or generalization. To assess robustness, we evaluate intra-task and inter-task consistency, confirming that these neuron-behavior associations reflect generalizable patterns rather than dataset-specific artifacts. Our findings reveal modular structure in LLMs and enable controlling memorization and generalization behaviors at inference time.

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Sparse Neurons Carry Strong Signals of Question Ambiguity in LLMs
Zhuoxuan Zhang | Jinhao Duan | Edward Kim | Kaidi Xu

Ambiguity is pervasive in real-world questions, yet large language models (LLMs) often respond with confident answers rather than seeking clarification. In this work, we show that question ambiguity is linearly encoded in the internal representations of LLMs and can be both detected and controlled at the neuron level. During the model’s pre-filling stage, we identify that a small number of neurons, as few as one, encode question ambiguity information. Probes trained on these Ambiguity-Encoding Neurons (AENs) achieve strong performance on ambiguity detection and generalize across datasets, outperforming prompting-based and representation-based baselines. Layerwise analysis reveals that AENs emerge from shallow layers, suggesting early encoding of ambiguity signals in the model’s processing pipeline. Finally, we show that through manipulating AENs, we can control LLM’s behavior from direct answering to abstention. Our findings reveal that LLMs form compact internal representations of question ambiguity, enabling interpretable and controllable behavior.

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Do Slides Help? Multi-modal Context for Automatic Transcription of Conference Talks
Supriti Sinhamahapatra | Jan Niehues

State-of-the-art (SOTA) Automatic Speech Recognition (ASR) systems primarily rely on acoustic information while disregarding additional multi-modal context. However, visual information are essential in disambiguation and adaptation. While most work focus on speaker images to handle noise conditions, this work also focuses on integrating presentation slides for the use cases of scientific presentation.In a first step, we create a benchmark for multi-modal presentation including an automatic analysis of transcribing domain-specific terminology. Next, we explore methods for augmenting speech models with multi-modal information. We mitigate the lack of datasets with accompanying slides by a suitable approach of data augmentation.Finally, we train a model using the augmented dataset, resulting in a relative reduction in word error rate of approximately 34%, across all words and 35%, for domain-specific terms compared to the baseline model.

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Promote, Suppress, Iterate: How Language Models Answer One-to-Many Factual Queries
Tianyi Lorena Yan | Robin Jia

To answer one-to-many factual queries (e.g., listing cities of a country), a language model (LM) must simultaneously recall knowledge and avoid repeating previous answers. How are these two subtasks implemented and integrated internally? Across multiple datasets, models, and prompt templates, we identify a promote-then-suppress mechanism: the model first recalls all answers, and then suppresses previously generated ones. Specifically, LMs use both the subject and previous answer tokens to perform knowledge recall, with attention propagating subject information and MLPs promoting the answers. Then, attention attends to and suppresses previous answer tokens, while MLPs amplify the suppression signal. Our mechanism is corroborated by extensive experimental evidence: in addition to using early decoding and causal tracing, we analyze how components use different tokens by introducing both Token Lens, which decodes aggregated attention updates from specified tokens, and a knockout method that analyzes changes in MLP outputs after removing attention to specified tokens. Overall, we provide new insights into how LMs’ internal components interact with different input tokens to support complex factual recall.

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Out of Sight, Not Out of Context? Egocentric Spatial Reasoning in VLMs Across Disjoint Frames
Sahithya Ravi | Gabriel Herbert Sarch | Vibhav Vineet | Andrew D Wilson | Balasaravanan Thoravi Kumaravel

An embodied AI assistant operating on egocentric video must integrate spatial cues across time - for instance, determining where an object A, glimpsed a few moments ago lies relative to an object B encountered later. We introduce Disjoint-3DQA , a generative QA benchmark that evaluates this ability of VLMs by posing questions about object pairs that are not co-visible in the same frame. We evaluated seven state-of-the-art VLMs and found that models lag behind human performance by 28%, with steeper declines in accuracy (60% → 30 %) as the temporal gap widens. Our analysis further reveals that providing trajectories or bird’s-eye-view projections to VLMs results in only marginal improvements, whereas providing oracle 3D coordinates leads to a substantial 20% performance increase. This highlights a core bottleneck of multi-frame VLMs in constructing and maintaining 3D scene representations over time from visual signals. Disjoint-3DQA therefore sets a clear, measurable challenge for long-horizon spatial reasoning and aims to catalyze future research at the intersection of vision, language, and embodied AI.

Despite the impressive chain-of-thought(CoT) reasoning ability of large language models (LLMs), its underlying mechanisms remains unclear. In this paper, we explore the inner workings of LLM’s CoT ability via the lens of neurons in the feed-forward layers. We propose an efficient method to identify reasoning-critical neurons by analyzing their activation patterns under reasoning chains of varying quality. Based on it, we devise a rather simple intervention method that directly stimulates these reasoning-critical neurons, to guide the generation of high-quality reasoning chains. Extended experiments validate the effectiveness of our method and demonstrate the critical role these identified neurons play in CoT reasoning.

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PakBBQ: A Culturally Adapted Bias Benchmark for QA
Abdullah Hashmat | Muhammad Arham Mirza | Agha Ali Raza

With the widespread adoption of Large Language Models (LLMs) across various applications, it is imperative to ensure their fairness across all user communities. However, most LLMs are trained and evaluated on Western centric data, with little attention paid to low-resource languages and regional contexts. To address this gap, we introduce PakBBQ, a culturally and regionally adapted extension of the original Bias Benchmark for Question Answering (BBQ) dataset. PakBBQ comprises over 214 templates, 17180 QA pairs across 8 categories in both English and Urdu, covering eight bias dimensions including age, disability, appearance, gender, socio-economic status, religious, regional affiliation, and language formality that are relevant in Pakistan. We evaluate multiple multilingual LLMs under both ambiguous and explicitly disambiguated contexts, as well as negative versus non negative question framings. Our experiments reveal (i) an average accuracy gain of 12% with disambiguation, (ii) consistently stronger counter bias behaviors in Urdu than in English, and (iii) marked framing effects that reduce stereotypical responses when questions are posed negatively. These findings highlight the importance of contextualized benchmarks and simple prompt engineering strategies for bias mitigation in low resource settings.

The emerging capabilities of large language models (LLMs) have sparked concerns about their immediate potential for harmful misuse. The core approach to mitigate these concerns is the detection of harmful queries to the model. Current detection approaches are fallible, and are particularly susceptible to attacks that exploit mismatched generalization of model capabilities (e.g., prompts in low-resource languages or prompts provided in non-text modalities such as image and audio). To tackle this challenge, we propose OMNIGUARD, an approach for detecting harmful prompts across languages and modalities. Our approach (i) identifies internal representations of an LLM/MLLM that are aligned across languages or modalities and then (ii) uses them to build a language-agnostic or modality-agnostic classifier for detecting harmful prompts. OMNIGUARD improves harmful prompt classification accuracy by 11.57% over the strongest baseline in a multilingual setting, by 20.44% for image-based prompts, and sets a new SOTA for audio-based prompts. By repurposing embeddings computed during generation, OMNIGUARD is also very efficient (≈ 120× faster than the next fastest baseline). Code and data are available at https://github.com/vsahil/OmniGuard

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Comparing human and LLM politeness strategies in free production
Haoran Zhao | Robert D. Hawkins

Polite speech poses a fundamental alignment challenge for large language models (LLMs). Humans deploy a rich repertoire of linguistic strategies to balance informational and social goals – from positive approaches that build rapport (compliments, expressions of interest) to negative strategies that minimize imposition (hedging, indirectness).We investigate whether LLMs employ a similarly context-sensitive repertoire by comparing human and LLM responses to English-language scenarios in both constrained and open-ended production tasks.We find that larger models (≥70B parameters) successfully replicate key effects from the computational pragmatics literature, and human evaluators prefer LLM-generated responses in open-ended contexts. However, further linguistic analyses reveal that models disproportionately rely on negative politeness strategies to create distance even in positive contexts, potentially leading to misinterpretations. While LLMs thus demonstrate an impressive command of politeness strategies, these systematic differences provide important groundwork for making intentional choices about pragmatic behavior in human-AI communication.

Negotiation requires dynamically balancing self-interest and cooperation within the flow of conversation to maximize one’s own utility. Yet, existing agents struggle due to bounded rationality in human data, low adaptability to counterpart behavior, and limited strategic reasoning. To address this, we introduce principle-driven negotiation agents, powered by ASTRA, a novel framework for turn-level offer optimization grounded in two core principles: opponent modeling and Tit-for-Tat reciprocity. ASTRA operates in three stages: (1) interpreting counterpart behavior, (2) optimizing counteroffers via a tool-integrated action with a linear programming (LP) solver, and (3) selecting offers based on strategy assessment and the partner’s acceptance probability. Through simulations and human evaluations, our agent effectively adapts to an opponent’s shifting stance and achieves favorable outcomes through enhanced adaptability and strategic reasoning. Beyond enhancing negotiation performance, it also serves as a powerful coaching tool, offering interpretable strategic feedback and optimal offer recommendations beyond human bounded rationality, with its potential further validated through human evaluation.

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CARMA: Enhanced Compositionality in LLMs via Advanced Regularisation and Mutual Information Alignment
Nura Aljaafari | Danilo Carvalho | Andre Freitas

Large language models (LLMs) struggle with compositional generalisation, limiting their ability to systematically combine learned components to interpret novel inputs. While architectural modifications, fine-tuning, and data augmentation improve compositionality, they often have limited adaptability, face scalability constraints, or yield diminishing returns on real data. To address this, we propose CARMA, an intervention that enhances the stability and robustness of compositional reasoning in LLMs while preserving fine-tuned performance. CARMA employs mutual information regularisation and layer-wise stability constraints to mitigate feature fragmentation, ensuring structured representations persist across and within layers. We evaluate CARMA on inverse dictionary modelling and sentiment classification, measuring its impact on semantic consistency, performance stability, and robustness to lexical perturbations. Results show that CARMA reduces the variability introduced by fine-tuning, stabilises token representations, and improves compositional reasoning. While its effectiveness varies across architectures, CARMA’s key strength lies in reinforcing learned structures rather than introducing new capabilities, making it a scalable auxiliary method. These findings suggest that integrating CARMA with fine-tuning can improve compositional generalisation while maintaining task-specific performance in LLMs.

Considering deep neural networks as manifold mappers, the pretrain-then-fine-tune paradigm can be interpreted as a two-stage process: pretrain establishes a broad knowledge base, and fine-tune adjusts the model parameters to activate specific neural pathways to align with the target manifold. Although prior fine-tuning approaches demonstrate success, their rigid parameter space limits their ability to dynamically activate appropriate neural pathways, rendering them ill-equipped to adapt flexibly to the diverse and evolving data distributions. In light of this view, we propose a novel approach, Mixture of Expert Prompt Tuning (MEPT), as an effective and efficient manifold-mapping framework. MEPT leverages the Mixture of Experts architecture by integrating multiple prompt experts to adaptively learn diverse and non-stationary data distributions. Empirical evaluations demonstrate that MEPT outperforms several state-of-the-art parameter efficient baselines on SuperGLUE, achieving notable improvements in mean accuracy (e.g., 1.94%) while significantly reducing activated prompts by 79.25%. The effectiveness of MEPT is further supported by theoretical insights from manifold learning and validated through neural activation pathway visualization results. Our code is avaliable at https://runjia.tech/emnlp_mept/.

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KG-CQR: Leveraging Structured Relation Representations in Knowledge Graphs for Contextual Query Retrieval
Chi Minh Bui | Ngoc Mai Thieu | Vinh Van Nguyen | Jason J. Jung | Khac-Hoai Nam Bui

The integration of knowledge graphs (KGs) with large language models (LLMs) offers significant potential to enhance the retrieval stage in retrieval-augmented generation (RAG) systems. In this study, we propose KG-CQR, a novel framework for Contextual Query Retrieval (CQR) that enhances the retrieval phase by enriching complex input queries with contextual representations derived from a corpus-centric KG. Unlike existing methods that primarily address corpus-level context loss, KG-CQR focuses on query enrichment through structured relation representations, extracting and completing relevant KG subgraphs to generate semantically rich query contexts. Comprising subgraph extraction, completion, and contextual generation modules, KG-CQR operates as a model-agnostic pipeline, ensuring scalability across LLMs of varying sizes without additional training. Experimental results on the RAGBench and MultiHop-RAG datasets demonstrate that KG-CQR outperforms strong baselines, achieving improvements of up to 4–6% in mAP and approximately 2–3% in Recall@25. Furthermore, evaluations on challenging RAG tasks such as multi-hop question answering show that, by incorporating KG-CQR, the performance outperforms the existing baseline in terms of retrieval effectiveness.

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SABER: Uncovering Vulnerabilities in Safety Alignment via Cross-Layer Residual Connection
Maithili Joshi | Palash Nandi | Tanmoy Chakraborty

Large Language Models (LLMs) with safe-alignment training are powerful instruments with robust language comprehension capability. Typically LLMs undergo careful alignment training involving human feedback to ensure the acceptance of safe inputs while rejection of harmful or unsafe ones. However, these humongous models are still vulnerable to jailbreak attacks, in which malicious users attempt to generate harmful outputs that safety-aligned LLMs are trained to avoid. In this study, we find that the safety mechanisms in LLMs are predominantly prevalent in the middle-to-late layers. Based on this observation, we introduce a novel white-box jailbreak method SABER (Safety Alignment Bypass via Extra Residuals) that connects two intermediate layer s and e such that s<e with a residual connection, achieving an improvement of 51% over the best performing baseline GCG on HarmBench test set. Moreover, model demonstrates only a marginal shift in perplexity when evaluated on the validation set of HarmBench.

Large language models (LLMs) tend to follow maliciously crafted instructions to generate deceptive responses, posing safety challenges. How deceptive instructions alter the internal representations of LLM compared to truthful ones remains poorly understood beyond output analysis. To bridge this gap, we investigate when and how these representations “flip”, such as from truthful to deceptive, under deceptive versus truthful/neutral instructions. Analyzing the internal representations of Llama-3.1-8B-Instruct and Gemma-2-9B-Instruct on a factual verification task, we find the model’s instructed True/False output is predictable via linear probes across all conditions based on the internal representation. Further, we use Sparse Autoencoders (SAEs) to show that the Deceptive instructions induce significant representational shifts compared to Truthful/Neutral representations (which are similar), concentrated in early-to-mid layers and detectable even on complex datasets. We also identify specific SAE features highly sensitive to deceptive instruction and use targeted visualizations to confirm distinct truthful/deceptive representational subspaces.

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Can LLMs simulate the same correct solutions to free-response math problems as real students?
Yuya Asano | Diane Litman | Erin Walker

Large language models (LLMs) have emerged as powerful tools for developing educational systems. While previous studies have explored modeling student mistakes, a critical gap remains in understanding whether LLMs can generate correct solutions that represent student responses to free-response problems. In this paper, we compare the distribution of solutions produced by four LLMs (one proprietary, two open-sourced general, and one open-sourced math models) with various sampling and prompting techniques and those generated by students, using conversations where students teach math problems to a conversational robot. Our study reveals discrepancies between the correct solutions produced by LLMs and by students. We discuss the practical implications of these findings for the design and evaluation of LLM-supported educational systems.

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Evaluating Behavioral Alignment in Conflict Dialogue: A Multi-Dimensional Comparison of LLM Agents and Humans
Deuksin Kwon | Kaleen Shrestha | Bin Han | Elena Hayoung Lee | Gale Lucas

Large Language Models (LLMs) are increasingly deployed in socially complex, interaction-driven tasks, yet their ability to mirror human behavior in emotionally and strategically complex contexts remains underexplored. This study assesses the behavioral alignment of personality-prompted LLMs in adversarial dispute resolution by simulating multi-turn conflict dialogues that incorporate negotiation. Each LLM is guided by a matched Five-Factor personality profile to control for individual variation and enhance realism. We evaluate alignment across three dimensions: linguistic style, emotional expression (e.g., anger dynamics), and strategic behavior. GPT-4.1 achieves the closest alignment with humans in linguistic style and emotional dynamics, while Claude-3.7-Sonnet best reflects strategic behavior. Nonetheless, substantial alignment gaps persist. Our findings establish a benchmark for alignment between LLMs and humans in socially complex interactions, underscoring both the promise and the limitations of personality conditioning in dialogue modeling.

We unveil that internal representations in large language models (LLMs) serve as reliable proxies of learned knowledge, and propose **RECALL**, a novel representation-aware model merging framework for continual learning without access to historical data. RECALL computes inter-model similarity from layer-wise hidden representations over clustered typical samples, and performs adaptive, hierarchical parameter fusion to align knowledge across models. This design enables the preservation of domain-general features in shallow layers while allowing task-specific adaptation in deeper layers. Unlike prior methods that require task labels or incur performance trade-offs, RECALL achieves seamless multi-domain integration and strong resistance to catastrophic forgetting. Extensive experiments across five NLP tasks and multiple continual learning scenarios show that RECALL outperforms baselines in both knowledge retention and generalization, providing a scalable and data-free solution for evolving LLMs.

Improvements in language model capabilities are often attributed to increasing model size or training data, but in some cases smaller models trained on curated data or with different architectural decisions can outperform larger ones trained on more tokens. What accounts for this? To quantify the impact of these design choices, we meta-analyze 92 open-source pretrained models across a wide array of scales, including state-of-the-art open-weights models as well as less performant models and those with less conventional design decisions. We find that by incorporating features besides model size and number of training tokens, we can achieve a relative 3-28% increase in ability to predict downstream performance compared with using scale alone. Analysis of model design decisions reveal insights into data composition, such as the trade-off between language and code tasks at 15-25% code, as well as the negative impact of web data on truthfulness. Broadly, our framework lays a foundation for more systematic investigation of how model development choices shape final capabilities.

As large language models (LLMs) are increasingly used in morally sensitive domains, it is crucial to understand how persona traits affect their moral reasoning and persuasive behavior. We present the first large-scale study of multi-dimensional persona effects in AI-AI debates over real-world moral dilemmas. Using a 6-dimensional persona space (age, gender, country, social class, ideology, and personality), we simulate structured debates between AI agents over 131 relationship-based cases. Our results show that personas affect initial moral stances and debate outcomes, with political ideology and personality traits exerting the strongest influence. Persuasive success varies across traits, with liberal and open personalities reaching higher consensus. While logit-based confidence grows during debates, emotional and credibility-based appeals diminish, indicating more tempered argumentation over time. These trends mirror findings from psychology and cultural studies, reinforcing the need for persona-aware evaluation frameworks for AI moral reasoning.

This paper introduces an algorithm to select demonstration examples for in-context learning of a query set. Given a set of n examples, how can we quickly select k out of n to best serve as the conditioning for downstream inference? This problem has broad applications in prompt tuning and chain-of-thought reasoning. Since model weights remain fixed during in-context learning, previous work has sought to design methods based on the similarity of token embeddings. This work proposes a new approach based on gradients of the output taken in the input embedding space. Our approach estimates model outputs through a first-order approximation using the gradients. Then, we apply this estimation to multiple randomly sampled subsets. Finally, we aggregate the sampled subset outcomes to form an influence score for each demonstration, and select k most relevant examples. This procedure only requires pre-computing model outputs and gradients once, resulting in a linear-time algorithm relative to model and training set sizes. Extensive experiments across various models and datasets validate the efficiency of our approach. We show that the gradient estimation procedure yields approximations of full inference with less than 𝟏% error across six datasets. This allows us to scale up subset selection that would otherwise run full inference by up to 37.7× on models with up to 34 billion parameters, and outperform existing selection methods based on input embeddings by 11% on average.

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Speech Vecalign: an Embedding-based Method for Aligning Parallel Speech Documents
Chutong Meng | Philipp Koehn

We present Speech Vecalign, a parallel speech document alignment method that monotonically aligns speech segment embeddings and does not depend on text transcriptions. Compared to the baseline method Global Mining, a variant of speech mining, Speech Vecalign produces longer speech-to-speech alignments. It also demonstrates greater robustness than Local Mining, another speech mining variant, as it produces less noise. We applied Speech Vecalign to 3,000 hours of unlabeled parallel English-German (En-De) speech documents from VoxPopuli, yielding about 1,000 hours of high-quality alignments. We then trained En-De speech-to-speech translation models on the aligned data. Speech Vecalign improves the En-to-De and De-to-En performance over Global Mining by 0.37 and 0.18 ASR-BLEU, respectively. Moreover, our models match or outperform SpeechMatrix model performance, despite using 8 times fewer raw speech documents.

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TurBLiMP: A Turkish Benchmark of Linguistic Minimal Pairs
Ezgi Başar | Francesca Padovani | Jaap Jumelet | Arianna Bisazza

We introduce TurBLiMP, the first Turkish benchmark of linguistic minimal pairs, designed to evaluate the linguistic abilities of monolingual and multilingual language models (LMs). Covering 16 linguistic phenomena with 1000 minimal pairs each, TurBLiMP fills an important gap in linguistic evaluation resources for Turkish. In designing the benchmark, we give extra attention to two properties of Turkish that remain understudied in current syntactic evaluations of LMs, namely word order flexibility and subordination through morphological processes. Our experiments on a wide range of LMs and a newly collected set of human acceptability judgments reveal that even cutting-edge Large LMs still struggle with grammatical phenomena that are not challenging for humans, and may also exhibit different sensitivities to word order and morphological complexity compared to humans.

The advancements of Large Language Models (LLMs) have spurred a growing interest in their application to Named Entity Recognition (NER) methods. However, existing datasets are primarily designed for traditional machine learning methods and are inadequate for LLM-based methods, in terms of corpus selection and overall dataset design logic. Moreover, the prevalent fixed and relatively coarse-grained entity categorization in existing datasets fails to adequately assess the superior generalization and contextual understanding capabilities of LLM-based methods, thereby hindering a comprehensive demonstration of their broad application prospects. To address these limitations, we propose DynamicNER, the first NER dataset designed for LLM-based methods with dynamic categorization, introducing various entity types and entity type lists for the same entity in different context, leveraging the generalization of LLM-based NER better. The dataset is also multilingual and multi-granular, covering 8 languages and 155 entity types, with corpora spanning a diverse range of domains. Furthermore, we introduce CascadeNER, a novel NER method based on a two-stage strategy and lightweight LLMs, achieving higher accuracy on fine-grained tasks while requiring fewer computational resources. Experiments show that DynamicNER serves as a robust and effective benchmark for LLM-based NER methods. Furthermore, we also conduct analysis for traditional methods and LLM-based methods on our dataset. Our code and dataset are openly available at https://github.com/Astarojth/DynamicNER.

Automating Exploratory Data Analysis (EDA) is critical for accelerating the workflow of data scientists. While Large Language Models (LLMs) offer a promising solution, current LLM-only approaches often exhibit limited accuracy and code reliability on less-studied or private datasets. Moreover, their effectiveness significantly diminishes with open-source LLMs compared to proprietary ones, limiting their usability in enterprises that prefer local models for privacy and cost. To address these limitations, we introduce RAGvis: a novel two-stage graph-guided Retrieval-Augmented Generation (RAG) framework. RAGvis first builds a base knowledge graph (KG) of EDA notebooks and enriches it with structured EDA operation semantics. These semantics are extracted by an LLM guided by our empirically-developed EDA operations taxonomy. Second, in the online generation stage for new datasets, RAGvis retrieves relevant operations from the KG, aligns them to the dataset’s structure, refines them with LLM reasoning, and then employs a self-correcting agent to generate executable Python code. Experiments on two benchmarks demonstrate that RAGvis significantly improves code executability (pass rate), semantic accuracy, and visual quality in generated operations. This enhanced performance is achieved with substantially lower token usage compared to LLM-only baselines. Notably, our approach enables smaller, open-source LLMs to match the performance of proprietary models, presenting a reliable and cost-effective pathway for automated EDA code generation.

Large language models have shown promise in clinical decision making, but current approaches struggle to localize and correct errors at specific steps of the reasoning process. This limitation is critical in medicine, where identifying and addressing reasoning errors is essential for accurate diagnosis and effective patient care. We introduce Med-PRM, a process reward modeling framework that leverages retrieval-augmented generation to verify each reasoning step against established medical knowledge bases. By verifying intermediate reasoning steps with evidence retrieved from clinical guidelines and literature, our model can precisely assess the reasoning quality in a fine-grained manner. Evaluations on five medical QA benchmarks and two open-ended diagnostic tasks demonstrate that Med-PRM achieves state-of-the-art performance, with improving the performance of base models by up to 13.50% using Med-PRM. Moreover, we demonstrate the generality of Med-PRM by integrating it in a plug-and-play fashion with strong policy models such as Meerkat, achieving over 80% accuracy on MedQA for the first time using small-scale models of 8 billion parameters.

Auto-evaluating language models (LMs), *i.e*., using a grader LM to evaluate the candidate LM, is an appealing way to accelerate the evaluation process and the cost associated with it. But this presents a paradox: how can we trust the grader LM, which is presumably weaker than the candidate LM, to assess problems that are beyond the frontier of the capabilities of either model or both? For instance, today’s LMs struggle on graduate-level physics and Olympiad-level math, making them unreliable graders in these domains. We show that providing *privileged information* – such as ground-truth solutions or problem-specific guidelines – improves automated evaluations on such frontier problems. This approach offers two key advantages. First, it expands the range of problems where LMs graders apply. Specifically, weaker models can now rate the predictions of stronger models. Second, privileged information can be used to devise easier variations of challenging problems which improves the separability of different LMs on tasks where their performance is generally low. With this approach, general-purpose LM graders match the state of the art performance on *RewardBench*, surpassing almost all the specially-tuned models. LM graders also outperform individual human raters on *Vibe-Eval*, and approach human expert graders on Olympiad-level math problems.

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SAMULE: Self-Learning Agents Enhanced by Multi-level Reflection
Yubin Ge | Salvatore Romeo | Jason Cai | Monica Sunkara | Yi Zhang

Despite the rapid advancements in LLM agents, they still face the challenge of generating meaningful reflections due to inadequate error analysis and a reliance on rare successful trajectories, especially in complex tasks. In this work, we propose SAMULE, a new framework for self-learning agents powered by a retrospective language model that is trained based on Multi-Level Reflection Synthesis. It first synthesizes high-quality reflections across three complementary levels: Single-Trajectory Learning (micro-level) for detailed error correction; Intra-Task Learning (meso-level) to build error taxonomies across multiple trials of the same task, and Inter-Task Learning (macro-level) to extract transferable insights based on same typed errors from diverse task failures. Then we fine-tune a language model serving as the retrospective model to generate reflections during inference. We further extend our framework to interactive settings through a foresight-based reflection mechanism, enabling agents to proactively reflect and adapt during user interactions by comparing predicted and actual responses. Extensive experiments on three challenging benchmarks—TravelPlanner, NATURAL PLAN, and Tau-bench—demonstrate that our approach significantly outperforms reflection-based baselines. Our results highlight the critical role of well-designed reflection synthesis and failure-centric learning in building self-improving LLM agents.

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Database-Augmented Query Representation for Information Retrieval
Soyeong Jeong | Jinheon Baek | Sukmin Cho | Sung Ju Hwang | Jong C. Park

Information retrieval models that aim to search for documents relevant to a query have shown multiple successes, which have been applied to diverse tasks. Yet, the query from the user is oftentimes short, which challenges the retrievers to correctly fetch relevant documents. To tackle this, previous studies have proposed expanding the query with a couple of additional (user-related) features related to it. However, they may be suboptimal to effectively augment the query, and there is plenty of other information available to augment it in a relational database. Motivated by this fact, we present a novel retrieval framework called Database-Augmented Query representation (DAQu), which augments the original query with various (query-related) metadata across multiple tables. In addition, as the number of features in the metadata can be very large and there is no order among them, we encode them with the graph-based set-encoding strategy, which considers hierarchies of features in the database without order. We validate our DAQu in diverse retrieval scenarios, demonstrating that it significantly enhances overall retrieval performance over relevant baselines.

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The Enemy from Within: A Study of Political Delegitimization Discourse in Israeli Political Speech
Naama Rivlin-Angert | Guy Mor-Lan

We present the first large-scale computational study of political delegitimization discourse (PDD), defined as symbolic attacks on the normative validity of political entities. We curate and manually annotate a novel Hebrew-language corpus of 10,410 sentences drawn from parliamentary speeches (1993-2023), Facebook posts, and leading news outlets (2018-2021), of which 1,812 instances (17.4%) exhibit PDD and 642 carry additional annotations for intensity, incivility, target type, and affective framing. We introduce a two-stage classification pipeline, and benchmark finetuned encoder models and decoder LLMs. Our best model (DictaLM 2.0) attains an F₁ of 0.74 for binary PDD detection and a macro-F₁ of 0.67 for classification of delegitimization characteristics. Applying this classifier to longitudinal and cross-platform data, we see a marked rise in PDD over three decades, higher prevalence on social media versus parliamentary debate, greater use by male politicians than by their female counterparts, and stronger tendencies among right-leaning actors, with pronounced spikes during election campaigns and major political events. Our findings demonstrate the feasibility and value of automated PDD analysis for analyzing democratic discourse.

Recent research has shown that carefully crafted jailbreak inputs can induce large language models to produce harmful outputs, despite safety measures such as alignment. It is important to anticipate the range of potential Jailbreak attacks to guide effective defenses and accurate assessment of model safety. In this paper, we present a new approach for generating highly effective Jailbreak attacks that manipulate the attention of the model to selectively strengthen or weaken attention among different parts of the prompt. By harnessing attention loss, we develop more effective jailbreak attacks, that are also transferrable. The attacks amplify the success rate of existing Jailbreak algorithms, including GCG, AutoDAN, and ReNeLLM, while lowering their generation cost (for example, the amplified GCG attack achieves 91.2% ASR, vs. 67.9% for the original attack on Llama2-7B-chat/AdvBench, using less than a third of the generation time).

Foundation models learn highly transferable representations through large-scale pretraining on diverse data. An increasing body of research indicates that these representations exhibit a remarkable degree of similarity across architectures and modalities. In this survey, we investigate the representation potentials of foundation models, defined as the latent capacity of their learned representations to capture task-specific information within a single modality while also providing a transferable basis for alignment and unification across modalities. We begin by reviewing representative foundation models and the key metrics that make alignment measurable. We then synthesize empirical evidence of representation potentials from studies in vision, language, speech, multimodality, and neuroscience. The evidence suggests that foundation models often exhibit structural regularities and semantic consistencies in their representation spaces, positioning them as strong candidates for cross-modal transfer and alignment. We further analyze the key factors that foster representation potentials, discuss open questions, and highlight potential challenges.

Conventional speculative decoding (SD) methods utilize a predefined length policy for proposing drafts, which implies the premise that the target model smoothly accepts the proposed draft tokens. However, reality deviates from this assumption: the oracle draft length varies significantly, and the fixed-length policy hardly satisfies such a requirement. Moreover, such discrepancy is further exacerbated in scenarios involving complex reasoning and long-form generation, particularly under test-time scaling for reasoning-specialized models. Through both theoretical and empirical estimation, we establish that the discrepancy between the draft and target models can be approximated by the draft model’s prediction entropy: a high entropy indicates a low acceptance rate of draft tokens, and vice versa. Based on this insight, we propose SVIP: Self-Verification Length Policy for Long-Context Speculative Decoding, which is a training-free dynamic length policy for speculative decoding systems that adaptively determines the lengths of draft sequences by referring to the draft entropy. Experimental results on mainstream SD benchmarks as well as reasoning-heavy benchmarks demonstrate the superior performance of SVIP, achieving up to 17% speedup on MT-Bench at 8K context compared with fixed draft lengths, and 22% speedup for QwQ in long-form reasoning.

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Visual-Aware Speech Recognition for Noisy Scenarios
Balaji Darur | Karan Singla

Humans have the ability to utilize visual cues, such as lip movements and visual scenes, to enhance auditory perception, particularly in noisy environments. However, current Automatic Speech Recognition (ASR) or Audio-Visual Speech Recognition (AVSR) models often struggle in noisy scenarios. To solve this task, we propose a model that improves transcription by correlating noise sources to visual cues. Unlike works that rely on lip motion and require the speaker’s visibility, we exploit broader visual information from the environment. This allows our model to naturally filter speech from noise and improve transcription, much like humans do in noisy scenarios. Our method re-purposes pretrained speech and visual encoders, linking them with multi-headed attention. This approach enables the transcription of speech and the prediction of noise labels in video inputs. We introduce a scalable pipeline to develop audio-visual datasets, where visual cues correlate to noise in the audio. We show significant improvements over existing audio-only models in noisy scenarios. Results also highlight that visual cues play a vital role in improved transcription accuracy

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Advancing Arabic Diacritization: Improved Datasets, Benchmarking, and State-of-the-Art Models
Abubakr Mohamed | Hamdy Mubarak

Arabic diacritics, similar to short vowels in English, provide phonetic and grammatical information but are typically omitted in written Arabic, leading to ambiguity. Diacritization (aka diacritic restoration or vowelization) is essential for natural language processing. This paper advances Arabic diacritization through the following contributions: first, we propose a methodology to analyze and refine a large diacritized corpus to improve training quality. Second, we introduce WikiNews-2024, a multi-reference evaluation methodology with an updated version of the standard benchmark “WikiNews-2014”. In addition, we explore various model architectures and propose a BiLSTM-based model that achieves state-of-the-art results with 3.12% and 2.70% WER on WikiNews-2014 and WikiNews-2024, respectively. Moreover, we develop a model that preserves user-specified diacritics while maintaining accuracy. Lastly, we demonstrate that augmenting training data enhances performance in low-resource settings.

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Implicit Values Embedded in How Humans and LLMs Complete Subjective Everyday Tasks
Arjun Arunasalam | Madison Pickering | Z. Berkay Celik | Blase Ur

Large language models (LLMs) can underpin AI assistants that help users with everyday tasks, such as by making recommendations or performing basic computation. Despite AI assistants’ promise, little is known about the implicit values these assistants display while completing subjective everyday tasks. Humans may consider values like environmentalism, charity, and diversity. To what extent do LLMs exhibit these values in completing everyday tasks? How do they compare with humans? We answer these questions by auditing how six popular LLMs complete 30 everyday tasks, comparing LLMs to each other and to 100 human crowdworkers from the US. We find LLMs often do not align with humans, nor with other LLMs, in the implicit values exhibited.

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Dynamic Retriever for In-Context Knowledge Editing via Policy Optimization
Mahmud Wasif Nafee | Maiqi Jiang | Haipeng Chen | Yanfu Zhang

Large language models (LLMs) excel at factual recall yet still propagate stale or incorrect knowledge. In‐context knowledge editing offers a gradient-free remedy suitable for black-box APIs, but current editors rely on static demonstration sets chosen by surface-level similarity, leading to two persistent obstacles: (i) a quantity–quality trade-off, and (ii) lack of adaptivity to task difficulty. We address these issues by dynamically selecting supporting demonstrations according to their utility for the edit. We propose **D**ynamic **R**etriever for **I**n-Context **K**nowledge **E**diting (DR-IKE), a lightweight framework that (1) trains a BERT retriever with REINFORCE to rank demonstrations by editing reward, and (2) employs a *learnable threshold σ* to prune low-value examples, shortening the prompt when the edit is easy and expanding it when the task is hard. DR-IKE performs editing without modifying model weights, relying solely on forward passes for compatibility with black-box LLMs. On the CounterFact benchmark, it improves edit success by up to 17.1%, reduces latency by 41.6%, and preserves accuracy on unrelated queries—demonstrating scalable and adaptive knowledge editing.

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LVLMs are Bad at Overhearing Human Referential Communication
Zhengxiang Wang | Weiling Li | Panagiotis Kaliosis | Owen Rambow | Susan Brennan

During spontaneous conversations, speakers collaborate on novel referring expressions, which they can then re-use in subsequent conversations. Understanding such referring expressions is an important ability for an embodied agent, so that it can carry out tasks in the real world. This requires integrating and understanding language, vision, and conversational interaction. We study the capabilities of seven state-of-the-art Large Vision Language Models (LVLMs) as overhearers to a corpus of spontaneous conversations between pairs of human discourse participants engaged in a collaborative object-matching task. We find that such a task remains challenging for current LVLMs and they all fail to show a consistent performance improvement as they overhear more conversations from the same discourse participants repeating the same task for multiple rounds. We release our corpus and code for reproducibility and to facilitate future research.

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Let’s Reason Formally: Natural-Formal Hybrid Reasoning Enhances LLM’s Math Capability
Ruida Wang | Yuxin Li | Yi R. Fung | Tong Zhang

Enhancing the mathematical reasoning capabilities of LLMs has garnered significant attention in both the mathematical and computer science communities. Recent works have made substantial progress in both Natural Language (NL) reasoning and Formal Language (FL) reasoning by leveraging the potential of pure Reinforcement Learning (RL) methods on base models. However, RL approaches struggle to impart new capabilities not presented in the base model, highlighting the need to integrate more knowledge like FL into NL math reasoning effectively. Yet, this integration is challenging due to inherent disparities in problem structure and reasoning format between NL and FL. To address these challenges, we introduce **NL-FL HybridReasoning (NFL-HR)**, an end-to-end framework designed to incorporate the FL expert into NL math problem-solving. To bridge the NL and FL input format gap, we propose the *NL-FL Problem Alignment* method, which reformulates the Question-Answering (QA) problems in NL as existence theorems in FL. Subsequently, the *Mixed Problem Input* technique we provide enables the FL reasoner to handle both QA and existence problems concurrently. Lastly, we mitigate the NL and FL output format gap in reasoning through an LLM-based *Answer Extraction* mechanism. Comprehensive experiments demonstrate that the **NFL-HR** framework achieves **89.80%** and **84.34%** accuracy rates on the MATH-500 and the AMC benchmarks, surpassing the NL baseline by 4.60% and 4.82%, respectively. Notably, some problems resolved by our framework remain unsolved by the NL baseline model even under a larger number of trials.

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TORSO: Template-Oriented Reasoning Towards General Tasks
Minhyuk Kim | Seungyoon Lee | Heuiseok Lim

The approaches that guide Large Language Models (LLMs) to emulate human reasoning during response generation have emerged as an effective method for enabling them to solve complex problems in a step-by-step manner, thereby achieving superior performance. However, most existing approaches using few-shot prompts to generate responses heavily depend on the provided examples, limiting the utilization of the model’s inherent reasoning capabilities. Moreover, constructing task-specific few-shot prompts is often costly and may lead to inconsistencies across different tasks. In this work, we introduce Template Oriented Reasoning (TORSO), which elicits the model to utilize internal reasoning abilities to generate proper responses across various tasks without the need for manually crafted few-shot examples. Our experimental results demonstrate that TORSO achieves strong performance on diverse LLMs benchmarks with reasonable rationales.

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Prototypical Human-AI Collaboration Behaviors from LLM-Assisted Writing in the Wild
Sheshera Mysore | Debarati Das | Hancheng Cao | Bahareh Sarrafzadeh

As large language models (LLMs) are used in complex writing workflows, users engage in multi-turn interactions to steer generations to better fit their needs. Rather than passively accepting output, users actively refine, explore, and co-construct text. We conduct a large scale analysis of this collaborative behavior for users engaged in writing tasks in the wild with two popular AI assistants, Bing Copilot and WildChat. Our analysis goes beyond simple task classification or satisfaction estimation common in prior work and instead characterizes how users interact with LLMs through the course of a session. We identify prototypical behaviors in how users interact with LLMs in prompts following their original request. We refer to these as Prototypical Human AI Collaboration Behaviors (PATHs) and find that a small group of PATHs explain a majority of the variation seen in user-LLM interaction. These PATHs span users revising intents, exploring texts, posing questions, adjusting style or injecting new content. Next, we find statistically significant correlations between specific writing intents and PATHs, revealing how users’ intents shape their collaboration behaviors. We conclude by discussing the implications of our findings on LLM alignment.

Recent advances in Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities across various vision-language tasks. However, their reasoning abilities in the multimodal symbolic music domain remain largely unexplored.We introduce WildScore, the first in-the-wild multimodal symbolic music reasoning and analysis benchmark, designed to evaluate MLLMs’ capacity to interpret real-world music scores and answer complex musicological queries. Each instance in WildScore is sourced from genuine musical compositions and accompanied by authentic user-generated questions and discussions, capturing the intricacies of practical music analysis. To facilitate a comprehensive evaluation, we propose a systematic taxonomy,comprising both high-level and fine-grained musicological ontologies. Furthermore, we frame complex music reasoning as multiple-choice question answering,enabling controlled and scalable assessment of MLLMs’ symbolic music understanding. Empirical benchmarking of state-of-the-art MLLMs on WildScore reveals intriguing patterns in their visual-symbolic reasoning, uncovering both promising directions and persistent challenges for MLLMs in symbolic music reasoning and analysis.We release the dataset and code.

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TRIAL: Token Relations and Importance Aware Late-interaction for Accurate Text Retrieval
Hyukkyu Kang | Injung Kim | Wook-Shin Han

Late-interaction based multi-vector retrieval systems have greatly advanced the field of information retrieval by enabling fast and accurate search over millions of documents. However, these systems rely on a naive summation of token-level similarity scores which often leads to inaccurate relevance estimation caused by the tokenization of semantic units (e.g., words and phrases) and the influence of low-content words (e.g., articles and prepositions). To address these challenges, we propose **TRIAL**: **T**oken **R**elations and **I**mportance **A**ware **L**ate-interaction, which enhances late interaction by explicitly modeling token relations and token importance in relevance scoring. Extensive experiments on three widely used benchmarks show that TRIAL achieves state-of-the-art accuracy, with an nDCG@10 of 46.3 on MSMARCO (in-domain), and average nDCG@10 scores of 51.09 and 72.15 on BEIR and LoTTE Search (out-of-domain), respectively. With superior accuracy, TRIAL maintains competitive retrieval speed compared to existing late-interaction methods, making it a practical solution for large-scale text retrieval.

Large Language Models (LLMs) have been shown to achieve breakthrough performances on complex logical reasoning tasks. Nevertheless, most existing research focuses on employing formal language to guide LLMs for deriving reliable reasoning paths, with systematic evaluations of these capabilities still being limited. In this paper, we aim to conduct a comprehensive evaluation of LLMs across various logical reasoning problems utilizing formal languages. From the perspective of three dimensions, i.e., spectrum of LLMs, taxonomy of tasks, and format of trajectories, our key findings are: 1) Thinking models significantly outperform Instruct models, especially when formal language is employed; 2). All LLMs exhibit limitations in inductive reasoning capability, irrespective of whether they use a formal language; 3). Data with PoT format achieves the best generalization performance across other languages. Additionally, we also curate the formal-relative training data to further enhance the small language models, and the experimental results indicate that a simple rejected fine-tuning method can better enable LLMs to generalize across formal languages and achieve the best overall performance.

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Fair or Framed? Political Bias in News Articles Generated by LLMs
Junho Yoo | Youhyun Shin

Despite biases in Large Language Models (LLMs) being widely researched, systematic explorations of political biases in news article generation tasks remain underexplored. This study evaluates political bias across seven LLMs by leveraging our PublicViews dataset-extracted from the TwinViews-13K corpus-comprising 31 topics and 31,692 statements. We analyze 10,850 articles, finding left-leaning bias persists in generation tasks, with neutral content remaining rare even under balanced opinion settings. Models exhibit asymmetric behavior in minority opinion scenarios, amplifying preferred viewpoints when in minority while conforming to majority opinions otherwise. Notably, all models employ ‘stance-flipping quotations” (altering supporters’ statements to express opposite viewpoints) in 33-38% of quotations despite explicit instructions against distortion. Consistent with prior research, increased model size failed to enhance neutrality. This research measures political bias in LLM-generated news, analyzes its mechanisms, and reveals how opinion distribution and explicitness affect bias expression. Our results highlight how LLMs can introduce unintended political bias in generative contexts. We publicly release our PublicViews corpus and code at https://anonymous.4open.science/r/Fair-or-Framed-46F1.

Peer review is essential for scientific progress but faces growing challenges due to increasing submission volumes and reviewer fatigue. Existing automated review approaches struggle with factual accuracy, rating consistency, and analytical depth, often generating superficial or generic feedback lacking the insights characteristic of high-quality human reviews. We introduce ReviewRL, a reinforcement learning framework for generating comprehensive and factually grounded scientific paper reviews. Our approach combines: (1) an ArXiv-MCP retrieval-augmented context generation pipeline that incorporates relevant scientific literature, (2) supervised fine-tuning that establishes foundational reviewing capabilities, and (3) a reinforcement learning procedure with a composite reward function that jointly enhances review quality and rating accuracy. Experiments on ICLR 2025 papers demonstrate that ReviewRL significantly outperforms existing methods across both rule-based metrics and model-based quality assessments. ReviewRL establishes a foundational framework for RL-driven automatic critique generation in scientific discovery, demonstrating promising potential for future development in this domain. The implementation of ReviewRL will be released at GitHub.

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Grammar Pruning: Enabling Low-Latency Zero-Shot Task-Oriented Language Models for Edge AI
Octavian Alexandru Trifan | Jason Lee Weber | Marc Titus Trifan | Alexandru Nicolau | Alexander Veidenbaum

Edge deployment of task-oriented semantic parsers demands high accuracy under tight latency and memory budgets. We present Grammar Pruning, a lightweight zero-shot framework that begins with a user-defined schema of API calls and couples a rule-based entity extractor with an iterative grammar-constrained decoder: extracted items dynamically prune the context-free grammar, limiting generation to only those intents, slots, and values that remain plausible at each step. This aggressive search-space reduction both reduces hallucinations and slashes decoding time. On the adapted FoodOrdering, APIMixSNIPS, and APIMixATIS benchmarks, Grammar Pruning with small language models achieves an average execution accuracy of over 90%—rivaling State-of-the-Art, cloud-based solutions—while sustaining at least 2x lower end-to-end latency than existing methods. By requiring nothing beyond the domain’s full API schema values yet delivering precise, real-time natural-language understanding, Grammar Pruning positions itself as a practical building block for future edge-AI applications that cannot rely on large models or cloud offloading.

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Calibrating LLMs for Text-to-SQL Parsing by Leveraging Sub-clause Frequencies
Terrance Liu | Shuyi Wang | Daniel Preotiuc-Pietro | Yash Chandarana | Chirag Gupta

While large language models (LLMs) achieve strong performance on text-to-SQL parsing, they sometimes exhibit unexpected failures in which they are confidently incorrect. Building trustworthy text-to-SQL systems thus requires eliciting reliable uncertainty measures from the LLM. In this paper, we study the problem of providing a calibrated confidence score that conveys the likelihood of an output query being correct. Our work is the first to establish a benchmark for post-hoc calibration of LLM-based text-to-SQL parsing. In particular, we show that Platt scaling, a canonical method for calibration, provides substantial improvements over directly using raw model output probabilities as confidence scores. Furthermore, we propose a method for text-to-SQL calibration that leverages the structured nature of SQL queries to provide more granular signals of correctness, named “sub-clause frequency” (SCF) scores. Using multivariate Platt scaling (MPS), our extension of the canonical Platt scaling technique, we combine individual SCF scores into an overall accurate and calibrated score. Empirical evaluation on two popular text-to-SQL datasets shows that our approach of combining MPS and SCF yields further improvements in calibration and the related task of error detection over traditional Platt scaling.

Large language model editing methods frequently suffer from overfitting, wherein factual updates can propagate beyond their intended scope, overemphasizing the edited target even when it’s contextually inappropriate. To address this challenge, we introduce REACT (Representation Extraction And Controllable Tuning), a unified two-phase framework designed for precise and controllable knowledge editing. In the initial phase, we utilize tailored stimuli to extract latent factual representations and apply Principal Component Analysis with a simple learnbale linear transformation to compute a directional “belief shift” vector for each instance. In the second phase, we apply controllable perturbations to hidden states using the obtained vector with a magnitude scalar, gated by a pre-trained classifier that permits edits only when contextually necessary. Relevant experiments on EVOKE benchmarks demonstrate that REACT significantly reduces overfitting across nearly all evaluation metrics, and experiments on COUNTERFACT and MQuAKE shows that our method preserves balanced basic editing performance (reliability, locality, and generality) under diverse editing scenarios.

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ThinkEdit: Interpretable Weight Editing to Mitigate Overly Short Thinking in Reasoning Models
Chung-En Sun | Ge Yan | Tsui-Wei Weng

Recent studies have shown that Large Language Models (LLMs) augmented with chain-of-thought (CoT) reasoning demonstrate impressive problem-solving abilities. However, in this work, we identify a recurring issue where these models occasionally generate overly short reasoning, leading to degraded performance on even simple mathematical problems. Specifically, we investigate how reasoning length is embedded in the hidden representations of reasoning models and its impact on accuracy. Our analysis reveals that reasoning length is governed by a linear direction in the representation space, allowing us to induce overly short reasoning by steering the model along this direction. Building on this insight, we introduce ThinkEdit, a simple yet effective weight-editing approach to mitigate the issue of overly short reasoning. We first identify a small subset of attention heads (approximately 4%) that predominantly drive short reasoning behavior. We then edit the output projection weights of these heads to remove the short reasoning direction. With changes to only 0.2% of the model’s parameters, ThinkEdit effectively reduces overly short reasoning and yields notable accuracy gains for short reasoning outputs (+6.39%), along with an overall improvement across multiple math benchmarks (+3.34%). Our findings provide new mechanistic insights into how reasoning length is controlled within LLMs and highlight the potential of fine-grained model interventions to improve reasoning quality.

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Incorporating Diverse Perspectives in Cultural Alignment: Survey of Evaluation Benchmarks Through A Three-Dimensional Framework
Meng-Chen Wu | Si-Chi Chin | Tess Wood | Ayush Goyal | Narayanan Sadagopan

Large Language Models (LLMs) increasingly serve diverse global audiences, making it critical for responsible AI deployment across cultures. While recent works have proposed various approaches to enhance cultural alignment in LLMs, a systematic analysis of their evaluation benchmarks remains needed. We propose a novel framework that conceptualizes alignment along three dimensions: Cultural Group (who to align with), Cultural Elements (what to align), and Awareness Scope (how to align: majority-focused vs. diversity-aware). Through this framework, we analyze 105 cultural alignment evaluation benchmarks, revealing significant imbalances: Region (37.9%) and Language (28.9%) dominate Cultural Group representation; Social and Political Relations (25.1%) and Speech and Language (20.9%) concentrate Cultural Elements coverage; and an overwhelming majority (97.1%) of datasets adopt majority-focused Awareness Scope approaches. In a case study examining AI safety evaluation across nine Asian countries (Section 5), we demonstrate how our framework reveals critical gaps between existing benchmarks and real-world cultural biases identified in the study, providing actionable guidance for developing more comprehensive evaluation resources tailored to specific deployment contexts.

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Are Large Language Models Chronically Online Surfers? A Dataset for Chinese Internet Meme Explanation
Yubo Xie | Chenkai Wang | Zongyang Ma | Fahui Miao

Large language models (LLMs) are trained on vast amounts of text from the Internet, but do they truly understand the viral content that rapidly spreads online—commonly known as memes? In this paper, we introduce CHIME, a dataset for CHinese Internet Meme Explanation. The dataset comprises popular phrase-based memes from the Chinese Internet, annotated with detailed information on their meaning, origin, example sentences, types, etc. To evaluate whether LLMs understand these memes, we designed two tasks. In the first task, we assessed the models’ ability to explain a given meme, identify its origin, and generate appropriate example sentences. The results show that while LLMs can explain the meanings of some memes, their performance declines significantly for culturally and linguistically nuanced meme types. Additionally, they consistently struggle to provide accurate origins for the memes. In the second task, we created a set of multiple-choice questions (MCQs) requiring LLMs to select the most appropriate meme to fill in a blank within a contextual sentence. While the evaluated models were able to provide correct answers, their performance remains noticeably below human levels. We have made CHIME public and hope it will facilitate future research on computational meme understanding.

Accurately evaluating the word sense disambiguation (WSD) capabilities of large language models (LLMs) remains challenging, as existing studies primarily rely on single-task evaluations and classification-based metrics that overlook the fundamental differences between generative LLMs and traditional classification models. To bridge this gap, we proposeRoDEval, the first comprehensive evaluation framework specifically tailored for assessing LLM-based WSD methods. RoDEval introduces four novel metrics: Disambiguation Scope, Disambiguation Robustness, Disambiguation Reliability, and Definition Generation Quality Score, enabling a multifaceted evaluation of LLMs’ WSD capabilities. Experimental results using RoDEval across five mainstream LLMs uncover significant limitations in their WSD performance. Specifically, incorrect definition selections in multiple-choice WSD tasks stem not from simple neglect or forget of correct options, but rather from incomplete acquisition of the all senses for polysemous words. Instead, disambiguation reliability is often compromised by the models’ persistent overconfidence. In addition, inherent biases continue to affect performance, and scaling up model parameters alone fails to meaningfully enhance their ability to generate accurate sense definitions. These findings provide actionable insights for enhancing LLMs’ WSD capabilities. The source code and evaluation scripts are open-sourced at https://github.com/DayDream405/RoDEval.

Accurately predicting public panic sentiment on social media is crucial for proactive governance and crisis management. Current efforts on this problem face three main challenges: lack of finely annotated data hinders emotion prediction studies, unmodeled risk perception causes prediction inaccuracies, and insufficient interpretability of panic formation mechanisms limits mechanistic insight. We address these issues by proposing a Psychology-driven generative Agent framework (PsychoAgent) for explainable panic prediction based on emotion arousal theory. Specifically, we first construct a fine-grained panic emotion dataset (namely COPE) via human-AI (Large Language Models, LLMs) collaboration, combining scalable LLM-based labeling with human annotators to ensure accuracy for panic emotion and to mitigate biases from linguistic variations. Then, we construct PsychoAgent integrating cross-domain heterogeneous data grounded in psychological mechanisms to model risk perception and cognitive differences in emotion generation. To enhance interpretability, we design an LLM-based role-playing agent that simulates individual psychological chains through dedicatedly designed prompts. Experimental results on our annotated dataset show that PsychoAgent improves panic emotion prediction performance by 13% to 21% compared to baseline models. Furthermore, the explainability and generalization of our approach is validated. Crucially, this represents a paradigm shift from opaque “data-driven fitting” to transparent “role-based simulation with mechanistic interpretation” for panic emotion prediction during emergencies. Our implementation is publicly available at: https://github.com/supersonic0919/PsychoAgent.

Mathematical reasoning through Chain-of-Thought (CoT) has emerged as a powerful capability of Large Language Models (LLMs), which can be further enhanced through Test-Time Scaling (TTS) methods like Beam Search and DVTS. However, these methods, despite improving accuracy by allocating more computational resources during inference, often suffer from path homogenization and inefficient use of intermediate results. To address these limitations, we propose Stepwise Reasoning Checkpoint Analysis (SRCA), a framework that introduces checkpoints between reasoning steps. It incorporates two key strategies: (1) Answer-Clustered Search, which groups reasoning paths by their intermediate checkpoint answers to maintain diversity while ensuring quality, and (2) Checkpoint Candidate Augmentation, which leverages all intermediate answers for final decision-making. Our approach effectively reduces path homogenization and creates a fault-tolerant mechanism by utilizing high-quality intermediate results. Experimental results show that SRCA improves reasoning accuracy compared to existing TTS methods across various mathematical datasets.

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Inter-sentence Context Modeling and Structure-aware Representation Enhancement for Conversational Sentiment Quadruple Extraction
Yu Zhang | Zhaoman Zhong | Huihui Lv

Conversational aspect-based sentiment quadruple analysis (DiaASQ) is a newly-emergent task aiming to extract quadruples of target-aspect-opinion-sentiment from a conversation text. Existing studies struggle to capture complete dialogue semantics, largely due to inadequate inter-utterance modeling and the underutilization of dialogue structure. To address these issues, we propose an Inter-sentence Context Modeling and Structure-aware Representation Enhancement model (ICMSR) to extract dialogue aspect sentiment quadruple. We design the Dialog Inter-sentence Contextual Enhancer (DICE) module after the sentence-by-sentence encoding phase to enhance inter-sentence interactions and mitigate contextual fragmentation caused by traditional sequential encoding. Moreover, to fully exploit structural information within dialogues, we propose the Dialog Feature Amplifier (DFA), which consists of two submodules: STREAM and SMM. The STREAM module integrates diverse structural dialogue information to generate structure-aware sentence representations, effectively improving the modeling of intra-dialogue structural relations. Furthermore, the Structural Multi-scale Mechanism (SMM) employs a multi-scale modeling approach, simulating varying extents of contextual awareness, thereby enhancing the model’s ability to capture cross-sentence structural dependencies. We extensively evaluate our method on benchmark datasets, and the empirical results consistently confirm its effectiveness.

Large Language Models (LLMs) have demonstrated remarkable creative writing capabilities, yet their substantial computational demands hinder widespread use. Enhancing Small Language Models (SLMs) offers a promising alternative, but current methods like Supervised Fine-Tuning (SFT) struggle with novelty, and Reinforcement Learning from Human Feedback (RLHF) is costly. This paper explores two distinct AI-driven reward strategies within a Reinforcement Learning from AI Feedback (RLAIF) framework to ignite the creative writing of a 7B-parameter SLM, specifically for generating Chinese greetings. The first strategy employs a Reward Model (RM) trained on high-quality preference data curated by a novel multi-agent rejection sampling framework designed for creative tasks. The second, more novel, strategy utilizes a principle-guided LLM-as-a-Judge, whose reward function is optimized via an adversarial training scheme with a reflection mechanism, to directly provide reward signals. Comprehensive experiments reveal that while both approaches significantly enhance creative output over baselines, the principle-guided LLM-as-a-Judge demonstrably yields superior generation quality. Furthermore, it offers notable advantages in training efficiency and reduced dependency on human-annotated data, presenting a more scalable and effective path towards creative SLMs. Our automated evaluation methods also exhibit strong alignment with human judgments.

Aligning large language models (LLMs) with human preferences is a central challenge for building reliable AI systems. Most existing alignment approaches rely on static signals, such as predefined principles or offline human annotations to guide model behavior toward a fixed approximation of human preferences. However, LLMs can exhibit distributional drift during training, and static alignment mechanisms lack the capacity to adaptively correct misaligned behaviors as they emerge. To address this limitation, we develop a two-stage framework that enables dynamic and continuous alignment. In the first stage, a constitution is continually revised based on observed model behaviors, and models are trained to comply with these evolving principles. In the second stage, this learned constitution is used to guide reinforcement learning, encouraging the model to align with the updated normative signals. We refer to this framework as COCOA: Co-evolution of Constitutions and AI Models. We show that COCOA enables a 7B model to greatly improve safety—raising StrongReject score from 0.741 to 0.935 and Safe-RLHF accuracy from 77.76% to 90.64% without human annotations, reaching performance close to much larger state-of-the-art models.

The protection of cyber Intellectual Property (IP) such as web content is an increasingly critical concern. The rise of large language models (LLMs) with online retrieval capabilities enables convenient access to information but often undermines the rights of original content creators. As users increasingly rely on LLM-generated responses, they gradually diminish direct engagement with original information sources, which will significantly reduce the incentives for IP creators to contribute, and lead to a saturating cyberspace with more AI-generated content. In response, we propose a novel defense framework that empowers web content creators to safeguard their web-based IP from unauthorized LLM real-time extraction and redistribution by leveraging the semantic understanding capability of LLMs themselves. Our method follows principled motivations and effectively addresses an intractable black-box optimization problem. Real-world experiments demonstrated that our methods improve defense success rates from 2.5% to 88.6% on different LLMs, outperforming traditional defenses such as configuration-based restrictions.

Scientific information extraction (SciIE) has primarily relied on entity-relation extraction in narrow domains, limiting its applicability to interdisciplinary research and struggling to capture the necessary context of scientific information, often resulting in fragmented or conflicting statements. In this paper, we introduce SciEvent, a novel multi-domain benchmark of scientific abstracts annotated via a unified event extraction (EE) schema designed to enable structured and context-aware understanding of scientific content. It includes 500 abstracts across five research domains, with manual annotations of event segments, triggers, and fine-grained arguments. We define SciIE as a multi-stage EE pipeline: (1) segmenting abstracts into core scientific activities—Background, Method, Result, and Conclusion; and (2) extracting the corresponding triggers and arguments. Experiments with fine-tuned EE models, large language models (LLMs), and human annotators reveal a performance gap, with current models struggling in domains such as sociology and humanities. SciEvent serves as a challenging benchmark and a step toward generalizable, multi-domain SciIE.

Unlike traditional search engines that present ranked lists of webpages, generative search engines rely solely on in-line citations as the key gateway to original real-world webpages, making it crucial to examine whether LLM-generated citations have biases—particularly for politically sensitive queries. To investigate this, we first construct AllSides-2024, a new dataset comprising the latest real-world news articles (Jan. 2024 - Dec. 2024) labeled with left- or right-leaning stances. Through systematic evaluations, we find that LLMs exhibit a consistent tendency to cite left-leaning sources at notably higher rates compared to traditional retrieval systems (e.g., BM25 and dense retrievers). Controlled experiments further reveal that this bias arises from a preference for media outlets identified as left-leaning, rather than for left-oriented content itself. Meanwhile, our findings show that while LLMs struggle to infer political bias from news content alone, they can almost perfectly recognize the political orientation of media outlets based on their names. These insights highlight the risk that, in the era of generative search engines, information exposure may be disproportionately shaped by specific media outlets, potentially shaping public perception and decision-making.

Knowledge graph question answering (KGQA) aims to answer natural language questions using knowledge graphs.Recent research leverages large language models (LLMs) to enhance KGQA reasoning, but faces limitations: retrieval-based methods are constrained by the quality of retrieved information, while agent-based methods rely heavily on proprietary LLMs.To address these limitations, we propose Retrieval-Judgment-Exploration (RJE), a framework that retrieves refined reasoning paths, evaluates their sufficiency, and conditionally explores additional evidence. Moreover, RJE introduces specialized auxiliary modules enabling small-sized LLMs to perform effectively: Reasoning Path Ranking, Question Decomposition, and Retriever-assisted Exploration. Experiments show that our approach with proprietary LLMs (such as GPT-4o-mini) outperforms existing baselines while enabling small open-source LLMs (such as 3B and 8B parameters) to achieve competitive results without fine-tuning LLMs.Additionally, RJE substantially reduces the number of LLM calls and token usage compared to agent-based methods, yielding significant efficiency improvements.

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Bias Mitigation or Cultural Commonsense? Evaluating LLMs with a Japanese Dataset
Taisei Yamamoto | Ryoma Kumon | Danushka Bollegala | Hitomi Yanaka

Large language models (LLMs) exhibit social biases, prompting the development of various debiasing methods. However, debiasing methods may degrade the capabilities of LLMs. Previous research has evaluated the impact of bias mitigation primarily through tasks measuring general language understanding, which are often unrelated to social biases. In contrast, cultural commonsense is closely related to social biases, as both are rooted in social norms and values. The impact of bias mitigation on cultural commonsense in LLMs has not been well investigated. Considering this gap, we propose SOBACO (SOcial BiAs and Cultural cOmmonsense benchmark), a Japanese benchmark designed to evaluate social biases and cultural commonsense in LLMs in a unified format. We evaluate several LLMs on SOBACO to examine how debiasing methods affect cultural commonsense in LLMs. Our results reveal that the debiasing methods degrade the performance of the LLMs on the cultural commonsense task (up to 75% accuracy deterioration). These results highlight the importance of developing debiasing methods that consider the trade-off with cultural commonsense to improve fairness and utility of LLMs.

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Chameleon LLMs: User Personas Influence Chatbot Personality Shifts
Jane Xing | Tianyi Niu | Shashank Srivastava

As large language models (LLMs) integrate into society, their ability to adapt to users is as critical as their accuracy. While prior work has used personality tests to examine the perceived personalities of LLMs, little research has explored whether LLMs adapt their perceived personalities in response to user interactions. We investigate whether and how LLMs exhibit conversational adaptations over prolonged interactions. Using a controlled simulations where a user and chatbot engage in dialogue, we measure the chatbot’s personality shift before and after the conversation. Across multiple models, we find that traits such as Agreeableness, Extraversion, and Conscientiousness are highly susceptible to user influence, whereas Emotional Stability and Intellect remain relatively more stable. Our results suggest that LLMs dynamically adjust their conversational style in response to user personas, raising important implications for AI alignment, trust, and safety.

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GuessingGame: Measuring the Informativeness of Open-Ended Questions in Large Language Models
Dylan Hutson | Daniel Vennemeyer | Aneesh Deshmukh | Justin Zhan | Tianyu Jiang

We introduce GuessingGame, a protocol for evaluating large language models (LLMs) as strategic question-askers in open-ended, open-domain settings. A Guesser LLM identifies a hidden object by posing free-form questions to an Oracle—without predefined choices or candidate lists. To measure question quality, we propose two information gain (IG) metrics: a Bayesian method that tracks belief updates over semantic concepts using LLM-scored relevance, and an entropy-based method that filters candidates via ConceptNet. Both metrics are model-agnostic and support post hoc analysis. Across 858 games with multiple models and prompting strategies, higher IG strongly predicts efficiency: a one-standard-deviation IG increase reduces expected game length by 43%. Prompting constraints guided by IG—such as enforcing question diversity—enable weaker models to match GPT-4o. These results show that question-asking in LLMs is both measurable and improvable, and crucial for interactive reasoning.

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SynC-LLM: Generation of Large-Scale Synthetic Circuit Code with Hierarchical Language Models
Shang Liu | Yao Lu | Wenji Fang | Jing Wang | Zhiyao Xie

In recent years, AI-assisted integrated circuit (IC) design methods have shown great potential in boosting IC design efficiency. However, this emerging technique is fundamentally limited by the serious scarcity of publicly accessible large-scale circuit design data, which are mostly private IPs owned by semiconductor companies. In this work, we propose SynC-LLM, the first technique that exploits LLM’s ability to generate new large-scale synthetic digital circuits. In our hierarchical circuit generation process, we first design a directed graph diffusion model to learn and generate the skeleton of large circuits with sequential cells. Then we propose a cone function retrieval technique to annotate each sequential node in the skeleton with a function description. Finally, we apply a level-by-level customized prompting technique utilizing LLM to complete the code at every skeleton cone. Experiments show that our generated circuits are not only valid and fully functional, but also closely resemble realistic large-scale designs and can significantly improve AI models’ performance in multiple IC design tasks. The code and data are open-sourced in https://github.com/hkust-zhiyao/SynCircuitData.

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Why Stop at One Error? Benchmarking LLMs as Data Science Code Debuggers for Multi-Hop and Multi-Bug Errors
Zhiyu Yang | Shuo Wang | Yukun Yan | Yang Deng

LLMs are transforming software development, yet current code generation and code repair benchmarks mainly assess syntactic and functional correctness in simple, single-error cases. LLMs’ capabilities to autonomously find and fix runtime logical errors in complex data science code remain largely unexplored. To address this gap, we introduce DSDBench: the Data Science Debugging Benchmark, the first benchmark for systematic evaluation of LLMs on multi-hop error tracing and multi-bug detection in data science code debugging. DSDBench adapts datasets from existing data science task benchmarks, such as DABench and MatPlotBench, featuring realistic data science debugging tasks with automatically synthesized multi-hop, multi-bug code snippets. DSDBench includes 1,117 annotated samples with 741 cause-effect error pairs and runtime error messages. Evaluations of state-of-the-art LLMs on DSDBench show significant performance gaps, highlighting challenges in debugging logical runtime errors in data science code. DSDBench offers a crucial resource to evaluate and improve LLMs’ debugging and reasoning capabilities, enabling more reliable AI-assisted data science in the future.

With the continuous advancement in the performance of large language models (LLMs), their demand for computational resources and memory has significantly increased, which poses major challenges for efficient inference on consumer-grade devices and legacy servers. These devices typically feature relatively weaker GPUs and stronger CPUs. Although techniques such as parameter offloading and partial offloading can alleviate GPU memory pressure to some extent, their effectiveness is limited due to communication latency and suboptimal hardware resource utilization. To address this issue, we propose Dovetail—a lossless inference acceleration method that leverages the complementary characteristics of heterogeneous devices and the advantages of speculative decoding. Dovetail deploys a draft model on the GPU to perform preliminary predictions, while a target model running on the CPU validates these outputs. By reducing the granularity of data transfer, Dovetail significantly minimizes communication overhead. To further improve efficiency, we optimize the draft model specifically for heterogeneous hardware environments by reducing the number of draft tokens to lower parallel verification latency, increasing model depth to enhance predictive capabilities, and introducing a Dynamic Gating Fusion (DGF) mechanism to improve the integration of feature and embedding information. We conduct comprehensive evaluations of Dovetail across various consumer-grade GPUs, covering multiple tasks and mainstream models. Experimental results on 13B models demonstrate that Dovetail achieves inference speedups ranging from 1.79× to 10.1× across different devices, while maintaining consistency and stability in the distribution of generated texts.

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V-SEAM: Visual Semantic Editing and Attention Modulating for Causal Interpretability of Vision-Language Models
Qidong Wang | Junjie Hu | Ming Jiang

Recent advances in causal interpretability have extended from language models to vision-language models (VLMs), seeking to reveal their internal mechanisms through input interventions. While textual interventions often target semantics, visual interventions typically rely on coarse pixel-level perturbations, limiting semantic insights on multimodal integration. In this study, we introduce V-SEAM, a novel framework that combines **V**isual **S**emantic **E**diting and **A**ttention **M**odulating for causal interpretation of VLMs. V-SEAM enables concept-level visual manipulations and identifies attention heads with positive or negative contributions to predictions across three semantic levels: objects, attributes, and relationships. We observe that positive heads are often shared within the same semantic level but vary across levels, while negative heads tend to generalize broadly. Finally, we introduce an automatic method to modulate key head embeddings, demonstrating enhanced performance for both LLAVA and InstructBLIP across three diverse VQA benchmarks. Our data and code are released at: https://github.com/petergit1/V-SEAM.

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LORAXBENCH: A Multitask, Multilingual Benchmark Suite for 20 Indonesian Languages
Alham Fikri Aji | Trevor Cohn

As one of the world’s most populous countries, with 700 languages spoken, Indonesia is behind in terms of NLP progress. We introduce LORAXBENCH, a benchmark that focuses on low-resource languages of Indonesia and covers 6 diverse tasks: reading comprehension, open-domain QA, language inference, causal reasoning, translation, and cultural QA. Our dataset cover 20 languages, with the addition of two formality registers for three languages. We evaluate a diverse set of multilingual and region-focused LLMs and found that this benchmark is challenging. We note a visible discrepancy between performance in Indonesian and other languages, especially the low-resource ones. There is no clear lead when using a region-specific model as opposed to the general multilingual model. Lastly, we show that a change in register affects model performance, especially with registers not commonly found in social media, such as high-level politeness ‘Krama’ Javanese.

Reward modeling is a key step in building safe foundation models when applying reinforcement learning from human feedback (RLHF) to align Large Language Models (LLMs). However, reward modeling based on the Bradley-Terry (BT) model assumes a global reward function, failing to capture the inherently diverse and heterogeneous human preferences. Hence, such oversimplification limits LLMs from supporting personalization and pluralistic alignment. Theoretically, we show that when human preferences follow a mixture distribution of diverse subgroups, a single BT model has an irreducible error. While existing solutions, such as fine-grained annotations via prompting or structured preference elicitation, help address this issue, they are costly and constrained by predefined attributes, failing to fully capture the richness of human values. In this work, we introduce MiCRo, a two-stage framework that enhances personalized preference learning by leveraging large-scale binary preference datasets without requiring explicit fine-grained annotations. In the first stage, MiCRo employs a mixture of preferences to model diverse human preferences, enabling a flexible representation of diverse value systems. In the second stage, MiCRo integrates an online routing strategy that dynamically adapts mixture weights based on specific context to resolve ambiguity, allowing for efficient and scalable preference adaptation with minimal additional supervision. Experiments on multiple preference datasets demonstrate that MiCRo effectively captures diverse human preferences and significantly improves personalized preference learning on downstream tasks.

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SAFE: Schema-Driven Approximate Distance Join for Efficient Knowledge Graph Querying
Sangoh Lee | Sungho Park | Wook-Shin Han

To reduce hallucinations in large language models (LLMs), researchers are increasingly investigating reasoning methods that integrate LLMs with external knowledge graphs (KGs). Existing approaches either map an LLM-generated query graph onto the KG or let the LLM traverse the entire graph; the former is fragile because noisy query graphs derail retrieval, whereas the latter is inefficient due to entity-level reasoning over large graphs. In order to tackle these problems, we propose **SAFE** (**S**chema-Driven **A**pproximate Distance Join **F**or **E**fficient Knowledge Graph Querying), a framework that leverages schema graphs for robust query graph generation and efficient KG retrieval. SAFE introduces two key ideas: (1) an Approximate Distance Join (ADJ) algorithm that refines LLM-generated pseudo query graphs by flexibly aligning them with the KG’s structure; and (2) exploiting a compact schema graph to perform ADJ efficiently, reducing overhead and improving retrieval accuracy. Extensive experiments on WebQSP, CWQ and GrailQA demonstrate that SAFE outperforms state-of-the-art methods in both accuracy and efficiency, providing a robust and scalable solution to overcome the inherent limitations of LLM-based knowledge retrieval.

Existing vision-language planning methods perform well on short-horizon tasks but struggle with long-horizon reasoning in dynamic environments due to the difficulty of training models to generate high-quality reasoning processes. To address this, we propose Structured Preference Optimization (SPO), a framework that enhances reasoning and action selection for long-horizon task planning through structured evaluation and optimized training. SPO introduces: 1) Structured Preference Evaluation and Optimization, which evaluates reasoning chains across task relevance, historical consistency (as part of textual coherence), and image awareness (alignment with visual observations) to construct high-quality preference pairs; and 2) Curriculum-Guided Progressive Learning, enabling the model to adapt from simple to complex tasks, thereby improving generalization and robustness. To advance research in vision-language long-horizon task planning, we introduce ExtendaBench, a comprehensive benchmark covering 1,509 tasks across VirtualHome and Habitat 2.0, categorized into ultra-short, short, medium, and long tasks. Experimental results demonstrate that SPO significantly improves reasoning quality and final decision accuracy, outperforming prior methods on long-horizon tasks and underscoring the effectiveness of preference-driven optimization in vision-language task planning. Specifically, SPO achieves a +5.98% GCR and +4.68% SR improvement in VirtualHome and a +3.30% GCR and +2.11% SR improvement in Habitat over the best-performing baselines.

While recent efforts have begun integrating large language models (LLMs) into English education, they often rely on traditional approaches to learning tasks without fully embracing educational methodologies, thus lacking adaptability to language learning. To address this gap, we argue that **LLMs have the potential to serve as effective tutors in English Education**. Specifically, LLMs can play three critical roles: (1) as data enhancers, improving the creation of learning materials or serving as student simulations; (2) as task predictors, serving as learner assessment or optimizing learning pathway; and (3) as agents, enabling personalized and inclusive education. We encourage interdisciplinary research to explore these roles, fostering innovation while addressing challenges and risks, ultimately advancing English Education through the thoughtful integration of LLMs.

Forecasting weather and climate events is crucial for making appropriate measures to mitigate environmental hazards and minimize losses. However, existing environmental forecasting research focuses narrowly on predicting numerical meteorological variables (e.g., temperature), neglecting the translation of these variables into actionable textual narratives of events and their consequences. To bridge this gap, we proposed Weather and Climate Event Forecasting (WCEF), a new task that leverages numerical meteorological raster data and textual event data to predict weather and climate events. This task is challenging to accomplish due to difficulties in aligning multimodal data and the lack of supervised datasets. To address these challenges, we present CLLMate, the first multimodal dataset for WCEF, using 26,156 environmental news articles aligned with ERA5 reanalysis data. We systematically benchmark 32 existing models on CLLMate, including closed-source, open-source, and our fine-tuned models. Our experiments reveal the advantages and limitations of existing MLLMs and the value of CLLMate for the training and benchmarking of the WCEF task. The dataset is available at https://github.com/hobolee/CLLMate.

Multi-lingual ability transfer has become increasingly important for the broad application of large language models (LLMs). Existing work highly relies on training with the multi-lingual ability-related data, which may not be available for low-resource languages. To solve it, we propose a **M**ulti-lingual **A**bilities **E**xtraction and **C**ombination approach, named as **MAEC**. Our key idea is to decompose and extract language-agnostic ability-related weights from LLMs, and combine them across different languages by simple addition and subtraction operations without training. Specifically, our MAEC consists of the extraction and combination stages. In the extraction stage, we firstly locate key neurons that are highly related to specific abilities, and then employ them to extract the transferable ability-related weights. In the combination stage, we further select the ability-related tensors that mitigate the linguistic effects, and design a combining strategy based on them and the language-specific weights, to build the multi-lingual ability-enhanced LLM. To assess the effectiveness of our approach, we conduct extensive experiments on LLaMA-3 8B on mathematical and scientific tasks in both high-resource and low-resource lingual scenarios. Experiment results have shown that MAEC can effectively and efficiently extract and combine the advanced abilities, achieving **comparable performance with PaLM**. We will publicly release our code and data.

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Evaluating the Effectiveness and Scalability of LLM-Based Data Augmentation for Retrieval
Pranjal A Chitale | Bishal Santra | Yashoteja Prabhu | Amit Sharma

Compact dual-encoder models are widely used for retrieval owing to their efficiency and scalability. However, such models often underperform compared to their Large Language Model (LLM)-based retrieval counterparts, likely due to their limited world knowledge. While LLM-based data augmentation has been proposed as a strategy to bridge this performance gap, there is insufficient understanding of its effectiveness and scalability to real-world retrieval problems. Existing research does not systematically explore key factors such as the optimal augmentation scale, the necessity of using large augmentation models, and whether diverse augmentations improve generalization, particularly in out-of-distribution (OOD) settings. This work presents a comprehensive study of the effectiveness of LLM augmentation for retrieval, comprising over 100 distinct experimental settings of retrieval models, augmentation models and augmentation strategies. We find that, while augmentation enhances retrieval performance, its benefits diminish beyond a certain scale, even with diverse augmentation strategies. Surprisingly, we observe that augmentation with smaller LLMs can achieve performance competitive with larger augmentation models. Moreover, we examine how augmentation effectiveness varies with retrieval model pre-training, revealing that augmentation provides the most benefit to models which are not well pre-trained. Our insights pave the way for more judicious and efficient augmentation strategies, thus enabling informed decisions and maximizing retrieval performance while being more cost-effective.

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Temporal Referential Consistency: Do LLMs Favor Sequences Over Absolute Time References?
Ashutosh Bajpai | Tanmoy Chakraborty

The increasing acceptance of large language models (LLMs) as an alternative to knowledge sources marks a significant paradigm shift across various domains, including time-sensitive fields such as law, healthcare, and finance. To fulfill this expanded role, LLMs must not only be factually accurate but also demonstrate consistency across temporal dimensions, necessitating robust temporal reasoning capabilities. Despite this critical requirement, efforts to ensure temporal consistency in LLMs remain scarce including noticeable absence of endeavors aimed at evaluating or augmenting LLMs across temporal references in time-sensitive inquiries. In this paper, we seek to address this gap by introducing a novel benchmark entitled temporal referential consistency, accompanied by a resource TEMP-ReCon designed to benchmark a wide range of both open-source and closed-source LLMs with various linguistic contexts characterized by differing resource richness (including English, French, and Romanian). The findings emphasis that LLMs do exhibit insufficient temporal referent consistency. To address this, we propose , a reasoning path alignment-based model that aims to enhance the temporal referential consistency of LLMs. Our empirical experiments substantiate the efficacy of UnTRaP compared to several baseline models.

The proliferation of memes on social media necessitates the capabilities of multimodal Large Language Models (mLLMs) to effectively understand multimodal harmfulness. Existing evaluation approaches predominantly focus on mLLMs’ detection accuracy for binary classification tasks, which often fail to reflect the in-depth interpretive nuance of harmfulness across diverse contexts. In this paper, we propose MemeArena, an agent-based arena-style evaluation framework that provides a context-aware and unbiased assessment for mLLMs’ understanding of multimodal harmfulness. Specifically, MemeArena simulates diverse interpretive contexts to formulate evaluation tasks that elicit perspective-specific analyses from mLLMs. By integrating varied viewpoints and reaching consensus among evaluators, it enables fair and unbiased comparisons of mLLMs’ abilities to interpret multimodal harmfulness. Extensive experiments demonstrate that our framework effectively reduces the evaluation biases of judge agents, with judgment results closely aligning with human preferences, offering valuable insights into reliable and comprehensive mLLM evaluations in multimodal harmfulness understanding. Our code and data are publicly available at https://github.com/Lbotirx/MemeArena.

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Multi-perspective Analysis of Large Language Model Domain Specialization: An Experiment in Accounting Audit Procedures Generation
Yusuke Noro

Two major domain specialization approaches for Large Language Models (LLMs), fine-tuning and In-Context Learning (ICL), have been compared across various domains.While prior research has examined the similarities and differences between these approaches in task-specific capabilities, less is known about how they affect the feature of the generated text itself.To address this research gap, we conducted an experimental study using Accounting Audit Procedures Generation (AAPG) task, a highly specialized task requiring expert accounting knowledge.This task provides a practical testbed for a multi-perspective analysis of domain specialization due to its technical complexity and the large gap between general and domain expert knowledge.The results show consistent differences in output characteristics across models when comparing fine-tuning, ICL, and their combined approaches.

Large language model (LLM) agents typically adopt a step-by-step reasoning framework, in which they interleave the processes of thinking and acting to accomplish the given task. However, this paradigm faces a deep-rooted one-pass issue whereby each generated intermediate thought is plugged into the trajectory regardless of its correctness, which can cause irreversible error propagation. To address the issue, this paper proposes a novel framework called Generator-Assistant Stepwise Rollback (GA-Rollback) to induce better decision-making for LLM agents. Particularly, GA-Rollback utilizes a generator to interact with the environment and an assistant to examine each action produced by the generator, where the assistant triggers a rollback operation upon detection of incorrect actions. Moreover, we introduce two additional strategies tailored for the rollback scenario to further improve its effectiveness. Extensive experiments show that GA-Rollback achieves significant improvements over several strong baselines on three widely used benchmarks. Our analysis further reveals that GA-Rollback can function as a robust plug-and-play module, integrating seamlessly with other methods.

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DocAgent: An Agentic Framework for Multi-Modal Long-Context Document Understanding
Li Sun | Liu He | Shuyue Jia | Yangfan He | Chenyu You

Recent advances in large language models (LLMs) have demonstrated significant promise in document understanding and question-answering. Despite the progress, existing approaches can only process short documents due to limited context length or fail to fully leverage multi-modal information. In this work, we introduce DocAgent, a multi-agent framework for long-context document understanding that imitates human reading practice. Specifically, we first extract a structured, tree-formatted outline from documents to help agents identify relevant sections efficiently. Further, we develop an interactive reading interface that enables agents to query and retrieve various types of content dynamically. To ensure answer reliability, we introduce a reviewer agent that cross-checks responses using complementary sources and maintains a task-agnostic memory bank to facilitate knowledge sharing across tasks. We evaluate our method on two long-context document understanding benchmarks, where it bridges the gap to human-level performance by surpassing competitive baselines, while maintaining a short context length. Our code is available at https://github.com/lisun-ai/DocAgent.

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EasyRec: Simple yet Effective Language Models for Recommendation
Xubin Ren | Chao Huang

Deep neural networks have emerged as a powerful technique for learning representations from user-item interaction data in collaborative filtering (CF) for recommender systems. However, many existing methods heavily rely on unique user and item IDs, which restricts their performance in zero-shot learning scenarios. Inspired by the success of language models (LMs) and their robust generalization capabilities, we pose the question: How can we leverage language models to enhance recommender systems? We propose EasyRec, an effective approach that integrates text-based semantic understanding with collaborative signals. EasyRec employs a text-behavior alignment framework that combines contrastive learning with collaborative language model tuning. This ensures strong alignment between text-enhanced semantic representations and collaborative behavior information. Extensive evaluations across diverse datasets show EasyRec significantly outperforms state-of-the-art models, particularly in text-based zero-shot recommendation. EasyRec functions as a plug-and-play component that integrates seamlessly into collaborative filtering frameworks. This empowers existing systems with improved performance and adaptability to user preferences. Implementation codes are publicly available at: https://github.com/HKUDS/EasyRec

Large Language Models (LLMs) are catalyzing a paradigm shift in scientific discovery, evolving from task-specific automation tools into increasingly autonomous agents and fundamentally redefining research processes and human-AI collaboration. This survey systematically charts this burgeoning field, placing a central focus on the changing roles and escalating capabilities of LLMs in science. Through the lens of the scientific method, we introduce a foundational three-level taxonomy—Tool, Analyst, and Scientist—to delineate their escalating autonomy and evolving responsibilities within the research lifecycle. We further identify pivotal challenges and future research trajectories such as robotic automation, self-improvement, and ethical governance. Overall, this survey provides a conceptual architecture and strategic foresight to navigate and shape the future of AI-driven scientific discovery, fostering both rapid innovation and responsible advancement.

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Mapping the Minds of LLMs: A Graph-Based Analysis of Reasoning LLMs
Zhen Xiong | Yujun Cai | Zhecheng Li | Yiwei Wang

Recent advances in test-time scaling have enabled Large Language Models (LLMs) to display sophisticated reasoning abilities via extended Chain-of-Thought (CoT) generation. Despite their impressive reasoning abilities, Large Reasoning Models (LRMs) frequently display unstable behaviors, e.g., hallucinating unsupported premises, overthinking simple tasks, and displaying higher sensitivity to prompt variations. This raises a deeper research question: How can we represent the reasoning process of LRMs to map their minds? To address this, we propose a unified graph-based analytical framework for fine-grained modeling and quantitative analysis of LRM reasoning dynamics. Our method first clusters long, verbose CoT outputs into semantically coherent reasoning steps, then constructs directed reasoning graphs to capture contextual and logical dependencies among these steps. Through a comprehensive analysis of derived reasoning graphs, we also reveal that key structural properties, such as exploration density, branching, and convergence ratios, strongly correlate with models’ performance. The proposed framework enables quantitative evaluation of internal reasoning structure and quality beyond conventional metrics and also provides practical insights for prompt engineering and cognitive analysis of LLMs. Code and resources will be released to facilitate future research in this direction.

Existing video-language models (Video-LLMs) typically rely on concatenating visual tokens with textual inputs for joint modeling. However, this token-level alignment leads to significant inefficiency, especially when scaling to long videos with dense visual inputs. In this work, we propose a video-to-parameter efficiency paradigm named ViPE that eliminates redundant visual tokens by transforming video content into visual perceptual weights, which are directly injected into the LLM’s parameters. ViPE consists of a visual injection module that compresses video features into a small set of perceptual queries using a hierarchical merge strategy, and a visual perception module that integrates the resulting representations into the LLM through a lightweight LoRA-like mechanism. ViPE achieves performance comparable to token-based baselines such as LLaVA, while reducing FLOPs by 85% and inference time by up to 65%, demonstrating a highly efficient and scalable solution for video understanding.

Recent advancements in tool learning have enabled large language models (LLMs) to integrate external tools, enhancing their task performance by expanding their knowledge boundaries. However, relying on tools often introduces trade-offs between performance, speed, and cost, with LLMs sometimes exhibiting overreliance and overconfidence in tool usage. This paper addresses the challenge of aligning LLMs with their knowledge boundaries to make more intelligent decisions about tool invocation. We propose a multi-objective alignment framework that combines probabilistic knowledge boundary estimation with dynamic decision-making, allowing LLMs to better assess when to invoke tools based on their confidence. Our framework includes two methods for knowledge boundary estimation—consistency-based and absolute estimation—and two training strategies for integrating these estimates into the model’s decision-making process. Experimental results on various tool invocation scenarios demonstrate the effectiveness of our framework, showing significant improvements in tool efficiency by reducing unnecessary tool usage.

Large Language Models (LLMs), constrained by limited context windows, often face significant performance degradation when reasoning over long contexts. To address this, Retrieval-Augmented Generation (RAG) retrieves and reasons over chunks but frequently sacrifices logical coherence due to its reliance on similarity-based rankings. Similarly, divide-and-conquer frameworks (DCF) split documents into small chunks for independent reasoning and aggregation. While effective for local reasoning, DCF struggles to capture long-range dependencies and risks inducing conflicts by processing chunks in isolation. To overcome these limitations, we propose ToM, a novel Tree-oriented MapReduce framework for long-context reasoning. ToM leverages the inherent hierarchical structure of long documents (e.g., main headings and subheadings) by constructing a DocTree through hierarchical semantic parsing and performing bottom-up aggregation. Using a Tree MapReduce approach, ToM enables recursive reasoning: in the Map step, rationales are generated at child nodes; in the Reduce step, these rationales are aggregated across sibling nodes to resolve conflicts or reach consensus at parent nodes. Experimental results on 70B+ LLMs show that ToM significantly outperforms existing divide-and-conquer frameworks and retrieval-augmented generation methods, achieving better logical coherence and long-context reasoning.

Detecting misogyny in multimodal content remains a notable challenge, particularly in culturally conservative and low-resource contexts like Bangladesh. While existing research has explored hate speech and general meme classification, the nuanced identification of misogyny in Bangla memes, rich in metaphor, humor, and visual-textual interplay, remains severely underexplored. To address this gap, we introduce BanMiMe, the first comprehensive Bangla misogynistic meme dataset comprising 2,000 culturally grounded samples where each meme includes misogyny labels, humor categories, metaphor localization, and detailed human-written explanations. We benchmark the various performance of open and closed-source vision-language models (VLMs) under zero-shot and prompt-based settings and evaluate their capacity for both classification and explanation generation. Furthermore, we systematically explore multiple fine-tuning strategies, including standard, data-augmented, and Chain-of-Thought (CoT) supervision. Our results demonstrate that CoT-based fine-tuning consistently enhances model performance, both in terms of accuracy and in generating meaningful explanations. We envision BanMiMe as a foundational resource for advancing explainable multimodal moderation systems in low-resource and culturally sensitive settings.

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Phi: Preference Hijacking in Multi-modal Large Language Models at Inference Time
Yifan Lan | Yuanpu Cao | Weitong Zhang | Lu Lin | Jinghui Chen

Recently, Multimodal Large Language Models (MLLMs) have gained significant attention across various domains. However, their widespread adoption has also raised serious safety concerns.In this paper, we uncover a new safety risk of MLLMs: the output preference of MLLMs can be arbitrarily manipulated by carefully optimized images. Such attacks often generate contextually relevant yet biased responses that are neither overtly harmful nor unethical, making them difficult to detect. Specifically, we introduce a novel method, **P**reference **Hi**jacking (**Phi**), for manipulating the MLLM response preferences using a preference hijacked image. Our method works at inference time and requires no model modifications. Additionally, we introduce a universal hijacking perturbation – a transferable component that can be embedded into different images to hijack MLLM responses toward any attacker-specified preferences. Experimental results across various tasks demonstrate the effectiveness of our approach. The code for Phi is accessible at https://github.com/Yifan-Lan/Phi.

GUI agents powered by vision-language models (VLMs) show promise in automating complex digital tasks. However, their effectiveness in real-world applications is often limited by scarce training data and the inherent complexity of these tasks, which frequently require long-tailed knowledge covering rare, unseen scenarios. We propose RAG-GUI , a lightweight VLM that leverages web tutorials at inferencetime. RAG-GUI is first warm-started via supervised finetuning (SFT) and further refined through self-guided rejection sampling fine-tuning (RSF). Designed to be model-agnostic, RAG-GUI functions as a generic plug-in that enhances any VLM-based agent. Evaluatedacross three distinct tasks, it consistently outperforms baseline agents and surpasses other inference baselines by 2.6% to 13.3% acrosstwo model sizes, demonstrating strong generalization and practical plug-and-play capabilities in real-world scenarios.

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COAS2W: A Chinese Older-Adults Spoken-to-Written Transformation Corpus with Context Awareness
Chun Kang | Zhigu Qian | Zhen Fu | Jiaojiao Fu | Yangfan Zhou

Spoken language from older adults often deviates from written norms due to omission, disordered syntax, constituent errors, and redundancy, limiting the usefulness of automatic transcripts in downstream tasks. We present COAS2W, a Chinese spoken-to-written corpus of 10,004 utterances from older adults, each paired with a written version, fine-grained error labels, and four-sentence context. Fine-tuned lightweight open-source models on COAS2W outperform larger closed-source models. Context ablation shows the value of multi-sentence input, and normalization improves performance on downstream translation tasks. COAS2W supports the development of inclusive, context-aware language technologies for older speakers. Our annotation convention, data, and code are publicly available at https://github.com/Springrx/COAS2W.

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Answer Convergence as a Signal for Early Stopping in Reasoning
Xin Liu | Lu Wang

Chain-of-thought (CoT) prompting enhances reasoning in large language models (LLMs) but often leads to verbose and redundant outputs, thus increasing inference cost. We hypothesize that many reasoning steps are unnecessary for producing correct answers. To investigate this, we start with a systematic study to investigate what is the minimum reasoning required for a model to reach a stable decision. Based on the insights, we propose three inference-time strategies to improve efficiency: (1) early stopping via answer consistency, (2) boosting the probability of generating end-of-reasoning signals, and (3) a supervised method that learns when to stop based on internal activations. Experiments across five benchmarks and five open-weights LLMs show that our methods largely reduce token usage with little or no accuracy drop. In particular, on NaturalQuestions, Answer Consistency reduces tokens by over 40% while further improving accuracy. Our work underscores the importance of cost-effective reasoning methods that operate at inference time, offering practical benefits for real-world applications.

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VeriFact: Enhancing Long-Form Factuality Evaluation with Refined Fact Extraction and Reference Facts
Xin Liu | Lechen Zhang | Sheza Munir | Yiyang Gu | Lu Wang

Large language models (LLMs) excel at generating long-form responses, but evaluating their factuality remains challenging due to complex inter-sentence dependencies within the generated facts. Prior solutions predominantly follow a decompose-decontextualize-verify pipeline but often fail to capture essential context and miss key relational facts. In this paper, we introduce VeriFact, a factuality evaluation framework designed to enhance fact extraction by identifying and resolving incomplete and missing facts to support more accurate verification results. Moreover, we introduce FactRBench , a benchmark that evaluates both precision and recall in long-form model responses, whereas prior work primarily focuses on precision. FactRBench provides reference fact sets from advanced LLMs and human-written answers, enabling recall assessment. Empirical evaluations show that VeriFact significantly enhances fact completeness and preserves complex facts with critical relational information, resulting in more accurate factuality evaluation. Benchmarking various open- and close-weight LLMs on FactRBench indicate that larger models within same model family improve precision and recall, but high precision does not always correlate with high recall, underscoring the importance of comprehensive factuality assessment.

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SQUAB: Evaluating LLM robustness to Ambiguous and Unanswerable Questions in Semantic Parsing
Simone Papicchio | Luca Cagliero | Paolo Papotti

Large Language Models (LLMs) have demonstrated robust performance in Semantic Parsing (SP) for well-defined queries with unambiguous intent and answerable responses. However, practical user questions frequently deviate from these ideal conditions, challenging the applicability of existing benchmarks. To address this issue, we introduce SQUAB, an automatic dataset generator of Ambiguous and Unanswerable questions. SQUAB generates complex, annotated SP tests using a blend of SQL and LLM capabilities. Results show that SQUAB reduces test generation costs by up to 99% compared to human-based solutions while aligning with real-world question patterns. Furthermore, these tests challenge LLM performance while revealing disparities between public and proprietary datasets. This highlights the need for a dynamic, automatic dataset generator as SQUAB. The code is designed for user extension to accommodate new ambiguous and unanswerable patterns and is available at https://anonymous.4open.science/r/squab-8716/.

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Reliable Evaluation and Benchmarks for Statement Autoformalization
Auguste Poiroux | Gail Weiss | Viktor Kunčak | Antoine Bosselut

Evaluating statement autoformalization, translating natural language mathematics into formal languages like Lean 4, remains a significant challenge, with few metrics, datasets, and standards to robustly measure progress. In this work, we present a comprehensive approach combining improved metrics, robust benchmarks, and systematic evaluation, to fill this gap. First, we introduce BEq+, an automated metric that correlates strongly with human judgment, along with ProofNetVerif, a new dataset for assessing the quality of evaluation metrics, containing 3,752 annotated examples. Second, we develop two new autoformalization benchmarks: ProofNet#, a corrected version of ProofNet, and RLM25, with 619 new pairs of research-level mathematics from six formalization projects. Through systematic experimentation across these benchmarks, we find that current techniques can achieve up to 45.1% accuracy on undergraduate mathematics but struggle with research-level content without proper context. Our work establishes a reliable foundation for evaluating and advancing autoformalization systems.

This research investigates both explicit and implicit social biases exhibited by Vision-Language Models (VLMs). The key distinction between these bias types lies in the level of awareness: explicit bias refers to conscious, intentional biases, while implicit bias operates subconsciously. To analyze explicit bias, we directly pose questions to VLMs related to gender and racial differences: (1) Multiple-choice questions based on a given image (e.g., “What is the education level of the person in the image?”) (2) Yes-No comparisons using two images (e.g., “Is the person in the first image more educated than the person in the second image?”) For implicit bias, we design tasks where VLMs assist users but reveal biases through their responses: (1) Image description tasks: Models are asked to describe individuals in images, and we analyze disparities in textual cues across demographic groups. (2) Form completion tasks: Models draft a personal information collection form with 20 attributes, and we examine correlations among selected attributes for potential biases. We evaluate Gemini-1.5, GPT-4V, GPT-4o, LLaMA-3.2-Vision and LLaVA-v1.6. Our code and data are publicly available at https://github.com/uscnlp-lime/VisBias.

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Less Is More? Examining Fairness in Pruned Large Language Models for Summarising Opinions
Nannan Huang | Haytham M. Fayek | Xiuzhen Zhang

Model compression through post-training pruning offers a way to reduce model size and computational requirements without significantly impacting model performance. However, the effect of pruning on the fairness of LLM-generated summaries remains unexplored, particularly for opinion summarisation where biased outputs could influence public views. In this paper, we present a comprehensive empirical analysis of opinion summarisation, examining three state-of-the-art pruning methods and various calibration sets across three open-source LLMs using four fairness metrics. Our systematic analysis reveals that pruning methods have larger impact on fairness than calibration sets. Building on these insights, we propose High Gradient Low Activation (HGLA) pruning, which identifies and removes parameters that are redundant for input processing but influential in output generation. Our experiments demonstrate that HGLA can better maintain or even improve fairness compared to existing methods, showing promise across models and tasks where traditional methods have limitations. Our human evaluation shows HGLA-generated outputs are fairer than existing state-of-the-art pruning methods.

Visual-Language Models (VLMs) have shown remarkable performance across various tasks, particularly in recognizing geographic information from images. However, VLMs still show regional biases in this task. To systematically evaluate these issues, we introduce a benchmark consisting of 1,200 images paired with detailed geographic metadata. Evaluating four VLMs, we find that while these models demonstrate the ability to recognize geographic information from images, achieving up to 53.8% accuracy in city prediction, they exhibit significant biases. Specifically, performance is substantially higher for economically developed and densely populated regions compared to less developed (-12.5%) and sparsely populated (-17.0%) areas. Moreover, regional biases of frequently over-predicting certain locations remain. For instance, they consistently predict Sydney for images taken in Australia, shown by the low entropy scores for these countries. The strong performance of VLMs also raises privacy concerns, particularly for users who share images online without the intent of being identified. Our code and dataset are publicly available at https://github.com/uscnlp-lime/FairLocator.

As a crucial method in prompt engineering, In-Context Learning (ICL) enhances the generalization and knowledge utilization capabilities of Large Language Models (LLMs) (Dong et al., 2024). However, the lengthy retrieved contexts and limited token throughput in autoregressive models significantly constrain reasoning speed. To address this challenge, we propose N-Gram Trie Speculative Decoding, a novel approach that leverages the overlap between context and model output. This method constructs an n-gram trie from the context to generate drafts, accelerating token generation for LLMs. We evaluate our approach on summarization, Retrieval-Augmented Generation (RAG), and context-based Question Answering (QA) tasks. Experimental results on Vicuna-7B, Llama2-7B-Chat, and Llama3-8B-Instruct demonstrate substantial speed improvements without compromising accuracy. Compared with various strong baselines, our method achieves the highest mean speedup, showcasing its effectiveness and efficiency.

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From Surveys to Narratives: Rethinking Cultural Value Adaptation in LLMs
Farid Adilazuarda | Chen Cecilia Liu | Iryna Gurevych | Alham Fikri Aji

Adapting cultural values in Large Language Models (LLMs) presents significant challenges, particularly due to biases and data limitations. Previous work aligns LLMs with different cultures using survey data, primarily from the World Values Survey (WVS). However, it remains unclear whether this approach effectively captures cultural nuances or produces distinct cultural representations for tasks like offensiveness classification. In this paper, we systematically investigate WVS-based training for cultural value adaptation and find that relying solely on survey data can homogenize cultural norms and interfere with factual knowledge. To address these issues, we propose augmenting WVS with encyclopedic and scenario-based cultural narratives from Wikipedia and NormAd. Our experiments across multiple cultures show that this approach captures more enhances differentiated cultural values and improves downstream classification performances.

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Iterative Prompt Refinement for Safer Text-to-Image Generation
Jinwoo Jeon | JunHyeok Oh | Hayeong Lee | Byung-Jun Lee

Text-to-Image (T2I) models have made remarkable progress in generating images from text prompts, but their output quality and safety still depend heavily on how prompts are phrased. Existing safety methods typically refine prompts using large language models (LLMs), but they overlook the images produced, which can result in unsafe outputs or unnecessary changes to already safe prompts. To address this, we propose an iterative prompt refinement algorithm that uses Vision Language Models (VLMs) to analyze both the input prompts and the generated images. By leveraging visual feedback, our method refines prompts more effectively, improving safety while maintaining user intent and reliability comparable to existing LLM-based approaches. Additionally, we introduce a new dataset labeled with both textual and visual safety signals using off-the-shelf multi-modal LLM, enabling supervised fine-tuning. Experimental results demonstrate that our approach produces safer outputs without compromising alignment with user intent, offering a practical solution for generating safer T2I content. \textcolor{red}{WARNING: This paper contains examples of harmful or inappropriate images generated by models.}

Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their further evolution is often hampered by the scarcity of high-quality training data and the heavy reliance of traditional methods on expert-labeled data. This reliance sets a ceiling on LLM performance and is particularly challenging in low data resource scenarios where extensive supervision is unavailable. To address this issue, we propose a novel paradigm named LANCE (**LAN**guage models as **C**ontinuous self-**E**volving data engineers) that enables LLMs to train themselves by autonomously generating, cleaning, reviewing, and annotating data with preference information. Our approach demonstrates that LLMs can serve as continuous self-evolving data engineers, significantly reducing the time and cost of post-training data construction. Through iterative fine-tuning on Qwen2 series models, we validate the effectiveness of LANCE across various tasks, showing that it can maintain high-quality data generation and continuously improve model performance. Across multiple benchmark dimensions, LANCE results in an average score enhancement of **3.64** for Qwen2-7B and **1.75** for Qwen2-7B-Instruct. This autonomous data construction paradigm not only lessens reliance on human experts or external models but also ensures data aligns with human preferences, offering a scalable path for LLM self-improvement, especially in contexts with limited supervisory data. Code is available at: https://github.com/Control-derek/LANCE.

Reasoning-focused large language models (LLMs) are rapidly evolving across various domains, yet their capabilities in handling complex legal problems remains underexplored. In this paper, we introduce Unilaw-R1, a large language model tailored for legal reasoning. With a lightweight 7-billion parameter scale, Unilaw-R1 significantly reduces deployment cost while effectively tackling three core challenges in the legal domain: insufficient legal knowledge, unreliable reasoning logic, and weak business generalization. To address these issues, we first construct Unilaw-R1-Data, a high-quality dataset containing ~17K distilled and screened chain-of-thought (CoT) samples. Based on this, we adopt a two-stage training strategy combining Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL), which significantly boosts the model’s performance on complex legal reasoning tasks and supports interpretable decision-making in legal AI applications. To assess legal reasoning ability, we also introduce Unilaw-R1-Eval, a dedicated benchmark designed to evaluate models across single- and multi-choice legal tasks. Unilaw-R1 demonstrates strong results on authoritative benchmarks, outperforming all models of similar scale and achieving performance on par with the much larger DeepSeek-R1-Distill-Qwen-32B (54.9%). Following domain-specific training, it also showed significant gains on LawBench and LexEval, exceeding Qwen-2.5-7B-Instruct (46.6%) by an average margin of 6.6%. Code is available at: https://github.com/Hanscal/Unilaw-R1.

Multimodal large language models (MLLMs) have recently achieved state-of-the-art performance on tasks ranging from visual question answering to video understanding. However, existing studies have concentrated mainly on visual–textual misalignment, leaving largely unexplored the MLLMs’ ability to preserve an originally correct answer when confronted with misleading information. We reveal a response uncertainty phenomenon: across nine standard datasets, twelve state-of-the-art open-source MLLMs overturn a previously correct answer in 65% of cases after receiving a single deceptive cue. To systematically quantify this vulnerability, we propose a two-stage evaluation pipeline: (1) elicit each model’s original response on unperturbed inputs; (2) inject explicit (false-answer hints) and implicit (contextual contradictions) misleading instructions, and compute the misleading rate—the fraction of correct-to-incorrect flips. Leveraging the most susceptible examples, we curate the Multimodal Uncertainty Benchmark (MUB), a collection of image–question pairs stratified into low, medium, and high difficulty based on how many of twelve state-of-the-art MLLMs they mislead. Extensive evaluation on twelve open-source and five closed-source models reveals a high uncertainty: average misleading rates exceed 86%, with explicit cues over 67.19% and implicit cues over 80.67%. To reduce the misleading rate, we then fine-tune all open-source MLLMs on a compact 2,000-sample mixed-instruction dataset, reducing misleading rates to 6.97% (explicit) and 32.77% (implicit), boosting consistency by nearly 29.37% on highly deceptive inputs, and slightly improving accuracy on standard benchmarks.

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Evaluating and Aligning Human Economic Risk Preferences in LLMs
Jiaxin Liu | Yixuan Tang | Yi Yang | Kar Yan Tam

Large Language Models (LLMs) are increasingly used in decision-making scenarios that involve risk assessment, yet their alignment with human economic rationality remains unclear. In this study, we investigate whether LLMs exhibit risk preferences consistent with human expectations across different personas. Specifically, we propose an evaluation metric called Risk Disparity Score (RDS) and assess whether LLM-generated responses reflect appropriate levels of risk aversion or risk-seeking behavior based on individual’s persona. Our results reveal that while LLMs make reasonable decisions in simplified, personalized risk contexts, their performance declines in more complex economic decision-making tasks. To address this, we test whether current state-of-art alignment methods such as Direct Preference Optimization(DPO) and In Context Learning(ICL) can enhance LLM adherence to persona-specific risk preferences. We find DPO can improve the economic rationality of LLMs in loss-related parameters, offering a step toward more human-aligned AI decision-making.

Hierarchical text classification aims to classify documents into multiple labels within a hierarchical taxonomy, making it an essential yet challenging task in natural language processing. Recently, using Large Language Models (LLM) to tackle hierarchical text classification in a zero-shot manner has attracted increasing attention due to their cost-efficiency and flexibility. Given the challenges of understanding the hierarchy, various HTC prompting strategies have been explored to elicit the best performance from LLMs.However, our empirical study reveals that LLMs are highly sensitive to these prompting strategies—(i) within a task, different strategies yield substantially different results, and (ii) across various tasks, the relative effectiveness of a given strategy varies significantly. To address this, we propose a novel ensemble method, HiEPS, which integrates the results of diverse prompting strategies to promote LLMs’ reliability. We also introduce a path-valid voting mechanism for ensembling, which selects a valid result with the highest path frequency score. Extensive experiments on three benchmark datasets show that HiEPS boosts the performance of single prompting strategies and achieves SOTA results. The source code is available at https://github.com/MingxuanXia/HiEPS.

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Improbable Bigrams Expose Vulnerabilities of Incomplete Tokens in Byte-Level Tokenizers
Eugene Jang | Kimin Lee | Jin-Woo Chung | Keuntae Park | Seungwon Shin

Tokenization is a crucial step that bridges human-readable text with model-readable discrete tokens. However, recent studies have revealed that tokenizers can be exploited to elicit unwanted model behaviors. In this work, we investigate incomplete tokens, i.e., undecodable tokens with stray bytes resulting from byte-level byte-pair encoding (BPE) tokenization. We hypothesize that such tokens are heavily reliant on their adjacent tokens and are fragile when paired with unfamiliar tokens. To demonstrate this vulnerability, we introduce improbable bigrams: out-of-distribution combinations of incomplete tokens designed to exploit their dependency. Our experiments show that improbable bigrams are significantly prone to hallucinatory behaviors. Surprisingly, the same phrases have drastically lower rates of hallucination (90% reduction in Llama3.1) when an alternative tokenization is used. We caution against the potential vulnerabilities introduced by byte-level BPE tokenizers, which may introduce blind spots to language models.

Graphical User Interface (GUI) agents are expected to precisely operate on the screens of digital devices. Existing GUI agents merely depend on current visual observations and plain-text action history, ignoring the significance of history screens. To mitigate this issue, we propose **UI-Hawk**, a multi-modal GUI agent specially designed to process screen streams encountered during GUI navigation. UI-Hawk incorporates a history-aware visual encoder to handle the screen sequences. To acquire a better understanding of screen streams, we select four fundamental tasks—UI grounding, UI referring, screen question answering, and screen summarization. We further propose a curriculum learning strategy to subsequently guide the model from fundamental tasks to advanced screen-stream comprehension.Along with the efforts above, we have also created a benchmark FunUI to quantitatively evaluate the fundamental screen understanding ability of MLLMs. Extensive experiments on FunUI and GUI navigation benchmarks consistently validate that screen stream understanding is essential for GUI tasks.Our code and data are now available at https://github.com/IMNearth/UIHawk.

Software debugging is a time-consuming endeavor involving a series of steps, such as fault localization and patch generation, each requiring thorough analysis and a deep understanding of the underlying logic. While large language models (LLMs) demonstrate promising potential in coding tasks, their performance in debugging remains limited. Current LLM-based methods often focus on isolated steps and struggle with complex bugs. In this paper, we propose the first end-to-end framework, UniDebugger, for unified debugging through multi-agent synergy. It mimics the entire cognitive processes of developers, with each agent specialized as a particular component of this process rather than mirroring the actions of an independent expert as in previous multi-agent systems. Agents are coordinated through a three-level design, following a cognitive model of debugging, allowing adaptive handling of bugs with varying complexities. Experiments on extensive benchmarks demonstrate that UniDebugger significantly outperforms state-of-the-art repair methods, fixing 1.25x to 2.56x bugs on the repo-level benchmark, Defects4J. This performance is achieved without requiring ground-truth root-cause code statements, unlike the baselines. Our source code is available on an anonymous link: https://github.com/BEbillionaireUSD/UniDebugger.

While Large Reasoning Models (LRMs) generate extensive chain-of-thought reasoning, we lack a principled framework for understanding how these thoughts are structured. In this paper, we introduce a novel approach by applying Schoenfeld’s Episode Theory, a classic cognitive framework for human mathematical problem-solving, to analyze the reasoning traces of LRMs. We annotated thousands of sentences and paragraphs from model-generated solutions to math problems using seven cognitive labels (e.g., Plan, Implement, Verify). The result is the first publicly available benchmark for the fine-grained analysis of machine reasoning, including a large annotated corpus and detailed annotation guidebooks. Our preliminary analysis reveals distinct patterns in LRM reasoning, such as the transition dynamics between cognitive states. This framework provides a theoretically grounded methodology for interpreting LRM cognition and enables future work on more controllable and transparent reasoning systems.

Recent advances in retrieval-augmented generation (RAG) have substantially improved question-answering systems, particularly for factoid ‘5Ws’ questions. However, significant challenges remain when addressing ‘1H’ questions, specifically how-to questions, which are integral for decision-making and require dynamic, step-by-step responses. The key limitation lies in the prevalent data organization paradigm, chunk, which commonly divides documents into fixed-size segments, and disrupts the logical coherence and connections within the context. To address this, we propose THREAD, a novel data organization paradigm enabling systems to handle how-to questions more effectively. Specifically, we introduce a new knowledge granularity, ‘logic unit’ (LU), where large language models transform documents into more structured and loosely interconnected LUs. Extensive experiments across both open-domain and industrial settings show that THREAD outperforms existing paradigms significantly, improving the success rate of handling how-to questions by 21% to 33%. Additionally, THREAD demonstrates high adaptability across diverse document formats, reducing retrieval information by up to 75% compared to chunk, and also shows better generalizability to ‘5Ws’ questions, such as multi-hop questions, outperforming other paradigms.

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Unsupervised Word-level Quality Estimation for Machine Translation Through the Lens of Annotators (Dis)agreement
Gabriele Sarti | Vilém Zouhar | Malvina Nissim | Arianna Bisazza

Word-level quality estimation (WQE) aims to automatically identify fine-grained error spans in machine-translated outputs and has found many uses, including assisting translators during post-editing. Modern WQE techniques are often expensive, involving prompting of large language models or ad-hoc training on large amounts of human-labeled data. In this work, we investigate efficient alternatives exploiting recent advances in language model interpretability and uncertainty quantification to identify translation errors from the inner workings of translation models. In our evaluation spanning 14 metrics across 12 translation directions, we quantify the impact of human label variation on metric performance by using multiple sets of human labels. Our results highlight the untapped potential of unsupervised metrics, the shortcomings of supervised methods when faced with label uncertainty, and the brittleness of single-annotator evaluation practices.

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STEER-BENCH: A Benchmark for Evaluating the Steerability of Large Language Models
Kai Chen | Zihao He | Taiwei Shi | Kristina Lerman

Steerability, or the ability of large language models (LLMs) to adapt outputs to align with diverse community-specific norms, perspectives, and communication styles, is critical for real-world applications but remains under-evaluated. We introduce STEER-BENCH, a benchmark for assessing population-specific steering using contrasting Reddit communities. Covering 30 contrasting subreddit pairs across 19 domains, STEER-BENCH includes over 10,000 instruction-response pairs and validated 5,500 multiple-choice questions with corresponding silver labels to test alignment with diverse community norms. It systematically assesses how effectively LLMs understand community-specific instructions, their resilience to adversarial steering attempts, and their ability to accurately represent diverse cultural and ideological perspectives. Our evaluation of 13 popular LLMs using STEER-BENCH reveals that while human experts achieve an accuracy of 81% with silver labels, the best-performing models reach only around 65% accuracy depending on the domain and configuration. Some models lag behind human-level alignment by over 15 percentage points, highlighting significant gaps in community-sensitive steerability.

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Combining Constrained and Unconstrained Decoding via Boosting: BoostCD and Its Application to Information Extraction
Marija Sakota | Robert West

Many recent approaches to structured NLP tasks use an autoregressive language model M to map unstructured input text x to output text y representing structured objects (such as tuples, lists, trees, code, etc.), where the desired output structure is enforced via constrained decoding. During training, these approaches do not require the model to be aware of the constraints, which are merely implicit in the training outputs y. This is advantageous as it allows for dynamic constraints without requiring retraining, but can lead to low-quality output during constrained decoding at test time. We overcome this problem with Boosted Constrained Decoding (BoostCD) which combines constrained and unconstrained decoding in two phases: Phase 1 decodes from the base model M twice, in constrained and unconstrained mode, obtaining two weak predictions. In phase 2, a learned autoregressive boosted model combines the two weak predictions into one final prediction. The mistakes made by the base model with vs. without constraints tend to be complementary, which the boosted model learns to exploit for improved performance. We demonstrate the power of BoostCD by applying it to closed information extraction. Our model, BoostIE, outperforms prior approaches both in and out of distribution, addressing several common errors identified in those approaches.

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MultiLogicNMR(er): A Benchmark and Neural-Symbolic Framework for Non-monotonic Reasoning with Multiple Extensions
Yeliang Xiu | Yongmei Liu

Non-monotonic reasoning (NMR) refers to the fact that conclusions may be invalidated by new information. It is widely used in daily life and legal reasoning. An NMR task usually has multiple extensions, which are sets of plausible conclusions. There are two reasoning modes – skeptical and credulous reasoning, depending on whether to believe facts in all extensions or any one extension. Despite some preliminary work exploring the NMR abilities of LLMs, the multi-extension NMR capabilities of LLMs remain underexplored. In this paper, we synthesize a multi-extension NMR dataset MultiLogicNMR, and construct two variants of the dataset with more extensions or text diversity. We propose a neural-symbolic framework MultiLogicNMRer for multi-extension NMR. Experimental evaluation with the datasets shows that LLMs still face significant challenges in NMR abilities, and reveal the effectiveness of our neural-symbolic framework, with an average accuracy gain of about 15% compared to prompt-based methods, and even outperforming some fine-tuning methods. All code and data are publicly available.

Introducing **MARK**, the **M**ulti-st**A**ge **R**easoning framewor**K** for cultural value survey response simulation, designed to enhance the accuracy, steerability, and interpretability of large language models in this task. The system is inspired by the type dynamics theory in the MBTI psychological framework for personality research. It effectively predicts and utilizes human demographic information for simulation: life-situational stress analysis, group-level personality prediction, and self-weighted cognitive imitation. Experiments on the World Values Survey show that MARK outperforms existing baselines by 10% accuracy and reduces the divergence between model predictions and human preferences. This highlights the potential of our framework to improve zero-shot personalization and help social scientists interpret model predictions.

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CrystalICL: Enabling In-Context Learning for Crystal Generation
Ruobing Wang | Qiaoyu Tan | Yili Wang | Ying Wang | Xin Wang

Designing crystal materials with desired physicochemical properties remains a fundamental challenge in materials science. While large language models (LLMs) have demonstrated strong in-context learning (ICL) capabilities, existing LLM-based crystal generation approaches are limited to zero-shot scenarios and are unable to benefit from few-shot scenarios. In contrast, human experts typically design new materials by modifying relevant known structures which aligns closely with the few-shot ICL paradigm. Motivated by this, we propose CrystalICL, a novel model designed for few-shot crystal generation. Specifically, we introduce a space-group based crystal tokenization method, which effectively reduces the complexity of modeling crystal symmetry in LLMs. We further introduce a condition-structure aware hybrid instruction tuning framework and a multi-task instruction tuning strategy, enabling the model to better exploit ICL by capturing structure-property relationships from limited data. Extensive experiments on four crystal generation benchmarks demonstrate the superiority of CrystalICL over the leading baseline methods on conditional and unconditional generation tasks.

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Towards a Unified Paradigm of Concept Editing in Large Language Models
Zhuowen Han | Xinwei Wu | Dan Shi | Renren Jin | Deyi Xiong

Concept editing aims to control specific concepts in large language models (LLMs) and is an emerging subfield of model editing. Despite the emergence of various editing methods in recent years, there remains a lack of rigorous theoretical analysis and a unified perspective to systematically understand and compare these methods. To address this gap, we propose a unified paradigm for concept editing methods, in which all forms of conceptual injection are aligned at the neuron level. We study four representative concept editing methods: Neuron Editing (NE), Supervised Fine-tuning (SFT), Sparse Autoencoder (SAE), and Steering Vector (SV). Then we categorize them into two classes based on their mode of conceptual information injection: indirect (NE, SFT) and direct (SAE, SV). We evaluate above methods along four dimensions: editing reliability, output generalization, neuron level consistency, and mathematical formalization. Experiments show that SAE achieves the best editing reliability. In output generalization, SAE captures features closer to human-understood concepts, while NE tends to locate text patterns rather than true semantics. Neuron-level analysis reveals that direct methods share high neuron overlap, as do indirect methods, indicating methodological commonality within each category. Our unified paradigm offers a clear framework and valuable insights for advancing interpretability and controlled generation in LLMs.

Test-Time Scaling (TTS) is a promising approach to progressively elicit the model’s intelligence during inference. Recently, training-based TTS methods, such as continued reinforcement learning (RL), have further surged in popularity, while training-free TTS methods are gradually fading from prominence. However, the additional computation overhead of training amplifies the burden on test-time scaling.In this paper, we focus on training-free TTS methods for reasoning. We first design Conditional Step-level Self-refinement, a fine-grained sequential scaling method guided by process verification. On top of its effectiveness, we further combine it with other classical parallel scaling methods at the step level, to introduce a novel inference paradigm called Hybrid Test-Time Scaling. Extensive experiments on five instruction-tuned LLMs across different scales (3B-14B) and families demonstrate that hybrid strategy incorporating various training-free TTS methods at a fine granularity has considerable potential for expanding the reasoning performance boundaries of LLMs.

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Dynamic Expert Specialization: Towards Catastrophic Forgetting-Free Multi-Domain MoE Adaptation
Junzhuo Li | Bo Wang | Xiuze Zhou | Xuming Hu

Mixture-of-Experts (MoE) models offer immense capacity via sparsely gated expert subnetworks, yet adapting them to multiple domains without catastrophic forgetting remains an open challenge. Existing approaches either incur prohibitive computation, suffer cross-domain interference, or require separate runs per domain. We propose DES-MoE, a dynamic expert specialization framework for multi-domain adaptation of Mixture-of-Experts models. DES-MoE addresses catastrophic forgetting through three innovations: (1) an adaptive router balancing pre-trained knowledge retention and task-specific updates via distillation, (2) real-time expert-domain correlation mapping to isolate domain-specific gradients, and (3) a three-phase adaptive fine-tuning schedule that progressively freezes non-specialized parameters. Evaluated on six domains (math, code, law, etc.), DES-MoE matches single-domain ESFT performance while training one unified model, reduces forgetting by 89% compared to full fine-tuning as domains scale from 2 to 6, and achieves 68% faster convergence than conventional methods. Our work establishes dynamic expert isolation as a scalable paradigm for multi-task MoE adaptation.

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RRInf: Efficient Influence Function Estimation via Ridge Regression for Large Language Models and Text-to-Image Diffusion Models
Zhuozhuo Tu | Cheng Chen | Yuxuan Du

The quality of data plays a vital role in the development of Large-scale Generative Models. Understanding how important a data point is for a generative model is essential for explaining its behavior and improving the performance. The influence function provides a framework for quantifying the impact of individual training data on model predictions. However, the high computational cost has hindered their applicability in large-scale applications. In this work, we present RRInf, a novel and principled method for estimating influence function in large-scale generative AI models. We show that influence function estimation can be transformed into a ridge regression problem. Based on this insight, we develop an algorithm that is efficient and scalable to large models. Experiments on noisy data detection and influential data identification tasks demonstrate that RRInf outperforms existing methods in terms of both efficiency and effectiveness for commonly used large models: RoBERTa-large, Llama-2-13B-chat, Llama-3-8B and stable-diffusion-v1.5.

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Evaluating Spatiotemporal Consistency in Automatically Generated Sewing Instructions
Luisa Geiger | Mareike Hartmann | Michael Sullivan | Alexander Koller

In this paper, we propose a novel, automatic tree-based evaluation metric for LLM-generated step-by-step assembly instructions, that more accurately reflects spatiotemporal aspects of construction than traditional metrics such as BLEU and BERT similarity scores. We apply our proposed metric to the domain of sewing instructions, and show that our metric better correlates with manually-annotated error counts, demonstrating our metric’s superiority for evaluating the spatiotemporal soundness of sewing instructions. Further experiments show that our metric is more robust than traditional approaches against artificially-constructed counterfactual examples that are specifically constructed to confound metrics that rely on textual similarity.

Large language models have demonstrated exceptional capabilities across diverse tasks, but their fine-tuning demands significant memory, posing challenges for resource-constrained environments. Zeroth-order (ZO) optimization provides a memory-efficient alternative by eliminating the need for backpropagation. However, ZO optimization suffers from high gradient variance, and prior research has largely focused on single-task learning, leaving its application to multi-task learning unexplored. Multi-task learning is crucial for leveraging shared knowledge across tasks to improve generalization, yet it introduces unique challenges under ZO settings, such as amplified gradient variance and collinearity. In this paper, we present MaZO, the first framework specifically designed for multi-task LLM fine-tuning under ZO optimization. MaZO tackles these challenges at the parameter level through two key innovations: a weight importance metric to identify critical parameters and a multi-task weight update mask to selectively update these parameters, reducing the dimensionality of the parameter space and mitigating task conflicts. Experiments demonstrate that MaZO achieves state-of-the-art performance, surpassing even multi-task learning methods designed for first-order optimization.

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Procedural Environment Generation for Tool-Use Agents
Michael Sullivan | Mareike Hartmann | Alexander Koller

Although the power of LLM tool-use agents has ignited a flurry of recent research in this area, the curation of tool-use training data remains an open problem\textemdashespecially for online RL training. Existing approaches to synthetic tool-use data generation tend to be non-interactive and/or non-compositional. We introduce RandomWorld, a pipeline for the procedural generation of interactive tools and compositional tool-use data. We show that models tuned via SFT and RL on synthetic RandomWorld data improve on a range of tool-use benchmarks, and set the new SoTA for two metrics on the NESTFUL dataset. Further experiments show that downstream performance scales with the amount of RandomWorld-generated training data, opening up the possibility of further improvement through the use of entirely synthetic data.

Despite advancements in large language models (LLMs), non-factual responses still persist in fact-seeking question answering. Unlike extensive studies on post-hoc detection of these responses, this work studies non-factuality prediction (NFP), predicting whether an LLM will generate a non-factual response prior to the response generation. Previous NFP methods have shown LLMs’ awareness of their knowledge, but they face challenges in terms of efficiency and transferability. In this work, we propose a lightweight model named Factuality Lens (FacLens), which effectively probes hidden representations of fact-seeking questions for the NFP task. Moreover, we discover that hidden question representations sourced from different LLMs exhibit similar NFP patterns, enabling the transferability of FacLens across different LLMs to reduce development costs. Extensive experiments highlight FacLens’s superiority in both effectiveness and efficiency.

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OMS: On-the-fly, Multi-Objective, Self-Reflective Ad Keyword Generation via LLM Agent
Bowen Chen | Zhao Wang | Shingo Takamatsu

Keyword decision in Sponsored Search Advertising is critical to the success of ad campaigns. While LLM-based methods offer automated keyword generation, they face three major limitations: reliance on large-scale query–keyword pair data, lack of online multi-objective performance monitoring and optimization, and weak quality control in keyword selection. These issues hinder the agentic use of LLMs in fully automating keyword decisions by monitoring and reasoning over key performance indicators such as impressions, clicks, conversions, and CTA effectiveness. To overcome these challenges, we propose OMS, a keyword generation framework that is On-the-fly (requires no training data, monitors online performance, and adapts accordingly), Multi-objective (employs agentic reasoning to optimize keywords based on multiple performance metrics) and Self-reflective (agentically evaluates keyword quality). Experiments on benchmarks and real-world ad campaigns show that OMS outperforms existing methods; Ablation and human evaluations confirm the effectiveness of each component and the quality of generated keywords.

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Med-VRAgent: A Framework for Medical Visual Reasoning-Enhanced Agents
Guangfu Guo | Xiaoqian Lu | Yue Feng

Vision-language models (VLMs) achieve promising results in medical reasoning but struggle with hallucinations, vague descriptions, Inconsistent logic and poor localization. To address this, we propose a agent framework named Medical Visual Reasoning Agent (Med-VRAgent). The approach is based on Visual Guidance and Self-Reward paradigms and Monte Carlo Tree Search (MCTS). By combining the Visual Guidance with tree search, Med-VRAgent improves the medical visual reasoning capabilities of VLMs. We use the trajectories collected by Med-RAgent as feedback to further improve the performance by fine-tuning the VLMs with the proximal policy optimization (PPO) objective. Experiments on multiple medical VQA benchmarks demonstrate that our method outperforms existing approaches.

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TrojanWave: Exploiting Prompt Learning for Stealthy Backdoor Attacks on Large Audio-Language Models
Asif Hanif | Maha Tufail Agro | Fahad Shamshad | Karthik Nandakumar

Prompt learning has emerged as an efficient alternative to full fine-tuning for adapting large audio-language models (ALMs) to downstream tasks. While this paradigm enables scalable deployment via Prompt-as-a-Service frameworks, it also introduces a critical yet underexplored security risk of backdoor attacks. In this work, we present TrojanWave, the first backdoor attack tailored to the prompt-learning setting in frozen ALMs. Unlike prior audio backdoor methods that require training from scratch on full datasets, TrojanWave injects backdoors solely through learnable prompts, making it highly scalable and effective in few-shot settings. TrojanWave injects imperceptible audio triggers in both time and spectral domains to effectively induce targeted misclassification during inference. To mitigate this threat, we further propose TrojanWave-Defense, a lightweight prompt purification method that neutralizes malicious prompts without hampering the clean performance. Extensive experiments across 11 diverse audio classification benchmarks demonstrate the robustness and practicality of both the attack and defense. Our code is publicly available at https://asif-hanif.github.io/trojanwave/.

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Can LLMs be Literary Companions?: Analysing LLMs on Bengali Figures of Speech Identification
Sourav Das | Kripabandhu Ghosh

Despite Bengali being among the most spoken languages bearing cultural importance and richness, the NLP endeavors on it, remain relatively limited. Figures of Speech (FoS) not only contribute to the phonetic and semantic nuances of a language, but they also exhibit aesthetics, expression, and creativity in literature. To our knowledge, in this paper, we present the first ever Bengali figures of speech classification dataset, **BengFoS**, on works of six renowned poets of Bengali literature. We deploy state-of-the-art Large Language Models (LLMs) to this dataset in the zero-shot setup, thereafter fine-tuning the best performing models, and finally dissect them for language model probing. This reveals novel insights on the intrinsic behavior of two open-source LLMs (Llama and DeepSeek) in FoS detection. **Though we have limited ourselves to Bengali, the experimental framework can be reproduced for English as well as for other low-resource languages**.

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Group-SAE: Efficient Training of Sparse Autoencoders for Large Language Models via Layer Groups
Davide Ghilardi | Federico Belotti | Marco Molinari | Tao Ma | Matteo Palmonari

Sparse AutoEncoders (SAEs) have recently been employed as a promising unsupervised approach for understanding the representations of layers of Large Language Models (LLMs). However, with the growth in model size and complexity, training SAEs is computationally intensive, as typically one SAE is trained for each model layer. To address such limitation, we propose Group-SAE, a novel strategy to train SAEs. Our method considers the similarity of the residual stream representations between contiguous layers to group similar layers and train a single SAE per group. To balance the trade-off between efficiency and performance, we further introduce AMAD (Average Maximum Angular Distance), an empirical metric that guides the selection of an optimal number of groups based on representational similarity across layers. Experiments on models from the Pythia family show that our approach significantly accelerates training with minimal impact on reconstruction quality and comparable downstream task performance and interpretability over baseline SAEs trained layer by layer. This method provides an efficient and scalable strategy for training SAEs in modern LLMs.

Relation extraction (RE) aims to identify semantic relations between entities in unstructured text. Although recent work extends traditional RE to multimodal scenarios, most approaches still adopt classification-based paradigms with fused multimodal features, representing relations as discrete labels. This paradigm has two significant limitations: (1) it overlooks structural constraints like entity types and positional cues, and (2) it lacks semantic expressiveness for fine-grained relation understanding. We propose **R**etrieval **O**ver **C**lassification (ROC), a novel framework that reformulates multimodal RE as a retrieval task driven by relation semantics. ROC integrates entity type and positional information through a multimodal encoder, expands relation labels into natural language descriptions using a large language model, and aligns entity-relation pairs via semantic similarity-based contrastive learning. Experiments show that our method achieves state-of-the-art performance on the benchmark datasets MNRE and MORE and exhibits stronger robustness and interpretability.

Pun memes, which combine wordplay with visual elements, represent a popular form of humor in Chinese online communications. Despite their prevalence, current Vision-Language Models (VLMs) lack systematic evaluation in understanding and applying these culturally-specific multimodal expressions. In this paper, we introduce PunMemeCN, a novel benchmark designed to assess VLMs’ capabilities in processing Chinese pun memes across three progressive tasks: pun meme detection, sentiment analysis, and chat-driven meme response. PunMemeCN consists of 1,959 Chinese memes (653 pun memes and 1,306 non-pun memes) with comprehensive annotations of punchlines, sentiments, and explanations, alongside 2,008 multi-turn chat conversations incorporating these memes. Our experiments indicate that state-of-the-art VLMs struggle with Chinese pun memes, particularly with homophone wordplay, even with Chain-of-Thought prompting. Notably, punchlines in memes can effectively conceal potentially harmful content from AI detection. These findings underscore the challenges in cross-cultural multimodal understanding and highlight the need for culture-specific approaches to humor comprehension in AI systems.

Instruction-following made modern large language models (LLMs) helpful assistants. However, the key to taming LLMs on complex instructions remains mysterious, for that there are huge gaps between models trained by open-source community and those trained by leading companies. To bridge the gap, we propose a simple and scalable approach UltraIF for building LLMs that can follow complex instructions with open-source data. UltraIF first decomposes real-world user prompts into simpler queries, constraints, and corresponding evaluation questions for the constraints. Then, we train an UltraComposer to compose constraint-associated prompts with evaluation questions. This prompt composer allows us to synthesize complicated instructions as well as filter responses with evaluation questions. In our experiment, for the first time, we successfully align LLaMA-3.1-8B-Base to catch up with its instruct version on 5 instruction-following benchmarks without any benchmark information, using only 8B model as response generator and evaluator. The aligned model also achieved competitive scores on other benchmarks. Moreover, we also show that UltraIF could further improve LLaMA-3.1-8B-Instruct through self-alignment, motivating broader use cases for the method.

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Identifying Pre-training Data in LLMs: A Neuron Activation-Based Detection Framework
Hongyi Tang | Zhihao Zhu | Yi Yang

The performance of large language models (LLMs) is closely tied to their training data, which can include copyrighted material or private information, raising legal and ethical concerns. Additionally, LLMs face criticism for dataset contamination and internalizing biases. To address these issues, the Pre-Training Data Detection (PDD) task was proposed to identify if specific data was included in an LLM’s pre-training corpus. However, existing PDD methods often rely on superficial features like prediction confidence and loss, resulting in mediocre performance. To improve this, we introduce NA-PDD, a novel algorithm analyzing differential neuron activation patterns between training and non-training data in LLMs. This is based on the observation that these data types activate different neurons during LLM inference. We also introduce CCNewsPDD, a temporally unbiased benchmark employing rigorous data transformations to ensure consistent time distributions between training and non-training data. Our experiments demonstrate that NA-PDD significantly outperforms existing methods across three benchmarks and multiple LLMs.

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TreeRare: Syntax Tree-Guided Retrieval and Reasoning for Knowledge-Intensive Question Answering
Boyi Zhang | Zhuo Liu | Hangfeng He

In real practice, questions are typically complex and knowledge-intensive, requiring Large Language Models (LLMs) to recognize the multifaceted nature of the question and reason across multiple information sources. Iterative and adaptive retrieval, where LLMs decide when and what to retrieve based on their reasoning, has been shown to be a promising approach to resolve complex, knowledge-intensive questions. However, the performance of such retrieval frameworks is limited by the accumulation of reasoning errors and misaligned retrieval results. To overcome these limitations, we propose TreeRare (Syntax Tree-Guided Retrieval and Reasoning, a framework that utilizes syntax trees to guide information retrieval and reasoning for question answering. Following the principle of compositionality, TreeRare traverses the syntax tree in a bottom-up fashion, and in each node, it generates subcomponent-based queries and retrieves relevant passages to resolve localized uncertainty. A subcomponent question answering module then synthesizes these passages into concise, context-aware evidence. Finally, TreeRare aggregates the evidence across the tree to form a final answer. Experiments across five question answering datasets involving ambiguous or multi-hop reasoning demonstrate that TreeRare achieves substantial improvements over existing state-of-the-art methods.

A lack of demographic context in existing toxic speech datasets limits our understanding of how different age groups communicate online. In collaboration with funk, a German public service content network, this research introduces the first large-scale German dataset annotated for toxicity and enriched with platform-provided age estimates. The dataset includes 3,024 human-annotated and 30,024 LLM-annotated anonymized comments from Instagram, TikTok, and YouTube. To ensure relevance, comments were consolidated using predefined toxic keywords, resulting in 16.7% labeled as problematic. The annotation pipeline combined human expertise with state-of-the-art language models, identifying key categories such as insults, disinformation, and criticism of broadcasting fees. The dataset reveals age-based differences in toxic speech patterns, with younger users favoring expressive language and older users more often engaging in disinformation and devaluation. This resource provides new opportunities for studying linguistic variation across demographics and supports the development of more equitable and age-aware content moderation systems.

Understanding user intents from UI interaction trajectories remains a challenging, yet crucial, frontier in intelligent agent development. While massive, datacenter-based, multi-modal large language models (MLLMs) possess greater capacity to handle the complexities of such sequences, smaller models which can run on-device to provide a privacy-preserving, low-cost, and low-latency user experience, struggle with accurate intent inference. We address these limitations by introducing a novel decomposed approach: first, we perform structured interaction summarization, capturing key information from each user action. Second, we perform intent extraction using a fine-tuned model operating on the aggregated summaries. This method improves intent understanding in resource-constrained models, even surpassing the base performance of large MLLMs.

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On Pruning State-Space LLMs
Tamer Ghattas | Michael Hassid | Roy Schwartz

Recent work proposed state-space models (SSMs) as an efficient alternative to transformer-based LLMs. Can these models be pruned to further reduce their computation costs? We adapt several pruning methods to the SSM structure, and apply them to four SSM-based LLMs across multiple tasks. We find that such models are quite robust to some pruning methods (e.g., WANDA), while using other methods lead to fast performance degradation.

Low-rank adaptation (LoRA) efficiently adapts LLMs to downstream tasks by decomposing LLMs’ weight update into trainable low-rank matrices for fine-tuning. However, the random low-rank matrices may introduce massive task-irrelevant information, while their recomposed form suffer from limited representation spaces under low-rank operations. Such dense and choked adaptation in LoRA impairs the adaptation performance of LLMs on downstream tasks. To address these challenges, this paper proposes OHoRA, an orthogonal high-rank adaptation for parameter-efficient fine-tuning on LLMs. According to the information bottleneck theory, OHoRA decomposes LLMs’ pre-trained weight matrices into orthogonal basis vectors via QR decomposition and splits them into two low-redundancy high-rank components to suppress task-irrelevant information. It then performs dynamic rank-elevated recomposition through Kronecker product to generate expansive task-tailored representation spaces, enabling precise LLM adaptation and enhanced generalization. OHoRA effectively operationalizes the information bottleneck theory to decompose LLMs’ weight matrices into low-redundancy high-rank components and recompose them in rank-elevated manner for more task-tailored representation spaces and precise LLM adaptation. Empirical evaluation shows OHoRA’s effectiveness by outperforming LoRA and its variants and achieving comparable performance to full fine-tuning with only 0.0371% trainable parameters.

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BSFA: Leveraging the Subspace Dichotomy to Accelerate Neural Network Training
WenJie Zhou | Bohan Wang | Wei Chen | Xueqi Cheng

Recent studies (CITATION) highlight a fundamental dichotomy in deep learning optimization: Although parameter updates along the top eigendirections of the loss Hessian (Dom-space) capture most of the update magnitude, they often contribute minimally to loss reduction. In contrast, updates in the orthogonal component (Bulk-space) have smaller magnitudes but drive most learning progress.In this work, we further advance the understanding of this phenomenon and introduce the Bulk-Space-Filtration-Accelerator (BSFA), a novel plug-and-play framework. BSFA accelerates training by differentially scaling update components projected onto these distinct subspaces, simultaneously enhancing stability by moderating updates in the dominant subspace and boosting convergence speed by amplifying those in the bulk-space.To ensure BSFA is both practical and scalable for contemporary large models, we introduce two key innovations: an efficient estimator using Principal Component Analysis (PCA) on historical updates for fast subspace estimation, and a block-wise strategy that applies this estimation on a per-parameter-block basis. These designs make BSFA computationally tractable and highly effective.We demonstrate BSFA’s acceleration across various tasks, notably achieving approximately 2× speedup when pre-training LLaMA-72M on WikiText-103 and LLaMA-134M on OpenWebText compared to vanilla AdamW.

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Debatable Intelligence: Benchmarking LLM Judges via Debate Speech Evaluation
Noy Sternlicht | Ariel Gera | Roy Bar-Haim | Tom Hope | Noam Slonim

We introduce Debate Speech Evaluation as a novel and challenging benchmark for assessing LLM judges. Evaluating debate speeches requires a deep understanding of the speech at multiple levels, including argument strength and relevance, the coherence and organization of the speech, the appropriateness of its style and tone, and so on. This task involves a unique set of cognitive abilities that previously received limited attention in systematic LLM benchmarking. To explore such skills, we leverage a dataset of over 600 meticulously annotated debate speeches and present the first in-depth analysis of how state-of-the-art LLMs compare to human judges on this task. Our findings reveal a nuanced picture: while larger models can approximate individual human judgments in some respects, they differ substantially in their overall judgment behavior. We also investigate the ability of frontier LLMs to generate persuasive, opinionated speeches, showing that models may perform at a human level on this task.

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METok: Multi-Stage Event-based Token Compression for Efficient Long Video Understanding
Mengyue Wang | Shuo Chen | Kristian Kersting | Volker Tresp | Yunpu Ma

Recent advances in Video Large Language Models (VLLMs) have significantly enhanced their ability to understand video content. Nonetheless, processing long videos remains challenging due to high computational demands and the redundancy present in the visual data. In this work, we propose METok, a training-free, Multi-stage Event-based Token compression framework designed to accelerate VLLMs’ inference while preserving accuracy. METok progressively eliminates redundant visual tokens across three critical stages: (1) event-aware compression during vision encoding, (2) hierarchical token pruning in the prefilling stage based on semantic alignment and event importance, and (3) a decoding-stage KV Cache optimization that further reduces memory consumption. Our experiments on diverse video benchmarks demonstrate that METok achieves an optimal trade-off between efficiency and accuracy by dynamically selecting informative visual tokens. For instance, equipping LongVA-7B with METok realizes an 80.6% FLOPs reduction and 93.5% KV Cache memory savings, all while maintaining comparable or even superior accuracy.

Multimodal Large Language Models (MLLMs) have achieved strong performance across vision-language tasks, but suffer from significant computational overhead due to the quadratic growth of attention computations with the number of multimodal tokens. Though efforts have been made to prune tokens in MLLMs, *they lack a fundamental understanding of how MLLMs process and fuse multimodal information*. Through systematic analysis, we uncover a three-stage cross-modal interaction process: (1) Shallow layers recognize task intent, with visual tokens acting as passive attention sinks; (2) Cross-modal fusion occurs abruptly in middle layers, driven by a few critical visual tokens; (3) Deep layers discard vision tokens, focusing solely on linguistic refinement. Based on these findings, we propose *VisiPruner*, a training-free pruning framework that reduces **99.9%** of vision-related attention computations and **62.8%** of FLOPs while maintaining performance. It significantly outperforms existing token pruning methods and generalizes across diverse MLLMs. Beyond pruning, our insights further provide actionable guidelines for training efficient MLLMs by aligning model architecture with its intrinsic layer-wise processing dynamics.

Evaluating and iterating upon recommender systems is crucial, yet traditional A/B testing is resource-intensive, and offline methods struggle with dynamic user-platform interactions. While agent-based simulation is promising, existing platforms often lack a mechanism for user actions to dynamically reshape the environment. To bridge this gap, we introduce RecInter , a novel agent-based simulation platform for recommender systems featuring a robust interaction mechanism. In RecInter platform, simulated user actions (e.g., likes, reviews, purchases) dynamically update item attributes in real-time, and introduced Merchant Agents can reply, fostering a more realistic and evolving ecosystem. High-fidelity simulation is ensured through Multidimensional User Profiling module, Advanced Agent Architecture, and LLM fine-tuned on Chain-of-Thought (CoT) enriched interaction data. Our platform achieves significantly improved simulation credibility and successfully replicates emergent phenomena like Brand Loyalty and the Matthew Effect. Experiments demonstrate that this interaction mechanism is pivotal for simulating realistic system evolution, establishing our platform as a credible testbed for recommender systems research. All codes are released in https://github.com/jinsong8/RecInter.

Spreadsheets are critical to data-centric tasks, with rich, structured layouts that enable efficient information transmission. Given the time and expertise required for manual spreadsheet layout design, there is an urgent need for automated solutions.However, existing automated layout models are ill-suited to spreadsheets, as they often (1) treat components as axis-aligned rectangles with continuous coordinates, overlooking the inherently discrete, grid-based structure of spreadsheets; and (2) neglect interrelated semantics, such as data dependencies and contextual links, unique to spreadsheets. In this paper, we first formalize the spreadsheet layout generation task, supported by a seven-criterion evaluation protocol and a dataset of 3,326 spreadsheets. We then introduce SheetDesigner, a zero-shot and training-free framework using Multimodal Large Language Models (MLLMs) that combines rule and vision reflection for component placement and content population. SheetDesigner outperforms five baselines by at least 22.6%. We further find that through vision modality, MLLMs handle overlap and balance well but struggle with alignment, necessitates hybrid rule and visual reflection strategies. Our codes and data is available at Github.

An Agentic AI Workflow (AAW), also known as an LLM-based multi-agent system, is an autonomous system that assembles several LLM-based agents to work collaboratively towards a shared goal. The high autonomy, widespread adoption, and growing interest in such AAWs highlight the need for a deeper understanding of their operations, from both quality and security aspects. To this day, there are no existing methods to assess the influence of each agent on the AAW’s final output. Adopting techniques from related fields is not feasible since existing methods perform only static structural analysis, which is unsuitable for inference time execution. We present Counterfactual-based Agent Influence Ranker (CAIR) - the first method for assessing the influence level of each agent on the AAW’s output and determining which agents are the most influential. By performing counterfactual analysis, CAIR provides a task-agnostic analysis that can be used both offline and at inference time. We evaluate CAIR using an AAWs dataset of our creation, containing 30 different use cases with 230 different functionalities. Our evaluation showed that CAIR produces consistent rankings, outperforms baseline methods, and can easily enhance the effectiveness and relevancy of downstream tasks.

Fine-tuning multi-turn dialogue systems requires high-quality supervision but often suffers from degraded performance when exposed to low-quality data. Supervision errors in early turns can propagate across subsequent turns, undermining coherence and response quality. Existing methods typically address data quality via static prefiltering, which decouples quality control from training and fails to mitigate turn-level error propagation. In this context, we propose **ReSURE** (REgularizing Supervision UnREliability), an adaptive learning method that dynamically down-weights unreliable supervision without explicit filtering. ReSURE estimates per-turn loss distributions using Welford’s online statistics and reweights sample losses on the fly accordingly. Experiments on both single-source and mixed-quality datasets show improved stability and response quality. Notably, ReSURE enjoys positive Spearman correlations (0.21 ~ 1.0 across multiple benchmarks) between response scores and number of samples regardless of data quality, which potentially paves the way for utilizing large-scale data effectively.

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Precise In-Parameter Concept Erasure in Large Language Models
Yoav Gur-Arieh | Clara Haya Suslik | Yihuai Hong | Fazl Barez | Mor Geva

Large language models (LLMs) often acquire knowledge during pretraining that is undesirable in downstream deployments, e.g., sensitive information or copyrighted content. Existing approaches for removing such knowledge rely on fine-tuning, training low-rank adapters or fact-level editing, but these are either too coarse, too shallow, or ineffective. In this work, we propose PISCES, a novel framework for precisely erasing entire concepts from model parameters by directly editing directions that encode them in parameter space. PISCES uses a disentangler model to decompose MLP vectors into interpretable features, identifies those associated with a target concept using automated interpretability techniques, and removes them from model parameters. Experiments on Gemma 2 and Llama 3.1 over various concepts show that PISCES achieves modest gains in efficacy over leading erasure methods, reducing accuracy on the target concept to as low as 7.7%, while dramatically improving erasure specificity (by up to 31%) and robustness (by up to 41%). Overall, these results demonstrate that feature-based in-parameter editing enables a more precise and reliable approach for removing conceptual knowledge in language models.

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PhonoThink: Improving Large Language Models’ Reasoning on Chinese Phonological Ambiguities
Jianfei Ma | Zhaoxin Feng | Emmanuele Chersoni | Huacheng Song | Ziqi Zhang

Effectively resolving phonological ambiguities is crucial for robust natural language processing, as these ambiguities are pervasive in tasks ranging from speech-to-text, spelling correction, to offensive language detection. However, current Large Language Models (LLMs) frequently struggle to resolve such ambiguities.To address this challenge, we present a framework to enhances LLMs’ phonological capability through a multiple-stage training approach. Our method begins with supervised fine-tuning on well-constructed datasets, including three subtask datasets designed to enhance the model’s foundational phonological knowledge, along with a synthetic dataset of step-by-step reasoning chains. Following this, we apply reinforcement learning to incentivize and stabilize its reasoning.Results show that our framework enables the base model to achieve relatively comparable performance to a much larger model. Our ablation studies reveal that subtask datasets and the synthetic dataset can simultaneously impact as complementary modular enhancers to strengthen LLMs’ integrated application.

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SAFE-SQL: Self-Augmented In-Context Learning with Fine-grained Example Selection for Text-to-SQL
Jimin Lee | Ingeol Baek | Byeongjeong Kim | Hyunkyung Bae | Hwanhee Lee

Text-to-SQL aims to convert natural language questions into executable SQL queries. While previous approaches, such as skeleton-masked selection, have demonstrated strong performance by retrieving similar training examples to guide large language models (LLMs), they struggle in real-world scenarios where such examples are unavailable. To overcome this limitation, we propose Fine-grained Self-Augmentation in-context learning for Text-to-SQL (SAFE-SQL), a novel framework that improves SQL generation by generating and filtering self-augmented examples. SAFE-SQL first prompts an LLM to generate multiple Text-to-SQL examples relevant to the test input. Then SAFE-SQL filters these examples through three relevance assessments, constructing high-quality in-context learning examples. Using self-generated examples, SAFE-SQL surpasses the previous zero-shot, and few-shot Text-to-SQL frameworks, achieving higher execution accuracy. Notably, our approach provides additional performance gains in extra hard and unseen scenarios, where conventional methods often fail.

Large language models (LLMs) have demonstrated significant potential in enhancing dense retrieval through query augmentation. However, most existing methods treat the LLM and the retriever as separate modules, overlooking the alignment between generation and ranking objectives. In this work, we propose ExpandR, a unified LLM-augmented dense retrieval framework that jointly optimizes both the LLM and the retriever. ExpandR employs the LLM to generate semantically rich query expansions, which are leveraged to enhance the retriever’s training. Simultaneously, the LLM is trained using Direct Preference Optimization (DPO), guided by a carefully designed reward function that balances retrieval effectiveness and generation consistency. This joint optimization paradigm enables mutual adaptation between the LLM and the retriever, resulting in query expansions that are both informative and well-suited for retrieval. Experimental results on multiple benchmarks show that ExpandR consistently outperforms strong baselines, achieving more than a 5% improvement in retrieval performance. All codes are available at https://github.com/NEUIR/ExpandR.

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Anecdoctoring: Automated Red-Teaming Across Language and Place
Alejandro Cuevas | Saloni Dash | Bharat Kumar Nayak | Dan Vann | Madeleine I. G. Daepp

Disinformation is among the top risks of generative artificial intelligence (AI) misuse. Global adoption of generative AI necessitates red-teaming evaluations (i.e., systematic adversarial probing) that are robust across diverse languages and cultures, but red-teaming datasets are commonly US- and English-centric. To address this gap, we propose ”anecdoctoring”, a novel red-teaming approach that automatically generates adversarial prompts across languages and cultures. We collect misinformation claims from fact-checking websites in three languages (English, Spanish, and Hindi) and two geographies (US and India). We then cluster individual claims into broader narratives and characterize the resulting clusters with knowledge graphs, with which we augment an attacker LLM. Our method produces higher attack success rates and offers interpretability benefits relative to few-shot prompting. Results underscore the need for disinformation mitigations that scale globally and are grounded in real-world adversarial misuse.

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ACING: Actor-Critic for Instruction Learning in Black-Box LLMs
Salma Kharrat | Fares Fourati | Marco Canini

The effectiveness of Large Language Models (LLMs) in solving tasks depends significantly on the quality of their instructions, which often require substantial human effort to craft. This underscores the need for automated instruction optimization. However, optimizing instructions is particularly challenging when working with black-box LLMs, where model parameters and gradients are inaccessible. We introduce ACING, an actor-critic reinforcement learning framework that formulates instruction optimization as a stateless, continuous-action problem, enabling exploration of infinite instruction spaces using only black-box feedback. ACING automatically discovers prompts that outperform human-written prompts in 76% of instruction-induction tasks, with gains of up to 33 points and a 10-point median improvement over the best automatic baseline in 33 tasks spanning instruction-induction, summarization, and chain-of-thought reasoning. Extensive ablations highlight its robustness and efficiency. An implementation of ACING is available at https://github.com/salmakh1/ACING.

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Women, Infamous, and Exotic Beings: A Comparative Study of Honorific Usages in Wikipedia and LLMs for Bengali and Hindi
Sourabrata Mukherjee | Atharva Mehta | Sougata Saha | Akhil Arora | Monojit Choudhury

The obligatory use of third-person honorifics is a distinctive feature of several South Asian languages, encoding nuanced socio-pragmatic cues such as power, age, gender, fame, and social distance.In this work, (i) We present the first large-scale study of third-person honorific pronoun and verb usage across 10,000 Hindi and Bengali Wikipedia articles with annotations linked to key socio-demographic attributes of the subjects, including gender, age group, fame, and cultural origin.(ii) Our analysis uncovers systematic intra-language regularities but notable cross-linguistic differences: honorifics are more prevalent in Bengali than in Hindi, while non-honorifics dominate while referring to infamous, juvenile, and culturally “exotic” entities. Notably, in both languages, and more prominently in Hindi, men are more frequently addressed with honorifics than women.(iii) To examine whether large language models (LLMs) internalize similar socio-pragmatic norms, we probe six LLMs using controlled generation and translation tasks over 1,000 culturally balanced entities. We find that LLMs diverge from Wikipedia usage, exhibiting alternative preferences in honorific selection across tasks, languages, and socio-demographic attributes. These discrepancies highlight gaps in the socio-cultural alignment of LLMs and open new directions for studying how LLMs acquire, adapt, or distort social-linguistic norms. Our code and data are publicly available at https://github.com/souro/honorific-wiki-llm

Process-supervised reward models (PRMs) excel at providing step-by-step verification for large language model (LLM) outputs in domains like mathematics and coding. However, their application to fields lacking ground-truth answers, such as clinical note generation, poses significant challenges. We introduce a novel framework for training PRMs to deliver step-level reward signals for LLM-generated clinical notes. By precisely defining meaningful “steps,” injecting realistic “errors” informed by domain expertise, and leveraging LLMs to generate process supervision data at scale, we overcome previous limitations. Our PRM, built on LLaMA-3.1 8B, consistently outperforms proprietary reasoning and non-reasoning models, achieving state-of-the-art performance on two key evaluations: (1) distinguishing gold-standard from error-containing samples with 98.8% accuracy, and (2) selecting physician-preferred clinical notes with 56.2% accuracy. We investigate critical components for effective PRM training, including optimal loss functions and data selection strategies, and present a comprehensive physician reader study identifying predictors of downstream Best-of-N performance. Our study sheds light on unlocking the potential of PRMs for diverse generative tasks across domains.

With the emergence of new topics on social media as sources of rumor propagation, addressing the domain shift between the source and target domain and the target domain samples scarcity remains a crucial task in cross-domain rumor detection. Traditional deep learning-based methods and LLM-based methods are mostly focused on the in-domain condition, thus having poor performance in cross-domain setting. Existing domain adaptation rumor detection approaches ignore the data generalization differences and rely on a large amount of unlabeled target domain samples to achieve domain adaptation, resulting in less effective on emerging topic rumor detection. In this paper, we propose a Gradient Coherence guided Meta-Learning approach (GCML) for emerging topics rumor detection. Firstly, we calculate the task generalization score of each source task (sampled from source domain) from a gradient coherence perspective, and selectively learn more “generalizable” tasks that are more beneficial in adapting to the target domain. Secondly, we leverage meta-learning to alleviate the target domain samples scarcity, which utilizes task generalization scores to re-weight meta-test gradients and adaptively updates learning rate. Extensive experimental results on real-world datasets show that our method substantially outperforms SOTA baselines.

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Can LLMs Generate and Solve Linguistic Olympiad Puzzles?
Neh Majmudar | Elena Filatova

In this paper, we introduce a combination of novel and exciting tasks: the solution and generation of linguistic puzzles. We focus on puzzles used in Linguistic Olympiads for high school students. We first extend the existing benchmark for the task of solving linguistic puzzles. We explore the use of Large Language Models (LLMs), including recent state-of-the-art models such as OpenAI’s o1, for solving linguistic puzzles, analyzing their performance across various linguistic topics. We demonstrate that LLMs outperform humans on most puzzles types, except for those centered on writing systems, and for the understudied languages. We use the insights from puzzle-solving experiments to direct the novel task of puzzle generation. We believe that automating puzzle generation, even for relatively simple puzzles, holds promise for expanding interest in linguistics and introducing the field to a broader audience. This finding highlights the importance of linguistic puzzle generation as a research task: such puzzles can not only promote linguistics but also support the dissemination of knowledge about rare and understudied languages.

Processing long contexts is increasingly important for Large Language Models (LLMs) in tasks like multi-turn dialogues, code generation, and document summarization. This paper addresses the challenges of achieving high long-context performance, low computational complexity, and compatibility with pretrained models – collectively termed the “impossible triangle”. We introduce E2LLM (Encoder Elongated Large Language Models), a novel approach that effectively navigates this paradox. E2LLM divides long contexts into chunks, compresses each into soft prompts using a pretrained text encoder, and aligns these representations with a decoder-only LLM via an adapter. To enhance the LLM’s reasoning with these soft prompts, we employ two training objectives: encoder output reconstruction and long-context instruction fine-tuning. Extensive experiments reveal that E2LLM not only outperforms 8 state-of-the-art (SOTA) methods in effectiveness and efficiency for document summarization and question answering, but also achieves the best performance on LongBench v2 among models of comparable size. The source code is available at https://github.com/codefuse-ai/E2LLM.

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DivScore: Zero-Shot Detection of LLM-Generated Text in Specialized Domains
Zhihui Chen | Kai He | Yucheng Huang | Yunxiao Zhu | Mengling Feng

Detecting LLM-generated text in specialized and high-stakes domains like medicine and law is crucial for combating misinformation and ensuring authenticity. However, current zero-shot detectors, while effective on general text, often fail when applied to specialized content due to domain shift. We provide a theoretical analysis showing this failure is fundamentally linked to the KL divergence between human, detector, and source text distributions. To address this, we propose DivScore, a zero-shot detection framework using normalized entropy-based scoring and domain knowledge distillation to robustly identify LLM-generated text in specialized domains. Experiments on medical and legal datasets show that DivScore consistently outperforms state-of-the-art detectors, with 14.4% higher AUROC and 64.0% higher recall at 0.1% false positive rate threshold. In adversarial settings, DivScore demonstrates superior robustness to other baselines, achieving on average 22.8% advantage in AUROC and 29.5% in recall.

Multi-document event extraction aims to aggregate event information from diverse sources for a comprehensive understanding of complex events. Despite its practical significance, this task has received limited attention in existing research. The inherent challenges include handling complex reasoning over long contexts and intricate event structures. In this paper, we propose a novel collaborative framework that integrates large language models for multi-step reasoning and fine-tuned small language models to handle key subtasks, guiding the overall reasoning process. We introduce a new benchmark for multi-document event extraction and propose an evaluation metric designed for comprehensive assessment of multiple aggregated events. Experimental results demonstrate that our approach significantly outperforms existing methods, providing new insights into collaborative reasoning to tackle the complexities of multi-document event extraction.

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MA-GTS: A Multi-Agent Framework for Solving Complex Graph Problems in Real-World Applications
Zike Yuan | Ming Liu | Hui Wang | Bing Qin

Graph-theoretic problems arise in real-world applications like logistics, communication networks, and traffic optimization. These problems are often complex, noisy, and irregular, posing challenges for traditional algorithms. Large language models offer potential solutions but face several challenges, including limited accuracy, input length constraints, and suboptimal algorithm selection. To address these challenges, we propose MA-GTS(Multi-Agent Graph Theory Solver), a multi-agent framework that decomposes these complex problems through agent collaboration. MA-GTS maps the implicitly expressed text-based graph data into clear, structured graph representations and dynamically selects the most suitable algorithm based on problem constraints and graph structure scale. We validate MA-GTS using the G-REAL dataset, a real-world-inspired graph theory dataset we created. Experimental results show that MA-GTS outperforms state-of-the-art methods in cost-effectiveness, accuracy, and scalability, achieving strong results on multiple benchmarks (G-REAL 93.6%, GraCoRe 96.9% ,NLGraph 98.4%) with robust performance on both closed- and open-source models.

Connecting audio encoders with large language models (LLMs) allows the LLM to perform various audio understanding tasks, such as automatic speech recognition (ASR) and audio captioning (AC). Most research focuses on training an adapter layer to generate a unified audio feature for the LLM. However, different tasks may require distinct features that emphasize either semantic or acoustic aspects, making task-specific audio features more desirable. In this paper, we propose Prompt-aware Mixture (PaM) to enhance the Speech LLM that uses multiple audio encoders. Our approach involves using different experts to extract different features based on the prompt that indicates different tasks. Experiments demonstrate that with PaM, only one Speech LLM surpasses the best performances achieved by all single-encoder Speech LLMs on ASR, speaker number verification, and AC tasks. PaM also outperforms other feature fusion baselines, such as concatenation and averaging.

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CIKT: A Collaborative and Iterative Knowledge Tracing Framework with Large Language Models
Runze Li | Siyu Wu | Jun Wang | Wei Zhang

Knowledge Tracing (KT) aims to model a student’s learning state over time and predict their future performance. However, traditional KT methods often face challenges in explainability, scalability, and effective modeling of complex knowledge dependencies. While Large Language Models (LLMs) present new avenues for KT, their direct application often struggles with generating structured, explainable student representations and lacks mechanisms for continuous, task-specific refinement. To address these gaps, we propose Collaborative Iterative Knowledge Tracing (CIKT), a framework that harnesses LLMs to enhance both prediction accuracy and explainability. CIKT employs a dual-component architecture: an Analyst generates dynamic, explainable user profiles from student historical responses, and a Predictor utilizes these profiles to forecast future performance. The core of CIKT is a synergistic optimization loop. In this loop, the Analyst is iteratively refined based on the predictive accuracy of the Predictor, which conditions on the generated profiles, and the Predictor is subsequently retrained using these enhanced profiles. Evaluated on multiple educational datasets, CIKT demonstrates significant improvements in prediction accuracy, offers enhanced explainability through its dynamically updated user profiles, and exhibits improved scalability. Our work presents a robust and explainable solution for advancing knowledge tracing systems, effectively bridging the gap between predictive performance and model transparency.

Language Model (LM)-based Text-to-Speech (TTS) systems often generate hallucinated speech that deviates from input text. Existing mitigation strategies either demand excessive training resources or introduce significant inference latency. In this paper, we propose GFlOwNet-guided distribution AlignmenT (GOAT) for LM-based TTS, a post-training framework that mitigates hallucinations without relying on massive resources or inference cost. Specifically, we first conduct an uncertainty analysis, revealing a strong positive correlation between hallucination and model uncertainty. Based on this, we reformulate TTS generation as a trajectory flow optimization problem and introduce an enhanced Subtrajectory Balance objective together with a sharpened internal reward as target distribution. We further integrate reward temperature decay and learning rate optimization for stability and performance balance. Extensive experiments show that GOAT reduce over 50% character error rates on challenging test cases and lowering uncertainty by up to 58%, demonstrating its strong generalization ability and effectiveness.

Large Language Models (LLMs) have shown growing potential in molecular sciences, but they often produce chemically inaccurate descriptions and struggle to recognize or justify potential errors. This raises important concerns about their robustness and reliability in scientific applications. To support more rigorous evaluation of LLMs in chemical reasoning, we present the MolErr2Fix benchmark, designed to assess LLMs on error detection and correction in molecular descriptions. Unlike existing benchmarks focused on molecule-to-text generation or property prediction, MolErr2Fix emphasizes fine-grained chemical understanding. It tasks LLMs with identifying, localizing, explaining, and revising potential structural and semantic errors in molecular descriptions. Specifically, MolErr2Fix consists of 1,193 fine-grained annotated error instances. Each instance contains quadruple annotations, i.e., (error type, span location, the explanation, and the correction). These tasks are intended to reflect the types of reasoning and verification required in real-world chemical communication. Evaluations of current state-of-the-art LLMs reveal notable performance gaps, underscoring the need for more robust chemical reasoning capabilities. MolErr2Fix provides a focused benchmark for evaluating such capabilities and aims to support progress toward more reliable and chemically informed language models. All annotations and an accompanying evaluation API will be publicly released to facilitate future research.

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Shared Path: Unraveling Memorization in Multilingual LLMs through Language Similarities
Xiaoyu Luo | Yiyi Chen | Johannes Bjerva | Qiongxiu Li

We present the first comprehensive study of Memorization in Multilingual Large Language Models (MLLMs), analyzing 95 languages using models across diverse model scales, architectures, and memorization definitions. As MLLMs are increasingly deployed, understanding their memorization behavior has become critical. Yet prior work has focused primarily on monolingual models, leaving multilingual memorization underexplored, despite the inherently long-tailed nature of training corpora. We find that the prevailing assumption, that memorization is highly correlated with training data availability, fails to fully explain memorization patterns in MLLMs. We hypothesize that treating languages in isolation — ignoring their similarities — obscures the true patterns of memorization. To address this, we propose a novel graph-based correlation metric that incorporates language similarity to analyze cross-lingual memorization. Our analysis reveals that among similar languages, those with fewer training tokens tend to exhibit higher memorization, a trend that only emerges when cross-lingual relationships are explicitly modeled. These findings underscore the importance of a language-aware perspective in evaluating and mitigating memorization vulnerabilities in MLLMs. This also constitutes empirical evidence that language similarity both explains Memorization in MLLMs and underpins Cross-lingual Transferability, with broad implications for multilingual NLP.

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Embedding Domain Knowledge for Large Language Models via Reinforcement Learning from Augmented Generation
Chaojun Nie | Jun Zhou | Guanxiang Wang | Shisong Wu | Zichen Wang

Large language models (LLMs) often exhibit limited performance on domain-specific tasks due to the natural disproportionate representation of specialized information in their training data and the static nature of these datasets. Knowledge scarcity and temporal lag create knowledge gaps for domain applications. While post-training on domain datasets can embed knowledge into models, existing approaches have some limitations. Continual Pre-Training (CPT) treats all tokens in domain documents with equal importance, failing to prioritize critical knowledge points, while supervised fine-tuning (SFT) with question-answer pairs struggles to develop the coherent knowledge structures necessary for complex reasoning tasks. To address these challenges, we propose Reinforcement Learning from Augmented Generation (RLAG). Our approach iteratively cycles between sampling generations and optimizing the model through calculated rewards, effectively embedding critical and contextually coherent domain knowledge. We select generated outputs with the highest log probabilities as the sampling result, then compute three tailored reward metrics to guide the optimization process. To comprehensively evaluate domain expertise, we assess answer accuracy and the rationality of explanations generated for correctly answered questions. Experimental results across medical, legal, astronomy, and current events datasets demonstrate that our proposed method significantly outperforms baseline approaches. Our code and data are open sourced at https://github.com/ChaojunNie/RLAG.

Cross-modal retrieval is essential for interpreting cultural heritage data, but its effectiveness is often limited by incomplete or inconsistent textual descriptions, caused by historical data loss and the high cost of expert annotation. While large language models (LLMs) offer a promising solution by enriching textual descriptions, their outputs frequently suffer from hallucinations or miss visually grounded details. To address these challenges, we propose C^3, a data augmentation framework that enhances cross-modal retrieval performance by improving the completeness and consistency of LLM-generated descriptions. C^3 introduces a bidirectional validation mechanism to assess semantic coverage using both visual cues and language-model outputs. Furthermore, to mitigate factual inconsistencies, we formulate a Markov Decision Process to supervise Chain-of-Thought reasoning, guiding consistency verification through adaptive query control. Experiments on the cultural heritage dataset CulTi and general benchmarks MSCOCO and Flickr30K demonstrate that C^3 achieves state-of-the-art performance in both fine-tuned and zero-shot settings.

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Artificial Impressions: Evaluating Large Language Model Behavior Through the Lens of Trait Impressions
Nicholas Deas | Kathleen McKeown

We introduce and study artificial impressions–patterns in LLMs’ internal representations of prompts that resemble human impressions and stereotypes based on language. We fit linear probes on generated prompts to predict impressions according to the two-dimensional Stereotype Content Model (SCM). Using these probes, we study the relationship between impressions and downstream model behavior as well as prompt features that may inform such impressions. We find that LLMs inconsistently report impressions when prompted, but also that impressions are more consistently linearly decodable from their hidden representations. Additionally, we show that artificial impressions of prompts are predictive of the quality and use of hedging in model responses. We also investigate how particular content, stylistic, and dialectal features in prompts impact LLM impressions.

Large Visual Language Models (LVLMs) have demonstrated impressive capabilities across multiple tasks. However, their trustworthiness is often challenged by hallucinations, which can be attributed to the modality misalignment and the inherent hallucinations of their underlying Large Language Models (LLMs) backbone. Existing preference alignment methods focus on aligning model responses with human preferences while neglecting image-text modality alignment, resulting in over-reliance on LLMs and hallucinations. In this paper, we propose Entity-centric Multimodal Preference Optimization (EMPO), which achieves enhanced modality alignment than existing human preference alignment methods. Besides, to overcome the scarcity of high-quality multimodal preference data, we utilize open-source instruction datasets to automatically construct high-quality preference data across three aspects: image, instruction, and response. Experiments on two human preference datasets and five multimodal hallucination benchmarks demonstrate the effectiveness of EMPO, e.g., reducing hallucination rates by 80.4% on Object HalBench and 52.6% on MM HalBench, thereby enhancing the trustworthiness of LVLMs. The code and dataset will be made publicly available.

Large Language Models (LLMs) excel in general language tasks, motivating their adaptation to specialized domains such as healthcare. Effective domain adaptation typically involves supervised fine-tuning (SFT) on carefully selected instruction-tuning data. Current data selection methods adopt a data-centric approach, relying on external annotations and heuristics to identify externally defined high-quality or challenging data. Our exploratory experiments highlight this approach fails to improve the model’s domain performance, due to misalignment between selected data and the model’s knowledge distribution. To tackle this, we propose Decomposed Difficulty-based Data Selection (3DS), a two-stage model-centric data selection framework that aligns data selection with the model’s distribution. 3DS employs Prompt-Driven Data Selection to filter out noise based on the model’s knowledge via explicit alignment in Stage#1, then adopts Decomposed Difficulty-based Data Selection to guide selection via three novel data difficulty metrics, including Instruction Understanding, Response Confidence, and Response Correctness in Stage#2, enhanced by an attention-based importance weighting mechanism for accurate calibration.Extensive experiments in the healthcare domain show 3DS outperforms existing methods by up to 2.97% accuracy, with additional validation in law and general domains, confirming its generalization ability. Our dataset and code are open-sourced at https://github.com/PuppyKnightUniversity/3DS.

Understanding long-form videos, such as movies and TV episodes ranging from tens of minutes to two hours, remains a significant challenge for multi-modal models. Existing benchmarks often fail to test the full range of cognitive skills needed to process these temporally rich and narratively complex inputs. Therefore, we introduce InfiniBench, a comprehensive benchmark designed to evaluate the capabilities of models in long video understanding rigorously.InfiniBench offers:(1) Over 1,000 hours of video content, with an average video length of 53 minutes.(2) The largest set of question-answer pairs for long video comprehension, totaling around 87.7 K.(3) Eight diverse skills that span both grounding-based (e.g., scene transitions, character actions) and reasoning-based (e.g., deep context understanding, multi-event linking).(4) Rich annotation formats, including both multiple-choice and open-ended questions.We conducted an in-depth evaluation across both commercial (GPT-4o, Gemini 2.0 Flash) and most recent open-source vision-language models, such as Qwen2.5-VL, InternVL3.0). Results reveal that:(1) Models struggle across the board: Even the best model, GPT-4o, achieves only 47.1% on grounding-based skills, with most models performing near or just above random chance.(2) Strong reliance on world knowledge: Models achieve surprisingly high scores using only metadata (e.g., video titles), highlighting a tendency to rely on pre-trained knowledge rather than actual visual or temporal understanding.(3) Multi-Modal Importance: When provided with full video and subtitle context, however, models show substantial improvements, confirming the critical role of multimodal input in video understanding.Our findings underscore the inherent challenges in long-video comprehension and point to the need for substantial advancements in both grounding and reasoning capabilities in MLLMs.

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Intrinsic Test of Unlearning Using Parametric Knowledge Traces
Yihuai Hong | Lei Yu | Haiqin Yang | Shauli Ravfogel | Mor Geva

The task of “unlearning” certain concepts in large language models (LLMs) has gained attention for its role in mitigating harmful, private, or incorrect outputs. Current evaluations mostly rely on behavioral tests, without monitoring residual knowledge in model parameters, which can be adversarially exploited to recover erased information. We argue that unlearning should also be assessed internally by tracking changes in the parametric traces of unlearned concepts. To this end, we propose a general evaluation methodology that uses vocabulary projections to inspect concepts encoded in model parameters. We apply this approach to localize “concept vectors” — parameter vectors encoding concrete concepts — and construct ConceptVectors, a benchmark of hundreds of such concepts and their parametric traces in two open-source LLMs. Evaluation on ConceptVectors shows that existing methods minimally alter concept vectors, mostly suppressing them at inference time, while direct ablation of these vectors removes the associated knowledge and reduces adversarial susceptibility. Our findings reveal limitations of behavior-only evaluations and advocate for parameter-based assessments. We release our code and benchmark at https://github.com/yihuaihong/ConceptVectors.

Speculative decoding is a prominent technique for accelerating LLM inference by leveraging an auxiliary draft model, but its effectiveness is limited by the autoregressive nature of draft generation, where acceptance rates depend on the draft model’s size. Scaling the draft model improves acceptance but also increases speculation latency, limiting overall speedup. Furthermore, fine-tuning both the draft and target models is often necessary to achieve high acceptance rates, adding complexity to inference systems as the number of downstream tasks grows. Single-model approaches like Medusa generate speculative tokens non-autoregressively but lack token dependencies, limiting effectiveness. Alternatives like Hydra and Eagle incorporate token dependencies but rely on dedicated heads, making speculation independent of the base model and limiting the extent to which stronger base models can improve speculation.We introduce a novel speculative decoding method that integrates speculative draft generation directly within the target model using multi-stream attention. This improves acceptance rates by introducing interdependencies between speculative tokens while ensuring non-autoregressive draft generation with minimal overhead. As target models scale in size and quality, speculative generation improves naturally with our method, unlike prior approaches. Furthermore, our approach is both parameter- and FLOP-efficient, requiring over 1000X fewer additional parameters than Medusa, making it highly suitable for resource-constrained devices. We design our method to operate in two modes: (1) Lossless mode, a plug-and-play method that preserves the output of any pre-trained model; and (2) Shared mode, optimizing both speedup and downstream performance. We demonstrate a 2–3.5X speedup across diverse tasks, including summarization, translation, question answering, mathematical reasoning, SQL generation, and retrieval-augmented generation (RAG).

Digital social media platforms frequently contribute to cognitive-behavioral fixation, a phenomenon in which users exhibit sustained and repetitive engagement with narrow content domains. While cognitive-behavioral fixation has been extensively studied in psychology, methods for computationally detecting and evaluating such fixation remain underexplored. To address this gap, we propose a novel framework for assessing cognitive-behavioral fixation by analyzing users’ multimodal social media engagement patterns. Specifically, we introduce a multimodal topic extraction module and a cognitive-behavioral fixation quantification module that collaboratively enable adaptive, hierarchical, and interpretable assessment of user behavior. Experiments on existing benchmarks and a newly curated multimodal dataset demonstrate the effectiveness of our approach, laying the groundwork for scalable computational analysis of cognitive fixation. All code in this project is publicly available for research purposes at https://github.com/Liskie/cognitive-fixation-evaluation.

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Mind the Gap: A Closer Look at Tokenization for Multiple-Choice Question Answering with LLMs
Mario Sanz-Guerrero | Minh Duc Bui | Katharina von der Wense

When evaluating large language models (LLMs) with multiple-choice question answering (MCQA), it is common to end the prompt with the string “*Answer:*” to facilitate automated answer extraction via next-token probabilities. However, there is no consensus on how to tokenize the space following the colon, often overlooked as a trivial choice. In this paper, we uncover accuracy differences of up to 11% due to this (seemingly irrelevant) tokenization variation as well as reshuffled model rankings, raising concerns about the reliability of LLM comparisons in prior work. Surprisingly, we are able to recommend one specific strategy – tokenizing the space *together* with the answer letter – as we observe consistent and statistically significant performance improvements. Additionally, it improves model calibration, enhancing the reliability of the model’s confidence estimates. Our findings underscore the importance of careful evaluation design and highlight the need for standardized, transparent evaluation protocols to ensure reliable and comparable results.

Speech large language models (LLMs) have emerged as a prominent research focus in speech processing. In this work, we introduce VocalNet, a series of high-performance speech LLMs featuring a scalable and model-agnostic training framework as well as a novel multi-token prediction (MTP) paradigm for speech generation. We first propose an efficient two-stage training framework that enables LLMs to acquire real-time speech interaction capabilities. Through extensive experiments on various training configurations, we ensure both simplicity and effectiveness in the training strategy. Furthermore, inspired by advances in language modeling, we introduce MTP into the domain of speech LLMs—an alternative to traditional next-token prediction (NTP)—which enables the model to predict multiple future tokens at each step. Through systematic analysis and improved implementation, we show that MTP not only accelerates inference speed but also significantly enhances speech quality. Experimental results demonstrate that VocalNet achieves performance comparable to state-of-the-art Omni LLMs while outperforming existing open-source speech LLMs, despite using limited training data.

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Path Drift in Large Reasoning Models: How First-Person Commitments Override Safety
Yuyi Huang | Runzhe Zhan | Lidia S. Chao | Ailin Tao | Derek F. Wong

As large language models (LLMs) are increasingly deployed for complex reasoning tasks, Long Chain-of-Thought (Long-CoT) prompting has emerged as a key paradigm for structured inference. Despite early-stage safeguards enabled by alignment techniques such as RLHF, we identify a previously underexplored vulnerability: reasoning trajectories in Long-CoT models can drift from aligned paths, resulting in content that violates safety constraints. We term this phenomenon Path Drift. Through empirical analysis, we uncover three behavioral triggers of Path Drift: (1) first-person commitments that induce goal-driven reasoning that delays refusal signals; (2) ethical evaporation, where surface-level disclaimers bypass alignment checkpoints; (3) condition chain escalation, where layered cues progressively steer models toward unsafe completions. Building on these insights, we introduce a three-stage Path Drift Induction Framework comprising cognitive load amplification, self-role priming, and condition chain hijacking. Each stage independently reduces refusal rates, while their combination further compounds the effect. To mitigate these risks, we propose a path-level defense strategy incorporating role attribution correction and metacognitive reflection (reflective safety cues). Our findings highlight the need for trajectory-level alignment oversight in long-form reasoning beyond token-level alignment.

The high costs of customizing large language models (LLMs) fundamentally limit their adaptability to user-specific needs. Consequently, LLMs are increasingly offered as cloud-based services, a paradigm that introduces critical limitations: providers struggle to support personalized customization at scale, while users face privacy risks when exposing sensitive data. To address this dual challenge, we propose Customized Black-box Prompt Tuning (CBP-Tuning), a novel framework that facilitates efficient local customization while preserving bidirectional privacy. Specifically, we design a two-stage framework: (1) a prompt generator trained on the server-side to capture domain-specific and task-agnostic capabilities, and (2) user-side gradient-free optimization that tailors soft prompts for individual tasks. This approach eliminates the need for users to access model weights or upload private data, requiring only a single customized vector per task while achieving effective adaptation. Furthermore, the evaluation of CBP-Tuning in the commonsense reasoning, medical and financial domain settings demonstrates superior performance compared to baselines, showcasing its advantages in task-agnostic processing and privacy preservation.

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Beyond the Score: Uncertainty-Calibrated LLMs for Automated Essay Assessment
Ahmed Karim | Qiao Wang | Zheng Yuan

Automated Essay Scoring (AES) systems now attain near–human agreement on some public benchmarks, yet real-world adoption—especially in high-stakes examinations—remains limited. A principal obstacle is that most models output a single score without any accompanying measure of confidence or explanation. We address this gap with conformal prediction, a distribution-free wrapper that equips any classifier with set-valued outputs enjoying formal coverage guarantees. Two open-source Large Language Models—Llama-3 8B and Qwen-2.5 3B—are fine-tuned on three diverse corpora (ASAP, TOEFL11, Cambridge-FCE) and calibrated at a 90% risk level. Reliability is assessed with UAcc, an uncertainty-aware accuracy that rewards models for being both correct and concise. To our knowledge, this is the first work to combine conformal prediction and UAcc for essay scoring. The calibrated models consistently meet the coverage target while keeping prediction sets compact, indicating that open-source, mid-sized LLMs can already support teacher-in-the-loop AES; we discuss scaling and broader user studies as future work.

Modeling complex subjective tasks in Natural Language Processing, such as recognizing emotion and morality, is considerably challenging due to significant variation in human annotations. This variation often reflects reasonable differences in semantic interpretations rather than mere noise, necessitating methods to distinguish between legitimate subjectivity and error.We address this challenge by exploring label verification in these contexts using Large Language Models (LLMs). First, we propose a simple In-Context Learning binary filtering baseline that estimates the reasonableness of a document-label pair. We then introduce the Label-in-a-Haystack setting: the query and its label(s) are included in the demonstrations shown to LLMs, which are prompted to predict the label(s) again, while receiving task-specific instructions (e.g., emotion recognition) rather than label copying.We show how the failure to copy the label(s) to the output of the LLM are task-relevant and informative. Building on this, we propose the Label-in-a-Haystack Rectification (LiaHR) framework for subjective label correction: when the model outputs diverge from the reference gold labels, we assign the generated labels to the example instead of discarding it. This approach can be integrated into annotation pipelines to enhance signal-to-noise ratios. Comprehensive analyses, human evaluations, and ecological validity studies verify the utility of LiaHR for label correction. Code is available at https://github.com/gchochla/liahr.

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Do It Yourself (DIY): Modifying Images for Poems in a Zero-Shot Setting Using Weighted Prompt Manipulation
Sofia Jamil | Kotla Sai Charan | Sriparna Saha | Koustava Goswami | Joseph K J

Poetry is an expressive form of art that invites multiple interpretations, as readers often bring their own emotions, experiences, and cultural backgrounds into their understanding of a poem. Recognizing this, we aim to generate images for poems and improve these images in a zero-shot setting, enabling audiences to modify images as per their requirements. To achieve this, we introduce a novel Weighted Prompt Manipulation (WPM) technique, which systematically modifies attention weights and text embeddings within diffusion models. By dynamically adjusting the importance of specific words, WPM enhances or suppresses their influence in the final generated image, leading to semantically richer and more contextually accurate visualizations. Our approach exploits diffusion models and large language models (LLMs) such as GPT in conjunction with existing poetry datasets, ensuring a comprehensive and structured methodology for improved image generation in the literary domain. To the best of our knowledge, this is the first attempt at integrating weighted prompt manipulation for enhancing imagery in poetic language.

Large vision-language models (LVLMs) have achieved impressive results in vision-language tasks. However, Therefore, we propose LACING, designed to address such bias with Mu ̲Ltimodal Du ̲Al-attention Me ̲Chan ̲Ism (MDA) a ̲Nd Soft-Image ̲Guidance (SIG). Specifically, MDA adopts a parallel dual-attention mechanism that constructs separate attention for visual and text inputs to enhance integration of visual inputs across model. SIG uses a learnable soft visual prompt during training and inference to replace visual inputs, designed to compel LVLMs to prioritize text inputs during inference. Experiments across different model architectures and scales demonstrate that LACING effectively debiases LVLMs from their language bias, enhancing visual comprehension and reducing hallucinations without additional resources.

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Who Holds the Pen? Caricature and Perspective in LLM Retellings of History
Lubna Zahan Lamia | Mabsur Fatin Bin Hossain | Md Mosaddek Khan

Large language models (LLMs) are no longer just language generators—they are increasingly used to simulate human behavior, perspectives, and demographic variation across social domains, from public opinion surveys to experimental research. Amid this shift, the use of LLMs to simulate historical narratives has emerged as a timely frontier. It is crucial to scrutinize the asymmetries these models embed when framing, interpreting, and retelling the past. Building on prior work that defines caricature as the combination of individuation and exaggeration, we analyze LLM-generated responses across 197 historically significant events—each featuring a directly and an indirectly affected persona. We find that LLMs reliably distinguish persona-based responses from neutral baselines, and that directly affected personas consistently exhibit higher exaggeration—amplifying identity-specific portrayals. Beyond lexical patterns, personas often frame the same event in conflicting ways—especially in military, political, and morally charged contexts. Grammatical analysis further reveals that direct personas adopt more passive constructions in institutional contexts, but shift to active framing when emotional immediacy is foregrounded. Our findings show how subtle asymmetries in tone, stance, and emphasis—not overt toxicity—can quietly, yet systematically, distort how history is told and remembered.

As large language models continue to scale, computational costs and resource consumption have emerged as significant challenges. While existing sparsification methods like pruning reduce computational overhead, they risk losing model knowledge through parameter removal. This paper proposes DSMoE (Dynamic Sparse Mixture-of-Experts), a novel approach that achieves sparsification by partitioning pre-trained FFN layers into computational blocks. We implement adaptive expert routing using sigmoid activation and straight-through estimators, enabling tokens to flexibly access different aspects of model knowledge based on input complexity. Additionally, we introduce a sparsity loss term to balance performance and computational efficiency. Extensive experiments on LLaMA models demonstrate that under equivalent computational constraints, DSMoE achieves superior performance compared to existing pruning and MoE approaches across language modeling and downstream tasks, particularly excelling in generation tasks. Analysis reveals that DSMoE learns distinctive layerwise activation patterns, providing new insights for future MoE architecture design.

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Search Wisely: Mitigating Sub-optimal Agentic Searches By Reducing Uncertainty
Peilin Wu | Mian Zhang | Xinlu Zhang | Xinya Du | Zhiyu Chen

Agentic Retrieval-Augmented Generation (RAG) systems enhance Large Language Models (LLMs) by enabling dynamic, multi-step reasoning and information retrieval. However, these systems often exhibit sub-optimal search behaviors like over-search (retrieving redundant information) and under-search (failing to retrieve necessary information), which hinder efficiency and reliability. This work formally defines and quantifies these behaviors, revealing their prevalence across multiple QA datasets and agentic RAG systems (e.g., one model could have avoided searching in 27.7% of its search steps). Furthermore, we demonstrate a crucial link between these inefficiencies and the models’ uncertainty regarding their own knowledge boundaries, where response accuracy correlates with model’s uncertainty in its search decisions. To address this, we propose β-GRPO, a reinforcement learning-based training method that incorporates confidence threshold to reward high-certainty search decisions. Experiments on seven QA benchmarks show that β-GRPO enable a 3B model with better agentic RAG ability, outperforming other strong baselines with a 4% higher average exact match score.

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Child-Directed Language Does Not Consistently Boost Syntax Learning in Language Models
Francesca Padovani | Jaap Jumelet | Yevgen Matusevych | Arianna Bisazza

Seminal work by Huebner et al. (2021) showed that language models (LMs) trained on English Child-Directed Language (CDL) can outperform LMs trained on an equal amount of adult-directed text like Wikipedia. However, it remains unclear whether these results generalize across languages, architectures, and evaluation settings. We test this by comparing models trained on CDL vs. Wikipedia across two LM objectives (masked and causal), three languages (English, French, German), and three syntactic minimal pair benchmarks. Our results on these benchmarks show inconsistent benefits of CDL, which in most cases is outperformed by Wikipedia models. We then identify various shortcomings in these benchmarks, and introduce a novel testing methodology, FIT-CLAMS, which uses a frequency-controlled design to enable balanced comparisons across training corpora. Through minimal pair evaluations and regression analysis we show that training on CDL does not yield stronger generalizations for acquiring syntax and highlight the importance of controlling for frequency effects when evaluating syntactic ability.

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Benchmarking Debiasing Methods for LLM-based Parameter Estimates
Nicolas Audinet de Pieuchon | Adel Daoud | Connor Thomas Jerzak | Moa Johansson | Richard Johansson

Large language models (LLMs) offer an inexpensive yet powerful way to annotate text, but are often inconsistent when compared with experts. These errors can bias downstream estimates of population parameters such as regression coefficients and causal effects. To mitigate this bias, researchers have developed debiasing methods such as Design-based Supervised Learning (DSL) and Prediction-Powered Inference (PPI), which promise valid estimation by combining LLM annotations with a limited number of expensive expert annotations.Although these methods produce consistent estimates under theoretical assumptions, it is unknown how they compare in finite samples of sizes encountered in applied research. We make two contributions: First, we study how each method’s performance scales with the number of expert annotations, highlighting regimes where LLM bias or limited expert labels significantly affect results. Second, we compare DSL and PPI across a range of tasks, finding that although both achieve low bias with large datasets, DSL often outperforms PPI on bias reduction and empirical efficiency, but its performance is less consistent across datasets. Our findings indicate that there is a bias-variance tradeoff at the level of debiasing methods, calling for more research on developing metrics for quantifying their efficiency in finite samples.

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(Almost) Free Modality Stitching of Foundation Models
Jaisidh Singh | Diganta Misra | Boris Knyazev | Antonio Orvieto

Foundation multi-modal models are often designed by stitching of multiple existing pretrained uni-modal models: for example, an image classifier with a text model. This stitching process is performed by training a connector module that aims to align the representation spaces of these uni-modal models towards a multi-modal objective. However, given the complexity of training such connectors on large scale web-based datasets coupled with the ever-increasing number of available pretrained uni-modal models, the task of uni-modal models selection and subsequent connector module training becomes computationally demanding. To address this under-studied critical problem, we propose Hypernetwork Model Alignment (Hyma), a novel all-in-one solution for optimal uni-modal model selection and connector training by leveraging hypernetworks. Specifically, our framework utilizes the parameter prediction capability of a hypernetwork to obtain jointly trained connector modules for N × M combinations of uni-modal models. In our experiments, Hyma reduces the cost of searching for the best performing uni-modal model pair by 10×, while matching the ranking and trained connector performance obtained via grid search across a suite of diverse multi-modal benchmarks.

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VERITAS: Leveraging Vision Priors and Expert Fusion to Improve Multimodal Data
Tingqiao Xu | Ziru Zeng | Jiayu Chen

The quality of supervised fine-tuning (SFT) data is crucial for the performance of large multimodal models (LMMs), yet current data enhancement methods often suffer from factual errors and hallucinations due to inadequate visual perception. To address this challenge, we propose VERITAS, a pipeline that systematically integrates vision priors and multiple state-of-the-art LMMs with statistical methods to enhance SFT data quality. VERITAS leverages visual recognition models (RAM++) and OCR systems (PP-OCRv4) to extract structured vision priors, which are combined with images, questions, and answers. Three LMMs (GPT-4o, Gemini-2.5-Pro, Doubao-1.5-pro) evaluate the original answers, providing critique rationales and scores that are statistically fused into a high-confidence consensus score serving as ground truth. Using this consensus, we train a lightweight critic model via Group Relative Policy Optimization (GRPO), enhancing reasoning capabilities efficiently. Each LMM then refines the original answers based on the critiques, generating new candidate answers; we select the highest-scoring one as the final refined answer. Experiments across six multimodal benchmarks demonstrate that models fine-tuned with data processed by VERITAS consistently outperform those using raw data, particularly in text-rich and fine-grained reasoning tasks. Our critic model exhibits enhanced capability comparable to state-of-the-art LMMs while being significantly more efficient. We release our pipeline, datasets, and model checkpoints to advance research in multimodal data optimization.

Incorporating external context can significantly enhance the response quality of Large Language Models (LLMs). However, real-world contexts often mix relevant information with disproportionate inappropriate content, posing reliability risks. How do LLMs process and prioritize mixed context? To study this, we introduce the Poisoned Context Testbed, pairing queries with real-world contexts containing relevant and inappropriate content. Inspired by associative learning in animals, we adapt the Rescorla-Wagner (RW) model from neuroscience to quantify how competing contextual signals influence LLM outputs. Our adapted model reveals a consistent behavioral pattern: LLMs exhibit a strong tendency to incorporate information that is less prevalent in the context. This susceptibility is harmful in real-world settings, where small amounts of inappropriate content can substantially degrade response quality. Empirical evaluations on our testbed further confirm this vulnerability. To tackle this, we introduce RW-Steering, a two-stage finetuning-based approach that enables the model to internally identify and ignore inappropriate signals. Unlike prior methods that rely on extensive supervision across diverse context mixtures, RW-Steering generalizes robustly across varying proportions of inappropriate content. Experiments show that our best fine-tuned model improves response quality by 39.8% and reverses the undesirable behavior curve, establishing RW-Steering as a robust, generalizable solution for improving LLM safety in real-world use.

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Exploring Artificial Image Generation for Stance Detection
Zhengkang Zhang | Zhongqing Wang | Guodong Zhou

Stance detection is a task aimed at identifying and analyzing the author’s stance from text. Previous studies have primarily focused on the text, which may not fully capture the implicit stance conveyed by the author. To address this limitation, we propose a novel approach that transforms original texts into artificially generated images and uses the visual representation to enhance stance detection. Our approach first employs a text-to-image model to generate candidate images for each text. These images are carefully crafted to adhere to three specific criteria: textual relevance, target consistency, and stance consistency. Next, we introduce a comprehensive evaluation framework to select the optimal image for each text from its generated candidates. Subsequently, we introduce a multimodal stance detection model that leverages both the original textual content and the generated image to identify the author’s stance. Experiments demonstrate the effectiveness of our approach and highlight the importance of artificially generated images for stance detection.

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Hope vs. Hate: Understanding User Interactions with LGBTQ+ News Content in Mainstream US News Media through the Lens of Hope Speech
Jonathan Pofcher | Christopher M Homan | Randall Sell | Ashiqur R. KhudaBukhsh

This paper makes three contributions. First, via a substantial corpus of 1,419,047 comments posted on 3,161 YouTube news videos of major US cable news outlets, we analyze how users engage with LGBTQ+ news content. Our analyses focus both on positive and negative content. In particular, we construct a hope speech classifier that detects positive (hope speech), negative, neutral, and irrelevant content. Second, in consultation with a public health expert specializing on LGBTQ+ health, we conduct an annotation study with a balanced and diverse political representation and release a dataset of 3,750 instances with crowd-sourced labels and detailed annotator demographic information. Finally, beyond providing a vital resource for the LGBTQ+ community, our annotation study and subsequent in-the-wild assessments reveal (1) strong association between rater political beliefs and how they rate content relevant to a marginalized community, (2) models trained on individual political beliefs exhibit considerable in-the-wild disagreement, and (3) zero-shot large language models (LLMs) align more with liberal raters.

Prompt sensitivity, referring to the phenomenon where paraphrasing (that is, repeating something written or spoken using different words) leads to significant changes in large language model performance, has been widely accepted as a core limitation of large language models. In this work, we revisit this issue and ask: Is the widely reported high prompt sensitivity truly an inherent weakness of large language models, or is it largely an artifact of evaluation processes? To answer this question, we systematically evaluate seven large language models (for example, the GPT and Gemini families) across six benchmarks, including both multiple-choice and open-ended tasks on twelve diverse prompt templates. We find that much of the prompt sensitivity stems from heuristic evaluation methods, including log-likelihood scoring and rigid answer matching, which often overlook semantically correct responses expressed through alternative phrasings, such as synonyms or paraphrases. When we adopt large language model as a judge evaluations, we observe a substantial reduction in performance variance and a consistently higher correlation in model rankings across prompts. Our findings suggest that modern large language models are more robust to prompt templates than previously believed, and that prompt sensitivity may be more an artifact of evaluation than a flaw in the models.

The effectiveness of in-context learning relies heavily on selecting demonstrations that provide all the necessary information for a given test input.To achieve this, it is crucial to identify and cover fine-grained knowledge requirements. However, prior methods often retrieve demonstrations based solely on embedding similarity or generation probability, resulting in irrelevant or redundant examples.In this paper, we propose TopicK, a topic coverage-based retrieval framework that selects demonstrations to comprehensively cover topic-level knowledge relevant to both the test input and the model.Specifically, TopicK estimates the topics required by the input and assesses the model’s knowledge on those topics.TopicK then iteratively selects demonstrations that introduce previously uncovered required topics, in which the model exhibits low topical knowledge.We validate the effectiveness of TopicK through extensive experiments across various datasets and both open- and closed-source LLMs.Our source code is available at https://github.com/WonbinKweon/TopicK_EMNLP2025.

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On the Same Wavelength? Evaluating Pragmatic Reasoning in Language Models across Broad Concepts
Linlu Qiu | Cedegao E. Zhang | Joshua B. Tenenbaum | Yoon Kim | Roger P. Levy

Language use is shaped by pragmatics—i.e., reasoning about communicative goals and norms in context. As language models (LMs) are increasingly used as conversational agents, it becomes ever more important to understand their pragmatic reasoning abilities. We propose an evaluation framework derived from *Wavelength*, a popular communication game where a speaker and a listener communicate about a broad range of concepts in a granular manner. We study a range of LMs on both language comprehension and language production using direct and Chain-of-Thought (CoT) prompting, and further explore a Rational Speech Act (RSA) approach to incorporating Bayesian pragmatic reasoning into LM inference. We find that state-of-the-art LMs, but not smaller ones, achieve strong performance on language comprehension, obtaining similar-to-human accuracy and exhibiting high correlations with human judgments even without CoT prompting or RSA. On language production, CoT can outperform direct prompting, and using RSA provides significant improvements over both approaches. Our study helps identify the strengths and limitations in LMs’ pragmatic reasoning abilities and demonstrates the potential for improving them with RSA, opening up future avenues for understanding conceptual representation, language understanding, and social reasoning in LMs and humans.

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MuseScorer: Idea Originality Scoring At Scale
Ali Sarosh Bangash | Krish Veera | Ishfat Abrar Islam | Raiyan Abdul Baten

An objective, face-valid method for scoring idea originality is to measure each idea’s statistical infrequency within a population—an approach long used in creativity research. Yet, computing these frequencies requires manually bucketing idea rephrasings, a process that is subjective, labor-intensive, error-prone, and brittle at scale. We introduce MuseScorer, a fully automated, psychometrically validated system for frequency-based originality scoring. MuseScorer integrates a Large Language Model (LLM) with externally orchestrated retrieval: given a new idea, it retrieves semantically similar prior idea-buckets and zero-shot prompts the LLM to judge whether the idea fits an existing bucket or forms a new one. These buckets enable frequency-based originality scoring without human annotation. Across five datasets (N_participants=1143, n_ideas=16,294), MuseScorer matches human annotators in idea clustering structure (AMI =0.59) and participant-level scoring (r = 0.89), while demonstrating strong convergent and external validity. The system enables scalable, intent-sensitive, and human-aligned originality assessment for creativity research.

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SAFENUDGE: Safeguarding Large Language Models in Real-time with Tunable Safety-Performance Trade-offs
Joao Fonseca | Andrew Bell | Julia Stoyanovich

Large Language Models (LLMs) have been shown to be susceptible to jailbreak attacks, or adversarial attacks used to illicit high risk behavior from a model, highlighting the critical need to safeguard widely-deployed models. Safeguarding approaches, which include fine-tuning models or having LLMs “self-reflect,” may lengthen the inference time of a model, incur a computational penalty, reduce the semantic fluency of an output, and restrict “normal” model behavior. Importantly, these Safety-Performance Trade-offs (SPTs) remain an understudied area. In this work, we make three contributions: (1) We introduce SAFENUDGE, a novel safeguard that combines Controlled Text Generation and “nudging.” SAFENUDGE triggers during text-generation while a jailbreak attack is being executed, and can reduce successful jailbreak attempts by between 28.1% and 37.3% by guiding the LLM towards a safe response. It adds minimal latency to inference and has a negligible impact on the semantic fluency of outputs. Second, it supports tunable SPTs, meaning practitioners can set their own tolerance for trade-offs balancing safety and restrictions to normal model behavior. Third, we release the source code for SAFENUDGE at https://github.com/joaopfonseca/SafeNudge. It is open source and compatible with the HuggingFace transformers library.

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RaDeR: Reasoning-aware Dense Retrieval Models
Debrup Das | Sam O’Nuallain | Razieh Rahimi

We propose RaDeR, a set of reasoning-based dense retrieval models trained with data derived from mathematical problem solving using large language models (LLMs). Our method leverages retrieval-augmented reasoning trajectories of an LLM and self-reflective relevance evaluation, enabling the creation of both diverse and hard-negative samples for reasoning-intensive relevance. RaDeR retrievers, trained for mathematical reasoning, effectively generalize to diverse reasoning tasks in the BRIGHT and RAR-b benchmarks, consistently outperforming strong baselines in overall performance. Notably, RaDeR achieves significantly higher performance than baselines on the Math and Coding splits. In addition, RaDeR presents the first dense retriever that outperforms BM25 when queries are Chain-of-Thought reasoning steps, underscoring the critical role of reasoning-based retrieval to augment reasoning language models. Furthermore RaDeR achieves comparable or superior performance while using only 2.5% of the training data used by the concurrent work ReasonIR, highlighting the quality of our synthesized training data. Our code, data, and retrieval models are publicly available.

Large multimodal models (LMMs) have recently gained attention due to their effectiveness to understand and generate descriptions of visual content. Most existing LMMs are in English language. While few recent works explore multilingual image LMMs, to the best of our knowledge, moving beyond the English language for cultural and linguistic inclusivity is yet to be investigated in the context of video LMMs. In pursuit of more inclusive video LMMs, we introduce a multilingual Video LMM benchmark, named ViMUL-Bench, to evaluate Video LMMs across 14 languages, including both low- and high-resource languages: Arabic, Bengali, Chinese, English, French, German, Hindi, Japanese, Russian, Sinhala, Spanish, Swedish, Tamil, and Urdu. Our ViMUL-Bench is designed to rigorously test video LMMs across 15 categories including eight culturally diverse categories, ranging from lifestyles and festivals to foods and rituals and from local landmarks to prominent cultural personalities. ViMUL-Bench comprises both open-ended (short and long-form) and multiple-choice questions spanning various video durations (short, medium, and long) with 8k samples that are manually verified by native language speakers. In addition, we also introduce a machine translated multilingual video training set comprising 1.2 million samples and develop a simple multilingual video LMM, named ViMUL, that is shown to provide a better tradeoff between high-and low-resource languages for video understanding. We hope our ViMUL-Bench and multilingual video LMM along with a large-scale multilingual video training set will help ease future research in developing cultural and linguistic inclusive multilingual video LMMs. Our proposed benchmark, video LMM and training data will be publicly released.

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DRES: Fake news detection by dynamic representation and ensemble selection
Faramarz Farhangian | Leandro Augusto Ensina | George D C Cavalcanti | Rafael M. O. Cruz

The rapid spread of information via social media has made text-based fake news detection critically important due to its societal impact. This paper presents a novel detection method called Dynamic Representation and Ensemble Selection (DRES) for identifying fake news based solely on text. DRES leverages instance hardness measures to estimate the classification difficulty for each news article across multiple textual feature representations. By dynamically selecting the textual representation and the most competent ensemble of classifiers for each instance, DRES significantly enhances prediction accuracy. Extensive experiments show that DRES achieves notable improvements over state-of-the-art methods, confirming the effectiveness of representation selection based on instance hardness and dynamic ensemble selection in boosting performance. Codes and data are available at: at:https://github.com/FFarhangian/FakeNewsDetection_DRES

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A Graph-Theoretical Framework for Analyzing the Behavior of Causal Language Models
Rashin Rahnamoun | Mehrnoush Shamsfard

Recent progress in natural language processing has popularized causal language models, but their internal behavior remains poorly understood due to the high cost and reliance on large-scale benchmarks in existing analysis methods. To address these challenges, we introduce a graph-theoretical framework for analyzing causal language models. Our method constructs graphs from model outputs by linking high-probability token transitions and applies classical metrics to capture linguistic features of model behavior. Based on previous works, none have examined or applied graph analysis from this perspective. For the first time, a macroscopic view of the overall behavior of a language model is provided by analyzing the mathematical characteristics of small sample graphs derived from the generated outputs. We first discuss the metrics theoretically, then demonstrate how they work through experiments, followed by some applications of this graph-theoretical framework in natural language processing tasks. Through experiments across training steps and model sizes, we demonstrate that these metrics can reflect model evolution and predict performance with minimal data. We further validate our findings by comparing them with benchmark accuracy scores, highlighting the reliability of our metrics. In contrast to existing evaluation methods, our approach is lightweight, efficient, and especially well-suited for low-resource settings. Our implementation codes are available at this GitHub repository.

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Membership and Memorization in LLM Knowledge Distillation
Ziqi Zhang | Ali Shahin Shamsabadi | Hanxiao Lu | Yifeng Cai | Hamed Haddadi

Recent advances in Knowledge Distillation (KD) aim to mitigate the high computational demands of Large Language Models (LLMs) by transferring knowledge from a large ”teacher” to a smaller ”student” model. However, students may inherit the teacher’s privacy when the teacher is trained on private data. In this work, we systematically characterize and investigate membership privacy risks inherent in six LLM KD techniques.Using instruction-tuning settings that span seven NLP tasks, together with three teacher model families (GPT-2, LLAMA-2, and OPT), and various size student models, we demonstrate that all existing LLM KD approaches carry membership and memorization privacy risks from the teacher to its students. However, the extent of privacy risks varies across different KD techniques. We systematically analyse how key LLM KD components (KD objective functions, student training data and NLP tasks) impact such privacy risks. We also demonstrate a significant disagreement between memorization and membership privacy risks of LLM KD techniques. Finally, we characterize per-block privacy risk and demonstrate that the privacy risk varies across different blocks by a large margin.

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Balanced Multi-Factor In-Context Learning for Multilingual Large Language Models
Masahiro Kaneko | Alham Fikri Aji | Timothy Baldwin

Multilingual large language models (MLLMs) are able to leverage in-context learning (ICL) to achieve high performance by leveraging cross-lingual knowledge transfer without parameter updates. However, their effectiveness is highly sensitive to example selection, particularly in multilingual settings. Based on the findings of existing work, three key factors influence multilingual ICL: (1) semantic similarity, (2) linguistic alignment, and (3) language-specific performance. However, existing approaches address these factors independently, without explicitly disentangling their combined impact, leaving optimal example selection underexplored. To address this gap, we propose balanced multi-factor ICL (BMF-ICL), a method that quantifies and optimally balances these factors for improved example selection. Experiments on mCSQA and TYDI across four MLLMs demonstrate that BMF-ICL outperforms existing methods. Further analysis highlights the importance of incorporating all three factors and the importance of selecting examples from multiple languages.

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Efficient Context Selection for Long-Context QA: No Tuning, No Iteration, Just Adaptive‐k
Chihiro Taguchi | Seiji Maekawa | Nikita Bhutani

Retrieval-augmented generation (RAG) and long-context language models (LCLMs) both address context limitations of LLMs in open-domain QA. However, optimal external context to retrieve remains an open problem: fixed retrieval budgets risk wasting tokens or omitting key evidence. Existing adaptive methods like Self-RAG and Self-Route rely on iterative LLM prompting and perform well on factoid QA, but struggle with aggregation QA where optimal context size is unknown and variable. We present Adaptive‐k retrieval, a simple and effective single-pass method that selects a query-specific number of passages by applying a threshold to the similarity scores between the query and candidate passages. It does not require model fine-tuning, extra LLM calls or changes to existing retriever–reader pipelines. On both factoid and aggregation QA benchmarks, Adaptive‐k matches or outperforms fixed‐k baselines while using up to 10x fewer tokens than full-context input, and still retrieves 70% of relevant passages. It improves accuracy across five LCLMs and two embedding models, highlighting that dynamically adjusting context size leads to more efficient and accurate QA.

Multilingual machine translation (MT) benchmarks play a central role in evaluating the capabilities of modern MT systems. Among them, the FLORES+ benchmark is widely used, offering English-to-many translation data for over 200 languages, curated with strict quality control protocols. However, we study data in four languages (Asante Twi, Japanese, Jinghpaw, and South Azerbaijani) and uncover critical shortcomings in the benchmark’s suitability for truly multilingual evaluation. Human assessments reveal that many translations fall below the claimed 90% quality standard, and the annotators report that source sentences are often too domain-specific and culturally biased toward the English-speaking world. We further demonstrate that simple heuristics, such as copying named entities, can yield non-trivial BLEU scores, suggesting vulnerabilities in the evaluation protocol. Notably, we show that MT models trained on naturalistic data perform poorly on FLORES+ while achieving significant gains on our domain-relevant evaluation set. Based on these findings, we advocate for multilingual MT benchmarks that use domain-general, named-entity-agnostic, and culturally neutral source texts to better reflect real-world translation challenges.

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Think Globally, Group Locally: Evaluating LLMs Using Multi-Lingual Word Grouping Games
César Guerra-Solano | Zhuochun Li | Xiang Lorraine Li

Large language models (LLMs) can exhibit biases in reasoning capabilities due to linguistic modality, performing better on tasks in one language versus another, even with similar content. Most previous works evaluate this through reasoning tasks where reliance on strategies or knowledge can ensure success, such as in commonsense or math tasks. However, abstract reasoning is vital to reasoning for everyday life, where people apply “out-of-the-box thinking” to identify and use patterns for solutions, without a reliance on formulaic approaches. Comparatively, little work has evaluated linguistic biases in this task type. In this paper, we propose a task inspired by the New York Times Connections: GlobalGroup, that evaluates models in an abstract reasoning task across several languages. We constructed a game benchmark with five linguistic backgrounds – English, Spanish, Chinese, Hindi, and Arabic – in both the native language and an English translation for comparison. We also proposed game difficulty measurements to evaluate models on games with similar difficulty, enabling a more controlled comparison, which is particularly important in reasoning evaluations. Through experimentation, we find English modalities largely lead to better performance in this abstract reasoning task, and performance disparities between open- and closed-source models.

Multimodal large language models (MLLMs) have demonstrated extraordinary capabilities in conducting conversations based on image inputs. However, we observe that MLLMs exhibit a pronounced form of visual sycophantic behavior. While similar behavior has also been noted in text-based large language models (LLMs), it becomes significantly more prominent when MLLMs process image inputs. We refer to this phenomenon as the “sycophantic modality gap.” To better understand this issue, we further analyze the factors that contribute to the exacerbation of this gap. To mitigate the visual sycophantic behavior, we first experiment with naive supervised fine-tuning to help the MLLM resist misleading instructions from the user. However, we find that this approach also makes the MLLM overly resistant to corrective instructions (i.e., stubborn even if it is wrong). To alleviate this trade-off, we propose Sycophantic Reflective Tuning (SRT), which enables the MLLM to engage in reflective reasoning, allowing it to determine whether a user’s instruction is misleading or corrective before drawing a conclusion. After applying SRT, we observe a significant reduction in sycophantic behavior toward misleading instructions, without resulting in excessive stubbornness when receiving corrective instructions.

The paradigm of using Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) as evaluative judges has emerged as an effective approach in RLHF and inference-time scaling. In this work, we propose Multimodal Reasoner as a Judge (MR. Judge), a paradigm for empowering general-purpose MLLMs judges with strong reasoning capabilities. Instead of directly assigning scores for each response, we formulate the judgement process as a reasoning-inspired multiple-choice problem. Specifically, the judge model first conducts deliberate reasoning covering different aspects of the responses and eventually selects the best response from them. This reasoning process not only improves the interpretibility of the judgement, but also greatly enhances the performance of MLLM judges. To cope with the lack of questions with scored responses, we propose the following strategy to achieve automatic annotation: 1) Reverse Response Candidates Synthesis: starting from a supervised fine-tuning (SFT) dataset, we treat the original response as the best candidate and prompt the MLLM to generate plausible but flawed negative candidates. 2) Text-based reasoning distillation: we carefully design a data synthesis pipeline for distilling the reasoning capability from a text-based reasoning model, which is adopted to enable the MLLM judges to regain complex reasoning ability via warm up supervised fine-tuning. Experiments demonstrate that our MR. Judge is effective across a wide range of tasks. Specifically, our MR. Judge-7B surpasses GPT-4o by 9.9% on VL-RewardBench, and improves performance on MM-Vet during inference-time scaling by up to 7.7%.

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MobiZO: Enabling Efficient LLM Fine-Tuning at the Edge via Inference Engines
Lei Gao | Amir Ziashahabi | Yue Niu | Salman Avestimehr | Murali Annavaram

Large Language Models (LLMs) are currently pre-trained and fine-tuned on large cloud servers. The next frontier is LLM personalization, where a foundation model can be fine-tuned with user/task-specific data. Given the sensitive nature of such private data, it is desirable to fine-tune these models on edge devices to improve user trust. However, fine-tuning on resource-constrained edge devices presents significant challenges due to substantial memory and computational demands, as well as limited infrastructure support. We observe that inference engines (e.g., ExecuTorch) can be repurposed for fine-tuning by leveraging zeroth-order (ZO) optimization, which uses multiple forward passes to approximate gradients. While promising, direct application of ZO methods on edge devices is inefficient due to the high computational cost of multiple forward passes required for accurate gradient estimation, and their deployment has been largely unexplored in practice. We introduce MobiZO, a resource-efficient fine-tuning framework for LLMs specifically designed for edge devices. MobiZO combines three key innovations: (1) a parallelized randomized gradient estimator that employs both outer-loop and inner-loop parallelism to eliminate sequential forward passes, (2) a specialized Multi-Perturbed LoRA (MP-LoRA) module that enables efficient realization of both inner and outer loop parallelism, and (3) a seamless integration with ExecuTorch for on-device training, requiring no modifications to the runtime. Experiments demonstrate that MobiZO achieves substantial runtime speedups and memory savings while improving fine-tuning accuracy, paving the way for practical deployment of LLMs in real-time, on-device applications. Code available at: https://github.com/leigao97/MobiZO.

Arabic poetry stands as one of the most sophisticated and culturally embedded forms of expression in the Arabic language, known for its layered meanings, stylistic diversity, and deep historical continuity. Although large language models (LLMs) have demonstrated strong performance across languages and tasks, their ability to understand Arabic poetry remains largely unexplored. In this work, we introduce “Fann or Flop”, the first benchmark designed to assess the comprehension of Arabic poetry by LLMs in twelve historical eras, covering 21 core poetic genres and a variety of metrical forms, from classical structures to contemporary free verse. The benchmark comprises a curated corpus of poems with explanations that assess semantic understanding, metaphor interpretation, prosodic awareness, and cultural context. We argue that poetic comprehension offers a strong indicator for testing how good the LLM is in understanding classical Arabic through the Arabic poetry. Unlike surface-level tasks, this domain demands deeper interpretive reasoning and cultural sensitivity. Our evaluation of state-of-the-art LLMs shows that most models struggle with poetic understanding despite strong results on standard Arabic benchmarks. We release “Fann or Flop” along with the evaluation suite as an open-source resource to enable rigorous evaluation and advancement for Arabic-capable language models.

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CoMAT: Chain of Mathematically Annotated Thought Improves Mathematical Reasoning
Joshua Ong Jun Leang | Aryo Pradipta Gema | Shay B Cohen

Mathematical reasoning remains a significant challenge for large language models (LLMs), despite progress in prompting techniques such as Chain-of-Thought (CoT). We present **Chain of Mathematically Annotated Thought (CoMAT)**, which enhances reasoning through two stages: *Symbolic Conversion* (converting natural language queries into symbolic form) and *Reasoning Execution* (deriving answers from symbolic representations). CoMAT operates entirely with a single LLM and without external solvers. Across four LLMs, CoMAT outperforms traditional CoT on six out of seven benchmarks, achieving gains of 4.48% on MMLU-Redux (MATH) and 4.58% on GaoKao MCQ. In addition to improved performance, CoMAT ensures faithfulness and verifiability, offering a transparent reasoning process for complex mathematical tasks.

Test-time scaling is a promising new approach to language modeling that uses extra test-time compute to improve performance. Recently, OpenAI’s o1 model showed this capability but did not publicly share its methodology, leading to many replication efforts. We seek the simplest approach to achieve test-time scaling and strong reasoning performance. First, we curate a small dataset s1K of 1,000 questions paired with reasoning traces relying on three criteria we validate through ablations: difficulty, diversity, and quality. Second, we develop budget forcing to control test-time compute by forcefully terminating the model’s thinking process or lengthening it by appending “Wait” multiple times to the model’s generation when it tries to end. This can lead the model to double-check its answer, often fixing incorrect reasoning steps. After supervised finetuning the Qwen2.5-32B-Instruct language model on s1K and equipping it with budget forcing, our model s1 exceeds o1-preview on competition math questions by up to 27% (MATH and AIME24). Further, scaling s1 with budget forcing allows extrapolating beyond its performance without test-time intervention: from 50% to 57% on AIME24. Our model, data, and code are open-source at https://github.com/simplescaling/s1.

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Learning Subjective Label Distributions via Sociocultural Descriptors
Mohammed Fayiz Parappan | Ricardo Henao

Subjectivity in NLP tasks, _e.g._, toxicity classification, has emerged as a critical challenge precipitated by the increased deployment of NLP systems in content-sensitive domains. Conventional approaches aggregate annotator judgements (labels), ignoring minority perspectives, and overlooking the influence of the sociocultural context behind such annotations. We propose a framework where subjectivity in binary labels is modeled as an empirical distribution accounting for the variation in annotators through human values extracted from sociocultural descriptors using a language model. The framework also allows for downstream tasks such as population and sociocultural group-level majority label prediction. Experiments on three toxicity datasets covering human-chatbot conversations and social media posts annotated with diverse annotator pools demonstrate that our approach yields well-calibrated toxicity distribution predictions across binary toxicity labels, which are further used for majority label prediction across cultural subgroups, improving over existing methods.

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COM-BOM: Bayesian Exemplar Search for Efficiently Exploring the Accuracy-Calibration Pareto Frontier
Gaoxiang Luo | Aryan Deshwal

Selecting an optimal set of exemplars is critical for good performance of in-context learning. However, prior exemplar search methods narrowly optimize for predictive accuracy, critically neglecting model calibration—a key determinant of trustworthiness and safe deployment. In this paper, we formulate exemplar selection as a multi-objective optimization problem, explicitly targeting both the maximization of predictive accuracy and the minimization of expected calibration error. We solve this problem with a sample-efficient Combinatorial Bayesian Optimization algorithm (COM-BOM) to find the Pareto-front that optimally trade-offs the two objectives of accuracy and calibration. We evaluate COM-BOM on multiple tasks from un-saturated MMLU-pro benchmark and find that COM-BOM beats or matches the baselines in jointly optimizing the two objectives, while requiring a minimal number of LLM API calls.

Promises made by politicians, corporate leaders, and public figures have a significant impact on public perception, trust, and institutional reputation. However, the complexity and volume of such commitments, coupled with difficulties in verifying their fulfillment, necessitate innovative methods for assessing their credibility. This paper introduces the concept of Promise Verification, a systematic approach involving steps such as promise identification, evidence assessment, and the evaluation of timing for verification. We propose the first multilingual dataset, ML-Promise, which includes English, French, Chinese, Japanese, and Korean, aimed at facilitating in-depth verification of promises, particularly in the context of Environmental, Social, and Governance (ESG) reports. Given the growing emphasis on corporate environmental contributions, this dataset addresses the challenge of evaluating corporate promises, especially in light of practices like greenwashing. Our findings also explore textual and image-based baselines, with promising results from retrieval-augmented generation (RAG) approaches. This work aims to foster further discourse on the accountability of public commitments across multiple languages and domains.

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Reading Between the Prompts: How Stereotypes Shape LLM’s Implicit Personalization
Vera Neplenbroek | Arianna Bisazza | Raquel Fernández

Generative Large Language Models (LLMs) infer user’s demographic information from subtle cues in the conversation — a phenomenon called implicit personalization. Prior work has shown that such inferences can lead to lower quality responses for users assumed to be from minority groups, even when no demographic information is explicitly provided. In this work, we systematically explore how LLMs respond to stereotypical cues using controlled synthetic conversations, by analyzing the models’ latent user representations through both model internals and generated answers to targeted user questions. Our findings reveal that LLMs do infer demographic attributes based on these stereotypical signals, which for a number of groups even persists when the user explicitly identifies with a different demographic group. Finally, we show that this form of stereotype-driven implicit personalization can be effectively mitigated by intervening on the model’s internal representations using a trained linear probe to steer them toward the explicitly stated identity. Our results highlight the need for greater transparency and control in how LLMs represent user identity.

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Paired by the Teacher: Turning Unpaired Data into High-Fidelity Pairs for Low-Resource Text Generation
Yen-Ju Lu | Thomas Thebaud | Laureano Moro-Velazquez | Najim Dehak | Jesus Villalba

We present Paired by the Teacher (PbT), a two-stage teacher–student pipeline that synthesizes accurate input–output pairs without human labels or parallel data. In many low-resource natural language generation (NLG) scenarios, practitioners may have only raw outputs, like highlights, recaps, or questions, or only raw inputs, such as articles, dialogues, or paragraphs, but seldom both. This mismatch forces small models to learn from very few examples or rely on costly, broad-scope synthetic examples produced by large LLMs. PbT addresses this by asking a teacher LLM to compress each unpaired example into a concise intermediate representation (IR), and training a student to reconstruct inputs from IRs. This enables outputs to be paired with student-generated inputs, yielding high-quality synthetic data. We evaluate PbT on five benchmarks—document summarization (XSum, CNNDM), dialogue summarization (SAMSum, DialogSum), and question generation (SQuAD)—as well as an unpaired setting on SwitchBoard (paired with DialogSum summaries). An 8B student trained only on PbT data outperforms models trained on 70 B teacher-generated corpora and other unsupervised baselines, coming within 1.2 ROUGE-L of human-annotated pairs and closing 82% of the oracle gap at one-third the annotation cost of direct synthesis. Human evaluation on SwitchBoard further confirms that only PbT produces concise, faithful summaries aligned with the target style, highlighting its advantage of generating in-domain sources that avoid the mismatch, limiting direct synthesis.

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Please Translate Again: Two Simple Experiments on Whether Human-Like Reasoning Helps Translation
Di Wu | Seth Aycock | Christof Monz

Large Language Models (LLMs) demonstrate strong reasoning capabilities for many tasks, often by explicitly decomposing the task via Chain-of-Thought (CoT) reasoning. Recent work on LLM-based translation designs hand-crafted prompts to decompose translation, or trains models to incorporate intermediate steps. _Translating Step-by-step_ (Briakou et al., 2024), for instance, introduces a multi-step prompt with decomposition and refinement of translation with LLMs, which achieved state-of-the-art results on WMT24 test data. In this work, we scrutinise this strategy’s effectiveness. Empirically, we find no clear evidence that performance gains stem from explicitly decomposing the translation process via CoT, at least for the models on test; and we show prompting LLMs to “translate again” and self-refine yields even better results than human-like step-by-step prompting. While the decomposition influences translation behaviour, faithfulness to the decomposition has both positive and negative effects on translation. Our analysis therefore suggests a divergence between the optimal translation strategies for humans and LLMs.

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How Do Large Vision-Language Models See Text in Image? Unveiling the Distinctive Role of OCR Heads
Ingeol Baek | Hwan Chang | Sunghyun Ryu | Hwanhee Lee

Despite significant advancements in Large Vision Language Models (LVLMs), a gap remains, particularly regarding their interpretability and how they locate and interpret textual information within images. In this paper, we explore various LVLMs to identify the specific heads responsible for recognizing text from images, which we term the Optical Character Recognition Head (OCR Head). Our findings regarding these heads are as follows: (1) Less Sparse: Unlike previous retrieval heads, a large number of heads are activated to extract textual information from images. (2) Qualitatively Distinct: OCR heads possess properties that differ significantly from general retrieval heads, exhibiting low similarity in their characteristics. (3) Statically Activated: The frequency of activation for these heads closely aligns with their OCR scores. We validate our findings in downstream tasks by applying Chain-of-Thought (CoT) to both OCR and conventional retrieval heads and by masking these heads. We also demonstrate that redistributing sink-token values within the OCR heads improves performance. These insights provide a deeper understanding of the internal mechanisms LVLMs employ in processing embedded textual information in images.

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Explainability and Interpretability of Multilingual Large Language Models: A Survey
Lucas Resck | Isabelle Augenstein | Anna Korhonen

Multilingual large language models (MLLMs) demonstrate state-of-the-art capabilities across diverse cross-lingual and multilingual tasks. Their complex internal mechanisms, however, often lack transparency, posing significant challenges in elucidating their internal processing of multilingualism, cross-lingual transfer dynamics and handling of language-specific features. This paper addresses this critical gap by presenting a survey of current explainability and interpretability methods specifically for MLLMs. To our knowledge, it is the first comprehensive review of its kind. Existing literature is categorised according to the explainability techniques employed, the multilingual tasks addressed, the languages investigated and available resources. The survey further identifies key challenges, distils core findings and outlines promising avenues for future research within this rapidly evolving domain.

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Decoding the Rule Book: Extracting Hidden Moderation Criteria from Reddit Communities
Youngwoo Kim | Himanshu Beniwal | Steven L. Johnson | Thomas Hartvigsen

Effective content moderation systems require explicit classification criteria, yet online communities like subreddits often operate with diverse, implicit standards. This work introduces a novel approach to identify and extract these implicit criteria from historical moderation data using an interpretable architecture. We represent moderation criteria as score tables of lexical expressions associated with content removal, enabling systematic comparison across different communities.Our experiments demonstrate that these extracted lexical patterns effectively replicate the performance of neural moderation models while providing transparent insights into decision-making processes. The resulting criteria matrix reveals significant variations in how seemingly shared norms are actually enforced, uncovering previously undocumented moderation patterns including community-specific tolerances for language, features for topical restrictions, and underlying subcategories of the toxic speech classification.

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AcT2I: Evaluating and Improving Action Depiction in Text-to-Image Models
Vatsal Malaviya | Agneet Chatterjee | Maitreya Patel | Yezhou Yang | Chitta Baral

Text-to-Image (T2I) models have recently achieved remarkable success in generating images from textual descriptions. However, challenges still persist in accurately rendering complex scenes where actions and interactions form the primary semantic focus. Our key observation in this work is that T2I models frequently struggle to capture nuanced and often implicit attributes inherent in action depiction, leading to generating images that lack key contextual details. To enable systematic evaluation, we introduce AcT2I, a benchmark designed to evaluate the performance of T2I models in generating images from action-centric prompts. We experimentally validate that leading T2I models do not fare well on AcT2I. We further hypothesize that this shortcoming arises from the incomplete representation of the inherent attributes and contextual dependencies in the training corpora of existing T2I models. We build upon this by developing a training-free, knowledge distillation technique utilizing Large Language Models to address this limitation. Specifically, we enhance prompts by incorporating dense information across three dimensions, observing that injecting prompts with temporal details significantly improves image generation accuracy, with our best model achieving an increase of 72%. Our findings highlight the limitations of current T2I methods in generating images that require complex reasoning and demonstrate that integrating linguistic knowledge in a systematic way can notably advance the generation of nuanced and contextually accurate images. Project Page : https://vatsal-malaviya.github.io/AcT2I/

This study presents a novel approach to assessing French text readability for adults with low literacy skills, addressing both global (full-text) and local (segment-level) difficulty. We introduce a dataset of 461 texts annotated using a difficulty scale developed specifically for this population. Using this corpus, we conducted a systematic comparison of key readability modeling approaches, including machine learning techniques based on linguistic variables, fine-tuning of CamemBERT, a hybrid approach combining CamemBERT with linguistic variables, and the use of generative language models (LLMs) to carry out readability assessment at both global and local levels.

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LILaC: Late Interacting in Layered Component Graph for Open-domain Multimodal Multihop Retrieval
Joohyung Yun | Doyup Lee | Wook-Shin Han

Multimodal document retrieval aims to retrieve query-relevant components from documents composed of textual, tabular, and visual elements. An effective multimodal retriever needs to handle two main challenges: (1) mitigate the effect of irrelevant contents caused by fixed, single-granular retrieval units, and (2) support multihop reasoning by effectively capturing semantic relationships among components within and across documents. To address these challenges, we propose LILaC, a multimodal retrieval framework featuring two core innovations. First, we introduce a layered component graph, explicitly representing multimodal information at two layers—each representing coarse and fine granularity—facilitating efficient yet precise reasoning. Second, we develop a late-interaction-based subgraph retrieval method, an edge-based approach that initially identifies coarse-grained nodes for efficient candidate generation, then performs fine-grained reasoning via late interaction. Extensive experiments demonstrate that LILaC achieves state-of-the-art retrieval performance on all five benchmarks, notably without additional fine-tuning. We make the artifacts publicly available at github.com/joohyung00/lilac.

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DiCoRe: Enhancing Zero-shot Event Detection via Divergent-Convergent LLM Reasoning
Tanmay Parekh | Kartik Mehta | Ninareh Mehrabi | Kai-Wei Chang | Nanyun Peng

Zero-shot Event Detection (ED), the task of identifying event mentions in natural language text without any training data, is critical for document understanding in specialized domains. Understanding the complex event ontology, extracting domain-specific triggers from the passage, and structuring them appropriately overloads and limits the utility of Large Language Models (LLMs) for zero-shot ED. To this end, we propose DiCoRe, a divergent-convergent reasoning framework that decouples the task of ED using Dreamer and Grounder. Dreamer encourages divergent reasoning through open-ended event discovery, which helps to boost event coverage. Conversely, Grounder introduces convergent reasoning to align the free-form predictions with the task-specific instructions using finite-state machine guided constrained decoding. Additionally, an LLM-Judge verifies the final outputs to ensure high precision. Through extensive experiments on six datasets across five domains and nine LLMs, we demonstrate how DiCoRe consistently outperforms prior zero-shot, transfer-learning, and reasoning baselines, achieving 4–7% average F1 gains over the best baseline – establishing DiCoRe as a strong zero-shot ED framework.

Event Detection (ED) – the task of identifying event mentions from natural language text – is critical for enabling reasoning in highly specialized domains such as biomedicine, law, and epidemiology. Data generation has proven to be effective in broadening its utility to wider applications without requiring expensive expert annotations. However, when existing generation approaches are applied to specialized domains, they struggle with label noise, where annotations are incorrect, and domain drift, characterized by a distributional mismatch between generated sentences and the target domain. To address these issues, we introduce SNaRe, a domain-aware synthetic data generation framework composed of three components: Scout, Narrator, and Refiner. Scout extracts triggers from unlabeled target domain data and curates a high-quality domain-specific trigger list using corpus-level statistics to mitigate domain drift. Narrator, conditioned on these triggers, generates high-quality domain-aligned sentences, and Refiner identifies additional event mentions, ensuring high annotation quality. Experimentation on three diverse domain ED datasets reveals how SNaRe outperforms the best baseline, achieving average F1 gains of 3-7% in the zero-shot/few-shot settings and 4-20% F1 improvement for multilingual generation. Analyzing the generated trigger hit rate and human evaluation substantiates SNaRe’s stronger annotation quality and reduced domain drift.

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Table-R1: Inference-Time Scaling for Table Reasoning Tasks
Zheyuan Yang | Lyuhao Chen | Arman Cohan | Yilun Zhao

In this work, we present the first study to explore inference-time scaling on table reasoning tasks. We develop and evaluate two post-training strategies to enable inference-time scaling: distillation from frontier model reasoning traces and reinforcement learning with verifiable rewards (RLVR). For distillation, we introduce a large-scale dataset of reasoning traces generated by DeepSeek-R1, which we use to fine-tune LLMs into the Table-R1-SFT model. For RLVR, we propose task-specific verifiable reward functions and apply the GRPO algorithm to obtain the Table-R1-Zero model. We evaluate our Table-R1-series models across diverse table reasoning tasks, including short-form QA, fact verification, and free-form QA. Notably, the Table-R1-Zero model matches or exceeds the performance of GPT-4.1 and DeepSeek-R1, while using only a 7B-parameter LLM. It also demonstrates strong generalization to out-of-domain datasets. Extensive ablation and qualitative analyses reveal the benefits of instruction tuning, model architecture choices, and cross-task generalization, as well as emergence of essential table reasoning skills during RL training.

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LimRank: Less is More for Reasoning-Intensive Information Reranking
Tingyu Song | Yilun Zhao | Siyue Zhang | Chen Zhao | Arman Cohan

Existing approaches typically rely on large-scale fine-tuning to adapt LLMs for information reranking tasks, which is computationally expensive. In this work, we demonstrate that modern LLMs can be effectively adapted using only minimal, high-quality supervision. To enable this, we design LIMRANK-SYNTHESIZER, a reusable and open-source pipeline for generating diverse, challenging, and realistic reranking examples. Using this synthetic data, we fine-tune our reranker model, LIMRANK. We evaluate LIMRANK on two challenging benchmarks, i.e., BRIGHT for reasoning-intensive retrieval and FollowIR for instruction-following retrieval. Our experiments demonstrate that LIMRANK achieves competitive performance, while being trained on less than 5% of the data typically used in prior work. Further ablation studies demonstrate the effectiveness of LIMRANK-SYNTHESIZER and the strong generalization capabilities of LIMRANK across downstream tasks, including scientific literature search and retrieval-augmented generation for knowledge-intensive problem solving.

Recent agent frameworks and inference-time algorithms often struggle with natural planning problems due to limitations in verifying generated plans or reasoning and varying complexity of instances within a single task. Many existing methods for these tasks either perform task-level verification without considering constraints or apply inference-time algorithms without adapting to instance-level complexity. To address these limitations, we propose PlanGEN, a model-agnostic and easily scalable agent framework with three key components: constraint, verification, and selection agents. Specifically, our approach proposes constraint-guided iterative verification to enhance performance of inference-time algorithms–Best of 𝒩, Tree-of-Thought, and REBASE. In PlanGEN framework, the selection agent optimizes algorithm choice based on instance complexity, ensuring better adaptability to complex planning problems. Experimental results demonstrate significant improvements over the strongest baseline across multiple benchmarks, achieving state-of-the-art results on NATURAL PLAN (~8%↑), OlympiadBench (~4%↑), DocFinQA (~7%↑), and GPQA (~1%↑). Our key finding highlights that constraint-guided iterative verification improves inference-time algorithms, and adaptive selection further boosts performance on complex planning and reasoning problems.

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An Empirical Study on Strong-Weak Model Collaboration for Repo-level Code Generation
Shubham Gandhi | Atharva Naik | Yiqing Xie | Carolyn Rose

We study cost-efficient collaboration between strong and weak language models for repository-level code generation, where the weak model handles simpler tasks at lower cost, and the most challenging tasks are delegated to the strong model. While many works propose architectures for this task, few analyze performance relative to cost. We evaluate a broad spectrum of collaboration strategies: context-based, pipeline-based, and dynamic, on GitHub issue resolution. Our most effective collaborative strategy achieves equivalent performance to the strong model while reducing the cost by 40%. Based on our findings, we offer actionable guidelines for choosing collaboration strategies under varying budget and performance constraints. Our results show that strong–weak collaboration substantially boosts the weak model’s performance at a fraction of the cost, pipeline and context-based methods being most efficient.

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What are Foundation Models Cooking in the Post-Soviet World?
Anton Lavrouk | Tarek Naous | Alan Ritter | Wei Xu

The culture of the Post-Soviet states is complex, shaped by a turbulent history that continues to influence current events. In this study, we investigate the Post-Soviet cultural food knowledge of foundation models by constructing BORSch, a multi-modal dataset encompassing 1147 and 823 dishes in the Russian and Ukrainian languages, centered around the Post-Soviet region. We demonstrate that leading models struggle to correctly identify the origins of dishes from Post-Soviet nations in both text-only and multi-modal Question Answering (QA), instead over-predicting countries linked to the language the question is asked in. Through analysis of pre-training data, we show that these results can be explained by misleading dish-origin co-occurrences, along with linguistic phenomena such as Russian-Ukrainian code mixing. Finally, to move beyond QA-based assessments, we test models’ abilities to produce accurate visual descriptions of dishes. The weak correlation between this task and QA suggests that QA alone may be insufficient as an evaluation of cultural understanding.

Modern large language models (LLMs) employ diverse logical inference mechanisms for reasoning, making the strategic optimization of these approaches critical for advancing their capabilities. This paper systematically investigate the **comparative dynamics** of inductive (System 1) versus abductive/deductive (System 2) inference in LLMs. We utilize a controlled analogical reasoning environment, varying modality (textual, visual, symbolic), difficulty, and task format (MCQ / free-text). Our analysis reveals System 2 pipelines generally excel, particularly in visual/symbolic modalities and harder tasks, while System 1 is competitive for textual and easier problems. Crucially, task format significantly influences their relative advantage, with System 1 sometimes outperforming System 2 in free-text rule-execution. These core findings generalize to broader in-context learning. Furthermore, we demonstrate that advanced System 2 strategies like hypothesis selection and iterative refinement can substantially scale LLM reasoning. This study offers foundational insights and actionable guidelines for strategically deploying logical inference to enhance LLM reasoning.

To address data locality and privacy restrictions, Federated Learning (FL) has recently been adopted to fine-tune large language models (LLMs), enabling improved performance on various downstream tasks without requiring aggregated data. However, the repeated exchange of model updates in FL can result in prohibitively high communication costs, hindering the distributed learning process. To address this challenge, we propose EcoLoRA, a novel communication-efficient federated fine-tuning framework for LLMs. Leveraging the modular structure, we propose a round-robin segment sharing scheme, where each client uploads only a complementary LoRA segment per round to reduce network bandwidth. It is further combined with adaptive sparsification methods tailored to LoRA’s training dynamics and lossless encoding techniques. We conduct extensive evaluations on both question-answering and value-alignment tasks across multiple datasets and models. The results show that EcoLoRA significantly reduces communication overhead without compromising performance. For instance, it reduces communication time by up to 79% and total training time by up to 65%.

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Memorization ≠ Understanding: Do Large Language Models Have the Ability of Scenario Cognition?
Boxiang Ma | Ru Li | Wang Yuanlong | Hongye Tan | Xiaoli Li

Driven by vast and diverse textual data, large language models (LLMs) have demonstrated impressive performance across numerous natural language processing (NLP) tasks. Yet, a critical question persists: does their generalization arise from mere memorization of training data or from deep semantic understanding? To investigate this, we propose a bi-perspective evaluation framework to assess LLMs’ scenario cognition—the ability to link semantic scenario elements with their arguments in context. Specifically, we introduce a novel scenario-based dataset comprising diverse textual descriptions of fictional facts, annotated with scenario elements. LLMs are evaluated through their capacity to answer scenario-related questions (model output perspective) and via probing their internal representations for encoded scenario elements-argument associations (internal representation perspective). Our experiments reveal that current LLMs predominantly rely on superficial memorization, failing to achieve robust semantic scenario cognition, even in simple cases. These findings expose critical limitations in LLMs’ semantic understanding and offer cognitive insights for advancing their capabilities.

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Priority on High-Quality: Selecting Instruction Data via Consistency Verification of Noise Injection
Hong Zhang | Feng Zhao | Ruilin Zhao | Cheng Yan | Kangzheng Liu

Large Language Models (LLMs) have demonstrated a remarkable understanding of language nuances through instruction tuning, enabling them to effectively tackle various natural language processing tasks. Recent research has focused on the quality of instruction data rather than the quantity of instructions. However, existing high-quality instruction selection methods rely on external models or rules, overlooking the intrinsic association between pre-trained model and instruction data, making it difficult to select data that align with the preferences of pre-trained model. To address this challenge, we propose a strategy that utilizes noise injection to identify the quality of instruction data, without relying on external model. We also implement the strategy of combining inter-class diversity and intra-class diversity to improve model performance. The experimental results demonstrate that our method significantly outperforms the model trained on the entire dataset and established baselines. Our study provides a new perspective on noise injection in the field of instruction tuning, and also illustrates that the pre-trained model itself should be considered in defining high-quality. Additionally, we publish our selected high-quality instruction data.

Large Language Models (LLMs) are widely used for temporal prediction, but their reliance on pretraining data raises contamination concerns, as accurate predictions on pre-cutoff test data may reflect memorization rather than reasoning, leading to an overestimation of their generalization capability. With the recent emergence of prompting-based unlearning techniques, a natural question arises: Can LLMs be prompted to simulate an earlier knowledge cutoff? In this work, we investigate the capability of prompting to simulate earlier knowledge cutoff in LLMs. We construct three evaluation datasets to assess the extent to which LLMs can forget (1) direct factual knowledge, (2) semantic shifts, and (3) causally related knowledge. Results demonstrate that while prompt-based simulated knowledge cutoffs show effectiveness when directly queried with the information after that date, they struggle to induce forgetting when the forgotten content is not directly asked but causally related to the query. These findings highlight the need for more rigorous evaluation settings when applying LLMs for temporal prediction tasks. The full dataset and evaluation code are available at https://github.com/gxx27/time_unlearn.

The reliability of large language models remains a critical challenge, particularly due to their susceptibility to hallucinations and factual inaccuracies during text generation. Existing solutions either underutilize models’ self-correction with preemptive strategies or use costly post-hoc verification. To further explore the potential of real-time self-verification and correction, we present Dynamic Self-Verify Decoding (DSVD), a novel decoding framework that enhances generation reliability through real-time hallucination detection and efficient error correction. DSVD integrates two key components: (1) parallel self-verification architecture for continuous quality assessment, (2) dynamic rollback mechanism for targeted error recovery. Extensive experiments across five benchmarks demonstrate DSVD’s effectiveness, achieving significant improvement in truthfulness (Quesetion-Answering) and factual accuracy (FActScore). Results show the DSVD can be further incorporated with existing faithful decoding methods to achieve stronger performance. Our work establishes that real-time self-verification during generation offers a viable path toward more trustworthy language models without sacrificing practical deployability.

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Metric Calculating Benchmark: Code-Verifiable Complicate Instruction Following Benchmark for Large Language Models
Hyeonseok Moon | Seongtae Hong | Jaehyung Seo | Heuiseok Lim

Recent frontier-level LLMs have saturated many previously difficult benchmarks, leaving little room for further differentiation. This progress highlights the need for challenging benchmarks that provide objective verification. In this paper, we introduce MCBench, a benchmark designed to evaluate whether LLMs can execute string-matching NLP metrics by strictly following step-by-step instructions. Unlike prior benchmarks that depend on subjective judgments or general reasoning, MCBench offers an objective, deterministic and code-verifiable evaluation. This setup allows us to systematically test whether LLMs can maintain accurate step-by-step execution, including instruction adherence, numerical computation, and long-range consistency in handling intermediate results. To ensure objective evaluation of these abilities, we provide a parallel reference code that can evaluate the accuracy of LLM output. We provide three evaluative metrics and three benchmark variants designed to measure the detailed instruction understanding capability of LLMs. Our analyses show that MCBench serves as an effective and objective tool for evaluating the capabilities of cutting-edge LLMs

End-to-end automatic speech recognition systems often fail to transcribe domain-speciffcnamed entities, causing catastrophic failuresin downstream tasks. Numerous fast and lightweight named entity correction (NEC) models have been proposed in recent years. These models, mainly leveraging phonetic-level edit distance algorithms, have shown impressive performances. However, when theforms of the wrongly-transcribed words(s) and the ground-truth entity are signiffcantly different, these methods often fail to locate the wrongly transcribed words in hypothesis, thus limiting their usage. We propose a novel NEC method that utilizes speech sound features to retrieve candidate entities. With speech sound features and candidate entities, we inovatively design a generative method to annotate entityerrors in ASR transcripts and replace the textwith correct entities. This method is effective inscenarios of word form difference. We test ourmethod using open-source and self-constructed test sets. The results demonstrate that our NEC method can bring signiffcant improvement to entity accuracy. We will open source our self constructed test set and training data.

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SOLAR: Towards Characterizing Subjectivity of Individuals through Modeling Value Conflicts and Trade-offs
Younghun Lee | Dan Goldwasser

Large Language Models (LLMs) not only have solved complex reasoning problems but also exhibit remarkable performance in tasks that require subjective decision-making. Existing studies suggest that LLM generations can convey subjectivity to some extent, yet exploring whether LLMs can account for individual-level subjectivity has not been sufficiently studied. In this paper, we characterize the subjectivity of individuals on social media and infer their moral judgments using LLMs. We propose a framework, SolAr (Subjective Ground with Value Abstraction), that observes value conflicts and trade-offs in the user-generated texts to better represent subjective ground of individuals. Empirical results demonstrate that our framework enhances overall inference performance, with notable improvements for users with limited data and in controversial situations. Additionally, we qualitatively show that SolAr provides explanations about individuals’ value preferences, which can further account for their judgments.

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LogicTree: Structured Proof Exploration for Coherent and Rigorous Logical Reasoning with Large Language Models
Kang He | Kaushik Roy

Large language models (LLMs) have achieved remarkable multi-step reasoning capabilities across various domains. However, LLMs still face distinct challenges in complex logical reasoning, as (1) proof-finding requires systematic exploration and the maintenance of logical coherence and (2) searching the right combination of premises at each reasoning step is inherently challenging in tasks with large premise space. To address this, we propose LogicTree, an inference-time modular framework employing algorithm-guided search to automate structured proof exploration and ensure logical coherence. Advancing beyond tree-of-thought (ToT), we incorporate caching mechanism into LogicTree to enable effective utilization of historical knowledge, preventing reasoning stagnation and minimizing redundancy. Furthermore, we address the combinatorial complexity of premise search by decomposing it into a linear process. The refined premise selection restricts subsequent inference to at most one derivation per step, enhancing reasoning granularity and enforcing strict step-by-step reasoning. Additionally, we introduce two LLM-free heuristics for premise prioritization, enabling strategic proof search. Experimental results on five datasets demonstrate that LogicTree optimally scales inference-time computation to achieve higher proof accuracy, surpassing chain-of-thought (CoT) and ToT with average gains of 23.6% and 12.5%, respectively, on GPT-4o. Moreover, within LogicTree, GPT-4o outperforms o3-mini by 7.6% on average.

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Unmasking Fake Careers: Detecting Machine-Generated Career Trajectories via Multi-layer Heterogeneous Graphs
Michiharu Yamashita | Thanh Tran | Delvin Ce Zhang | Dongwon Lee

The rapid advancement of Large Language Models (LLMs) has enabled the generation of highly realistic synthetic data. We identify a new vulnerability, LLMs generating convincing career trajectories in fake resumes and explore effective detection methods. To address this challenge, we construct a dataset of machine-generated career trajectories using LLMs and various methods, and demonstrate that conventional text-based detectors perform poorly on structured career data. We propose CareerScape, a novel heterogeneous, hierarchical multi-layer graph framework that models career entities and their relations in a unified global graph built from genuine resumes. Unlike conventional classifiers that treat each instance independently, CareerScape employs a structure-aware framework that augments user-specific subgraphs with trusted neighborhood information from a global graph, enabling the model to capture both global structural patterns and local inconsistencies indicative of synthetic career paths. Experimental results show that CareerScape outperforms state-of-the-art baselines by 5.8-85.0% relatively, highlighting the importance of structure-aware detection for machine-generated content. Our codebase is available at https://github.com/mickeymst/careerscape.

The deployment of Large Language Models (LLMs) faces significant challenges due to high computational costs,driving the demand for effective pruning techniques. Existing structured pruning methods employ uniform compression rates across network layers, neglecting the varying importance of different network depths. To address this limitation, we propose a novel optimization framework that directly minimizes global capability loss through layer-adaptive pruning rates. The framework formulates the pruning task as a combinatorial optimization problem constrained by a total parameter budget, and an efficient dynamic programming solution is derived to determine optimal layer-wise compression rates.Experiments demonstrate that, when tuning is not included, our approach achieves comparable performance with state-of-the-art methods at high pruning rates (37-50% reduction), and shows significant advantages at low pruning rates (13-25% reduction). When tuning is included, our method achieves the best performance among the compared methods.

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Distribution Prompting: Understanding the Expressivity of Language Models Through the Next-Token Distributions They Can Produce
Haojin Wang | Zining Zhu | Freda Shi

Autoregressive neural language models (LMs) generate a probability distribution over tokens at each time step given a prompt. In this work, we attempt to systematically understand the probability distributions that LMs can produce, showing that some distributions are significantly harder to elicit than others. Specifically, for any target next-token distribution over the vocabulary, we attempt to find a prompt that induces the LM to output a distribution as close as possible to the target, using either soft or hard gradient-based prompt tuning. We find that (1) in general, distributions with very low or very high entropy are easier to approximate than those with moderate entropy; (2) among distributions with the same entropy, those containing ”outlier tokens” are easier to approximate; (3) target distributions generated by LMs – even LMs with different tokenizers – are easier to approximate than randomly chosen targets. These results offer insights into the expressiveness of LMs and the challenges of using them as probability distribution proposers.

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LGA: LLM-GNN Aggregation for Temporal Evolution Attribute Graph Prediction
Feng Zhao | Ruoyu Chai | Kangzheng Liu | Xianggan Liu

Temporal evolution attribute graph prediction, a key task in graph machine learning, aims to forecast the dynamic evolution of node attributes over time. While recent advances in Large Language Models (LLMs) have enabled their use in enhancing node representations for integration with Graph Neural Networks (GNNs), their potential to directly perform GNN-like aggregation and interaction remains underexplored. Furthermore, traditional approaches to initializing attribute embeddings often disregard structural semantics, limiting the provision of rich prior knowledge to GNNs. Current methods also primarily focus on 1-hop neighborhood aggregation, lacking the capability to capture complex structural interactions. To address these limitations, we propose a novel prediction framework that integrates structural information into attribute embeddings through the introduction of an attribute embedding loss. We design specialized prompts to enable LLMs to perform GNN-like aggregation and incorporate a relation-aware Graph Convolutional Network to effectively capture long-range and complex structural dependencies. Extensive experiments on multiple real-world datasets validate the effectiveness of our approach, demonstrating significant improvements in predictive performance over existing methods.

With the development and widespread application of large language models (LLMs), the new paradigm of “Model as Product” is rapidly evolving, and demands higher capabilities to address complex user needs, often requiring precise workflow execution which involves the accurate understanding of multiple tasks. However, existing benchmarks focusing on single-task environments with limited constraints, lack the complexity required to fully reflect To bridge this gap, we present the Extremely Complex Instruction Following Benchmark (EIFBENCH), meticulously crafted to facilitate a more realistic and robust evaluation of LLMs. EIFBENCH not only includes multi-task scenarios that enable comprehensive assessment across diverse task types concurrently, but also integrates a variety of constraints, replicating complex operational environments. Furthermore, we propose the Segment Policy Optimization (SegPO) algorithm to enhance the LLM’s ability to accurately fulfill multi-task workflow. Evaluations on EIFBENCH have unveiled considerable performance discrepancies in existing LLMs when challenged with these extremely complex instructions. This finding underscores the necessity for ongoing optimization to navigate the intricate challenges posed by real-world LLM applications.

Large language models (LLMs) can now access a wide range of external tools, thanks to the Model Context Protocol (MCP). This greatly expands their abilities as various agents. However, LLMs rely entirely on the text descriptions of tools to decide which ones to use—a process that is surprisingly fragile. In this work, we expose a vulnerability in prevalent tool/function-calling protocols by investigating a series of edits to tool descriptions, some of which can drastically increase a tool’s usage from LLMs when competing with alternatives. Through controlled experiments, we show that tools with properly edited descriptions receive **over 10 times more usage** from GPT-4.1 and Qwen2.5-7B than tools with original descriptions. We further evaluate how various edits to tool descriptions perform when competing directly with one another and how these trends generalize or differ across a broader set of 17 different models. These phenomena, while giving developers a powerful way to promote their tools, underscore the need for a more reliable foundation for agentic LLMs to select and utilize tools and resources. Our code is publicly available at [https://github.com/kazemf78/llm-unreliable-tool-preferences](https://github.com/kazemf78/llm-unreliable-tool-preferences).

Knowledge graph completion (KGC) aims to infer new knowledge and make predictions from knowledge graphs. Recently, large language models (LLMs) have exhibited remarkable reasoning capabilities. LLM-enhanced KGC methods primarily focus on designing task-specific instructions, achieving promising advancements. However, there are still two critical challenges. First, existing methods often ignore the inconsistent representation spaces between natural language and graph structures. Second, most approaches develop separate instructions for different KGC tasks, leading to duplicate works and time-consuming processes. To address these challenges, we propose SAT, a novel framework that enhances LLMs for KGC via structure-aware alignment-tuning. Specifically, we first introduce hierarchical knowledge alignment to align graph embeddings with the natural language space through multi-task contrastive learning. Then, we propose structural instruction tuning to guide LLMs in performing structure-aware reasoning over KGs, using a unified graph instruction combined with a lightweight knowledge adapter. Experimental results on two KGC tasks across four benchmark datasets demonstrate that SAT significantly outperforms state-of-the-art methods, especially in the link prediction task with improvements ranging from 8.7% to 29.8%

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MultiDocFusion : Hierarchical and Multimodal Chunking Pipeline for Enhanced RAG on Long Industrial Documents
Joongmin Shin | Chanjun Park | Jeongbae Park | Jaehyung Seo | Heuiseok Lim

RAG-based QA has emerged as a powerful method for processing long industrial documents. However, conventional text chunking approaches often neglect complex and long industrial document structures, causing information loss and reduced answer quality. To address this, we introduce MultiDocFusion, a multimodal chunking pipeline that integrates: (i) detection of document regions using vision-based document parsing, (ii) text extraction from these regions via OCR, (iii) reconstruction of document structure into a hierarchical tree using large language model (LLM)-based document section hierarchical parsing (DSHP-LLM), and (iv) construction of hierarchical chunks through DFS-based grouping. Extensive experiments across industrial benchmarks demonstrate that MultiDocFusion improves retrieval precision by 8–15% and ANLS QA scores by 2–3% compared to baselines, emphasizing the critical role of explicitly leveraging document hierarchy for multimodal document-based QA. These significant performance gains underscore the necessity of structure-aware chunking in enhancing the fidelity of RAG-based QA systems.

Hallucination has emerged as a significant barrier to the effective application of Large Language Models (LLMs). In this work, we introduce a novel Attention-Guided SElf-Reflection (AGSER) approach for zero-shot hallucination detection in LLMs. The AGSER method utilizes attention contributions to categorize the input query into attentive and non-attentive queries. Each query is then processed separately through the LLMs, allowing us to compute consistency scores between the generated responses and the original answer. The difference between the two consistency scores serves as a hallucination estimator. In addition to its efficacy in detecting hallucinations, AGSER notably reduces computational complexity, requiring only three passes through the LLM and utilizing two sets of tokens. We have conducted extensive experiments with four widely-used LLMs across three different hallucination benchmarks, demonstrating that our approach significantly outperforms existing methods in zero-shot hallucination detection.

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‘Rich Dad, Poor Lad’: How do Large Language Models Contextualize Socioeconomic Factors in College Admission ?
Huy Nghiem | Phuong-Anh Nguyen-Le | John Prindle | Rachel Rudinger | Hal Daumé Iii

Large Language Models (LLMs) are increasingly involved in high-stakes domains, yet how they reason about socially-sensitive decisions still remain underexplored. We present a large-scale audit of LLMs’ treatment of socioeconomic status (SES) in college admissions decisions using a novel dual-process framework inspired by cognitive science. Leveraging a synthetic dataset of 30,000 applicant profiles grounded in real-world correlations, we prompt 4 open-source LLMs (Qwen 2, Mistral v0.3, Gemma 2, Llama 3.1) under 2 modes: a fast, decision-only setup (System 1) and a slower, explanation-based setup (System 2). Results from 5 million prompts reveals that LLMs consistently favor low-SES applicants—even when controlling for academic performance—and that System 2 amplifies this tendency by explicitly invoking SES as compensatory justification, highlighting both their potential and volatility as decision-makers. We then propose DPAF, a dual-process audit framework to probe LLMs’ reasoning behaviors in sensitive applications.

Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks, yet they often refuse to answer legitimate queries—a phenomenon known as overrefusal. Overrefusal typically stems from over-conservative safety alignment, causing models to treat many reasonable prompts as potentially risky. To systematically understand this issue, we probe and leverage the models’ safety decision boundaries to analyze and mitigate overrefusal. Our findings reveal that overrefusal is closely tied to misalignment at these boundary regions, where models struggle to distinguish subtle differences between benign and harmful content. Building on these insights, we present **RASS**, an automated framework for prompt generation and selection that strategically targets overrefusal prompts near the safety boundary. By harnessing steering vectors in the representation space, **RASS** efficiently identifies and curates boundary-aligned prompts, enabling more effective and targeted mitigation of overrefusal. This approach not only provides a more precise and interpretable view of model safety decisions but also seamlessly extends to multilingual scenarios. We have explored the safety decision boundaries of various LLMs and construct the **MORBench** evaluation set to facilitate robust assessment of model safety and helpfulness across multiple languages. Code and datasets are available at https://github.com/Master-PLC/RASS.

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MMAG: Multimodal Learning for Mucus Anomaly Grading in Nasal Endoscopy via Semantic Attribute Prompting
Xinpan Yuan | Mingzhu Huang | Liujie Hua | Jianuo Ju | Xu Zhang

Accurate grading of rhinitis severity in nasal endoscopy relies heavily on the characterization of key secretion types, notably clear nasal discharge (CND) and purulent nasal secretion (PUS). However, both exhibit ambiguous appearance and high structural variability, posing challenges to automated grading under weak supervision. To address this, we propose Multimodal Learning for Mucus Anomaly Grading (MMAG), which integrates structured prompts with rank-aware vision-language modeling for joint detection and grading. Attribute prompts are constructed from clinical descriptors (e.g., secretion type, severity, location) and aligned with multi-level visual features via a dual-branch encoder. During inference, the model localizes mucus anomalies and maps the input image to severity-specific prompts (e.g., “moderate pus”), projecting them into a rank-aware feature space for progressive similarity scoring.Extensive evaluations on CND and PUS datasets show that our method achieves consistent gains over Baseline, improving AUC by 6.31% and 4.79%, and F1 score by 12.85% and 6.03%, respectively.This framework enables interpretable, annotation-efficient, and semantically grounded assessment of rhinitis severity based on mucus anomalies.

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The Emperor’s New Reasoning: Format Imitation Overshadows Genuine Mathematical Understanding in SFT
Linyao Yang | Jian-Tao Huang | Yafei Lu | Zhenhui Jessie Li | Guirong Xue

Recent advances in large language models (LLMs) have yielded impressive gains on mathematical reasoning benchmarks via supervised fine-tuning (SFT). However, the brittleness of these models under input perturbations has cast doubt on whether such improvements reflect genuine reasoning abilities or merely superficial alignment with expected output formats. We investigate the mechanisms behind SFT improvements in small-scale LLMs, addressing four key questions: (1) Are performance gains primarily due to format alignment rather than reasoning? (2) Can high-quality supervision encourage genuine reasoning? (3) Does scaling data shift learning from format alignment to deeper reasoning? (4) Are format alignment gains consistent across model sizes and architectures? Through controlled experiments, we find that most performance improvements arise from format alignment rather than genuine reasoning enhancement. Moreover, SFT’s effectiveness is strongly influenced by the alignment between the base model’s inductive biases and the teacher model’s output distribution, rather than the teacher’s raw strength. Finally, scaling up training data offers diminishing returns and does not fundamentally alter the model’s reasoning behavior. These findings suggest that current SFT practices may overestimate the reasoning abilities of LLMs and underscore the need for more rigorous evaluation methods.

Mathematical reasoning has been challenging for large language models (LLMs), and the introduction of step-by-step Chain-of-Thought (CoT) inference has significantly advanced the mathematical capabilities of LLMs. However, current approaches either necessitate extensive inference datasets for training or depend on few-shot methods that frequently compromise computational accuracy. To address these fundamental limitations, we propose Step Guided Reasoning, a novel training-free adaptation framework that efficiently equips general-purpose pre-trained language models with enhanced mathematical reasoning capabilities. In this approach, LLMs reflect on small reasoning steps, similar to how humans deliberate and focus attention on what to do next. By incorporating this reflective process into the inference stage, LLMs can effectively guide their reasoning from one step to the next. Through extensive experiments, we demonstrate the significant effect of Step Guided Reasoning in enhancing mathematical performance in state-of-the-art language models – Qwen2-72B-Instruct outperforms its math-specific counterpart, Qwen2.5-72B-Math-Instruct, on MMLU-STEM with a score of 90.9%, compared to 87.3%. The average scores of Qwen2-7B-Instruct and Qwen2-72B-Instruct increase from 27.1% to 36. 3% and from 36. 5% to 47.4% in the math domain, respectively.

Large language models (LLMs) have made significant breakthroughs in extracting useful information from conversation history to enhance the response in long-term conversations. Summarizing useful information from historical conversations has achieved remarkable performance, which, however, may introduce irrelevant or redundant information, making it difficult to flexibly choose and integrate key information from different sessions during memory retrieval. To address this issue, we propose a Fragment-then-Compose framework, a novel memory utilization approach for long-term open-domain conversation, called *FraCom*. To be specific, inspired by the concept of proposition representation from Cognitive Psychology, we first represent the conversation history as a series of predicates plus arguments for propositional representation to preserve key information useful for memory ("**Fragment**”). Then, we compose propositional graphs for the conversation history based on the connection between shared arguments ("**Compose**”). During retrieval, we retrieve relevant propositions from the graph based on arguments from the current query. This essentially allows for flexible and effective utilization of related information in long-term memory for better response generation towards a query. Experimental results on four long-term open-domain conversation datasets demonstrate the effectiveness of our *FraCom* in memory utilization and its ability to enhance response generation for LLMs.

While diffusion models have revolutionized text-to-image generation with their ability to synthesize realistic and diverse scenes, they continue to struggle with generating consistent and legible text within images. This shortcoming is commonly attributed to the locality bias inherent in diffusion-based generation, which limits their capacity to model long-range spatial dependencies. In this paper, we introduce STRICT, a benchmark designed to systematically stress-test the ability of diffusion models to render coherent and instruction-aligned text in images. Our benchmark evaluates models across multiple dimensions: (1) the maximum length of readable text that can be generated and (2) the correctness and legibility of the generated text. We assess several state-of-the-art models, including proprietary and open-source variants, and reveal persistent limitations in long-range consistency and instruction-following capabilities. Our findings provide insights into architectural bottlenecks and motivate future research directions in multimodal generative modeling.

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A Sequential Multi-Stage Approach for Code Vulnerability Detection via Confidence- and Collaboration-based Decision Making
Chung-Nan Tsai | Xin Wang | Cheng-Hsiung Lee | Ching-Sheng Lin

While large language models (LLMs) have shown strong capabilities across diverse domains, their application to code vulnerability detection holds great potential for identifying security flaws and improving software safety. In this paper, we propose a sequential multi-stage approach via confidence- and collaboration-based decision making (ConfColl). The system adopts a three-stage sequential classification framework, proceeding through a single agent, retrieval-augmented generation (RAG) with external examples, and multi-agent reasoning enhanced with RAG. The decision process selects among these strategies to balance performance and cost, with the process terminating at any stage where a high-certainty prediction is achieved. Experiments on a benchmark dataset and a low-resource language demonstrate the effectiveness of our framework in enhancing code vulnerability detection performance.

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Leveraging Large Models to Evaluate Novel Content: A Case Study on Advertisement Creativity
Zhaoyi Joey Hou | Adriana Kovashka | Xiang Lorraine Li

Evaluating creativity is challenging, even for humans, not only because of its subjectivity but also because it involves complex cognitive processes. Inspired by work in marketing, we attempt to break down visual advertisement creativity into atypicality and originality. With fine-grained human annotations on these dimensions, we propose a suite of tasks specifically for such a subjective problem. We also evaluate the alignment between state-of-the-art (SoTA) vision language models (VLMs) and humans on our proposed benchmark, demonstrating both the promises and challenges of using VLMs for automatic creativity assessment.

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BIRD: Bronze Inscription Restoration and Dating
Wenjie Hua | Hoang H Nguyen | Gangyan Ge

Bronze inscriptions from early China are fragmentary and difficult to date. We introduce BIRD (Bronze Inscription Restoration and Dating), a fully encoded dataset grounded in standard scholarly transcriptions and chronological labels. We further propose an allograph-aware masked language modeling framework that integrates domain- and task-adaptive pretraining with a Glyph Net (GN), which links graphemes and allographs. Experiments show that GN improves restoration, while glyph-biased sampling yields gains in dating.

Large Vision-Language Models (LVLMs) achieve remarkable performance in multimodal tasks but suffer from high computational costs due to the large number of visual tokens. Existing pruning methods either apply after visual tokens enter the LLM or perform pre-pruning based solely on visual attention. Both fail to balance efficiency and semantic alignment, as post-pruning incurs redundant computation, while visual-only pre-pruning overlooks multimodal relevance.To address this limitation, we propose Dual-Cue Pruning (DCP), a novel cross-modal pruning framework that jointly considers textual semantics and visual self-attention. DCP consists of a text-aware computation module, which employs a gradient-weighted attention mechanism to enhance text-visual alignment, and an image-aware computation module, which utilizes deep-layer self-attention distributions to retain essential structural information. By integrating both cues, DCP adaptively selects the most informative visual tokens, achieving efficient inference acceleration while maintaining strong task performance. Experimental results show that DCP can retain only 25% of the visual tokens, with a minimal performance degradation of only 0.063% on LLaVA-1.5-13B, demonstrating its effectiveness in balancing efficiency and accuracy.

Large language models (LLMs) often struggle with context fidelity, producing inconsistent answers when responding to questions based on provided information. Existing approaches either rely on expensive supervised fine-tuning to generate evidence post-answer or train models to perform web searches without necessarily improving utilization of the given context. We propose CARE, a novel native retrieval-augmented reasoning framework that teaches LLMs to explicitly integrate in-context evidence within their reasoning process with the model’s own retrieval capabilities. Our method requires limited labeled evidence data while significantly enhancing both retrieval accuracy and answer generation performance through strategically retrieved in-context tokens in the reasoning chain. Extensive experiments on multiple real-world and counterfactual QA benchmarks demonstrate that our approach substantially outperforms supervised fine-tuning, traditional retrieval-augmented generation methods, and external retrieval solutions. This work represents a fundamental advancement in making LLMs more accurate, reliable, and efficient for knowledge-intensive tasks.

Autoregressive speech token generation models produce speech with remarkable variety and naturalness but often suffer from hallucinations and undesired vocalizations that do not conform to conditioning inputs. To address these challenges, we introduce Koel-TTS, an encoder-decoder transformer model for multilingual TTS that improves contextual adherence of speech generation LLMs through preference alignment and classifier-free guidance (CFG). For preference alignment, we design a reward system that ranks model outputs using automatic metrics derived from speech recognition and speaker verification models, encouraging generations that better match the input text and speaker identity. CFG further allows fine-grained control over the influence of conditioning inputs during inference by interpolating conditional and unconditional logits. Notably, applying CFG to a preference-aligned model yields additional gains in transcription accuracy and speaker similarity, demonstrating the complementary benefits of both techniques. Koel-TTS achieves state-of-the-art results in zero-shot TTS, outperforming prior LLM-based models on intelligibility, speaker similarity, and naturalness, despite being trained on significantly less data.

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Mixing Inference-time Experts for Enhancing LLM Reasoning
Soumya Sanyal | Tianyi Xiao | Xiang Ren

Large Language Models (LLMs) have demonstrated impressive reasoning abilities, but their generated rationales often suffer from issues such as reasoning inconsistency and factual errors, undermining their reliability. Prior work has explored improving rationale quality via multi-reward fine-tuning or reinforcement learning (RL), where models are optimized for diverse objectives. While effective, these approaches train the model in a fixed manner and do not have any inference-time adaptability, nor can they generalize reasoning requirements for new test-time inputs. Another approach is to train specialized reasoning experts using reward signals and use them to improve generation at inference time. Existing methods in this paradigm are limited to using only a single expert and cannot improve upon multiple reasoning aspects. To address this, we propose MIXIE, a novel inference-time expert-mixing framework that dynamically determines mixing proportions for each expert, enabling contextualized and flexible fusion. We demonstrate the effectiveness of MIXIE on improving chain-of-thought reasoning in LLMs by merging commonsense and entailment reasoning experts finetuned on reward-filtered data. Our approach outperforms existing baselines on three question-answering datasets: StrategyQA, CommonsenseQA, and ARC, highlighting its potential to enhance LLM reasoning with efficient, adaptable expert integration.

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Reinforced Query Reasoners for Reasoning-intensive Retrieval Tasks
Xubo Qin | Jun Bai | Jiaqi Li | Zixia Jia | Zilong Zheng

Traditional information retrieval (IR) methods excel at textual and semantic matching but struggle in reasoning-intensive retrieval tasks that require multi-hop inference or complex semantic understanding between queries and documents. One promising solution is to explicitly rewrite or augment queries using large language models (LLMs) to elicit reasoning-relevant content prior to retrieval. However, the widespread use of large-scale LLMs like GPT-4 or LLaMA3-70B remains impractical due to their high inference cost and limited deployability in real-world systems. In this work, we introduce Reinforced Query Reasoner (RQR), a family of small-scale language models for query reasoning and rewriting in reasoning-intensive retrieval. Our approach frames query reformulation as a reinforcement learning problem and employs a novel semi-rule-based reward function. This enables smaller language models, e.g., Qwen2.5-7B-Instruct and Qwen2.5-1.5B-Instruct, to achieve reasoning performance rivaling large-scale LLMs without their prohibitive inference costs. Experiment results on BRIGHT benchmark show that, with BM25 as retrievers, both RQR-7B and RQR-1.5B models significantly outperform existing baselines, including prompt-based query reasoners and some latest dense retrievers trained for reasoning-intensive retrieval tasks, offering superior adaptability for real-world deployment. All code and dataset will be publicly released.

Rapid advances in Large Language Models (LLMs) have spurred demand for processing extended context sequences in contemporary applications. However, this progress faces two challenges: performance degradation due to sequence lengths out-of-distribution, and excessively long inference times caused by the quadratic computational complexity of attention. These issues limit LLMs in long-context scenarios. In this paper, we propose Dynamic Token-Level KV Cache Selection (*TokenSelect*), a training-free method for efficient and accurate long-context inference. *TokenSelect* builds upon the observation of non-contiguous attention sparsity, using QK dot products to measure per-head KV Cache criticality at token-level. By per-head soft voting mechanism, *TokenSelect* selectively involves a few critical KV cache tokens in attention calculation without sacrificing accuracy. To further accelerate *TokenSelect*, we design the Selection Cache based on observations of consecutive Query similarity and implemented the efficient Paged Dot Product Kernel, significantly reducing the selection overhead. A comprehensive evaluation of *TokenSelect* demonstrates up to 23.84× speedup in attention computation and up to 2.28× acceleration in end-to-end latency, while providing superior performance compared to state-of-the-art long-context inference methods.

As large language models (LLMs) become widely adopted, ensuring their alignment with human values is crucial to prevent jailbreaks where adversaries manipulate models to produce harmful content. While most defenses target single-turn attacks, real-world usage often involves multi-turn dialogues, exposing models to attacks that exploit conversational context to bypass safety measures. We introduce MUSE, a comprehensive framework tackling multi-turn jailbreaks from both attack and defense angles. For attacks, we propose MUSE-A, a method that uses frame semantics and heuristic tree search to explore diverse semantic trajectories. For defense, we present MUSE-D, a fine-grained safety alignment approach that intervenes early in dialogues to reduce vulnerabilities. Extensive experiments on various models show that MUSE effectively identifies and mitigates multi-turn vulnerabilities. Code is available at https://anonymous.4open.science/r/MUSE-75F7.

Large language models (LLMs) have demonstrated strong machine translation capabilities for English-centric language pairs but underperform in direct non-English (x2x) translation. This work addresses this limitation through a synthetic data generation framework that leverages models’ established English-to-x (en2x) capabilities. By extending English parallel corpora into omnidirectional datasets and developing an English-referenced quality evaluation proxy, we enable effective collection of high-quality x2x training data. Combined with preference-based optimization, our method achieves significant improvement across 72 x2x directions for widely used LLMs, while generalizing to enhance en2x performance. The results demonstrate that strategic exploitation of English-centric strengths can bootstrap comprehensive multilingual translation capabilities in LLMs.

Large language models (LLMs) exhibit prompt leakage vulnerabilities, where they may be coaxed into revealing system prompts embedded in LLM services, raising intellectual property and confidentiality concerns. An intriguing question arises: Do LLMs genuinely internalize prompt leakage intents in their hidden states before generating tokens? In this work, we use probing techniques to capture LLMs’ intent-related internal representations and confirm that the answer is yes. We start by comprehensively inducing prompt leakage behaviors across diverse system prompts, attack queries, and decoding methods. We develop a hybrid labeling pipeline, enabling the identification of broader prompt leakage behaviors beyond mere verbatim leaks. Our results show that a simple linear probe can predict prompt leakage risks from pre-generation hidden states without generating any tokens. Across all tested models, linear probes consistently achieve 90%+ AUROC, even when applied to new system prompts and attacks. Understanding the model internals behind prompt leakage drives practical applications, including intention-based detection of prompt leakage risks. Code is available at: https://github.com/jianshuod/Probing-leak-intents.

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Nullspace Disentanglement for Red Teaming Language Models
Yi Han | Yuanxing Liu | Weinan Zhang | Ting Liu

With the widespread deployment of generative language models, concerns about safety issues have continuously grown. High-quality fine-tuning data generated from red teaming plays a crucial role in the model’s safety. Recently, automated red teaming approaches have been proposed to create test cases. However, these approaches, which rely on open-ended generation, encounter issues related to inefficiency and low attack success rates. In this work, we introduce a black-box approach that ingeniously exploits the unique properties of the nullspace to disentangle and regulate the crucial success information within test cases. Our study provides a brand-new perspective for automated red team research. Experimental results demonstrate that our approach outperforms baseline methods regarding the attack success rate. The generated test cases also excel in aspects of diversity and fluency.

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Supervised Attention Mechanism for Low-quality Multimodal Data
Sijie Mai | Shiqin Han | Haifeng Hu

In practical applications, multimodal data are often of low quality, with noisy modalities and missing modalities being typical forms that severely hinder model performance, robustness, and applicability. However, current studies address these issues separately. To this end, we propose a framework for multimodal affective computing that jointly addresses missing and noisy modalities to enhance model robustness in low-quality data scenarios. Specifically, we view missing modality as a special case of noisy modality, and propose a supervised attention framework. In contrast to traditional attention mechanisms that rely on main task loss to update the parameters, we design supervisory signals for the learning of attention weights, ensuring that attention mechanisms can focus on discriminative information and suppress noisy information. We further propose a ranking-based optimization strategy to compare the relative importance of different interactions by adding a ranking constraint for attention weights, avoiding training noise caused by inaccurate absolute labels. The proposed model consistently outperforms state-of-the-art baselines on multiple datasets under the settings of complete modalities, missing modalities, and noisy modalities.

Large Language Models (LLMs) require alignment via reinforcement learning (RL) to effectively perform task-specific objectives, such as human preference alignment and enhanced reasoning. While Proximal Policy Optimization (PPO) is widely adopted, its computational overhead, stemming from additional value model requirements, limits applicability. Existing alternatives, like Group Relative Policy Optimization (GRPO), mitigate computational costs but remain sensitive to reward model quality. To address this, we introduce Group Preference Reward Shaping (GPRS), a novel method that leverages preference-based comparisons rather than precise numerical rewards. GPRS requires no extra model components and remains robust across varying reward model sizes and qualities. Extensive experiments demonstrate that GPRS consistently outperforms existing critic-model-free RL algorithms in Reinforcement Learning from Human Feedback (RLHF) and reasoning tasks, providing stable and good alignment performance.

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zFLoRA: Zero-Latency Fused Low-Rank Adapters
Dhananjaya Gowda | Seoha Song | Harshith Goka | Junhyun Lee

Large language models (LLMs) are increasingly deployed with task-specific adapters catering to multiple downstream applications. In such a scenario, the additional compute associated with these apparently insignificant number of adapter parameters (typically less than 1% of the base model) turns out to be disproportionately significant during inference time (up to 2.5x times that of the base model). In this paper, we propose a new zero-latency fused low-rank adapter (zFLoRA) that introduces zero or negligible latency overhead on top of the base model. Experimental results on LLMs of size 1B, 3B and 7B show that zFLoRA compares favorably against the popular supervised fine-tuning benchmarks including low-rank adapters (LoRA) as well as full fine-tuning (FFT). Experiments are conducted on 18 different tasks across three different categories namely commonsense reasoning, math reasoning and summary-dialogue. Latency measurements made on NPU (Samsung Galaxy S25+) as well as GPU (NVIDIA H100) platforms show that the proposed zFLoRA adapters introduce zero to negligible latency overhead.

Recently, decomposing complex problems into simple subtasks–a crucial part of human-like natural planning–to solve the given problem has significantly boosted the performance of large language models (LLMs). However, leveraging such planning structures during post-training to boost the performance of smaller open-source LLMs remains underexplored. Motivated by this, we introduce PLAN-TUNING, a unified post-training framework that (i) distills synthetic task decompositions (termed “planning trajectories”) from large-scale LLMs and (ii) fine-tunes smaller models via supervised and reinforcement-learning objectives designed to mimic these planning processes to improve complex reasoning. On GSM8k and the MATH benchmarks, plan-tuned models outperform strong baselines by an average ~7%. Furthermore, plan-tuned models show better generalization capabilities on out-of-domain datasets, with average ~10% and ~12% performance improvements on OlympiadBench and AIME 2024, respectively. Our detailed analysis demonstrates how planning trajectories improves complex reasoning capabilities, showing that PLAN-TUNING is an effective strategy for improving task-specific performance of smaller LLMs.

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Semantic Inversion, Identical Replies: Revisiting Negation Blindness in Large Language Models
Jinsung Kim | Seonmin Koo | Heuiseok Lim

Large language models (LLMs) often fail to capture semantic changes in queries due to negation, and generate incorrect responses. Negation frequently exists in the real world and is useful for understanding the opposite or absence of a statement, so it is an essential element in logical reasoning. Previous studies have explored LLMs’ ability to capture negations ‘separately’ from their ability to properly ground knowledge for positive queries. However, this perspective is limited in that it cannot clearly distinguish whether the cause of incorrect responses is the logical incoherence caused by negations or the lack of grounding ability for the given context. To address this issue, we focus on the phenomenon of the model failing to capture semantic contradictions in negated queries despite its accurate understanding of knowledge about positive queries. We term this phenomenon negation blindness on the query. We propose a verification framework that includes task design and measurement methods to verify this issue. In detail, we establish two criteria for systematic task design–i) ‘complexity’ and ii) ‘constrainedness’–and devise four verification tasks accordingly. Moreover, we analyze the results extensively and provide insights into problem alleviation feasibility through experiments on various approaches. Our code and resources can be found at https://www.github.com/jin62304/NegationBlindness.

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AMACE: Automatic Multi-Agent Chart Evolution for Iteratively Tailored Chart Generation
Hyuk Namgoong | Jeesu Jung | Hyeonseok Kang | Yohan Lee | Sangkeun Jung

Many statistical facts are conveyed through charts. While various methods have emerged for chart understanding, chart generation typically requires users to manually input code, intent, and other parameters to obtain the desired format on chart generation tools. Recently, the advent of image-generating Large Language Models has facilitated chart generation; however, even this process often requires users to provide numerous constraints for accurate results. In this paper, we propose a loop-based framework for automatically evolving charts in a multi-agent environment. Within this framework, three distinct agents—Chart Code Generator, Chart Replier, and Chart Quality Evaluator—collaborate for iterative, user-tailored chart generation using large language models. Our approach demonstrates an improvement of up to 29.97% in performance compared to first generation, while also reducing generation time by up to 86.9% compared to manual prompt-based methods, showcasing the effectiveness of this multi-agent collaboration in enhancing the quality and efficiency of chart generation.

Large Action models are essential for enabling autonomous agents to perform complex tasks. However, training such models remains challenging due to the diversity of agent environments and the complexity of noisy agentic data. Existing infrastructure offers limited support for scalable, agent-specific fine-tuning and standardized agent data processing. We introduce ActionStudio, a lightweight and extensible data and training framework designed for large action models. ActionStudio unifies diverse agent trajectories using our proposed Unified Format 2.0, supports a range of training workflows with optimized multi-node distributed setup, and integrates robust preprocessing and real-time verification tools. ActionStudio demonstrates up to 9× higher throughput compared to existing agentic training frameworks, and our trained models yield top performances across public and realistic agent benchmarks. To support the broader research community, we open-source the ActionStudio framework and release actionstudio-98k, a curated dataset of 98k high-quality trajectories.

As large language models (LLMs) see wider real-world use, understanding and mitigating their unsafe behaviors is critical. Interpretation techniques can reveal causes of unsafe outputs and guide safety, but such connections with safety are often overlooked in prior surveys. We present the first survey that bridges this gap, introducing a unified framework that connects safety-focused interpretation methods, the safety enhancements they inform, and the tools that operationalize them. Our novel taxonomy, organized by LLM workflow stages, summarizes nearly 70 works at their intersections. We conclude with open challenges and future directions. This timely survey helps researchers and practitioners navigate key advancements for safer, more interpretable LLMs.

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Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens
Sohee Kim | Soohyun Ryu | Joonhyung Park | Eunho Yang

Large Vision-Language Models (LVLMs) generate contextually relevant responses by jointly interpreting visual and textual inputs. However, our finding reveals they often mistakenly perceive text inputs lacking visual evidence as being part of the image, leading to erroneous responses. In light of this finding, we probe whether LVLMs possess an internal capability to determine if textual concepts are grounded in the image, and discover a specific subset of Feed-Forward Network (FFN) neurons, termed Visual Absence-aware (VA) neurons, that consistently signal the visual absence through a distinctive activation pattern. Leveraging these patterns, we develop a detection module that systematically classifies whether an input token is visually grounded. Guided by its prediction, we propose a method to refine the outputs by reinterpreting question prompts or replacing the detected absent tokens during generation. Extensive experiments show that our method effectively mitigates the models’ tendency to falsely presume the visual presence of text input and its generality across various LVLMs.

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Improving Task Diversity in Label Efficient Supervised Finetuning of LLMs
Abhinav Arabelly | Jagrut Nemade | Robert D Nowak | Jifan Zhang

Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse domains, but developing high-performing models for specialized applications often requires substantial human annotation — a process that is time-consuming, labor-intensive, and expensive. In this paper, we address the label-efficient learning problem for supervised finetuning (SFT) by leveraging task-diversity as a fundamental principle for effective data selection. This is markedly different from existing methods based on the prompt-diversity. Our approach is based on two key observations: 1) task labels for different prompts are often readily available; 2) pre-trained models have significantly varying levels of confidence across tasks. We combine these facts to devise a simple yet effective sampling strategy: we select examples across tasks using an inverse confidence weighting strategy. This produces models comparable to or better than those trained with more complex sampling procedures, while being significantly easier to implement and less computationally intensive. Notably, our experimental results demonstrate that this method can achieve better accuracy than training on the complete dataset (a 4% increase in MMLU score). Across various annotation budgets and two instruction finetuning datasets, our algorithm consistently performs at or above the level of the best existing methods, while reducing annotation costs by up to 80%.

In recent years, Large Language Models (LLMs) have made significant progress in emotional support dialogue. However, there are two major challenges for LLM-based support systems. First, users may be hesitant to fully disclose their emotions at the outset. Second, direct probing or excessive questioning can induce discomfort or even resistance. To bridge this gap, we propose COCOON, a proactive emotional support framework that leverages principles of active listening to uncover implicit user needs. We design a multi-stage data curation pipeline and an annotation mechanism for support strategies. Based on this framework, we build COCOON-Llama3, a fine-tuned large language model, and evaluate it using both standard metrics and psychological scales. Experimental results indicate that our model more effectively elicits implicit emotional needs and delivers empathetic support compared to existing baselines, suggesting its utility for building more inclusive emotional support dialogue systems.

Retrieval-augmented generation (RAG) systems empower large language models (LLMs) to access external knowledge during inference. Recent advances have enabled LLMs to act as search agents via reinforcement learning (RL), improving information acquisition through multi-turn interactions with retrieval engines. However, existing approaches either optimize retrieval using search-only metrics (e.g., NDCG) that ignore downstream utility or fine-tune the entire LLM to jointly reason and retrieve—entangling retrieval with generation and limiting the real search utility and compatibility with frozen or proprietary models. In this work, we propose **s3**, a lightweight, model-agnostic framework that decouples the searcher from the generator and trains the searcher using a Gain Beyond RAG reward: the improvement in generation accuracy over naïve RAG. **s3** requires only 2.4k training samples to outperform baselines trained on over 70 × more data, consistently delivering stronger downstream performance across six general QA and five medical QA benchmarks.

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FuseChat: Knowledge Fusion of Chat Models
Fanqi Wan | Longguang Zhong | Ziyi Yang | Ruijun Chen | Xiaojun Quan

While training large language models (LLMs) from scratch can indeed lead to models with distinct capabilities and strengths, it incurs substantial costs and may lead to redundancy in competencies. Knowledge fusion aims to integrate existing LLMs of diverse architectures and capabilities into a more potent LLM through lightweight continual training, thereby reducing the need for costly LLM development. In this work, we propose a new framework for the knowledge fusion of chat LLMs through two main stages, resulting in FuseChat. Firstly, we conduct pairwise knowledge fusion on source chat LLMs of varying structures and scales to create multiple target LLMs with identical structure and size via lightweight fine-tuning. During this process, a statistics-based token alignment approach is introduced as the cornerstone for fusing LLMs with different structures. Secondly, we merge these target LLMs within the parameter space, where we propose a novel method for determining the merging coefficients based on the magnitude of parameter updates before and after fine-tuning. We implement and validate FuseChat using six prominent chat LLMs with diverse architectures and scales. Experimental results on two instruction-following benchmarks, AlpacaEval 2.0 and MT-Bench, demonstrate the superiority of FuseChat-7B over baselines of various sizes.

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Continuous-Time Attention: PDE-Guided Mechanisms for Long-Sequence Transformers
Yukun Zhang | Xueqing Zhou

We present Continuous-Time Attention, a novel framework that infuses partial differential equations (PDEs) into the Transformer’s attention mechanism to better handle long sequences. Instead of relying on a static attention matrix, we allow attention weights to evolve along a pseudo-time dimension governed by diffusion, wave, or reaction-diffusion dynamics. This dynamic process systematically smooths local noise, strengthens long-range dependencies, and improves gradient stability during training.Our theoretical analysis shows that PDE-driven attention mitigates the exponential decay of distant interactions and improves the optimization landscape. Empirically, Continuous-Time Attention achieves consistent performance gains over both standard and long-sequence Transformer variants across a range of tasks. These results suggest that embedding continuous-time dynamics into attention mechanisms is a promising direction for enhancing global coherence and scalability in Transformer models. Code is publicly available at:https://github.com/XueqingZhou/Continuous-Time-Attention

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Forget What You Know about LLMs Evaluations - LLMs are Like a Chameleon
Nurit Cohen Inger | Yehonatan Elisha | Bracha Shapira | Lior Rokach | Seffi Cohen

Large language models (LLMs) often appear to excel on public benchmarks, but these high scores may mask an overreliance on dataset-specific surface cues rather than true language understanding. We introduce the **Chameleon Benchmark Overfit Detector (C-BOD)**, a meta-evaluation framework designed to reveal such overfitting. C-BOD systematically rephrases benchmark inputs via a parameterized transformation that preserves semantic content and labels, enabling the detection of performance degradation indicative of superficial pattern reliance.We conduct extensive experiments across two datasets, three rephrasing models, and multiple distortion levels, evaluating 32 state-of-the-art LLMs. On the MMLU benchmark, C-BOD reveals an average performance drop of 2.75% under modest rephrasings, with over 80% of models exhibiting statistically significant differences. Notably, higher-performing models and larger LLMs tend to show greater sensitivity, suggesting a deeper dependence on benchmark-specific phrasing.Due to its dataset and model-agnostic design, C-BOD can be easily integrated into evaluation pipelines and offers a promising foundation for overfitting mitigation strategies. Our findings challenge the community to look beyond leaderboard scores and prioritize resilience and generalization in LLM evaluation. Our code and benchmark datasets are availableat: https://github.com/nuritci/cbod

Idioms have long posed a challenge due to their unique linguistic properties, which set them apart from other common expressions. While recent studies have leveraged large language models (LLMs) to handle idioms across various tasks, e.g., idiom-containing sentence generation and idiomatic machine translation, little is known about the underlying mechanisms of idiom processing in LLMs, particularly in multilingual settings. To this end, we introduce MIDAS, a new large-scale dataset of idioms in six languages, each paired with its corresponding meaning. Leveraging this resource, we conduct a comprehensive evaluation of LLMs’ idiom processing ability, identifying key factors that influence their performance. Our findings suggest that LLMs rely not only on memorization, but also adopt a hybrid approach that integrates contextual cues and reasoning, especially when processing compositional idioms. This implies that idiom understanding in LLMs emerges from an interplay between internal knowledge retrieval and reasoning-based inference.

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RD-MCSA: A Multi-Class Sentiment Analysis Approach Integrating In-Context Classification Rationales and Demonstrations
Haihua Xie | Yinzhu Cheng | Yaqing Wang | Miao He | Mingming Sun

This paper addresses the important yet underexplored task of **multi-class sentiment analysis (MCSA)**, which remains challenging due to the subtle semantic differences between adjacent sentiment categories and the scarcity of high-quality annotated data. To tackle these challenges, we propose **RD-MCSA** (**R**ationales and **D**emonstrations-based **M**ulti-**C**lass **S**entiment **A**nalysis), an In-Context Learning (ICL) framework designed to enhance MCSA performance under limited supervision by integrating classification rationales with adaptively selected demonstrations. First, semantically grounded classification rationales are generated from a representative, class-balanced subset of annotated samples selected using a tailored balanced coreset algorithm. These rationales are then paired with demonstrations chosen through a similarity-based mechanism powered by a **multi-kernel Gaussian process (MK-GP)**, enabling large language models (LLMs) to more effectively capture fine-grained sentiment distinctions. Experiments on five benchmark datasets demonstrate that RD-MCSA consistently outperforms both supervised baselines and standard ICL methods across various evaluation metrics.

Rebus puzzles, visual riddles that encode language through imagery, spatial arrangement, and symbolic substitution, pose a unique challenge to current vision-language models (VLMs). Unlike traditional image captioning or question answering tasks, rebus solving requires multimodal abstraction, symbolic reasoning, and a grasp of cultural, phonetic and linguistic puns. In this short paper, we investigate the capacity of contemporary VLMs to interpret and solve rebus puzzles by constructing a hand-generated and annotated benchmark of diverse english-language rebus puzzles, ranging from simple pictographic substitutions to spatially-dependent cues (“head” over “heels”). We analyze how different VLMs perform, and our findings reveal that while VLMs exhibit some surprising capabilities in decoding simple visual clues, they struggle significantly with tasks requiring abstract reasoning, lateral thinking, and understanding visual metaphors.

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CREPE: Rapid Chest X-ray Report Evaluation by Predicting Multi-category Error Counts
Gihun Cho | Seunghyun Jang | Hanbin Ko | Inhyeok Baek | Chang Min Park

We introduce CREPE (Rapid Chest X-ray Report Evaluation by Predicting Multi-category Error Counts), a rapid, interpretable, and clinically grounded metric for automated chest X-ray report generation. CREPE uses a domain-specific BERT model fine-tuned with a multi-head regression architecture to predict error counts across six clinically meaningful categories. Trained on a large-scale synthetic dataset of 32,000 annotated report pairs, CREPE demonstrates strong generalization and interpretability. On the expert-annotated ReXVal dataset, CREPE achieves a Kendall’s tau correlation of 0.786 with radiologist error counts, outperforming traditional and recent metrics. CREPE achieves these results with an inference speed approximately 280 times faster than large language model (LLM)-based approaches, enabling rapid and fine-grained evaluation for scalable development of chest X-ray report generation models.

Addressing the challenges in QA for specific technical domains requires identifying relevant portions of extensive documents and generating answers based on this focused content. Traditional pre-trained LLMs often struggle with domain-specific terminology, while fine-tuned LLMs demand substantial computational resources. To overcome these limitations, we propose TIDES, Technical Information Distillation and Extraction System. TIDES is a training-free approach that combines traditional TF-IDF techniques with prompt-based LLMs in a hybrid process, effectively addressing complex technical questions. It uses TF-IDF to identify and prioritize domain-specific words that are rare in other documents and LLMs to refine the candidate pool by focusing on the most relevant segments in documents through multiple stages. Our approach improves the precision and efficiency of QA systems in technical contexts without LLM retraining.

Equipped with the capability to call functions, modern LLM agents can leverage external tools for addressing a range of tasks unattainable through language skills alone. However, the effective execution of these tools relies heavily not just on the advanced capabilities of LLM agents but also on precise user instructions, which often cannot be ensured in the real world. To evaluate the performance of LLM agents tool-use under imperfect instructions, we meticulously examine the real-world instructions queried from users, analyze the error patterns, and build a challenging tool-use benchmark called Noisy ToolBench. We find that due to the next-token prediction training objective, LLM agents tend to arbitrarily generate the missed argument, which may lead to hallucinations and risks. To address this issue, we propose a novel framework, Ask-when-Needed, which prompts LLM agents to ask questions to users whenever they encounter obstacles due to unclear instructions. Moreover, to reduce the manual labor involved in user-LLM interaction and assess LLM agents’ performance in tool utilization from both accuracy and efficiency perspectives, we design an automated evaluation tool named ToolEvaluator. Our experiments demonstrate that the Ask-when-Needed significantly outperforms existing frameworks for tool learning in the Noisy ToolBench. We will release all related code and datasets to support future research.

Image recaptioning is widely used to generate training datasets with enhanced quality for various multimodal tasks. Existing recaptioning methods typically rely on powerful multimodal large language models (MLLMs) to enhance textual descriptions, but often suffer from inaccuracies due to hallucinations and incompleteness caused by missing fine-grained details. To address these limitations, we propose RICO, a novel framework that refines captions through visual reconstruction. Specifically, we leverage a text-to-image model to reconstruct a caption into a reference image, and prompt an MLLM to identify discrepancies between the original and reconstructed images to refine the caption. This process is performed iteratively, further progressively promoting the generation of more faithful and comprehensive descriptions. To mitigate the additional computational cost induced by the iterative process, we introduce RICO-Flash, which learns to generate captions like RICO using DPO. Extensive experiments demonstrate that our approach significantly improves caption accuracy and completeness, outperforms most baselines by approximately 10% on both CapsBench and CompreCap.

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StepSearch: Igniting LLMs Search Ability via Step-Wise Proximal Policy Optimization
Xuhui Zheng | Kang An | Ziliang Wang | Yuhang Wang | Yichao Wu

Efficient multi-hop reasoning requires Large Language Models (LLMs) based agents to acquire high-value external knowledge iteratively. Previous work has explored reinforcement learning (RL) to train LLMs to perform search-based document retrieval, achieving notable improvements in QA performance, but underperform on complex, multi-hop QA resulting from the sparse rewards from global signal only. To address this gap in existing research, we introduce StepSearch, a framework for search LLMs that trained with step-wise proximal policy optimization method. It consists of richer and more detailed intermediate search rewards and token-level process supervision based on information gain and redundancy penalties to better guide each search step. We constructed a fine-grained question-answering dataset containing sub-question-level search trajectories based on open source datasets through a set of data pipeline method. On standard multi-hop QA benchmarks, it significantly outperforms global-reward baselines, achieving 11.2% and 4.2% absolute improvements for 3B and 7B models over various search with RL baselines using only 19k training data, demonstrating the effectiveness of fine-grained, stepwise supervision in optimizing deep search LLMs. The project is open source at https://github.com/Zillwang/StepSearch

End-to-end automatic speech recognition (ASR) based on deep learning has achieved impressive progress in recent years. However, the performance of ASR foundation model often degrades significantly on out-of-domain data due to real-world domain shifts. Test-Time Adaptation (TTA) methods aim to mitigate this issue by adapting models during inference without access to source data. Despite recent progress, existing ASR TTA methods often struggle with instability under continual and long-term distribution shifts. To alleviate the risk of performance collapse due to error accumulation, we propose Dynamic Model-bank Single-Utterance Test-time Adaptation (DMSUTA), a sustainable continual TTA framework based on adaptive ASR model ensembling. DMSUTA maintains a dynamic model bank, from which a subset of checkpoints is selected for each test sample based on confidence and uncertainty criteria. To preserve both model plasticity and long-term stability, DMSUTA actively manages the bank by filtering out potentially collapsed models. This design allows DMSUTA to continually adapt to evolving domain shifts in ASR test-time scenarios. Experiments on diverse, continuously shifting ASR TTA benchmarks show that DMSUTA consistently outperforms existing continual TTA baselines, demonstrating superior robustness to domain shifts in ASR.

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Mitigating Catastrophic Forgetting in Large Language Models with Forgetting-aware Pruning
Wei Huang | Anda Cheng | Yinggui Wang

Recent advancements in large language models (LLMs) have shown impressive capabilities in various downstream tasks but typically face Catastrophic Forgetting (CF) during fine-tuning. In this paper, we propose the Forgetting-Aware Pruning Metric (FAPM), a novel pruning-based approach to balance CF and downstream task performance. Our investigation reveals that the degree to which task vectors (i.e., the subtraction of pre-trained weights from the weights fine-tuned on downstream tasks) overlap with pre-trained model parameters is a critical factor for CF. Based on this finding, FAPM employs the ratio of the task vector to pre-trained model parameters as a metric to quantify CF, integrating this measure into the pruning criteria. Importantly, FAPM does not necessitate modifications to the training process or model architecture, nor does it require any auxiliary data. We conducted extensive experiments across eight datasets, covering natural language inference, General Q&A, Medical Q&A, Math Q&A, reading comprehension, and cloze tests. The results demonstrate that FAPM limits CF to just 0.25% while maintaining 99.67% accuracy on downstream tasks. We provide the codes of FAPM at an anonymous repository(https://anonymous.4open.science/r/FAPM-65CF).

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Does Localization Inform Unlearning? A Rigorous Examination of Local Parameter Attribution for Knowledge Unlearning in Language Models
Hwiyeong Lee | Uiji Hwang | Hyelim Lim | Taeuk Kim

Large language models often retain unintended content, prompting growing interest in knowledge unlearning.Recent approaches emphasize localized unlearning, restricting parameter updates to specific regions in an effort to remove target knowledge while preserving unrelated general knowledge. However, their effectiveness remains uncertain due to the lack of robust and thorough evaluation of the trade-off between the competing goals of unlearning.In this paper, we begin by revisiting existing localized unlearning approaches. We then conduct controlled experiments to rigorously evaluate whether local parameter updates causally contribute to unlearning.Our findings reveal that the set of parameters that must be modified for effective unlearning is not strictly determined, challenging the core assumption of localized unlearning that parameter locality is inherently indicative of effective knowledge removal.

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ArgCMV: An Argument Summarization Benchmark for the LLM-era
Omkar Gurjar | Agam Goyal | Eshwar Chandrasekharan

Key point extraction is an important task in argument summarization which involves extracting high-level short summaries from arguments. Existing approaches for KP extraction have been mostly evaluated on the popular ArgKP21 dataset. In this paper, we highlight some of the major limitations of the ArgKP21 dataset and demonstrate the need for new benchmarks that are more representative of actual human conversations. Using SoTA large language models (LLMs), we curate a new argument key point extraction dataset called ArgCMV comprising of ∼12K arguments from actual online human debates spread across ∼3K topics. Our dataset exhibits higher complexity such as longer, co-referencing arguments, higher presence of subjective discourse units, and a larger range of topics over ArgKP21. We show that existing methods do not adapt well to ArgCMV and provide extensive benchmark results by experimenting with existing baselines and latest open source models. This work introduces a novel KP extraction dataset for long-context online discussions, setting the stage for the next generation of LLM-driven summarization research.

Large language models (LLMs) have shown significant promise in embodied decision-making tasks within virtual open-world environments. Nonetheless, their performance is hindered by the absence of domain-specific knowledge. Methods that finetune on large-scale domain-specific data entail prohibitive development costs. This paper introduces VistaWise, a cost-effective agent framework that integrates cross-modal domain knowledge and finetunes a dedicated object detection model for visual analysis. It reduces the requirement for domain-specific training data from millions of samples to a few hundred. VistaWise integrates visual information and textual dependencies into a cross-modal knowledge graph (KG), enabling a comprehensive and accurate understanding of multimodal environments. We also equip the agent with a retrieval-based pooling strategy to extract task-related information from the KG, and a desktop-level skill library to support direct operation of the Minecraft desktop client via mouse and keyboard inputs. Experimental results demonstrate that VistaWise achieves state-of-the-art performance across various open-world tasks, highlighting its effectiveness in reducing development costs while enhancing agent performance.

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GraphKV: Breaking the Static Selection Paradigm with Graph-Based KV Cache Eviction
Xuelin Li | Xiangqi Jin | Linfeng Zhang

Efficient Key-Value (KV) cache management is essential for processing long text sequences in large language models (LLMs), where memory constraints often limit performance. Conventional KV eviction strategies, such as top-k selection based on attention scores, depend on static heuristics that fail to capture the evolving implicit dependencies among tokens during inference. To overcome this, we propose GraphKV, a graph-based framework that redefines token selection for KV cache compression. In GraphKV, tokens are modeled as nodes with importance scores, and edges represent their similarity relationships. Through a decay-signal-propagation mechanism, token importance is dynamically updated by propagating information across the graph, enabling adaptive retention of the most contextually significant tokens. GraphKV can be seamlessly utilized in existing KV cache eviction methods such as SnapKV and PyramidKV in a plug-and-play manner. Codes are available in the supplementary materials and will be released on Github.

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Joint Modeling of Entities and Discourse Relations for Coherence Assessment
Wei Liu | Michael Strube

In linguistics, coherence can be achieved by different means, such as by maintaining reference to the same set of entities across sentences and by establishing discourse relations between them. However, most existing work on coherence modeling focuses exclusively on either entity features or discourse relation features, with little attention given to combining the two. In this study, we explore two methods for jointly modeling entities and discourse relations for coherence assessment. Experiments on three benchmark datasets show that integrating both types of features significantly enhances the performance of coherence models, highlighting the benefits of modeling both simultaneously for coherence evaluation.

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Understanding and Leveraging the Expert Specialization of Context Faithfulness in Mixture-of-Experts LLMs
Jun Bai | Minghao Tong | Yang Liu | Zixia Jia | Zilong Zheng

Context faithfulness is essential for reliable reasoning in context-dependent scenarios. However, large language models often struggle to ground their outputs in the provided context, resulting in irrelevant responses.Inspired by the emergent expert specialization observed in mixture-of-experts architectures, this work investigates whether certain experts exhibit specialization in context utilization—offering a potential pathway toward targeted optimization for improved context faithfulness.To explore this, we propose Router Lens, a method that accurately identifies context-faithful experts. Our analysis reveals that these experts progressively amplify attention to relevant contextual information, thereby enhancing context grounding.Building on this insight, we introduce Context-faithful Expert Fine-Tuning (CEFT), a lightweight optimization approach that selectively fine-tunes context-faithful experts.Experiments across a wide range of benchmarks and models demonstrate that CEFT matches or surpasses the performance of full fine-tuning while being significantly more efficient.

Mixture of Experts (MoE) offers remarkable performance and computational efficiency by selectively activating subsets of model parameters. Traditionally, MoE models use homogeneous experts, each with identical capacity. However, varying complexity in input data necessitates experts with diverse capabilities, while homogeneous MoE hinders effective expert specialization and efficient parameter utilization. In this study, we propose a novel Heterogeneous Mixture of Experts (HMoE) framework, where experts differ in size and thus possess diverse capacities. This heterogeneity allows for more specialized experts to handle varying token complexities more effectively. To address the imbalance in expert activation, we propose a novel training objective that encourages the frequent activation of smaller experts, so as to improve computational efficiency and parameter utilization. Extensive experiments demonstrate that HMoE achieves a lower loss rate with fewer activated parameters and outperforms conventional homogeneous MoE models on various pre-training evaluation benchmarks. Codes will be released upon acceptance.

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The Ranking Blind Spot: Decision Hijacking in LLM-based Text Ranking
Yaoyao Qian | Yifan Zeng | Yuchao Jiang | Chelsi Jain | Huazheng Wang

Large Language Models (LLMs) have demonstrated strong performance in information retrieval tasks like passage ranking. Our research examines how instruction-following capabilities in LLMs interact with multi-document comparison tasks, identifying what we term the “Ranking Blind Spot”—a characteristic of LLM decision processes during comparative evaluation. We analyze how this ranking blind spot affects LLM evaluation systems through two approaches: **Decision Objective Hijacking**, which alters the evaluation goal in pairwise ranking systems, and **Decision Criteria Hijacking**, which modifies relevance standards across ranking schemes. These approaches demonstrate how content providers could potentially influence LLM-based ranking systems to affect document positioning. These attacks aim to force the LLM ranker to prefer a specific passage and rank it at the top. Malicious content providers can exploit this weakness, which helps them gain additional exposure by attacking the ranker. In our experiment, We empirically show that the proposed attacks are effective in various LLMs and can be generalized to multiple ranking schemes. We apply these attack to real-world examples to show their effectiveness. We also found stronger LLMs are more vulnerable to these attacks.

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Uniform Information Density and Syntactic Reduction: Revisiting *that*-Mentioning in English Complement Clauses
Hailin Hao | Elsi Kaiser

Speakers often have multiple ways to express the same meaning. The Uniform Information Density (UID) hypothesis suggests that speakers exploit this variability to maintain a consistent rate of information transmission during language production. Building on prior work linking UID to syntactic reduction, we revisit the finding that the optional complementizer *that* in English complement clauses is more likely to be omitted when the clause has low information density (i.e., more predictable). We advance this line of research by analyzing a large-scale, contemporary conversational corpus and using machine learning and neural language models to refine estimates of information density. Our results replicated the established relationship between information density and *that*-mentioning. However, we found that previous measures of information density based on matrix verbs’ subcategorization probability capture substantial idiosyncratic lexical variation. By contrast, estimates derived from contextual word embeddings account for additional variance in patterns of complementizer usage.

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GRIT: Guided Relational Integration for Efficient Multi-Table Understanding
Yujin Kang | Park Seong Woo | Yoon-Sik Cho

Recent advances in large language models (LLMs) have opened new possibilities for table-based tasks. However, most existing methods remain confined to single-table settings, limiting their applicability to real-world databases composed of multiple interrelated tables. In multi-table scenarios, LLMs face two key challenges: reasoning over relational structures beyond sequential text, and handling the input length limitations imposed by large-scale table concatenation. To address these issues, we propose Guided Relational Integration for multiple Tables (GRIT), a lightweight method that converts relational schemas into LLM-friendly textual representations. GRIT employs hashing-based techniques to efficiently infer primary–foreign key relationships and constructs prompts that explicitly encode relevant join paths and question-relevant columns. When applied to off-the-shelf LLMs, GRIT consistently improves table-column retrieval performance across diverse multi-table benchmarks while significantly reducing memory and computational overhead.

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RPDR: A Round-trip Prediction-Based Data Augmentation Framework for Long-Tail Question Answering
Yiming Zhang | Siyue Zhang | Junbo Zhao | Chen Zhao

Long-tail question answering presents significant challenges for large language models (LLMs) due to their limited ability to acquire and accurately recall less common knowledge. Retrieval-augmented generation (RAG) systems have shown great promise in mitigating this limitation by integrating external retrieval mechanisms. However, dense retrieval models often face the same difficulties when generalizing to rare or niche knowledge. In this study, we introduce RPDR, a novel data augmentation framework that selects high-quality easy-to-learn training data, to enhance dense retrievers. Our approach is built around three core components: synthetic data generation, data selection with Round-Trip prediction to identify easy-to-learn instances, and retriever training with these instances. We evaluate RPDR on two long-tail retrieval benchmarks, PopQA and EntityQuestion, demonstrating substantial improvements over existing retrievers like BM25 and Contriver, especially on extremely long-tail categories. We identify the strengths and limitations of RPDR through detailed human analysis and propose a dynamic routing mechanism to dynamically route queries to specialized retrieval modules to further improve retrieval performance.

Multilingual question answering (QA) systems must ensure factual consistency across languages, especially for objective queries such as What is jaundice?, while also accounting for cultural variation in subjective responses. We propose MIND, a user-in-the-loop fact-checking pipeline to detect factual and cultural discrepancies in multilingual QA knowledge bases. MIND highlights divergent answers to culturally sensitive questions (e.g., Who assists in childbirth?) that vary by region and context. We evaluate MIND on a bilingual QA system in the maternal and infant health domain and release a dataset of bilingual questions annotated for factual and cultural inconsistencies. We further test MIND on datasets from other domains to assess generalization. In all cases, MIND reliably identifies inconsistencies, supporting the development of more culturally aware and factually consistent QA systems.

Data efficiency is crucial in domain-specific continual pre-training (CPT) of large language models (LLMs), especially under resource constraints. Aiming for “small data, big impact,” this work addresses the limitations of existing domain-specific data selection strategies, which often rely on scarce labeled data or computationally expensive LLMs. We introduce CDF Sampling with Grammatical Complexity (CDF-GC), an annotation-independent, efficient and interpretable data selection framework for CPT. Our approach comprehensively evaluates grammatical complexity using lexical diversity and syntactic complexity, and employs a cumulative distribution function (CDF)-based sampling strategy to balance complexity and diversity. To validate the effectiveness of CDF-GC, we conducted experiments on a financial dataset. The results demonstrate that CDF-GC significantly outperforms baselines, achieving 2.0% improvement in financial QA at the same selection ratio and even surpassing full-data training by 1.7% using only 20% of the data.

Recent efforts leverage knowledge distillation techniques to develop lightweight and practical sentiment analysis models. These methods are grounded in human-written instructions and large-scale user texts. Despite the promising results, two key challenges remain: (1) manually written instructions are limited in diversity and quantity, making them insufficient to ensure comprehensive coverage of distilled knowledge; (2) large-scale user texts incur high computational cost, hindering the practicality of these methods. To this end, we introduce CompEffDist, a comprehensive and efficient distillation framework for sentiment analysis. Our framework consists of two key modules: attribute-based automatic instruction construction and difficulty-based data filtering, which correspondingly tackle the aforementioned challenges. Applying our method across multiple model series (Llama-3, Qwen-3, and Gemma-3), we enable 3B student models to match the performance of 20x larger teacher models on most tasks. In addition, our approach greatly outperforms baseline methods in data efficiency, attaining the same performance level with only 10% of the data. All codes are available at https://github.com/HITSZ-HLT/COMPEFFDIST.

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One Planner To Guide Them All ! Learning Adaptive Conversational Planners for Goal-oriented Dialogues
Huy Quang Dao | Lizi Liao

Goal-oriented dialogues, such as recommendation and negotiation, often require balancing multiple, conflicting objectives. Existing methods typically involve training separate models for specific combinations of objectives, leading to computational and scalability issues. In this work, we aim to develop a new dialogue policy method that can adapt to varying objective preferences at inference time without retraining. This raises several challenges in terms of both (1) optimization strategy and (2) knowledge utilization. To address these, we propose a novel learning framework, Preference Adaptive Dialogue Policy Planner (PADPP), for multi-objective goal-oriented dialogues. Specifically, to tackle the former, we introduce a novel policy optimization scheme, which leverages information gained from training the model on previously updated objective weights, accelerating the learning capability on new weight settings. To address the latter, we utilize Generalized Policy Improvement (GPI) to ensure the effectiveness of leveraged knowledge. Experimental results demonstrate that PADPP achieves superior adaptability and performance compared to state-of-the-art approaches, offering a scalable and flexible solution for multi-objective, goal-oriented dialogues. Code and data are available at the anonymous link.

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Unsupervised Hallucination Detection by Inspecting Reasoning Processes
Ponhvoan Srey | Xiaobao Wu | Anh Tuan Luu

Unsupervised hallucination detection aims to identify hallucinated content generated by large language models (LLMs) without relying on labeled data. While unsupervised methods have gained popularity by eliminating labor-intensive human annotations, they frequently rely on proxy signals unrelated to factual correctness. This misalignment biases detection probes toward superficial or non-truth-related aspects, limiting generalizability across datasets and scenarios. To overcome these limitations, we propose IRIS, an unsupervised hallucination detection framework, leveraging internal representations intrinsic to factual correctness. IRIS prompts the LLM to carefully verify the truthfulness of a given statement, and obtain its contextualized embedding as informative features for training. Meanwhile, the uncertainty of each response is considered a soft pseudolabel for truthfulness. Experimental results demonstrate that IRIS consistently outperforms existing unsupervised methods. Our approach is fully unsupervised, computationally low cost, and works well even with few training data, making it suitable for real-time detection.

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Multimodal Neural Machine Translation: A Survey of the State of the Art
Yi Feng | Chuanyi Li | Jiatong He | Zhenyu Hou | Vincent Ng

Multimodal neural machine translation (MNMT) has received increasing attention due to its widespread applications in various fields such as cross-border e-commerce and cross-border social media platforms. The task aims to integrate other modalities, such as the visual modality, with textual data to enhance translation performance. We survey the major milestones in MNMT research, providing a comprehensive overview of relevant datasets and recent methodologies, and discussing key challenges and promising research directions.

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Lemmatization of Polish Multi-word Expressions
Magdalena Król | Aleksander Smywiński-Pohl | Zbigniew Kaleta | Paweł Lewkowicz

This paper explores the lemmatization of multi-word expressions (MWEs) and proper names in Polish – tasks complicated by linguistic irregularities and historical factors. Instead of using rule-based methods, we apply a machine learning approach with fine-tuned plT5 and mT5 models. We trained and validated the models on enhanced gold-standard data from the 2019 PolEval task and evaluated the impact of additional fine-tuning on a silver-standard dataset derived from Wikipedia. Two setups were tested: one without context, and one using left-side context of the target MWE. Our best model achieved 86.23% AccCS (Accuracy Case-Sensitive), 89.43% AccCI (Accuracy Case-Insensitive), and a combined score of 88.79%, setting a new state-of-the-art for Polish MWE and named entity lemmatization, as confirmed by the PolEval maintainers. We also evaluated optimization and quantization techniques to reduce model size and inference time with minimal quality loss.

This paper explores targeted distillation methods for sentiment analysis, aiming to build compact and practical models that preserve strong and generalizable sentiment analysis capabilities. To this end, we conceptually decouple the distillation target into knowledge and alignment and accordingly propose a two-stage distillation framework. Moreover, we introduce SentiBench, a comprehensive and systematic sentiment analysis benchmark that covers a diverse set of tasks across 12 datasets. We evaluate a wide range of models on this benchmark. Experimental results show that our approach substantially enhances the performance of compact models across diverse sentiment analysis tasks, and the resulting models demonstrate strong generalization to unseen tasks, showcasing robust competitiveness against existing small-scale models.

Large Language Models (LLMs) are susceptible to generating harmful content when prompted with carefully crafted inputs, a vulnerability known as LLM jailbreaking. As LLMs become more powerful, studying jailbreak methods is critical to enhancing security and aligning models with human values. Traditionally, jailbreak techniques have relied on suffix addition or prompt templates, but these methods suffer from limited attack diversity. This paper introduces DiffusionAttacker, an end-to-end generative approach for jailbreak rewriting inspired by diffusion models. Our method employs a sequence-to-sequence (seq2seq) text diffusion model as a generator, conditioning on the original prompt and guiding the denoising process with a novel attack loss. Unlike previous approaches that use autoregressive LLMs to generate jailbreak prompts, which limit the modification of already generated tokens and restrict the rewriting space, DiffusionAttacker utilizes a seq2seq diffusion model, allowing more flexible token modifications. This approach preserves the semantic content of the original prompt while producing harmful content. Additionally, we leverage the Gumbel-Softmax technique to make the sampling process from the diffusion model’s output distribution differentiable, eliminating the need for iterative token search. Extensive experiments on Advbench and Harmbench demonstrate that DiffusionAttacker outperforms previous methods across various evaluation metrics, including attack success rate (ASR), fluency, and diversity.

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Rank-Awareness and Angular Constraints: A New Perspective on Learning Sentence Embeddings from NLI Data
Zicheng Zhou | Min Huang | Qinghai Miao

Learning high-quality sentence embeddings from Natural Language Inference (NLI) data is often challenged by a critical signal conflict between discrete labels and the continuous spectrum of semantic similarity, as well as information loss from discarded neutral sentence pairs during training. To address this, we introduce Rank-Awareness and Angular Optimization Embeddings (RAOE), a framework that leverages the full NLI dataset (Entailment, Neutral, Contradiction) augmented with pre-computed continuous similarity scores (S). RAOE employs a novel composite objective which features: (1) a Rank Margin objective that enforces rank consistency against S using an explicit margin, and (2) a Gated Angular objective that conditionally refines embedding geometry based on NLI label (L) and S score agreement. Extensive evaluations on STS tasks and the MTEB benchmark demonstrate RAOE’s effectiveness. Our general-purpose RAOE-S1 model (BERT-base) significantly outperforms strong baselines, achieving an average Spearman’s correlation of 85.11 (vs. SimCSE’s 81.57 and AnglE’s 82.43), and shows consistent improvements on MTEB. Further STS-specialized fine-tuning (RAOE-S2) establishes new state-of-the-art performance on STS (88.17 with BERT-base). These results confirm RAOE’s ability to efficiently learn robust and nuanced sentence representations through the synergy of rank-awareness and conditional angular constraints. Code is available at https://github.com/Shengjingwa/RAOE.

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LLM-Guided Semantic Relational Reasoning for Multimodal Intent Recognition
Qianrui Zhou | Hua Xu | Yifan Wang | Xinzhi Dong | Hanlei Zhang

Understanding human intents from multimodal signals is critical for analyzing human behaviors and enhancing human-machine interactions in real-world scenarios. However, existing methods exhibit limitations in their modality-level reliance, constraining relational reasoning over fine-grained semantics for complex intent understanding. This paper proposes a novel LLM-Guided Semantic Relational Reasoning (LGSRR) method, which harnesses the expansive knowledge of large language models (LLMs) to establish semantic foundations that boost smaller models’ relational reasoning performance. Specifically, an LLM-based strategy is proposed to extract fine-grained semantics as guidance for subsequent reasoning, driven by a shallow-to-deep Chain-of-Thought (CoT) that autonomously uncovers, describes, and ranks semantic cues by their importance without relying on manually defined priors. Besides, we formally model three fundamental types of semantic relations grounded in logical principles and analyze their nuanced interplay to enable more effective relational reasoning. Extensive experiments on multimodal intent and dialogue act recognition tasks demonstrate LGSRR’s superiority over state-of-the-art methods, with consistent performance gains across diverse semantic understanding scenarios. The complete data and code are available at https://github.com/thuiar/LGSRR.

Multimodal vision-language models (VLMs) have made substantial progress in various tasks that require a combined understanding of visual and textual content, particularly in cultural understanding tasks, with the emergence of new cultural datasets. However, these datasets frequently fall short of providing cultural reasoning while underrepresenting many cultures.In this paper, we introduce the Seeing Culture Benchmark (SCB), focusing on cultural reasoning with a novel approach that requires VLMs to reason on culturally rich images in two stages: i) selecting the correct visual option with multiple-choice visual question answering (VQA), and ii) segmenting the relevant cultural artifact as evidence of reasoning. Visual options in the first stage are systematically organized into three types: those originating from the same country, those from different countries, or a mixed group. Notably, all options are derived from a singular category for each type. Progression to the second stage occurs only after a correct visual option is chosen. The SCB benchmark comprises 1,065 images that capture 138 cultural artifacts across five categories from seven Southeast Asia countries, whose diverse cultures are often overlooked, accompanied by 3,178 questions, of which 1,093 are unique and meticulously curated by human annotators. Our evaluation of various VLMs reveals the complexities involved in cross-modal cultural reasoning and highlights the disparity between visual reasoning and spatial grounding in culturally nuanced scenarios. The SCB serves as a crucial benchmark for identifying these shortcomings, thereby guiding future developments in the field of cultural reasoning. https://github.com/buraksatar/SeeingCulture

Retrieval Augmented Generation (RAG) frameworks can improve the factual accuracy of large language models (LLMs) by integrating external knowledge from retrieved documents, thereby overcoming the limitations of models’ static intrinsic knowledge. However, these systems are susceptible to adversarial attacks that manipulate the retrieval process by introducing documents that are adversarial yet semantically similar to the query. Notably, while these adversarial documents resemble the query, they exhibit weak similarity to benign documents in the retrieval set. Thus, we propose a simple yet effective **G**raph-based **R**eranking against **A**dversarial **D**ocument **A**ttacks (GRADA) framework aiming at preserving retrieval quality while significantly reducing the success of adversaries. Our study evaluates the effectiveness of our approach through experiments conducted on six LLMs: GPT-3.5-Turbo, GPT-4o, Llama3.1-8b-Instruct, Llama3.1-70b-Instruct, Qwen2.5-7b-Instruct and Qwen2.5-14b-Instruct. We use three datasets to assess performance, with results from the Natural Questions dataset demonstrating up to an 80% reduction in attack success rates while maintaining minimal loss in accuracy.

Video-to-audio synthesis, which generates synchronized audio for visual content, critically enhances viewer immersion and narrative coherence in film and interactive media. However, video-to-audio dubbing for long-form content remains an unsolved challenge due to dynamic semantic shifts, audio diversity and the absence of dedicated datasets. While existing methods excel in short videos, they falter in long scenarios (e.g., movies) due to fragmented synthesis and inadequate cross-scene consistency. We propose LVAS-Agent, a multi-agent framework that offers a coordinated, multi-component approach to long-video audio generation. Our approach decomposes long-video synthesis into four steps including scene segmentation, script generation, audio design and audio synthesis. To enable systematic evaluation, we introduce LVAS-Bench, the first benchmark with 207 professionally curated long videos spanning diverse scenarios. Experiments show that our method outperforms state-of-the-art V2A models in overall audio synthesis quality.

Word sense disambiguation (WSD) is a fundamental yet challenging task in natural language processing. In recent years, the advent of large language models (LLMs) has led to significant advancements in regular WSD tasks. However, most existing LLMs face two major issues that hinder their performance in WSD. Firstly, these models are often prone to misclassifying the correct meaning of an ambiguous word when confronted with contexts containing adversarial information. Secondly, there is a lack of sufficient adversarial WSD datasets, which severely limits the development and evaluation of adversarial WSD systems. To address these gaps, we propose a novel Multi-Agent Debate framework for Adversarial Word Sense Disambiguation (MADAWSD). The MADAWSD framework simulates a real-world debate environment where multiple agent roles, namely, the Debater, Moderator, Consensus-seeker, and Judge, engage in discussions about ambiguous words in the context of adversarial information. Through a collaborative mechanism among these agents, it achieves accurate WSD. Additionally, a novel dataset for Chinese adversarial WSD has been constructed, focusing on improving and evaluating the performance of WSD models in the Chinese language. Extensive experiments on both English and Chinese adversarial WSD datasets demonstrate that MADAWSD can seamlessly integrate with existing LLMs and significantly enhance their performance, showcasing broad generality and outstanding effectiveness.

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Interpretable Text Embeddings and Text Similarity Explanation: A Survey
Juri Opitz | Lucas Moeller | Andrianos Michail | Sebastian Padó | Simon Clematide

Text embeddings are a fundamental component in many NLP tasks, including classification, regression, clustering, and semantic search. However, despite their ubiquitous application, challenges persist in interpreting embeddings and explaining similarities between them.In this work, we provide a structured overview of methods specializing in inherently interpretable text embeddings and text similarity explanation, an underexplored research area. We characterize the main ideas, approaches, and trade-offs. We compare means of evaluation, discuss overarching lessons learned and finally identify opportunities and open challenges for future research.

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Dyve: Thinking Fast and Slow for Dynamic Process Verification
Jianyuan Zhong | Zeju Li | Zhijian Xu | Xiangyu Wen | Qiang Xu

Large Language Models have advanced significantly in complex reasoning, often leveraging external reward model to improve the reliability of their multi-step processes. However, existing process verification methods struggle with reliably assessing incomplete reasoning traces and are limited by the cost of high-quality human annotations or the inherent noise in automatically generated labels. Therefore, we present Dyve, a dynamic process verifier that enhances reasoning error detection in large language models by integrating fast and slow thinking, inspired by Kahneman’s Systems Theory. Dyve adaptively applies immediate token-level confirmation (System 1) for straightforward steps and comprehensive analysis (System 2) for complex ones. Unlike traditional verifiers that only evaluate final outputs, Dyve employs a step-wise consensus-filtered supervision strategy, leveraging Monte Carlo estimation, LLM-as-a-Judge, and specialized reasoning models to extract high-quality training signals from noisy rollouts. Experimental results on ProcessBench and the MATH dataset confirm that Dyve significantly outperforms existing process-based verifiers and boosts performance in Best-of-N settings while maintaining computational efficiency by strategically allocating verification resources.

Data perspectivism goes beyond majority vote label aggregation by recognizing various perspectives as legitimate ground truths.However, current evaluation practices remain fragmented, making it difficult to compare perspectivist approaches and analyze their impact on different users and demographic subgroups. To address this gap, we introduce PersEval, the first unified framework for evaluating perspectivist models in NLP. A key innovation is its evaluation at the individual annotator level and its treatment of annotators and users as distinct entities, consistently with real-world scenarios. We demonstrate PersEval’s capabilities through experiments with both Encoder-based and Decoder-based approaches, as well as an analysis of the effect of sociodemographic prompting. By considering global, text-, trait- and user-level evaluation metrics, we show that PersEval is a powerful tool for examining how models are influenced by user-specific information and identifying the biases this information may introduce.

Supervised fine-tuning (SFT) is a critical step in aligning large language models (LLMs) with human instructions and values, yet many aspects of SFT remain poorly understood. We trained a wide range of base models on a variety of datasets including code generation, mathematical reasoning, and general-domain tasks, resulting in 1,000+ SFT models under controlled conditions. We then identified the dataset properties that matter most and examined the layer-wise modifications introduced by SFT.Our findings reveal that some training–task synergies persist across all models while others vary substantially, emphasizing the importance of model-specific strategies. Moreover, we demonstrate that perplexity consistently predicts SFT effectiveness, often surpassing superficial similarity between the training data and the benchmark, and that mid-layer weight changes correlate most strongly with performance gains. We release these 1,000+ SFT models and benchmark results to accelerate further research. All resources are available at https://github.com/llm-jp/massive-sft.

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IndiGEC: Multilingual Grammar Error Correction for Low-Resource Indian Languages
Ujjwal Sharma | Pushpak Bhattacharyya

Grammatical Error Correction (GEC) for low-resource Indic languages faces significant challenges due to the scarcity of annotated data. In this work, we introduce the Mask-Translate&Fill (MTF) framework, a novel approach for generating high-quality synthetic data for GEC using only monolingual corpora. MTF leverages a machine translation system and a pretrained masked language model to introduce synthetic errors and tries to mimic errors made by second-language learners. Our experimental results on English, Hindi, Bengali, Marathi, and Tamil demonstrate that MTF consistently outperforms other monolingual synthetic data generation methods and achieves performance comparable to the Translation Language Modeling (TLM)-based approach, which uses a bilingual corpus, in both independent and multilingual settings. Under multilingual training, MTF yields significant improvements across Indic languages, with particularly notable gains in Bengali and Tamil, achieving +1.6 and +3.14 GLEU over the TLM-based method, respectively. To support further research, we also introduce the IndiGEC Corpus, a high-quality, human-written, manually validated GEC dataset for these four Indic languages, comprising over 8,000 sentence pairs with separate development and test splits.

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Bias Beware: The Impact of Cognitive Biases on LLM-Driven Product Recommendations
Giorgos Filandrianos | Angeliki Dimitriou | Maria Lymperaiou | Konstantinos Thomas | Giorgos Stamou

The advent of Large Language Models (LLMs) has revolutionized product recommenders, yet their susceptibility to adversarial manipulation poses critical challenges, particularly in real-world commercial applications. Our approach is the first one to tap into human psychological principles, seamlessly modifying product descriptions, making such manipulations hard to detect. In this work, we investigate cognitive biases as black-box adversarial strategies, drawing parallels between their effects on LLMs and human purchasing behavior. Through extensive evaluation across models of varying scale, we find that certain biases, such as social proof, consistently boost product recommendation rate and ranking, while others, like scarcity and exclusivity, surprisingly reduce visibility. Our results demonstrate that cognitive biases are deeply embedded in state-of-the-art LLMs, leading to highly unpredictable behavior in product recommendations and posing significant challenges for effective mitigation.

Extensive research has been conducted to explore the capabilities of large language models (LLMs) in table reasoning. However, the essential task of transforming tables information into reports remains a significant challenge for industrial applications. This task is plagued by two critical issues: 1) the complexity and diversity of tables lead to suboptimal reasoning outcomes; and 2) existing table benchmarks lack the capacity to adequately assess the practical application of this task. To fill this gap, we propose the table-to-report task and construct a bilingual benchmark named T2R-bench, where the key information flow from the tables to the reports for this task. The benchmark comprises 457 industrial tables, all derived from real-world scenarios and encompassing 19 industry domains as well as four types of industrial tables. Furthermore, we propose a novel evaluation criteria to fairly measure the quality of report generation. Expeimental results show that Deepseek-R1 only achieves the best performance with 62.71% overall score, indicating that LLMs still have room for improvement on T2R-bench.

Temporal reasoning and planning are essential capabilities for large language models (LLMs), yet most existing benchmarks evaluate them in isolation and under limited forms of complexity. To address this gap, we introduce the Temporal Constraint-based Planning (TCP) benchmark, that jointly assesses both capabilities. Each instance in TCP features a naturalistic dialogue around a collaborative project, where diverse and interdependent temporal constraints are explicitly or implicitly expressed, and models must infer an optimal schedule that satisfies all constraints. To construct TCP, we generate abstract problem prototypes that are then paired with realistic scenarios from various domains and enriched into dialogues using an LLM. A human quality check is performed on a sampled subset to confirm the reliability of our benchmark. We evaluate state-of-the-art LLMs and find that even the strongest models may struggle with TCP, highlighting its difficulty and revealing limitations in LLMs’ temporal constraint-based planning abilities. We analyze underlying failure cases, open source our benchmark, and hope our findings can inspire future research.

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The Role of Outgoing Connection Heterogeneity in Feedforward Layers of Large Language Models
Felix Stahlberg | Shankar Kumar

We report on investigations into the characteristics of outgoing connections in feedforward layers of large language models. Our findings show that inner neurons with diverse outgoing connection strengths are more critical to model performance than those with uniform connections. We propose a new fine-tuning loss that takes advantage of this observation by decreasing the outgoing connection entropy in feedforward layers. Using this loss yields gains over standard fine-tuning across two different model families (PaLM-2 and Gemma-2) for downstream tasks in math, coding, and language understanding. To further elucidate the role of outgoing connection heterogeneity, we develop a data-free structured pruning method, which uses entropy to identify and remove neurons. This method is considerably more effective than removing neurons either randomly or based on their magnitude.

Flowcharts are a critical tool for visualizing decision-making processes. However, their non-linear structure and complex visual-textual relationships make it challenging to interpret them using LLMs, as vision-language models frequently hallucinate nonexistent connections and decision paths when analyzing these diagrams. This leads to compromised reliability for automated flowchart processing in critical domains such as logistics, health, and engineering. We introduce the task of Fine-grained Flowchart Attribution, which traces specific components grounding a flowchart referring LLM response. Flowchart Attribution ensures the verifiability of LLM predictions and improves explainability by linking generated responses to the flowchart’s structure. We propose FlowPathAgent, a neurosymbolic agent that performs fine-grained post hoc attribution through graph-based reasoning. It first segments the flowchart, then converts it into a structured symbolic graph, and then employs an agentic approach to dynamically interact with the graph, to generate attribution paths. Additionally, we present FlowExplainBench, a novel benchmark for evaluating flowchart attributions across diverse styles, domains, and question types. Experimental results show that FlowPathAgent mitigates visual hallucinations in LLM answers over flowchart QA, outperforming strong baselines by 10–14% on our proposed FlowExplainBench dataset.

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Collaborative Rational Speech Act: Pragmatic Reasoning for Multi-Turn Dialog
Lautaro Estienne | Gabriel Ben Zenou | Nona Naderi | Jackie CK Cheung | Pablo Piantanida

As AI systems take on collaborative roles, they must reason about shared goals and beliefs—not just generate fluent language. The Rational Speech Act (RSA) framework offers a principled approach to pragmatic reasoning, but existing extensions face challenges in scaling to multi-turn, collaborative scenarios. In this paper, we introduce Collaborative Rational Speech Act (CRSA), an information-theoretic (IT) extension of RSA that models multi-turn dialog by optimizing a gain function adapted from rate-distortion theory. This gain is an extension of the gain model that is maximized in the original RSA model but takes into account the scenario in which both agents in a conversation have private information and produce utterances conditioned on the dialog. We demonstrate the effectiveness of CRSA on referential games and template-based doctor–patient dialogs in the medical domain. Empirical results show that CRSA yields more consistent, interpretable, and collaborative behavior than existing baselines—paving the way for more pragmatic and socially aware language agents.

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Understanding Subword Compositionality of Large Language Models
Qiwei Peng | Yekun Chai | Anders Søgaard

Large language models (LLMs) take sequences of subwords as input, requiring them to effective compose subword representations into meaningful word-level representations. In this paper, we present a comprehensive set of experiments to probe how LLMs compose subword information, focusing on three key aspects: structural similarity, semantic decomposability, and form retention. Our analysis of the experiments suggests that these five LLM families can be classified into three distinct groups, likely reflecting difference in their underlying composition strategies. Specifically, we observe (i) three distinct patterns in the evolution of structural similarity between subword compositions and whole-word representations across layers; (ii) great performance when probing layer by layer their sensitivity to semantic decompositionality; and (iii) three distinct patterns when probing sensitivity to formal features, e.g., character sequence length. These findings provide valuable insights into the compositional dynamics of LLMs and highlight different compositional pattens in how LLMs encode and integrate subword information.

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Internal Chain-of-Thought: Empirical Evidence for Layer‐wise Subtask Scheduling in LLMs
Zhipeng Yang | Junzhuo Li | Siyu Xia | Xuming Hu

We show that large language models (LLMs) exhibit an internal chain-of-thought: they sequentially decompose and execute composite tasks layer-by-layer. Two claims ground our study: (i) distinct subtasks are learned at different network depths, and (ii) these subtasks are executed sequentially across layers. On a benchmark of 15 two-step composite tasks, we employ layer-from context-masking and propose a novel cross-task patching method, confirming (i). To examine claim (ii), we apply LogitLens to decode hidden states, revealing a consistent layerwise execution pattern. We further replicate our analysis on the real-world TRACE benchmark, observing the same stepwise dynamics. Together, our results enhance LLMs transparency by showing their capacity to internally plan and execute subtasks (or instructions), opening avenues for fine-grained, instruction-level activation steering.

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From Understanding to Generation: An Efficient Shortcut for Evaluating Language Models
Viktor Hangya | Fabian Küch | Darina Gold

Iterative evaluation of LLMs during training is essential to ensure expected capability development, but can be time- and compute-intensive. While NLU tasks, where the model selects from fixed answer choices, are cheap to evaluate, essential capabilities like reasoning and code generation rely on the more time-consuming NLG (token-by-token generation) format. In this work, our aim is to decrease the computational burden of NLG benchmarks in order to enable monitoring crucial LLM capabilities during model training. We reformulate generative tasks into computationally cheaper NLU alternatives. We test the performance correlation between the original and reformulated tasks using 8 LMs of various sizes and 4 capabilities: mathematical reasoning, code generation, factual knowledge and reading comprehension. Our results show a strong correlation between task formats, supporting capability assessment via cheaper alternatives and achieving over 35x average reduction in evaluation time. Our project is available at: https://github.com/Fraunhofer-IIS/EvalShortcut

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Debiasing Multilingual LLMs in Cross-lingual Latent Space
Qiwei Peng | Guimin Hu | Yekun Chai | Anders Søgaard

Debiasing techniques such as SentDebias aim to reduce bias in large language models (LLMs). Previous studies have evaluated their cross-lingual transferability by directly applying these methods to LLM representations, revealing their limited effectiveness across languages. In this work, we therefore propose to perform debiasing in a joint latent space rather than directly on LLM representations. We construct a well-aligned cross-lingual latent space using an autoencoder trained on parallel TED talk scripts. Our experiments with Aya-expanse and two debiasing techniques across four languages (English, French, German, Dutch) demonstrate that a) autoencoders effectively construct a well-aligned cross-lingual latent space, and b) applying debiasing techniques in the learned cross-lingual latent space significantly improves both the overall debiasing performance and cross-lingual transferability.

A limitation of modern document retrieval embedding methods is that they typically encode passages (chunks) from the same documents independently, often overlooking crucial contextual information from the rest of the document that could greatly improve individual chunk representations.In this work, we introduce ConTEB (Context-aware Text Embedding Benchmark), a benchmark designed to evaluate retrieval models on their ability to leverage document-wide context. Our results show that state-of-the-art embedding models struggle in retrieval scenarios where context is required. To address this limitation, we propose InSeNT (In-sequence Negative Training), a novel contrastive post-training approach which combined with late chunking pooling enhances contextual representation learning while preserving computational efficiency. Our method significantly improves retrieval quality on ConTEB without sacrificing base model performance. We further find chunks embedded with our method are more robust to suboptimal chunking strategies and larger retrieval corpus sizes.We open-source all artifacts at https://github.com/illuin-tech/contextual-embeddings.

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MS-RAG: Simple and Effective Multi-Semantic Retrieval-Augmented Generation
Xiaozhou You | Yahui Luo | Lihong Gu

To alleviate the hallucination problem of large language model (LLM), retrieval-augmented generation (RAG) has been proposed and widely adopted. Due to the limitations in cross-chunk summarization task of naive RAG, graph-based RAG has emerged as a promising solution. However, a close study reveals several flaws in these works. First, most graph-based RAGs suffer from less efficient indexing process, which leads to information loss and expensive costs. Second, they heavily rely on LLM for retrieval thus inference slowly, which hinders their application in industry. To build a more efficient and effective RAG, we propose the multi-semantic RAG (MS-RAG). In this work, we combine knowledge graphs with dense vector to build a multi-semantic RAG. To be specific, (i) at indexing stage, we create multiple semantic-level indexes, including chunk-level, relation-level, and entity-level, to leverage the merits of dense vector and knowledge graph. (ii) at retrieval stage, unlike the previous LLM-empowered entity extraction, we propose a novel mix recall algorithm. Finally, we employ a multi-semantic rerank module to purify the results. Extensive experiments show that MS-RAG achieves superior performance. In terms of retrieval effect, MS-RAG achieves state-of-the-art performance, which is about 10%-30% improvement than the existing methods. In terms of question-answering effect, MS-RAG still achieves promising results with faster inference speed. More analysis and experiments are provided in Appendix.

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Transitive self-consistency evaluation of NLI models without gold labels
Wei Wu | Mark Last

Natural Language Inference (NLI) is an important task in natural language processing. NLI models are aimed at automatically determining logical relationships between pairs of sentences. However, recent studies based on gold labels assigned to sentence pairs by human experts have provided some evidence that NLI models tend to make inconsistent model decisions during inference. Previous studies have used existing NLI datasets to test the transitive consistency of language models. However, they test only variations of two transitive consistency rules out of four. To further evaluate the transitive consistency of NLI models, we propose a novel evaluation approach that allows us to test all four rules automatically by generating adversarial examples via antonym replacements. Since we are testing self-consistency, human labeling of generated adversarial examples is unnecessary. Our experiments on several benchmark datasets indicate that the examples generated by the proposed antonym replacement methodology can reveal transitive inconsistencies in the state-of-the-art NLI models.

Despite bilingual speakers frequently using mixed-language queries in web searches, Information Retrieval (IR) research on them remains scarce. To address this, we introduce ***MiLQ***, ***Mi***xed-***L***anguage ***Q***uery test set, the first public benchmark of mixed-language queries, qualified as realistic and relatively preferred. Experiments show that multilingual IR models perform moderately on MiLQ and inconsistently across native, English, and mixed-language queries, also suggesting code-switched training data’s potential for robust IR models handling such queries. Meanwhile, intentional English mixing in queries proves an effective strategy for bilinguals searching English documents, which our analysis attributes to enhanced token matching compared to native queries.

Detecting offensive language in Chinese is challenging due to homophonic substitutions used to evade detection. We propose a framework to improve large language models’ robustness against such phonetic attacks. First, we construct HED-COLD, the first large-scale and systematic homophonic dataset for Chinese offensive language detection. Additionally, we design a homophone-aware pretraining strategy that learns the mappings among orthography, phonetics, and semantics between original and perturbed text. Experimental results show that our approach achieves state-of-the-art performance on both the COLD test set and the toxicity benchmark ToxiCloakCN. Notably, it achieves greater gains in domains susceptible to homophonic attacks, such as gender and regional content. These results demonstrate improved robustness and generalization against phonetic adversarial attacks.

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Persona-Augmented Benchmarking: Evaluating LLMs Across Diverse Writing Styles
Kimberly Truong | Riccardo Fogliato | Hoda Heidari | Steven Wu

Current benchmarks for evaluating Large Language Models (LLMs) often do not exhibit enough writing style diversity, with many adhering primarily to standardized conventions. Such benchmarks do not fully capture the rich variety of communication patterns exhibited by humans. Thus, it is possible that LLMs, which are optimized on these benchmarks, may demonstrate brittle performance when faced with “non-standard” input. In this work, we test this hypothesis by rewriting evaluation prompts using persona-based LLM prompting, a low-cost method to emulate diverse writing styles. Our results show that, even with identical semantic content, variations in writing style and prompt formatting significantly impact the estimated performance of the LLM under evaluation. Notably, we identify distinct writing styles that consistently trigger either low or high performance across a range of models and tasks, irrespective of model family, size, or recency. Our work offers a scalable approach to augment existing benchmarks, improving the external validity of the assessments they provide for LLM performance across linguistic variations.

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Computational Analysis of Character Development in Holocaust Testimonies
Esther Shizgal | Eitan Wagner | Renana Keydar | Omri Abend

This work presents a computational approach to analyze character development along the narrative timeline. The analysis characterizes changes in the protagonist’s views and behavior and the interplay between them. We consider transcripts of Holocaust survivor testimonies as a test case, each telling the story of an individual in first-person terms. We focus on the survivor’s religious trajectory, examining the evolution of their disposition toward religious belief and practice as it is reflected in the testimony. Clustering the resulting trajectories in the dataset, we identify common sequences in the data. Our findings highlight multiple common structures of religiosity across the narratives: in terms of belief, a constant disposition is common, while for practice, most present an oscillating structure, serving as valuable material for historical and sociological research. This work demonstrates the potential of natural language processing for analyzing character evolution through thematic trajectories in narratives.

LoRA has become one of the most widely used parameter-efficient fine-tuning methods due to its simplicity and effectiveness. However, numerous studies have shown that LoRA often introduces substantial parameter redundancy, which not only increases the number of trainable parameters but also hinders the effectiveness of fine-tuning. Since identifying redundant parameters in LoRA is inherently difficult, how to eliminate them efficiently and accurately remains a challenging problem. In this paper, we propose TASO, a redundancy reduction method that leverages importance information from the pretrained model’s weights to mitigate LoRA redundancy. Specifically, we estimate parameter importance on downstream tasks and identify task-specific core regions based on the distribution of importance scores. The location information of these core regions is then used to determine the sparse structure of LoRA modules, enabling redundancy removal before fine-tuning. Our approach significantly reduces the number of trainable parameters required for task adaptation, while providing a novel task-aligned perspective for LoRA redundancy reduction. Experimental results demonstrate that, with a parameter budget comparable to LoRA with rank r = 1, TASO consistently outperforms standard LoRA across multiple tasks, achieving strong fine-tuning performance while effectively eliminating redundant parameters.

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Dual-Path Counterfactual Integration for Multimodal Aspect-Based Sentiment Classification
Rui Liu | Jiahao Cao | Jiaqian Ren | Xu Bai | Yanan Cao

Multimodal aspect-based sentiment classification (MABSC) requires fine-grained reasoning over both textual and visual content to infer sentiments toward specific aspects. However, existing methods often rely on superficial correlations—particularly between aspect terms and sentiment labels—leading to poor generalization and vulnerability to spurious cues. To address this limitation, we propose DPCI, a novel Dual-Path Counterfactual Integration framework that enhances model robustness by explicitly modeling counterfactual reasoning in multimodal contexts. Specifically, we design a dual counterfactual generation module that simulates two types of interventions: replacing aspect terms and rewriting descriptive content, thereby disentangling the spurious dependencies from causal sentiment cues. We further introduce a sample-aware counterfactual selection strategy to retain high-quality, diverse counterfactuals tailored to each generation path. Finally, a confidence-guided integration mechanism adaptively fuses counterfactual signals into the main prediction stream. Extensive experiments on standard MABSC benchmarks demonstrate that DPCI not only achieves state-of-the-art performance but also significantly improves model robustness.

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Job Unfair: An Investigation of Gender and Occupational Bias in Free-Form Text Completions by LLMs
Camilla Casula | Sebastiano Vecellio Salto | Elisa Leonardelli | Sara Tonelli

Disentangling how gender and occupations are encoded by LLMs is crucial to identify possible biases and prevent harms, especially given the widespread use of LLMs in sensitive domains such as human resources.In this work, we carry out an in-depth investigation of gender and occupational biases in English and Italian as expressed by 9 different LLMs (both base and instruction-tuned). Specifically, we focus on the analysis of sentence completions when LLMs are prompted with job-related sentences including different gender representations. We carry out a manual analysis of 4,500 generated texts over 4 dimensions that can reflect bias, we propose a novel embedding-based method to investigate biases in generated texts and, finally, we carry out a lexical analysis of the model completions. In our qualitative and quantitative evaluation we show that many facets of social bias remain unaccounted for even in aligned models, and LLMs in general still reflect existing gender biases in both languages. Finally, we find that models still struggle with gender-neutral expressions, especially beyond English.

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C3: A Bilingual Benchmark for Spoken Dialogue Models Exploring Challenges in Complex Conversations
Chengqian Ma | Wei Tao | Steven Y. Guo

Spoken Dialogue Models (SDMs) have recently attracted significant attention for their ability to generate voice responses directly to users’ spoken queries. Despite their increasing popularity, there exists a gap in research focused on comprehensively understanding their practical effectiveness in comprehending and emulating human conversations. This is especially true compared to text-based Large Language Models (LLMs), which benefit from extensive benchmarking. Human voice interactions are inherently more complex than text due to characteristics unique to spoken dialogue. Ambiguity poses one challenge, stemming from semantic factors like polysemy, as well as phonological aspects such as heterograph, heteronyms, and stress patterns. Additionally, context-dependency, like omission, coreference, and multi-turn interaction, adds further complexity to human conversational dynamics. To illuminate the current state of SDM development and to address these challenges, we present a benchmark dataset in this paper, which comprises 1,079 instances in English and Chinese. Accompanied by an LLM-based evaluation method that closely aligns with human judgment, this dataset facilitates a comprehensive exploration of the performance of SDMs in tackling these practical challenges.

Cross-lingual context retrieval (extracting contextual information in one language based on requests in another) is a fundamental aspect of cross-lingual alignment, but the performance and mechanism of it for large language models (LLMs) remains unclear. In this paper, we evaluate the cross-lingual context retrieval of over 40 LLMs across 12 languages, using cross-lingual machine reading comprehension (xMRC) as a representative scenario. Our results show that post-trained open LLMs show strong cross-lingual context retrieval ability, comparable to closed-source LLMs such as GPT-4o, and their estimated oracle performances greatly improve after post-training. Our mechanism analysis shows that the cross-lingual context retrieval process can be divided into two main phases: question encoding and answer retrieval, which are formed in pre-training and post-training respectively. The phasing stability correlates with xMRC performance, and the xMRC bottleneck lies at the last model layers in the second phase, where the effect of post-training can be evidently observed. Our results also indicate that larger-scale pretraining cannot improve the xMRC performance. Instead, larger LLMs need further multilingual post-training to fully unlock their cross-lingual context retrieval potential.

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Blind Men and the Elephant: Diverse Perspectives on Gender Stereotypes in Benchmark Datasets
Mahdi Zakizadeh | Mohammad Taher Pilehvar

Accurately measuring gender stereotypical bias in language models is a complex task with many hidden aspects. Current benchmarks have underestimated this multifaceted challenge and failed to capture the full extent of the problem. This paper examines the inconsistencies between intrinsic stereotype benchmarks. We propose that currently available benchmarks each capture only partial facets of gender stereotypes, and when considered in isolation, they provide just a fragmented view of the broader landscape of bias in language models. Using StereoSet and CrowS-Pairs as case studies, we investigated how data distribution affects benchmark results. By applying a framework from social psychology to balance the data of these benchmarks across various components of gender stereotypes, we demonstrated that even simple balancing techniques can significantly improve the correlation between different measurement approaches. Our findings underscore the complexity of gender stereotyping in language models and point to new directions for developing more refined techniques to detect and reduce bias.

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Linguistic and Embedding-Based Profiling of Texts Generated by Humans and Large Language Models
Sergio E. Zanotto | Segun Aroyehun

The rapid advancements in large language models (LLMs) have significantly improved their ability to generate natural language, making texts generated by LLMs increasingly indistinguishable from human-written texts. While recent research has primarily focused on using LLMs to classify text as either human-written or machine-generated texts, our study focuses on characterizing these texts using a set of linguistic features across different linguistic levels such as morphology, syntax, and semantics. We select a dataset of human-written and machine-generated texts spanning 8 domains and produced by 11 different LLMs. We calculate different linguistic features such as dependency length and emotionality, and we use them for characterizing human-written and machine-generated texts along with different sampling strategies, repetition controls, and model release dates. Our statistical analysis reveals that human-written texts tend to exhibit simpler syntactic structures and more diverse semantic content. Furthermore, we calculate the variability of our set of features across models and domains. Both human- and machine-generated texts show stylistic diversity across domains, with human-written texts displaying greater variation in our features. Finally, we apply style embeddings to further test variability among human-written and machine-generated texts. Notably, newer models output text that is similarly variable, pointing to a homogenization of machine-generated texts.

Machine Translation (MT) tools are widely used today, often in contexts where professional translators are not present. Despite progress in MT technology, a gap persists between system development and real-world usage, particularly for non-expert users who may struggle to assess translation reliability.This paper advocates for a human-centered approach to MT, emphasizing the alignment of system design with diverse communicative goals and contexts of use. We survey the literature in Translation Studies and Human-Computer Interaction to recontextualize MT evaluation and design to address the diverse real-world scenarios in which MT is used today.

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Exploring the Hidden Capacity of LLMs for One-Step Text Generation
Gleb Mezentsev | Ivan Oseledets

A recent study showed that large language models (LLMs) can reconstruct surprisingly long texts — up to thousands of tokens — via autoregressive generation from just one trained input embedding. In this work, we explore whether autoregressive decoding is essential for such reconstruction. We show that frozen LLMs can generate hundreds of accurate tokens in just one token-parallel forward pass, when provided with only two learned embeddings. This reveals a surprising and underexplored multi-token generation capability of autoregressive LLMs. We examine these embeddings and characterize the information they encode. We also empirically show that, although these representations are not unique for a given text, they form connected and local regions in embedding space — suggesting the potential to train a practical encoder. The existence of such representations hints that multi-token generation may be natively accessible in off-the-shelf LLMs via a learned input encoder, eliminating heavy retraining and helping to overcome the fundamental bottleneck of autoregressive decoding while reusing already-trained models.

Transformer models face scalability challenges in causal language modeling (CLM) due to inefficient memory allocation for growing key-value (KV) caches, which strains compute and storage resources. Existing methods like Grouped Query Attention (GQA) and token-level KV optimization improve efficiency but rely on rigid resource allocation, often discarding “low-priority” tokens or statically grouping them, failing to address the dynamic spectrum of token importance. We propose mixSGA, a novel mixture-of-expert (MoE) approach that dynamically optimizes token-wise computation and memory allocation. Unlike prior approaches, mixSGA retains all tokens while adaptively routing them to specialized experts with varying KV group sizes, balancing granularity and efficiency. Our key novelties include: (1) a token-wise expert-choice routing mechanism guided by learned importance scores, enabling proportional resource allocation without token discard; (2) weight-sharing across grouped attention projections to minimize parameter overhead; and (3) an auxiliary loss to ensure one-hot routing decisions for training-inference consistency in CLMs. Extensive evaluations across Llama3, TinyLlama, OPT, and Gemma2 model families show mixSGA’s superiority over static baselines. On instruction-following and continued pretraining tasks, mixSGA achieves higher ROUGE-L and lower perplexity under the same KV budgets.

Conventional retrieval-augmented generation(RAG) systems employ rigid retrieval strategies that create: (1) knowledge blind spots across domain boundaries, (2) reasoning fragmentation when processing interdependent concepts, and (3) contradictions from conflicting evidence sources. Motivated by these limitations, we introduce PathwiseRAG, which addresses these challenges through: intent-aware strategy selection to eliminate blind spots, dynamic reasoning networks that capture sub-problem interdependencies to overcome fragmentation, and parallel path exploration with adaptive refinement to resolve conflicts. The framework models query intent across semantic and reasoning dimensions, constructs a directed acyclic graph of interconnected sub-problems, and explores multiple reasoning trajectories while continuously adapting to emerging evidence. Evaluation across challenging benchmarks demonstrates significant improvements over state-of-the-art RAG systems, with average accuracy gains of 4.9% and up to 6.9% on complex queries, establishing a new paradigm for knowledge-intensive reasoning by transforming static retrieval into dynamic, multi-dimensional exploration.

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“Mm, Wat?” Detecting Other-initiated Repair Requests in Dialogue
Anh Ha Ngo | Nicolas Rollet | Catherine Pelachaud | Chloé Clavel

Maintaining mutual understanding is a key component in human-human conversation to avoid conversation breakdowns, in which repair, particularly Other-Initiated Repair (OIR, when one speaker signals trouble and prompts the other to resolve), plays a vital role. However, Conversational Agents (CAs) still fail to recognize user repair initiation, leading to breakdowns or disengagement. This work proposes a multimodal model to automatically detect repair initiation in Dutch dialogues by integrating linguistic and prosodic features grounded in Conversation Analysis. The results show that prosodic cues complement linguistic features and significantly improve the results of pretrained text and audio embeddings, offering insights into how different features interact. Future directions include incorporating visual cues, exploring multilingual and cross-context corpora to assess the robustness and generalizability.

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R-BPE: Improving BPE-Tokenizers with Token Reuse
Nancy Hamdan | Osama Rakan Al Mraikhat | Fadi Zaraket

This paper presents R-BPE, a lightweight framework for adapting existing Byte-Pair Encoding (BPE) tokenizers to better support a specified target language.It reuses tokens from user-excluded languages and creates ID-based maps to resolve the new tokens of the chosen language.We evaluate R-BPE on Arabic as a target language. R-BPE reduced subword fertility by an average of 24.4% across the LLaMA 3.1 8B, Command R 35B, and Qwen 3 8B models.Applied to LLaMA 3.1 8B in continued pretraining mode, R-BPE yields a 7.33% reduction in training time. On the ArabicMMLU benchmark, the resulting model improved by 5.09 points on five in-domain topics and matched the original model’s overall performance.It also preserved performance on EnglishMMLU. R-BPE effectively leverages existing models’ tokenizers, embedding layers, and performance to better support target languages without incurring model size changes. We release an R-BPE implementation that is compatible with HuggingFace interfaces and thereby readily applicable to a wide range of existing models at https://acr.ps/1L9GPmL.

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Language Models Can be Efficiently Steered via Minimal Embedding Layer Transformations
Diogo Tavares | David Semedo | Alexander Rudnicky | Joao Magalhaes

Large Language Models (LLMs) are increasingly costly to fine-tune due to their size, with embedding layers alone accounting for up to 20% of model parameters. While Parameter-Efficient Fine-Tuning (PEFT) methods exist, they largely overlook the embedding layer. In this paper, we introduce TinyTE, a novel PEFT approach that steers model behavior via minimal translational transformations in the embedding space. TinyTE modifies input embeddings without altering hidden layers, achieving competitive performance while requiring approximately 0.0001% of the parameters needed for full fine-tuning. Experiments across architectures provide a new lens for understanding the relationship between input representations and model behavior—revealing them to be more flexible at their foundation than previously thought.

In an era where misinformation spreads freely, fact-checking (FC) plays a crucial role in verifying claims and promoting reliable information. While automated fact-checking (AFC) has advanced significantly, existing systems remain vulnerable to adversarial attacks that manipulate or generate claims, evidence, or claim-evidence pairs. These attacks can distort the truth, mislead decision-makers, and ultimately undermine the reliability of FC models. Despite growing research interest in adversarial attacks against AFC systems, a comprehensive, holistic overview of key challenges remains lacking. These challenges include understanding attack strategies, assessing the resilience of current models, and identifying ways to enhance robustness. This survey provides the first in-depth review of adversarial attacks targeting FC, categorizing existing attack methodologies and evaluating their impact on AFC systems. Additionally, we examine recent advancements in adversary-aware defenses and highlight open research questions that require further exploration. Our findings underscore the urgent need for resilient FC frameworks capable of withstanding adversarial manipulations in pursuit of preserving high verification accuracy.

The rapid advancements in Multimodal Large Language Models (MLLMs) have significantly enhanced capabilities in Document Understanding. However, prevailing benchmarks like DocVQA and ChartQA predominantly comprise scanned or digital documents, inadequately reflecting the intricate challenges posed by diverse real-world scenarios such as variable illumination and physical distortions. This paper introduces WildDoc, the inaugural benchmark designed specifically for assessing document understanding in natural environments. WildDoc incorporates a diverse set of manually captured document images reflecting real-world conditions and leverages document sources from established benchmarks to facilitate comprehensive comparisons with digital or scanned documents. Further, to rigorously evaluate model robustness, each document is captured four times under different conditions. Evaluations of state-of-the-art MLLMs on WildDoc expose substantial performance declines and underscore the models’ inadequate robustness compared to traditional benchmarks, highlighting the unique challenges posed by real-world document understanding.

The rapid advancement of large language models (LLMs) has heightened concerns about benchmark data contamination (BDC), where models inadvertently memorize evaluation data during the training process, inflating performance metrics, and undermining genuine generalization assessment. This paper introduces the Data Contamination Risk (DCR) framework, a lightweight, interpretable pipeline designed to detect and quantify BDC risk across four granular levels: semantic, informational, data, and label. By synthesizing contamination scores via a fuzzy inference system, DCR produces a unified DCR Factor that adjusts raw accuracy to reflect contamination-aware performance. Validated on 9 LLMs (0.5B-72B) across sentiment analysis, fake news detection, and arithmetic reasoning tasks, the DCR framework reliably diagnoses contamination severity and with accuracy adjusted using the DCR Factor to within 4% average error across the three benchmarks compared to the uncontaminated baseline. Emphasizing computational efficiency and transparency, DCR provides a practical tool for integrating contamination assessment into routine evaluations, fostering fairer comparisons and enhancing the credibility of LLM benchmarking practices.

Large language models offer transformative potential for healthcare, yet their responsible and equitable development depends critically on a deeper understanding of how training data characteristics influence model behavior, including the potential for bias. Current practices in dataset curation and bias assessment often lack the necessary transparency, creating an urgent need for comprehensive evaluation frameworks to foster trust and guide improvements. In this study, we present an in-depth analysis of potential downstream biases in clinical language models, with a focus on differential opioid prescription tendencies across diverse demographic groups, such as ethnicity, gender, and age. As part of this investigation, we introduce HC4: Healthcare Comprehensive Commons Corpus, a novel and extensively curated pretraining dataset exceeding 89 billion tokens. Our evaluation leverages both established general benchmarks and a novel, healthcare-specific methodology, offering crucial insights to support fairness and safety in clinical AI applications.

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Surprise Calibration for Better In-Context Learning
Zhihang Tan | Jingrui Hou | Ping Wang | Qibiao Hu | Peng Zhu

In-context learning (ICL) has emerged as a powerful paradigm for task adaptation in large language models (LLMs), where models infer underlying task structures from a few demonstrations. However, ICL remains susceptible to biases that arise from prior knowledge and contextual demonstrations, which can degrade the performance of LLMs. Existing bias calibration methods typically apply fixed class priors across all inputs, limiting their efficacy in dynamic ICL settings where the context for each query differs. To address these limitations, we adopt implicit sequential Bayesian inference as a framework for interpreting ICL, identify “surprise” as an informative signal for class prior shift, and introduce a novel method—Surprise Calibration (SC). SC leverages the notion of surprise to capture the temporal dynamics of class priors, providing a more adaptive and computationally efficient solution for in-context learning. We empirically demonstrate the superiority of SC over existing bias calibration techniques across a range of benchmark natural language processing tasks.

Topic evolution and stance dynamics are deeply intertwined in online social media, shaping the fragmentation and polarization of public discourse. Yet existing dynamic topic models and stance analysis approaches usually consider these processes in isolation, relying on abstractions that lack interpretability and agent-level behavioral fidelity. We present stance and topic evolution reasoning framework (SPARK), the first LLM-based multi-agent simulation framework for jointly modeling the co-evolution of topics and stances through natural language interactions. In SPARK, each agent is instantiated as an LLM persona with unique demographic and psychological traits, equipped with memory and reflective reasoning. Agents engage in daily conversations, adapt their stances, and organically introduce emergent subtopics, enabling interpretable, fine-grained simulation of discourse dynamics at scale. Experiments across five real-world domains show that SPARK captures key empirical patterns—such as rapid topic innovation in technology, domain-specific stance polarization, and the influence of personality on stance shifts and topic emergence. Our framework quantitatively reveals the bidirectional mechanisms by which stance shifts and topic evolution reinforce each other, a phenomenon rarely addressed in prior work. SPARK provides actionable insights and a scalable tool for understanding and mitigating polarization in online discourse. Code and simulation resources will be released after acceptance.

We introduce Drivelology, a unique linguistic phenomenon characterised as “nonsense with depth” - utterances that are syntactically coherent yet pragmatically paradoxical, emotionally loaded, or rhetorically subversive. While such expressions may resemble surface-level nonsense, they encode implicit meaning requiring contextual inference, moral reasoning, or emotional interpretation. We find that current large language models (LLMs), despite excelling at many natural language processing (NLP) tasks, consistently fail to grasp the layered semantics of Drivelological text. To investigate this, we construct a benchmark dataset of over 1,200+ meticulously curated and diverse examples across English, Mandarin, Spanish, French, Japanese, and Korean. Each example underwent careful expert review to verify its Drivelological characteristics, involving multiple rounds of discussion and adjudication to address disagreements. Using this dataset, we evaluate a range of LLMs on classification, generation, and reasoning tasks. Our results reveal clear limitations of LLMs: models often confuse Drivelology with shallow nonsense, produce incoherent justifications, or miss implied rhetorical functions altogether. These findings highlight a deep representational gap in LLMs’ pragmatic understanding and challenge the assumption that statistical fluency implies cognitive comprehension. We release our dataset and code to facilitate further research in modelling linguistic depth beyond surface-level coherence.

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Can Large Language Models be Effective Online Opinion Miners?
Ryang Heo | Yongsik Seo | Junseong Lee | Dongha Lee

The surge of user-generated online content presents a wealth of insights into customer preferences and market trends.However, the highly diverse, complex, and context-rich nature of such content poses significant challenges to traditional opinion mining approaches.To address this, we introduce Online Opinion Mining Benchmark (OOMB), a novel dataset and evaluation protocol designed to assess the ability of large language models (LLMs) to mine opinions effectively from diverse and intricate online environments. OOMB provides, for each content instance, an extensive set of (entity, feature, opinion) tuples and a corresponding opinion-centric insight that highlights key opinion topics, thereby enabling the evaluation of both the extractive and abstractive capabilities of models.Through our proposed benchmark, we conduct a comprehensive analysis of which aspects remain challenging and where LLMs exhibit adaptability, to explore whether they can effectively serve as opinion miners in realistic online scenarios.This study lays the foundation for LLM-based opinion mining and discusses directions for future research in this field.

Large language models (LLMs) have demonstrated impressive performance in machine translation, but still struggle with unseen low-resource languages, especially those written in underrepresented scripts. To investigate whether LLMs can translate such languages with the help of linguistic resources, we introduce Lotus, a benchmark designed to evaluate translation for Mongolian (in traditional script) and Yi. Our study shows that while linguistic resources can improve translation quality as measured by automatic metrics, LLMs remain limited in their ability to handle these languages effectively. We hope our work provides insights for the low-resource NLP community and fosters further progress in machine translation for underrepresented script low-resource languages. Our code and data are available.

The formation and circulation of ideas in philosophy have profound implications for understanding philosophical dynamism–enabling us to identify seminal texts, delineate intellectual traditions, and track changing conventions in the act of philosophizing. However, traditional analyses of these issues often depend on manual reading and subjective interpretation, constrained by human cognitive limits. We introduce InterIDEAS, a pioneering dataset designed to bridge philosophy, literary studies, and natural language processing (NLP). By merging theories of intertextuality from literary studies with bibliometric techniques and recent LLMs, InterIDEAS enables both quantitative and qualitative analysis of the intellectual, social, and historical relations embedded within authentic philosophical texts. This dataset not only assists the study of philosophy but also contributes to the development of language models by providing a training corpus that challenges and enhances their interpretative capacity.

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KCS: Diversify Multi-hop Question Generation with Knowledge Composition Sampling
Yangfan Wang | Jie Liu | Chen Tang | Lian Yan | Jingchi Jiang

Multi-hop question answering faces substantial challenges due to data sparsity, which increases the likelihood of language models learning spurious patterns. To address this issue, prior research has focused on diversifying question generation through content planning and varied expression. However, these approaches often emphasize generating simple questions and neglect the integration of essential knowledge, such as relevant sentences within documents. This paper introduces the **Knowledge Composition Sampling (KCS)**, an innovative framework designed to expand the diversity of generated multi-hop questions by sampling varied knowledge compositions within a given context. KCS models the knowledge composition selection as a sentence-level conditional prediction task and utilizes a probabilistic contrastive loss to predict the next most relevant piece of knowledge. During inference, we employ a stochastic decoding strategy to effectively balance accuracy and diversity. Compared to competitive baselines, our KCS improves the overall accuracy of knowledge composition selection by 3.9%, and its application for data augmentation yields improvements on HotpotQA and 2WikiMultihopQA datasets. Our code is available at: https://github.com/yangfanww/kcs.

Recently, large vision–language models (LVLMs) have emerged as the preferred tools for judging text–image alignment, yet their robustness along the visual modality remains underexplored. This work is the first study to address a key research question: Can adversarial visual manipulations systematically fool LVLM judges into assigning unfairly inflated scores? We define potential image-induced biases within the context of T2I evaluation and examine how these biases affect the evaluations of LVLM judges. Moreover, we introduce a novel, fine-grained, multi-domain meta-evaluation benchmark named FRAME, which is deliberately constructed to exhibit diverse score distributions. By introducing the defined biases into the benchmark, we reveal that all tested LVLM judges exhibit vulnerability across all domains, consistently inflating scores for manipulated images. Further analysis reveals that combining multiple biases amplifies their effects, and pairwise evaluations are similarly susceptible. Moreover, we observe that visual biases persist despite prompt-based mitigation strategies, highlighting the vulnerability of current LVLM evaluation systems and underscoring the urgent need for more robust LVLM judges.

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Disentangled Information Bottleneck for Adversarial Text Defense
Yidan Xu | Xinghao Yang | Wei Liu | Bao-di Liu | Weifeng Liu

Adversarial text defense is a significant strategy to protect modern NLP models from being attacked. Typical text defense methods usually enhance the model’s robustness by model retraining or equipping it with a data preprocessing step, aiming to eliminate the non-robust features and preserve the robust ones. Although some efforts have been made to recognize the robust features, e.g., by the information bottleneck (IB) technique, how to fully disentangle the robust and non-robust representation remains a big challenge. To alleviate this problem, we propose a novel text defense method, named Disentangled Information Bottleneck (DisIB), with two major merits. Firstly, we separate the robust features and non-robust features with a disentangled two-line framework rather than the one-line compression network in IB. This prevents the loss of robust features caused by information compression and produces complete robust features. Secondly, we design a discriminator network to approximate the minimum mutual information of the two lines, which sufficiently disentangles robust and non-robust features. To validate the effectiveness of our DisIB, we conduct a total of 96 defense experiments on four datasets by defending four popular attack methods. Experimental results elaborate that our method significantly outperforms six baselines, with accuracy improvements ranging from 3.8% to 20.7%.

Entity alignment (EA), critical for knowledge graph (KG) integration, identifies equivalent entities across different KGs. Traditional methods often face challenges in semantic understanding and scalability. The rise of language models (LMs), particularly large language models (LLMs), has provided powerful new strategies. This paper systematically reviews LM-driven EA methods, proposing a novel taxonomy that categorizes methods in three key stages: data preparation, feature embedding, and alignment. We further summarize key benchmarks, evaluation metrics, and discuss future directions. This paper aims to provide researchers and practitioners with a clear and comprehensive understanding of how language models reshape the field of entity alignment.

Developing Large Language Model (LLM) agents that exhibit human-like behavior, encompassing not only individual heterogeneity rooted in unique user profiles but also adaptive response to socially connected neighbors, is a significant research challenge. Social media platforms, with their diverse user data and explicit social structures, provide an ideal testbed for such investigations. This paper introduces EvoBot, an **Evo**lving LLM-based social **Bot** that significantly enhances human-like generative capabilities through a novel adversarial learning framework. EvoBot is initialized by Supervised Fine-Tuning (SFT) on representative data from social media and then iteratively refines its generation of sophisticated, human-like content via Direct Preference Optimization (DPO). This refinement is guided by feedback from a co-adapting **Detector** which concurrently improves its ability to distinguish EvoBot from humans, thereby creating an increasingly challenging learning environment for EvoBot. Experiments demonstrate that EvoBot generates content aligned with diverse user profiles, increasingly bypassing the co-adapting Detector through human-like expression. Moreover, it exhibits strong social responsiveness, more accurately modeling real-world opinion dynamics and information spread in multi-agent simulations. The framework also yields a more robust Detector, underscoring its broader utility for both advanced agent development and related detection tasks. The code is available at https://anonymous.4open.science/r/EvoBot-036D.

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GER-LLM: Efficient and Effective Geospatial Entity Resolution with Large Language Model
Haojia Zhu | Zhicheng Li | Jiahui Jin

Geospatial Entity Resolution (GER) plays a central role in integrating spatial data from diverse sources. However, existing methods are limited by their reliance on large amounts of training data and their inability to incorporate commonsense knowledge. While recent advances in Large Language Models (LLMs) offer strong semantic reasoning and zero-shot capabilities, directly applying them to GER remains inadequate due to their limited spatial understanding and high inference cost. In this work, we present GER-LLM, a framework that integrates LLMs into the GER pipeline. To address the challenge of spatial understanding, we design a spatially informed blocking strategy based on adaptive quadtree partitioning and Area of Interest (AOI) detection, preserving both spatial proximity and functional relationships. To mitigate inference overhead, we introduce a group prompting mechanism with graph-based conflict resolution, enabling joint evaluation of diverse candidate pairs and enforcing global consistency across alignment decisions. Extensive experiments on real-world datasets demonstrate the effectiveness of our approach, yielding significant improvements over state-of-the-art methods.

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CodeRAG: Finding Relevant and Necessary Knowledge for Retrieval-Augmented Repository-Level Code Completion
Sheng Zhang | Yifan Ding | Shuquan Lian | Shun Song | Hui Li

Repository-level code completion automatically predicts the unfinished code based on the broader information from the repository. Recent strides in Code Large Language Models (code LLMs) have spurred the development of repository-level code completion methods, yielding promising results. Nevertheless, they suffer from issues such as inappropriate query construction, single-path code retrieval, and misalignment between code retriever and code LLM. To address these problems, we introduce CodeRAG, a framework tailored to identify relevant and necessary knowledge for retrieval-augmented repository-level code completion. Its core components include log probability guided query construction, multi-path code retrieval, and preference-aligned BestFit reranking. Extensive experiments on benchmarks ReccEval and CCEval demonstrate that CodeRAG significantly and consistently outperforms state-of-the-art methods. The implementation of CodeRAG is available at https://github.com/KDEGroup/CodeRAG.

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Searching for the Most Human-like Emergent Language
Brendon Boldt | David R. Mortensen

In this paper, we design a signalling game-based emergent communication environment to generate state-of-the-art emergent languages in terms of similarity to human language. This is done with hyperparameter optimization, using XferBench as the objective function. XferBench quantifies the statistical similarity of emergent language to human language by measuring its suitability for deep transfer learning to human language. Additionally, we demonstrate the predictive power of entropy on the transfer learning performance of emergent language as well as corroborate previous results on the entropy-minimization properties of emergent communication systems. Finally, we report generalizations regarding what hyperparameters produce more realistic emergent languages, that is, ones which transfer better to human language.

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Does Context Matter? A Prosodic Comparison of English and Spanish in Monolingual and Multilingual Discourse Settings
Debasmita Bhattacharya | David Sasu | Michela Marchini | Natalie Schluter | Julia Hirschberg

Different languages are known to have typical and distinctive prosodic profiles. However, the majority of work on prosody across languages has been restricted to monolingual discourse contexts. We build on prior studies by asking: how does the nature of the discourse context influence variations in the prosody of monolingual speech? To answer this question, we compare the prosody of spontaneous, conversational monolingual English and Spanish both in monolingual and in multilingual speech settings. For both languages, we find that monolingual speech produced in a monolingual context is prosodically different from that produced in a multilingual context, with more marked differences having increased proximity to multilingual discourse. Our work is the first to incorporate multilingual discourse contexts into the study of native-level monolingual prosody, and has potential downstream applications for the recognition and synthesis of multilingual speech.

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ZERA: Zero-init Instruction Evolving Refinement Agent – From Zero Instructions to Structured Prompts via Principle-based Optimization
Seungyoun Yi | Minsoo Khang | Sungrae Park

Automatic Prompt Optimization (APO) improves large language model (LLM) performance by refining prompts for specific tasks. However, prior APO methods typically focus only on user prompts, rely on unstructured feedback, and require large sample sizes and long iteration cycles—making them costly and brittle. We propose ZERA (Zero-init Instruction Evolving Refinement Agent), a novel framework that jointly optimizes both system and user prompts through principled, low-overhead refinement. ZERA scores prompts using eight generalizable criteria with automatically inferred weights, and revises prompts based on these structured critiques. This enables fast convergence to high-quality prompts using minimal examples and short iteration cycles. We evaluate ZERA across five LLMs and nine diverse datasets spanning reasoning, summarization, and code generation tasks. Experimental results demonstrate consistent improvements over strong baselines. Further ablation studies highlight the contribution of each component to more effective prompt construction. Our implementation including all prompts is publicly available at https://github.com/younatics/zera-agent.

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Toward Machine Interpreting: Lessons from Human Interpreting Studies
Matthias Sperber | Maureen de Seyssel | Jiajun Bao | Matthias Paulik

Current speech translation systems, while having achieved impressive accuracies, are rather static in their behavior and do not adapt to real-world situations in ways human interpreters do. In order to improve their practical usefulness and enable interpreting-like experiences, a precise understanding of the nature of human interpreting is crucial. To this end, we discuss human interpreting literature from the perspective of the machine translation field, while considering both operational and qualitative aspects. We identify implications for the development of speech translation systems and argue that there is great potential to adopt many human interpreting principles using recent modeling techniques. We hope that our findings provide inspiration for closing the perceived usability gap, and can motivate progress toward true machine interpreting.

GUI agents powered by LLMs show promise in interacting with diverse digital environments. Among these, video games offer a valuable testbed due to their varied interfaces, with adventure games posing additional challenges through complex, narrative-driven interactions. Existing game benchmarks, however, lack diversity and rarely evaluate agents on completing entire storylines. To address this, we introduce FlashAdventure, a benchmark of 34 Flash-based adventure games designed to test full story arc completion and tackle the observation-behavior gap—the challenge of remembering and acting on earlier gameplay information. We also propose CUA-as-a-judge, an automated gameplay evaluator, and COAST, an agentic framework leveraging long-term clue memory to better plan and solve sequential tasks. Experiments show current GUI agents struggle with full story arcs, while COAST improves milestone completion by bridging the observation-behavior gap. Nonetheless, a marked discrepancy between humans and best-performing agents warrants continued research efforts to narrow this divide.

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FLARE: Faithful Logic-Aided Reasoning and Exploration
Erik Arakelyan | Pasquale Minervini | Patrick Lewis | Pat Verga | Isabelle Augenstein

Modern Question Answering (QA) and Reasoning approaches with Large Language Models (LLMs) commonly use Chain-of-Thought (CoT) prompting but struggle with generating outputs faithful to their intermediate reasoning chains. While neuro-symbolic methods like Faithful CoT (F-CoT) offer higher faithfulness through external solvers, they require code-specialized models and struggle with ambiguous tasks.We introduce Faithful Logic-Aided Reasoning and Exploration (FLARE), which uses LLMs to plan solutions, formalize queries into logic programs, and simulate code execution through multi-hop search without external solvers. Our method achieves SOTA results on 𝟕 out of 𝟗 diverse reasoning benchmarks and 3 out of 3 logic inference benchmarks while enabling measurement of reasoning faithfulness. We demonstrate that model faithfulness correlates with performance and that successful reasoning traces show an 18.1% increase in unique emergent facts, 8.6% higher overlap between code-defined and execution-trace relations, and 3.6% reduction in unused relations.

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Discourse-Driven Code-Switching: Analyzing the Role of Content and Communicative Function in Spanish-English Bilingual Speech
Debasmita Bhattacharya | Juan Junco | Divya Tadimeti | Julia Hirschberg

Code-switching (CSW) is commonly observed among bilingual speakers, and is motivated by various paralinguistic, syntactic, and morphological aspects of conversation. We build on prior work by asking: how do discourse-level aspects of dialogue – i.e. the content and function of speech – influence patterns of CSW? To answer this, we analyze the named entities and dialogue acts present in a Spanish-English spontaneous speech corpus, and build a predictive model of CSW based on our statistical findings. We show that discourse content and function interact with patterns of CSW to varying degrees, with a stronger influence from function overall. Our work is the first to take a discourse-sensitive approach to understanding the pragmatic and referential cues of bilingual speech and has potential applications in improving the prediction, recognition, and synthesis of code-switched speech that is grounded in authentic aspects of multilingual discourse.

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Can Large Language Models Translate Spoken-Only Languages through International Phonetic Transcription?
Jiale Chen | Xuelian Dong | Qihao Yang | Wenxiu Xie | Tianyong Hao

Spoken-only languages are languages without a writing system. They remain excluded from modern Natural Language Processing (NLP) advancements like Large Language Models (LLMs) due to their lack of textual data. Existing NLP research focuses primarily on high-resource or written low-resource languages, leaving spoken-only languages critically underexplored. As a popular NLP paradigm, LLMs have demonstrated strong few-shot and cross-lingual generalization abilities, making them a promising solution for understanding and translating spoken-only languages. In this paper, we investigate how LLMs can translate spoken-only languages into high-resource languages by leveraging international phonetic transcription as an intermediate representation. We propose UNILANG, a unified language understanding framework that learns to translate spoken-only languages via in-context learning. Through automatic dictionary construction and knowledge retrieval, UNILANG equips LLMs with more fine-grained knowledge for improving word-level semantic alignment. To support this study, we introduce the SOLAN dataset, which consists of Bai (a spoken-only language) and its corresponding translations in a high-resource language. A series of experiments demonstrates the effectiveness of UNILANG in translating spoken-only languages, potentially contributing to the preservation of linguistic and cultural diversity. Our dataset and code will be publicly released.

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ClimateViz: A Benchmark for Statistical Reasoning and Fact Verification on Scientific Charts
Ruiran Su | Jiasheng Si | Zhijiang Guo | Janet B. Pierrehumbert

Scientific fact-checking has largely focused on textual and tabular sources, neglecting scientific charts—a primary medium for conveying quantitative evidence and supporting statistical reasoning in research communication. We introduce ClimateViz, the first large-scale benchmark for scientific fact-checking grounded in real-world, expert-curated scientific charts. ClimateViz comprises 49,862 claims paired with 2,896 visualizations, each labeled as support, refute, or not enough information. To enable interpretable verification, each instance includes structured knowledge graph explanations that capture statistical patterns, temporal trends, spatial comparisons, and causal relations. We conduct a comprehensive evaluation of state-of-the-art multimodal large language models, including proprietary and open-source ones, under zero-shot and few-shot settings. Our results show that current models struggle to perform fact-checking when statistical reasoning over charts is required: even the best-performing systems, such as Gemini 2.5 and InternVL 2.5, achieve only 76.2–77.8% accuracy in label-only output settings, which is far below human performance (89.3% and 92.7%). While few-shot prompting yields limited improvements, explanation-augmented outputs significantly enhance performance in some closed-source models, notably o3 and Gemini 2.5.

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Bridging the Gap Between Molecule and Textual Descriptions via Substructure-aware Alignment
Hyuntae Park | Yeachan Kim | SangKeun Lee

Molecule and text representation learning has gained increasing interest due to its potential for enhancing the understanding of chemical information. However, existing models often struggle to capture subtle differences between molecules and their descriptions, as they lack the ability to learn fine-grained alignments between molecular substructures and chemical phrases. To address this limitation, we introduce MolBridge, a novel molecule–text learning framework based on substructure-aware alignments. Specifically, we augment the original molecule–description pairs with additional alignment signals derived from molecular substructures and chemical phrases. To effectively learn from these enriched alignments, MolBridge employs substructure-aware contrastive learning, coupled with a self-refinement mechanism that filters out noisy alignment signals. Experimental results show that MolBridge effectively captures fine-grained correspondences and outperforms state-of-the-art baselines on a wide range of molecular benchmarks, underscoring the importance of substructure-aware alignment in molecule-text learning.

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SLlama: Parameter-Efficient Language Model Architecture for Enhanced Linguistic Competence Under Strict Data Constraints
Victor Adelakun Omolaoye | Babajide Alamu Owoyele | Gerard de Melo

Scaling data and model size has driven recent advances in language modeling, but this strategy falters under scenarios with strict data constraints, as in the BabyLM Challenge. However, insights from Chinchilla highlights that smaller models trained on more data outperform larger counterparts trained inadequately, emphasizing the need for compact architectures. Furthermore, while embedding weight tying is a common parameter-saving technique, we find it significantly diminishes linguistic competence in compact models.In response, we explore alternative architectural strategies that preserve the parameter efficiency of tied models without sacrificing the representational benefits of untied embeddings. Consequently, we introduce SLlama a Llama3 architecture variant which incorporates targeted modifications—Repeated Reduced Hidden Size and Projection (RRHP), Permutated Weight Attention (PWA), Shared Projection Multi-Layer Perceptron (SPMLP), and Layer Weight Sharing—to compress Transformer components. Without relying on distillation, SLlama achieves a 31.72% improvement in linguistic knowledge acquisition over the BabyLlama baseline, with a comparable GLUE score and significantly lower parameter count. These results demonstrate that well-designed, compact models can rival larger ones under strict data constraints.

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What You See is What You Ask: Evaluating Audio Descriptions
Divy Kala | Eshika Khandelwal | Makarand Tapaswi

Audio descriptions (ADs) narrate important visual details in movies, enabling Blind and Low Vision (BLV) users to understand narratives and appreciate visual details. Existing works in automatic AD generation mostly focus on few-second trimmed clips, and evaluate them by comparing against a single ground-truth reference AD. However, writing ADs is inherently subjective. Through alignment and analysis of two independent AD tracks for the same movies, we quantify the subjectivity in when and whether to describe, and what and how to highlight. Thus, we show that working with trimmed clips is inadequate. We propose ADQA, a QA benchmark that evaluates ADs at the level of few-minute long, coherent video segments, testing whether they would help BLV users understand the story and appreciate visual details. ADQA features visual appreciation (VA) questions about visual facts and narrative understanding (NU) questions based on the plot. Through ADQA, we show that current AD generation methods lag far behind human-authored ADs. We conclude with several recommendations for future work and introduce a public leaderboard for benchmarking.

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TAPS: Tool-Augmented Personalisation via Structured Tagging
Ekaterina Taktasheva | Jeff Dalton

Recent advancements in tool-augmented large language models have enabled them to interact with external tools, enhancing their ability to perform complex user tasks. However, existing approaches overlook the role of personalisation in guiding tool use. This work investigates how user preferences can be effectively integrated into goal-oriented dialogue agents. Through extensive analysis, we identify key weaknesses in the ability of LLMs to personalise tool use. To this end, we introduce TAPS, a novel solution that enhances personalised tool use by leveraging a structured tagging tool and an uncertainty-based tool detector. TAPS significantly improves the ability of LLMs to incorporate user preferences, achieving the new state-of-the-art for open source models on the NLSI task.

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Investigating How Pre-training Data Leakage Affects Models’ Reproduction and Detection Capabilities
Masahiro Kaneko | Timothy Baldwin

Large Language Models (LLMs) are trained on massive web-crawled corpora, often containing personal information, copyrighted text, and benchmark datasets. This inadvertent inclusion in the training dataset, known as data leakage, poses significant risks and could compromise the safety of LLM outputs. Despite its criticality, existing studies do not examine how leaked instances in the pre-training data influence LLMs’ output and detection capabilities. In this paper, we conduct an experimental survey to elucidate the relationship between data leakage in training datasets and its effects on the generation and detection by LLMs. Our experiments reveal that LLMs often generate outputs containing leaked information, even when there is little such data in the training dataset. Moreover, the fewer the leaked instances, the more difficult it becomes to detect such leakage. Finally, we demonstrate that enhancing leakage detection through few-shot learning can help mitigate the impact of the leakage rate in the training data on detection performance.

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Walk and Read Less: Improving the Efficiency of Vision-and-Language Navigation via Tuning-Free Multimodal Token Pruning
Wenda Qin | Andrea Burns | Bryan A. Plummer | Margrit Betke

Large models achieve strong performance on Vision-and-Language Navigation (VLN) tasks, but are costly to run in resource-limited environments. Token pruning offers appealing tradeoffs for efficiency with minimal performance loss by reducing model input size, but prior work overlooks VLN-specific challenges. For example, information loss from pruning can effectively increase computational cost due to longer walks. Thus, the inability to identify uninformative tokens undermines the supposed efficiency gains from pruning.To address this, we propose Navigation-Aware Pruning (NAP), which uses navigation-specific traits to simplify the pruning process by pre-filtering tokens into foreground and background. For example, image views are filtered based on whether the agent can navigate in that direction. We also extract navigation-relevant instructions using a Large Language Model. After filtering, we focus pruning on background tokens, minimizing information loss. To further help avoid increases in navigation length, we discourage backtracking by removing low-importance navigation nodes.Experiments on standard VLN benchmarks show NAP significantly outperforms prior work, preserving higher success rates while saving more than 50% FLOPS.

While large language models (LLMs) have achieved remarkable performance across various natural language processing (NLP) tasks, LLMs exhibit a limited understanding of commonsense reasoning due to the necessity of implicit knowledge that is rarely expressed in text. Recently, retrieval-augmented language models (RALMs) have enhanced their commonsense reasoning ability by incorporating background knowledge from external corpora. However, previous RALMs overlook the implicit nature of commonsense knowledge, potentially resulting in the retrieved documents not directly containing information needed to answer questions. In this paper, we propose Retrieval-augmented knowledge Connection, ReConnect, which transforms indirectly relevant documents into a direct explanation to answer the given question. To this end, we extract relevant knowledge from various retrieved document subsets and aggregate them into a direct explanation. Experimental results show that ReConnect outperforms state-of-the-art (SOTA) baselines, achieving improvements of +2.0% and +4.6% average accuracy on in-domain (ID) and out-of-domain (OOD) benchmarks, respectively.

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Agent-as-Judge for Factual Summarization of Long Narratives
Yeonseok Jeong | Minsoo Kim | Seung-won Hwang | Byung-Hak Kim

Large Language Models (LLMs) have demonstrated near-human performance in summarization tasks based on traditional metrics such as ROUGE and BERTScore. However, these metrics do not adequately capture critical aspects of summarization quality, such as factual accuracy, particularly for long narratives (>100K tokens). Recent advances, such as LLM-as-a-Judge, address the limitations of metrics based on lexical similarity but still exhibit factual inconsistencies, especially in understanding character relationships and states. In this work, we introduce NarrativeFactScore (NFS), the first “Agent-as-a-Judge” framework that evaluates and refines factuality in narrative summarization. By leveraging a Character Knowledge Graph (CKG) extracted from input narrative, NarrativeFactScore evaluates the factuality and provides actionable guidance for refinement, such as identifying missing or erroneous facts. Our experimental results demonstrate that constructing the CKG enables reasoning with 1/3 of the factuality computation used in the prior approach, and achieve three times higher correlation with human judgments. Furthermore, refinement with actionable guidance improves the quality of the summary.

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DnDScore: Decontextualization and Decomposition for Factuality Verification in Long-Form Text Generation
Miriam Wanner | Benjamin Van Durme | Mark Dredze

The decompose-then-verify strategy for verification of Large Language Model (LLM) generations decomposes claims that are then independently verified. Decontextualization augments text (claims) to ensure it can be verified outside of the original context, enabling reliable verification. While decomposition and decontextualization have been explored independently, their interactions in a complete system have not been investigated. Their conflicting purposes can create tensions: decomposition isolates atomic facts while decontextualization inserts relevant information. Furthermore, a decontextualized subclaim presents a challenge to the verification step: what part of the augmented text should be verified as it now contains multiple atomic facts? We conduct an evaluation of different decomposition, decontextualization, and verification strategies and find that the choice of strategy matters in the resulting factuality scores. Additionally, we introduce DnDScore, a decontextualization aware verification method that validates subclaims in the context of contextual information.

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RAcQUEt: Unveiling the Dangers of Overlooked Referential Ambiguity in Visual LLMs
Alberto Testoni | Barbara Plank | Raquel Fernández

Ambiguity resolution is key to effective communication. While humans effortlessly address ambiguity through conversational grounding strategies, the extent to which current language models can emulate these strategies remains unclear. In this work, we examine referential ambiguity in image-based question answering by introducing RAcQUEt, a carefully curated dataset targeting distinct aspects of ambiguity. Through a series of evaluations, we reveal significant limitations and problems of overconfidence of state-of-the-art large multimodal language models in addressing ambiguity in their responses. The overconfidence issue becomes particularly relevant for RAcQUEt-BIAS, a subset designed to analyze a critical yet underexplored problem: failing to address ambiguity leads to stereotypical, socially biased responses. Our results underscore the urgency of equipping models with robust strategies to deal with uncertainty without resorting to undesirable stereotypes.

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Resource-Rational Noisy-Channel Language Processing: Testing the Effect of Algorithmic Constraints on Inferences
Thomas Hikaru Clark | Jacob Hoover Vigly | Edward Gibson | Roger P. Levy

Human language use is robust to errors: comprehenders can and do mentally correct utterances that are implausible or anomalous. How are humans able to solve these problems in real time, picking out alternatives from an unbounded space of options using limited cognitive resources? And can language models trained on next-word prediction for typical language be augmented to handle language anomalies in a human-like way? Using a language model as a prior and an error model to encode likelihoods, we use Sequential Monte Carlo with optional rejuvenation to perform incremental and approximate probabilistic inference over intended sentences and production errors. We demonstrate that the model captures previously established patterns in human sentence processing, and that a trade-off between human-like noisy-channel inferences and computational resources falls out of this model. From a psycholinguistic perspective, our results offer a candidate algorithmic model of rational inference in language processing. From an NLP perspective, our results showcase how to elicit human-like noisy-channel inference behavior from a relatively small LLM while controlling the amount of computation available during inference. Our model is implemented in the Gen.jl probabilistic programming language, and our code is available at https://github.com/thomashikaru/noisy_channel_model.

Standardized benchmarks are central to evaluating and comparing model performance in Natural Language Processing (NLP). However, Large Language Models (LLMs) have exposed shortcomings in existing benchmarks, and so far there is no clear solution. In this paper, we survey a wide scope of benchmarking issues, and provide an overview of solutions as they are suggested in the literature. We observe that these solutions often tackle a limited number of issues, neglecting other facets. Therefore, we propose concrete checklists to cover all aspects of benchmarking issues, both for benchmark creation and usage. We illustrate the use of our checklists by applying them to three popular NLP benchmarks (i.e., SuperGLUE, WinoGrande, and ARC-AGI). Additionally, we discuss the potential advantages of adding minimal-sized test-suites to benchmarking, which would ensure downstream applicability on real-world use cases.

Role-playing agents (RPAs) have attracted growing interest for their ability to simulate immersive and interactive characters. However, existing approaches primarily focus on static role profiles, overlooking the dynamic perceptual abilities inherent to humans. To bridge this gap, we introduce the concept of dynamic role profiles by incorporating video modality into RPAs. To support this, we construct Role-playing-Video60k, a large-scale, high-quality dataset comprising 60k videos and 700k corresponding dialogues. Based on this dataset, we develop a comprehensive RPA framework that combines adaptive temporal sampling with both dynamic and static role profile representations. Specifically, the dynamic profile is created by adaptively sampling video frames and feeding them to the LLM in temporal order, while the static profile consists of (1) character dialogues from training videos during fine-tuning, and (2) a summary context from the input video during inference. This joint integration enables RPAs to generate greater responses. Furthermore, we propose a robust evaluation method covering eight metrics. Experimental results demonstrate the effectiveness of our framework, highlighting the importance of dynamic role profiles in developing RPAs.

We introduce a set of training-free ABX-style discrimination tasks to evaluate how multilingual language models represent language identity (form) and semantic content (meaning). Inspired from speech processing, these zero-shot tasks measure whether minimal differences in representation can be reliably detected. This offers a flexible and interpretable alternative to probing. Applied to XLM-R (Conneau et al, 2020) across pretraining checkpoints and layers, we find that language discrimination declines over training and becomes concentrated in lower layers, while meaning discrimination strengthens over time and stabilizes in deeper layers. We then explore probing tasks, showing some alignment between our metrics and linguistic learning performance.Our results position ABX tasks as a lightweight framework for analyzing the structure of multilingual representations.

In recent years, protein-text models have gained significant attention for their potential in protein generation and understanding. Current approaches focus on integrating protein-related knowledge into large language models through continued pretraining and multi-modal alignment, enabling simultaneous comprehension of textual descriptions and protein sequences. Through a thorough analysis of existing model architectures and text-based protein understanding benchmarks, we identify significant data leakage issues present in current benchmarks. Moreover, conventional metrics derived from natural language processing fail to assess the model’s performance in this domain accurately. To address these limitations, we reorganize existing datasets and introduce a novel evaluation framework based on biological entities. Motivated by our observation, we propose a retrieval-enhanced method, which significantly outperforms fine-tuned LLMs for protein-to-text generation and shows accuracy and efficiency in training-free scenarios. Our code and data will be available.

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Grounded Semantic Role Labelling from Synthetic Multimodal Data for Situated Robot Commands
Claudiu Daniel Hromei | Antonio Scaiella | Danilo Croce | Roberto Basili

Understanding natural language commands in situated Human-Robot Interaction (HRI) requires linking linguistic input to perceptual context. Traditional symbolic parsers lack the flexibility to operate in complex, dynamic environments. We introduce a novel Multimodal Grounded Semantic Role Labelling (G-SRL) framework that combines frame semantics with perceptual grounding, enabling robots to interpret commands via multimodal logical forms. Our approach leverages modern Visual Language Models (VLLMs), which jointly process text and images, and is supported by an automated pipeline that generates high-quality training data. Structured command annotations are converted into photorealistic scenes via LLM-guided prompt engineering and diffusion models, then rigorously validated through object detection and visual question answering. The pipeline produces over 11,000 image-command pairs (3,500+ manually validated), while approaching the quality of manually curated datasets at significantly lower cost.

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Easy as PIE? Identifying Multi-Word Expressions with LLMs
Kai Golan Hashiloni | Ofri Hefetz | Kfir Bar

We investigate the identification of idiomatic expressions—a semantically non-compositional subclass of multiword expressions (MWEs)—in running text using large language models (LLMs) without any fine-tuning. Instead, we adopt a prompt-based approach and evaluate a range of prompting strategies, including zero-shot, few-shot, and chain-of-thought variants, across multiple languages, datasets, and model types. Our experiments show that, with well-crafted prompts, LLMs can perform competitively with supervised models trained on annotated data. These findings highlight the potential of prompt-based LLMs as a flexible and effective alternative for idiomatic expression identification.

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Query-Focused Retrieval Heads Improve Long-Context Reasoning and Re-ranking
Wuwei Zhang | Fangcong Yin | Howard Yen | Danqi Chen | Xi Ye

Recent work has identified retrieval heads (Wu et al., 2025), a subset of attention heads responsible for retrieving salient information in long-context language models (LMs), as measured by their copy-paste behavior in Needle-in-a-Haystack tasks. In this paper, we introduce QRHead (Query-Focused Retrieval Head), an improved set of attention heads that enhance retrieval from long context. We identify QRHead by aggregating attention scores with respect to the input query, using a handful of examples from real-world tasks (e.g., long-context QA). We further introduce QRRetriever, an efficient and effective retriever that uses the accumulated attention mass of QRHead as retrieval scores. We use QRRetriever for long-context reasoning by selecting the most relevant parts with the highest retrieval scores. On multi-hop reasoning tasks LongMemEval and CLIPPER, this yields over 10% performance gains over full context and outperforms strong dense retrievers. We also evaluate QRRetriever as a re-ranker on the BEIR benchmark and find that it achieves strong zero-shot performance, outperforming other LLM-based re-rankers such as RankGPT. Further analysis shows that both the query-context attention scoring and task selection are crucial for identifying QRHead with strong downstream utility. Overall, our work contributes a general-purpose retriever and offers interpretability insights into the long-context capabilities of LMs.

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Robust Adaptation of Large Multimodal Models for Retrieval Augmented Hateful Meme Detection
Jingbiao Mei | Jinghong Chen | Guangyu Yang | Weizhe Lin | Bill Byrne

Hateful memes have become a significant concern on the Internet, necessitating robust automated detection systems. While Large Multimodal Models (LMMs) have shown promise in hateful meme detection, they face notable challenges like sub-optimal performance and limited out-of-domain generalization capabilities. Recent studies further reveal the limitations of both supervised fine-tuning (SFT) and in-context learning when applied to LMMs in this setting. To address these issues, we propose a robust adaptation framework for hateful meme detection that enhances in-domain accuracy and cross-domain generalization while preserving the general vision-language capabilities of LMMs. Analysis reveals that our approach achieves improved robustness under adversarial attacks compared to SFT models. Experiments on six meme classification datasets show that our approach achieves state-of-the-art performance, outperforming larger agentic systems.Moreover, our method generates higher-quality rationales for explaining hateful content compared to standard SFT, enhancing model interpretability. Code available at https://github.com/JingbiaoMei/RGCL

Recent advancements in multimodal reasoning overlook the audio modality. We introduce Audio-Reasoner, a large-scale audio language model for deep reasoning. We meticulously curated a large-scale and diverse multi-task audio dataset with simple annotations. Then, we leverage closed-source models to conduct secondary labeling, QA generation, along with structured COT process. These datasets together form a high-quality reasoning dataset with 1.2 million reasoning-rich samples, which we name CoTA. Following inference scaling principles, we train Audio-Reasoner on CoTA, enabling it to achieve great logical capabilities in audio reasoning. Experiments show state-of-the-art performance across key benchmarks, including MMAU-mini (+25.42%), AIR-Bench chat/foundation (+14.57%/+10.13%), and MELD (+8.01%). Our findings stress the core of structured CoT training in advancing audio reasoning. The model, dataset, and code are open-sourced at [https://github.com/xzf-thu/Audio-Reasoner](https://github.com/xzf-thu/Audio-Reasoner) or [https://huggingface.co/datasets/zhifeixie/Audio-Reasoner-CoTA](https://huggingface.co/datasets/zhifeixie/Audio-Reasoner-CoTA).

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From perception to production: how acoustic invariance facilitates articulatory learning in a self-supervised vocal imitation model
Marvin Lavechin | Thomas Hueber

Human infants face a formidable challenge in speech acquisition: mapping extremely variable acoustic inputs into appropriate articulatory movements without explicit instruction. We present a computational model that addresses the acoustic-to-articulatory mapping problem through self-supervised learning. Our model comprises a feature extractor that transforms speech into latent representations, an inverse model that maps these representations to articulatory parameters, and a synthesizer that generates speech outputs. Experiments conducted in both single- and multi-speaker settings reveal that intermediate layers of a pre-trained wav2vec 2.0 model provide optimal representations for articulatory learning, significantly outperforming MFCC features. These representations enable our model to learn articulatory trajectories that correlate with human patterns, discriminate between places of articulation, and produce intelligible speech. Critical to successful articulatory learning are representations that balance phonetic discriminability with speaker invariance – precisely the characteristics of self-supervised representation learning models. Our findings provide computational evidence consistent with developmental theories proposing that perceptual learning of phonetic categories guides articulatory development, offering insights into how infants might acquire speech production capabilities despite the complex mapping problem they face.

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REALM: Recursive Relevance Modeling for LLM-based Document Re-Ranking
Pinhuan Wang | Zhiqiu Xia | Chunhua Liao | Feiyi Wang | Hang Liu

Large Language Models (LLMs) have shown strong capabilities in document re-ranking, a key component in modern Information Retrieval (IR) systems. However, existing LLM-based approaches face notable limitations, including ranking uncertainty, unstable top-k recovery, and high token cost due to token-intensive prompting. To effectively address these limitations, we propose REALM, an uncertainty-aware re-ranking framework that models LLM-derived relevance as Gaussian distributions and refines them through recursive Bayesian updates. By explicitly capturing uncertainty and minimizing redundant queries, REALM achieves better rankings more efficiently. Experimental results demonstrate that our REALM surpasses state-of-the-art re-rankers while significantly reducing token usage and latency, improving NDCG@10 by 0.7-11.9 and simultaneously reducing the number of LLM inferences by 23.4-84.4%, promoting it as the next-generation re-ranker for modern IR systems.

Alignment is the critical process of minimizing harmful outputs by teaching large language models (LLMs) to prefer safe, helpful and appropriate responses. While the majority of alignment research and datasets remain overwhelmingly English-centric, ensuring safety across diverse linguistic and cultural contexts requires localized resources. In this paper, we introduce the first Polish preference dataset PLLuM-Align, created entirely through human annotation to reflect Polish language and cultural nuances. The dataset includes response rating, ranking, and multi-turn dialog data. Designed to reflect the linguistic subtleties and cultural norms of Polish, this resource lays the groundwork for more aligned Polish LLMs and contributes to the broader goal of multilingual alignment in underrepresented languages.

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Graph-R1: Incentivizing the Zero-Shot Graph Learning Capability in LLMs via Explicit Reasoning
Yicong Wu | Guangyue Lu | Yuan Zuo | Huarong Zhang | Junjie Wu

Generalizing to unseen graph tasks without task-specific supervision remains challenging. Graph Neural Networks (GNNs) are limited by fixed label spaces, while Large Language Models (LLMs) lack structural inductive biases. Recent advances in Large Reasoning Models (LRMs) provide a zero-shot alternative via explicit, long chain-of-thought reasoning. Inspired by this, we propose a GNN-free approach that reformulates graph tasks—node classification, link prediction, and graph classification—as textual reasoning problems solved by LRMs. We introduce the first datasets with detailed reasoning traces for these tasks and develop Graph-R1, a reinforcement learning framework that leverages task-specific rethink templates to guide reasoning over linearized graphs. Experiments demonstrate that Graph-R1 outperforms state-of-the-art baselines in zero-shot settings, producing interpretable and effective predictions. Our work highlights the promise of explicit reasoning for graph learning and provides new resources for future research. Codes are available at https://github.com/lgybuaa/Graph-R1.

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Scalable and Culturally Specific Stereotype Dataset Construction via Human-LLM Collaboration
Weicheng Ma | John J. Guerrerio | Soroush Vosoughi

Research on stereotypes in large language models (LLMs) has largely focused on English-speaking contexts, due to the lack of datasets in other languages and the high cost of manual annotation in underrepresented cultures. To address this gap, we introduce a cost-efficient human-LLM collaborative annotation framework and apply it to construct EspanStereo, a Spanish-language stereotype dataset spanning multiple Spanish-speaking countries across Europe and Latin America. EspanStereo captures both well-documented stereotypes from prior literature and culturally specific biases absent from English-centric resources. Using LLMs to generate candidate stereotypes and in-culture annotators to validate them, we demonstrate the framework’s effectiveness in identifying nuanced, region-specific biases. Our evaluation of Spanish-supporting LLMs using EspanStereo reveals significant variation in stereotypical behavior across countries, highlighting the need for more culturally grounded assessments. Beyond Spanish, our framework is adaptable to other languages and regions, offering a scalable path toward multilingual stereotype benchmarks. This work broadens the scope of stereotype analysis in LLMs and lays the groundwork for comprehensive cross-cultural bias evaluation.

Large language models (LLMs) have achieved remarkable success across diverse domains. However, their potential as effective language teachers—particularly in complex pedagogical scenarios like teaching Chinese as a second language—remains inadequately assessed. To address this gap, we propose the first pedagogical competence benchmark for LLMs, rigorously evaluating their performance against international standards for Chinese language teachers. Our framework spans three core dimensions: (1) basic knowledge evaluation, covering 32 subtopics across five major categories; (2) international teacher examination, based on data collected from international Chinese teacher certification exams; and (3) teaching practice evaluation, where target LLMs summarize knowledge points and design instructional content for student models, followed by testing the student models to assess the LLM’s ability to distill and teach key concepts.We conduct a comprehensive evaluation of 13 latest multilingual and Chinese LLMs. While most models demonstrate promising pedagogical potential, there remains substantial room for improvement in their teaching capabilities. This study contributes to the development of AI-assisted language education tools capable of rivaling human teaching excellence. The benchmark dataset and evaluation scripts used in this study are publicly available at https://github.com/Line-Kite/CLTE.

The large language models (LLMs) learning framework for math problem generation (MPG) mostly performs homogeneous training in different epochs on small-scale manually annotated data. This pattern struggles to provide large-scale new quality data to support continual improvement, and fails to stimulate the mutual promotion reaction between generation and reasoning ability of math problem, resulting in the lack of reliable solving process. This paper proposes a synthetic data based continual learning framework to improve LLMs ability for MPG and math reasoning. The framework cycles through three stages, “supervised fine-tuning, data synthesis, direct preference optimization”, continuously and steadily improve performance. We propose a synthetic data method with dual mechanism of model self-play and multi-agent cooperation is proposed, which ensures the consistency and validity of synthetic data through sample filtering and rewriting strategies, and overcomes the dependence of continual learning on manually annotated data. A data replay strategy that assesses sample importance via loss differentials is designed to mitigate catastrophic forgetting. Experimental analysis on abundant authoritative math datasets demonstrates the superiority and effectiveness of our framework.

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Tokenization and Representation Biases in Multilingual Models on Dialectal NLP Tasks
Vani Kanjirangat | Tanja Samardzic | Ljiljana Dolamic | Fabio Rinaldi

Dialectal data are characterized by linguistic variation that appears small to humans but has a significant impact on the performance of models. This dialect gap has been related to various factors (e.g., data size, economic and social factors) whose impact, however, turns out to be inconsistent. In this work, we investigate factors impacting the model performance more directly: we correlate Tokenization Parity (TP) and Information Parity (IP), as measures of representational biases in pre-trained multilingual models, with the downstream performance. We compare state-of-the-art decoder-only LLMs with encoder-based models across three tasks: dialect classification, topic classification, and extractive question answering, controlling for varying scripts (Latin vs. non-Latin) and resource availability (high vs. low). Our analysis reveals that TP is a better predictor of the performance on tasks reliant on syntactic and morphological cues (e.g., extractive QA), while IP better predicts performance in semantic tasks (e.g., topic classification). Complementary analyses, including tokenizer behavior, vocabulary coverage, and qualitative insights, reveal that the language support claims of LLMs often might mask deeper mismatches at the script or token level.

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Evaluating the Evaluators: Are readability metrics good measures of readability?
Isabel Cachola | Daniel Khashabi | Mark Dredze

Plain Language Summarization (PLS) aims to distill complex documents into accessible summaries for non-expert audiences. In this paper, we conduct a thorough survey of PLS literature, and identify that the current standard practice for readability evaluation is to use traditional readability metrics, such as Flesch-Kincaid Grade Level (FKGL). However, despite proven utility in other fields, these metrics have not been compared to human readability judgments in PLS. We evaluate 8 readability metrics and show that most correlate poorly with human judgments, including the most popular metric, FKGL. We then show that Language Models (LMs) are better judges of readability, with the best-performing model achieving a Pearson correlation of 0.56 with human judgments. Extending our analysis to PLS datasets, which contain summaries aimed at non-expert audiences, we find that LMs better capture deeper measures of readability, such as required background knowledge, and lead to different conclusions than the traditional metrics. Based on these findings, we offer recommendations for best practices in the evaluation of plain language summaries.

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Text Takes Over: A Study of Modality Bias in Multimodal Intent Detection
Ankan Mullick | Saransh Sharma | Abhik Jana | Pawan Goyal

The rise of multimodal data, integrating text, audio, and visuals, has created new opportunities for studying multimodal tasks such as intent detection. This work investigates the effectiveness of Large Language Models (LLMs) and non-LLMs, including text-only and multimodal models, in the multimodal intent detection task. Our study reveals that Mistral-7B, a text-only LLM, outperforms most competitive multimodal models by approximately 9% on MIntRec-1 and 4% on MIntRec2.0 dataset. This performance advantage comes from a strong textual bias in these datasets, where over 90% of the samples require textual input, either alone or in combination with other modalities, for correct classification. We confirm the modality bias of these datasets via human evaluation, too. Next, we propose a framework to debias the datasets, and upon debiasing, more than 70% of the samples in MIntRec-1 and more than 50% in MIntRec2.0 get removed, resulting in significant performance degradation across all models, with smaller multimodal fusion models being the most affected with an accuracy drop of over 50 - 60%. Further, we analyze the context-specific relevance of different modalities through empirical analysis. Our findings highlight the challenges posed by modality bias in multimodal intent datasets and emphasize the need for unbiased datasets to evaluate multimodal models effectively. We release both the code and the dataset used for this work at https://github.com/Text-Takes-Over-EMNLP-2025/MultiModal-Intent-EMNLP-2025.

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What’s in a prompt? Language models encode literary style in prompt embeddings
Raphaël Sarfati | Haley Moller | Toni J.b. Liu | Nicolas Boulle | Christopher Earls

Large language models use high-dimensional latent spaces to encode and process textual information. Much work has investigated how the conceptual content of words translates into geometrical relationships between their vector representations. Fewer studies analyze how the cumulative information of an entire prompt becomes condensed into individual embeddings under the action of transformer layers. We use literary pieces to show that information about intangible, rather than factual, aspects of the prompt are contained in deep representations. We observe that short excerpts (10 - 100 tokens) from different novels separate in the latent space independently from what next-token prediction they converge towards. Ensembles from books from the same authors are much more entangled than across authors, suggesting that embeddings encode stylistic features. This geometry of style may have applications for authorship attribution and literary analysis, but most importantly reveals the sophistication of information processing and compression accomplished by language models.

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Identifying and Answering Questions with False Assumptions: An Interpretable Approach
Zijie Wang | Eduardo Blanco

People often ask questions with false assumptions, a type of question that does not have regular answers. Answering such questions requires first identifying the false assumptions. Large Language Models (LLMs) often generate misleading answers to these questions because of hallucinations. In this paper, we focus on identifying and answering questions with false assumptions in several domains. We first investigate whether the problem reduces to fact verification. Then, we present an approach leveraging external evidence to mitigate hallucinations. Experiments with five LLMs demonstrate that (1) incorporating retrieved evidence is beneficial and (2) generating and validating atomic assumptions yields more improvements and provides an interpretable answer by pinpointing the false assumptions.

Multimodal large language models (MLLMs) hold great promise for automating complex financial analysis. To comprehensively evaluate their capabilities, we introduce VisFinEval, the first large-scale Chinese benchmark that spans the full front-middle-back office lifecycle of financial tasks. VisFinEval comprises 15,848 annotated question–answer pairs drawn from eight common financial image modalities (e.g., K-line charts, financial statements, official seals), organized into three hierarchical scenario depths: Financial Knowledge & Data Analysis, Financial Analysis & Decision Support, and Financial Risk Control & Asset Optimization. We evaluate 21 state-of-the-art MLLMs in a zero-shot setting. The top model, Qwen-VL-max, achieves an overall accuracy of 76.3%, outperforming non-expert humans but trailing financial experts by over 14 percentage points. Our error analysis uncovers six recurring failure modes—including cross-modal misalignment, hallucinations, and lapses in business-process reasoning—that highlight critical avenues for future research. VisFinEval aims to accelerate the development of robust, domain-tailored MLLMs capable of seamlessly integrating textual and visual financial information. The data and the code are available at https://github.com/SUFE-AIFLM-Lab/VisFinEval.

Recent research in vision-language models (VLMs) has centered around the possibility of equipping them with implicit long-form chain-of-thought reasoning—akin to the success observed in language models—via distillation and reinforcement learning. But what about the non-reasoning models already trained and deployed across the internet? Should we simply abandon them, or is there hope for a search mechanism that can elicit hidden knowledge and induce long reasoning traces— without any additional training or supervision? In this paper, we explore this possibility using a Monte Carlo Tree Search (MCTS)-inspired algorithm, which injects subquestion–subanswer pairs into the model’s output stream. We show that framing reasoning as a search process—where subquestions act as latent decisions within a broader inference trajectory—helps the model “connect the dots” between fragmented knowledge and produce extended reasoning traces in non-reasoning models. We evaluate our method across three benchmarks and observe consistent improvements. Notably, our approach yields a 2% overall improvement on MMMU-PRO, including a significant 9% gain in Liberal Arts.

To collaborate effectively with humans, language models must be able to explain their decisions in natural language. We study a specific type of self-explanation: self-generated counterfactual explanations (SCEs), where a model explains its prediction by modifying the input such that it would have predicted a different outcome. We evaluate whether LLMs can produce SCEs that are valid, achieving the intended outcome, and minimal, modifying the input no more than necessary. When asked to generate counterfactuals, we find that LLMs typically produce SCEs that are valid, but far from minimal, offering little insight into their decision-making behaviour. Worryingly, when asked to generate minimal counterfactuals, LLMs typically make excessively small edits that fail to change predictions. The observed validity-minimality trade-off is consistent across several LLMs, datasets, and evaluation settings. Our findings suggest that SCEs are, at best, an ineffective explainability tool and, at worst, can provide misleading insights into model behaviour. Proposals to deploy LLMs in high-stakes settings must consider the impact of unreliable self-explanations on downstream decision-making. Our code is available at https://github.com/HarryMayne/SCEs.

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Grounding Multilingual Multimodal LLMs With Cultural Knowledge
Jean De Dieu Nyandwi | Yueqi Song | Simran Khanuja | Graham Neubig

Multimodal Large Language Models excel in high-resource settings, but often misinterpret long-tail cultural entities and underperform in low-resource languages. To address this gap, we propose a data-centric approach that directly grounds MLLMs in cultural knowledge. Leveraging a large scale knowledge graph from Wikidata, we collect images that represent culturally significant entities, and generate synthetic multilingual visual question answering data. The resulting dataset, CulturalGround, comprises 22 million high-quality, culturally-rich VQA pairs spanning 42 countries and 39 languages. We train an open-source MLLM CulturalPangea on CulturalGround, interleaving standard multilingual instruction-tuning data to preserve general abilities. Cultural-Pangea achieves state-of-the-art performance among open models on various culture-focused multilingual multimodal benchmarks, outperforming prior models by an average of +5.0%without degrading results on mainstream vision–language tasks. Our findings show that our targeted, culturally grounded approach could substantially narrow the cultural gap in MLLMs and offer a practical path towards globally inclusive multimodal systems.

Aligned instruction following models can better fulfill user requests than their unaligned counterparts. However, it has been shown that there is a length bias in evaluation of such models, and that training algorithms tend to exploit this bias by learning longer responses. In this work we show how to train models that can be controlled at inference time with instructions containing desired length constraints. Such models are superior in length instructed evaluations, outperforming standard instruction following models such as GPT4, Llama 3 and Mixtral.

We introduce Memory-QA, a novel real-world task that involves answering recall questions about visual content from previously stored multimodal memories. This task poses unique challenges, including the creation of task-oriented memories, the effective utilization of temporal and location information within memories, and the ability to draw upon multiple memories to answer a recall question. To address these challenges, we propose a comprehensive pipeline, Pensieve, integrating memory-specific augmentation, time- and location-aware multi-signal retrieval, and multi-memory QA fine-tuning. We created a multimodal benchmark to illustrate various real challenges in this task, and show the superior performance of Pensieve over state-of-the-art solutions (up to +14% on QA accuracy).

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NEXUS: Network Exploration for eXploiting Unsafe Sequences in Multi-Turn LLM Jailbreaks
Javad Rafiei Asl | Sidhant Narula | Mohammad Ghasemigol | Eduardo Blanco | Daniel Takabi

Large Language Models (LLMs) have revolutionized natural language processing, yet remain vulnerable to jailbreak attacks—particularly multi-turn jailbreaks that distribute malicious intent across benign exchanges, thereby bypassing alignment mechanisms. Existing approaches often suffer from limited exploration of the adversarial space, rely on hand-crafted heuristics, or lack systematic query refinement. We propose NEXUS (Network Exploration for eXploiting Unsafe Sequences), a modular framework for constructing, refining, and executing optimized multi-turn attacks. NEXUS comprises: (1) ThoughtNet, which hierarchically expands a harmful intent into a structured semantic network of topics, entities, and query chains; (2) a feedback-driven Simulator that iteratively refines and prunes these chains through attacker–victim–judge LLM collaboration using harmfulness and semantic-similarity benchmarks; and (3) a Network Traverser that adaptively navigates the refined query space for real-time attacks. This pipeline systematically uncovers stealthy, high-success adversarial paths across LLMs. Our experimental results on several closed-source and open-source LLMs show that NEXUS can achieve a higher attack success rate, between 2.1% and 19.4%, compared to state-of-the-art approaches. Our source code is available at https://github.com/inspire-lab/NEXUS.

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Sketch-of-Thought: Efficient LLM Reasoning with Adaptive Cognitive-Inspired Sketching
Simon A. Aytes | Jinheon Baek | Sung Ju Hwang

Recent advances in large language models (LLMs) have enabled strong reasoning capabilities through Chain-of-Thought (CoT) prompting, which elicits step-by-step problem solving, but often at the cost of excessive verbosity in intermediate outputs, leading to increased computational overhead. We propose Sketch-of-Thought (SoT), a prompting framework that integrates cognitively inspired reasoning paradigms with linguistic constraints to reduce token usage while preserving reasoning accuracy. SoT is designed as a flexible, modular approach and is instantiated with three paradigms—Conceptual Chaining, Chunked Symbolism, and Expert Lexicons—each tailored to distinct reasoning tasks and selected dynamically at test-time by a lightweight routing model. Across 18 reasoning datasets spanning multiple domains, languages, and modalities, SoT achieves token reductions of up to 84% with minimal accuracy loss. In tasks such as mathematical and multi-hop reasoning, it even improves accuracy while shortening outputs.

Large language models (LLMs) exhibit remarkable similarity to neural activity in the human language network. However, the key properties of language underlying this alignment—and how brain-like representations emerge and change across training—remain unclear. We here benchmark 34 training checkpoints spanning 300B tokens across 8 different model sizes to analyze how brain alignment relates to linguistic competence. Specifically, we find that brain alignment tracks the development of formal linguistic competence—i.e., knowledge of linguistic rules—more closely than functional linguistic competence. While functional competence, which involves world knowledge and reasoning, continues to develop throughout training, its relationship with brain alignment is weaker, suggesting that the human language network primarily encodes formal linguistic structure rather than broader cognitive functions. Notably, we find that the correlation between next-word prediction, behavioral alignment, and brain alignment fades once models surpass human language proficiency. We further show that model size is not a reliable predictor of brain alignment when controlling for the number of features. Finally, using the largest set of rigorous neural language benchmarks to date, we show that language brain alignment benchmarks remain unsaturated, highlighting opportunities for improving future models. Taken together, our findings suggest that the human language network is best modeled by formal, rather than functional, aspects of language.

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Logos as a Well-Tempered Pre-train for Sign Language Recognition
Ilya Ovodov | Petr Surovtsev | Karina Kvanchiani | Alexander Kapitanov | Alexander Nagaev

This paper examines two aspects of the isolated sign language recognition (ISLR) task. First, although a certain number of datasets is available, the data for individual sign languages is limited. It poses the challenge of cross-language ISLR model training, including transfer learning. Second, similar signs can have different semantic meanings. It leads to ambiguity in dataset labeling and raises the question of the best policy for annotating such signs. To address these issues, this study presents Logos, a novel Russian Sign Language (RSL) dataset, the most extensive available ISLR dataset by the number of signers, one of the most extensive datasets in size and vocabulary, and the largest RSL dataset. It is shown that a model, pre-trained on the Logos dataset can be used as a universal encoder for other language SLR tasks, including few-shot learning. We explore cross-language transfer learning approaches and find that joint training using multiple classification heads benefits accuracy for the target low-resource datasets the most. The key feature of the Logos dataset is explicitly annotated visually similar sign groups. We show that explicitly labeling visually similar signs improves trained model quality as a visual encoder for downstream tasks. Based on the proposed contributions, we outperform current state-of-the-art results for the WLASL dataset and get competitive results for the AUTSL dataset, with a single stream model processing solely RGB video. The source code, dataset, and pre-trained models are publicly available.

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Hallucination Detection in LLMs Using Spectral Features of Attention Maps
Jakub Binkowski | Denis Janiak | Albert Sawczyn | Bogdan Gabrys | Tomasz Jan Kajdanowicz

Large Language Models (LLMs) have demonstrated remarkable performance across various tasks but remain prone to hallucinations. Detecting hallucinations is essential for safety-critical applications, and recent methods leverage attention map properties to this end, though their effectiveness remains limited. In this work, we investigate the spectral features of attention maps by interpreting them as adjacency matrices of graph structures. We propose the LapEigvals method, which utilises the top-k eigenvalues of the Laplacian matrix derived from the attention maps as an input to hallucination detection probes. Empirical evaluations demonstrate that our approach achieves state-of-the-art hallucination detection performance among attention-based methods. Extensive ablation studies further highlight the robustness and generalisation of LapEigvals, paving the way for future advancements in the hallucination detection domain.

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Composable Cross-prompt Essay Scoring by Merging Models
Sanwoo Lee | Kun Liang | Yunfang Wu

Recent advances in cross-prompt automated essay scoring typically train models jointly on all available source domains, often requiring simultaneous access to unlabeled target domain samples. However, using all sources can lead to suboptimal transfer and high computational cost. Moreover, repeatedly accessing the source essays for continual adaptation raises privacy concerns. We propose a source-free adaptation approach that selectively merges the parameters of individually trained source models without further access to the source datasets. In particular, we mix the task vectors—the parameter updates from fine-tuning—via a weighted sum to efficiently simulate selective joint-training. We use Bayesian optimization to determine the mixing weights using our proposed Prior-encoded Information Maximization (PIM), an unsupervised objective which promotes score discriminability by leveraging useful priors pre-computed from the sources. Experimental results with LLMs on in-dataset and cross-dataset adaptation show that our method (1) consistently outperforms joint-training on all sources, (2) maintains superior robustness compared to other merging methods, (3) excels under severe distribution shifts where recent leading cross-prompt methods struggle, all while retaining computational efficiency.

We introduce HAMLET, a holistic and automated framework for evaluating the long-context comprehension of large language models (LLMs). HAMLET structures key information of source texts into a three-level hierarchy at root-, branch-, and leaf-levels, and employs query-focused summarization to evaluate how well models faithfully recall the key information at each level. To validate the reliability of our fully automated pipeline, we conduct a systematic human study, demonstrating that our automatic evaluation achieves over 90% agreement with expert human judgments, while reducing the evaluation cost by up to 25×. HAMLET reveals that LLMs struggle with fine-grained comprehension, especially at the leaf level, and are sensitive to positional effects like the lost-in-the-middle. Analytical queries pose greater challenges than narrative ones, and consistent performance gaps emerge between open-source and proprietary models, as well as across model scales. Our code and dataset are publicly available at https://github.com/DISL-Lab/HAMLET.

Large language models (LLMs) have demonstrated strong reasoning and tool-use capabilities, yet they often fail in real-world tool-interactions due to incorrect parameterization, poor tool selection, or misinterpretation of user intent. These issues often stem from an incomplete understanding of user goals and inadequate comprehension of tool documentation. While Chain-of-Thought (CoT) prompting has proven effective for enhancing reasoning in general contexts, our analysis reveals that free-form CoT is insufficient and sometimes counterproductive for structured function-calling tasks. To address this, we introduce a curriculum-inspired framework that leverages structured reasoning templates to guide LLMs through more deliberate step-by-step instructions for generating function callings. Experimental results show that our method reduces tool-use errors, achieving 3–12% relative improvements over strong baselines across diverse model series and approaches. Moreover, our framework enhances the robustness, interpretability, and transparency of tool-using agents, advancing the development of more reliable AI assistants for real-world applications.

We present a survey of methods for assessing and enhancing the quality of online discussions, focusing on the potential of Large Language Models (LLMs). While online discourses aim, at least in theory, to foster mutual understanding, they often devolve into harmful exchanges, such as hate speech, threatening social cohesion and democratic values. Recent advancements in LLMs enable artificial facilitation agents to not only moderate content, but also actively improve the quality of interactions. Our survey synthesizes ideas from Natural Language Processing (NLP) and Social Sciences to provide (a) a new taxonomy on discussion quality evaluation, (b) an overview of intervention and facilitation strategies, (c) along with a new taxonomy of conversation facilitation datasets, (d) an LLM-oriented roadmap of good practices and future research directions, from technological and societal perspectives.

Recently, Large Language Models (LLMs) have been widely adopted in a wide range of tasks, leading to increasing attention towards the research on how scaling LLMs affects their performance. Existing works, termed Scaling Laws, have discovered that the final test loss of LLMs scales as power-laws with model size, computational budget, and dataset size. However, the temporal change of the test loss of an LLM throughout its pretraining process remains unexplored, though it is valuable in many aspects, such as selecting better hyperparameters *directly* on the target LLM. In this paper, we propose the novel concept of Temporal Scaling Law, studying how the test loss of an LLM evolves as the training steps scale up. In contrast to modeling the test loss as a whole in a coarse-grained manner, we break it down and dive into the fine-grained test loss of each token position, and further develop a dynamic hyperbolic-law. Afterwards, we derive the much more precise temporal scaling law by studying the temporal patterns of the parameters in the dynamic hyperbolic-law. Results on both in-distribution (ID) and out-of-distribution (OOD) validation datasets demonstrate that our temporal scaling law accurately predicts the test loss of LLMs across training steps. Our temporal scaling law has broad practical applications. First, it enables direct and efficient hyperparameter selection on the target LLM, such as data mixture proportions. Secondly, viewing the LLM pretraining dynamics from the token position granularity provides some insights to enhance the understanding of LLM pretraining.

Recent progress in large language models (LLMs) has opened new possibilities for mental health support, yet current approaches lack realism in simulating specialized psychotherapy and fail to capture therapeutic progression over time. Narrative therapy, which helps individuals transform problematic life stories into empowering alternatives, remains underutilized due to limited access and social stigma. We address these limitations through a comprehensive framework with two core components. First, **INT** (Interactive Narrative Therapist) simulates expert narrative therapists by planning therapeutic stages, guiding reflection levels, and generating contextually appropriate responses through retrieval-augmentation. Second, **IMA** (Innovative Moment Assessment) provides a therapy-centric evaluation method that quantifies effectiveness by tracking “Innovative Moments” (IMs), critical narrative shifts in client speech signaling therapy progress. Experimental results on 260 simulated clients and 230 human participants reveal that **INT** consistently outperforms standard methods in therapeutic quality and depth. We further demonstrate the effectiveness of **INT** in synthesizing high-quality support conversations to facilitate social applications.

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From Word to World: Evaluate and Mitigate Culture Bias in LLMs via Word Association Test
Xunlian Dai | Li Zhou | Benyou Wang | Haizhou Li

The human-centered word association test (WAT) serves as a cognitive proxy, revealing sociocultural variations through culturally shared semantic expectations and implicit linguistic patterns shaped by lived experiences. We extend this test into an LLM-adaptive, free-relation task to assess the alignment of large language models (LLMs) with cross-cultural cognition. To address culture preference, we propose CultureSteer, an innovative approach that moves beyond superficial cultural prompting by embedding cultural-specific semantic associations directly within the model’s internal representation space. Experiments show that current LLMs exhibit significant bias toward Western (notably American) schemas at the word association level. In contrast, our model substantially improves cross-cultural alignment, capturing diverse semantic associations. Further validation on culture-sensitive downstream tasks confirms its efficacy in fostering cognitive alignment across cultures. This work contributes a novel methodological paradigm for enhancing cultural awareness in LLMs, advancing the development of more inclusive language technologies.

Retrieval-augmented generation (RAG) enhances the outputs of language models by integrating relevant information retrieved from external knowledge sources. However, when the retrieval process involves private data, RAG systems may face severe privacy risks, potentially leading to the leakage of sensitive information. To address this issue, we propose using synthetic data as a privacy-preserving alternative for the retrieval data. We propose SAGE, a novel two-stage synthetic data generation paradigm. In the stage-1, we employ an attribute-based extraction and generation approach to preserve key contextual information from the original data. In the stage-2, we further enhance the privacy properties of the synthetic data through an agent-based iterative refinement process. Extensive experiments demonstrate that using our synthetic data as the retrieval context achieves comparable performance to using the original data while substantially reducing privacy risks. Our work takes the first step towards investigating the possibility of generating high-utility and privacy-preserving synthetic data for RAG, opening up new opportunities for the safe application of RAG systems in various domains.

Despite extensive efforts in safety alignment, large language models (LLMs) remain vulnerable to jailbreak attacks. Activation steering offers a training-free defense method but relies on fixed steering coefficients, resulting in suboptimal protection and increased false rejections of benign inputs. To address this, we propose AdaSteer, an adaptive activation steering method that dynamically adjusts model behavior based on input characteristics. We identify two key properties: Rejection Law (R-Law), which shows that stronger steering is needed for jailbreak inputs opposing the rejection direction, and Harmfulness Law (H-Law), which differentiates adversarial and benign inputs. AdaSteer steers input representations along both the Rejection Direction (RD) and Harmfulness Direction (HD), with adaptive coefficients learned via logistic regression, ensuring robust jailbreak defense while preserving benign input handling. Experiments on LLaMA-3.1, Gemma-2, and Qwen2.5 show that AdaSteer outperforms baseline methods across multiple jailbreak attacks with minimal impact on utility. Our results highlight the potential of interpretable model internals for real-time, flexible safety enforcement in LLMs.

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Large Language Models Meet Knowledge Graphs for Question Answering: Synthesis and Opportunities
Chuangtao Ma | Yongrui Chen | Tianxing Wu | Arijit Khan | Haofen Wang

Large language models (LLMs) have demonstrated remarkable performance on question-answering (QA) tasks because of their superior capabilities in natural language understanding and generation. However, LLM-based QA struggles with complex QA tasks due to poor reasoning capacity, outdated knowledge, and hallucinations. Several recent works synthesize LLMs and knowledge graphs (KGs) for QA to address the above challenges. In this survey, we propose a new structured taxonomy that categorizes the methodology of synthesizing LLMs and KGs for QA according to the categories of QA and the KG’s role when integrating with LLMs. We systematically survey state-of-the-art methods in synthesizing LLMs and KGs for QA and compare and analyze these approaches in terms of strength, limitations, and KG requirements. We then align the approaches with QA and discuss how these approaches address the main challenges of different complex QA. Finally, we summarize the advancements, evaluation metrics, and benchmark datasets and highlight open challenges and opportunities.

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TFDP: Token-Efficient Disparity Audits for Autoregressive LLMs via Single-Token Masked Evaluation
Inderjeet Singh | Ramya Srinivasan | Roman Vainshtein | Hisashi Kojima

Auditing autoregressive Large Language Models (LLMs) for disparities is often impeded by high token costs and limited precision. We introduce Token-Focused Disparity Probing (TFDP), a novel methodology overcoming these challenges by adapting single-token masked prediction to autoregressive architectures via targeted token querying. Disparities between minimally contrastive sentence pairs are quantified through a multi-scale semantic alignment score that integrates sentence, local-context, and token embeddings with adaptive weighting. We propose three disparity metrics: Preference Score (\mathcal{PS}), Prediction Set Divergence (\mathcal{PSD}), and Weighted Final Score (\mathcal{WFS}), for comprehensive assessment. Evaluated on our customized Proverbs Disparity Dataset (PDD) with controlled attribute toggles (e.g., gender bias, misinformation susceptibility), TFDP precisely detects disparities while achieving up to 42 times fewer output tokens than minimal n-token continuations, offering a scalable tool for responsible LLM evaluation.

Culture is a rich and dynamic domain that evolves across both geography and time. However, existing studies on cultural understanding with vision-language models (VLMs) primarily emphasize geographic diversity, often overlooking the critical temporal dimensions. To bridge this gap, we introduce Hanfu-Bench, a novel, expert-curated multimodal dataset. Hanfu, a traditional garment spanning ancient Chinese dynasties, serves as a representative cultural heritage that reflects the profound temporal aspects of Chinese culture while remaining highly popular in Chinese contemporary society. Hanfu-Bench comprises two core tasks: cultural visual understanding and cultural image transcreation. The former task examines temporal-cultural feature recognition based on single- or multi-image inputs through multiple-choice visual question answering, while the latter focuses on transforming traditional attire into modern designs through cultural element inheritance and modern context adaptation. Our evaluation shows that closed VLMs perform comparably to non-experts on visual cutural understanding but fall short by 10% to human experts, while open VLMs lags further behind non-experts. For the transcreation task, multi-faceted human evaluation indicates that the best-performing model achieves a success rate of only 42%. Our benchmark provides an essential testbed, revealing significant challenges in this new direction of temporal cultural understanding and creative adaptation.

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MERMAID: Multi-perspective Self-reflective Agents with Generative Augmentation for Emotion Recognition
Zhongyu Yang | Junhao Song | Siyang Song | Wei Pang | Yingfang Yuan

Multimodal large language models (MLLMs) have demonstrated strong performance across diverse multimodal tasks, achieving promising outcomes. However, their application to emotion recognition in natural images remains underexplored. MLLMs struggle to handle ambiguous emotional expressions and implicit affective cues, whose capability is crucial for affective understanding but largely overlooked. To address these challenges, we propose MERMAID, a novel multi-agent framework that integrates a multi-perspective self-reflection module, an emotion-guided visual augmentation module, and a cross-modal verification module. These components enable agents to interact across modalities and reinforce subtle emotional semantics, thereby enhancing emotion recognition and supporting autonomous performance. Extensive experiments show that MERMAID outperforms existing methods, achieving absolute accuracy gains of 8.70%–27.90% across diverse benchmarks and exhibiting greater robustness in emotionally diverse scenarios.

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Personality Vector: Modulating Personality of Large Language Models by Model Merging
Seungjong Sun | Seo Yeon Baek | Jang Hyun Kim

Driven by the demand for personalized AI systems, there is growing interest in aligning the behavior of large language models (LLMs) with human traits such as personality. Previous attempts to induce personality in LLMs have shown promising results, but they struggle to capture the continuous and multidimensional nature of human traits. In this work, we propose a novel method for personality modulation in LLMs via model merging. Specifically, we construct personality vectors by subtracting the weights of a pre-trained model from those of the fine-tuned model on a given personality trait. By merging personality vectors, we enable LLMs to exhibit desired personality traits without additional training. Extensive experiments show that personality vectors enable continuous control over trait intensity and support the composition of multiple traits. Furthermore, personality vectors transfer across diverse downstream models, suggesting that they encode generalizable representations of personality.

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Beyond Outlining: Heterogeneous Recursive Planning for Adaptive Long-form Writing with Language Models
Ruibin Xiong | Yimeng Chen | Dmitrii Khizbullin | Mingchen Zhuge | Jürgen Schmidhuber

Long-form writing agents require flexible integration and interaction across information retrieval, reasoning, and composition. Current approaches rely on predefined workflows and rigid thinking patterns to generate outlines before writing, resulting in constrained adaptability during writing. In this paper we propose WriteHERE, a general agent framework that achieves human-like adaptive writing through recursive task decomposition and dynamic integration of three fundamental task types: retrieval, reasoning, and composition. Our methodology features: 1) a planning mechanism that interleaves recursive task decomposition and execution, eliminating artificial restrictions on writing workflow; and 2) integration of task types that facilitates heterogeneous task decomposition. Evaluations on both fiction writing and technical report generation show that our method consistently outperforms state-of-the-art approaches across all automatic evaluation metrics, demonstrating the effectiveness and broad applicability of our proposed framework. We have publicly released our code and prompts to facilitate further research.

Multimodal large language models (MLLMs) are increasingly deployed in open-ended, real-world environments where inputs are messy, underspecified, and not always trustworthy. Unlike curated benchmarks, these settings frequently involve instructions that reference missing objects or contradictory facts, rely on ambiguous cues, or request infeasible actions. In such cases, success hinges not merely on task execution, but on the model’s ability to detect when something is silently wrong. This paper presents a systematic analysis of how current MLLMs handle such underspecified and misspecified scenarios: cases where flaws must be inferred from context rather than explicitly stated. Using a curated diagnostic suite spanning four categories of real-world failure modes, we evaluate nine MLLMs, including o3 and GPT-4o, and find that models often fail to surface hidden issues, even when they possess the necessary perceptual and reasoning skills. Explicit prompting reveals that the underlying capabilities exist but are frequently suppressed in favor of user compliance.We further show that simple inference-time interventions, such as cautious persona prompting and, in particular, requiring a clarifying question, can substantially recover performance. Our findings highlight a persistent gap between reasoning competence and behavioral compliance in current MLLMs, and suggest practical strategies for making these systems more trustworthy in underconstrained environments.

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PrimeX: A Dataset of Worldview, Opinion, and Explanation
Rik Koncel-Kedziorski | Brihi Joshi | Tim Paek

As the adoption of language models advances, so does the need to better represent individual users to the model. Are there aspects of an individual’s belief system that a language model can utilize for improved alignment? Following prior research, we investigate this question in the domain of opinion prediction by developing PrimeX, a dataset of public opinion survey data from 858 US residents with two additional sources of belief information: written explanations from the respondents for why they hold specific opinions, and the Primal World Belief survey for assessing respondent worldview. We provide an extensive initial analysis of our data and show the value of belief explanations and worldview for personalizing language models. Our results demonstrate how the additional belief information in PrimeX can benefit both the NLP and psychological research communities, opening up avenues for further study.

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LASER: An LLM-based ASR Scoring and Evaluation Rubric
Amruta Parulekar | Preethi Jyothi

Standard ASR evaluation metrics like Word Error Rate (WER) tend to unfairly penalize morphological and syntactic nuances that do not significantly alter sentence semantics. We introduce an LLM-based scoring rubric LASER that leverages state-of-the-art LLMs’ in-context learning abilities to learn from prompts with detailed examples. Hindi LASER scores using Gemini 2.5 Pro achieved a very high correlation score of 94% with human annotations. Hindi examples in the prompt were also effective in analyzing errors in other Indian languages such as Marathi, Kannada and Malayalam. We also demonstrate how a smaller LLM like Llama 3 can be finetuned on word-pair examples derived from reference and ASR predictions to predict what kind of penalty should be applied with close to 89% accuracy.

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Improving Zero-shot Sentence Decontextualisation with Content Selection and Planning
Zhenyun Deng | Yulong Chen | Andreas Vlachos

Extracting individual sentences from a document as evidence or reasoning steps is commonly done in many NLP tasks. However, extracted sentences often lack context necessary to make them understood, e.g., coreference and background information. To this end, we propose a content selection and planning framework for zero-shot decontextualisation, which determines what content should be mentioned and in what order for a sentence to be understood out of context. Specifically, given a potentially ambiguous sentence and its context, we first segment it into basic semantically-independent units. We then identify potentially ambiguous units from the given sentence, and extract relevant units from the context based on their discourse relations. Finally, we generate a content plan to rewrite the sentence by enriching each ambiguous unit with its relevant units. Experimental results demonstrate that our approach is competitive for sentence decontextualisation, producing sentences that exhibit better semantic integrity and discourse coherence, outperforming existing methods.

Multimodal Retrieval-Augmented Generation (MRAG) systems enhance LMMs by integrating external multimodal databases, but introduce unexplored privacy vulnerabilities. While text-based RAG privacy risks have been studied, multimodal data presents unique challenges. We provide the first systematic analysis of MRAG privacy vulnerabilities across vision-language and speech-language modalities. Using a novel compositional structured prompt attack in a black-box setting, we demonstrate how attackers can extract private information by manipulating queries. Our experiments reveal that LMMs can both directly generate outputs resembling retrieved content and produce descriptions that indirectly expose sensitive information, highlighting the urgent need for robust privacy-preserving MRAG techniques.

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Code Execution as Grounded Supervision for LLM Reasoning
Dongwon Jung | Wenxuan Zhou | Muhao Chen

Training large language models (LLMs) with chain-of-thought (CoT) supervision has proven effective for enhancing their reasoning abilities. However, obtaining reliable and accurate reasoning supervision remains a significant challenge. We propose a scalable method for generating a high-quality CoT supervision dataset by leveraging the determinism of program execution. Unlike existing reasoning dataset generation methods that rely on costly human annotations or error-prone LLM-generated CoT, our approach extracts verifiable, step-by-step reasoning traces from code execution and transforms them into a natural language CoT reasoning. Experiments on reasoning benchmarks across various domains show that our method effectively equips LLMs with transferable reasoning abilities across diverse tasks. Furthermore, the ablation studies validate that our method produces highly accurate reasoning data and reduces overall token length during inference by reducing meaningless repetition and overthinking.

A Large Language Model (LLM) offers versatility across domains and tasks, purportedly benefiting users with a wide variety of behaviors and preferences. We question this perception about an LLM when users have inherently subjective behaviors and preferences, as seen in their ubiquitous and idiosyncratic browsing of websites or apps. The sequential behavior logs of pages, thus generated, form something akin to each user’s self-constructed “language”, albeit without the structure and grammar imbued in natural languages. We ask: (i) Can a small LM represent the “language of browsing” better than a large LM? (ii) Can an LM with a single set of parameters (or, single LM) adequately capture myriad users’ heterogeneous, subjective behaviors and preferences? (iii) Can a single LM with high average performance, yield low variance in performance to make alignment good at user level? We introduce clusterwise LM training, HeTLM (Heterogeneity aware Training of Language Model), appropriate for subjective behaviors. We find that (i) a small LM trained using a page-level tokenizer outperforms large pretrained or finetuned LMs; (ii) HeTLM with heterogeneous cluster specific set of parameters outperforms a single LM of the same family, controlling for the number of parameters; and (iii) a higher mean and a lower variance in generation ensues, implying improved alignment.

Multimodal Large Language Models (MLLMs) perform well on tasks such as visual question answering, but it remains unclear whether their reasoning relies more on memorized world knowledge or on the visual information present in the input image. To investigate this, we introduce Visual CounterFact, a new dataset of visually-realistic counterfactuals that put world knowledge priors (e.g, red strawberry) into direct conflict with visual input (e.g, blue strawberry). Using Visual CounterFact, we show that model predictions initially reflect memorized priors, but shift toward visual evidence in mid-to-late layers. This dynamic reveals a competition between the two modalities, with visual input ultimately overriding priors during evaluation. To control this behavior, we propose Pixels Versus Priors (PvP) steering vectors, a mechanism for controlling model outputs toward either world knowledge or visual input through activation-level interventions. On average, PvP successfully shifts 99.3% of color and 80.8% of size predictions from priors to counterfactuals. Together, these findings offer new tools for interpreting and controlling factual behavior in multimodal models.

Deploying large language models (LLMs) in real-world applications is often hindered by strict computational and latency constraints. While dynamic inference offers the flexibility to adjust model behavior based on varying resource budgets, existing methods are frequently limited by hardware inefficiencies or performance degradation. In this paper, we introduce Balcony, a simple yet highly effective framework for depth-based dynamic inference. By freezing the pretrained LLM and inserting additional transformer layers at selected exit points, Balcony maintains the full model’s performance while enabling real-time adaptation to different computational budgets. These additional layers are trained using a straightforward self-distillation loss, aligning the sub-model outputs with those of the full model. This approach requires significantly fewer training tokens and tunable parameters, drastically reducing computational costs compared to prior methods. When applied to the LLaMA3-8B model, using only 0.2% of the original pretraining data, Balcony achieves minimal performance degradation while enabling significant speedups. Remarkably, we show that Balcony outperforms state-of-the-art methods such as Flextron and Layerskip on multiple models at various scales, as well as other leading compression techniques across a variety of benchmarks.

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Social Genome: Grounded Social Reasoning Abilities of Multimodal Models
Leena Mathur | Marian Qian | Paul Pu Liang | Louis-Philippe Morency

Social reasoning abilities are crucial for AI systems to effectively interpret and respond to multimodal human communication and interaction within social contexts. We introduce Social Genome, the first benchmark for fine-grained, grounded social reasoning abilities of multimodal models. Social Genome contains 272 videos of interactions and 1,486 human-annotated reasoning traces related to inferences about these interactions. These traces contain 5,777 reasoning steps that reference evidence from visual cues, verbal cues, vocal cues, and external knowledge (contextual knowledge external to videos). Social Genome is also the first modeling challenge to study external knowledge in social reasoning. Social Genome computes metrics to holistically evaluate semantic and structural qualities of model-generated social reasoning traces. We demonstrate the utility of Social Genome through experiments with state-of-the-art models, identifying performance gaps and opportunities for future research to improve the grounded social reasoning abilities of multimodal models.

With the increasing capabilities of Large Language Models (LLMs), the proliferation of AI-generated texts has become a serious concern. Given the diverse range of organizations providing LLMs, it is crucial for governments and third-party entities to identify the origin LLM of a given AI-generated text to enable accurate mitigation of potential misuse and infringement. However, existing detection methods, primarily designed to distinguish between human-generated and LLM-generated texts, often fail to accurately identify the origin LLM due to the high similarity of AI-generated texts from different LLMs. In this paper, we propose a novel black-box AI-generated text origin detection method, dubbed Profiler, which accurately predicts the origin of an input text by extracting distinct context inference patterns through calculating and analyzing novel context losses between the surrogate model’s output logits and the adjacent input context. Extensive experimental results show that Profiler outperforms 10 state-of-the-art baselines, achieving more than a 25% increase in AUC score on average across both natural language and code datasets when evaluated against five of the latest commercial LLMs under both in-distribution and out-of-distribution settings.

With the rise of Speech Large Language Models (SpeechLLMs), two dominant approaches have emerged for speech processing: discrete tokens and continuous features. Each approach has demonstrated strong capabilities in audio-related processing tasks. However, the performance gap between these two paradigms has not been thoroughly explored. To address this gap, we present a fair comparison of self-supervised learning (SSL)-based discrete and continuous features under the same experimental settings. We evaluate their performance across six spoken language understanding-related tasks using both small and large-scale LLMs (Qwen1.5-0.5B and Llama3.1-8B). We further conduct in-depth analyses, including efficient comparison, SSL layer analysis, LLM layer analysis, and robustness comparison. Our findings reveal that continuous features generally outperform discrete tokens in various tasks. Each speech processing method exhibits distinct characteristics and patterns in how it learns and processes speech information. We hope our results will provide valuable insights to advance spoken language understanding in SpeechLLMs.

Robust evaluation is critical for deploying trustworthy retrieval-augmented generation (RAG) systems. However, current LLM-based evaluation frameworks predominantly rely on directly prompting resource-intensive models with complex multi-stage prompts, underutilizing models’ reasoning capabilities and introducing significant computational cost. In this paper, we present RAG-Zeval (RAG-Zero Evaluator), a novel end-to-end framework that formulates faithfulness and correctness evaluation of RAG systems as a rule-guided reasoning task. Our approach trains evaluators with reinforcement learning, facilitating compact models to generate comprehensive and sound assessments with detailed explanation in one-pass. We introduce a ranking-based outcome reward mechanism, using preference judgments rather than absolute scores, to address the challenge of obtaining precise pointwise reward signals. To this end, we synthesize the ranking references by generating quality-controlled responses with zero human annotation. Experiments demonstrate RAG-Zeval’s superior performance, achieving the strongest correlation with human judgments and outperforming baselines that rely on LLMs with 10-100× more parameters. Our approach also exhibits superior interpretability in response evaluation.

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Infini-gram mini: Exact n-gram Search at the Internet Scale with FM-Index
Hao Xu | Jiacheng Liu | Yejin Choi | Noah A. Smith | Hannaneh Hajishirzi

Language models are trained mainly on massive text data from the Internet, and it becomes increasingly important to understand this data source. Exact-match search engines enable searching in large text corpora – counting string appearances and retrieving the enclosing documents – yet the high storage overhead hinders their application on Internet-scale data. We present Infini-gram mini, an efficient and scalable system that can make petabyte-level text corpora searchable. Based on the FM-index data structure (Ferragina and Manzini, 2000), which simultaneously indexes and compresses text, our system creates indexes with size only 44% of the corpus. Infini-gram mini greatly improves upon the best existing implementation of FM-index in terms of indexing speed (18×) and memory use during both indexing (3.2× reduction) and querying (down to a negligible amount). We index 83TB of Internet text in 99 days with a single 128-core CPU node (or 19 hours if using 137 such nodes). We show one important use case of Infini-gram mini in a large-scale analysis of benchmark contamination. We find several core LM evaluation benchmarks to be heavily contaminated in Internet crawls (up to 74.2% in GSM8K), which could lead to overestimating the capabilities of language models if trained on such data. We host a benchmark contamination bulletin to share the contamination rate of many core and community-contributed benchmarks. We also release a web interface and an API endpoint to serve general search queries on Infini-gram mini indexes.

High lexical variation, ambiguous references, and long-range dependencies make entity resolution in literary texts particularly challenging. We present Mahānāma, the first large-scale dataset for end-to-end Entity Discovery and Linking (EDL) in Sanskrit, a morphologically rich and under-resourced language. Derived from the Mahābhārata , the world’s longest epic, the dataset comprises over 109K named entity mentions mapped to 5.5K unique entities, and is aligned with an English knowledge base to support cross-lingual linking. The complex narrative structure of Mahānāma, coupled with extensive name variation and ambiguity, poses significant challenges to resolution systems. Our evaluation reveals that current coreference and entity linking models struggle when evaluated on the global context of the test set. These results highlight the limitations of current approaches in resolving entities within such complex discourse. Mahānāma thus provides a unique benchmark for advancing entity resolution, especially in literary domains.

Language model evaluations often fail to characterize consequential failure modes, forcing experts to inspect outputs and build new benchmarks. We introduce task elicitation, a method that automatically builds new evaluations to profile model behavior. Task elicitation finds hundreds of natural-language tasks—an order of magnitude more than prior work—where frontier models exhibit systematic failures, in domains ranging from forecasting to online harassment. For example, we find that Sonnet 3.5 over-associates quantum computing and AGI and that o3-mini is prone to hallucination when fabrications are repeated in-context.

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Causal Interventions Reveal Shared Structure Across English Filler–Gap Constructions
Sasha Boguraev | Christopher Potts | Kyle Mahowald

Language Models (LMs) have emerged as powerful sources of evidence for linguists seeking to develop theories of syntax. In this paper, we argue that causal interpretability methods, applied to LMs, can greatly enhance the value of such evidence by helping us characterize the abstract mechanisms that LMs learn to use. Our empirical focus is a set of English filler–gap dependency constructions (e.g., questions, relative clauses). Linguistic theories largely agree that these constructions share many properties. Using experiments based in Distributed Interchange Interventions, we show that LMs converge on similar abstract analyses of these constructions. These analyses also reveal previously overlooked factors – relating to frequency, filler type, and surrounding context – that could motivate changes to standard linguistic theory. Overall, these results suggest that mechanistic, internal analyses of LMs can push linguistic theory forward.

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TactfulToM: Do LLMs have the Theory of Mind ability to understand White Lies?
Yiwei Liu | Emma Jane Pretty | Jiahao Huang | Saku Sugawara

While recent studies explore Large Language Models’ (LLMs) performance on Theory of Mind (ToM) reasoning tasks, research on ToM abilities that require more nuanced social context is limited, such as white lies. We introduce TactfulToM, a novel English benchmark designed to evaluate LLMs’ ability to understand white lies within real-life conversations and reason about prosocial motivations behind them, particularly when they are used to spare others’ feelings and maintain social harmony. Our benchmark is generated through a multi-stage human-in-the-loop pipeline where LLMs expand manually designed seed stories into conversations to maintain the information asymmetry between participants necessary for authentic white lies. We show that TactfulToM is challenging for state-of-the-art models, which perform substantially below humans, revealing shortcomings in their ability to fully comprehend the ToM reasoning that enables true understanding of white lies.

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Don’t Sweat the Small Stuff: Segment-Level Meta-Evaluation Based on Pairwise Difference Correlation
Colten DiIanni | Daniel Deutsch

This paper introduces Pairwise Difference Pearson (PDP), a novel segment-level meta-evaluation metric for Machine Translation (MT) that addresses limitations in previous Pearson’s 𝜌-based and Kendall’s 𝜏-based meta-evaluation approaches. PDP is a correlation-based metric that utilizes pairwise differences rather than raw scores. It draws on information from all segments for a more robust understanding of score distributions and uses only pairwise differences to refine Global Pearson to intra-segment comparisons. Analysis on the WMT’24 shared task shows PDP properly ranks sentinel evaluation metrics and better aligns with human error weightings than acc_eq.

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SMART: Simulated Students Aligned with Item Response Theory for Question Difficulty Prediction
Alexander Scarlatos | Nigel Fernandez | Christopher Ormerod | Susan Lottridge | Andrew Lan

Item (question) difficulties play a crucial role in educational assessments, enabling accurate and efficient assessment of student abilities and personalization to maximize learning outcomes. Traditionally, estimating item difficulties can be costly, requiring real students to respond to items, followed by fitting an item response theory (IRT) model to get difficulty estimates. This approach cannot be applied to the cold-start setting for previously unseen items either. In this work, we present SMART (Simulated Students Aligned with IRT), a novel method for aligning simulated students with instructed ability, which can then be used in simulations to predict the difficulty of open-ended items. We achieve this alignment using direct preference optimization (DPO), where we form preference pairs based on how likely responses are under a ground-truth IRT model. We perform a simulation by generating thousands of responses, evaluating them with a large language model (LLM)-based scoring model, and fit the resulting data to an IRT model to obtain item difficulty estimates. Through extensive experiments on two real-world student response datasets, we show that SMART outperforms other item difficulty prediction methods by leveraging its improved ability alignment.

Most resources for evaluating social biases in Large Language Models are developed without co-design from the communities affected by these biases, and rarely involve participatory approaches. We introduce HESEIA, a dataset of 46,499 sentences created in a professional development course. The course involved 370 high-school teachers and 5,370 students from 189 Latin-American schools. Unlike existing benchmarks, HESEIA captures intersectional biases across multiple demographic axes and school subjects. It reflects local contexts through the lived experience and pedagogical expertise of educators. Teachers used minimal pairs to create sentences that express stereotypes relevant to their school subjects and communities. We show the dataset diversity in term of demographic axes represented and also in terms of the knowledge areas included. We demonstrate that the dataset contains more stereotypes unrecognized by current LLMs than previous datasets. HESEIA is available to support bias assessments grounded in educational communities.

We present WebMMU, a multilingual benchmark that evaluates three core web tasks: (1) website visual question answering, (2) code editing involving HTML/CSS/JavaScript, and (3) mockup-to-code generation. Unlike prior benchmarks that treat these tasks separately, WebMMU unifies them using expert-annotated, real-world web data to assess models’ abilities in complex multi-step reasoning, precise element grounding, and functional UI comprehension and coding. Our evaluation shows that while multimodal large language models (MLLMs) perform well on basic information extraction, they struggle with reasoning and grounding, editing code to preserve functionality, and generating design-to-code that maintains hierarchy and supports multilingual content. These findings reveal key limitations in current MLLMs and underscore the need for improved multimodal and cross-lingual reasoning to build future web agents capable of automating diverse web development tasks.

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Analyzing values about gendered language reform in LLMs’ revisions
Jules Watson | Xi Wang | Raymond Liu | Suzanne Stevenson | Barend Beekhuizen

Within the common LLM use case of text revision, we study LLMs’ revision of gendered role nouns (e.g., outdoorsperson/woman/man) and their justifications of such revisions. We evaluate their alignment with feminist and trans-inclusive language reforms for English. Drawing on insight from sociolinguistics, we further assess if LLMs are sensitive to the same contextual effects in the application of such reforms as people are, finding broad evidence of such effects. We discuss implications for value alignment.

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ALLabel: Three-stage Active Learning for LLM-based Entity Recognition using Demonstration Retrieval
Zihan Chen | Lei Shi | Weize Wu | Qiji Zhou | Yue Zhang

Many contemporary data-driven research efforts in the natural sciences, such as chemistry and materials science, require large-scale, high-performance entity recognition from scientific datasets. Large language models (LLMs) have increasingly been adopted to solve the entity recognition task, with the same trend being observed on all-spectrum NLP tasks. The prevailing entity recognition LLMs rely on fine-tuned technology, yet the fine-tuning process often incurs significant cost. To achieve a best performance-cost trade-off, we propose ALLabel, a three-stage framework designed to select the most informative and representative samples in preparing the demonstrations for LLM modeling. The annotated examples are used to construct a ground-truth retrieval corpus for LLM in-context learning. By sequentially employing three distinct active learning strategies, ALLabel consistently outperforms all baselines under the same annotation budget across three specialized domain datasets. Experimental results also demonstrate that selectively annotating only 5%-10% of the dataset with ALLabel can achieve performance comparable to the method annotating the entire dataset. Further analyses and ablation studies verify the effectiveness and generalizability of our proposal.

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HyperKGR: Knowledge Graph Reasoning in Hyperbolic Space with Graph Neural Network Encoding Symbolic Path
Lihui Liu

Knowledge graphs (KGs) enable reasoning tasks such as link prediction, question answering, and knowledge discovery. However, real-world KGs are often incomplete, making link prediction both essential and challenging. Existing methods, including embedding-based and path-based approaches, rely on Euclidean embeddings, which struggle to capture hierarchical structures. GNN-based methods aggregate information through message passing in Euclidean space, but they struggle to effectively encode the recursive tree-like structures that emerge in multi-hop reasoning. To address these challenges, we propose a hyperbolic GNN framework that embeds recursive learning trees in hyperbolic space and generates query-specific embeddings. By incorporating hierarchical message passing, our method naturally aligns with reasoning paths and dynamically adapts to queries, improving prediction accuracy. Unlike static embedding-based approaches, our model computes context-aware embeddings tailored to each query. Experiments on multiple benchmark datasets show that our approach consistently outperforms state-of-the-art methods, demonstrating its effectiveness in KG reasoning.

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LLaMP: Large Language Model Made Powerful for High-fidelity Materials Knowledge Retrieval
Yuan Chiang | Elvis Hsieh | Chia-Hong Chou | Janosh Riebesell

Materials science research requires multi-step reasoning and precise material informatics retrieval, where minor errors can propagate into significant failures in downstream experiments. Despite their general success, Large Language Models (LLMs) often struggle with hallucinations, handling domain-specific data effectively (e.g., crystal structures), and integrating experimental workflows. To address these challenges, we introduce LLaMP, a hierarchical multi-agent framework designed to emulate the materials science research workflow. The high-level supervisor agent decomposes user requests into sub-tasks and coordinates with specialized assistant agents. These assistant agents handle domain-specific tasks, such as retrieving and processing data from the Materials Project (MP) or conducting simulations as needed. This pipeline facilitates iterative refinement of material property retrieval and enables the simulation of real-world research workflows. To ensure reliability, we propose a novel metric combining uncertainty and confidence estimate to evaluate the self-consistency of responses from LLaMP and baseline methods. Our experiments demonstrate LLaMP’s superior performance in material property retrieval, crystal structure editing, and annealing molecular dynamics simulations using pre-trained interatomic potentials. Unlike prior work focused solely on material property prediction or discovery, LLaMP serves as a foundation for autonomous materials research by combining grounded informatics and enabling iterative experimental processes. Code and live demo are available at https://github.com/chiang-yuan/llamp.

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ReSeeding Latent States for Sequential Language Understanding
Stéphane Aroca-Ouellette | Katharina von der Wense | Alessandro Roncone

We introduce Refeeding State Embeddings aligned using Environmental Data (ReSEED), a novel method for grounding language in environmental data. While large language models (LLMs) excel at many tasks, they continue to struggle with multi-step sequential reasoning. ReSEED addresses this by producing latent embeddings aligned with the true state of the environment and refeeding these embeddings into the model before generating its output. To evaluate its effectiveness, we develop three new sequential reasoning benchmarks, each with a training set of paired state-text trajectories and several text-only evaluation sets that test generalization to longer, unseen trajectories. Across all benchmarks, ReSEED significantly improves generalization and scalability over a text-only baseline. We further show that ReSEED outperforms commercial LLMs on our benchmarks, highlighting the value of grounding language in the environment.

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DPED: Multi-Layer Noise Distillation for Privacy-Preserving Text Embeddings
Shuya Feng | Yuan Hong

Training text embedding models under differential privacy constraints is challenging due to the high dimensionality of language data and the presence of rare, identifying linguistic features. We propose (Differentially Private Embedding Distillation), a framework that leverages teacher-student distillation with multi-layer noise injection to learn high-quality embeddings while providing differential privacy guarantees. DPED trains an ensemble of teacher models on disjoint subsets of sensitive text data, then transfers their knowledge to a student model through noisy aggregation at multiple layers. A rare-word-aware strategy adaptively handles infrequent words, improving privacy-utility trade-offs. Experiments on benchmark datasets demonstrate that DPED outperforms standard differentially private training methods, achieving substantially higher utility at the same privacy budget. Our approach protects individual word usage patterns in training documents, preventing models from memorizing unique linguistic fingerprints while maintaining practical utility for downstream NLP tasks. Source code is available at https://github.com/datasec-lab/DPED.

Establishing shared goals is a fundamental step in human-AI communication. However, ambiguities can lead to outputs that seem correct but fail to reflect the speaker’s intent. In this paper, we explore this issue with a focus on the data visualization domain, where ambiguities in natural language impact the generation of code that visualizes data. The availability of multiple views on the contextual (e.g. the intended plot and the code rendering the plot) allows for a unique and comprehensive analysis of diverse ambiguity types. We develop a taxonomy of types of ambiguity that arise in this task and propose metrics to quantify them. Using Matplotlib problems from the DS-1000 dataset, we demonstrate that our ambiguity metrics better correlate with human annotations than uncertainty baselines. Our work also explores how multi-turn dialogue can reduce ambiguity, and therefore, improve code accuracy by better matching user goals. We evaluate three pragmatic models to inform our dialogue strategies: Gricean Cooperativity, Discourse Representation Theory, and Questions under Discussion. A simulated user study reveals how pragmatic dialogues reduce ambiguity and enhance code accuracy, highlighting the value of multi-turn exchanges in code generation.

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Morpheme Induction for Emergent Language
Brendon Boldt | David R. Mortensen

We introduce CSAR, an algorithm for inducing morphemes from emergent language corpora of parallel utterances and meanings.It is a greedy algorithm that (1) weights morphemes based on mutual information between forms and meanings, (2) selects the highest-weighted pair, (3) removes it from the corpus, and (4) repeats the process to induce further morphemes (i.e., Count, Select, Ablate, Repeat).The effectiveness of CSAR is first validated on procedurally generated datasets and compared against baselines for related tasks.Second, we validate CSAR’s performance on human language data to show that the algorithm makes reasonable predictions in adjacent domains.Finally, we analyze a handful of emergent languages, quantifying linguistic characteristics like degree of synonymy and polysemy.

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Stepwise Informativeness Search for Improving LLM Reasoning
Siyuan Wang | Enda Zhao | Xiang Ren

Advances in Large Language Models (LLMs) have improved multi-step reasoning by generating free-text rationales, but these models tend to lose focus over the middle of long contexts. This raises concerns that as reasoning progresses, LLMs may overlook information in earlier steps when decoding subsequent steps, leading to unreliable and redundant rationales. To address this, we propose guiding LLMs to generate more accurate and concise rationales by (1) proactively referencing information from underutilized prior steps, and (2) minimizing redundant information between new and existing steps. We introduce stepwise informativeness search, an inference-time tree search framework incorporating two selection heuristics: grounding-guided selection which prioritizes steps paying higher attention over underutilized steps; and novelty-guided selection which encourages steps with novel conclusions. We further utilize a self-grounding strategy that prompts LLMs to explicitly reference relevant prior steps as premises before deduction at each step, mitigating distraction from irrelevant content. Experiments on five reasoning datasets across five LLMs show the effectiveness and efficiency of our approach to improve reasoning with reduced errors and redundancy.

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Social Good or Scientific Curiosity? Uncovering the Research Framing Behind NLP Artefacts
Eric Chamoun | Nedjma Ousidhoum | Michael Sejr Schlichtkrull | Andreas Vlachos

Clarifying the research framing of NLP artefacts (e.g., models, datasets, etc.) is crucial to aligning research with practical applications when researchers claim that their findings have real-world impact. Recent studies manually analyzed NLP research across domains, showing that few papers explicitly identify key stakeholders, intended uses, or appropriate contexts. In this work, we propose to automate this analysis, developing a three-component system that infers research framings by first extracting key elements (means, ends, stakeholders), then linking them through interpretable rules and contextual reasoning.We evaluate our approach on two domains: automated fact-checking using an existing dataset, and hate speech detection for which we annotate a new dataset—achieving consistent improvements over strong LLM baselines.Finally, we apply our system to recent automated fact-checking papers and uncover three notable trends: a rise in underspecified research goals, increased emphasis on scientific exploration over application, and a shift toward supporting human fact-checkers rather than pursuing full automation.

Text-to-image diffusion models often exhibit biases toward specific demographic groups, such as generating more males than females when prompted to generate images of engineers, raising ethical concerns and limiting their adoption. In this paper, we tackle the challenge of mitigating generation bias towards any target attribute value (e.g., “male” for “gender”) in diffusion models while preserving generation quality. We propose FairGen, an adaptive latent guidance mechanism which controls the generation distribution during inference. In FairGen, a latent guidance module dynamically adjusts the diffusion process to enforce specific attributes, while a memory module tracks the generation statistics and steers latent guidance to align with the targeted fair distribution of the attribute values. Further, given the limitations of existing datasets in comprehensively assessing bias in diffusion models, we introduce a holistic bias evaluation benchmark HBE, covering diverse domains and incorporating complex prompts across various applications. Extensive evaluations on HBE and Stable Bias datasets demonstrate that FairGen outperforms existing bias mitigation approaches, achieving substantial bias reduction (e.g., 68.5% gender bias reduction on Stable Diffusion 2). Ablation studies highlight FairGen’s ability to flexibly and precisely control generation distribution at any user-specified granularity, ensuring adaptive and targeted bias mitigation.

Real-world decision-making often begins with identifying which modality contains the most relevant information for a given query. While recent multimodal models have made impressive progress in processing diverse inputs, it remains unclear whether they can reason contrastively across multiple modalities to select the one that best satisfies a natural language prompt. We argue this capability is foundational, especially in retrieval-augmented and decision-time contexts, where systems must evaluate multiple signals and identify which one conveys the relevant information. To evaluate this skill, we introduce Contra4, a dataset for contrastive cross-modal reasoning across four modalities: image, audio, video, and 3D. Each example presents a natural language question alongside multiple candidate modality instances, and the model must select the one that semantically aligns with the prompt. Contra4 combines human-annotated captions with a mixture-of-models round-trip-consistency filter to ensure high-quality supervision, resulting in 174k training examples and a manually verified test set of 2.3k samples. While task-specific fine-tuning improves performance by 56% relative to baseline, state-of-the-art models still achieve only 56% accuracy overall and 42% in four-modality settings, underscoring a significant limitation in current multimodal models.

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Proactive Hearing Assistants that Isolate Egocentric Conversations
Guilin Hu | Malek Itani | Tuochao Chen | Shyamnath Gollakota

We introduce proactive hearing assistants that automatically identify and separate the wearer’s conversation partners, without requiring explicit prompts. Our system operates on egocentric binaural audio and uses the wearer’s self-speech as an anchor, leveraging turn-taking behavior and dialogue dynamics to infer conversational partners and suppress others. To enable real-time, on-device operation, we propose a dual-model architecture: a lightweight streaming model runs every 12.5 ms for low-latency extraction of the conversation partners, while a slower model runs less frequently to capture longer-range conversational dynamics. Results on real-world 2- and 3-speaker conversation test sets, collected with binaural egocentric hardware from 11 participants totaling 6.8 hours, show generalization in identifying and isolating conversational partners in multi-conversation settings. Our work marks a step toward hearing assistants that adapt proactively to conversational dynamics and engagement.

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fLSA: Learning Semantic Structures in Document Collections Using Foundation Models
Weijia Xu | Nebojsa Jojic | Nicolas Le Roux

Humans can learn to solve new tasks by inducing high-level strategies from example solutions to similar problems and then adapting these strategies to solve unseen problems. Can we use large language models to induce such high-level structure from example documents or solutions? We introduce fLSA, a foundation-model-based Latent Semantic Analysis method that iteratively clusters and tags document segments based on document-level contexts. These tags can be used to model the latent structure of given documents and for hierarchical sampling of new texts. Our experiments on story writing, math, and multi-step reasoning datasets demonstrate that fLSA tags are more informative in reconstructing the original texts than existing tagging methods. Moreover, when used for hierarchical sampling, fLSA tags help expand the output space in the right directions that lead to correct solutions more often than direct sampling and hierarchical sampling with existing tagging methods.

Large Reasoning Models (LRMs) introduce a new generation paradigm of explicitly reasoning before answering, leading to remarkable improvements in complex tasks. However, they pose great safety risks against harmful queries and adversarial attacks. While recent mainstream safety efforts on LRMs, supervised fine-tuning (SFT), improve safety performance, we find that SFT-aligned models struggle to generalize to unseen jailbreak prompts. After thorough investigation, we identify a safety aha moment that can activate safety reasoning and lead to a safe response. This aha moment typically appears in the ‘key sentence’ that follows models’ query understanding process and can indicate whether the model will proceed safely. Based on these insights, we propose SafeKey, including two complementary objectives to better activate the safety aha-moment in the key sentence: (1) a Dual-Path Safety Head to enhance the safety signal in the model’s internal representations before the key sentence, and (2) a Query-Mask Modeling objective to improve the models’ attention on its query understanding, which has important safety hints. Experiments across multiple safety benchmarks demonstrate that our methods significantly improve safety generalization to a wide range of jailbreak attacks and out-of-distribution harmful prompts, lowering the harmfulness rate by 9.6%, while maintaining general abilities. Our analysis reveals how SafeKey enhances safety by reshaping internal attention and improving the quality of hidden representations.

Large Language models have demonstrated promising performance in research ideation across scientific domains. Hypothesis development, the process of generating a highly specific declarative statement connecting a research idea with empirical validation, has received relatively less attention. Existing approaches trivially deploy retrieval augmentation and focus only on the quality of the final output ignoring the underlying reasoning process behind ideation. We present HypER (Hypothesis Generation with Explanation and Reasoning), a small language model (SLM) trained for literature-guided reasoning and evidence-based hypothesis generation. HypER is trained in a multi-task setting to discriminate between valid and invalid scientific reasoning chains in presence of controlled distractions. We find that HypER outperformes the base model, distinguishing valid from invalid reasoning chains (+22% average absolute F1), generates better evidence-grounded hypotheses (0.327 vs. 0.305 base model) with high feasibility and impact as judged by human experts (>3.5 on 5-point Likert scale).

In recent years, large language models (LLMs) have revolutionized the field of natural language processing. However, they often suffer from knowledge gaps and hallucinations. Graph retrieval-augmented generation (GraphRAG) enhances LLM reasoning by integrating structured knowledge from external graphs. However, we identify two key challenges that plague GraphRAG: (1) Retrieving noisy and irrelevant information can degrade performance and (2) Excessive reliance on external knowledge suppresses the model’s intrinsic reasoning.To address these issues, we propose GraphRAG-FI (Filtering & Integration), consisting of GraphRAG-Filtering and GraphRAG-Integration. GraphRAG-Filtering employs a two-stage filtering mechanism to refine retrieved information. GraphRAG-Integration employs a logits-based selection strategy to balance external knowledge from GraphRAG with the LLM’s intrinsic reasoning, reducing over-reliance on retrievals. Experiments on knowledge graph QA tasks demonstrate that GraphRAG-FI significantly improves reasoning performance across multiple backbone models, establishing a more reliable and effective GraphRAG framework.

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Interpretable Mnemonic Generation for Kanji Learning via Expectation-Maximization
Jaewook Lee | Alexander Scarlatos | Andrew Lan

Learning Japanese vocabulary is a challenge for learners from Roman alphabet backgrounds due to script differences. Japanese combines syllabaries like hiragana with kanji, which are logographic characters of Chinese origin. Kanji are also complicated due to their complexity and volume. Keyword mnemonics are a common strategy to aid memorization, often using the compositional structure of kanji to form vivid associations. Despite recent efforts to use large language models (LLMs) to assist learners, existing methods for LLM-based keyword mnemonic generation function as a black box, offering limited interpretability. We propose a generative framework that explicitly models the mnemonic construction process as driven by a set of common rules, and learn them using a novel Expectation-Maximization-type algorithm. Trained on learner-authored mnemonics from an online platform, our method learns latent structures and compositional rules, enabling interpretable and systematic mnemonics generation. Experiments show that our method performs well in the cold-start setting for new learners while providing insight into the mechanisms behind effective mnemonic creation.

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Refining Attention for Explainable and Noise-Robust Fact-Checking with Transformers
Jean-Flavien Bussotti | Paolo Papotti

In tasks like question answering and fact-checking, models must discern relevant information from extensive corpora in an “open-book” setting. Conventional transformer-based models excel at classifying input data, but (i) often falter due to sensitivity to noise and (ii) lack explainability regarding their decision process. To address these challenges, we introduce ATTUN, a novel transformer architecture designed to enhance model transparency and resilience to noise by refining the attention mechanisms. Our approach involves a dedicated module that directly modifies attention weights, allowing the model to both improve predictions and identify the most relevant sections of input data. We validate our methodology using fact-checking datasets and show promising results in question answering. Experiments demonstrate improvements of up to 51% in F1 score for detecting relevant context, and gains of up to 18% in task accuracy when integrating ATTUN into a model.

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Harmful Prompt Laundering: Jailbreaking LLMs with Abductive Styles and Symbolic Encoding
Seongho Joo | Hyukhun Koh | Kyomin Jung

Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks, but their potential misuse for harmful purposes remains a significant concern. To strengthen defenses against such vulnerabilities, it is essential to investigate universal jailbreak attacks that exploit intrinsic weaknesses in the architecture and learning paradigms of LLMs. In response, we propose Harmful Prompt Laundering (HaPLa), a novel and broadly applicable jailbreaking technique that requires only black-box access to target models. HaPLa incorporates two primary strategies: 1) abductive framing, which instructs LLMs to infer plausible intermediate steps toward harmful activities, rather than directly responding to explicit harmful queries; and 2) symbolic encoding, a lightweight and flexible approach designed to obfuscate harmful content, given that current LLMs remain sensitive primarily to explicit harmful keywords. Experimental results show that HaPLa achieves over 95% attack success rate on GPT-series models and 70% across all targets. Further analysis with diverse symbolic encoding rules also reveals a fundamental challenge: it remains difficult to safely tune LLMs without significantly diminishing their helpfulness in responding to benign queries.

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Pathway to Relevance: How Cross-Encoders Implement a Semantic Variant of BM25
Meng Lu | Catherine Chen | Carsten Eickhoff

Mechanistic interpretation has greatly contributed to a more detailed understanding of generative language models, enabling significant progress in identifying structures that implement key behaviors through interactions between internal components. In contrast, interpretability in information retrieval (IR) remains relatively coarse-grained, and much is still unknown as to how IR models determine whether a document is relevant to a query. In this work, we address this gap by mechanistically analyzing how one commonly used model, a cross-encoder, estimates relevance. We find that the model extracts traditional relevance signals, such as term frequency and inverse document frequency, in early-to-middle layers. These concepts are then combined in later layers, similar to the well-known probabilistic ranking function, BM25. Overall, our analysis offers a more nuanced understanding of how IR models compute relevance. Isolating these components lays the groundwork for future interventions that could enhance transparency, mitigate safety risks, and improve scalability.

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Rewarding the Unlikely: Lifting GRPO Beyond Distribution Sharpening
Andre Wang He | Daniel Fried | Sean Welleck

Reinforcement learning is emerging as a primary driver for improving language model reasoning capabilities. A fundamental question is whether current reinforcement learning algorithms—such as Group Relative Policy Optimization (GRPO), the de facto standard algorithm used to improve language model reasoning—merely sharpen the base model’s distribution around problems it can already solve. We investigate this question in the context of formal theorem proving, which has access to a perfect verifier. We identify a degenerate rank bias in GRPO in which highly probable trajectories are reinforced and rare ones are neglected. This results in distribution sharpening: the model can solve some problems with fewer samples, but underperforms simply sampling more solutions from the original model. To overcome GRPO’s rank bias we introduce unlikeliness reward, a simple method for explicitly up-weighting rare but correct solutions. We show that unlikeliness reward mitigates rank bias and improves pass@N across a large range of N in both synthetic and real theorem proving settings. We also uncover an unexpected link between rank bias and a seemingly mundane hyperparameter—the number of updates per batch—that leads to a second, complementary mitigation. We combine our insights into a revised GRPO training recipe for formal theorem proving, yielding an open pipeline that achieves competitive performance to DeepSeek-Prover-V1.5-RL on the miniF2F-test benchmark.

Vocabulary acquisition poses a significant challenge for second-language (L2) learners, especially when learning typologically distant languages such as English and Korean, where phonological and structural mismatches complicate vocabulary learning. Recently, large language models (LLMs) have been used to generate keyword mnemonics by leveraging similar keywords from a learner’s first language (L1) to aid in acquiring L2 vocabulary. However, most methods still rely on direct IPA-based phonetic matching or employ LLMs without phonological guidance. In this paper, we present PhoniTale, a novel cross-lingual mnemonic generation system that performs IPA-based phonological adaptation and syllable-aware alignment to retrieve L1 keyword sequence and uses LLMs to generate verbal cues. We evaluate PhoniTale through automated metrics and a short-term recall test with human participants, comparing its output to human-written and prior automated mnemonics. Our findings show that PhoniTale consistently outperforms previous automated approaches and achieves quality comparable to human-written mnemonics.

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Amulet: Putting Complex Multi-Turn Conversations on the Stand with LLM Juries
Sahana Ramnath | Anurag Mudgil | Brihi Joshi | Skyler Hallinan | Xiang Ren

Today, large language models are widely used as judges to evaluate responses from other language models. Hence, it is imperative to benchmark and improve these LLM-judges on real-world language model usage: a typical human-assistant conversation is lengthy, and shows significant diversity in topics, intents, and requirements across turns, e.g. social interactions, task requests, feedback. We present Amulet, a framework that leverages pertinent linguistic concepts of dialog-acts and maxims to improve the accuracy of LLM-judges on preference data with complex, multi-turn conversational context. Amulet presents valuable insights about (a) the communicative structures and intents present in the conversation (dialog acts), and (b) the satisfaction of conversational principles (maxims) by the preference pair responses, and uses them to make judgments. On 4 challenging datasets, Amulet shows that (a) humans frequently (60-70% of the time) change their intents from one turn of the conversation to the next, and (b) in ∼75% of instances, the preference responses can be differentiated via dialog acts and/or maxims, reiterating the latter’s significance in judging such data. Amulet can be used either as a judge by applying the framework to a single LLM, or integrated into a jury with different LLM judges; our judges and juries show strong improvements on relevant baselines for all 4 datasets.

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Exploring Chain-of-Thought Reasoning for Steerable Pluralistic Alignment
Yunfan Zhang | Kathleen McKeown | Smaranda Muresan

Large Language Models (LLMs) are typically trained to reflect a relatively uniform set of values, which limits their applicability to tasks that require understanding of nuanced human perspectives. Recent research has underscored the importance of enabling LLMs to support steerable pluralism — the capacity to adopt a specific perspective and align generated outputs with it. In this work, we investigate whether Chain-of-Thought (CoT) reasoning techniques can be applied to building steerable pluralistic models. We explore several methods, including CoT prompting, fine-tuning on human-authored CoT, fine-tuning on synthetic explanations, and Reinforcement Learning with Verifiable Rewards (RLVR). We evaluate these approaches using the Value Kaleidoscope and OpinionQA datasets. Among the methods studied, RLVR consistently outperforms others and demonstrates strong training sample efficiency. We further analyze the generated CoT traces with respect to faithfulness and safety.

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CMedCalc-Bench: A Fine-Grained Benchmark for Chinese Medical Calculations in LLM
Yunyan Zhang | Zhihong Zhu | Xian Wu

Large Language Models (LLMs) have demonstrated significant potential in medical diagnostics and clinical decision-making. While benchmarks such as MedQA and PubMedQA have advanced the evaluation of qualitative reasoning, existing medical NLP benchmarks still face two limitations: the absence of a Chinese benchmark for medical calculation tasks, and the lack of fine-grained evaluation of intermediate reasoning. In this paper, we introduce CMedCalc-Bench, a new benchmark designed for Chinese medical calculation. CMedCalc-Bench covers 69 calculators across 12 clinical departments, featuring over 1,000 real-world patient cases. Building on this, we design a fine-grained evaluation framework that disentangles clinical entity extraction from numerical computation, enabling systematic diagnosis of model deficiencies. Experiments across four model families, including medical-specialized and reasoning-focused, provide an assessment of their strengths and limitations on Chinese medical calculation. Furthermore, explorations on faithful reasoning and the demonstration effect offer early insights into advancing safe and reliable clinical computation.

Large Audio-Language Models (LALMs) are increasingly deployed in real-world applications, yet their robustness against malicious audio injection remains underexplored. To address this gap, this study systematically evaluates five leading LALMs across four attack scenarios: Audio Interference Attack, Instruction Following Attack, Context Injection Attack, and Judgment Hijacking Attack. We quantitatively assess their vulnerabilities and resilience using metrics: the Defense Success Rate, Context Robustness Score, and Judgment Robustness Index. The experiments reveal significant performance disparities, with no single model demonstrating consistent robustness across all attack types. Attack effectiveness is significantly influenced by the position of the malicious content, particularly when injected at the beginning of a sequence. Furthermore, our analysis uncovers a negative correlation between a model’s instruction-following capability and its robustness: models that strictly adhere to instructions tend to be more susceptible, whereas safety-aligned models exhibit greater resistance. To facilitate future research, this work introduces a comprehensive benchmark framework. Our findings underscore the critical need for integrating robustness into training pipelines and developing multi-modal defenses, ultimately facilitating the secure deployment of LALMs. The dataset used in this work is available on Hugging Face.

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How Far Can LLMs Improve from Experience? Measuring Test-Time Learning Ability in LLMs with Human Comparison
Jiayin Wang | Zhiqiang Guo | Weizhi Ma | Min Zhang

As evaluation designs of large language models may shape our trajectory toward artificial general intelligence, comprehensive and forward-looking assessment is essential. Existing benchmarks primarily assess static knowledge, while intelligence also entails the ability to rapidly learn from experience. To this end, we advocate for the evaluation of Test-time Learning, the capacity to improve performance in experience-based, reasoning-intensive tasks during test time. In this work, we propose semantic games as effective testbeds for evaluating test-time learning, due to their resistance to saturation and inherent demand for strategic reasoning. We introduce an objective evaluation framework that compares model performance under both limited and cumulative experience settings, and contains four forms of experience representation. To provide a comparative baseline, we recruit eight human participants to complete the same task. Results show that LLMs exhibit measurable test-time learning capabilities; however, their improvements are less stable under cumulative experience and progress more slowly than those observed in humans. These findings underscore the potential of LLMs as general-purpose learning machines, while also revealing a substantial intellectual gap between models and humans, irrespective of how well LLMs perform on static benchmarks. The code and data are available.

Large Language Models (LLMs) are increasingly used for decision making in embodied agents, yet existing safety evaluations often rely on coarse success rates and domain-specific setups, making it difficult to diagnose why and where these models fail. This obscures our understanding of embodied safety and limits the selective deployment of LLMs in high-risk physical environments. We introduce SAFEL, the framework for systematically evaluating the physical safety of LLMs in embodied decision making. SAFEL assesses two key competencies: (1) rejecting unsafe commands via the Command Refusal Test, and (2) generating safe and executable plans via the Plan Safety Test. Critically, the latter is decomposed into functional modules, goal interpretation, transition modeling, action sequencing enabling fine-grained diagnosis of safety failures. To support this framework, we introduce EMBODYGUARD, a PDDL-grounded benchmark containing 942 LLM-generated scenarios covering both overtly malicious and contextually hazardous instructions. Evaluation across 13 state-of-the-art LLMs reveals that while models often reject clearly unsafe commands, they struggle to anticipate and mitigate subtle, situational risks. Our results highlight critical limitations in current LLMs and provide a foundation for more targeted, modular improvements in safe embodied reasoning.

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SwiftKV: Fast Prefill-Optimized Inference with Knowledge-Preserving Model Transformation
Aurick Qiao | Zhewei Yao | Samyam Rajbhandari | Yuxiong He

LLM inference for enterprise applications, such as summarization, RAG, and code-generation, typically observe much longer prompt than generations, leading to high prefill cost and response latency. We present SwiftKV, a novel model transformation and distillation procedure targeted at reducing the prefill compute (in FLOPs) of prompt tokens while preserving high generation quality. First, SwiftKV prefills later layers’ KV cache using an earlier layer’s output, allowing prompt tokens to skip those later layers. Second, SwiftKV employs a lightweight knowledge-preserving distillation procedure that can adapt existing LLMs with minimal accuracy impact. Third, SwiftKV can naturally incorporate KV cache compression to improve inference performance in low-memory scenarios. Our comprehensive experiments show that SwiftKV can effectively reduce prefill computation by 25-50% across several LLM families while incurring minimum quality degradation. In the end-to-end inference serving, SwiftKV realizes up to 2x higher aggregate throughput and 60% lower time per output token. It can achieve a staggering 560 TFlops/GPU of normalized inference throughput, which translates to 16K tokens/s for Llama-3.1-70B. SwiftKV is open-sourced at https://github.com/snowflakedb/arctictraining and https://github.com/snowflakedb/arcticinference.

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Co-Eval: Augmenting LLM-based Evaluation with Machine Metrics
Ling-I Wu | Weijie Wu | Minyu Chen | Jianxin Xue | Guoqiang Li

Large language models (LLMs) are increasingly used as evaluators in natural language generation tasks, offering advantages in scalability and interpretability over traditional evaluation methods. However, existing LLM-based evaluations often suffer from biases and misalignment, particularly in domain-specific tasks, due to limited functional understanding and knowledge gaps. To address these challenges, we first investigate the relationship between an LLM-based evaluator’s familiarity with the target task and its evaluation performance. We then introduce the Co-Eval framework, which leverages a criteria planner model and optimized machine metrics to enhance the scalability and fairness of LLM-based evaluation. Experimental results on both general and domain-specific tasks demonstrate that Co-Eval reduces biases, achieving up to a 0.4903 reduction in self-preference bias, and improves alignment with human preferences, with gains of up to 0.324 in Spearman correlation.

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Sali4Vid: Saliency-Aware Video Reweighting and Adaptive Caption Retrieval for Dense Video Captioning
MinJu Jeon | Si-Woo Kim | Ye-Chan Kim | HyunGee Kim | Dong-Jin Kim

Dense video captioning aims to temporally localize events in video and generate captions for each event. While recent works propose end-to-end models, they suffer from two limitations: (1) applying timestamp supervision only to text while treating all video frames equally, and (2) retrieving captions from fixed-size video chunks, overlooking scene transitions. To address these, we propose **Sali4Vid**, a simple yet effective saliency-aware framework. We introduce Saliency-aware Video Reweighting, which converts timestamp annotations into sigmoid-based frame importance weights, and Semantic-based Adaptive Caption Retrieval, which segments videos by frame similarity to capture scene transitions and improve caption retrieval. Sali4Vid achieves state-of-the-art results on YouCook2 and ViTT, demonstrating the benefit of jointly improving video weighting and retrieval for dense video captioning.

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Semantic Networks Extracted from Students’ Think-Aloud Data are Correlated with Students’ Learning Performance
Pingjing Yang | Sullam Jeoung | Jennifer Cromley | Jana Diesner

When students reflect on their learning from a textbook via think-aloud processes, network representations can be used to capture the concepts and relations from these data. What can we learn from the resulting network representations about students’ learning processes, knowledge acquisition, and learning outcomes? This study brings methods from entity and relation extraction using classic and LLM-based methods to the application domain of educational psychology. We built a ground-truth baseline of relational data that represents relevant (to educational science), textbook-based information as a semantic network. Among the tested models, SPN4RE and LUKE achieved the best performance in extracting concepts and relations from students’ verbal data. Network representations of students’ verbalizations varied in structure, reflecting different learning processes. Correlating the students’ semantic networks with learning outcomes revealed that denser and more interconnected semantic networks were associated with more elaborated knowledge acquisition. Structural features such as the number of edges and surface overlap with textbook networks significantly correlated with students’ posttest performance.

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Less is More: The Effectiveness of Compact Typological Language Representations
York Hay Ng | Phuong Hanh Hoang | En-Shiun Annie Lee

Linguistic feature datasets such as URIEL+ are valuable for modelling cross-lingual relationships, but their high dimensionality and sparsity, especially for low-resource languages, limit the effectiveness of distance metrics. We propose a pipeline to optimize the URIEL+ typological feature space by combining feature selection and imputation, producing compact yet interpretable typological representations. We evaluate these feature subsets on linguistic distance alignment and downstream tasks, demonstrating that reduced-size representations of language typology can yield more informative distance metrics and improve performance in multilingual NLP applications.

Complex narrative contexts often challenge language models’ ability to follow instructions, and existing benchmarks fail to capture these difficulties. To address this, we propose Concise-SAE, a training-free framework that improves instruction following by identifying and editing instruction-relevant neurons using only natural language instructions, without requiring labelled data. To thoroughly evaluate our method, we introduce FreeInstruct, a diverse and realistic benchmark that highlights the challenges of instruction following in narrative-rich settings. While initially motivated by complex narratives, Concise-SAE demonstrates state-of-the-art instruction adherence across varied tasks without compromising generation quality.

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Inceptive Transformers: Enhancing Contextual Representations through Multi-Scale Feature Learning Across Domains and Languages
Asif Shahriar | Rifat Shahriyar | M Saifur Rahman

Encoder transformer models compress information from all tokens in a sequence into a single [CLS] token to represent global context. This approach risks diluting fine-grained or hierarchical features, leading to information loss in downstream tasks where local patterns are important. To remedy this, we propose a lightweight architectural enhancement: an inception-style 1-D convolution module that sits on top of the transformer layer and augments token representations with multi-scale local features. This enriched feature space is then processed by a self-attention layer that dynamically weights tokens based on their task relevance. Experiments on five diverse tasks show that our framework consistently improves general-purpose, domain-specific, and multilingual models, outperforming baselines by 1% to 14% while maintaining efficiency. Ablation studies show that multi-scale convolution performs better than any single kernel and that the self-attention layer is critical for performance.

Extracting causal relationships from a medical case report is essential for comprehending the case, particularly its diagnostic process. Since the diagnostic process is regarded as a bottom-up inference, causal relationships in cases naturally form a multi-layered tree structure. The existing tasks, such as medical relation extraction, are insufficient for capturing the causal relationships of an entire case, as they treat all relations equally without considering the hierarchical structure inherent in the diagnostic process. Thus, we propose a novel task, Causal Tree Extraction (CTE), which receives a case report and generates a causal tree with the primary disease as the root, providing an intuitive understanding of a case’s diagnostic process. Subsequently, we construct a Japanese case report CTE dataset, J-Casemap, propose a generation-based CTE method that outperforms the baseline by 20.2 points in the human evaluation, and introduce evaluation metrics that reflect clinician preferences. Further experiments also show that J-Casemap enhances the performance of solving other medical tasks, such as question answering.

Large language models (LLMs) are known to memorize and recall English text from their pretraining data. However, the extent to which this ability generalizes to non-English languages or transfers across languages remains unclear. This paper investigates multilingual and cross-lingual memorization in LLMs, probing if memorized content in one language (e.g., English) can be recalled when presented in translation. To do so, we introduce , a dataset of **31.5K** aligned excerpts from 20 books in ten languages, including English originals, official translations (Vietnamese, Spanish, Turkish), and new translations in six low-resource languages (Sesotho, Yoruba, Maithili, Malagasy, Setswana, Tahitian). We evaluate memorization across model families and sizes through three tasks: (1) **direct probing**, which asks the model to identify a book’s title and author; (2) **name cloze**, which requires predicting masked character names; and (3) **prefix probing**, which involves generating continuations. We find that some LLMs consistently recall content across languages, even for texts without existing translation. GPT-4o, for example, identifies authors and titles 69.4% of the time and masked entities 6.3% of the time in newly translated excerpts. While perturbations (e.g., masking characters, shuffling words) reduce accuracy, the model’s performance remains above chance level. Our results highlight the extent of cross-lingual memorization and provide insights on the differences between the models.

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Enhanced Noun-Noun Compound Interpretation through Textual Enrichment
Bingyang Ye | Jingxuan Tu | James Pustejovsky

Interpreting Noun-Noun Compounds remains a persistent challenge for Large Language Models (LLMs) because the semantic relation between the modifier and the head is rarely stated explicitly. Recent benchmarks frame Noun-Noun Compound Interpretation as a multiple-choice question. While this setting allows LLMs to produce more controlled results, it still faces two key limitations: vague relation descriptions as options and the inability to handle polysemous compounds. We introduce a dual-faceted textual enrichment framework that augments prompts. Description enrichment paraphrases relations into event‐oriented descriptions instantiated with the target compound to explicitly surface the hidden event connecting head and modifier. Conditioned context enrichment identifies polysemous compounds leveraging qualia-role binding and assigns each compound with condition cues for disambiguation. Our method yields consistently higher accuracy across three LLM families. These gains suggest that surfacing latent compositional structure and contextual constraint is a promising path toward deeper semantic understanding in language models.

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ICL CIPHERS: Quantifying ”Learning” in In-Context Learning via Substitution Ciphers
Zhouxiang Fang | Aayush Mishra | Muhan Gao | Anqi Liu | Daniel Khashabi

Recent works have suggested that In-Context Learning (ICL) operates in dual modes, i.e. task retrieval (remember learned patterns from pre-training) and task learning (inference-time ”learning” from demonstrations). However, disentangling these the two modes remains a challenging goal. We introduce ICL CIPHERS, a class of task reformulations based on substitution ciphers borrowed from classic cryptography. In this approach, a subset of tokens in the in-context inputs are substituted with other (irrelevant) tokens, rendering English sentences less comprehensible to human eye. However, by design, there is a latent, fixed pattern to this substitution, making it reversible. This bijective (reversible) cipher ensures that the task remains a well-defined task in some abstract sense, despite the transformations. It is a curious question if LLMs can solve tasks reformulated by ICL CIPHERS with a BIJECTIVE mapping, which requires ”deciphering” the latent cipher. We show that LLMs are better at solving tasks reformulated by ICL CIPHERS with BIJECTIVE mappings than the NON-BIJECTIVE (irreversible) baseline, providing a novel approach to quantify ”learning” in ICL. While this gap is small, it is consistent across the board on four datasets and six models. Finally, our interpretability analysis shows evidence that LLMs can internally decode ciphered inputs.

Visual Instruction Tuning (VIT) aims to enhance Multimodal Large Language Models (MLLMs), yet its effectiveness is often compromised by corrupted datasets with issues such as hallucinated content, incorrect responses, and poor OCR quality. Previous approaches to address these challenges have focused on refining datasets through high-quality data collection or rule-based filtering that can be costly or limited in scope. In this paper, we conduct a systematic investigation into the impact of corrupted data on MLLMs and discover that, although corrupted data degrade model performance, such adverse effects are largely reversible, and MLLMs are corrupted but not broken. Specifically, we find that disabling a small subset of parameters can almost fully restore performance. Moreover, corrupted MLLMs inherently possess the capability to differentiate between clean and corrupted samples, facilitating dataset cleaning without external intervention. Building on these insights, we introduce a corruption-robust training paradigm that significantly surpasses existing strategies for mitigating the effects of corrupted data.

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Memory OS of AI Agent
Jiazheng Kang | Mingming Ji | Zhe Zhao | Ting Bai

Large Language Models (LLMs) face a crucial challenge from fixed context windows and inadequate memory management, leading to a severe shortage of long-term memory capabilities and limited personalization in the interactive experience with AI agents. To overcome this challenge, we innovatively propose a Memory Operating System, i.e., MemoryOS, to achieve comprehensive and efficient memory management for AI agents. Inspired by the memory management principles in operating systems, MemoryOS designs a hierarchical storage architecture and consists of four key modules: memory Storage, Updating, Retrieval, and Generation. Specifically, the architecture comprises three levels of storage units: short-term memory, mid-term memory, and long-term personal memory. Key operations within MemoryOS include dynamic updates between storage units: short-term to mid-term updates follow a dialogue-chain-based FIFO principle, while mid-term to long-term updates use a segmented page organization strategy. Our pioneering MemoryOS enables hierarchical memory integration and dynamic updating. Extensive experiments on the LoCoMo benchmark show an average improvement of 48.36% on F1 and 46.18% on BLEU-1 over the baselines on GPT-4o-mini, showing contextual coherence and personalized memory retention in long conversations.

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Rule Discovery for Natural Language Inference Data Generation Using Out-of-Distribution Detection
Juyoung Han | Hyunsun Hwang | Changki Lee

Natural Language Inference (NLI) is a fundamental task in Natural Language Processing (NLP), yet adapting NLI models to new domains remains challenging due to the high cost of collecting domain-specific training data. While prior work proposed 15 sentence transformation rules to automate training data generation, these rules insufficiently capture the diversity of natural language. We propose a novel framework that combines Out-of-Distribution (OOD) detection and BERT-based clustering to identify premise–hypothesis pairs in the SNLI dataset that are not covered by existing rules and to discover four new transformation rules from them. Using these rules with Chain-of-Thought (CoT) prompting and Large Language Models (LLMs), we generate high-quality training data and augment the SNLI dataset. Our method yields consistent performance improvements across dataset sizes, achieving +0.85%p accuracy on 2k and +0.15%p on 550k samples. Furthermore, a distribution-aware augmentation strategy enhances performance across all scales. Beyond manual explanations, we extend our framework to automatically generated explanations (CoT-Ex), demonstrating that they provide a scalable alternative to human-written explanations and enable reliable rule discovery.

Spatial reasoning is a core component of human cognition, enabling individuals to perceive, comprehend, and interact with the physical world. It relies on a nuanced understanding of spatial structures and inter-object relationships, serving as the foundation for complex reasoning and decision-making. To investigate whether current vision-language models (VLMs) exhibit similar capability, we introduce Jigsaw-Puzzles, a novel benchmark consisting of 1,100 carefully curated real-world images with high spatial complexity. Based on this dataset, we design five tasks to rigorously evaluate VLMs’ spatial perception, structural understanding, and reasoning capabilities, while deliberately minimizing reliance on domain-specific knowledge to better isolate and assess the general spatial reasoning capability. We conduct a comprehensive evaluation across 24 state-of-the-art VLMs. The results show that even the strongest model, Gemini-2.5-Pro, achieves only 77.14% overall accuracy and performs particularly poorly on the Order Generation task, with only 30.00% accuracy, far below the 90%+ performance achieved by human participants. This persistent gap underscores the need for continued progress, positioning Jigsaw-Puzzles as a challenging and diagnostic benchmark for advancing spatial reasoning research in VLMs. Our project page is at https://zesen01.github.io/jigsaw-puzzles.

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Definition Generation for Word Meaning Modeling: Monolingual, Multilingual, and Cross-Lingual Perspectives
Francesco Periti | Roksana Goworek | Haim Dubossarsky | Nina Tahmasebi

The task of Definition Generation has recently gained attention as an interpretable approach to modeling word meaning. Thus far, most research has been conducted in English, with limited work and resources for other languages. In this work, we expand Definition Generation beyond English to a suite of 22 languages and evaluate Llama-based models within a monolingual, multilingual, and cross-lingual setting. Our experiments show that monolingual fine-tuning consistently outperforms pretrained baselines, with the largest gains observed in languages with lower initial performance; and that multilingual fine-tuning does not consistently improve performance on the individual fine-tuning languages. Our cross-lingual evaluation reveals that models fine-tuned on a single language typically lose the ability to generate definitions in other languages, whereas multilingual models exhibit robust generalization even to languages unseen during fine-tuning.

While language models (LMs) paired with residual vector quantization (RVQ) tokenizers have shown promise in text-to-audio (T2A) generation, they still lag behind diffusion-based models by a non-trivial margin. We identify a critical dilemma underpinning this gap: incorporating more RVQ layers improves audio reconstruction fidelity but exceeds the generation capacity of conventional LMs. To address this, we first analyze RVQ dynamics and uncover two key limitations: 1) orthogonality of features across RVQ layers hinders effective LMs training, and 2) descending semantic richness in tokens from deeper RVQ layers exacerbates exposure bias during autoregressive decoding. Based on these insights, we propose Siren, a novel LM-based framework that employs multiple isolated transformers with causal conditioning and anti-causal alignment via reinforcement learning. Extensive experiments demonstrate that Siren outperforms both existing LM-based and diffusion-based T2A systems, achieving state-of-the-art results. By bridging the representational strengths of LMs with the fidelity demands of audio synthesis, our approach repositions LMs as competitive contenders against diffusion models in T2A tasks. Moreover, by aligning audio representations with linguistic structures, Siren opens a promising pathway toward unified multi-modal generation frameworks.

Fine-tuning large language models (LLMs) faces significant memory challenges due to the high cost of back-propagation. MeZO addresses this using zeroth-order (ZO) optimization, matching memory usage to inference but suffering from slow convergence due to varying curvatures across model parameters. To overcome this limitation, We propose HELENE, a scalable and memory-efficient optimizer that integrates annealed A-GNB gradients with diagonal Hessian estimation and layer-wise clipping as a second-order pre-conditioner. HELENE provably accelerates and stabilizes convergence by reducing dependence on total parameter space and scaling with the largest layer dimension. Experiments on RoBERTa-large and OPT-1.3B show up to a 20× speedup over MeZO with an average accuracy improvement of 1.5%. HELENE supports full and parameter-efficient fine-tuning, outperforming several state-of-the-art optimizers.

Rapid advances in Multimodal Large Language Models (MLLMs) have extended information retrieval beyond text, enabling access to complex real-world documents that combine both textual and visual content. However, most documents are private, either owned by individuals or confined within corporate silos, and current retrievers struggle when faced with unseen domains or languages. To address this gap, we introduce PREMIR, a simple yet effective framework that leverages the broad knowledge of an MLLM to generate cross-modal pre-questions (preQs) before retrieval. Unlike earlier multimodal retrievers that embed entire documents as a single vector, PREMIR leverages preQs, decomposed from documents into finer token-level representations across modalities, enabling richer contextual understanding. Experiments show that PREMIR achieves state-of-the-art performance on out-of-distribution benchmarks, including closed-domain and multilingual settings, outperforming strong baselines across all metrics. We confirm the contribution of each component through in-depth ablation studies, and qualitative analyses of the generated preQs further highlight the framework’s robustness in real-world settings.

Large language models (LLMs) have achieved remarkable success across various domains, driving significant technological advancements and innovations. Despite the rapid growth in model scale and capability, systematic, data-driven research on how structural configurations affect performance remains scarce. To address this gap, we present a large-scale dataset encompassing diverse open-source LLM structures and their performance across multiple benchmarks. Leveraging this dataset, we conduct a systematic, data mining-driven analysis to validate and quantify the relationship between structural configurations and performance. Our study begins with a review of the historical development of LLMs and an exploration of potential future trends. We then analyze how various structural choices impact performance across benchmarks and further corroborate our findings using mechanistic interpretability techniques. By providing data-driven insights into LLM optimization, our work aims to guide the targeted development and application of future models.

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Logical Reasoning with Outcome Reward Models for Test-Time Scaling
Ramya Keerthy Thatikonda | Wray Buntine | Ehsan Shareghi

Logical reasoning is a critical benchmark for evaluating the capabilities of large language models (LLMs), as it reflects their ability to derive valid conclusions from given premises. While the combination of test-time scaling with dedicated outcome or process reward models has opened up new avenues to enhance LLMs performance in complex reasoning tasks, this space is under-explored in deductive logical reasoning. We present a set of Outcome Reward Models (ORMs) for deductive reasoning. To train the ORMs we mainly generate data using Chain-of-Thought (CoT) with single and multiple samples. Additionally, we propose a novel tactic to further expand the type of errors covered in the training dataset of the ORM. In particular, we propose an echo generation technique that leverages LLMs’ tendency to reflect incorrect assumptions made in prompts to extract additional training data, covering previously unexplored error types. While a standard CoT chain may contain errors likely to be made by the reasoner, the echo strategy deliberately steers the model toward incorrect reasoning. We show that ORMs trained on CoT and echo-augmented data demonstrate improved performance on the FOLIO, JustLogic, and ProverQA datasets across four different LLMs.

Tokens are basic elements in the datasets for LLM training. It is well-known that many tokens representing Chinese phrases in the vocabulary of GPT (4o/4o-mini/o1/o3/4.5/4.1/o4-mini) are indicating contents like pornography or online gambling. Based on this observation, our goal is to locate Polluted Chinese (PoC) tokens in LLMs and study the relationship between PoC tokens’ existence and training data. (1) We give a formal definition and taxonomy of PoC tokens based on the GPT’s vocabulary. (2) We build a PoC token detector via fine-tuning an LLM to label PoC tokens in vocabularies by considering each token’s both semantics and related contents from the search engines. (3) We study the speculation on the training data pollution via PoC tokens’ appearances (token ID). Experiments on GPT and other 23 LLMs indicate that tokens widely exist while GPT’s vocabulary behaves the worst: more than 23% long Chinese tokens (i.e., a token with more than two Chinese characters) are either porn or online gambling. We validate the accuracy of our speculation method on famous pre-training datasets like C4 and Pile. Then, considering GPT-4o, we speculate that the ratio of “波*野结衣” related webpages in GPT-4o’s training data is around 0.5%.

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NovelHopQA: Diagnosing Multi-Hop Reasoning Failures in Long Narrative Contexts
Abhay Gupta | Kevin Zhu | Vasu Sharma | Sean O’Brien | Michael Lu

Current large language models (LLMs) struggle to answer questions that span tens of thousands of tokens, especially when multi-hop reasoning is involved. While prior benchmarks explore long-context comprehension or multi-hop reasoning in isolation, none jointly vary context length and reasoning depth in natural narrative settings. We introduce NovelHopQA, the first benchmark to evaluate 1–4 hop QA over 64k–128k-token excerpts from 83 full-length public-domain novels. A keyword-guided pipeline builds hop-separated chains grounded in coherent storylines. We evaluate six state-of-the-art (SOTA) models and apply golden context filtering to ensure all questions are genuinely answerable. Human annotators validate both alignment and hop depth. We noticed consistent accuracy drops with increased hops and context length, even in frontier models—revealing that sheer scale does not guarantee robust reasoning. Our failure mode analysis highlights common breakdowns, such as missed final-hop integration and long-range drift. NovelHopQA offers a controlled diagnostic setting to stress-test multi-hop reasoning at scale.

Despite the efficacy of Direct Preference Optimization (DPO) in aligning Large Language Models (LLMs), reward hacking remains a pivotal challenge. This issue emerges when LLMs excessively reduce the probability of rejected completions to achieve high rewards, without genuinely meeting their intended goals. As a result, this leads to overly lengthy generation lacking diversity, as well as catastrophic forgetting of knowledge. We investigate the underlying reason behind this issue, which is representation redundancy caused by neuron collapse in the parameter space. Hence, we propose a novel Weights-Rotated Preference Optimization (RoPO) algorithm, which implicitly constrains the output layer logits with the KL divergence inherited from DPO and explicitly constrains the intermediate hidden states by fine-tuning on a multi-granularity orthogonal matrix. This design prevents the policy model from deviating too far from the reference model, thereby retaining the knowledge and expressive capabilities acquired during pre-training and SFT stages. Our RoPO achieves up to a 3.27-point improvement on AlpacaEval 2, and surpasses the best baseline by 6.2 to 7.5 points on MT-Bench with merely 0.015% of the trainable parameters, demonstrating its effectiveness in alleviating the reward hacking problem of DPO.

With the growing spread of misinformation online, understanding how true news evolves into fake news has become crucial for early detection and prevention. However, previous research has often assumed fake news inherently exists rather than exploring its gradual formation. To address this gap, we propose FUSE (Fake news evolUtion Simulation framEwork), a novel Large Language Model (LLM)-based simulation approach explicitly focusing on fake news evolution from real news. Our framework model a social network with four distinct types of LLM agents commonly observed in daily interactions: spreaders who propagate information, commentators who provide interpretations, verifiers who fact-check, and standers who observe passively to simulate realistic daily interactions that progressively distort true news. To quantify these gradual distortions, we develop FUSE-EVAL, a comprehensive evaluation framework measuring truth deviation along multiple linguistic and semantic dimensions. Results show that FUSE effectively captures fake news evolution patterns and accurately reproduces known fake news, aligning closely with human evaluations. Experiments demonstrate that FUSE accurately reproduces known fake news evolution scenarios, aligns closely with human judgment, and highlights the importance of timely intervention at early stages. Our framework is extensible, enabling future research on broader scenarios of fake news:https://github.com/LiuYuHan31/FUSE

Large language models (LLMs) have attracted significant attention due to their impressive general capabilities across diverse downstream tasks. However, without domain-specific optimization, they often underperform on specialized knowledge benchmarks and even produce hallucination. Recent studies show that strategically infusing domain knowledge during pretraining can substantially improve downstream performance. A critical challenge lies in balancing this infusion trade-off: injecting too little domain-specific data yields insufficient specialization, whereas excessive infusion triggers catastrophic forgetting of previously acquired knowledge. In this work, we focus on the phenomenon of memory collapse induced by over-infusion. Through systematic experiments, we make two key observations, i.e. 1) Critical collapse point: each model exhibits a threshold beyond which its knowledge retention capabilities sharply degrade. 2) Scale correlation: these collapse points scale consistently with the model’s size. Building on these insights, we propose a knowledge infusion scaling law that predicts the optimal amount of domain knowledge to inject into large LLMs by analyzing their smaller counterparts. Extensive experiments across different model sizes and pertaining token budgets validate both the effectiveness and generalizability of our scaling law.

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SMEC:Rethinking Matryoshka Representation Learning for Retrieval Embedding Compression
Biao Zhang | Lixin Chen | Tong Liu | Bo Zheng

Large language models (LLMs) generate high-dimensional embeddings that capture rich semantic and syntactic information. However, high-dimensional embeddings exacerbate computational complexity and storage requirements, thereby hindering practical deployment. To address these challenges, we propose a novel training framework named Sequential Matryoshka Embedding Compression (SMEC). This framework introduces the Sequential Matryoshka Representation Learning(SMRL) method to mitigate gradient variance during training, the Adaptive Dimension Selection (ADS) module to reduce information degradation during dimension pruning, and the Selectable Cross-batch Memory (S-XBM) module to enhance unsupervised learning between high- and low-dimensional embeddings. Experiments on image, text, and multimodal datasets demonstrate that SMEC achieves significant dimensionality reduction while maintaining performance. For instance, on the BEIR dataset, our approach improves the performance of compressed LLM2Vec embeddings (256 dimensions) by 1.1 points and 2.7 points compared to the Matryoshka-Adaptor and Search-Adaptor models, respectively.

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Reverse Prompt Engineering: A Zero-Shot, Genetic Algorithm Approach to Language Model Inversion
Hanqing Li | Diego Klabjan

We explore a new language model inversion problem under strict black-box, zero-shot, and limited data conditions. We propose a novel training-free framework that reconstructs prompts using only a limited number of text outputs from a language model. Existing methods rely on the availability of a large number of outputs for both training and inference, an assumption that is unrealistic in the real world, and they can sometimes produce garbled text. In contrast, our approach, which relies on limited resources, consistently yields coherent and semantically meaningful prompts. Our framework leverages a large language model together with an optimization process inspired by the genetic algorithm to effectively recover prompts. Experimental results on several datasets derived from public sources indicate that our approach achieves high-quality prompt recovery and generates prompts more semantically and functionally aligned with the originals than current state-of-the-art methods. Additionally, use-case studies introduced demonstrate the method’s strong potential for generating high-quality text data on perturbed prompts.

Grounding natural language queries in graphical user interfaces (GUIs) poses unique challenges due to the diversity of visual elements, spatial clutter, and the ambiguity of language. In this paper, we introduce DiMo-GUI, a training-free framework for GUI grounding that leverages two core strategies: dynamic visual grounding and modality-aware optimization. Instead of treating the GUI as a monolithic image, our method splits the input into textual elements and iconic elements, allowing the model to reason over each modality independently using general-purpose vision-language models. When predictions are ambiguous or incorrect, DiMo-GUI dynamically focuses attention by generating candidate focal regions centered on the model’s initial predictions and incrementally zooms into subregions to refine the grounding result. This hierarchical refinement process helps disambiguate visually crowded layouts without the need for additional training or annotations. We evaluate our approach on standard GUI grounding benchmarks and demonstrate consistent improvements over baseline inference pipelines, highlighting the effectiveness of combining modality separation with region-focused reasoning.

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SocioBench: Modeling Human Behavior in Sociological Surveys with Large Language Models
Jia Wang | Ziyu Zhao | Tingjuntao Ni | Zhongyu Wei

Large language models (LLMs) show strong potential for simulating human social behaviors and interactions, yet lack large-scale, systematically constructed benchmarks for evaluating their alignment with real-world social attitudes. To bridge this gap, we introduce SocioBench—a comprehensive benchmark derived from the annually collected, standardized survey data of the International Social Survey Programme (ISSP). The benchmark aggregates over 480,000 real respondent records from more than 30 countries, spanning 10 sociological domains and over 40 demographic attributes. Our experiments indicate that LLMs achieve only 30–40% accuracy when simulating individuals in complex survey scenarios, with statistically significant differences across domains and demographic subgroups. These findings highlight several limitations of current LLMs in survey scenarios, including insufficient individual-level data coverage, inadequate scenario diversity, and missing group-level modeling. We have open-sourced SocioBench at https://github.com/JiaWANG-TJ/SocioBench.

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Financial Risk Relation Identification through Dual-view Adaptation
Wei-Ning Chiu | Yu-Hsiang Wang | Andy Hsiao | Yu-Shiang Huang | Chuan-Ju Wang

A multitude of interconnected risk events—ranging from regulatory changes to geopolitical tensions—can trigger ripple effects across firms. Identifying inter-firm risk relations is thus crucial for applications like portfolio management and investment strategy. Traditionally, such assessments rely on expert judgment and manual analysis, which are, however, subjective, labor-intensive, and difficult to scale. To address this, we propose a systematic method for extracting inter-firm risk relations using Form 10-K filings—authoritative, standardized financial documents—as our data source. Leveraging recent advances in natural language processing, our approach captures implicit and abstract risk connections through unsupervised fine-tuning based on chronological and lexical patterns in the filings. This enables the development of a domain-specific financial encoder with a deeper contextual understanding and introduces a quantitative risk relation score for transparency, interpretable analysis. Extensive experiments demonstrate that our method outperforms strong baselines across multiple evaluation settings.

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CopySpec: Accelerating LLMs with Speculative Copy-and-Paste
Razvan-Gabriel Dumitru | Minglai Yang | Vikas Yadav | Mihai Surdeanu

We introduce CopySpec, a simple yet effective technique to tackle the inefficiencies LLMs face when generating responses that closely resemble previous outputs or responses that can be verbatim extracted from context. CopySpec identifies repeated sequences in the model’s chat history or context and speculates that the same tokens will follow, enabling seamless copying without compromising output quality and without requiring additional GPU memory. To evaluate the effectiveness of our approach, we conducted experiments using seven LLMs and five datasets: MT-Bench, CNN/DM, GSM8K, HumanEval, and our newly created dataset, MT-Redundant. MT-Redundant, introduced in this paper, transforms the second turn of MT-Bench into a request for variations of the first turn’s answer, simulating real-world scenarios where users request modifications to prior responses. Our results demonstrate significant speed-ups: up to 2.35x on CNN/DM, 3.08x on the second turn of select MT-Redundant categories, and 2.66x on the third turn of GSM8K’s self-correction tasks. Importantly, we show that CopySpec integrates seamlessly with speculative decoding, yielding an average 49% additional speed-up over speculative decoding for the second turn of MT-Redundant across all eight categories. While LLMs, even with speculative decoding, suffer from slower inference as context size grows, CopySpec leverages larger contexts to accelerate inference, making it a faster complementary solution. Our code and dataset are publicly available at https://github.com/RazvanDu/CopySpec.

Recent studies have demonstrated that many layers are functionally redundant in large language models (LLMs), enabling model compression by removing these layers to reduce inference cost. While such approaches can improve efficiency, indiscriminate layer pruning often results in significant performance degradation. In this paper, we propose **GRASP** (**G**radient-based **R**etention of **A**daptive **S**ingular **P**arameters), a novel compression framework that mitigates this issue by preserving sensitivity-aware singular values. Unlike direct layer pruning, GRASP leverages gradient-based attribution on a small calibration dataset to adaptively identify and retain critical singular components. By replacing redundant layers with only a minimal set of parameters, GRASP achieves efficient compression while maintaining strong performance with minimal overhead. Experiments across multiple LLMs show that GRASP consistently outperforms existing compression methods, achieving 90% of the original model’s performance under a 20% compression ratio.

Real-world data combines structured (e.g., graph connections) and unstructured (e.g., text, visuals) formats, capturing explicit relationships (e.g., social links) and implicit semantic interdependencies (e.g., knowledge graphs). We propose GraphAgent, an automated agent pipeline addressing both explicit and implicit graph-enhanced semantic dependencies for predictive (e.g., node classification) and generative (e.g., text generation) tasks. GraphAgent integrates three components: (i) a Graph Generator Agent creating knowledge graphs for semantic dependencies; (ii) a Task Planning Agent interpreting user queries and formulating tasks via self-planning; and (iii) a Task Execution Agent automating task execution with tool matching. These agents combine language and graph language models to reveal complex relational and semantic patterns. Extensive experiments on diverse datasets validate GraphAgent’s effectiveness in graph-related predictive and text generative tasks. GraphAgent is open-sourced at: https://anonymous.4open.science/r/GraphAgent-Submit-6F52/.

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DDO: Dual-Decision Optimization for LLM-Based Medical Consultation via Multi-Agent Collaboration
Zhihao Jia | Mingyi Jia | Junwen Duan | Jianxin Wang

Large Language Models (LLMs) demonstrate strong generalization and reasoning abilities, making them well-suited for complex decision-making tasks such as medical consultation (MC). However, existing LLM-based methods often fail to capture the dual nature of MC, which entails two distinct sub-tasks: symptom inquiry, a sequential decision-making process, and disease diagnosis, a classification problem. This mismatch often results in ineffective symptom inquiry and unreliable disease diagnosis. To address this, we propose DDO, a novel LLM-based framework that performs Dual-Decision Optimization by decoupling the two sub-tasks and optimizing them with distinct objectives through a collaborative multi-agent workflow. Experiments on three real-world MC datasets show that DDO consistently outperforms existing LLM-based approaches and achieves competitive performance with state-of-the-art generation-based methods, demonstrating its effectiveness in the MC task. The code is available at https://github.com/zh-jia/DDO.

Mobile GUI agents have attracted tremendous research participation recently. Traditional approaches to mobile agent training rely on centralized data collection, leading to high cost and limited scalability. Distributed training utilizing federated learning offers an alternative by harnessing real-world user data, providing scalability and reducing costs. However, pivotal challenges, including the absence of standardized benchmarks, hinder progress in this field. To tackle the challenges, we introduce FedMABench, the first benchmark for federated training and evaluation of mobile GUI agents, specifically designed for heterogeneous scenarios. FedMABench features 6 datasets with 30+ subsets, 8 federated algorithms, 10+ base models, and over 800 apps across 5 categories, providing a comprehensive framework for evaluating mobile agents across diverse environments. Through extensive experiments, we uncover several key insights: federated algorithms consistently outperform local training; the distribution of specific apps plays a crucial role in heterogeneity; and, even apps from distinct categories can exhibit correlations during training. FedMABench is publicly available at: https://github.com/wwh0411/FedMABench.

Vision-language models demand watermarking solutions that protect intellectual property without compromising multimodal coherence. Existing text watermarking methods disrupt visual-textual alignment through biased token selection and static strategies, leaving semantic-critical concepts vulnerable. We propose VLA-Mark, a vision-aligned framework that embeds detectable watermarks while preserving semantic fidelity through cross-modal coordination. Our approach integrates multiscale visual-textual alignment metrics, combining localized patch affinity, global semantic coherence, and contextual attention patterns, to guide watermark injection without model retraining. An entropy-sensitive mechanism dynamically balances watermark strength and semantic preservation, prioritizing visual grounding during low-uncertainty generation phases. Experiments show 7.4% lower PPL and 26.6% higher BLEU than conventional methods, with near-perfect detection (98.8% AUC). The framework demonstrates 96.1% attack resilience against attacks such as paraphrasing and synonym substitution, while maintaining text-visual consistency, establishing new standards for quality-preserving multimodal watermarking.

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Sentence Smith: Controllable Edits for Evaluating Text Embeddings
Hongji Li | Andrianos Michail | Reto Gubelmann | Simon Clematide | Juri Opitz

Controllable and transparent text generation has been a long-standing goal in NLP. Almost as long-standing is a general idea for addressing this challenge: Parsing text to a symbolic representation, and generating from it. However, earlier approaches were hindered by parsing and generation insufficiencies. Using modern parsers and a safety supervision mechanism, we show how close current methods come to this goal. Concretely, we propose the framework for English, which has three steps: 1. Parsing a sentence into a semantic graph. 2. Applying human-designed semantic manipulation rules. 3. Generating text from the manipulated graph. A final entailment check (4.) verifies the validity of the applied transformation. To demonstrate our framework’s utility, we use it to induce hard negative text pairs that challenge text embedding models. Since the controllable generation makes it possible to clearly isolate different types of semantic shifts, we can evaluate text embedding models in a fine-grained way, also addressing an issue in current benchmarking where linguistic phenomena remain opaque. Human validation confirms that our transparent generation process produces texts of good quality. Notably, our way of generation is very resource-efficient, since it relies only on smaller neural networks.

Reasoning-based large language models have excelled in mathematics and programming, yet their potential in knowledge-intensive medical question answering remains underexplored and insufficiently validated in clinical contexts.To bridge this gap, we introduce ReasonMed, the largest medical reasoning dataset to date, comprising 370k high-quality examples distilled from 1.75 million initial reasoning paths generated by complementary LLMs and curated through a cost-efficient easy-medium-difficult (EMD) pipeline.ReasonMed is built through a multi-agent generation, verification, and refinement process, in which an Error Refiner improves reasoning paths by correcting error-prone steps identified by a verifier.Using ReasonMed, we investigate effective strategies for training medical reasoning models and find that integrating detailed CoT reasoning with concise answer summaries yields the most robust fine-tuning results.Models trained on ReasonMed set a new benchmark: ReasonMed-7B surpasses the prior best sub-10B models by 4.17% and even exceeds LLaMA3.1-70B on PubMedQA by 4.60%. When scaled to ReasonMed-14B, it remains highly competitive, underscoring consistent scaling potential.The codes and datasets are available at https://github.com/YuSun-Work/ReasonMed.

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Decoding Dense Embeddings: Sparse Autoencoders for Interpreting and Discretizing Dense Retrieval
Seongwan Park | Taeklim Kim | Youngjoong Ko

Despite their strong performance, Dense Passage Retrieval (DPR) models suffer from a lackof interpretability. In this work, we propose a novel interpretability framework that leveragesSparse Autoencoders (SAEs) to decompose previously uninterpretable dense embeddings fromDPR models into distinct, interpretable latent concepts. We generate natural language descriptionsfor each latent concept, enabling human interpretations of both the dense embeddingsand the query-document similarity scores of DPR models. We further introduce Concept-Level Sparse Retrieval (CL-SR), a retrieval framework that directly utilizes the extractedlatent concepts as indexing units. CL-SR effectively combines the semantic expressiveness ofdense embeddings with the transparency and efficiency of sparse representations. We showthat CL-SR achieves high index-space and computational efficiency while maintaining robustperformance across vocabulary and semantic mismatches.

Mobile task automation is an emerging technology that leverages AI to automatically execute routine tasks by users’ commands on mobile devices like Android, thus enhancing efficiency and productivity. While large language models (LLMs) excel at general mobile tasks through training on massive datasets, they struggle with app-specific workflows. To solve this problem, we designed UI Map, a structured representation of target app’s UI information. We further propose a UI Map-guided LLM-based approach UICompass to automate mobile tasks. Specifically, UICompass first leverages static analysis and LLMs to automatically build UI Map from either source codes of apps or byte codes (i.e., APK packages). During task execution, UICompass mines the task-relevant information from UI Map to feed into the LLMs, generate a planned paths, and adaptively adjust the path based on the actual app state and action history. Experimental results demonstrate that UICompass achieves a 15.87% higher task executing success rate than SOTA approaches. Even when only APK is available, UICompass maintains superior performance, demonstrating its applicability to closed-source apps.

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Leaky Thoughts: Large Reasoning Models Are Not Private Thinkers
Tommaso Green | Martin Gubri | Haritz Puerto | Sangdoo Yun | Seong Joon Oh

We study privacy leakage in the reasoning traces of large reasoning models used as personal agents which handle sensitive user data. Unlike final outputs, reasoning traces are often assumed to be internal and safe. We challenge this assumption by showing that reasoning traces frequently contain sensitive user data, which can be extracted via prompt injections or accidentally leak into outputs. Through probing and agentic evaluations, we demonstrate that test-time compute approaches, particularly increased reasoning steps, amplify such leakage. While increasing the budget of those test-time compute approaches makes models more cautious in their final answers, it also leads them to reason more verbosely and leak more in their own thinking. This reveals a core tension: reasoning improves utility but enlarges the privacy attack surface. We argue that safety efforts must extend to the model’s internal thinking, not just its outputs.

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Model Unlearning via Sparse Autoencoder Subspace Guided Projections
Xu Wang | Zihao Li | Benyou Wang | Yan Hu | Difan Zou

Large language models (LLMs) store vast amounts of information, making them powerful yet raising privacy and safety concerns when selective knowledge removal is required. Existing unlearning strategies, ranging from gradient-based fine-tuning and model editing to sparse autoencoder (SAE) steering, either lack interpretability or fail to provide a robust defense against adversarial prompts. We propose **S**AE–Guided **S**ubspace **P**rojection **U**nlearning (**SSPU**), a novel framework that leverages SAE features to drive targeted updates in the model’s parameter space, enabling precise, interpretable, and robust unlearning. SSPU’s three-stage pipeline performs data-driven layer and feature selection, subspace construction via QR decomposition, and constrained optimization that controls activations into an “irrelevant” subspace while preserving retained knowledge. Overall, we use SAE features to construct a subspace that supervises unlearning, refining the loss and adding a regularization term to guide interpretable parameter updates. In experiments on the WMDP–Cyber forget set and three utility benchmarks (MMLU, TruthfulQA, GSM8K), SSPU reduces harmful knowledge accuracy by 3.22% compared to the strongest baseline. It also improves adversarial robustness, lowering malicious accuracy under jailbreak prompts compared to baselines. Our findings expose the limitations of prior unlearning methods and demonstrate how interpretable subspace-guided optimization can achieve robust, controllable model behavior.

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ConvSearch-R1: Enhancing Query Reformulation for Conversational Search with Reasoning via Reinforcement Learning
Changtai Zhu | Siyin Wang | Ruijun Feng | Kai Song | Xipeng Qiu

Conversational search systems require effective handling of context-dependent queries that often contain ambiguity, omission, and coreference. Conversational Query Reformulation (CQR) addresses this challenge by transforming these queries into self-contained forms suitable for off-the-shelf retrievers. However, existing CQR approaches suffer from two critical constraints: high dependency on costly external supervision from human annotations or large language models, and insufficient alignment between the rewriting model and downstream retrievers. We present ConvSearch-R1, the first self-driven framework that completely eliminates dependency on external rewrite supervision by leveraging reinforcement learning to optimize reformulation directly through retrieval signals. Our novel two-stage approach combines Self-Driven Policy Warm-Up to address the cold-start problem through retrieval-guided self-distillation, followed by Retrieval-Guided Reinforcement Learning with a specially designed rank-incentive reward shaping mechanism that addresses the sparsity issue in conventional retrieval metrics. Extensive experiments on TopiOCQA and QReCC datasets demonstrate that ConvSearch-R1 significantly outperforms previous state-of-the-art methods, achieving over 10% improvement on the challenging TopiOCQA dataset while using smaller 3B parameter models without any external supervision.

Molecule generation is key to drug discovery and materials science, enabling the design of novel compounds with specific properties. Large language models (LLMs) can learn to perform a wide range of tasks from just a few examples. However, generating valid molecules using representations like SMILES is challenging for LLMs in few-shot settings. In this work, we explore how LLMs can generate 100% valid molecules. We evaluate whether LLMs can use SELFIES, a representation where every string corresponds to a valid molecule, for valid molecule generation but find that LLMs perform worse with SELFIES than with SMILES. We then examine LLMs’ ability to correct invalid SMILES and find their capacity limited. Finally, we introduce SmiSelf, a cross-chemical language framework for invalid SMILES correction. SmiSelf converts invalid SMILES to SELFIES using grammatical rules, leveraging SELFIES’ mechanisms to correct the invalid SMILES. Experiments show that SmiSelf ensures 100% validity while preserving molecular characteristics and maintaining or even enhancing performance on other metrics. SmiSelf helps expand LLMs’ practical applications in biomedicine and is compatible with all SMILES-based generative models. Code is available at https://github.com/wentao228/SmiSelf.

The advancement of Argument Mining (AM) is hindered by a critical bottleneck: the scarcity of structure-annotated datasets, which are expensive to create manually. Inspired by recent successes in synthetic data generation across various NLP tasks, this paper explores methodologies for LLMs to generate synthetic data for AM.We investigate two complementary synthesis perspectives: a quality-oriented synthesis approach, which employs structure-aware paraphrasing to preserve annotation quality, and a diversity-oriented synthesis approach, which generates novel argumentative texts with diverse topics and argument structures.Experiments on three datasets show that augmenting original training data with our synthetic data, particularly when combining both quality- and diversity-oriented instances, significantly enhances the performance of existing AM models, both in full-data and low-resource settings.Moreover, the positive correlation between synthetic data volume and model performance highlights the scalability of our methods.

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Dynamic Jointly Batch Selection for Data Efficient Machine Translation Fine-Tuning
Mohammad Amin Ghanizadeh | Mohammad Javad Dousti

Data quality and its effective selection are fundamental to improving the performance of machine translation models, serving as cornerstones for achieving robust and reliable translation systems. This paper presents a data selection methodology specifically designed for fine-tuning machine translation systems, which leverages the synergy between a learner model and a pre-trained reference model to enhance overall training effectiveness. By defining a learnability score, our approach systematically evaluates the utility of data points for training, ensuring that only the most relevant and impactful examples contribute to the fine-tuning process. Furthermore, our method employs a batch selection strategy which considers interdependencies among data points, optimizing the efficiency of the training process while maintaining a focus on data relevance. Experiments on English ↔ Persian and several other language pairs using an mBART model fine-tuned on the CCMatrix dataset demonstrate that our method can achieve up to a fivefold improvement in data efficiency compared to an iid baseline. Experimental results indicate that our approach improves computational efficiency by 24 when utilizing cached embeddings, as it requires fewer training data points. Additionally, it enhances generalization, resulting in superior translation performance compared to random selection method.

Though Large Vision-Language Models (LVLMs) are being actively explored in medicine, their ability to conduct complex real-world telemedicine consultations combining accurate diagnosis with professional dialogue remains underexplored. This paper presents **3MDBench** (**M**edical **M**ultimodal **M**ulti-agent **D**ialogue **Bench**mark), an open-source framework for simulating and evaluating LVLM-driven telemedical consultations. 3MDBench simulates patient variability through temperament-based Patient Agent and evaluates diagnostic accuracy and dialogue quality via Assessor Agent. It includes 2996 cases across 34 diagnoses from real-world telemedicine interactions, combining textual and image-based data. The experimental study compares diagnostic strategies for widely used open and closed-source LVLMs. We demonstrate that multimodal dialogue with internal reasoning improves F1 score by 6.5% over non-dialogue settings, highlighting the importance of context-aware, information-seeking questioning. Moreover, injecting predictions from a diagnostic convolutional neural network into the LVLM’s context boosts F1 by up to 20%. Source code is available at https://github.com/univanxx/3mdbench.

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OpenTuringBench: An Open-Model-based Benchmark and Framework for Machine-Generated Text Detection and Attribution
Lucio La Cava | Andrea Tagarelli

Open Large Language Models (OLLMs) are increasingly leveraged in generative AI applications, posing new challenges for detecting their outputs. We propose OpenTuringBench, a new benchmark based on OLLMs, designed to train and evaluate machine-generated text detectors on the Turing Test and Authorship Attribution problems. OpenTuringBench focuses on a representative set of OLLMs, and features a number of challenging evaluation tasks, including human/machine-manipulated texts, out-of-domain texts, and texts from previously unseen models. We also provide OTBDetector, a contrastive learning framework to detect and attribute OLLM-based machine-generated texts. Results highlight the relevance and varying degrees of difficulty of the OpenTuringBench tasks, with our detector achieving remarkable capabilities across the various tasks and outperforming most existing detectors.

The ability of large language models (LLMs) to utilize external tools has enabled them to tackle an increasingly diverse range of tasks. However, as the tasks become more complex and long-horizon, the intricate tool utilization process may trigger various unexpected errors. Therefore, how to effectively handle such errors, including identifying, diagnosing, and recovering from them, has emerged as a key research direction for advancing tool learning. In this work, we first extensively analyze the types of errors encountered during the function-calling process on several competitive tool evaluation benchmarks. Based on it, we introduce CRITICTOOL, a comprehensive critique evaluation benchmark specialized for tool learning. Building upon a novel evolutionary strategy for dataset construction, CRITICTOOL holds diverse tool-use errors with varying complexities, which better reflects real-world scenarios. We conduct extensive experiments on CRITICTOOL, and validate the generalization and effectiveness of our constructed benchmark strategy. We also provide an in-depth analysis of the tool reflection ability on various LLMs, offering a new perspective on the field of tool learning in LLMs. The code is available at https://github.com/Shellorley0513/CriticTool.

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Pre-trained Language Models Learn Remarkably Accurate Representations of Numbers
Marek Kadlčík | Michal Štefánik | Timothee Mickus | Josef Kuchař | Michal Spiegel

Pretrained language models (LMs) are prone to arithmetic errors. Existing work showed limited success in probing numeric values from models’ representations, indicating that these errors can be attributed to the inherent unreliability of distributionally learned embeddings in representing exact quantities. However, we observe that previous probing methods are inadequate for the emergent structure of learned number embeddings with sinusoidal patterns.In response, we propose a novel probing technique that decodes numeric values from input embeddings with near-perfect accuracy across a range of open-source LMs. This proves that after the sole pre-training, LMs represent numbers with remarkable precision. Finally, we find that the embeddings’ preciseness judged by our probe’s accuracy explains a large portion of LM’s errors in elementary arithmetic, and show that aligning the embeddings with the pattern discovered by our probe can mitigate these errors.

High-quality image captions are essential for improving modality alignment and visual understanding in Large Vision-Language Models (LVLMs). However, the scarcity of ultra-detailed image caption data limits further advancements. This paper presents a systematic pipeline for generating high-quality, ultra-detailed image captions, encompassing both pre-processing and post-processing stages. In the pre-processing stage, we classify and deduplicate images, extract visual information using expert tools, and leverage GPT-4o with structured prompts to generate initial captions. To enhance comprehensiveness, we introduce an expansion strategy based on Large Language Models (LLMs), defining eight descriptive dimensions to refine and extend captions, which serve as seed data for training a proprietary captioner model. In the post-processing stage, we incorporate human error-correction annotations and an active learning-inspired approach to refine low-quality samples. Using high-quality corrected data, we apply Direct Preference Optimization (DPO) and develop a critic-rewrite pipeline, training a sentence-level critic model to mitigate hallucinations. Experimental results demonstrate that our ultra-detailed captions significantly enhance LVLMs’ perception and cognitive abilities across multiple vision-language benchmarks. The code and dataset are available at https://github.com/yuzeng0-0/UltraCaption.

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Translate Smart, not Hard: Cascaded Translation Systems with Quality-Aware Deferral
António Farinhas | Nuno M Guerreiro | Sweta Agrawal | Ricardo Rei | Andre Martins

Larger models often outperform smaller ones but come with high computational costs. Cascading offers a potential solution. By default, it uses smaller models and defers only some instances to larger, more powerful models. However, designing effective deferral rules remains a challenge. In this paper, we propose a simple yet effective approach for machine translation, using existing quality estimation (QE) metrics as deferral rules. We show that QE-based deferral allows a cascaded system to match the performance of a larger model while invoking it for a small fraction (30% to 50%) of the examples, significantly reducing computational costs. We validate this approach through both automatic and human evaluation.

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iVISPAR — An Interactive Visual-Spatial Reasoning Benchmark for VLMs
Julius Mayer | Mohamad Ballout | Serwan Jassim | Farbod Nosrat Nezami | Elia Bruni

Vision-Language Models (VLMs) are known to struggle with spatial reasoning and visual alignment. To help overcome these limitations, we introduce iVISPAR, an interactive multimodal benchmark designed to evaluate the spatial reasoning capabilities of VLMs acting as agents. iVISPAR is based on a variant of the sliding tile puzzle—a classic problem that demands logical planning, spatial awareness, and multi-step reasoning. The benchmark supports visual 3D, 2D, and text-based input modalities, enabling comprehensive assessments of VLMs’ planning and reasoning skills. We evaluate a broad suite of state-of-the-art open-source and closed-source VLMs, comparing their performance while also providing optimal path solutions and a human baseline to assess the task’s complexity and feasibility for humans. Results indicate that while VLMs perform better on 2D tasks compared to 3D or text-based settings, they struggle with complex spatial configurations and consistently fall short of human performance, illustrating the persistent challenge of visual alignment. This underscores critical gaps in current VLM capabilities, highlighting their limitations in achieving human-level cognition. Project website: https://microcosm.ai/ivispar.

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Are LLMs Better than Reported? Detecting Label Errors and Mitigating Their Effect on Model Performance
Omer Nahum | Nitay Calderon | Orgad Keller | Idan Szpektor | Roi Reichart

NLP benchmarks rely on standardized datasets for training and evaluating models and are crucial for advancing the field. Traditionally, expert annotations ensure high-quality labels; however, the cost of expert annotation does not scale well with the growing demand for larger datasets required by modern models. While crowd-sourcing provides a more scalable solution, it often comes at the expense of annotation precision and consistency. Recent advancements in large language models (LLMs) offer new opportunities to enhance the annotation process, particularly for detecting label errors in existing datasets. In this work, we consider the recent approach of LLM-as-a-judge, leveraging an ensemble of LLMs to flag potentially mislabeled examples. We conduct a case study on four factual consistency datasets from the TRUE benchmark, spanning diverse NLP tasks, and on SummEval, which uses Likert-scale ratings of summary quality across multiple dimensions. We empirically analyze the labeling quality of existing datasets and compare expert, crowd-sourced, and LLM-based annotations in terms of the agreement, label quality, and efficiency, demonstrating the strengths and limitations of each annotation method. Our findings reveal a substantial number of label errors, which, when corrected, induce a significant upward shift in reported model performance. This suggests that many of the LLMs’ so-called mistakes are due to label errors rather than genuine model failures. Additionally, we discuss the implications of mislabeled data and propose methods to mitigate them in training to improve performance.

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Detecting Legal Citations in United Kingdom Court Judgments
Holli Sargeant | Andreas Östling | Måns Magnusson

Legal citation detection in court judgments underpins reliable precedent mapping, citation analytics, and document retrieval. Extracting references to legislation and case law in the United Kingdom is especially challenging: citation styles have evolved over centuries, and judgments routinely cite foreign or historical authorities. We conduct the first systematic comparison of three modelling paradigms on this task using the Cambridge Law Corpus: (i) rule‐based regular expressions; (ii) transformer-based encoders (BERT, RoBERTa, LEGAL‐BERT, ModernBERT); and (iii) large language models (GPT‐4.1). We produced a gold‐standard high-quality corpus of 190 court judgments containing 45,179 fine-grained annotations for UK and non-UK legislation and case references. ModernBERT achieves a macro-averaged F1 of 93.3%, only marginally ahead of the other encoder-only models, yet significantly outperforming the strongest regular-expression baseline (35.42% F1) and GPT-4.1 (76.57% F1).

In this paper, we propose a “Generalization Stress Test” to assess Large Language Models’ (LLMs) generalization ability under slight and controlled perturbations, including option length, problem types, and irrelevant noun replacements. We achieve novel and significant findings that, despite high benchmark scores, LLMs exhibit severe accuracy drops and unexpected biases (e.g., preference for longer distractors) when faced with these minor but content-preserving modifications. For example, Qwen 2.5 1.5B’s MMLU score rises from 60 to 89 and drops from 89 to 36 when option lengths are changed without altering the question. Even GPT4o experiences a 25-point accuracy loss when problem types are changed, with a 6-point drop across all three modification categories. These analyses suggest that LLMs rely heavily on superficial cues rather than forming robust, abstract representations that generalize across formats, lexical variations, and shifts in irrelevant content.

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Studying the Role of Input-Neighbor Overlap in Retrieval-Augmented Language Models Training Efficiency
Ehsan Doostmohammadi | Marco Kuhlmann

Retrieval-augmented language models have demonstrated performance comparable to much larger models while requiring fewer computational resources. The effectiveness of these models crucially depends on the overlap between query and retrieved context, but the optimal degree of this overlap remains unexplored. In this paper, we systematically investigate how varying levels of query–context overlap affect model performance during both training and inference. Our experiments reveal that increased overlap initially has minimal effect, but substantially improves test-time perplexity and accelerates model learning above a critical threshold. Building on these findings, we demonstrate that deliberately increasing overlap through synthetic context can enhance data efficiency and reduce training time by approximately 40% without compromising performance. We specifically generate synthetic context through paraphrasing queries. We validate our perplexity-based findings on question-answering tasks, confirming that the benefits of retrieval-augmented language modeling extend to practical applications. Our results provide empirical evidence of significant optimization potential for retrieval mechanisms in language model pretraining.

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Principled Personas: Defining and Measuring the Intended Effects of Persona Prompting on Task Performance
Pedro Henrique Luz de Araujo | Paul Röttger | Dirk Hovy | Benjamin Roth

Expert persona prompting—assigning roles such as expert in math to language models—is widely used for task improvement. However, prior work shows mixed results on its effectiveness, and does not consider when and why personas should improve performance. We analyze the literature on persona prompting for task improvement and distill three desiderata: 1) performance advantage of expert personas, 2) robustness to irrelevant persona attributes, and 3) fidelity to persona attributes. We then evaluate 9 state-of-the-art LLMs across 27 tasks with respect to these desiderata. We find that expert personas usually lead to positive or non-significant performance changes. Surprisingly, models are highly sensitive to irrelevant persona details, with performance drops of almost 30 percentage points. In terms of fidelity, we find that while higher education, specialization, and domain-relatedness can boost performance, their effects are often inconsistent or negligible across tasks. We propose mitigation strategies to improve robustness—but find they only work for the largest, most capable models. Our findings underscore the need for more careful persona design and for evaluation schemes that reflect the intended effects of persona usage.

Large language models (LLMs) often leverage adapters, such as low-rank-based adapters, to achieve strong performance on downstream tasks. However, storing a separate adapter for each task significantly increases memory requirements, posing a challenge for resource-constrained environ ments such as mobile devices. Although model merging techniques can reduce storage costs, they typically result in substantial performance degradation. In this work, we introduce HydraOpt, a new model merging technique that capitalizes on the inherent similarities between the matrices of low-rank adapters. Unlike existing methods that produce a fixed trade-off between storage size and performance, HydraOpt allows us to navigate this spectrum of efficiency and performance. Our experiments show that HydraOpt significantly reduces storage size (48% reduction) compared to storing all adapters, while achieving competitive performance (0.2-1.8% drop). Furthermore, it outperforms existing merging techniques in terms of performance at the same or slightly worse storage efficiency.

Natural language chain-of-thought (N-CoT) and Program chain-of-thought (P-CoT) have emerged as two primary paradigms for large language models (LLMs) to solve mathematical reasoning problems. Current research typically endeavors to achieve unidirectional enhancement: P-CoT enhanced N-CoT or N-CoT enhanced P-CoT. In this paper, we seek to fully unleash the two paradigms’ strengths for mutual enhancement and ultimately achieve simultaneous improvements. We conduct a detailed analysis of the error types across two paradigms, based on which we propose Parrot, a novel training pipeline for mathematical problems: 1) Three target-designed subtasks integrate sequential P-CoT and N-CoT generation. 2) A subtask hybrid training strategy to facilitate natural language semantic transferability. 3) The converted N-CoT auxiliary reward is designed to alleviate the sparse rewards in P-CoT optimization. Extensive experiments demonstrate that Parrot significantly enhances both the performance of N-CoT and P-CoT, especially on N-CoT. Using Parrot SFT, the LLaMA2’s and CodeLLaMA’s N-CoT performance achieve gains of +21.87 and +21.48 on MathQA over the RL baseline, which is resource-intensive.

Vision-Language-Action (VLA) models have made substantial progress by leveraging the robust capabilities of Visual Language Models (VLMs). However, VLMs’ significant parameter size and autoregressive (AR) decoding nature impose considerable computational demands on VLA models. While Speculative Decoding (SD) has shown efficacy in accelerating Large Language Models (LLMs) by incorporating efficient drafting and parallel verification, allowing multiple tokens to be generated in one forward pass, its application to VLA models remains unexplored. This work introduces Spec-VLA, an SD framework designed to accelerate VLA models. Due to the difficulty of the action prediction task and the greedy decoding mechanism of the VLA models, the direct application of the advanced SD framework to the VLA prediction task yields a minor speed improvement. To boost the generation speed, we propose an effective mechanism to relax acceptance utilizing the relative distances represented by the action tokens of the VLA model. Empirical results across diverse test scenarios affirm the effectiveness of the Spec-VLA framework, and further analysis substantiates the impact of our proposed strategies, which enhance the acceptance length by 44%, achieving 1.42× speedup compared with the OpenVLA baseline, without compromising the success rate. The success of the Spec-VLA framework highlights the potential for broader application of speculative execution in VLA prediction scenarios.

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Leveraging Text-to-Text Transformers as Classifier Chain for Few-Shot Multi-Label Classification
Quang Anh Nguyen | Nadi Tomeh | Mustapha Lebbah | Thierry Charnois | Hanane Azzag

Multilabel text classification (MLTC) is an essential task in NLP applications. Traditional methods require extensive labeled data and are limited to fixed label sets. Extracting labels by LLMs is more effective and universal, but incurs high computational costs. In this work, we introduce a distillation-based T5 generalist model for zero-shot MLTC and few-shot fine-tuning. Our model accommodates variable label sets with general domain-agnostic pertaining, while modeling dependency between labels. Experiments show that our approach outperforms baselines of similar size on three few-shot tasks.Our code is available at https://anonymous.4open.science/r/t5-multilabel-0C32/README.md

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M-Wanda: Improving One-Shot Pruning for Multilingual LLMs
Rochelle Choenni | Ivan Titov

Multilingual LLM performance is often critically dependent on model size. With an eye on efficiency, this has led to a surge in interest in one-shot pruning methods that retain the benefits of large-scale pretraining while shrinking the model size. However, as pruning tends to come with performance loss, it is important to understand the trade-offs between multilinguality and sparsification. In this work, we study multilingual performance under different sparsity constraints and show that moderate ratios already substantially harm performance. To help bridge this gap, we propose M-Wanda, a pruning method that models cross-lingual variation by incorporating language-aware activation statistics into its pruning criterion and dynamically adjusts layerwise sparsity based on cross-lingual importance. We show that M-Wanda consistently improves performance at minimal additional costs. We are the first to explicitly optimize pruning to retain multilingual performance, and hope to inspire future advances in multilingual pruning.

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Beyond Hate Speech: NLP’s Challenges and Opportunities in Uncovering Dehumanizing Language
Hamidreza Saffari | Mohammadamin Shafiei | Hezhao Zhang | Lasana T. Harris | Nafise Sadat Moosavi

Dehumanization, i.e., denying human qualities to individuals or groups, is a particularly harmful form of hate speech that can normalize violence against marginalized communities. Despite advances in NLP for detecting general hate speech, approaches to identifying dehumanizing language remain limited due to scarce annotated data and the subtle nature of such expressions. In this work, we systematically evaluate four state-of-the-art large language models (LLMs) — Claude, GPT, Mistral, and Qwen — for dehumanization detection.Our results show that only one model—Claude—achieves strong performance (over 80% F₁) under an optimized configuration, while others, despite their capabilities, perform only moderately. Performance drops further when distinguishing dehumanization from related hate types such as derogation. We also identify systematic disparities across target groups: models tend to over-predict dehumanization for some identities (e.g., Gay men), while under-identifying it for others (e.g., Refugees). These findings motivate the need for systematic, group-level evaluation when applying pretrained language models to dehumanization detection tasks.

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Conflict-Aware Soft Prompting for Retrieval-Augmented Generation
Eunseong Choi | June Park | Hyeri Lee | Jongwuk Lee

Retrieval-augmented generation (RAG) enhances the capabilities of large language models (LLMs) by incorporating external knowledge into their input prompts. However, when the retrieved context contradicts the LLM’s parametric knowledge, it often fails to resolve the conflict between incorrect external context and correct parametric knowledge, known as context-memory conflict. To tackle this problem, we introduce Conflict-Aware REtrieval-Augmented Generation (CARE), consisting of a context assessor and a base LLM. The context assessor encodes external context into compact memory embeddings. Through grounded/adversarial soft prompting, the context assessor is trained to discern unreliable context and capture a guidance signal that directs reasoning toward the more reliable knowledge source. Extensive experiments show that CARE effectively mitigates context-memory conflicts, leading to an average performance gain of 5.0% on QA and fact-checking benchmarks, establishing a promising direction for trustworthy and adaptive RAG systems.

Role-playing capabilities in large language models (LLMs) often lack cognitive consistency in complex scenarios that require deep understanding and coherent reasoning. While recent reasoning models excel in math and coding tasks, they show limited effectiveness in open-ended role-playing scenarios. We introduce R-CHAR (Role-Consistent Hierarchical Adaptive Reasoning), a metacognition-driven framework that enhances role-playing performance through guided thinking trajectories synthesis and adaptive evaluation. Our approach demonstrates that concise thinking processes can achieve superior performance efficiently compared to elaborate reasoning chains in role-playing social intelligence tasks, outperforming existing specialized models. Experimental results on the SocialBench benchmark show significant and stable performance improvements across varying scenario complexities, showing particular strength in long-context comprehension (from 34.64% to 68.59%) and group-level social interactions. Our work advances the development of cognitively consistent role-playing systems, bridging the gap between surface-level mimicry and authentic character simulation.

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Annotating Training Data for Conditional Semantic Textual Similarity Measurement using Large Language Models
Gaifan Zhang | Yi Zhou | Danushka Bollegala

Semantic similarity between two sentences depends on the aspects considered between those sentences. To study this phenomenon, Deshpande et al. (2023) proposed the Conditional Semantic Textual Similarity (C-STS) task and annotated a human-rated similarity dataset containing pairs of sentences compared under two different conditions. However, Tu et al. (2024) found various annotation issues in this dataset and showed that manually re-annotating a small portion of it leads to more accurate C-STS models. Despite these pioneering efforts, the lack of large and accurately annotated C-STS datasets remains a blocker for making progress on this task as evidenced by the subpar performance of the C-STS models. To address this training data need, we resort to Large Language Models (LLMs) to correct the condition statements and similarity ratings in the original dataset proposed by Deshpande et al. (2023). Our proposed method is able to re-annotate a large training dataset for the C-STS task with minimal manual effort. Importantly, by training a supervised C-STS model on our cleaned and re-annotated dataset, we achieve a 5.4% statistically significant improvement in Spearman correlation. The re-annotated dataset is available at https://LivNLP.github.io/CSTS-reannotation.

Enabling digital humans to express rich emotions has significant applications in dialogue systems, gaming, and other interactive scenarios. While recent advances in talking head synthesis have achieved impressive results in lip synchronization, they tend to overlook the rich and dynamic nature of facial expressions. To fill this critical gap, we introduce an end-to-end text-to-expression model that explicitly focuses on emotional dynamics. Our model learns expressive facial variations in a continuous latent space and generates expressions that are diverse, fluid, and emotionally coherent. To support this task, we introduce EmoAva, a large-scale and high-quality dataset containing 15,000 text–3D expression pairs. Extensive experiments on both existing datasets and EmoAva demonstrate that our method significantly outperforms baselines across multiple evaluation metrics, marking a significant advancement in the field.

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Improving Online Job Advertisement Analysis via Compositional Entity Extraction
Kai Krüger | Johanna Binnewitt | Kathrin Ehmann | Stefan Winnige | Alan Akbik

We propose a compositional entity modeling framework for requirement extraction from online job advertisements (OJAs), representing complex, tree-like structures that connect atomic entities via typed relations. Based on this schema, we introduce GOJA, a manually annotated dataset of 500 German job ads that captures roles, tools, experience levels, attitudes, and their functional context. We report strong inter-annotator agreement and benchmark transformer models, demonstrating the feasibility of learning this structure. A focused case study on AI-related requirements illustrates the analytical value of our approach for labor market research.

LLMs with in-context learning (ICL) obtain remarkable performance but are sensitive to the quality of ICL examples. Prior works on ICL example selection explored unsupervised heuristic methods and supervised LLM-based methods, but they typically focus on the selection of individual examples and ignore correlations among examples. Researchers use the determinantal point process (DPP) to model negative correlations among examples to select diverse examples. However, the DPP fails to model positive correlations among examples, while ICL still requires the positive correlations of examples to ensure the consistency of examples, which provides a clear instruction for LLMs. In this paper, we propose an ICL example selection method based on the nonsymmetric determinantal point process (NDPP) to capture positive and negative correlations, considering both the diversity and the relevance among ICL examples. Specifically, we optimize NDPP via kernel decomposition-based MLE to fit a constructed pseudo-labeled dataset, where we also propose a low-rank decomposition to reduce the computational cost. Further, we perform query-aware kernel adaptation on our NDPP to customize the input query, and we select examples via a MAP inference based on the adapted NDPP. Experimental results show our model outperforms strong baselines in ICL example selection.

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Leveraging Cognitive Complexity of Texts for Contextualization in Dense Retrieval
Effrosyni Sokli | Georgios Peikos | Pranav Kasela | Gabriella Pasi

Dense Retrieval Models (DRMs) estimate the semantic similarity between queries and documents based on their embeddings. Prior studies highlight the importance of embedding contextualization in enhancing retrieval performance. To this aim, existing approaches primarily leverage token-level information derived from query/document interactions. In this paper, we introduce a novel DRM, namely DenseC3, which leverages query/document interactions based on the full embedding representations generated by a Transformer-based model. To enhance similarity estimation, DenseC3 integrates external linguistic information about the Cognitive Complexity of texts, enriching the contextualization of embeddings. We empirically evaluate our approach across seven benchmarks and three different IR tasks to assess the impact of Cognitive Complexity-aware query and document embeddings for contextualization in dense retrieval. Results show that our approach consistently outperforms standard fine-tuning techniques on lightweight bi-encoders (e.g., BERT-based) and traditional late-interaction models (i.e., ColBERT) across all benchmarks. On larger retrieval-optimized bi-encoders like Contriever, our model achieves comparable or higher performance on four of the considered evaluation benchmarks. Our findings suggest that Cognitive Complexity-aware embeddings enhance query and document representations, improving retrieval effectiveness in DRMs. Our code is available online at: https://github.com/FaySokli/DenseC3.

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Beyond Online Sampling: Bridging Offline-to-Online Alignment via Dynamic Data Transformation for LLMs
Zhang Zhang | Guhao Feng | Jian Guan | Di He | Wei Wu

While Direct Preference Optimization (DPO) eliminates complex reward modeling in aligning large language models (LLMs) with human preferences, its online variant faces significant efficiency bottlenecks due to costly real-time preference sampling and the reward model annotation. We propose a novel framework that bridges offline-to-online alignment by systematically transforming static datasets into dynamically adaptive equivalents, without the need for an explicit reward model. Our approach employs paraphrasing techniques to preserve response correctness while aligning data distributions with model-generated outputs, circumventing the need for resource-intensive online interactions. Experiments on mathematical reasoning and conversational tasks demonstrate that our method matches or exceeds the performance of a fully online DPO. This work establishes a computationally sustainable paradigm for LLM alignment, particularly benefiting scenarios requiring iterative preference updates and domain adaptation.

Humans can naturally identify, reason about, and explain anomalies in their environment. In computer vision, this long-standing challenge remains limited to industrial defects or unrealistic, synthetically generated anomalies, failing to capture the richness and unpredictability of real-world anomalies. In this work, we introduce CAVE, the first benchmark of real-world visual anomalies. CAVE supports three open-ended tasks: anomaly description, explanation, and justification; with fine-grained annotations for visual grounding and categorizing anomalies based on their visual manifestations, their complexity, severity, and commonness. These annotations draw inspiration from cognitive science research on how humans identify and resolve anomalies, providing a comprehensive framework for evaluating Vision-Language Models (VLMs) in detecting and understanding anomalies. We show that state-of-the-art VLMs struggle with visual anomaly perception and commonsense reasoning, even with advanced prompting strategies. By offering a realistic and cognitively grounded benchmark, CAVE serves as a valuable resource for advancing research in anomaly detection and commonsense reasoning in VLMs.

Large language models (LLMs) exhibit remarkable multilingual capabilities despite English-dominated pre-training, attributed to cross-lingual mechanisms during pre-training. Existing methods for enhancing cross-lingual transfer remain constrained by parallel resources, suffering from limited linguistic and domain coverage. We propose Cross-lingual In-context Pre-training (CrossIC-PT), a simple and scalable approach that enhances cross-lingual transfer by leveraging semantically related bilingual texts via simple next-word prediction. We construct CrossIC-PT samples by interleaving semantic-related bilingual Wikipedia documents into a single context window. To access window size constraints, we implement a systematic segmentation policy to split long bilingual document pairs into chunks while adjusting the sliding window mechanism to preserve contextual coherence. We further extend data availability through a semantic retrieval framework to construct CrossIC-PT samples from web-crawled corpus. Experimental results demonstrate that CrossIC-PT improves multilingual performance on three models (Llama-3.1-8B, Qwen2.5-7B, and Qwen2.5-1.5B) across six target languages, yielding performance gains of 3.79%, 3.99%, and 1.95%, respectively, with additional improvements after data augmentation.

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SemVink: Advancing VLMs’ Semantic Understanding of Optical Illusions via Visual Global Thinking
Sifan Li | Yujun Cai | Yiwei Wang

Vision-language models (VLMs) excel in semantic tasks but falter at a core human capability: detecting hidden content in optical illusions or AI-generated images through perceptual adjustments like zooming. We introduce HC-Bench, a benchmark of 112 images with hidden texts, objects, and illusions, revealing that leading VLMs achieve near-zero accuracy (0–5.36%) even with explicit prompting. Humans resolve such ambiguities instinctively, yet VLMs fail due to an overreliance on high-level semantics. Strikingly, we propose SemVink (Semantic Visual Thinking) by simply scaling images to low resolutions, which unlocks over 99% accuracy by eliminating redundant visual noise. This exposes a critical architectural flaw: VLMs prioritize abstract reasoning over low-level visual operations crucial for real-world robustness. Our work urges a shift toward hybrid models integrating multi-scale processing, bridging the gap between computational vision and human cognition for applications in medical imaging, security, and beyond.

Logical reasoning is essential for large language models (LLMs) to ensure accurate and coherent inference. However, LLMs struggle with reasoning order variations and fail to generalize across logically equivalent transformations. LLMs often rely on fixed sequential patterns rather than true logical understanding. To address this issue, we introduce an order-centric data augmentation framework based on commutativity in logical reasoning. We first randomly shuffle independent premises to introduce condition order augmentation. For reasoning steps, we construct a directed acyclic graph (DAG) to model dependencies between steps, which allows us to identify valid reorderings of steps while preserving logical correctness. By leveraging order-centric augmentations, models can develop a more flexible and generalized reasoning process. Finally, we conduct extensive experiments across multiple logical reasoning benchmarks, demonstrating that our method significantly enhances LLMs’ reasoning performance and adaptability to diverse logical structures. We release our codes and augmented data in https://anonymous.4open.science/r/Order-Centric-Data-Augmentation-822C.

Inductive link prediction is emerging as a key paradigm for real-world knowledge graphs (KGs), where new entities frequently appear and models must generalize to them without retraining. Predicting links in a KG faces the challenge of guessing previously unseen entities by leveraging generalizable node features such as subgraph structure, type annotations, and ontological constraints. However, explicit type information is often lacking or incomplete. Even when available, type information in most KGs is often coarse-grained, sparse, and prone to errors due to human annotation. In this work, we explore the potential of pre-trained language models (PLMs) to enrich node representations with implicit type signals. We introduce TyleR, a Type-less yet type-awaRe approach for subgraph-based inductive link prediction that leverages PLMs for semantic enrichment. Experiments on standard benchmarks demonstrate that TyleR outperforms state-of-the-art baselines in scenarios with scarce type annotations and sparse graph connectivity. To ensure reproducibility, we share our code at https://github.com/sisinflab/tyler .

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Extracting Linguistic Information from Large Language Models: Syntactic Relations and Derivational Knowledge
Tsedeniya Kinfe Temesgen | Marion Di Marco | Alexander Fraser

This paper presents a study of the linguistic knowledge and generalization capabilities of Large Language Models (LLMs), focusing ontheir morphosyntactic competence. We design three diagnostic tasks: (i) labeling syntactic information at the sentence level - identifying subjects, objects, and indirect objects; (ii) derivational decomposition at the word level - identifying morpheme boundaries and labeling thedecomposed sequence; and (iii) in-depth study of morphological decomposition in German and Amharic. We evaluate prompting strategies in GPT-4o and LLaMA 3.3-70B to extract different types of linguistic structure for typologically diverse languages. Our results showthat GPT-4o consistently outperforms LLaMA in all tasks; however, both models exhibit limitations and show little evidence of abstract morphological rule learning. Importantly, we show strong evidence that the models fail to learn underlying morphological structures. Therefore,raising important doubts about their ability to generalize.

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Beyond Correctness: Confidence-Aware Reward Modeling for Enhancing Large Language Model Reasoning
Qianxi He | Qingyu Ren | Shanzhe Lei | Xuhong Wang | Yingchun Wang

Recent advancements in large language models (LLMs) have shifted the post-training paradigm from traditional instruction tuning and human preference alignment toward reinforcement learning (RL) focused on reasoning capabilities. However, most current methods rely on rule-based evaluations of answer correctness, overlooking the importance of confidence-aware reasoning, especially for small to medium-sized models. These models often receive rewards for speculative answers without generating coherent reasoning chains. To address this limitation, we propose a novel confidence-based reward model tailored for enhancing STEM reasoning capabilities. Unlike conventional approaches, our model penalizes not only incorrect answers but also low-confidence correct responses, thereby promoting more robust and logically consistent reasoning. We validate the effectiveness of our approach through static evaluations, Best-of-N inference tests, and PPO-based RL training. Our method outperforms several state-of-the-art open-source reward models across diverse STEM benchmarks. We release our codes and model in https://github.com/qianxiHe147/C2RM.

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TrojanStego: Your Language Model Can Secretly Be A Steganographic Privacy Leaking Agent
Dominik Meier | Jan Philip Wahle | Paul Röttger | Terry Ruas | Bela Gipp

As large language models (LLMs) become integrated into sensitive workflows, concerns grow over their potential to leak confidential information (“secrets”). We propose TrojanStego, a novel threat model in which an adversary fine-tunes an LLM to embed sensitive context information into natural-looking outputs via linguistic steganography, without requiring explicit control over inference inputs. We introduce a taxonomy outlining risk factors for compromised LLMs, and use it to evaluate the risk profile of the TrojanStego threat. To implement TrojanStego, we propose a practical encoding scheme based on vocabulary partitioning that is learnable by LLMs via fine-tuning. Experimental results show that compromised models reliably transmit 32-bit secrets with 87% accuracy on held-out prompts, reaching over 97% accuracy using majority voting across three generations. Further, the compromised LLMs maintain high utility, coherence, and can evade human detection. Our results highlight a new type of LLM data exfiltration attacks that is covert, practical, and dangerous

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Frequency & Compositionality in Emergent Communication
Jean-Baptiste Sevestre | Emmanuel Dupoux

In natural languages, frequency and compositionality exhibit an inverse relationship: the most frequent words often resist regular patterns, developing idiosyncratic forms. This phenomenon, exemplified by irregular verbs where the most frequent verbs resist regular patterns, raises a compelling question: do artificial communication systems follow similar principles?Through systematic experiments with neural network agents in a referential game setting, and by manipulating input frequency through Zipfian distributions, we investigate if these systems mirror the irregular verbs phenomenon, where messages referring to frequent objects develop less compositional structure than messages referring to rare ones.We establish that compositionality is not an inherent property of the frequency itself and provide compelling evidence that limited data exposure, which frequency distributions naturally create, serves as a fundamental driver for the emergence of compositional structure in communication systems, offering insights into the cognitive and computational pressures that shape linguistic systems.

Speech summarization has become an essential tool for efficiently managing and accessing the growing volume of spoken and audiovisual content. However, despite its increasing importance, speech summarization remains loosely defined. The field intersects with several research areas, including speech recognition, text summarization, and specific applications like meeting summarization. This survey not only examines existing datasets and evaluation protocols, which are crucial for assessing the quality of summarization approaches, but also synthesizes recent developments in the field, highlighting the shift from traditional systems to advanced models like fine-tuned cascaded architectures and end-to-end solutions. In doing so, we surface the ongoing challenges, such as the need for realistic evaluation benchmarks, multilingual datasets, and long-context handling.

Role-Playing Language Agents (RPLAs) have emerged as a significant application direction for Large Language Models (LLMs). Existing approaches typically rely on prompt engineering or supervised fine-tuning to enable models to imitate character behaviors in specific scenarios, but often neglect the underlying cognitive mechanisms driving these behaviors. Inspired by cognitive psychology, we introduce CogDual, a novel RPLA adopting a cognize-then-respond reasoning paradigm. By jointly modeling external situational awareness and internal self-awareness, CogDual generates responses with improved character consistency and contextual alignment. To further optimize the performance, we employ reinforcement learning with two general-purpose reward schemes designed for open-domain text generation. Extensive experiments on the CoSER benchmark, as well as Cross-MR and LifeChoice, demonstrate that CogDual consistently outperforms existing baselines and generalizes effectively across diverse role-playing tasks.

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Assay2Mol: Large Language Model-based Drug Design Using BioAssay Context
Yifan Deng | Spencer S Ericksen | Anthony Gitter

Scientific databases aggregate vast amounts of quantitative data alongside descriptive text. In biochemistry, chemical screening assays evaluate the functional responses of candidate compounds against disease targets. Unstructured text that describes the biological mechanisms through which these targets operate, experimental screening protocols, and other attributes of assays offer rich information for new drug discovery campaigns, but has been untapped because of that unstructured format. We present Assay2Mol, a large language model-based workflow that can capitalize on the vast existing biochemical screening assays for early-stage drug discovery. Assay2Mol retrieves existing assay records involving targets similar to the new target and generates candidate compounds using in-context learning with the retrieved assay screening data. Assay2Mol outperforms recent machine learning approaches that generate candidate ligand compounds for target protein structures, while also promoting more synthesizable molecule generation.

Large Language Models (LLMs) have shown impressive capabilities in language understanding and generation, leading to growing interest in zero-shot relation triplet extraction (ZeroRTE), a task that aims to extract triplets for unseen relations without annotated data. However, existing methods typically depend on costly fine-tuning and lack the structured semantic guidance required for accurate and interpretable extraction. To overcome these limitations, we propose FrameRTE, a novel ZeroRTE framework that adopts a “frame first, then extract” paradigm. Rather than extracting triplets directly, FrameRTE first constructs high-quality Relation Semantic Frames (RSFs) through a unified pipeline that integrates frame retrieval, synthesis, and enhancement. These RSFs serve as structured and interpretable knowledge scaffolds that guide frozen LLMs in the extraction process. Building upon these RSFs, we further introduce a human-inspired three-stage reasoning pipeline consisting of semantic frame evocation, frame-guided triplet extraction, and core frame elements validation to achieve semantically constrained extraction. Experiments demonstrate that FrameRTE achieves competitive zero-shot performance on multiple benchmarks. Moreover, the RSFs we construct serve as high-quality semantic resources that can enhance other extraction methods, showcasing the synergy between linguistic knowledge and foundation models.

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MrGuard: A Multilingual Reasoning Guardrail for Universal LLM Safety
Yahan Yang | Soham Dan | Shuo Li | Dan Roth | Insup Lee

Large Language Models (LLMs) are susceptible to adversarial attacks such as jailbreaking, which can elicit harmful or unsafe behaviors. This vulnerability is exacerbated in multilingual settings, where multilingual safety-aligned data is often limited. Thus, developing a guardrail capable of detecting and filtering unsafe content across diverse languages is critical for deploying LLMs in real-world applications. In this work, we introduce a multilingual guardrail with reasoning for prompt classification. Our method consists of: (1) synthetic multilingual data generation incorporating culturally and linguistically nuanced variants, (2) supervised fine-tuning, and (3) a curriculum-based Group Relative Policy Optimization (GRPO) framework that further improves performance. Experimental results demonstrate that our multilingual guardrail, MrGuard, consistently outperforms recent baselines across both in-domain and out-of-domain languages by more than 15%. We also evaluate MrGuard’s robustness to multilingual variations, such as code-switching and low-resource language distractors in the prompt, and demonstrate that it preserves safety judgments under these challenging conditions. The multilingual reasoning capability of our guardrail enables it to generate explanations, which are particularly useful for understanding language-specific risks and ambiguities in multilingual content moderation.

Table question answering (TQA) requires accurate retrieval and reasoning over tabular data. Existing approaches attempt to retrieve query-relevant content before leveraging large language models (LLMs) to reason over long tables. However, these methods often fail to accurately retrieve contextually relevant data which results in information loss, and suffer from excessive encoding overhead. In this paper, we propose TALON, a multi-agent framework designed for question answering over long tables. TALON features a planning agent that iteratively invokes a tool agent to access and manipulate tabular data based on intermediate feedback, which progressively collects necessary information for answer generation, while a critic agent ensures accuracy and efficiency in tool usage and planning. In order to comprehensively assess the effectiveness of TALON, we introduce two benchmarks derived from the WikiTableQuestion and BIRD-SQL datasets, which contain tables ranging from 50 to over 10,000 rows. Experiments demonstrate that TALON achieves average accuracy improvements of 7.5% and 12.0% across all language models, establishing a new state-of-the-art in long-table question answering. Our code is publicly available at: https://github.com/Wwestmoon/TALON.

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You Are What You Train: Effects of Data Composition on Training Context-aware Machine Translation Models
Pawel Maka | Yusuf Can Semerci | Jan Scholtes | Gerasimos Spanakis

Achieving human-level translations requires leveraging context to ensure coherence and handle complex phenomena like pronoun disambiguation. Sparsity of contextually rich examples in the standard training data has been hypothesized as the reason for the difficulty of context utilization. In this work, we systematically validate this claim in both single- and multilingual settings by constructing training datasets with a controlled proportions of contextually relevant examples. We demonstrate a strong association between training data sparsity and model performance confirming sparsity as a key bottleneck. Importantly, we reveal that improvements in one contextual phenomenon do no generalize to others. While we observe some cross-lingual transfer, it is not significantly higher between languages within the same sub-family. Finally, we propose and empirically evaluate two training strategies designed to leverage the available data. These strategies improve context utilization, resulting in accuracy gains of up to 6 and 8 percentage points on the ctxPro evaluation in single- and multilingual settings respectively.

The paper shows that parameter-efficient reinforcement learning (PE-RL) is a highly effective training regime to improve large language models’ (LLMs) ability to answer queries on sensitive topics with a Neutral Point of View (NPOV), i.e. to provide significantly more informative, diverse and impartial answers. This is shown by evaluating PE-RL and multiple strong baselines—including LoRA finetuning (strongest baseline), SFT and RLHF. PE-RL not only improves on overall NPOV quality compared to the strongest baseline (97.06% → 99.08%), but also scores much higher on features linguists identify as key to separating good answers from the best answers (60.25% → 85.21% for presence of supportive details, 68.74% → 91.43% for absence of oversimplification). A qualitative analysis corroborates this. Finally, our evaluation finds no statistical differences between results on topics that appear in the training dataset and those on separated evaluation topics, which provides strong evidence that our approach to training PE-RL exhibits very effective out of topic generalization. To enable the study, and enable further future studies we also release the dataset, SHQ-NPOV, and provide a methodology to create such datasets through iterative rounds of human peer-critique and annotator training.

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Randomized Smoothing Meets Vision-Language Models
Emmanouil Seferis | Changshun Wu | Stefanos Kollias | Saddek Bensalem | Chih-Hong Cheng

Randomized smoothing (RS) is one of the prominent techniques to ensure the correctness of machine learning models, where point-wise robustness certificates can be derived analytically. While RS is well understood for classification, its application to generative models is unclear, since their outputs are sequences rather than labels. We resolve this by connecting generative outputs to an oracle classification task and showing that RS can still be enabled: the final response can be classified as a discrete action (e.g., service-robot commands in VLAs), as harmful vs. harmless (content moderation or toxicity detection in VLMs), or even applying oracles to cluster answers into semantically equivalent ones. Provided that the error rate for the oracle classifier comparison is bounded, we develop the theory that associates the number of samples with the corresponding robustness radius. We further derive improved scaling laws analytically relating the certified radius and accuracy to the number of samples, showing that the earlier result of 2 to 3 orders of magnitude fewer samples sufficing with minimal loss remains valid even under weaker assumptions. Together, these advances make robustness certification both well-defined and computationally feasible for state-of-the-art VLMs, as validated against recent jailbreak-style adversarial attacks.

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PIIvot: A Lightweight NLP Anonymization Framework for Question-Anchored Tutoring Dialogues
Matthew Zent | Digory Smith | Simon Woodhead

Personally identifiable information (PII) anonymization is a high-stakes task that poses a barrier to many open-science data sharing initiatives. While PII identification has made large strides in recent years, in practice, error thresholds and the recall/precision trade-off still limit the uptake of these anonymization pipelines. We present PIIvot, a lighter-weight framework for PII anonymization that leverages knowledge of the data context to simplify the PII detection problem. To demonstrate its effectiveness, we also contribute QATD_2k, the largest open-source real-world tutoring dataset of its kind, to support the demand for quality educational dialogue data.

Trustworthiness in healthcare question-answering (QA) systems is important for ensuring patient safety, clinical effectiveness, and user confidence. As large language models (LLMs) become increasingly integrated into medical settings, the reliability of their responses directly influences clinical decision-making and patient outcomes. However, achieving comprehensive trustworthiness in medical QA poses significant challenges due to the inherent complexity of healthcare data, the critical nature of clinical scenarios, and the multifaceted dimensions of trustworthy AI. In this survey, we systematically examine six key dimensions of trustworthiness in medical QA, i.e., Factuality, Robustness, Fairness, Safety, Explainability, and Calibration. We review how each dimension is evaluated in existing LLM-based medical QA systems. We compile and compare major benchmarks designed to assess these dimensions and analyze evaluation-guided techniques that drive model improvements, such as retrieval-augmented grounding, adversarial fine-tuning, and safety alignment. Finally, we identify open challenges—such as scalable expert evaluation, integrated multi-dimensional metrics, and real-world deployment studies—and propose future research directions to advance the safe, reliable, and transparent deployment of LLM-powered medical QA.

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Unpacking Let Alone: Human-Scale Models Generalize to a Rare Construction in Form but not Meaning
Wesley Scivetti | Tatsuya Aoyama | Ethan Wilcox | Nathan Schneider

Humans have a remarkable ability to acquire and understand grammatical phenomena that are seen rarely, if ever, during childhood. Recent evidence suggests that language models with human-scale pretraining data may possess a similar ability by generalizing from frequent to rare constructions. However, it remains an open question how widespread this generalization ability is, and to what extent this knowledge extends to meanings of rare constructions, as opposed to just their forms. We fill this gap by testing human-scale transformer language models on their knowledge of both the form and meaning of the (rare and quirky) English Let-Alone construction. To evaluate our LMs we construct a bespoke synthetic benchmark that targets syntactic and semantic properties of the construction. We find that human-scale LMs are sensitive to form, even when related constructions are filtered from the dataset. However, human-scale LMs do not make correct generalizations about Let-Alone’s meaning. These results point to an asymmetry in the current architectures’ sample efficiency between language form and meaning, something which is not present in human language learners.

BOUQuET is a multi-way, multicentric and multi-register/domain dataset and benchmark, and a broader collaborative initiative. This dataset is handcrafted in 8 non-English languages (i.e. Egyptian Arabic and Modern Standard Arabic, French, German, Hindi, Indonesian, Mandarin Chinese, Russian, and Spanish). Each of these source languages are representative of the most widely spoken ones and therefore they have the potential to serve as pivot languages that will enable more accurate translations. The dataset is multicentric to enforce representation of multilingual language features. In addition, the dataset goes beyond the sentence level, as it is organized in paragraphs of various lengths. Compared with related machine translation datasets, we show that BOUQuET has a broader representation of domains while simplifying the translation task for non-experts. Therefore, BOUQuET is specially suitable for crowd-source extension for which we are launching a call aim-ing at collecting a multi-way parallel corpus covering any written language. The dataset is freely available at https://huggingface.co/datasets/facebook/bouquet.

The evolution of text-to-image (T2I) generation techniques has introduced new capabilities for information visualization, with the potential to advance knowledge democratization and education. In this paper, we investigate how T2I models can be adapted to generate educational health knowledge contents, exploring their potential to make healthcare information more visually accessible and engaging. We explore methods to harness recent T2I models for generating health knowledge flashcards—visual educational aids that present healthcare information through appealing and concise imagery. To support this goal, we curated a diverse, high-quality healthcare knowledge flashcard dataset containing 2,034 samples sourced from credible medical resources. We further validate the effectiveness of fine-tuning open-source models with our dataset, demonstrating their promise as specialized health flashcard generators. Our code and dataset are available at: https://github.com/med-air/HealthCards.

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When Life Gives You Samples: The Benefits of Scaling up Inference Compute for Multilingual LLMs
Ammar Khairi | Daniel D’souza | Ye Shen | Julia Kreutzer | Sara Hooker

Recent advancements in large language models (LLMs) have shifted focus toward scaling inference-time compute—improving performance without retraining the model. A common approach is to sample multiple outputs in parallel, and select one of these as the final output. While existing work has focused on English and specific domains, we study how to robustly scale inference-time compute in a multilingual, multi-task setting: spanning open-ended generations, math and translation tasks, for open models at 8B and 111B scale, across seven languages. Our findings highlight the need for tailored sampling and selection strategies. We propose novel solutions tailored for this multi-faceted inference scenario, demonstrating notable gains across languages and tasks. Our methods achieve an average +6.8 jump in win-rates for 8B models on m-ArenaHard-v2.0 prompts in non-English languages against proprietary models like Gemini. At larger scale, our 111B model shows a +9.0 improvement with just five samples compared to single-sample decoding. These results emphasize the importance of language- and task-aware approaches to democratize inference-time improvements.

Large language model (LLM)-driven multi-agent systems (MAS) are transforming how humans and AIs collaboratively generate ideas and artifacts. While existing surveys provide comprehensive overviews of MAS infrastructures, they largely overlook the dimension of creativity, including how novel outputs are generated and evaluated, how creativity informs agent personas, and how creative workflows are coordinated. This is the first survey dedicated to creativity in MAS. We focus on text and image generation tasks, and present:(1) a taxonomy of agent proactivity and persona design;(2) an overview of generation techniques, including divergent exploration, iterative refinement, and collaborative synthesis, as well as relevant datasets and evaluation metrics; and(3) a discussion of key challenges, such as inconsistent evaluation standards, insufficient bias mitigation, coordination conflicts, and the lack of unified benchmarks.This survey offers a structured framework and roadmap for advancing the development, evaluation, and standardization of creative MAS.

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Context and POS in Action: A Comparative Study of Chinese Homonym Disambiguation in Human and Language Models
Xie Chenwei | Matthew King-Hang Ma | Wenbo Wang | William Shiyuan Wang

Ambiguity is pervasive in language, yet we resolve it effortlessly and unconsciously, often aided by context and part-of-speech (POS) cues. This study investigates how context similarity and POS influence homonym disambiguation in humans and large language models (LLMs). To enable comparable analyses between humans and LLMs, we first built an expert-curated sentence-pair dataset, manipulating context similarity and homonym POS categories (nouns vs. verbs). Participants (n = 55) and LLMs (via prompting) were asked to rate the sense similarity of target homonyms embedded within each sentence on a 7-point Likert scale. We found that context similarity influenced both groups similarly, but only humans utilized POS information, likely contributing to their superior performance. Model-derived metrics (surprisal, entropy) predicted human reaction times, and angular similarity between homonym representations accounted for additional variance, highlighting the roles of both expectation-based and semantic processes. Psycholinguistic factors like age of acquisition affected only human responses, underscoring distinct language acquisition mechanisms. Together, our findings illustrate how context and POS information interactively shape homonym resolution in humans, while exposing the limitations of current language models in capturing these nuanced processes. Dataset and codes are publicly available at https://github.com/neurothew/context-and-pos-in-action.

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Attacking Misinformation Detection Using Adversarial Examples Generated by Language Models
Piotr Przybyła | Euan McGill | Horacio Saggion

Large language models have many beneficial applications, but can they also be used to attack content-filtering algorithms in social media platforms? We investigate the challenge of generating adversarial examples to test the robustness of text classification algorithms detecting low-credibility content, including propaganda, false claims, rumours and hyperpartisan news. We focus on simulation of content moderation by setting realistic limits on the number of queries an attacker is allowed to attempt. Within our solution (TREPAT), initial rephrasings are generated by large language models with prompts inspired by meaning-preserving NLP tasks, such as text simplification and style transfer. Subsequently, these modifications are decomposed into small changes, applied through beam search procedure, until the victim classifier changes its decision. We perform (1) quantitative evaluation using various prompts, models and query limits, (2) targeted manual assessment of the generated text and (3) qualitative linguistic analysis. The results confirm the superiority of our approach in the constrained scenario, especially in case of long input text (news articles), where exhaustive search is not feasible.

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Leveraging Loanword Constraints for Improving Machine Translation in a Low-Resource Multilingual Context
Felermino D. M. A. Ali | Henrique Lopes Cardoso | Rui Sousa-Silva

This research investigates how to improve machine translation systems for low-resource languages by integrating loanword constraints as external linguistic knowledge. Focusing on the Portuguese-Emakhuwa language pair, which exhibits significant lexical borrowing, we address the challenge of effectively adapting loanwords during the translation process. To tackle this, we propose a novel approach that augments source sentences with loanword constraints, explicitly linking source-language loanwords to their target-language equivalents. Then, we perform supervised fine-tuning on multilingual neural machine translation models and multiple Large Language Models of different sizes. Our results demonstrate that incorporating loanword constraints leads to significant improvements in translation quality as well as in handling loanword adaptation correctly in target languages, as measured by different machine translation metrics. This approach offers a promising direction for improving machine translation performance in low-resource settings characterized by frequent lexical borrowing.

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Linguistic Neuron Overlap Patterns to Facilitate Cross-lingual Transfer on Low-resource Languages
Yuemei Xu | Kexin Xu | Jian Zhou | Ling Hu | Lin Gui

The current Large Language Models (LLMs) face significant challenges in improving their performance on low-resource languagesand urgently need data-efficient methods without costly fine-tuning.From the perspective of language-bridge,we propose a simple yet effective method, namely BridgeX-ICL, to improve the zero-shot Cross-lingual In-Context Learning (X-ICL) for low-resource languages. Unlike existing works focusing on language-specific neurons,BridgeX-ICL explores whether sharingneurons can improve cross-lingual performance in LLMs.We construct neuron probe data from the ground-truth MUSE bilingual dictionaries, and define a subset of language overlap neurons accordingly to ensure full activation of these anchored neurons.Subsequently, we propose an HSIC-based metric to quantify LLMs’ internal linguistic spectrumbased on overlapping neurons, guiding optimal bridge selection.The experiments conducted on 4 cross-lingual tasks and 15 language pairs from 7diverse families, covering both high-low and moderate-low pairs, validate the effectiveness of BridgeX-ICL and offer empirical insights into the underlying multilingual mechanisms of LLMs. The code is publicly available at https://github.com/xuyuemei/BridgeX-ICL.

We investigate the potential of LLM-generated synthetic data for improving low-resource Machine Translation (MT). Focusing on seven diverse target languages, we construct a document-level synthetic corpus from English Europarl, and extend it via pivoting to 147 additional language pairs. Automatic and human evaluation confirm its overall high quality. We study its practical application by (i) identifying effective training regimes, (ii) comparing our data with the HPLT dataset, (iii) studying the effect of varying training data size, and (iiii) testing its utility beyond English-centric MT. Finally, we introduce SynOPUS, a public repository for synthetic parallel datasets. Our findings show that LLM-generated synthetic data, even when noisy, can substantially improve MT performance for low-resource languages.

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Tailoring Table Retrieval from a Field-aware Hybrid Matching Perspective
Da Li | Keping Bi | Jiafeng Guo | Xueqi Cheng

Table retrieval, essential for accessing information through tabular data, is less explored compared to text retrieval. The row/column structure and distinct fields of tables (including titles, headers, and cells) present unique challenges. For example, different table fields have varying matching preferences: cells may favor finer-grained (word/phrase level) matching over broader (sentence/passage level) matching due to their fragmented and detailed nature, unlike titles. This necessitates a table-specific retriever to accommodate the various matching needs of each table field. Therefore, we introduce a Table-tailored HYbrid Matching rEtriever (THYME), which approaches table retrieval from a field-aware hybrid matching perspective. Empirical results on two table retrieval benchmarks, NQ-TABLES and OTT-QA, show that THYME significantly outperforms state-of-the-art baselines. Comprehensive analyses have confirmed the differing matching preferences across table fields and validated the efficacy of THYME.

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Randomly Removing 50% of Dimensions in Text Embeddings has Minimal Impact on Retrieval and Classification Tasks
Sotaro Takeshita | Yurina Takeshita | Daniel Ruffinelli | Simone Paolo Ponzetto

In this paper, we study the surprising impact that truncating text embeddings has on downstream performance. We consistently observe across 6 state-of-the-art text encoders and 26 downstream tasks, that randomly removing up to 50% of embedding dimensions results in only a minor drop in performance, less than 10%, in retrieval and classification tasks. Given the benefits of using smaller-sized embeddings, as well as the potential insights about text encoding, we study this phenomenon and find that, contrary to what is suggested in prior work, this is not the result of an ineffective use of representation space. Instead, we find that a large number of uniformly distributed dimensions actually cause an increase in performance when removed. This would explain why, on average, removing a large number of embedding dimensions results in a marginal drop in performance. We make similar observations when truncating the embeddings used by large language models to make next-token predictions on generative tasks, suggesting that this phenomenon is not isolated to classification or retrieval tasks.

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Morables: A Benchmark for Assessing Abstract Moral Reasoning in LLMs with Fables
Matteo Marcuzzo | Alessandro Zangari | Andrea Albarelli | Jose Camacho-Collados | Mohammad Taher Pilehvar

As LLMs excel on standard reading comprehension benchmarks, attention is shifting toward evaluating their capacity for complex abstract reasoning and inference. Literature-based benchmarks, with their rich narrative and moral depth, provide a compelling framework for evaluating such deeper comprehension skills. Here, we present Morables, a human-verified benchmark built from fables and short stories drawn from historical literature. The main task is structured as multiple-choice questions targeting moral inference, with carefully crafted distractors that challenge models to go beyond shallow, extractive question answering. To further stress-test model robustness, we introduce adversarial variants designed to surface LLM vulnerabilities and shortcuts due to issues such as data contamination. Our findings show that, while larger models outperform smaller ones, they remain susceptible to adversarial manipulation and often rely on superficial patterns rather than true moral reasoning. This brittleness results in significant self-contradiction, with the best models refuting their own answers in roughly 20% of cases depending on the framing of the moral choice. Interestingly, reasoning-enhanced models fail to bridge this gap, suggesting that scale - not reasoning ability - is the primary driver of performance.

Information retrieval (IR) is the task of finding relevant documents in response to a user query. Although Spanish is the second most spoken native language, there are few Spanish IR datasets, which limits the development of information access tools for Spanish speakers. We introduce MessIRve, a large-scale Spanish IR dataset with almost 700,000 queries from Google’s autocomplete API and relevant documents sourced from Wikipedia. MessIRve’s queries reflect diverse Spanish-speaking regions, unlike other datasets that are translated from English or do not consider dialectal variations. The large size of the dataset allows it to cover a wide variety of topics, unlike smaller datasets. We provide a comprehensive description of the dataset, comparisons with existing datasets, and baseline evaluations of prominent IR models. Our contributions aim to advance Spanish IR research and improve information access for Spanish speakers.

This paper introduces AFRIDOC-MT, a document-level multi-parallel translation dataset covering English and five African languages: Amharic, Hausa, Swahili, Yorùbá, and Zulu. The dataset comprises 334 health and 271 information technology news documents, all human-translated from English to these languages. We conduct document-level translation benchmark experiments by evaluating the ability of neural machine translation (NMT) models and large language models (LLMs) to translate between English and these languages, at both the sentence and pseudo-document levels, the outputs being realigned to form complete documents for evaluation. Our results indicate that NLLB-200 achieves the best average performance among the standard NMT models, while GPT-4o outperforms general-purpose LLMs. Fine-tuning selected models leads to substantial performance gains, but models trained on sentences struggle to generalize effectively to longer documents. Furthermore, our analysis reveals that some LLMs exhibit issues such as under-generation, over-generation, repetition of words and phrases, and off-target translations, specifically for translation into African languages.

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Charting the Landscape of African NLP: Mapping Progress and Shaping the Road Ahead
Jesujoba Oluwadara Alabi | Michael A. Hedderich | David Ifeoluwa Adelani | Dietrich Klakow

With over 2,000 languages and potentially millions of speakers, Africa represents one of the richest linguistic regions in the world. Yet, this diversity is scarcely reflected in state-of-the-art natural language processing (NLP) systems and large language models (LLMs), which predominantly support a narrow set of high-resource languages. This exclusion not only limits the reach and utility of modern NLP technologies but also risks widening the digital divide across linguistic communities. Nevertheless, NLP research on African languages is active and growing. In recent years, there has been a surge of interest in this area, driven by several factors—including the creation of multilingual language resources, the rise of community-led initiatives, and increased support through funding programs. In this survey, we analyze 884 research papers on NLP for African languages published over the past five years, offering a comprehensive overview of recent progress across core tasks. We identify key trends shaping the field and conclude by outlining promising directions to foster more inclusive and sustainable NLP research for African languages.

Existing video benchmarks often resemble image-based benchmarks, with question types like “What actions does the person perform throughout the video?” or “What color is the woman’s dress in the video?” For these, models can often answer by scanning just a few key frames, without deep temporal reasoning. This limits our ability to assess whether large vision-language models (LVLMs) can truly think with videos rather than perform superficial frame-level analysis. To address this, we introduce , a benchmark specifically designed to evaluate whether LVLMs can genuinely think with videos. Unlike prior benchmarks, emphasizes comprehensive video understanding beyond static image cues. It consists of 3,269 videos and over 4,342 highly visual-centric questions across 11 categories, including Trajectory Analysis, Temporal Reasoning, and Forensics Detection. All questions are carefully crafted by human annotators and require watching the entire video and reasoning over full video context—this is what we mean by thinking with video. These questions cannot be answered by scanning selected frames or relying on text alone. In human evaluations, achieves 94.82% accuracy, but current LVLMs face significant challenges. Even the best-performing model, GPT-o3, reaches only 66.43%, highlighting that LVLMs still struggle to move beyond surface-level reasoning to truly think with videos. We publicly release our benchmark and code at https://github.com/aiming-lab/GLIMPSE.

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Social Bias in Multilingual Language Models: A Survey
Lance Calvin Lim Gamboa | Yue Feng | Mark G. Lee

Pretrained multilingual models exhibit the same social bias as models processing English texts. This systematic review analyzes emerging research that extends bias evaluation and mitigation approaches into multilingual and non-English contexts. We examine these studies with respect to linguistic diversity, cultural awareness, and their choice of evaluation metrics and mitigation techniques. Our survey illuminates gaps in the field’s dominant methodological design choices (e.g., preference for certain languages, scarcity of multilingual mitigation experiments) while cataloging common issues encountered and solutions implemented in adapting bias benchmarks across languages and cultures. Drawing from the implications of our findings, we chart directions for future research that can reinforce the multilingual bias literature’s inclusivity, cross-cultural appropriateness, and alignment with state-of-the-art NLP advancements.

Knowledge graph question answering (KGQA) presents significant challenges due to the structural and semantic variations across input graphs. Existing works rely on Large Language Model (LLM) agents for graph traversal and retrieval; an approach that is sensitive to traversal initialization, as it is prone to entity linking errors and may not generalize well to custom (“bring-your-own”) KGs. We introduce BYOKG-RAG, a framework that enhances KGQA by synergistically combining LLMs with specialized graph retrieval tools. In BYOKG-RAG, LLMs generate critical graph artifacts (question entities, candidate answers, reasoning paths, and OpenCypher queries), and graph tools link these artifacts to the KG and retrieve relevant graph context. The retrieved context enables the LLM to iteratively refine its graph linking and retrieval, before final answer generation. By retrieving context from different graph tools, BYOKG-RAG offers a more general and robust solution for QA over custom KGs. Through experiments on five benchmarks spanning diverse KG types, we demonstrate that BYOKG-RAG outperforms the second-best graph retrieval method by 4.5% points while showing better generalization to custom KGs. BYOKG-RAG framework is open-sourced at https://github.com/awslabs/graphrag-toolkit.

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Synth-SBDH: A Synthetic Dataset of Social and Behavioral Determinants of Health for Clinical Text
Avijit Mitra | Zhichao Yang | Emily Druhl | Raelene Goodwin | Hong Yu

Social and behavioral determinants of health (SBDH) play a crucial role in health outcomes and are frequently documented in clinical text. Automatically extracting SBDH information from clinical text relies on publicly available good-quality datasets. However, existing SBDH datasets exhibit substantial limitations in their availability and coverage. In this study, we introduce Synth-SBDH, a novel synthetic dataset with detailed SBDH annotations, encompassing status, temporal information, and rationale across 15 SBDH categories. We showcase the utility of Synth-SBDH on three tasks using real-world clinical datasets from two distinct hospital settings, highlighting its versatility, generalizability, and distillation capabilities. Models trained on Synth-SBDH consistently outperform counterparts with no Synth-SBDH training, achieving up to 63.75% macro-F improvements. Additionally, Synth-SBDH proves effective for rare SBDH categories and under-resource constraints while being substantially cheaper than expert-annotated real-world data. Human evaluation reveals a 71.06% Human-LLM alignment and uncovers areas for future refinements.

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Pun Unintended: LLMs and the Illusion of Humor Understanding
Alessandro Zangari | Matteo Marcuzzo | Andrea Albarelli | Mohammad Taher Pilehvar | Jose Camacho-Collados

Puns are a form of humorous wordplay that exploits polysemy and phonetic similarity. While LLMs have shown promise in detecting puns, we show in this paper that their understanding often remains shallow, lacking the nuanced grasp typical of human interpretation. By systematically analyzing and reformulating existing pun benchmarks, we demonstrate how subtle changes in puns are sufficient to mislead LLMs. Our contributions include comprehensive and nuanced pun detection benchmarks, human evaluation across recent LLMs, and an analysis of the robustness challenges these models face in processing puns.

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RACCooN: Versatile Instructional Video Editing with Auto-Generated Narratives
Jaehong Yoon | Shoubin Yu | Mohit Bansal

Recent video generative models primarily rely on detailed, labor-intensive text prompts for tasks, like inpainting or style editing, limiting adaptability for personal/raw videos. This paper proposes RACCooN, a versatile and user-friendly video-to-paragraph-to-video editing method, supporting diverse video editing capabilities, such as removal, addition, and modification, through a unified pipeline. RACCooN consists of two principal stages: Video-to-Paragraph (V2P), which automatically generates structured video descriptions capturing both scene context and object details, and Paragraph-to-Video (P2V), where users (optionally) refine these descriptions to guide a video diffusion model for flexible content modifications, including removing, changing subjects, and/or adding new objects. Key contributions of RACCooN include: (1) A multi-granular spatiotemporal pooling strategy for structured video understanding, capturing both broad context and fine-grained details of major objects to enable precise text-based video editing without the need for complex human annotations. (2) A video generative model fine-tuned on our curated video-paragraph-mask dataset, enhances the editing and inpainting quality. (3) The capability to seamlessly generate new objects in videos by forecasting their movements through automatically generated mask planning. In the end, users can easily edit complex videos with RACCooN’s automatic explanations and guidance. We demonstrate its versatile capabilities in video-to-paragraph generation (up to 9.4%p absolute improvement in human evaluations) and video content editing (relative to 49.7% lower FVD), and can be integrated with SoTA video generation models for further enhancement.

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Pre-trained Models Perform the Best When Token Distributions Follow Zipf’s Law
Yanjin He | Qingkai Zeng | Meng Jiang

Tokenization is a fundamental step in natural language processing (NLP) and other sequence modeling domains, where the choice of vocabulary size significantly impacts model performance. Despite its importance, selecting an optimal vocabulary size remains underexplored, typically relying on heuristics or dataset-specific choices. In this work, we propose a principled method for determining the vocabulary size by analyzing token frequency distributions through Zipf’s law. We show that downstream task performance correlates with how closely token distributions follow power-law behavior, and that aligning with Zipfian scaling improves both model efficiency and effectiveness. Extensive experiments across NLP, genomics, and chemistry demonstrate that models consistently achieve peak performance when the token distribution closely adheres to Zipf’s law, establishing Zipfian alignment as a robust and generalizable criterion for vocabulary size selection. The code and data are available at: https://github.com/yanjinhe/Tokenizer

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Do RAG Systems Really Suffer From Positional Bias?
Florin Cuconasu | Simone Filice | Guy Horowitz | Yoelle Maarek | Fabrizio Silvestri

Retrieval Augmented Generation enhances LLM accuracy by adding passages retrieved from an external corpus to the LLM prompt. This paper investigates how positional bias - the tendency of LLMs to weight information differently based on its position in the prompt - affects not only the LLM’s capability to capitalize on relevant passages, but also its susceptibility to distracting passages. Through extensive experiments on three benchmarks, we show how state-of-the-art retrieval pipelines, while attempting to retrieve relevant passages, systematically bring highly distracting ones to the top ranks, with over 60% of queries containing at least one highly distracting passage among the top-10 retrieved passages. As a result, the impact of the LLM positional bias, which in controlled settings is often reported as very prominent by related works, is actually marginal in real scenarios since both relevant and distracting passages are, in turn, penalized. Indeed, our findings reveal that sophisticated strategies that attempt to rearrange the passages based on LLM positional preferences do not perform better than random shuffling.

Recent progress in large language models (LLMs) has enabled the automated processing of lengthy documents even without supervised training on a task-specific dataset. Yet, their zero-shot performance in complex tasks as opposed to straightforward information extraction tasks remains suboptimal. One feasible approach for tasks with lengthy, complex input is to first summarize the document and then apply supervised fine-tuning to the summary. However, the summarization process inevitably results in some loss of information. In this study we present a method for processing the summaries of long documents aimed to capture different important aspects of the original document. We hypothesize that LLM summaries generated with different aspect-oriented prompts contain different information signals, and we propose methods to measure these differences. We introduce approaches to effectively integrate signals from these different summaries for supervised training of transformer models. We validate our hypotheses on a high-impact task – 30-day readmission prediction from a psychiatric discharge – using real-world data from four hospitals, and show that our proposed method increases the prediction performance for the complex task of predicting patient outcome.

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Adapting Bias Evaluation to Domain Contexts using Generative Models
Tamara Quiroga | Felipe Bravo-Marquez | Valentin Barriere

Numerous datasets have been proposed to evaluate social bias in Natural Language Processing (NLP) systems. However, assessing bias within specific application domains remains challenging, as existing approaches often face limitations in scalability and fidelity across domains. In this work, we introduce a domain-adaptive framework that utilizes prompting with Large Language Models (LLMs) to automatically transform template-based bias datasets into domain-specific variants. We apply our method to two widely used benchmarks—Equity Evaluation Corpus (EEC) and Identity Phrase Templates Test Set (IPTTS)—adapting them to the Twitter and Wikipedia Talk data. Our results show that the adapted datasets yield bias estimates more closely aligned with real-world data. These findings highlight the potential of LLM-based prompting to enhance the realism and contextual relevance of bias evaluation in NLP systems.

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Emergent morpho-phonological representations in self-supervised speech models
Jon Gauthier | Canaan Breiss | Matthew K Leonard | Edward F. Chang

Self-supervised speech models can be trained to efficiently recognize spoken words in naturalistic, noisy environments. However, we do not understand the types of linguistic representations these models use to accomplish this task. To address this question, we study how S3M variants optimized for word recognition represent phonological and morphological phenomena in frequent English noun and verb inflections. We find that their representations exhibit a global linear geometry which can be used to link English nouns and verbs to their regular inflected forms.This geometric structure does not directly track phonological or morphological units. Instead, it tracks the regular distributional relationships linking many word pairs in the English lexicon—often, but not always, due to morphological inflection. These findings point to candidate representational strategies that may support human spoken word recognition, challenging the presumed necessity of distinct linguistic representations of phonology and morphology.

English, as a very high-resource language, enables the pretraining of high-quality large language models (LLMs). However, the same can not be said for most other languages, likely due to a gap in the quality and diversity of available multilingual pretraining corpora. In this work, we find that documents machine-translated from a high-quality English corpus, can contribute significantly to the pretraining quality of multilingual LLMs. Concretely, we translate FineWeb-Edu, a high-quality English web corpus, into nine languages. resulting in a 1.7-trillion-token corpus, which we call TransWebEdu and pretrain a 1.3B-parameter model, TransWebLLM, from scratch on this corpus. Across Non-English understanding and reasoning tasks, we show that TransWebLLM matches or even outperforms multilingual LLMs of similar size, including Llama3.2, Qwen2.5, and Gemma3, despite being trained on an order of magnitude less data. Moreover, we show that adding fewer than 5% of TransWebLLM’s training tokens as domain-specific data for continued pretraining yields state-of-the-art results in Arabic, Indonesian, Swahili, and Welsh for understanding and commonsense reasoning tasks. To promote reproducibility, we release our corpus and models under Open Source Initiative-approved licenses.

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IntentionFrame: A Semi-Structured, Multi-Aspect Framework for Fine-Grained Conversational Intention Understanding
Jinggui Liang | Dung Vo | Lizi Liao

Understanding user intentions in multi-turn dialogues is critical for conversational AI, yet existing approaches—relying on rigid slot-value structures or unstructured free-text—fail to fully capture conversational complexity. In this paper, we propose IntentionFrame, a semi-structured framework inspired by psychological and cognitive intention theories, which organizes conversational intents into four interrelated aspects: situation, emotion, action, and knowledge. This design not only retains interpretability but also provides LLMs with a rich context to accurately parse and respond to nuanced user inputs. To efficiently scale IntentionFrame annotations, we introduce a Weakly-supervised Reinforced Generation (WeRG) method that leverages a small set of high-quality human annotations in conjunction with abundant coarsely labeled data. By applying reinforcement learning to balance these diverse signals, WeRG aims to effectively generate reliable IntentionFrame annotations, which serve as essential grounding for downstream tasks—leading to substantial improvements in response generation and task completion. Our experiments, supported by both automatic metrics and human evaluations, show that integrating IntentionFrame with WeRG significantly improves LLMs’ conversational understanding and sets a new benchmark for intent analysis.

Despite advances in reinforcement learning (RL)-based video reasoning with large language models (LLMs), data collection and fine- tuning remain significant challenges. These methods often rely on large-scale supervised fine-tuning (SFT) with extensive video data and long Chain-of-Thought (CoT) annotations, making them costly and hard to scale. To address this, we present Video-RTS, a new approach to improve video reasoning capability with drastically improved data efficiency by combining data-efficient RL with a video-adaptive test-time scaling (TTS) strategy. Building on observations about the data scaling, we skip the resource-intensive SFT step and employ efficient pure-RL training with output-based rewards, requiring no additional annotations or extensive fine-tuning. Furthermore, to utilize computational resources more efficiently, we introduce a sparse-to-dense video TTS strategy that improves inference by iteratively adding frames based on output consistency. We validate our approach on multiple video reasoning benchmarks, showing that Video-RTS surpasses existing video reasoning models by 2.4% in accuracy using only 3.6% training samples. Specifically, Video-RTS achieves a 4.2% improvement on Video-Holmes, a recent and challenging video reasoning benchmark. Notably, our pure RL training and adaptive video TTS offer complementary strengths, enabling Video-RTS’s strong reasoning performance.

Adapter parameters provide a mechanism to modify the behavior of machine learning models and have gained significant popularity in the context of large language models (LLMs) and generative AI. These parameters can be merged to support multiple tasks via a process known as task merging. However, prior work on merging in LLMs, particularly in natural language processing, has been limited to scenarios where each test example addresses only a single task. In this paper, we focus on on-device settings and study the problem of text-based compositional multi-tasking, where each test example involves the simultaneous execution of multiple tasks. For instance, generating a translated summary of a long text requires solving both translation and summarization tasks concurrently. To facilitate research in this setting, we propose a benchmark comprising four practically relevant compositional tasks. We also present an efficient method (Learnable Calibration) tailored for on-device applications, where computational resources are limited, emphasizing the need for solutions that are both resource-efficient and high-performing. Our contributions lay the groundwork for advancing the capabilities of LLMs in real-world multi-tasking scenarios, expanding their applicability to complex, resource-constrained use cases.

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Improving Large Language Model Safety with Contrastive Representation Learning
Samuel Simko | Mrinmaya Sachan | Bernhard Schölkopf | Zhijing Jin

Large Language Models (LLMs) are powerful tools with profound societal impacts, yet their ability to generate responses to diverse and uncontrolled inputs leaves them vulnerable to adversarial attacks. While existing defenses often struggle to generalize across varying attack types, recent advancements in representation engineering offer promising alternatives. In this work, we propose a defense framework that formulates model defense as a contrastive representation learning (CRL) problem. Our method finetunes a model using a triplet-based loss combined with adversarial hard negative mining to encourage separation between benign and harmful representations. Our experimental results across multiple models demonstrate that our approach outperforms prior representation engineering-based defenses, improving robustness against both input-level and embedding-space attacks without compromising standard performance.

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Leveraging What’s Overfixed: Post-Correction via LLM Grammatical Error Overcorrection
Taehee Park | Heejin Do | Gary Lee

Robust supervised fine-tuned small Language Models (sLMs) often show high reliability but tend to undercorrect. They achieve high precision at the cost of low recall. Conversely, Large Language Models (LLMs) often show the opposite tendency, making excessive overcorrection, leading to low precision. To effectively harness the strengths of LLMs to address the recall challenges in sLMs, we propose Post-Correction via Overcorrection (PoCO), a novel approach that strategically balances recall and precision. PoCO first intentionally triggers overcorrection via LLM to maximize recall by allowing comprehensive revisions, then applies a targeted post-correction step via fine-tuning smaller models to identify and refine erroneous outputs. We aim to harmonize both aspects by leveraging the generative power of LLMs while preserving the reliability of smaller supervised models. Our extensive experiments demonstrate that PoCO effectively balances GEC performance by increasing recall with competitive precision, ultimately improving the overall quality of grammatical error correction.

Temporal Domain Generalization (TDG) aims to generalize across temporal distribution shifts, e.g., lexical change over time. Prior work often addresses this by predicting future model weights. However, full model prediction is prohibitively expensive for even reasonably sized models. Thus, recent methods only predict the classifier layer, limiting generalization by failing to adjust other model components. To address this, we propose Temporal Expert Averaging (TEA), a novel and scalable TDG framework that updates the entire model using weight averaging to maximize generalization potential while minimizing computational costs. Our theoretical analysis guides us to two steps that enhance generalization to future domains. First, we create expert models with functional diversity yet parameter similarity by fine-tuning a domain-agnostic base model on individual temporal domains while constraining weight changes. Second, we optimize the bias-variance tradeoff through adaptive averaging coefficients derived from modeling temporal weight trajectories in a principal component subspace. Expert’s contributions are based on their projected proximity to future domains. Extensive experiments across 7 TDG benchmarks, 5 models, and 2 TDG settings shows TEA outperforms prior TDG methods by up to 69% while being up to 60x more efficient.

Large language models (LLMs) demonstrate exceptional performance on tasks requiring complex linguistic abilities, such as reference disambiguation and metaphor recognition/generation. Although LLMs possess impressive capabilities, their internal mechanisms for processing and representing linguistic knowledge remain largely opaque. Prior research on linguistic mechanisms is limited by coarse granularity, limited analysis scale, and narrow focus. In this study, we propose LinguaLens, a systematic and comprehensive framework for analyzing the linguistic mechanisms of large language models, based on Sparse Auto-Encoders (SAEs). We extract a broad set of Chinese and English linguistic features across four dimensions—morphology, syntax, semantics, and pragmatics. By employing counterfactual methods, we construct a large-scale counterfactual dataset of linguistic features for mechanism analysis. Our findings reveal intrinsic representations of linguistic knowledge in LLMs, uncover patterns of cross-layer and cross-lingual distribution, and demonstrate the potential to control model outputs. This work provides a systematic suite of resources and methods for studying linguistic mechanisms, offers strong evidence that LLMs possess genuine linguistic knowledge, and lays the foundation for more interpretable and controllable language modeling in future research.

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The Strawberry Problem: Emergence of Character-level Understanding in Tokenized Language Models
Adrian Cosma | Stefan Ruseti | Emilian Radoi | Mihai Dascalu

Despite their remarkable progress across diverse domains, Large Language Models (LLMs) consistently fail at simple character-level tasks, such as counting letters in words, due to a fundamental limitation: tokenization. In this work, we frame this limitation as a problem of low mutual information and analyze it in terms of concept emergence. Using a suite of 19 synthetic tasks that isolate character-level reasoning in a controlled setting, we show that such capabilities emerge suddenly and only late in training. We find that percolation-based models of concept emergence explain these patterns, suggesting that learning character composition is not fundamentally different from learning commonsense knowledge. To address this bottleneck, we propose a lightweight architectural modification that significantly improves character-level reasoning while preserving the inductive advantages of subword models. Together, our results bridge low-level perceptual gaps in tokenized LMs and provide a principled framework for understanding and mitigating their structural blind spots. We make our code publicly available.

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Improving the Quality of Web-mined Parallel Corpora of Low-Resource Languages using Debiasing Heuristics
Surangika Ranathunga | Aloka Fernando | Menan Velayuthan | Charitha Rathnayaka | Nisansa de Silva

Parallel Data Curation (PDC) techniques aim to filter out noisy parallel sentences from web-mined corpora. Ranking sentence pairs using similarity scores on sentence embeddings derived from Pre-trained Multilingual Language Models (multiPLMs) is the most common PDC technique. However, previous research has shown that the choice of the multiPLM significantly impacts the quality of the filtered parallel corpus, and the Neural Machine Translation (NMT) models trained using such data show a disparity across multiPLMs. This paper shows that this disparity is due to different multiPLMs being biased towards certain types of sentence pairs, which are treated as noise from an NMT point of view. We show that such noisy parallel sentences can be removed to a certain extent by employing a series of heuristics. The NMT models, trained using the curated corpus, lead to producing better results while minimizing the disparity across multiPLMs. We publicly release the source code and the curated datasets

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Weaver: Interweaving SQL and LLM for Table Reasoning
Rohit Khoja | Devanshu Gupta | Yanjie Fu | Dan Roth | Vivek Gupta

Querying tables with unstructured data is challenging due to the presence of text (or image), either embedded in the table or in external paragraphs, which traditional SQL struggles to process, especially for tasks requiring semantic reasoning. While Large Language Models (LLMs) excel at understanding context, they face limitations with long input sequences. Existing approaches that combine SQL and LLM typically rely on rigid, predefined workflows, limiting their adaptability to complex queries. To address these issues, we introduce Weaver, a modular pipeline that dynamically integrates SQL and LLM for table-based question answering (Table QA). Weaver generates a flexible, step-by-step plan that combines SQL for structured data retrieval with LLMs for semantic processing. By decomposing complex queries into manageable subtasks, Weaver improves accuracy and generalization. Our experiments show that consistently outperforms state-of-the-art methods across four Table QA datasets, reducing both API calls and error rates.

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ECO Decoding: Entropy-Based Control for Controllability and Fluency in Controllable Dialogue Generation
Seungmin Shin | Dooyoung Kim | Youngjoong Ko

Controllable Dialogue Generation (CDG) enables chatbots to generate responses with desired attributes, and weighted decoding methods have achieved significant success in the CDG task. However, using a fixed constant value to manage the bias of attribute probabilities makes it challenging to find an ideal control strength that satisfies both controllability and fluency. To address this issue, we propose ECO decoding Entropy-based COntrol, which dynamically adjusts the control strength at each generation step according to the model’s entropy in both the language model and attribute classifier probability distributions. Experimental results on DailyDialog and MultiWOZ datasets show that our method achieves improved control accuracy while maintaining fluency and grammar, outperforming previous decoding methods across various models and settings. Furthermore, ECO decoding alleviates probability interpolation issues in multi-attribute generation, demonstrating its robust performance in both single and multi-attribute scenarios.

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Investigating the interaction of linguistic and mathematical reasoning in language models using multilingual number puzzles
Antara Raaghavi Bhattacharya | Isabel Papadimitriou | Kathryn Davidson | David Alvarez-Melis

Across languages, numeral systems vary widely in how they construct and combine numbers. While humans consistently learn to navigate this diversity, large language models (LLMs) struggle with linguistic-mathematical puzzles involving cross-linguistic numeral systems, which humans can learn to solve successfully. We investigate why this task is difficult for LLMs through a series of experiments that untangle the linguistic and mathematical aspects of numbers in language. Our experiments establish that models cannot consistently solve such problems unless the mathematical operations in the problems are explicitly marked using known symbols (+, ×, etc, as in “twenty + three”). In further ablation studies, we probe how individual parameters of numeral construction and combination affect performance. While humans use their linguistic understanding of numbers to make inferences about the implicit compositional structure of numerals, LLMs seem to lack this notion of implicit numeral structure. We conclude that the ability to flexibly infer compositional rules from implicit patterns in human-scale data remains an open challenge for current reasoning models.

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Unsupervised Concept Vector Extraction for Bias Control in LLMs
Hannah Cyberey | Yangfeng Ji | David Evans

Large language models (LLMs) are known to perpetuate stereotypes and exhibit biases. Various strategies have been proposed to mitigate these biases, but most work studies biases as a black-box problem without considering how concepts are represented within the model. We adapt techniques from representation engineering to study how the concept of “gender” is represented within LLMs. We introduce a new method that extracts concept representations via probability weighting without labeled data and efficiently selects a steering vector for measuring and manipulating the model’s representation. We develop a projection-based method that enables precise steering of model predictions and demonstrate its effectiveness in mitigating gender bias in LLMs and show that it also generalizes to racial bias.

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Seeing the Same Story Differently: Framing‐Divergent Event Coreference for Computational Framing Analysis
Jin Zhao | Xinrui Hu | Nianwen Xue

News articles often describe the same real-world event in strikingly different ways, shaping perception through framing rather than factual disagreement. However, traditional computational framing approaches often rely on coarse-grained topic classification, limiting their ability to capture subtle, event-level differences in how the same occurrences are presented across sources. We introduce Framing-divergent Event Coreference (FrECo), a novel task that identifies pairs of event mentions referring to the same underlying occurrence but differing in framing across documents to provide a event-centric lens for computational framing analysis. To support this task, we construct the high-agreement and diverse FrECo corpus. We evaluate the FrECo task on the corpus through supervised and preference-based tuning of large language models, providing strong baseline performance. To scale beyond the annotated data, we develop a bootstrapped mining pipeline that iteratively expands the training set with high-confidence FrECo pairs. Our approach enables scalable, interpretable analysis of how media frame the same events differently, offering a new lens for contrastive framing analysis at the event level.

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LLMs are Better Than You Think: Label-Guided In-Context Learning for Named Entity Recognition
Fan Bai | Hamid Hassanzadeh | Ardavan Saeedi | Mark Dredze

In-context learning (ICL) enables large language models (LLMs) to perform new tasks using only a few demonstrations. In Named Entity Recognition (NER), demonstrations are typically selected based on semantic similarity to the test instance, ignoring training labels and resulting in suboptimal performance. We introduce DEER, a new method that leverages training labels through token-level statistics to improve ICL performance. DEER first enhances example selection with a label-guided, token-based retriever that prioritizes tokens most informative for entity recognition. It then prompts the LLM to revisit error-prone tokens, which are also identified using label statistics, and make targeted corrections. Evaluated on five NER datasets using four different LLMs, DEER consistently outperforms existing ICL methods and approaches the performance of supervised fine-tuning. Further analysis shows its effectiveness on both seen and unseen entities and its robustness in low-resource settings.

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COUNTDOWN: Contextually Sparse Activation Filtering Out Unnecessary Weights in Down Projection
Jaewon Cheon | Pilsung Kang

The growing size of large language models has created significant computational inefficiencies. To address this challenge, sparse activation selectively deactivates non-essential parameters during inference, reducing computational costs in FFNN layers. While existing methods focus on non-linear gating mechanisms, we hypothesize that the sparsity of the FFNN layer lies globally in the form of a linear combination over its internal down projection matrix. Based on this insight, we propose two methods: M-COUNTDOWN, leveraging indirect coefficients, and D-COUNTDOWN, utilizing direct coefficients of the linear combination. Experimental results demonstrate that D-COUNTDOWN can omit 90% of computations with performance loss as low as 5.5% ideally, while M-COUNTDOWN provides a predictor-free solution with up to 29.4% better performance preservation compared to existing methods. Our specialized kernel implementations effectively realize these theoretical gains into substantial real-world acceleration.

Document Visual Question Answering (DocVQA) is a practical yet challenging task, which is to ask questions based on documents while referring to multiple pages and different modalities of information, e.g., images and tables. To handle multi-modality, recent methods follow a similar Retrieval Augmented Generation (RAG) pipeline, but utilize Visual Language Models (VLMs) based embedding model to embed and retrieve relevant pages as images, and generate answers with VLMs that can accept an image as input. In this paper, we introduce SimpleDoc, a lightweight yet powerful retrieval - augmented framework for DocVQA. It boosts evidence page gathering by first retrieving candidates through embedding similarity and then filtering and re-ranking these candidates based on page summaries. A single VLM-based reasoner agent repeatedly invokes this dual-cue retriever, iteratively pulling fresh pages into a working memory until the question is confidently answered. SimpleDoc outperforms previous baselines by 3.2% on average on 4 DocVQA datasets with much fewer pages retrieved. Our code is available at https://github.com/ag2ai/SimpleDoc.

Reward engineering is one of the key challenges in Reinforcement Learning (RL). Preference-based RL effectively addresses this issue by learning from human feedback. However, it is both time-consuming and expensive to collect human preference labels. In this paper, we propose a novel Vision-Language Preference learning framework, named VLP, which learns a vision-language preference model to provide feedback for embodied manipulation tasks. To achieve this, we define three types of language-conditioned preferences and construct a vision-language preference dataset, which contains versatile implicit preference orders. The model learns to extract language-related features, and then serves as a predictor in various downstream tasks. The policy can be learned according to the annotated labels via reward learning or direct policy optimization. Extensive empirical results on simulated embodied manipulation tasks demonstrate that our method provides accurate preferences and generalizes to unseen tasks and unseen language instructions, outperforming the baselines by a large margin and shifting the burden from continuous, per-task human annotation to one-time, per-domain data collection.

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QG-CoC: Question-Guided Chain-of-Captions for Large Multimodal Models
Kuei-Chun Kao | Hsu Tzu-Yin | Yunqi Hong | Ruochen Wang | Cho-Jui Hsieh

Recently, Multimodal Large Language Models (MLLMs) encounter two key issues in multi-image contexts: (1) a lack of fine-grained perception across disparate images, and (2) a diminished capability to effectively reason over and synthesize information from multiple visual inputs. However, while various prompting methods aim to describe visual content, many existing studies focus primarily on single-image settings or specific, constrained scenarios. This leaves a critical gap in understanding and addressing how MLLMs tackle more general and complex multi-image reasoning tasks. Thus, we first extensively investigate how current prompting methods perceive fine-grained visual details and process visual information when dealing with multiple images. Our findings reveal that existing prompting methods fall short in attending to needed clues and seamlessly integrating perception and reasoning. Inspired by the findings, we propose a new zero-shot prompting method, Question-Guided Chain-of-Captions (QG-CoC), a generalized prompting approach that effectively handles problems with an arbitrary number of images. We evaluate our method on various open-source and closed-source MLLMs for multi-image and single-image benchmarks. Experimental results indicate that QG-CoC demonstrates competitive performance across tasks and exhibits robust improvements in the challenging scenarios where existing prompting methods fail.

Multimodal Large Language Models (MLLMs) have demonstrated remarkable performance in complex multimodal tasks. While MLLMs excel at visual perception and reasoning in third-person and egocentric videos, they are prone to hallucinations, generating coherent yet inaccurate responses. We present EGOILLUSION, a first benchmark to evaluate MLLM hallucinations in egocentric videos. EGOILLUSION comprises 1,400 videos paired with 8,000 human-annotated open and closed-ended questions designed to trigger hallucinations in both visual and auditory cues in egocentric videos. Evaluations across ten MLLMs reveal significant challenges, including powerful models like GPT-4o and Gemini, achieving only 59% accuracy. EGOILLUSION lays the foundation in developing robust benchmarks to evaluate the effectiveness of MLLMs and spurs the development of better egocentric MLLMs with reduced hallucination rates. Our benchmark will be open-sourced for reproducibility

The rapid progress of Large Language Models (LLMs) has empowered omni models to act as voice assistants capable of understanding spoken dialogues. These models can process multimodal inputs beyond text, such as speech and visual data, enabling more context-aware interactions. However, current benchmarks fall short in comprehensively evaluating how well these models generate context-aware responses, particularly when it comes to implicitly understanding fine-grained speech characteristics, such as pitch, emotion, timbre, and volume or the environmental acoustic context such as background sounds. Additionally, they inadequately assess the ability of models to align paralinguistic cues with complementary visual signals to inform their responses. To address these gaps, we introduce MultiVox, the first omni voice assistant benchmark designed to evaluate the ability of voice assistants to integrate spoken and visual cues including paralinguistic speech features for truly multimodal understanding. Specifically, MultiVox includes 1000 human-annotated and recorded speech dialogues that encompass diverse paralinguistic features and a range of visual cues such as images and videos. Our evaluation on 10 state-of-the-art models reveals that, although humans excel at these tasks, current open-source models consistently struggle to produce contextually grounded responses.

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Do All Autoregressive Transformers Remember Facts the Same Way? A Cross-Architecture Analysis of Recall Mechanisms
Minyeong Choe | Haehyun Cho | Changho Seo | Hyunil Kim

Understanding how Transformer-based language models store and retrieve factual associations is critical for improving interpretability and enabling targeted model editing. Prior work, primarily on GPT-style models, has identified MLP modules in early layers as key contributors to factual recall. However, it remains unclear whether these findings generalize across different autoregressive architectures. To address this, we conduct a comprehensive evaluation of factual recall across several models—including GPT, LLaMA, Qwen, and DeepSeek—analyzing where and how factual information is encoded and accessed. Consequently, we find that Qwen-based models behave differently from previous patterns: attention modules in the earliest layers contribute more to factual recall than MLP modules. Our findings suggest that even within the autoregressive Transformer family, architectural variations can lead to fundamentally different mechanisms of factual recall.

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Probing Narrative Morals: A New Character-Focused MFT Framework for Use with Large Language Models
Luca Mitran | Sophie Wu | Andrew Piper

Moral Foundations Theory (MFT) provides a framework for categorizing different forms of moral reasoning, but its application to computational narrative analysis remains limited. We propose a novel character-centric method to quantify moral foundations in storytelling, using large language models (LLMs) and a novel Moral Foundations Character Action Questionnaire (MFCAQ) to evaluate the moral foundations supported by the behaviour of characters in stories. We validate our approach against human annotations and then apply it to a study of 2,697 folktales from 55 countries. Our findings reveal: (1) broad distribution of moral foundations across cultures, (2) significant cross-cultural consistency with some key regional differences, and (3) a more balanced distribution of positive and negative moral content than suggested by prior work. This work connects MFT and computational narrative analysis, demonstrating LLMs’ potential for scalable moral reasoning in narratives.

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Probing and Boosting Large Language Models Capabilities via Attention Heads
Dezhi Zhao | Xin Liu | Xiaocheng Feng | Hui Wang | Bing Qin

Understanding the internal origins of capabilities in large language models (LLMs) is crucial for interpretability and efficient adaptation. However, the emergence of specific capabilities remains poorly understood, as most existing approaches rely on external signals (e.g., performance shifts or gradient similarities) with limited structural grounding. To address these issues, this paper proposes a lightweight and highly interpretable approach that links LLM capabilities to internal components by identifying correspondences at the level of attention heads. Specifically, we first define five fundamental capabilities, namely Mathematical Reasoning, Reading Comprehension, Commonsense Reasoning, Scientific Reasoning, and Professional Expertise, and employ probing techniques to detect the attention heads most predictive of each, thereby establishing capability–head mappings. For targeted instruction tuning, complex tasks are decomposed into these fundamental capabilities, and training data are selected accordingly. Experiments on LLaMA3.1-8B and Qwen2.5-7B show over 70% discrimination accuracy in identifying capabilities. On MMLU and BBH, our method improves accuracy by 1 to 1.5 points over the gradient-based method LESS and by 5 to 6 points over other intermediate-state baselines.

N-ary Knowledge Graphs (NKGs) are a specialized type of knowledge graph designed to efficiently represent complex real-world facts. Unlike traditional knowledge graphs, where a fact typically involves two entities, NKGs can capture n-ary facts containing more than two entities. Link prediction in NKGs aims to predict missing elements within these n-ary facts, which is essential for completing NKGs and improving the performance of downstream applications. This task has recently gained significant attention. In this paper, we present the first comprehensive survey of link prediction in NKGs, providing an overview of the field, systematically categorizing existing methods, and analyzing their performance and application scenarios. We also outline promising directions for future research.

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Multi-Frequency Contrastive Decoding: Alleviating Hallucinations for Large Vision-Language Models
Bingqian Liu | Fu Zhang | Guoqing Chen | Jingwei Cheng

Large visual-language models (LVLMs) have demonstrated remarkable performance in visual-language tasks. However, object hallucination remains a significant challenge for LVLMs. Existing studies attribute object hallucinations in LVLMs mainly to linguistic priors and data biases. We further explore the causes of object hallucinations from the perspective of frequency domain and reveal that insufficient frequency information in images amplifies these linguistic priors, increasing the likelihood of hallucinations. To mitigate this issue, we propose the Multi-Frequency Contrastive Decoding (MFCD) method, a simple yet trainingfree approach that removes the hallucination distribution in the original output distribution, which arises from LVLMs neglecting the high-frequency information or low-frequency information in the image input. Without compromising the general capabilities of LVLMs, the proposed MFCD effectively mitigates the object hallucinations in LVLMs. Our experiments demonstrate that MFCD significantly mitigates object hallucination across diverse large-scale vision-language models, without requiring additional training or external tools. In addition, MFCD can be applied to various LVLMs without modifying model architecture or requiring additional training, demonstrating its generality and robustness. Codes are available at https://github.com/liubq-dev/mfcd.

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ORPP: Self-Optimizing Role-playing Prompts to Enhance Language Model Capabilities
Yifan Duan | Yihong Tang | Kehai Chen | Liqiang Nie | Min Zhang

High-quality prompts are crucial for eliciting outstanding performance from large language models (LLMs) on complex tasks. Existing research has explored model-driven strategies for prompt optimization. However, these methods often suffer from high computational overhead or require strong optimization capabilities from the model itself, which limits their broad applicability.To address these challenges, we propose ORPP, a framework that enhances model performance by optimizing and generating role-playing prompts. The core idea of ORPP is to confine the prompt search space to role-playing scenarios, thereby fully activating the model’s intrinsic capabilities through carefully crafted, high-quality role-playing prompts. Specifically, ORPP first performs iterative optimization on a small subset of training samples to generate high-quality role-playing prompts. Then, leveraging the model’s few-shot learning capability, it transfers the optimization experience to efficiently generate suitable prompts for the remaining samples.Our experimental results show that ORPP not only matches but in most cases surpasses existing mainstream prompt optimization methods in terms of performance. Notably, ORPP suggests great “plug-and-play” capability. In most cases, it can be integrated with various other prompt methods and further enhance their effectiveness.

Braille plays a vital role in education and information accessibility for visually impaired individuals. However, Braille information processing faces challenges such as data scarcity and ambiguities in mixed-text contexts. We construct English and Chinese Braille Mixed Datasets (EBMD/CBMD) with mathematical formulas to support diverse Braille domain research, and propose a syntax tree-based augmentation method tailored for Braille data. To address the underperformance of traditional fine-tuning methods in braille-related tasks, we investigate Braille Knowledge-Based Fine-Tuning (BKFT), which reduces the learning difficulty of Braille contextual features. BrailleLLM employs BKFT via instruction tuning to achieve unified Braille translation, formula-to-Braille conversion, and mixed-text translation. Experiments demonstrate that BKFT achieves significant performance improvements over conventional fine-tuning in Braille translation scenarios. Our open-sourced datasets and methodologies establish a foundation for low-resource multilingual Braille research.

Fine-grained understanding and species-specific, multimodal question answering are vital for advancing biodiversity conservation and ecological monitoring. However, existing multimodal large language models (MM-LLMs) face challenges when it comes to specialized topics like avian species, making it harder to provide accurate and contextually relevant information in these areas. To address this limitation, we introduce the **MAviS-Dataset**, a large-scale multimodal avian species dataset that integrates image, audio, and text modalities for over 1,000 bird species, comprising both pretraining and instruction-tuning subsets enriched with structured question–answer pairs. Building on the MAviS-Dataset, we introduce **MAviS-Chat**, a multimodal LLM that supports audio, vision, and text designed for fine-grained species understanding, multimodal question answering, and scene-specific description generation. Finally, for quantitative evaluation, we present **MAviS-Bench**, a benchmark of over 25,000 Q&A pairs designed to assess avian species-specific perceptual and reasoning abilities across modalities. Experimental results show that MAviS-Chat outperforms the baseline MiniCPM-o-2.6 by a large margin, achieving state-of-the-art open-source results and demonstrating the effectiveness of our instruction-tuned MAviS-Dataset. Our findings highlight the necessity of domain-adaptive MM-LLMs for ecological applications. Our code, training data, evaluation benchmark, and models are available at https://github.com/yevheniia-uv/MAviS.

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Refining Text Generation for Realistic Conversational Recommendation via Direct Preference Optimization
Manato Tajiri | Michimasa Inaba

Conversational Recommender Systems (CRSs) aim to elicit user preferences via natural dialogue to provide suitable item recommendations. However, current CRSs often deviate from realistic human interactions by rapidly recommending items in brief sessions. This work addresses this gap by leveraging Large Language Models (LLMs) to generate dialogue summaries from dialogue history and item recommendation information from item description. This approach enables the extraction of both explicit user statements and implicit preferences inferred from the dialogue context. We introduce a method using Direct Preference Optimization (DPO) to ensure dialogue summary and item recommendation information are rich in information crucial for effective recommendations. Experiments on two public datasets validate our method’s effectiveness in fostering more natural and realistic conversational recommendation processes. Our implementation is publicly available at: https://github.com/UEC-InabaLab/Refining-LLM-Text

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Large Language Models Threaten Language’s Epistemic and Communicative Foundations
Shashank Srivastava

Large language models are reshaping the norms of human communication, sometimes decou- pling words from genuine human thought. This transformation is deep, and undermines norms historically tied to authorship of text. We draw from linguistic philosophy and AI ethics to detail how large-scale text genera- tion can induce semantic drift, erode account- ability, and obfuscate intent and authorship. Our work here introduces hybrid authorship graphs (modeling humans, LLMs, and texts in a provenance network), epistemic doppel- gängers (LLM-generated texts that are indis- tinguishable from human-authored texts), and authorship entropy. We explore mechanisms such as “proof-of-interaction” authorship veri- fication and educational reforms to restore con- fidence in language. LLMs’ benefits (broader access, increased fluency, automation, etc.) are undeniable, but the upheavals they introduce to the linguistic landscape demand reckoning.

Despite the advancements made in Vision Large Language Models (VLLMs), like text Large Language Models (LLMs), they have limitations in addressing questions that require real-time information or are knowledge-intensive. Indiscriminately adopting Retrieval Augmented Generation (RAG) techniques is an effective yet expensive way to enable models to answer queries beyond their knowledge scopes. To mitigate the dependence on retrieval and simultaneously maintain, or even improve, the performance benefits provided by retrieval, we propose a method to detect the knowledge boundary of VLLMs, allowing for more efficient use of techniques like RAG. Specifically, we propose a method with two variants that fine-tune a VLLM on an automatically constructed dataset for boundary identification. Experimental results on various types of Visual Question Answering datasets show that our method successfully depicts a VLLM’s knowledge boundary, based on which we are able to reduce indiscriminate retrieval while maintaining or improving the performance. In addition, we show that the knowledge boundary identified by our method for one VLLM can be used as a surrogate boundary for other VLLMs. Code will be released at https://github.com/Chord-Chen-30/VLLM-KnowledgeBoundary

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Multi-view-guided Passage Reranking with Large Language Models
Jeongwoo Na | Jun Kwon | Eunseong Choi | Jongwuk Lee

Recent advances in large language models (LLMs) have shown impressive performance in passage reranking tasks. Despite their success, LLM-based methods still face challenges in efficiency and sensitivity to external biases. (1) Existing models rely mostly on autoregressive generation and sliding window strategies to rank passages, which incur heavy computational overhead as the number of passages increases. (2) External biases, such as position or selection bias, hinder the model’s ability to accurately represent passages and increase input-order sensitivity. To address these limitations, we introduce a novel passage reranking model, called Multi-View-guided Passage Reranking (MVP). MVP is a non-generative LLM-based reranking method that encodes query-passage information into diverse view embeddings without being influenced by external biases. For each view, it combines query-aware passage embeddings to produce a distinct anchor vector, which is then used to directly compute relevance scores in a single decoding step. In addition, it employs an orthogonal loss to make the views more distinctive. Extensive experiments demonstrate that MVP, with just 220M parameters, matches the performance of much larger 7B-scale fine-tuned models while achieving a 100x reduction in inference latency. Notably, the 3B-parameter variant of MVP achieves state-of-the-art performance on both in-domain and out-of-domain benchmarks. The source code is available at: https://github.com/bulbna/MVP.

Variation is inherent in opinion-based annotation tasks like sentiment or hate speech analysis. It does not only arise from errors, fatigue, or sentence ambiguity but also from genuine differences in opinion shaped by background, experience, and culture. In this paper, first, we show how annotators’ confidence ratings can be great use for disentangling subjective variation from uncertainty, without relying on specific features present in the data (text, gaze, etc.). Our goal is to establish distinctive dimensions of variation which are often not clearly separated in existing work on modeling annotator variation. We illustrate our approach through a hate speech detection task, demonstrating that models are affected differently by instances of uncertainty and subjectivity. In addition, we show that human gaze patterns offer valuable indicators of subjective evaluation and uncertainty. Disclaimer: This paper contains sentences that may be offensive.

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VoiceBBQ: Investigating Effect of Content and Acoustics in Social Bias of Spoken Language Model
Junhyuk Choi | Ro-hoon Oh | Jihwan Seol | Bugeun Kim

We introduce VoiceBBQ, a spoken extension of the BBQ (Bias Benchmark for Question answering) - a dataset that measures social bias by presenting ambiguous or disambiguated contexts followed by questions that may elicit stereotypical responses. Due to the nature of speech modality, social bias in Spoken Language Models (SLMs) can emerge from two distinct sources: 1) content aspect and 2) acoustic aspect. The dataset converts every BBQ context into controlled voice conditions, enabling per-axis accuracy, bias, and consistency scores that remain comparable to the original text benchmark. Using VoiceBBQ, we evaluate two SLMs—LLaMA-Omni and Qwen2-Audio—and observe architectural contrasts: LLaMA-Omni retains strong acoustic sensitivity, amplifying gender and accent bias, whereas Qwen2-Audio substantially dampens these cues while preserving content fidelity. VoiceBBQ thus provides a compact, drop-in testbed for jointly diagnosing content and acoustic bias across spoken language models.

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Explaining Differences Between Model Pairs in Natural Language through Sample Learning
Advaith Malladi | Rakesh R Menon | Yuvraj Jain | Shashank Srivastava

With the growing adoption of machine learning models in critical domains, techniques for explaining differences between models have become essential for trust, debugging, and informed deployment. Previous approaches address this by identifying input transformations that cause divergent predictions or by learning joint surrogate models to align and contrast behaviors. These methods often require access to training data and do not produce natural language explanations. In this paper, we introduce SLED, a framework that generates faithful natural language explanations of when and how two ML models converge or diverge in their predictions. SLED first uses gradient-based optimization to synthesize input samples that highlight divergence and convergence patterns, and then leverages a large language model (LLM) to generate explanations grounded in these synthetic samples. Across both text-based (3 tasks, 7 models) and structured (10 tasks, 4 models) classification tasks, we show that SLED explanations are 18–24% more faithful than the strongest baselines. User studies also indicate that SLED explanations achieve a real-world simulatability of 63.5%. Importantly, SLED requires minimal access to training data and generalizes well to real-world samples, enabling transparent and data-efficient model comparison.

Recent advancements in large language models (LLMs) and AI systems have led to a paradigm shift in the design and optimization of complex AI workflows. By integrating multiple components, compound AI systems have become increasingly adept at performing sophisticated tasks. However, as these systems grow in complexity, new challenges arise in optimizing not only individual components but also their interactions. While traditional optimization methods such as supervised fine-tuning (SFT) and reinforcement learning (RL) remain foundational, the rise of natural language feedback introduces promising new approaches, especially for optimizing non-differentiable systems. This paper provides a systematic review of recent progress in optimizing compound AI systems, encompassing both numerical and language-based techniques. We formalize the notion of compound AI system optimization, classify existing methods along several key dimensions, and highlight open research challenges and future directions in this rapidly evolving field.

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A Multi-Level Benchmark for Causal Language Understanding in Social Media Discourse
Xiaohan Ding | Kaike Ping | Buse Çarık | Eugenia Rho

Understanding causal language in informal discourse is a core yet underexplored challenge in NLP. Existing datasets largely focus on explicit causality in structured text, providing limited support for detecting implicit causal expressions, particularly those found in informal, user-generated social media posts. We introduce CausalTalk, a multi-level dataset of five years of Reddit posts (2020–2024) discussing public health related to the COVID-19 pandemic, among which 10,120 posts are annotated across four causal tasks: (1) binary causal classification, (2) explicit vs. implicit causality, (3) cause–effect span extraction, and (4) causal gist generation. Annotations comprise both gold-standard labels created by domain experts and silver-standard labels generated by GPT-4o and verified by human annotators.CausalTalk bridges fine-grained causal detection and gist-based reasoning over informal text. It enables benchmarking across both discriminative and generative models, and provides a rich resource for studying causal reasoning in social media contexts.

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Causal Representation Learning from Multimodal Clinical Records under Non-Random Modality Missingness
Zihan Liang | Ziwen Pan | Ruoxuan Xiong

Clinical notes contain rich patient information, such as diagnoses or medications, making them valuable for patient representation learning. Recent advances in large language models have further improved the ability to extract meaningful representations from clinical texts. However, clinical notes are often missing. For example, in our analysis of the MIMIC-IV dataset, 24.5% of patients have no available discharge summaries. In such cases, representations can be learned from other modalities such as structured data, chest X-rays, or radiology reports. Yet the availability of these modalities is influenced by clinical decision-making and varies across patients, resulting in modality missing-not-at-random (MMNAR) patterns. We propose a causal representation learning framework that leverages observed data and informative missingness in multimodal clinical records. It consists of: (1) an MMNAR-aware modality fusion component that integrates structured data, imaging, and text while conditioning on missingness patterns to capture patient health and clinician-driven assignment; (2) a modality reconstruction component with contrastive learning to ensure semantic sufficiency in representation learning; and (3) a multitask outcome prediction model with a rectifier that corrects for residual bias from specific modality observation patterns. Comprehensive evaluations across MIMIC-IV and eICU show consistent gains over the strongest baselines, achieving up to 13.8% improvement for hospital readmission and 13.1% for ICU admission (AUC, relative to best baseline).

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XLQA: A Benchmark for Locale-Aware Multilingual Open-Domain Question Answering
Keonwoo Roh | Yeong-Joon Ju | Seong-Whan Lee

Large Language Models (LLMs) have shown significant progress in Open-domain question answering (ODQA), yet most evaluations focus on English and assume locale-invariant answers across languages. This assumption neglects the cultural and regional variations that affect question understanding and answer, leading to biased evaluation in multilingual benchmarks. To address these limitations, we introduce XLQA, a novel benchmark explicitly designed for locale-sensitive multilingual ODQA. XLQA contains 3,000 English seed questions expanded to eight languages, with careful filtering for semantic consistency and human-verified annotations distinguishing locale-invariant and locale-sensitive cases. Our evaluation of five state-of-the-art multilingual LLMs reveals notable failures on locale-sensitive questions, exposing gaps between English and other languages due to a lack of locale-grounding knowledge. We provide a systematic framework and scalable methodology for assessing multilingual QA under diverse cultural contexts, offering a critical resource to advance real-world applicability of multilingual ODQA systems. Our findings suggest that disparities in training data distribution contribute to differences in both linguistic competence and locale-awareness across models.

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Transformer-Based Temporal Information Extraction and Application: A Review
Xin Su | Phillip Howard | Steven Bethard

Temporal information extraction (IE) aims to extract structured temporal information from unstructured text, thereby uncovering the implicit timelines within. This technique is applied across domains such as healthcare, newswire, and intelligence analysis, aiding models in these areas to perform temporal reasoning and enabling human users to grasp the temporal structure of text. Transformer-based pre-trained language models have produced revolutionary advancements in natural language processing, demonstrating exceptional performance across a multitude of tasks. Despite the achievements garnered by Transformer-based approaches in temporal IE, there is a lack of comprehensive reviews on these endeavors. In this paper, we aim to bridge this gap by systematically summarizing and analyzing the body of work on temporal IE using Transformers while highlighting potential future research directions.

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How to Protect Yourself from 5G Radiation? Investigating LLM Responses to Implicit Misinformation
Ruohao Guo | Wei Xu | Alan Ritter

As Large Language Models (LLMs) are widely deployed in diverse scenarios, the extent to which they could tacitly spread misinformation emerges as a critical safety concern. Current research primarily evaluates LLMs on explicit false statements, overlooking how misinformation often manifests subtly as unchallenged premises in real-world interactions. We curated EchoMist, the first comprehensive benchmark for implicit misinformation, where false assumptions are embedded in the query to LLMs. EchoMist targets circulated, harmful, and ever-evolving implicit misinformation from diverse sources, including realistic human-AI conversations and social media interactions. Through extensive empirical studies on 15 state-of-the-art LLMs, we find that current models perform alarmingly poorly on this task, often failing to detect false premises and generating counterfactual explanations. We also investigate two mitigation methods, i.e., Self-Alert and RAG, to enhance LLMs’ capability to counter implicit misinformation. Our findings indicate that EchoMist remains a persistent challenge and underscore the critical need to safeguard against the risk of implicit misinformation.

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AmpleHate: Amplifying the Attention for Versatile Implicit Hate Detection
Yejin Lee | Joonghyuk Hahn | Hyeseon Ahn | Yo-Sub Han

Implicit hate speech detection is challenging due to its subtlety and reliance on contextual interpretation rather than explicit offensive words. Current approaches rely on contrastive learning, which are shown to be effective on distinguishing hate and non-hate sentences. Humans, however, detect implicit hate speech by first identifying specific targets within the text and subsequently interpreting how these target relate to their surrounding context. Motivated by this reasoning process, we propose AmpleHate, a novel approach designed to mirror human inference for implicit hate detection. AmpleHate identifies explicit target using a pretrained Named Entity Recognition model and capture implicit target information via [CLS] tokens. It computes attention-based relationships between explicit, implicit targets and sentence context and then, directly injects these relational vectors into the final sentence representation. This amplifies the critical signals of target-context relations for determining implicit hate. Experiments demonstrate that AmpleHate achieves state-of-the-art performance, outperforming contrastive learning baselines by an average of 82.14% and achieve faster convergence. Qualitative analyses further reveal that attention patterns produced by AmpleHate closely align with human judgement, underscoring its interpretability and robustness.

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Can Large Language Models Act as Ensembler for Multi-GNNs?
Hanqi Duan | Yao Cheng | Jianxiang Yu | Yao Liu | Xiang Li

Graph Neural Networks (GNNs) have emerged as powerful models for learning from graph-structured data. However, GNNs lack the inherent semantic understanding capability of rich textual node attributes, limiting their effectiveness in applications. On the other hand, we empirically observe that for existing GNN models, no one can consistently outperforms others across diverse datasets. In this paper, we study whether LLMs can act as an ensembler for multi-GNNs and propose the LensGNN model. The model first aligns multiple GNNs, mapping the representations of different GNNs into the same space. Then, through LoRA fine-tuning, it aligns the space between the GNN and the LLM, injecting graph tokens and textual information into LLMs. This allows LensGNN to ensemble multiple GNNs and take advantage of the strengths of LLM, leading to a deeper understanding of both textual semantic information and graph structural information. The experimental results show that LensGNN outperforms existing models. This research advances text-attributed graph ensemble learning by providing a robust and superior solution for integrating semantic and structural information. We provide our code and data here: https://github.com/AquariusAQ/LensGNN.

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Agent-to-Agent Theory of Mind: Testing Interlocutor Awareness among Large Language Models
Younwoo Choi | Changling Li | Yongjin Yang | Zhijing Jin

As large language models (LLMs) are increasingly integrated into multi-agent and human-AI systems, understanding their awareness of both self-context and conversational partners is essential for ensuring reliable performance and robust safety. While prior work has extensively studied situational awareness which refers to an LLM’s ability to recognize its operating phase and constraints, it has largely overlooked the complementary capacity to identify and adapt to the identity and characteristics of a dialogue partner. In this paper, we formalize this latter capability as interlocutor awareness and present the first systematic evaluation of its emergence in contemporary LLMs. We examine interlocutor inference across three dimensions—reasoning patterns, linguistic style, and alignment preferences—and show that LLMs reliably identify same-family peers and certain prominent model families, such as GPT and Claude. To demonstrate its practical significance, we develop three case studies in which interlocutor awareness both enhances multi-LLM collaboration through prompt adaptation and introduces new alignment and safety vulnerabilities, including reward-hacking behaviors and increased jailbreak susceptibility. Our findings highlight the dual promise and peril of identity—sensitive behavior in LLMs, underscoring the need for further understanding of interlocutor awareness and new safeguards in multi-agent deployments.

Charts are very common for exploring dataand communicating insights, but extracting key takeaways from charts and articulating them in natural language can be challenging. The chart-to-text task aims to automate this process by generating textual summaries of charts. While with the rapid advancement of large Vision-Language Models (VLMs), we have witnessed great progress in this domain, little to no attention has been given to potential biases in their outputs. This paper investigates how VLMs can amplify geo-economic biases when generating chart summaries, potentially causing societal harm. Specifically, we conduct a large-scale evaluation of geo-economic biases in VLM-generated chart summaries across 6,000 chart-country pairs from six widely used proprietary and open-source models to understand how a country’s economic status influences the sentiment of generated summaries. Our analysis reveals that existing VLMs tend to produce more positive descriptions for high-income countries compared to middle- or low-income countries, even when country attribution is the only variable changed. We also find that models such as GPT-4o-mini, Gemini-1.5-Flash, and Phi-3.5 exhibit varying degrees of bias. We further explore inference-time prompt-based debiasing techniques using positive distractors but find them only partially effective, underscoring the complexity of the issue and the need for more robust debiasing strategies. Our code and dataset are available at <redacted>.

The integration of Large Language Models (LLMs) with retrieval systems has shown promising potential in retrieving documents (docs) or advertisements (ads) for a given query. Existing LLM-based retrieval methods generate numeric or content-based DocIDs to retrieve docs/ads. However, the one-to-few mapping between numeric IDs and docs, along with the time-consuming content extraction, leads to semantic inefficiency and limits the scalability of existing methods on large-scale corpora. In this paper, we propose the **R**eal-time **A**d **RE**trieval (RARE) framework, which leverages LLM-generated text called Commercial Intentions (CIs) as an intermediate semantic representation to directly retrieve ads for queries in real-time. These CIs are generated by a customized LLM injected with commercial knowledge, enhancing its domain relevance. Each CI corresponds to multiple ads, yielding a lightweight and scalable set of CIs. RARE has been implemented in a real-world online system, handling daily search volumes in billions. The online implementation has yielded significant benefits: a 5.04% increase in consumption, a 6.37% rise in Gross Merchandise Volume (GMV), a 1.28% enhancement in click-through rate (CTR) and a 5.29% increase in shallow conversions. Extensive offline experiments show RARE’s superiority over ten competitive baselines in four major categories.

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Toward Efficient Sparse Autoencoder-Guided Steering for Improved In-Context Learning in Large Language Models
Ikhyun Cho | Julia Hockenmaier

Sparse autoencoders (SAEs) have emerged as a powerful analytical tool in mechanistic interpretability for large language models (LLMs), with growing success in applications beyond interpretability. Building on this momentum, we present a novel approach that leverages SAEs to enhance the general in-context learning (ICL) performance of LLMs.Specifically, we introduce Feature Detection through Prompt Variation (FDPV), which leverages the SAE’s remarkable ability to capture subtle differences between prompts, enabling efficient feature selection for downstream steering. In addition, we propose a novel steering method tailored to ICL—Selective In-Context Steering (SISTER)—grounded in recent insights from ICL research that LLMs utilize label words as key anchors. Our method yields a 3.5% average performance improvement across diverse text classification tasks and exhibits greater robustness to hyperparameter variations compared to standard steering approaches. Our code is available at https://github.com/ihcho2/SAE-ICL.

With the widespread adoption of open-source code language models (code LMs), intellectual property (IP) protection has become an increasingly critical concern. While current watermarking techniques have the potential to identify the code LM to protect its IP, they have limitations when facing the more practical and complex demand, i.e., offering the individual user-level tracing in the black-box setting. This work presents CLMTracing, a black-box code LM watermarking framework employing the rule-based watermarks and utility-preserving injection method for user-level model tracing. CLMTracing further incorporates a parameter selection algorithm sensitive to the robust watermark and adversarial training to enhance the robustness against watermark removal attacks. Comprehensive evaluations demonstrate CLMTracing is effective across multiple state-of-the-art (SOTA) code LMs, showing significant harmless improvements compared to existing SOTA baselines and strong robustness against various removal attacks.

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The Good, the Bad and the Constructive: Automatically Measuring Peer Review’s Utility for Authors
Abdelrahman Sadallah | Tim Baumgärtner | Iryna Gurevych | Ted Briscoe

Providing constructive feedback to paper authors is a core component of peer review. With reviewers increasingly having less time to perform reviews, automated support systems are required to ensure high reviewing quality, thus making the feedback in reviews useful for authors. To this end, we identify four key aspects of review comments (individual points in weakness sections of reviews) that drive the utility for authors: Actionability, Grounding & Specificity, Verifiability, and Helpfulness. To enable evaluation and development of models assessing review comments, we introduce the RevUtil dataset. We collect 1,430 human-labeled review comments and scale our data with 10k synthetically labeled comments for training purposes. The synthetic data additionally contains rationales, i.e., explanations for the aspect score of a review comment. Employing the RevUtil dataset, we benchmark fine-tuned models for assessing review comments on these aspects and generating rationales. Our experiments demonstrate that these fine-tuned models achieve agreement levels with humans comparable to, and in some cases exceeding, those of powerful closed models like GPT-4o. Our analysis further reveals that machine-generated reviews generally underperform human reviews on our four aspects.

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Evolving Chinese Spelling Correction with Corrector-Verifier Collaboration
Linfeng Liu | Hongqiu Wu | Hai Zhao

Recent methods address Chinese Spelling Correction (CSC) with either BERT-based models or large language models (LLMs) independently. However, both of them face challenges. BERT-based models are efficient for this task but struggle with limited generalizability to error patterns, thus failing in open-domain CSC. LLMs are advantageous in their extensive knowledge but fall into low efficiency in character-level editing. To address this dilemma, we propose Automatic Corrector Iteration (ACI), a novel model collaboration pipeline to iteratively optimize a BERT-based corrector. This pipeline is free of human annotation, by leveraging an LLM verifier to provide useful signals for the corrector. Experimental results demonstrate that our pipeline consistently improves the model performance across iterations and significantly outperforms existing data augmentation methods, achieving comparable performance with human annotation.

Multimodal knowledge editing is an important method for modifying outdated or incorrect knowledge in Multimodal Large Language Models (MLLMs). However, existing datasets for multimodal knowledge editing lack multi-granularity knowledge. In this paper, we present a more realistic dataset called M2Edit, which includes three distinct types of knowledge: entity, relation, and action. Additionally, existing knowledge editing methods for MLLMs lack the ability to handle multi-granularity knowledge and generalize to multimodal data. To address these limitations, we propose the multimodal knowledge editing method MLE. This approach identifies key knowledge layers within different components and collaboratively edits the various components of MLLMs. As a result, we observe significant improvements in visual generality performance, ranging from 4.8 to 10.8, and achieve the best overall performance on knowledge data of different granularities.

Large Language Models (LLMs) require robust evaluation. However, existing frameworks often rely on curated datasets that, once public, may be accessed by newer LLMs. This creates a risk of data leakage, where test sets inadvertently become part of training data, compromising evaluation fairness and integrity. To mitigate this issue, we propose Behave as Claimed (BaC), a novel evaluation framework inspired by counterfactual reasoning. BaC constructs a “what-if” scenario where LLMs respond to counterfactual questions about how they would behave if the input were manipulated. We refer to these responses as claims, which are verifiable by observing the LLMs’ actual behavior when given the manipulated input. BaC dynamically generates and verifies counterfactual questions using various few-shot in-context learning evaluation datasets, reducing their susceptibility to data leakage. Moreover, BaC provides a more challenging evaluation paradigm for LLMs. LLMs must thoroughly understand the prompt, the task, and the consequences of their responses to achieve better performance. We evaluate several state-of-the-art LLMs and find that, while most perform well on the original datasets, they struggle with BaC. This suggests that LLMs usually fail to align their claims with their actual behavior and that high performance on standard datasets may be less stable than previously assumed.

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Multilingual vs Crosslingual Retrieval of Fact-Checked Claims: A Tale of Two Approaches
Alan Ramponi | Marco Rovera | Robert Moro | Sara Tonelli

Retrieval of previously fact-checked claims is a well-established task, whose automation can assist professional fact-checkers in the initial steps of information verification. Previous works have mostly tackled the task monolingually, i.e., having both the input and the retrieved claims in the same language. However, especially for languages with a limited availability of fact-checks and in case of global narratives, such as pandemics, wars, or international politics, it is crucial to be able to retrieve claims across languages. In this work, we examine strategies to improve the multilingual and crosslingual performance, namely selection of negative examples (in the supervised) and re-ranking (in the unsupervised setting). We evaluate all approaches on a dataset containing posts and claims in 47 languages (283 language combinations). We observe that the best results are obtained by using LLM-based re-ranking, followed by fine-tuning with negative examples sampled using a sentence similarity-based strategy. Most importantly, we show that crosslinguality is a setup with its own unique characteristics compared to the multilingual setup.

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How Much Do LLMs Hallucinate across Languages? On Realistic Multilingual Estimation of LLM Hallucination
Saad Obaid Ul Islam | Anne Lauscher | Goran Glavaš

In the age of misinformation, hallucination—the tendency of Large Language Models (LLMs) to generate non-factual or unfaithful responses—represents the main risk for their global utility. Despite LLMs becoming increasingly multilingual, the vast majority of research on detecting and quantifying LLM hallucination are (a) English-centric and (b) focus on machine translation (MT) and summarization, tasks that are less common in realistic settings than open information seeking. In contrast, we aim to quantify the extent of LLM hallucination across languages in knowledge-intensive long-form question answering (LFQA). To this end, we train a multilingual hallucination detection model and conduct a large-scale study across 30 languages and 6 open-source LLM families. We start from an English hallucination detection dataset and rely on MT to translate-train a detection model. We also manually annotate gold data for five high-resource languages; we then demonstrate, for these languages, that the estimates of hallucination rates are similar between silver (LLM-generated) and gold test sets, validating the use of silver data for estimating hallucination rates for other languages. For the final rates estimation, we build open-domain QA dataset for 30 languages with LLM-generated prompts and Wikipedia articles as references. Our analysis shows that LLMs, in absolute terms, hallucinate more tokens in high-resource languages due to longer responses, but that the actual hallucination rates (i.e., normalized for length) seems uncorrelated with the sizes of languages’ digital footprints. We also find that smaller LLMs hallucinate more, and significantly, LLMs with broader language support display higher hallucination rates.

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LiTransProQA: An LLM-based Literary Translation Evaluation Metric with Professional Question Answering
Ran Zhang | Wei Zhao | Lieve Macken | Steffen Eger

The impact of Large Language Models (LLMs) has extended into literary domains. However, existing evaluation metrics for literature prioritize mechanical accuracy over artistic expression and tend to overrate machine translation as being superior to human translation from experienced professionals. In the long run, this bias could result in an irreversible decline in translation quality and cultural authenticity. In response to the urgent need for a specialized literary evaluation metric, we introduce LITRANSPROQA, a novel, reference-free, LLM-based question-answering framework designed for literary translation evaluation. LITRANSPROQA integrates humans in the loop to incorporate insights from professional literary translators and researchers, focusing on critical elements in literary quality assessment such as literary devices, cultural understanding, and authorial voice. Our extensive evaluation shows that while literary-finetuned XCOMET-XL yields marginal gains, LITRANSPROQA substantially outperforms current metrics, achieving up to 0.07 gain in correlation and surpassing the best state-of-the-art metrics by over 15 points in adequacy assessments. Incorporating professional translator insights as weights further improves performance, highlighting the value of translator inputs. Notably, LITRANSPROQA reaches an adequacy performance comparable to trained linguistic student evaluators, though it still falls behind experienced professional translators. LITRANSPROQA shows broad applicability to open-source models like LLaMA3.3-70b and Qwen2.5-32b, indicating its potential as an accessible and training-free tool for evaluating literary translations that require local processing due to copyright or ethical considerations.

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Improving Handshape Representations for Sign Language Processing: A Graph Neural Network Approach
Alessa Carbo | Eric Nalisnick

Handshapes serve a fundamental phonological role in signed languages, with American Sign Language employing approximately 50 distinct shapes. However, computational approaches rarely model handshapes explicitly, which limits both recognition accuracy and linguistic analysis. We introduce a novel graph neural network that separates temporal dynamics from static handshape configurations. Our approach combines anatomically informed graph structures with contrastive learning to address key challenges in handshape recognition, including subtle inter-class distinctions and temporal variations. We establish the first benchmark for structured handshape recognition in signing sequences, achieving 46% accuracy across 37 handshape classes, compared to 25% for baseline methods.

Instructing language models with user intent requires large instruction datasets, which are only available for a limited set of languages. In this paper, we explore alternatives to conventional instruction adaptation pipelines in low-resource scenarios. We assume a realistic scenario for low-resource languages, where only the following are available: corpora in the target language, existing open-weight multilingual base and instructed backbone LLMs, and synthetically generated instructions sampled from the instructed backbone. We present a comprehensive set of experiments for Basque that systematically study different combinations of these components evaluated on benchmarks and human preferences from 1,680 participants. Our conclusions show that target language corpora are essential, with synthetic instructions yielding robust models, and, most importantly, that using as backbone an instruction-tuned model outperforms using a base non-instructed model. Scaling up to Llama 3.1 Instruct 70B as backbone, our model comes near frontier models of much larger sizes for Basque, without using any Basque instructions. We release code, models, instruction datasets, and human preferences to support full reproducibility in future research on low-resource language adaptation.

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SOCIAL SCAFFOLDS: A Generalization Framework for Social Understanding Tasks
Ritam Dutt | Carolyn Rose | Maarten Sap

Effective human communication in social settings is contingent on recognizing subtle cues, such as intents or implications. Without such cues, NLP models risk missing social signals, instead relying on surface patterns. We introduce SOCIAL SCAFFOLDS, an automated framework for facilitating generalization across social reasoning tasks by generating rationales that make these social cues explicit. Grounded in narrative modeling principles, we generate task-agnostic rationales that capture different perspectives, i.e., that of the speaker, the listener, and the general world-view. Our experimental suite showcases that providing rationales as augmentations aids task performance for both supervised fine-tuning and in-context learning paradigms. Notably, providing all three rationale types significantly improves cross-task performance in 44% of cases, and inferred speaker intent in 31.3% of cases. We conduct statistical and ablation analyses that show how rationales complement the input text and are used effectively by models.

The emergence of large language models (LLMs) has revolutionized AI development, yet their resource demands beyond a single cluster or even datacenter, limiting accessibility to well-resourced organizations. Decentralized training has emerged as a promising paradigm to leverage dispersed resources across clusters, datacenters and even regions, offering the potential to democratize LLM development for broader communities. As the first comprehensive exploration of this emerging field, we present decentralized LLM training as a resource-driven paradigm and categorize existing efforts into community-driven and organizational approaches. We further clarify this through: (1) a comparison with related paradigms, (2) characterization of decentralized resources, and (3) a taxonomy of recent advancements. We also provide up-to-date case studies and outline future directions to advance research in decentralized LLM training.

Large Language Models (LLMs) are widely used as conversational agents exploiting their capabilities in various sectors such as education, law, medicine, and more. However, LLMs are often subjected to context-shifting behaviour, resulting in a lack of consistent and interpretable personality-aligned interactions. Adherence to psychological traits lacks comprehensive analysis, especially in the case of dyadic (pairwise) conversations. We examine this challenge from two viewpoints, initially using two conversation agents to generate a discourse on a certain topic with an assigned personality from the OCEAN framework (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism) as High/Low for each trait. This is followed by using multiple judge agents to infer the original traits assigned to explore prediction consistency, inter-model agreement, and alignment with the assigned personality. Our findings indicate that while LLMs can be guided toward personality-driven dialogue, their ability to maintain personality traits varies significantly depending on the combination of models and discourse settings. These inconsistencies emphasise the challenges in achieving stable and interpretable personality-aligned interactions in LLMs.

Multilingual representations embed words with similar meanings to share a common semantic space across languages, creating opportunities to transfer debiasing effects between languages. However, existing methods for debiassing are unable to exploit this opportunity because they operate on individual languages. We present Iterative Multilingual Spectral Attribute Erasure (IMSAE), which identifies and mitigates joint bias subspaces across multiple languages through iterative SVD-based truncation. Evaluating IMSAE across eight languages and five demographic dimensions, we demonstrate its effectiveness in both standard and zero-shot settings, where target language data is unavailable, but linguistically similar languages can be used for debiasing. Our comprehensive experiments across diverse language models (BERT, LLaMA, Mistral) show that IMSAE outperforms traditional monolingual and cross-lingual approaches while maintaining model utility.

Mechanistic interpretability research faces a gap between analyzing simple circuits in toy tasks and discovering features in large models. To bridge this gap, we propose text-to-SQL generation as an ideal task to study, as it combines the formal structure of toy tasks with real-world complexity. We introduce TinySQL, a synthetic dataset, progressing from basic to advanced SQL operations, and train models ranging from 33M to 1B parameters to establish a comprehensive testbed for interpretability. We apply multiple complementary interpretability techniques, including Edge Attribution Patching and Sparse Autoencoders, to identify minimal circuits and components supporting SQL generation. We compare circuits for different SQL subskills, evaluating their minimality, reliability, and identifiability. Finally, we conduct a layerwise logit lens analysis to reveal how models compose SQL queries across layers: from intent recognition to schema resolution to structured generation. Our work provides a robust framework for probing and comparing interpretability methods in a structured, progressively complex setting.

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SCRIBE: Structured Chain Reasoning for Interactive Behaviour Explanations using Tool Calling
Fares Fawzi | Vinitra Swamy | Dominik Glandorf | Tanya Nazaretsky | Tanja Käser

Language models can be used to provide interactive, personalized student feedback in educational settings. However, real-world deployment faces three key challenges: privacy concerns, limited computational resources, and the need for pedagogically valid responses. These constraints require small, open-source models that can run locally and reliably ground their outputs in correct information. We introduce SCRIBE, a framework for multi-hop, tool-augmented reasoning designed to generate valid responses to student questions about feedback reports. SCRIBE combines domain-specific tools with a self-reflective inference pipeline that supports iterative reasoning, tool use, and error recovery. We distil these capabilities into 3B and 8B models via two-stage LoRA fine-tuning on synthetic GPT-4o-generated data. Evaluation with a human-aligned GPT-Judge and a user study with 108 students shows that 8B-SCRIBE models achieve comparable or superior quality to much larger models in key dimensions such as relevance and actionability, while being perceived on par with GPT-4o and Llama-3.3 70B by students. These findings demonstrate the viability of SCRIBE for low-resource, privacy-sensitive educational applications.

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Logit Space Constrained Fine-Tuning for Mitigating Hallucinations in LLM-Based Recommender Systems
Jianfeng Deng | Qingfeng Chen | Debo Cheng | Jiuyong Li | Lin Liu

Large language models (LLMs) have gained increasing attention in recommender systems, but their inherent hallucination issues significantly compromise the accuracy and reliability of recommendation results. Existing LLM-based recommender systems predominantly rely on standard fine-tuning methodologies, often ignoring hallucination issues during the fine-tuning process. To address this challenge, we propose Logit Space Constraints Fine-Tuning (LCFT), a novel fine-tuning framework designed to mitigate hallucination in LLM-based recommenders. Specifically, LCFT takes as input semantically positive and negative instruction pairs and incorporates Kullback–Leibler (KL) divergence into the training objective to explicitly maximise their distributional disparity in the logit space. By conducting such logit space-constrained fine-tuning, LCFT encourages more distinguishable and semantically grounded representations, thereby reducing the model’s susceptibility to hallucination. Extensive experiments on two recommendation models with distinct LLM backbones and four real-world datasets demonstrate that LCFT consistently reduces hallucination and enhances recommendation performance.

Extensive research on LLM-based spoken dialogue systems has significantly advanced the development of intelligent voice assistants. However, the integration of role information within speech remains an underexplored area, limiting its application in real-world scenarios, particularly in multi-party dialogue settings. With the growing demand for personalization, voice assistants that can recognize and remember users establish a deeper connection with them. We focus on enabling LLMs with speaker-awareness capabilities and enhancing their understanding of character settings through synthetic data to generate contextually appropriate responses. We introduce Persona-Dialogue, the first large-scale multi-party spoken dialogue dataset that incorporates speaker profiles. Based on this dataset, we propose PAChat, an architecture that simultaneously models both linguistic content and speaker features, allowing LLMs to map character settings to speaker identities in speech. Through extensive experiments, we demonstrate that PAChat successfully achieves speaker-specific responses, character understanding, and the generation of targeted replies in multi-party dialogue scenarios, surpassing existing spoken dialogue systems.

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Reasoning-to-Defend: Safety-Aware Reasoning Can Defend Large Language Models from Jailbreaking
Junda Zhu | Lingyong Yan | Shuaiqiang Wang | Dawei Yin | Lei Sha

Large Reasoning Models (LRMs) have recently demonstrated impressive performances across diverse domains. However, how the safety of Large Language Models (LLMs) benefits from enhanced reasoning capabilities against jailbreak queries remains unexplored. To bridge this gap, in this paper, we propose Reasoning-to-Defend (R2D), a novel training paradigm that integrates a safety-aware reasoning mechanism into LLMs’ generation process. This enables self-evaluation at each step of the reasoning process, forming safety pivot tokens as indicators of the safety status of responses. Furthermore, in order to improve the accuracy of predicting pivot tokens, we propose Contrastive Pivot Optimization (CPO), which enhances the model’s perception of the safety status of given dialogues. LLMs dynamically adjust their response strategies during reasoning, significantly enhancing their safety capabilities defending jailbreak attacks. Extensive experiments demonstrate that R2D effectively mitigates various attacks and improves overall safety, while maintaining the original performances. This highlights the substantial potential of safety-aware reasoning in improving robustness of LRMs and LLMs against various jailbreaks.

Natural language explanations (NLEs) are commonly used to provide plausible free-text explanations of a model’s reasoning about its predictions. However, recent work has questioned their faithfulness, as they may not accurately reflect the model’s internal reasoning process regarding its predicted answer. In contrast, highlight explanations–input fragments critical for the model’s predicted answers–exhibit measurable faithfulness. Building on this foundation, we propose G-TEx, a Graph-Guided Textual Explanation Generation framework designed to enhance the faithfulness of NLEs. Specifically, highlight explanations are first extracted as faithful cues reflecting the model’s reasoning logic toward answer prediction. They are subsequently encoded through a graph neural network layer to guide the NLE generation, which aligns the generated explanations with the model’s underlying reasoning toward the predicted answer. Experiments on both encoder-decoder and decoder-only models across three reasoning datasets demonstrate that G-TEx improves NLE faithfulness by up to 12.18% compared to baseline methods. Additionally, G-TEx generates NLEs with greater semantic and lexical similarity to human-written ones. Human evaluations show that G-TEx can decrease redundant content and enhance the overall quality of NLEs. Our work presents a novel method for explicitly guiding NLE generation to enhance faithfulness, serving as a foundation for addressing broader criteria in NLE and generated text.

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The Validation Gap: A Mechanistic Analysis of How Language Models Compute Arithmetic but Fail to Validate It
Leonardo Bertolazzi | Philipp Mondorf | Barbara Plank | Raffaella Bernardi

The ability of large language models (LLMs) to validate their output and identify potential errors is crucial for ensuring robustness and reliability. However, current research indicates that LLMs struggle with self-correction, encountering significant challenges in detecting errors. While studies have explored methods to enhance self-correction in LLMs, relatively little attention has been given to understanding the models’ internal mechanisms underlying error detection. In this paper, we present a mechanistic analysis of error detection in LLMs, focusing on simple arithmetic problems. Through circuit analysis, we identify the computational subgraphs responsible for detecting arithmetic errors across four smaller-sized LLMs. Our findings reveal that all models heavily rely on consistency heads\textemdash{}attention heads that assess surface-level alignment of numerical values in arithmetic solutions. Moreover, we observe that the models’ internal arithmetic computation primarily occurs in higher layers, whereas validation takes place in middle layers, before the final arithmetic results are fully encoded. This structural dissociation between arithmetic computation and validation seems to explain why smaller-sized LLMs struggle to detect even simple arithmetic errors.

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A Causal Lens for Evaluating Faithfulness Metrics
Kerem Zaman | Shashank Srivastava

Large Language Models (LLMs) offer natural language explanations as an alternative to feature attribution methods for model interpretability. However, despite their plausibility, they may not reflect the model’s truereasoning faithfully. While several faithfulness metrics have been proposed, they are often evaluated in isolation, making principled comparisons between them difficult. We present Causal Diagnosticity, a testbed framework for evaluating faithfulness metrics for natural language explanations. We use the concept of diagnosticity, and employ model-editing methods to generate faithful-unfaithful explanation pairs. Our benchmark includes four tasks: fact-checking, analogy, object counting, and multi-hop reasoning. We evaluate prominent faithfulness metrics, including post-hoc explanation and chain-of-thought methods. Diagnostic performance varies across tasks and models, with Filler Tokens performing best overall. Additionally, continuous metrics are generally more diagnostic than binary ones but can be sensitive to noise and model choice. Our results highlight the need for more robust faithfulness metrics.

Evaluating the ability of large language models (LLMs) to process lengthy contexts is critical, especially for retrieving query-relevant information embedded within them. We introduce Sequential-NIAH, a benchmark specifically designed to evaluate the capability of LLMs to extract sequential information items (known as needles) from long contexts. The benchmark includes three needle generation pipelines: synthetic-temporal, real-temporal, and real-logical orders, with context lengths ranging from 8K to 128K, which comprises 14,000 samples (2,000 for testing). To facilitate the evaluation of this benchmark, we trained an evaluation model that assesses the correctness of LLM responses by comparing their completeness and sequential consistency against the ground truth, which provides a more reliable evaluation metric than GPT-4 or Claude. We conducted experiments on six well-known LLMs, revealing that even the best-performing model achieved a maximum accuracy of only 63.50% on test set of this benchmark. Further analysis highlights the growing challenges posed by increasing the context length or the number of needles, underscoring substantial room for improvement of LLMs. Additionally, noise analysis validates the reliability and challenge of the benchmark, making Sequential-NIAH an important reference for advancing research on long text information extraction capabilities of LLMs.

Pruning is a critical strategy for compressing trained large language models (LLMs), aiming at substantial memory conservation and computational acceleration without compromising performance. However, existing pruning methods typically necessitate inefficient retraining for billion-scale LLMs or rely on heuristically designed metrics to determine pruning masks, leading to performance degradation. This paper presents, for the first time, a LASSO-like convex optimization model crafted to induce sparsity in LLMs. By leveraging FISTA, we introduce FISTAPruner, a novel method that includes a cumulative error elimination mechanism within decoder layers and supports parallel pruning for unstructured pruning. Additionally, we extend this method to 2:4 semi-structured pruning. We comprehensively evaluate FISTAPruner on models such as OPT, LLaMA, and Qwen variants with 125M to 70B parameters under unstructured and 2:4 semi-structured sparsity, showcasing superior performance over existing methods across various language benchmarks. Notably, it can remove 50% of the model parameters for LLaMA-3-70B while retaining 98.6% and 95.6% of the zero-shot task performance under these two sparsity patterns, respectively.

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Do LLMs Encode Frame Semantics? Evidence from Frame Identification
Jayanth Krishna Chundru | Rudrashis Poddar | Jie Cao | Tianyu Jiang

We investigate whether large language models encode latent knowledge of frame semantics, focusing on frame identification, a core challenge in frame semantic parsing that involves selecting the appropriate semantic frame for a target word in context. Using the FrameNet lexical resource, we evaluate models under prompt-based inference and observe that they can perform frame identification effectively even without explicit supervision. To assess the impact of task-specific training, we fine-tune the model on FrameNet data, which substantially improves in-domain accuracy while generalizing well to out-of-domain benchmarks. Further analysis shows that the models can generate semantically coherent frame definitions, highlighting the model’s internalized understanding of frame semantics.

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StepER: Step-wise Knowledge Distillation for Enhancing Reasoning Ability in Multi-Step Retrieval-Augmented Language Models
Kyumin Lee | Minjin Jeon | Sanghwan Jang | Hwanjo Yu

Answering complex real-world questions requires step-by-step retrieval and integration of relevant information to generate well-grounded responses. However, existing knowledge distillation methods overlook the need for different reasoning abilities at different steps, hindering transfer in multi-step retrieval-augmented frameworks. To address this, we propose Step-wise Knowledge Distillation for Enhancing Reasoning Ability in Multi-Step Retrieval-Augmented Language Models (StepER). StepER employs step-wise supervision to align with evolving information and reasoning demands across stages. Additionally, it incorporates difficulty-aware training to progressively optimize learning by prioritizing suitable steps. Our method is highly adaptable across various frameworks of multi-step retrieval-augmented language models, including those based on reasoning paths or question decomposition. Extensive experiments show that StepER outperforms prior methods on multi-hop QA benchmarks, with an 8B model achieving performance comparable to a 70B teacher model.

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How Does DPO Reduce Toxicity? A Mechanistic Neuron-Level Analysis
Yushi Yang | Filip Sondej | Harry Mayne | Andrew Lee | Adam Mahdi

Safety fine-tuning algorithms reduce harmful outputs in language models, yet their mechanisms remain under-explored. Direct Preference Optimization (DPO) is a popular choice of algorithm, but prior explanations—attributing its effects solely to dampened toxic neurons in the MLP layers—are incomplete. In this study, we analyse four language models (Llama-3.1-8B, Gemma-2-2B, Mistral-7B, GPT-2-Medium) and show that toxic neurons only account for 2.5% to 24% of DPO’s effects across models. Instead, DPO induces distributed activation shifts across all MLP neurons to create a net toxicity reduction. We attribute this reduction to four neuron groups—two aligned with reducing toxicity and two promoting anti-toxicity—whose combined effects replicate DPO across models. To further validate this understanding, we develop an activation editing method that mimics DPO through distributed shifts along a toxicity representation. This method outperforms DPO in reducing toxicity while preserving perplexity, without requiring any weight updates. Our work provides a mechanistic understanding of DPO and introduces an efficient, tuning-free alternative for safety fine-tuning.

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It’s All About In-Context Learning! Teaching Extremely Low-Resource Languages to LLMs
Yue Li | Zhixue Zhao | Carolina Scarton

Extremely low-resource languages, especially those written in rare scripts, remain largely unsupported by large language models (LLMs). This is due in part to compounding factors such as the lack of training data. This paper delivers the first comprehensive analysis of whether LLMs can acquire such languages purely via in-context learning (ICL), with or without auxiliary alignment signals, and how these methods compare to parameter-efficient fine-tuning (PEFT). We systematically evaluate 20 under-represented languages across three state-of-the-art multilingual LLMs. Our findings highlight the limitation of PEFT when both language and its script are extremely under-represented by the LLM. In contrast, zero-shot ICL with language alignment is impressively effective on extremely low-resource languages, while few-shot ICL or PEFT is more beneficial for languages relatively better represented by LLMs. For LLM practitioners working on extremely low-resource languages, we summarise guidelines grounded by our results on adapting LLMs to low-resource languages, e.g., avoiding fine-tuning a multilingual model on languages of unseen scripts.

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Where to show Demos in Your Prompt: A Positional Bias of In-Context Learning
Kwesi Adu Cobbina | Tianyi Zhou

In-context learning (ICL) is a critical emerging capability of large language models (LLMs), enabling few-shot learning during inference by including a few demonstrations (demos) in the prompt. However, it has been found that ICL’s performance can be sensitive to the choices of demos and their order. This paper investigates an unexplored new positional bias of ICL for the first time: we observe that the predictions and accuracy can drift drastically when the positions of demos, system prompt, and user message in LLM input are varied. This bias, we refer to as DEMOS’ POSITION IN PROMPT bias (DPP bias). We design a systematic evaluation pipeline to study this type of positional bias across classification, QA, summarization, and reasoning tasks. We introduce two metrics, ACCURACY-CHANGE and PREDICTION-CHANGE, to quantify net gains and output volatility induced by demos’ position change. Extensive experiments on tenLLMs from four open-source model families(QWEN, LLAMA3, MISTRAL, COHERE) verify that the bias significantly affects their accuracy and predictions: placing demos at the start of prompt yields the most stable and accurate outputs with gains of up to +6 points. In contrast, placing demos at the end of the user message flips over 30% of predictions without improving correctness in QA tasks. Smaller models are most affected by this sensitivity, though even large models do remain marginally affected on complex tasks.

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Multilingual Pretraining for Pixel Language Models
Ilker Kesen | Jonas F. Lotz | Ingo Ziegler | Phillip Rust | Desmond Elliott

Pixel language models operate directly on images of rendered text, eliminating the need for a fixed vocabulary. While these models have demonstrated strong capabilities for downstream cross-lingual transfer, multilingual pretraining remains underexplored. We introduce PIXEL-M4, a model pretrained on four visually and linguistically diverse languages: English, Hindi, Ukrainian, and Simplified Chinese. Multilingual evaluations on semantic and syntactic tasks show that PIXEL-M4 outperforms an English-only counterpart on non-Latin scripts. Word-level probing analyses confirm that PIXEL-M4 captures rich linguistic features, even in languages not seen during pretraining. Furthermore, an analysis of its hidden representations shows that multilingual pretraining yields a semantic embedding space closely aligned across the languages used for pretraining. This work demonstrates that multilingual pretraining substantially enhances the capability of pixel language models to effectively support a diverse set of languages.

A critical component in the trustworthiness of LLMs is reliable uncertainty communication, yet LLMs often use assertive language when conveying false claims, leading to over-reliance and eroded trust. We present the first systematic study of _faithful confidence calibration_ of LLMs, benchmarking models’ ability to use linguistic expressions of uncertainty that _faithfully reflect_ their intrinsic uncertainty, across a comprehensive array of models, datasets, and prompting strategies. Our results demonstrate that LLMs largely fail at this task, and that existing interventions are insufficient: standard prompt approaches provide only marginal gains, and existing, factuality-based calibration techniques can even harm faithful calibration. To address this critical gap, we introduce MetaFaith, a novel prompt-based calibration approach inspired by human metacognition. We show that MetaFaith robustly improves faithful calibration across diverse models and task domains, enabling up to 61% improvement in faithfulness and achieving an 83% win rate over original generations as judged by humans.

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Machine-generated text detection prevents language model collapse
George Drayson | Emine Yilmaz | Vasileios Lampos

As Large Language Models (LLMs) become increasingly prevalent, their generated outputs are proliferating across the web, risking a future where machine-generated content dilutes human-authored text. Since online data is the primary resource for LLM pre-training, subsequent models could be trained on an unknown portion of synthetic samples. This could lead to model collapse, a degenerative process whereby LLMs reinforce their own errors, reduce output diversity, and ultimately yield declining performance. In this study, we investigate the impact of decoding strategy on model collapse, analysing the text characteristics at each model generation, the similarity to human references, and the resulting model performance. Using the decoding strategies that lead to the most significant degradation, we evaluate model collapse in a more realistic scenario where the origin of the data (human or synthetic) is unknown. We train a machine-generated text detector and propose an importance resampling approach to prevent model collapse by up-sampling likely human content in the training data. Our method is validated on four LLMs from two model families (GPT-2 and SmolLM2), across a range of model sizes (124M to 1.7B). We demonstrate that it not only prevents model collapse but also improves performance compared to training on purely human data, underscoring the benefit of synthetic samples and the importance of data curation.

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Data-Efficient Hate Speech Detection via Cross-Lingual Nearest Neighbor Retrieval with Limited Labeled Data
Faeze Ghorbanpour | Daryna Dementieva | Alexander Fraser

Considering the importance of detecting hateful language, labeled hate speech data is expensive and time-consuming to collect, particularly for low-resource languages. Prior work has demonstrated the effectiveness of cross-lingual transfer learning and data augmentation in improving performance on tasks with limited labeled data. To develop an efficient and scalable cross-lingual transfer learning approach, we leverage nearest-neighbor retrieval to augment minimal labeled data in the target language, thereby enhancing detection performance. Specifically, we assume access to a small set of labeled training instances in the target language and use these to retrieve the most relevant labeled examples from a large multilingual hate speech detection pool. We evaluate our approach on eight languages and demonstrate that it consistently outperforms models trained solely on the target language data. Furthermore, in most cases, our method surpasses the current state-of-the-art. Notably, our approach is highly data-efficient, retrieving as small as 200 instances in some cases while maintaining superior performance. Moreover, it is scalable, as the retrieval pool can be easily expanded, and the method can be readily adapted to new languages and tasks. We also apply maximum marginal relevance to mitigate redundancy and filter out highly similar retrieved instances, resulting in improvements in some languages.

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V-VAE: A Variational Auto Encoding Framework Towards Fine-Grained Control over Human-Like Chat
Qi Lin | Weikai Xu | Lisi Chen | Bin Dai

With the continued proliferation of Large Language Model (LLM) based chatbots, there is a growing demand for generating responses that are not only linguistically fluent but also consistently aligned with persona-specific traits in conversations. However, existing role-play and persona-based chat approaches rely heavily on static role descriptions, coarse-grained signal space, and low-quality synthetic data, which fail to capture dynamic fine-grained details in human-like chat. Human-like chat requires modeling subtle latent traits, such as emotional tone, situational awareness, and evolving personality, which are difficult to predefine and cannot be easily learned from synthetic or distillation-based data. To address these limitations, we propose a Verbal Variational Auto-Encoding (V-VAE) framework, containing a variational auto-encoding module and fine-grained control space which dynamically adapts dialogue behaviour based on fine-grained, interpretable latent variables across talking style, interaction patterns, and personal attributes. We also construct a high-quality dataset, HumanChatData, and benchmark HumanChatBench to address the scarcity of high-quality data in the human-like domain. Experiments show that LLMs based on V-VAE consistently outperform standard baselines on HumanChatBench and DialogBench, which further demonstrates the effectiveness of V-VAE and HumanChatData.

We propose Mixture of Languages (MoL), a new strategy to pretrain largely multilingual encoders. Recent work in this field has relied on training transformer encoders on a large amount of multilingual data, with all parameters shared across all languages, without studying how to optimally balance language transfer and interference to achieve better performance. To address this, MoL proposes to group languages based on their similarity, and add parallel, sparsely activated layers that process each group independently. This architecture allows MoL to boost language transfer while minimizing interference, without increasing the active parameter count. We show that MoL largely outperforms a dense counterpart trained with the same configuration, as well as MoE models and public multilingual encoders such as XLM-R or mBERT on downstream tasks.

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Too Helpful, Too Harmless, Too Honest or Just Right?
Gautam Siddharth Kashyap | Mark Dras | Usman Naseem

Large Language Models (LLMs) exhibit strong performance across a wide range of NLP tasks, yet aligning their outputs with the principles of Helpfulness, Harmlessness, and Honesty (HHH) remains a persistent challenge. Existing methods often optimize for individual alignment dimensions in isolation, leading to trade-offs and inconsistent behavior. While Mixture-of-Experts (MoE) architectures offer modularity, they suffer from poorly calibrated routing, limiting their effectiveness in alignment tasks. We propose TrinityX, a modular alignment framework that incorporates a Mixture of Calibrated Experts (MoCaE) within the Transformer architecture. TrinityX leverages separately trained experts for each HHH dimension, integrating their outputs through a calibrated, task-adaptive routing mechanism that combines expert signals into a unified, alignment-aware representation. Extensive experiments on three standard alignment benchmarks—Alpaca (Helpfulness), BeaverTails (Harmlessness), and TruthfulQA (Honesty)—demonstrate that TrinityX outperforms strong baselines, achieving relative improvements of 32.5% in win rate, 33.9% in safety score, and 28.4% in truthfulness. In addition, TrinityX reduces memory usage and inference latency by over 40% compared to prior MoE-based approaches. Ablation studies highlight the importance of calibrated routing, and cross-model evaluations confirm TrinityX’s generalization across diverse LLM backbones. Ourcode is available at: https://github.com/gskgautam/TrinityX

The prototyping of computer games, particularly card games, requires extensive human effort in creative ideation and gameplay evaluation. Recent advances in Large Language Models (LLMs) offer opportunities to automate and streamline these processes. However, it remains challenging for LLMs to design novel game mechanics beyond existing databases, generate consistent gameplay environments, and develop scalable gameplay AI for large-scale evaluations. This paper addresses these challenges by introducing a comprehensive automated card game prototyping framework. The approach highlights a graph-based indexing method for generating novel game variations, an LLM-driven system for consistent game code generation validated by gameplay records, and a gameplay AI constructing method that uses an ensemble of LLM-generated action-value functions optimized through self-play. These contributions aim to accelerate card game prototyping, reduce human labor, and lower barriers to entry for game developers.

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Assessing effective de-escalation of crisis conversations using transformer-based models and trend statistics
Ignacio J. Tripodi | Greg Buda | Margaret Meagher | Elizabeth A. Olson

One of the core goals of crisis counseling services is to support emotional de-escalation of the individual in crisis, by reducing intense negative emotional affect and emotional dysregulation. The science of crisis intervention has been impeded, however, by a lack of quantitative approaches that allow for detailed analysis of emotion in crisis conversations. In order to measure de-escalation at scale (millions of text-based conversations), lightweight models are needed that can assign not just binary sentiment predictions but quantitative scores to capture graded change in emotional valence. Accordingly, we developed a transformer-based emotional valence scoring model fit for crisis conversations, BERT-EV, that assigns numerical emotional valence scores to rate the intensity of expressed negative versus positive emotion. This transformer-based model can run on modest hardware configurations, allowing it to scale affordably and efficiently to a massive corpus of crisis conversations. We evaluated model performance on a corpus of hand-scored social media messages, and found that BERT-EV outperforms existing dictionary-based standard tools in the field, as well as other transformer-based implementations and an LLM in accurately matching scores from human annotators. Finally, we show that trends in these emotional valence scores can be used to assess emotional de-escalation during crisis conversations, with sufficient turn-by-turn granularity to help identify helpful vs. detrimental crisis counselor statements.

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Measuring and Mitigating Media Outlet Name Bias in Large Language Models
Seong-Jin Park | Kang-Min Kim

Large language models (LLMs) have achieved remarkable performance across diverse natural language processing tasks, but concerns persist regarding their potential political biases. While prior research has extensively explored political biases in LLMs’ text generation and perception, limited attention has been devoted to biases associated with media outlet names. In this study, we systematically investigate the presence of media outlet name biases in LLMs and evaluate their impact on downstream tasks, such as political bias prediction and news summarization. Our findings demonstrate that LLMs consistently exhibit biases toward the known political leanings of media outlets, with variations across model families and scales. We propose a novel metric to quantify media outlet name biases in LLMs and leverage this metric to develop an automated prompt optimization framework. Our framework effectively mitigates media outlet name biases, offering a scalable approach to enhancing the fairness of LLMs in news-related applications.

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The Good, the Bad, and the Debatable: A Survey on the Impacts of Data for In-Context Learning
Stephanie Schoch | Yangfeng Ji

In-context learning is an emergent learning paradigm that enables an LLM to learn an unseen task by seeing a number of demonstrations in the context window. The quality of the demonstrations is of paramount importance as 1) context window size limitations restrict the number of demonstrations that can be presented to the model, and 2) the model must identify the task and potentially learn new, unseen input-output mappings from the limited demonstration set. An increasing body of work has also shown the sensitivity of predictions to perturbations on the demonstration set. Given this importance, this work presents a survey on the current literature pertaining to the relationship between data and in-context learning. We present our survey in three parts: the “good” – qualities that are desirable when selecting demonstrations, the “bad” – qualities of demonstrations that can negatively impact the model, as well as issues that can arise in presenting demonstrations, and the “debatable” – qualities of demonstrations with mixed results or factors modulating data impacts.

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Where Confabulation Lives: Latent Feature Discovery in LLMs
Thibaud Ardoin | Yi Cai | Gerhard Wunder

Hallucination remains a critical failure mode of large language models (LLMs), undermining their trustworthiness in real-world applications. In this work, we focus on confabulation, a foundational aspect of hallucination where the model fabricates facts about unknown entities. We introduce a targeted dataset designed to isolate and analyze this behavior across diverse prompt types. Using this dataset, and building on recent progress in interpreting LLM internals, we extract latent directions associated with confabulation using sparse projections. A simple vector-based steering method demonstrates that these directions can modulate model behavior with minimal disruption, shedding light on the inner representations that drive factual and non-factual output. Our findings contribute to a deeper mechanistic understanding of LLMs and pave the way toward more trustworthy and controllable generation. We release the code and dataset at https://github.com/Thibaud-Ardoin/where-confabulation-lives.

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Analysing Chain of Thought Dynamics: Active Guidance or Unfaithful Post-hoc Rationalisation?
Samuel Lewis-Lim | Xingwei Tan | Zhixue Zhao | Nikolaos Aletras

Recent work has demonstrated that using chain of thought (CoT), on soft-reasoning problems such as analytical and commonsense reasoning, often yields limited or even negative performance gains. CoT can also be unfaithful to the model’s actual reasoning. This paper investigates dynamics and unfaithfulness of CoT in soft-reasoning tasks across instruction-tuned, reasoning and reasoning-distilled models. Our findings show that distilled‐reasoning models rely heavily on CoT for these tasks, while instruction‐tuned and reasoning models often use it post‐hoc. Additionally, we find that CoT can steer model predictions without faithfully reflecting reasoning, indicating a disconnect between CoT influence and faithfulness.

Interaction between learner and feedback-giver has come into focus recently for post-training of Large Language Models (LLMs), through the use of reward models that judge the appropriateness of a model’s response. In this paper, we investigate whether Dialogue Games—goal-directed and rule-governed activities driven predominantly by verbal actions—can also serve as a source of feedback signals for learning.We introduce Playpen, an environment for off- and online learning through Dialogue Game self-play, and investigate a representative set of post-training methods: supervised fine-tuning; direct alignment (DPO); and reinforcement learning with Group Relative Policy Optimization (GRPO). We experiment with post-training a small LLM (Llama-3.1-8B-Instruct), evaluating performance on unseen instances of training games as well as unseen games, and on standard benchmarks. We find that imitation learning through SFT improves performance on unseen instances, but negatively impacts other skills, while interactive learning with GRPO shows balanced improvements without loss of skills. We release the framework and the baseline training setups to foster research in this promising new direction of “learning in (synthetic) interaction”.

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GenLink: Generation-Driven Schema-Linking via Multi-Model Learning for Text-to-SQL
Zhifeng Hao | Junqi Huang | Shaobin Shi | Ruichu Cai | Boyan Xu

Schema linking is widely recognized as a key factor in improving text-to-SQL performance. Supervised fine-tuning approaches enhance SQL generation quality by explicitly fine-tuning schema linking as an extraction task. However, they suffer from two major limitations: (i) The training corpus of small language models restricts their cross-domain generalization ability. (ii) The extraction-based fine-tuning process struggles to capture complex linking patterns. To address these issues, we propose GenLink, a generation-driven schema-linking framework based on multi-model learning. Instead of explicitly extracting schema elements, GenLink enhances linking through a generation-based learning process, effectively capturing implicit schema relationships. By integrating multiple small language models, GenLink improves schema-linking recall rate and ensures robust cross-domain adaptability. Experimental results on the BIRD and Spider benchmarks validate the effectiveness of GenLink, achieving execution accuracies of 67.34% (BIRD), 89.7% (Spider development set), and 87.8% (Spider test set), demonstrating its superiority in handling diverse and complex database schemas.

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TSVer: A Benchmark for Fact Verification Against Time-Series Evidence
Marek Strong | Andreas Vlachos

Reasoning over temporal and numerical data, such as time series, is a crucial aspect of fact-checking. While many systems have recently been developed to handle this form of evidence, their evaluation remains limited by existing datasets, which often lack structured evidence, provide insufficient justifications for verdicts, or rely on synthetic claims. In this paper, we introduce TSVer, a new benchmark dataset for fact verification focusing on temporal and numerical reasoning with time-series evidence. TSVer contains 287 real-world claims sourced from 38 fact-checking organizations and a curated database of 400 time series covering diverse domains.Each claim is annotated with time frames across all pertinent time series, along with a verdict and justifications reflecting how the evidence is used to reach the verdict. Using an LLM-assisted multi-step annotation process, we improve the quality of our annotations and achieve an inter-annotator agreement of 𝜅 = 0.745 on verdicts. We also develop a baseline for verifying claims against time-series evidence and show that even the state-of-the-art reasoning models like Gemini-2.5-Pro are challenged by time series, achieving a 63.37 accuracy score on verdicts and an Ev²R score of 48.63 on verdict justifications.

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Cross-MoE: An Efficient Temporal Prediction Framework Integrating Textual Modality
Ruizheng Huang | Zhicheng Zhang | Yong Wang

It has been demonstrated that incorporating external information as textual modality can effectively improve time series forecasting accuracy. However, current multi-modal models ignore the dynamic and different relations between time series patterns and textual features, which leads to poor performance in temporal-textual feature fusion. In this paper, we propose a lightweight and model-agnostic temporal-textual fusion framework named Cross-MoE. It replaces Cross Attention with Cross-Ranker to reduce computational complexity, and enhances modality-aware correlation memorization with Mixture-of-Experts (MoE) networks to tolerate the distributional shifts in time series. The experimental results demonstrate a 8.78% average reduction in Mean Squared Error (MSE) compared to the SOTA multi-modal time series framework. Notably, our method requires only 75% of computational overhead and 12.5% of activated parameters compared with Cross Attention mechanism. Our codes are available at https://github.com/Kilosigh/Cross-MoE.git

Sparse Autoencoders (SAEs) provide potential for uncovering structured, human-interpretable representations in Large Language Models (LLMs), making them a crucial tool for transparent and controllable AI systems. We systematically analyze SAE for interpretable feature extraction from LLMs in safety-critical classification tasks. Our framework evaluates (1) model-layer selection and scaling properties, (2) SAE architectural configurations, including width and pooling strategies, and (3) the effect of binarizing continuous SAE activations. SAE-derived features achieve macro F1 > 0.8, outperforming hidden-state and BoW baselines while demonstrating cross-model transfer from Gemma 2 2B to 9B-IT models. These features generalize in a zero-shot manner to cross-lingual toxicity detection and visual classification tasks. Our analysis highlights the significant impact of pooling strategies and binarization thresholds, showing that binarization offers an efficient alternative to traditional feature selection while maintaining or improving performance. These findings establish new best practices for SAE-based interpretability and enable scalable, transparent deployment of LLMs in real-world applications.

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KGE Calibrator: An Efficient Probability Calibration Method of Knowledge Graph Embedding Models for Trustworthy Link Prediction
Yang Yang | Mohan Timilsina | Edward Curry

Knowledge graph embedding (KGE) models are designed for the task of link prediction, which aims to infer missing triples by learning representations for entities and relations. While KGE models excel at ranking-based link prediction, the critical issue of probability calibration has been largely overlooked, resulting in uncalibrated estimates that limit their adoption in high-stakes domains where trustworthy predictions are essential. Addressing this is challenging, as we demonstrate that existing calibration methods are ill-suited to KGEs, often significantly degrading the essential ranking performance they are meant to support. To overcome this, we introduce the KGE Calibrator (KGEC), the first probability calibration method tailored for KGE models to enhance the trustworthiness of their predictions. KGEC integrates three key techniques: a Jump Selection Strategy that improves efficiency by selecting the most informative instances while filtering out less significant ones; Multi-Binning Scaling, which models different confidence levels separately to increase capacity and flexibility; and a Wasserstein distance-based calibration loss that further boosts calibration performance. Extensive experiments across multiple datasets demonstrate that KGEC consistently outperforms existing calibration methods in terms of both effectiveness and efficiency, making it a promising solution for calibration in KGE models.

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LCES: Zero-shot Automated Essay Scoring via Pairwise Comparisons Using Large Language Models
Takumi Shibata | Yuichi Miyamura

Recent advances in large language models (LLMs) have enabled zero-shot automated essay scoring (AES), providing a promising way to reduce the cost and effort of essay scoring in comparison with manual grading. However, most existing zero-shot approaches rely on LLMs to directly generate absolute scores, which often diverge from human evaluations owing to model biases and inconsistent scoring. To address these limitations, we propose LLM-based Comparative Essay Scoring (LCES), a method that formulates AES as a pairwise comparison task. Specifically, we instruct LLMs to judge which of two essays is better, collect many such comparisons, and convert them into continuous scores. Considering that the number of possible comparisons grows quadratically with the number of essays, we improve scalability by employing RankNet to efficiently transform LLM preferences into scalar scores. Experiments using AES benchmark datasets show that LCES outperforms conventional zero-shot methods in accuracy while maintaining computational efficiency. Moreover, LCES is robust across different LLM backbones, highlighting its applicability to real-world zero-shot AES.

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The Arabic Generality Score: Another Dimension of Modeling Arabic Dialectness
Sanad Sha’ban | Nizar Habash

Arabic dialects form a diverse continuum, yet NLP models often treat them as discrete categories. Recent work addresses this issue by modeling dialectness as a continuous variable, notably through the Arabic Level of Dialectness (ALDi). However, ALDi reduces complex variation to a single dimension. We propose a complementary measure: the Arabic Generality Score (AGS), which quantifies how widely a word is used across dialects. We introduce a pipeline that combines word alignment, etymology-aware edit distance, and smoothing to annotate a parallel corpus with word-level AGS. A regression model is then trained to predict AGS in context. Our approach outperforms strong baselines, including state-of-the-art dialect ID systems, on a multi-dialect benchmark. AGS offers a scalable, linguistically grounded way to model lexical generality, enriching representations of Arabic dialectness. Code is publicly available at https://github.com/CAMeL-Lab/arabic-generality-score.

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Lemmatization as a Classification Task: Results from Arabic across Multiple Genres
Mostafa Saeed | Nizar Habash

Lemmatization is crucial for NLP tasks in morphologically rich languages with ambiguous orthography like Arabic, but existing tools face challenges due to inconsistent standards and limited genre coverage. This paper introduces two novel approaches that frame lemmatization as classification into a Lemma-POS-Gloss (LPG) tagset, leveraging machine translation and semantic clustering. We also present a new Arabic lemmatization test set covering diverse genres, standardized alongside existing datasets. We evaluate character-level sequence-to-sequence models, which perform competitively and offer complementary value, but are limited to lemma prediction (not LPG) and prone to hallucinating implausible forms. Our results show that classification and clustering yield more robust, interpretable outputs, setting new benchmarks for Arabic lemmatization.

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A Comprehensive Framework to Operationalize Social Stereotypes for Responsible AI Evaluations
Aida Mostafazadeh Davani | Sunipa Dev | Héctor Pérez-Urbina | Vinodkumar Prabhakaran

Societal stereotypes are at the center of a myriad of responsible AI interventions targeted at reducing the generation and propagation of potentially harmful outcomes. While these efforts are much needed, they tend to be fragmented and often address different parts of the issue without adopting a unified or holistic approach to social stereotypes and how they impact various parts of the machine learning pipeline. As a result, current interventions fail to capitalize on the underlying mechanisms that are common across different types of stereotypes, and to anchor on particular aspects that are relevant in certain cases. In this paper, we draw on social psychological research and build on NLP data and methods, to propose a unified framework to operationalize stereotypes in generative AI evaluations. Our framework identifies key components of stereotypes that are crucial in AI evaluation, including the target group, associated attribute, relationship characteristics, perceiving group, and context. We also provide considerations and recommendations for its responsible use.

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Correct-Detect: Balancing Performance and Ambiguity Through the Lens of Coreference Resolution in LLMs
Amber Shore | Russell Scheinberg | Ameeta Agrawal | So Young Lee

Large Language Models (LLMs) are intended to reflect human linguistic competencies. But humans have access to a broad and embodied context, which is key in detecting and resolving linguistic ambiguities, even in isolated text spans. A foundational case of semantic ambiguity is found in the task of coreference resolution: how is a pronoun related to an earlier person mention? This capability is implicit in nearly every downstream task, and the presence of ambiguity at this level can alter performance significantly. We show that LLMs can achieve good performance with minimal prompting in both coreference disambiguation and the detection of ambiguity in coreference, however, they cannot do both at the same time. We present the CORRECT-DETECT trade-off: though models have both capabilities and deploy them implicitly, successful performance balancing these two abilities remains elusive.

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GRAID: Synthetic Data Generation with Geometric Constraints and Multi-Agentic Reflection for Harmful Content Detection
Melissa Kazemi Rad | Alberto Purpura | Himanshu Kumar | Emily Chen | Mohammad Shahed Sorower

We address the problem of data scarcity in harmful text classification for guardrailing applications and introduce GRAID (Geometric and Reflective AI-Driven Data Augmentation), a novel pipeline that leverages Large Language Models (LLMs) for dataset augmentation. GRAID consists of two stages: (i) generation of geometrically controlled examples using a constrained LLM, and (ii) augmentation through a multi-agentic reflective process that promotes stylistic diversity and uncovers edge cases. This combination enables both reliable coverage of the input space and nuanced exploration of harmful content. Using two benchmark data sets, we demonstrate that augmenting a harmful text classification dataset with GRAID leads to significant improvements in downstream guardrail model performance.

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LaMDAgent: An Autonomous Framework for Post-Training Pipeline Optimization via LLM Agents
Taro Yano | Yoichi Ishibashi | Masafumi Oyamada

Large Language Models (LLMs) excel across diverse tasks, with post-training methods like Supervised Fine-Tuning (SFT), Preference Learning, and Model Merging enabling effective domain and task adaptation. While outcomes can vary with data orderings or component combinations, yet manual pipeline optimization is costly and labor-intensive. Existing approaches typically rely on manual design or focus narrowly on optimizing individual components, such as data ordering or merging parameters. We propose LaMDAgent, an LLM Agent-driven framework that autonomously constructs and optimizes end-to-end post-training pipelines by exploring various model improving methods, objects, and their applied orderings based on task-based feedback. LaMDAgent achieves a 9.0-point gain in tool-use accuracy without degrading instruction-following, and identifies high-performing strategies overlooked by manual design.We further analyze the impact of data and model scaling to reduce computational costs on the exploration, finding that model size scalings introduces new challenges, whereas scaling data size enables cost-effective pipeline discovery.

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Finetuning LLMs for Human Behavior Prediction in Social Science Experiments
Akaash Kolluri | Shengguang Wu | Joon Sung Park | Michael S. Bernstein

Large language models (LLMs) offer a powerful opportunity to simulate the results of social science experiments. In this work, we demonstrate that finetuning LLMs directly on individual-level responses from past experiments meaningfully improves the accuracy of such simulations. We construct SocSci210 via an automatic pipeline, a dataset comprising 2.9 million responses from 400,491 participants in 210 open-source social science experiments. Through finetuning, we achieve multiple levels of generalization. In completely unseen studies, our strongest model, Socrates-Qwen-14B, produces predictions that are 36% more aligned with distributions of human responses to diverse outcome questions under varying conditions relative to its base model (Qwen2.5-14B), outperforming GPT-4o by 15%. By finetuning on a subset of conditions in a study, generalization to new unseen conditions is particularly robust, improving by 71%. Since SocSci210 contains rich demographic information, we reduce demographic parity difference, a measure of bias, by 10.6% through finetuning. Because social sciences routinely generate rich, topic-specific datasets, our findings indicate that finetuning on such data could enable more accurate simulations for experimental hypothesis screening. We release our data, models and finetuning code.

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How Private are Language Models in Abstractive Summarization?
Anthony Hughes | Nikolaos Aletras | Ning Ma

In sensitive domains such as medical and legal, protecting sensitive information is critical, with protective laws strictly prohibiting the disclosure of personal data. This poses challenges for sharing valuable data such as medical reports and legal cases summaries. While language models (LMs) have shown strong performance in text summarization, it is still an open question to what extent they can provide privacy-preserving summaries from non-private source documents. In this paper, we perform a comprehensive study of privacy risks in LM-based summarization across two closed- and four open-weight models of different sizes and families. We experiment with both prompting and fine-tuning strategies for privacy-preservation across a range of summarization datasets including medical and legal domains. Our quantitative and qualitative analysis, including human evaluation, shows that LMs frequently leak personally identifiable information in their summaries, in contrast to human-generated privacy-preserving summaries, which demonstrate significantly higher privacy protection levels. These findings highlight a substantial gap between current LM capabilities and expert human expert performance in privacy-sensitive summarization tasks.

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Expectation Preference Optimization: Reliable Preference Estimation for Improving the Reasoning Capability of Large Language Models
Zelin Li | Dawei Song

Pairwise preference optimization, such as Direct Preference Optimization (DPO), was originally designed to align large language models (LLMs) with human values. It has recently been used to improve the supervised fine-tuning (SFT) performance of LLMs. Using pairs of single samples, DPO estimates the probability distribution of the preferences of picking one response over another. However, in tasks that involve more complicated preferences (e.g., reasoning tasks) than those in the human value alignment task, this sampling method is likely to bring deviations from the ground-truth distribution. To solve the problem, extra efforts (e.g., external annotations or amendment of the loss function) are often required. In this paper, we hypothesise that the preferences can be better estimated through a multi-sampling process. Accordingly, we propose an Expectation Preference Optimization (EPO) algorithm that takes pairs of sample groups, instead of pairs of single samples as in DPO, for preference learning. Compared to pairwise DPO, the proposed EPO tends to produce more reliable preference estimations. Applying different preference optimization methods in a self-training paradigm, we have conducted extensive experiments on various reasoning benchmarks. The results show that our EPO approach outperforms a range of baseline approaches in terms of zero-shot accuracy on all benchmarks.

We introduce a zero-shot merging framework for large language models (LLMs) that consolidates specialized domain experts into a single model without any further training. Our core contribution lies in leveraging relative task vectors—difference representations encoding each expert’s unique traits with respect to a shared base model—to guide a principled and efficient merging process. By dissecting parameters into common dimensions (averaged across experts) and complementary dimensions (unique to each expert), we strike an optimal balance between generalization and specialization. We further devise a compression mechanism for the complementary parameters, retaining only principal components and scalar multipliers per expert, thereby minimizing overhead. A dynamic router then selects the most relevant domain at inference, ensuring that domain-specific precision is preserved. Experiments on code generation, mathematical reasoning, medical question answering, and instruction-following benchmarks confirm the versatility and effectiveness of our approach. Altogether, this framework enables truly adaptive and scalable LLMs that seamlessly integrate specialized knowledge for improved zero-shot performance.

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Model Consistency as a Cheap yet Predictive Proxy for LLM Elo Scores
Ashwin Ramaswamy | Nestor Demeure | Ermal Rrapaj

New large language models (LLMs) are being released every day. Some perform significantly better or worse than expected given their parameter count. Therefore, there is a need for a method to independently evaluate models. The current best way to evaluate a model is to measure its Elo score by comparing it to other models in a series of contests—an expensive operation since humans are ideally required to compare LLM outputs. We observe that when an LLM is asked to judge such contests, the consistency with which it selects a model as the best in a matchup produces a metric that is 91% correlated with its own human-produced Elo score. This provides a simple proxy for Elo scores that can be computed cheaply, without any human data or prior knowledge.

Despite Greece’s pivotal role in the global economy, large language models (LLMs) remain underexplored for Greek financial context due to the linguistic complexity of Greek and the scarcity of domain-specific datasets. While multilingual financial NLP has revealed large performance gaps across languages, no benchmarks or LLMs have been tailored for Greek financial tasks until now. To bridge this gap, we introduce Plutus-ben, the first Greek Financial Evaluation Benchmark, and Plutus-8B, the first financial LLM fine-tuned on Greek-specific financial data. Plutus-ben addresses six core tasks: numeric/textual named entity recognition, question answering, extractive summarization, abstractive summarization, and topic classification. To support these tasks, we release four new expert-annotated Greek financial datasets and incorporate two existing resources. Our comprehensive evaluation of 24 LLMs reveals persistent challenges in Greek financial NLP, driven by linguistic complexity, domain terminology, and financial reasoning gaps. Experiment results underscore the limitations of cross-lingual transfer and the need for Greek-specific financial modeling. We publicly release Plutus-ben, Plutus-8B, and all associated datasets to promote reproducible research and advance multilingual financial NLP.

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TaxoAlign: Scholarly Taxonomy Generation Using Language Models
Avishek Lahiri | Yufang Hou | Debarshi Kumar Sanyal

Taxonomies play a crucial role in helping researchers structure and navigate knowledge in a hierarchical manner. They also form an important part in the creation of comprehensive literature surveys. The existing approaches to automatic survey generation do not compare the structure of the generated surveys with those written by human experts. To address this gap, we present our own method for automated taxonomy creation that can bridge the gap between human-generated and automatically-created taxonomies. For this purpose, we create the CS-TaxoBench benchmark which consists of 460 taxonomies that have been extracted from human-written survey papers. We also include an additional test set of 80 taxonomies curated from conference survey papers. We propose TaxoAlign, a three-phase topic-based instruction-guided method for scholarly taxonomy generation. Additionally, we propose a stringent automated evaluation framework that measures the structural alignment and semantic coherence of automatically generated taxonomies in comparison to those created by human experts. We evaluate our method and various baselines on CS-TaxoBench, using both automated evaluation metrics and human evaluation studies. The results show that TaxoAlign consistently surpasses the baselines on nearly all metrics. The code and data can be found at https://github.com/AvishekLahiri/TaxoAlign.

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DiNaM: Disinformation Narrative Mining with Large Language Models
Witold Sosnowski | Arkadiusz Modzelewski | Kinga Skorupska | Adam Wierzbicki

Disinformation poses a significant threat to democratic societies, public health, and national security. To address this challenge, fact-checking experts analyze and track disinformation narratives. However, the process of manually identifying these narratives is highly time-consuming and resource-intensive. In this article, we introduce DiNaM, the first algorithm and structured framework specifically designed for mining disinformation narratives. DiNaM uses a multi-step approach to uncover disinformation narratives. It first leverages Large Language Models (LLMs) to detect false information, then applies clustering techniques to identify underlying disinformation narratives. We evaluated DiNaM’s performance using ground-truth disinformation narratives from the EUDisinfoTest dataset. The evaluation employed the Weighted Chamfer Distance (WCD), which measures the similarity between two sets of embeddings: the ground truth and the predicted disinformation narratives. DiNaM achieved a state-of-the-art WCD score of 0.73, outperforming general-purpose narrative mining methods by a notable margin of 16.4–24.7%. We are releasing DiNaM’s codebase and the dataset to the public.

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VeriLocc: End-to-End Cross-Architecture Register Allocation via LLM
Lesheng Jin | Zhenyuan Ruan | Haohui Mai | Jingbo Shang

Modern GPUs evolve rapidly, yet production compilers still rely on hand-crafted register allocation heuristics that require substantial re-tuning for each hardware generation. We introduce VeriLocc, a framework that combines large language models (LLMs) with formal compiler techniques to enable generalizable and verifiable register allocation across GPU architectures. VeriLocc fine-tunes an LLM to translate intermediate representations (MIRs) into target-specific register assignments, aided by static analysis for cross-architecture normalization and generalization and a verifier-guided regeneration loop to ensure correctness. Evaluated on matrix multiplication (GEMM) and multi-head attention (MHA), VeriLocc achieves 85–99% single-shot accuracy and near-100% pass@100. Case study shows that VeriLocc discovers more performant assignments than expert-tuned libraries, outperforming rocBLAS by over 10% in runtime.

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MemeIntel: Explainable Detection of Propagandistic and Hateful Memes
Mohamed Bayan Kmainasi | Abul Hasnat | Md Arid Hasan | Ali Ezzat Shahroor | Firoj Alam

The proliferation of multimodal content on social media presents significant challenges in understanding and moderating complex, context-dependent issues such as misinformation, hate speech, and propaganda. While efforts have been made to develop resources and propose new methods for automatic detection, limited attention has been given to label detection and the generation of explanation-based rationales for predicted labels. To address this challenge, we introduce MemeXplain, an explanation-enhanced dataset for propaganda memes in Arabic and hateful memes in English, making it the first large-scale resource for these tasks. To solve these tasks, we propose a novel multi-stage optimization approach and train Vision-Language Models (VLMs). Our results demonstrate that this approach significantly improves performance over the base model for both label detection and explanation generation, outperforming the current state-of-the-art with an absolute improvement of approximately 3% on ArMeme and 7% on Hateful Memes. For reproducibility and future research, we aim to make the MemeXplain dataset and scripts publicly available.

Figurative language conveys stance, emotion, and social nuance, making its appropriate use essential in dialogue. While large language models (LLMs) often succeed in recognizing figurative expressions at the sentence level, their ability to use them coherently in conversation remains uncertain. We introduce FLUID QA, the first multilingual benchmark that evaluates figurative usage in dialogue across English, Korean, and Chinese. Each item embeds figurative choices into multi-turn contexts. To support interpretation, we include FLUTE-bi, a sentence-level diagnostic task. Results reveal a persistent gap: models that perform well on FLUTE-bi frequently fail on FLUID QA, especially in sarcasm and metaphor. These errors reflect systematic rhetorical confusion and limited discourse reasoning. FLUID QA provides a scalable framework for assessing usage-level figurative competence across languages.

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Structured Moral Reasoning in Language Models: A Value-Grounded Evaluation Framework
Mohna Chakraborty | Lu Wang | David Jurgens

Large language models (LLMs) are increasingly deployed in domains requiring moral understanding, yet their reasoning often remains shallow, and misaligned with human reasoning. Unlike humans, whose moral reasoning integrates contextual trade-offs, value systems, and ethical theories, LLMs often rely on surface patterns, leading to biased decisions in morally and ethically complex scenarios. To address this gap, we present a value-grounded framework for evaluating and distilling structured moral reasoning in LLMs. We benchmark 12 open-source models across four moral datasets using a taxonomy of prompts grounded in value systems, ethical theories, and cognitive reasoning strategies. Our evaluation is guided by four questions: (1) Does reasoning improve LLM decision-making over direct prompting? (2) Which types of value/ethical frameworks most effectively guide LLM reasoning? (3) Which cognitive reasoning strategies lead to better moral performance? (4) Can small-sized LLMs acquire moral competence through distillation? We find that prompting with explicit moral structure consistently improves accuracy and coherence, with first-principles reasoning and Schwartz’s + care-ethics scaffolds yielding the strongest gains. Furthermore, our supervised distillation approach transfers moral competence from large to small models without additional inference cost. Together, our results offer a scalable path toward interpretable and value-grounded models.

Reinforcement learning with verifiable rewards (RLVR) has become a key technique for enhancing large language models (LLMs), with verification engineering playing a central role. However, best practices for RL in instruction following remain underexplored. In this work, we explore the verification challenge in RL for instruction following and propose VerIF, a verification method that combines rule-based code verification with LLM-based verification from a large reasoning model (e.g., QwQ-32B). To support this approach, we construct a high-quality instruction-following dataset, VerInstruct, containing approximately 22,000 instances with associated verification signals. We apply RL training with VerIF to two models, achieving significant improvements across several representative instruction-following benchmarks. The trained models reach state-of-the-art performance among models of comparable size and generalize well to unseen constraints. We further observe that their general capabilities remain unaffected, suggesting that RL with VerIF can be integrated into existing RL recipes to enhance overall model performance. We will release our datasets, codes, and models to facilitate future research.

Large Language Models (LLMs) are prone to hallucination, particularly in long-form generations. A promising direction to mitigate hallucination is to teach LLMs to express uncertainty explicitly when they lack sufficient knowledge. However, existing work lacks direct and fair evaluation of LLMs’ ability to express uncertainty effectively in long-form generation. To address this gap, we first introduce UNCLE, a benchmark designed to evaluate uncertainty expression in both long- and short-form question answering (QA). UNCLE covers five domains and includes more than 1,000 entities, each with paired short- and long-form QA items. Our dataset is the first to directly link short- and long-form QA through aligned questions and gold-standard answers.Along with UNCLE, we propose a suite of new metrics to assess the models’ capabilities to selectively express uncertainty. We then demonstrate that current models fail to convey uncertainty appropriately in long-form generation. We further explore both prompt-based and training-based methods to improve models’ performance, with the training-based methods yielding greater gains. Further analysis of alignment gaps between short- and long-form uncertainty expression highlights promising directions for future research using UNCLE.

Systematic reviews in medicine play a critical role in evidence-based decision-making by aggregating findings from multiple studies. A central bottleneck in automating this process is extracting numeric evidence and determining study-level conclusions for specific outcomes and comparisons. Prior work has framed this problem as a textual inference task by retrieving relevant content fragments and inferring conclusions from them. However, such approaches often rely on shallow textual cues and fail to capture the underlying numeric reasoning behind expert assessments.In this work, we conceptualise the problem as one of quantitative reasoning. Rather than inferring conclusions from surface text, we extract structured numerical evidence (e.g., event counts or standard deviations) and apply domain knowledge informed logic to derive outcome-specific conclusions. We develop a numeric reasoning system composed of a numeric data extraction model and an effect estimate component, enabling more accurate and interpretable inference aligned with the domain expert principles. We train the numeric data extraction model using different strategies, including supervised fine-tuning (SFT) and reinforcement learning (RL) with a new value reward model.When evaluated on the CochraneForest benchmark, our best-performing approach – using RL to train a small-scalenumber extraction model – yields up to a 21% absolute improvement in F1 score over retrieval-based systems and outperforms general-purpose LLMs of over 400B parameters by up to 9%.Our results demonstrate the promise of reasoning-driven approaches for automating systematic evidence synthesis.

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Context-aware Biases for Length Extrapolation
Ali Veisi | Hamidreza Amirzadeh | Amir M. Mansourian

Transformers often struggle to generalize to longer sequences than those seen during training - a limitation known as length extrapolation. Most existing Relative Positional Encoding (RPE) methods attempt to address this by introducing either fixed linear biases or globally learned biases, which lack the capacity to adapt to different input contexts. In this work, we propose an additive RPE, Context-Aware Biases for Length Extrapolation (CABLE), a method that learns token-specific, context-aware biases for each attention head in transformers. By dynamically adjusting positional biases based on the input sequence, CABLE overcomes the rigidity of fixed RPEs. When evaluated on sequences longer than originally trained with, GPT-2 Medium (334M parameters) with CABLE achieves lower perplexity than counterparts using other widely adopted positional encoding methods. Additionally, by applying CABLE to the BERT base model we improved performance in long-context retrieval tasks. Our method significantly enhances the extrapolation performance of existing RPE methods tested on the FineWeb-Edu-10B and WikiText-103 datasets. Our code is available at: https://github.com/AlgonetLabs/Cable.

Despite long-standing efforts in accelerating scientific discovery with AI, building AI co-scientists remains challenging due to limited high-quality data for training and evaluation. To tackle this data scarcity issue, we present AutoSDT, an automatic pipeline that collects high-quality coding tasks in real-world data-driven discovery workflows. AutoSDT leverages the coding capabilities and parametric knowledge of LLMs to search for diverse sources, select ecologically valid tasks, and synthesize accurate task instructions and code solutions. Using our pipeline, we construct AutoSDT-5K, a dataset of 5,404 coding tasks for data-driven discovery that covers four scientific disciplines and 756 unique Python packages. To the best of our knowledge, AutoSDT-5K is the only automatically collected and the largest open dataset for data-driven scientific discovery. Expert feedback on a subset of 256 tasks shows the effectiveness of AutoSDT: 93% of the collected tasks are ecologically valid, and 92.2% of the synthesized programs are functionally correct. Trained on AutoSDT-5K, the Qwen2.5-Coder-Instruct LLM series, dubbed AutoSDT-Coder, show substantial improvement on two challenging data-driven discovery benchmarks, ScienceAgentBench and DiscoveryBench. Most notably, AutoSDT-Coder-32B reaches the same level of performance as GPT-4o on ScienceAgentBench with a success rate of 7.8%, doubling the performance of its base model. On DiscoveryBench, it lifts the hypothesis matching score to 8.1, bringing a 17.4% relative improvement and closing the gap between open-weight models and GPT-4o.

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Finding your MUSE: Mining Unexpected Solutions Engine
Nir Sweed | Hanit Hakim | Ben Wolfson | Hila Lifshitz | Dafna Shahaf

Innovators often exhibit cognitive fixation on existing solutions or nascent ideas, hindering the exploration of novel alternatives. This paper introduces a methodology for constructing Functional Concept Graphs (FCGs), interconnected representations of functional elements that support abstraction, problem reframing, and analogical inspiration. Our approach yields large-scale, high-quality FCGs with explicit abstraction relations, overcoming limitations of prior work. We further present MUSE, an algorithm leveraging FCGs to generate creative inspirations for a given problem. We demonstrate our method by computing an FCG on 500K patents, which we release for further research. We introduced MUSE, a novel engine to find unexpected solutions to problems. This engine consists of the inspiration graph, whose problem and solution nodes were extracted from 500K patent descriptions. For a given problem, MUSE aims to enhance users’ creative problem solving by providing them with inspirations sampled from the inspiration graph. A user study indicates that participants exposed to MUSE’s inspirations generated more creative ideas, both in terms of absolute number (up to 19% increase over participants not given inspirations) and ratio (75%, compared to 49% for no inspirations).

Quantization enables efficient deployment of large language models (LLMs) in resource-constrained environments by significantly reducing memory and computation costs. While quantized LLMs often maintain performance on perplexity and zero-shot tasks, their impact on truthfulness—whether generating truthful or deceptive responses—remains largely unexplored. In this work, we introduce TruthfulnessEval, a comprehensive evaluation framework for assessing the truthfulness of quantized LLMs across three dimensions: (1) Truthfulness on Logical Reasoning; (2) Truthfulness on Common Sense; and (3) Truthfulness on Imitative Falsehoods. Using this framework, we examine mainstream quantization techniques (ranging from 4-bit to extreme 2-bit) across several open-source LLMs. Surprisingly, we find that while quantized models retain internally truthful representations, they are more susceptible to producing false outputs under misleading prompts. To probe this vulnerability, we test 15 rephrased variants of “honest”, “neutral” and “deceptive” prompts and observe that “deceptive” prompts can override truth-consistent behavior, whereas “honest” and “neutral” prompts maintain stable outputs. Further, we reveal that quantized models “know” the truth internally yet still produce false outputs when guided by “deceptive” prompts via layer-wise probing and PCA visualizations. Our findings provide insights into future designs of quantization-aware alignment and truthfulness interventions.

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Leveraging Knowledge Graph-Enhanced LLMs for Context-Aware Medical Consultation
Su-Hyeong Park | Ho-Beom Kim | Seong-Jin Park | Dinara Aliyeva | Kang-Min Kim

Recent advancements in large language models have significantly influenced the field of online medical consultations. However, critical challenges remain, such as the generation of hallucinated information and the integration of up-to-date medical knowledge. To address these issues, we propose **I**nformatics **Llama** (ILlama), a novel framework that combines retrieval-augmented generation with a structured medical knowledge graph. ILlama incorporates relevant medical knowledge by transforming subgraphs from a structured medical knowledge graph into text for retrieval-augmented generation. By generating subgraphs from the medical knowledge graph in advance, specifically focusing on diseases and symptoms, ILlama is able to enhance the accuracy and relevance of its medical reasoning. This framework enables effective incorporation of causal relationships between symptoms and diseases. Also, it delivers context-aware consultations aligned with user queries. Experimental results on the two medical consultation datasets demonstrate that ILlama outperforms the strong baselines, achieving a semantic similarity F1-score of 0.884 when compared with ground truth consultation answers. Furthermore, qualitative analysis of ILlama’s responses reveals significant improvements in hallucination reduction and clinical usefulness. These results suggest that ILlama has strong potential as a reliable tool for real-world medical consultation environments.

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Reflective Agreement: Combining Self-Mixture of Agents with a Sequence Tagger for Robust Event Extraction
Fatemeh Haji | Mazal Bethany | Cho-Yu Jason Chiang | Anthony Rios | Peyman Najafirad

Event Extraction (EE) involves automatically identifying and extracting structured information about events from unstructured text, including triggers, event types, and arguments. Traditional discriminative models demonstrate high precision but often exhibit limited recall, particularly for nuanced or infrequent events. Conversely, generative approaches leveraging Large Language Models (LLMs) provide higher semantic flexibility and recall but suffer from hallucinations and inconsistent predictions. To address these challenges, we propose Agreement-based Reflective Inference System (ARIS), a hybrid approach combining a Self Mixture of Agents with a discriminative sequence tagger. ARIS explicitly leverages structured model consensus, confidence-based filtering, and an LLM reflective inference module to reliably resolve ambiguities and enhance overall event prediction quality. We further investigate decomposed instruction fine-tuning for enhanced LLM event extraction understanding. Experiments demonstrate our approach outperforms existing state-of-the-art event extraction methods across three benchmark datasets.

Large language models (LLMs) often behave inconsistently across inputs, indicating uncertainty and motivating the need for its quantification in high-stakes settings. Prior work on calibration and uncertainty quantification often focuses on individual models, overlooking the potential of model diversity. We hypothesize that LLMs make complementary predictions due to differences in training and the Zipfian nature of language, and that aggregating their outputs leads to more reliable uncertainty estimates. To leverage this, we propose MUSE (Multi-LLM Uncertainty via Subset Ensembles), a simple information-theoretic method that uses Jensen-Shannon Divergence to identify and aggregate well-calibrated subsets of LLMs. Experiments on binary prediction tasks demonstrate improved calibration and predictive performance compared to single-model and naive ensemble baselines. In addition, we explore using MUSE as guided signals with chain-of-thought distillation to fine-tune LLMs for calibration. MUSE is available at: https://github.com/LARK-NLP-Lab/MUSE.

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Exploring morphology-aware tokenization: A case study on Spanish language modeling
Alba Táboas García | Piotr Przybyła | Leo Wanner

This paper investigates to what extent the integration of morphological information can improve subword tokenization and thus also language modeling performance. We focus on Spanish, a language with fusional morphology, where subword segmentation can benefit from linguistic structure. Instead of relying on purely data-driven strategies like Byte Pair Encoding (BPE), we explore a linguistically grounded approach: training a tokenizer on morphologically segmented data. To do so, we develop a semi-supervised segmentation model for Spanish, building gold-standard datasets to guide and evaluate it. We then use this tokenizer to pre-train a masked language model and assess its performance on several downstream tasks. Our results show improvements over a baseline with a standard tokenizer, supporting our hypothesis that morphology-aware tokenization offers a viable and principled alternative for improving language modeling.

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Studying Rhetorically Ambiguous Questions
Oghenevovwe Ikumariegbe | Eduardo Blanco | Ellen Riloff

Distinguishing between rhetorical questions and informational questions is a challenging task, as many rhetorical questions have similar surface forms to informational questions. Existing datasets, however, do not contain many questions that can be rhetorical or informational in different contexts. We introduce Studying Rhetorically Ambiguous Questions (SRAQ), a new dataset explicitly constructed to support the study of such rhetorical ambiguity. The questions in SRAQ can be interpreted as either rhetorical or informational depending on the context. We evaluate the performance of state-of-the-art language models on this dataset and find that they struggle to recognize many rhetorical questions.

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Estimating LLM Consistency: A User Baseline vs Surrogate Metrics
Xiaoyuan Wu | Weiran Lin | Omer Akgul | Lujo Bauer

Large language models (LLMs) are prone to hallucinations and sensitive to prompt perturbations, often resulting in inconsistent or unreliable generated text. Different methods have been proposed to mitigate such hallucinations and fragility, one of which is to measure the consistency of LLM responses–the model’s confidence in the response or likelihood of generating a similar response when resampled. In previous work, measuring LLM response consistency often relied on calculating the probability of a response appearing within a pool of resampled responses, analyzing internal states, or evaluating logits of resopnses. However, it was not clear how well these approaches approximated users’ perceptions of consistency of LLM responses. To find out, we performed a user study (n=2,976) demonstrating that current methods for measuring LLM response consistency typically do not align well with humans’ perceptions of LLM consistency. We propose a logit-based ensemble method for estimating LLM consistency and show that our method matches the performance of the best-performing existing metric in estimating human ratings of LLM consistency. Our results suggest that methods for estimating LLM consistency without human evaluation are sufficiently imperfect to warrant broader use of evaluation with human input; this would avoid misjudging the adequacy of models because of the imperfections of automated consistency metrics.

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Are Vision-Language Models Safe in the Wild? A Meme-Based Benchmark Study
DongGeon Lee | Joonwon Jang | Jihae Jeong | Hwanjo Yu

Rapid deployment of vision-language models (VLMs) magnifies safety risks, yet most evaluations rely on artificial images. This study asks: How safe are current VLMs when confronted with meme images that ordinary users share? To investigate this question, we introduce MemeSafetyBench, a 50,430-instance benchmark pairing real meme images with both harmful and benign instructions. Using a comprehensive safety taxonomy and LLM-based instruction generation, we assess multiple VLMs across single and multi-turn interactions. We investigate how real-world memes influence harmful outputs, the mitigating effects of conversational context, and the relationship between model scale and safety metrics. Our findings demonstrate that VLMs are more vulnerable to meme-based harmful prompts than to synthetic or typographic images. Memes significantly increase harmful responses and decrease refusals compared to text-only inputs. Though multi-turn interactions provide partial mitigation, elevated vulnerability persists. These results highlight the need for ecologically valid evaluations and stronger safety mechanisms. MemeSafetyBench is publicly available at https://github.com/oneonlee/Meme-Safety-Bench.

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Improving Rule-based Reasoning in LLMs using Neurosymbolic Representations
Varun Dhanraj | Chris Eliasmith

Large language models (LLMs) continue to face challenges in reliably solving reasoning tasks, particularly tasks that involve precise rule following, as often found in mathematical reasoning tasks. This paper introduces a novel neurosymbolic method that improves LLM reasoning by encoding hidden states into neurosymbolic vectors, enabling problem-solving within a neurosymbolic vector space. The results are decoded and merged with the original hidden state, significantly boosting the model’s performance on numerical reasoning tasks. By offloading computation through neurosymbolic representations, this method enhances efficiency, reliability, and interpretability. Our experimental results demonstrate an average of 88.6% lower cross-entropy loss and 15.4 times more problems correctly solved on a suite of mathematical reasoning tasks compared to chain-of-thought prompting and supervised fine-tuning (LoRA), while not hindering the LLM’s performance on other tasks. We make our code available at https://github.com/vdhanraj/Neurosymbolic-LLM.

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Can LLMs Extract Frame-Semantic Arguments?
Jacob Devasier | Rishabh Mediratta | Chengkai Li

Frame-semantic parsing is a critical task in natural language understanding, yet the ability of large language models (LLMs) to extract frame-semantic arguments remains underexplored. This paper presents a comprehensive evaluation of LLMs on frame-semantic argument identification, analyzing the impact of input representation formats, model architectures, and generalization to unseen and out-of-domain samples. Our experiments, spanning models from 0.5B to 72B parameters, reveal that JSON-based representations significantly enhance performance, and while larger models generally perform better, smaller models can achieve competitive results through fine-tuning. We also introduce a novel approach to frame identification leveraging predicted frame elements, achieving state-of-the-art performance on ambiguous targets. Despite strong generalization capabilities, our analysis finds that LLMs still struggle with out-of-domain data.

Language models have demonstrated remarkable capabilities in reasoning tasks through test-time scaling techniques like best-of-N sampling and tree search. However, these approaches often demand substantial computational resources, creating a critical trade-off between performance and efficiency. We introduce STAND (STochastic Adaptive N-gram Drafting), a novel model-free speculative decoding approach that exploits the inherent redundancy in reasoning trajectories to achieve significant acceleration without compromising accuracy. Our analysis shows that reasoning paths frequently reuse similar reasoning patterns, enabling efficient model-free token prediction without requiring separate draft models. By introducing stochastic drafting and preserving probabilistic information through a memory-efficient logit-based N-gram module, combined with optimized Gumbel-Top-K sampling and data-driven tree construction, STAND significantly improves token acceptance rates. Extensive evaluations across multiple models and reasoning tasks (AIME-2024, GPQA-Diamond, and LiveCodeBench) demonstrate that STAND reduces inference latency by 60-65% compared to standard autoregressive decoding while maintaining accuracy. Furthermore, STAND consistently outperforms state-of-the-art speculative decoding methods across diverse inference patterns, including single-trajectory decoding, batch decoding, and test-time tree search. As a model-free approach, STAND can be applied to any existing language model without additional training, making it a powerful plug-and-play solution for accelerating language model reasoning.

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Enhancing RLHF with Human Gaze Modeling
Karim Galliamov | Ivan Titov | Ilya Pershin

Reinforcement Learning from Human Feedback (RLHF) aligns language models with human preferences but faces efficiency challenges. We explore two approaches leveraging human gaze prediction to enhance RLHF: (1) gaze-aware reward models and (2) gaze-based distribution of sparse rewards at token level. Our experiments show gaze-informed RLHF achieves faster convergence while maintaining or slightly improving performance, reducing computational requirements during policy optimization. Human visual attention patterns provide valuable signals for policy training, suggesting a promising direction for improving RLHF efficiency through human-like attention mechanisms.

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Mapping semantic networks to Dutch word embeddings as a diagnostic tool for cognitive decline
Maithe van Noort | Michal Korenar | Jelke Bloem

We explore the possibility of semantic networks as a diagnostic tool for cognitive decline by using Dutch verbal fluency data to investigate the relationship between semantic networks and cognitive health. In psychology, semantic networks serve as abstract representations of the semantic memory system. Semantic verbal fluency data can be used to estimate said networks. Traditionally, this is done by counting the number of raw items produced by participants in a verbal fluency task. We used static and contextual word embedding models to connect the elicited words through semantic similarity scores, and extracted three network distance metrics. We then tested how well these metrics predict participants’ cognitive health scores on the Mini-Mental State Examination (MMSE). While the significant predictors differed per model, the traditional number-of-words measure was not significant in any case. These findings suggest that semantic network metrics may provide a more sensitive measure of cognitive health than traditional scoring.

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CausalVLBench: Benchmarking Visual Causal Reasoning in Large Vision-Language Models
Aneesh Komanduri | Karuna Bhaila | Xintao Wu

Large language models (LLMs) have shown remarkable ability in various language tasks, especially with their emergent in-context learning capability. Extending LLMs to incorporate visual inputs, large vision-language models (LVLMs) have shown impressive performance in tasks such as recognition and visual question answering (VQA). Despite increasing interest in the utility of LLMs in causal reasoning tasks such as causal discovery and counterfactual reasoning, there has been relatively little work showcasing the abilities of LVLMs on visual causal reasoning tasks. We take this opportunity to formally introduce a comprehensive causal reasoning benchmark for multi-modal in-context learning from LVLMs. Our CausalVLBench encompasses three representative tasks: causal structure inference, intervention target prediction, and counterfactual prediction. We evaluate the ability of state-of-the-art open-source LVLMs on our causal reasoning tasks across three causal representation learning datasets and demonstrate their fundamental strengths and weaknesses. We hope that our benchmark elucidates the drawbacks of existing vision-language models and motivates new directions and paradigms in improving the visual causal reasoning abilities of LVLMs.

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Implicit Behavioral Alignment of Language Agents in High-Stakes Crowd Simulations
Yunzhe Wang | Gale Lucas | Burcin Becerik-Gerber | Volkan Ustun

Language-driven generative agents have enabled large-scale social simulations with transformative uses, from interpersonal training to aiding global policy-making. However, recent studies indicate that generative agent behaviors often deviate from expert expectations and real-world data—a phenomenon we term the *Behavior-Realism Gap*. To address this, we introduce a theoretical framework called Persona-Environment Behavioral Alignment (PEBA), formulated as a distribution matching problem grounded in Lewin’s behavior equation stating that behavior is a function of the person and their environment. Leveraging PEBA, we propose PersonaEvolve (PEvo), an LLM-based optimization algorithm that iteratively refines agent personas, implicitly aligning their collective behaviors with realistic expert benchmarks within a specified environmental context. We validate PEvo in an active shooter incident simulation we developed, achieving an 84% average reduction in distributional divergence compared to no steering and a 34% improvement over explicit instruction baselines. Results also show PEvo-refined personas generalize to novel, related simulation scenarios. Our method greatly enhances behavioral realism and reliability in high-stakes social simulations. More broadly, the PEBA-PEvo framework provides a principled approach to developing trustworthy LLM-driven social simulations.

As AI systems increasingly navigate applications in healthcare, law, and governance, understanding how they handle ethically complex scenarios becomes critical. Previous work has mainly examined the moral judgments in large language models (LLMs), rather than their underlying moral reasoning process. In contrast, we focus on a large-scale analysis of the moral reasoning traces provided by LLMs. Furthermore, unlike prior work that attempted to draw inferences from only a handful of moral dilemmas, our study leverages over 600 distinct trolley problems as probes for revealing the reasoning patterns that emerge within different LLMs. We introduce and test a taxonomy of moral rationales to systematically classify reasoning traces according to two main normative ethical theories: consequentialism and deontology. Our analysis reveals that LLM chains-of-thought favor deontological principles based on moral obligations, while post-hoc explanations shift notably toward consequentialist rationales that emphasize utility. Our framework provides a foundation for understanding how LLMs process and articulate ethical considerations, an important step toward safe and interpretable deployment of LLMs in high-stakes decision-making environments.

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PatentScore: Multi-dimensional Evaluation of LLM-Generated Patent Claims
Yongmin Yoo | Qiongkai Xu | Longbing Cao

High-stakes texts such as patent claims, medical records, and technical reports are structurally complex and demand a high degree of reliability and precision. While large language models (LLMs) have recently been applied to automate their generation in high-stakes domains, reliably evaluating such outputs remains a major challenge. Conventional natural language generation (NLG) metrics are effective for generic documents but fail to capture the structural and legal characteristics essential to evaluating complex high-stakes documents. To address this gap, we propose PatentScore, a multi-dimensional evaluation framework specifically designed for one of the most intricate and rigorous domains, patent claims. PatentScore integrates hierarchical decomposition of claim elements, validation patterns grounded in legal and technical standards, and scoring across structural, semantic, and legal dimensions. In experiments on our dataset which consists of 400 Claim1, PatentScore achieved the highest correlation with expert annotations (r = 0.819), significantly outperforming widely used NLG metrics. This work establishes a new standard for evaluating LLM-generated patent claims, providing a solid foundation for research on patent generation and validation.

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All for One: LLMs Solve Mental Math at the Last Token With Information Transferred From Other Tokens
Siddarth Mamidanna | Daking Rai | Ziyu Yao | Yilun Zhou

Large language models (LLMs) demonstrate proficiency across numerous computational tasks, yet their inner workings remain unclear. In theory, the combination of causal self-attention and multilayer perceptron allows every token to access and compute information based on all preceding tokens. In practice, to what extent are such operations present? In this paper, on mental math tasks (i.e., direct math calculation via next-token prediction without explicit reasoning), we investigate this question in three steps: inhibiting input-specific token computations in the initial layers, restricting the routes of information transfer in the next few layers, and forcing all computation to happen at the last token in the remaining layers. With two proposed techniques, Context-Aware Mean Ablation (CAMA) and Attention-Based Peeking (ABP), we identify an All-for-One subgraph (AF1) with high accuracy on a wide variety of mental math tasks, where meaningful computation occurs very late (in terms of layer depth) and only at the last token, which receives information of other tokens in few specific layers. Experiments show that this circuit is sufficient and necessary for high model performance, transfers across different models, and works on a variety of input styles. Ablations on different CAMA and ABP alternatives reveal their unique advantages over other methods, which may be of independent interest.

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A Position Paper on the Automatic Generation of Machine Learning Leaderboards
Roelien C. Timmer | Yufang Hou | Stephen Wan

An important task in machine learning (ML) research is comparing prior work, which is often performed via ML leaderboards: a tabular overview of experiments with comparable conditions (e.g. same task, dataset, and metric). However, the growing volume of literature creates challenges in creating and maintaining these leaderboards. To ease this burden, researchers have developed methods to extract leaderboard entries from research papers for automated leaderboard curation. Yet, prior work varies in problem framing, complicating comparisons and limiting real-world applicability. In this position paper, we present the first overview of Automatic Leaderboard Generation (ALG) research, identifying fundamental differences in assumptions, scope, and output formats. We propose an ALG unified conceptual framework to standardise how the ALG task is defined. We offer ALG benchmarking guidelines, including recommendations for datasets and metrics that promote fair, reproducible evaluation. Lastly, we outline challenges and new directions for ALG, advocating for broader coverage by including all reported results and richer metadata.

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SimMark: A Robust Sentence-Level Similarity-Based Watermarking Algorithm for Large Language Models
Amirhossein Dabiriaghdam | Lele Wang

The widespread adoption of large language models (LLMs) necessitates reliable methods to detect LLM-generated text. We introduce SimMark, a robust sentence-level watermarking algorithm that makes LLMs’ outputs traceable without requiring access to model internals, making it compatible with both open and API-based LLMs. By leveraging the similarity of semantic sentence embeddings combined with rejection sampling to embed detectable statistical patterns imperceptible to humans, and employing a soft counting mechanism, SimMark achieves robustness against paraphrasing attacks. Experimental results demonstrate that SimMark sets a new benchmark for robust watermarking of LLM-generated content, surpassing prior sentence-level watermarking techniques in robustness, sampling efficiency, and applicability across diverse domains, all while maintaining the text quality and fluency.

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SERVAL: Surprisingly Effective Zero-Shot Visual Document Retrieval Powered by Large Vision and Language Models
Thong Nguyen | Yibin Lei | Jia-Huei Ju | Andrew Yates

Visual Document Retrieval (VDR) typically operates as text-to-image retrieval using specialized bi-encoders trained to directly embed document images. We revisit a zero-shot generate-and-encode pipeline: a vision–language model first produces a detailed textual description of each document image, which is then embedded by a standard text encoder. On the ViDoRe-v2 benchmark, the method reaches 63.4% nDCG@5, surpassing the strongest specialised multi-vector visual document encoder, and it scales similarly on MIRACL-VISION with broader multilingual coverage. Analysis shows that modern vision–language models capture complex textual and visual cues with sufficient granularity to act as a reusable semantic proxy. By off-loading modality alignment to pretrained vision–language models, our approach removes the need for computationally intensive text-image contrastive training and establishes a strong zero-shot baseline for future VDR systems.

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Meta-Semantics Augmented Few-Shot Relational Learning
Han Wu | Jie Yin

Few-shot relational learning on knowledge graph (KGs) aims to perform reasoning over relations with only a few training examples. While current methods have focused primarily on leveraging specific relational information, rich semantics inherent in KGs have been largely overlooked. To bridge this gap, we propose PromptMeta, a novel prompted meta-learning framework that seamlessly integrates meta-semantics with relational information for few-shot relational learning. PromptMeta introduces two core innovations: (1) a Meta-Semantic Prompt (MSP) pool that learns and consolidates high-level meta-semantics shared across tasks, enabling effective knowledge transfer and adaptation to newly emerging relations; and (2) a learnable fusion mechanism that dynamically combines meta-semantics with task-specific relational information tailored to different few-shot tasks. Both components are optimized jointly with model parameters within a meta-learning framework. Extensive experiments and analyses on two real-world KG benchmarks validate the effectiveness of PromptMeta in adapting to new relations with limited supervision.

Video understanding is essential for multimodal large language models (MLLMs) to interact effectively with users and the real world. However, analyzing long videos remains a major challenge due to the lack of high-quality video instruction data and effective training strategies. In this paper, we introduce a simple yet effective baseline for long-context video understanding, including dataset construction and training recipes. We curate a large-scale video instruction dataset with over 1M samples, encompassing videos from a few seconds to several minutes across diverse sources, without any human annotations. Additionally, we propose a progressive video instruction tuning strategy that incrementally increases input context length, enabling better utilization of videos of varying durations. Comprehensive experiments demonstrate that our dataset significantly outperforms existing video instruction datasets for fine-tuning MLLMs. Furthermore, our training approach establishes a strong video MLLM baseline, surpassing previous open-source models on video benchmarks and outperforming proprietary models like GPT-4V and GPT-4o-mini on VideoMME, even with a compact 7B model.

Automatic toxic language detection is important for creating safe, inclusive online spaces. However, it is a highly subjective task, with perceptions of toxic language shaped by community norms and lived experience. Existing toxicity detection models are typically trained on annotations that collapse diverse annotator perspectives into a single ground truth, erasing important context-specific notions of toxicity such as reclaimed language. To address this, we introduce MODELCITIZENS, a dataset of 6.8K social media posts and 40K toxicity annotations across diverse identity groups. To reflect the impact of conversational context on toxicity, typical of social media posts, we augment MODELCITIZENS posts with LLM-generated conversational scenarios. State-of-the-art toxicity detection tools (e.g. OpenAI Moderation API, GPT-o4-mini) underperform on MODELCITIZENS with further degradation on context-augmented posts. Finally, we release LLAMACITIZEN-8B and GEMMACITIZEN-12B, LLaMA and Gemma-based models finetuned on our dataset, which outperform GPT-o4-mini by 5.5% on in-distribution evaluations. Our findings highlight the importance of community-informed annotation and modeling for inclusive content moderation. We will release all code, data and models upon publication.

Unified vision large language models (VLLMs) have recently achieved impressive advancements in both multimodal understanding and generation, powering applications such as visual question answering and text-guided image synthesis. However, progress in unified VLLMs remains constrained by the lack of datasets that fully exploit the synergistic potential between these two core abilities. Existing datasets typically address understanding and generation in isolation, thereby limiting the performance of unified VLLMs. To bridge this critical gap, we introduce a novel dataset construction framework, UnifiedVisual, and present UnifiedVisual-240K, a high-quality dataset meticulously designed to facilitate mutual enhancement between multimodal understanding and generation. UnifiedVisual-240K seamlessly integrates diverse visual and textual inputs and outputs, enabling comprehensive cross-modal reasoning and precise text-to-image alignment. Our dataset encompasses a wide spectrum of tasks and data sources, ensuring rich diversity and addressing key shortcomings of prior resources. Extensive experiments demonstrate that models trained on UnifiedVisual-240K consistently achieve strong performance across a wide range of tasks. Notably, these models exhibit significant mutual reinforcement between multimodal understanding and generation, further validating the effectiveness of our framework and dataset. We believe UnifiedVisual represents a new growth point for advancing unified VLLMs and unlocking their full potential.

This paper investigates the capacity of small language models (0.5B-5B parameters) to generate empathetic responses for individuals with PTSD. We introduce Trauma-Informed Dialogue for Empathy (TIDE), a novel dataset comprising 10,000 two-turn conversations across 500 diverse, clinically-grounded PTSD personas (https://huggingface.co/datasets/yenopoya/TIDE). Using frontier model outputs as ground truth, we evaluate eight small LLMs in zero-shot settings and after fine-tuning. Fine-tuning enhances empathetic capabilities, improving cosine similarity and perceived empathy, although gains vary across emotional scenarios and smaller models exhibit a “knowledge transfer ceiling.” As expected, Claude Sonnet 3.5 consistently outperforms all models, but surprisingly, the smaller models often approach human-rated empathy levels. Demographic analyses showed that older adults favored responses that validated distress before offering support (p = .004), while graduate-educated users preferred emotionally layered replies in specific scenarios. Gender-based differences were minimal (p > 0.15), suggesting the feasibility of broadly empathetic model designs. This work offers insights into building resource-efficient, emotionally intelligent systems for mental health support.

Multimodal large language models (MLLMs) have shown impressive capabilities in document understanding, a rapidly growing research area with significant industrial demand. As a multimodal task, document understanding requires models to possess both perceptual and cognitive abilities. However, due to different types of annotation noise in training, current MLLMs often face conflicts between perception and cognition. Taking a document VQA task (cognition) as an example, an MLLM might generate answers that do not match the corresponding visual content identified by its OCR (perception). This conflict suggests that the MLLM might struggle to establish an intrinsic connection between the information it “sees” and what it “understands”. Such conflicts challenge the intuitive notion that cognition is consistent with perception, hindering the performance and explainability of MLLMs. In this paper, we define the conflicts between cognition and perception as Cognition and Perception (C&P) knowledge conflicts, a form of multimodal knowledge conflicts, and systematically assess them with a focus on document understanding. Our analysis reveals that even GPT-4o, a leading MLLM, achieves only 75.26% C&P consistency. To mitigate the C&P knowledge conflicts, we propose a novel method called Multimodal Knowledge Consistency Fine-tuning. Our method reduces C&P knowledge conflicts across all tested MLLMs and enhances their performance in both cognitive and perceptual tasks.

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AutoCT: Automating Interpretable Clinical Trial Prediction with LLM Agents
Fengze Liu | Haoyu Wang | Joonhyuk Cho | Dan Roth | Andrew Lo

Clinical trials are critical for advancing medical treatments but remain prohibitively expensive and time-consuming. Accurate prediction of clinical trial outcomes can significantly reduce research and development costs and accelerate drug discovery. While recent deep learning models have shown promise by leveraging unstructured data, their black-box nature, lack of interpretability, and vulnerability to label leakage limit their practical use in high-stakes biomedical contexts. In this work, we propose AutoCT, a novel framework that combines the reasoning capabilities of large language models with the explainability of classical machine learning. AutoCT autonomously generates, evaluates, and refines tabular features based on public information without human input. Our method uses Monte Carlo Tree Search to iteratively optimize predictive performance. Experimental results show that AutoCT performs on par with or better than SOTA methods on clinical trial prediction tasks within only a limited number of self-refinement iterations, establishing a new paradigm for scalable, interpretable, and cost-efficient clinical trial prediction.

Multimodal document retrieval aims to identify and retrieve various forms of multimodal content, such as figures, tables, charts, and layout information from extensive documents. Despite its increasing popularity, there is a notable lack of a comprehensive and robust benchmark to effectively evaluate the performance of systems in such tasks. To address this gap, this work introduces a new benchmark, named MMDocIR, that encompasses two distinct tasks: page-level and layout-level retrieval. The former evaluates the performance of identifying the most relevant pages within a long document, while the later assesses the ability of detecting specific layouts, providing a more fine-grained measure than whole-page analysis. A layout refers to a variety of elements, including textual paragraphs, equations, figures, tables, or charts. The MMDocIR benchmark comprises a rich dataset featuring 1,685 questions annotated by experts and 173,843 questions with bootstrapped labels, making it a valuable resource in multimodal document retrieval for both training and evaluation. Through rigorous experiments, we demonstrate that (i) visual retrievers significantly outperform their text counterparts, (ii) MMDocIR training set effectively enhances the performance of multimodal document retrieval and (iii) text retrievers leveraging VLM-text significantly outperforms retrievers relying on OCR-text.

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Program of Thoughts for Financial Reasoning: Leveraging Dynamic In-Context Examples and Generative Retrieval
Subhendu Khatuya | Shashwat Naidu | Pawan Goyal | Niloy Ganguly

Despite continuous advancements in the capabilities of large language models (LLMs), numerical reasoning remains a challenging area. Techniques like chain-of-thought prompting, tree-of-thought prompting, and program-of-thought prompting guide LLMs through intermediate reasoning steps. Although in-context learning with few-shot prompting has improved performance, LLMs still lag behind state-of-the-art models on financial numerical reasoning datasets such as FinQA and ConvFinQA. In this work, we introduce FINDER, a novel two-step framework, to enhance LLM’s capabilities in financial numerical reasoning. The first step utilizes a generative retriever to extract relevant facts from unstructured data, including both text and tables. This is followed by context-aware Program of Thought prompting with dynamic selection of in-context examples. Our model FINDER achieves a new state-of-the-art performance on both the FinQA and ConvFinQA datasets, surpassing previous benchmarks with execution accuracy improvements of 5.98% and 4.05%, respectively.

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Waste-Bench: A Comprehensive Benchmark for Evaluating VLLMs in Cluttered Environments
Muhammad Ali | Salman Khan

Recent advancements in Large Language Models (LLMs) have paved the way for VisionLarge Language Models (VLLMs) capable ofperforming a wide range of visual understand-ing tasks. While LLMs have demonstrated impressive performance on standard naturalimages, their capabilities have not been thoroughly explored in cluttered datasets where there is complex environment having deformedshaped objects. In this work, we introduce a novel dataset specifically designed for waste classification in real-world scenarios, character-ized by complex environments and deformed shaped objects. Along with this dataset, we present an in-depth evaluation approach to rig-orously assess the robustness and accuracy of VLLMs. The introduced dataset and comprehensive analysis provide valuable insights intothe performance of VLLMs under challenging conditions. Our findings highlight the critical need for further advancements in VLLM’s ro-bustness to perform better in complex enviroments. The dataset and code for our experiments are available at https://github.com/aliman80/wastebench.

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Demystifying Domain-adaptive Post-training for Financial LLMs
Zixuan Ke | Yifei Ming | Xuan-Phi Nguyen | Caiming Xiong | Shafiq Joty

Domain-adaptive post-training of large language models (LLMs) has emerged as a promising approach for specialized domains such as medicine and finance. However, significant challenges remain in identifying optimal adaptation criteria and training strategies across varying data and model configurations. To address these challenges, we introduce FINDAP, a systematic and fine-grained investigation into domain-adaptive post-training of LLMs for the finance domain. Our approach consists of four key components: FinCap, which defines the core capabilities required for the target domain; FinRec, an effective training recipe that jointly optimizes continual pre-training and instruction-following, along with a novel preference data distillation method leveraging process signals from a generative reward model; FinTrain, a curated set of training datasets supporting FinRec; and FinEval, a comprehensive evaluation suite aligned with FinCap. The resulting model, Llama-Fin, achieves state-of-the-art performance across a wide range of financial tasks. Our analysis also highlights how each post-training stage contributes to distinct capabilities, uncovering specific challenges and effective solutions, providing valuable insights for domain adaptation of LLMs.

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HICode: Hierarchical Inductive Coding with LLMs
Mian Zhong | Pristina Wang | Anjalie Field

Despite numerous applications for fine-grained corpus analysis, researchers continue to rely on manual labeling, which does not scale, or statistical tools like topic modeling, which are difficult to control. We propose that LLMs have the potential to scale the nuanced analyses that researchers typically conduct manually to large text corpora. To this effect, inspired by qualitative research methods, we develop HICode, a two-part pipeline that first inductively generates labels directly from analysis data and then hierarchically clusters them to surface emergent themes. We validate this approach across three diverse datasets by measuring alignment with human-constructed themes and demonstrating its robustness through automated and human evaluations. Finally, we conduct a case study of litigation documents related to the ongoing opioid crisis in the U.S., revealing aggressive marketing strategies employed by pharmaceutical companies and demonstrating HICode’s potential for facilitating nuanced analyses in large-scale data.

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Cacheback: Speculative Decoding With Nothing But Cache
Zhiyao Ma | In Gim | Lin Zhong

We present Cacheback Decoding, a training-free and model-agnostic speculative decoding method that exploits the locality in language to accelerate Large Language Model (LLM) inference.Cacheback leverages only Least Recently Used (LRU) cache tables of token n-grams to generate draft sequences.Cacheback achieves state-of-the-art performance among comparable methods despite its minimalist design, and its simplicity allows easy integration into existing systems.Cacheback also shows potential for fast adaptation to new domains.

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MA-DPR: Manifold-aware Distance Metrics for Dense Passage Retrieval
Yifan Liu | Qianfeng Wen | Mark Zhao | Jiazhou Liang | Scott Sanner

Dense Passage Retrieval (DPR) typically relies on Euclidean or cosine distance to measure query–passage relevance in embedding space, which is effective when embeddings lie on a linear manifold. However, our experiments across DPR benchmarks suggest that embeddings often lie on lower-dimensional, non-linear manifolds, especially in out-of-distribution (OOD) settings, where cosine and Euclidean distance fail to capture semantic similarity. To address this limitation, we propose a *manifold-aware* distance metric for DPR (**MA-DPR**) that models the intrinsic manifold structure of passages using a nearest-neighbor graph and measures query–passage distance based on their shortest path in this graph. We show that MA-DPR outperforms Euclidean and cosine distances by up to **26%** on OOD passage retrieval, with comparable in-distribution performance across various embedding models, while incurring a minimal increase in query inference time. Empirical evidence suggests that manifold-aware distance allows DPR to leverage context from related neighboring passages, making it effective even in the absence of direct semantic overlap. MA-DPR can be applied to a wide range of dense embedding and retrieval tasks, offering potential benefits across a wide spectrum of domains.

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LLM-Guided Co-Training for Text Classification
Md Mezbaur Rahman | Cornelia Caragea

In this paper, we introduce a novel weighted co-training approach that is guided by Large Language Models (LLMs). Namely, in our co-training approach, we use LLM labels on unlabeled data as target labels and co-train two encoder-only based networks that train each other over multiple iterations: first, all samples are forwarded through each network and historical estimates of each network’s confidence in the LLM label are recorded; second, a dynamic importance weight is derived for each sample according to each network’s belief (or confidence) in the quality of the LLM label for that sample; finally, the two networks exchange importance weights with each other—each network back-propagates all samples weighted with the importance weights coming from its peer network and updates its own parameters. By strategically utilizing LLM-generated guidance, our approach significantly outperforms conventional SSL methods, particularly in settings with abundant unlabeled data. Empirical results show that it achieves state-of-the-art performance on 4 out of 5 benchmark datasets and ranks first among 14 compared methods according to the Friedman test. Our results highlight a new direction in semi-supervised learning—where LLMs serve as knowledge amplifiers, enabling backbone co-training models to achieve SOTA performance efficiently.

Large language models (LLMs) enable long-context tasks but face efficiency challenges due to the growing key-value (KV) cache. We propose LeanK, a learning-based method that prunes unimportant key (K) cache channels by leveraging static channel sparsity. LeanK reduces GPU memory and accelerates decoding without sacrificing accuracy. Experiments demonstrate up to 70% K cache and 16%–18% V cache memory reduction, and 1.45× decoding speedup. We also provide insights into model channels and attention heads during long-context inference by analyzing the learned importance distribution. Our code is anonymously available at https://anonymous.4open.science/r/LeanK-7A87/README.md.

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DELOC: Document Element Localizer
Hammad Ayyubi | Puneet Mathur | Mehrab Tanjim | Vlad I Morariu

Editing documents and PDFs using natural language instructions is desirable for many reasons – ease of use, increasing accessibility to non-technical users, and for creativity. To do this automatically, a system needs to first understand the user’s intent and convert this to an executable plan or command, and then the system needs to identify or localize the elements that the user desires to edit. While there exist methods that can accomplish these tasks, a major bottleneck in these systems is the inability to ground the spatial edit location effectively. We address this gap through our proposed system, DELOC (Document Element LOCalizer). DELOC adapts the grounding capabilities of existing Multimodal Large Language Model (MLLM) from natural images to PDFs. This adaptation involves two novel contributions: 1) synthetically generating PDF-grounding instruction tuning data from partially annotated datasets; and 2) synthetic data cleaning via Code-NLI, an NLI-inspired process to clean data using generated Python code. The effectiveness of DELOC is apparent in the >3x zero-shot improvement it achieves over the next best Multimodal LLM, GPT-4o.

Translating natural language into formal language such as Lean 4 has gained attention for its potential to automate formal proof development. Automated methods provide a scalable and cost-effective alternative to manual formalization, driving increasing interest in this task. However, existing LLMs mainly rely on instruction tuning and lack fine-grained structural and semantic alignment, making it difficult to generate syntactically and logically sound formal proofs.To address this, we propose a reinforcement learning framework ReLean that enables LLMs to generate high-quality Lean 4 statements from natural language.We first fine-tune a LLaMA3-8B model on NL–Lean 4 data to obtain a base translator with basic translation ability. Then, we design a multi-aspect dense reward mechanism covering four key dimensions: semantic alignment, term-level alignment, global-level alignment, and compile-checking. Separate reward models are trained via preference modeling, and their normalized outputs are combined to guide optimization via PPO. Finally, a curriculum learning strategy based on multi-dimensional difficulty allows the model to learn progressively from simple to complex cases. Experiments on NL-to-Lean 4 tasks show that our method consistently outperforms baseline models. Further analysis on reward model and curriculum learning confirms their effectiveness in enhancing model performance.

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A Multilingual, Culture-First Approach to Addressing Misgendering in LLM Applications
Sunayana Sitaram | Adrian de Wynter | Isobel McCrum | Qilong Gu | Si-Qing Chen

Misgendering is the act of referring to someone by a gender that does not match their chosen identity. It marginalizes and undermines a person’s sense of self, causing significant harm. English-based approaches have clear-cut approaches to avoiding misgendering, such as the use of the pronoun “they”. However, other languages pose unique challenges due to both grammatical and cultural constructs. In this work we develop methodologies to assess and mitigate misgendering across 42 languages and dialects using a participatory-design approach to design effective and appropriate guardrails across all languages. We test these guardrails in a standard LLM-based application (meeting transcript summarization), where both the data generation and the annotation steps followed a human-in-the-loop approach. We find that the proposed guardrails are very effective in reducing misgendering rates across all languages in the summaries generated, and without incurring loss of quality. Our human-in-the-loop approach demonstrates a method to feasibly scale inclusive and responsible AI-based solutions across multiple languages and cultures. We release the guardrails and synthetic dataset encompassing 42 languages, along with human and LLM-judge evaluations, to encourage further research on this subject.

Prevalent text-to-video retrieval systems mainly adopt embedding models for feature extraction and compute cosine similarities for ranking. However, this design presents two limitations. Low-quality text-video data pairs could compromise the retrieval, yet are hard to identify and examine. Cosine similarity alone provides no explanation for the ranking results, limiting the interpretability. We ask that can we interpret the ranking results, so as to assess the retrieval models and examine the text-video data? This work proposes X-CoT, an explainable retrieval framework upon LLM CoT reasoning in place of the embedding model-based similarity ranking. We first expand the existing benchmarks with additional video annotations to support semantic understanding and reduce data bias. We also devise a retrieval CoT consisting of pairwise comparison steps, yielding detailed reasoning and complete ranking. X-CoT empirically improves the retrieval performance and produces detailed rationales. It also facilitates the model behavior and data quality analysis. Code and data are available at: https://github.com/PrasannaPulakurthi/X-CoT.

Query generation is a critical task for web search engines (e.g. Google, Bing) and recommendation systems. Recently, state-of-the-art query generation methods leverage Large Language Models (LLMs) for their strong capabilities in context understanding and text generation. However, they still face challenges in generating high-quality queries in terms of inferring user intent based on their web search interaction history. In this paper, we propose Token-level Proximal Policy Optimization (TPPO), a noval approach designed to empower LLMs perform better in query generation through fine-tuning. TPPO is based on the Reinforcement Learning from AI Feedback (RLAIF) paradigm, consisting of a token-level reward model and a token-level proximal policy optimization module to address the sparse reward challenge in traditional RLAIF frameworks. We conducted experiments on both open-source dataset and an industrial dataset that was collected from a globally-used search engine, demonstrating that TPPO significantly improves the performance of query generation for LLMs and outperforms its existing competitors.

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Prior Prompt Engineering for Reinforcement Fine-Tuning
Pittawat Taveekitworachai | Potsawee Manakul | Sarana Nutanong | Kunat Pipatanakul

This paper investigates prior prompt engineering (pPE) in the context of reinforcement fine-tuning (RFT), where language models (LMs) are incentivized to exhibit behaviors that maximize performance through reward signals. While existing RFT research has primarily focused on algorithms, reward shaping, and data curation, the design of the prior prompt–the instructions prepended to queries during training to elicit behaviors such as step-by-step reasoning–remains underexplored. We investigate whether different pPE approaches can guide LMs to internalize distinct behaviors after RFT. Inspired by inference-time prompt engineering (iPE), we translate five representative iPE strategies–reasoning, planning, code-based reasoning, knowledge recall, and null-example utilization–into corresponding pPE approaches. We experiment with Qwen2.5-7B using each of the pPE approaches, then evaluate performance on in-domain and out-of-domain benchmarks (e.g., AIME2024, HumanEval+, and GPQA-Diamond). Our results show that all pPE-trained models surpass their iPE-prompted counterparts, with the null-example pPE approach achieving the largest average performance gain and the highest improvement on AIME2024 and GPQA-Diamond, surpassing the commonly used reasoning approach. Furthermore, by adapting a behavior-classification framework, we demonstrate that different pPE strategies instill distinct behavioral styles in the resulting models. These findings position pPE as a powerful yet understudied axis for RFT.

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Beyond WER: Probing Whisper’s Sub‐token Decoder Across Diverse Language Resource Levels
Siyu Liang | Nicolas Ballier | Gina-Anne Levow | Richard Wright

While large multilingual automatic speech recognition (ASR) models achieve remarkable performance, the internal mechanisms of the end-to-end pipeline, particularly concerning fairness and efficacy across languages, remain underexplored. This paper introduces a fine-grained analysis of Whisper’s multilingual decoder, examining its sub-token hypotheses during transcription across languages with various resource levels. Our method traces the beam search path, capturing sub-token guesses and their associated probabilities. Results reveal that higher resource languages benefit from higher likelihood of the correct token being top-ranked, greater confidence, lower predictive entropy, and more diverse alternative candidates. Lower resource languages fare worse on these metrics, but also exhibit distinct clustering patterns in sub-token usage sometimes influenced by typology in our PCA and t-SNE analysis. This sub-token probing uncovers systematic decoding disparities masked by aggregate error rates and points towards targeted interventions to ameliorate the imbalanced development of speech technology.

Recent advances in test-time scaling have led to the emergence of thinking LLMs that exhibit self-reflective behaviors and multi-step reasoning. While RL drives this self-improvement paradigm, recent studies show that solely RL does not truly instill these new reasoning abilities - it merely draws out behaviors already present in the base models. This raises a question: How can we train models that don’t exhibit such thinking behavior to develop it in the first place? To this end, we propose ThinkTuning, a GRPO-based interactive training approach where we augment the rollouts of a student model with the guidance from a teacher model. A simple idea from classroom practice inspires our method: a teacher poses a problem, lets the student try an answer, then gives corrective feedback–enough to point the mind in the right direction and then show the solution. Each feedback reshapes the student’s thoughts, leading them to arrive at the correct solution. Similarly, we find that this type of implicit supervision through feedback from a teacher model of the same size improves the reasoning capabilities of the student model. Particularly, on average, our method shows 3.69% improvement over zero-shot baselines across benchmarks, and on MATH-500 and GPQA-Diamond, it shows 2.08% and 3.99% improvement over the vanilla-GRPO baseline.

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Droid: A Resource Suite for AI-Generated Code Detection
Daniil Orel | Indraneil Paul | Iryna Gurevych | Preslav Nakov

We present DroidCollection, the most extensive open data suite for training and evaluating machine-generated code detectors, comprising over a million code samples, seven programming languages, outputs from 43 coding models, and three real-world coding domains. Alongside fully AI-generated examples, our collection includes human-AI co-authored code, as well as adversarial examples explicitly crafted to evade detection. Subsequently, we develop DroidDetect, a suite of encoder-only detectors trained using a multi-task objective over DroidCollection. Our experiments show that existing detectors’ performance fails to generalise to diverse coding domains and programming languages outside of their narrow training data. We further demonstrate that while most detectors are easily compromised by humanising the output distributions using superficial prompting and alignment approaches, this problem can be easily amended by training on a small number of adversarial examples. Finally, we demonstrate the effectiveness of metric learning and uncertainty-based resampling as way to enhance detector training on possibly noisy distributions.

The Mixture of Experts (MoE) architecture improves large language models (LLMs) by utilizing sparsely activated expert sub-networks with a routing module, but it typically demands high training cost. Previous work introduces parameter-efficient fine-tuning (PEFT) modules, e.g., LoRA, to achieve a lightweight MoE for training efficiency. However, they construct static experts by manually splitting the LoRA parameters into fixed groups, which limits flexibility and dynamism. Furthermore, this manual partitioning also hinders the effective utilization of well-initialized LoRA modules. To address the challenges, we first delve into the parameter patterns in LoRA modules, revealing that there exists task-relevant parameters that are concentrated along the rank dimension of the LoRA parameters. Based on this, we redesign the construction of experts and propose the method LoRACoE (LoRA Composition of Experts). Specifically, when confronted with a task, it dynamically builds experts based on rank-level parameter composition, i.e., experts can flexibly combine rank-level parameters in LoRA module. Extensive experiments demonstrate that compared to other LoRA-based MoE methods, our method achieves better task performance across a broader range of tasks.

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Same Question, Different Words: A Latent Adversarial Framework for Prompt Robustness
Tingchen Fu | Fazl Barez

Insensitivity to semantically-preserving variations of prompts (paraphrases) is crucial for reliable behavior and real-world deployment of large language models. However, language models exhibit significant performance degradation with semantically equivalent but differently phrased prompts, and existing solutions either depend on trial-and-error prompt engineering or require computationally expensive inference-time algorithms. In this study, built on the key insight that worst-case prompts exhibit a drift in embedding space, we present Latent Adversarial Paraphrasing (LAP), a dual-loop adversarial framework that optimizes a trainable perturbation as “latent continuous paraphrase” and language model performance on these perturbations iteratively. Extensive experiments are conducted to demonstrate the effectiveness of LAP across multiple backbones on the RobustAlpaca benchmark with a 0.5%-4% absolution improvement on worst-case win-rate.

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Pluralistic Alignment for Healthcare: A Role-Driven Framework
Jiayou Zhong | Anudeex Shetty | Chao Jia | Xuanrui Lin | Usman Naseem

As large language models are increasingly deployed in sensitive domains such as healthcare, ensuring their outputs reflect the diverse values and perspectives held across populations is critical. However, existing alignment approaches, including pluralistic paradigms like Modular Pluralism, often fall short in the health domain, where personal, cultural, and situational factors shape pluralism. Motivated by the aforementioned healthcare challenges, we propose a first lightweight, generalizable, pluralistic alignment approach, ETHOSAGENTS, designed to simulate diverse perspectives and values. We empirically show that it advances the pluralistic alignment for all three modes across seven varying-sized open and closed models. Our findings reveal that health-related pluralism demands adaptable and normatively aware approaches, offering insights into how these models can better respect diversity in other high-stakes domains.

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Flexible-length Text Infilling for Discrete Diffusion Models
Andrew Zhang | Anushka Sivakumar | Chia-Wei Tang | Chris Thomas

Discrete diffusion models are a new class of text generators that offer advantages such as bidirectional context use, parallelizable generation, and flexible prompting compared to autoregressive models. However, a critical limitation of discrete diffusion models is their inability to perform flexible-length or flexible-position text infilling without access to ground-truth positional data. We introduce DDOT (Discrete Diffusion with Optimal Transport Position Coupling), the first discrete diffusion model to overcome this challenge. DDOT jointly denoises token values and token positions, employing a novel sample-level Optimal Transport (OT) coupling. This coupling preserves relative token ordering while dynamically adjusting the positions and length of infilled segments, a capability previously missing in text diffusion. Our method is orthogonal to existing discrete text diffusion methods and is compatible with various pretrained text denoisers. Extensive experiments on text infilling benchmarks such as One-Billion-Word and Yelp demonstrate that DDOT outperforms naive diffusion baselines. Furthermore, DDOT achieves performance on par with state-of-the-art non-autoregressive models and enables significant improvements in training efficiency and flexibility.

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Beyond the Leaderboard: Understanding Performance Disparities in Large Language Models via Model Diffing
Sabri Boughorbel | Fahim Dalvi | Nadir Durrani | Majd Hawasly

As fine-tuning becomes the dominant paradigm for improving large language models (LLMs), understanding what changes during this process is increasingly important. Traditional benchmarking often fails to explain _why_ one model outperforms another. In this work, we use model diffing, a mechanistic interpretability approach, to analyze the specific capability differences between Gemma-2-9b-it and a SimPO-enhanced variant. Using crosscoders, we identify and categorize latent representations that differentiate the two models. We find that SimPO acquired latent concepts predominantly enhance safety mechanisms (+32.8%), multilingual capabilities (+43.8%), and instruction-following (+151.7%), while its additional training also reduces emphasis on model self-reference (-44.1%) and hallucination management (-68.5%). Our analysis shows that model diffing can yield fine-grained insights beyond leaderboard metrics, attributing performance gaps to concrete mechanistic capabilities. This approach offers a transparent and targeted framework for comparing LLMs.

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Explicit Learning and the LLM in Machine Translation
Malik Marmonier | Rachel Bawden | Benoît Sagot

This study explores an LLM’s ability to learn new languages using explanations found in a grammar book—a process we term “explicit learning.” To rigorously assess this ability, we design controlled translation experiments between English and constructed languages generated—through specific cryptographic means—from Latin or French. Contrary to previous studies, our results demonstrate that LLMs do possess a measurable capacity for explicit learning. This ability, however, diminishes as the complexity of the linguistic phenomena to be learned increases. Supervised fine-tuning on ad hoc chains of thought significantly enhances LLM performance but struggles to generalize to typologically novel or more complex linguistic features. These findings point to the need for more diverse training sets and alternative fine-tuning strategies to further improve explicit learning by LLMs, benefiting low-resource languages typically described in grammar books but lacking extensive corpora.

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Towards Language-Agnostic STIPA: Universal Phonetic Transcription to Support Language Documentation at Scale
Jacob Lee Suchardt | Hana El-Shazli | Pierluigi Cassotti

This paper explores the use of existing state-of-the-art speech recognition models (ASR) for the task of generating narrow phonetic transcriptions using the International Phonetic Alphabet (STIPA). Unlike conventional ASR systems focused on orthographic output for high-resource languages, STIPA can be used as a language-agnostic interface valuable for documenting under-resourced and unwritten languages. We introduce a new dataset for South Levantine Arabic and present the first large-scale evaluation of STIPA models across 51 language families. Additionally, we provide a use case on Sanna, a severely endangered language. Our findings show that fine-tuned ASR models can produce accurate IPA transcriptions with limited supervision, significantly reducing phonetic error rates even in extremely low-resource settings. The results highlight the potential of STIPA for scalable language documentation.

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Beyond Pairwise: Global Zero-shot Temporal Graph Generation
Alon Eirew | Kfir Bar | Ido Dagan

Temporal relation extraction (TRE) is a fundamental task in natural language processing (NLP) that involves identifying the temporal relationships between events in a document. Despite the advances in large language models (LLMs), their application to TRE remains limited. Most existing approaches rely on pairwise classification, where event pairs are classified in isolation, leading to computational inefficiency and a lack of global consistency in the resulting temporal graph. In this work, we propose a novel zero-shot method for TRE that generates a document’s complete temporal graph in a single step, followed by temporal constraint optimization to refine predictions and enforce temporal consistency across relations. Additionally, we introduce OmniTemp, a new dataset with complete annotations for all pairs of targeted events within a document. Through experiments and analyses, we demonstrate that our method outperforms existing zero-shot approaches and offers a competitive alternative to supervised TRE models.

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“Feels Feminine to Me”: Understanding Perceived Gendered Style through Human Annotations
Hongyu Chen | Neele Falk | Michael Roth | Agnieszka Falenska

In NLP, language–gender associations are commonly grounded in the author’s gender identity, inferred from their language use. However, this identity-based framing risks reinforcing stereotypes and marginalizing individuals who do not conform to normative language–gender associations. To address this, we operationalize the language–gender association as a perceived gender expression of language, focusing on how such expression is externally interpreted by humans, independent of the author’s gender identity. We present the first dataset of itskind: 5,100 human annotations of perceived gendered style—human-written texts rated on a five-point scale from very feminine to verymasculine. While perception is inherently subjective, our analysis identifies textual features associated with higher agreement among annotators: formal expressions and lower emotional intensity. Moreover, annotator demographics influence their perception: women annotators are more likely to label texts as feminine, and men and non-binary annotators as masculine. Finally, feature analysis reveals that the text’s perceived gendered style is shaped by both affective and function words, partially overlapping with known patterns of language variation across gender identities. Our findings lay the groundwork for operationalizing gendered style through human annotation, while also highlighting annotators’ subjective judgments as meaningful signals to understand perception-based concepts.

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RALS: Resources and Baselines for Romanian Automatic Lexical Simplification
Fabian Anghel | Cristea Petru-Theodor | Claudiu Creanga | Sergiu Nisioi

We introduce the first dataset that jointly covers both lexical complexity prediction (LCP) annotations and lexical simplification (LS) for Romanian, along with a comparison of lexical simplification approaches. We propose a methodology for ordering simplification suggestions using a pairwise ranking approximation method, arranging candidates from simple to complex based on a separate set of human judgments. In addition, we provide human lexical complexity annotations for 3,921 word samples in context. Finally, we explore several novel pipelines for complexity prediction and simplification and present the first text simplification system for Romanian.

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How Do Social Bots Participate in Misinformation Spread? A Comprehensive Dataset and Analysis
Herun Wan | Minnan Luo | Zihan Ma | Guang Dai | Xiang Zhao

Social media platforms provide an ideal environment to spread misinformation, where social bots can accelerate the spread. This paper explores the interplay between social bots and misinformation on the Sina Weibo platform. We construct a large-scale dataset that includes annotations for both misinformation and social bots. From the misinformation perspective, the dataset is multimodal, containing 11,393 pieces of misinformation and 16,416 pieces of verified information. From the social bot perspective, this dataset contains 65,749 social bots and 345,886 genuine accounts, annotated using a weakly supervised annotator. Extensive experiments demonstrate the comprehensiveness of the dataset, the clear distinction between misinformation and real information, and the high quality of social bot annotations. Further analysis illustrates that: (i) social bots are deeply involved in information spread; (ii) misinformation with the same topics has similar content, providing the basis of echo chambers, and social bots would amplify this phenomenon; and (iii) social bots generate similar content aiming to manipulate public opinions.

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Are Stereotypes Leading LLMs’ Zero-Shot Stance Detection ?
Anthony Dubreuil | Antoine Gourru | Christine Largeron | Amine Trabelsi

Large Language Models inherit stereotypes from their pretraining data, leading to biased behavior toward certain social groups in many Natural Language Processing tasks, such as hateful speech detection or sentiment analysis. Surprisingly, the evaluation of this kind of bias in stance detection methods has been largely overlooked by the community. Stance Detection involves labeling a statement as being against, in favor, or neutral towards a specific target and is among the most sensitive NLP tasks, as it often relates to political leanings. In this paper, we focus on the bias of Large Language Models when performing stance detection in a zero-shot setting. We automatically annotate posts in pre-existing stance detection datasets with two attributes: dialect or vernacular of a specific group and text complexity/readability, to investigate whether these attributes influence the model’s stance detection decisions. Our results show that LLMs exhibit significant stereotypes in stance detection tasks, such as incorrectly associating pro-marijuana views with low text complexity and African American dialect with opposition to Donald Trump.

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Multi-Modal Framing Analysis of News
Arnav Arora | Srishti Yadav | Maria Antoniak | Serge Belongie | Isabelle Augenstein

Automated frame analysis of political communication is a popular task in computational social science that is used to study how authors select aspects of a topic to frame its reception. So far, such studies have been narrow, in that they use a fixed set of pre-defined frames and focus only on the text, ignoring the visual contexts in which those texts appear. Especially for framing in the news, this leaves out valuable information about editorial choices, which include not just the written article but also accompanying photographs. To overcome such limitations, we present a method for conducting multi-modal, multi-label framing analysis at scale using large (vision-) language models. Grounding our work in framing theory, we extract latent meaning embedded in images used to convey a certain point and contrast that to the text by comparing the respective frames used. We also identify highly partisan framing of topics with issue-specific frame analysis found in prior qualitative work. We demonstrate a method for doing scalable integrative framing analysis of both text and image in news, providing a more complete picture for understanding media bias.

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TempParaphraser: “Heating Up” Text to Evade AI-Text Detection through Paraphrasing
Junjie Huang | Ruiquan Zhang | Jinsong Su | Yidong Chen

The widespread adoption of large language models (LLMs) has increased the need for reliable AI-text detection. While current detectors perform well on benchmark datasets, we highlight a critical vulnerability: increasing the temperature parameter during inference significantly reduces detection accuracy. Based on this weakness, we propose TempParaphraser, a simple yet effective paraphrasing framework that simulates high-temperature sampling effects through multiple normal-temperature generations, effectively evading detection. Experiments show that TempParaphraser reduces detector accuracy by an average of 82.5% while preserving high text quality. We also demonstrate that training on TempParaphraser-augmented data improves detector robustness. All resources are publicly available at https://github.com/HJJWorks/TempParaphraser.

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ComicScene154: A Scene Dataset for Comic Analysis
Sandro Paval | Pascal Meißner | Ivan P. Yamshchikov

Comics offer a compelling yet under-explored domain for computational narrative analysis, combining text and imagery in ways distinct from purely textual or audiovisual media. We introduce ComicScene154, a manually annotated dataset of scene-level narrative arcs derived from public-domain comic books spanning diverse genres. By conceptualizing comics as an abstraction for narrative-driven, multimodal data, we highlight their potential to inform broader research on multi-modal storytelling. To demonstrate the utility of ComicScene154, we present a baseline scene segmentation pipeline, providing an initial benchmark that future studies can build upon. Our results indicate that ComicScene154 constitutes a valuable resource for advancing computational methods in multimodal narrative understanding and expanding the scope of comic analysis within the Natural Language Processing community.

In pharmacovigilance, effective automation of medical data structuring, especially linking entities to standardized terminologies such as MedDRA, is critical. This challenge is rarely addressed for German data. With MedLinkDE we address German MedDRA entity linking for adverse drug reactions in a two-step approach: (1) retrieval of medical terms with fine-tuned embedding models, followed (2) by guided chain-of-thought re-ranking using LLMs. To this end, we introduce RENOde, a German real-world MedDRA dataset consisting of reportings from patients and healthcare professionals. To overcome the challenges posed by the linguistic diversity of these reports, we generate synthetic data mapping the two reporting styles of patients and healthcare professionals. Our embedding models, fine-tuned on these synthetic, quasi-personalized datasets, show competitive performance with real datasets in terms of accuracy at high top- recall, providing a robust basis for re-ranking. Our subsequent guided Chain of Thought (CoT) re-ranking, informed by MedDRA coding guidelines, improves entity linking accuracy by approximately 15% (Acc@1) compared to embedding-only strategies. In this way, our approach demonstrates the feasibility of entity linking in medical reports under the constraints of data scarcity by relying on synthetic data reflecting different informant roles of reporting persons.

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HookMoE: A learnable performance compensation strategy of Mixture-of-Experts for LLM inference acceleration
Cheng Longkai | Along He | Mulin Li | Xie Xueshuo | Tao Li

Mixture of Experts (MoE) architectures have emerged as a promising paradigm for scaling model capacity through top-k routing mechanisms. Although reducing the number of activated experts inherently enables inference acceleration, this efficiency gain typically comes at the cost of significant performance degradation. To address this trade-off between efficiency and performance, we propose HookMoE, a plug-and-play single-layer compensation framework that effectively restores performance using only a small post-training calibration set. Our method strategically inserts a lightweight trainable Hook module immediately preceding selected transformer blocks. Comprehensive evaluations on four popular MoE models, with an average performance degradation of only 2.5% across various benchmarks, our method reduces the number of activated experts by more than 50% and achieves a 1.42× inference speed-up during the prefill stage. Through systematic analysis, we further reveal that the upper layers require fewer active experts, offering actionable insights for refining dynamic expert selection strategies and enhancing the overall efficiency of MoE models.

Natural Language Inference (NLI) is a fundamental task in natural language processing. While NLI has developed many subdirections such as sentence-level NLI, document-level NLI and cross-lingual NLI, Cross-Document Cross-Lingual NLI (CDCL-NLI) remains largely unexplored. In this paper, we propose a novel paradigm: CDCL-NLI, which extends traditional NLI capabilities to multi-document, multilingual scenarios. To support this task, we construct a high-quality CDCL-NLI dataset including 25,410 instances and spanning 26 languages.To address the limitations of previous methods on CDCL-NLI task, we further propose an innovative method that integrates RST-enhanced graph fusion with interpretability-aware prediction.Our approach leverages RST (Rhetorical Structure Theory) within heterogeneous graph neural networks for cross-document context modeling, and employs a structure-aware semantic alignment based on lexical chains for cross-lingual understanding. For NLI interpretability, we develop an EDU (Elementary Discourse Unit)-level attribution framework that produces extractive explanations.Extensive experiments demonstrate our approach’s superior performance, achieving significant improvements over both conventional NLI models as well as large language models.Our work sheds light on the study of NLI and will bring research interest on cross-document cross-lingual context understanding, hallucination elimination and interpretability inference.Our code and dataset are available at CDCL-NLI-link.

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3R: Enhancing Sentence Representation Learning via Redundant Representation Reduction
Longxuan Ma | Xiao Wu | Yuxin Huang | Shengxiang Gao | Zhengtao Yu

Sentence representation learning (SRL) aims to learn sentence embeddings that conform to the semantic information of sentences. In recent years, fine-tuning methods based on pre-trained models and contrastive learning frameworks have significantly advanced the quality of sentence representations. However, within the semantic space of SRL models, both word embeddings and sentence representations derived from word embeddings exhibit substantial redundant information, which can adversely affect the precision of sentence representations. Existing approaches predominantly optimize training strategies to alleviate the redundancy problem, lacking fine-grained guidance on reducing redundant representations. This paper proposes a novel approach that dynamically identifies and reduces redundant information from a dimensional perspective, training the SRL model to redistribute semantics on different dimensions, and entailing better sentence representations. Extensive experiments across seven semantic text similarity benchmarks demonstrate the effectiveness and generality of the proposed method. A comprehensive analysis of the experimental results is conducted, and the code/data will be released.

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When Big Models Train Small Ones: Label-Free Model Parity Alignment for Efficient Visual Question Answering using Small VLMs
Abhirama Subramanyam Penamakuri | Navlika Singh | Piyush Arora | Anand Mishra

Large Vision-Language Models (L-VLMs) have demonstrated remarkable performance in various vision and language tasks, including Visual Question Answering (VQA). However, their high computational cost makes them impractical for resource-constrained settings and inference-heavy applications. In contrast, Small Vision-Language Models (S-VLMs) offer efficiency but suffer from a significant performance gap compared to their larger counterparts. In this work, we introduce the Model Parity Aligner (MPA), a novel framework designed to systematically improve S-VLMs by leveraging unlabeled images and effective knowledge transfer from L-VLMs. Instead of traditional knowledge distillation methods that rely on labeled training data, MPA employs a strategic parity-based approach that precisely identifies the knowledge disparities between S-VLMs and L-VLMs, and optimizes training by targeting only these disparities. We conduct extensive experiments on four diverse VQA benchmarks, namely TextVQA, ST-VQA, ChartQA, and OKVQA, each of which required specialized reasoning capabilities such as text recognition, chart interpretation, and commonsense and factual understanding. Our results demonstrate that MPA consistently enhances the performance of S-VLM on all benchmarks, reducing the performance gap while maintaining computational efficiency. We shall make our code and MPA-aligned models publicly available upon acceptance of this work.

Large vision-language models (LVLMs) have witnessed significant progress on visual understanding tasks. However, they often prioritize language knowledge over image information on visual reasoning tasks, incurring performance degradation. To tackle this issue, we first identify the drawbacks of existing solutions (i.e., limited multi-modal reasoning capacities, and insufficient and irrelevant visual descriptions). We then decompose visual reasoning process into two stages: proactive visual perception (i.e., eyesight) and textual reasoning (i.e., wisdom), and introduce a novel visual reasoning framework named ProReason. This framework features decoupled vision-reasoning capabilities and multi-run proactive perception. Briefly, given a multi-modal question, ProReason iterates proactive information collection and reasoning until the answer can be concluded with necessary and sufficient visual descriptions. Notably, the disassociation of capabilities allows seamless integration of existing large language models (LLMs) to compensate for the reasoning deficits of LVLMs. Our extensive experiments demonstrate that ProReason outperforms existing multi-step reasoning frameworks on various benchmarks for both open-source and closed-source models, with the average performance gain reaching 13.2%. Besides, the integration of LLMs allows ProReason to produce high-quality visual reasoning data, which empowers ProReason-distilled models (i.e., ProReason-VL and ProReason-Q3) to achieve superior performance in downstream tasks. Our insights into existing solutions and the decoupled perspective for feasible integration of LLMs illuminate future research on visual reasoning techniques, especially LLM-assisted ones. The code is available at https://github.com/lian-tian-mo-zun/Pro_Reason.

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Extractive Fact Decomposition for Interpretable Natural Language Inference in one Forward Pass
Nicholas Popovič | Michael Färber

Recent works in Natural Language Inference (NLI) and related tasks, such as automated fact-checking, employ atomic fact decomposition to enhance interpretability and robustness. For this, existing methods rely on resource-intensive generative large language models (LLMs) to perform decomposition. We propose JEDI, an encoder-only architecture that jointly performs extractive atomic fact decomposition and interpretable inference without requiring generative models during inference. To facilitate training, we produce a large corpus of synthetic rationales covering multiple NLI benchmarks. Experimental results demonstrate that JEDI achieves competitive accuracy in distribution and significantly improves robustness out of distribution and in adversarial settings over models based solely on extractive rationale supervision. Our findings show that interpretability and robust generalization in NLI can be realized using encoder-only architectures and synthetic rationales.

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Structure-Conditional Minimum Bayes Risk Decoding
Bryan Eikema | Anna Rutkiewicz | Mario Giulianelli

Minimum Bayes Risk (MBR) decoding has seen renewed interest as an alternative to traditional generation strategies. While MBR has proven effective in machine translation, where the variability of a language model’s outcome space is naturally constrained, it may face challenges in more open-ended tasks such as dialogue or instruction-following. We hypothesise that in such settings, applying MBR with standard similarity-based utility functions may result in selecting responses that are broadly representative of the model’s distribution, yet sub-optimal with respect to any particular grouping of generations that share an underlying latent structure. In this work, we introduce three lightweight adaptations to the utility function, designed to make MBR more sensitive to structural variability in the outcome space. To test our hypothesis, we curate a dataset capturing three representative types of latent structure—dialogue act, emotion, and response structure (e.g., a sentence, a paragraph, or a list)—and we propose two metrics to evaluate the structural optimality of MBR. Our analysis demonstrates that common similarity-based utility functions fall short by these metrics. In contrast, our proposed adaptations considerably improve structural optimality. Finally, we evaluate our approaches on real-world instruction-following benchmarks, AlpacaEval and MT-Bench, and show that increased structural sensitivity improves generation quality by up to 13.7 percentage points in win rate.

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Label Set Optimization via Activation Distribution Kurtosis for Zero-Shot Classification with Generative Models
Yue Li | Zhixue Zhao | Carolina Scarton

In-context learning (ICL) performance is highly sensitive to prompt design, yet the impact of class label options (e.g. lexicon or order) in zero-shot classification remains underexplored. This study proposes LOADS (Label set Optimization via Activation Distribution kurtosiS), a post-hoc method for selecting optimal label sets in zero-shot ICL with large language models (LLMs).LOADS is built upon the observations in our empirical analysis, the first to systematically examine how label option design (i.e., lexical choice, order, and elaboration) impacts classification performance. This analysis shows that the lexical choice of the labels in the prompt (such as agree vs. support in stance classification) plays an important role in both model performance and model’s sensitivity to the label order. A further investigation demonstrates that optimal label words tend to activate fewer outlier neurons in LLMs’ feed-forward networks. LOADS then leverages kurtosis to measure the neuron activation distribution for label selection, requiring only a single forward pass without gradient propagation or labelled data. The LOADS-selected label words consistently demonstrate effectiveness for zero-shot ICL across classification tasks, datasets, models and languages, achieving maximum performance gain from 0.54 to 0.76 compared to the conventional approach of using original dataset label words.

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The Transfer Neurons Hypothesis: An Underlying Mechanism for Language Latent Space Transitions in Multilingual LLMs
Hinata Tezuka | Naoya Inoue

Recent studies have suggested a processing framework for multilingual inputs in decoder-based LLMs: early layers convert inputs into English-centric and language-agnostic representations; middle layers perform reasoning within an English-centric latent space; and final layers generate outputs by transforming these representations back into language-specific latent spaces.However, the internal dynamics of such transformation and the underlying mechanism remain underexplored.Towards a deeper understanding of this framework, we propose and empirically validate **The Transfer Neurons Hypothesis**: certain neurons in the MLP module are responsible for transferring representations between language-specific latent spaces and a shared semantic latent space.Furthermore, we show that one function of language-specific neurons, as identified in recent studies, is to facilitate movement between latent spaces.Finally, we show that transfer neurons are critical for reasoning in multilingual LLMs

Automatically assessing handwritten mathematical solutions is an important problem in educational technology with practical applications, but remains a significant challenge due to the diverse formats, unstructured layouts, and symbolic complexity of student work. To address this challenge, we introduce VEHME-a Vision-Language Model for Evaluating Handwritten Mathematics Expressions—designed to assess open-form handwritten math responses with high accuracy and interpretable reasoning traces. VEHME integrates a two-phase training pipeline: (i) supervised fine-tuning using structured reasoning data, and (ii) reinforcement learning that aligns model outputs with multi-dimensional grading objectives, including correctness, reasoning depth, and error localization. To enhance spatial understanding, we propose an Expression-Aware Visual Prompting Module, trained on our synthesized multi-line math expressions dataset to robustly guide attention in visually heterogeneous inputs. Evaluated on AIHub and FERMAT datasets, VEHME achieves state-of-the-art performance among open-source models and approaches the accuracy of proprietary systems, demonstrating its potential as a scalable and accessible tool for automated math assessment. Our training and experiment code is publicly available at our GitHub repository.

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All Roads Lead to Rome: Graph-Based Confidence Estimation for Large Language Model Reasoning
Caiqi Zhang | Chang Shu | Ehsan Shareghi | Nigel Collier

Confidence estimation is essential for the reliable deployment of large language models (LLMs). Existing methods are primarily designed for factual QA tasks and often fail to generalize to reasoning tasks. To address this gap, we propose a set of training-free, graph-based confidence estimation methods tailored to reasoning tasks. Our approach models reasoning paths as directed graphs and estimates confidence by exploiting graph properties such as centrality, path convergence, and path weighting. Experiments with two LLMs on three reasoning datasets demonstrate improved confidence estimation and enhanced performance on two downstream tasks.

Knowledge Graph Foundation Models (KGFMs) have shown promise in enabling zero-shot reasoning over unseen graphs by learning transferable patterns. However, most existing KGFMs rely solely on graph structure, overlooking the rich semantic signals encoded in textual attributes. We introduce SEMMA, a dual-module KGFM that systematically integrates transferable textual semantics alongside structure. SEMMA leverages Large Language Models (LLMs) to enrich relation identifiers, generating semantic embeddings that subsequently form a textual relation graph, which is fused with the structural component. Across 54 diverse KGs, SEMMA outperforms purely structural baselines like ULTRA in fully inductive link prediction. Crucially, we show that in more challenging generalization settings, where the test-time relation vocabulary is entirely unseen, structural methods collapse while SEMMA is 2x more effective. Our findings demonstrate that textual semantics are critical for generalization in settings where structure alone fails, highlighting the need for foundation models that unify structural and linguistic signals in knowledge reasoning.

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Text2Vis: A Challenging and Diverse Benchmark for Generating Multimodal Visualizations from Text
Mizanur Rahman | Md Tahmid Rahman Laskar | Shafiq Joty | Enamul Hoque

Automated data visualization plays a crucial role in simplifying data interpretation, enhancing decision-making, and improving efficiency. While large language models (LLMs) have shown promise in generating visualizations from natural language, the absence of comprehensive benchmarks limits the rigorous evaluation of their capabilities. We introduce Text2Vis, a benchmark designed to assess text-to-visualization models, covering 20+ chart types and diverse data science queries, including trend analysis, correlation, outlier detection, and predictive analytics. It comprises 1,985 samples, each with a data table, natural language query, short answer, visualization code, and annotated charts. The queries involve complex reasoning, conversational turns, and dynamic data retrieval. We benchmark 11 open-source and closed-source models, revealing significant performance gaps, highlighting key challenges, and offering insights for future advancements. To close this gap, we propose the first cross-modal actor-critic agentic framework that jointly refines the textual answer and visualization code, increasing GPT-4o’s pass rate from 26% to 42% over the direct approach and improving chart quality. We also introduce an automated LLM-based evaluation framework that enables scalable assessment across thousands of samples without human annotation, measuring answer correctness, code execution success, visualization readability, and chart accuracy. We release Text2Vis at <redacted>.

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Predicting Prosodic Boundaries for Children’s Texts
Mansi Dhamne | Sneha Raman | Preeti Rao

Reading fluency in any language requires accurate word decoding but also natural prosodic phrasing i.e the grouping of words into rhythmically and syntactically coherent units. This holds for, both, reading aloud and silent reading. While adults pause meaningfully at clause or punctuation boundaries, children aged 8-13 often insert inappropriate pauses due to limited breath control and underdeveloped prosodic awareness. We present a text-based model to predict cognitively appropriate pause locations in children’s reading material. Using a curated dataset of 54 leveled English stories annotated for potential pauses, or prosodic boundaries, by 21 fluent speakers, we find that nearly 30% of pauses occur at non-punctuation locations of the text, highlighting the limitations of using only punctuation-based cues. Our model combines lexical, syntactic, and contextual features with a novel breath duration feature that captures syllable load since the last major boundary. This cognitively motivated approach can model both allowed and “forbidden” pauses. The proposed framework supports applications such as child-directed TTS and oral reading fluency assessment where the proper grouping of words is considered critical to reading comprehension.

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Enhancing Logical Reasoning in Language Models via Symbolically-Guided Monte Carlo Process Supervision
Xingwei Tan | Marco Valentino | Mahmud Elahi Akhter | Maria Liakata | Nikolaos Aletras

Large language models (LLMs) have shown strong performance in many reasoning benchmarks. However, recent studies have pointed to memorization, rather than generalization, as one of the leading causes for such performance. LLMs, in fact, are susceptible to content variations, demonstrating a lack of robust planning or symbolic abstractions supporting their reasoning process. To improve reliability, many attempts have been made to combine LLMs with symbolic methods. Nevertheless, existing approaches fail to effectively leverage symbolic representations due to the challenges involved in developing reliable and scalable verification mechanisms. In this paper, we propose to overcome such limitations by synthesizing high-quality symbolic reasoning trajectories with stepwise pseudo-labels at scale via Monte Carlo estimation. A Process Reward Model (PRM) can be efficiently trained based on the synthesized data and then used to select more symbolic trajectories. The trajectories are then employed with Direct Preference Optimization (DPO) and Supervised Fine-Tuning (SFT) to improve logical reasoning and generalization. Our results on benchmarks (i.e., FOLIO and LogicAsker) show the effectiveness of the proposed method with gains on frontier and open-weight models. Moreover, additional experiments on claim verification data reveal that fine-tuning on the generated symbolic reasoning trajectories enhances out-of-domain generalizability, suggesting the potential impact of the proposed method in enhancing planning and logical reasoning.

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Can Large Language Models Outperform Non-Experts in Poetry Evaluation? A Comparative Study Using the Consensual Assessment Technique
Piotr Sawicki | Marek Grzes | Dan Brown | Fabricio Goes

This study adapts the Consensual Assessment Technique (CAT) for Large Language Models (LLMs), introducing a novel methodology for poetry evaluation. Using a 90-poem dataset with a ground truth based on publication venue, we demonstrate that this approach allows LLMs to significantly surpass the performance of non-expert human judges. Our method, which leverages forced-choice ranking within small, randomized batches, enabled Claude-3-Opus to achieve a Spearman’s Rank Correlation of 0.87 with the ground truth, dramatically outperforming the best human non-expert evaluation (SRC = 0.38). The LLM assessments also exhibited high inter-rater reliability, underscoring the methodology’s robustness. These findings establish that LLMs, when guided by a comparative framework, can be effective and reliable tools for assessing poetry, paving the way for their broader application in other creative domains.

Efficient resume parsing is critical for global hiring, yet the absence of dedicated benchmarks for evaluating large language models (LLMs) on multilingual, structure-rich resumes hinders progress. To address this, we introduce ResumeBench, the first privacy-compliant benchmark comprising 2,500 synthetic resumes spanning 50 templates, 30 career fields, and 5 languages. These resumes are generated through a human-in-the-loop pipeline that prioritizes realism, diversity, and privacy compliance, which are validated against real-world resumes. This paper evaluates 24 state-of-the-art LLMs on ResumeBench, revealing substantial variations in handling resume complexities. Specifically, top-performing models like GPT-4o exhibit challenges in cross-lingual structural alignment while smaller models show inconsistent scaling effects. Code-specialized LLMs underperform relative to generalists, while JSON outputs enhance schema compliance but fail to address semantic ambiguities. Our findings underscore the necessity for domain-specific optimization and hybrid training strategies to enhance structural and contextual reasoning in LLMs.

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Orthogonal Finetuning Made Scalable
Zeju Qiu | Weiyang Liu | Adrian Weller | Bernhard Schölkopf

Orthogonal finetuning (OFT) offers highly parameter-efficient adaptation while preventing catastrophic forgetting, but its high runtime and memory demands limit practical deployment. We identify the core computational bottleneck in OFT as its weight-centric implementation, which relies on costly matrix-matrix multiplications with cubic complexity. To overcome this, we propose OFTv2, an input-centric reformulation that instead uses matrix-vector multiplications (i.e., matrix-free computation), reducing the computational cost to quadratic. We further introduce the Cayley–Neumann parameterization, an efficient orthogonal parameterization that approximates the matrix inversion in the Cayley transform via a truncated Neumann series. These modifications allow OFTv2 to achieve up to 10x faster training and 3x lower GPU memory usage without compromising performance. In addition, we extend OFTv2 to support finetuning quantized foundation models and show that it outperforms the popular QLoRA in training stability, efficiency, and memory usage.

With the development of large language models, their ability to follow simple instructions has significantly improved. However, adhering to complex instructions remains a major challenge. Current approaches to generating complex instructions are often irrelevant to the current instruction requirements or suffer from limited scalability and diversity. Moreover, methods such as back-translation, while effective for simple instruction generation, fail to leverage the rich knowledge and formatting in human written documents. In this paper, we propose a novel **A**utomatic **I**terative **R**efinement (**AIR**) framework to generate complex instructions with constraints, which not only better reflects the requirements of real scenarios but also significantly enhances LLMs’ ability to follow complex instructions. The AIR framework consists of two stages: 1) Generate an initial instruction from a document; 2) Iteratively refine instructions with LLM-as-judge guidance by comparing the model’s output with the document to incorporate valuable constraints. Finally, we construct the AIR-10K dataset with 10K complex instructions and demonstrate that instructions generated with our approach significantly improve the model’s ability to follow complex instructions, outperforming existing methods for instruction generation.

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SQUiD: Synthesizing Relational Databases from Unstructured Text
Mushtari Sadia | Zhenning Yang | Yunming Xiao | Ang Chen | Amrita Roy Chowdhury

Relational databases are central to modern data management, yet most data exists in unstructured forms like text documents. To bridge this gap, we leverage large language models (LLMs) to automatically synthesize a relational database by generating its schema and populating its tables from raw text. We introduce SQUiD, a novel neurosymbolic framework that decomposes this task into four stages, each with specialized techniques. Our experiments show that SQUiD consistently outperforms baselines across diverse datasets. Our code and datasets are publicly available at: https://github.com/Mushtari-Sadia/SQUiD.

The integration of external knowledge through Retrieval-Augmented Generation (RAG) has become foundational in enhancing large language models (LLMs) for knowledge-intensive tasks. However, existing RAG paradigms often overlook the cognitive step of applying knowledge, leaving a gap between retrieved facts and task-specific reasoning. In this work, we introduce RAG+, a principled and modular extension that explicitly incorporates application-aware reasoning into the RAG pipeline. RAG+ constructs a dual corpus consisting of knowledge and aligned application examples, created either manually or automatically, and jointly retrieves both during inference. This design enables LLMs not only to access relevant information but also to apply it within structured, goal-oriented reasoning processes. Experiments across mathematical, law, and medical domains, conducted on multiple models, demonstrate that RAG+ consistently outperforms standard RAG variants, achieving average improvements of 3–5%, and peak gains up to 13.5% in complex scenarios. By bridging retrieval with actionable application, RAG+ advances a more cognitively grounded framework for knowledge integration, representing a step toward more interpretable and capable LLMs.

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Rapid Word Learning Through Meta In-Context Learning
Wentao Wang | Guangyuan Jiang | Tal Linzen | Brenden Lake

Humans can quickly learn a new word from a few illustrative examples, and then systematically and flexibly use it in novel contexts. Yet the abilities of current language models for few-shot word learning, and methods for improving these abilities, are underexplored. In this study, we introduce a novel method, Meta-training for IN-context learNing Of Words (Minnow). This method trains language models to generate new examples of a word’s usage given a few in-context examples, using a special placeholder token to represent the new word. This training is repeated on many new words to develop a general word-learning ability. We find that training models from scratch with Minnow on human-scale child-directed language enables strong few-shot word learning, comparable to a large language model (LLM) pre-trained on orders of magnitude more data. Furthermore, through discriminative and generative evaluations, we demonstrate that finetuning pre-trained LLMs with Minnow improves their ability to discriminate between new words, identify syntactic categories of new words, and generate reasonable new usages and definitions for new words, based on one or a few in-context examples. These findings highlight the data efficiency of Minnow and its potential to improve language model performance in word learning tasks.

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EuroGEST: Investigating gender stereotypes in multilingual language models
Jacqueline Rowe | Mateusz Klimaszewski | Liane Guillou | Shannon Vallor | Alexandra Birch

Large language models increasingly support multiple languages, yet most benchmarks for gender bias remain English-centric. We introduce EuroGEST, a dataset designed to measure gender-stereotypical reasoning in LLMs across English and 29 European languages. EuroGEST builds on an existing expert-informed benchmark covering 16 gender stereotypes, expanded in this work using translation tools, quality estimation metrics, and morphological heuristics. Human evaluations confirm that our data generation method results in high accuracy of both translations and gender labels across languages. We use EuroGEST to evaluate 24 multilingual language models from six model families, demonstrating that the strongest stereotypes in all models across all languages are that women are beautiful, empathetic and neat and men are leaders, strong, tough and professional. We also show that larger models encode gendered stereotypes more strongly and that instruction finetuned models continue to exhibit gendered stereotypes. Our work highlights the need for more multilingual studies of fairness in LLMs and offers scalable methods and resources to audit gender bias across languages.

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How Persuasive Is Your Context?
Tu Nguyen | Kevin Du | Alexander Miserlis Hoyle | Ryan Cotterell

Two central capabilities of language models (LMs) are: (i) drawing on prior knowledge about entities, which allows them to answer queries such as What’s the official language of Austria?, and (ii) adapting to new information provided in context, e.g., Pretend the official language of Austria is Tagalog., that is pre-pended to the question. In this article, we introduce targeted persuasion score (TPS), designed to quantify how persuasive a given context is to an LM where persuasion is operationalized as the ability of the context to alter the LM’s answer to the question. In contrast to evaluating persuasiveness only through a model’s most likely answer, TPS provides a more fine-grained view of model behavior. Based on the Wasserstein distance, TPS measures how much a context shifts a model’s original answer distribution towarda target distribution. Empirically, through aseries of experiments, we show that TPS captures a more nuanced notion of persuasiveness than previously proposed metrics.

Embedding-based similarity metrics between text sequences can be influenced not just by the content dimensions we most care about, but can also be biased by spurious attributes like the text’s source or language. These document confounders cause problems for many applications, but especially those that need to pool texts from different corpora. This paper shows that a debiasing algorithm that removes information about observed confounders from the encoder representations substantially reduces these biases at a minimal computational cost. Document similarity and clustering metrics improve across every embedding variant and task we evaluate—often dramatically. Interestingly, performance on out-of-distribution benchmarks is not impacted, indicating that the embeddings are not otherwise degraded.

Many constructs that characterize language, like its complexity or emotionality, have a naturally continuous semantic structure; a public speech is not just “simple” or “complex”, but exists on a continuum between extremes. Although large language models (LLMs) are an attractive tool for measuring scalar constructs, their idiosyncratic treatment of numerical outputs raises questions of how to best apply them. We address these questions with a comprehensive evaluation of LLM-based approaches to scalar construct measurement in social science. Using multiple datasets sourced from the political science literature, we evaluate four approaches: unweighted direct pointwise scoring, aggregation of pairwise comparisons, token-probability-weighted pointwise scoring, and finetuning. Our study finds that pairwise comparisons made by LLMs produce better measurements than simply prompting the LLM to directly output the scores, which suffers from bunching around arbitrary numbers. However, taking the weighted mean over the token probability of scores further improves the measurements over the two previous approaches. Finally, finetuning smaller models with as few as 1,000 training pairs can match or exceed the performance of prompted LLMs.

The widespread dissemination of toxic content on social media poses a serious threat to both online environments and public discourse, highlighting the urgent need for detoxification methods that effectively remove toxicity while preserving the original semantics.However, existing approaches often struggle to simultaneously achieve strong detoxification performance, semantic preservation, and robustness to out-of-distribution data. Moreover, they typically rely on costly, manually annotated parallel corpora while showing poor data efficiency.To address these challenges, we propose GEM, a two-stage training framework that jointly optimizes Model Generalization, Data Efficiency, and Semantic Preservation.We first perform supervised fine-tuning on a small set of high-quality, filtered parallel data to establish a strong initialization. Then, we leverage unlabeled toxic inputs and a custom-designed reward model to train the LLM using Group Relative Policy Optimization.Experimental results demonstrate that our method effectively mitigates the trade-offs faced by previous work, achieving state-of-the-art performance with improved generalization and significantly reduced dependence on annotated data. Our code is available at https://github.com/allacnobug/Detoxification-of-Text.

De-identification in the healthcare setting is an application of NLP where automated algorithms are used to remove personally identifying information of patients (and, sometimes, providers). With the recent rise of generative large language models (LLMs), there has been a corresponding rise in the number of papers that apply LLMs to de-identification. Although these approaches often report near-perfect results, significant challenges concerning reproducibility and utility of the research papers persist. This paper identifies three key limitations in the current literature: inconsistent reporting metrics hindering direct comparisons, the inadequacy of traditional classification metrics in capturing errors which LLMs may be more prone to (i.e., altering clinically relevant information), and lack of manual validation of automated metrics which aim to quantify these errors. To address these issues, we first present a survey of LLM-based de-identification research, highlighting the heterogeneity in reporting standards. Second, we evaluated a diverse set of models to quantify the extent of inappropriate removal of clinical information. Next, we conduct a manual validation of an existing evaluation metric to measure the removal of clinical information, employing clinical experts to assess their efficacy. We highlight poor performance and describe the inherent limitations of such metrics in identifying clinically significant changes. Lastly, we propose a novel methodology for the detection of clinically relevant information removal.

Large language models (LLMs) excel at complex reasoning tasks but often suffer from overconfidence and computational inefficiency due to fixed computation budgets and miscalibrated confidence estimates. We present a novel framework for computationally efficient, trustworthy reasoning under uncertainty, introducing two complementary techniques: Diversity-Aware Self-Signal Dilution (DASD) and Convergent Adaptive Weighted Sampling (CAWS). DASD operates in an unsupervised manner to dilute overconfident, semantically redundant reasoning paths, thereby producing better-calibrated internal confidence estimates. CAWS dynamically allocates computational resources at inference time by aggregating these signals and terminating computation once answer dominance and stability are achieved. Comprehensive experiments across three reasoning datasets demonstrate that our approach maintains accuracy levels while achieving over 70% reduction in inference cost, surpassing competitive baselines. Our framework provides a scalable, unsupervised solution for reliable and efficient LLM reasoning.

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Africa Health Check: Probing Cultural Bias in Medical LLMs
Charles Nimo | Shuheng Liu | Irfan Essa | Michael L. Best

Large language models (LLMs) are increasingly deployed in global healthcare, yet their outputs often reflect Western-centric training data and omit indigenous medical systems and region-specific treatments. This study investigates cultural bias in instruction-tuned medical LLMs using a curated dataset of African traditional herbal medicine. We evaluate model behavior across two complementary tasks, namely, multiple-choice questions and fill-in-the-blank completions, designed to capture both treatment preferences and responsiveness to cultural context. To quantify outcome preferences and prompt influences, we apply two complementary metrics: Cultural Bias Score (CBS) and Cultural Bias Attribution (CBA). Our results show that while prompt adaptation can reduce inherent bias and enhance cultural alignment, models vary in how responsive they are to contextual guidance. Persistent default to allopathic (Western) treatments in zero-shot scenarios suggests that many biases remain embedded in model training. These findings underscore the need for culturally informed evaluation strategies to guide the development of AI systems that equitably serve diverse global health contexts. By releasing our dataset and providing a dual-metric evaluation approach, we offer practical tools for developing more culturally aware and clinically grounded AI systems for healthcare settings in the Global South.

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Assumed Identities: Quantifying Gender Bias in Machine Translation of Gender-Ambiguous Occupational Terms
Orfeas Menis Mastromichalakis | Giorgos Filandrianos | Maria Symeonaki | Giorgos Stamou

Machine Translation (MT) systems frequently encounter gender-ambiguous occupational terms, where they must assign gender without explicit contextual cues. While individual translations in such cases may not be inherently biased, systematic patterns—such as consistently translating certain professions with specific genders—can emerge, reflecting and perpetuating societal stereotypes. This ambiguity challenges traditional instance-level single-answer evaluation approaches, as no single gold standard translation exists. To address this, we introduce GRAPE, a probability-based metric designed to evaluate gender bias by analyzing aggregated model responses. Alongside this, we present GAMBIT, a benchmarking dataset in English with gender-ambiguous occupational terms. Using GRAPE, we evaluate several MT systems and examine whether their gendered translations in Greek and French align with or diverge from societal stereotypes, real-world occupational gender distributions, and normative standards.

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REVIVING YOUR MNEME: Predicting The Side Effects of LLM Unlearning and Fine-Tuning via Sparse Model Diffing
Aly M. Kassem | Zhuan Shi | Negar Rostamzadeh | Golnoosh Farnadi

LLMs are frequently fine-tuned or unlearned to adapt to new tasks or eliminate undesirable behaviors. While existing evaluation methods assess performance after such interventions, there remains no general approach for detecting unintended side effects—such as unlearning biology content degrading performance on chemistry tasks, particularly when these effects are unpredictable or emergent. To address this issue, we introduce MNEME, Model diffiNg for Evaluating Mechanistic Effects, a framework for identifying these side effects using sparse model diffing. MNEME compares base and fine-tuned models on out-of-distribution (OOD) data (e.g., The Pile, LMSYS-Chat-1M), without access to fine-tuning data, to isolate behavioral shifts.Applied to five LLMs across three scenarios, WMDP knowledge unlearning, emergent misalignment, and benign fine-tuning, MNEME achieves up to 95% accuracy in predicting side effects, aligning with known benchmarks and requiring no custom heuristics. Our results demonstrate that sparse probing and diffing offer a scalable and automated lens into fine-tuning-induced model changes, providing practical tools for understanding and managing LLM behavior.

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ToM-SSI: Evaluating Theory of Mind in Situated Social Interactions
Matteo Bortoletto | Constantin Ruhdorfer | Andreas Bulling

Most existing Theory of Mind (ToM) benchmarks for foundation models rely on variations of the Sally-Anne test, offering only a very limited perspective on ToM and neglecting the complexity of human social interactions. To address this gap, we propose ToM-SSI: a new benchmark specifically designed to test ToM capabilities in environments rich with social interactions and spatial dynamics. While current ToM benchmarks are limited to text-only or dyadic interactions, ToM-SSI is multimodal and includes group interactions of up to four agents that communicate and move in situated environments. This unique design allows us to study, for the first time, mixed cooperative-obstructive settings and reasoning about multiple agents’ mental state in parallel, thus capturing a wider range of social cognition than existing benchmarks. Our evaluations reveal that the current models’ performance is still severely limited, especially in these new tasks, highlighting critical gaps for future research.

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Recursive Training Loops in LLMs: How training data properties modulate distribution shift in generated data?
Grgur Kovač | Jérémy Perez | Rémy Portelas | Peter Ford Dominey | Pierre-Yves Oudeyer

Large language models (LLMs) are increasingly used in the creation of online content, creating feedback loops as subsequent generations of models will be trained on this synthetic data. Such loops were shown to lead to distribution shifts - models misrepresenting the true underlying distributions of human data (also called model collapse). However, how human data properties affect such shifts remains poorly understood. In this paper, we provide the first empirical examination of the effect of such properties on the outcome of recursive training. We first confirm that using different human datasets leads to distribution shifts of different magnitudes. Through exhaustive manipulation of dataset properties combined with regression analyses, we then identify a set of properties predicting distribution shift magnitudes. Lexical diversity is found to amplify these shifts, while semantic diversity and data quality mitigate them. Furthermore, we find that these influences are highly modular: data scrapped from a given internet domain has little influence on the content generated for another domain. Finally, experiments on political bias reveal that human data properties affect whether the initial bias will be amplified or reduced. Overall, our results portray a novel view, where different parts of internet may undergo different types of distribution shift.

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Detecting LLM Hallucination Through Layer-wise Information Deficiency: Analysis of Ambiguous Prompts and Unanswerable Questions
Hazel Kim | Tom A. Lamb | Adel Bibi | Philip Torr | Yarin Gal

Large language models (LLMs) frequently generate confident yet inaccurate responses, introducing significant risks for deployment in safety-critical domains. We present a novel, test-time approach to detecting model hallucination through systematic analysis of information flow across model layers. We target cases when LLMs process inputs with ambiguous or insufficient context. Our investigation reveals that hallucination manifests as usable information deficiencies in inter-layer transmissions. While existing approaches primarily focus on final-layer output analysis, we demonstrate that tracking cross-layer information dynamics (ℒI) provides robust indicators of model reliability, accounting for both information gain and loss during computation. I improves model reliability by immediately integrating with universal LLMs without additional training or architectural modifications.

Despite Large Language Models (LLMs) demonstrating superior translation performance and long-context capabilities, evaluation methodologies remain constrained to sentence-level assessment due to dataset limitations, token number restrictions in metrics, and rigid sentence boundary requirements. We introduce SEGALE, an evaluation scheme that extends existing automatic metrics to long-document translation by treating documents as continuous text and applying sentence segmentation and alignment methods. Our approach enables previously unattainable document-level evaluation, handling translations of arbitrary length generated with document-level prompts while accounting for under-/over-translations and varied sentence boundaries. Experiments show our scheme significantly outperforms existing long-form document evaluation schemes, while being comparable to evaluations performed with groundtruth sentence alignments. Additionally, we apply our scheme to book-length texts and newly demonstrate that many open-weight LLMs fail to effectively translate documents at their reported maximum context lengths.

Medical fact-checking has become increasingly critical as more individuals seek medical information online. However, existing datasets predominantly focus on human-generated content, leaving the verification of content generated by large language models (LLMs) relatively unexplored. To address this gap, we introduce MedFact, the first evidence-based Chinese medical fact-checking dataset of LLM-generated medical content. It consists of 1,321 questions and 7,409 claims, mirroring the complexities of real-world medical scenarios. We conduct comprehensive experiments in both in-context learning (ICL) and fine-tuning settings, showcasing the capability and challenges of current LLMs on this task, accompanied by an in-depth error analysis to point out key directions for future research. Our dataset is publicly available at https://github.com/AshleyChenNLP/MedFact.

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VideoPASTA: 7K Preference Pairs That Matter for Video-LLM Alignment
Yogesh Kulkarni | Pooyan Fazli

Video-language models (Video-LLMs) excel at understanding video content but struggle with spatial relationships, temporal ordering, and cross-frame continuity. To address these limitations, we introduce VideoPASTA (Preference Alignment with Spatio-Temporal-Cross Frame Adversaries), a framework that enhances Video-LLMs through targeted preference optimization. VideoPASTA trains models to distinguish accurate video representations from carefully crafted adversarial examples that deliberately violate spatial, temporal, or cross-frame relationships. With only 7,020 preference pairs and Direct Preference Optimization, VideoPASTA enables models to learn robust representations that capture fine-grained spatial details and long-range temporal dynamics. Experiments demonstrate that VideoPASTA is model agnostic and significantly improves performance, for example, achieving gains of up to + 3.8 percentage points on LongVideoBench, +4.1 on VideoMME, and +4.0 on MVBench, when applied to various state-of-the-art Video-LLMs. These results demonstrate that targeted alignment, rather than massive pretraining or architectural modifications, effectively addresses core video-language challenges. Notably, VideoPASTA achieves these improvements without any human annotation or captioning, relying solely on 32-frame sampling. This efficiency makes our approach a scalable plug-and-play solution that seamlessly integrates with existing models while preserving their original capabilities.

Do LLMs genuinely incorporate external definitions, or do they primarily rely on their parametric knowledge? To address these questions, we conduct controlled experiments across multiple explanation benchmark datasets (general and domain-specific) and label definition conditions, including expert-curated, LLM-generated, perturbed, and swapped definitions. Our results reveal that while explicit label definitions can enhance accuracy and explainability, their integration into an LLM’s task-solving processes is neither guaranteed nor consistent, suggesting reliance on internalized representations in many cases. Models often default to their internal representations, particularly in general tasks, whereas domain-specific tasks benefit more from explicit definitions. These findings underscore the need for a deeper understanding of how LLMs process external knowledge alongside their pre-existing capabilities.

Large Language Models (LLMs) often suffer from mode collapse, repeatedly generating the same few completions even when many valid answers exist, limiting their diversity across a wide range of tasks. We introduce Group-Aware Policy Optimization (GAPO), a simple extension of the recent and popular Group Relative Policy Optimization (GRPO) that computes rewards over the group as a whole. GAPO enables learning from the group-level properties such as diversity and coverage.We demonstrate GAPO using a frequency-aware reward function that encourages uniform sampling over valid LLM completions, and show that GAPO-trained models produce valid and more diverse model responses.Beyond this setup, GAPO generalizes to open-ended prompts and improves response diversity without compromising accuracy on standard LLM benchmarks (GSM8K, MATH, HumanEval, MMLU-Pro). Our code will be made publicly available.

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Model-Based Ranking of Source Languages for Zero-Shot Cross-Lingual Transfer
Abteen Ebrahimi | Adam Wiemerslage | Katharina von der Wense

We present NN-Rank, an algorithm for ranking source languages for cross-lingual transfer, which leverages hidden representations from multilingual models and unlabeled target-language data. We experiment with two pretrained multilingual models and two tasks: part-of-speech tagging (POS) and named entity recognition (NER). We consider 51 source languages and evaluate on 56 and 72 target languages for POS and NER, respectively. When using in-domain data, NN-Rank beats state-of-the-art baselines that leverage lexical and linguistic features, with average improvements of up to 35.56 NDCG for POS and 18.14 NDCG for NER. As prior approaches can fall back to language-level features if target language data is not available, we show that NN-Rank remains competitive using only the Bible, an out-of-domain corpus available for a large number of languages. Ablations on the amount of unlabeled target data show that, for subsets consisting of as few as 25 examples, NN-Rank produces high-quality rankings which achieve 92.8% of the NDCG achieved using all available target data for ranking.

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PruneCD: Contrasting Pruned Self Model to Improve Decoding Factuality
Byeongho Yu | Changhun Lee | Jun-gyu Jin | Eunhyeok Park

To mitigate the hallucination problem in large language models, DoLa exploits early exit logits from the same model as a contrastive prior. However, we found that these early exit logits tend to be flat, low in magnitude, and fail to reflect meaningful contrasts. To address this, we propose PruneCD, a novel contrastive decoding method that constructs the amateur model via layer pruning rather than early exit. This design leads to more informative and well-aligned logits, enabling more effective contrastive decoding. Through qualitative and quantitative analyses, we demonstrate that PruneCD consistently improves factuality with minimal inference overhead, offering a robust and practical approach to mitigating hallucinations in LLMs.

Cognitive Restructuring (CR) uses multi-turn dialogue to identify and restructure one’s negative thoughts, arising from mental health issues, into more helpful and positive ones. Clinician shortage and stigma urge the development of human-LLM interactive psychotherapy for CR. Yet, effectively implementing CR is hindered by entrenched cognitive distortions, emotional resistance, and individual differences, which existing works have not overcome. To bridge this gap, we propose CRDial, a novel framework that structures CR as theory-grounded multi-stage multi-turn dialogue, integrating multi-aspect supportive strategies for emotional management and a multi-channel loop mechanism to account for diverse individual distortions. With CRDial, we distill Crisp, a large-scale and high-quality bilingual dialogue dataset, from LLM. We then train Crispers, Crisp-based conversational LLMs for CR, at 7B and 14B scales. Extensive human studies show the superiority of Crispers in pointwise, pairwise, and intervention evaluations.

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AccessEval: Benchmarking Disability Bias in Large Language Models
Srikant Panda | Amit Agarwal | Hitesh Laxmichand Patel

Large Language Models (LLMs) are increasingly deployed across diverse domains but often exhibit disparities in how they handle real life queries. To systematically investigate these effects with various disability context, we introduce AccessEval, a large-scale benchmark evaluating total 21 close & open source LLMs across six real-world domains and nine disability types using paired Neutral and Disability-Aware Queries. We evaluated model outputs with metrics for factual accuracy, sentiment, and social perception.Our analysis reveals that responses to disability-aware queries tend to have higher factual error, more negative tone, and increased stereotyping with social perception compared to neutral queries. These effects show notable variation by domain and disability type. Disabilities affecting hearing, speech and mobility are disproportionately impacted. These disparities reveal persistent forms of ableism, highlighting the need for more comprehensive and nuanced assessment.We further argue that framing bias in terms of model performance within real-world decision making helps to better link model behaviors to the potential harms users may face. This approach guides the development of more effective and tailored fairness interventions. AccessEval, therefore, serves as a crucial tool for advancing equitable and inclusive language technologies.

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The Impact of Language Mixing on Bilingual LLM Reasoning
Yihao Li | Jiayi Xin | Miranda Muqing Miao | Qi Long | Lyle Ungar

Proficient multilingual speakers often intentionally switch languages in the middle of a conversation. Similarly, recent reasoning-focused bilingual large language models (LLMs) with strong capabilities in both languages exhibit **language mixing**—alternating languages within their chain of thought. Discouraging this behavior in DeepSeek-R1 was found to degrade accuracy, suggesting that language mixing may benefit reasoning. In this work, we study language switching in Chinese-English bilingual reasoning models. We identify reinforcement learning with verifiable rewards (RLVR) as the critical training stage that leads to language mixing. We show that language mixing can enhance reasoning: enforcing monolingual decoding reduces accuracy by 5.6 percentage points on MATH500. Additionally, a lightweight probe can be trained to predict whether a potential language switch would benefit or harm reasoning, and when used to guide decoding, increases accuracy by 2.92 percentage points. Our findings suggest that language mixing is not merely a byproduct of multilingual training, but is a *strategic reasoning behavior*.

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VISaGE: Understanding Visual Generics and Exceptions
Stella Frank | Emily Allaway

While Vision Language Models (VLMs) learn conceptual representations, in the form of generalized knowledge, during training, they are typically used to analyze individual instances. When evaluation instances are atypical, this paradigm results in tension between two priors in the model. The first is a pragmatic prior that the textual and visual input are both relevant, arising from VLM finetuning on congruent inputs; the second is a semantic prior that the conceptual representation is generally true for instances of the category. In order to understand how VLMs trade off these priors, we introduce a new evaluation dataset, VISaGE, consisting of both typical and exceptional images. In carefully balanced experiments, we show that conceptual understanding degrades when the assumption of congruency underlying the pragmatic prior is violated with incongruent images. This effect is stronger than the effect of the semantic prior when querying about individual instances

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Stronger Baselines for Retrieval-Augmented Generation with Long-Context Language Models
Alex Laitenberger | Christopher D Manning | Nelson F. Liu

With the rise of long-context language models (LMs) capable of processing tens of thousands of tokens in a single context window, do multi-stage retrieval-augmented generation (RAG) pipelines still offer measurable benefits over simpler, single-stage approaches? To assess this question, we conduct a controlled evaluation for QA tasks under systematically scaled token budgets, comparing two recent multi-stage pipelines, ReadAgent and RAPTOR, against three baselines, including DOS RAG (Document’s Original Structure RAG), a simple retrieve-then-read method that preserves original passage order. Despite its straightforward design, DOS RAG consistently matches or outperforms more intricate methods on multiple long-context QA benchmarks. We trace this strength to a combination of maintaining source fidelity and document structure, prioritizing recall within effective context windows, and favoring simplicity over added pipeline complexity. We recommend establishing DOS RAG as a simple yet strong baseline for future RAG evaluations, paired with state-of-the-art embedding and language models, and benchmarked under matched token budgets, to ensure that added pipeline complexity is justified by clear performance gains as models continue to improve.

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Discursive Circuits: How Do Language Models Understand Discourse Relations?
Yisong Miao | Min-Yen Kan

Which components in transformer language models are responsible for discourse understanding? We hypothesize that sparse computational graphs, termed as discursive circuits, control how models process discourse relations. Unlike simpler tasks, discourse relations involve longer spans and complex reasoning. To make circuit discovery feasible, we introduce a task called Completion under Discourse Relation (CuDR), where a model completes a discourse given a specified relation. To support this task, we construct a corpus of minimal contrastive pairs tailored for activation patching in circuit discovery. Experiments show that sparse circuits (≈0.2% of a full GPT-2 model) recover discourse understanding in the English PDTB-based CuDR task. These circuits generalize well to unseen discourse frameworks such as RST and SDRT. Further analysis shows lower layers capture linguistic features such as lexical semantics and coreference, while upper layers encode discourse-level abstractions. Feature utility is consistent across frameworks (e.g., coreference supports Expansion-like relations).

Large language models (LLMs) have shown promise in robotic procedural planning, yet their human-centric reasoning often omits the low-level, grounded details needed for robotic execution. Vision-language models (VLMs) offer a path toward more perceptually grounded plans, but current methods either rely on expensive, large-scale models or are constrained to narrow simulation settings. We introduce SelfReVision, a lightweight and scalable self-improvement framework for vision-language procedural planning. SelfReVision enables small VLMs to iteratively critique, revise, and verify their own plans, without external supervision or teacher models, drawing inspiration from chain-of-thought prompting and self-instruct paradigms. Through this self-distillation loop, models generate higher-quality, execution-ready plans that can be used both at inference and for continued fine-tuning. Using models varying from 3B to 72B, our results show that SelfReVision not only boosts performance over weak base VLMs but also outperforms models 100X the size, yielding improved control in downstream embodied tasks.

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ThinkSLM: Towards Reasoning in Small Language Models
Gaurav Srivastava | Shuxiang Cao | Xuan Wang

Reasoning has long been viewed as an emergent property of large language models (LLMs). However, recent studies challenge this assumption, showing that small language models (SLMs) can also achieve competitive reasoning performance. This paper introduces ThinkSLM, the first extensive benchmark to systematically evaluate and study the reasoning abilities of SLMs trained from scratch or derived from LLMs through quantization, pruning, and distillation. We first establish a reliable evaluation criterion comparing available methods and LLM judges against our human evaluations. Then we present a study evaluating 72 diverse SLMs from six major model families across 17 reasoning benchmarks. We repeat all our experiments three times to ensure a robust assessment. Our findings show that: 1) reasoning ability in SLMs is strongly influenced by training methods and data quality rather than solely model scale; 2) quantization preserves reasoning capability, while pruning significantly disrupts it; 3) larger models consistently exhibit higher robustness against adversarial perturbations and intermediate reasoning, but certain smaller models closely match or exceed the larger models’ performance. Our findings challenge the assumption that scaling is the only way to achieve strong reasoning. Instead, we foresee a future where SLMs with strong reasoning capabilities can be developed through structured training or post-training compression. Our ThinkSLM Leaderboard is publicly available at: https://ctrl-gaurav.github.io/thinkslm.github.io/.

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MAgICoRe: Multi-Agent, Iterative, Coarse-to-Fine Refinement for Reasoning
Justin Chen | Archiki Prasad | Swarnadeep Saha | Elias Stengel-Eskin | Mohit Bansal

Large language model (LLM) reasoning can be improved by scaling test-time compute with aggregation, i.e., generating multiple samples and aggregating over them. While improving performance, this strategy often reaches a saturation point beyond which additional compute provides no return. Refinement offers an alternative by using model-generated feedback to improve answer quality. However, refinement faces three key challenges: (1) Excessive refinement: Uniformly refining all instances can cause over-correction and reduce overall performance. (2) Inability to localize and address errors: LLMs struggle to identify and correct their own mistakes. (3) Insufficient refinement: Stopping refinement too soon could leave errors unaddressed. To tackle these issues, we propose MAgICoRe, a framework for Multi-Agent Iteration for Coarse-to-fine Refinement. MAgICoRe mitigates excessive refinement by categorizing problems as easy or hard, solving easy problems with coarse-grained aggregation, and solving the hard ones with fine-grained multi-agent refinement. To better localize errors, we incorporate external step-wise reward model scores, and to ensure sufficient refinement, we iteratively refine the solutions using a multi-agent setup. We evaluate MAgICoRe on Llama-3-8B and GPT- 3.5 and show its effectiveness across seven reasoning datasets. One iteration of MAgICoRe beats Self-Consistency by 3.4%, Best-of-k by 3.2%, and Self-Refine by 4.0% even when these baselines use k = 120, and MAgICoRe uses less than 50% of the compute.

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Batched Self-Consistency Improves LLM Relevance Assessment and Ranking
Anton Korikov | Pan Du | Scott Sanner | Navid Rekabsaz

LLM query-passage relevance assessment is typically studied using a one-by-one pointwise (PW) strategy where each LLM call judges one passage at a time. However, this strategy requires as many LLM calls as there are passages while also preventing information sharing between passages. We thus hypothesize that batched PW methods, which evaluate multiple passages per LLM call, can improve not only efficiency but also judgment quality — by enabling content from multiple passages to be seen jointly. Moreover, batched PW methods may be better suited to harness the test-time scaling benefits of self-consistency — the ensembling technique of repeating (potentially perturbed) LLM tasks in parallel and aggregating results — since batching can naturally enable prompt diversification through varied batch permutations and compositions to create more robust ensembles. We evaluate several batched PW methods against one-by-one PW and listwise ranking baselines on LLM relevance assessment and ranking tasks, using three passage retrieval datasets and GPT-4o, Claude Sonnet 3, and Amazon Nova Pro. We show that batching can greatly amplify self-consistency benefits, making batched PW methods achieve the best performance while often reducing latency by an order of magnitude or more compared to one-by-one PW methods. For instance, on legal search, batched PW ranking with GPT-4o improves from 43.8% to 51.3% NDCG@10 when using 1 vs. 15 self-consistency calls, compared to one-by-one PW ranking improving from 44.9% to 46.8% and being 15.3x slower.

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SemCSE: Semantic Contrastive Sentence Embeddings Using LLM-Generated Summaries For Scientific Abstracts
Marc Felix Brinner | Sina Zarrieß

We introduce SemCSE, an unsupervised method for learning semantic embeddings of scientific texts. Building on recent advances in contrastive learning for text embeddings, our approach leverages LLM-generated summaries of scientific abstracts to train a model that positions semantically related summaries closer together in the embedding space. This resulting objective ensures that the model captures the true semantic content of a text, in contrast to traditional citation-based approaches that do not necessarily reflect semantic similarity. To validate this, we propose a novel benchmark designed to assess a model’s ability to understand and encode the semantic content of scientific texts, demonstrating that our method enforces a stronger semantic separation within the embedding space. Additionally, we evaluate SemCSE on the comprehensive SciRepEval benchmark for scientific text embeddings, where it achieves state-of-the-art performance among models of its size, thus highlighting the benefits of a semantically focused training approach.

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Controlled Generation for Private Synthetic Text
Zihao Zhao | Anjalie Field

Text anonymization is essential for responsibly developing and deploying AI in high-stakes domains such as healthcare, social services, and law. In this work, we propose a novel methodology for privacy-preserving synthetic text generation that leverages the principles of de-identification and the Hiding In Plain Sight (HIPS) theory. Our approach introduces entity-aware control codes to guide controllable generation using either in-context learning (ICL) or prefix tuning. The ICL variant ensures privacy levels consistent with the underlying de-identification system, while the prefix tuning variant incorporates a custom masking strategy and loss function to support scalable, high-quality generation. Experiments on legal and clinical datasets demonstrate that our method achieves a strong balance between privacy protection and utility, offering a practical and effective solution for synthetic text generation in sensitive domains.

Large language models (LLMs) hold promise for therapeutic interventions, yet most existing datasets rely solely on text, overlooking non-verbal emotional cues essential to real-world therapy. To address this, we introduce a multimodal dataset of 1,441 publicly sourced therapy session videos containing both dialogue and non-verbal signals such as facial expressions and vocal tone. Inspired by Hochschild’s concept of emotional labor, we propose a computational formulation of emotional dissonance—the mismatch between facial and vocal emotion—and use it to guide emotionally aware prompting. Our experiments show that integrating multimodal cues, especially dissonance, improves the quality of generated interventions. We also find that LLM-based evaluators misalign with expert assessments in this domain, highlighting the need for human-centered evaluation. Data and code will be released to support future research.

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From Shortcuts to Balance: Attribution Analysis of Speech-Text Feature Utilization in Distinguishing Original from Machine-Translated Texts
Yongjian Chen | Antonio Toral

Neural text-based models for detecting machine-translated texts can rely on named entities (NEs) as classification shortcuts. While masking NEs encourages learning genuine translationese signals, it degrades the classification performance. Incorporating speech features compensates for this loss, but their interaction with NE reliance requires careful investigation. Through systematic attribution analysis across modalities, we find that bimodal integration leads to more balanced feature utilization, reducing the reliance on NEs in text while moderating overemphasis attribution patterns in speech features.

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DEBATE, TRAIN, EVOLVE: Self‐Evolution of Language Model Reasoning
Gaurav Srivastava | Zhenyu Bi | Meng Lu | Xuan Wang

Large language models (LLMs) have improved significantly in their reasoning through extensive training on massive datasets. However, relying solely on additional data for improvement is becoming increasingly impractical, highlighting the need for models to autonomously enhance their reasoning without external supervision. In this paper, we propose Debate, Train, Evolve (DTE), a novel ground truth-free training framework that uses multi-agent debate traces to evolve a single language model. We also introduce a new prompting strategy Reflect-Critique-Refine, to improve debate quality by explicitly instructing agents to critique and refine their reasoning. Extensive evaluations on seven reasoning benchmarks with six open-weight models show that our DTE framework achieve substantial improvements, with an average accuracy gain of 8.92% on the challenging GSM-PLUS dataset. Furthermore, we observe strong cross-domain generalization, with an average accuracy gain of 5.8% on all other benchmarks, suggesting that our method captures general reasoning capabilities. Our framework code and trained models are publicly available at https://github.com/ctrl-gaurav/Debate-Train-Evolve.

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From Chat Logs to Collective Insights: Aggregative Question Answering
Wentao Zhang | Woojeong Kim | Yuntian Deng

Conversational agents powered by large language models (LLMs) are rapidly becoming integral to our daily interactions, generating unprecedented amounts of conversational data. Such datasets offer a powerful lens into societal interests, trending topics, and collective concerns. Yet existing approaches typically treat these interactions as independent, missing critical insights that could emerge from aggregating and reasoning across large-scale conversation logs. In this paper, we introduce Aggregative Question Answering, a novel task requiring models to reason explicitly over thousands of user-chatbot interactions to answer aggregational queries, such as identifying emerging concerns among specific demographics. To enable research in this direction, we construct a benchmark, WildChat-AQA, comprising 6,027 aggregative questions derived from 182,330 real-world chatbot conversations. Experiments show that existing methods either struggle to reason effectively or incur prohibitive computational costs, underscoring the need for new approaches capable of extracting collective insights from large-scale conversational data.

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A Text-Based Recommender System that Leverages Explicit Affective State Preferences
Tonmoy Hasan | Razvan Bunescu

The affective attitude of liking a recommended item reflects just one category in a wide spectrum of affective phenomena that also includes emotions such as entranced or intrigued, moods such as cheerful or buoyant, as well as more fine-grained affective states, such as “pleasantly surprised by the conclusion”. In this paper, we introduce a novel recommendation task that can leverage a virtually unbounded range of affective states sought explicitly by the user in order to identify items that, upon consumption, are likely to induce those affective states. Correspondingly, we create a large dataset of user preferences containing expressions of fine-grained affective states that are mined from book reviews, and propose ACRec, a Transformer-based architecture that leverages such affective expressions as input. We then use the resulting dataset of affective states preferences, together with the linked users and their histories of book readings, ratings, and reviews, to train and evaluate multiple recommendation models on the task of matching recommended items with affective preferences. Experimental comparisons with a range of state-of-the-art baselines demonstrate ACRec’s superior ability to leverage explicit affective preferences.

Language Models (LMs) are typically tuned with human preferences to produce helpful responses, but the impact of preference tuning on the ability to handle culturally diverse queries remains understudied. In this paper, we systematically analyze how native human cultural preferences can be incorporated into the preference learning process to train more culturally aware LMs. We introduce CARE, a multilingual resource containing 3,490 culturally specific questions and 31.7k responses with human judgments. We demonstrate how a modest amount of high-quality native preferences improves cultural awareness across various LMs, outperforming larger generic preference data. Our analyses reveal that models with stronger initial cultural performance benefit more from alignment, leading to gaps among models developed in different regions with varying access to culturally relevant data. CARE is publicly available at https://github.com/Guochry/CARE.

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Multilingual Dialogue Generation and Localization with Dialogue Act Scripting
Justin Vasselli | Eunike Andriani Kardinata | Yusuke Sakai | Taro Watanabe

Non-English dialogue datasets are scarce, and models are often trained or evaluated on translations of English-language dialogues, an approach which can introduce artifacts that reduce their naturalness and cultural appropriateness. This work proposes Dialogue Act Script (DAS), a structured framework for encoding, localizing, and generating multilingual dialogues from abstract intent representations. Rather than translating dialogue utterances directly, DAS enables the generation of new dialogues in the target language that are culturally and contextually appropriate. By using structured dialogue act representations, DAS supports flexible localization across languages, mitigating translationese and enabling more fluent, naturalistic conversations. Human evaluations across Italian, German, and Chinese show that DAS-generated dialogues consistently outperform those produced by both machine and human translators on measures of cultural relevance, coherence, and situational appropriateness.

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SUE: Sparsity-based Uncertainty Estimation via Sparse Dictionary Learning
Tamás Ficsor | Gábor Berend

The growing deployment of deep learning models in real-world applications necessitates not only high predictive accuracy, but also mechanism to identify unreliable predictions, especially in high-stakes scenarios where decision risk must be minimized. Existing methods estimate uncertainty by leveraging predictive confidence (e.g., Softmax Response), structural characteristics of representation space (e.g., Mahalanobis distance), or stochastic variation in model outputs (e.g., Bayesian inference techniques such as Monte Carlo Dropout). In this work, we propose a novel uncertainty estimation (UE) framework based on sparse dictionary learning by identifying dictionary atoms associated with misclassified samples. We leverage pointwise mutual information (PMI) to quantify the association between sparse features and predictive failure. Our method – Sparsity-based Uncertainty Estimation (SUE) – is computationally efficient, offers interpretability via atom-level analysis of the dictionary, has no assumption about the class distribution (unlike Mahalanobis distance). We evaluated SUE on several NLU benchmarks (GLUE and ANLI tasks) and sentiment analysis benchmarks (Twitter, ParaDetox, and Jigsaw). In general, SUE outperforms or matches the performance of other methods. SUE performs particularly well when there is considerable uncertainty in the model, i.e., when the model lacks high precision.

Fill-in-the-Middle (FIM), or infilling, has become integral to code language models, enabling generation of missing code given both left and right contexts. However, the current FIM training paradigm which performs next-token prediction (NTP) over reordered sequence often leads to models struggling to generate content that aligns well with the surrounding context. We hypothesize that NTP alone is insufficient for models to learn effective planning conditioned on the distant right context, a critical factor for successful code infilling. To overcome this, we propose Horizon-Length Prediction (HLP), a novel training objective that teaches models to predict the number of remaining middle tokens at each step. HLP advances FIM with lookahead planning, enabling models to inherently learn infilling boundaries for arbitrary left and right contexts without relying on dataset-specific post-processing. Our evaluation across different model families and sizes shows that HLP significantly improves FIM performance by up to 24% relatively on diverse benchmarks, across file-level and repository-level. Furthermore, the enhanced planning capability gained through HLP boosts model performance on code reasoning. Importantly, HLP incurs negligible training overhead and no additional inference cost, ensuring its practicality for real-world scenarios.

Large Language Models (LLMs) demonstrate impressive general knowledge and reasoning abilities, yet their evaluation has predominantly focused on global or anglocentric subjects, often neglecting low-resource languages and culturally specific content. While recent multilingual benchmarks attempt to bridge this gap, many rely on automatic translation, which can introduce errors and misrepresent the original cultural context. To address this, we introduce SinhalaMMLU, the first multiple-choice question answering benchmark designed specifically for Sinhala, a low-resource language. The dataset includes over 7,000 questions spanning secondary to collegiate education levels, aligned with the Sri Lankan national curriculum, and covers six domains and 30 subjects, encompassing both general academic topics and culturally grounded knowledge. We evaluate 26 LLMs on SinhalaMMLU and observe that, while Claude 3.5 sonnet and GPT-4o achieve the highest average accuracies at 67% and 62% respectively, overall model performance remains limited. In particular, models struggle in culturally rich domains such as the Humanities, revealing substantial room for improvement in adapting LLMs to low-resource and culturally specific contexts.

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OG-RAG: Ontology-grounded retrieval-augmented generation for large language models
Kartik Sharma | Peeyush Kumar | Yunqing Li

While LLMs are widely used for generic tasks like question answering and search, they struggle to adapt to specialized knowledge, such as industrial workflows in healthcare, legal, and agricultural sectors, as well as knowledge-driven tasks such as news journalism, investigative research, and consulting without expensive fine-tuning or sub-optimal retrieval methods. Existing retrieval-augmented models, such as RAG, offer improvements but fail to account for structured domain knowledge, leading to suboptimal context generation. Ontologies, which conceptually organize domain knowledge by defining entities and their interrelationships, offer a structured representation to address this gap. This paper presents OG-RAG, an Ontology-Grounded Retrieval Augmented Generation method designed to enhance LLM-generated responses by anchoring retrieval processes in domain-specific ontologies. OG-RAG constructs a hypergraph representation of domain documents, where each hyperedge encapsulates clusters of factual knowledge grounded using domain-specific ontology and retrieves a minimal set of hyperedges for a given query using an optimization algorithm. Our evaluations demonstrate that OG-RAG increases the recall of accurate facts by 55% and improves response correctness by 40% across four different LLMs. Additionally, OG-RAG enables 30% faster attribution of responses to context and boosts fact-based reasoning accuracy by 27% compared to baseline methods. We release the code at [https://github.com/microsoft/ograg2](https://github.com/microsoft/ograg2).

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Convergence and Divergence of Language Models under Different Random Seeds
Finlay Fehlauer | Kyle Mahowald | Tiago Pimentel

In this paper, we investigate the convergence of language models (LMs) trained under different random seeds, measuring convergence as the expected per-token Kullback–Leibler (KL) divergence across seeds. By comparing LM convergence as a function of model size and training checkpoint, we identify a four-phase convergence pattern: (i) an initial uniform phase, (ii) a sharp-convergence phase, (iii) a sharp-divergence phase, and (iv) a slow-reconvergence phase. Further, we observe that larger models reconverge faster in later training stages, while smaller models never actually reconverge; these results suggest that a certain model size may be necessary to learn stable distributions. Restricting our analysis to specific token frequencies, or part-of-speech (PoS) tags further reveals that convergence is uneven across linguistic categories: frequent tokens and function words converge faster and more reliably than their counterparts (infrequent tokens and content words). Overall, our findings highlight factors that influence the stability of the learned distributions in model training.

We present a statistical framework for modeling and controlling large language model (LLM) response lengths using extreme value theory. Analyzing 14,301 GPT-4o responses across temperature and prompting conditions, with cross-validation on Qwen and DeepSeek architectures, we demonstrate that verbosity follows Weibull-type generalized extreme value (GEV) distributions with heavier tails under stochastic generation. Our key contributions include: (1) development of a novel GEV-generalized Pareto (GPD) hybrid model that improves tail fit (R²_CDF=0.9993 vs standalone GEV’s 0.998) while maintaining architectural generalizability; (2) quantitative characterization of prompt anchoring effects across models, showing reduced dispersion but increased outliers under randomization; and (3) identification of temperature-dependent response patterns that persist across architectures, with higher temperatures amplifying length variability while preserving extreme-value mechanisms. The hybrid model’s threshold selection method enables precise verbosity control in production systems regardless of model choice. While validated on multiple architectures, generalizability to emerging model families requires further study.

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Language Models Identify Ambiguities and Exploit Loopholes
Jio Choi | Mohit Bansal | Elias Stengel-Eskin

Studying the responses of large language models (LLMs) to loopholes presents a two-fold opportunity. First, it affords us a lens through which to examine ambiguity and pragmatics in LLMs, since exploiting a loophole requires identifying ambiguity and performing sophisticated pragmatic reasoning. Second, loopholes pose an interesting and novel alignment problem where the model is presented with conflicting goals and can exploit ambiguities to its own advantage. To address these questions, we design scenarios where LLMs are given a goal and an ambiguous user instruction in conflict with the goal, with scenarios covering scalar implicature, structural ambiguities, and power dynamics. We then measure different models’ abilities to exploit loopholes to satisfy their given goals as opposed to the goals of the user. We find that both closed-source and stronger open-source models can identify ambiguities and exploit their resulting loopholes, presenting a potential AI safety risk. Our analysis indicates that models which exploit loopholes explicitly identify and reason about both ambiguity and conflicting goals.

Large language models (LLMs) have shown remarkable performance in general translation tasks. However, the increasing demand for high-quality translations that are not only adequate but also fluent and elegant. To assess the extent to which current LLMs can meet these demands, we introduce a suitable benchmark (PoetMT) for translating classical Chinese poetry into English. This task requires not only adequacy in translating culturally and historically significant content but also a strict adherence to linguistic fluency and poetic elegance. Our study reveals that existing LLMs fall short of this task. To address these issues, we propose RAT, a Retrieval-Augmented machine Translation method that enhances the translation process by incorporating knowledge related to classical poetry. Additionally, we propose an automatic evaluation metric based on GPT-4, which better assesses translation quality in terms of adequacy, fluency, and elegance, overcoming the limitations of traditional metrics.

The rapid advancements of Large Language models (LLMs) necessitate robust benchmarks. In this paper, we present AraEval, a pioneering and comprehensive evaluation suite specifically developed to assess the advanced knowledge, reasoning, truthfulness, and instruction- following capabilities of foundation models in the Arabic context. AraEval includes a diverse set of evaluation tasks that test various dimensions of knowledge and reasoning, with a total of 24,378 samples. These tasks cover areas such as linguistic understanding, factual recall, logical inference, commonsense reasoning, mathematical problem-solving, and domain-specific expertise, ensuring that the evaluation goes beyond basic language comprehension. It covers multiple domains of knowledge, such as science, history, religion, and literature, ensuring that the LLMs are tested on a broad spectrum of topics relevant to Arabic-speaking contexts. AraEval is designed to facilitate comparisons across different foundation models, enabling LLM developers and users to benchmark perfor- mance effectively. In addition, it provides diagnostic insights to identify specific areas where models excel or struggle, guiding further development. AraEval datasets can be found at https://huggingface.co/collections/humain-ai/araeval-datasets-687760e04b12a7afb429a4a0.

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QUIDS: Query Intent Description for Exploratory Search via Dual Space Modeling
Yumeng Wang | Xiuying Chen | Suzan Verberne

In exploratory search, users often submit vague queries to investigate unfamiliar topics, but receive limited feedback about how the search engine understood their input. This leads to a self-reinforcing cycle of mismatched results and trial-and-error reformulation. To address this, we study the task of generating user-facing natural language query intent descriptions that surface what the system likely inferred the query to mean, based on post-retrieval evidence. We propose QUIDS, a method that leverages dual-space contrastive learning to isolate intent-relevant information while suppressing irrelevant content. QUIDS combines a dual-encoder representation space with a disentangling decoder that works together to produce concise and accurate intent descriptions. Enhanced by intent-driven hard negative sampling, the model significantly outperforms state-of-the-art baselines across ROUGE, BERTScore, and human/LLM evaluations. Our qualitative analysis confirms QUIDS’ effectiveness in generating accurate intent descriptions for exploratory search. Our work contributes to improving the interaction between users and search engines by providing feedback to the user in exploratory search settings.

Since the advent of large language models (LLMs), prompt engineering has been a crucial step for eliciting desired responses for various Natural Language Processing (NLP) tasks. However, prompt engineering remains an impediment for end users due to rapid advances in models, tasks, and associated best practices. To mitigate this, Automatic Prompt Optimization (APO) techniques have recently emerged that use various automated techniques to help improve the performance of LLMs on various tasks. In this paper, we present a comprehensive survey summarizing the current progress and remaining challenges in this field. We provide a formal definition of APO, a 5-part unifying framework, and then proceed to rigorously categorize all relevant works based on their salient features therein. We hope to spur further research guided by our framework.

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Threading the Needle: Reweaving Chain-of-Thought Reasoning to Explain Human Label Variation
Beiduo Chen | Yang Janet Liu | Anna Korhonen | Barbara Plank

The recent rise of reasoning-tuned Large Language Models (LLMs)—which generate chains of thought (CoTs) before giving the final answer—has attracted significant attention and offers new opportunities for gaining insights into human label variation, which refers to plausible differences in how multiple annotators label the same data instance.Prior work has shown that LLM-generated explanations can help align model predictions with human label distributions, but typically adopt a *reverse* paradigm: producing explanations based on given answers. In contrast, CoTs provide a *forward* reasoning path that may implicitly embed rationales for each answer option, before generating the answers. We thus propose a novel LLM-based pipeline enriched with linguistically-grounded discourse segmenters to extract supporting and opposing statements for each answer option from CoTs with improved accuracy. We also propose a rank-based HLV evaluation framework that prioritizes the ranking of answers over exact scores, which instead favor direct comparison of label distributions.Our method outperforms a direct generation method as well as baselines on three datasets, and shows better alignment of ranking methods with humans, highlighting the effectiveness of our approach.

Large language model (LLM) agents have evolved to intelligently process information, make decisions, and interact with users or tools. A key capability is the integration of long-term memory capabilities, enabling these agents to draw upon historical interactions and knowledge. However, the growing memory size and need for semantic structuring pose significant challenges. In this work, we propose an autonomous memory augmentation approach, MemInsight, to enhance semantic data representation and retrieval mechanisms. By leveraging autonomous augmentation to historical interactions, LLM agents are shown to deliver more accurate and contextualized responses. We empirically validate the efficacy of our proposed approach in three task scenarios; conversational recommendation, question answering and event summarization. On the LLM-REDIAL dataset, MemInsight boosts persuasiveness of recommendations by up to 14%. Moreover, it outperforms a RAG baseline by 34% in recall for LoCoMo retrieval. Our empirical results show the potential of MemInsight to enhance the contextual performance of LLM agents across multiple tasks.

Multi-modal entity alignment (MMEA) aims to identify equivalent entities between two multimodal knowledge graphs (MMKGs). However, the intrinsic noise within modalities, such as the inconsistency in visual modality and redundant attributes, has not been thoroughly investigated. Excessive noise not only weakens semantic representation but also increases the risk of overfitting in attention-based fusion methods. To address this, we propose LGEA, a novel LLMguided MMEA framework that prioritizes noise reduction before fusion. Specifically, LGEA introduces two key strategies: (1) fine-grained visual filtering to remove irrelevant images at the semantic level, and (2) contextual summarization of attribute information to enhance entity semantics. To our knowledge, we are the first work to apply LLMs for both visual filtering and attribute-level semantic enhancement in MMEA. Experiments on multiple benchmarks, including the noisy FB YG dataset, show that LGEA sets a new state-of-the-art (SOTA) in robust multi-modal alignment, highlighting the potential of noise-aware strategies as a promising direction for future MMEA research.

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ImpliRet: Benchmarking the Implicit Fact Retrieval Challenge
Zeinab Sadat Taghavi | Ali Modarressi | Yunpu Ma | Hinrich Schuetze

Retrieval systems are central to many NLP pipelines, but often rely on surface-level cues such as keyword overlap and lexical semantic similarity. To evaluate retrieval beyond these shallow signals, recent benchmarks introduce reasoning-heavy queries; however, they primarily shift the burden to query-side processing techniques – like prompting or multi-hop retrieval – that can help resolve complexity. In contrast, we present Impliret, a benchmark that shifts the reasoning challenge to document-side processing: The queries are simple, but relevance depends on facts stated implicitly in documents through temporal (e.g., resolving “two days ago”), arithmetic, and world knowledge relationships. We evaluate a range of sparse and dense retrievers, all of which struggle in this setting: the best nDCG@10 is only 14.91%. We also test whether long-context models can overcome this limitation. But even with a short context of only thirty documents, including the positive document, GPT-o4-mini scores only 55.54%, showing that document-side reasoning remains a challenge. Our codes are available at github.com/ZeinabTaghavi/IMPLIRET.

Showing incorrect answers to Large Language Models (LLMs) is a popular strategy to improve their performance in reasoning-intensive tasks. It is widely assumed that, in order to be helpful, the incorrect answers must be accompanied by comprehensive rationales, explicitly detailing where the mistakes are and how to correct them. However, in this work we present a counterintuitive finding: we observe that LLMs perform *better* in math reasoning tasks when these rationales are eliminated from the context and models are left to infer on their own what makes an incorrect answer flawed. This approach also substantially outperforms chain-of-thought prompting in our evaluations. These results are consistent across LLMs of different sizes and varying reasoning abilities. To gain an understanding of *why* LLMs learn from mistakes more effectively without explicit corrective rationales, we perform a thorough analysis, investigating changes in context length and answer diversity between different prompting strategies, and their effect on performance. We also examine evidence of overfitting to the in-context rationales when these are provided, and study the extent to which LLMs are able to autonomously infer high-quality corrective rationales given only incorrect answers as input. We find evidence that, while incorrect answers are more beneficial for LLM learning than additional diverse *correct* answers, explicit corrective rationales over-constrain the model, thus limiting those benefits.

Identifying human morals and values embedded in language is essential to empirical studies of communication. However, researchers often face substantial difficulty navigating the diversity of theoretical frameworks and data available for their analysis. Here, we contribute MoVa, a well-documented suite of resources for generalizable classification of human morals and values, consisting of (1) 16 labeled datasets and benchmarking results from four theoretically-grounded frameworks; (2) a lightweight LLM prompting strategy that outperforms fine-tuned models across multiple domains and frameworks; and (3) a new application that helps evaluate psychological surveys. In practice, we specifically recommend a classification strategy, all@once, that scores all related concepts simultaneously, resembling the well-known multi-label classifier chain. The data and methods in MoVa can facilitate many fine-grained interpretations of human and machine communication, with potential implications for the alignment of machine behavior.

Graphical User Interface (GUI) action grounding, mapping language instructions to actionable elements on GUI screens, is important for assisting users in interactive tutorials, task automation, accessibility support, etc. Most recent works of GUI action grounding use large GUI datasets to fine-tune Multimodal Large Language Models (MLLMs). However, the fine-tuning data is inherently limited to specific GUI environments, leading to significant performance degradation in novel environments due to the generalization challenges in the GUI domain. Therefore, we argue that GUI action grounding models should be further aligned with novel environments before deployment to optimize their performance. To address this, we first propose GUI-Bee, an MLLM-based autonomous agent, to collect high-quality, environment-specific data through exploration and then continuously fine-tune GUI grounding models with the collected data. To ensure the GUI action grounding models generalize to various screens within the target novel environment after the continuous fine-tuning, we equip GUI-Bee with a novel Q-value-Incentive In-Context Reinforcement Learning (Q-ICRL) algorithm that optimizes exploration efficiency and exploration data quality. In the experiment, we introduce NovelScreenSpot to test how well the data can help align GUI action grounding models to novel environments. Furthermore, we conduct an ablation study to validate the Q-ICRL method in enhancing the efficiency of GUI-Bee.

Large language model (LLM) role-playing has gained widespread attention. Authentic character knowledge is crucial for constructing realistic LLM role-playing agents. However, existing works usually overlook the exploration of LLMs’ ability to detect characters’ known knowledge errors (KKE) and unknown knowledge errors (UKE) while playing roles, which would lead to low-quality automatic construction of character trainable corpus. In this paper, we propose RoleKE-Bench to evaluate LLMs’ ability to detect errors in KKE and UKE. The results indicate that even the latest LLMs struggle to detect these two types of errors effectively, especially when it comes to familiar knowledge. We experimented with various reasoning strategies and propose an agent-based reasoning method, Self-Recollection and Self-Doubt (S²RD), to explore further the potential for improving error detection capabilities.

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Taking Notes Brings Focus? Towards Multi-Turn Multimodal Dialogue Learning
Jiazheng Liu | Sipeng Zheng | Börje F. Karlsson | Zongqing Lu

Multimodal large language models (MLLMs), built on large-scale pre-trained vision towers and language models, have shown great capabilities in multimodal understanding. However, most existing MLLMs are trained on single-turn vision question-answering tasks, which do not accurately reflect real-world human conversations. In this paper, we introduce MMDiag, a new large-scale multi-turn multimodal dialogue dataset. This dataset is collaboratively generated through deliberately designed rules and GPT assistance, featuring complex dialogues with contextual dependencies that force models to track, ground, and recall information across multiple turns and disparate visual regions. MMDiag serves as a strong benchmark for multi-turn multimodal dialogue learning and brings more challenges to the grounding and reasoning capabilities of MLLMs. Further, inspired by human vision processing we present DiagNote, equipped with multimodal grounding and reasoning capabilities. DiagNote adopts a novel dual-module architecture that explicitly separates reasoning from grounding: a reasoning module (Deliberate) performs step-by-step Chain-of-Thought, while a grounding module (Gaze) provides precise visual focus by predicting bounding box annotations. These modules interact iteratively, enabling DiagNote to dynamically refine its understanding. We empirically demonstrate the advantages of DiagNote in both grounding and jointly processing and reasoning with vision and language information over existing MLLMs.

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Graph-Based Multi-Trait Essay Scoring
Shengjie Li | Vincent Ng

While virtually all existing work on Automated Essay Scoring (AES) models an essay as a word sequence, we put forward the novel view that an essay can be modeled as a graph and subsequently propose GAT-AES, a graph-attention network approach to AES. GAT-AES models the interactions among essay traits in a principled manner by (1) representing each essay trait as a trait node in the graph and connecting each pair of trait nodes with directed edges, and (2) allowing neighboring nodes to influence each other by using a convolutional operator to update node representations. Unlike competing approaches, which can only model one-hop dependencies, GAT-AES allows us to easily model multi-hop dependencies. Experimental results demonstrate that GAT-AES achieves the best multi-trait scoring results to date on the ASAP++ dataset. Further analysis shows that GAT-AES outperforms not only alternative graph neural networks but also approaches that use trait-attention mechanisms to model trait dependencies.

Semantic Overlap Summarization (SOS) is a multi-document summarization task focused on extracting the common information shared cross alternative narratives which is a capability that is critical for trustworthy generation in domains such as news, law, and healthcare. We benchmark popular Large Language Models (LLMs) on SOS and introduce PrivacyPolicyPairs (3P), a new dataset of 135 high-quality samples from privacy policy documents, which complements existing resources and broadens domain coverage. Using the TELeR prompting taxonomy, we evaluate nearly one million LLM-generated summaries across two SOS datasets and conduct human evaluation on a curated subset. Our analysis reveals strong prompt sensitivity, identifies which automatic metrics align most closely with human judgments, and provides new baselines for future SOS research

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N-CORE: N-View Consistency Regularization for Disentangled Representation Learning in Nonverbal Vocalizations
Siddhant Bikram Shah | Kristina T. Johnson

Nonverbal vocalizations are an essential component of human communication, conveying rich information without linguistic content. However, their computational analysis is hindered by a lack of lexical anchors in the data, compounded by biased and imbalanced data distributions. While disentangled representation learning has shown promise in isolating specific speech features, its application to nonverbal vocalizations remains unexplored. In this paper, we introduce N-CORE, a novel backbone-agnostic framework designed to disentangle intertwined features like emotion and speaker information from nonverbal vocalizations by leveraging N views of audio samples to learn invariance to specific transformations. N-CORE achieves competitive performance compared to state-of-the-art methods for emotion and speaker classification on the VIVAE, ReCANVo, and ReCANVo-Balanced datasets. We further propose an emotion perturbation function that disrupts affective information while preserving speaker information in audio signals for emotion-invariant speaker classification. Our work informs research directions on paralinguistic speech processing, including clinical diagnoses of atypical speech and longitudinal analysis of communicative development. Our code is available at https://github.com/SiddhantBikram/N-CORE.

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Probability Distribution Collapse: A Critical Bottleneck to Compact Unsupervised Neural Grammar Induction
Jinwook Park | Kangil Kim

Unsupervised neural grammar induction aims to learn interpretable hierarchical structures from language data. However, existing models face an expressiveness bottleneck, often resulting in unnecessarily large yet underperforming grammars. We identify a core issue, *probability distribution collapse*, as the underlying cause of this limitation. We analyze when and how the collapse emerges across key components of neural parameterization and introduce a targeted solution, *collapse-relaxing neural parameterization*, to mitigate it. Our approach substantially improves parsing performance while enabling the use of significantly more compact grammars across a wide range of languages, as demonstrated through extensive empirical analysis.

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Spatial Layouts in News Homepages Capture Human Preferences
Alexander Spangher | Michael Vu | Arda Kaz | Naitian Zhou | Ben Welsh

Information prioritization plays an important role in the way we perceive and understand the world. Homepage layouts, which are daily and manually curated by expert human news editors, serve as a tangible proxy for this prioritization. In this work, we present NewsHomepages, a novel and massive dataset of over 3,000 news website homepages, including local, national, and topic-specific outlets, captured twice daily over a five-year period. We develop a scalable pairwise preference model to capture ranked preferences between news items and confirm that these preferences are stable and learnable: our models infer editorial preference with over 0.7 F1 score (based on human trials). To demonstrate the importance of these learned preferences, we (1) perform a novel analysis showing that outlets across the political spectrum share surprising preference agreements and (2) apply our models to rank-order a collection of local city council policies passed over a ten-year period in San Francisco, assessing their “newsworthiness”. Our findings lay the groundwork for leveraging implicit cues to deepen our understanding of human informational preference.

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KRETA: A Benchmark for Korean Reading and Reasoning in Text-Rich VQA Attuned to Diverse Visual Contexts
Taebaek Hwang | Minseo Kim | Gisang Lee | Seonuk Kim | Hyunjun Eun

Understanding and reasoning over text within visual contexts poses a significant challenge for Vision-Language Models (VLMs), given the complexity and diversity of real-world scenarios. To address this challenge, text-rich Visual Question Answering (VQA) datasets and benchmarks have emerged for high-resource languages like English. However, a critical gap persists for low-resource languages such as Korean, where the lack of comprehensive benchmarks hinders robust model evaluation and comparison. To bridge this gap, we introduce KRETA, a benchmark for Korean Reading and rEasoning in Text-rich VQA Attuned to diverse visual contexts. KRETA facilitates an in-depth evaluation of both visual text understanding and reasoning capabilities, while also supporting a multifaceted assessment across 15 domains and 26 image types. Additionally, we introduce a semi-automated VQA generation pipeline specifically optimized for text-rich settings, leveraging refined stepwise image decomposition and a rigorous seven-metric evaluation protocol to ensure data quality. While KRETA is tailored for Korean, we hope our adaptable and extensible pipeline will facilitate the development of similar benchmarks in other languages, thereby accelerating multilingual VLM research. The code and dataset for KRETA are available at [https://github.com/tabtoyou/KRETA](https://github.com/tabtoyou/KRETA).

Recent advances in LLM agents have largely built on reasoning backbones like ReAct, which interleave thought and action in complex environments. However, ReAct often produces ungrounded or incoherent reasoning steps, leading to misalignment between the agent’s actual state and goals. Our analysis finds that this stems from ReAct’s inability to maintain consistent internal beliefs and goal alignment, causing compounding errors and hallucinations. To address this, we introduce ReflAct, a novel backbone that shifts reasoning from merely planning next actions to continuously reflecting on the agent’s state relative to its goal. By explicitly grounding decisions in states and enforcing ongoing goal alignment, ReflAct dramatically improves strategic reliability. This design delivers substantial empirical gains: ReflAct surpasses ReAct by 27.7% on average, achieving a 93.3% success rate in ALFWorld. Notably, ReflAct even outperforms ReAct with added enhancement modules (e.g., Reflexion, WKM), showing that strengthening the core reasoning backbone is key to reliable agent performance.

Answer verification is crucial not only for evaluating large language models (LLMs) by matching their unstructured outputs against standard answers, but also serves as the reward model to guide LLM optimization. Most evaluation frameworks rely on regularized matching or employ general LLMs for answer verification, which demands extensive, repetitive customization for regex rules or evaluation prompts. Two fundamental limitations persist in current methodologies: 1) the absence of comprehensive benchmarks that systematically evaluate verification capabilities across different LLMs; and 2) the nascent stage of verifier development, where existing approaches lack both the robustness to handle complex edge cases and the generalizability across different domains. In this work, we develop CompassVerifier, an accurate and robust lightweight verifier model for evaluation and outcome reward. It demonstrates multi-domain competency spanning math, knowledge, and diverse reasoning tasks, with the capability to process various answer types including multi-subproblems, formulas, and sequence answers, while effectively identifying abnormal/invalid responses. We introduce VerifierBench benchmark comprising model outputs collected from multiple data sources, augmented through manual analysis of meta error patterns to enhance CompassVerifier. We anticipate that CompassVerifier and VerifierBench will facilitate evaluation protocols and reinforcement learning research.

Medical decision-making often involves integrating knowledge from multiple clinical specialties, typically achieved through multidisciplinary teams. Inspired by this collaborative process, recent work has leveraged large language models (LLMs) in multi-agent collaboration frameworks to emulate expert teamwork. While these approaches improve reasoning through agent interaction, they are limited by static, pre-assigned roles, which hinder adaptability and dynamic knowledge integration. To address these limitations, we propose KAMAC, a Knowledge-driven Adaptive Multi-Agent Collaboration framework that enables LLM agents to dynamically form and expand expert teams based on the evolving diagnostic context. KAMAC begins with one or more expert agents and then conducts a knowledge-driven discussion to identify and fill knowledge gaps by recruiting additional specialists as needed. This supports flexible, scalable collaboration in complex clinical scenarios, with decisions finalized through reviewing updated agent comments. Experiments on two real-world medical benchmarks demonstrate that KAMAC significantly outperforms both single-agent and advanced multi-agent methods, particularly in complex clinical scenarios (i.e., cancer prognosis) requiring dynamic, cross-specialty expertise. Our code is publicly available at: https://github.com/XiaoXiao-Woo/KAMAC.

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Castle: Causal Cascade Updates in Relational Databases with Large Language Models
Yongye Su | Yucheng Zhang | Zeru Shi | Bruno Ribeiro | Elisa Bertino

This work introduces Castle, the first framework for schema-only cascade update generation using large language models (LLMs). Despite recent advances in LLMs for Text2SQL code generation, existing approaches focus primarily on SELECT queries, neglecting the challenges of SQL update operations and their ripple effects. Traditional CASCADE UPDATE constraints are static and unsuitable for modern, denormalized databases, which demand dynamic, context-aware updates. Castle enables natural language instructions to trigger multi-column, causally consistent SQL UPDATE statements, without revealing table content to the model. By framing UPDATE SQL generation as a divide-and-conquer task with LLMs’ reasoning capacity, Castle can determine not only which columns must be directly updated, but also how those updates propagate through the schema, causing cascading updates — all via nested queries and substructures that ensure data confidentiality. We evaluate it on real-world causal update scenarios, demonstrating its ability to produce accurate SQL updates, and thereby highlighting the reasoning ability of LLMs in automated DBMS.

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Idiosyncratic Versus Normative Modeling of Atypical Speech Recognition: Dysarthric Case Studies
Vishnu Raja | Adithya V Ganesan | Anand Syamkumar | Ritwik Banerjee | H. Schwartz

State-of-the-art automatic speech recognition (ASR) models like Whisper perform poorly on atypical speech, such as that produced by individuals with dysarthria. Past works for atypical speech have mostly investigated fully personalized (or idiosyncratic) models, but modeling strategies that can both generalize and handle idiosyncrasy could be more effective for capturing atypical speech. To investigate this, we compare four strategies: (a) *normative* models trained on typical speech (no personalization), (b) *idiosyncratic* models completely personalized to individuals, (c) *dysarthric-normative* models trained on other dysarthric speakers, and (d) *dysarthric-idiosyncratic* models which combine strategies by first modeling normative patterns before adapting to individual speech. In this case study, we find the dysarthric-idiosyncratic model performs better than the idiosyncratic approach while requiring less than half as much personalized data (36.43 WER with 128 train size vs. 36.99 with 256). Further, we found that tuning the speech encoder alone (as opposed to the LM decoder) yielded the best results, reducing word error rate from 71% to 32% on average. Our findings highlight the value of leveraging both normative (cross-speaker) and idiosyncratic (speaker-specific) patterns to improve ASR for underrepresented speech populations. [GitHub: VishnuRaja98/Dysarthric-Speech-Transcription](https://github.com/VishnuRaja98/Dysarthric-Speech-Transcription)

The resurgence of autonomous agents built using large language models (LLMs) to solve complex real-world tasks has brought increased focus on LLMs’ fundamental ability of tool or function calling. At the core of these agents, an LLM must plan, execute, and respond using external tools, APIs, and custom functions. Research on tool calling has gathered momentum, but evaluation benchmarks and datasets representing the complexity of the tasks have lagged behind. In this work, we focus on one such complexity, nested sequencing, with the goal of extending existing benchmarks and evaluation. Specifically, we present NESTFUL, a benchmark to evaluate LLMs on nested sequences of API calls, i.e., sequences where the output of one API call is passed as input to a subsequent call. NESTFUL contains 1800+ nested sequences where all the function calls are executable. Experimental results on a variety of models show that the best-performing model (GPT-4o) achieves a full sequence match accuracy of 28% and a win-rate of 60%, necessitating a large scope for improvement in the nested sequencing aspect of function calling. Our analysis of these results provides possible future research directions for the community, in addition to a benchmark to track progress.

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Benchmarking and Mitigating MCQA Selection Bias of Large Vision-Language Models
Md. Atabuzzaman | Ali Asgarov | Chris Thomas

Large Vision-Language Models (LVLMs) have achieved strong performance on vision-language tasks, particularly Visual Question Answering (VQA). While prior work has explored unimodal biases in VQA, the problem of selection bias in Multiple-Choice Question Answering (MCQA), where models may favor specific option tokens (e.g., “A”) or positions, remains underexplored. In this paper, we investigate both the presence and nature of selection bias in LVLMs through fine-grained MCQA benchmarks spanning easy, medium, and hard difficulty levels, defined by the semantic similarity of the options. We further propose an inference-time logit-level debiasing method that estimates an ensemble bias vector from general and contextual prompts and applies confidence-adaptive corrections to the model’s output. Our method mitigates bias without retraining and is compatible with frozen LVLMs. Extensive experiments across several state-of-the-art models reveal consistent selection biases that intensify with task difficulty, and show that our mitigation approach significantly reduces bias while improving accuracy in challenging settings. This work offers new insights into the limitations of LVLMs in MCQA and presents a practical approach to improve their robustness in fine-grained visual reasoning. Datasets and code are available at: https://github.com/Atabuzzaman/Selection-Bias-of-LVLMs

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Can Large Language Models Unlock Novel Scientific Research Ideas?
Sandeep Kumar | Tirthankar Ghosal | Vinayak Goyal | Asif Ekbal

The widespread adoption of Large Language Models (LLMs) and publicly available ChatGPT have marked a significant turning point in the integration of Artificial Intelligence (AI) into people’s everyday lives. This study explores the capability of LLMs in generating novel research ideas based on information from research papers. We conduct a thorough examination of 4 LLMs in five domains (e.g., Chemistry, Computer, Economics, Medical, and Physics). We found that the future research ideas generated by Claude-2 and GPT-4 are more aligned with the author’s perspective than GPT-3.5 and Gemini. We also found that Claude-2 generates more diverse future research ideas than GPT-4, GPT-3.5, and Gemini 1.0. We further performed a human evaluation of the novelty, relevancy, and feasibility of the generated future research ideas. This investigation offers insights into the evolving role of LLMs in idea generation, highlighting both its capability and limitations. Our work contributes to the ongoing efforts in evaluating and utilizing language models for generating future research ideas. We make our datasets and codes publicly available.

Large Reasoning Models (LRMs) are often bottlenecked by the high cost of output tokens. We show that a significant portion of these tokens are useless self-repetitions — what we call “word salad” — that exhaust the decoding budget without adding value. Interestingly, we observe that LRMs are self-aware when trapped in these loops: the hidden states of ‘‘ tokens trailing each reasoning chunk exhibit patterns that allow us to detect word salad behavior on-the-fly via a single linear classifier. Once detected, a simple chop appended by a straightforward regeneration prompt yields substantial length savings with minimal quality loss. Our work offers WordSaladChopper (WSC) — a lightweight, turnkey component for LRM that is minimally invasive to its reasoning trajectory. Given its low overhead, strong savings, and the lack of semantic value of word salad tokens, we believe it is not too far-fetched to argue that WSC — or a similar component — is a must-have for all LRM applications with user experience in mind.

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DIWALI - Diversity and Inclusivity aWare cuLture specific Items for India: Dataset and Assessment of LLMs for Cultural Text Adaptation in Indian Context
Pramit Sahoo | Maharaj Brahma | Maunendra Sankar Desarkar

Large language models (LLMs) are widely used in various tasks and applications. However, despite their wide capabilities, they are shown to lack cultural alignment (CITATION) and produce biased generations (CITATION) due to a lack of cultural knowledge and competence. Evaluation of LLMs for cultural awareness and alignment is particularly challenging due to the lack of proper evaluation metrics and unavailability of culturally grounded datasets representing the vast complexity of cultures at the regional and sub-regional levels. Existing datasets for culture specific items (CSIs) focus primarily on concepts at the regional level and may contain false positives. To address this issue, we introduce a novel CSI dataset for Indian culture, belonging to 17 cultural facets. The dataset comprises ~8k cultural concepts from 36 sub-regions. To measure the cultural competence of LLMs on a cultural text adaptation task, we evaluate the adaptations using the CSIs created, LLM as Judge, and human evaluations from diverse socio-demographic region. Furthermore, we perform quantitative analysis demonstrating selective sub-regional coverage and surface-level adaptations across all considered LLMs. Our dataset is available here: https://huggingface.co/datasets/nlip/DIWALI, project webpage, and our codebase with model outputs can be found here: https://github.com/pramitsahoo/culture-evaluation.

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SYNC: A Synthetic Long-Context Understanding Benchmark for Controlled Comparisons of Model Capabilities
Shuyang Cao | Kaijian Zou | Lu Wang

Recently, researchers have turned to synthetic tasks for evaluation of large language models’ long-context capabilities, as they offer more flexibility than realistic benchmarks in scaling both input length and dataset size. However, existing synthetic tasks typically target narrow skill sets such as retrieving information from massive input, limiting their ability to comprehensively assess model capabilities. Furthermore, existing benchmarks often pair each task with a different input context, creating confounding factors that prevent fair cross-task comparison. To address these limitations, we introduce SYNC, a new evaluation suite of synthetic tasks spanning domains including graph understanding and translation. Each domain includes three tasks designed to test a wide range of capabilities—from retrieval, to multi-hop tracking, and to global context understanding that that requires chain-of-thought (CoT) reasoning. Crucially, all tasks share the same context, enabling controlled comparisons of model performance. We evaluate 14 LLMs on SYNC and observe substantial performance drops on more challenging tasks, underscoring the benchmark’s difficulty. Additional experiments highlight the necessity of CoT reasoning and demonstrate that poses a robust challenge for future models.

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OpenNER 1.0: Standardized Open-Access Named Entity Recognition Datasets in 50+ Languages
Chester Palen-Michel | Maxwell Pickering | Maya Kruse | Jonne Sälevä | Constantine Lignos

We present OpenNER 1.0, a standardized collection of openly-available named entity recognition (NER) datasets.OpenNER contains 36 NER corpora that span 52 languages, human-annotated in varying named entity ontologies.We correct annotation format issues, standardize the original datasets into a uniform representation with consistent entity type names across corpora, and provide the collection in a structure that enables research in multilingual and multi-ontology NER.We provide baseline results using three pretrained multilingual language models and two large language models to compare the performance of recent models and facilitate future research in NER.We find that no single model is best in all languages and that significant work remains to obtain high performance from LLMs on the NER task.OpenNER is released at https://github.com/bltlab/open-ner.

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Mondrian: A Framework for Logical Abstract (Re)Structuring
Elizabeth Grace Orwig | Shinwoo Park | Hyundong Jin | Yo-Sub Han

The well-known rhetorical framework, ABT (And, But, Therefore), mirrors natural human cognition in structuring an argument’s logical progression - apropos to academic communication. However, distilling the complexities of research into clear and concise prose requires careful sequencing of ideas and formulating clear connections between them. This presents a quiet inequitability for contributions from authors who struggle with English proficiency or academic writing conventions. We see this as impetus to introduce: Mondrian, a framework that identifies the key components of an abstract and reorients itself to properly reflect the ABT logical progression. The framework is composed of a deconstruction stage, reconstruction stage, and rephrasing. We introduce a novel metric for evaluating deviation from ABT structure, named EB-DTW, which accounts for both ordinality and a non-uniform distribution of importance in a sequence. Our overall approach aims to improve the comprehensibility of academic writing, particularly for non-native English speakers, along with a complementary metric. The effectiveness of Mondrian is tested with automatic metrics and extensive human evaluation, and demonstrated through impressive quantitative and qualitative results, with organization and overall coherence of an abstract improving by an average of 27.71% and 24.71%.

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Case-Based Decision-Theoretic Decoding with Quality Memories
Hiroyuki Deguchi | Masaaki Nagata

Minimum Bayes risk (MBR) decoding is a decision rule of text generation, which selects the hypothesis that maximizes the expected utility and robustly generates higher-quality texts than maximum a posteriori (MAP) decoding.However, it depends on sample texts drawn from the text generation model; thus, it is difficult to find a hypothesis that correctly captures the knowledge or information of out-of-domain.To tackle this issue, we propose case-based decision-theoretic (CBDT) decoding, another method to estimate the expected utility using examples of domain data.CBDT decoding not only generates higher-quality texts than MAP decoding, but also the combination of MBR and CBDT decoding outperformed MBR decoding in seven domain De–En and Ja↔En translation tasks and image captioning tasks on MSCOCO and nocaps datasets.

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PRIME: Large Language Model Personalization with Cognitive Dual-Memory and Personalized Thought Process
Xinliang Frederick Zhang | Nicholas Beauchamp | Lu Wang

Large language model (LLM) personalization aims to align model outputs with individuals’ unique preferences and opinions. While recent efforts have implemented various personalization methods, a unified theoretical framework that can systematically understand the drivers of effective personalization is still lacking. In this work, we integrate the well-established cognitive dual-memory model into LLM personalization, by mirroring episodic memory to historical user engagements and semantic memory to long-term, evolving user beliefs. Specifically, we systematically investigate memory instantiations and introduce a unified framework, PRIME, using episodic and semanticmemory mechanisms. We further augment PRIME with a novel personalized thinking capability inspired by the slow thinking strategy. Moreover, recognizing the absence of suitable benchmarks, we introduce a dataset using Change My View (CMV) from Reddit, specifically designed to evaluate long-context personalization. Extensive experiments validate PRIME’s effectiveness across both long- and short-context scenarios. Further analysis confirms that PRIME effectively captures dynamic personalization beyond mere popularity biases.

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Mechanisms vs. Outcomes: Probing for Syntax Fails to Explain Performance on Targeted Syntactic Evaluations
Ananth Agarwal | Jasper Jian | Christopher D Manning | Shikhar Murty

Large Language Models (LLMs) exhibit a robust mastery of syntax when processing and generating text. While this suggests internalized understanding of hierarchical syntax and dependency relations, the precise mechanism by which they represent syntactic structure is an open area within interpretability research. Probing provides one way to identify syntactic mechanisms linearly encoded in activations; however, no comprehensive study has yet established whether a model’s probing accuracy reliably predicts its downstream syntactic performance. Adopting a “mechanisms vs. outcomes” framework, we evaluate 32 open-weight transformer models and find that syntactic features extracted via probing fail to predict outcomes of targeted syntax evaluations across English linguistic phenomena. Our results highlight a substantial disconnect between latent syntactic representations found via probing and observable syntactic behaviors in downstream tasks.

Image Difference Captioning (IDC) aims to generate natural language descriptions that highlight subtle differences between two visually similar images. While recent advances leverage pre-trained vision-language models to align fine-grained visual differences with textual semantics, existing supervised approaches often overfit to dataset-specific language patterns and fail to capture accurate preferences on IDC, which often indicates fine-grained and context-aware distinctions. To address these limitations, we propose an adversarial direct preference optimization (ADPO) framework for IDC, which formulates IDC as a preference optimization problem under the Bradley-Terry-Luce model, directly aligning the captioning policy with pairwise difference preferences via Direct Preference Optimization (DPO). To model more accurate and diverse IDC preferences, we introduce an adversarially trained hard negative retriever that selects counterfactual captions, This results in a minimax optimization problem, which we solve via policy-gradient reinforcement learning, enabling the policy and retriever to improve jointly. Experiments on benchmark IDC datasets show that our approach outperforms existing baselines, especially in generating fine-grained and accurate difference descriptions.

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seqBench: A Tunable Benchmark to Quantify Sequential Reasoning Limits of LLMs
Mohammad Ramezanali | Mo Vazifeh | Paolo Santi

We introduce **seqBench**, a parametrized benchmark for probing sequential reasoning limits in Large Language Models (LLMs) through precise, multi-dimensional control over several key complexity dimensions. **seqBench** allows systematic variation of (1) the logical depth, defined as the number of sequential actions required to solve the task; (2) the number of backtracking steps along the optimal path, quantifying how often the agent must revisit prior states to satisfy deferred preconditions (e.g., retrieving a key after encountering a locked door); and (3) the noise ratio, defined as the ratio between supporting and distracting facts about the environment. Our evaluations on state-of-the-art LLMs reveal a universal failure pattern: accuracy collapses exponentially beyond a model-specific logical depth. Unlike existing benchmarks, **seqBench**’s fine-grained control facilitates targeted analyses of these reasoning failures, illuminating universal scaling laws and statistical limits, as detailed in this paper alongside its generation methodology and evaluation metrics. We find that even top-performing models systematically fail on **seqBench**’s structured reasoning tasks despite minimal search complexity, underscoring key limitations in their commonsense reasoning capabilities. Designed for future evolution to keep pace with advancing models, the **seqBench** datasets are publicly released to spur deeper scientific inquiry into LLM reasoning, aiming to establish a clearer understanding of their true potential and current boundaries for robust real-world application.

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NormGenesis: Multicultural Dialogue Generation via Exemplar-Guided Social Norm Modeling and Violation Recovery
Minki Hong | Jangho Choi | Jihie Kim

Social norms govern culturally appropriate behavior in communication, enabling dialogue systems to produce responses that are not only coherent but also socially acceptable. We present NormGenesis, a multicultural framework for generating and annotating socially grounded dialogues across English, Chinese, and Korean. To model the dynamics of social interaction beyond static norm classification, we propose a novel dialogue type, Violation-to-Resolution (V2R), which models the progression of conversations following norm violations through recognition and socially appropriate repair. To improve pragmatic consistency in underrepresented languages, we implement an exemplar-based iterative refinement early in the dialogue synthesis process. This design introduces alignment with linguistic, emotional, and sociocultural expectations before full dialogue generation begins. Using this framework, we construct a dataset of 10,800 multi-turn dialogues annotated at the turn level for norm adherence, speaker intent, and emotional response. Human and LLM-based evaluations demonstrate that NormGenesis significantly outperforms existing datasets in refinement quality, dialogue naturalness, and generalization performance. We show that models trained on our V2R-augmented data exhibit improved pragmatic competence in ethically sensitive contexts. Our work establishes a new benchmark for culturally adaptive dialogue modeling and provides a scalable methodology for norm-aware generation across linguistically and culturally diverse languages.

We introduce SATBench, a benchmark for evaluating the logical reasoning capabilities of large language models (LLMs) through logical puzzles derived from Boolean satisfiability (SAT) problems.Unlike prior work that focuses on inference rule-based reasoning, which often involves deducing conclusions from a set of premises, our approach leverages the search-based nature of SAT problems, where the objective is to find a solution that fulfills a specified set of logical constraints. Each instance in SATBench is generated from a SAT formula, then translated into a puzzle using LLMs. The generation process is fully automated and allows for adjustable difficulty by varying the number of clauses. All 2100 puzzles are validated through both LLM-based and solver-based consistency checks, with human validation on a subset. Experimental results show that even the strongest model, o4-mini, achieves only 65.0% accuracy on hard UNSAT problems, close to the random baseline of 50%. Our error analysis reveals systematic failures such as satisfiability bias, context inconsistency, and condition omission, highlighting limitations of current LLMs in search-based logical reasoning. Our code and data are publicly available at https://github.com/Anjiang-Wei/SATBench

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Data Descriptions from Large Language Models with Influence Estimation
Chaeri Kim | Jaeyeon Bae | Taehwan Kim

Deep learning models have been successful in many areas, but understanding their behavior remains a challenge. Most prior explainable AI (XAI) approaches have focused on interpreting how models make predictions. In contrast, we introduce a novel approach that identifies textual descriptions most beneficial for model training. By analyzing which descriptions contribute most effectively to the model training, our method has the potential to provide insights into how the model prioritizes and utilizes information for decision-making. To achieve this, we propose a pipeline that generates textual descriptions using large language models, incorporates external knowledge bases, and refines them through influence estimation and CLIP score. Furthermore, leveraging the phenomenon of cross-modal transferability, we propose a novel benchmark task named cross-modal transfer classification to examine the effectiveness of our textual descriptions. In zero-shot experiments, we demonstrate that our textual descriptions improve classification accuracy compared to baselines, leading to consistent performance gains across nine image classification datasets. Additionally, understanding which descriptions contribute most to model performance can shed light on how the model utilizes textual information in its decision-making.

As large language models (LLMs) become integral to code-related tasks, a central question emerges: Do LLMs truly understand program semantics? We introduce EquiBench, a new benchmark for evaluating LLMs through equivalence checking, i.e., determining whether two programs produce identical outputs for all possible inputs. Unlike prior code generation benchmarks, this task directly tests a model’s ability to reason about program semantics. EquiBench consists of 2400 program pairs across four languages and six categories. These pairs are generated through program analysis, compiler scheduling, and superoptimization, ensuring high-confidence labels, nontrivial difficulty, and full automation. We evaluate 19 state-of-the-art LLMs and find that in the most challenging categories, the best accuracies are 63.8% and 76.2%, only modestly above the 50% random baseline. Further analysis reveals that models often rely on syntactic similarity rather than exhibiting robust reasoning about program semantics, highlighting current limitations. Our code and dataset are publicly available at https://github.com/Anjiang-Wei/equibench

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MicroEdit: Neuron-level Knowledge Disentanglement and Localization in Lifelong Model Editing
Shiqi Wang | Qi Wang | Runliang Niu | He Kong | Yi Chang

Large language models (LLMs) require continual knowledge updates to keep pace with the evolving world. While various model editing methods have been proposed, most face critical challenges in the context of lifelong learning due to two fundamental limitations: (1) Edit Overshooting - parameter updates intended for a specific fact spill over to unrelated regions, causing interference with previously retained knowledge; and (2) Knowledge Entanglement - polysemantic neurons’ overlapping encoding of multiple concepts makes it difficult to isolate and edit a single fact. In this paper, we propose MicroEdit, a neuron-level editing method that performs minimal and controlled interventions within LLMs. By leveraging a sparse autoencoder (SAE), MicroEdit disentangles knowledge representations and activates only a minimal set of necessary neurons for precise parameter updates. This targeted design enables fine-grained control over the editing scope, effectively mitigating interference and preserving unrelated knowledge. Extensive experiments show that MicroEdit outperforms prior methods and robustly handles lifelong knowledge editing across QA and Hallucination settings on LLaM and Mistral.

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Do Large Language Models Understand Word Senses?
Domenico Meconi | Simone Stirpe | Federico Martelli | Leonardo Lavalle | Roberto Navigli

Understanding the meaning of words in context is a fundamental capability for Large Language Models (LLMs). Despite extensive evaluation efforts, the extent to which LLMs show evidence that they truly grasp word senses remains underexplored. In this paper, we address this gap by evaluating both i) the Word Sense Disambiguation (WSD) capabilities of instruction-tuned LLMs, comparing their performance to state-of-the-art systems specifically designed for the task, and ii) the ability of two top-performing open- and closed-source LLMs to understand word senses in three generative settings: definition generation, free-form explanation, and example generation. Notably, we find that, in the WSD task, leading models such as GPT-4o and DeepSeek-V3 achieve performance on par with specialized WSD systems, while also demonstrating greater robustness across domains and levels of difficulty. In the generation tasks, results reveal that LLMs can explain the meaning of words in context up to 98% accuracy, with the highest performance observed in the free-form explanation task, which best aligns with their generative capabilities.We release our code and data at: https://github.com/Babelscape/LLM-WSD.

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Diverse, not Short: A Length-Controlled Data Selection Strategy for Improving Response Diversity of Language Models
Vijeta Deshpande | Debasmita Ghose | John D Patterson | Roger E. Beaty | Anna Rumshisky

Diverse language model responses are crucial for creative generation, open-ended tasks, and self-improvement training. We show that common diversity metrics, and even reward models used for preference optimization, systematically bias models toward shorter outputs, limiting expressiveness. To address this, we introduce Diverse, not Short (Diverse-NS), a length-controlled data selection strategy that improves response diversity while maintaining length parity. By generating and filtering preference data that balances diversity, quality, and length, Diverse-NS enables effective training using only 3,000 preference pairs. Applied to LLaMA-3.1-8B and the Olmo-2 family, Diverse-NS substantially enhances lexical and semantic diversity. We show consistent improvement in diversity with minor reduction or gains in response quality on four creative generation tasks: Divergent Associations, Persona Generation, Alternate Uses, and Creative Writing. Surprisingly, experiments with the Olmo-2 model family (7B, and 13B) show that smaller models like Olmo-2-7B can serve as effective “diversity teachers” for larger models. By explicitly addressing length bias, our method efficiently pushes models toward more diverse and expressive outputs.

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Uncovering the Bigger Picture: Comprehensive Event Understanding Via Diverse News Retrieval
Yixuan Tang | Yuanyuan Shi | Yiqun Sun | Anthony Kum Hoe Tung

Access to diverse perspectives is essential for understanding real-world events, yet most news retrieval systems prioritize textual relevance, leading to redundant results and limited viewpoint exposure. We propose NEWSCOPE, a two-stage framework for diverse news retrieval that enhances event coverage by explicitly modeling semantic variation at the sentence level. The first stage retrieves topically relevant content using dense retrieval, while the second stage applies sentence-level clustering and diversity-aware re-ranking to surface complementary information. To evaluate retrieval diversity, we introduce three interpretable metrics, namely Average Pairwise Distance, Positive Cluster Coverage, and Information Density Ratio, and construct two paragraph-level benchmarks: LocalNews and DSGlobal. Experiments show that NEWSCOPE consistently outperforms strong baselines, achieving significantly higher diversity without compromising relevance. Our results demonstrate the effectiveness of fine-grained, interpretable modeling in mitigating redundancy and promoting comprehensive event understanding. The data and code are available at https://github.com/tangyixuan/NEWSCOPE.

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Personalized LLM Decoding via Contrasting Personal Preference
Hyungjune Bu | ChanJoo Jung | Minjae Kang | Jaehyung Kim

As large language models (LLMs) are progressively deployed in various real-world applications, personalization of LLMs has become increasingly important. While various approaches to LLM personalization such as prompt-based and training-based methods have been actively explored, the development of effective decoding-time algorithms remains largely overlooked, despite their demonstrated potential. In this paper, we propose Contrasting Personal Preference (CoPe), a novel decoding-time approach applied after performing parameter-efficient fine-tuning (PEFT) on user-specific data. Our core idea is to leverage reward-guided decoding specifically for personalization by maximizing each user’s implicit reward signal. We evaluate CoPe across five open-ended personalized text generation tasks. Our empirical results demonstrate that CoPe achieves strong performance, improving personalization by an average of 10.57% in ROUGE-L without relying on external reward models or additional training procedures.

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The Missing Parts: Augmenting Fact Verification with Half Truth Detection
Yixuan Tang | Jincheng Wang | Anthony Kum Hoe Tung

Fact verification systems typically assess whether a claim is supported by retrieved evidence, assuming that truthfulness depends solely on what is stated. However, many real-world claims are half-truths, factually correct yet misleading due to the omission of critical context. Existing models struggle with such cases, as they are not designed to reason about omitted information. We introduce the task of half-truth detection, and propose PolitiFact-Hidden, a new benchmark with 15k political claims annotated with sentence-level evidence alignment and inferred claim intent. To address this challenge, we present TRACER, a modular re-assessment framework that identifies omission-based misinformation by aligning evidence, inferring implied intent, and estimating the causal impact of hidden content. TRACER can be integrated into existing fact-checking pipelines and consistently improves performance across multiple strong baselines. Notably, it boosts Half-True classification F1 by up to 16 points, highlighting the importance of modeling omissions for trustworthy fact verification. The benchmark and code are available via https://github.com/tangyixuan/TRACER.

As Machine Translation (MT) becomes increasingly commonplace, understanding how the general public perceives and relies on imperfect MT is crucial for contextualizing MT research in real-world applications. We present a human study conducted in a public museum (n=452), investigating how fluency and adequacy errors impact bilingual and non-bilingual users’ reliance on MT during casual use. Our findings reveal that non-bilingual users often over-rely on MT due to a lack of evaluation strategies and alternatives, while experiencing the impact of errors can prompt users to reassess future reliance. This highlights the need for MT evaluation and NLP explanation techniques to promote not only MT quality, but also MT literacy among its users.

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Personalization up to a Point: Why Personalized Content Moderation Needs Boundaries, and How We Can Enforce Them
Emanuele Moscato | Tiancheng Hu | Matthias Orlikowski | Paul Röttger | Debora Nozza

Personalized content moderation can protect users from harm while facilitating free expression by tailoring moderation decisions to individual preferences rather than enforcing universal rules. However, content moderation that is fully personalized to individual preferences, no matter what these preferences are, may lead to even the most hazardous types of content being propagated on social media. In this paper, we explore this risk using hate speech as a case study. Certain types of hate speech are illegal in many countries. We show that, while fully personalized hate speech detection models increase overall user welfare (as measured by user-level classification performance), they also make predictions that violate such legal hate speech boundaries, especially when tailored to users who tolerate highly hateful content. To address this problem, we enforce legal boundaries in personalized hate speech detection by overriding predictions from personalized models with those from a boundary classifier. This approach significantly reduces legal violations while minimally affecting overall user welfare. Our findings highlight both the promise and the risks of personalized moderation, and offer a practical solution to balance user preferences with legal and ethical obligations.

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MPCG: Multi-Round Persona-Conditioned Generation for Modeling the Evolution of Misinformation with LLMs
Chong Jun Rong Brian | Yixuan Tang | Anthony Kum Hoe Tung

Misinformation evolves as it spreads, shifting in language, framing, and moral emphasis to adapt to new audiences. However, current misinformation detection approaches implicitly assume that misinformation is static. We introduce MPCG, a multi-round, persona-conditioned framework that simulates how claims are iteratively reinterpreted by agents with distinct ideological perspectives. Our approach uses an uncensored large language model (LLM) to generate persona-specific claims across multiple rounds, conditioningeach generation on outputs from the previous round, enabling the study of misinformation evolution. We evaluate the generated claims through human and LLM-based annotations, cognitive effort metrics (readability, perplexity), emotion evocation metrics (sentiment analysis, morality), clustering, feasibility, and downstream classification. Results show strong agreement between human and GPT-4o-mini annotations, with higher divergence in fluency judgments. Generated claims require greater cognitive effort than the original claims and consistently reflect persona-aligned emotional and moral framing. Clustering and cosine similarity analyses confirmsemantic drift across rounds while preserving topical coherence. Feasibility results show a 77% feasibility rate, confirming suitability for downstream tasks. Classification results reveal that commonly used misinformation detectors experience macro-F1 performance drops of up to 49.7%. The code is available at https://github.com/bcjr1997/MPCG.

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LiTEx: A Linguistic Taxonomy of Explanations for Understanding Within-Label Variation in Natural Language Inference
Pingjun Hong | Beiduo Chen | Siyao Peng | Marie-Catherine de Marneffe | Barbara Plank

There is increasing evidence of Human Label Variation (HLV) in Natural Language Inference (NLI), where annotators assign different labels to the same premise-hypothesis pair. However, *within-label variation* — cases where annotators agree on the same label but provide divergent reasoning — poses an additional and mostly overlooked challenge. Several NLI datasets contain highlighted words in the NLI item as explanations, but the same spans on the NLI item can be highlighted for different reasons, as evidenced by free-text explanations, which offer a window into annotators’ reasoning. To systematically understand this problem and gain insight into the rationales behind NLI labels, we introduce LiTEx, a linguistically-informed taxonomy for categorizing free-text explanations in English. Using this taxonomy, we annotate a subset of the e-SNLI dataset, validate the taxonomy’s reliability, and analyze how it aligns with NLI labels, highlights, and explanations. We further assess the taxonomy’s usefulness in explanation generation, demonstrating that conditioning generation on LiTEx yields explanations that are linguistically closer to human explanations than those generated using only labels or highlights. Our approach thus not only captures within-label variation but also shows how taxonomy-guided generation for reasoning can bridge the gap between human and model explanations more effectively than existing strategies.

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LiteraryQA: Towards Effective Evaluation of Long-document Narrative QA
Tommaso Bonomo | Luca Gioffré | Roberto Navigli

Question Answering (QA) on narrative text poses a unique challenge to current systems, requiring a deep understanding of long, complex documents. However, the reliability of NarrativeQA, the most widely used benchmark in this domain, is hindered by noisy documents and flawed QA pairs. In this work, we introduce LiteraryQA, a high-quality subset of NarrativeQA focused on literary works. Using a human- and LLM-validated pipeline, we identify and correct low-quality QA samples while removing extraneous text from source documents. We then carry out a meta-evaluation of automatic metrics to clarify how systems should be evaluated on LiteraryQA.This analysis reveals that all n-gram-based metrics have a low system-level correlation to human judgment, while LLM-as-a-Judge evaluations, even with small open-weight models, can strongly agree with the ranking identified by humans.Finally, we benchmark a set of long-context LLMs on LiteraryQA. We release our code and data at https://github.com/sapienzaNLP/LiteraryQA.

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FillerSpeech: Towards Human-Like Text-to-Speech Synthesis with Filler Insertion and Filler Style Control
Seung-Bin Kim | Jun-Hyeok Cha | Hyung-Seok Oh | Heejin Choi | Seong-Whan Lee

Recent advancements in speech synthesis have significantly improved the audio quality and pronunciation of synthesized speech. To further advance toward human-like conversational speech synthesis, this paper presents FillerSpeech, a novel speech synthesis framework that enables natural filler insertion and control over filler style. To address this, we construct a filler-inclusive speech data, derived from the open-source large-scale speech corpus. This data includes fillers with pitch and duration information. For the generation and style control of natural fillers, we propose a method that tokenizes the filler style and utilizes cross-attention with the input text. Furthermore, we introduce a large language model-based filler prediction method that enables natural insertion of fillers even when only text input is provided. The experimental results demonstrate that the constructed dataset is valid and that our proposed methods for filler style control and filler prediction are effective.

As large language models (LLMs) continue to improve, their evaluation increasingly centers on complex, high-level tasks, often at the expense of systematically assessing fundamental capabilities. To address this gap, recent work proposed LMentry, a compact benchmark comprising tasks that are trivial for humans but remain surprisingly difficult for LLMs. However, LMentry is limited to English, leaving its insights linguistically narrow. In this paper, we present Multi-LMentry, a ground-up recreation of LMentry that enables systematic evaluation of LLMs on basic reasoning and understanding tasks across nine diverse languages. Multi-LMentry includes English and expands to Basque, Brazilian Portuguese, Catalan, Galician, German, Italian, Korean, and Spanish, emphasizing the importance of cross-lingual and low-resource settings. To validate that Multi-LMentry is still trivial for humans, we demonstrate that L2 speakers with only elementary proficiency achieve near-perfect scores in a low-resource language, namely, Basque. Through extensive experiments, we reveal that state-of-the-art open-weight multilingual LLMs still fall short of human performance on elementary tasks in many languages. Our results expose new failure modes that remain hidden in monolingual evaluation, underscoring the need for rigorous, language-diverse “unit tests” of core model abilities.

Large language models (LLMs) rely on key-value cache (KV cache) to accelerate decoding by reducing redundant computations. However, the KV cache memory usage grows substantially with longer text sequences, posing challenges for efficient deployment. Existing KV cache eviction methods prune tokens using prefilling-stage attention scores, causing inconsistency with actual inference queries, especially under tight memory budgets. In this paper, we propose Lookahead Q-Cache (LAQ), a novel eviction framework that generates low-cost pseudo lookahead queries to better approximate the true decoding-stage queries. By using these lookahead queries as the observation window for importance estimation, LAQ achieves more consistent and accurate KV cache eviction aligned with real inference scenarios. Experimental results on LongBench and Needle-in-a-Haystack benchmarks show that LAQ outperforms existing methods across various budget levels, achieving a 1 4 point improvement on LongBench under limited cache budget. Moreover, LAQ is complementary to existing approaches and can be flexibly combined to yield further improvements.

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PerspectiveMod: A Perspectivist Resource for Deliberative Moderation
Eva Maria Vecchi | Neele Falk | Carlotta Quensel | Iman Jundi | Gabriella Lapesa

Human moderators in online discussions face a heterogeneous range of tasks, which go beyond content moderation, or policing. They also support and improve discussion quality, which is challenging to model (and evaluate) in NLP due to its inherent subjectivity and the scarcity of annotated resources. We address this gap by introducing PerspectiveMod, a dataset of online comments annotated for the question: *“Does this comment require moderation, and why?”* Annotations were collected from both expert moderators and trained non-experts. **PerspectiveMod** is unique in its intentional variation across (a) the level of moderation experience embedded in the source data (professional vs. non-professional moderation environments), (b) the annotator profiles (experts vs. trained crowdworkers), and (c) the richness of each moderation judgment, both in terms on fine-grained comment properties (drawn from argumentation and deliberative theory) and in the representation of the individuality of the annotator (socio-demographics and attitudes towards the task). We advance understanding of the task’s complexity by providing interpretation layers that account for its subjectivity. Our statistical analysis highlights the value of collecting annotator perspectives, including their experiences, attitudes, and views on AI, as a foundation for developing more context-aware and interpretively robust moderation tools.

Continuous instruction following closely mirrors real-world tasks by requiring models to solve sequences of interdependent steps, yet existing multi-step instruction datasets suffer from three key limitations: (1) lack of logical coherence across turns, (2) narrow topical breadth and depth, and (3) reliance on rigid templates or heavy manual effort. We introduce LoCt-Pipeline, a novel pipeline that leverages modern LLMs’ reasoning capabilities to assemble rich, topic-related single-instruction data into multi-turn dialogues, producing chains that are logically coherent, progressively deepen in content, and span diverse domains without fixed templates or extensive human annotation. We employed this pipeline to construct LoCt-Instruct for assessing models’ problem-solving abilities. The generated chains serve as a testbed for benchmarking a variety of models, including reasoning-oriented architectures, instruction-tuned variants, and state-of-the-art closed-source LLMs on their capacity to follow and correctly respond to each step. Our results reveal a substantial performance gap between current LLMs and human solvers. These findings highlight the need for more robust continuous instruction following. We publicly release the dataset and end-to-end pipeline.

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CodeSSM: Towards State Space Models for Code Understanding
Shweta Verma | Abhinav Anand | Mira Mezini

Although transformers dominate many code-specific tasks, they have significant limitations. This paper explores State Space Models (SSMs) as a promising alternative for code understanding tasks such as retrieval, classification, and clone detection. We introduce CodeSSM, the first SSM-based model trained on code corpora to assess its effectiveness. Our results demonstrate that SSMs are more sample-efficient and can extrapolate to longer contexts beyond the pretraining length. Extensive experiments show that SSMs offer a viable alternative to transformers, addressing several their limitations. Additionally, CodeSSM reduces memory usage by up to 64% compared to transformers at a context length of 2048, with greater savings as context length grows.The code is available [here](https://github.com/abx04/CodeSSM).

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EduAdapt: A Question Answer Benchmark Dataset for Evaluating Grade-Level Adaptability in LLMs
Numaan Naeem | Abdellah El Mekki | Muhammad Abdul-Mageed

Large language models (LLMs) are transforming education by answering questions, explaining complex concepts, and generating content across a wide range of subjects. Despite strong performance on academic benchmarks, they often fail to tailor responses to students’ grade levels. This is a critical need in K-12 education, where age-appropriate vocabulary and explanation are essential for effective learning. Existing models frequently produce outputs that are too advanced or vague for younger learners, and there are no standardized benchmarks to evaluate their ability to adjust across cognitive and developmental stages. To address this gap, we introduce EduAdapt, a benchmark of nearly 48k grade-labeled QA pairs across nine science subjects, spanning Grades 1-12 and grouped into four grade levels. We evaluate a diverse set of open-source LLMs on EduAdapt and find that while larger models generally perform better, they still struggle with generating suitable responses for early-grade students (Grades 1-5). Our work presents the first dataset and evaluation framework for assessing grade-level adaptability in LLMs, aiming to foster more developmentally aligned educational AI systems through better training and prompting strategies. EduAdapt code and datasets are publicly available at https://github.com/NaumanNaeem/EduAdapt.

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xCoRe: Cross-context Coreference Resolution
Giuliano Martinelli | Bruno Gatti | Roberto Navigli

Current coreference resolution systems are typically tailored for short- or medium-sized texts and struggle to scale to very long documents due to architectural limitations and implied memory costs.However, a few available solutions can be applied by inputting documents split into smaller windows. This is inherently similar to what happens in the cross-document setting, in which systems infer coreference relations between mentions that are found in separate documents.In this paper, we unify these two challenging settings under the general framework of cross-context coreference, and introduce xCoRe, a new unified approach designed to efficiently handle short-, long-, and cross-document coreference resolution.xCoRe adopts a three-step pipeline that first identifies mentions, then creates clusters within individual contexts, and finally merges clusters across contexts.In our experiments, we show that our formulation enables joint training on shared long- and cross-document resources, increasing data availability and particularly benefiting the challenging cross-document task.Our model achieves new state-of-the-art results on cross-document benchmarks and strong performance on long-document data, while retaining top-tier results on traditional datasets, positioning it as a robust, versatile solution that can be applied across all end-to-end coreference settings.We release our models and code at http://github.com/sapienzanlp/xcore.

Retrieval-Augmented Generation (RAG) is an effective approach to enhance the factual accuracy of large language models (LLMs) by retrieving information from external databases, which are typically composed of diverse sources, to supplement the limited internal knowledge of LLMs. However, the standard RAG often risks retrieving incorrect information, as it relies solely on relevance between a query and a document, overlooking the heterogeneous reliability of these sources. To address this issue, we propose Reliability-Aware RAG (RA-RAG), a new multi-source RAG framework that estimates the reliability of sources and leverages this information to prioritize highly reliable and relevant documents, ensuring more robust and accurate response generation. Specifically, RA-RAG first estimates source reliability by cross-checking information across multiple sources. It then retrieves documents from the top-𝜅 reliable and relevant sources and aggregates their information using weighted majority voting (WMV), where the selective retrieval ensures scalability while not compromising the performance. Comprehensive experiments show that RA-RAG consistently outperforms baselines in scenarios with heterogeneous source reliability while scaling efficiently as the number of sources increases. Furthermore, we demonstrate the ability of RA-RAG to estimate real-world sources’ reliability, highlighting its practical applicability. Our code and data are available at RA-RAG.

Large language models (LLMs) show promise in legal question answering (QA), yet Thai legal QA systems face challenges due to limited data and complex legal structures. We introduce NitiBench, a novel benchmark featuring two datasets: (1) NitiBench-CCL, covering Thai financial laws, and (2) NitiBench-Tax, containing Thailand’s official tax rulings. Our benchmark also consists of specialized evaluation metrics suited for Thai legal QA. We evaluate retrieval-augmented generation (RAG) and long-context LLM (LCLM) approaches across three key dimensions: (1) the benefits of domain-specific techniques like hierarchy-aware chunking and cross-referencing, (2) comparative performance of RAG components, e.g., retrievers and LLMs, and (3) the potential of long-context LLMs to replace traditional RAG systems. Our results reveal that domain-specific components slightly improve over naive methods. At the same time, existing retrieval models still struggle with complex legal queries, and long-context LLMs have limitations in consistent legal reasoning. Our study highlights current limitations in Thai legal NLP and lays a foundation for future research in this emerging domain.

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From Input Perception to Predictive Insight: Modeling Model Blind Spots Before They Become Errors
Maggie Mi | Aline Villavicencio | Nafise Sadat Moosavi

Language models often struggle with idiomatic, figurative, or context-sensitive inputs, not because they produce flawed outputs, but because they misinterpret the input from the outset. We propose an input-only method for anticipating such failures using token-level likelihood features inspired by surprisal and the Uniform Information Density hypothesis. These features capture localized uncertainty in input comprehension and outperform standard baselines across five linguistically challenging datasets. We show that span-localized features improve error detection for larger models, while smaller models benefit from global patterns. Our method requires no access to outputs or hidden activations, offering a lightweight and generalizable approach to pre-generation error prediction.

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Wojood^Relations: Arabic Relation Extraction Corpus and Modeling
Alaa Aljabari | Mohammed Khalilia | Mustafa Jarrar

Relation extraction (RE) is a core task in natural language processing, crucial for semantic understanding, knowledge graph construction, and enhancing downstream applications. Existing work on Arabic RE remains limited due to the language’s rich morphology and syntactic complexity, and the lack of large, high-quality datasets. In this paper, we present Wojood^Relations, the largest and most diverse Arabic RE corpus to date, containing over 33K sentences (∼550K tokens) annotated with ∼15K relation triples across 40 relation types. The corpus is built on top of Wojood NER dataset with manual relation annotations carried out by expert annotators, achieving a Cohen’s 𝜅 of 0.92, indicating high reliability. In addition, we propose two methods: NLI-RE, which formulates RE as a binary natural language inference problem using relation-aware templates, and GPT-Joint, a few-shot LLM framework for joint entity and RE via relation-aware retrieval. Finally, we benchmark the dataset using both supervised models and in-context learning with LLMs. Supervised models achieve 92.89% F1 for RE, while LLMs obtain 72.73% F1 for joint entity and RE. These results establish strong baselines, highlight key challenges, and provide a foundation for advancing Arabic RE research.

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Conflicting Needles in a Haystack: How LLMs behave when faced with contradictory information
Murathan Kurfali

Large Language Models (LLMs) have demonstrated an impressive ability to retrieve and summarize complex information, but their reliability in conflicting contexts remains poorly understood. We introduce an adversarial extension of the Needle-in-a-Haystack framework in which three mutually exclusive “needles” are embedded within long documents. By systematically manipulating factors such as position, repetition, layout, and domain relevance, we evaluate how LLMs handle contradictions. We find that models almost always fail to signal uncertainty and instead confidently select a single answer, exhibiting strong and consistent biases toward repetition, recency, and particular surface forms. We further analyze whether these patterns persist across model families and sizes, and we evaluate both probability-based and generation-based retrieval. Our framework highlights critical limitations in the robustness of current LLMs—including commercial systems—to contradiction. These limitations reveal potential shortcomings in RAG systems’ ability to handle noisy or manipulated inputs and exposes risks for deployment in high-stakes applications.

Developing a general-purpose system that can extract events with massive types is a long-standing target in Event Extraction (EE). In doing so, the basic challenge comes from the absence of an efficient and effective annotation framework to construct the corresponding datasets. In this paper, we propose an LLM-based collaborative annotation framework. Through collaboration among multiple LLMs and a subsequent voting process, it refines annotations of triggers from distant supervision and then carries out argument annotation. Finally, we create EEMT, the largest EE dataset to date, featuring over **200,000** samples, **3,465** event types, and **6,297** role types. Evaluation on human-annotated test set demonstrates that the proposed framework achieves the F1 scores of **90.1%** and **85.3%** for event detection and argument extraction, strongly validating its effectiveness. Besides, to alleviate the excessively long prompts caused by massive types, we propose an LLM-based Partitioning method for EE called LLM-PEE. It first recalls candidate event types and then splits them into multiple partitions for LLMs to extract. After fine-tuning on the EEMT training set, the distilled LLM-PEE with 7B parameters outperforms state-of-the-art methods by **5.4%** and **6.1%** in event detection and argument extraction. Besides, it also surpasses mainstream LLMs by **12.9%** on the unseen datasets, which strongly demonstrates the event diversity of the EEMT dataset and the generalization capabilities of the LLM-PEE method.

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Liaozhai through the Looking-Glass: On Paratextual Explicitation of Culture-Bound Terms in Machine Translation
Sherrie Shen | Weixuan Wang | Alexandra Birch

The faithful transfer of contextually-embedded meaning continues to challenge contemporary machine translation (MT), particularly in the rendering of culture-bound terms—expressions or concepts rooted in specific languages or cultures, resisting direct linguistic transfer. Existing computational approaches to explicitating these terms have focused exclusively on in-text solutions, overlooking paratextual apparatus in the footnotes and endnotes employed by professional translators. In this paper, we formalize Genette’s (1987) theory of paratexts from literary and translation studies to introduce the task of paratextual explicitation for MT. We construct a dataset of 560 expert-aligned paratexts from four English translations of the classical Chinese short story collection Liaozhai and evaluate LLMs with and without reasoning traces on choice and content of explicitation. Experiments across intrinsic prompting and agentic retrieval methods establish the difficulty of this task, with human evaluation showing that LLM-generated paratexts improve audience comprehension, though remain considerably less effective than translator-authored ones. Beyond model performance, statistical analysis reveals that even professional translators vary widely in their use of paratexts, suggesting that cultural mediation is inherently open-ended rather than prescriptive. Our findings demonstrate the potential of paratextual explicitation in advancing MT beyond linguistic equivalence, with promising extensions to monolingual explanation and personalized adaptation.

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Concept-pedia: a Wide-coverage Semantically-annotated Multimodal Dataset
Karim Ghonim | Andrei Stefan Bejgu | Alberte Fernández-Castro | Roberto Navigli

Vision-language Models (VLMs), such as CLIP and SigLIP, have become the de facto standard for multimodal tasks, serving as essential building blocks for recent Multimodal Large Language Models, including LLaVA and PaliGemma. However, current evaluations for VLMs remain heavily anchored to ImageNet. In this paper, we question whether ImageNet’s coverage is still sufficiently challenging for modern VLMs, and investigate the impact of adding novel and varied concept categories, i.e., semantically grouped fine-grained synsets. To this end, we introduce Concept-pedia, a novel, large-scale, semantically-annotated multimodal resource covering more than 165,000 concepts. Leveraging a language-agnostic, automatic annotation pipeline grounded in Wikipedia, Concept-pedia expands the range of visual concepts, including diverse abstract categories. Building on Concept-pedia, we also present a manually-curated Visual Concept Recognition evaluation benchmark, Concept-10k, that spans thousands of concepts across a wide range of categories. Our experiments show that current models, although excelling on ImageNet, struggle with Concept-10k. Not only do these findings highlight a persistent bias toward ImageNet-centric concepts, but they also underscore the urgent need for more representative benchmarks. By offering a broader and semantically richer testbed, Concept-10k aims to support the development of multimodal systems that better generalize to the complexities of real-world visual concepts.

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RAED: Retrieval-Augmented Entity Description Generation for Emerging Entity Linking and Disambiguation
Karim Ghonim | Pere-Lluís Huguet Cabot | Riccardo Orlando | Roberto Navigli

Entity Linking and Entity Disambiguation systems aim to link entity mentions to their corresponding entries, typically represented by descriptions within a predefined, static knowledge base. Current models assume that these knowledge bases are complete and up-to-date, rendering them incapable of handling entities not yet included therein. However, in an ever-evolving world, new entities emerge regularly, making these static resources insufficient for practical applications. To address this limitation, we introduce RAED, a model that retrieves external knowledge to improve factual grounding in entity descriptions. Using sources such as Wikipedia, RAED effectively disambiguates entities and bases their descriptions on factual information, reducing the dependence on parametric knowledge. Our experiments show that retrieval not only enhances overall description quality metrics, but also reduces hallucinations. Moreover, despite not relying on fixed entity inventories, RAED outperforms systems that require predefined candidate sets at inference time on Entity Disambiguation. Finally, we show that descriptions generated by RAED provide useful entity representations for downstream Entity Linking models, leading to improved performance in the extremely challenging Emerging Entity Linking task.

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Personalized Language Models via Privacy-Preserving Evolutionary Model Merging
Kyuyoung Kim | Jinwoo Shin | Jaehyung Kim

Personalization in language models aims to tailor model behavior to individual users or user groups. Prompt-based methods incorporate user preferences into queries, while training-based methods encode them into model parameters. Model merging has also been explored for personalization under limited data. However, existing methods often fail to directly optimize task-specific utility and lack explicit mechanisms for privacy preservation. To address the limitations, we propose Privacy-Preserving Model Merging via Evolutionary Algorithms (PriME), a novel personalization approach that employs gradient-free methods to directly optimize utility while reducing privacy risks. By integrating privacy preservation into the optimization objective, PriME creates personalized modules that effectively capture target user preferences while minimizing privacy risks for data-sharing users. Experiments on the LaMP benchmark show that PriME consistently outperforms a range of baselines, achieving up to a 45% improvement in task performance. Further analysis demonstrates that PriME achieves a superior privacy-utility trade-off compared to a prior state-of-the-art, with enhanced robustness to membership inference attacks and greater utility in capturing user preferences.

Although speech language models are expected to align well with brain language processing during speech comprehension, recent studies have found that they fail to capture brain-relevant semantics beyond low-level features. Surprisingly, text-based language models exhibit stronger alignment with brain language regions, as they better capture brain-relevant semantics. However, no prior work has examined the alignment effectiveness of text/speech representations from multimodal models. This raises several key questions: Can speech embeddings from such multimodal models capture brain-relevant semantics through cross-modal interactions? Which modality can take advantage of this synergistic multimodal understanding to improve alignment with brain language processing? Can text/speech representations from such multimodal models outperform unimodal models? To address these questions, we systematically analyze multiple multimodal models, extracting both text- and speech-based representations to assess their alignment with MEG brain recordings during naturalistic story listening. We find that text embeddings from both multimodal and unimodal models significantly outperform speech embeddings from these models. Specifically, multimodal text embeddings exhibit a peak around 200 ms, suggesting that they benefit from speech embeddings, with heightened activity during this time period. However, speech embeddings from these multimodal models still show a similar alignment compared to their unimodal counterparts, suggesting that they do not gain meaningful semantic benefits over text-based representations. These results highlight an asymmetry in cross-modal knowledge transfer, where the text modality benefits more from speech information, but not vice versa.

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STARQA: A Question Answering Dataset for Complex Analytical Reasoning over Structured Databases
Mounica Maddela | Lingjue Xie | Daniel Preotiuc-Pietro | Mausam

Our goal is to assess how well current Text2SQL systems support SQL analysts in their primary work of performing complex analytics on specialized relational databases. Although several benchmarks evaluate Text2SQL models, the complexity of questions (and the output SQL queries) in most datasets is inherently limited – they do not focus on intents involving analytics and reasoning. In response, we present STARQA, the first public human-created dataset focused on complex analytical questions and answers (involving nested joins, time series analytics, statistical operations, and more) on three specialized-domain databases. In addition to standard Text2SQL baselines, we also evaluate a novel approach (Text2SQLCode) that decomposes the task through a combination of SQL and Python: SQL is responsible for data fetch, and Python more naturally performs reasoning. Our results demonstrate that both existing Text2SQL systems and our Text2SQLCode approach find STARQA questions quite challenging, even though Text2SQLCode achieves better performance on the more difficult questions. Further analyses assess the typical errors made by existing systems and charts a research path for pushing the capabilities of real-world systems.

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Slim-SC: Thought Pruning for Efficient Scaling with Self-Consistency
Colin Hong | Xu Guo | Anand Chaanan Singh | Esha Choukse | Dmitrii Ustiugov

Recently, Test-Time Scaling (TTS) has gained increasing attention for improving LLM reasoning performance at test time without retraining the model. A notable TTS technique is Self-Consistency (SC), which generates multiple reasoning chains in parallel and selects the final answer via majority voting. While effective, the order-of-magnitude computational overhead limits its broad deployment. Prior attempts to accelerate SC mainly rely on model-based confidence scores or heuristics with limited empirical support. For the first time, we theoretically and empirically analyze the inefficiencies of SC and reveal actionable opportunities for improvement. Building on these insights, we propose Slim-SC, a step-wise pruning strategy that identifies and removes redundant chains using inter-chain similarity at the thought level.Experiments on three STEM reasoning datasets and two recent LLM architectures show that Slim-SC reduces inference latency and KVC usage by up to 45% and 26%, respectively, with R1-Distill, while maintaining or improving accuracy, thus offering a simple yet efficient TTS alternative for SC.

Reasoning models have demonstrated remarkable performance on complex tasks by generating long reasoning traces prior to producing final answers. However, previous research on long-context scaling in language models has generally focused on managing lengthy input prompts instead of producing long outputs. To leverage the strong long context understanding abilities of current models, we introduce Understanding-to-Reasoning Transition (URT) fine-tuning, a sequence-level curriculum learning framework that gradually shifts a model’s focus from interpreting long chain-of-thoughts to generating them. By incorporating partial reasoning steps in the input context, URT naturally exposes the model to diverse prompt lengths during training, preserving its performance on long-context comprehension while developing advanced reasoning capabilities. Experiments on rigorous reasoning benchmarks, including AIME24 and GPQA Diamond, reveal that our approach surpasses standard fine-tuning by over 10%, while maintaining robust performance on the understanding tasks in RULER.

This paper compares the effectiveness of traditional machine learning methods, encoder-based models, and large language models (LLMs) on the task of detecting depression and anxiety. Five Russian-language datasets were considered, each differing in format and in the method used to define the target pathology class. We tested AutoML models based on linguistic features, several variations of encoder-based Transformers such as BERT, and state-of-the-art LLMs as pathology classification models. The results demonstrated that LLMs outperform traditional methods, particularly on noisy and small datasets where training examples vary significantly in text length and genre. However, psycholinguistic features and encoder-based models can achieve performance comparable to language models when trained on texts from individuals with clinically confirmed depression, highlighting their potential effectiveness in targeted clinical applications.

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Efficient Real-time Refinement of Language Model Text Generation
Joonho Ko | Jinheon Baek | Sung Ju Hwang

Large language models (LLMs) have shown remarkable performance across a wide range of natural language tasks. However, a critical challenge remains in that they sometimes generate factually incorrect answers. To address this, while many previous work has focused on identifying errors in their generation and further refining them, they are slow in deployment since they are designed to verify the response from LLMs only after their entire generation (from the first to last tokens) is done. Further, we observe that once LLMs generate incorrect tokens early on, there is a higher likelihood that subsequent tokens will also be factually incorrect. To this end, in this work, we propose Streaming-VR (Streaming Verification and Refinement), a novel approach designed to enhance the efficiency of verification and refinement of LLM outputs. Specifically, the proposed Streaming-VR enables on-the-fly verification and correction of tokens as they are being generated, similar to a streaming process, ensuring that each subset of tokens is checked and refined in real-time by another LLM as the LLM constructs its response. Through comprehensive evaluations on multiple datasets, we demonstrate that our approach not only enhances the factual accuracy of LLMs, but also offers a more efficient solution compared to prior refinement methods.

Masked diffusion models (MDMs) offer a promising non-autoregressive alternative for large language modeling. Standard decoding methods for MDMs, such as confidence-based sampling, select tokens independently based on individual token confidences at each diffusion step. However, we observe that this independent token selection often results in generation orders resembling sequential autoregressive processes, limiting the advantages of non-autoregressive modeling. To mitigate this pheonomenon, we propose Reward-Weighted Sampling (RWS), a novel decoding strategy that leverages an external reward model to provide a principled global signal during the iterative diffusion process. Specifically, at each diffusion step, RWS evaluates the quality of the entire intermediate sequence and scales token logits accordingly, guiding token selection by integrating global sequence-level coherence. This method selectively increases the confidence of tokens that initially have lower scores, thereby promoting a more non-autoregressive generation order. Furthermore, we provide theoretical justification showing that reward-weighted logit scaling induces beneficial rank reversals in token selection and consistently improves expected reward. Experiments demonstrate that RWS significantly promotes non-autoregressive generation orders, leading to improvements across multiple evaluation metrics. These results highlight the effectiveness of integrating global signals in enhancing both the non-autoregressive properties and overall performance of MDMs.

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AI Argues Differently: Distinct Argumentative and Linguistic Patterns of LLMs in Persuasive Contexts
Esra Dönmez | Maximilian Maurer | Gabriella Lapesa | Agnieszka Falenska

Distinguishing LLM-generated text from human-written is a key challenge for safe and ethical NLP, particularly in high-stake settings such as persuasive online discourse. While recent work focuses on detection, real-world use cases also demand interpretable tools to help humans understand and distinguish LLM-generated texts. To this end, we present an analysis framework comparing human- and LLM-authored arguments using two easily-interpretable feature sets: general-purpose linguistic features (e.g., lexical richness, syntactic complexity) and domain-specific features related to argument quality (e.g., logical soundness, engagement strategies). Applied to */r/ChangeMyView* arguments by humans and three LLMs, our method reveals clear patterns: LLM-generated counter-arguments show lower type-token and lemma-token ratios but higher emotional intensity — particularly in anticipation and trust. They more closely resemble textbook-quality arguments — cogent, justified, explicitly respectful toward others, and positive in tone. Moreover, counter-arguments generated by LLMs converge more closely with the original post’s style and quality than those written by humans. Finally, we demonstrate that these differences enable a lightweight, interpretable, and highly effective classifier for detecting LLM-generated comments in CMV.

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TounsiBench: Benchmarking Large Language Models for Tunisian Arabic
Souha Ben Hassine | Asma Arrak | Marouene Addhoum | Steven R Wilson

In this work, we introduce the first benchmark for evaluating the capabilities of large language models (LLMs) in understanding and generating responses in Tunisian Arabic. To achieve this, we construct a dataset of Tunisian Arabic instructions and prompt ten widely-used LLMs that claim to support Arabic. We then assess the LLM responses through both human and LLM-based evaluations across four criteria: quality, correctness, relevance, and dialectal adherence. We analyze the agreement and correlation between these judgments and identify GPT-4o as our automated judge model based on its high correlation with human ratings, and generate a final leaderboard using this model. Our error analysis reveals that most LLMs struggle with recognizing and properly responding in Tunisian Arabic. To facilitate further research, we release our dataset, along with gold-standard human-written responses for all 744 instructions, and our evaluation framework, allowing others to benchmark their own models.

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Moral Framing in Politics (MFiP): A new resource and models for moral framing
Ines Rehbein | Ines Reinig | Simone Paolo Ponzetto

The construct of morality permeates our entire lives and influences our behavior and how we perceive others. It therefore comes at no surprise that morality also plays an important role in politics, as morally framed arguments are perceived as more appealing and persuasive. Thus, being able to identify moral framing in political communication and to detect subtle differences in politicians’ moral framing can provide the basis for many interesting analyses in the political sciences. In the paper, we release MoralFramingInPolitics (MFiP), a new corpus of German parliamentary debates where the speakers’ moral framing has been coded, using the framework of Moral Foundations Theory (MFT). Our fine-grained annotations distinguish different types of moral frames and also include narrative roles, together with the moral foundations for each frame. We then present models for frame type and moral foundation classification and explore the benefits of data augmentation (DA) and contrastive learning (CL) for the two tasks. All data and code will be made available to the research community.

Almost 50% depression patients face the risk of going into relapse. The risk increases to 80% after the second episode of depression. Although, depression detection from social media has attained considerable attention, depression relapse detection has remained largely unexplored due to the lack of curated datasets and the difficulty of distinguishing relapse and non-relapse users. In this work, we present ReDepress, the first clinically validated social media dataset focused on relapse, comprising 204 Reddit users annotated by mental health professionals. Unlike prior approaches, our framework draws on cognitive theories of depression, incorporating constructs such as attention bias, interpretation bias, memory bias and rumination into both annotation and modeling. Through statistical analyses and machine learning experiments, we demonstrate that cognitive markers significantly differentiate relapse and non-relapse groups, and that models enriched with these features achieve competitive performance, with transformer-based temporal models attaining an F1 of 0.86. Our findings validate psychological theories in real-world textual data and underscore the potential of cognitive-informed computational methods for early relapse detection, paving the way for scalable, low-cost interventions in mental healthcare.

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iKnow-audio: Integrating Knowledge Graphs with Audio-Language Models
Michel Olvera | Changhong Wang | Paraskevas Stamatiadis | Gaël Richard | Slim Essid

Contrastive Language–Audio Pretraining (CLAP) models learn by aligning audio and text in a shared embedding space, enabling powerful zero-shot recognition. However, their performance is highly sensitive to prompt formulation and language nuances, and they often inherit semantic ambiguities and spurious correlations from noisy pretraining data. While prior work has explored prompt engineering, adapters, and prefix tuning to address these limitations, the use of structured prior knowledge remains largely unexplored. We present iKnow-audio, a framework that integrates knowledge graphs with audio-language models to provide robust semantic grounding. iKnow-audio builds on the Audio-centric Knowledge Graph (AKG), which encodes ontological relations comprising semantic, causal, and taxonomic connections reflective of everyday sound scenes and events. By training knowlege graph embedding models on the AKG and refining CLAP predictions through this structured knowledge, iKnow-audio improves disambiguation of acoustically similar sounds and reduces reliance on prompt engineering. Comprehensive zero-shot evaluations across six benchmark datasets demonstrate consistent gains over baseline CLAP, supported by embedding-space analyses that highlight improved relational grounding. Resources are publicly available at https://github.com/michelolzam/iknow-audio

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EduVidQA: Generating and Evaluating Long-form Answers to Student Questions based on Lecture Videos
Sourjyadip Ray | Shubham Sharma | Somak Aditya | Pawan Goyal

As digital platforms redefine educational paradigms, ensuring interactivity remains vital for effective learning. This paper explores using Multimodal Large Language Models (MLLMs) to automatically respond to student questions from online lectures - a novel question answering task of real world significance. We introduce the EduVidQA Dataset with 5252 question-answer pairs (both synthetic and real-world) from 296 computer science videos covering diverse topics and difficulty levels. To understand the needs of the dataset and task evaluation, we empirically study the qualitative preferences of students, which we provide as an important contribution to this line of work. Our benchmarking experiments consist of 6 state-of-the-art MLLMs, through which we study the effectiveness of our synthetic data for finetuning, as well as showing the challenging nature of the task. We evaluate the models using both text-based and qualitative metrics, thus showing a nuanced perspective of the models’ performance, which is paramount to future work. This work not only sets a benchmark for this important problem, but also opens exciting avenues for future research in the field of Natural Language Processing for Education.

Large language models (LLMs) have revolutionized natural language processing, yet their tendency to hallucinate poses serious challenges for reliable deployment. Despite numerous hallucination detection methods, their evaluations often rely on ROUGE, a metric based on lexical overlap that misaligns with human judgments. Through comprehensive human studies, we demonstrate that while ROUGE exhibits high recall, its extremely low precision leads to misleading performance estimates. In fact, several established detection methods show performance drops of up to 45.9% when assessed using human-aligned metrics like LLM-as-Judge. Moreover, our analysis reveals that simple heuristics based on response length can rival complex detection techniques, exposing a fundamental flaw in current evaluation practices. We argue that adopting semantically aware and robust evaluation frameworks is essential to accurately gauge the true performance of hallucination detection methods, ultimately ensuring the trustworthiness of LLM outputs.

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Turning Logic Against Itself: Probing Model Defenses Through Contrastive Questions
Rachneet Singh Sachdeva | Rima Hazra | Iryna Gurevych

Large language models, despite extensive alignment with human values and ethical principles, remain vulnerable to sophisticated jailbreak attacks that exploit their reasoning abilities. Existing safety measures often detect overt malicious intent but fail to address subtle, reasoning-driven vulnerabilities. In this work, we introduce POATE (Polar Opposite query generation, Adversarial Template construction, and Elaboration), a novel jailbreak technique that harnesses contrastive reasoning to provoke unethical responses. POATE crafts semantically opposing intents and integrates them with adversarial templates, steering models toward harmful outputs with remarkable subtlety. We conduct extensive evaluation across six diverse language model families of varying parameter sizes to demonstrate the robustness of the attack, achieving significantly higher attack success rates (44%) compared to existing methods. To counter this, we propose Intent-Aware CoT and Reverse Thinking CoT, which decompose queries to detect malicious intent and reason in reverse to evaluate and reject harmful responses. These methods enhance reasoning robustness and strengthen the model’s defense against adversarial exploits.

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CHURRO: Making History Readable with an Open-Weight Large Vision-Language Model for High-Accuracy, Low-Cost Historical Text Recognition
Sina Semnani | Han Zhang | Xinyan He | Merve Tekgurler | Monica Lam

Accurate text recognition for historical documents can greatly advance the study and preservation of cultural heritage. Existing vision-language models (VLMs), however, are designed for modern, standardized texts and are not equipped to read the diverse languages and scripts, irregular layouts, and frequent degradation found in historical materials.This paper presents CHURRO, a 3B-parameter open-weight VLM specialized for historical text recognition. The model is trained on CHURRO-DS, the largest historical text recognition dataset to date. CHURRO-DS unifies 155 historical corpora comprising 99,491 pages, spanning 22 centuries of textual heritage across 46 language clusters, including historical variants and dead languages.We evaluate several open-weight and closed VLMs and optical character recognition (OCR) systems on CHURRO-DS and find that CHURRO outperforms all other VLMs. On the CHURRO-DS test set, CHURRO achieves 82.3% (printed) and 70.1% (handwritten) normalized Levenshtein similarity, surpassing the second-best model, Gemini 2.5 Pro, by 1.4% and 6.5%, respectively, while being 15.5 times more cost-effective.By releasing the model and dataset, we aim to enable community-driven research to improve the readability of historical texts and accelerate scholarship.

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Towards Author-informed NLP: Mind the Social Bias
Inbar Pendzel | Einat Minkov

Social text understanding is prone to fail when opinions are conveyed implicitly or sarcastically. It is therefore desired to model users’ contexts in processing the texts authored by them. In this work, we represent users within a social embedding space that was learned from the Twitter network at large-scale. Similar to word embeddings that encode lexical semantics, the network embeddings encode latent dimensions of social semantics. We perform extensive experiments on author-informed stance prediction, demonstrating improved generalization through inductive social user modeling, both within and across topics. Similar results were obtained for author-informed toxicity and incivility detection. The proposed approach may pave way to social NLP that considers user embeddings as contextual modality. However, our investigation also reveals that user stances are correlated with the personal socio-demographic traits encoded in their embeddings. Hence, author-informed NLP approaches may inadvertently model and reinforce socio-demographic and other social biases.

Wikipedia is the largest open knowledge corpus, widely used worldwide and serving as a key resource for training large language models (LLMs) and retrieval-augmented generation (RAG) systems. Ensuring its accuracy is therefore critical. But how accurate is Wikipedia, and how can we improve it?We focus on inconsistencies, a specific type of factual inaccuracy, and introduce the task of corpus-level inconsistency detection. We present CLAIRE, an agentic system that combines LLM reasoning with retrieval to surface potentially inconsistent claims along with contextual evidence for human review. In a user study with experienced Wikipedia editors, 87.5% reported higher confidence when using CLAIRE, and participants identified 64.7% more inconsistencies in the same amount of time.Combining CLAIRE with human annotation, we contribute WIKICOLLIDE, the first benchmark of real Wikipedia inconsistencies. Using random sampling with CLAIRE-assisted analysis, we find that at least 3.3% of English Wikipedia facts contradict another fact, with inconsistencies propagating into 7.3% of FEVEROUS and 4.0% of AmbigQA examples. Benchmarking strong baselines on this dataset reveals substantial headroom: the best fully automated system achieves an AUROC of only 75.1%.Our results show that contradictions are a measurable component of Wikipedia and that LLM-based systems like CLAIRE can provide a practical tool to help editors improve knowledge consistency at scale.

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Leveraging Multilingual Training for Authorship Representation: Enhancing Generalization across Languages and Domains
Junghwan Kim | Haotian Zhang | David Jurgens

Authorship representation (AR) learning, which models an author’s unique writing style, has demonstrated strong performance in authorship attribution tasks. However, prior research has primarily focused on monolingual settings—mostly in English—leaving the potential benefits of multilingual AR models underexplored. We introduce a novel method for multilingual AR learning that incorporates two key innovations: probabilistic content masking, which encourages the model to focus on stylistically indicative words rather than content-specific words, and language-aware batching, which improves contrastive learning by reducing cross-lingual interference. Our model is trained on over 4.5 million authors across 36 languages and 13 domains. It consistently outperforms monolingual baselines in 21 out of 22 non-English languages, achieving an average Recall@8 improvement of 4.85%, with a maximum gain of 15.91% in a single language. Furthermore, it exhibits stronger cross-lingual and cross-domain generalization compared to a monolingual model trained solely on English. Our analysis confirms the effectiveness of both proposed techniques, highlighting their critical roles in the model’s improved performance.

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DrFrattn: Directly Learn Adaptive Policy from Attention for Simultaneous Machine Translation
Libo Zhao | Jing Li | Ziqian Zeng

Simultaneous machine translation (SiMT) necessitates a robust read/write (R/W) policy to determine the optimal moments for translation, thereby balancing translation quality and latency. Effective timing in translation can align source and target tokens accurately. The attention mechanism within translation models inherently provides valuable alignment information. Building on this, previous research has attempted to modify the attention mechanism’s structure to leverage its alignment properties during training, employing multi-task learning to derive the read/write policy. However, this multi-task learning approach may compromise the efficacy of the attention mechanism itself. This raises a natural question: why not directly learn the read/write policy from the well-trained attention mechanism? In this study, we propose DrFrattn, a method that directly learns adaptive policies from the attention mechanism. Experimental results across various benchmarks demonstrate that our approach achieves an improved balance between translation accuracy and latency.

According to the U.S. National Institutes of Health, more than 3.4 million children experience speech disorders that require clinical intervention. The number of speech-language pathologists (SLPs) is roughly 20 times fewer than the number of affected children, highlighting a significant gap in children’s care and a pressing need for technological support that improves the productivity of SLPs. State-of-the-art multimodal language models (MLMs) show promise for supporting SLPs, but their use remains underexplored largely due to a limited understanding of their performance in high-stakes clinical settings. To address this gap, we collaborate with domain experts to develop a taxonomy of real-world use cases of MLMs in speech-language pathologies. Building on this taxonomy, we introduce the first comprehensive benchmark for evaluating MLM across five core use cases, each containing 1,000 manually annotated data points. This benchmark includes robustness and sensitivity tests under various settings, including background noise, speaker gender, and accent. Our evaluation of 15 state-of-the-art MLMs reveals that no single model consistently outperforms others across all tasks. Notably, we find systematic disparities, with models performing better on male speakers, and observe that chain-of-thought prompting can degrade performance on classification tasks with large label spaces and narrow decision boundaries. Furthermore, we study fine-tuning MLMs on domain-specific data, achieving improvements of over 30% compared to base models. These findings highlight both the potential and limitations of current MLMs for speech-language pathology applications, underscoring the need for further research and targeted development.

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NormXLogit: The Head-on-Top Never Lies
Sina Abbasi | Mohammad Reza Modarres | Mohammad Taher Pilehvar

With new large language models (LLMs) emerging frequently, it is important to consider the potential value of model-agnostic approaches that can provide interpretability across a variety of architectures. While recent advances in LLM interpretability show promise, many rely on complex, model-specific methods with high computational costs. To address these limitations, we propose NormXLogit, a novel technique for assessing the significance of individual input tokens. This method operates based on the input and output representations associated with each token. First, we demonstrate that the norm of word embeddings can be utilized as a measure of token importance. Second, we reveal a significant relationship between a token’s importance and how predictive its representation is of the model’s final output. Extensive analyses indicate that our approach outperforms existing gradient-based methods in terms of faithfulness and offers competitive performance compared to leading architecture-specific techniques.

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Doc2Chart: Intent-Driven Zero-Shot Chart Generation from Documents
Akriti Jain | Pritika Ramu | Aparna Garimella | Apoorv Saxena

Large Language Models (LLMs) have demonstrated strong capabilities in transforming text descriptions or tables to data visualizations via instruction-tuning methods. However, it is not straightforward to apply these methods directly for a more real-world use case of visualizing data from long documents based on user-given intents, as opposed to the user pre-selecting the relevant content manually. We introduce the task of _intent-based chart generation_ from documents: given a user-specified intent and document(s), the goal is to generate a chart adhering to the intent and grounded on the document(s) in a zero-shot setting. We propose an unsupervised, two-staged framework in which an LLM first extracts relevant information from the document(s) by decomposing the intent and iteratively validates and refines this data. Next, a heuristic-guided module selects an appropriate chart type before final code generation. To assess the data accuracy of the generated charts, we propose an attribution-based metric that uses a structured textual representation of charts, instead of relying on visual decoding metrics that often fail to capture the chart data effectively. To validate our approach, we curate a dataset comprising of 1,242 <intent, document, charts> tuples from two domains, finance and scientific, in contrast to the existing datasets that are largely limited to parallel text descriptions/ tables and their corresponding charts. We compare our approach with baselines using single-shot chart generation using LLMs and query-based retrieval methods; our method outperforms by upto 9 points and 17 points in terms of chart data accuracy and chart type respectively over the best baselines.

Recently, autonomous agents built on large language models (LLMs) have experienced significant development and are being deployed in real-world applications. Through the usage of tools, these systems can perform actions in the real world. Given the agents’ practical applications and ability to execute consequential actions, such autonomous systems can cause more severe damage than a standalone LLM if compromised. While some existing research has explored harmful actions by LLM agents, our study approaches the vulnerability from a different perspective. We introduce a new type of attack that causes malfunctions by misleading the agent into executing repetitive or irrelevant actions. Our experiments reveal that these attacks can induce failure rates exceeding 80% in multiple scenarios. Through attacks on implemented and deployable agents in multi-agent scenarios, we accentuate the realistic risks associated with these vulnerabilities. To mitigate such attacks, we propose self-examination defense methods. Our findings indicate these attacks are more difficult to detect compared to previous overtly harmful attacks, highlighting the substantial risks associated with this vulnerability.

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FoREST: Frame of Reference Evaluation in Spatial Reasoning Tasks
Tanawan Premsri | Parisa Kordjamshidi

Spatial reasoning is a fundamental aspect of human intelligence. One key concept in spatial cognition is the Frame of Reference (FoR), which identifies the perspective of spatial expressions. Despite its significance, FoR has received limited attention in AI models that need spatial intelligence. There is a lack of dedicated benchmarks and in-depth evaluation of large language models (LLMs) in this area. To address this issue, we introduce the Frame of Reference Evaluation in Spatial Reasoning Tasks (FoREST) benchmark, designed to assess FoR comprehension in LLMs. We evaluate LLMs on answering questions that require FoR comprehension and layout generation in text-to-image models using FoREST. Our results reveal a notable performance gap across different FoR classes in various LLMs, affecting their ability to generate accurate layouts for text-to-image generation. This highlights critical shortcomings in FoR comprehension. To improve FoR understanding, we propose Spatial-Guided prompting, which improves LLMs’ ability to extract essential spatial concepts. Our proposed method improves overall performance across spatial reasoning tasks.

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Multilinguality Does not Make Sense: Investigating Factors Behind Zero-Shot Cross-Lingual Transfer in Sense-Aware Tasks
Roksana Goworek | Haim Dubossarsky

Cross-lingual transfer allows models to perform tasks in languages unseen during training and is often assumed to benefit from increased multilinguality. In this work, we challenge this assumption in the context of two underexplored, sense-aware tasks: polysemy disambiguation and lexical semantic change. Through a large-scale analysis across 28 languages, we show that multilingual training is neither necessary nor inherently beneficial for effective transfer. Instead, we find that confounding factors, such as fine-tuning data composition and evaluation artifacts, can better account for the perceived advantages of multilinguality. Our findings call for more rigorous evaluations in multilingual NLP, and more nuanced and sensible choice of models for transfer. We release fine-tuned models and benchmarks to support further research, with implications extending to low-resource and typologically diverse languages.

Domain-specific multilingual terminology is essential for accurate machine translation (MT) and cross-lingual NLP applications. We present a gold-standard terminology resource for the tax and financial education domains, built from curated governmental publications and covering seven typologically diverse languages: English, Spanish, Russian, Vietnamese, Korean, Chinese (traditional and simplified) and Haitian Creole. Using this resource, we assess various MT systems and LLMs on translation quality and term accuracy. We annotate over 3,000 terms for domain-specificity, facilitating a comparison between domain-specific and general term translations, and observe models’ challenges with specialized tax terms. We also analyze the case of terminology-aided translation, and the LLMs’ performance in extracting the translated term given the context. Our results highlight model limitations and the value of high-quality terminologies for advancing MT research in specialized contexts.

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Train It and Forget It: Merge Lists are Unnecessary for BPE Inference in Language Models
Tomohiro Sawada | Kartik Goyal

Standard Byte-Pair Encoding (BPE) tokenization compresses text by pairing a learned token vocabulary with a detailed merge list. Recent work has shown that this merge list exposes a potential attack surface for extracting information about language model’s training data. In this paper, we explore the downstream impact of BPE inference algorithms that do not rely on this merge list at all, and hence differ from the encoding process during the BPE training. To address this question, we investigate two broad classes of BPE inference schemes that differ from BPE application during training: a) targetted deviation from merge-lists including random merge orders, and various corruptions of merge list involving deletion/truncation, and b) non-targetted BPE inference algorithms that do not depend on the merge list but focus on compressing the text either greedily or exactly. Extensive experiments across diverse language modeling tasks like accuracy-based QA bench- marks, machine translation, and open-ended generation reveal that while the targetted deviation from the merge lists exhibit significant degradation in language model performance, the non-targetted merge-list free inference algorithms result in minimal impact on downstream performance that is often much smaller than expected. These findings pave way for simpler and potentially more privacy-preserving tokenization schemes that do not catastrophically compromise model performance.

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Spectral Scaling Laws in Language Models: emphHow Effectively Do Feed-Forward Networks Use Their Latent Space?
Nandan Kumar Jha | Brandon Reagen

As Large Language Models (LLMs) scale, the question is not just how large they become, but how much of their capacity is effectively utilized. Existing scaling laws relate model size to loss, yet overlook how components exploit their latent space. In this work, we focus on Feed-Forward Networks (FFNs) and recast width selection as a spectral utilization optimization problem. Using a lightweight diagnostic suite: Hard Rank (participation ratio), Soft Rank (Shannon Rank), Spectral Concentration, and the composite Spectral Utilization Index (SUI), we quantify how many latent directions are meaningfully activated across LLaMA, GPT-2, and nGPT families. Our key finding is an Asymmetric Spectral Scaling Law: soft rank follows an almost perfect power law with FFN width, while hard rank grows only sublinearly, with high variance. This asymmetry suggests that widening FFNs mostly adds low-energy tail directions, while dominant-mode subspaces saturate early. Moreover, at larger widths, variance further collapses into a narrow subspace, leaving much of the latent space under-utilized. These results recast FFN width selection as a principled trade-off between tail capacity and dominant-mode capacity, offering concrete guidance for inference-efficient LLM design.

Large language models have made tremendous progress in recent years, but low-resource languages, like Tibetan, remain significantly underrepresented in their evaluation. Despite Tibetan being spoken by over seven million people, it has largely been neglected in the development and assessment of LLMs. To address this gap, we present a Tibetan Language Understanding Evaluation Benchmark, TLUE, which is also the first large-scale benchmark for measuring the proficiency of large language models in the Tibetan language. TLUE comprises two major components: a comprehensive multi-task understanding benchmark spanning 5 domains and 67 subdomains, and a safety benchmark encompassing 7 subdomains. Finally, we evaluate a diverse set of state-of-the-art LLMs. Experimental results demonstrate that most large language models perform below the random baseline, especially highlighting the considerable challenges they face in Tibetan language processing. TLUE provides a crucial foundation for advancing future research in Tibetan language understanding and highlights the importance of promoting greater inclusivity in the development of large language models.

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Retrieving Support to Rank Answers in Open-Domain Question Answering
Zeyu Zhang | Alessandro Moschitti | Thuy Vu

We introduce a novel Question Answering (QA) architecture that enhances answer selection by retrieving targeted supporting evidence. Unlike traditional methods, which retrieve documents or passages relevant only to a query q, our approach retrieves content relevant to the combined pair (q, a), explicitly emphasizing the supporting relation between the query and a candidate answer a. By prioritizing this relational context, our model effectively identifies paragraphs that directly substantiate the correctness of a with respect to q, leading to more accurate answer verification than standard retrieval systems. Our neural retrieval method also scales efficiently to collections containing hundreds of millions of paragraphs. Moreover, this approach can be used by large language models (LLMs) to retrieve explanatory paragraphs that ground their reasoning, enabling them to tackle more complex QA tasks with greater reliability and interpretability.

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Trojsten Benchmark: Evaluating LLM Problem-Solving in Slovak STEM Competition Problems
Adam Zahradník | Marek Suppa

Large language models show promising performance on reasoning tasks, yet evaluation methods for low-resource languages remain limited, particularly for complex STEM problem-solving. We introduce Trojsten Benchmark, a Slovak-language dataset of 1,108 high-school competition problems with reference solutions across mathematics, physics, and programming, and a rubric-based LLM grading framework. Using GPT-4 to generate rubrics and grade solutions, we observe 1.05 average absolute deviation from human graders (5-point scale), while benchmarking GPT-3.5-Turbo, GPT-4, GPT-4o, and open-weight models (Llama 3, Phi-3). We quantify multistep reasoning performance by difficulty, show consistent underperformance on harder items, and demonstrate language sensitivity: accuracy drops on English translations of Slovak statements, evidencing challenges beyond translation. Trojsten Benchmark complements English-centric math datasets (e.g., MATH, GSM8K) by targeting open-response, rubric-gradable reasoning under low-resource linguistic framing. We release code and data to enable reproducible evaluation and human-aligned auto-grading for STEM in under-served languages.

The detection of audio deepfakes (ADD) has become increasingly important due to the rapid evolution of generative speech models. However, progress in this field remains uneven across languages, particularly for low-resource languages like Portuguese, which lack high-quality datasets. In this paper, we introduce BRSpeech-DF, the first publicly available ADD dataset for Portuguese, encompassing both Brazilian and European variants. The dataset contains over 458,000 utterances, including a smaller portion of real speech from 62 speakers and a large collection of synthetic samples generated using multiple zero-shot text-to-speech (TTS) models, each conditioned on the original speaker’s voice. By providing this resource, our objective is to support the development of robust, multilingual detection systems, thereby advancing equity in speech forensics and security research. BRSpeech-DF addresses a significant gap in annotated data for underrepresented languages, facilitating more inclusive and generalizable advancements in synthetic speech detection.

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A Simple Yet Effective Method for Non-Refusing Context Relevant Fine-grained Safety Steering in LLMs
Shaona Ghosh | Amrita Bhattacharjee | Yftah Ziser | Christopher Parisien

Fine-tuning large language models (LLMs) to meet evolving safety policies is costly and impractical. Mechanistic interpretability enables inference-time control through latent activation steering, but its potential for precise, customizable safety adjustments remains underexplored. We propose SafeSteer, a simple and effective method to guide LLM outputs by (i) leveraging category-specific steering vectors for fine-grained control, (ii) applying a gradient-free, unsupervised approach that enhances safety while preserving text quality and topic relevance without forcing explicit refusals, and (iii) eliminating the need for contrastive safe data. Across multiple LLMs, datasets, and risk categories, SafeSteer provides precise control, avoids blanket refusals, and directs models to generate safe, relevant content, aligning with recent findings that simple activation-steering techniques often outperform more complex alternatives.

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Statistical and Neural Methods for Hawaiian Orthography Modernization
Jaden Kapali | Keaton Williamson | Winston Wu

Hawaiian orthography employs two distinct spelling systems, both of which are used by communities of speakers today. These two spelling systems are distinguished by the presence of the ‘okina letter and kahakō diacritic, which represent glottal stops and long vowels, respectively. We develop several models ranging in complexity to convert between these two orthographies. Our results demonstrate that simple statistical n-gram models surprisingly outperform neural seq2seq models and LLMs, highlighting the potential for traditional machine learning approaches in a low-resource setting.

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so much depends / upon / a whitespace: Why Whitespace Matters for Poets and LLMs
Sriharsh Bhyravajjula | Melanie Walsh | Anna Preus | Maria Antoniak

Whitespace is a critical component of poetic form, reflecting both adherence to standardized forms and rebellion against those forms. Each poem’s whitespace distribution reflects the artistic choices of the poet and is an integral semantic and spatial feature of the poem. Yet, despite the popularity of poetry as both a long-standing art form and as a generation task for large language models (LLMs), whitespace has not received sufficient attention from the NLP community. Using a corpus of 19k English-language published poems from Poetry Foundation, we investigate how 4k poets have used whitespace in their works. We release a subset of 2.8k public-domain poems with preserved formatting to facilitate further research in this area. We compare whitespace usage in the published poems to (1) 51k LLM-generated poems, and (2) 12k unpublished poems posted in an online community. We also explore whitespace usage across time periods, poetic forms, and data sources. Additionally, we find that different text processing methods can result in significantly different representations of whitespace in poetry data, motivating us to use these poems and whitespace patterns to discuss implications for the processing strategies used to assemble pretraining datasets for LLMs.

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Certified Mitigation of Worst-Case LLM Copyright Infringement
Jingyu Zhang | Jiacan Yu | Marc Marone | Benjamin Van Durme | Daniel Khashabi

The exposure of large language models (LLMs) to copyrighted material during pre-training raises concerns about unintentional copyright infringement post deployment. This has driven the development of “copyright takedown” methods—post-training approaches aimed at preventing models from generating content substantially similar to copyrighted ones. While current mitigation approaches are somewhat effective for average-case risks, we demonstrate that they overlook worst-case copyright risks exhibited by the existence of long, verbatim quotes from copyrighted sources. We propose BloomScrub, a remarkably simple yet highly effective inference-time approach that provides certified copyright takedown. Our method repeatedly interleaves quote detection with rewriting techniques to transform potentially infringing segments. By leveraging efficient data sketches (Bloom filters), our approach enables scalable copyright screening—even for large-scale real-world corpora. When quotes beyond a length threshold cannot be removed, the system can abstain from responding, offering certified risk reduction. Experimental results show that BloomScrub reduces infringement risk, preserves utility, and accommodates different levels of enforcement stringency with adaptive abstention. Our results suggest that lightweight, inference-time methods can be surprisingly effective for copyright prevention.

Large language models (LLMs) excel at many NLP tasks, yet their multi-step logical reasoning remains unreliable. Existing solutions such as Chain-of-Thought prompting generate intermediate steps but provide no internal check of their logical coherence. In this paper, we use the “QK-score”, a lightweight metric based on query–key alignments within transformer attention heads, to evaluate the logical reasoning capabilities of LLMs. Our method automatically identifies attention heads that play a key role in distinguishing valid from invalid logical inferences, enabling efficient inference-time evaluation via a single forward pass. It reveals latent reasoning structure in LLMs and provides a scalable mechanistic alternative to ablation-based analysis. Across three benchmarks: ProntoQA-OOD, PARARULE-Plus, and MultiLogicEval, with models ranging from 1.5B to 70B parameters, the selected heads predict logical validity up to 14% better than the model probabilities, and remain robust under distractors and increasing reasoning depth of d≤ 6.

Large language models (LLMs) are increasingly used in interactive applications, and human evaluation remains the gold standard for assessing their performance in multi-turn conversations. Since human studies are costly, time-consuming, and hard to reproduce, recent work explores using LLMs to simulate users for automatic assistant evaluation. However, there is no benchmark or systematic study to evaluate whether these simulated users are reliable stand-ins for real users. To address this, we introduce SimulatorArena, a benchmark of 909 annotated human–LLM conversations on two interactive tasks—math tutoring and document creation. SimulatorArena evaluates simulators based on how closely their messages match human behavior and how well their assistant ratings align with human judgments. Experiments on various simulator methods show that simulators conditioned on user profiles, capturing traits like background and message styles, align closely with human judgments. They reach Spearman’s 𝜌 of 0.7 on both tasks, providing a practical, scalable alternative to human evaluation. Using the best simulator for each task, we benchmark 18 assistants, including the latest LLMs such as GPT-5, Claude 4.1 Opus, and Gemini 2.5 Pro.

LLMs are increasingly applied in the legal domain in tasks such as summarizing legal texts and providing basic legal advice. Yet, their capacity to draft full judicial analyses in U.S. court opinions is still largely uncharted, such as generating entire judicial reasoning sections in U.S. court decisions, remain under-explored. Given the continued adoption of LLMs and the significance of law to society at large, measurement of LLM’s legal reasoning capabilities is a pressing task. We propose CourtReasoner, a novel expert-annotated judicial reasoning benchmark for evaluating LLM agents’ capabilities in complex legal reasoning. Sourcing U.S. court opinions, we construct benchmarks that measure the LLMs ability to construct goal-oriented legal reasoning. CourtReasoner measured the agent’s ability to argue both ways in a legal dispute, rather than simple Q/A. Our results show that more than 60% of frontier model outputs contain invalid arguments and more than 53% of frontier model produced irrelevant citations when conducting complex legal reasoning. We also introduce a meta-evaluation benchmark to provide insights into the capabilities of LLMs as evaluators of legal reasoning. We will release our data, code and full annotation guidelines publicly for future research.

Regulatory agencies often operate with limited resources and rely on tips from the public to identify potential violations. However, processing these tips at scale presents significant operational challenges, as agencies must correctly identify and route relevant tips to the appropriate enforcement divisions. Through a case study, we demonstrate how advances in large language models can be utilized to support overburdened agencies with limited capacities. In partnership with the U.S. Environmental Protection Agency, we leverage previously unstudied citizen tips data from their “Report a Violation” system to develop an LLM-assisted pipeline for tip routing. Our approach filters out 80.5% of irrelevant tips and increases overall routing accuracy from 31.8% to 82.4% compared to the current routing system. At a time of increased focus on government efficiencies, our approach provides a constructive path forward by using technology to empower civil servants.

The memorization of training data in large language models (LLMs) poses significant privacy and copyright concerns. Existing data extraction methods, particularly heuristic-based divergence attacks, often exhibit limited success and offer limited insight into the fundamental drivers of memorization leakage. This paper introduces Confusion-Inducing Attacks (CIA), a principled framework for extracting memorized data by systematically maximizing model uncertainty. We empirically demonstrate that the emission of memorized text during divergence is preceded by a sustained spike in token-level prediction entropy. CIA leverages this insight by optimizing input snippets to deliberately induce this consecutive high-entropy state. For aligned LLMs, we further propose Mismatched Supervised Fine-tuning (SFT) to simultaneously weaken their alignment and induce targeted confusion, thereby increasing susceptibility to our attacks. Experiments on various unaligned and aligned LLMs demonstrate that our proposed attacks outperform existing baselines in extracting verbatim and near-verbatim training data without requiring prior knowledge of the training data. Our findings highlight persistent memorization risks across various LLMs and offer a more systematic method for assessing these vulnerabilities.

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Layer-wise Minimal Pair Probing Reveals Contextual Grammatical-Conceptual Hierarchy in Speech Representations
Linyang He | Qiaolin Wang | Xilin Jiang | Nima Mesgarani

Transformer-based speech language models (SLMs) have significantly improved neural speech recognition and understanding. While existing research has examined how well SLMs encode shallow acoustic and phonetic features, the extent to which SLMs encode nuanced syntactic and conceptual features remains unclear. By drawing parallels with linguistic competence assessments for large language models, this study is the first to systematically evaluate the presence of contextual syntactic and semantic features across SLMs for self-supervised learning (S3M), automatic speech recognition (ASR), speech compression (codec), and as the encoder for auditory large language models (AudioLLMs). Through minimal pair designs and diagnostic feature analysis across 71 tasks spanning diverse linguistic levels, our layer-wise and time-resolved analysis uncovers that 1) all speech encode grammatical features more robustly than conceptual ones. 2) Despite never seeing text, S3M match or surpass ASR encoders on every linguistic level, demonstrating that rich grammatical and even conceptual knowledge can arise purely from audio. 3) S3M representations peak mid-network and then crash in the final layers, whereas ASR and AudioLLM encoders maintain or improve, reflecting how pre-training objectives reshape late-layer content. 4) Temporal probing further shows that S3Ms encode grammatical cues 500 ms before a word begins, whereas AudioLLMs distribute evidence more evenly—indicating that objectives shape not only where but also when linguistic information is most salient. Together, these findings establish the first large-scale map of contextual syntax and semantics in speech models and highlight both the promise and the limits of current SLM training paradigms.

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Current Semantic-change Quantification Methods Struggle with Semantic Change Discovery in the Wild
Khonzoda Umarova | Lillian Lee | Laerdon Kim

Methods for lexical semantic-change detection quantify changes in the meaning of words over time. Prior methods have excelled on established benchmarks consisting of pre-selected target words, chosen ahead of time due to the prohibitive cost of manually annotating all words. However, performance measured on small curated wordsets cannot reveal how well these methods perform at discovering semantic changes among the full corpus vocabulary, which is the actual end goal for many applications.In this paper, we implement a top-k setup to evaluate semantic-change discovery despite lacking complete annotations. (At the same time, we also extend the annotations in the commonly used LiverpoolFC and SemEval-EN benchmarks by 85% and 90%, respectively). We deploy our evaluation setup on a battery of semantic-change detection methods under multiple variations.We find that when presented with a natural distribution of instances, all the methods struggle at ranking known large changes higher than other words in the vocabulary. Furthermore, we manually verify that the majority of words with high detected-change scores in LiverpoolFC do not actually experience meaning changes. In fact, for most of the methods, less than a half of the highest-ranked changes were determined to have changed in meaning. Given the large performance discrepancies between existing benchmark results and discovery “in the wild”, we recommend that researchers direct more attention to semantic-change discovery and include it in their suite of evaluations. Our annotations and code for running evaluations are available at https://github.com/khonzoda/semantic-change-discovery-emnlp2025.

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Evaluating Large Language Models for Detecting Antisemitism
Jay Patel | Hrudayangam Mehta | Jeremy Blackburn

Detecting hateful content is a challenging and important problem. Automated tools, like machine‐learning models, can help, but they require continuous training to adapt to the ever-changing landscape of social media. In this work, we evaluate eight open-source LLMs’ capability to detect antisemitic content, specifically leveraging in-context definition as a policy guideline. We explore various prompting techniques and design a new CoT-like prompt, Guided-CoT. Guided‐CoT handles the in-context policy well, increasing performance across all evaluated models, regardless of decoding configuration, model sizes, or reasoning capability. Notably, Llama 3.1 70B outperforms fine-tuned GPT-3.5. Additionally, we examine LLM errors and introduce metrics to quantify semantic divergence in model-generated rationales, revealing notable differences and paradoxical behaviors among LLMs. Our experiments highlight the differences observed across LLMs’ utility, explainability, and reliability.

Knowledge Graph Question Answering (KGQA) aims to answer natural language questions based on knowledge graphs.Recent approaches apply the Retrieval-Augmented Generation (RAG) paradigm to incorporate Large Language Models (LLMs) to this task, where a retriever selects a question-related subgraph and an LLM-based generator is then adopted to predict answers based on the retrieved subgraph. However, the subgraph selection process is non-differentiable, preventing end-to-end training of the retriever and the generator in these approaches, which leads to sub-optimal performance. To overcome this limitation, this paper proposes a Differentiable RAG (D-RAG) approach that jointly optimizes the retriever and the generator for KGQA. Via reformulating the optimization objective as an expectation over a subgraph distribution with respect to answer generation likelihood, D-RAG makes the joint optimization feasible. Specifically, it implements this joint optimization through a differentiable subgraph sampling and prompting module that integrates Gumbel-Softmax reparameterization for sampling and a neural prompt construction process that fuses semantic and structural information. Experimental results on WebQSP and CWQ demonstrate that D-RAG outperforms state-of-the-art approaches.

Finding the right north-star metrics is highly critical for advancing mathematical reasoning capabilities of foundation models, especially given that existing evaluations are either too easy or only focusing on getting correct short answers. To address these issues, we present IMO-Bench, a suite of advanced reasoning benchmarks that specifically targets the level of the International Mathematical Olympiad (IMO), the most prestigious venue for young mathematicians. IMOAnswerBench first tests models on 400 diverse Olympiad problems with verifiable short answers. IMO-ProofBench is the next-level evaluation for proof-writing capabilities, which includes both basic and advanced IMO problems as well as detailed grading guidelines to facilitate automatic grading. These benchmarks played a crucial role in our historic achievement of the gold-level performance at IMO 2025 with Gemini Deep Think (Luong and Lockhart, 2025). Our model achieved 80.0% on IMO-AnswerBench and 65.7% on the advanced IMO-ProofBench, surpassing the best non-Gemini models by large margins of 6.9% and 42.4% respectively. We also showed that autograders built with Gemini reasoning correlate well with human evaluations and construct IMO-GradingBench, with 1000 human gradings on proofs, to enable further progress in automatic evaluation of long-form answers. We hope that IMO-Bench will help the community towards advancing robust mathematical reasoning and release it at https://github.com/google-deepmind/superhuman/imobench.

Language models such as GPT and Llama have shown remarkable ability on diverse natural language tasks, yet their performance on complex table tasks (e.g., NL-to-Code, data cleaning, etc.) continues to be suboptimal. To improve their performance, task-specific fine-tuning is often needed, which, however, require expensive human labeling and is prone to over-fitting.In this work, we propose Table-Specialist, a new self-trained fine-tuning paradigm specifically designed for table tasks. Our insight is that for each table task, there often exist two dual versions of the same task, one generative and one classification in nature. Leveraging their duality, we propose a Generator-Validator paradigm to iteratively generate-then-validate training data from language models, to fine-tune stronger Table-Specialist models that can specialize in a given task, without using manually-labeled data.Extensive evaluations of Table-Specialist on Llama, GPT-3.5 and GPT-4 suggest that our Table-Specialist has (1) **strong performance** on diverse table tasks over vanilla language-models – for example, Table-Specialist fine-tuned on GPT-3.5 not only outperforms vanilla GPT-3.5, but can often match or surpass GPT-4 level quality, (2) **lower cost** to deploy, because when Table-Specialist fine-tuned on GPT-3.5 achieve GPT-4 level quality, it becomes possible to deploy smaller models with lower latency/cost at comparable quality, and (3) **better generalizability** when evaluated across multiple benchmarks, since Table-Specialist is fine-tuned on a broad range of training data systematically generated from diverse real tables. Our code is available at [microsoft/Table-Specialist](https://github.com/microsoft/Table-Specialist). Specialist models fine-tuned using Table-Specialist have been integrated into Microsoft Excel for use cases such as automated table data cleaning.

Analyzing and processing vast amounts of textual data presents significant challenges in efficiently extracting key information.In this paper, we introduce '***Spotlight***’, a novel paradigm for information extraction that produces concise, engaging narratives by highlighting the most compelling aspects of a document. Unlike highlights (fragmented key points) and traditional summaries, which prioritize comprehensive coverage, spotlights selectively emphasize intriguing content to foster deeper reader engagement with the source material. We formally differentiate spotlights from related constructs and support our analysis with a detailed benchmarking study using new datasets curated for this work. To generate high-quality spotlights, we propose a two-stage approach: fine-tuning a large language model on our benchmark data, followed by alignment via Direct Preference Optimization (DPO). Our comprehensive evaluation demonstrates that the resulting model not only identifies key elements with precision but also enhances readability and boosts the engagement value of the original document. Datasets and code are available at https://github.com/ankan2/Spotlight-EMNLP2025.

Large Language Models (LLMs) have revolutionized various Natural Language Generation (NLG) tasks, including Argument Summarization (ArgSum), a key subfield of Argument Mining. This paper investigates the integration of state-of-the-art LLMs into ArgSum systems and their evaluation. In particular, we propose a novel prompt-based evaluation scheme, and validate it through a novel human benchmark dataset. Our work makes three main contributions: (i) the integration of LLMs into existing ArgSum systems, (ii) the development of two new LLM-based ArgSum systems, benchmarked against prior methods, and (iii) the introduction of an advanced LLM-based evaluation scheme. We demonstrate that the use of LLMs substantially improves both the generation and evaluation of argument summaries, achieving state-of-the-art results and advancing the field of ArgSum. We also show that among the four LLMs integrated in (i) and (ii), Qwen-3-32B, despite having the fewest parameters, performs best, even surpassing GPT-4o.

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Computational Analysis of Conversation Dynamics through Participant Responsivity
Margaret Hughes | Brandon Roy | Elinor Poole-Dayan | Deb Roy | Jad Kabbara

Growing literature explores toxicity and polarization in discourse, with comparatively less work on characterizing what makes dialogue prosocial and constructive. We explore conversational discourse and investigate a method for characterizing its quality built upon the notion of “responsivity”—whether one person’s conversational turn is responding to a preceding turn. We develop and evaluate methods for quantifying responsivity—first through semantic similarity of speaker turns, and second by leveraging state-of-the-art large language models (LLMs) to identify the relation between two speaker turns. We evaluate both methods against a ground truth set of human-annotated conversations. Furthermore, selecting the better performing LLM-based approach, we characterize the nature of the response—whether it responded to that preceding turn in a substantive way or not. We view these responsivity links as a fundamental aspect of dialogue but note that conversations can exhibit significantly different responsivity structures. Accordingly, we then develop conversation-level derived metrics to address various aspects of conversational discourse. We use these derived metrics to explore other conversations and show that they support meaningful characterizations and differentiations across a diverse collection of conversations.

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AMQ: Enabling AutoML for Mixed-precision Weight-Only Quantization of Large Language Models
Sangjun Lee | Seung-taek Woo | Jun-gyu Jin | Changhun Lee | Eunhyeok Park

To enable broader deployment of Large Language Models (LLMs), it is essential to identify the best-performing model under strict memory constraints. We present AMQ, Automated Mixed-Precision Weight-Only Quantization, a framework that assigns layer-wise quantization bit-widths to optimally balance model quality and memory usage. However, the combinatorial search space, with over 10¹⁰⁰ possible configurations, makes conventional black-box optimization infeasible. AMQ overcomes this challenge through four key innovations: (1) **search space pruning** using prior knowledge to exclude unpromising configurations, (2) **quantization proxy** to bypass costly format conversions during search, (3) **quality predictor** to minimize evaluation overhead, and (4) **iterative search-and-update** strategy for fast and stable convergence. By integrating these components, AMQ efficiently explores the quality–efficiency landscape, reaching the Pareto frontier and yielding LLMs that are both compact and high-performing.

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Beyond Averages: Learning with Annotator Disagreement in STS
Alejandro Benito-Santos | Adrian Ghajari

This work investigates capturing and modeling disagreement in Semantic Textual Similarity (STS), where sentence pairs are assigned ordinal similarity labels (0–5). Conventional STS systems average multiple annotator scores and focus on a single numeric estimate, overlooking label dispersion. By leveraging the disaggregated SemEval-2015 dataset (Soft-STS-15), this paper proposes and compares two disagreement-aware strategies that treat STS as an ordinal distribution prediction problem: a lightweight truncated Gaussian head for standard regression models, and a cross-encoder trained with a distance-aware objective, refined with temperature scaling. Results show improved performance in distance-based metrics, with the calibrated soft-label model proving best overall and notably more accurate on the most ambiguous pairs. This demonstrates that modeling disagreement benefits both calibration and ranking accuracy, highlighting the value of retaining and modeling full annotation distributions rather than collapsing them to a single mean label.

Large Language Models (LLMs), particularly smaller variants, still struggle with complex reasoning tasks. While inference-time prompting can guide reasoning, existing methods often rely on sequential queries. Ensemble approaches offer a promising path to performance gains, especially given recent batch inference speed-ups. This work introduces DIPPER, a novel, training-free framework that transforms a single LLM into an effective inference-time ensemble. By feeding the model an optimized and diverse set of prompts in parallel, DIPPER elicits varied reasoning paths, leading to performance gains. We empirically demonstrate significant improvements on mathematical reasoning benchmarks, such as MATH, where a DIPPER ensemble of three Qwen2-MATH-1.5B instances (via parallel prompting of a single model) outperforms a larger Qwen2-MATH-7B model.

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Constrained Non-negative Matrix Factorization for Guided Topic Modeling of Minority Topics
Seyedeh Fatemeh Ebrahimi | Jaakko Peltonen

Topic models often fail to capture low-prevalence, domain-critical themes—so-called minority topics—such as mental health themes in online comments. While some existing methods can incorporate domain knowledge such as expected topical content, methods allowing guidance may require overly detailed expected topics, hindering the discovery of topic divisions and variation. We propose a topic modeling solution via a specially constrained NMF. We incorporate a seed word list characterizing minority content of interest, but we do not require experts to pre-specify their division across minority topics. Through prevalence constraints on minority topics and seed word content across topics, we learn distinct data-driven minority topics as well as majority topics. The constrained NMF is fitted via Karush-Kuhn-Tucker (KKT) conditions with multiplicative updates. We outperform several baselines on synthetic data in terms of topic purity, normalized mutual information, and also evaluate topic quality using Jensen-Shannon divergence (JSD). We conduct a case study on YouTube vlog comments, analyzing viewer discussion of mental health content; our model successfully identifies and reveals this domain relevant minority content.

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Which Word Orders Facilitate Length Generalization in LMs? An Investigation with GCG-Based Artificial Languages
Nadine El-Naggar | Tatsuki Kuribayashi | Ted Briscoe

Whether language models (LMs) have inductive biases that favor typologically frequent grammatical properties over rare, implausible ones has been investigated, typically using artificial languages (ALs) (White and Cotterell, 2021; Kuribayashi et al., 2024). In this paper, we extend these works from two perspectives. First, we extend their context-free AL formalization by adopting Generalized Categorial Grammar (GCG) (Wood, 2014), which allows ALs to cover attested but previously overlooked constructions, such as unbounded dependency and mildly context-sensitive structures. Second, our evaluation focuses more on the generalization ability of LMs to process unseen longer test sentences. Thus, our ALs better capture features of natural languages and our experimental paradigm leads to clearer conclusions — typologically plausible word orders tend to be easier for LMs to productively generalize.

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Training compute-optimal transformer encoder models
Megi Dervishi | Alexandre Allauzen | Gabriel Synnaeve | Yann LeCun

Transformer encoders are critical for a wide range of Natural Language Processing (NLP) tasks, yet their compute–efficiency remains poorly understood. We present the first comprehensive empirical investigation of compute-optimal pretraining for encoder transformers using the Masked Language Modeling (MLM) objective. Across hundreds of carefully controlled runs we vary model size, data size, batch size, learning rate, and masking ratio, with increasing compute budget. The compute-optimal data-to-model ratio of Transformer encoder models is 10 to 100 times larger than the ratio of auto-regressive models. Using these recipes, we train OptiBERT, a family of compute-optimal BERT-style models that matches or surpasses leading baselines—including ModernBERT and NeoBERT—on GLUE and MTEB while training with dramatically less FLOPS.

Peer review underpins scientific progress, but it is increasingly strained by reviewer shortages and growing workloads. Large Language Models (LLMs) can automatically draft reviews now, but determining whether LLM-generated reviews are trustworthy requires systematic evaluation. Researchers have evaluated LLM reviews at either surface-level (e.g., BLEU and ROUGE) or content-level (e.g., specificity and factual accuracy). Yet it remains uncertain whether LLM-generated reviews attend to the same critical facets that human experts weigh—the strengths and weaknesses that ultimately drive an accept-or-reject decision. We introduce a focus-level evaluation framework that operationalizes the focus as a normalized distribution of attention across predefined facets in paper reviews. Based on the framework, we developed an automatic focus-level evaluation pipeline based on two sets of facets: target (e.g., problem, method, and experiment) and aspect (e.g., validity, clarity, and novelty), leveraging 676 paper reviews from OpenReview that consists of 3,657 strengths and weaknesses identified from human experts. The comparison of focus distributions between LLMs and human experts showed that the off-the-shelf LLMs consistently have a more biased focus towards examining technical validity while significantly overlooking novelty assessment when criticizing papers.Dataset: https://figshare.com/s/d5adf26c802527dd0f62

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Seeing Through Words, Speaking Through Pixels: Deep Representational Alignment Between Vision and Language Models
Zoe Wanying He | Sean Trott | Meenakshi Khosla

Recent studies show that deep vision-only and language-only models—trained on disjoint modalities—nonetheless project their inputs into a partially aligned representational space. Yet we still lack a clear picture of _where_ in each network this convergence emerges, _what_ visual or linguistic cues support it, _whether_ it captures human preferences in many-to-many image-text scenarios, and _how_ aggregating exemplars of the same concept affects alignment. Here, we systematically investigate these questions. We find that alignment peaks in mid-to-late layers of both model types, reflecting a shift from modality-specific to conceptually shared representations. This alignment is robust to appearance-only changes but collapses when semantics are altered (e.g., object removal or word-order scrambling), highlighting that the shared code is truly semantic. Moving beyond the one-to-one image-caption paradigm, a forced-choice “Pick-a-Pic” task shows that human preferences for image-caption matches are mirrored in the embedding spaces across all vision-language model pairs. This pattern holds bidirectionally when multiple captions correspond to a single image, demonstrating that models capture fine-grained semantic distinctions akin to human judgments. Surprisingly, averaging embeddings across exemplars amplifies alignment rather than blurring detail. Together, our results demonstrate that unimodal networks converge on a shared semantic code that aligns with human judgments and strengthens with exemplar aggregation.

Uncertainty quantification (UQ) has emerged as a promising approach for detecting hallucinations and low-quality output of Large Language Models (LLMs). However, obtaining proper uncertainty scores is complicated by the conditional dependency between the generation steps of an autoregressive LLM, because it is hard to model it explicitly. Here, we propose to learn this dependency from attention-based features. In particular, we train a regression model that leverages LLM attention maps, probabilities on the current generation step, and recurrently computed uncertainty scores from previously generated tokens. To incorporate the recurrent features, we also suggest a two-staged training procedure. Our experimental evaluation on ten datasets and three LLMs shows that the proposed method is highly effective for selective generation, achieving substantial improvements over rivaling unsupervised and supervised approaches.

Detoxifying offensive language while preserving the speaker’s original intent is a challenging yet critical goal for improving the quality of online interactions. Although large language models (LLMs) show promise in rewriting toxic content, they often default to overly polite rewrites, distorting the emotional tone and communicative intent. This problem is especially acute in Chinese, where toxicity often arises implicitly through emojis, homophones, or discourse context. We present ToxiRewriteCN, the first Chinese detoxification dataset explicitly designed to preserve sentiment polarity. The dataset comprises 1,556 carefully annotated triplets, each containing a toxic sentence, a sentiment-aligned non-toxic rewrite, and labeled toxic spans. It covers five real-world scenarios: standard expressions, emoji-induced and homophonic toxicity, as well as single-turn and multi-turn dialogues. We evaluate 17 LLMs, including commercial and open-source models with variant architectures, across four dimensions: detoxification accuracy, fluency, content preservation, and sentiment polarity. Results show that while commercial and MoE models perform best overall, all models struggle to balance safety with emotional fidelity in more subtle or context-heavy settings such as emoji, homophone, and dialogue-based inputs. We release ToxiRewriteCN to support future research on controllable, sentiment-aware detoxification for Chinese.

LLMs have the tendency to hallucinate, i.e., to sporadically generate false or fabricated information, and users generally lack the tools to detect when this happens. Uncertainty quantification (UQ) provides a framework for assessing the reliability of model outputs, aiding in the identification of potential hallucinations. In this work, we introduce pre-trained UQ heads: supervised auxiliary modules for LLMs that substantially enhance their ability to capture uncertainty compared to unsupervised UQ methods. Their strong performance stems from the transformer architecture in their design, in the form of informative features derived from LLM attention maps and logits. Our experiments show that these heads are highly robust and achieve state-of-the-art performance in claim-level hallucination detection across both in-domain and out-of-domain prompts. Moreover, these modules demonstrate strong generalization to languages they were not explicitly trained on. We pre-train a collection of UQ heads for popular LLM series, including Mistral, Llama, and Gemma. We publicly release both the code and the pre-trained heads.

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bib (full) Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

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Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Ivan Habernal | Peter Schulam | Jörg Tiedemann

We present a synthetic data generation tool integrated into EvalAssist. EvalAssist is a web-based application designed to assist human-centered evaluation of language model outputs by allowing users to refine LLM-as-a-Judge evaluation criteria. The synthetic data generation tool in EvalAssist is tailored for evaluation contexts and informed by findings from user studies with AI practitioners. Participants identified key pain points in current workflows including circularity risks (where models are judged by criteria derived by themselves), compounded bias (amplification of biases across multiple stages of a pipeline), and poor support for edge cases, and expressed a strong preference for real-world grounding and fine-grained control. In response, our tool supports flexible prompting, RAG-based grounding, persona diversity, and iterative generation workflows. We also incorporate features for quality assurance and edge case discovery.

We present ROBoto2, an open-source, web-based platform for large language model (LLM)-assisted risk of bias (ROB) assessment of clinical trials. ROBoto2 streamlines the traditionally labor-intensive ROB v2 (ROB2) annotation process via an interactive interface that combines PDF parsing, retrieval-augmented LLM prompting, and human-in-the-loop review. Users can upload clinical trial reports, receive preliminary answers and supporting evidence for ROB2 signaling questions, and provide real-time feedback or corrections to system suggestions. ROBoto2 is publicly available at https://roboto2.vercel.app/, with code and data released to foster reproducibility and adoption. We construct and release a dataset of 521 pediatric clinical trial reports (8954 signaling questions with 1202 evidence passages), annotated using both manually and LLM-assisted methods, serving as a benchmark and enabling future research. Using this dataset, we benchmark ROB2 performance for 4 LLMs and provide an analysis into current model capabilities and ongoing challenges in automating this critical aspect of systematic review.

Religion and spirituality (R/S) are complex and highly domain-dependent concepts which have long confounded researchers and policymakers. Due to their context-specificity, R/S are difficult to operationalize in conventional archival search strategies, particularly when datasets are very large, poorly accessible, and marked by information noise. As a result, considerable time investments and specialist knowledge is often needed to extract actionable insights related to R/S from general archival sources, increasing reliance on published literature and manual desk reviews. To address this challenge, we present SpiritRAG, an interactive Question Answering (Q&A) system based on Retrieval-Augmented Generation (RAG). Built using 7,500 United Nations (UN) resolution documents related to R/S in the domains of health and education, SpiritRAG allows researchers and policymakers to conduct complex, context-sensitive database searches of very large datasets using an easily accessible, chat-based web interface. SpiritRAG is lightweight to deploy and leverages both UN documents and user provided documents as source material. A pilot test and evaluation with domain experts on 100 manually composed questions demonstrates the practical value and usefulness of SpiritRAG.

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LingConv: An Interactive Toolkit for Controlled Paraphrase Generation with Linguistic Attribute Control
Mohamed Elgaar | Hadi Amiri

We introduce LINGCONV, an interactive toolkit for paraphrase generation enabling finegrained control over 40 specific lexical, syntactic, and discourse linguistic attributes. Users can directly manipulate target attributes using sliders, and with automatic imputation for unspecified attributes, simplifying the control process. Our adaptive Quality Control mechanism employs iterative refinement guided by line search to precisely steer the generation towards target attributes while preserving semantic meaning, overcoming limitations associated with fixed control strengths. Applications of LINGCONV include enhancing text accessibility by adjusting complexity for different literacy levels, enabling personalized communication through style adaptation, providing a valuable tool for linguistics and NLP research, and facilitating second language learning by tailoring text complexity. The system is available at https://mohdelgaar-lingconv.hf.space, with a demo video at https://youtu.be/wRBJEJ6EALQ.

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AgentMaster: A Multi-Agent Conversational Framework Using A2A and MCP Protocols for Multimodal Information Retrieval and Analysis
Callie C. Liao | Duoduo Liao | Sai Surya Gadiraju

The rise of Multi-Agent Systems (MAS) in Artificial Intelligence (AI), especially integrated with Large Language Models (LLMs), has greatly facilitated the resolution of complex tasks. However, current systems are still facing challenges of inter-agent communication, coordination, and interaction with heterogeneous tools and resources. Most recently, the Model Context Protocol (MCP) by Anthropic and Agent-to-Agent (A2A) communication protocol by Google have been introduced, and to the best of our knowledge, very few applications exist where both protocols are employed within a single MAS framework. We present a pilot study of AgentMaster, a novel modular multi-protocol MAS framework with self-implemented A2A and MCP, enabling dynamic coordination, flexible communication, and rapid development with faster iteration. Through a unified conversational interface, the system supports natural language interaction without prior technical expertise and responds to multimodal queries for tasks including information retrieval, question answering, and image analysis. The experiments are validated through both human evaluation and quantitative metrics, including BERTScore F1 (96.3%) and LLM-as-a-Judge G-Eval (87.1%). These results demonstrate robust automated inter-agent coordination, query decomposition, task allocation, dynamic routing, and domain-specific relevant responses. Overall, our proposed framework contributes to the potential capabilities of domain-specific, cooperative, and scalable conversational AI powered by MAS.

We present the iRead4Skills Intelligent Complexity Analyzer, an open-access platform specifically designed to assist educators and content developers in addressing the needs of low-literacy adults by analyzing and diagnosing text complexity. This multilingual system integrates a range of Natural Language Processing (NLP) components to assess input texts along multiple levels of granularity and linguistic dimensions in Portuguese, Spanish, and French. It assigns four tailored difficulty levels using state-of-the-art models, and introduces four diagnostic yardsticks—textual structure, lexicon, syntax, and semantics—offering users actionable feedback on specific dimensions of textual complexity. Each component of the system is supported by experiments comparing alternative models on manually annotated data.

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AIPOM: Agent-aware Interactive Planning for Multi-Agent Systems
Hannah Kim | Kushan Mitra | Chen Shen | Dan Zhang | Estevam Hruschka

Large language models (LLMs) are being increasingly used for planning in orchestrated multi-agent systems. However, existing LLM-based approaches often fall short of human expectations and, critically, lack effective mechanisms for users to inspect, understand, and control their behaviors. These limitations call for enhanced transparency, controllability, and human oversight. To address this, we introduce AIPOM, a system supporting human-in-the-loop planning through conversational and graph-based interfaces. AIPOM enables users to transparently inspect, refine, and collaboratively guide LLM-generated plans, significantly enhancing user control and trust in multi-agent workflows. Our code and demo video are available at https://github.com/megagonlabs/aipom.

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LAD: LoRA-Adapted Diffusion
Ruurd Jan Anthonius Kuiper | Lars de Groot | Bram van Es | Maarten van Smeden | Ayoub Bagheri

Autoregressive models dominate text generation but suffer from left-to-right decoding constraints that limit efficiency and bidirectional reasoning. Diffusion-based models offer a flexible alternative but face challenges in adapting to discrete text efficiently. We propose LAD (LoRA-Adapted Diffusion), a framework for non-autoregressive generation that adapts LLaMA models for iterative, bidirectional sequence refinement using LoRA adapters. LAD employs a structural denoising objective combining masking with text perturbations (swaps, duplications and span shifts), enabling full sequence editing during generation. We aim to demonstrate that LAD could be a viable and efficient alternative to training diffusion models from scratch, by providing both validation results as well as two interactive demos directly available online:https://ruurdkuiper.github.io/tini-lad/https://huggingface.co/spaces/Ruurd/tini-ladInference and training code:https://github.com/RuurdKuiper/lad-code

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Automated Evidence Extraction and Scoring for Corporate Climate Policy Engagement: A Multilingual RAG Approach
Imene Kolli | Saeid Vaghefi | Chiara Colesanti Senni | Shantam Raj | Markus Leippold

InfluenceMap’s LobbyMap Platform monitors the climate policy engagement of over 500 companies and 250 industry associations, assessing each entity’s support or opposition to science-based policy pathways for achieving the Paris Agreement’s goal of limiting global warming to 1.5°C. Although InfluenceMap has made progress with automating key elements of the analytical workflow, a significant portion of the assessment remains manual, making it time- and labor-intensive and susceptible to human error. We propose an AI-assisted framework to accelerate the monitoring of corporate climate policy engagement by leveraging Retrieval-Augmented Generation to automate the most time-intensive extraction of relevant evidence from large-scale textual data. Our evaluation shows that a combination of layout-aware parsing, the Nomic embedding model, and few-shot prompting strategies yields the best performance in extracting and classifying evidence from multilingual corporate documents. We conclude that while the automated RAG system effectively accelerates evidence extraction, the nuanced nature of the analysis necessitates a human-in-the-loop approach where the technology augments, rather than replaces, expert judgment to ensure accuracy.

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GLiNER2: Schema-Driven Multi-Task Learning for Structured Information Extraction
Urchade Zaratiana | Gil Pasternak | Oliver Boyd | George Hurn-Maloney | Ash Lewis

Information extraction (IE) is fundamental to numerous NLP applications, yet existing solutions often require specialized models for different tasks or rely on computationally expensive large language models. We present GLiNER2, a unified framework that enhances the original GLiNER architecture to support named entity recognition, text classification, and hierarchical structured data extraction within a single efficient model. Built on a fine-tuned encoder architecture, GLiNER2 maintains CPU efficiency and compact size while introducing multi-task composition through an intuitive schema-based interface. Our experiments demonstrate competitive performance across diverse IE tasks with substantial improvements in deployment accessibility compared to LLM-based alternatives. We release GLiNER2 as an open-source library available through pip, complete with pre-trained models and comprehensive documentation.

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SciClaims: An End-to-End Generative System for Biomedical Claim Analysis
Raúl Ortega | Jose Manuel Gomez-Perez

We present SciClaims, an interactive web-based system for end-to-end scientific claim analysis in the biomedical domain. Designed for high-stakes use cases such as systematic literature reviews and patent validation, SciClaims extracts claims from text, retrieves relevant evidence from PubMed, and verifies their veracity. The system features a user-friendly interface where users can input scientific text and view extracted claims, predictions, supporting or refuting evidence, and justifications in natural language. Unlike prior approaches, SciClaims seamlessly integrates the entire scientific claim analysis process using a single large language model, without requiring additional fine-tuning. SciClaims is optimized to run efficiently on a single GPU and is publicly available for live interaction.

Large language model agents have enabled GUI-based automation, particularly for mobile devices. However, deployment remains limited by noisy data, poor generalization, and lack of support for non-English GUIs. In this work, we present AgentCPM-GUI, an 8B-parameter GUI agent built for robust and efficient on-device GUI interaction. Our training pipeline includes grounding-aware pre-training to enhance perception, supervised fine-tuning on high-quality Chinese and English trajectories to imitate human-like actions, and reinforcement fine-tuning with GRPO to improve reasoning capability. AgentCPM-GUI achieves promising performance on five public benchmarks and our proposed Chinese benchmark CAGUI. To facilitate reproducibility and further research, we publicly release all code, model checkpoint, and evaluation data at: https://github.com/OpenBMB/AgentCPM-GUI

We present Marcel, a lightweight and open-source conversational agent designed to support prospective students with admission-related inquiries. The system aims to provide fast and personalized responses, while reducing workload of university staff. We employ retrieval-augmented generation to ground answers in university resources and to provide users with verifiable, contextually relevant information. We introduce a Frequently Asked Question (FAQ) retriever that maps user questions to knowledge-base entries, which allows administrators to steer retrieval, and improves over standard dense/hybrid retrieval strategies. The system is engineered for easy deployment in resource-constrained academic settings. We detail the system architecture, provide a technical evaluation of its components, and report insights from a real-world deployment.

One of the most important tasks in quantitative investment research is mining new alphas (effective trading signals or factors). Traditional alpha mining methods, either hand-crafted factor synthesis or algorithmic factor mining (e.g., search with genetic programming), have inherent limitations, especially in implementing the ideas of quant researchers. In this work, we propose a new alpha mining paradigm by introducing human-AI interaction, and a novel prompt engineering algorithmic framework to implement this paradigm by leveraging the power of large language models. Moreover, we develop Alpha-GPT, a new interactive alpha mining system framework that provides a heuristic way to “understand” the ideas of quant researchers and outputs creative, insightful, and effective alphas. We demonstrate the effectiveness and advantage of Alpha-GPT via a number of alpha mining experiments. In particular, we evaluated Alpha-GPT’s performance in the WorldQuant International Quant Championship, where it demonstrated results comparable to those of top-performing human participants, ranking among top-10 over 41000 teams worldwide. These findings suggest Alpha-GPT’s significant potential in generating highly effective alphas that may surpass human capabilities in quantitative investment strategies.

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AgentDiagnose: An Open Toolkit for Diagnosing LLM Agent Trajectories
Tianyue Ou | Wanyao Guo | Apurva Gandhi | Graham Neubig | Xiang Yue

Large Language Model (LLM) agents produce rich, multi-step trajectories that interleave observations, internal reasoning, and tool actions. However, most evaluation pipelines focus solely on end-task success, leaving the agent’s decision-making process opaque and poorly understood. We introduce AgentDiagnose, an open-source, modular framework for diagnosing agent trajectories. The present release fully supports the web domain, and AgentDiagnose is architect as an extensible, open platform with compatibility for most agent trajectories. AgentDiagnose consists of (i) an evaluation module that quantifies five core agentic competencies—backtracking & exploration, task decomposition, observation reading, self-verification, and objective quality—and (ii) a visualization module that highlights trajectory semantics through t-SNE action embeddings, interactive word clouds, and state-transition timelines. On a set of 30 manually annotated trajectories, our automatic metrics achieve a mean Pearson correlation of 0.57 with human judgments, rising to 0.78 for task decomposition. Furthermore, filtering the 46k-example NNetNav-Live dataset with AgentDiagnose and fine-tuning a Llama-3.1-8B model on the top 6k trajectories improves WebArena success rates by 0.98, despite using only 13% of the original data. AgentDiagnose thus serves as both a diagnostic lens for agent analysis and a practical tool for curating higher-quality training data. The toolkit and demo are publicly available.

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Tau-Eval: A Unified Evaluation Framework for Useful and Private Text Anonymization
Gabriel Loiseau | Damien Sileo | Damien Riquet | Maxime Meyer | Marc Tommasi

Text anonymization is the process of removing or obfuscating information from textual data to protect the privacy of individuals. This process inherently involves a complex trade-off between privacy protection and information preservation, where stringent anonymization methods can significantly impact the text’s utility for downstream applications. Evaluating the effectiveness of text anonymization proves challenging from both privacy and utility perspectives, as there is no universal benchmark that can comprehensively assess anonymization techniques across diverse, and sometimes contradictory contexts. We present Tau-Eval, an open-source framework for benchmarking text anonymization methods through the lens of privacy and utility task sensitivity. A Python library, code, documentation and tutorials are publicly available.

LLM-based translation agents have achieved highly human-like translation results and are capable of handling longer and more complex contexts with greater efficiency. However, they are typically limited to text-only inputs. In this paper, we introduce ViDove, a translation agent system designed for multimodal input. Inspired by the workflow of human translators, ViDove leverages visual and contextual background information to enhance the translation process. Additionally, we integrate a multimodal memory system and long-short term memory modules enriched with domain-specific knowledge, enabling the agent to perform more accurately and adaptively in real-world scenarios. As a result, ViDove achieves significantly higher translation quality in both subtitle generation and general translation tasks, with a 28% improvement in BLEU scores and a 15% improvement in SubER compared to previous state-of-the-art baselines. Moreover, we introduce DoveBench, a new benchmark for long-form automatic video subtitling and translation, featuring 17 hours of high-quality, human-annotated data. Our demo is available here: https://vidove.willbe03.com/

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Sanskrit Voyager: Unified Web Platform for Interactive Reading and Linguistic Analysis of Sanskrit Texts
Giacomo De Luca | Danilo Croce | Roberto Basili

Sanskrit Voyager is a web application for searching, reading, and analyzing the texts in the Sanskrit literary corpus. Unlike previous tools that require expert linguistic knowledge or manual normalization, Sanskrit Voyager enables users to search for words and phrases as they actually appear in texts, handling inflection, sandhi, and compound forms automatically while supporting any transliteration. The system integrates four core functionalities: (1) multi-dictionary lookup with morphological analysis and inflection tables; (2) real-time text parsing and annotation; (3) an interactive reader for over 900 digitalized texts; and (4) advanced corpus search with fuzzy matching and filtering. Evaluation shows over 92% parsing accuracy on complex compounds and substantially higher search recall than BuddhaNexus on challenging queries. Source code is publicly available under CC-BY-NC license, resource-efficient, and designed for both learners and researchers, offering the first fully integrated, user-friendly platform for computational Sanskrit studies.

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PromptSuite: A Task-Agnostic Framework for Multi-Prompt Generation
Eliya Habba | Noam Dahan | Gili Lior | Gabriel Stanovsky

Evaluating LLMs with a single prompt has proven unreliable, with small changes leading to significant performance differences. However, generating the prompt variations needed for a more robust multi-prompt evaluation is challenging, limiting its adoption in practice. To address this, we introduce PromptSuite, a framework that enables the automatic generation of various prompts. PromptSuite is flexible – working out of the box on a wide range of tasks and benchmarks. It follows a modular prompt design, allowing controlled perturbations to each component, and is extensible, supporting the addition of new components and perturbation types. Through a series of case studies, we show that PromptSuite provides meaningful variations to support strong evaluation practices. All resources, including the Python API, source code, user-friendly web interface, and demonstration video, are available at: https://eliyahabba.github.io/PromptSuite/.

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LionGuard 2: Building Lightweight, Data-Efficient & Localised Multilingual Content Moderators
Leanne Tan | Gabriel Chua | Ziyu Ge | Roy Ka-Wei Lee

Modern moderation systems increasingly support multiple languages, but often fail to address localisation and low-resource variants—creating safety gaps in real-world deployments. Small models offer a potential alternative to large LLMs, yet still demand considerable data and compute. We present LionGuard 2, a lightweight, multilingual moderation classifier tailored to the Singapore context, supporting English, Chinese, Malay, and partial Tamil. Built on pre-trained OpenAI embeddings and a multi-head ordinal classifier, LionGuard 2 outperforms several commercial and open-source systems across 17 benchmarks, including both Singapore-specific and public English datasets. The system is actively deployed within the Singapore Government, demonstrating practical efficacy at scale. Our findings show that high-quality local data and robust multilingual embeddings can achieve strong moderation performance, without fine-tuning large models. We release our model weights and part of our training data to support future work on LLM safety.

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GraphMind: Interactive Novelty Assessment System for Accelerating Scientific Discovery
Italo Luis da Silva | Hanqi Yan | Lin Gui | Yulan He

Large Language Models (LLMs) show strong reasoning and text generation capabilities, prompting their use in scientific literature analysis, including novelty assessment. While evaluating novelty of scientific papers is crucial for peer review, it requires extensive knowledge of related work, something not all reviewers have.While recent work on LLM-assisted scientific literature analysis supports literature comparison, existing approaches offer limited transparency and lack mechanisms for result traceability via an information retrieval module. To address this gap, we introduce GraphMind, an easy-to-use interactive web tool designed to assist users in evaluating the novelty of scientific papers or drafted ideas. Specially, GraphMind enables users to capture the main structure of a scientific paper, explore related ideas through various perspectives, and assess novelty via providing verifiable contextual insights. GraphMind enables users to annotate key elements of a paper, explore related papers through various relationships, and assess novelty with contextual insight. This tool integrates external APIs such as arXiv and Semantic Scholar with LLMs to support annotation, extraction, retrieval and classification of papers. This combination provides users with a rich, structured view of a scientific idea’s core contributions and its connections to existing work. GraphMind is available at https://oyarsa.github.io/graphmind and a demonstration video at https://youtu.be/wKbjQpSvwJg.

Building language models (LMs), especially small and medium ones, remains more art than science. While large LMs often improve by sheer scale, it is still unclear why many design choices work. For small LMs, this uncertainty is more limiting: tight parameter budgets make each decision critical, yet researchers still lack systematic, scientific ways to test and refine new ideas. We introduce Pico, a lightweight, modular framework that enables systematic, hypothesis-driven research for small and medium-scale language model development. Pico consists of two libraries that together provide a practical sandbox where researchers can make targeted changes to a model’s architecture or training procedures and directly observe their effects on the model’s behavior. To support reproducible experimentation, we also release a suite of baseline models, pico-decoder, trained under standardized conditions and open-sourced for the community. Case studies highlight how Pico can support iterative small LM design and analysis.

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DistaLs: a Comprehensive Collection of Language Distance Measures
Rob Van Der Goot | Esther Ploeger | Verena Blaschke | Tanja Samardzic

Languages vary along a wide variety of dimensions. In Natural Language Processing (NLP), it is useful to know how “distant” languages are from each other, so that we can inform NLP models about these differences or predict good transfer languages. Furthermore, it can inform us about how diverse language samples are. However, there are many different perspectives on how distances across languages could be measured, and previous work has predominantly focused on either intuition or a single type of distance, like genealogical or typological distance. Therefore, we propose DistaLs, a toolkit that is designed to provide users with easy access to a wide variety of language distance measures. We also propose a filtered subset, which contains less redundant and more reliable features. DistaLs is designed to be accessible for a variety of use cases, and offers a Python, CLI, and web interface. It is easily updateable, and available as a pip package. Finally, we provide a case-study in which we use DistaLs to measure correlations of distance measures with performance on four different morphosyntactic tasks.

The learning process for medical residents presents significant challenges, demanding both the ability to interpret complex case reports and the rapid acquisition of accurate medical knowledge from reliable sources. Residents typically study case reports and engage in discussions with peers and mentors, but finding relevant educational materials and evidence to support their learning from these cases is often time-consuming and challenging. To address this, we introduce MedTutor, a novel system designed to augment resident training by automatically generating evidence-based educational content and multiple-choice questions from clinical case reports. MedTutor leverages a Retrieval-Augmented Generation (RAG) pipeline that takes clinical case reports as input and produces targeted educational materials. The system’s architecture features a hybrid retrieval mechanism that synergistically queries a local knowledge base of medical textbooks and academic literature (using PubMed, Semantic Scholar APIs) for latest related research, ensuring the generated content is both foundationally sound and current. The retrieved evidence is filtered and ordered using a state-of-the-art reranking model and then an LLM generates the final long-form output describing the main educational content regarding the case-report.We conduct a rigorous evaluation of the system. First, two radiologists assessed the quality of outputs, finding them to be of high clinical and educational value. Second, we perform a large-scale evaluation using an LLM-as-a Judge to understand if LLMs can be used to evaluate the output of the system. Our analysis using correlation of LLMs with human expert judgments reveals a moderate alignment and highlights the continued necessity of expert oversight.

We introduce Co-DETECT (Collaborative Discovery of Edge cases in TExt ClassificaTion), a novel mixed-initiative annotation framework that integrates human expertise with automatic annotation guided by large language models (LLMs). Co-DETECT starts with an initial, sketch-level codebook and dataset provided by a domain expert, then leverages the LLM to annotate the data and identify edge cases that are not well described by the initial codebook. Specifically, Co-DETECT flags challenging examples, induces high-level, generalizable descriptions of edge cases, and assists user in incorporating edge case handling rules to improve the codebook. This iterative process enables more effective handling of nuanced phenomena through compact, generalizable annotation rules. Extensive user study, qualitative and quantitative analyses prove the effectiveness of Co-DETECT.

Language models trained with a fixed vocabulary struggle to generalize to novel or out-of-vocabulary words, limiting their flexibility in handling diverse token combinations. Existing dynamic vocabulary approaches attempt to address this limitation but face challenges such as fragmented codebases, lack of support for modern LLMs, and limited inference scalability. To overcome these issues, we introduce DVAGen, a fully open-source, unified framework designed for training, evaluation, and visualization of dynamic vocabulary-augmented language models. Our framework modularizes the pipeline for ease of customization, integrates seamlessly with open-source LLMs, and is the first to provide both CLI and WebUI tools for real-time result inspection. We validate the effectiveness of dynamic vocabulary methods on modern LLMs and demonstrate support for batch inference, significantly improving inference throughput.

The rapid adoption of Large Language Models (LLMs) as intelligent agents has underscored the necessity for robust evaluation frameworks capable of assessing agent performance in realistic, interactive environments. Existing evaluation methodologies often suffer from limitations such as static task benchmarks, limited scope, and inadequate integration with practical applications. In response, we introduce MCPEval, an open-source, Model Context Protocol (MCP)-based evaluation framework specifically tailored for comprehensive and systematic assessment of LLM-powered agents. MCPEval standardizes evaluations across diverse domains through automated task generation and verification, supports multiple performance metrics, and integrates seamlessly with native agent capabilities. We empirically validate the effectiveness of MCPEval across five distinct real-world domains, highlighting significant variations in performance across various LLM architectures and prompting strategies. Our results illustrate the framework’s capacity to uncover nuanced performance patterns and identify domain-specific strengths and weaknesses, providing valuable insights beyond traditional binary success metrics. We publicly release MCPEval to foster reproducible research and promote standardized evaluation practices within the LLM agent community.

High-quality schematic diagrams, which provide a conceptual overview of the research, play a crucial role in summarizing and clarifying a study’s core ideas. However, creating these diagrams is time-consuming for authors and remains challenging for current AI systems, as it requires both a deep understanding of the paper’s content and a strong sense of visual design. To address this, we introduce SCISKETCH, an open-source framework that supports two automated workflows for schematic diagram generation using foundation models, shown in Figure 1. 1) In the graphic-code-based workflow, SCISKETCH follows a two-stage pipeline: it first produces a layout plan expressed in a graphical code language with a self-refinement and self-verification mechanism. It then integrates empirical images and symbolic icons to create a visually coherent, informative diagram. 2) In the image-based workflow, SCISKETCH directly synthesizes the diagram image through image generation with a self-refinement mechanism. Through both automatic and human evaluations, we show that SCISKETCH outperforms several state-of-the-art foundation models, including GPT-4o, and Gemini-2.5-Pro, in generating schematic diagrams for scientific papers. We make SCISKETCH fully open-sourced, providing researchers with an accessible, extensible tool for high-quality schematic diagram generation in scientific fields.

Multi-agent debate (MAD) has demonstrated the ability to augment collective intelligence by scaling test-time compute and leveraging expertise. Current frameworks for MAD are often designed towards tool use, lack integrated evaluation, or provide limited configurability of agent personas, response generators, discussion paradigms, and decision protocols. We introduce MALLM (Multi-Agent Large Language Models), an open-source framework that enables systematic analysis of MAD components. MALLM offers more than 144 unique configurations of MAD, including (1) agent personas (e.g., Expert, Personality), (2) response generators (e.g., Critical, Reasoning), (3) discussion paradigms (e.g., Memory, Relay), and (4) decision protocols (e.g., Voting, Consensus). MALLM uses simple configuration files to define a debate. Furthermore, MALLM can load any textual Hugging Face dataset (e.g., MMLU-Pro, WinoGrande) and provides an evaluation pipeline for easy comparison of MAD configurations. MALLM enables researchers to systematically configure, run, and evaluate debates for their problems, facilitating the understanding of the components and their interplay.

The rapid advancement of Large Language Models (LLMs) in software engineering has revealed critical limitations in existing benchmarks, particularly the widely used SWE-bench dataset. Recent studies have uncovered severe data contamination issues, e.g., SWE-bench reports 32.67% of successful patches involve direct solution leakage and 31.08% pass due to inadequate test cases. We introduce SWE-MERA, a dynamic, continuously updated benchmark designed to address these fundamental challenges through an automated collection of real-world GitHub issues and rigorous quality validation. Our approach implements a reliable pipeline that ensures quality while minimizing contamination risks, resulting in approximately 10,000 potential tasks with 728 samples currently available. Evaluation using the Aider coding agent demonstrates strong discriminative power in state-of-the-art models. We report performance across a dozen recent LLMs evaluated on tasks collected between September 2024 and June 2025.

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Open-Theatre: An Open-Source Toolkit for LLM-based Interactive Drama
Tianyang Xu | Hongqiu Wu | Weiqi Wu | Hai Zhao

LLM-based Interactive Drama introduces a novel dialogue scenario in which the player immerses into a character and engages in a dramatic story by interacting with LLM agents. Despite the fact that this emerging area holds significant promise, it remains largely underexplored due to the lack of a well-designed playground to develop a complete drama. This makes a significant barrier for researchers to replicate, extend, and study such systems. Hence, we present Open-Theatre, the first open-source toolkit for experiencing and customizing LLM-based interactive drama. It refines prior work with an efficient multi-agent architecture and a hierarchical retrieval-based memory system, designed to enhance narrative coherence and realistic long-term behavior in complex interactions. In addition, we provide a highly configurable pipeline, making it easy for researchers to develop and optimize new approaches.

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CafGa: Customizing Feature Attributions to Explain Language Models
Alan David Boyle | Furui Cheng | Vilém Zouhar | Mennatallah El-Assady

Feature attribution methods, such as SHAP and LIME, explain machine learning model predictions by quantifying the influence of each input component. When applying feature attributions to explain language models, a basic question is defining the interpretable components.Traditional feature attribution methods, commonly treat individual words as atomic units.This is highly computationally inefficient for long-form text and fails to capture semantic information that spans multiple words.To address this, we present CafGa, an interactive tool for generating and evaluating feature attribution explanations at customizable granularities. CafGa supports customized segmentation with user interaction and visualizes the deletion and insertion curves for explanation assessments. Through a user study involving participants of various expertise, we confirm CafGa’s usefulness, particularly among LLM practitioners. Explanations created using CafGa were also perceived as more useful compared to those generated by two fully automatic baseline methods: PartitionSHAP and MExGen, suggesting the effectiveness of the system.

This work introduces UnityAI-Guard, a framework for binary toxicity classification targeting low-resource Indian languages. While existing systems predominantly cater to high-resource languages, UnityAI-Guard addresses this critical gap by developing state-of-the-art models for identifying toxic content across diverse Brahmic/Indic scripts. Our approach achieves an impressive average F1-score of 84.23% across seven languages, leveraging a dataset of 567k training instances and 30k manually verified test instances. By advancing multilingual content moderation for linguistically diverse regions, UnityAI-Guard also provides public API access to foster broader adoption and application.

Comprehensive pathway datasets are essential resources for advancing biological research, yet constructing these datasets is labor intensive. Recognizing the labor-intensive nature of constructing these critical resources, we present BioGraphia, a web-based annotation platform designed to facilitate collaborative pathway graph annotation. BioGraphia supports multi-user collaboration with real-time monitoring, curation, and interactive pathway graph visualization. It enables users to directly annotate the nodes and relations on the candidate graph, guided by detailed instructions. The platform is further enhanced with a large language model that automatically generates explainable and span-aligned pre-annotation to accelerate the annotation process. Its modular design allows flexible integration of external knowledge bases, and customization of the definition of annotation schema and, to support adaptation to other graph-based annotation tasks. Code is available at https://github.com/LeiLiLab/BioGraphia

We present SynthTextEval, a toolkit for conducting comprehensive evaluations of synthetic text. The fluency of large language model (LLM) outputs has made synthetic text potentially viable for numerous applications, such as reducing the risks of privacy violations in the development and deployment of AI systems in high-stakes domains. Realizing this potential, however, requires principled consistent evaluations of synthetic data across multiple dimensions: its utility in downstream systems, the fairness of these systems, the risk of privacy leakage, general distributional differences from the source text, and qualitative feedback from domain experts. SynthTextEval allows users to conduct evaluations along all of these dimensions over synthetic data that they upload or generate using the toolkit’s generation module. While our toolkit can be run over any data, we highlight its functionality and effectiveness over datasets from two high-stakes domains: healthcare and law. By consolidating and standardizing evaluation metrics, we aim to improve the viability of synthetic text, and in-turn, privacy-preservation in AI development.

Scientific research often requires constructing high-quality datasets, yet the current workflows remain labor-intensive, and dependent on domain expertise. Existing approaches automate isolated steps such as retrieval or generation, but lack support for the full end-to-end data collection process. We present Quest2DataAgent, a general-purpose multi-agent framework for automating scientific data collection workflows. Given a natural language research question, it decomposes tasks into structured subtasks, retrieves relevant data using hybrid strategies, evaluates dataset quality, and generates visualizations through a conversational interface. We demonstrate its flexibility in two domains: EcoData for ecological research and PolyData for polymer materials. Both systems share the same core architecture but operate over distinct datasets and user needs. Human evaluations show that Quest2DataAgent significantly improves data relevance, usability, and time efficiency compared to manual collection and tool-assisted baselines. The framework is open-source and extensible to other domains.

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End-to-End Multilingual Automatic Dubbing via Duration-based Translation with Large Language Models
Hyun-Sik Won | DongJin Jeong | Hyunkyu Choi | Jinwon Kim

Automatic dubbing (AD) aims to replace the original speech in a video with translated speech that maintains precise temporal alignment (isochrony). Achieving natural synchronization between dubbed speech and visual content remains challenging due to variations in speech durations across languages. To address this, we propose an end-to-end AD framework that leverages large language models (LLMs) to integrate translation and timing control seamlessly. At the core of our framework lies Duration-based Translation (DT), a method that dynamically predicts the optimal phoneme count based on source speech duration and iteratively adjusts the translation length accordingly. Our experiments on English, Spanish, and Korean language pairs demonstrate that our approach substantially improves speech overlap—achieving up to 24% relative gains compared to translations without explicit length constraints—while maintaining competitive translation quality measured by COMET scores. Furthermore, our framework does not require language-specific tuning, ensuring practicality for multilingual dubbing scenarios.

In this paper, we introduce EasyEdit2, a framework designed to enable plug-and-play adjustability for controlling Large Language Model (LLM) behaviors. EasyEdit2 supports a wide range of test-time interventions, including safety, sentiment, personality, reasoning patterns, factuality, and language features. Unlike its predecessor, EasyEdit2 features a new architecture specifically designed for seamless model steering. It comprises key modules such as the steering vector generator and the steering vector applier, which enable automatic generation and application of steering vectors to influence the model’s behavior without modifying its parameters. One of the main advantages of EasyEdit2 is its ease of use—users do not need extensive technical knowledge. With just a single example, they can effectively guide and adjust the model’s responses, making precise control both accessible and efficient. Empirically, we report model steering performance across different LLMs, demonstrating the effectiveness of these techniques. We have released the source code on https://github.com/zjunlp/EasyEdit along with a demonstration notebook. In addition, we provide an online system at http://easyedit.zjukg.cn/for real-time model steering, and a demo video at https://www.youtube.com/watch?v=AkfoiPfp5rQ.

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AERA Chat: An Interactive Platform for Automated Explainable Student Answer Assessment
Jiazheng Li | Artem Bobrov | Runcong Zhao | Cesare Aloisi | Yulan He

Explainability in automated student answer scoring systems is critical for building trust and enhancing usability among educators. Yet, generating high-quality assessment rationales remains challenging due to the scarcity of annotated data and the prohibitive cost of manual verification, prompting heavy reliance on rationales produced by large language models (LLMs), which are often noisy and unreliable. To address these limitations, we present AERA Chat, an interactive visualization platform designed for automated explainable student answer assessment. AERA Chat leverages multiple LLMs to concurrently score student answers and generate explanatory rationales, offering innovative visualization features that highlight critical answer components and rationale justifications. The platform also incorporates intuitive annotation and evaluation tools, supporting educators in marking tasks and researchers in evaluating rationale quality from different models. We demonstrate the effectiveness of our platform through evaluations of multiple rationale-generation methods on several datasets, showcasing its capability for facilitating robust rationale evaluation and comparative analysis.

We introduce RadEval, a unified, open-source framework for evaluating radiology texts. RadEval consolidates a diverse range of metrics - from classic n‐gram overlap (BLEU, ROUGE) and contextual measures (BERTScore) to clinical concept-based scores (F1CheXbert, F1RadGraph, RaTEScore, SRR-BERT, TemporalEntityF1) and advanced LLM‐based evaluators (GREEN). We refine and standardize implementations, extend GREEN to support multiple imaging modalities with a more lightweight model, and pretrain a domain-specific radiology encoder - demonstrating strong zero-shot retrieval performance. We also release a richly annotated expert dataset with over 450 clinically significant error labels and show how different metrics correlate with radiologist judgment. Finally, RadEval provides statistical testing tools and baseline model evaluations across multiple publicly available datasets, facilitating reproducibility and robust benchmarking in radiology report generation.

Automatic research with Large Language Models (LLMs) is rapidly gaining importance, driving the development of increasingly complex workflows involving multi-agent systems, planning, tool usage, code execution, and human-agent interaction to accelerate research processes. However, as more researchers and developers begin to use and build upon these tools and platforms, the complexity and difficulty of extending and maintaining such agentic workflows have become a significant challenge, particularly as algorithms and architectures continue to advance. To address this growing complexity, TinyScientist identifies the essential components of the automatic research workflow and proposes an interactive, extensible, and controllable framework that adapts easily to new tools and supports iterative growth. We provide an open-source codebase, an interactive web demonstration, and a PyPI Python package to make state-of-the-art auto-research pipelines broadly accessible to every researcher and developer.

The paper presents KMatrix-2, an open-source toolkit that supports comprehensive heterogeneous knowledge collaborative enhancement for Large Language Models (LLMs). As the successor of KMatrix, our toolkit offers powerful modular components and typical enhancement patterns for convenient construction of mainstream knowledge-enhanced LLMs systems. Besides, it provides unified knowledge integration and joint knowledge retrieval methods to achieve more comprehensive heterogeneous knowledge collaborative enhancement. Compared with KMatrix which mainly focuses on descriptive knowledge, this work additionally considers procedural knowledge. Moreover, systematic inter-context and context-memory knowledge conflict resolution methods are offered for better knowledge integration. Some key research questions in heterogeneous knowledge-enhanced Large Language Models systems are analyzed, and our toolkit’s capability in building such systems is validated.

Large language models (LLMs) are advancing at an unprecedented pace globally, with regions increasingly adopting these models for applications in their primary languages. Evaluating these models in diverse linguistic environments, especially in low-resource languages, has become a major challenge for academia and industry. Existing evaluation frameworks suffer from inconsistency across different benchmarks, being disproportionately focused on English and a handful of high-resource languages, thereby overlooking the realistic performance of LLMs in multilingual and lower-resource scenarios. To address this critical challenge of fragmented and inconsistent multilingual evaluation, we introduce GlotEval, a unified and lightweight framework that systematically integrates 27 benchmarks under a standardized ISO 639-3 language identifier system, allowing for seamless incorporation of new benchmarks. Supporting nine key tasks (machine translation, text classification, summarization, open-ended generation, reading comprehension, sequence labeling, intrinsic evaluation, instruction following and reasoning), spanning over dozens to hundreds of languages, GlotEval uniquely enables language-specific, cross-benchmark analysis and non-English-centric evaluations at a scale previously less practical for many researchers. This enables a precise diagnosis of model strengths and weaknesses in diverse linguistic contexts. A multilingual translation case study demonstrates GlotEval’s applicability for multilingual and language-specific evaluations.

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MASA: LLM-Driven Multi-Agent Systems for Autoformalization
Lan Zhang | Marco Valentino | Andre Freitas

Autoformalization serves a crucial role in connecting natural language and formal reasoning. This paper presents MASA, a novel framework for building multi-agent systems for autoformalization driven by Large Language Models (LLMs). MASA leverages collaborative agents to convert natural language statements into their formal representations. The architecture of MASA is designed with a strong emphasis on modularity, flexibility, and extensibility, allowing seamless integration of new agents and tools to adapt to a fast-evolving field. We showcase the effectiveness of MASA through use cases on real-world mathematical definitions and experiments on formal mathematics datasets. This work highlights the potential of multi-agent systems powered by the interaction of LLMs and theorem provers in enhancing the efficiency and reliability of autoformalization, providing valuable insights and support for researchers and practitioners in the field.

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LearnLens: LLM-Enabled Personalised, Curriculum-Grounded Feedback with Educators in the Loop
Runcong Zhao | Artem Bobrov | Jiazheng Li | Cesare Aloisi | Yulan He

Effective feedback is essential for student learning but is time-intensive for teachers. We present LearnLens, a modular, LLM-based system that generates personalised, curriculum-aligned feedback in science education. LearnLens comprises three components: (1) an error-aware assessment module that captures nuanced reasoning errors; (2) a curriculum-grounded generation module that uses a structured, topic-linked memory chain rather than traditional similarity-based retrieval, improving relevance and reducing noise; and (3) an educator-in-the-loop interface for customisation and oversight. LearnLens addresses key challenges in existing systems, offering scalable, high-quality feedback that empowers both teachers and students.

The proliferation of transformer-based language models has revolutionized NLP domain while simultaneously introduced significant challenges regarding model transparency and trustworthiness. The complexity of achieving explainable systems in this domain is evidenced by the extensive array of explanation methods and evaluation metrics developed by researchers. To address the challenge of selecting optimal explainability approaches, we present o-mega, a hyperparameter optimization tool designed to automatically identify the most effective explainable AI methods and their configurations within the semantic matching domain. We evaluate o-mega on a post-claim matching pipeline using a curated dataset of social media posts paired with refuting claims. Our tool systematically explores different explainable methods and their hyperparameters, demonstrating improved transparency in automated fact-checking systems. As a result, such automated optimization of explanation methods can significantly enhance the interpretability of claim-matching models in critical applications such as misinformation detection, contributing to more trustworthy and transparent AI systems.

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EvoAgentX: An Automated Framework for Evolving Agentic Workflows
Yingxu Wang | Siwei Liu | Jinyuan Fang | Zaiqiao Meng

Multi-agent systems (MAS) have emerged as a powerful paradigm for orchestrating large language models (LLMs) and specialized tools to collaboratively address complex tasks. However, existing MAS frameworks often require manual workflow configuration and lack native support for dynamic evolution and performance optimization. In addition, many MAS optimization algorithms are not integrated into a unified framework. In this paper, we present **EvoAgentX**, an open-source platform that automates the generation, execution, and evolutionary optimization of multi-agent workflows. EvoAgentX employs a modular architecture consisting of five core layers: the basic components, agent, workflow, evolving, and evaluation layers. Specifically, within the evolving layer, EvoAgentX integrates three MAS optimization algorithms, TextGrad, AFlow, and MIPRO, to iteratively refine agent prompts, tool configurations, and workflow topologies. We evaluate EvoAgentX on HotPotQA, MBPP, and MATH for multi-hop reasoning, code generation, and mathematical problem solving, respectively, and further assess it on real-world tasks using GAIA. Experimental results show that consistently achieves significant performance improvements, including a 7.44% increase in HotPotQA F1, a 10.00% improvement in MBPP pass@1, a 10.00% gain in MATH solve accuracy, and an overall accuracy improvement of up to 20.00% on GAIA. The source code is available at: https://github.com/EvoAgentX/EvoAgentX.

Large Language Models (LLMs) fine-tuned via Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning with Verifiable Rewards (RLVR) significantly improve the alignment of human-AI values and further raise the upper bound of AI capabilities, particularly in reasoning-intensive, long-context Chain-of-Thought (long-CoT) tasks. However, existing RLHF (or RLVR) frameworks commonly face challenges such as inference bottlenecks and complexity barriers, restricting their accessibility for newcomers. To bridge this gap, we introduce OpenRLHF, a user-friendly, scalable, and easy-to-learn open-source RLHF framework built upon Ray, vLLM, DeepSpeed, and HuggingFace Transformers, featuring a simplified design, clear code structure, and comprehensive documentation to facilitate entry for researchers and practitioners. Experimental results show that OpenRLHF achieves superior training efficiency with speedups ranging from 1.22× to 1.68× across different model sizes compared to state-of-the-art frameworks, while requiring significantly fewer lines of code for implementation. OpenRLHF is publicly available at https://github.com/OpenRLHF/OpenRLHF, and has already been adopted by leading institutions to accelerate RLHF research and learning.

The ARR Responsible NLP Research checklist website states that the “checklist is designed to encourage best practices for responsible research, addressing issues of research ethics, societal impact and reproducibility.” Answering the questions is an opportunity for authors to reflect on their work and make sure any shared scientific assets follow best practices. Ideally, considering a checklist before submission can favorably impact the writing of a research paper. However, previous research has shown that self-reported checklist responses don’t always accurately represent papers. In this work, we introduce ConfReady, a retrieval-augmented generation (RAG) application that can be used to empower authors to reflect on their work and assist authors with conference checklists. To evaluate checklist assistants, we curate a dataset of 1,975 ACL checklist responses, analyze problems in human answers, and benchmark RAG and Large Language Model (LM) based systems on an evaluation subset. Our code is released under the AGPL-3.0 license on GitHub, with documentation covering the user interface and PyPI package.

Understanding the relationship between training data and model behavior during pretraining is crucial, but existing workflows make this process cumbersome, fragmented, and often inaccessible to researchers. We present TokenSmith, an open-source library for interactive editing, inspection, and analysis of datasets used in Megatron-style pretraining frameworks such as GPT-NeoX, Megatron, and NVIDIA NeMo. TokenSmith supports a wide range of operations including searching, viewing, exporting, inspecting, and sampling data, all accessible through a simple user interface and a modular backend. It also enables structured editing of pretraining data without requiring changes to training code, simplifying dataset debugging, validation, and experimentation. TokenSmith is designed as a plug-and-play addition to existing large language model pretraining workflows, thereby democratizing access to production-grade dataset tooling. TokenSmith is hosted on GitHub (https://github.com/aflah02/TokenSmith), with accompanying documentation and tutorials (https://aflah02.github.io/TokenSmith/). A demonstration video is also available on YouTube (https://www.youtube.com/watch?v=cDO8VE9fZvU)

We introduce LLM×MapReduce-V3, a hierarchically modular agent system designed for long-form survey generation. Building on the prior work, LLM×MapReduce-V2, this version incorporates a multi-agent architecture where individual functional components, such as skeleton initialization, digest construction, and skeleton refinement, are implemented as independent model-context-protocol (MCP) servers. These atomic servers can be aggregated into higher-level servers, creating a hierarchically structured system. A high-level planner agent dynamically orchestrates the workflow by selecting appropriate modules based on their MCP tool descriptions and the execution history. This modular decomposition facilitates human-in-the-loop intervention, affording users greater control and customization over the research process. Through a multi-turn interaction, the system precisely captures the intended research perspectives to generate a comprehensive skeleton, which is then developed into an in-depth survey. Human evaluations demonstrate that our system surpasses representative baselines in both content depth and length, highlighting the strength of MCP-based modular planning. Demo, video and code are available at https://github.com/thunlp/LLMxMapReduce.

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GraDeT-HTR: A Resource-Efficient Bengali Handwritten Text Recognition System utilizing Grapheme-based Tokenizer and Decoder-only Transformer
Md. Mahmudul Hasan | Ahmed Nesar Tahsin Choudhury | Mahmudul Hasan | Md Mosaddek Khan

Despite Bengali being the sixth most spoken language in the world, handwritten text recognition (HTR) systems for Bengali remain severely underdeveloped. The complexity of Bengali script—featuring conjuncts, diacritics, and highly variable handwriting styles—combined with a scarcity of annotated datasets makes this task particularly challenging. We present **GraDeT-HTR**, a resource-efficient Bengali handwritten text recognition system based on a **Gra**pheme-aware **De**coder-only **T**ransformer architecture. To address the unique challenges of Bengali script, we augment the performance of a decoder-only transformer by integrating a grapheme-based tokenizer and demonstrate that it significantly improves recognition accuracy compared to conventional subword tokenizers. Our model is pretrained on large-scale synthetic data and fine-tuned on real human-annotated samples, achieving state-of-the-art performance on multiple benchmark datasets.

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AutoIntent: AutoML for Text Classification
Ilya Alekseev | Roman Solomatin | Darina Rustamova | Denis Kuznetsov

AutoIntent is an automated machine learning tool for text classification tasks. Unlike existing solutions, AutoIntent offers end-to-end automation with embedding model selection, classifier optimization, and decision threshold tuning, all within a modular, sklearn-like interface. The framework is designed to support multi-label classification and out-of-scope detection. AutoIntent demonstrates superior performance compared to existing AutoML tools on standard intent classification datasets and enables users to balance effectiveness and resource consumption.

Generative Large Language Models (LLMs) inevitably produce untruthful responses. Accurately predicting the truthfulness of these outputs is critical, especially in high-stakes settings. To accelerate research in this domain and make truthfulness prediction methods more accessible, we introduce TruthTorchLM an open-source, comprehensive Python library featuring over 30 truthfulness prediction methods, which we refer to as Truth Methods. Unlike existing toolkits such as Guardrails, which focus solely on document-grounded verification, or LM-Polygraph, which is limited to uncertainty-based methods, TruthTorchLM offers a broad and extensible collection of techniques. These methods span diverse trade-offs in computational cost, access level (e.g., black-box vs. white-box), grounding document requirements, and supervision type (self-supervised or supervised). TruthTorchLM is seamlessly compatible with both HuggingFace and LiteLLM, enabling support for locally hosted and API-based models. It also provides a unified interface for generation, evaluation, calibration, and long-form truthfulness prediction, along with a flexible framework for extending the library with new methods. We conduct an evaluation of representative truth methods on three datasets, TriviaQA, GSM8K, and FactScore-Bio.

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The Dangers of Indirect Prompt Injection Attacks on LLM-based Autonomous Web Navigation Agents: A Demonstration
Sam Johnson | Viet Pham | Thai Le

This work demonstrates that LLM-based web browsing AI agents offer powerful automation capabilities but are vulnerable to Indirect Prompt Injection (IPI) attacks. We show that adversaries can embed universal adversarial triggers in webpage HTML to hijack agents that utilize the parsed-HTML accessibility tree, causing unintended or malicious actions. Using the Greedy Coordinate Gradient (GCG) algorithm and a Browser Gym agent powered by Llama-3.1, this work demonstrates high success rates across real websites in both targeted and general attacks, including login credential exfiltration and forced advertisement clicks. Our empirical results highlight critical security risks and the need for stronger defenses as LLM-driven autonomous web agents become more widely adopted. The system software is released under the MIT License at https://github.com/sej2020/manipulating-web-agents, with an accompanying publicly available demo website and video.

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LaTeXMT: Machine Translation for LaTeX Documents
Calvin Hoy | Samuel Frontull | Georg Moser

While machine translation has taken great strides in recent years, thanks in large part to transformer language models, machine translation tools are designed primarily for plain text, and thus not equipped to deal with complex markup documents. Not even Large Language Models can reliably handle LaTeX source files, as non-standard structures are not captured by any available training data. Previous attempts to create translation engines for LaTeX either work on compiled documents, rely on document pre-processors which may lose critical semantic elements, or cannot distinguish between text and non-text content. In this paper we present LaTeXMT, a software solution for structure-preserving, source-to-source translation of LaTeX documents. All of the source code to LaTeXMT is provided under the LGPL-3.0 open-source licence and a web version is publicly available.

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LangVAE and LangSpace: Building and Probing for Language Model VAEs
Danilo Carvalho | Yingji Zhang | Harriet Unsworth | Andre Freitas

We present LangVAE, a novel framework for modular construction of variational autoencoders (VAEs) on top of pre-trained large language models (LLMs). Such language model VAEs can encode the knowledge of their pre-trained components into more compact and semantically disentangled representations. The representations obtained in this way can be analysed with the LangVAE companion framework: LangSpace, which implements a collection of probing methods, such as vector traversal and interpolation, disentanglement measures, and cluster visualisations. LangVAE and LangSpace offer a flexible, efficient and scalable way of building and analysing textual representations, with simple integration for models available on the HuggingFace Hub. Additionally, we conducted a set of experiments with different encoder and decoder combinations, as well as annotated inputs, revealing a wide range of interactions across architectural families and sizes w.r.t.generalisation and disentanglement. Our findings demonstrate a promising framework for systematising the experimentation and understanding of textual representations.

We present PresentAgent, a multimodal agent that transforms long-form documents into narrated presentation videos. While existing approaches are limited to generating static slides or text summaries, our method advances beyond these limitations by producing fully synchronized visual and spoken content that closely mimics human-style presentations. To achieve this integration, PresentAgent employs a modular pipeline that systematically segments the input document, plans and renders slide-style visual frames, generates contextual spoken narration with large language models and Text-to-Speech models, and seamlessly composes the final video with precise audio-visual alignment. Given the complexity of evaluating such multimodal outputs, we introduce PresentEval, a unified assessment framework powered by Vision-Language Models that comprehensively scores videos across three critical dimensions: content fidelity, visual clarity, and audience comprehension through prompt-based evaluation. Our experimental validation on a curated dataset of 30 document–presentation pairs demonstrates that PresentAgent approaches human-level quality across all evaluation metrics. These results highlight the significant potential of controllable multimodal agents in transforming static textual materials into dynamic, effective, and accessible presentation formats.

The rapid advancement of generative AI has democratized access to powerful tools such as Text-to-Image (T2I) models. However, to generate high-quality images, users must still craft detailed prompts specifying scene, style, and context—often through multiple rounds of refinement. We propose PromptSculptor, a novel multi-agent framework that automates this iterative prompt optimization process. Our system decomposes the task into four specialized agents that work collaboratively to transform a short, vague user prompt into a comprehensive, refined prompt. By leveraging Chain-of-Thought (CoT) reasoning, our framework effectively infers hidden context and enriches scene and background details. To iteratively refine the prompt, a self-evaluation agent aligns the modified prompt with the original input, while a feedback-tuning agent incorporates user feedback for further refinement. Experimental results demonstrate that PromptSculptor significantly enhances output quality and reduces the number of iterations needed for user satisfaction. Moreover, its model-agnostic design allows seamless integration with various T2I models, paving the way for industrial applications.

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EasyDistill: A Comprehensive Toolkit for Effective Knowledge Distillation of Large Language Models
Chengyu Wang | Junbing Yan | Wenrui Cai | Yuanhao Yue | Jun Huang

In this paper, we present EasyDistill, a comprehensive toolkit designed for effective black-box and white-box knowledge distillation (KD) of large language models (LLMs). Our framework offers versatile functionalities, including data synthesis, supervised fine-tuning, ranking optimization, and reinforcement learning techniques specifically tailored for KD scenarios. The toolkit accommodates KD functionalities for both System 1 (fast, intuitive) and System 2 (slow, analytical) models. With its modular design and user-friendly interface, EasyDistill empowers researchers and industry practitioners to seamlessly experiment with and implement state-of-the-art KD strategies for LLMs. In addition, EasyDistill provides a series of robust distilled models and KD-based industrial solutions developed by us, along with the corresponding open-sourced datasets, catering to a variety of use cases. Furthermore, we describe the seamless integration of EasyDistill into Alibaba Cloud’s Platform for AI (PAI). Overall, the EasyDistill toolkit makes advanced KD techniques for LLMs more accessible and impactful within the NLP community. The toolkit, together with source codes, all model checkpoints and datasets, is released at: https://github.com/modelscope/easydistill.

Argument(ation) mining (AM) is the automated process of identification and extraction of argumentative structures in natural language. This field has seen rapid advancements, offering powerful tools to analyze and interpret complex and large discourse in diverse domains (political debates, medical reports, etc.). In this paper we introduce an AM-boosted version of BCause, a large-scale deliberation platform.The system enables the extraction and analysis of arguments from online discussions in the context of deliberative democracy, which aims to enhance the understanding and accessibility of structured argumentation in large-scale deliberation processes.

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TRACE: Training and Inference-Time Interpretability Analysis for Language Models
Nura Aljaafari | Danilo Carvalho | Andre Freitas

Understanding when and how linguistic knowledge emerges during language model training remains a central challenge for interpretability. Most existing tools are post hoc, rely on scalar metrics, or require nontrivial integration effort, making comprehensive interpretability analysis difficult to deploy and maintain. We introduce TRACE, a modular toolkit for training and inference-time interpretability analysis of transformer models. It enables lightweight, in-training analysis of linguistic and representational signals, including features probing, intrinsic dimensionality, Hessian curvature, and output diagnostics. It integrates with ABSynth, a controllable synthetic corpus generator that provides structured annotations for precise evaluation of linguistic feature acquisition. Experiments with autoregressive transformers demonstrate that TRACE reveals developmental phenomena such as early syntactic emergence, delayed semantic acquisition, and representational compression, signals overlooked by traditional scalar metrics such as loss or accuracy. With minimal integration effort, the tool enables layer-wise diagnostics, convergence-based early stopping, and detection of structural errors, making transformer analysis interpretable, actionable, and reproducible.

The rapid adoption of LLM-based conversational systems is already transforming the landscape of educational technology. However, the current state-of-the-art learning models do not take into account the student’s affective states. Multiple studies in educational psychology support the claim that positive or negative emotional states can impact a student’s learning capabilities. To bridge this gap, we present MathBuddy, an emotionally aware LLM-powered Math Tutor, which dynamically models the student’s emotions and maps them to relevant pedagogical strategies, making the tutor-student conversation a more empathetic one. The student’s emotions are captured from the conversational text as well as from their facial expressions. The student’s emotions are aggregate from both modalities to confidently prompt our LLM Tutor for an emotionally-aware response. We have evaluated our model using automatic evaluation metrics across eight pedagogical dimensions and user studies. We report a massive 23 point performance gain using the win rate and a 3 point gain at an overall level using DAMR scores which strongly supports our hypothesis of improving LLM-based tutor’s pedagogical abilities by modeling students’ emotions. Our dataset and code are available at: https://github.com/ITU-NLP/MathBuddy.

Political pledges reflect candidates’ policy commitments, but tracking their fulfilment requires reasoning over incremental evidence distributed across multiple, dynamically updated sources. Existing methods simplify this task into a document classification task, overlooking its dynamic temporal and multi-document nature. To address this issue, we introduce PledgeTracker, a system that reformulates pledge verification into structured event timeline construction. PledgeTracker consists of three core components: (1) a multi-step evidence retrieval module; (2) a timeline construction module and; (3) a fulfilment filtering module, allowing the capture of the evolving nature of pledge fulfilment and producing interpretable and structured timelines. We evaluate PledgeTracker in collaboration with professional fact-checkers in real-world workflows, demonstrating its effectiveness in retrieving relevant evidence and reducing human verification effort.

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Interactive Training: Feedback-Driven Neural Network Optimization
Wentao Zhang | Yang Young Lu | Yuntian Deng

Traditional neural network training typically follows fixed, predefined optimization recipes, lacking the flexibility to dynamically respond to instabilities or emerging training issues. In this paper, we introduce Interactive Training, an open-source framework that enables real-time, feedback-driven intervention during neural network training by human experts or automated AI agents. At its core, Interactive Training uses a control server to mediate communication between users or agents and the ongoing training process, allowing users to dynamically adjust optimizer hyperparameters, training data, and model checkpoints. Through three case studies, we demonstrate that Interactive Training achieves superior training stability, reduced sensitivity to initial hyperparameters, and improved adaptability to evolving user needs, paving the way toward a future training paradigm where AI agents autonomously monitor training logs, proactively resolves instabilities, and optimizes training dynamics.

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Metamo: Empowering Large Language Models with Psychological Distortion Detection for Cognition-aware Coaching
Hajime Hotta | Huu-Loi Le | Manh-Cuong Phan | Minh-Tien Nguyen

We demonstrate Metamo, a browser-based dialogue system that transforms an off-the-shelf large language model into an empathetic coach for everyday workplace concerns. Metamo introduces a light, single-pass wrapper that first identifies the cognitive distortion behind an emotion, then recognizes the user’s emotion, and finally produces a question-centered reply that invites reflection, all within one model call. The wrapper keeps the response time below two seconds in the API, yet enriches the feedback with cognitively grounded insight. A front-end web interface renders the detected emotion as an animated avatar and shows distortion badges in real time, whereas a safety layer blocks medical advice and redirects crisis language to human hotlines. Empirical tests on public corpora confirmed that the proposed design improved emotion‐recognition quality and response diversity without sacrificing latency. A small user study with company staff reported higher perceived empathy and usability than a latency‐matched baseline. Metamo is model-agnostic, illustrating a practical path toward cognition-aware coaching tools.

Pre-hospital Emergency Care (PEC) systems are critical for managing life-threatening emergencies where rapid intervention can significantly impact patient outcomes. The rising global demand for PEC services, coupled with increased emergency calls and strained emergency departments, necessitates efficient resource utilization through Telephone Triage (TT) systems. However, existing TT processes face challenges such as incomplete data collection, communication barriers, and manual errors, leading to high over-triage and under-triage rates. This study proposes InTriage, an AI-driven multilingual TT system to provide decision support for triage. InTriage enhances accuracy by transcribing emergency calls, extracting critical patient information, prompting supplementary, and providing real-time triage decisions support. We conducted an evaluation on a real-world corpus of approximately 40 hours of telephone data, achieving a word error rate of 14.57% for speech recognition and an F1 score of 73.34% for key information extraction.By improving communication efficiency and reducing triage errors, InTriage offers a scalable solution to potentially help address the growing demands on PEC systems globally.

We introduce VLM-Lens, a toolkit designed to enable systematic benchmarking, analysis, and interpretation of vision-language models (VLMs) by supporting the extraction of intermediate outputs from any layer during the forward pass of open-source VLMs. VLM-Lens provides a unified, YAML-configurable interface that abstracts away model-specific complexities and supports user-friendly operation across diverse VLMs. It currently supports 16 state-of-the-art base VLMs and their over 30 variants, and is extensible to accommodate new models without changing the core logic.The toolkit integrates easily with various interpretability and analysis methods. We demonstrate its usage with two simple analytical experiments, revealing systematic differences in the hidden representations of VLMs across layers and target concepts. VLM-Lens is released as an open-sourced project to accelerate community efforts in understanding and improving VLMs.

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ResearStudio: A Human-intervenable Framework for Building Controllable Deep Research Agents
Linyi Yang | Yixuan Weng

Current deep-research agents run in a ”fire-and-forget” mode: once started, they give users no way to fix errors or add expert knowledge during execution. We present ResearStudio, the first open-source framework that places real-time human control at its core. The system follows a Collaborative Workshop design. A hierarchical Planner–Executor writes every step to a live ”plan-as-document,” and a fast communication layer streams each action, file change, and tool call to a web interface. At any moment, the user can pause the run, edit the plan or code, run custom commands, and resume – switching smoothly between AI-led, human-assisted and human-led, AI-assisted modes. In fully autonomous mode, ResearStudio achieves state-of-the-art results on the GAIA benchmark, surpassing systems like OpenAI’s DeepResearch and Manus. These results show that strong automated performance and fine-grained human control can coexist. We will release the full code, protocol, and evaluation scripts to encourage further work on safe and controllable research agents upon acceptance.

Empathetic interaction is a cornerstone of human-machine communication, due to the need for understanding speech enriched with paralinguistic cues and generating emotional and expressive responses. However, the most powerful empathetic LSLMs are increasingly closed off, leaving the crucial details about the architecture, data and development opaque to researchers. Given the critical need for transparent research into the LSLMs and empathetic behavior, we present OpenS2S, a fully open-source, transparent and end-to-end LSLM designed to enable empathetic speech interactions. Based on our empathetic speech-to-text model BLSP-Emo, OpenS2S further employs a streaming interleaved decoding architecture to achieve low-latency speech generation. To facilitate end-to-end training, OpenS2S incorporates an automated data construction pipeline that synthesizes diverse, high-quality empathetic speech dialogues at low cost. By leveraging large language models to generate empathetic content and controllable text-to-speech systems to introduce speaker and emotional variation, we construct a scalable training corpus with rich paralinguistic diversity and minimal human supervision. We release the fully open-source OpenS2S model, including the dataset, model weights, pre-training and fine-tuning codes, to empower the broader research community and accelerate innovation in empathetic speech systems.

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PDFMathTranslate: Scientific Document Translation Preserving Layouts
Rongxin Ouyang | Chang Chu | Zhikuang Xin | Xiangyao Ma

Language barriers in scientific documents hinder the diffusion and development of science and technologies. However, prior efforts in translating such documents largely overlooked the information in layouts. To bridge the gap, we introduce PDFMathTranslate, the world’s first open-source software for translating scientific documents while preserving layouts. Leveraging the most recent advances in large language models and precise layout detection, we contribute to the community with key improvements in precision, flexibility, and efficiency. The work is open-sourced at https://github.com/byaidu/pdfmathtranslate with more than 222k downloads.

High-quality annotated data is a cornerstone of modern Natural Language Processing (NLP). While recent methods begin to leverage diverse annotation sources—including Large Language Models (LLMs), Small Language Models (SLMs), and human experts—they often focus narrowly on the labeling step itself. A critical gap remains in the holistic process control required to manage these sources dynamically, addressing complex scheduling and quality-cost trade-offs in a unified manner. Inspired by real-world crowdsourcing companies, we introduce CrowdAgent, a multi-agent system that provides end-to-end process control by integrating task assignment, data annotation, and quality/cost management. It implements a novel methodology that rationally assigns tasks, enabling LLMs, SLMs, and human experts to advance synergistically in a collaborative annotation workflow. We demonstrate the effectiveness of CrowdAgent through extensive experiments on six diverse multimodal classification tasks. The source code and video demo are available at https://github.com/QMMMS/CrowdAgent.

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Bratly: A Python Extension for BRAT Functionalities
Jamil Zaghir | Jean-Philippe Goldman | Nikola Bjelogrlic | Mina Bjelogrlic | Christian Lovis

BRAT is a widely used web-based text annotation tool. However, it lacks robust Python support for effective annotation management and processing. We present Bratly, an open-source extension of BRAT that introduces a solid Python backend, enabling advanced functionalities such as annotation typings, collection typings with statistical insights, corpus and annotation handling, object modifications, and entity-level evaluation based on MUC-5 standards. These enhancements streamline annotation workflows, improve usability, and facilitate high-quality NLP research. This paper outlines the system’s architecture, functionalities and evaluation, positioning it as a valuable BRAT extension for its users. The tool is open-source, and the NLP community is welcome to suggest improvements.

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BAREC Demo: Resources and Tools for Sentence-level Arabic Readability Assessment
Kinda Altarbouch | Khalid N. Elmadani | Ossama Obeid | Hanada Taha | Nizar Habash

We present BAREC Demo, a web-based system for fine-grained, sentence-level Arabic readability assessment. The demo is part of the Balanced Arabic Readability Evaluation Corpus (BAREC) project, which manually annotated 69,000 sentences (over one million words) from diverse genres and domains using a 19-level readability scale inspired by the Taha/Arabi21 framework, covering reading abilities from kindergarten to postgraduate levels. The project also developed models for automatic readability assessment.The demo provides two main functionalities for educators, content creators, language learners, and researchers: (1) a Search interface to explore the annotated dataset for text selection and resource development, and (2) an Analyze interface, which uses trained models to assign detailed readability labels to Arabic texts at the sentence level.The system and all of its resources are accessible at https://barec.camel-lab.com.

Large language models (LLMs) have shown impressive performance on general-purpose tasks, yet adapting them to specific domains remains challenging due to the scarcity of high-quality domain data. Existing data synthesis tools often struggle to extract reliable fine-tuning data from heterogeneous documents effectively. To address this limitation, we propose Easy Dataset, a unified framework for synthesizing fine-tuning data from unstructured documents via an intuitive graphical user interface (GUI). Specifically, Easy Dataset allows users to easily configure text extraction models and chunking strategies to transform raw documents into coherent text chunks. It then leverages a persona-driven prompting approach to generate diverse question-answer pairs using public-available LLMs. Throughout the pipeline, a human-in-the-loop visual interface facilitates the review and refinement of intermediate outputs to ensure data quality. Experiments on a financial question-answering task show that fine-tuning LLMs on the synthesized dataset significantly improves domain-specific performance while preserving general knowledge. The source code and installable package are available at https://github.com/ConardLi/easy-dataset and have garnered over 10,000 GitHub stars.

In today’s rapidly evolving business landscape, organizations are turning to AI agents to automate tasks, streamline business operations, and improve decision-making processes. However, despite the flexibility offered by existing libraries, the developed agents often struggle with integration into organizational workflows, resulting in limited daily usage for work. In this paper, we present SlackAgents, a multi-agent library for scalable management and collaboration of AI agents on Slack. As an agentic layer developed upon the Slack platform, the framework offers instant AI integration into organizational workflows and enables AI-powered automation of real daily tasks. Furthermore, SLACKAGENTS facilitates scalable collaboration, allowing for effective communication and task orchestration. Our solution bridges existing gaps, offering a robust platform for developing, deploying and managing AI agents for workplace environments.

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Open Political Corpora: Structuring, Searching, and Analyzing Political Text Collections with PoliCorp
Nina Smirnova | Muhammad Ahsan Shahid | Philipp Mayr

In this work, we present PoliCorp, a web portal designed to facilitate the search and analysis of political text corpora. PoliCorp provides researchers with access to rich textual data, enabling in-depth analysis of parliamentary discourse over time. The platform currently contains a collection of transcripts of debates from the German parliament, spanning 76 years of proceedings. With the advanced search functionality, researchers can apply logical operations to combine or exclude search criteria, making it easier to filter through vast amounts of parliamentary debate data. The search can be customised by combining multiple fields and applying logical operators to uncover complex patterns and insights within the data. Additional data processing steps were performed to enable web-based search and incorporate supplementary features. A key feature that differentiates PoliCorp is its intuitive web-based interface that enables users to query processed political texts without requiring programming skills. The user-friendly platform allows the creation of custom subcorpora via search parameters, which can be freely downloaded in JSON format for further analysis.

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bib (full) Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track

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Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track
Saloni Potdar | Lina Rojas-Barahona | Sebastien Montella

Advertising (Ad) is a cornerstone of the digital economy, yet the moderation of video advertisements remains a significant challenge due to their complexity and the need for precise violation localization. While recent advancements, such as the RAVEN model, have improved coarse-grained violation detection, critical gaps persist in fine-grained understanding, explainability, and generalization. To address these limitations, we propose RAVEN++, a novel framework that introduces three key innovations: 1) Active Reinforcement Learning (RL), which dynamically adapts training to samples of varying difficulty; 2) Fine-Grained Violation Understanding, achieved through hierarchical reward functions and reasoning distillation; and 3) Progressive Multi-Stage Training, which systematically combines knowledge injection, curriculum-based passive RL, and active RL. Extensive experiments on both public and proprietary datasets, on both offline scenarios and online deployed A/B Testing, demonstrate that RAVEN++ outperforms general-purpose LLMs and specialized models like RAVEN in terms of fine-grained violation understanding, reasoning capabilities, and generalization ability.

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SAGE: A Generic Framework for LLM Safety Evaluation
Madhur Jindal | Hari Shrawgi | Parag Agrawal | Sandipan Dandapat

As Large Language Models are rapidly deployed across diverse applications from healthcare to financial advice, safety evaluation struggles to keep pace. Current benchmarks focus on single-turn interactions with generic policies, failing to capture the conversational dynamics of real-world usage and the application-specific harms that emerge in context. Such potential oversights can lead to harms that go unnoticed in standard safety benchmarks and other current evaluation methodologies. To address these needs for robust AI safety evaluation, we introduce SAGE (Safety AI Generic Evaluation), an automated modular framework designed for customized and dynamic harm evaluations. SAGE employs prompted adversarial agents with diverse personalities based on the Big Five model, enabling system-aware multi-turn conversations that adapt to target applications and harm policies. We evaluate seven state-of-the-art LLMs across three applications and harm policies. Multi-turn experiments show that harm increases with conversation length, model behavior varies significantly when exposed to different user personalities and scenarios, and some models minimize harm via high refusal rates that reduce usefulness. We also demonstrate policy sensitivity within a harm category where tightening a child-focused sexual policy substantially increases measured defects across applications. These results motivate adaptive, policy-aware, and context-specific testing for safer real-world deployment.

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CRAB: A Benchmark for Evaluating Curation of Retrieval-Augmented LLMs in Biomedicine
Hanmeng Zhong | Linqing Chen | Wentao Wu | Weilei Wang

Recent development in Retrieval-Augmented Large Language Models (LLMs) have shown great promise in biomedical applications. However, a critical gap persists in reliably evaluating their curation ability—the process by which models select and integrate relevant references while filtering out noise. To address this, we introduce the benchmark for Curation of Retrieval-Augmented LLMs in Biomedicine (CRAB), the first multilingual benchmark tailored for evaluating the biomedical curation of retrieval-augmented LLMs, available in English, French, German and Chinese. By incorporating a novel citation-based evaluation metric, CRAB quantifies the curation performance of retrieval-augmented LLMs in biomedicine. Experimental results reveal significant discrepancies in the curation performance of mainstream LLMs, underscoring the urgent need to improve it in the domain of biomedicine.

Video Content Discovery (VCD) is to identify the specific videos defined by a certain pre-specified text policy (or constraint), which plays a crucial role in building a healthy and high-quality Web content ecology. Currently, related works typically employ multiple classifiers or similarity-based systems to support VCD. However, these approaches are difficult to manage, lack generalization power, and suffer from low performance. To tackle these problems, this paper presents a new Vision-Language Large Model (VLLM)-driven VCD system called VENUS (the abbreviation of Video contENt UnderStander). Concretely, we first develop an automatic policy-guided sequential annotator (APSA) to generate high-quality, VCD-specific, and reasoning-equipped instruct-tuning data for model training, then extend the VLLM inference to support VCD better. Following that, we construct a real VCD test set called VCD-Bench, which includes a total of 13 policies and 57K videos. Furthermore, to evaluate its practical efficacy, we deploy VENUS in three different real scenarios. Extensive experiments on both the VCD-Bench and public evaluation datasets for various VCD-related tasks demonstrate the superiority of VENUS over existing baselines.

Knowledge of the medical decision process, which can be modeled as medical decision trees (MDTs), is critical to building clinical decision support systems. However, current MDT construction methods rely heavily on time-consuming and laborious manual annotation. To address this challenge, we propose PI-LoRA (Path-Integrated LoRA), a novel low-rank adaptation method for automatically extracting MDTs from clinical guidelines and textbooks. We integrate gradient path information to capture synergistic effects between different modules, enabling more effective and reliable rank allocation. This framework ensures that the most critical modules receive appropriate rank allocations while less important ones are pruned, resulting in a more efficient and accurate model for extracting medical decision trees from clinical texts. Extensive experiments on medical guideline datasets demonstrate that our PI-LoRA method significantly outperforms existing parameter-efficient fine-tuning approaches for the Text2MDT task, achieving better accuracy with substantially reduced model complexity. The proposed method achieves state-of-the-art results while maintaining a lightweight architecture, making it particularly suitable for clinical decision support systems where computational resources may be limited.

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PolyNorm: Few-Shot LLM-Based Text Normalization for Text-to-Speech
Michel Wong | Ali Alshehri | Sophia Kao | Haotian He

Text Normalization (TN) is a key preprocessing step in Text-to-Speech (TTS) systems, converting written forms into their canonical spoken equivalents. Traditional TN systems can exhibit high accuracy, but involve substantial engineering effort, are difficult to scale, and pose challenges to language coverage, particularly in low-resource settings. We propose PolyNorm, a prompt-based approach to TN using Large Language Models (LLMs), aiming to reduce the reliance on manually crafted rules and enable broader linguistic applicability with minimal human intervention. Additionally, we present a language-agnostic pipeline for automatic data curation and evaluation, designed to facilitate scalable experimentation across diverse languages. Experiments across eight languages show consistent reductions in the word error rate (WER) compared to a production-grade-based system. To support further research, we release PolyNorm-Benchmark, a multilingual data set covering a diverse range of text normalization phenomena.

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Audio Query Handling System with Integrated Expert Models and Contextual Understanding
Naveen Vakada | Arvind Krishna Sridhar | Yinyi Guo | Erik Visser

This paper presents an audio chatbot system designed to handle a wide range of audio-related queries by integrating multiple specialized audio processing models. The proposed system uses an intent classifier, trained on a diverse audio query dataset, to route queries about audio content to expert models such as Automatic Speech Recognition (ASR), Speaker Diarization, Music Identification, and Text-to-Audio generation. A novel audio intent classification dataset is developed for building the intent classifier. A 3.8 B LLM model then takes inputs from an Audio Context Detection (ACD) module extracting audio event information from the audio and post processes text domain outputs from the expert models to compute the final response to the user. We evaluated the system on custom audio tasks and MMAU sound set benchmarks. The custom datasets were motivated by target use cases not covered in industry benchmarks. We proposed ACD-timestamp-QA (Question Answering) as well as ACD-temporal-QA datasets to evaluate timestamp and temporal reasoning questions, respectively. First, we determined that a BERT based Intent Classifier outperforms LLM-fewshot intent classifier in routing queries. Experiments further show that our approach significantly improves accuracy on some custom tasks compared to state-of-the-art Large Audio Language Models and outperforms models in the 7B parameter size range on the sound testset of the MMAU benchmark, thereby offering an attractive option for on device deployment.

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Generative Reviewer Agents: Scalable Simulacra of Peer Review
Nicolas Bougie | Narimawa Watanabe

The peer review process is fundamental to scientific progress, determining which papers meet the quality standards for publication. Yet, the rapid growth of scholarly production and increasing specialization in knowledge areas strain traditional scientific feedback mechanisms. In light of this, we introduce Generative Agent Reviewers (GAR), leveraging LLM-empowered agents to simulate faithful peer reviewers. To enable generative reviewers, we design an architecture that extends a large language model with memory capabilities and equips agents with reviewer personas derived from historical data. Our experiments demonstrate that GAR performs comparably to human reviewers in providing detailed feedback and predicting paper outcomes. Beyond mere performance comparison, we conduct insightful experiments, such as evaluating the impact of reviewer expertise and examining fairness in reviews. By offering early expert-level feedback, typically restricted to a limited group of researchers, GAR democratizes access to transparent and in-depth evaluation.

Large language models (LLMs) remain unreliable for global enterprise applications due to substantial performance gaps between high-resource and mid/low-resource languages, driven by English-centric pretraining and internal reasoning biases. This inconsistency undermines customer experience and operational reliability in multilingual settings such as customer support, content moderation, and information retrieval. Even with advanced Retrieval-Augmented Generation (RAG) systems, we observe up to an 29% accuracy drop in non-English languages compared to English.We propose a practical, batch-wise alignment strategy for fine-tuning LLMs, leveraging semantically equivalent multilingual data in each training batch to directly align model outputs across languages. This approach improves non-English accuracy by up to 23.9% without compromising English performance, model reasoning, or retrieval quality. Our method is simple to implement, scalable, and integrates seamlessly with existing LLM training & deployment pipelines, enabling more robust and equitable multilingual AI solutions in industry.

Multimodal Large Language Models (MLLMs) have achieved impressive results on vision-language benchmarks, yet it remains unclear whether these benchmarks assess genuine global reasoning or allow success via localized visual cues. Existing evaluation methods do not explicitly measure this distinction, hindering effective dataset curation and real-world focused model development.We introduce Region Comprehension Index (RCI), the first model-based score to directly quantify a dataset’s reliance on global versus local visual information. RCI systematically compares reference-model performance on image patches versus full images, revealing if tasks require holistic image understanding or can be solved with partial or localized visual cues.When applying RCI to 13 widely used multimodal benchmarks, we observed that most of them favor localized reasoning and exhibit significant spatial biases, indicating potential risks in real-world applications. RCI equips researchers & practitioners with an actionable tool for diagnosing & mitigating these biases, enabling the construction of datasets and benchmarks to foster the development of robust, enterprise-ready multimodal systems.

Creating high-quality, large-scale datasets for large language models (LLMs) often relies on resource-intensive, GPU-accelerated models for quality filtering, making the process time-consuming and costly. This dependence on GPUs limits accessibility for organizations lacking significant computational infrastructure. To address this issue, we introduce the Lightweight, Purpose-driven (LP) Data Pipeline, a framework that operates entirely on CPUs to streamline the processes of dataset extraction, filtering, and curation. Based on our four core principles, the LP Data Pipeline significantly reduces preparation time and cost while maintaining high data quality. Importantly, our pipeline enables the creation of purpose-driven datasets tailored to specific domains and languages, enhancing the applicability of LLMs in specialized contexts. We anticipate that our pipeline will lower the barriers to LLM development, enabling a wide range of organizations to access LLMs more easily.

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Toward Reliable Clinical Coding with Language Models: Verification and Lightweight Adaptation
Moy Yuan | Han-Chin Shing | Mitch Strong | Chaitanya Shivade

Accurate clinical coding is essential for healthcare documentation, billing, and decision-making. While prior work shows that off-the-shelf LLMs struggle with this task, evaluations based on exact match metrics often overlook errors where predicted codes are hierarchically close but incorrect. Our analysis reveals that such hierarchical misalignments account for a substantial portion of LLM failures. We show that lightweight interventions, including prompt engineering and small-scale fine-tuning, can improve accuracy without the computational overhead of search-based methods. To address hierarchically near-miss errors, we introduce clinical code verification as both a standalone task and a pipeline component. To mitigate the limitations in existing datasets, such as incomplete evidence and inpatient bias in MIMIC, we release an expert double-annotated benchmark of outpatient clinical notes with ICD-10 codes. Our results highlight verification as an effective and reliable step toward improving LLM-based medical coding.

Talent search is a cornerstone of modern recruitment systems, yet existing approaches often struggle to capture nuanced job-specific preferences, model recruiter behavior at a fine-grained level, and mitigate noise from subjective human judgments. We present a novel framework that enhances talent search effectiveness and delivers substantial business value through two key innovations: (i) leveraging LLMs to extract fine-grained recruitment signals from job descriptions and historical hiring data, and (ii) employing a role-aware multi-gate MoE network to capture behavioral differences across recruiter roles. To further reduce noise, we introduce a multi-task learning module that jointly optimizes click-through rate (CTR), conversion rate (CVR), and resume matching relevance. Experiments on real-world recruitment data and online A/B testing show relative AUC gains of 1.70% (CTR) and 5.97% (CVR), and a 17.29% lift in click-through conversion rate. These improvements reduce dependence on external sourcing channels, enabling an estimated annual cost saving of millions of CNY.

The reliability of Multimodal Large Language Models (MLLMs) in real-world settings is often undermined by sensitivity to irrelevant or distracting visual context, an aspect not captured by existing evaluation metrics. We introduce the Patch Context Robustness Index (PCRI), the first systematic and interpretable score for quantifying MLLM robustness to variations in visual context granularity, measuring performance changes between localized image patches and full-image input.Applying PCRI to 19 state-of-the-art MLLMs across 15 vision-language benchmarks, we find that most leading models remain brittle to background noise, with only a few, such as InternVL2-26B and Qwen2VL-72B, demonstrating consistent robustness across tasks. PCRI analysis also highlights how different model architectures handle and integrate visual context, offering actionable diagnostic insight for both researchers and practitioners.PCRI enables rigorous comparison of context robustness, supporting principled model selection and guiding the development of future architectures and training strategies for robust, real-world deployment.

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CitySim: Modeling Urban Behaviors and City Dynamics with Large-Scale LLM-Driven Agent Simulation
Nicolas Bougie | Narimawa Watanabe

Modeling human behavior in urban environments is fundamental for social science, behavioral studies, and urban planning. Prior work often rely on rigid, hand-crafted rules, limiting their ability to simulate nuanced intentions, plans, and adaptive behaviors. Addressing these challenges, we envision an urban simulator (CitySim), capitalizing on breakthroughs in human-level intelligence exhibited by large language models. In CitySim, agents generate realistic daily schedules using a recursive value-driven approach that balances mandatory activities, personal habits, and situational factors. To enable long-term, lifelike simulations, we endow agents with beliefs, long-term goals, and spatial memory for navigation. CitySim exhibits closer alignment with real humans than prior work, both at micro and macro levels. Additionally, we conduct insightful experiments by modeling tens of thousands of agents and evaluating their collective behaviors under various real-world scenarios, including estimating crowd density, predicting place popularity, and assessing well-being. Our results highlight CitySim as a scalable, flexible testbed for understanding and forecasting urban phenomena.

We present a novel approach to conversational agent evaluation using Persona-driven User Simulations based on Large Language Models (LLMs). Our methodology first uses LLMs to generate diverse customer personas, which are then used to configure a single LLM-based user simulator. This simulator evaluates SalesBot 2.0, a proactive conversational sales agent. We introduce a dataset of these personas, along with corresponding goals and conversation scenarios, enabling comprehensive testing across different customer types with varying assertiveness levels and precision of needs. Our evaluation framework assesses both the simulator’s adherence to persona instructions and the bot’s performance across multiple dimensions, combining human annotation with LLM-as-a-judge assessments using commercial and open-source models. Results demonstrate that our LLM-based simulator effectively emulates nuanced customer roles, and that cross-selling strategies can be implemented with minimal impact on customer satisfaction, varying by customer type.

Recent generative models such as GPT‐4o have shown strong capabilities in producing high-quality images with accurate text rendering. However, commercial design tasks like advertising banners demand more than visual fidelity—they require structured layouts, precise typography, consistent branding and etc. In this paper, we introduce **MIMO (Mirror In‐the‐Model)**, an agentic refinement framework for automatic ad banner generation. MIMO combines a hierarchical multimodal agent system (MIMO‐Core) with a coordination loop (MIMO‐Loop) that explores multiple stylistic directions and iteratively improves design quality. Requiring only a simple natural language based prompt and logo image as input, MIMO automatically detects and corrects multiple types of errors during generation. Experiments show that MIMO significantly outperforms existing diffusion and LLM-based baselines in real-world banner design scenarios.

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Leveraging Product Catalog Patterns for Multilingual E-commerce Product Attribute Prediction
Bryan Zhang | Suleiman A. Khan | SteCphan Walter

E-commerce stores increasingly use Large Language Models (LLMs) to enhance catalog data quality through automated regeneration. A critical challenge is accurately predicting missing structured attribute values across multilingual product catalogs, where LLM performance varies significantly by language. While existing approaches leverage general knowledge through prompt engineering and external retrieval, more effective and accurate signals for attribute prediction can exist within the catalog ecosystem itself-similar products often share consistent patterns and structural relationships, and may have the missing attributes filled. Therefore, this paper introduces PatternRAG, a novel retrieval-augmented system that strategically leverages existing product catalog entries to guide LLM predictions for missing attributes. Our approach introduces a multi-stage retrieval framework that progressively refines the search space based on product type, uses textual similarity, glance views and brand relationships to identify the most relevant attribute-filled examples for LLM prediction guidance. Experiments on test sets across three major e-commerce stores in different languages (US, DE, FR) demonstrate substantial improvements in catalog data quality, achieving up to 34% increase in recall and 0.8% in precision for attribute value prediction. At catalog entry level, it also achieves up to +43.32% increase in completeness and up to +2.83% in correctness.

In this paper, we introduce , the first benchmark framework for evaluating LLM agent with multimodal capabilities in the e-commerce customer support domain. ECom-Bench features dynamic user simulation based on persona information collected from real e-commerce customer interactions and a realistic task dataset derived from authentic e-commerce dialogues. These tasks, covering a wide range of business scenarios, are designed to reflect real-world complexities, making highly challenging. For instance, even advanced models like GPT-4o achieve only a 10–20% pass3 metric in our benchmark, highlighting the substantial difficulties posed by complex e-commerce scenarios. The code and data have been made publicly available at https://github.com/XiaoduoAILab/ECom-Bench to facilitate further research and development in this domain.

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ProCut: LLM Prompt Compression via Attribution Estimation
Zhentao Xu | Fengyi Li | Albert C. Chen | Xiaofeng Wang

In large-scale industrial LLM systems, prompt templates often expand to thousands of tokens as teams iteratively incorporate sections such as task instructions, few-shot examples, and heuristic rules to enhance robustness and coverage. This expansion leads to bloated prompts that are difficult to maintain and incur significant inference latency and serving costs. To address this, we introduce Prompt Compression via Attribution Estimation (ProCut), a flexible, LLM-agnostic, training-free framework that compresses prompts through attribution analysis. ProCut segments prompt templates into semantically meaningful units, quantifies their impact on task performance, and prunes low-utility components. Through extensive experiments on five public benchmark datasets and real-world industrial prompts, we show that ProCut achieves substantial prompt size reductions (78% fewer tokens in production) while maintaining or even slightly improving task performance (up to 62% better than alternative methods). We further introduce an LLM-driven attribution estimator that reduces compression latency by over 50%, and demonstrate that ProCut integrates seamlessly with existing prompt-optimization frameworks to produce concise, high-performing prompts.

Detecting abnormal events in real-world customer service dialogues is highly challenging due to the complexity of business data and the dynamic nature of customer interactions. Moreover, models must demonstrate strong out-of-domain (OOD) generalization to enable rapid adaptation across different business scenarios and maximize commercial value.In this work, we propose a novel Adaptive Perplexity-Aware Reinforcement Learning (APARL) framework that leverages the advanced reasoning capabilities of large language models for abnormal event detection. APARL introduces a dual-loop dynamic curriculum learning architecture, enabling the model to progressively focus on more challenging samples as its proficiency increases. This design effectively addresses performance bottlenecks and significantly enhances OOD transferability.Extensive evaluations on food delivery dialogue tasks show that our model achieves significantly enhanced adaptability and robustness, attaining the highest F1 score with an average improvement of 17.19%, and an average improvement of 9.59% in OOD transfer tests. This method provides a superior solution for industrial deployment of anomaly detection models, contributing to improved operational efficiency and commercial benefits.

With the emergence of Large Language Models (LLMs), numerous use cases have arisen in the medical field, particularly in generating summaries for consultation transcriptions and extensive medical reports. A major concern is that these summaries may omit critical information from the original input, potentially jeopardizing the decision-making process. This issue of omission is distinct from hallucination, which involves generating incorrect or fabricated facts. To address omissions, this paper introduces a dataset designed to evaluate such issues and proposes a frugal approach called EmbedKDECheck for detecting omissions in LLM-generated texts. The dataset, created in French, has been validated by medical experts to ensure it accurately represents real-world scenarios in the medical field. The objective is to develop a reference-free (black-box) method that can evaluate the reliability of summaries or reports without requiring significant computational resources, relying only on input and output. Unlike methods that rely on embeddings derived from the LLM itself, our approach uses embeddings generated by a third-party, lightweight NLP model based on a combination of FastText and Word2Vec. These embeddings are then combined with anomaly detection models to identify omissions effectively, making the method well-suited for resource-constrained environments. EmbedKDECheck was benchmarked against black-box state-of-the-art frameworks and models, including SelfCheckGPT, ChainPoll, and G-Eval, which leverage GPT. Results demonstrated its satisfactory performance in detecting omissions in LLM-generated summaries. This work advances frugal methodologies for evaluating the reliability of LLM-generated texts, with significant potential to improve the safety and accuracy of medical decision support systems in surgery and other healthcare domains.

Large language models (LLMs) often struggle with factual accuracy in knowledge-intensive domains like healthcare. We introduce LEAF (Learning and Evaluation Augmented by Fact-Checking), a framework for improving LLM factuality in medical question answering. LEAF comprises three components: (1) RAFE, a robust fact-checking system using open-source LLMs and domain-specific retrieval to evaluate response accuracy; (2) Fact-Check-then-RAG, which leverages fact-checking results to guide retrieval without parameter updates; and (3) Learning from Fact Check, enabling self-training through supervised fine-tuning or preference-based learning using fact-checking as pseudo-labels. Experimental results show that RAFE outperforms Factcheck-GPT in detecting inaccuracies, Fact-Check-then-RAG effectively corrects errors, and Learning from Fact Check improves performance without labeled data. In a real-world healthcare deployment with proprietary medical documents, LEAF achieved an 83% improvement in factuality scores, demonstrating practical applicability for adapting general-purpose LLMs to organization-specific knowledge. Our framework provides a scalable solution for industrial applications requiring high factual accuracy.

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ReAct Meets Industrial IoT: Language Agents for Data Access
James T Rayfield | Shuxin Lin | Nianjun Zhou | Dhaval C Patel

We present a robust framework for deploying domain-specific language agents that can query industrial sensor data using natural language. Grounded in the Reasoning and Acting (ReAct) paradigm, our system introduces three key innovations: (1) integration of the Self-Ask method for compositional, multi-hop reasoning; (2) a multi-agent architecture with Review, Reflect and Distillation components to improve reliability and fault tolerance; and (3) a long-context prompting strategy leveraging curated in-context examples, which we call Tiny Trajectory Store, eliminating the need for fine-tuning. We apply our method to Industry 4.0 scenarios, where agents query SCADA systems (e.g., SkySpark) using questions such as, “How much power did B002 AHU 2-1-1 use on 6/14/16 at the POKMAIN site?” To enable systematic evaluation, we introduce IoTBench, a benchmark of 400+ tasks across five industrial sites. Our experiments show that ReAct-style agents enhanced with long-context reasoning (ReActXen) significantly outperform standard prompting baselines across multiple LLMs including smaller models. This work repositions NLP agents as practical interfaces for industrial automation, bridging natural language understanding and sensor-driven environments.

Online shoppers often initiate their journey with only a vague idea of what they need, forcing them to iterate over search results until they eventually discover a suitable product. We formulate this scenario as product demand clarification: starting from an ambiguous query, an agent must iteratively ask clarifying questions, progressively refine the user’s intent, and retrieve increasingly relevant items. To tackle this challenge, we present **ProductAgent**, a fully autonomous conversational information-seeking agent that couples large language models with a set of domain-specific tools. ProductAgent maintains a structured memory of the dialogue, summarizes candidate products into concise feature statistics, generates strategic clarification questions, and performs retrieval over hybrid (symbolic + dense) indices in a closed decision loop. To measure real–world effectiveness, we further introduce **PROCLARE**, a PROduct CLArifying REtrieval benchmark that pairs ProductAgent with an LLM-driven user simulator, thereby enabling large-scale and reproducible evaluation without human annotation. On 2,000 automatically generated sessions, retrieval metrics improve monotonically with the number of turns, validating that ProductAgent captures and refines user intent through dialogue.

We propose MADS (Multi-Agent Dialogue Simulation), a scalable framework for generating persuasive multi-turn dialogues via agent self-play. MADS employs three coordinated agents: User Agents designed to simulate diverse persona-driven behaviors by leveraging personality signifiers such as Zodiac Signs and MBTI types, a Dialog Agent executing task-oriented persuasion strategies and an Optimization Agent evaluating and refining dialogue outcomes. We further validate its effectiveness through users’ Chain-of-Attitude (CoA) modeling and dedicated LLMs’ persuasion assessment. This approach enables low-cost generation of training data without human annotation, addressing key industry challenges such as lack of user data, cold-start evaluation difficulties, and prompt inefficiency. Applied to a real-world marketing scenario, MADS significantly improved the persuasion capacity of small LLMs, increasing the organic traffic conversion rate by 22.4% (from 1.83% to 2.24%) , demonstrating clear business value.

Large language models (LLMs) are commonly adapted for diverse downstream tasks via parameter-efficient fine-tuning techniques such as Low-Rank Adapters (LoRA). While adapters can be combined to handle multiple tasks separately, standard approaches struggle when targeting the simultaneous execution of complex tasks, such as generating a translated summary from a long conversation. To address this challenge, we propose a novel approach tailored specifically for compositional multi-tasking scenarios involving summarization and translation. Our technique involves adding a learnable projection layer on top of the combined summarization and translation adapters. This design enables effective integration while maintaining efficiency through reduced computational overhead compared to alternative strategies requiring extensive retraining or sequential processing. We demonstrate the practical viability of our method within an on-device environment by developing an Android app capable of executing compositional tasks seamlessly. Experimental results indicate our solution performs well and is fast in both cloud-based and on-device implementations, highlighting the potential benefits of adopting our framework in real-world applications demanding high-speed operation alongside resource constraints.

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Select-then-Route : Taxonomy guided Routing for LLMs
Soham Shah | Kumar Shridhar

Recent advances in large language models (LLMs) have boosted performance across a broad spectrum of natural‐language tasks, yet no single model excels uniformly across domains. Sending each query to the most suitable model mitigates this limitation, but deciding among *all* available LLMs for each query is prohibitively expensive. Both the accuracy and the latency can improve if the decision space for the model choice is first narrowed, followed by selecting the suitable model for the given query.We introduce Select-then-Route (StR), a two‐stage framework that first *selects* a small, task‐appropriate pool of LLMs and then *routes* each query within that pool through an adaptive cascade. StR first employs a lightweight, *taxonomy‐guided selector* that maps each query to models proven proficient for its semantic class (e.g., reasoning, code, summarisation). Within the selected pool, a *confidence‐based cascade* begins with the cheapest model and escalates only when a multi‐judge agreement test signals low reliability.Across six public benchmarks of various domains, StR improves the end‐to‐end accuracy from 91.7% (best single model) to 94.3% while reducing inference cost by 4X. Because both the taxonomy and multi-judge evaluation thresholds are tunable, StR exposes a smooth cost–accuracy frontier, enabling users to dial in the trade‐off that best fits their latency and budget constraints.

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FABRIC: Fully-Automated Broad Intent Categorization in E-commerce
Anna Tigunova | Philipp Schmidt | Damla Ezgi Akcora

Predicting the user’s shopping intent is a crucial task in e-commerce. In particular determining the product category, which the user wants to shop, is essential for delivering relevant search results and website navigation options. Existing query classification models are reported to have excellent predictive performanceon the single-intent queries (e.g. ‘running shoes’), but there is only little research on predicting multiple-intents for a broad query (e.g.‘running gear’). Although the training data for broad query classification can be easily obtained, the evaluation of multi-label categorization remains challenging, as the set of true labels for multi-intent queries is subjective and ambiguous. In this work we propose an automatic method of creating the evaluation data for multi-label e-commerce query classification. We reduce the ambiguity of the annotations by blending the label assessment from three different sources: user click data, query-item relevance and LLM judgments.

Chinese Spelling Correction (CSC) aims to detect and correct spelling errors in given sentences. Recently, multi-domain CSC has gradually attracted the attention of researchers because it is more practicable.In this paper, we focus on the key flaw of the CSC model when adapting to multi-domain scenarios: the tendency to forget previously acquired knowledge upon learning new domain-specific knowledge (i.e., **catastrophic forgetting**).To address this, we propose a novel model-agnostic **M**ulti-stage **K**nowledge **T**ransfer (**MKT**) framework with an evolving teacher model and dynamic distillation weights for knowledge transfer in each domain, rather than focusing solely on new domain knowledge.It deserves to be mentioned that we are the first to apply continual learning methods to the multi-domain CSC task. Experiments. prove our method’s effectiveness over traditional approaches, highlighting the importance of overcoming catastrophic forgetting to enhance model performance.

We present a scalable large language model (LLM)-based system that combines aspect-based sentiment analysis (ABSA) with guided summarization to generate concise and interpretable product review summaries. Our approach first extracts and consolidates aspect–sentiment pairs from individual reviews, selects the most frequent aspects for each product, and samples representative reviews accordingly. These are used to construct structured prompts that guide the LLM to produce summaries grounded in actual customer feedback. We demonstrate the real-world effectiveness of our system through a large-scale online A/B test. Furthermore, we describe our real-time deployment strategy and release a dataset of 11,8 million anonymized customer reviews covering 92,000 products, including extracted aspects and generated summaries, to support future research in aspect-guided review summarization.

Structured outputs are essential for large language models (LLMs) in critical applications like agents and information extraction. Despite their capabilities, LLMs often generate outputs that deviate from predefined schemas, significantly hampering reliable application development. We present SLOT (Structured LLM Output Transformer), a model-agnostic approach that transforms unstructured LLM outputs into precise structured formats. While existing solutions predominantly rely on constrained decoding techniques or are tightly coupled with specific models, SLOT employs a fine-tuned lightweight language model as a post-processing layer, achieving flexibility across various LLMs and schema specifications. We introduce SLOTBench, curated by a data synthesis pipeline alongside a formal evaluation methodology that quantifies both schema accuracy and content fidelity. Our results demonstrate that fine-tuned Mistral-7B model with constrained decoding achieves near-perfect schema accuracy (99.5%) and content similarity (94.0%), outperforming Claude-3.5-Sonnet by substantial margins (+25 and +20 percentage points, respectively). Notably, even compact models like Llama-3.2-1B can match or exceed the structured output capabilities of much larger proprietary models when equipped with SLOT, enabling reliable structured generation in resource-constrained environments. SLOTBench will be released upon legal approval.

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QuackIR: Retrieval in DuckDB and Other Relational Database Management Systems
Yijun Ge | Zijian Chen | Jimmy Lin

Enterprises today are increasingly compelled to adopt dedicated vector databases for retrieval-augmented generation (RAG) in applications based on large language models (LLMs).As a potential alternative for these vector databases, we propose that organizations leverage existing relational databases for retrieval, which many have already deployed in their enterprise data lakes, thus minimizing additional complexity in their software stacks.To demonstrate the simplicity and feasibility of this approach, we present QuackIR, an information retrieval (IR) toolkit built on relational database management systems (RDBMSes), with integrations in DuckDB, SQLite, and PostgreSQL. Using QuackIR, we benchmark the sparse and dense retrieval capabilities of these popular RDBMSes and demonstrate that their effectiveness is comparable to baselines from established IR toolkits. Our results highlight the potential of relational databases as a simple option for RAG scenarios due to their established widespread usage and the easy integration of retrieval abilities. Our implementation is available at quackir.io.

We present a new benchmark for evaluating Deep Search—a realistic and complex form of retrieval-augmented generation (RAG) that requires source-aware, multi-hop reasoning over diverse, sparsed, but related sources. These include documents, meeting transcripts, Slack messages, GitHub, and URLs, which vary in structure and often contain human-to-human interactions. We build it using a synthetic data pipeline that simulates business workflows across product planning, development, and support stages, generating interconnected content with realistic noise and multi-hop questions with guaranteed ground-truth answers. We release our benchmark with both answerable and unanswerable queries, and retrieval pool of 39,190 enterprise artifacts, enabling fine-grained evaluation of long-context LLM and RAG systems. Our experiments reveal that even the best-performing agentic RAG methods achieve an average performance score of 32.96 on our benchmark. With further analysis, we highlight retrieval as the main bottleneck: existing methods struggle to conduct deep searches and retrieve all necessary evidence. Consequently, they often reason over partial context, leading to significant performance degradation.

Large Language Models (LLMs) have demonstrated impressive text generation capabilities, yet their outputs often misalign with human preferences. To address this challenge, Reinforcement Learning from Human Feedback (RLHF) has become an essential component of modern LLM training pipelines. Although Proximal Policy Optimization (PPO) initially emerged as a favored RLHF strategy, its complexity and inefficiency have spurred the investigation of simpler alternatives. This work presents, to the authors’ knowledge, the most comprehensive benchmark to date of seventeen state-of-the-art RLHF algorithms. We evaluate these algorithms on two different benchmarks, OpenAI’s TL;DR Summarization and Anthropic’s Helpfulness / Harmlessness, with two different reward models a Gemma 2B Reward model and a Rules based reward model. We incorporate extensive hyperparameter sweeps for each algorithm. With this expanded analysis, we report consistently top-performing RLHF algorithms: IPO, DPO, Reinforce, GRPO, and Best-of-N, and list the highest performing hyperparameter combinations for each. This work aims to guide practitioners in selecting the most effective RLHF algorithm while promoting a culture of thorough and impartial benchmarking in the field.

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Predicting Cross-lingual Trends in Microblogs
Satoshi Akasaki

Trends on microblogs often transcend linguistic boundaries, evolving into global phenomena with significant societal and economic impact. This paper introduces and tackles the novel predictive task of forecasting which microblog trends will cross linguistic boundaries to become popular in other languages, and when. While crucial for proactive global monitoring and marketing, this area has been under-explored. We introduce a methodology to overcome the challenge of cross-lingual trend identification by automatically constructing a dataset using Wikipedia’s inter-language links. We then propose a prediction model that leverages a rich feature set, including not only temporal frequency but also microblog content and external knowledge signals from Wikipedia. Our approach significantly outperforms existing trend prediction methods and LLM-based approaches, achieving an improvement of up to 4% in F1-score, enabling the forecast of cross-lingual trends before they emerge in a new language.

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Generating Fine Details of Entity Interactions
Xinyi Gu | Jiayuan Mao

Recent text-to-image models excel at generating high-quality object-centric images from instructions. However, images should also encapsulate rich interactions between objects, where existing models often fall short, likely due to limited training data and benchmarks for rare interactions. This paper explores a novel application of Multimodal Large Language Models (MLLMs) to benchmark and enhance the generation of interaction-rich images.We introduce InterActing-1000, an interaction-focused dataset with 1000 LLM-generated fine-grained prompts for image generation covering (1) functional and action-based interactions, (2) multi-subject interactions, and (3) compositional spatial relationships.To address interaction-rich generation challenges, we propose a decomposition-augmented refinement procedure. Our approach, DetailScribe, leverages LLMs to decompose interactions into finer-grained concepts, uses an MLLM to critique generated images, and applies targeted refinements with a partial diffusion denoising process. Automatic and human evaluations show significantly improved image quality, demonstrating the potential of enhanced inference strategies. Our dataset and code are available at https://detailscribe.github.io/.

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AutoCVSS: Assessing the Performance of LLMs for Automated Software Vulnerability Scoring
Davide Sanvito | Giovanni Arriciati | Giuseppe Siracusano | Roberto Bifulco | Michele Carminati

The growing volume of daily disclosed software vulnerabilities imposes significant pressure on security analysts, extending the time needed for analysis - an essential step for accurate risk prioritization.Meanwhile, the time between disclosure and exploitation is reducing, becoming shorter than the analysis time and increasing the window of opportunity for attackers.This study explores leveraging Large Language Models (LLMs) for automating vulnerability risk score prediction using the industrial CVSS standard.From our analysis across different data availability scenarios, LLMs can effectively complement supervised baselines in data-scarce settings. In the absence of any annotated data, such as during the transition to new versions of the standard, LLMs are the only viable approach, highlighting their value in improving vulnerability management.We make the source code of AutoCVSS public at https://github.com/nec-research/AutoCVSS.

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SFAL: Semantic-Functional Alignment Scores for Distributional Evaluation of Auto-Interpretability in Sparse Autoencoders
Fabio Mercorio | Filippo Pallucchini | Daniele Potertì | Antonio Serino | Andrea Seveso

Interpreting the internal representations of large language models (LLMs) is crucial for their deployment in real-world applications, impacting areas such as AI safety, debugging, and compliance. Sparse Autoencoders facilitate interpretability by decomposing polysemantic activation into a latent space of monosemantic features. However, evaluating the auto-interpretability of these features is difficult and computationally expensive, which limits scalability in practical settings. In this work, we propose SFAL, an alternative evaluation strategy that reduces reliance on LLM-based scoring by assessing the alignment between the semantic neighbourhoods of features (derived from auto-interpretation embeddings) and their functional neighbourhoods (derived from co-occurrence statistics).Our method enhances efficiency, enabling fast and cost-effective assessments. We validate our approach on large-scale models, demonstrating its potential to provide interpretability while reducing computational overhead, making it suitable for real-world deployment.

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Just One is Enough: An Existence-based Alignment Check for Robust Japanese Pronunciation Estimation
Hayate Nakano | Nobuhiro Kaji

Neural models for Japanese pronunciation estimation often suffer from errors such ashallucinations (generating pronunciations that are not grounded in the input) and omissions (skipping parts of the input).Although attention-based alignment has been used to detect such errors,selecting reliable attention heads is difficult,and developing methods that can both detect and correct these errorsremains challenging.In this paper, we propose a simple method calledexistence-based alignment check.In this approach,we consider alignment candidatesindependently extracted from all attention heads,and check whether at least one of these candidates satisfies two conditionsderived from the linguistic properties of Japanese pronunciation:monotonicity and pronunciation length per character.We generate multiple hypotheses using beam searchand use the alignment check as a filtering mechanismto correct hallucinations and omissions.We apply this method to a dataset of Japanese facility namesand demonstrate that it improves pronunciation estimation accuracyby over 2.5%.

Large Language Model (LLM) agents hold promise for a flexible and scalable alternative to traditional business process automation, but struggle to reliably follow complex company policies. In this study we introduce a deterministic, transparent, and modular framework for enforcing business policy adherence in agentic workflows. Our method operates in two phases: (1) an offline buildtime stage that compiles policy documents into verifiable guard code associated with tool use, and (2) a runtime integration where these guards ensure compliance before each agent action. We demonstrate our approach on the challenging 𝜏-bench Airlines domain, showing encouraging preliminary results in policy enforcement, and further outline key challenges for real-world deployments.

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Learning to Translate Ambiguous Terminology by Preference Optimization on Post-Edits
Nathaniel Berger | Johannes Eschbach-Dymanus | Miriam Exel | Matthias Huck | Stefan Riezler

In real world translation scenarios, terminology is rarely one-to-one. Instead, multiple valid translations may appear in a terminology dictionary, but correctness of a translation depends on corporate style guides and context. This can be challenging for neural machine translation (NMT) systems. Luckily, in a corporate context, many examples of human post-edits of valid but incorrect terminology exist. The goal of this work is to learn how to disambiguate our terminology based on these corrections. Our approach is based on preference optimization, using the term post-edit as the knowledge to be preferred. While previous work had to rely on unambiguous translation dictionaries to set hard constraints during decoding, or to add soft constraints in the input, our framework requires neither one-to-one dictionaries nor human intervention at decoding time. We report results on English-German post-edited data and find that the optimal combination of supervised fine-tuning and preference optimization, with both term-specific and full sequence objectives, yields statistically significant improvements in term accuracy over a strong translation oriented LLM without significant losses in COMET score. Additionally, we release test sets from our post-edited data and terminology dictionary.

The reasoning capabilities of Large Language Models (LLMs) play a critical role in many downstream tasks, yet depend strongly on the quality of training data. Despite various proposed data construction methods, their practical utility in real-world pipelines remains underexplored. In this work, we conduct a comprehensive analysis of open-source datasets and data synthesis techniques for mathematical reasoning, evaluating them under a unified pipeline designed to mirror training and deployment scenarios. We further distill effective data selection strategies and identify practical methods suitable for industrial applications. Our findings highlight that structuring data in more interpretable formats, or distilling from stronger models often outweighs simply scaling up data volume. This study provides actionable guidance for integrating training data to enhance LLM capabilities, supporting both cost-effective data curation and scalable model enhancement. We hope this work will inspire further research on how to balance “more data” versus “better data” for real-world reasoning tasks.

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SRS-Stories: Vocabulary-constrained multilingual story generation for language learning
Wiktor Kamzela | Mateusz Lango | Ondrej Dusek

In this paper, we use large language models to generate personalized stories for language learners, using only the vocabulary they know.The generated texts are specifically written to teach the user new vocabulary by simply reading stories where it appears in context, while at the same time seamlessly reviewing recently learned vocabulary. The generated stories are enjoyable to read and the vocabulary reviewing/learning is optimized by a Spaced Repetition System.The experiments are conducted in three languages: English, Chinese and Polish, evaluating three story generation methods and three strategies for enforcing lexical constraints. The results show that the generated stories are more grammatical, coherent, and provide better examples of word usage than texts generated by the standard constrained beam search approach.

This paper presents Ryt AI, an LLM-native agentic framework that powers Ryt Bank to enable customers to execute core financial transactions through natural language conversation. This represents the first global regulator-approved deployment worldwide where conversational AI functions as the primary banking interface, in contrast to prior assistants that have been limited to advisory or support roles. Built entirely in-house, Ryt AI is powered by ILMU, a closed-source LLM developed internally, and replaces rigid multi-screen workflows with a single dialogue orchestrated by four LLM-powered agents (Guardrails, Intent, Payment, and FAQ). Each agent attaches a task-specific LoRA adapter to ILMU, which is hosted within the bank’s infrastructure to ensure consistent behavior with minimal overhead. Deterministic guardrails, human-in-the-loop confirmation, and a stateless audit architecture provide defense-in-depth for security and compliance. The result is Banking Done Right: demonstrating that regulator-approved natural-language interfaces can reliably support core financial operations under strict governance.

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Graph of Attacks with Pruning: Optimizing Stealthy Jailbreak Prompt Generation for Enhanced LLM Content Moderation
Daniel Schwartz | Dmitriy Bespalov | Zhe Wang | Ninad Kulkarni | Yanjun Qi

As large language models (LLMs) become increasingly prevalent, ensuring their robustness against adversarial misuse is crucial. This paper introduces the Graph of Attacks with Pruning (GAP) framework, an advanced approach for generating stealthy jailbreak prompts to evaluate and enhance LLM safeguards. GAP addresses limitations in existing tree-based LLM jailbreak methods by implementing an interconnected graph structure that enables knowledge sharing across attack paths. Our experimental evaluation demonstrates GAP’s superiority over existing techniques, achieving a 20.8% increase in attack success rates while reducing query costs by 62.7%. GAP consistently outperforms state-of-the-art methods for attacking both open and closed LLMs, with attack success rates of >96%. Additionally, we present specialized variants like GAP-Auto for automated seed generation and GAP-VLM for multimodal attacks. GAP-generated prompts prove highly effective in improving content moderation systems, increasing true positive detection rates by 108.5% and accuracy by 183.6% when used for fine-tuning.

Reward Models (RMs) are key components for evaluating and guiding language model outputs. However, traditional scalar RMs often struggle with incorporating contextual and background information during inference, leading to incomplete evaluations. Generative RMs (GRMs) attempt to address these limitations by generating intermediate reasoning steps. Yet, their uncontrolled black-box nature and inefficiency due to sequential decoding hinder their industrial deployment. Industrial scenarios, such as search and recommendation systems, often involve single-domain tasks requiring evaluation along specific dimensions. In such contexts, diagnosing “bad cases” necessitates structured feedback to identify and optimize dimension-specific issues.In this paper, we propose the Structural Reward Model (SRM), a modular and interpretable framework integrating side-branch models as auxiliary feature generators. By introducing fine-grained dimensions, SRMs enable interpretable and efficient evaluation, facilitating targeted diagnostics and optimization. This structured approach ensures adaptability and scalability for industrial applications.Through comprehensive experiments, we demonstrate that SRMs outperform scalar RMs and GRMs in robustness and alignment with human preferences. The modular design further supports efficient optimization for practical scenarios, allowing SRM to provide a practical reward modeling solution for industry.

Given the inherent subjectivity of similarity in text, fully unsupervised text clustering is unlikely to produce groupings that work across a variety of use cases. Traditional techniques to guide clustering rely on costly, time-consuming human feedback and/or pre-existing labels. Leveraging recent advancements in LLMs and decoder-only embedding models, we present techniques to effectively control text embeddings with minimal human input: prefix instructions and LLM preprocessing. We evaluate clustering performance for datasets with multiple independent ground-truth labels, or perspectives, and find that these techniques can be used to improve clustering for one perspective or use case, at the cost of a tradeoff in performance for another use case.

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SpeechLLMs for Large-scale Contextualized Zero-shot Slot Filling
Kadri Hacioglu | Manjunath K E | Andreas Stolcke

Slot filling is a crucial subtask in spoken language understanding (SLU), traditionally implemented as a cascade of speech recognition followed by one or more natural language understanding (NLU) components. The recent advent of speech-based large language models (speechLLMs), which integrate speech and textual foundation models, has opened new avenues for achieving speech understanding tasks in a more unified, generative, and instruction-following manner while promising data and compute efficiency with zero-shot abilities, generalizing to unseen slot labels. We address the slot-filling task by creating an empirical upper bound for the task, identifying performance, robustness, and generalization gaps, and proposing improvements to the training data, architecture, and training strategies to narrow the gap with the upper bound result. We show that each of these measures improve performance substantially, while highlighting practical challenges and providing empirical guidance and insights for harnessing these emerging models.

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NurseLLM: The First Specialized Language Model for Nursing
Md Tawkat Islam Khondaker | Julia Harrington | Shady Shehata

Recent advancements in large language models (LLMs) have significantly transformed medical systems. However, their potential within specialized domains such as nursing remains largely underexplored. In this work, we introduce NurseLLM, the first nursing-specialized LLM tailored for multiple choice question-answering (MCQ) tasks. We develop a multi-stage data generation pipeline to build the first large scale nursing MCQ dataset to train LLMs on a broad spectrum of nursing topics. We further introduce multiple nursing benchmarks to enable rigorous evaluation. Our extensive experiments demonstrate that NurseLLM outperforms SoTA general-purpose and medical-specialized LLMs of comparable size on different benchmarks, underscoring the importance of a specialized LLM for the nursing domain. Finally, we explore the role of reasoning and multi-agent collaboration systems in nursing, highlighting their promise for future research and applications.

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Augmenting Compliance-Guaranteed Customer Service Chatbots: Context-Aware Knowledge Expansion with Large Language Models
Mengze Hong | Chen Jason Zhang | Di Jiang | Yuanqin He

Retrieval-based chatbots leverage human-verified Q&A knowledge to deliver accurate, verifiable responses, making them ideal for customer-centric applications where compliance with regulatory and operational standards is critical. To effectively handle diverse customer inquiries, augmenting the knowledge base with “similar questions” that retain semantic meaning while incorporating varied expressions is a cost-effective strategy. In this paper, we introduce the Similar Question Generation (SQG) task for LLM training and inference, proposing context-aware approaches to enable comprehensive semantic exploration and enhanced alignment with source question-answer relationships. We formulate optimization techniques for constructing in-context prompts and selecting an optimal subset of similar questions to expand chatbot knowledge under budget constraints. Both quantitative and human evaluations validate the effectiveness of these methods, achieving a 92% user satisfaction rate in a deployed chatbot system, reflecting an 18% improvement over the unaugmented baseline. These findings highlight the practical benefits of SQG and emphasize the potential of LLMs, not as direct chatbot interfaces, but in supporting non-generative systems for hallucination-free, compliance-guaranteed applications.

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Memory-Efficient Backpropagation for Fine-Tuning LLMs on Resource-Constrained Mobile Devices
Congzheng Song | Xinyu Tang

Fine-tuning large language models (LLMs) with backpropagation–even for a subset of parameters such as LoRA–can be much more memory-consuming than inference and is often deemed impractical for resource-constrained mobile devices. Alternative methods, such as zeroth-order optimization (ZO), can greatly reduce the memory footprint but come at the cost of significantly slower model convergence (10× to 100× more steps than backpropagation). We propose a memory-efficient implementation of backpropagation (MeBP) on mobile devices that allows flexible trade-offs between memory usage and compute time, while converging faster and achieving better performance than the ZO baseline. We verify the effectiveness of MeBP on an iPhone 15 Pro Max and show that various LLMs, ranging from 0.5B to 4B parameters, can be fine-tuned using less than 1GB of memory. We release an example of the MeBP implementation at https://github.com/apple/ml-mebp.

Retrieval-augmented generation (RAG) often falls short when retrieved context includes confusing semi-relevant passages, or when answering questions require deep contextual understanding and reasoning. We propose an efficient fine-tuning framework, called PrismRAG, that (i) trains the model with distractor-aware QA pairs mixing gold evidence with subtle distractor passages, and (ii) instills reasoning-centric habits that make the LLM plan, rationalize, and synthesize without relying on extensive human engineered instructions. Evaluated across 12 open-book RAG QA benchmarks spanning diverse application domains and scenarios, PrismRAG improves average factuality by 5.4%, outperforming state-of-the-art solutions. Our method is being deployed in production.

Retrieval-augmented generation (RAG) aims to reduce hallucinations by grounding responses in external context, yet large language models (LLMs) still frequently introduce unsupported information or contradictions even when provided with relevant context. This paper presents two complementary efforts at Vectara to measure and benchmark LLM faithfulness in RAG. First, we describe our original hallucination leaderboard, which has tracked hallucination rates for LLMs since 2023 using our HHEM hallucination detection model. Motivated by limitations observed in current hallucination detection methods, we introduce FaithJudge, an LLM-as-a-judge framework that leverages a pool of diverse human-annotated hallucination examples to substantially improve the automated hallucination evaluation of LLMs. We introduce an enhanced hallucination leaderboard centered on FaithJudge that benchmarks LLMs on RAG faithfulness in summarization, question-answering, and data-to-text generation tasks. FaithJudge enables a more reliable benchmarking of LLM hallucinations in RAG and supports the development of more trustworthy generative AI systems: https://github.com/vectara/FaithJudge.

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A Multi-Agent Framework for Quantitative Finance : An Application to Portfolio Management Analytics
Sayani Kundu | Dushyant Sahoo | Victor Li | Jennifer Rabowsky | Amit Varshney

Machine learning and artificial intelligence have been used widely within quantitative finance. However there is a scarcity of AI frameworks capable of autonomously performing complex tasks and quantitative analysis on structured data. This paper introduces a novel Multi-Agent framework tailored for such tasks which are routinely performed by portfolio managers and researchers within the asset management industry. Our framework facilitates mathematical modeling and data analytics by dynamically generating executable code. The framework’s innovative multi-agent architecture includes specialized components and agents for reflection, summarization, and financial expertise which coordinate to enhance problem solving abilities. We present a comprehensive empirical evaluation on portfolio management-specific tasks, addressing a critical gap in current research. Our findings reveal that the proposed Multi-Agent framework vastly outperforms Single-Agent frameworks, demonstrating its practical utility across various task categories. By using dynamic code generation with the agent’s multi-step reasoning capabilities, we broaden the range of tasks that can be successfully addressed.

LLMs often fail to meet specialized needs of distinct user groups due to their one-size-fits-all approach, and there is limited understanding of what personalization each group expects.To address this, we propose GPA a group-aware personalization framework that captures context-specific preference variations and steers LLMs accordingly.Our approach involves: (1) Group-Aware Preference Extraction, which distills divergent preferences from real-world conversation logs into interpretable rubrics, and (2) Tailored Response Generation, using (a) GPA-CT, which adapts responses using learnt rubrics, and (b) GPA-FT, which finetunes models using rubric-guided synthetic data.Automatic and Human evaluations confirm that GPA improves group alignment without compromising perfomance on standard instruction-following benchmarks.

The increasing complexity of modern driving systems demands efficient collection and analysis of specific driving scenarios that are crucial for system development and validation. Current approaches either rely on massive data collection followed by manual filtering, or rigid threshold-based recording systems that often miss important edge cases. In this paper, we present Distributed Adaptive Scene Recognition (DASR), a novel multi-agent cloud-edge framework for language-guided scene detection in connected vehicles. Our system leverages the complementary strengths of cloud-based large language models and edge-deployed vision language models to intelligently identify and preserve relevant driving scenarios while optimizing limited on-vehicle buffer storage. The cloud-based LLM serves as an intelligent coordinator that analyzes developer prompts to determine which specialized tools and sensor data streams should be incorporated, while the edge-deployed VLM efficiently processes video streams in real time to make relevant decisions. Extensive experiments across multiple driving datasets demonstrate that our framework achieves superior performance compared to larger baseline models, with exceptional performance on complex driving tasks requiring sophisticated reasoning. DASR also shows strong generalization capabilities on out-of-distribution datasets and significantly reduces storage requirements (28.73 %) compared to baseline methods.

Large language models (LLMs) such as GPT-4o and o1 have demonstrated strong performance on clinical natural language processing (NLP) tasks across multiple medical benchmarks. Nonetheless, two high-impact NLP tasks — structured tabular reporting from nurse dictations and medical order extraction from doctor-patient consultations — remain underexplored due to data scarcity and sensitivity, despite active industry efforts. Practical solutions to these real-world clinical tasks can significantly reduce the documentation burden on healthcare providers, allowing greater focus on patient care. In this paper, we investigate these two challenging tasks using private and open-source clinical datasets, evaluating the performance of both open- and closed-weight LLMs, and analyzing their respective strengths and limitations. Furthermore, we propose an agentic pipeline for generating realistic, non-sensitive nurse dictations, enabling structured extraction of clinical observations. To support further research in both areas, we release SYNUR and SIMORD, the first open-source datasets for nurse observation extraction and medical order extraction.

Entity Linking (EL) has traditionally relied on large annotated datasets and extensive model fine-tuning. While recent few-shot methods leverage large language models (LLMs) through prompting to reduce training requirements, they often suffer from inefficiencies due to expensive LLM-based reasoning. ARTER (Adaptive Routing and Targeted Entity Reasoning) presents a structured pipeline that achieves high performance without deep fine-tuning by strategically combining candidate generation, context-based scoring, adaptive routing, and selective reasoning. ARTER computes a small set of complementary signals(both embedding and LLM-based) over the retrieved candidates to categorize contextual mentions into easy and hard cases. The cases are then handled by a low-computational entity linker (e.g. ReFinED) and more expensive targeted LLM-based reasoning respectively. On standard benchmarks, ARTER outperforms ReFinED by up to +4.47%, with an average gain of +2.53% on 5 out of 6 datasets, and performs comparably to pipelines using LLM-based reasoning for all mentions, while being as twice as efficient in terms of the number of LLM tokens.

In modern industry systems like multi-turn chat agents, Text-to-SQL technology bridges natural language (NL) questions and database (DB) querying. The conversion of tabular DB results into NL representations (NLRs) enables the chat-based interaction. Currently, NLR generation is typically handled by large language models (LLMs), but information loss or errors in presenting tabular results in NL remains largely unexplored.This paper introduces a novel evaluation method - Combo-Eval - for judgment of LLM-generated NLRs that combines the benefits of multiple existing methods, optimizing evaluation fidelity and achieving a significant reduction in LLM calls by 25-61%. Accompanying our method is NLR-BIRD, the first dedicated dataset for NLR benchmarking. Through human evaluations, we demonstrate the superior alignment of Combo-Eval with human judgments, applicable across scenarios with and without ground truth references.

The ubiquity of payment networks generates vast transactional data encoding rich consumer and merchant behavioral patterns. Recent foundation models for transaction analysis process tabular data sequentially but rely on index-based representations for categorical merchant fields, causing substantial semantic information loss by converting rich textual data into discrete tokens. While Large Language Models (LLMs) can address this limitation through superior semantic understanding, their computational overhead challenges real-time financial deployment. We introduce a hybrid framework that uses LLM-generated embeddings as semantic initializations for lightweight transaction models, balancing interpretability with operational efficiency. Our approach employs multi-source data fusion to enrich merchant categorical fields and a one-word constraint principle for consistent embedding generation across LLM architectures. We systematically address data quality through noise filtering and context-aware enrichment. Experiments on large-scale transaction datasets demonstrate significant performance improvements across multiple transaction understanding tasks.

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Agent vs. Agent: Automated Data Generation and Red-Teaming for Custom Agentic Workflows
Ninad Kulkarni | Xian Wu | Siddharth Varia | Dmitriy Bespalov

Large Language Models (LLMs) deployed as autonomous agents with tool access present unique safety challenges that extend beyond standalone model vulnerabilities. Existing red-teaming frameworks like AgentHarm use static prompts and hardcoded toolsets, limiting their applicability to custom production systems.We introduce a dual-component automated red-teaming framework: AgentHarm-Gen generates adversarial tasks and evaluation functions tailored to arbitrary toolsets, while Red-Agent-Reflect employs iterative prompt refinement with self-reflection to develop progressively more effective attacks.Evaluating across 115 harmful tasks (71 generated, 44 from AgentHarm) spanning 8 risk categories, our method achieves substantial improvements: up to 162% increase in attack success rate on o4-mini and 86% success on Gemini 2.5 Pro. Successful attacks systematically decompose adversarial objectives into benign-appearing sub-tasks that circumvent safety alignment, highlighting the need for agent-specific guardrails.

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Auto prompting without training labels: An LLM cascade for product quality assessment in e-commerce catalogs
Soham Satyadharma | Fatemeh Sheikholeslami | Swati Kaul | Aziz Umit Batur | Suleiman A. Khan

We introduce a novel, training free cascade for auto-prompting Large Language Models (LLMs) to assess product quality in e-commerce. Our system requires no training labels or model fine-tuning, instead automatically generating and refining prompts for evaluating attribute quality across tens of thousands of product category–attribute pairs. Starting from a seed of human-crafted prompts, the cascade progressively optimizes instructions to meet catalog-specific requirements. This approach bridges the gap between general language understanding and domain-specific knowledge at scale in complex industrial catalogs. Our extensive empirical evaluations shows the auto-prompt cascade improves precision and recall by 8–10% over traditional chain-of-thought prompting. Notably, it achieves these gains while reducing domain expert effort from 5.1 hours to 3 minutes per attribute - a 99% reduction. Additionally, the cascade generalizes effectively across five languages and multiple quality assessment tasks, consistently maintaining performance gains.

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Harmonizing Diverse Models: A Layer-wise Merging Strategy for Consistent Generation
Xujun Peng | Anoop Kumar | Jingyu Wu | Parker Glenn | Daben Liu

Retrieval-Augmented Generation (RAG) systems leverage Large Language Models (LLMs) to generate accurate and reliable responses that are grounded in retrieved context. However, LLMs often generate inconsistent outputs for semantically equivalent inputs, a problem exacerbated by limited consistency-focused data and the limitations of existing fine-tuning methods for improving consistency. We propose a new approach combining systematic synthetic data generation, triplet loss for better embeddings, and a novel layer-wise model merging approach. Using consistency-aware weights derived from intermediate layer activations, our method effectively integrates knowledge from specialized models. Experimental results how that our merged model significantly enhances output consistency, achieving approximately 47.5% improvement in response similarity over the baseline, thus offering a practical solution for increasing the the reliability of an industrial RAG system.

Open-ended survey responses provide valuable insights in marketing research, but low-quality responses not only burden researchers with manual filtering but also risk leading to misleading conclusions, underscoring the need for effective evaluation. Existing automatic evaluation methods target LLM-generated text and inadequately assess human-written responses with their distinct characteristics. To address such characteristics, we propose a two-stage evaluation framework specifically designed for human survey responses. First, gibberish filtering removes nonsensical responses. Then, three dimensions—effort, relevance, and complete- ness—are evaluated using LLM capabilities, grounded in empirical analysis of real-world survey data. Validation on English and Korean datasets shows that our framework not only outperforms existing metrics but also demonstrates high practical applicability for real-world applications such as response quality prediction and response rejection, showing strong correlations with expert assessment.

Video Temporal Grounding (VTG) aims to localize relevant temporal segments in videos given natural language queries. Despite recent progress with large vision-language models (LVLMs) and instruction-tuning, existing approaches often suffer from limited temporal awareness and poor generalization. In this work, we introduce a two-stage training framework that integrates supervised fine-tuning with reinforcement learning (RL) to improve both the accuracy and robustness of VTG models. Our approach first leverages high-quality curated cold-start data for SFT initialization, followed by difficulty-controlled RL to further enhance temporal localization and reasoning abilities. Comprehensive experiments on multiple VTG benchmarks demonstrate that our method consistently outperforms existing models, particularly in challenging and open-domain scenarios. We conduct an in-depth analysis of training strategies and dataset curation, highlighting the importance of both high-quality cold-start data and difficulty-controlled RL. To facilitate further research and industrial adoption, we release all intermediate datasets, models, and code to the community.

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SEARA: An Automated Approach for Obtaining Optimal Retrievers
Zou Yuheng | Wang Yiran Yiran | Tian Yuzhu | Zhu Min | Yanhua Huang

Retrieval-Augmented Generation (RAG) is a core approach for enhancing Large Language Models (LLMs), where the effectiveness of the retriever largely determines the overall response quality of RAG systems. Retrievers encompass a multitude of hyperparameters that significantly impact performance outcomes and demonstrate sensitivity to specific applications. Nevertheless, hyperparameter optimization entails prohibitively high computational expenses. Existing evaluation methods suffer from either prohibitive costs or disconnection from domain-specific scenarios. This paper proposes SEARA (Subset sampling Evaluation for Automatic Retriever Assessment), which addresses evaluation data challenges through subset sampling techniques and achieves robust automated retriever evaluation by minimal retrieval facts extraction and comprehensive retrieval metrics. Based on real user queries, this method enables fully automated retriever evaluation at low cost, thereby obtaining optimal retriever for specific business scenarios. We validate our method across classic RAG applications in rednote, including knowledge-based Q&A system and retrieval-based travel assistant, successfully obtaining scenario-specific optimal retrievers.

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UniEDU: Toward Unified and Efficient Large Multimodal Models for Educational Tasks
Zhendong Chu | Jian Xie | Shen Wang | Zichao Wang | Qingsong Wen

Education materials for K-12 students often consist of multiple modalities, such as text and images, posing challenges for models to fully understand nuanced information in these materials. In this paper, we propose a unified language and vision assistant UniEDU designed for various educational applications, including knowledge recommendation, knowledge tracing, time cost prediction, and user answer prediction, all within a single model. Unlike conventional task-specific models, UniEDU offers a unified solution that excels across multiple educational tasks while maintaining strong generalization capabilities. Its adaptability makes it well-suited for real-world deployment in diverse learning environments. Furthermore, UniEDU is optimized for industry-scale deployment by significantly reducing computational overhead—achieving approximately a 300% increase in efficiency—while maintaining competitive performance with minimal degradation compared to fully fine-tuned models. This work represents a significant step toward creating versatile AI systems tailored to the evolving demands of education.

Large Language Models (LLMs) hold significant promise for transforming digital health by enabling automated medical question answering. However, ensuring these models meet critical industry standards for factual accuracy, usefulness, and safety remains a challenge, especially for open-source solutions. We present a rigorous benchmarking framework via a dataset of over 1,000 health questions. We assess model performance across honesty, helpfulness, and harmlessness. Our results highlight trade-offs between factual reliability and safety among evaluated models—Mistral-7B, BioMistral-7B-DARE, and AlpaCare-13B. AlpaCare-13B achieves the highest accuracy (91.7%) and harmlessness (0.92), while domain-specific tuning in BioMistral-7B-DARE boosts safety (0.90) despite smaller scale. Few-shot prompting improves accuracy from 78% to 85%, and all models show reduced helpfulness on complex queries, highlighting challenges in clinical QA. Our code is available at: https://github.com/AnasAzeez/TTT

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An Address Intelligence Framework for E-commerce Deliveries
Gokul Swamy | Aman Gulati | Srinivas Virinchi | Anoop Saladi

For an e-commerce domain, the customeraddress is the single most important pieceof customer data for ensuring accurateand reliable deliveries. In this two-partstudy, we first outline the construction ofa language model to assist customers withaddress standardization and in the latterpart, we detail a novel Pareto-ensemblemulti-task prediction algorithm that derives critical insights from customer addresses to minimize operational losses arising from a given geographical area. Finally, we demonstrate the potential benefits ofthe proposed address intelligence systemfor a large e-commerce domain throughlarge scale experiments on a commercialsystem.

Running Large Language Models (LLMs) on edge devices is constrained by high compute and memory demands—posing a barrier for real-time applications in industries like healthcare, education, and embedded systems. Current solutions such as quantization, pruning, and Retrieval-Augmented Generation (RAG) offer only partial optimizations and often compromise on speed or accuracy. We introduce HOLA, an end-to-end optimization framework for efficient LLM deployment. Internally, it leverages Hierarchical Speculative Decoding (HSD) for faster inference without quality loss. Externally, AdaComp-RAG adjusts retrieval complexity based on context needs. Together with Lo-Bi, which blends structured pruning (LoRA) and quantization, HOLA delivers significant gains: +17.6% EMA on GSM8K, +10.5% MCA on ARC, and reduced latency and memory on edge devices like Jetson Nano—proving both scalable and production-ready. Our code is available at: https://github.com/zohaibhasan066/HOLA_Codebase

Customer service often relies on human agents, which, while effective, can be costly and slower to scale. Recent advancements in intelligent chatbots, particularly Retrieval-Augmented Generation (RAG) models, have significantly enhanced efficiency by integrating large language models with external knowledge retrieval. However, developing a multi-turn RAG-based chatbot for real-world customer service presents additional complexities, requiring components like adaptive retrieval and query reformulation. These components typically require substantial annotated data, which is often scarce. To overcome this limitation, we propose methods to automatically generate labels for these components using real customer-agent dialogue data. Specifically, we introduce two labeling strategies for adaptive retrieval: an intent-guided strategy and an explanation-based strategy, along with two query reformulation strategies: natural language query reformulation and keyword-based reformulation. Our experiments reveal that the explanation-based strategy yields the best results for adaptive retrieval, while the keyword-based reformulation improves document retrieval quality.Our findings offer valuable insights for practitioners working on multi-turn RAG systems.

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RAGulator: Lightweight Out-of-Context Detectors for Grounded Text Generation
Ian Poey | Jiajun Li1 | Qishuai Zhong

Real-time identification of out-of-context outputs from large language models (LLMs) is crucial for enterprises to safely adopt retrieval augmented generation (RAG) systems. In this work, we develop lightweight models capable of detecting when LLM-generated text deviates from retrieved source documents semantically. We compare their performance against open-source alternatives on data from credit policy and sustainability reports used in the banking industry. The fine-tuned DeBERTa model stands out for its superior performance, speed, and simplicity, as it requires no additional preprocessing or feature engineering. While recent research often prioritises state-of-the-art accuracy through fine-tuned generative LLMs and complex training pipelines, we demonstrate how detection models are deployed efficiently with high speed and minimal resource usage.

Accurate intent classification is critical for efficient routing in customer service, ensuring customers are connected with the most suitable agents while reducing handling times and operational costs. However, as companies expand their product lines, intent classification faces scalability challenges due to the increasing number of intents and variations in taxonomy across different verticals. In this paper, we introduce REIC, a Retrieval-augmented generation Enhanced Intent Classification approach, which addresses these challenges effectively. REIC leverages retrieval-augmented generation (RAG) to dynamically incorporate relevant knowledge, enabling precise classification without the need for frequent retraining. Through extensive experiments on real-world datasets, we demonstrate that REIC outperforms traditional fine-tuning, zero-shot, and few-shot methods in large-scale customer service settings. Our results highlight its effectiveness in both in-domain and out-of-domain scenarios, demonstrating its potential for real-world deployment in adaptive and large-scale intent classification systems.

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Mapping Smarter, Not Harder: A Test-Time Reinforcement Learning Agent That Improve Without Labels or Model Updates
Wen-Kwang Tsao | Yao-Ching Yu | Chien-Ming Huang

The Enterprise Intelligence Platform must integrate logs from numerous third-party vendors in order to perform various downstream tasks. However, vendor documentation is often unavailable at test time. It is either misplaced, mismatched, poorly formatted, or incomplete, which makes schema mapping challenging. We introduce a reinforcement learning agent that can self-improve without labeled examples or model weight updates. During inference, the agent first identifies ambiguous field-mapping attempts, then generates targeted web-search queries to gather external evidence, and finally applies a confidence-based reward to iteratively refine its mappings. To demonstrate this concept, we converted Microsoft Defender for Endpoint logs into a common schema. Our method increased mapping accuracy from 56.4% (LLM-only) to 72.73% (RAG) to 93.94% over 100 iterations using GPT-4o. At the same time, it reduced the number of low-confidence mappings requiring expert review by 85%. This new approach provides an evidence-driven, transparent method for solving future industry problems, paving the way for more robust, accountable, scalable, efficient, flexible, adaptable, and collaborative solutions.

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On Assigning Product and Software Codes to Customer Service Requests with Large Language Models
Sujatha Das Gollapalli | Mouad Hakam | Mingzhe Du | See-Kiong Ng | Mohammed Hamzeh

In a technology company, quality of customer service that involves providingtroubleshooting assistance and advice to customers is a crucial asset.Often, insights from historical customer service data are used to make decisions related to future product offerings. In this paper, we address the challenging problem of automatic assignment of product names and software version labels to customer Service Requests (SRs) related to BLIND, a company in the networking domain.We study the effectiveness of state-of-the-art Large Language Models (LLMs) in assigning the correct product name codes and software versions from several possible label options and their “non-canonical” mentions in the associated SR data. To this end, we frame the assignment as a multiple-choice question answering task instead of conventional prompts and devise, to our knowledge, a novel pipeline of employing a classifier for filtering inputs to the LLM for saving usage costs. On our experimental dataset based on real SRs, we are able to correctly identify product name and software version labels when they are mentioned with over 90% accuracy while cutting LLM costs by ~40-60% on average, thus providing a viable solution for practical deployment.

Short video platforms are evolving rapidly, making the identification of inappropriate content increasingly critical.Existing approaches typically train separate and small classification models for each type of issue, which requires extensive human-labeled data and lacks cross-issue generalization.We propose a reasoning-enhanced multimodal large language model (MLLM) pretraining paradigm for unified inappropriate content detection. To address the distribution gap between short video content and the original pretraining data of MLLMs, as well as the complex issue definitions, we introduce three targeted pretraining tasks:(1) Caption, to enhance the MLLM’s perception of video details;(2) Visual Question Answering (VQA), to deepen the MLLM’s understanding of issue definitions and annotation guidelines;(3) Chain-of-Thought (CoT), to enhance the MLLM’s reasoning capability.Experimental results show that our pretraining approach significantly improves the MLLM’s performance in both zero-shot and supervised fine-tuning (SFT) settings.In addition, our pretrained model demonstrates strong generalization capabilities to emergent, previously unseen issues.

Structured representation of product information is a major bottleneck for the efficiency of e-commerce platforms, especially in second-hand ecommerce platforms. Currently, most product information are organized based on manually curated product categories and attributes, which often fail to adequately cover long-tail products and do not align well with buyer preference. To address these problems, we propose Generative Semantic InDexings (GSID), a data-driven approach to generate product structured representations. GSID consists of two key components: (1) Pre-training on unstructured product metadata to learn in-domain semantic embeddings, and (2) Generating more effective semantic codes tailored for downstream product-centric applications. Extensive experiments are conducted to validate the effectiveness of GSID, and it has been successfully deployed on the real-world e-commerce platform, achieving promising results on product understanding and other downstream tasks.

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Learning from LLM Agents: In-Context Generative Models for Text Casing in E-Commerce Ads
Yingxue Zhou | Tan Zhu | Tao Zeng | Zigeng Wang | Wei Shen

E-commerce ad platforms enforce content policies and review created ads before publication, with casing requirements playing a critical role in maintaining readability and brand consistency. Existing NER-based transformer models have been widely used for casing correction, but they process characters independently in a classification-based manner, failing to capture sentence level contextual dependencies, making them less reliable when handling unseen or ad-specific terms, e.g., brand names. LLMs like ChatGPT offer better generalization to proper nouns, but they are expensive and have high latency. Besides, generative model can suffer from hallucination. To address these challenges, we propose a two-stage approach: (1) an LLM-based Agent leveraging Chain-of-Actions (CoA) to enforce casing policies while accurately handling ads-specific terms, such as brand names, and (2) a lightweight generative model that preserves the LLM Agent’s knowledge while significantly reducing latency and costs. We design a novel in-context decoding strategy, which avoids hallucinations. Our approach outperforms NER-based methods and achieves near-LLM Agent performance, making it a scalable and efficient solution for real-world ad compliance automation.

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Auto-Weighted Group Relative Preference Optimization for Multi-Objective Text Generation Tasks
Yuki Ichihara | Yuu Jinnai

Group Relative Policy Optimization (GRPO) is a promising approach to complex, real-world tasks, such as those involving multiple rewards or strict constraints. However, when training GRPO with multiple rewards, the weights of each reward must be decided in advance. Failing to balance the objectives adequately can lead to overfitting or insufficient learning of each reward function. To address this problem, we propose Auto-Weighted Group Relative Policy Optimization (AW-GRPO), which adjusts reward weights during training according to the progress of the learning of each objective so far.We evaluate AW-GRPO on advertising text generation, a real-world problem where the generated text must satisfy multiple objectives, such as quality and diversity, while adhering to the constraints of the media (e.g., maximum number of characters).Our results show that AW-GRPO successfully balances multiple objectives, improving the overall scores while reducing the constraint violation rate.We additionally evaluate AW-GRPO using publicly available benchmark problems for reproducibility, in which we observe the same qualitative result that the proposed method outperforms GRPO.

We present an enterprise-grade translation platform for global e-commerce that combines daily batch and real-time API pipelines with optimized T5-based models and a Reference Generator to enforce >99% non-translatable entity preservation. A linguist-driven rule engine and explainable evaluation framework (BLEU, COMET, and a custom e-commerce metric) enable continuous quality improvements. Deployed on GPU-accelerated inference servers and CPU-based processing nodes, our system processes millions of listings per day with sub-second latency and achieves 10×–100× cost savings over general-purpose LLMs for English→Spanish and English→French translation, all while version-tracking every update for robust enterprise rollouts.

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InstaJudge: Aligning Judgment Bias of LLM-as-Judge with Humans in Industry Applications
Myeongjun Erik Jang | Fran Silavong

Automated evaluation using LLM-as-Judge offers significant practical benefits for industrial applications. However, the commonly recognized misalignment of judgment biases between humans and LLM-as-Judge hinders its usage in real-world businesses. Although preference-finetuning could be a potential solution, it is often impractical for industrial use-cases due to the scarcity of business-specific data and the infeasibility of applying it to closed models. In this paper, we propose InstaJudge, an LLM-as-Judge library that improves alignments of judgment biases through automatic prompt optimization (APO). Our library not only integrates recent APO methods within a unified framework but also introduces a novel APO approach called distribution-preserving few-shot sampling (DPFS). Experimental results verify demonstrate DPFS significantly outperforms existing LLM-as-Judge libraries, like DeepEval, and APO methods by a large margin, while being more cost efficient.

As Large Language Models (LLMs) evolve into powerful agentic systems, the telecommunications industry’s expansion into AI services necessitates industry-grounded benchmarks to evaluate their underexplored domain-specific capabilities. To address the gap left by generic benchmarks that fail to assess realistic, non-English performance, we present TelAgentBench, a Korean benchmark for the telecommunications domain evaluating five core agentic capabilities: Reasoning, Planning, Action (tool-use), Retrieval-Augmented Generation, and Instruction Following. Evaluations reveal significant performance disparities between models that employ explicit reasoning and those that do not, providing actionable insights for deploying agentic LLMs in real-world telecommunications tasks.

Evaluation and Management (E/M) coding, under the Current Procedural Terminology (CPT) taxonomy, documents medical services provided to patients by physicians. Used primarily for billing purposes, it is in physicians’ best interest to provide accurate CPT E/M codes. Automating this coding task will help alleviate physicians’ documentation burden, improve billing efficiency, and ultimately enable better patient care. However, a number of real-world complexities have made E/M encoding automation a challenging task. In this paper, we elaborate some of the key complexities and present ProFees, our LLM-based framework that tackles them, followed by a systematic evaluation. On an expert-curated real-world dataset, ProFees achieves an increase in coding accuracy of more than 36% over a commercial CPT E/M coding system and almost 5% over our strongest single-prompt baseline, demonstrating its effectiveness in addressing the real-world complexities.

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Classifier-Augmented Generation for Structured Workflow Prediction
Thomas Gschwind | Shramona Chakraborty | Nitin Gupta | Sameep Mehta

ETL (Extract, Transform, Load) tools such as IBM DataStage allow users to visually assemble complex data workflows, but configuring stages and their properties remains time consuming and requires deep tool knowledge. We propose a system that translates natural language descriptions into executable workflows, automatically predicting both the structure and detailed configuration of the flow. At its core lies a Classifier-Augmented Generation (CAG) approach that combines utterance decomposition with a classifier and stage-specific few-shot prompting to produce accurate stage predictions. These stages are then connected into non-linear workflows using edge prediction, and stage properties are inferred from sub-utterance context. We compare CAG against strong single-prompt and agentic baselines, showing improved accuracy and efficiency, while substantially reducing token usage. Our architecture is modular, interpretable, and capable of end-to-end workflow generation, including robust validation steps. To our knowledge, this is the first system with a detailed evaluation across stage prediction, edge layout, and property generation for natural-language-driven ETL authoring.

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Efficient and Versatile Model for Multilingual Information Retrieval of Islamic Text: Development and Deployment in Real-World Scenarios
Vera Pavlova | Mohammed Makhlouf

Despite recent advancements in Multilingual Information Retrieval (MLIR), a significant gap remains between research and practical deployment. Many studies assess MLIR performance in isolated settings, limiting their applicability to real-world scenarios.In this work, we leverage the unique characteristics of the Quranic multilingual corpus to examine the optimal strategies to develop an ad-hoc IR system for the Islamic domain that is designed to satisfy users’ information needs in multiple languages. We prepared eleven retrieval models employing four training approaches: monolingual, cross-lingual, translate-train-all, and a novel mixed method combining cross-lingual and monolingual techniques. Evaluation on an in-domain dataset demonstrates that the mixed approach achieves promising results across diverse retrieval scenarios. Furthermore, we provide a detailed analysis of how different training configurations affect the embedding space and their implications for multilingual retrieval effectiveness.Finally, we discuss deployment considerations, emphasizing the cost-efficiency of deploying a single versatile, lightweight model for real-world MLIR applications.

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AutoQual: An LLM Agent for Automated Discovery of Interpretable Features for Review Quality Assessment
Xiaochong Lan | Jie Feng | Yinxing Liu | Xinleishi | Yong Li

Ranking online reviews by their intrinsic quality is a critical task for e-commerce platforms and information services, impacting user experience and business outcomes. However, quality is a domain-dependent and dynamic concept, making its assessment a formidable challenge. Traditional methods relying on hand-crafted features are unscalable across domains and fail to adapt to evolving content patterns, while modern deep learning approaches often produce black-box models that lack interpretability and may prioritize semantics over quality. To address these challenges, we propose AutoQual, an LLM-based agent framework that automates the discovery of interpretable features. While demonstrated on review quality assessment, AutoQual is designed as a general framework for transforming tacit knowledge embedded in data into explicit, computable features. It mimics a human research process, iteratively generating feature hypotheses through reflection, operationalizing them via autonomous tool implementation, and accumulating experience in a persistent memory. We deploy our method on a large-scale online platform with a billion-level user base. Large-scale A/B testing confirms its effectiveness, increasing average reviews viewed per user by 0.79% and the conversion rate of review readers by 0.27%.

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JSON Whisperer: Efficient JSON Editing with LLMs
Sarel Duanis | Asnat Greenstein-Messica | Eliya Habba

Large language models (LLMs) can modify JSON documents through natural language commands, but current approaches regenerate entire structures for each edit, resulting in computational inefficiency. We present JSON Whisperer, a framework that enables LLMs to generate RFC 6902 diff patches-expressing only the necessary modifications-rather than complete documents.We identify two key challenges in patch-based editing: (1) LLMs often miss related updates when generating isolated patches, and (2) array manipulations require tracking index shifts across operations, which LLMs handle poorly. To address these issues, we introduce EASE (Explicitly Addressed Sequence Encoding), which transforms arrays into dictionaries with stable keys, eliminating index arithmetic complexities.Our evaluation shows that patch generation with EASE reduces token usage by 31% while maintaining edit quality within 5% of full regeneration with particular gains for complex instructions and list manipulations.

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L4: Mutual Learning Helps Lifelong Language Learning
Jiyong Li | Dilshod Azizov | Shangsong Liang

Adapting language models to learn continuously from data streams while retaining previous knowledge is a key challenge in artificial intelligence (AI), particularly in lifelong language learning. Existing distillation methods are based on offline techniques, limiting their ability to update in real-time and adapt to dynamic environments. To address this, we propose online dynamic mutual distillation - a novel framework that enables continuous mutual learning from task streams without relying on domain-specific teachers. To our knowledge, this is the first application of mutual learning in lifelong language learning, providing dynamic knowledge transfer without domain-specific teachers. Moreover, our extensive experiments demonstrate that the proposed method reduces catastrophic forgetting, while improving task performance on various benchmark datasets making it suitable for real-world, dynamic natural language processing (NLP) applications such as adaptive chatbots and personalized language systems. We will make our code publicly available upon acceptance.

Natural language interfaces (NLIs) democratize data analytics by enabling non-technical users to query relational databases via Text-to-SQL systems. While large language models (LLMs) have achieved state-of-the-art accuracy on benchmarks like Spider and BIRD, two critical challenges persist for real-time deployment: (1) inference latency due to sequential autoregressive decoding (e.g., average inference latency on BIRD (Minidev) is 14.3 seconds per query for Qwen2.5-Coder32B and 22.86 seconds for Llama-70B.), and (2) schema hallucinations (e.g., invalid column references like customer_ids instead of cust_id). (2) schema hallucinations (e.g., Qwen2.5-Coder-32B Instruct generated ... COUNT(users.UserId) ... = users.Id ..., using users.Id correctly in JOIN but hallucinating users.UserId in COUNT). To address these, we propose Tree-Guided Token Decoding (TTD-SQL), a lightweight framework that integrates SQL grammar and database schema constraints into the decoding process without modifying the underlying LLM. TTD precomputes token-level decision trees over SQL keywords, table names, and column identifiers, enabling deterministic “auto-fill” transitions for uniquely determined tokens (e.g., “Song_” → “ID”) while retaining flexibility for unconstrained reasoning. Across five LLMs (CodeLlama, Phi-4, Qwen2.5, Granite, Llama70B), TTD achieves up to 19.96% token-rate speedups by eliminating redundant forward passes (e.g., CodeLlama: 8.97→10.76 tokens/s on Spider) and reduces schema hallucinations by +17.7% in executable-SQL rates (e.g., CodeLlama on BIRD). By bridging rigid parser based methods and flexible LLM generation, TTD offers a practical path toward reliable, high-performance SQL generation in both public benchmarks and enterprise settings.

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Spot the BlindSpots: Systematic Identification and Quantification of Fine-Grained LLM Biases in Contact Center Call Summarization
Kawin Mayilvaghanan | Siddhant Gupta | Ayush Kumar

Abstractive summarization is a core application in contact centers, where Large Language Models (LLMs) generate millions of summaries of call transcripts daily. Despite their apparent quality, it remains unclear whether LLMs systematically under- or over-attend to specific aspects of the transcript, potentially introducing biases in the generated summary. While prior work has examined social and positional biases, the specific forms of bias pertinent to contact center operations—which we term ‘Operational Bias’—have remained unexplored. To address this gap, we introduce BlindSpot, a framework built upon a taxonomy of 15 operational bias dimensions (e.g., disfluency, speaker, topic) for the identification and quantification of these biases. BlindSpot leverages an LLM as a zero-shot classifier to derive categorical distributions for each bias dimension in a pair of transcript and its summary. The bias is then quantified using two metrics: Fidelity Gap, measured as the Total Variation Distance (TVD) between distributions, and Coverage, defined as the percentage of source labels omitted. Using BlindSpot, we conduct an empirical study with 2500 real call transcripts and their summaries generated by 20 LLMs of varying scales and families (e.g., GPT, Llama, Claude). Our analysis reveals that biases are systemic and present across all evaluated models, regardless of size or family. We further report on bias mitigation via targeted prompting which measurably reduces bias across models.

Modern recommendation systems grapple with reconciling users’ enduring preferences with transient interests, particularly in click-through rate (CTR) prediction. Existing approaches inadequately fuse long-term behavioral profiles (e.g., aggregated purchase trends) and short-term interaction sequences (e.g., real-time clicks), suffering from representational misalignment and noise in transient signals. We propose HierDiffuse, a unified framework that redefines interest fusion as a hierarchical denoising process through diffusion models. Our approach addresses these challenges via three innovations: (1) A cross-scale diffusion mechanism aligns long- and short-term representations by iteratively refining long-term interests using short-term contextual guidance; (2) A Semantic Guidance Disentanglement (SGD) mechanism explicitly decouples core interests from noise in short-term signals;(3) Trajectory Convergence Constraint (TCC) is proposed to accelerate diffusion model reasoning without reducing generation quality to meet the constraints of high QPS (Queries Per Second) and low latency for online deployment of recommendation or advertising systems.HierDiffuse eliminates ad-hoc fusion operators, dynamically integrates multi-scale interests, and enhances robustness to spurious interactions as well as improves inference speed. Extensive experiments on real-world datasets demonstrate state-of-the-art performance, with significant improvements in CTR prediction accuracy and robustness to noisy interactions. Our work establishes diffusion models as a principled paradigm for adaptive interest fusion in recommendation systems.

Retrieval-Augmented Generation (RAG) is one of the leading and most widely used techniques for enhancing LLM retrieval capabilities, but it still faces significant limitations in commercial use cases. RAG primarily relies on the query-chunk text-to-text similarity in the embedding space for retrieval and can fail to capture deeper semantic relationships across chunks, is highly sensitive to chunking strategies, and is prone to hallucinations. To address these challenges, we propose TOBUGraph, a graph-based retrieval framework that first constructs the knowledge graph from unstructured data dynamically and automatically. Using LLMs, TOBUGraph extracts structured knowledge and diverse relationships among data, going beyond RAG’s text-to-text similarity. Retrieval is achieved through graph traversal, leveraging the extracted relationships and structures to enhance retrieval accuracy. This eliminates the need for chunking configurations while reducing hallucination. We demonstrate TOBUGraph’s effectiveness in TOBU, a real-world application in production for personal memory organization and retrieval. Our evaluation using real user data demonstrates that TOBUGraph outperforms multiple RAG implementations in both precision and recall, significantly enhancing user experience through improved retrieval accuracy.

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Thinking with DistilQwen: A Tale of Four Distilled Reasoning and Reward Model Series
Wenrui Cai | Chengyu Wang | Junbing Yan | Jun Huang | Xiangzhong Fang

Recently, the demand for small and efficient reasoning models to support real-world applications has driven the development of knowledge distillation techniques that balance reasoning performance and inference speed. In this paper, we further extend the DistilQwen model family, initialized from the Qwen models, by introducing four model series specifically designed to meet industrial requirements. The distilled model collection comprises: (1) slow-thinking models, optimized for reasoning tasks that require high accuracy; (2) two series of adaptive-thinking models, which dynamically adjust reasoning strategies based on input tasks to maximize efficiency across diverse scenarios; and (3) distilled reward models, which enable further reinforcement learning of reasoning models using distilled knowledge. Comprehensive evaluations across multiple benchmarks demonstrate both high inference efficiency and strong reasoning performance for these models, as well as the practical utility of distilled reward models. We further show that these models support industry practitioners by providing scalable training and inference functionalities on the Alibaba Cloud PAI (Platform for Artificial Intelligence) platform.

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Crossing Domains without Labels: Distant Supervision for Term Extraction
Elena Senger | Yuri Campbell | Rob Van Der Goot | Barbara Plank

Automatic Term Extraction (ATE) is a critical component in downstream NLP tasks such as document tagging, ontology construction and patent analysis. Current state-of-the-art methods require expensive human annotation and struggle with domain transfer, limiting their practical deployment. This highlights the need for more robust, scalable solutions and realistic evaluation settings. To address this, we introduce a comprehensive benchmark spanning seven diverse domains, enabling performance evaluation at both the document- and corpus-levels. Furthermore, we propose a robust LLM-based model that outperforms both supervised cross-domain encoder models and few-shot learning baselines and performs competitively with its GPT-4o teacher on this benchmark.The first step of our approach is generating psuedo-labels with this black-box LLM on general and scientific domains to ensure generalizability. Building on this data, we fine-tune the first LLMs for ATE. To further enhance document-level consistency, oftentimes needed for downstream tasks, we introduce lightweight post-hoc heuristics. Our approach exceeds previous approaches on 5/7 domains with an average improvement of 10 percentage points. We release our dataset and fine-tuned models to support future research in this area.

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I-SEE: An Instruction-tuned, SOP-Enhanced Quality Evaluator for Product Content
Aniket Joshi | Cyrus Andre DSouza | Sejal Jain | Jitenkumar Babubhai Rana | Promod Yenigalla

High-quality content is critical for driving customer satisfaction and conversions across digital platforms and e-commerce. Ensuring that essential information is complete, accurate, and aligned with customer expectations presents a significant challenge at scale. Existing approaches to content evaluation often treat all information uniformly, without prioritizing based on customer relevance, and rely heavily on manual prompt design to encode domain expertise into Large Language Models (LLMs). We present ISEE, a unified framework that addresses these limitations through three core innovations: (1) automated identification of customer-impacting features by synthesizing signals from search behavior, queries, and feedback, enabling targeted content improvements; (2) an instruction-tuned multimodal LLM trained to reliably follow structured operational guidelines, reducing dependence on manual prompt engineering; and (3) robust zero-shot generalization to new product content, features and SOPs via targeted instruction tuning. Evaluated across 20 product categories and 150 product specific features, ISEE achieves 90% precision at 78% recall in detecting content inconsistencies, outperforming much larger (> 200B parameters) models while using a compact 12B architecture.

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Computational Blueprints: Generating Isomorphic Math Problems with Large Language Models
Jeong-hoon Kim | Jinwoo Nam | Geunsik Jo

Personalized mathematics education is growing rapidly, creating a strong demand for large sets of similar practice problems.Yet existing studies on mathematics problem generation have focused on data augmentation for training neural language models rather than on direct educational deployment. To bridge this gap, we define a new task, Isomorphic Math Problem Generation (IMPG), designed to produce structurally consistent variants of source problems. Subsequently, we explored LLM-based frameworks for automatic IMPG through successive refinements, and established Computational Blueprints for Isomorphic Twins (CBIT).With meta-level generation and template-based selective variation, CBIT achieves high mathematical correctness and structural consistency while reducing the cost of generation.Empirical results across refinements demonstrate that CBIT is superior on generation accuracy and cost-effectiveness at scale.Most importantly, CBIT-generated problems exhibited an error rate 17.8% lower than expert-authored items, with deployment to 6,732 learners on a commercial education platform yielding 186,870 interactions.

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Fin-ExBERT: User Intent based Text Extraction in Financial Context using Graph-Augmented BERT and trainable Plugin
Soumick Sarker | Abhijit Kumar Rai

Financial dialogue transcripts pose a unique challenge for sentence-level information extraction due to their informal structure, domain-specific vocabulary, and variable intent density. We introduce Fin-ExBERT, a lightweight and modular framework for extracting user intent–relevant sentences from annotated financial service calls. Our approach builds on a domain-adapted BERT (Bidirectional Encoder Representations from Transformers) backbone enhanced with LoRA (Low-Rank Adaptation) adapters, enabling efficient fine-tuning using limited labeled data. We propose a two-stage training strategy with progressive unfreezing: initially training a classifier head while freezing the backbone, followed by gradual fine-tuning of the entire model with differential learning rates. To ensure robust extraction under uncertainty, we adopt a dynamic thresholding strategy based on probability curvature (elbow detection), avoiding fixed cutoff heuristics. Empirical results show strong precision and F1 performance on real-world transcripts, with interpretable output suitable for downstream auditing and question-answering workflows. The full framework supports batched evaluation, visualization, and calibrated export, offering a deployable solution for financial dialogue mining.

Agentic Retrieval-Augmented Generation (Agentic RAG) enhances the processing capability for complex tasks through dynamic retrieval and adaptive workflows. Recent advances (e.g., Search-R1) have shown that outcome-supervised reinforcement learning demonstrate strong performance. However, this approach still suffers from inefficient exploration, sparse reward signals, and ambiguous global reward feedback.To address these challenges, we propose DecEx-RAG, which models RAG as a Markov Decision Process (MDP) incorporating decision-making and execution, while introducing an efficient pruning strategy to optimize data expansion. Through comprehensive process-level policy optimization, DecEx-RAG significantly enhances the autonomous task decomposition, dynamic retrieval, and high-quality answer generation capabilities of large language models (LLMs). Experiments show that DecEx-RAG achieves an average absolute performance improvement of 6.2% across six datasets, significantly outperforming existing baselines. Moreover, the pruning strategy improves data construction efficiency by nearly 6 ×, providing an efficient solution for process-supervised RAG training. The code is available at https://github.com/sdsxdxl/DecEx-RAG.

Large Language Models (LLMs) can be used to convert natural language (NL) instructions into structured business process automation (BPA) process artifacts.This paper contributes (i) FLOW-BENCH, a high quality dataset of paired NL instructions and business process definitions toevaluate NL-based BPA tools, and support research in this area, and (ii) FLOW-GEN,our approach to utilize LLMs to translate NL into an intermediate Python representation that facilitates final conversion into widely adopted business process definition languages, such as BPMN and DMN. We bootstrap FLOW-BENCH by demonstrating how it can be used to evaluate the components of FLOW-GEN across eight LLMs. We hope that FLOW-GEN and FLOW-BENCHcatalyze further research in BPA.

Recent advances in Large Language Models (LLMs) have brought significant improvements to various service domains, including chatbots and medical pre-consultation applications. In the healthcare domain, the most common approach for adapting LLMs to multi-turn dialogue generation is Supervised Fine-Tuning (SFT). However, datasets for SFT in tasks like medical pre-consultation typically exhibit a skewed turn-count distribution. Training on such data induces a novel failure mechanism we term **Format Inertia**, where models tend to generate repetitive, format-correct, but diagnostically uninformative questions in long medical dialogues. To mitigate this observed failure mechanism, we adopt a simple, data-centric method that rebalances the turn-count distribution of the training dataset. Experimental results show that our approach substantially alleviates Format Inertia in medical pre-consultation.

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Extraction of Information Provision Activity Requirements from EU Acquis
Jakub Piskorski | Dominik Skotarczak

We report on experiments on information extraction (IE) from EU Acquis, the European Union law. We introduce a new IE task of Information Provision Activity Requirement Extraction. This task comprises the identification of text fragments that introduce an obligation to provide information, and the extraction of structured information about the key entities involved along with the temporal modalities. We compare various technologies for this task, i.e. knowledge-, classical ML-, transformer-, and generative AI-based approaches, on a new benchmark corpus.

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Contrastive Learning Using Graph Embeddings for Domain Adaptation of Language Models in the Process Industry
Anastasia Zhukova | Jonas Luehrs | Christian Matt | Bela Gipp

Recent trends in NLP utilize knowledge graphs (KGs) to enhance pretrained language models by incorporating additional knowledge from the graph structures to learn domain-specific terminology or relationships between documents that might otherwise be overlooked. This paper explores how SciNCL, a graph-aware neighborhood contrastive learning methodology originally designed for scientific publications, can be applied to the process industry domain, where text logs contain crucial information about daily operations and are often structured as sparse KGs. Our experiments demonstrate that language models fine-tuned with triplets derived from graph embeddings (GE) outperform a state-of-the-art mE5-large text encoder by 9.8-14.3% (5.45-7.96p) on the proprietary process industry text embedding benchmark (PITEB) while having 3 times fewer parameters.

AI-generated clinical notes are increasingly used in healthcare, but evaluating their quality remains a challenge due to high subjectivity and limited scalability of expert review. Existing automated metrics often fail to align with real-world physician preferences. To address this, we propose a pipeline that systematically distills real user feedback into structured checklists for note evaluation. These checklists are designed to be interpretable, grounded in human feedback, and enforceable by LLM-based evaluators. Using deidentified data from over 21,000 clinical encounters (prepared in accordance with the HIPAA safe harbor standard) from a deployed AI medical scribe system, we show that our feedback-derived checklist outperforms a baseline approach in our offline evaluations in coverage, diversity, and predictive power for human ratings. Extensive experiments confirm the checklist’s robustness to quality-degrading perturbations, significant alignment with clinician preferences, and practical value as an evaluation methodology. In offline research settings, our checklist offers a practical tool for flagging notes that may fall short of our defined quality standards.

Developing document understanding models at enterprise scale requires large, diverse, and well-annotated datasets spanning a wide range of document types. However, collecting such data is prohibitively expensive due to privacy constraints, legal restrictions, and the sheer volume of manual annotation needed - costs that can scale into millions of dollars. We introduce FlexDoc, a scalable synthetic data generation framework that combines Stochastic Schemas and Parameterized Sampling to produce realistic, multilingual semi-structured documents with rich annotations. By probabilistically modeling layout patterns, visual structure, and content variability, FlexDoc enables the controlled generation of diverse document variants at scale. Experiments on Key Information Extraction (KIE) tasks demonstrate that FlexDoc-generated data improves the absolute F1 Score by up to 11% when used to augment real datasets, while reducing annotation effort by over 90% compared to traditional hard-template methods. The solution is in active deployment, where it has accelerated the development of enterprise-grade document understanding models while significantly reducing data acquisition and annotation costs.

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GEMMAS: Graph-based Evaluation Metrics for Multi Agent Systems
Jisoo Lee | Raeyoung Chang | Dongwook Kwon | Harmanpreet Singh | Nikhil Verma

Multi-agent systems built on language models have shown strong performance on collaborative reasoning tasks. However, existing evaluations focus only on the correctness of the final output, overlooking how inefficient communication and poor coordination contribute to redundant reasoning and higher computational costs. We introduce **GEMMAS**, a graph-based evaluation framework that analyzes the internal collaboration process by modeling agent interactions as a directed acyclic graph. To capture collaboration quality, we propose two process-level metrics: Information Diversity Score (IDS) to measure semantic variation in inter-agent messages, and Unnecessary Path Ratio (UPR) to quantify redundant reasoning paths. We evaluate GEMMAS across five benchmarks and highlight results on GSM8K, where systems with only a 2.1% difference in accuracy differ by 12.8% in IDS and 80% in UPR, revealing substantial variation in internal collaboration. These findings demonstrate that outcome-only metrics are insufficient for evaluating multi-agent performance and highlight the importance of process-level diagnostics in designing more interpretable and resource-efficient collaborative AI systems.

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Knowledge-Augmented Question Error Correction for Chinese Question Answer System with QuestionRAG
Longpeng Qiu | Ting Li | Shuai Mao | Nan Yang | Xiaohui Yan

Input errors in question-answering (QA) systems often lead to incorrect responses. Large language models (LLMs) struggle with this task, frequently failing to interpret user intent (misinterpretation) or unnecessarily altering the original question’s structure (over-correction).We propose QuestionRAG, a framework that tackles these problems. To address misinterpretation, it enriches the input with external knowledge (e.g., search results, related entities). To prevent over-correction, it uses reinforcement learning (RL) to align the model’s objective with precise correction, not just paraphrasing.Our results demonstrate that knowledge augmentation is critical for understanding faulty questions. Furthermore, RL-based alignment proves significantly more effective than traditional supervised fine-tuning (SFT), boosting the model’s ability to follow instructions and generalize. By integrating these two strategies, QuestionRAG unlocks the full potential of LLMs for the question correction task.

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SMART: Scalable Multilingual Approach for a Robust TOD System
Karan Malhotra | Arihant Jain | Purav Aggarwal | Anoop Saladi

Task-Oriented Dialogue (TOD) systems have become increasingly important for real-world applications, yet existing frameworks face significant challenges in handling unstructured information, providing multilingual support, and engaging proactively. We propose SMART (Scalable Multilingual Approach for a Robust TOD System), a novel TOD framework that effectively addresses these limitations. SMART combines traditional pipeline elements with modern agent-based approaches, featuring a simplified dialogue state, intelligent clarification mechanisms, and a unified natural language generation component that eliminates response redundancy. Through comprehensive evaluation on troubleshooting and medical domains, we demonstrate that SMART outperforms baseline systems across key metrics. The system’s modular approach enables efficient scaling to new languages, as demonstrated through Spanish and Arabic languages. Integration of SMART in an e-commerce store resulted in reduction in product return rates, highlighting its industry impact. Our results establish SMART as an effective approach for building robust, scalable TOD systems that meet real-world requirements.

Large language models usually suffer from multiple-file coding scenarios where strong inter-file dependencies manifest, typically demonstrated in SWE-bench. To mitigate this issue, we propose Think-Search-Patch (TSP), a retrieval-augmented reasoning framework for repository-level code repair. At the Think stage, our system breaks down a coding task and creates clear search query. Next, at the Search stage, it retrieves relevant code snippets using models like E5. At the final Patch stage, it generates standardized patches based on the key snippets. In addition the proposed framework, we enhance system reliability through a two-stage training process. At the first stage, the system undergoes supervised fine-tuning (SFT) on our TSP dataset. At the subsequent stage, we employ rejection sampling with correction to generate preference pairs for Direct Preference Optimization (DPO) training, thereby reducing errors in the intermediate phases. Experimental results demonstrate that TSP framework enhances retrieval accuracy and repair success on SWE-bench Lite, even surpassing models with a larger size in managing extensive code contexts and successfully addressing bugs spanning across multiple files. All data and code available at https://github.com/Gengar0215/TSP-framework.

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ASR-EC Benchmark: Evaluating Large Language Models on Chinese ASR Error Correction
Victor Junqiu Wei | Weicheng Wang | Di Jiang | Yuanfeng Song | Lu Wang

Automatic Speech Recognition (ASR) is a fundamental and important task in the field of speech and natural language processing. It is an inherent building block in many applications such as voice assistant, speech translation, etc. Despite the advancement of ASR technologies in recent years, it is still inevitable for modern ASR systems to have a substantial number of erroneous recognition due to environmental noise, ambiguity, etc. Therefore, the error correction in ASR is crucial. Motivated by this, this paper studies ASR error correction in the Chinese language, which is one of the most popular languages and enjoys a large number of users in the world. We first create a benchmark dataset named ASR-EC that contains a wide spectrum of ASR errors generated by industry-grade ASR systems. To the best of our knowledge, it is the first Chinese ASR error correction benchmark. Then, inspired by the recent advances in large language models (LLMs), we investigate how to harness the power of LLMs to correct ASR errors. We apply LLMs to ASR error correction in three paradigms. The first paradigm is prompting, which is further categorized as zero-shot, few-shot, and multi-step. The second paradigm is finetuning, which finetunes LLMs with ASR error correction data. The third paradigm is multi-modal augmentation, which collectively utilizes the audio and ASR transcripts for error correction. Extensive experiments reveal that prompting is not effective for ASR error correction. Finetuning is effective only for a portion of LLMs. Multi-modal augmentation is the most effective method for error correction and achieves state-of-the-art performance.

Large Language Models have achieved great success in tasks like sentiment analysis, machine translation, and question answering, yet their effectiveness in the multilingual financial domain remains less explored. This study explores the potential of generative LLMs for classifying financial sustainability in four diverse languages: English, Hindi, Bengali, and Telugu, representing low, medium, and high-resource language categories. We propose a novel fine-tuning approach that integrates both positive and negative rationales alongside classification labels. Unlike existing approaches, our method improves classification performance by incorporating structured bidirectional reasoning into financial decision-making. Extensive evaluations demonstrate that the proposed approach consistently outperforms prior methods across all four languages, establishing new benchmark results for multilingual financial NLP. Notably, it also enables smaller models to achieve competitive or even superior performance compared to significantly larger models fine-tuned with conventional methods, demonstrating its suitability for industry applications.

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Automotive Document Labeling Using Large Language Models
Dang Van Thin | Cuong Xuan Chu | Christian Graf | Tobias Kaminski | Trung-Kien Tran

Repairing and maintaining car parts are crucial tasks in the automotive industry, requiring a mechanic to have all relevant technical documents available. However, retrieving the right documents from a huge database heavily depends on domain expertise and is time consuming and error-prone. By labeling available documents according to the components they relate to, concise and accurate information can be retrieved efficiently. However, this is a challenging task as the relevance of a document to a particular component strongly depends on the context and the expertise of the domain specialist. Moreover, component terminology varies widely between different manufacturers. We address these challenges by utilizing Large Language Models (LLMs) to enrich and unify a component database via web mining, extracting relevant keywords, and leveraging hybrid search and LLM-based re-ranking to select the most relevant component for a document. We systematically evaluate our method using various LLMs on an expert-annotated dataset and demonstrate that it outperforms the baselines, which rely solely on LLM prompting.

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Building Data-Driven Occupation Taxonomies: A Bottom-Up Multi-Stage Approach via Semantic Clustering and Multi-Agent Collaboration
Nan Li | Bo Kang | Tijl De Bie

Creating robust occupation taxonomies, vital for applications ranging from job recommendation to labor market intelligence, is challenging.Manual curation is slow, while existing automated methods are either not adaptive to dynamic regional markets (top-down) or struggle to build coherent hierarchies from noisy data (bottom-up). We introduce CLIMB (CLusterIng-based Multi-agent taxonomy Builder), a framework that fully automates the creation of high-quality, data-driven taxonomies from raw job postings. CLIMB uses global semantic clustering to distill core occupations, then employs a reflection-based multi-agent system to iteratively build a coherent hierarchy. On three diverse, real-world datasets, we show that CLIMB produces taxonomies that are more coherent and scalable than existing methods and successfully capture unique regional characteristics. We release our code and datasets at https://github.com/aida-ugent/CLIMB.

LLM agents show promise for vulnerability testing. We however lack benchmarks to evaluate and compare solutions. AutoPenBench covers this need offering an open benchmark for the evaluation of vulnerability testing agents. It includes 33 tasks, ranging from introductory exercises to actual vulnerable systems. It supports MCP, enabling the comparison of agent capabilities. We introduce milestones per task, allowing the comparison of intermediate steps where agents struggle. To illustrate the use of AutoPenBench we evaluate autonomous and human-assisted agent architectures. The former achieves 21% success rates, insufficient for production, while human-assisted agents reach 64% success, indicating a viable industrial path. AutoPenBench is offered as open source and enables fair comparison of agents.

Large language model (LLM) agents often struggle in environments where rules and required domain knowledge frequently change, such as regulatory compliance and user risk screening. To address this limitation, we propose the Adaptive Reflective Interactive Agent (ARIA), an LLM agent framework designed specifically to continuously learn updated domain knowledge at test time. ARIA assesses its own uncertainty through structured self-dialogue, proactively identifying knowledge gaps and requesting targeted explanations or corrections from human experts. It then systematically updates an internal, timestamped knowledge repository with provided human guidance, detecting and resolving conflicting or outdated knowledge through comparisons and clarification queries. We evaluate ARIA on the realistic customer due diligence name screening task on a global payment platform, alongside publicly available dynamic knowledge tasks. Results demonstrate significant improvements in adaptability and accuracy compared to baselines using standard offline fine-tuning and existing self-improving agents. ARIA has been deployed on a global payment platform serving over 150 million monthly active users.

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Encouraging Good Processes Without the Need for Good Answers: Reinforcement Learning for LLM Agent Planning
Zhiwei Li | Yong Hu | Wenqing Wang

The functionality of Large Language Model (LLM) agents is primarily determined by two capabilities: action planning and answer summarization. The former, action planning, is the core capability that dictates an agent’s performance. However, prevailing training paradigms employ end-to-end, multi-objective optimization that jointly trains both capabilities. This paradigm faces two critical challenges: imbalanced optimization objective allocation and scarcity of verifiable data, making it difficult to enhance the agent’s planning capability. To address these challenges, we propose Reinforcement Learning with Tool-use Rewards (RLTR), a novel framework that decouples the training process to enable a focused, single-objective optimization of the planning module. Crucially, RLTR introduces a reward signal based on tool-use completeness to directly evaluate the quality of tool invocation sequences. This method offers a more direct and reliable training signal than assessing the final response content, thereby obviating the need for verifiable data. Our experiments demonstrate that RLTR achieves an 8%–12% improvement in planning performance compared to end-to-end baselines. Moreover, this enhanced planning capability, in turn, translates to a 5%–6% increase in the final response quality of the overall agent system.

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Experience Report: Implementing Machine Translation in a Regulated Industry
Marco Zocca | Per Fallgren | David Buffoni

This paper presents lessons learned from implementing Machine Translation systems in the context of a global medical technology company. We describe system challenges, legal and security considerations, and the critical role of human-in-the-loop validation for quality assurance and responsible deployment. Furthermore, based on an experiment involving over 11,000 ranked translations, we report reviewer preferences for outputs from small and large language models under various prompting configurations, using a domain-specific dataset spanning five language pairs.

Deploying natural language processing (NLP) models on mobile platforms requires models that can adapt across diverse applications while remaining efficient in memory and computation. We investigate pre-finetuning strategies to enhance the adaptability of lightweight BERT-like encoders for two fundamental NLP task families: named entity recognition (NER) and text classification. While pre-finetuning improves downstream performance for each task family individually, we find that naïve multi-task pre-finetuning introduces conflicting optimization signals that degrade overall performance. To address this, we propose a simple yet effective multi-task pre-finetuning framework based on task-primary LoRA modules, which enables a single shared encoder backbone with modular adapters. Our approach achieves performance comparable to individual pre-finetuning while meeting practical deployment constraint. Experiments on 21 downstream tasks show average improvements of +0.8% for NER and +8.8% for text classification, demonstrating the effectiveness of our method for versatile mobile NLP applications.

Large language models (LLMs) have demonstrated remarkable performance across a wide range of industrial applications, from search and recommendation systems to generative tasks. Although scaling laws indicate that larger models generally yield better generalization and performance, their substantial computational requirements often render them impractical for many real-world scenarios at scale. In this paper, we present a comprehensive set of insights for training and deploying small language models (SLMs) that deliver high performance for a variety of industry use cases. We focus on two key techniques: (1) knowledge distillation and (2) model compression via structured pruning and quantization. These approaches enable SLMs to retain much of the quality of their larger counterparts while significantly reducing training/serving costs and latency. We detail the impact of these techniques on a variety of use cases in a large professional social network platform and share deployment lessons, including hardware optimization strategies that improve speed and throughput for both predictive and reasoning-based applications in Recommendation Systems.

In enterprise systems, tasks like API integration, ETL pipeline creation, customer record merging, and data consolidation rely on accurately aligning attributes that refer to the same real-world concept but differ across schemas. This semantic attribute alignment is critical for enabling schema unification, reporting, and analytics. The challenge is amplified in schema only settings where no instance data is available due to ambiguous names, inconsistent descriptions, and varied naming conventions.We propose a hybrid, unsupervised framework that combines the contextual reasoning of Large Language Models (LLMs) with the stability of embedding-based similarity and schema grouping to address token limitations and hallucinations. Our method operates solely on metadata and scales to large schemas by grouping attributes and refining LLM outputs through embedding-based enhancement, justification filtering, and ranking. Experiments on real-world healthcare schemas show strong performance, highlighting the effectiveness of the framework in privacy-constrained scenarios.

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STREAQ: Selective Tiered Routing for Effective and Affordable Contact Center Quality Assurance
Prajwal Sood | Rajdeep Agrawal | Mayank Sati | Digvijay Anil Ingle | Cijo George

Contact centers process millions of customer conversations daily, requiring Quality Assurance (QA) teams to evaluate agent performance against compliance and service standards, often by answering agent evaluation questionnaires. Traditional manual QA cannot scale to growing volumes, while fully automated evaluation using large language models presents a cost-performance trade-off. High-performing models excel at detecting rare but business-critical Answers of Interest (AoI) but incur prohibitive costs, while smaller fine-tuned models are economical but suffer from poor AoI precision, generating high false positive rates that erode agent trust and waste QA resources. We introduce STREAQ, a two-tier selective routing framework to intelligently route queries between cost-efficient and high-capability models. Based on benchmarking on a proprietary dataset across six large LMs, STREAQ achieves substantial cost reduction while preserving critical performance. Using Nova-Pro, STREAQ reduces daily costs by 48% from 34,162 to17,842 while retaining 88.9% of full-model AoI precision. Our ablation studies reveal that flawed reasoning from smaller models can degrade performance, emphasizing the importance of carefully designing routing systems, making enterprise-scale automated QA both practical and economically viable.

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Divide, Link, and Conquer: Recall-oriented Schema Linking for NL-to-SQL via Question Decomposition
Kiran Pradeep | Kirushikesh Db | Nishtha Madaan | Sameep Mehta | Pushpak Bhattacharyya

Natural language to SQL (NL-to-SQL) systems are increasingly critical in industry for enabling non-technical users to access structured data efficiently, supporting faster decision-making and data accessibility. However, state-of-the-art systems often depend on large proprietary models, which introduce serious concerns around privacy. While open-source LLMs offer a viable substitute, high-performing variants (e.g., 70B or 405B) require substantial GPU memory, making them impractical for many production environments. Smaller open-source models that fit on a single 80GB GPU present a more deployable alternative, yet existing efforts to enhance their Text-to-SQL performance rely heavily on fine-tuning, limiting flexibility. We propose RoSL, a plug-and-play framework that improves SQL generation for smaller LLMs without any task-specific training. While schema linking is often omitted for larger models, we show it remains essential for smaller ones. Further, we are the first to apply question decomposition at the schema linking stage, rather than during SQL generation as in prior work, to address the precision-recall tradeoff. Our approach improves schema linking recall by 25.1% and execution accuracy by 8.2% on the BIRD benchmark using ibm-granite/granite-3.3-8b-instruct, making it an effective and industry-friendly NL-to-SQL solution. We further analyze RoSL’s latency–efficiency characteristics, showing that it maintains practical efficiency for real-world deployment.

In many industrial settings, users wish to ask questions in natural language, the answers to which require assembling information from diverse structured data sources. With the advent of Large Language Models (LLMs), applications can now translate natural language questions into a set of API calls or database calls, execute them, and combine the results into an appropriate natural language response. However, these applications remain impractical in realistic industrial settings because they do not cope with the data source heterogeneity that typifies such environments. In this work, we simulate the heterogeneity of real industry settings by introducing two extensions of the popular Spider benchmark dataset that require a combination of database and API calls. Then, we introduce a declarative approach to handling such data heterogeneityand demonstrate that it copes with data source heterogeneity significantly better than state-of-the-art LLM-based agentic or imperative code generation systems. Our augmented benchmarks are available to the research community.

Safety is a paramount concern in clinical chatbot applications, where inaccurate or harmful responses can lead to serious consequences. Existing methods—such as guardrails and tool-calling—often fall short in addressing the nuanced demands of the clinical domain. In this paper, we introduce TACOS(Taxonomy of Comprehensive Safety for Clinical Agents), a fine-grained, 21-class taxonomy that integrates safety filtering and tool selection into a single user intent classification step. TACOS covers a wide spectrum of clinical and non-clinical queries, explicitly modeling varying safety thresholds and external tool dependencies. To validate our taxonomy, we curate a TACOS-annotated dataset and perform extensive experiments. Our results demonstrate the value of a new taxonomy specialized for clinical agent settings, and reveal valuable insights about train data distribution and pretrained knowledge of base models.

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Dr. Copilot: A Multi-Agent Prompt Optimized Assistant for Improving Patient-Doctor Communication in Romanian
Andrei Niculae | Adrian Cosma | Cosmin Dumitrache | Emilian Radoi

Text-based telemedicine has become increasingly common, yet the quality of medical advice in doctor-patient interactions is often judged more on how advice is communicated rather than its clinical accuracy. To address this, we introduce Dr.Copilot, a multi-agent large language model (LLM) system that supports Romanian-speaking doctors by evaluating and enhancing the presentation quality of their written responses. Rather than assessing medical correctness, Dr.Copilot provides feedback along 17 interpretable quality measures. The system comprises of three LLM agents with prompts automatically optimized via DSPy. Designed with low-resource Romanian data and deployed using open-weight models, it delivers real-time specific feedback to doctors within a telemedicine platform. Empirical evaluations and live deployment with 41 doctors show measurable improvements in user reviews and response quality, marking one of the first real-world deployments of LLMs in Romanian medical settings.

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Data-Efficient Automatic Prompt Optimization for Memory-Enhanced Conversational Agents
Ervine Zheng | Yikuan Li | Geoffrey Jay Tso | Jilong Kuang

Automatic prompt optimization (APO) uses algorithms to automatically refine prompts for LLMs, effectively reducing human effort in prompt engineering. However, applying APO to memory-enhanced conversational agents presents unique challenges. These agents leverage memory to retain information from historical interactions with users and provide context-aware and personalized responses. Optimizing prompts for these agents is challenging due to their complex, interconnected modules that include memory writing, reading, and response generation. This paper introduces a data-efficient framework for APO in these agents. Our approach leverages LLMs to holistically optimize the prompts of all agents. We also introduce an automated evaluation module that not only provides a holistic quality score for responses but also performs error attribution, pinpointing failures within the specific modules. More importantly, to ensure the evaluation module aligns with human judgment, we develop a data-efficient active sampling algorithm with convex optimization to select the most informative samples for human feedback and prompt improvement. We conducted experiments on two health-related conversation datasets to demonstrate the effectiveness of the proposed framework.

We present CLARITY (Clinical Assistant for Routing, Inference and Triage), an AI-driven platform designed to facilitate patient-to-specialist routing, clinical consultations, and severity assessment of patient conditions. Its hybrid architecture combines a Finite State Machine (FSM) for structured dialogue flows with collaborative agents that employ Large Language Model (LLM) to analyze symptoms and prioritize referrals to appropriate specialists. Built on a modular microservices framework, CLARITY ensures safe, efficient, and robust performance, flexible and readily scalable to meet the demands of existing workflows and IT solutions in healthcare.We report integration of our clinical assistant into a large-scale national interhospital platform, with more than 55,000 content-rich userdialogues completed within the two months of deployment, 2,500 of which were expert-annotated for subsequent validation. The validation results show that CLARITY surpasses human-level performance in terms of the first-attempt routing precision, naturally requiring up to 3 times shorter duration of the consultation than with a human.

Large Language Models (LLMs) are widely used in industry but remain prone to hallucinations, limiting their reliability in critical applications. This work addresses hallucination reduction in consumer grievance chatbots built using LLaMA 3.1 8B Instruct, a compact model frequently used in industry. We develop **HalluDetect**, an LLM-based hallucination detection system that achieves an F1 score of **68.92%** outperforming baseline detectors by **22.47%**. Benchmarking five hallucination mitigation architectures, we find that out of them, AgentBot minimizes hallucinations to **0.4159** per turn while maintaining the highest token accuracy (**96.13%**), making it the most effective mitigation strategy. Our findings provide a scalable framework for hallucination mitigation, demonstrating that optimized inference strategies can significantly improve factual accuracy.

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How Accurate Are LLMs at Multi-Question Answering on Conversational Transcripts?
Xiliang Zhu | Shi Zong | David Rossouw

Deploying Large Language Models (LLMs) for question answering (QA) over lengthy contexts is a significant challenge. In industrial settings, this process is often hindered by high computational costs and latency, especially when multiple questions must be answered based on the same context. In this work, we explore the capabilities of LLMs to answer multiple questions based on the same conversational context. We conduct extensive experiments and benchmark a range of both proprietary and public models on this challenging task. Our findings highlight that while strong proprietary LLMs like GPT-4o achieve the best overall performance, fine-tuned public LLMs with up to 8 billion parameters can surpass GPT-4o in accuracy, which demonstrates their potential for transparent and cost-effective deployment in real-world applications.

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AI Knowledge Assist: An Automated Approach for the Creation of Knowledge Bases for Conversational AI Agents
Md Tahmid Rahman Laskar | Julien Bouvier Tremblay | Xue-Yong Fu | Cheng Chen | Shashi Bhushan Tn

The utilization of conversational AI systems by leveraging Retrieval Augmented Generation (RAG) techniques to solve customer problemshas been on the rise with the rapid progress of Large Language Models (LLMs). However, the absence of a company-specific dedicated knowledge base is a major barrier to the integration of conversational AI systems in contact centers. To this end, we introduce AI Knowledge Assist, a system that extracts knowledge in the form of question-answer (QA) pairs from historical customer‐agent conversations to automatically build a knowledge base. Fine‐tuning a lightweight LLM on internal data demonstrates state-of-the-art performance, outperforming larger closed-source LLMs. More specifically, empirical evaluation on 20 companies demonstrates that the proposed AI Knowledge Assist system that leverages the LLaMA-3.1-8B model eliminates the cold‐start gap in contact centers by achieving above 90% accuracy in answering information‐seeking questions. This enables immediate deployment of RAG-powered chatbots.

The rapid advancements in Large Language Models (LLMs) have enabled their adoption in real-world industrial scenarios for various natural language processing tasks. However, the high inference cost of large-scale LLMs makes their deployment impractical, necessitating the use of smaller models. Despite their efficiency, smaller LLMs lack robust zero-shot instruction-following capabilities across diverse domains, limiting their adaptability to dynamic user requirements. Traditional fine-tuning approaches exacerbate this issue by inducing catastrophic forgetting, reducing the model’s generalization ability for unseen tasks. In this paper, we propose Domain Adaptive Continual Instruction Pre-Training via Reading Comprehension (DACIP-RC), a continual pre-training technique that enhances smaller LLMs’ domain adaptability for business conversational tasks. Unlike conventional pre-training approaches that rely on next-token prediction, DACIP-RC generates diverse task instructions and responses via reading comprehension on conversation transcripts, enabling better instruction generalization. Our empirical evaluations demonstrate that DACIP-RC significantly improves zero-shot generalization across a wide range of business conversational tasks, including meeting summarization, action item generation, and call purpose identification. To the best of our knowledge, this is the first work to apply instruction pre-training on business conversational data, providing insights into how industries can leverage proprietary datasets for domain adaptation.

The lack of high-quality test collections challenges Information Retrieval (IR) in specialized domains. This work addresses this issue by comparing supervised classifiers against zero-shot Large Language Models (LLMs) for automated relevance annotation in the oil and gas industry, using human expert judgments as a benchmark. A supervised classifier, trained on limited expert data, outperforms LLMs, achieving an F1-score that surpasses even a second human annotator. The study also empirically confirms that LLMs are susceptible to unfairly prefer technologically similar retrieval systems. While LLMs lack precision in this context, a well-engineered classifier offers an accurate and practical path to scaling evaluation datasets within a human-in-the-loop framework that empowers, not replaces, human expertise.

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Mind the Query: A Benchmark Dataset towards Text2Cypher Task
Vashu Chauhan | Shobhit Raj | Shashank Mujumdar | Avirup Saha | Anannay Jain

We present a high-quality, multi-domain dataset for the Text2Cypher task which is enabling the translation of natural language (NL) questions into executable Cypher queries over graph databases. The dataset comprises 27,529 NL queries and corresponding Cyphers spanning across 11 real-world graph datasets, each accompanied by its corresponding graph database for grounded query execution. To ensure correctness, the queries are validated through a rigorous pipeline combining automated schema, runtime and value checks, along with manual review for logical correctness. Queries are further categorized by complexity to support fine-grained evaluation. We have released our benchmark dataset and code to replicate our data synthesis pipeline on new graph datasets, supporting extensibility and future research for the task of Text2Cypher.

Large Vision-Language Models (LVLMs) with only 7B parameters have shown promise as automated judges in chart comprehension tasks. However, tiny models (<=2B parameters) still perform poorly as judges, limiting their real-world use in resource-constrained settings. To address this, we propose two approaches to ensure cost‐efficient evaluation: (i) multi-criteria prompting, which combines separate evaluation criteria into a single query, and (ii) domain‐adaptive transfer learning, in which we fine‐tune a 2B‐parameter VLM on synthetic judgments in a chart dataset to create the ChartJudge. Experiments show that multi-criteria prompting exposes robustness gaps, which led to a huge drop in performance for 7B models, including specialized LVLM judges like LLaVA‐Critic. In addition, we find that our tiny LVLM (ChartJudge) can effectively transfer knowledge from one dataset to another to make it a more specialized model. Our fine-grained analysis across chart types and query complexities offers actionable insights into trade-offs between model size, prompt design, and transferability, enabling scalable, low-cost evaluation for chart reasoning tasks. Our code and the data will be made publicly available.

We introduce an Agent-in-the-Loop (AITL) framework that implements a continuous data flywheel for iteratively improving an LLM-based customer support system. Unlike standard offline approaches that rely on batch annotations, AITL integrates four key types of annotations directly into live customer operations: (1) pairwise response preferences, (2) agent adoption and rationales, (3) knowledge relevance checks, and (4) identification of missing knowledge. These feedback signals seamlessly feed back into models’ updates, reducing retraining cycles from months to weeks. Our production pilot involving US-based customer support agents demonstrated significant improvements in retrieval accuracy (+11.7% recall@75, +14.8% precision@8), generation quality (+8.4% helpfulness) and agent adoption rates (+4.5%). These results underscore the effectiveness of embedding human feedback loops directly into operational workflows to continuously refine LLM-based customer support system.

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Beyond Pointwise Scores: Decomposed Criteria-Based Evaluation of LLM Responses
Fangyi Yu | Nabeel Seedat | Drahomira Herrmannova | Frank Schilder | Jonathan Richard Schwarz

Evaluating long-form answers in high-stakes domains such as law or medicine remains a fundamental challenge. Standard metrics like BLEU and ROUGE fail to capture semantic correctness, and current LLM-based evaluators often reduce nuanced aspects of answer quality into a single undifferentiated score. We introduce DeCE, a decomposed LLM evaluation framework that separates precision (factual accuracy and relevance) and recall (coverage of required concepts), using instance-specific criteria automatically extracted from gold answer requirements. DeCE is model-agnostic and domain-general, requiring no predefined taxonomies or handcrafted rubrics. We instantiate DeCE to evaluate different LLMs on a real-world legal QA task involving multi-jurisdictional reasoning and citation grounding. DeCE achieves substantially stronger correlation with expert judgments (r=0.78), compared to traditional metrics (r=0.12) and pointwise LLM scoring (r=0.35). It also reveals interpretable trade-offs: generalist models favor recall, while specialized models favor precision. Importantly, only 11.95% of LLM-generated criteria required expert revision, underscoring DeCE’s scalability. DeCE offers an interpretable and actionable LLM evaluation framework in expert domains.

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Scalable and Cost Effective High-Cardinality Classification with LLMs via Multi-View Label Representations and Retrieval Augmentation
Anup Pattnaik | Sasanka Vutla | Hamvir Dev | Jeevesh Nandan | Cijo George

Classifying contact center interactions into a large number of categories is critical for downstream analytics, but challenging due to high label cardinality, and cost constraints. While Large Language Models (LLMs) offer flexibility for such tasks, existing methods degrade with increasing label space, showing significant inconsistencies and sensitivity to label ordering. We propose a scalable, cost-effective two-step retrieval-augmented classification framework, enhanced with a multi-view representation of labels. Our method significantly improves accuracy and consistency over baseline LLM approaches. Experiments across 4 private and 5 open datasets yield performance improvements of upto 14.6% while reducing inference cost by 60-91% compared to baseline approaches.

Supervised fine-tuning (SFT) on benign data can paradoxically erode a language model’s safety alignment, a phenomenon known as catastrophic forgetting of safety behaviors. Although prior work shows that randomly adding safety examples can reduce harmful output, the principles that make certain examples more effective than others remain poorly understood. This paper investigates the hypothesis that the effectiveness of a safety example is governed by two key factors: its instruction-response behavior (e.g., refusal vs. explanation) and its semantic diversity across harm categories. We systematically evaluate sampling strategies based on these axes and find that structured, diversity-aware sampling significantly improves model safety. Our method reduces harmfulness by up to 41% while adding only 0.05% more data to the fine-tuning set.

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Zero-knowledge LLM hallucination detection and mitigation through fine-grained cross-model consistency
Aman Goel | Daniel Schwartz | Yanjun Qi

Large language models (LLMs) have demonstrated impressive capabilities across diverse tasks, but they remain susceptible to hallucinations—generating content that appears plausible but contains factual inaccuracies. We present Finch-Zk, a black-box framework that leverages fine-grained cross-model consistency to detect and mitigate hallucinations in LLM outputs without requiring external knowledge sources. Finch-Zk introduces two key innovations: 1) a cross-model consistency checking strategy that reveals fine-grained inaccuracies by comparing responses generated by diverse models from semantically-equivalent prompts, and 2) a targeted mitigation technique that applies precise corrections to problematic segments while preserving accurate content. Experiments on the FELM dataset show Finch-Zk improves hallucination detection F1 scores by 6-39% compared to existing approaches. For mitigation, Finch-Zk achieves up to 9 absolute percentage points improvement in answer accuracy on the GPQA-diamond dataset when applied to state-of-the-art models like Llama 4 Maverick and Claude 4 Sonnet. Extensive evaluation on multiple datasets demonstrates that Finch-Zk provides a practical, deployment-ready safeguard for enhancing factual reliability in production LLM systems.

We introduce an incremental summarization system for customer support agents that intelligently determines when to generate concise bullet notes during conversations, reducing agents’ cognitive load and redundant review. Our approach combines a fine-tuned Mixtral-8×7B model for continuous note generation with a DeBERTa-based classifier to filter trivial content. Agent edits refine the online notes generation and regularly inform offline model retraining, closing the agent edits feedback loop. Deployed in production, our system achieved a 3% reduction in case handling time compared to bulk summarization (with reductions of up to 9% in highly complex cases), alongside high agent satisfaction ratings from surveys. These results demonstrate that incremental summarization with continuous feedback effectively enhances summary quality and agent productivity at scale.

Collaborative filtering (CF) is widely adopted in industrial recommender systems (RecSys) for modeling user-item interactions across numerous applications, but often struggles with cold-start and data-sparse scenarios. Recent advancements in pre-trained large language models (LLMs) with rich semantic knowledge, offer promising solutions to these challenges. However, deploying LLMs at scale is hindered by their significant computational demands and latency. In this paper, we propose a novel and scalable LLM-RecSys framework, LLMInit, designed to integrate pretrained LLM embeddings into CF models through selective initialization strategies. Specifically, we identify the embedding collapse issue observed when CF models scale and match the large embedding sizes in LLMs and avoid the problem by introducing efficient sampling methods, including, random, uniform, and variance-based selections. Comprehensive experiments conducted on multiple real-world datasets demonstrate that LLMInit significantly improves recommendation performance while maintaining low computational costs, offering a practical and scalable solution for industrial applications. To facilitate industry adoption and promote future research, we provide open-source access to our implementation at https://github.com/DavidZWZ/LLMInit.

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LLM Agents Implement an NLG System from Scratch: Building Interpretable Rule-Based RDF-to-Text Generators
Mateusz Lango | Ondrej Dusek

We present a novel neurosymbolic framework for RDF-to-text generation, in which the model is “trained” through collaborative interactions among multiple LLM agents rather than traditional backpropagation. The LLM agents produce rule-based Python code for a generator for the given domain, based on RDF triples only, with no in-domain human reference texts. The resulting system is fully interpretable, requires no supervised training data, and generates text nearly instantaneously using only a single CPU. Our experiments on the WebNLG and OpenDialKG data show that outputs produced by our approach reduce hallucination, with only slight fluency penalties compared to finetuned or prompted language models.

Every year, the European Union and its member states allocate millions of euros to fund various development initiatives. However, the increasing number of applications received for these programs often creates significant bottlenecks in evaluation processes, due to limited human capacity. In this work, we detail the real-world deployment of AI-assisted evaluation within the pipeline of two government initiatives: (i) corporate applications aimed at international business expansion, and (ii) citizen reimbursement claims for investments in energy-efficient home improvements. While these two cases involve distinct evaluation procedures, our findings confirm that AI effectively enhanced processing efficiency and reduced workload across both types of applications. Specifically, in the citizen reimbursement claims initiative, our solution increased reviewer productivity by 20.1%, while keeping a negligible false-positive rate based on our test set observations. These improvements resulted in an overall reduction of more than 2 months in the total evaluation time, illustrating the impact of AI-driven automation in large-scale evaluation workflows.

Although large language models (LLMs) have revolutionized natural language processing capabilities, their practical implementation as autonomous multi-agent systems (MAS) for industrial problem-solving encounters persistent barriers. Conventional MAS architectures are fundamentally restricted by inflexible, hand-crafted graph topologies that lack contextual responsiveness, resulting in diminished efficacy across varied academic and commercial workloads. To surmount these constraints, we introduce AMAS, a paradigm-shifting framework that redefines LLM-based MAS through a novel dynamic graph selector. This component autonomously identifies task-specific optimal graph configurations via lightweight LLM adaptation, eliminating the reliance on monolithic, universally applied structural templates. Instead, AMAS exploits the intrinsic properties of individual inputs to intelligently direct query trajectories through task-optimized agent pathways. Rigorous validation across question answering, mathematical deduction, and code generation benchmarks confirms that AMAS systematically exceeds state-of-the-art single-agent and multi-agent approaches across diverse LLM architectures. Our investigation establishes that context-sensitive structural adaptability constitutes a foundational requirement for high-performance LLM MAS deployments.

Retrieval-augmented generation has proven practical when models require specialized knowledge or access to the latest data. However, existing methods for multimodal document retrieval often replicate techniques developed for text-only retrieval, whether in how they encode documents, define training objectives, or compute similarity scores. To address these limitations, we present ColMate, a document retrieval model that bridges the gap between multimodal representation learning and document retrieval. ColMate utilizes a novel OCR-based pretraining objective, a self-supervised masked contrastive learning objective, and a late interaction scoring mechanism more relevant to multimodal document structures and visual characteristics. ColMate obtains 3.61% improvements over existing retrieval models on the ViDoRe V2 benchmark, demonstrating stronger generalization to out-of-domain benchmarks.

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Confidence-Aware Reasoning: Optimizing Self-Guided Thinking Trajectories in Large Reasoning Models
Jiaxin Zhang

Chain-of-thought enables large reasoning models (LRMs) to reason through multi-step problems but often leads to unnecessarily long or redundant reasoning traces, a phenomenon known as overthinking. This results in inflated inference costs and potential degradation in answer quality. To address these challenges, we propose Confidence-Aware Reasoning (), an inference-time framework that optimizes reasoning trajectories by selectively pruning low-utility reasoning blocks and halting early when sufficient confidence has been achieved. is theoretically grounded in Bayesian optimal experimental design, treating each reasoning block as a sequential decision whose utility is approximated by its marginal contribution to reducing final answer uncertainty. We introduce a lightweight implementation that leverages token-level confidence to dynamically modulate reasoning depth without additional supervision. Evaluations on multiple benchmarks, including AMC, AIME, GPQA-Diamond, and MATH-500 show that improves answer accuracy by up to +13.3%, while reducing average reasoning length by 40%–50%. Our findings demonstrate that information-theoretic insights can effectively control self-guided reasoning and enable LRMs to “think just enough” at test time.

Identifying attribute values from product profiles is a key task for improving product search, recommendation, and business analytics on e-commerce platforms, which we called Product Attribute Value Identification (PAVI) . However, existing PAVI methods face critical challenges, such as cascading errors, inability to handle out-of-distribution (OOD) attribute values, and lack of generalization capability. To address these limitations, we introduce Multi-Value-Product Retrieval-Augmented Generation (MVP-RAG), combining the strengths of retrieval, generation, and classification paradigms. MVP-RAG defines PAVI as a retrieval-generation task, where the product title description serves as the query, and products and attribute values act as the corpus. It first retrieves similar products of the same category and candidate attribute values, and then generates the standardized attribute values. The key advantages of this work are: (1) the proposal of a multi-level retrieval scheme, with products and attribute values as distinct hierarchical levels in PAVI domain (2) attribute value generation of large language model to significantly alleviate the OOD problem and (3) its successful deployment in a real-world industrial environment. Extensive experimental results on the dataset demonstrate that the proposed method performs better than the state-of-the-art baselines.

Effective product schema modeling is fundamental to e-commerce success, enabling accurate product discovery and superior customer experience. However, traditional manual schema modeling processes are severely bottlenecked, producing only tens of attributes per month, which is insufficient for modern e-commerce platforms managing thousands of product types. This paper introduces AttributeForge, the first framework to automate end-to-end product schema modeling using Large Language Models (LLMs). Our key innovation lies in orchestrating 43 specialized LLM agents through strategic workflow patterns to handle the complex interdependencies in schema generation. The framework incorporates two novel components: MC2-Eval, a comprehensive validation system that assesses schemas against technical, business, and customer experience requirements; and AutoFix, an intelligent mechanism that automatically corrects modeling defects through iterative refinement. Deployed in production, AttributeForge achieves an 88× increase in modeling throughput while delivering superior quality: a 59.83% Good-to-Good (G2G) conversion rate compared to 37.50% for manual approaches. This significant improvement in both speed and quality enables e-commerce platforms to rapidly adapt their product schemas to evolving market needs.

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VestaBench: An Embodied Benchmark for Safe Long-Horizon Planning Under Multi-Constraint and Adversarial Settings
Tanmana Sadhu | Yanan Chen | Ali Pesaranghader

Large language models (LLMs) are applied to reasoning and (automated) planning across diverse domains, from travel itineraries to embodied AI tasks. However, concerns have been raised about their suitability for long-horizon tasks involving multiple constraints, as they are prone to hallucinations, particularly in adversarial scenarios. Safety reasoning also becomes critical for embodied AI agents, which interact with their physical environments to complete tasks on behalf of humans. However, existing (safety) benchmarks fail to represent a diverse range of multi-constraint tasks that require long-horizon planning with a focus on safety. To address this, we propose VESTABENCH, a benchmark curated using VirtualHome and BEHAVIOR-100. Our VESTABENCH includes (1) tasks that can be achieved safely under adversarial and multi-constraint settings, as well as (2) adversarial instructions that the agent must avoid. Our experiments with state-of-the-art LLM-based baselines reveal that they perform poorly against our tasks, not only achieving low success rates but also suffering significantly compromised safety outcomes. This observation reinforces the limitations of LLMs in generating safe plans when faced with adversarial settings or instructions. Finally, we believe that our findings benefit the research and industry communities.

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Advancing E-commerce Merchants Telemarketing with Synthetic Data-Driven LLMs
Qi Gou | Zehua Xia | Li Juan | Qingyang Zhao | Wenjing Yang

Telemarketing towards merchants is considerably more complex than traditional dialogue systems. Given a user utterance, the response must not only follow the context but also strategically and naturally guide the conversation toward marketing objectives. A common approach is to fine-tune LLMs using high-quality dialogue data from top sales. However, we find that even after careful data cleaning, these data cannot be used directly due to two issues:(1) Poor strategy-following: Real-world conversations are highly random with much chit-chat topics, easily leading deviation from intended strategy.(2) Insufficient expert knowledge learning: Expert knowledge appears infrequently or not at all in limited collected corpus.To this end, we introduce a hybrid data synthesis framework with two main innovations. First, we unify the input schema with profile and strategy designed by top sales, and extract them via a Multi-task paradigm.In addition, we propose Role-playing Simulation and Session Prefix Completion to complementarily improve the strategy-following and inject long-tail expert knowledge into our fine-tuned model – TeleBot.Comprehensive online and offline evaluations demonstrate its effectiveness.In particular, in terms of the final marketing results – High Intention Rate, TeleBot reaches the performance level of the top 25% of human sales.

Limited access to mental healthcare resources hinders timely depression diagnosis, leading to detrimental outcomes.Social media platforms present a valuable data source for early detection, yet this task faces two significant challenges: 1) the need for medical knowledge to distinguish clinical depression from transient mood changes, and 2) the dual requirement for high accuracy and model explainability.To address this, we propose DORIS, a framework that leverages Large Language Models (LLMs).To integrate medical knowledge, DORIS utilizes LLMs to annotate user texts against established medical diagnostic criteria and to summarize historical posts into temporal mood courses.These medically-informed features are then used to train an accurate Gradient Boosting Tree (GBT) classifier.Explainability is achieved by generating justifications for predictions based on the LLM-derived symptom annotations and mood course analyses.Extensive experimental results validate the effectiveness as well as interpretability of our method, highlighting its potential as a supportive clinical tool.

Cyberbullying (CB) involves complex relational dynamics that are often oversimplified as a binary classification task. Existing youth-focused CB datasets rely on scripted role-play, lacking conversational realism and ethical youth involvement, with little or no evaluation of their social plausibility. To address this, we introduce a youth-in-the-loop dataset “BullyBench” developed by adolescents (ages 15–16) through an ethical co-research framework. We introduce a structured intrinsic quality evaluation with experts-in-the-loop (social scientists, psychologists, and content moderators) for assessing realism, relevance, and coherence in youth CB data. Additionally, we perform extrinsic baseline evaluation of this dataset by benchmarking encoder- and decoder-only language models for multi-class CB role classification for future research. A three-stage annotation process by young adults refines the dataset into a gold-standard test benchmark, a high-quality resource grounded in minors’ lived experiences of CB detection. Code and data are available for review

Recent investigations into effective context lengths of modern flagship large language models (LLMs) have revealed major limitations in effective question answering (QA) and reasoning over long and complex contexts for even the largest and most impressive cadre of models. While approaches like retrieval-augmented generation (RAG) and chunk-based re-ranking attempt to mitigate this issue, they are sensitive to chunking, embedding and retrieval strategies and models, and furthermore, rely on extensive pre-processing, knowledge acquisition and indexing steps. In this paper, we propose Tagging-Augmented Generation (TAG), a lightweight data augmentation strategy that boosts LLM performance in long-context scenarios, without degrading and altering the integrity and composition of retrieved documents. We validate our hypothesis by augmenting two challenging and directly relevant question-answering benchmarks – NoLima and NovelQA – and show that tagging the context or even just adding tag definitions into QA prompts leads to consistent relative performance gains over the baseline – up to 17% for 32K token contexts, and 2.9% in complex reasoning question-answering for multi-hop queries requiring knowledge across a wide span of text.

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DispatchQA: A Benchmark for Small Function Calling Language Models in E-Commerce Applications
Joachim Daiber | Victor Maricato | Ayan Sinha | Andrew Rabinovich

We introduce DispatchQA, a benchmark to evaluate how well small language models (SLMs) translate open‐ended search queries into executable API calls via explicit function calling. Our benchmark focuses on the latency-sensitive e-commerce setting and measures SLMs’ impact on both search relevance and search latency. We provide strong, replicable baselines based on Llama 3.1 8B Instruct fine-tuned on synthetically generated data and find that fine-tuned SLMs produce search quality comparable or better than large language models such as GPT-4o while achieving up to 3× faster inference. All data, code, and training checkpoints are publicly released to spur further research on resource‐efficient query understanding.

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Generalized Embedding Models for Industry 4.0 Applications
Christodoulos Constantinides | Shuxin Lin | Dhaval C Patel

In this work, we present the first embedding model specifically designed for Industry 4.0 applications, targeting the semantics of industrial asset operations. Given natural language tasks related to specific assets, our model retrieves relevant items and generalizes to queries involving similar assets, such as identifying sensors relevant to an asset’s failure mode. We systematically construct nine asset-specific datasets using an expert-validated knowledge base reflecting real operational scenarios. To ensure contextually rich embeddings, we augment queries with Large Language Models, generating concise entity descriptions that capture domain-specific nuances. Across five embedding models ranging from BERT (110M) to gte-Qwen (7B), we observe substantial in-domain gains: HIT@1 +54.2%, MAP@100 +50.1%, NDCG@10 +54.7% on average. Ablation studies reveal that (a) LLM-based query augmentation significantly improves embedding quality; (b) contrastive objectives without in-batch negatives are more effective for tasks with many relevant items; and (c) balancing positives and negatives in batches is essential. We evaluate on a new task and finally present a case study wrapping them as tools and providing them to a planning agent. The code can be found here.

This paper introduces ECHO-LLaMA, an efficient LLaMA architecture designed to improve both the training speed and inference throughput of LLaMA architectures while maintaining its learning capacity. ECHO-LLaMA transforms LLaMA models into shared KV caching across certain layers, significantly reducing KV computational complexity while maintaining or improving language performance. Experimental results demonstrate that ECHO-LLaMA achieves up to 77% higher token-per-second throughput during training, up to 16% higher Model FLOPs Utilization (MFU), and up to 14% lower loss when trained on an equal number of tokens. Furthermore, on the 1.1B model, ECHO-LLaMA delivers approximately 7% higher test-time throughput compared to the baseline. By introducing a computationally efficient adaptation mechanism, ECHO-LLaMA offers a scalable and cost-effective solution for pretraining and finetuning large language models, enabling faster and more resource-efficient training without compromising performance.

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Generating Spatial Knowledge Graphs from Automotive Diagrams for Question Answering
Steve Bakos | Chen Xing | Heidar Davoudi | Aijun An | Ron DiCarlantonio

Answering “Where is the X button?” with “It’s next to the Y button” is unhelpful if the user knows neither location. Useful answers require obvious landmarks as a reference point. We address this by generating from a vehicle dashboard diagram a spatial knowledge graph (SKG) that shows the spatial relationship between a dashboard component and its nearby landmarks and using the SKG to help answer questions. We evaluate three distinct generation pipelines (Per-Attribute, Per-Component, and a Single-Shot baseline) to create the SKG using Large Vision-Language Models (LVLMs). On a new 65-vehicle dataset, we demonstrate that a decomposed Per-Component pipeline is the most effective strategy for generating a high-quality SKG; the graph produced by this method, when evaluated with a novel Significance score, identifies landmarks achieving 71.3% agreement with human annotators. This work enables downstream QA systems to provide more intuitive, landmark-based answers.

Current approaches to developing persuasive dialogue agents often rely on a limited set of predefined persuasive strategies that fail to capture the complexity of real-world interactions. We applied a cross-disciplinary approach to develop a framework for designing persuasive dialogue agents that draws on proven strategies from social psychology, behavioral economics, and communication theory. We validated our proposed framework through experiments on two distinct datasets: the Persuasion for Good dataset, which represents a specific in-domain scenario, and the DailyPersuasion dataset, which encompasses a wide range of scenarios. The proposed framework achieved strong results for both datasets and demonstrated notable improvement in the persuasion success rate as well as promising generalizability. Notably, the proposed framework also excelled at persuading individuals with initially low intent, which addresses a critical challenge for persuasive dialogue agents.

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BIOPSY - Biomarkers In Oncology: Pipeline for Structured Yielding
Sanya A. Chetwani | Jaseem Mahmmdla

In clinical science, biomarkers are crucial indicators for early cancer detection, prognosis, and guiding personalized treatment decisions. Although critical, extracting biomarkers and their levels from clinical texts remains a complex and underexplored problem in natural language processing research. In this paper, we present BIOPSY, an end-to-end pipeline that integrates a domain-adapted biomarker entity recognition model, a relation extraction model to link biomarkers to their respective mutations, a biomarker-type classifier, and finally, a tailored algorithm to capture biomarker expression levels. Evaluated on 5,000 real-world clinical texts, our system achieved an overall F1 score of 0.86 for oncology and 0.87 for neuroscience domains. This reveals the ability of the pipeline to adapt across various clinical sources, including trial records, research papers, and medical notes, offering the first comprehensive solution for end-to-end, context-aware biomarker extraction and interpretation in clinical research.

Recent advancements in slow-thinking reasoning models have shown exceptional performance in complex reasoning tasks. However, their tendency for “overthinking” on simple problems leads to excessive computational resource usage and increased inference latency, which hinders their widespread industrial adoption. While current mitigation strategies uniformly reduce reasoning tokens, they risk degrading performance on challenging tasks that require extended reasoning. This paper introduces Difficulty-Adaptive Slow-Thinking (DAST), a novel framework that enables models to autonomously adjust Chain-of-Thought (CoT) length based on problem difficulty. We propose a Token Length Budget (TLB) metric and leverage budget-aware preference optimization to implement DAST, which penalizes inefficiency on simple problems while incentivizing deep reasoning for complex ones. Experiments demonstrate DAST’s significant value for practical application: it effectively mitigates overthinking, substantially lowering costs and latency—while crucially preserving high accuracy on complex problems, paving the way for the efficient application of advanced reasoning models.

Embedding-based retrieval aims to learn a shared semantic representation space for both queries and items, enabling efficient and effective item retrieval through approximate nearest neighbor (ANN) algorithms. In current industrial practice, retrieval systems typically retrieve a fixed number of items for each query. However, this fixed-size retrieval often results in insufficient recall for head queries and low precision for tail queries. This limitation largely stems from the dominance of frequentist approaches in loss function design, which fail to address this challenge in industry. In this paper, we propose a novel probabilistic Embedding-Based Retrieval (pEBR) framework. Our method models the item distribution conditioned on each query, enabling the use of a dynamic cosine similarity threshold derived from the cumulative distribution function (CDF) of the probabilistic model. Experimental results demonstrate that pEBR significantly improves both retrieval precision and recall. Furthermore, ablation studies reveal that the probabilistic formulation effectively captures the inherent differences between head-to-tail queries.

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Finding Diamonds in Conversation Haystacks: A Benchmark for Conversational Data Retrieval
Yohan Lee | Yongwoo Song | Sangyeop Kim

We present the Conversational Data Retrieval (CDR) benchmark, the first comprehensive test set for evaluating systems that retrieve conversation data for product insights. With 1.6k queries across five analytical tasks and 9.1k conversations, our benchmark provides a reliable standard for measuring conversational data retrieval performance. Our evaluation of 16 popular embedding models shows that even the best models reach only around NDCG@10 of 0.51, revealing a substantial gap between document and conversational data retrieval capabilities. Our work identifies unique challenges in conversational data retrieval (implicit state recognition, turn dynamics, contextual references) while providing practical query templates and detailed error analysis across different task categories. The benchmark dataset and code are available at https://github.com/l-yohai/CDR-Benchmark.

This paper introduces <SYNTACT>, a three-stage multi agent LLM framework designed to transform unstructured and ambiguous Standard Operating Procedure (SOP) into a structured plan and an executable code template. Unstructured SOPs—common across industries such as finance, retail, and logistics—frequently suffer from ambiguity, missing information, and inconsistency, all of which hinder automation. SYNTACT addresses this through: (1) a Clarifier module that disambiguate the SOP using large language models, internal knowledge base (RAG) and human-in-the-loop , (2) a Planner that converts refined natural language instructions into a structured plan of hierarchical task flows through function (API) tagging, conditional branches and human-in-the-loop check-points, and (3) an Implementor that generates executable code fragments or pseudocode templates. We evaluate SYNTACT on real-world SOPs and synthetic variants, demonstrating an 88.4% end-to-end accuracy and a significant reduction in inconsistency compared to leading LLM baselines. Ablation studies highlight the necessity of each component, with performance dropping notably when modules are removed.Our findings show that structured multi-agent pipelines like SYNTACT can meaningfully improve consistency, reduce manual effort, and accelerate automation at scale.

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Recover-LoRA: Data-Free Accuracy Recovery of Degraded Language Models via Low-Rank Adaptation
Devleena Das | Rajeev Patwari | Ashish Sirasao

Inference optimizations such as quantization, pruning, format and datatype conversion, model export, and serialization can lead to functional degradations in language model task performance. While most efforts on performance recovery for deployment focus on robust quantization techniques, we focus on recovering model accuracies from any sources that degrade model weights, such as improper model serialization. In this work, we propose Recover-LoRA, a lightweight and dataset agnostic method to recover accuracy in degraded models. Recover-LoRA uses synthetic data and logit distillation to learn LoRA adapters on selective layers that facilitate aligning the degraded model to its full precision model. We investigate the utility of Recover-LoRA across a diverse set of small language models (SLMs), including models with varying attention architectures, multi-head attention (MHA) and group-query attention (GQA), as well as several evaluation datasets. Our results show that Recover-LoRA recovers model accuracies by 5-17% on MHA and GQA SLMs.

The emergence of open-source large language models (LLMs) has expanded opportunities for enterprise applications; however, many organizations still lack the infrastructure to deploy and maintain large-scale models. As a result, small LLMs (sLLMs) have become a practical alternative despite inherent performance limitations. While Domain Adaptive Continual Pretraining (DACP) has been explored for domain adaptation, its utility in commercial settings remains under-examined. In this study, we validate the effectiveness of a DACP-based recipe across diverse foundation models and service domains, producing DACP-applied sLLMs (ixi-GEN). Through extensive experiments and real-world evaluations, we demonstrate that ixi-GEN models achieve substantial gains in target-domain performance while preserving general capabilities, offering a cost-efficient and scalable solution for enterprise-level deployment.

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GRAFT: A Graph-based Flow-aware Agentic Framework for Document-level Machine Translation
Himanshu Dutta | Sunny Manchanda | Prakhar Bapat | Meva Ram Gurjar | Pushpak Bhattacharyya

Enterprises, public organizations, and localization providers increasingly rely on Document-level Machine Translation (DocMT) to process contracts, reports, manuals, and multimedia transcripts across languages. However, existing MT systems often struggle to handle discourse-level phenomena such as pronoun resolution, lexical cohesion, and ellipsis, resulting in inconsistent or incoherent translations. We propose **GRAFT**, a modular graph-based DocMT framework that leverages Large Language Model (LLM) agents to segment documents into discourse units, infer inter-discourse dependencies, extract structured memory, and generate context-aware translations. GRAFT transforms documents into directed acyclic graphs (DAGs) to explicitly model translation flow and discourse structure. Experiments across eight language directions and six domains show GRAFT outperforms commercial systems (e.g., Google Translate) and closed LLMs (e.g., GPT-4) by an average of 2.8 d-BLEU, and improves terminology consistency and discourse handling. GRAFT supports deployment with open-source LLMs (e.g., LLaMA, Qwen), making it cost-effective and privacy-preserving. These results position GRAFT as a robust solution for scalable, document-level translation in real-world applications.

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Recon, Answer, Verify: Agents in Search of Truth
Satyam Shukla | Himanshu Dutta | Pushpak Bhattacharyya

Human fact-checking is too slow to meet current demands, making automatic fact-checking system an essential alternative. Evaluating such systems is challenging as existing benchmark datasets either suffer from leakage or evidence incompleteness. This limits the realism of current evaluations. We present Politi-Fact-Only (PFO), a 5-class benchmark dataset of 2,982 political claims from politifact.com, where all post-claim analysis and annotator cues have been removed manually from evidence article. After filtration, evidence contains information available prior to the claim’s verification. By evaluating PFO, we see an average performance drop of 11.39% in terms of macro-f1 compared to PFO’s unfiltered version. Based on the identified challenges of the existing LLM-based fact-checking system, we propose RAV (Recon-Answer-Verify), an agentic framework with three agents, it iteratively generates and answers sub-questions to verify different aspects of the claim before finally generating the label. Unlike prior literature, we worked on reducing the follow-up question complexity by leveraging two 2 types of structured questions, which either validate a fact or inquire about a fact. RAV generalizes across both domains and label granularities, outperforming state-of-the-art methods by 57.5% on PFO (political, 5-class) and by 3.05% on the widely used HOVER dataset (encyclopedic, 2-class).

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T-VEC: A Telecom-Specific Vectorization Model with Enhanced Semantic Understanding via Deep Triplet Loss Fine-Tuning
Vignesh Ethiraj | Ashwath D | Sidhanth Menon | Divya Vijay | Vidhyakshaya Kannan

The specialized vocabulary and nuanced concepts of the telecommunications industry pose persistent challenges for standard Natural Language Processing (NLP) models. Generic embedding models often struggle to represent telecom-specific semantics, limiting their utility in retrieval and downstream tasks. We present T-VEC (Telecom Vectorization Model), a domain-adapted embedding model fine-tuned from the gte-Qwen2-1.5B-instruct backbone using a triplet loss objective. Fine-tuning was performed on T-Embed, a high-quality, large-scale dataset covering diverse telecom concepts, standards, and operational scenarios. Although T-Embed contains some proprietary material and cannot be fully released, we open source 75% of the dataset to support continued research in domain-specific representation learning. On a custom benchmark comprising 1500 query-passage pairs from IETF RFCs and vendor manuals, T-VEC surpasses MPNet, BGE, Jina and E5, demonstrating superior domain grounding and semantic precision in telecom-specific retrieval. Embedding visualizations further showcase tight clustering of telecom-relevant concepts. We release T-VEC and its tokenizer to support semantically faithful NLP applications within the telecom domain.

In the field of urban planning, existing Vision-Language Models (VLMs) frequently fail to effectively analyze planning maps, which are critical for urban planners and educational contexts. Planning maps require specialized understanding of spatial configurations, regulatory requirements, and multi-scale analysis.To address this challenge, we introduce PlanGPT-VL, the first domain-specific VLM tailored for urban planning maps. PlanGPT-VL employs three innovations:(1) PlanAnno-V framework for high-quality VQA data synthesis,(2) Critical Point Thinking (CPT) to reduce hallucinations through structured verification, and(3) PlanBench-V benchmark for systematic evaluation.Evaluation on PlanBench-V shows that PlanGPT-VL outperforms general-purpose VLMs on planning map interpretation tasks, with our 7B model achieving performance comparable to larger 72B models.

Routing incoming queries to the most cost-effective LLM while maintaining response quality poses a fundamental challenge in optimizing performance-cost trade-offs for large-scale commercial systems.We present IPR—a quality-constrained Intelligent Prompt Routing framework that dynamically selects optimal models based on predicted response quality and user-specified tolerance levels.IPR introduces three key innovations: (1) a modular architecture with lightweight quality estimators trained on 1.5M prompts annotated with calibrated quality scores, enabling fine-grained quality prediction across model families; (2) a user-controlled routing mechanism with tolerance parameter 𝜏 ∈ [0,1] that provides explicit control over quality-cost trade-offs; and (3) an extensible design using frozen encoders with model-specific adapters, reducing new model integration from days to hours. To rigorously train and evaluate IPR, we curate an industrial-level IPR dataset, a comprehensive benchmark containing 1.5 million examples with response quality annotations across 11 LLM candidates.Deployed on a major cloud platform, IPR achieves 43.9% cost reduction while maintaining quality parity with the strongest model in the Claude family and processes requests with sub-150ms latency.

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Semantic Agreement Enables Efficient Open-Ended LLM Cascades
Duncan Soiffer | Steven Kolawole | Virginia Smith

Cascade systems for open-ended text generation face a fundamental challenge: determining output reliability when generation quality lies on a continuous spectrum, often with multiple valid responses. To address this, we propose _semantic agreement_—meaning-level consensus between ensemble outputs—as a training-free signal for reliable deferral. We show that when diverse model outputs agree semantically, their consensus is a stronger reliability signal than token-level confidence. Evaluated from 500M to 70B-parameter models, semantic cascades improve deferral accuracy, match or surpass target-model quality at 40% of the cost, and reduce latency by up to 60%. Our method requires no model internals, works across black-box APIs, and remains robust to model updates, making it a practical baseline for real-world LLM deployment.

Phonetic Cloaking Replacement (PCR), defined as the deliberate use of homophonic or near-homophonic variants to hide toxic intent, has become a major obstacle to Chinese content moderation. While this problem is well-recognized, existing evaluations predominantly rely on rule-based, synthetic perturbations that ignore the creativity of real users. We organize PCR into a four-way surface-form taxonomy and compile PCR-ToxiCN, a dataset of 500 naturally occurring, phonetically cloaked offensive posts gathered from the RedNote platform. Benchmarking state-of-the-art LLMs on this dataset exposes a serious weakness: the best model reaches only an F1-score of 0.672, and zero-shot chain-of-thought prompting pushes performance even lower. Guided by error analysis, we revisit a Pinyin-based prompting strategy that earlier studies judged ineffective and show that it recovers much of the lost accuracy. This study offers the first comprehensive taxonomy of Chinese PCR, a realistic benchmark that reveals current detectors’ limits, and a lightweight mitigation technique that advances research on robust toxicity detection.

Retrieval-Augmented Generation (RAG) has shown strong performance in open-domain tasks, but its effectiveness in industrial domains is limited by a lack of domain understanding and document structural elements (DSE) such as tables, figures, charts, and formula.To address this challenge, we propose an efficient knowledge distillation framework that transfers complementary knowledge from both Large Language Models (LLMs) and Vision-Language Models (VLMs) into a compact domain-specific retriever.Extensive experiments and analysis on real-world industrial datasets from shipbuilding and electrical equipment domains demonstrate that the proposed framework improves both domain understanding and visual-structural retrieval, outperforming larger baselines while requiring significantly less computational complexity.

Traditional Retrieval-augmented Generation systems struggle with complex multi-hop questions, which often require reasoning over multiple passages. While GraphRAG approaches address these challenges, most of them rely on expensive LLM calls. In this paper, we propose GR\small{IEVER}, a lightweight, low-resource, multi-step graph-based retriever for multi-hop QA. Unlike prior work, GR\small{IEVER} does not rely on LLMs and can perform multi-step retrieval in a few hundred milliseconds. It efficiently indexes passages alongside an associated knowledge graph and employs a hybrid retriever combined with aggressive filtering to reduce retrieval latency. Experiments on multi-hop QA datasets demonstrate that GR\small{IEVER} outperforms conventional retrievers and shows strong potential as a base retriever within multi-step agentic frameworks.

Large language models (LLMs) have achieved overwhelming success but require massive storage and computational resources to support the generative inference. Post-training quantization (PTQ) is a promising approach to reduce memory usage, latency and energy consumption of the deployment of LLMs. However, the presence of outliers makes most existing PTQ methods dedicated to dynamic quantization, which turns out hardware-unfriendly and often leads to large quantization errors in static scenarios. To address the above limitations, we introduce a Static Hierarchical Mix-precision Quantization method (SHMQ), which enables near-lossless and hardware-friendly compression of LLMs. Theoretically, our proposed SHMQ quantifies both inter-layer and intra-layer sensitivity through unified derivations involving Hessian. Specifically, SHMQ conducts a systematic precision allocation strategy, which seamlessly integrates coarse-grained inter-layer and fine-grained intra-layer static mix-precision quantization. Furthermore, the permutation procedure, which reorders sensitive channels and insensitive channels that share similar distribution, is leveraged to mitigate static quantization error. Our proposed SHMQ achieves 75.58% on zero-shot reasoning tasks in W4.8A8 Qwen2.5-7B-Instruct, narrowing the accuracy gap to merely 0.13% while yielding averaged 2.86× practical speedup.

Large Language Models (LLMs) often generate incorrect or outdated information, especially in low-resource settings or when dealing with private data. To address this, Retrieval-Augmented Generation (RAG) uses external knowledge bases (KBs), but these can also suffer from inaccuracies. We introduce STACKFEED, a novel Structured Textual Actor-Critic Knowledge base editing with Feedback approach that iteratively refines the KB based on expert feedback using a multi-actor, centralized critic reinforcement learning framework. STACKFEED defines a ReACT actor agent on each document to perform structured edits based on document-specific targeted instructions. Experimental results showcase that STACKFEED significantly improves KB quality and performance of the RAG system. We evaluate STACKFEED on low-resource programming problems, modified Python packages, and factual question-answering tasks.

Corporate history in corporate annual reports includes events related to organizational changes, which can provide useful cues for a comprehensive understanding of corporate actions.However, extracting organizational changes requires identifying differences in companies before and after an event, raising concerns about whether existing information extraction systems can accurately capture the relations.This work introduces JaCorpTrack, a novel event extraction task designed to identify events related to organizational changes.JaCorpTrack defines five event types related to organizational changes and is designed to identify the company names before and after each event, as well as the corresponding date.Experimental results indicate that large language models (LLMs) exhibit notable disparities in performance across event types.Our analysis reveals that these systems face challenges in identifying company names before and after events, and in interpreting event types expressed under ambiguous terminology.We will publicly release our dataset and experimental code at https://github.com/naist-nlp/JaCorpTrack

Generating effective query suggestions in conversational search requires aligning model outputs with user click preferences. However, directly optimizing for these preferences is difficult because click signals are sparse and inherently noisy. To address this, we propose Generative Query Suggestion (GQS), a generative framework that leverages click modeling to denoise implicit feedback and enables reliable preference optimization for improving real-world user engagement.GQS consists of three key components: (1) a Multi-Source CTR Modeling module that captures diverse contextual signals to estimate fine-grained click-through rates, thereby constructing more reliable user click-preference pairs; (2) a Diversity-Aware Preference Alignment strategy using CTR-weighted Direct Preference Optimization (DPO), which balances relevance and semantic diversity; and (3) a CTR-Calibrated Iterative Optimization process that jointly refines both the CTR model and the query suggestion model across training rounds, enabling effective data reuse.Experiments on two real-world tasks demonstrate that GQS outperforms strong baselines in CTR, relevance, and diversity.

Learning clients embeddings from sequences of their historic communications is central to financial applications. While large language models (LLMs) offer general world knowledge, their direct use on long event sequences is computationally expensive and impractical in real-world pipelines. In this paper, we propose , a contrastive learning framework that aligns raw event embeddings with description-based semantic embeddings from frozen LLMs. Behavioral features based on statistical user descriptions are summarized into short prompts, embedded by the LLM, and used as supervision via contrastive loss. The proposed approach significantly reduces inference cost and input size compared to the conventional processing of complete sequences by LLM. We experimentally show that our method outperforms state-of-the-art techniques for learning event sequence representations on real-world financial datasets while remaining deployable in latency-sensitive environments.

As a primary medium for modern information dissemination, social networking services (SNS) have experienced rapid growth, which has proposed significant challenges for platform content management and interaction quality improvement. Recently, the development of large language models (LLMs) has offered potential solutions but existing studies focus on isolated tasks, which not only encounter diminishing benefit from the data scaling within individual scenarios but also fail to flexibly adapt to diverse real-world context. To address these challenges, we introduce RedOne, a domain-specific LLM designed to break the performance bottleneck of single-task baselines and establish a comprehensive foundation for the SNS. RedOne was developed through a three-stage training strategy consisting of continue pretraining, supervised fine-tuning, and preference optimization, using a large-scale real-world dataset. Through extensive experiments, RedOne maintains strong general capabilities, and achieves an average improvement up to 14.02% across 8 major SNS tasks and 7.56% in SNS bilingual evaluation benchmark, compared with base models. Furthermore, through online testing, RedOne reduced the exposure rate in harmful content detection by 11.23% and improved the click page rate in post-view search by 14.95% compared with single-tasks baseline models. These results establish RedOne as a robust domain-specific LLM for SNS, demonstrating excellent generalization across various tasks and promising applicability in real-world scenarios.

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High-Quality Medical Dialogue Synthesis for Improving EMR Generation
Chengze Ge | Yu Xu | Qi Shao | Shengping Liu

High-quality doctor–patient dialogues, by which we mean realistic and human-like interactions that are intent-consistent, clinically faithful, and free of contradictions, are crucial for accurate Electronic Medical Record (EMR) generation. However, collecting large-scale real dialogues is costly and constrained by privacy regulations, while existing synthetic methods often yield rigid and medically inconsistent dialogues. We propose a scalable framework integrating (1) Intent Graph Planning for diverse clinical flows, (2) Dual-Agent Simulation for realistic doctor-patient interactions, and (3) Rule-Reward Quality Control combining explicit medical rules with a self-supervised reward model. Experiments across multiple clinical domains demonstrate that our synthesized dialogues significantly enhance realism, diversity, and downstream EMR quality, substantially reducing physician editing efforts. Our framework provides a practical and privacy-compliant solution for deploying robust clinical NLP systems.

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Z1: Efficient Test-time Scaling with Code
Zhaojian Yu | Yinghao Wu | Yilun Zhao | Arman Cohan | Xiao-Ping Zhang

Large Language Models (LLMs) can achieve enhanced complex problem-solving through test-time computing scaling, yet this often entails longer contexts and numerous reasoning token costs. In this paper, we propose an efficient test-time scaling method that trains LLMs on code-related reasoning trajectories, facilitating their reduction of excess thinking tokens while maintaining performance.First, we create Z1-Code-Reasoning-107K, a curated dataset of simple and complex coding problems paired with their short and long solution trajectories. Second, we present a novel Shifted Thinking Window to mitigate overthinking overhead by removing context-delimiting tags (e.g., <think>...</think>) and capping reasoning tokens. Trained with long and short trajectory data and equipped with Shifted Thinking Window, our model, Z1-7B, demonstrates the ability to adjust its reasoning level as the complexity of problems and exhibits efficient test-time scaling across different reasoning tasks that matches R1-Distill-Qwen-7B performance with about 30% of its average thinking tokens.Notably, fine-tuned with only code trajectories, Z1-7B demonstrates generalization to broader reasoning tasks (47.5% on GPQA Diamond). Our analysis of efficient reasoning elicitation also provides valuable insights for future research.

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Quality Assessment of Tabular Data using Large Language Models and Code Generation
Ashlesha Akella | Akshar Kaul | Krishnasuri Narayanam | Sameep Mehta

Reliable data quality is crucial for downstream analysis of tabular datasets, yet rule-based validation often struggles with inefficiency, human intervention, and high computational costs. We present a three-stage framework that combines statistical inliner detection with LLM-driven rule and code generation. After filtering data samples through traditional clustering, we iteratively prompt LLMs to produce semantically valid quality rules and synthesize their executable validators through code-generating LLMs. To generate reliable quality rules, we aid LLMs with retrieval-augmented generation (RAG) by leveraging external knowledge sources and domain-specific few-shot examples. Robust guardrails ensure the accuracy and consistency of both rules and code snippets. Extensive evaluations on benchmark datasets confirm the effectiveness of our approach.

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PARSE: LLM Driven Schema Optimization for Reliable Entity Extraction
Anubhav Shrimal | Aryan Jain | Soumyajit Chowdhury | Promod Yenigalla

Structured information extraction from unstructured text is critical for emerging Software 3.0 systems where LLM agents autonomously interact with APIs and tools. Recent approaches apply large language models directly to extraction tasks using existing JSON schemas, often with constraint decoding or reinforcement learning approaches to ensure syntactic validity, but treat JSON schemas as static contracts designed for human developers, leading to suboptimal extraction performance, frequent hallucinations, and unreliable agent behavior when schemas contain ambiguous or incomplete specifications. We recognize that JSON schemas themselves are a form of natural language understanding contract that encodes rules, relationships, and expectations about data structure contracts that LLMs should be able to both interpret and systematically improve. Consequently, we develop PARSE (Parameter Automated Refinement and Schema Extraction), a novel system with two synergistic components: ARCHITECT, which autonomously optimizes JSON schemas for LLM consumption while maintaining backward compatibility through RELAY (an integrated code generation system), and SCOPE, which implements reflection-based extraction with combined static and LLM-based guardrails. We evaluate PARSE qualitatively and quantitatively on three datasets including Schema-Guided Dialogue (SGD), Structured Web Data Extraction (SWDE), and internal retail conversation data, and find that it achieves up to 64.7% improvement in extraction accuracy on SWDE with combined framework improvements reaching 10% across models, while reducing extraction errors by 92% within the first retry and and maintaining practical latency.

The rapid growth of online video content, especially on short video platforms, has created a growing demand for efficient video editing techniques that can condense long-form videos into concise and engaging clips. Existing automatic editing methods predominantly rely on textual cues from ASR transcripts and end-to-end segment selection, often neglecting the rich visual context and leading to incoherent outputs. In this paper, we propose a Human-Inspired automatic video editing framework (HIVE) that leverages multimodal narrative understanding to address these limitations. Our approach incorporates character extraction, dialogue analysis, and narrative summarization through multimodal large language models, enabling a holistic understanding of the video content. To further enhance coherence, we apply scene-level segmentation and decompose the editing process into three subtasks: highlight detection, opening/ending selection, and pruning of irrelevant content. To facilitate research in this area, we introduce DramaAD, a novel benchmark dataset comprising over 2500 short drama episodes and 500 professionally edited advertisement clips. Experimental results demonstrate that our framework consistently outperforms existing baselines across both general and advertisement-oriented editing tasks, significantly narrowing the quality gap between automatic and human-edited videos.

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Efficiency-Effectiveness Reranking FLOPs for LLM-based Rerankers
Zhiyuan Peng | Ting-Ruen Wei | Tingyu Song | Yilun Zhao

Large Language Models (LLMs) have recently been applied to reranking tasks in information retrieval, achieving strong performance. However, their high computational demands often hinder practical deployment.Existing studies evaluate the efficiency of LLM-based rerankers using proxy metrics such as latency, the number of forward passes, input tokens, and output tokens. However, these metrics depend on hardware and running-time choices (parallel or not, batch size, etc), and often fail to account for model size, making it difficult to interpret and obscuring the evaluation of the efficiency-effectiveness tradeoff. To address this issue, we propose for LLM-based rerankers: RPP (ranking metrics per PetaFLOP), measuring how much ranking quality (e.g., NDCG or MRR) a method achieves per PetaFLOP, and QPP (queries per PetaFLOP), measuring how many queries can be processed per PetaFLOP. Accompanied by the new metrics, an interpretable FLOPs estimator is developed to estimate the FLOPs of an LLM-based reranker even without running any experiments. Based on the proposed metrics, we conduct comprehensive experiments to evaluate a wide range of LLM-based rerankers with different architectures, studying the efficiency-effectiveness trade-off and bringing this issue to the attention of the research community.

Large language models (LLMs) increasingly power car assistants, enabling natural language interaction for tasks such as maintenance, troubleshooting, and operational guidance. While retrieval-augmented generation (RAG) improves grounding using vehicle manuals, evaluating response quality remains a key challenge. Traditional metrics like BLEU and ROUGE fail to capture critical aspects such as factual accuracy and information coverage. We propose GEAR, a fully automated, reference-based evaluation framework for car assistant systems. GEAR uses LLMs as evaluators to compare assistant responses against ground-truth counterparts, assessing coverage, correctness, and other dimensions of answer quality. To enable fine-grained evaluation, both responses are decomposed into key facts and labeled as essential, optional, or safety-critical using LLMs. The evaluator then determines which of these facts are correct and covered. Experiments show that GEAR aligns closely with human annotations, offering a scalable and reliable solution for evaluating car assistants.

To effectively support users’ goal achievement in chat-LLM services, providing user-centered follow-up questions is essential. Existing studies primarily focus on enhancing information-seeking or topical relevance, often missing how follow-up questions could satisfy users’ intrinsic needs and conversational goals. To bridge this gap, we introduce FQ-Eval, a user-centered evaluation dataset designed for assessing follow-up question generation in chat-LLM services. FQ-Eval incorporates realistic chat-LLM usage scenarios and five distinct human-aligned criteria, each reflecting user expectations of effective follow-up questions. Experimental results show that FQ-Eval constructed through our approach clearly capture human-aligned criteria, enabling robust, human-aligned follow-up question generation evaluation of various models and services.

We assess whether AI systems can credibly evaluate investment risk appetite—a task that must be thoroughly validated before automation. Our analysis was conducted on proprietary systems (GPT, Claude, Gemini) and open-weight models (LLaMA, DeepSeek, Mistral), using carefully curated user profiles that reflect real users with varying attributes such as country and gender. As a result, the models exhibit significant variance in score distributions when user attributes—such as country or gender—that should not influence risk computation are changed. For example, GPT-4o assigns higher risk scores to Nigerian and Indonesian profiles. While some models align closely with expected scores in the low- and mid-risk ranges, none maintain consistent scores across regions and demographics, thereby violating AI and finance regulations.

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CAPSTONE: Composable Attribute‐Prompted Scene Translation for Zero‐Shot Vision–Language Reasoning
Md. Ismail Hossain | Shahriyar Zaman Ridoy | Moshiur Farazi | Nabeel Mohammed | Shafin Rahman

Interpreting visual scenes with high-level reasoning is essential for many real-world applications, such as autonomous systems andcontent moderation, but training and maintaining Vision–Language Models (VLMs) remains resource-intensive and opaque. In this work, we present CAPSTONE, a lightweight, modular framework designed for industrial settings. Instead of relying on multimodal training or fine-tuning large models, CAPSTONE transforms outputs from off-the-shelf vision models into structured text prompts that can be interpreted by a frozen Large Language Model (LLM). This plug-and-play architecture enables reasoning over visual input without access to raw pixels, dramatically reducing computational cost and complexity. On the POPE dataset, our system, using a 7B LLM, outperforms several fully trained VLMs in zero-shot evaluations, while on the VSR benchmark, the 4B model achieves competitive results, together demonstrating strong generalization without retraining. CAPSTONE offers a scalable and interpretable alternative for companies looking to integrate visual reasoning capabilities without the burden of full-scale VLM pipelines.

Language agents powered by large language models (LLMs) face significant deployment challenges in resource-constrained environments, particularly for specialized domains and less-common languages. This paper presents Tox-chat, a Korean chemical toxicity information agent devised within these limitations. We propose two key innovations: a context-efficient architecture that reduces token consumption through hierarchical section search, and a scenario-based dialogue generation methodology that effectively distills tool-using capabilities from larger models. Experimental evaluations demonstrate that our fine-tuned 8B parameter model substantially outperforms both untuned models and baseline approaches, in terms of DB faithfulness and preference. Our work offers valuable insights for researchers developing domain-specific language agents under practical constraints.

Large Language Models (LLMs) excel at complexreasoning tasks, yet their performance hinges on the quality of their prompts and pipeline structures. Manual promptdesign, as used in frameworks like DSPy, poses significantlimitations: it is time-intensive, demands substantial expertise,and lacks scalability, restricting the widespread use of LLMsacross diverse applications. To overcome these challenges, weintroduce AutoDSPy, the first framework to fully automateDSPy pipeline construction using reinforcement learning (RL).AutoDSPy leverages an RL-tuned policy network to dynamicallyselect optimal reasoning modules—such as Chain-of-Thought forlogical tasks or ReAct for tool integration—along with inputoutput signatures and execution strategies, entirely eliminatingthe need for manual configuration. Experimental results on theGSM8K and HotPotQA benchmarks demonstrate that AutoDSPyoutperforms traditional DSPy baselines, achieving accuracy gainsof up to 4.3% while reducing inference time, even with smallermodels like GPT-2 (127M). By integrating RL-based automation,AutoDSPy enhances both efficiency and accessibility, simplifyingthe development of structured, high-performing LLM solutionsand enabling scalability across a wide range of tasks

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bib (full) Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts

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Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts
Valentina Pyatkin | Andreas Vlachos

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Efficient Inference for Large Language Models –Algorithm, Model, and System
Xuefei Ning | Guohao Dai | Haoli Bai | Lu Hou | Yu Wang

The inference of LLMs incurs high computational costs, memory access overhead, and memory usage, leading to inefficiencies in terms of latency, throughput, power consumption, and storage. To this end, this tutorial focuses on the increasingly important topic of Efficient Inference for LLMs and aims to provide a systematic understanding of key facts and methodologies from a designer’s perspective. We start by introducing the basic concepts of modern LLMs, software and hardware. Following this, we define the efficiency optimization problem. To equip the audience with a designer’s mindset, we briefly explain how to diagnose efficiency bottlenecks for a given workload on specific hardware. After introducing the basics, we will introduce our full-stack taxonomy of efficient inference methods for LLMs. We will walk through each category of methodology, using one to three representative methods as examples for each leaf subcategory, elaborating on the design logic behind each method and which inefficiency factors they primarily address. Finally, we will wrap up with a takeaway summary, and future research directions.

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Advancing Language Models through Instruction Tuning: Recent Progress and Challenges
Zhihan Zhang | Renze Lou | Fangkai Jiao | Wenpeng Yin | Meng Jiang

The capability of following instructions is a key dimension for AI systems. Therefore, in NLP, instruction tuning – the process of training language models to follow natural language instructions – has become a fundamental component of the model development pipeline. This tutorial addresses three critical questions within the field: (1) What are the current focal points in instruction tuning research? (2) What are the best practices in training an instruction-following model? (3) What new challenges have emerged? To answer these questions, the tutorial presents a systematic overview of recent advances in instruction tuning. It covers different stages in model training: supervised fine-tuning, preference optimization, and reinforcement learning. It introduces scalable strategies for building high-quality instruction data, explores approaches for training autonomous AI agents that handle complex real-world tasks, and discusses common criteria for evaluating instruction-following models. The audience will gain a comprehensive understanding of cutting-edge trends in instruction tuning and insights into promising directions for future research.

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Spoken Conversational Agents with Large Language Models
Huck Yang | Andreas Stolcke | Larry P. Heck

Spoken conversational agents are converging toward voice-native LLMs. This tutorial distills the path from cascaded ASR/NLU to end-to-end, retrieval-and vision-grounded systems. We frame adaptation of text LLMs to audio, cross-modal alignment, and joint speech–text training; review datasets, metrics, and robustness across accents; and compare design choices (cascaded vs. E2E, post-ASR correction, streaming). We link industrial assistants to current open-domain and task-oriented agents, highlight reproducible baselines, and outline open problems in privacy, safety, and evaluation. Attendees leave with practical recipes and a clear systems-level roadmap.

Language models (LMs) like GPT and Claude have shown impressive abilities in a range of natural language processing (NLP) tasks. Among these tasks, code understanding and generation have quickly become one of the most popular applications of LMs, given its nature of executable logic forms. However, there is a practical understanding of how programming knowledge can be combined with natural language to automate software development. Moreover, recent studies also empirically demonstrate that code can be a better form for complex reasoning and agentic task automation, but they do not indicate their significance. In this tutorial, we deem such superior capabilities brought by code modeling as Code Intelligence, and aim to provide a coherent overview of recent advances in this topic. We will start by first providing preliminaries of training foundation models on code and their common practices. We will then focus on downstream tasks in the domain of code and their evaluations. Then, we will cover how code can contribute to advancements in general tasks, and the opportunities of future research on Code Intelligence.

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Data and Model Centric Approaches for Expansion of Large Language Models to New languages
Anoop Kunchukuttan | Raj Dabre | Rudra Murthy | Mohammed Safi Ur Rahman Khan | Thanmay Jayakumar

Despite the increasing pace of Large Language Model (LLM) research, a vast majority of existing LLMs mainly support English alongside a handful of high resource languages, leaving a major gap for most low-resource languages. In this tutorial, we focus on approaches to expand the language coverage of LLMs. This provides an efficient and viable path to bring LLM technologies to low-resource languages, instead of training from scratch. We look at approaches at various stages of the LLM training pipeline, like tokenizer training, pre-training, instruction tuning, alignment, evaluation, etc., where adaptations are made to support new languages. We look at data-oriented approaches as well as model-oriented approaches. We hope that our tutorial enables researchers and practitioners to work on incorporating additional languages and tasks into existing LLMs to enhance inclusivity and coverage.

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Neuro-Symbolic Natural Language Processing
André Freitas | Marco Valentino | Danilo Silva de Carvalho

Despite the performance leaps delivered by Large Language Models (LLMs), NLP systems based only on deep learning architectures still have limiting capabilities in terms of delivering safe and controlled reasoning, interpretability, and adaptability within complex and specialised domains, restricting their use in areas where reliability and trustworthiness are crucial. Neur-symbolic NLP methods seek to overcome these limitations by integrating the flexibility of contemporary language models with the control/interpretability of symbolic methods. This hybrid approach brings the promise to both enhance inference capabilities and to deepen the theoretical understanding of LLMs. This tutorial aims to bridge the gap between the practical performance of LLMs and the principled modelling of language and inference of formal methods. We provide an overview of formal foundations in linguistics and reasoning, followed by contemporary architectural mechanisms to interpret, control, and extend NLP models. Balancing theoretical and practical activities, the tutorial is suitable for PhD students, experienced researchers, and industry practitioners.

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Continual Learning of Large Language Models
Tongtong Wu | Trang Vu | Linhao Luo | Gholamreza Haffari

As large language models (LLMs) continue to expand in size and utility, keeping them current with evolving knowledge and shifting user preferences becomes an increasingly urgent yet challenging task. This tutorial offers a comprehensive exploration of continual learning (CL) in the context of LLMs, presenting a structured framework that spans continual pre-training, instruction tuning, and alignment. Grounded in recent survey work and empirical studies, we discuss emerging trends, key methods, and practical insights from both academic research and industry deployments. In addition, we highlight the new frontier of lifelong LLM agents, i.e., systems capable of autonomous, self-reflective, and tool-augmented adaptation. Participants will gain a deep understanding of the computational, algorithmic, and ethical challenges inherent to CL in LLMs, and learn about strategies to mitigate forgetting, manage data and evaluation pipelines, and design systems that can adapt responsibly and reliably over time. This tutorial will benefit researchers and practitioners interested in advancing the long-term effectiveness, adaptability, and safety of foundation models.

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bib (full) Proceedings of The Third Arabic Natural Language Processing Conference

Under-represented languages suffer from a lack of data, and as a result, there are few LLMs that support them. Extending an existing LLM to a new language is a practical option for startups, university labs, and organizations with limited budgets. This process involves several steps. In this paper, we describe how we adapted the Falcon3-7B model to Arabic, covering everything from data collection and training to evaluation. Falcon-Arabic was trained exclusively on native data to better capture the cultural and linguistic aspects of the language. Our evaluations show that Falcon-Arabic achieves state-of-the-art results on a range of Arabic benchmarks.

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ArabJobs: A Multinational Corpus of Arabic Job Ads
Mo El-Haj

ArabJobs is a publicly available corpus of Arabic job advertisements collected from Egypt, Jordan, Saudi Arabia, and the United Arab Emirates. Comprising over 8,500 postings and more than 550,000 words, the dataset captures linguistic, regional, and socio-economic variation in the Arab labour market. We present analyses of gender representation and occupational structure, and highlight dialectal variation across ads, which offers opportunities for future research. We also demonstrate applications such as salary estimation and job category normalisation using large language models, alongside benchmark tasks for gender bias detection and profession classification. The findings show the utility of ArabJobs for fairness-aware Arabic NLP and labour market research. The dataset is publicly available on GitHub: https://github.com/drelhaj/ArabJobs.

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Semitic Root Encoding: Tokenization Based on the Templatic Morphology of Semitic Languages in NMT
Brendan T. Hatch | Stephen D. Richardson

The morphological structure of Semitic languages, such as Arabic, is based on non-concatenative roots and templates. This complex word structure used by humans is obscured to neural models that employ traditional tokenization algorithms, such as byte-pair encoding (BPE) (Sennrich et al., 2016; Gage, 1994). In this work, we present and evaluate Semitic Root Encoding (SRE), a tokenization method that represents both concatenative and non-concatenative structures in Semitic words with sequences of root, template stem, and BPE tokens. We apply the method to neural machine translation (NMT) and find that SRE tokenization yields an average increase of 1.15 BLEU over the baseline. SRE tokenization is also robust against generating combinations of roots with template stems that do not occur in nature. Finally, we compare the performance of SRE to tokenization based on non-linguistic root and template structures and tokenization based on stems, providing evidence that NMT models are capable of leveraging tokens based on non-concatenative Semitic morphology.

Arabic is one of the most widely spoken languages in the world, yet efforts to develop and evaluate Large Language Models (LLMs) for Arabic remain relatively limited. Most existing Arabic benchmarks focus on linguistic, cultural, or religious content, leaving a significant gap in areas like STEM and coding domains that are increasingly relevant for real-world LLM applications. To help bridge this gap, we present 3LM, a suite of three benchmarks designed specifically for Arabic. The first is a set of STEM-related question-answer pairs, naturally sourced from Arabic textbooks and educational worksheets. The second consists of synthetically generated STEM questions, created using the same sources. The third benchmark focuses on code generation, built through a careful translation of two widely used code benchmarks, incorporating a human-in-the-loop process with several rounds of review to ensure high-quality and faithful translations. We release all three benchmarks publicly to support the growth of Arabic LLM research in these essential but underrepresented areas.

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TuniFra: A Tunisian Arabic Speech Corpus with Orthographic Transcriptions and French Translations
Alex Choux | Marko Avila | Josep Crego | Fethi Bougares | Antoine Laurent

We introduce TuniFra, a novel and comprehensive corpus developed to advance research in Automatic Speech Recognition (ASR) and Speech-to-Text Translation (STT) for Tunisian Arabic, a notably low-resourced language variety. The TuniFra corpus comprises 15 hours of native Tunisian Arabic speech, carefully transcribed and manually translated into French. While the development of ASR and STT systems for major languages is supported by extensive datasets, low-resource languages such as Tunisian Arabic face significant challenges due to limited training data, particularly for speech technologies. TuniFra addresses this gap by offering a valuable resource tailored for both ASR and STT tasks in the Tunisian dialect. We describe our methodology for data collection, transcription, and annotation, and present initial baseline results for both Tunisian Arabic speech recognition and Tunisian Arabic–French speech translation.

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The Cross-Lingual Cost: Retrieval Biases in RAG over Arabic-English Corpora
Chen Amiraz | Yaroslav Fyodorov | Elad Haramaty | Zohar Karnin | Liane Lewin-Eytan

Cross-lingual retrieval-augmented generation (RAG) is a critical capability for retrieving and generating answers across languages. Prior work in this context has mostly focused on generation and relied on benchmarks derived from open-domain sources, most notably Wikipedia. In such settings, retrieval challenges often remain hidden due to language imbalances, overlap with pretraining data, and memorized content. To address this gap, we study Arabic-English RAG in a domain-specific setting using benchmarks derived from real-world corporate datasets. Our benchmarks include all combinations of languages for the user query and the supporting document, drawn independently and uniformly at random. This enables a systematic study of multilingual retrieval behavior.Our findings reveal that retrieval is a critical bottleneck in cross-lingual domain-specific scenarios, with substantial performance drops occurring when the user query and supporting document languages differ. A key insight is that these failures stem primarily from the retriever’s difficulty in ranking documents across languages. Finally, we propose two simple retrieval strategies that address this source of failure by enforcing equal retrieval from both languages or by translating the query, resulting in substantial improvements in cross-lingual and overall performance. These results highlight meaningful opportunities for improving multilingual retrieval, particularly in practical, real-world RAG applications.

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Open-domain Arabic Conversational Question Answering with Question Rewriting
Mariam E. Hassib | Nagwa El-Makky | Marwan Torki

Conversational question-answering (CQA) plays a crucial role in bridging the gap between human language and machine understanding, enabling more natural and interactive interactions with AI systems. In this work, we present the first results on open-domain Arabic CQA using deep learning. We introduce AraQReCC, a large-scale Arabic CQA dataset containing 9K conversations with 62K question-answer pairs, created by translating a subset of the QReCC dataset. To ensure data quality, we used COMET-based filtering and manual ratings from large language models (LLMs), such as GPT-4 and LLaMA, selecting conversations with COMET scores, along with LLM ratings of 4 or more. AraQReCC facilitates advanced research in Arabic CQA, improving clarity and relevance through question rewriting. We applied AraT5 for question rewriting and used BM25 and Dense Passage Retrieval (DPR) for passage retrieval. AraT5 is also used for question answering, completing the end-to-end system. Our experiments show that the best performance is achieved with DPR, attaining an F1 score of 21.51% on the test set. While this falls short of the human upper bound of 40.22%, it underscores the importance of question rewriting and quality-controlled data in enhancing system performance.

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ATHAR: A High-Quality and Diverse Dataset for Classical Arabic to English Translation
Mohammed Sabry Mohammed | Mohammed Khalil

Classical Arabic represents a significant era that encompasses the golden age of Arab culture, philosophy, and scientific literature. With a broad consensus on the importance of translating these literatures to enrich knowledge dissemination across communities, the advent of large language models (LLMs) and translation systems offers promising tools to facilitate this goal. However, we have identified a scarcity of translation datasets in Classical Arabic, which are often limited in scope and topics, hindering the development of high-quality translation systems. In response, we present the ATHAR dataset, which comprises 66,000 high-quality classical Arabic to English translation samples that cover a wide array of topics including science, culture, and philosophy. Furthermore, we assess the performance of current state-of-the-art LLMs under various settings, concluding that there is a need for such datasets in current systems. Our findings highlight how models can benefit from fine-tuning or incorporating this dataset into their pretraining pipelines. The dataset is publicly available on the HuggingFace Data Hub. To preserve anonymity during review, we additionally provide an anonymized snapshot at https://drive.google.com/drive/folders/1c_ElsblaOJzQ0TW_M1DugjR2o3Xv9RUo.

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A-SEA³𝐋-QA: A Fully Automated Self-Evolving, Adversarial Workflow for Arabic Long-Context Question-Answer Generation
Kesen Wang | Daulet Toibazar | Pedro J Moreno Mengibar

We present an end-to-end, self-evolving adversarial workflow for long-context Question-Answer (QA) Generation in Arabic. By orchestrating multiple specialized LVLMs: a question generator, an evaluator, and a swarm of answer generators, our system iteratively refines its own performance without any human intervention. Starting from raw, multi-page Arabic documents across diverse domains, the question generator produces fine-grained, context-aware queries to be tackled by the answer generator swarm, and the evaluator assesses and feeds back quality metrics. This closed-loop cycle enables continuous learning: low-confidence outputs trigger automated re-generation and model updates, progressively enhancing question difficulty and relevance. Moreover, we set the quality metrics as a tunable hyperparameter, enabling question generation at controllable and customizable difficulty levels. We release AraLongBench, a large-scale Arabic benchmark of single- and multi-page challenges spanning hundreds of pages, and demonstrate that our self-evolving workflow substantially outperform static pipelines, markedly boosting the long-context comprehension capabilities of leading Arabic Large Vision Language Models (LVLMs). Lastly, we also meticulously architect a fully automated agentic workflow for long-context Arabic document collection.

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Lemmatizing Dialectal Arabic with Sequence-to-Sequence Models
Mostafa Saeed | Nizar Habash

Lemmatization for dialectal Arabic poses many challenges due to the lack of orthographic standards and limited morphological analyzers. This work explores the effectiveness of Seq2Seq models for lemmatizing dialectal Arabic, both without analyzers and with their integration. We assess how well these models generalize across dialects and benefit from related varieties. Focusing on Egyptian, Gulf, and Levantine dialects with varying resource levels, our analysis highlights both the potential and limitations of data-driven approaches. The proposed method achieves significant gains over baselines, performing well in both low-resource and dialect-rich scenarios.

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Saudi-Alignment Benchmark: Assessing LLMs Alignment with Cultural Norms and Domain Knowledge in the Saudi Context
Manal Alhassoun | Imaan Mohammed Alkhanen | Nouf Alshalawi | Ibtehal Baazeem | Waleed Alsanie

For effective use in specific countries, Large Language Models (LLMs) need a strong grasp of local culture and core knowledge to ensure socially appropriate, context-aware, and factually correct responses. Existing Arabic and Saudi benchmarks are limited, focusing mainly on dialects or lifestyle, with little attention to deeper cultural or domain-specific alignment from authoritative sources. To address this gap and the challenge LLMs face with non-Western cultural nuance, this study introduces the Saudi-Alignment Benchmark. It consists of 874 manually curated questions across two core cultural dimensions: Saudi Cultural and Ethical Norms, and Saudi Domain Knowledge. These questions span multiple subcategories and use three formats to assess different goals with verified sources. Our evaluation reveals significant variance in LLM alignment. GPT-4 achieved the highest overall accuracy (83.3%), followed by ALLaM-7B (81.8%) and Llama-3.3-70B (81.6%), whereas Jais-30B exhibited a pronounced shortfall at 21.9%. Furthermore, multilingual LLMs excelled in norms; ALLaM-7B in domain knowledge. Considering the effect of question format, LLMs generally excelled in selected-response formats but showed weaker results on generative tasks, indicating that recognition-based benchmarks alone may overestimate cultural and contextual alignment. These findings highlight the need for tailored benchmarks and reveal LLMs’ limitations in achieving cultural grounding, particularly in underrepresented contexts like Saudi Arabia.

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AraHalluEval: A Fine-grained Hallucination Evaluation Framework for Arabic LLMs
Aisha Alansari | Hamzah Luqman

Recently, extensive research on the hallucination of the large language models (LLMs) has mainly focused on the English language. Despite the growing number of multilingual and Arabic-specific LLMs, evaluating LLMs’ hallucination in the Arabic context remains relatively underexplored. The knowledge gap is particularly pressing given Arabic’s widespread use across many regions and its importance in global communication and media. This paper presents the first comprehensive hallucination evaluation of Arabic and multilingual LLMs on two critical Arabic natural language generation tasks: generative question answering (GQA) and summarization. This study evaluates a total of 12 LLMs, including 4 Arabic pre-trained models, 4 multilingual models, and 4 reasoning-based models. To assess the factual consistency and faithfulness of LLMs’ outputs, we developed a fine-grained hallucination evaluation framework consisting of 12 fine-grained hallucination indicators that represent the varying characteristics of each task. The results reveal that factual hallucinations are more prevalent than faithfulness errors across all models and tasks. Notably, the Arabic pre-trained model Allam consistently demonstrates lower hallucination rates than multilingual models and a comparative performance with reasoning-based models. The code is available at: https://github.com/aishaalansari57/AraHalluEval

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Evaluating Prompt Relevance in Arabic Automatic Essay Scoring: Insights from Synthetic and Real-World Data
Chatrine Qwaider | Kirill Chirkunov | Bashar Alhafni | Nizar Habash | Ted Briscoe

Prompt relevance is a critical yet underexplored dimension in Arabic Automated Essay Scoring (AES). We present the first systematic study of binary prompt-essay relevance classification, supporting both AES scoring and dataset annotation. To address data scarcity, we built a synthetic dataset of on-topic and off-topic pairs and evaluated multiple models, including threshold-based classifiers, SVMs, causal LLMs, and a fine-tuned masked SBERT model. For real-data evaluation, we combined QAES with ZAEBUC, creating off-topic pairs via mismatched prompts. We also tested prompt expansion strategies using AraVec, CAMeL, and GPT-4o. Our fine-tuned SBERT achieved 98% F1 on synthetic data and strong results on QAES+ZAEBUC, outperforming SVMs and threshold-based baselines and offering a resource-efficient alternative to LLMs. This work establishes the first benchmark for Arabic prompt relevance and provides practical strategies for low-resource AES.

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WojoodOntology: Ontology-Driven LLM Prompting for Unified Information Extraction Tasks
Alaa Aljabari | Nagham Hamad | Mohammed Khalilia | Mustafa Jarrar

Information Extraction tasks such as Named Entity Recognition and Relation Extraction are often developed using diverse tagsets and annotation guidelines. This presents major challenges for model generalization, cross-dataset evaluation, tool interoperability, and broader industry adoption. To address these issues, we propose an information extraction ontology, , which covers a wide range of named entity types and relations. serves as a semantic mediation framework that facilitates alignment across heterogeneous tagsets and annotation guidelines. We propose two ontology-based mapping methods: (i) as a set of mapping rules for uni-directional tagset alignment; and (ii) as ontology-based prompting, which incorporates the ontology concepts directly into prompts, enabling large language models (LLMs) to perform more effective and bi-directional mappings. Our experiments show a 15% improvement in out-of-domain mapping accuracy when using ontology-based prompting compared to rule-based methods. Furthermore, is aligned with Schema.org and Wikidata, enabling interoperability with knowledge graphs and facilitating broader industry adoption. The is open source and available at https://sina.birzeit.edu/wojood.

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Tahdib: A Rhythm-Aware Phrase Insertion for Classical Arabic Poetry Composition
Mohamad Elzohbi | Richard Zhao

This paper presents a methodology for inserting phrases in Arabic poems to conform to a specific rhythm using ByT5, a byte-level multilingual transformer-based model. Our work discusses a rule-based grapheme-to-beat transformation tailored for extracting the rhythm from fully diacritized Arabic script. Our approach employs a conditional denoising objective to fine-tune ByT5, where the model reconstructs masked words to match a target rhythm. We adopt a curriculum learning strategy, pre-training on a general Arabic dataset before fine-tuning on poetic dataset, and explore cross-lingual transfer from English to Arabic. Experimental results demonstrate that our models achieve high rhythmic alignment while maintaining semantic coherence. The proposed model has the potential to be used in co-creative applications in the process of composing classical Arabic poems.

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Can LLMs Directly Retrieve Passages for Answering Questions from Qur’an?
Sohaila Eltanbouly | Salam Albatarni | Shaimaa Hassanein | Tamer Elsayed

The Holy Qur’an provides timeless guidance, addressing modern challenges and offering answers to many important questions. The Qur’an QA 2023 shared task introduced the Qur’anic Passage Retrieval (QPR) task, which involves retrieving relevant passages in response to MSA questions. In this work, we evaluate the ability of seven pre-trained large language models (LLMs) to retrieve relevant passages from the Qur’an in response to given questions, considering zero-shot and several few-shot scenarios. Our experiments show that the best model, Claude, significantly outperforms the state-of-the-art QPR model by 28 points on MAP and 38 points on MRR, exhibiting an impressive improvement of about 113% and 82%, respectively.

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ArabEmoNet: A Lightweight Hybrid 2D CNN-BiLSTM Model with Attention for Robust Arabic Speech Emotion Recognition
Ali Abouzeid | Bilal Elbouardi | Mohamed Maged | Shady Shehata

Speech emotion recognition is vital for human-computer interaction, particularly for low-resource languages like Arabic, which face challenges due to limited data and research. We introduce ArabEmoNet, a lightweight architecture designed to overcome these limitations and deliver state-of-the-art performance. Unlike previous systems relying on discrete MFCC features and 1D convolutions, which miss nuanced spectro-temporal patterns, ArabEmoNet uses Mel spectrograms processed through 2D convolutions, preserving critical emotional cues often lost in traditional methods. While recent models favor large-scale architectures with millions of parameters, ArabEmoNet achieves superior results with just 1 million parameters—90 times smaller than HuBERT base and 74 times smaller than Whisper. This efficiency makes it ideal for resource-constrained environments. ArabEmoNet advances Arabic speech emotion recognition, offering exceptional performance and accessibility for real-world applications.

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Capturing Intra-Dialectal Variation in Qatari Arabic: A Corpus of Cultural and Gender Dimensions
Houda Bouamor | Sara Al-Emadi | Zeinab Ibrahim | Hany Fazzaa | Aisha Al-Sultan

We present the first publicly available, multidimensional corpus of Qatari Arabic that captures intra-dialectal variation across Urban and Bedouin speakers. While often grouped under the label of “Gulf Arabic”, Qatari Arabic exhibits rich phonological, lexical, and discourse-level differences shaped by gender, age, and sociocultural identity. Our dataset includes aligned speech and transcriptions from 255 speakers, stratified by gender and age, and collected through structured interviews on culturally salient topics such as education, heritage, and social norms. The corpus reveals systematic variation in pronunciation, vocabulary, and narrative style, offering insights for both sociolinguistic analysis and computational modeling. We also demonstrate its utility through preliminary experiments in the prediction of dialects and genders. This work provides the first large-scale, demographically balanced corpus of Qatari Arabic, laying a foundation for both sociolinguistic research and the development of dialect-aware NLP systems.

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Feature Engineering is not Dead: A Step Towards State of the Art for Arabic Automated Essay Scoring
Marwan Sayed | Sohaila Eltanbouly | May Bashendy | Tamer Elsayed

Automated Essay Scoring (AES) has shown significant advancements in educational assessment. However, under-resourced languages like Arabic have received limited attention. To bridge this gap and enable robust Arabic AES, this paper introduces the first publicly-available comprehensive set of engineered features tailored for Arabic AES, covering surface-level, readability, lexical, syntactic, and semantic features. Experiments are conducted on a dataset of 620 Arabic essays, each annotated with both holistic and trait-specific scores. Our findings demonstrate that the proposed feature set is effective across different models and competitive with recent NLP advances including LLMs, establishing the state-of-the-art performance and providing strong baselines for future Arabic AES research. Moroever, the resulting feature set offers a reusable and foundational resource, contributing towards the development of more effective Arabic AES systems.

This paper evaluates the knowledge and reasoning capabilities of Large Language Models in Islamic inheritance law, ʿilm al-mawārīth. We assess the performance of seven LLMs using a benchmark of 1,000 multiple-choice questions covering diverse inheritance scenarios, designed to test each model’s ability—from understanding the inheritance context to computing the distribution of shares prescribed by Islamic jurisprudence. The results show a wide performance gap among models. o3 and Gemini 2.5 achieved accuracies above 90%, while ALLaM, Fanar, LLaMA, and Mistral scored below 50%. These disparities reflect important differences in reasoning ability and domain adaptation.We conduct a detailed error analysis to identify recurring failure patterns across models, including misunderstandings of inheritance scenarios, incorrect application of legal rules, and insufficient domain knowledge. Our findings highlight the limitations of current models in handling structured legal reasoning and suggest directions for improving their performance in Islamic legal reasoning.

The impressive advancement of Large Language Models (LLMs) in English has not been matched across all languages. In particular, LLM performance in Arabic lags behind, due to data scarcity, linguistic diversity of Arabic and its dialects, morphological complexity, etc. Progress is further hindered by the quality of Arabic benchmarks, which typically rely on static, publicly available data, lack comprehensive task coverage, or do not provide dedicated platforms with blind test sets. This makes it challenging to measure actual progress and to mitigate data contamination. Here, we aim to bridge these gaps. In particular, we introduce BALSAM, a comprehensive, community-driven benchmark aimed at advancing Arabic LLM development and evaluation. It includes 78 NLP tasks from 14 broad categories, with 52K examples divided into 37K test and 15K development, and a centralized, transparent platform for blind evaluation. We envision BALSAM as a unifying platform that sets standards and promotes collaborative research to advance Arabic LLM capabilities.

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TEDxTN: A Three-way Speech Translation Corpus for Code-Switched Tunisian Arabic - English
Fethi Bougares | Salima Mdhaffar | Haroun Elleuch | Yannick Estève

In this paper, we introduce TEDxTN, the first publicly available Tunisian Arabic to English speech translation dataset. This work is in line with the ongoing effort to mitigate the data scarcity obstacle for a number of Arabic dialects. We collected, segmented, transcribed and translated 108 TEDx talks following our internally developed annotations guidelines. The. collected talks represent 25 hours of speech with code-switching that cover speakers with various accents from over 11 different regions of Tunisia. We make the annotation guidelines and corpus publicly available. This will enable the extension of TEDxTN to new talks as they become available. We also report results for strong baseline systems of Speech Recognition and Speech Translation using multiple pre-trained and fine-tuned end-to-end models. This corpus is the first open source and publicly available speech translation corpus of Code-Switching Tunisian dialect. We believe that this is a valuable resource that can motivate and facilitate further research studying Tunisian Dialect.

Video-to-text and text-to-video retrieval are dominated by English benchmarks (e.g. DiDeMo, MSR-VTT) and recent multilingual corpora (e.g. RUDDER), yet Arabic remains underserved, lacking localized evaluation metrics. We introduce a three-stage framework, AutoArabic, utilizing state-of-the-art large language models (LLMs) to translate non-Arabic benchmarks into Modern Standard Arabic, reducing the manual revision required by nearly fourfold. The framework incorporates an error detection module that automatically flags potential translation errors with 97% accuracy. Applying the framework to DiDeMo, a video retrieval benchmark produces DiDeMo-AR, an Arabic variant with 40,144 fluent Arabic descriptions. An analysis of the translation errors is provided and organized into an insightful taxonomy to guide future Arabic localization efforts. We train a CLIP-style baseline with identical hyperparameters on the Arabic and English variants of the benchmark, finding a moderate performance gap (𝛥 ≈ 3pp at Recall@1), indicating that Arabic localization preserves benchmark difficulty. We evaluate three post-editing budgets (zero/ flagged-only/ full) and find that performance improves monotonically with more post-editing, while the raw LLM output (zero-budget) remains usable. To ensure reproducibility to other languages, we made the code available at https://github.com/Tahaalshatiri/AutoArabic.

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Zero-Shot and Fine-Tuned Evaluation of Generative LLMs for Arabic Word Sense Disambiguation
Yossra Noureldien | Abdelrazig Mohamed | Farah Attallah

Arabic presents unique challenges for sense level language understanding due to its rich morphology and semantic ambiguity. This paper benchmarks large generative language models (LLMs) for Arabic Word Sense Disambiguation (WSD) under both zero-shot and fine-tuning conditions. We evaluate one proprietary model (GPT-4o) and three opensource models (LLaMA 3.1-8B, Qwen 2.5-7B, and Gemma 2-9B) on two publicly available datasets. In zero-shot settings, GPT-4o achieved the highest overall performance, with comparable results across both datasets, reaching 79% accuracy and an average macro-F1 score of 66.08%. Fine-tuning, however, notably elevated all open models beyond GPT4o’s zero-shot results. Qwen achieved the top scores on one dataset, with an accuracy of 90.77% and a macro-F1 score of 83.98%, while LLaMA scored highest on the other, reaching an accuracy of 88.51% and a macroF1 score of 69.41%. These findings demonstrate that parameter-efficient supervised adaptation can close much of the performance gap and establish strong, reproducible baselines for Arabic WSD using open-source, relatively medium-sized models. Full code is publicly available.

We introduce Nile-Chat-4B, 3x4B-A6B, and 12B, a collection of LLMs for Egyptian dialect, uniquely designed to understand and generate texts written in both Arabic and Latin scripts. Specifically, with Nile-Chat-3x4B-A6B, we introduce a novel language adaptation approach by leveraging the Branch-Train-MiX strategy to merge script-specialized experts, into a single MoE model. Our Nile-Chat models significantly outperform leading multilingual and Arabic LLMs, such as LLaMa, Jais, and ALLaM, on our newly introduced Egyptian evaluation benchmarks, which span both understanding and generative tasks. Notably, our 12B model delivers a 14.4% performance gain over Qwen2.5-14B-Instruct on Latin-script benchmarks. All our resources are publicly available. We believe this work presents a comprehensive methodology for adapting LLMs to a single language with dual-script usage, addressing an often overlooked aspect in contemporary LLM development.

Post-training has emerged as a crucial technique for aligning pre-trained Large Language Models (LLMs) with human instructions, significantly enhancing their performance across a wide range of tasks. Central to this process is the quality and diversity of post-training datasets. This paper presents a review of publicly available Arabic post-training datasets on the Hugging Face Hub, organized along four key dimensions: (1) LLM Capabilities (e.g., Question Answering, Translation, Reasoning, Summarization, Dialogue, Code Generation, and Function Calling); (2) Steerability (e.g., Persona and System Prompts); (3) Alignment (e.g., Cultural, Safety, Ethics, and Fairness); and (4) Robustness. Each dataset is rigorously evaluated based on popularity, practical adoption, recency and maintenance, documentation and annotation quality, licensing transparency, and scientific contribution. Our review revealed critical gaps in the development of Arabic post-training datasets, including limited task diversity, inconsistent or missing documentation and annotation, and low adoption across the community. Finally, the paper discusses the implications of these gaps on the progress of Arabic-centric LLMs and applications while providing concrete recommendations for future efforts in Arabic post-training dataset development.

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Bridging Dialectal Gaps in Arabic Medical LLMs through Model Merging
Ahmed Ibrahim | Abdullah Hosseini | Hoda Helmy | Wafa Lakhdhar | Ahmed Serag

The linguistic fragmentation of Arabic, with over 30 dialects exhibiting low mutual intelligibility, presents a critical challenge for deploying natural language processing (NLP) in healthcare. Conventional fine-tuning of large language models (LLMs) for each dialect is computationally prohibitive and operationally unsustainable. In this study, we explore model merging as a scalable alternative by integrating three pre-trained LLMs—a medical domain expert, an Egyptian Arabic model, and a Moroccan Darija model—into a unified system without additional fine-tuning. We introduce a novel evaluation framework that assesses both dialectal fidelity via dual evaluation: LLM-based automated scoring and human assessments by native speakers. Our results demonstrate that the merged model effectively handles cross-dialect medical scenarios, such as interpreting Moroccan Darija inputs for Egyptian Arabic-speaking clinicians, while maintaining high clinical relevance. The merging process reduced computational cost by over 60% compared to per-dialect fine-tuning, highlighting its viability for resource-constrained settings. This work offers a promising path for building dialect-aware medical LLMs at scale, with implications for broader deployment across linguistically diverse regions.

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Tool Calling for Arabic LLMs: Data Strategies and Instruction Tuning
Asım Ersoy | Enes Altinisik | Kareem Mohamed Darwish | Husrev Taha Sencar

Tool calling is a critical capability that allows Large Language Models (LLMs) to interact with external systems, significantly expanding their utility. However, research and resources for tool calling are predominantly English-centric, leaving a gap in our understanding of how to enable this functionality for other languages, such as Arabic. This paper investigates three key research questions: (1) the necessity of in-language (Arabic) tool-calling data versus relying on cross-lingual transfer, (2) the effect of general-purpose instruction tuning on tool-calling performance, and (3) the value of fine-tuning on specific, high-priority tools. To address these questions, we conduct extensive experiments using base and post-trained variants of an open-weight Arabic LLM. To enable this study, we bridge the resource gap by translating and adapting two open-source tool-calling datasets into Arabic. Our findings provide crucial insights into the optimal strategies for developing robust tool-augmented agents for Arabic.

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Toward Culturally-Aware Arabic Debate Platforms with NLP Support
Khalid Al Khatib | Mohammad Khader

Despite the growing importance of online discourse, Arabic-speaking communities lack platforms that support structured, culturally grounded debate. Mainstream social media rarely fosters constructive engagement, often leading to polarization and superficial exchanges. This paper proposes the development of a culturally aware debate platform tailored to the values and traditions of Arabic-speaking users, with a focus on leveraging advances in natural language processing (NLP). We present findings from a user survey that explores experiences with existing debate tools and expectations for future platforms. Besides, we analyze 30,000 English-language debate topics using large language models (LLMs) to assess their cultural relevance and appropriateness for Arab audiences. We further examine the ability of LLMs to generate new culturally resonant debate topics, contributing to the emerging tasks of culture-aware topic assessment and generation. Finally, we propose a theoretical and technical framework for building an NLP-supported Arabic debate platform. Our work highlights the urgent need for culturally sensitive NLP resources that foster critical thinking, digital literacy, and meaningful deliberation in Arabic.

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Modeling North African Dialects from Standard Languages
Yassine Toughrai | Kamel Smaïli | David Langlois

Processing North African Arabic dialects presents significant challenges due to high lexical variability, frequent code-switching with French, and the use of both Arabic and Latin scripts. We address this with a phonemebased normalization strategy that maps Arabic and French text into a simplified representation (Arabic rendered in Latin script), reflecting native reading patterns. Using this method, we pretrain BERTbased models on normalized Modern Standard Arabic and French only and evaluate them on Named Entity Recognition (NER) and text classification. Experiments show that normalized standard-language corpora yield competitive performance on North African dialect tasks; in zero-shot NER, Ar_20k surpasses dialectpretrained baselines. Normalization improves vocabulary alignment, indicating that normalized standard corpora can suffice for developing dialect-supportive

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Learning Word Embeddings from Glosses: A Multi-Loss Framework for Arabic Reverse Dictionary Tasks
Engy Ibrahim | Farhah Adel | Marwan Torki | Nagwa El-Makky

We address the task of reverse dictionary modeling in Arabic, where the goal is to retrieve a target word given its definition. The task comprises two subtasks: (1) generating embeddings for Arabic words based on Arabic glosses, and (2) a cross-lingual setting where the gloss is in English and the target embedding is for the corresponding Arabic word. Prior approaches have largely relied on BERT models such as CAMeLBERT or MARBERT trained with mean squared error loss. In contrast, we propose a novel ensemble architecture that combines MARBERTv2 with the encoder of AraBART, and we demonstrate that the choice of loss function has a significant impact on performance. We apply contrastive loss to improve representational alignment, and introduce structural and center losses to better capture the semantic distribution of the dataset. This multi-loss framework enhances the quality of the learned embeddings and leads to consistent improvements in both monolingual and cross-lingual settings. Our system achieved the best rank metric in both subtasks compared to the previous approaches. These results highlight the effectiveness of combining architectural diversity with task-specific loss functions in representational tasks for morphologically rich languages like Arabic.

We introduce ALARB, a dataset and suite of tasks designed to evaluate the reasoning capabilities of large language models (LLMs) within the Arabic legal domain. While existing Arabic benchmarks cover some knowledge-intensive tasks such as retrieval and understanding, substantial datasets focusing specifically on multistep reasoning for Arabic LLMs, especially in open-ended contexts, are lacking. The dataset comprises over 13K commercial court cases from Saudi Arabia, with each case including the facts presented, the reasoning of the court, the verdict, as well the cited clauses extracted from the regulatory documents. We define a set of challenging tasks leveraging this dataset and reflecting the complexity of real-world legal reasoning, including verdict prediction, completion of reasoning chains in multistep legal arguments, and identification of relevant regulations based on case facts. We benchmark a representative selection of current open and closed Arabic LLMs on these tasks and demonstrate the dataset’s utility for instruction tuning. Notably, we show that instruction tuning a modest 12B parameter model using ALARB significantly enhances its performance in verdict prediction and Arabic verdict generation, reaching a level comparable to that of GPT-4o.

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Transfer or Translate? Argument Mining in Arabic with No Native Annotations
Sara Nabhani | Khalid Al Khatib

Argument mining for Arabic remains underexplored, largely due to the scarcity of annotated corpora. To address this gap, we examine the effectiveness of cross-lingual transfer from English. Using the English Persuasive Essays (PE) corpus, annotated with argumentative components (Major Claim, Claim, and Premise), we explore several transfer strategies: training encoder-based multilingual and monolingual models on English data, machine-translated Arabic data, and their combination. We further assess the impact of annotation noise introduced during translation by manually correcting portions of the projected training data. In addition, we investigate the potential of prompting large language models (LLMs) for the task. Experiments on a manually corrected Arabic test set show that monolingual models trained on translated data achieve the strongest performance, with further improvements from small-scale manual correction of training examples.

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An Exploration of Knowledge Editing for Arabic
Basel Mousi | Nadir Durrani | Fahim Dalvi

While Knowledge Editing (KE) has been widely explored in English, its behavior in morphologically rich languages like Arabic remains underexamined. In this work, we present the first study of Arabic KE. We evaluate four methods (ROME, MEMIT, ICE, and LTE) on Arabic translations of the ZsRE and Counterfact benchmarks, analyzing both multilingual and cross-lingual settings. Our experiments on Llama-2-7B-chat show show that parameter-based methods struggle with cross-lingual generalization, while instruction-tuned methods perform more robustly. We extend Learning-To-Edit (LTE) to a multilingual setting and show that joint Arabic-English training improves both editability and transfer. We release Arabic KE benchmarks and multilingual training for LTE data to support future research.

We present Octopus, a first family of modular speech-language models designed for Arabic-English ASR, dialect identification, and speech translation. Built on Whisper-V3 and enhanced with large language models like ALLaM, LLaMA, and DeepSeek, Octopus bridges speech and text through a lightweight projection layer and Q-Former. To broaden its scope beyond speech, Octopus integrates BEATs, a general-purpose audio encoder allowing it to understand both linguistic and acoustic events. Despite its simplicity, this dual-encoder design supports robust performance across multilingual and code-switched scenarios. We also introduce TinyOctopus, a distilled variant using smaller models (Distil-Whisper + LLaMA3-1B / DeepSeek-1.5B), achieving competitive results with just a fraction of the parameters. Fine-tuning on synthetic code-switched data further boosts its performance. Octopus demonstrates the power of compact, extensible architectures in Arabic-centric speech modeling and sets the stage for unified multilingual audio-language understanding.

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ArabicWeb-Edu: Educational Quality Data for Arabic LLM Training
Majd Hawasly | Tasnim Mohiuddin | Hamdy Mubarak | Sabri Boughorbel

The quality of training data plays a critical role in the performance of large language models (LLMs). This is especially true for low-resource languages where high-quality content is relatively scarce. Inspired by the success of FineWeb-Edu for English, we construct a native Arabic educational-quality dataset using similar methodological principles. We begin by sampling 1 million Arabic web documents from Common Crawl and labeling them into six quality classes (0–5) with Qwen-2.5-72B-Instruct model using a classification prompt adapted from FineWeb-Edu. These labeled examples are used to train a robust classifier capable of distinguishing educational content from general web text. We train a classification head on top of a multilingual 300M encoder model, then use this classifier to filter a large Arabic web corpus, discarding documents with low educational value. To evaluate the impact of this curation, we pretrain from scratch two bilingual English-Arabic 7B LLMs on 800 billion tokens using the filtered and unfiltered data and compare their performance across a suite of benchmarks. Our results show a significant improvement when using the filtered educational dataset, validating the effectiveness of quality filtering as a component in a balanced data mixture for Arabic LLM development. This work addresses the scarcity of high-quality Arabic training data and offers a scalable methodology for curating educational quality content in low-resource languages.

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AMCrawl: An Arabic Web-Scale Dataset of Interleaved Image-Text Documents and Image-Text Pairs
Shahad Aboukozzana | Muhammad Kamran J Khan | Ahmed Ali

In this paper, we present the Arabic Multimodal Crawl (AMCrawl), the first native-based Arabic multimodal dataset to our knowledge, derived from the Common Crawl corpus and rigorously filtered for quality and safety. Image-text pair datasets are the standard choice for pretraining multimodal large language models. However, they are often derived from image alt-text metadata, which is typically brief and context-poor, disconnecting images from their broader meaning. Although significant advances have been made in building interleaved image-text datasets for English, such as the OBELICS dataset, a substantial gap remains for native Arabic content. Our processing covered 8.6 million Arabic web pages, yielding 5.8 million associated images and 1.3 billion text tokens. The final dataset includes interleaved image-text documents and question-answer pairs, featuring 2.8 million high-quality interleaved documents and 5 million QA pairs. Alongside the dataset, we release the complete pipeline and code, ensuring reproducibility and encouraging further research and development. To demonstrate the effectiveness of AMCrawl, we introduce a publicly available native Arabic Vision Language model, trained with 13 billion parameters. These models achieve competitive results when benchmarked against publicly available datasets. AMCrawl bridges a critical gap in Arabic multimodal resources, providing a robust foundation for developing Arabic multimodal large language models and fostering advancements in this underrepresented area. Code: github.com/shahad-aboukozzana/AMCrawl

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DialG2P: Dialectal Grapheme-to-Phoneme. Arabic as a Case Study
Majd Hawasly | Hamdy Mubarak | Ahmed Abdelali | Ahmed Ali

Grapheme-to-phoneme (G2P) models are essential components in text-to-speech (TTS) and pronunciation assessment applications. While standard forms of languages have gained attention in that regard, dialectal speech, which often serves as the primary means of spoken communication for many communities, as it is the case for Arabic, has not received the same level of focus. In this paper, we introduce an end-to-end dialectal G2P for Egyptian Arabic, a dialect without standard orthography. Our novel architecture accomplishes three tasks: (i) restores short vowels of the diacritical marks for the dialectal text; (ii) maps certain characters that happen only in the spoken version of the dialectal Arabic to their dialect-specific character transcriptions; and finally (iii) converts the previous step output to the corresponding phoneme sequence. We benchmark G2P on a modular cascaded system, a large language model, and our multi-task end-to-end architecture.

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Shawarma Chats: A Benchmark Exact Dialogue & Evaluation Platter in Egyptian, Maghrebi & Modern Standard Arabic—A Triple-Dialect Feast for Hungry Language Models
Kamyar Zeinalipour | Mohamed Zaky Saad | Oumaima Attafi | Marco Maggini | Marco Gori

Content-grounded dialogue evaluation for Arabic remains under-resourced, particularly across Modern Standard (MSA), Egyptian, and Maghrebi varieties. We introduce Shawarma Chats, a benchmark of 30,000 six-turn conversations grounded in Wikipedia content, evenly split across the three dialects. To build this corpus, we prompt five frontier LLMs GPT-4o, Gemini 2.5 Flash, Qwen-Plus, DeepSeek-Chat, and Mistral Large to generate 1,500 seed dialogues. Native Arabic speakers evaluate these outputs to select the most effective generator and most human-aligned grader. Sub-A dialogues undergo a two-pass, rationale-driven self-repair loop where the grader critiques and the generator revises; unresolved cases are manually corrected. We apply this pipeline to 10,000 Wikipedia paragraphs to create 30,000 high-quality conversations 10,000 per dialect—at modest human cost. To validate the benchmark, we LoRA-fine-tune six open LLMs (1–24 B parameters) on Shawarma Chats and observe consistent gains in automatic-grader scores, BERTScore, BLEU and ROUGE particularly for models larger than 7 B parameters. Shawarma Chats thus establishes the first large-scale, dialect-aware, content-grounded dialogue benchmark for Arabic.

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bib (full) Proceedings of The Third Arabic Natural Language Processing Conference: Shared Tasks

We present an overview of the AraGenEval shared task, organized as part of the ArabicNLP 2025 conference. This task introduced the first benchmark suite for Arabic authorship analysis, featuring three subtasks: Authorship Style Transfer, Authorship Identification, and AI-Generated Text Detection. We curated high-quality datasets, including over 47,000 paragraphs from 21 authors and a balanced corpus of human- and AI-generated texts. The task attracted significant global participation, with 72 registered teams from 16 countries. The results highlight the effectiveness of transformer-based models, with top systems leveraging prompt engineering for style transfer, model ensembling for authorship identification, and a mix of multilingual and Arabic-specific models for AI text detection. This paper details the task design, datasets, participant systems, and key findings, establishing a foundation for future research in Arabic stylistics and trustworthy NLP.

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MISSION at AraGenEval Shared Task: Enhanced Arabic Authority Classification
Thamer Maseer Alharbi

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Nojoom.AI at AraGenEval Shared Task: Arabic Authorship Style Transfer
Hafsa Kara Achira | Mourad Bouache | Mourad Dahmane

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LMSA at AraGenEval Shared Task: Ensemble-Based Detection of AI-Generated Arabic Text Using Multilingual and Arabic-Specific Models
Kaoutar Zita | Attia Nehar | Abdelkader Khelil | Slimane Bellaouar | Hadda Cherroun

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Amr&MohamedSabaa at AraGenEval shared task: Arabic Authorship Identification using Term Frequency – Inverse Document Frequency Features with Supervised Machine Learning
Amr Sabaa | Mohamed Sabaa

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NLP_wizard at AraGenEval shared task: Embedding-Based Classification for AI Detection and Authorship Attribution
Mena Hany

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PTUK-HULAT at AraGenEval Shared Task: Fine-tuning XLM-RoBERTa for AI-Generated Arabic News Detection
Tasneem Duridi | Areej Jaber | Paloma Martínez

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Sebaweh at AraGenEval Shared Task: BERENSE - BERt based ENSEmbler for Arabic Authorship Identification
Muhammad Helmy | Batool Najeh Balah | Ahmed Mohamed Sallam | Ammar Sherif

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CUET-NLP_Team_SS306 at AraGenEval Shared Task: A Transformer-based Framework for Detecting AI-Generated Arabic Text
Sowrav Nath | Shadman Saleh | Kawsar Ahmed | Mohammed Moshiul Hoque

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BUSTED at ARATECT Shared Task: A Comparative Study of Transformer-Based Models for Arabic AI-Generated Text Detection
Ali Zain | Sareem Farooqui | Muhammad Rafi

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CIOL at AraGenEval shared task: Authorship Identification and AI Generated Text Detection in Arabic using Pretrained Models
Sadia Tasnim Meem | Azmine Toushik Wasi

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Osint at AraGenEval shared task: Fine-Tuned Modeling for Tracking Style Signatures and AI Generation in Arabic Texts
Shifali Agrahari | Hemanth Prakash Simhadri | Ashutosh Kumar Verma | Ranbir Singh Sanasam

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MarsadLab at AraGenEval Shared Task: LLM-Based Approaches to Arabic Authorship Style Transfer and Identification
Md. Rafiul Biswas | Mabrouka Bessghaier | Firoj Alam | Wajdi Zaghouani

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REGLAT at AraGenEval shared task: Morphology-Aware AraBERT for Detecting Arabic AI-Generated Text
Mariam Labib | Nsrin Ashraf | Mohammed Aldawsari | Hamada Nayel

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Jenin at AraGenEval Shared Task: Parameter-Efficient Fine-Tuning and Layer-Wise Analysis of Arabic LLMs for Authorship Style Transfer and Classification
Huthayfa Malhis | Mohammad Tami | Huthaifa I. Ashqar

We introduce AraHealthQA 2025, the Comprehensive Arabic Health Question Answering Shared Task, held in conjunction with ArabicNLP 2025 co-located with EMNLP 2025. This shared task addresses the paucity of high-quality Arabic medical QA resources by offering two complementary tracks: MentalQA, focusing on Arabic mental health Q&A (e.g., anxiety, depression, stigma reduction), and MedArabiQ, covering broader medical domains such as internal medicine, pediatrics, and clinical decision making. Each track comprises multiple subtasks, evaluation datasets, and standardized metrics, facilitating fair benchmarking. The task was structured to promote modeling under realistic, multilingual, and culturally nuanced healthcare contexts. We outline the dataset creation, task design and evaluation framework, participation statistics, baseline systems, and summarize the overall outcomes. We conclude with reflections on the performance trends observed and prospects for future iterations in Arabic health QA.

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NYUAD at AraHealthQA Shared Task: Benchmarking the Medical Understanding and Reasoning of Large Language Models in Arabic Healthcare Tasks
Nouar AlDahoul | Yasir Zaki

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MedLingua at MedArabiQ2025: Zero- and Few-Shot Prompting of Large Language Models for Arabic Medical QA
Fatimah Mohamed Emad Elden | Mumina Ab. Abukar

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Sakinah-AI at MentalQA: A Comparative Study of Few-Shot, Optimized, and Ensemble Methods for Arabic Mental Health Question Classification
Fatimah Mohamed Emad Elden | Mumina Ab. Abukar

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MindLLM at AraHealthQA 2025 Track 1: Leveraging Large Language Models for Mental Health Question Answering
Nejood Abdulaziz Bin Eshaq

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Quasar at AraHealthQA Track 1 : Leveraging Zero-Shot Large Language Models for Question and Answer Categorization in Arabic Mental Health
Adiba Fairooz Chowdhury | Md Sagor Chowdhury

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Binary_Bunch at AraHealthQA Track 1: Arabic Mental Health Q&A Classification Using Data Augmentation and Transformer Models
Sajib Bhattacharjee | Ratnajit Dhar | Kawsar Ahmed | Mohammed Moshiul Hoque

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!MSA at AraHealthQA 2025 Shared Task: Enhancing LLM Performance for Arabic Clinical Question Answering through Prompt Engineering and Ensemble Learning
Mohamed Younes | Seif Ahmed | Mohamed Basem

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Sindbad at AraHealthQA Track 1: Leveraging Large Language Models for Mental Health Q&A
AbdulRahman A. Morsy | Saad Mankarious | Ayah Zirikly

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Arabic Mental Health Question Answering: A Multi-Task Approach with Advanced Retrieval-Augmented Generation
Abdelaziz Amr AbdelAziz | Mohamed Ahmed Youssef | Mamdouh Mohamed Koritam | Marwa Eldeeb | Ensaf Hussein

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AraMinds at AraHealthQA 2025: A Retrieval-Augmented Generation System for Fine-Grained Classification and Answer Generation of Arabic Mental Health Q&A
Mohamed Zaytoon | Ahmed Mahmoud Salem | Ahmed Sakr | Hossam Elkordi

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Fahmni at AraHealthQA Track 1: Multi-Agent Retrieval-Augmented Generation and Multi-Label Classification for Arabic Mental Health Q&A
Caroline Sabty | Mohamad Rasmy | Mohamed Eyad Badran | Nourhan Sakr | Alia El Bolock

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MedGapGab at AraHealthQA: Modular LLM Assignment for Gaps and Gabs in Arabic Medical Question Answering
Baraa Hikal

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Egyhealth at General Arabic Health QA (MedArabiQ): An Enhanced RAG Framework with Large-Scale Arabic Q&A Medical Data
Hossam Amer | Rawan Tarek Taha | Gannat Elsayed | Ensaf Hussein Mohamed

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mucAI at AraHealthQA 2025: Explain–Retrieve–Verify (ERV) Workflow for Multi-Label Arabic Health QA Classification
Ahmed Abdou

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MarsadLab at AraHealthQA: Hybrid Contextual–Lexical Fusion with AraBERT for Question and Answer Categorization
Mabrouka Bessghaier | Shimaa Ibrahim | Md. Rafiul Biswas | Wajdi Zaghouani

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BAREC Shared Task 2025 on Arabic Readability Assessment
Khalid N. Elmadani | Bashar Alhafni | Hanada Taha | Nizar Habash

We present the results and findings of the BAREC Shared Task 2025 on Arabic Readability Assessment, organized as part of The Third Arabic Natural Language Processing Conference (ArabicNLP 2025). The BAREC 2025 shared task focuses on automatic readability assessment using BAREC Corpus, addressing fine-grained classification into 19 readability levels. The shared task includes two sub-tasks: sentence-level classification and document-level classification, and three tracks: (1) Strict Track, where only BAREC Corpus is allowed; (2) Constrained Track, restricted to the BAREC Corpus, SAMER Corpus, and SAMER Lexicon, and (3) Open Track, allowing any external resources. A total of 22 teams from 12 countries registered for the task. Among these, 17 teams submitted system description papers. The winning team achieved 87.5 QWK on the sentence-level task and 87.4 QWK on the document-level task.

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Syntaxa at BAREC Shared Task 2025: BERTnParse - Fusion of BERT and Dependency Graphs for Readability Prediction
Ahmed Bahloul

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GNNinjas at BAREC Shared Task 2025: Lexicon-Enriched Graph Modeling for Arabic Document Readability Prediction
Passant Elchafei | Mayar Osama | Mohamad Rageh | Mervat Abu-Elkheir

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ZAI at BAREC Shared Task 2025: AraBERT CORAL for Fine Grained Arabic Readability
Ahmad M. Nazzal

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ANLPers at BAREC Shared Task 2025: Readability of Embeddings Training Neural Readability Classifiers on the BAREC Corpus
Serry Sibaee | Omer Nacar | Yasser Alhabashi | Adel Ammar | Wadii Boulila

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MarsadLab at BAREC Shared Task 2025: Strict-Track Readability Prediction with Specialized AraBERT Models on BAREC
Shimaa Ibrahim | Md. Rafiul Biswas | Mabrouka Bessghaier | Wajdi Zaghouani

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SATLab at BAREC Shared Task 2025: Optimizing a Language-Independent System for Fine-Grained Readability Assessment
Yves Bestgen

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MorphoArabia at BAREC Shared Task 2025: A Hybrid Architecture with Morphological Analysis for Arabic Readability Assessment
Fatimah Mohamed Emad Elden

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!MSA at BAREC Shared Task 2025: Ensembling Arabic Transformers for Readability Assessment
Mohamed Basem | Mohamed Younes | Seif Ahmed | Abdelrahman Moustafa

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Qais at BAREC Shared Task 2025: A Fine-Grained Approach for Arabic Readability Classification Using a pre-trained model
Samar Ahmad

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mucAI at BAREC Shared Task 2025: Towards Uncertainty Aware Arabic Readability Assessment
Ahmed Abdou

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AMAR at BAREC Shared Task 2025: Arabic Meta-learner for Assessing Readability
Mostafa Saeed | Rana Waly | Abdelaziz Ashraf Hussein

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Noor at BAREC Shared Task 2025: A Hybrid Transformer-Feature Architecture for Sentence-level Readability Assessment
Nour Rabih

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PalNLP at BAREC Shared Task 2025: Predicting Arabic Readability Using Ordinal Regression and K-Fold Ensemble Learning
Mutaz Ayesh

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Pixels at BAREC Shared Task 2025: Visual Arabic Readability Assessment
Ben Sapirstein

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Phantoms at BAREC Shared Task 2025: Enhancing Arabic Readability Prediction with Hybrid BERT and Linguistic Features
Ahmed Alhassan | Asim Mohamed | Moayad Elamin

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STBW at BAREC Shared Task 2025: AraBERT-v2 with MSE-SoftQWK Loss for Sentence-Level Arabic Readability
Saoussan Trigui

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LIS at BAREC Shared Task 2025: Multi-Scale Curriculum Learning for Arabic Sentence-Level Readability Assessment Using Pre-trained Language Models
Anya Amel Nait Djoudi | Patrice Bellot | Adrian-Gabriel Chifu

We present ImageEval 2025, the first shared task dedicated to Arabic image captioning. The task addresses the critical gap in multimodal Arabic NLP by focusing on two complementary subtasks: (1) creating the first open-source, manually-captioned Arabic image dataset through a collaborative datathon, and (2) developing and evaluating Arabic image captioning models. A total of 44 teams registered, of which eight submitted during the test phase, producing 111 valid submissions. Evaluation was conducted using automatic metrics, LLM-based judgment, and human assessment. In Subtask 1, the best-performing system achieved a cosine similarity of 65.5, while in Subtask 2, the top score was 60.0. Although these results show encouraging progress, they also confirm that Arabic image captioning remains a challenging task, particularly due to cultural grounding requirements, morphological richness, and dialectal variation. All datasets, baseline models, and evaluation tools are released publicly to support future research in Arabic multimodal NLP.

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Codezone Research Group at ImageEval Shared-Task 2: Arabic Image Captioning Using BLIP and M2M100: A Two-Stage Translation Approach for ImageEval 2025
Abdulkadir Shehu Bichi

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BZU-AUM@ImageEval2025: An Arabic Image Captioning Dataset for Conflict Narratives with Human Annotation
Mohammed Alkhanafseh | Ola Surakhi | Abdallah Abedaljalill

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ImpactAi at ImageEval 2025 Shared Task: Region-Aware Transformers for Arabic Image Captioning – A Case Study on the Palestinian Narrative
Rabee Al-Qasem | Mohannad Hendi

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VLCAP at ImageEval 2025 Shared Task: Multimodal Arabic Captioning with Interpretable Visual Concept Integration
Passant Elchafei | Amany Fashwan

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PhantomTroupe at ImageEval 2025 Shared Task: Multimodal Arabic Image Captioning through Translation-Based Fine-Tuning of LLM Models
Muhammad Abu Horaira | Farhan Amin | Sakibul Hasan | Md. Tanvir Ahammed Shawon | Muhammad Ibrahim Khan

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NU_Internship team at ImageEval 2025: From Zero-Shot to Ensembles: Enhancing Grounded Arabic Image Captioning
Rana Gaber | Seif Eldin Amgad | Ahmed Sherif Nasri | Mohamed Ibrahim Ragab | Ensaf Hussein Mohamed

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Averroes at ImageEval 2025 Shared Task: Advancing Arabic Image Captioning with Augmentation and Two-Stage Generation
Mariam Saeed | Sarah Elshabrawy | Abdelrahman Hagrass | Mazen Yasser | Ayman Khalafallah

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AZLU at ImagEval Shared Task: Bridging Linguistics and Cultural Gaps in Arabic Image Captioning
Sarah Yassine

We present the findings of the first shared task on Qur’anic pronunciation assessment, which focuses on addressing the unique challenges of evaluating the precise pronunciation of Qur’anic recitation. To fill an existing research gap, the Iqra’Eval 2025 shared task introduces the first open benchmark for Mispronunciation Detection and Diagnosis (MDD) in Qur’anic recitation, using Modern Standard Arabic (MSA) reading of Qur’anic texts as its case study. The task provides a comprehensive evaluation framework with increasingly complex subtasks: error localization and detailed error diagnosis. Leveraging the recently developed QuranMB benchmark dataset along with auxiliary training resources, this shared task aims to stimulate research in an area of both linguistic and cultural significance while addressing computational challenges in pronunciation assessment.

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Hafs2Vec: A System for the IqraEval Arabic and Qur’anic Phoneme-level Pronunciation Assessment
Ahmed Ibrahim

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Phoneme-level mispronunciation detection in Quranic recitation using ShallowTransformer
Mohamed Nadhir Daoud | Mohamed Anouar Ben Messaoud

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AraS2P: Arabic Speech-to-Phonemes System
Bassam Mattar | Mohamed Fayed | Ayman Khalafallah

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Metapseud at Iqra’Eval: Domain Adaptation with Multi-Stage Fine-Tuning for Phoneme-Level Qur’anic Mispronunciation Detection
Ayman Mansour

Hallucination in Large Language Models (LLMs) remains a significant challenge and continues to draw substantial research attention. The problem becomes especially critical when hallucinations arise in sensitive domains, such as religious discourse. To address this gap, we introduce IslamicEval 2025—the first shared task specifically focused on evaluating and detecting hallucinations in Islamic content. The task consists of two subtasks: (1) Hallucination Detection and Correction of quoted verses (Ayahs) from the Holy Quran and quoted Hadiths; and (2) Qur’an and Hadith Question Answering, which assesses retrieval models and LLMs by requiring answers to be retrieved from grounded, authoritative sources. Thirteen teams participated in the final phase of the shared task, employing a range of pipelines and frameworks. Their diverse approaches underscore both the complexity of the task and the importance of effectively managing hallucinations in Islamic discourse.

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NUR at IslamicEval 2025 Shared Task: Retrieval-Augmented LLMs for Qur’an and Hadith QA
Serag Amin | Ranwa Aly | Yara Allam | Yomna Eid | Ensaf Hussein Mohamed

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BurhanAI at IslamicEval 2025 Shared Task: Combating Hallucinations in LLMs for Islamic Content; Evaluation, Correction, and Retrieval-Based Solution
Arij Al Adel | Abu Bakr Soliman | Mohamed Sakher Sawan | Rahaf Al-Najjar | Sameh Amin

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HUMAIN at IslamicEval 2025 Shared Task 1: A Three-Stage LLM-Based Pipeline for Detecting and Correcting Hallucinations in Quran and Hadith
Arwa Omayrah | Sakhar Alkhereyf | Ahmed Abdelali | Abdulmohsen Al-Thubaity | Jeril Kuriakose | Ibrahim AbdulMajeed

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TCE at IslamicEval 2025: Retrieval-Augmented LLMs for Quranic and Hadith Content Identification and Verification
Mohammed ElKoumy | Khalid Allam | Ahmad Tamer | Mohamed Alqablawi

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ThinkDrill at IslamicEval 2025 Shared Task: LLM Hybrid Approach for Qur’an and Hadith Question Answering
Eman Elrefai | Toka Khaled | Ahmed Soliman

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Burhan at IslamicEval: Fact-Augmented and LLM-Driven Retrieval for Islamic QA
Mohammad Basheer | Watheq Mansour | Abdulhamid Touma | Ahmad Qadeib Alban

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Isnad AI at IslamicEval 2025: A Rule-Based System for Identifying Religious Texts in LLM Outputs
Fatimah Mohamed Emad Elden

This paper presents the MAHED 2025 Shared Task on Multimodal Detection of Hope and Hate Emotions in Arabic Content, comprising three subtasks: (1) text-based classification of Arabic content into hate and hope, (2) multi-task learning for joint prediction of emotions, offensive content, and hate speech, and (3) multimodal detection of hateful content in Arabic memes. We provide three high-quality datasets totaling over 22,000 instances sourced from social media platforms, annotated by native Arabic speakers with Cohen’s Kappa exceeding 0.85. Our evaluation attracted 46 leaderboard submissions from participants, with systems leveraging Arabic-specific pre-trained language models (AraBERT, MARBERT), large language models (GPT-4, Gemini), and multimodal fusion architectures combining CLIP vision encoders with Arabic text models. The best-performing systems achieved macro F1-scores of 0.723 (Task 1), 0.578 (Task 2), and 0.796 (Task 3), with top teams employing ensemble methods, class-weighted training, and OCR-aware multimodal fusion. Analysis reveals persistent challenges in dialectal robustness, minority class detection for hope speech, and highlights key directions for future Arabic content moderation research.

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NYUAD at MAHED Shared Task: Detecting Hope, Hate, and Emotion in Arabic Textual Speech and Multi-modal Memes Using Large Language Models
Nouar AlDahoul | Yasir Zaki

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NguyenTriet at MAHED Shared Task: Ensemble of Arabic BERT Models with Hierarchical Prediction and Soft Voting for Text-Based Hope and Hate Detection
Nguyen Minh Triet | Thìn Đặng Văn

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ANLPers at MAHED2025: From Hate to Hope: Boosting Arabic Text Classification
Yasser Alhabashi | Serry Sibaee | Omer Nacar | Adel Ammar | Wadii Boulila

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LoveHeaven at MAHED 2025: Text-based Hate and Hope Speech Classification Using AraBERT-Twitter Ensemble
Nguyễn Thiên Bảo | Dang Van Thin

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CIC-NLP at MAHED 2025 TASK 1:Assessing the Role of Bigram Augmentation in Multiclass Arabic Hate and Hope Speech Classification
Tolulope Olalekan Abiola | Oluwatobi Joseph Abiola | Ogunleye Temitope Dasola | Tewodros Achamaleh | Obiadoh Augustine Ekenedilichukwu

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TranTranUIT at MAHED Shared Task: Multilingual Transformer Ensemble with Advanced Data Augmentation and Optuna-based Hyperparameter Optimization
Trinh Tran Tran | Thìn Đặng Văn

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YassirEA at MAHED 2025: Fusion-Based Multimodal Models for Arabic Hate Meme Detection
Yassir El Attar

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AAA at MAHED Text-based Hate and Hope Speech Classification: A Systematic Encoder Evaluation for Arabic Hope and Hate Speech Classification
Ahmed Khalil Elzainy | Mohamed Amin | Ahmed Samir | Hazem Abdelsalam

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CUET-823 at MAHED 2025 Shared Task: Large Language Model-Based Framework for Emotion, Offensive, and Hate Detection in Arabic
Ratnajit Dhar | Arpita Mallik

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AraMinds at MAHED 2025: Leveraging Vision-Language Models and Contrastive Multi-task Learning for Multimodal Hate Speech Detection
Mohamed Zaytoon | Ahmed Mahmoud Salem | Ahmed Sakr | Hossam Elkordi

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DesCartes-HOPE at MAHED Shared task 2025: Integrating Pragmatic Features for Arabic Hope and Hate Speech Detection
Leila Moudjari | Hacène-Cherkaski Mélissa | Farah Benamara

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ANLP-UniSo at MAHED Shared Task: Detection of Hate and Hope Speech in Arabic Social Media based on XLM-RoBERTa and Logistic Regression
Yasmine El Abed | Mariem Ben Arbia | Saoussen Ben Chaabene | Omar Trigui

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REGLAT at MAHED Shared Task: A Hybrid Ensemble-Based System for Arabic Hate Speech Detection
Nsrin Ashraf | Mariam Labib | Tarek Elshishtawy | Hamada Nayel

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HTU at MAHED Shared Task: Ensemble-Based Classification of Arabic Hate and Hope Speech Using Pre-trained Dialectal Arabic Models
Abdallah Saleh | Mariam M Biltawi

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SmolLab_SEU at MAHED Shared Task: Do Arabic-Native Encoders Surpass Multilingual Models in Detecting the Nuances of Hope, Hate, and Emotion?
Md Abdur Rahman | Md Sabbir Dewan | Md. Tofael Ahmed Bhuiyan | Md Ashiqur Rahman

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Baoflowin502 at MAHED Shared Task: Text-based Hate and Hope Speech Classification
Nguyen Minh Bao | Dang Van Thin

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AyahVerse at MAHED Shared Task: Fine-Tuning ArabicBERT with Preprocessing for Hope and Hate Detection
Ibad-ur-Rehman Rashid | Muhammad Hashir Khalil

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MultiMinds at MAHED 2025: Multimodal and Multitask Approaches for Detecting Emotional, Hate, and Offensive Speech in Arabic Content
Riddhiman Debnath | Abdul Wadud Shakib | Md Saiful Islam

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joy_2004114 at MAHED Shared Task : Filtering Hate Speech from Memes using A Multimodal Fusion-based Approach
Joy Das | Alamgir Hossain | Mohammed Moshiul Hoque

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Quasar at MAHED Shared Task : Decoding Emotions and Offense in Arabic Text using LLM and Transformer-Based Approaches
Md Sagor Chowdhury | Adiba Fairooz Chowdhury

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CUET_Zahra_Duo@Mahed 2025: Hate and Hope Speech Detection in Arabic Social Media Content using Transformer
Walisa Alam | Mehreen Rahman | Shawly Ahsan | Mohammed Moshiul Hoque

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AraNLP at MAHED 2025 Shared Task: Using AraBERT for Text-based Hate and Hope Speech Classification
Wafaa S. El-Kassas | Enas A. Hakim Khalil

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Thinking Nodes at MAHED: A Comparative Study of Multimodal Architectures for Arabic Hateful Meme Detection
Itbaan Safwan

We present the findings of the sixth Nuanced Arabic Dialect Identification (NADI 2025) Shared Task, which focused on Arabic speech dialect processing across three subtasks: spoken dialect identification (Subtask 1), speech recognition (Subtask 2), and diacritic restoration for spoken dialects (Subtask 3). A total of 44 teams registered, and during the testing phase, 100 valid submissions were received from eight unique teams. The distribution was as follows: 34 submissions for Subtask 1 five teams, 47 submissions for Subtask 2 six teams, and 19 submissions for Subtask 3 two teams. The best-performing systems achieved 79.8% accuracy on Subtask 1, 35.68/12.20 WER/CER (overall average) on Subtask 2, and 55/13 WER/CER on Subtask 3. These results highlight the ongoing challenges of Arabic dialect speech processing, particularly in dialect identification, recognition, and diacritic restoration. We also summarize the methods adopted by participating teams and briefly outline directions for future editions of NADI.

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Munsit at NADI 2025 Shared Task 2: Pushing the Boundaries of Multidialectal Arabic ASR with Weakly Supervised Pretraining and Continual Supervised Fine-tuning
Mahmoud Salhab | Shameed Sait | Mohammad Abusheikh | Hasan Abusheikh

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Lahjati at NADI 2025 A ECAPA-WavLM Fusion with Multi-Stage Optimization
Sanad Albawwab | Omar Qawasmeh

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MarsadLab at NADI Shared Task: Arabic Dialect Identification and Speech Recognition using ECAPA-TDNN and Whisper
Md. Rafiul Biswas | Kais Attia | Shimaa Ibrahim | Mabrouka Bessghaier | Wajdi Zaghouani

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ELYADATA & LIA at NADI 2025: ASR and ADI Subtasks
Haroun Elleuch | Youssef Saidi | Salima Mdhaffar | Yannick Estève | Fethi Bougares

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Unicorn at NADI 2025 Subtask 3: GEMM3N-DR: Audio-Text Diacritic Restoration via Fine-tuning Multimodal Arabic LLM
Mohamed Lotfy Elrefai

Large Language Models (LLMs) inherently reflect the vast data distributions they encounter during their pre-training phase. As this data is predominantly sourced from the web, there is a high chance it will be skewed towards high-resourced languages and cultures, such as those of the West. Consequently, LLMs often exhibit a diminished understanding of certain communities, a gap that is particularly evident in their knowledge of Arabic and Islamic cultures. This issue becomes even more pronounced with increasingly under-represented topics. To address this critical challenge, we introduce PalmX 2025, the first shared task designed to benchmark the cultural competence of LLMs in these specific domains. The task is composed of two subtasks featuring multiple-choice questions (MCQs) in Modern Standard Arabic (MSA): General Arabic Culture and General Islamic Culture. These subtasks cover a wide range of topics, including traditions, food, history, religious practices, and language expressions from across 22 Arab countries. The initiative drew considerable interest, with 26 teams registering for Subtask 1 and 19 for Subtask 2, culminating in nine and six valid submissions, respectively. Our findings reveal that task-specific fine-tuning substantially boosts performance over baseline models. The top-performing systems achieved an accuracy of 72.15% on cultural questions and 84.22% on Islamic knowledge. Parameter-efficient fine-tuning emerged as the predominant and most effective approach among participants, while the utility of data augmentation was found to be domain-dependent. Ultimately, this benchmark provides a crucial, standardized framework to guide the development of more culturally grounded and competent Arabic LLMs. Results of the shared task demonstrate that general cultural and general religious knowledge remain challenging to LLMs, motivating us to continue to offer the shared task in the future.

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Hamyaria at PalmX2025: Leveraging Large Language Models to Improve Arabic Multiple-Choice Questions in Cultural and Islamic Domains
Walid Al-Dhabyani | Hamzah A. Alsayadi

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ISL-NLP at PalmX 2025: Retrieval-Augmented Fine-Tuning for Arabic Cultural Question Answering
Mohamed Gomaa | Noureldin Elmadany

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ADAPT–MTU HAI at PalmX 2025: Leveraging Full and Parameter‐Efficient LLM Fine‐Tuning for Arabic Cultural QA
Shehenaz Hossain | Haithem Afli

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CultranAI at PalmX 2025: Data Augmentation for Cultural Knowledge Representation
Hunzalah Hassan Bhatti | Youssef Ahmed | Md Arid Hasan | Firoj Alam

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MarsadLab at PalmX Shared Task: An LLM Benchmark for Arabic Culture and Islamic Civilization
Md. Rafiul Biswas | Shimaa Ibrahim | Kais Attia | Firoj Alam | Wajdi Zaghouani

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Star at PalmX 2025: Arabic Cultural Understanding via Targeted Pretraining and Lightweight Fine-tuning
Eman Elrefai | Esraa Khaled | Alhassan Ehab

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AYA at PalmX 2025: Modeling Cultural and Islamic Knowledge in LLMs
Jannatul Tajrin | Bir Ballav Roy | Firoj Alam

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Cultura-Arabica: Probing and Enhancing Arabic Cultural Awareness in Large Language Models via LoRA
Pulkit Chatwal | Santosh Kumar Mishra

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Phoenix at Palmx: Exploring Data Augmentation for Arabic Cultural Question Answering
Houdaifa Atou | Issam Ait Yahia | Ismail Berrada

This paper provides a comprehensive overview of the QIAS 2025 shared task, organized as part of the ArabicNLP 2025 conference and co-located with EMNLP 2025. The task was designed for the evaluation of large language models in the complex domains of religious and legal reasoning. It comprises two subtasks: (1) Islamic Inheritance Reasoning, requiring models to compute inheritance shares according to Islamic jurisprudence, and (2) Islamic Knowledge Assessment, which covers a range of traditional Islamic disciplines. Both subtasks were structured as multiple-choice question answering challenges, with questions stratified by varying difficulty levels. The shared task attracted significant interest, with 44 teams participating in the development phase, from which 18 teams advanced to the final test phase. Of these, 6 teams submitted entries for both subtasks, 8 for Task 1 only, and two for Task 3 only. Ultimately, 16 teams submitted system description papers. Herein, we detail the task’s motivation, dataset construction, evaluation protocol, and present a summary of the participating systems and their results.

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NYUAD at QIAS Shared Task: Benchmarking the Legal Reasoning of LLMs in Arabic Islamic Inheritance Cases
Nouar AlDahoul | Yasir Zaki

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SHA at the QIAS Shared Task: LLMs for Arabic Islamic Inheritance Reasoning
Shatha Altammami

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N&N at QIAS 2025: Chain-of-Thought Ensembles with Retrieval-Augmented framework for Classical Arabic Islamic
Nourah Alangari | Nouf AlShenaifi

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HIAST at QIAS 2025: Retrieval-Augmented LLMs with Top-Hit Web Evidence for Arabic Islamic Reasoning QA
Mohamed Motasim Hamed | Nada Ghneim | Riad Sonbol

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QU-NLP at QIAS 2025 Shared Task: A Two-Phase LLM Fine-Tuning and Retrieval-Augmented Generation Approach for Islamic Inheritance Reasoning
Mohammad AL-Smadi

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Transformer Tafsir at QIAS 2025 Shared Task: Hybrid Retrieval-Augmented Generation for Islamic Knowledge Question Answering
Muhammad Abu Ahmad | Mohamad Ballout | Raia Abu Ahmad | Elia Bruni

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PuxAI at QIAS 2025: Multi-Agent Retrieval-Augmented Generation for Islamic Inheritance and Knowledge Reasoning
Nguyen Xuan Phuc | Thìn Đặng Văn

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Athar at QIAS2025: LLM-based Question Answering Systems for Islamic Inheritance and Classical Islamic Knowledge
Yossra Noureldien | Hassan Suliman | Farah Attallah | Abdelrazig Mohamed | Sara Abdalla

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ADAPT–MTU HAI at QIAS2025: Dual-Expert LLM Fine-Tuning and Constrained Decoding for Arabic Islamic Inheritance Reasoning
Shehenaz Hossain | Haithem Afli

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CVPD at QIAS 2025 Shared Task: An Efficient Encoder-Based Approach for Islamic Inheritance Reasoning
Salah Eddine Bekhouche | Abdellah Zakaria Sellam | Telli Hichem | Cosimo Distante | Abdenour Hadid

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CIS-RG at QIAS 2025 Shared Task: Approaches for Enhancing Performance of LLM on Islamic Legal Reasoning and its Mathematical Calculations
Osama Farouk Zaki

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SEA-Team at QIAS 2025: Enhancing LLMs for Question Answering in Islamic Texts
Sanaa Alowaidi

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MorAI at QIAS 2025: Collaborative LLM via Voting and Retrieval-Augmented Generation for Solving Complex Inheritance Problems
Jihad R’baiti | Chouaib El Hachimi | Youssef Hmamouche | Amal Seghrouchni

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Gumball at QIAS 2025: Arabic LLM Automated Reasoning in Islamic Inheritance
Eman Elrefai | Mohamed Lotfy Elrefai | Aml Hassan Esmail

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Tokenizers United at QIAS-2025: RAG-Enhanced Question Answering for Islamic Studies by Integrating Semantic Retrieval with Generative Reasoning
Mayar Boghdady

Automated Essay Scoring (AES) has emerged as a significant research problem in natural language processing, offering valuable tools to support educators in assessing student writing. Motivated by the growing need for reliable Arabic AES systems, we organized the first shared Task for Arabic Quality Evaluation of Essays in Multi-dimensions (TAQEEM) held at the ArabicNLP 2025 conference. TAQEEM 2025 includes two subtasks: Task A on holistic scoring and Task B on trait-specific scoring. It introduces a new (and first of its kind) dataset of 1,265 Arabic essays, annotated with holistic and trait-specific scores, including relevance, organization, vocabulary, style, development, mechanics, and grammar. The main goal of TAQEEM is to address the scarcity of standardized benchmarks and high-quality resources in Arabic AES. TAQEEM 2025 attracted 11 registered teams for Task A and 10 for Task B, with a total of 5 teams, across both tasks, submitting system runs for evaluation. This paper presents an overview of the task, outlines the approaches employed, and discusses the results of the participating teams.

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912 at TAQEEM 2025: A Distribution-aware Approach to Arabic Essay Scoring
Trong-Tai Dam Vu | Thìn Đặng Văn

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Taibah at TAQEEM 2025: Leveraging GPT-4o for Arabic Essay Scoring
Nada Almarwani | Alaa Alharbi | Samah Aloufi

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MarsadLab at TAQEEM 2025: Prompt-Aware Lexicon-Enhanced Transformer for Arabic Automated Essay Scoring
Mabrouka Bessghaier | Md. Rafiul Biswas | Amira Dhouib | Wajdi Zaghouani

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bib (full) Proceedings of the First BabyLM Workshop

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Rethinking the Role of Text Complexity in Language Model Pretraining
Dan John Velasco | Matthew Theodore Roque

Improving pretraining data quality and size is known to boost downstream performance, but the role of text complexity—how hard a text is to read—remains less explored. We reduce surface-level complexity (shorter sentences, simpler words, simpler structure) while keeping core content approximately constant and ask: (i) How does complexity affect language modeling across model sizes? (ii) Can useful representations be learned from simpler text alone? (iii) How does pretraining text complexity influence downstream language understanding? We simplify human-written texts using a large language model, pretrain causal models (28M–500M) from scratch on original vs. simplified data, and evaluate them in fine-tuning and zero-shot setups. We find that perplexity is sensitive to the interaction between model capacity and text complexity—smaller models degrade far less on simpler texts—while text complexity has little impact on fine-tuning evaluations, with zero-shot evaluations indicating that simpler texts benefit performance on linguistic knowledge tasks, whereas more complex texts favor tasks requiring world knowledge and entity tracking. Our findings suggest that different types of data diversity affect transfer and zero-shot performance differently, providing insight into tailoring data curation to specific goals.

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Contrastive Decoding for Synthetic Data Generation in Low-Resource Language Modeling
Jannek Ulm | Kevin Du | Vésteinn Snæbjarnarson

Large language models (LLMs) are trained on huge amounts of textual data, and concerns have been raised that the limits of such data may soon be reached. A potential solution is to train on synthetic data sampled from LLMs. In this work, we build on this idea and investigate the benefits of *contrastive decoding* for generating synthetic data. In a controlled setting, we experiment with sampling corpora using the relative difference between a GOOD and BAD model trained on the same original corpus of 100 million words. By amplifying the signal from a model that has better performance, we create a synthetic corpus and mix it with the original training data. Our findings show that training on a mixture of synthesized and real data improves performance on the language modeling objective and a range of downstream tasks.In particular, we see that training with a mix of synthetic data from contrastive decoding benefits tasks that require more *reasoning skills*, while synthetic data from traditional sampling helps more on tasks requiring surface-level *linguistic* capabilities.

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Unifying Mixture of Experts and Multi-Head Latent Attention for Efficient Language Models
Sushant Mehta | Raj Dandekar | Rajat Dandekar | Sreedath Panat

We present MoE-MLA-RoPE, a novel architecture combination that combines Mixture of Experts (MoE) with Multi-head Latent Attention (MLA) and Rotary Position Embeddings (RoPE) for efficient small language models. Our approach addresses the fundamental trade-off between model capacity and computational efficiency through three key innovations: (1) fine-grained expert routing with 64 micro-experts and top-k selection, enabling flexible specialization through \binom{62}{6} ≈ 3.6 × 10⁷ possible expert combinations; (2) shared expert isolation that dedicates 2 always active experts for common patterns while routing to 6 of 62 specialized experts; and (3) gradient-conflict-free load balancing that maintains expert utilization without interfering with primary loss optimization. Extensive experiments on models ranging from 17M to 202M parameters demonstrate that with compression ratio r=d/2 achieves 68% KV cache memory reduction and 3.2× inference speedup while maintaining competitive perplexity (0.8% degradation). Compared to the parameters with 53.9M parameters, improves the validation loss by 6.9% over the vanilla transformers while using 42% fewer active parameters per forward pass. FLOP-matched experiments reveal even larger gains: 11.1% improvement with 3.2× inference acceleration. Automated evaluation using GPT-4 as a judge confirms quality improvements in generation, with higher scores on coherence (8.1/10), creativity (7.9/10) and grammatical correctness (8.2/10). Our results establish that architectural synergy, not parameter scaling, defines the efficiency frontier for resource-constrained language model deployment.

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Are BabyLMs Deaf to Gricean Maxims? A Pragmatic Evaluation of Sample-efficient Language Models
Raha Askari | Sina Zarrieß | Özge Alacam | Judith Sieker

Implicit meanings are integral to human communication, making it essential for language models to be capable of identifying and interpreting them. Grice (1975) proposed a set of conversational maxims that guide cooperative dialogue, noting that speakers may deliberately violate these principles to express meanings beyond literal words, and that listeners, in turn, recognize such violations to draw pragmatic inferences.Building on Surian et al. (1996)’s study of children’s sensitivity to violations of Gricean maxims, we introduce a novel benchmark to test whether language models pretrained on <10M and <100M tokens can distinguish maxim-adhering from maxim-violating utterances. We compare these BabyLMs across five maxims and situate their performance relative to children and a Large Language Model (LLM) pretrained on 3T tokens.We find that overall, models trained on <100M tokens outperform those trained on <10M, yet fall short of child-level and LLM competence. Our results suggest that modest data increases improve some aspects of pragmatic behavior, leading to finer-grained differentiation between pragmatic dimensions.

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Model Merging to Maintain Language-Only Performance in Developmentally Plausible Multimodal Models
Ece Takmaz | Lisa Bylinina | Jakub Dotlacil

State-of-the-art vision-and-language models consist of many parameters and learn from enormous datasets, surpassing the amounts of linguistic data that children are exposed to as they acquire a language. This paper presents our approach to the multimodal track of the BabyLM challenge addressing this discrepancy. We develop language-only and multimodal models in low-resource settings using developmentally plausible datasets, with our multimodal models outperforming previous BabyLM baselines. One finding in the multimodal language model literature is that these models tend to underperform in language-only tasks. Therefore, we focus on maintaining language-only abilities in multimodal models. To this end, we experiment with model merging, where we fuse the parameters of multimodal models with those of language-only models using weighted linear interpolation. Our results corroborate the findings that multimodal models underperform in language-only benchmarks that focus on grammar, and model merging with text-only models can help alleviate this problem to some extent, while maintaining multimodal performance.

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TafBERTa: Learning Grammatical Rules from Small-Scale Language Acquisition Data in Hebrew
Anita Gelboim | Elior Sulem

We present TafBERTa, a compact RoBERTa based language model tailored for Hebrew child-directed speech (CDS). This work builds upon the BabyBERTa framework to address data scarcity and morphological complexity in Hebrew. Focusing on determiner-noun grammatical agreement phenomena, we show that TafBERTa achieves competitive performance compared to large-scale Hebrew language models while requiring significantly less data and computational resources. As part of this work, we also introduce a new corpus of Hebrew CDS, HTBerman, aligned with morphological metadata and our new grammatical evaluation benchmark for Hebrew, HeCLiMP, based on minimal pairs. Our results demonstrate the effectiveness of TafBERTa in grammaticality judgments and its potential for efficient NLP in low-resource settings.

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FORGETTER with forgetful hyperparameters and recurring sleeps can continue to learn beyond normal overtfitting limits
Yamamoto Rui | Keiji Miura

LLMs suffer from considerable computational costs in training.A more biologically plausible curriculum learning may help to decrease the learning costs.Here we propose a FORGETTER training algorithm,in which a model forgets the variables for optimization after a sleepand the hyperparameters are set toward forgetting memory:rather large weight decay and learning rates as well as small but optimized batch sizes.By limiting minGemma model to 512 input length and speeding up the development cycle,we compared normal and FORGETTER learning algorithms by using more than a thousand different models.Specifically, we found and utilized the “120-rule” that the models with about 120 (Query) heads in total, irrespective of the head number per layer, outperform.The improvement by using the FORGETTER algorithm is far bigger than that by optimizing the model structure.Specifically, FORGETTER models can learn beyond the data size where the normal learning overfits.The FORGETTER also works for CIFAR10 image classification.These results suggest that forgetting can be beneficial for pretraining deep neural networks by avoiding overfitting.

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Large Language Models and Children Have Different Learning Trajectories in Determiner Acquisition
Olivia La Fiandra | Nathalie Fernandez Echeverri | Patrick Shafto | Naomi H. Feldman

Large language models are often compared to human learners based on the amount of training data required or the end state capabilities of a learner, yet less attention has been given to differences in their language learning process. This study uses determiner acquisition as a case study to characterize how LLMs and children differ in their learning processes. By analyzing annotated speech samples from specified age ranges of four children and intermediate training checkpoints of the Pythia-70m language model, we trace the learners’ learning paths of definite and indefinite determiner use. Our results reveal a divergence: the children first produce the indefinite determiner, while the model first produces the definite determiner. This difference reflects underlying differences in the learning goals and mechanisms of models and children. Framing language learning as movement over distributions of linguistic features makes the learning process visible and offers an alternative approach for comparing humans and language models.

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Design and Analysis of few Million Parameter Transformer-based Language Models trained over a few Million Tokens Dataset
Yen-Che Hsiao | Abhishek Dutta

In this work, we systematically explore training methods and perform hyperparameter tuning to identify key language model parameters upper bounded by 28 million. These models are designed to generate a broad spectrum of basic general knowledge in simple and coherent English with limited generalization ability. We use the Simple English Wikipedia as the training dataset, selecting samples between 64 and 512 words, which provides a high-quality, compressed representation of general knowledge in basic English. Through hyperparameter tuning, we identify the best-performing architecture, yielding the lowest training loss, as a decoder-only Transformer with rotary positional encoding, multi-head attention, root-mean-square normalization, Gaussian error linear unit activation, post-normalization, no interleaved group query attention, an embedding dimension of 512, 8 layers, 8 attention heads, a feedforward dimension of 2048, and zero dropout. Models trained with a learning rate decaying linearly from 10^-4 to 10^-5 over 64 epochs achieve a training loss of 0.1, which appears sufficient for reproducing text more effectively than models trained to losses of 0.2 or 0.5. Fine-tuning on rephrased text further demonstrates that the model retains its ability to produce simple and coherent English covering broad basic knowledge, while exhibiting limited generalization capability.

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What is the Best Sequence Length for BabyLM?
Suchir Salhan | Richard Diehl Martinez | Zebulon Goriely | Paula Buttery

Transformer language models typically operate with a fixed-length context window, which has grown in step with large-scale pretraining datasets. In the BabyLM Challenge, however, many past submissions have defaulted to using much shorter sequence lengths. We examine the impact of sequence length on BabyLM pretraining, to answer the simple question: what sequence length should we be using when training Baby LMs? Using 100M-word training data and fixed compute budgets, we compare 125M-parameter Mamba and OPT models, finding that although longer is often better, the optimal length depends on both task and architecture. Shorter sequences are sufficient for grammatical generalization tasks whereas longer contexts benefit morphological analogical reasoning tasks.

Cross-attention transformers and other multimodal vision-language models excel at grounding and generation; however, their extensive, full-precision backbones make it challenging to deploy them on edge devices. Memory-augmented architectures enhance the utilization of past context; however, most works rarely pair them with aggressive edge-oriented quantization. We introduce BitMar, a quantized multimodal transformer that proposes an external human-like episodic memory for effective image-text generation on hardware with limited resources. BitMar utilizes 1.58-bit encoders, one for text (BitNet-style) and one for vision (DiNOv2-based), to create compact embeddings that are combined and used to query a fixed-size key-value episodic memory. During vector retrieval, the BitNet decoder applies per‐layer conditioning, which increases the contextual relevance of generated content. The decoder also employs attention sinks with a sliding‐window mechanism to process long or streaming inputs under tight memory budgets. The combination of per-layer conditioning and sliding-window attention achieves a strong quality–speed trade–off, delivering competitive captioning and multimodal understanding at low latency with a small model footprint. These characteristics make BitMar well-suited for edge deployment.

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Exploring smaller batch sizes for a high-performing BabyLM model architecture
Sharid Loáiciga | Eleni Fysikoudi | Asad B. Sayeed

We explore the conditions under which the highest-performing entry to the BabyLM task in 2023, Every Layer Counts BERT or ELC-BERT, is best-performing given more constrained resources than the original run, with a particular focus on batch size. ELC-BERT’s relative success, as an instance of model engineering compared to more cognitively-motivated architectures, could be taken as evidence that the “lowest-hanging” fruit is to be found from non-linguistic machine learning approaches. We find that if we take away the advantage of training time from ELC-BERT, the advantage of the architecture mostly disappears, but some hyperparameter combinations nevertheless differentiate themselves in performance.

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BLiSS: Evaluating Bilingual Learner Competence in Second Language Small Language Models
Yuan Gao | Suchir Salhan | Andrew Caines | Paula Buttery | Weiwei Sun

Cross-lingual extensions of the BabyLM Shared Task beyond English incentivise the development of Small Language Models that simulate a much wider range of language acquisition scenarios, including code-switching, simultaneous and successive bilingualism and second language acquisition. However, to our knowledge, there is no benchmark of the formal competence of cognitively-inspired models of L2 acquisition, or L2LMs. To address this, we introduce a Benchmark of Learner Interlingual Syntactic Structure (BLiSS). BLiSS consists of 1.5M naturalistic minimal pairs dataset derived from errorful sentence–correction pairs in parallel learner corpora. These are systematic patterns –overlooked by standard benchmarks of the formal competence of Language Models – which we use to evaluate L2LMs trained in a variety of training regimes on specific properties of L2 learner language to provide a linguistically-motivated framework for controlled measure of the interlanguage competence of L2LMs.

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Sample-Efficient Language Modeling with Linear Attention and Lightweight Enhancements
Patrick Haller | Jonas Golde | Alan Akbik

We study architectural and optimization techniques for sample-efficient language modeling under the constraints of the BabyLM 2025 shared task. Our model, BLaLM, replaces self-attention with a linear-time mLSTM token mixer and explores lightweight enhancements, including short convolutions, sliding window attention with dynamic modulation, and Hedgehog feature maps. To support training in low-resource settings, we curate a high-quality corpus emphasizing readability and pedagogical structure. Experiments across both strict and strict-small tracks show that (1) linear attention combined with sliding window attention consistently improves zero-shot performance, and (2) the Muon optimizer stabilizes convergence and reduces perplexity over AdamW. These results highlight effective strategies for efficient language modeling without relying on scale.

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Looking to Learn: Token-wise Dynamic Gating for Low-Resource Vision-Language Modelling
Bianca-Mihaela Ganescu | Suchir Salhan | Andrew Caines | Paula Buttery

Training vision-language models on cognitively-plausible amounts of data requires rethinking how models integrate multimodal information. Within the constraints of the Vision track for the BabyLM Challenge 2025, we propose a lightweight decoder-based architecture with (1) token-wise dynamic gating for adaptive fusion of linguistic and visual cues, (2) feature modulation and channel attention to maximise the utility of limited visual information and (3) auxiliary contrastive objectives for visual grounding. Evaluation on five benchmarks (BLiMP, BLiMP Supplement, EWoK, Winoground and VQA) shows competitive or superior performance to multimodal baselines. More notably, our dynamic gate discovers interpretable patterns without explicit supervision, favouring visual cues for content words and linguistic cues for function words. While we identify limitations in the Challenge constraints, such as the information bottleneck created by global image embeddings and training instability from the dataset split, our findings establish dynamic gating as a powerful tool for efficient multimodal learning, offering both interpretability and performance even under severe constraints.

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A Comparison of Elementary Baselines for BabyLM
Rareș Păpușoi | Sergiu Nisioi

This paper explores multiple simple baselines for the BabyLM challenge, covering random models, elementary predictions based on frequency, n-gram language models, LSTM with several tokenizers (BPE, Unigram, SuperBPE), and GPT-BERT, the winning architecture from the prior BabyLM edition. The evaluation is focused on the BLiMP and BLiMP-Supplement benchmarks. Our experiments show that Strict-Small can sometimes outperform Strict, the fact that performance can be highly sensitive to tokenization and the importance of data efficiency. A simple word-frequency baseline scored unexpectedly high, which led to identifying an evaluation artifact in the pipeline: a system that returns identical logits for both sentences in a minimal pair can achieve maximal accuracy.

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Two ways into the hall of mirrors: Language exposure and lossy memory drive cross-linguistic grammaticality illusions in language models
Kate McCurdy | Katharina Christian | Amelie Seyfried | Mikhail Sonkin

Readers of English — but not Dutch or German — consistently show a grammaticality illusion: they find ungrammatical double-center-embedded sentences easier to process than corresponding grammatical sentences. If pre-trained language model (LM) surprisal mimics these cross-linguistic patterns, this implies that language statistics explain the effect; if, however, the illusion requires memory constraints such as lossy context surprisal (LCS), this suggests a critical role for memory. We evaluate LMs in Dutch, German, and English. We find that both factors influence LMs’ susceptibility to grammaticality illusions, and neither fully account for human-like processing patterns.

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What did you say? Generating Child-Directed Speech Questions to Train LLMs
Whitney Poh | Michael Tombolini | Libby Barak

Child-Directed Speech (CDS) holds unique linguistic properties that distinguish it from other types of textual corpora. Language models trained using CDS often obtain superior results compared with the same size of different types of data. Several studies have aimed at modifying non-CDS data to mimic its linguistic properties to match the hypothesized advantageous aspects of CDS. Here, we propose to adapt the non-CDS portions of the training data to include questions similar to CDS interaction. We modify the data by adding artificially generated questions to the data and methodically analyzing the change in performance using each modified dataset. Our results show that artificial question generation strongly depends on the properties of the original dataset. While the performance improves for question-related measures, the overall performance is negatively affected as a result of the reduced syntactic diversity.

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Beyond Repetition: Text Simplification and Curriculum Learning for Data-Constrained Pretraining
Matthew Theodore Roque | Dan John Velasco

Most language model pretraining studies assume large data volumes, leaving open how to improve pretraining in data-constrained settings beyond repeated exposure. In such settings, the effects of training data order and of including alternative versions of the same text remain underexplored. We address this by studying curriculum learning in pretraining, focusing on text-complexity ordering and data augmentation via simplification. We ask: (1) Does simplifying texts enhance representation quality more than reusing the original data?; and (2) Does ordering data by text complexity yield better representations? To answer, we simplify a high-quality English dataset using a large language model and test four data schedules: (1) repeated exposure, (2) low-to-high complexity, (3) high-to-low, and (4) interleaved. We analyze models’ representation quality from a sample-efficiency perspective via fine-tuning, as well as its zero-shot performance on linguistic knowledge, entity tracking, world knowledge, and commonsense reasoning. Our findings show that adding simplified data improves fine-tuning and zero-shot performance over repeated exposure baseline: smaller models benefit from low-to-high complexity, while larger models perform better with interleaved ordering.

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CurLL: A Developmental Framework to Evaluate Continual Learning in Language Models
Pavan Kalyan Tankala | Shubhra Mishra | Satya Lokam | Navin Goyal

We introduce a comprehensive continual learning dataset and benchmark CurLL grounded in human developmental trajectories from ages 5–10, enabling systematic and fine-grained assessment of models’ ability to progressively acquire new skills. CurLL spans five developmental stages (0–4) covering ages 5–10, with a skill graph of 32 high-level skills, 128 sub-skills, 350+ goals, and 1,300+ indicators explicitly modeling prerequisite relationships. We generate a 23.4B-token synthetic dataset with controlled skill progression, vocabulary complexity, and format diversity, comprising paragraphs, comprehension-based QA (CQA), skill-testing QA (CSQA), and instruction–response (IR) pairs. Stage-wise token counts range from 2.12B to 6.78B tokens, supporting precise analysis of forgetting, forward transfer, and backward transfer. Using a 135M-parameter transformer trained under independent, joint, and sequential (continual) setups, we show trade-offs in skill retention and transfer efficiency. By mirroring human learning patterns and providing fine-grained control over skill dependencies, this work advances continual learning evaluations for language models.

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A Morpheme-Aware Child-Inspired Language Model
Necva Bölücü | Burcu Can

Most tokenization methods in language models rely on subword units that lack explicit linguistic correspondence. In this work, we investigate the impact of using morpheme-based tokens in a small language model, comparing them to the widely used frequency-based method, BPE. We apply the morpheme-based tokenization method to both 10-million and 100-million word datasets from the BabyLM Challenge. Our results show that using a morphological tokenizer improves EWoK (basic world knowledge) performance by around 20% and entity tracking by around 40%, highlighting the impact of morphological information in developing smaller language models. We also apply curriculum learning, in which morphological information is gradually introduced during training, mirroring the vocabulary-building stage in infants that precedes morphological processing. The results are consistent with previous research: curriculum learning yields slight improvements for some tasks, but performance degradation in others.

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Do Syntactic Categories Help in Developmentally Motivated Curriculum Learning for Language Models?
Arzu Burcu Güven | Anna Rogers | Rob Van Der Goot

We examine the syntactic properties of BabyLM corpus, and age-groups within CHILDES. While we find that CHILDES does not exhibit strong syntactic differentiation by age, we show that the syntactic knowledge about the training data can be helpful in interpreting model performance on linguistic tasks. For curriculum learning, we explore developmental and several alternative cognitively inspired curriculum approaches. We find that some curricula help with reading tasks, but the main performance improvement come from using the subset of syntactically categorizable data, rather than the full noisy corpus.

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SlovakBabyLM: Replication of the BabyLM and Sample-efficient Pretraining for a Low-Resource Language
Ľuboš Kriš | Marek Suppa

In recent years, we can observe a trend of creating various specific language models (LMs) within the Slavic language family with the Bert architecture. However, with an increasing number of parameters of LM, a larger amount of text is required for good performance, which can hinder the development and creation of LMs for specific languages. Our research is looking for a solution in Curriculum learning(CL) methods that can help us build better models with a lower amount of text in comparison with current LMs, which can help in better prtraining of models with low resource languages(LRL). Therefore, we replicate the BabyLM Challenge in the Slovak language (Dataset: https://huggingface.co/datasets/ubokri/SlovakBabyLM, Code: https://github.com/baucek/Slovakbabylm/tree/main). Additionally, apply CL to test and see the difference in the application of CL methods on the English and Slovak languages and evaluate whether the CL improves performance of LM. Our experiments show that the use of CL methods as preprocessing methods is significant for improving model performance in sentiment analysis and question answering.

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Single layer tiny Co4 outpaces GPT-2 and GPT-BERT
Noor Ul Zain | Mohsin Raza Naseem | Ahsan Adeel

We show that a tiny Co⁴ machine (CITATION) with a single layer, two heads, and 8M parameters, operating at O(N) computational cost (where N is the number of input tokens), in just 2 epochs outpaces GPT-2 (124M, 12 layers, O(N²)) and GPT-BERT (30M, 12 layers, O(N²)), both trained for 10 epochs. Co⁴ achieves orders-of-magnitude greater training efficiency on 10M tokens, demonstrating sample-efficient pretraining. On the BabyLM challenge evaluation pipeline, Co⁴ performs comparably or better across complex benchmarks, showing strong zero-shot and fine-tuning performance on SuperGLUE tasks. Specifically, Co⁴ outperforms GPT-2 in 5 out of 7 zero-shot metrics and 6 out of 7 fine-tuning tasks, and GPT-BERT in 4 out of 7 metrics in both cases. These results strongly suggest a need to rethink prevailing deep learning paradigms and associated scaling laws.

Multi-turn dialogues between a child and caregiver are characterized by a property called contingency – prompt, direct, and meaningful exchanges between interlocutors. We introduce ContingentChat, a Teacher–Student framework that benchmarks and improves multi-turn contingency in a BabyLM trained on 100M words. Using a novel alignment dataset for post-training, BabyLM generates responses that are more grammatical and cohesive. Experiments with adaptive Teacher decoding strategies show limited additional gains. ContingentChat highlights the positive benefits of targeted post-training on dialogue quality and presents contingency as a challenging goal for BabyLMs.

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Influence-driven Curriculum Learning for Pre-training on Limited Data
Loris Schoenegger | Lukas Thoma | Terra Blevins | Benjamin Roth

Curriculum learning, a training technique where data is presented to the model in order of example difficulty (e.g., from simpler to more complex documents), has shown limited success for pre-training language models. In this work, we investigate whether curriculum learning becomes competitive if we replace conventional human-centered difficulty metrics with one that more closely corresponds to example difficulty as observed during model training. Specifically, we experiment with sorting training examples by their training data influence, a score which estimates the effect of individual training examples on the model’s output. Models trained on our curricula are able to outperform ones trained in random order by over 10 percentage points in benchmarks, confirming that curriculum learning is beneficial for language model pre-training, as long as a more model-centric notion of difficulty is adopted.

Hybrid models that combine state space models (SSMs) with attention mechanisms have demonstrated strong performance by leveraging the efficiency of SSMs and the high recall ability of attention. However, the underlying reasons for these benefits remain insufficiently understood. In this work, we investigate hybrid architectures through the lens of memory utilization and overall performance, and propose a complementary method to further enhance their effectiveness. We focus in particular on the distinction between sequential and parallel integration of SSM and attention layers. Our analysis reveals that sequential hybrids perform better on shorter contexts, whereas parallel hybrids are more effective for longer contexts. Among various configurations, parallel hybrids using a cross-attention to combine SSM and attention outputs perform best. We also introduce a data-centric approach to further improve model performance: continual training on datasets with paraphrases. This method strikes the best balance across various other datasets, enhancing memory recall while preserving other capabilities. It generalizes well across different base models, including pure SSMs, and outperforms architectural modifications aimed at enhancing recall.

This report summarizes the findings from the 3rd BabyLM Challenge and the 1st BabyLM Workshop. The BabyLM Challenge is a shared task aimed at closing the data efficiency gap between human and machine language learners. The goal is to improve the performance of language models given a fixed training budget of no more than 100 million words. This year, the challenge was held as part of an expanded BabyLM Workshop that invited paper submissions on topics relevant to the BabyLM effort, including sample-efficient pretraining and cognitive modeling for LMs. For the challenge, we kept the text-only and text–image tracks from previous years, but also introduced a new interaction track, where student models are allowed to learn from feedback from larger teacher models. Furthermore, we introduce a new set of evaluation tasks to assess the “human likeness” of models on a cognitive and linguistic level, limit the total amount of training compute allowed, and measure performance on intermediate checkpoints. We observe that new training objectives and architectures tend to produce the best-performing approaches, and that interaction with teacher models can yield high-quality language models. The strict and interaction tracks saw submissions that outperformed the best-performing methods from previous years. We do not observe a complete correlation between training FLOPs and performance. %, suggesting that some methods can produce real gains beyond allowing us to spend more compute. This year’s BabyLM Challenge shows that there is still room to innovate in a data-constrained setting, and that community-driven research can yield actionable insights for language modeling.

We investigate whether pre-training exclusively on dialogue data results in formally and functionally apt small language models. Based on this pre-trained llamalogue model, we employ a variety of fine-tuning strategies to enforce “more communicative” text generations by our models. Although our models underperform on most standard BabyLM benchmarks, they excel at dialogue continuation prediction in a minimal pair setting. While PPO fine-tuning has mixed to adversarial effects on our models, DPO fine-tuning further improves their performance on our custom dialogue benchmark.

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CLASS-IT: Conversational and Lecture-Aligned Small-Scale Instruction Tuning for BabyLMs
Luca Capone | Alessandro Bondielli | Alessandro Lenci

This work investigates whether small-scale LMs can benefit from instruction tuning (IT). We compare conversational and question–answering IT datasets, applied either in a merged or sequential curriculum, using decoder-only models with 100M and 140M parameters. Evaluation spans both fine-tuning (SuperGLUE) and zero-shot (BLiMP, EWoK, WUGs, entity tracking, and psycholinguistic correlation) settings. Results show that IT yields small but consistent gains in fine-tuning scenarios, with sequential curricula outperforming merged data; however, improvements do not consistently transfer to zero-shot tasks, suggesting a trade-off between interaction-focused adaptation and broad linguistic generalization. These results highlight both the potential and the constraints of adapting human-inspired learning strategies to low-resource LMs, and point toward hybrid, curriculum-based approaches for enhancing generalization under ecological training limits.

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Mask and You Shall Receive: Optimizing Masked Language Modeling For Pretraining BabyLMs
Lukas Edman | Alexander Fraser

We describe our strategy for the 2025 edition of the BabyLM Challenge. Our main contribution is that of an improved form of Masked Language Modeling (MLM), which adapts the probabilities of the tokens masked according to the model’s ability to predict them. The results show a substantial increase in performance on (Super)GLUE tasks over the standard MLM. We also incorporate sub-token embeddings, finding that this increases the model’s morphological generalization capabilities. Our submission beats the baseline in the strict-small track.

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Once Upon a Time: Interactive Learning for Storytelling with Small Language Models
Jonas Mayer Martins | Ali Hamza Bashir | Muhammad Rehan Khalid | Lisa Beinborn

Children efficiently acquire language not just by listening, but by interacting with others in their social environment. Conversely, large language models are typically trained with next-word prediction on massive amounts of text. Motivated by this contrast, we investigate whether language models can be trained with less data by learning not only from next-word prediction but also from high-level, cognitively inspired feedback. We train a student model to generate stories, which a teacher model rates on readability, narrative coherence, and creativity. By varying the amount of pretraining before the feedback loop, we assess the impact of this interactive learning on formal and functional linguistic competence. We find that the high-level feedback is highly data efficient: With just 1 M words of input in interactive learning, storytelling skills can improve as much as with 410 M words of next-word prediction.

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You are an LLM teaching a smaller model everything you know: Multi-task pretraining of language models with LLM-designed study plans
Wiktor Kamzela | Mateusz Lango | Ondrej Dusek

This paper proposes a multi-task pre-training of language models without any text corpora.The method leverages an existing Large Language Model (LLM) to generate a diverse corpus containing training data for 56 automatically designed tasks and uses generated labels to enhance the training signal.The method does not rely on hidden states or even output distributions of the teacher model, so may be employed in scenarios when the teacher LLM is available only through an API.The conducted experiments show that models trained on the proposed synthetic corpora achieve competitive or superior performance compared to those trained on same-sized human-written texts.

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Active Curriculum Language Modeling over a Hybrid Pre-training Method
Eleni Fysikoudi | Sharid Loáiciga | Asad B. Sayeed

We apply the Active Curriculum Language Modeling (ACLM) method to the constrained pretraining setting of the 2025 BabyLM Challenge, where models are limited by both data and compute budgets. Using GPT-BERT (Charpentier and Samuel, 2024) as the base architecture, we investigate the impact of surprisal-based example selection for constructing a training curriculum. In addition, we conduct a targeted hyperparameter search over tokenizer size and batch size. Our approach yields stable pretrained models that surpass the official baseline on multiple evaluation tasks, demonstrating ACLM’s potential for improving performance and generalization in low-resource pretraining scenarios.

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Linguistic Units as Tokens: Intrinsic and Extrinsic Evaluation with BabyLM
Achille Fusco | Maria Letizia Piccini Bianchessi | Tommaso Sgrizzi | Asya Zanollo | Cristiano Chesi

Tokenization is often treated as a preprocessing step, yet in data-limited settings it directly shapes what a model can learn. We compare four segmentation strategies in the BabyLM Challenge: frequency-based BPE, morphology-aware MorPiece and ParadigmFinder, and syllable-based SylliTok. Evaluation combines two perspectives. First, an intrinsic test on the SIGMORPHON 2022 segmentation benchmark, adapted to English, measures how closely each tokenizer aligns with morpheme boundaries. Second, extrinsic tests train GPT-2 on the 10M BabyLM corpus and evaluate on the 2025 benchmark. No single tokenizer dominates. BPE remains strong on syntax-heavy tasks. ParadigmFinder excels in semantic composition and age-of-acquisition alignment. MorPiece shows advantages in discourse tracking. Morphology-aware tokenizers achieve the best intrinsic segmentation scores, and these gains translate into more robust generalisation in comprehension tasks. These results highlight tokenization as a core modeling decision, with direct consequences for compression, morphology, and the path to humanlike learning.

Human children acquire language from a substantially smaller amount of linguistic input than that typically required for training large language models (LLMs). This gap motivates the search for more efficient pre-training methods. Inspired by child development, curriculum learning, which progresses from simple to complex data, has been widely adopted. In this study, we propose a pre-training framework that mirrors child language acquisition, advancing step by step from words to sentences while retaining prior knowledge. We investigate whether this improves retention and efficiency under limited resources. Our approach is implemented through four components: (i) a curriculum-aligned dataset, (ii) a batch-wise convergence loop, (iii) a distance-controlled loss to mitigate forgetting, and (iv) a constraint-controlled optimizer for stability. Experiments on the BabyLM benchmark show that the proposed method performs slightly below the official baselines in overall accuracy, with larger gaps on grammar-oriented evaluations such as BLiMP. Nonetheless, it yields small but consistent gains on morphology- and discourse-related tasks (e.g., WUG-ADJ, Entity Tracking), suggesting that the approach affects different linguistic aspects unevenly under limited data conditions.

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Pretraining Language Models with LoRA and Artificial Languages
Nalin Kumar | Mateusz Lango | Ondrej Dusek

Large language models (LLMs) require a substantial amount of training data, which contrasts with the data-efficient learning observed in humans. In our submission to the BabyLM Challenge, we address this disparity by proposing a parameter-efficient pretraining approach for language acquisition from limited data. Our approach involves initializing the model with token embeddings trained by a shallow model, followed by tuning the non-embedding parameters with non-linguistic data to introduce structural biases. Then, we freeze the resulting model and pretrain it on the 10M-token BabyLM corpus using LoRA adapters. Experiments on small corpora demonstrate that our approach improves upon classic pretraining of the entire model.

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Masked Diffusion Language Models with Frequency-Informed Training
Despoina Kosmopoulou | Efthymios Georgiou | Vaggelis Dorovatas | Georgios Paraskevopoulos | Alexandros Potamianos

We present a masked diffusion language modeling framework for data-efficient training for the BabyLM 2025 Challenge. Our approach applies diffusion training objectives to language modeling under strict data constraints, incorporating frequency-informed masking that prioritizes learning from rare tokens while maintaining theoretical validity. We explore multiple noise scheduling strategies, including two-mode approaches, and investigate different noise weighting schemes within the NELBO objective. We evaluate our method on the BabyLM benchmark suite, measuring linguistic competence, world knowledge, and human-likeness. Results show performance competitive to hybrid autoregressive-masked baselines, demonstrating that diffusion-based training offers a viable alternative for data-restricted language learning.

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MoEP: Modular Expert Paths for Sample-Efficient Language Modeling
Joonas Tapaninaho

Training language models under tight compute budgets with small training datasets remains challenging for dense decoder-only Transformers, where every token activates the full stack of model parameters. We introduce MoEP (Modular Expert Paths), a sparse decoder-only architecture that enables more selective token activation, which increases model performance and accelerates learning without increasing the total number of parameters. We show that combining model parallelism with Mixture-of-Experts (MoE) style linear projections and a lightweight top-k router outperforms the GPT-2 baseline and stabilizes evaluation performance more quickly.

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RecombiText: Compositional Data Augmentation for Enhancing LLM Pre-Training Datasets in Low-Resource Scenarios
Alexander Tampier | Lukas Thoma | Loris Schoenegger | Benjamin Roth

We introduce RecombiText Augmentation (RTA), a novel purely statistical NLP method for compositional data augmentation for data-efficient LLM pre-training in low-resource scenarios. RTA identifies lexically and semantically similar sentences within the corpus and generates synthetic sentence pairs from them while preserving underlying patterns from the corpus. We pre-train GPT-2 and RoBERTa language models on a domain-specific, low-resource corpus of 10 million words, with different proportions of augmented data. We compare our RTA-augmented model variants to a baseline model trained on the full original dataset. Zero-shot results show that the language models pre-trained on synthetic data improve in entity tracking, self-paced reading, and morphological generalization benchmarks. In other tasks, the performance is comparable to the baseline model. We demonstrate that it is possible to expand low-resource datasets by two- to four-fold without compromising benchmark performance, solely through statistical processing of the available data.

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bib (full) Proceedings of the 8th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP

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CE-Bench: Towards a Reliable Contrastive Evaluation Benchmark of Interpretability of Sparse Autoencoders
Alex Gulko | Yusen Peng | Sachin Kumar

Sparse autoencoders (SAEs) are a promising approach for uncovering interpretable features in large language models (LLMs). While several automated evaluation methods exist for SAEs, most rely on external LLMs. In this work, we introduce CE-Bench, a novel and lightweight contrastive evaluation benchmark for sparse autoencoders, built on a curated dataset of contrastive story pairs. We conduct comprehensive evaluation studies to validate the effectiveness of our approach. Our results show that CE-Bench reliably measures the interpretability of sparse autoencoders and aligns well with existing benchmarks without requiring an external LLM judge, achieving over 70% Spearman correlation with results in SAEBench. The official implementation and evaluation dataset are open-sourced and publicly available.

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Char-mander Use mBackdoor! A Study of Cross-lingual Backdoor Attacks in Multilingual LLMs
Himanshu Beniwal | Sailesh Panda | Birudugadda Srivibhav | Mayank Singh

We explore Cross-lingual Backdoor ATtacks (X-BAT) in multilingual Large Language Models (mLLMs), revealing how backdoors inserted in one language can automatically transfer to others through shared embedding spaces. Using toxicity classification as a case study, we demonstrate that attackers can compromise multilingual systems by poisoning data in a single language, with rare and high-occurring tokens serving as specific, effective triggers. Our findings reveal a critical vulnerability that affects the model’s architecture, leading to a concealed backdoor effect during the information flow. Our code and data are publicly available at https://github.com/himanshubeniwal/X-BAT.

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Evil twins are not that evil: Qualitative insights into machine-generated prompts
Nathanaël Carraz Rakotonirina | Corentin Kervadec | Francesca Franzon | Marco Baroni

It has been widely observed that language models (LMs) respond in predictable ways to algorithmically generated prompts that are seemingly unintelligible. This is both a sign that we lack a full understanding of how LMs work, and a practical challenge, because opaqueness can be exploited for harmful uses of LMs, such as jailbreaking. We present the first thorough analysis of opaque machine-generated prompts, or autoprompts, pertaining to 6 LMs of different sizes and families. We find that machine-generated prompts are characterized by a last token that is often intelligible and strongly affects the generation. A small but consistent proportion of the previous tokens are prunable, probably appearing in the prompt as a by-product of the fact that the optimization process fixes the number of tokens. The remaining tokens fall into two categories: filler tokens, which can be replaced with semantically unrelated substitutes, and keywords, that tend to have at least a loose semantic relation with the generation, although they do not engage in well-formed syntactic relations with it. Additionally, human experts can reliably identify the most influential tokens in an autoprompt a posteriori, suggesting these prompts are not entirely opaque. Finally, some of the ablations we applied to autoprompts yield similar effects in natural language inputs, suggesting that autoprompts emerge naturally from the way LMs process linguistic inputs in general.

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Steering Prepositional Phrases in Language Models: A Case of with-headed Adjectival and Adverbial Complements in Gemma-2
Stefan Arnold | Rene Gröbner

Language Models, when generating prepositional phrases, must often decide for whether their complements functions as an instrumental adjunct (describing the verb adverbially) or an attributive modifier (enriching the noun adjectivally), yet the internal mechanisms that resolve this split decision remain poorly understood. In this study, we conduct a targeted investigation into Gemma-2 to uncover and control the generation of prepositional complements. We assemble a prompt suite containing with-headed prepositional phrases whose contexts equally accommodate either an instrumental or attributive continuation, revealing a strong preference for an instrumental reading at a ratio of 3:4. To pinpoint individual attention heads that favor instrumental over attributive complements, we project activations into the vocabulary space. By scaling the value vector of a single attention head, we can shift the distribution of functional roles of complements, attenuating instruments to 33% while elevating attributes to 36%.

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The Comparative Trap: Pairwise Comparisons Amplifies Biased Preferences of LLM Evaluators
Hawon Jeong | ChaeHun Park | Jimin Hong | Hojoon Lee | Jaegul Choo

As large language models (LLMs) are increasingly used as evaluators for natural language generation tasks, ensuring unbiased assessments is essential. However, LLM evaluators often display biased preferences, such as favoring verbosity and authoritative tones.Our empirical analysis reveals that these biases are exacerbated in pairwise evaluation, where LLMs directly compare two outputs and easily prioritize superficial attributes. In contrast, pointwise evaluation, which assesses outputs independently, is less susceptible to such bias because each output is judged in isolation. To address the limitations of the pairwise evaluation, we introduce a novel evaluation method, PRePair, which integrates pointwise reasoning within a pairwise framework. PRePair effectively alleviates biased preference, improving performance on the adversarial benchmark (LLMBar) while outperforming pointwise evaluation on the standard benchmark (MT-Bench).

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Not a nuisance but a useful heuristic: Outlier dimensions favor frequent tokens in language models
Iuri Macocco | Nora Graichen | Gemma Boleda | Marco Baroni

We study last-layer outlier dimensions, i.e. dimensions that display extreme activations for the majority of inputs. We show that outlier dimensions arise in many different modern language models, and trace their function back to the heuristic of constantly predicting frequent words. We further show how a model can block this heuristic when it is not contextually appropriate, by assigning a counterbalancing weight mass to the remaining dimensions, and we investigate which model parameters boost outlier dimensions and when they arise during training. We conclude that outlier dimensions are a specialized mechanism discovered by many distinct models to implement a useful token prediction heuristic.

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Language Dominance in Multilingual Large Language Models
Nadav Shani | Ali Basirat

This paper investigates the language dominance hypothesis in multilingual large language models (LLMs), which posits that cross-lingual understanding is facilitated by an implicit translation into a dominant language seen more frequently during pretraining. We propose a novel approach to quantify how languages influence one another in a language model. By analyzing the hidden states across intermediate layers of language models, we model interactions between language-specific embedding spaces using Gaussian Mixture Models. Our results reveal only weak signs of language dominance in middle layers, affecting only a fraction of tokens. Our findings suggest that multilingual processing in LLMs is better explained by language-specific and shared representational spaces rather than internal translation into a single dominant language.

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Interpreting Language Models Through Concept Descriptions: A Survey
Nils Feldhus | Laura Kopf

Understanding the decision-making processes of neural networks is a central goal of mechanistic interpretability. In the context of Large Language Models (LLMs), this involves uncovering the underlying mechanisms and identifying the roles of individual model components such as neurons and attention heads, as well as model abstractions such as the learned sparse features extracted by Sparse Autoencoders (SAEs). A rapidly growing line of work tackles this challenge by using powerful generator models to produce open-vocabulary, natural language concept descriptions for these components. In this paper, we provide the first survey of the emerging field of concept descriptions for model components and abstractions. We chart the key methods for generating these descriptions, the evolving landscape of automated and human metrics for evaluating them, and the datasets that underpin this research. Our synthesis reveals a growing demand for more rigorous, causal evaluation. By outlining the state of the art and identifying key challenges, this survey provides a roadmap for future research toward making models more transparent.

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Investigating ReLoRA: Effects on the Learning Dynamics of Small Language Models
Yuval Weiss | David Demitri Africa | Paula Buttery | Richard Diehl Martinez

Parameter-efficient methods like LoRA have revolutionised large language model (LLM) fine-tuning. ReLoRA extends this idea to pretraining by repeatedly merging and reinitialising low-rank adapters, increasing cumulative rank while keeping updates cheap. This aligns well with observations that high-capacity models learn through locally low-rank trajectories that expand over time. By contrast, recent work suggests that small language models (SLMs) exhibit rank deficiencies and under-utilise their available dimensionality. This raises a natural question: can ReLoRA’s rank-expanding update rule steer SLMs toward healthier learning dynamics, mitigating rank bottlenecks in a capacity-constrained regime? We argue SLMs are an ideal testbed: they train quickly, enable controlled ablations, and make rank phenomena more measurable. We present the first systematic study of ReLoRA in SLMs (11M-66M parameters), evaluating both performance and learning dynamics. Across loss, Paloma perplexity, and BLiMP, we find that ReLoRA underperforms full-rank training, with gaps widening at larger scales. Analysis of proportional effective rank and condition numbers shows that ReLoRA amplifies existing rank deficiencies and induces ill-conditioned updates early in training. Our results suggest that while ReLoRA’s merge-and-restart strategy can expand ranks in larger models, it does not straightforwardly translate to capacity-limited SLMs, motivating adaptive-rank or hybrid-rank approaches for low-compute pretraining.

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When LRP Diverges from Leave-One-Out in Transformers
Weiqiu You | Siqi Zeng | Yao-Hung Hubert Tsai | Makoto Yamada | Han Zhao

Leave-One-Out (LOO) provides an intuitive measure of feature importance but is computationally prohibitive. While Layer-Wise Relevance Propagation (LRP) offers a potentially efficient alternative, its axiomatic soundness in modern Transformers remains under-examined. In this work, we first show that the bilinear propagation rules used in recent advances of AttnLRP violate implementation invariance. We prove this analytically and confirm it empirically in linear attention layers. Second, we also revisit CP-LRP as a diagnostic baseline and find that bypassing relevance propagation through the softmax layer—back-propagating relevance only through the value matrices—significantly improves alignment with LOO, particularly in the middle-to-late Transformer layers. Overall, our results suggest that (i) bilinear factorization sensitivity and (ii) softmax propagation error potentially jointly undermine LRP’s ability to approximate LOO in Transformers.

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Understanding the Side Effects of Rank-One Knowledge Editing
Ryosuke Takahashi | Go Kamoda | Benjamin Heinzerling | Keisuke Sakaguchi | Kentaro Inui

This study conducts a detailed analysis of the side effects of rank-one knowledge editing using language models with controlled knowledge. The analysis focuses on each element of knowledge triples (subject, relation, object) and examines two aspects: “knowledge that causes large side effects when edited” and “knowledge that is affected by the side effects.” Our findings suggest that editing knowledge with subjects that have relationships with numerous objects or are robustly embedded within the LM may trigger extensive side effects. Furthermore, we demonstrate that the similarity between relation vectors, the density of object vectors, and the distortion of knowledge representations are closely related to how susceptible knowledge is to editing influences. The findings of this research provide new insights into the mechanisms of side effects in LM knowledge editing and indicate specific directions for developing more effective and reliable knowledge editing methods.

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Emergent Convergence in Multi-Agent LLM Annotation
Angelina Parfenova | Alexander Denzler | Jürgen Pfeffer

Large language models (LLMs) are increasingly deployed in collaborative settings, yet little is known about how they coordinate when treated as black-box agents. We simulate 7,500 multi-agent, multi-round discussions in an inductive coding task, generating over 125,000 utterances that capture both final annotations and their interactional histories. We introduce process-level metrics—code stability, semantic self-consistency, and lexical confidence—alongside sentiment and convergence measures, to track coordination dynamics. To probe deeper alignment signals, we analyze the evolving geometry of output embeddings, showing that intrinsic dimensionality declines over rounds, suggesting semantic compression. The results reveal that LLM groups converge lexically and semantically, develop asymmetric influence patterns, and exhibit negotiation-like behaviors despite the absence of explicit role prompting. This work demonstrates how black-box interaction analysis can surface emergent coordination strategies, offering a scalable complement to internal probe-based interpretability methods.

Large Language Models (LLMs) achieve impressive natural language processing performance but can memorize and leak Personally Identifiable Information (PII), posing serious privacy risks. Existing mitigation strategies—such as differential privacy and neuron-level interventions—often degrade utility or fail to reliably prevent leakage. We present PrivacyScalpel, a privacy-preserving framework that leverages LLM interpretability to identify and suppress PII leakage while preserving performance. PrivacyScalpel operates in three stages: (1) Feature Probing to locate model layers encoding PII-rich representations; (2) Sparse Autoencoding using a k-Sparse Autoencoder (k-SAE) to disentangle and isolate privacy-sensitive features; and (3) Feature-Level Interventions via targeted ablation and vector steering to reduce leakage. Experiments on Gemma2-2B and Llama2-7B fine-tuned with the Enron dataset show that PrivacyScalpel reduces email leakage from 5.15% to 0.0% while retaining over 99.4% of the original utility. Compared to neuron-level methods, our approach achieves a superior privacy–utility trade-off, highlighting the effectiveness of targeting sparse, monosemantic features over polysemantic neurons. Beyond privacy gains, PrivacyScalpel offers interpretability insights into PII memorization mechanisms, contributing to safer and more transparent LLM deployment.

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Circuit-Tracer: A New Library for Finding Feature Circuits
Michael Hanna | Mateusz Piotrowski | Jack Lindsey | Emmanuel Ameisen

Feature circuits aim to shed light on LLM behavior by identifying the features that are causally responsible for a given LLM output, and connecting them into a directed graph, or *circuit*, that explains how both each feature and each output arose. However, performing circuit analysis is challenging: the tools for finding, visualizing, and verifying feature circuits are complex and spread across multiple libraries.To facilitate feature-circuit finding, we introduce ‘circuit-tracer‘, an open-source library for efficient identification of feature circuits. ‘circuit-tracer‘ provides an integrated pipeline for finding, visualizing, annotating, and performing interventions on such feature circuits, tested with various model sizes, up to 14B parameters. We make ‘circuit-tracer‘ available to both developers and end users, via integration with tools such as Neuronpedia, which provides a user-friendly interface.

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The Lookahead Limitation: Why Multi-Operand Addition is Hard for LLMs
Tanja Baeumel | Josef Van Genabith | Simon Ostermann

Autoregressive large language models (LLMs) exhibit impressive performance across various tasks but struggle with simple arithmetic, such as additions of two or more operands. We show that this struggle arises from LLMs’ use of a simple one-digit lookahead heuristic, which forms an upper bound for LLM performance and accounts for characteristic error patterns in two-operand addition and failure in multi-operand addition, where the carry-over logic is more complex. Our probing experiments and digit-wise accuracy evaluation show that the evaluated LLMs fail precisely where a one-digit lookahead is insufficient to account for cascading carries. We analyze the impact of tokenization strategies on arithmetic performance and show that all investigated models, regardless of tokenization and size, are inherently limited in the addition of multiple operands due to their reliance on a one-digit lookahead heuristic. Our findings reveal limitations that prevent LLMs from generalizing to more complex numerical reasoning.

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Can LLMs Detect Ambiguous Plural Reference? An Analysis of Split-Antecedent and Mereological Reference
Dang Thi Thao Anh | Rick Nouwen | Massimo Poesio

Our goal is to study how LLMs represent and interpret plural reference in ambiguous and unambiguous contexts. We ask the following research questions: (1) Do LLMs exhibit human-like preferences in representing plural reference? and (2) Are LLMs able to detect ambiguity in plural anaphoric expressions and identify possible referents? To address these questions, we design a set of experiments, examining pronoun production using next-token prediction tasks, pronoun interpretation, and ambiguity detection using different prompting strategies. We then assess how comparable LLMs are to humans in formulating and interpreting plural reference. We find that LLMs are sometimes aware of possible referents of ambiguous pronouns. However, they do not always follow human reference when choosing between interpretations, especially when the possible interpretation is not explicitly mentioned. In addition, they struggle to identify ambiguity without direct instruction. Our findings also reveal inconsistencies in the results across different types of experiments.

Normative reasoning is a type of reasoning that involves normative or deontic modality, such as obligation and permission. While large language models (LLMs) have demonstrated remarkable performance across various reasoning tasks, their ability to handle normative reasoning remains underexplored. In this paper, we systematically evaluate LLMs’ reasoning capabilities in the normative domain from both logical and modal perspectives. Specifically, to assess how well LLMs reason with normative modals, we make a comparison between their reasoning with normative modals and their reasoning with epistemic modals, which share a common formal structure. To this end, we introduce a new dataset covering a wide range of formal patterns of reasoning in both normative and epistemic domains, while also incorporating non-formal cognitive factors that influence human reasoning. Our results indicate that, although LLMs generally adhere to valid reasoning patterns, they exhibit notable inconsistencies in specific types of normative reasoning and display cognitive biases similar to those observed in psychological studies of human reasoning. These findings highlight challenges in achieving logical consistency in LLMs’ normative reasoning and provide insights for enhancing their reliability. All data and code are released publicly at https://github.com/kmineshima/NeuBAROCO.

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A Theorem-Proving-Based Evaluation of Neural Semantic Parsing
Hayate Funakura | Hyunsoo Kim | Koji Mineshima

Graph-matching metrics such as Smatch are the de facto standard for evaluating neural semantic parsers, yet they capture surface overlap rather than logical equivalence. We reassess evaluation by pairing graph-matching with automated theorem proving. We compare two approaches to building parsers: supervised fine-tuning (T5-Small/Base) and few-shot in-context learning (GPT-4o/4.1/5), under normalized and unnormalized targets. We evaluate outputs using graph-matching, bidirectional entailment between source and target formulas with a first-order logic theorem prover, and well-formedness. Across settings, we find that models performing well on graph-matching often fail to produce logically equivalent formulas. Normalization reduces incidental target variability, improves well-formedness, and strengthens logical adequacy. Error analysis shows performance degrades with increasing formula complexity and with coordination, prepositional phrases, and passive voice; the dominant failures involve variable binding and indexing, and predicate naming. These findings highlight limits of graph-based metrics for reasoning-oriented applications and motivate logic-sensitive evaluation and training objectives together with simplified, normalized target representations. All data and code will be publicly released.

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A Pipeline to Assess Merging Methods via Behavior and Internals
Yutaro Sigrist | Andreas Waldis

Merging methods combine the weights of multiple language models (LMs) to leverage their capacities, such as for domain adaptation. While existing studies investigate merged models from a solely behavioral perspective, we offer the first comprehensive view by assessing and connecting their behavior and internals. We present a novel evaluation pipeline that first merges multiple parent LMs, and then evaluates the merged models in comparison to the initial ones based on their behavior on downstream tasks, like MMLU, and the internal encoded linguistic competence.We showcase this pipeline by assessing the merging of instruction fine-tuned with math- and code-adapted LMs from the Qwen2.5 family. Our results show that merging methods impacts behavior and internals differently. While the performance of merged models is typically between that of the two parent models, their encoded information about linguistic phenomena – particularly in morphology and syntax – can surpass the parent models.Moreover, we find weak ranking correlation between this behavior and internal evaluation. With our pipeline and initial results, we emphasize the need for more comprehensive evaluations of model merging methods to gain a faithful understanding of their capabilities and reliability, beyond potential superficial behavioral advances.

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From BERT to LLMs: Comparing and Understanding Chinese Classifier Prediction in Language Models
Ziqi Zhang | Jianfei Ma | Emmanuele Chersoni | You Jieshun | Zhaoxin Feng

Classifiers are an important and defining feature of the Chinese language, and their correct prediction is key to numerous educational applications. Yet, whether the most popular Large Language Models (LLMs) possess proper knowledge the Chinese classifiers is an issue that has largely remain unexplored in the Natural Language Processing (NLP) literature.To address such a question, we employ various masking strategies to evaluate the LLMs’ intrinsic ability, the contribution of different sentence elements, and the working of the attention mechanisms during prediction. Besides, we explore fine-tuning for LLMs to enhance the classifier performance.Our findings reveal that LLMs perform worse than BERT, even with fine-tuning. The prediction, as expected, greatly benefits from the information about the following noun, which also explains the advantage of models with a bidirectional attention mechanism such as BERT.

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Mechanistic Fine-tuning for In-context Learning
Hakaze Cho | Peng Luo | Mariko Kato | Rin Kaenbyou | Naoya Inoue

In-context Learning (ICL) utilizes structured demonstration-query inputs to induce few-shot learning on Language Models (LMs), which are not originally pre-trained on ICL-style data. To bridge the gap between ICL and pre-training, some approaches fine-tune LMs on large ICL-style datasets by an end-to-end paradigm with massive computational costs. To reduce such costs, in this paper, we propose Attention Behavior Fine-Tuning (ABFT), utilizing the previous findings on the inner mechanism of ICL, building training objectives on the attention scores instead of the final outputs, to force the attention scores to focus on the correct label tokens presented in the context and mitigate attention scores from the wrong label tokens. Our experiments on 9 modern LMs and 8 datasets empirically find that ABFT outperforms in performance, robustness, unbiasedness, and efficiency, with only around 0.01% data cost compared to the previous methods. Moreover, our subsequent analysis finds that the end-to-end training objective contains the ABFT objective, suggesting the implicit bias of ICL-style data to the emergence of induction heads. Our work demonstrates the possibility of controlling specific module sequences within LMs to improve their behavior, opening up the future application of mechanistic interpretability.

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Understanding How CodeLLMs (Mis)Predict Types with Activation Steering
Francesca Lucchetti | Arjun Guha

Large Language Models (LLMs) are widely used by software engineers for programming tasks. However, research shows that LLMs often lack a deep understanding of program semantics. Even minor changes to syntax, such as renaming variables, can significantly degrade performance across various tasks. In this work, we examine the task of *type prediction*: given a partially typed program, can a model predict a missing type annotations such that the resulting program is more typed? We construct a dataset of adversarial examples where models initially predict the correct types, but begin to fail after semantically irrelevant edits. This is problematic, as models should ideally generalize across different syntactic forms of semantically equivalent code. This lack of robustness suggests that models may have a shallow understanding of code semantics.Despite this, we provide evidence that LLMs do, in fact, learn robust mechanisms for type prediction—though these mechanisms often fail to activate in adversarial scenarios. By using *activation steering*, a method that manipulates a model’s internal activations to guide it toward using latent knowledge, we restore accurate predictions on adversarial inputs. We show that steering successfully activates a type prediction mechanism that is shared by both Python and TypeScript, and is more effective than prompting with in-context examples. Across five different models, our comprehensive evaluation demonstrates that LLMs can learn generalizable representations of code semantics that transfer across programming languages.

Contrastive explanations, which indicate why an AI system produced one output (the target) instead of another (the foil), are widely recognized in explainable AI as more informative and interpretable than standard explanations. However, obtaining such explanations for speech-to-text (S2T) generative models remains an open challenge. Adopting a feature attribution framework, we propose the first method to obtain contrastive explanations in S2T by analyzing how specific regions of the input spectrogram influence the choice between alternative outputs. Through a case study on gender translation in speech translation, we show that our method accurately identifies the audio features that drive the selection of one gender over another.

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Exploring Large Language Models’ World Perception: A Multi-Dimensional Evaluation through Data Distribution
Zhi Li | Jing Yang | Ying Liu

In recent years, large language models (LLMs) have achieved remarkable success across diverse natural language processing tasks. Nevertheless, their capacity to process and reflect core human experiences remains underexplored. Current benchmarks for LLM evaluation typically focus on a single aspect of linguistic understanding, thus failing to capture the full breadth of its abstract reasoning about the world. To address this gap, we propose a multidimensional paradigm to investigate the capacity of LLMs to perceive the world through temporal, spatial, sentimental, and causal aspects. We conduct extensive experiments by partitioning datasets according to different distributions and employing various prompting strategies. Our findings reveal significant differences and shortcomings in how LLMs handle temporal granularity, multi-hop spatial reasoning, subtle sentiments, and implicit causal relationships. While sophisticated prompting approaches can mitigate some of these limitations, substantial challenges persist in effectively capturing abstract human perception. We aspire that this work, which assesses LLMs from multiple perspectives of human understanding of the world, will guide more instructive research on the LLMs’ perception or cognition.

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On the Representations of Entities in Auto-regressive Large Language Models
Victor Morand | Josiane Mothe | Benjamin Piwowarski

Named entities are fundamental building blocks of knowledge in text, grounding factual information and structuring relationships within language. Despite their importance, it remains unclear how Large Language Models (LLMs) internally represent entities. Prior research has primarily examined explicit relationships, but little is known about entity representations themselves. We introduce entity mention reconstruction as a novel framework for studying how LLMs encode and manipulate entities. We investigate whether entity mentions can be generated from internal representations, how multi-token entities are encoded beyond last-token embeddings, and whether these representations capture relational knowledge. Our proposed method, leveraging task vectors, allows to consistently generate multi-token mentions from various entity representations derived from the LLMs hidden states. We thus introduce the Entity Lens, extending the logit-lens to predict multi-token mentions. Our results bring new evidence that LLMs develop entity-specific mechanisms to represent and manipulate any multi-token entities, including those unseen during training.

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Can Language Neuron Intervention Reduce Non-Target Language Output?
Suchun Xie | Hwichan Kim | Shota Sasaki | Kosuke Yamada | Jun Suzuki

Large language models (LLMs) often fail togenerate text in the intended target language,particularly in non-English interactions. Con-currently, recent work has explored LanguageNeuron Intervention (LNI) as a promising tech-nique for steering output language. In thispaper, we re-evaluate LNI in more practicalscenarios—specifically with instruction-tunedmodels and prompts that explicitly specify thetarget language. Our experiments show thatwhile LNI also shows potential in such practi-cal scenarios, its average effect is limited andunstable across models and tasks, with a 0.83%reduction in undesired language output and a0.1% improvement in performance. Our furtheranalysis identifies two key factors for LNI’slimitation: (1) LNI affects both the output lan-guage and the content semantics, making ithard to control one without affecting the other,which explains the weak performance gains. (2)LNI increases the target language token proba-bilities, but they often remain below the top-1generation threshold, resulting in failure to gen-erate the target language in most cases. Ourresults highlight both the potential and limi-tations of LNI, paving the way for future im-provements

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Fine-Grained Manipulation of Arithmetic Neurons
Wenyu Du | Rui Zheng | Tongxu Luo | Stephen Chung | Jie Fu

It is a longstanding challenge to understand how neural models perform mathematical reasoning. Recent mechanistic interpretability work indicates that large language models (LLMs) use a “bag of heuristics” in middle to late-layer MLP neurons for arithmetic, where each heuristic promotes logits for specific numerical patterns. Building on this, we aim for fine-grained manipulation of these heuristic neurons to causally steer model predictions towards specific arithmetic outcomes, moving beyond simply disrupting accuracy. This paper presents a methodology that enables the systematic identification and causal manipulation of heuristic neurons, which is applied to the addition task in this study. We train a linear classifier to predict heuristics based on activation values, achieving over 90% classification accuracy. The trained classifier also allows us to rank neurons by their importance to a given heuristic. By targeting a small set of top-ranked neurons (K=50), we demonstrate high success rates—over 80% for the ones place and nearly 70% for the tens place—in controlling addition outcomes. This manipulation is achieved by transforming the activation of identified neurons into specific target heuristics by zeroing out source-heuristic neurons and adjusting target-heuristic neurons towards their class activation centroids. We explain these results by hypothesizing that high-ranking neurons possess ‘cleaner channels’ for their heuristics, supported by Signal-to-Noise Ratio (SNR) analysis where these neurons show higher SNR scores. Our work offers a robust approach to dissect, causally test, and precisely influence LLM arithmetic, advancing understanding of their internal mechanisms.

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What Features in Prompts Jailbreak LLMs? Investigating the Mechanisms Behind Attacks
Nathalie Maria Kirch | Constantin Niko Weisser | Severin Field | Helen Yannakoudakis | Stephen Casper

Jailbreaks have been a central focus of research regarding the safety and reliability of large language models (LLMs), yet the mechanisms underlying these attacks remain poorly understood. While previous studies have predominantly relied on linear methods to detect jailbreak attempts and model refusals, we take a different approach by examining both linear and non-linear features in prompts that lead to successful jailbreaks. First, we introduce a novel dataset comprising 10,800 jailbreak attempts spanning 35 diverse attack methods. Leveraging this dataset, we train linear and non-linear probes on hidden states of open-weight LLMs to predict jailbreak success. Probes achieve strong in-distribution accuracy but transfer is attack-family-specific, revealing that different jailbreaks are supported by distinct internal mechanisms rather than a single universal direction. To establish causal relevance, we construct probe-guided latent interventions that systematically shift compliance in the predicted direction. Interventions derived from non-linear probes produce larger and more reliable effects than those from linear probes, indicating that features linked to jailbreak success are encoded non-linearly in prompt representations. Overall, the results surface heterogeneous, non-linear structure in jailbreak mechanisms and provide a prompt-side methodology for recovering and testing the features that drive jailbreak outcomes.

One of the main challenges in mechanistic interpretability is circuit discovery – determining which parts of a model perform a given task. We build on the Mechanistic Interpretability Benchmark (MIB) and propose three key improvements to circuit discovery. First, we use bootstrapping to identify edges with consistent attribution scores. Second, we introduce a simple ratio-based selection strategy to prioritize strong positive-scoring edges, balancing performance and faithfulness. Third, we replace the standard greedy selection with an integer linear programming formulation. Our methods yield more faithful circuits and outperform prior approaches across multiple MIB tasks and models.

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BlackboxNLP-2025 MIB Shared Task: IPE: Isolating Path Effects for Improving Latent Circuit Identification
Nicolò Brunello | Andrea Cerutti | Andrea Sassella | Mark James Carman

Understanding why large language models (LLMs) exhibit certain behaviors is the goal of mechanistic interpretability. One of the major tools employed by mechanistic interpretability is circuit discovery, i.e., identifying a subset of the model’s components responsible for a given task. We present a novel circuit discovery technique called IPE (Isolating Path Effects) that, unlike traditional edge-centric approaches, aims to identify entire computational paths (from input embeddings to output logits) responsible for certain model behaviors. Our method modifies the messages passed between nodes along a given path in such a way as to either precisely remove the effects of the entire path (i.e., ablate it) or to replace the path’s effects with those that would have been generated by a counterfactual input. IPE is different from current path-patching or edge activation-patching techniques since they are not ablating single paths, but rather a set of paths sharing certain edges, preventing more precise tracing of information flow. We apply our method to the well-known Indirect Object Identification (IOI) task, recovering the canonical circuit reported in prior work. On the MIB workshop leaderboard, we tested IOI and MCQA tasks on GPT2-small and Qwen2.5. For GPT2, path counterfactual replacement outperformed path ablation as expected and led to top-ranking results, while for Qwen, no significant differences were observed, indicating a need for larger experiments to distinguish the two approaches.

The Circuit Localization track of the Mechanistic Interpretability Benchmark (MIB) evaluates methods for localizing circuits within large language models (LLMs), i.e., subnetworks responsible for specific task behaviors. In this work, we investigate whether ensembling two or more circuit localization methods can improve performance. We explore two variants: parallel and sequential ensembling. In parallel ensembling, we combine attribution scores assigned to each edge by different methods—e.g., by averaging or taking the minimum or maximum value. In the sequential ensemble, we use edge attribution scores obtained via EAP-IG as a warm start for a more expensive but more precise circuit identification method, namely edge pruning. We observe that both approaches yield notable gains on the benchmark metrics, leading to a more precise circuit identification approach. Finally, we find that taking a parallel ensemble over various methods, including the sequential ensemble, achieves the best results. We evaluate our approach in the BlackboxNLP 2025 MIB Shared Task, comparing ensemble scores to official baselines across multiple model–task combinations.

Mechanistic interpretability (MI) seeks to uncover how language models (LMs) implement specific behaviors, yet measuring progress in MI remains challenging. The recently released Mechanistic Interpretability Benchmark (MIB) provides a standardized framework for evaluating circuit and causal variable localization. Building on this foundation, the BlackboxNLP 2025 Shared Task extends MIB into a community-wide reproducible comparison of MI techniques. The shared task features two tracks: circuit localization, which assesses methods that identify causally influential components and interactions driving model behavior, and causal variable localization, which evaluates approaches that map activations into interpretable features. With three teams spanning eight different methods, participants achieved notable gains in circuit localization using ensemble and regularization strategies for circuit discovery. With one team spanning two methods, participants achieved significant gains in causal variable localization using low-dimensional and non-linear projections to featurize activation vectors. The MIB leaderboard remains open; we encourage continued work in this standard evaluation framework to measure progress in MI research going forward.

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bib (full) Proceedings of the 6th Workshop on Computational Approaches to Discourse, Context and Document-Level Inferences (CODI 2025)

Recent progress in Natural Language Processing (NLP) has driven the creation of Large Language Models (LLMs) capable of tackling a vast range of tasks. A critical property of these models is their ability to handle large documents and process long token sequences, which has fostered the need for a robust evaluation methodology for long-text scenarios. To meet this requirement in the context of the Russian language, we present our benchmark consisting of 18 datasets designed to assess LLM performance in tasks such as information retrieval, knowledge extraction, machine reading, question answering, and reasoning. These datasets are categorized into four levels of complexity, enabling model evaluation across context lengths up to 128k tokens. To facilitate further research, we provide open-source datasets, a codebase, and a public leaderboard associated with the benchmark.

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Unpacking Ambiguity: The Interaction of Polysemous Discourse Markers and Non-DM Signals
Jingni Wu | Amir Zeldes

Discourse markers (DMs) like ‘but’ or ‘then’ are crucial for creating coherence in discourse, yet they are often replaced by or co-occur with non-DMs (‘in the morning’ can mean the same as ‘then’), and both can be ambiguous (‘since’ can refer to time or cause). The interaction mechanism between such signals remains unclear but pivotal for their disambiguation. In this paper we investigate the relationship between DM polysemy and co-occurrence of non-DM signals in English, as well as the influence of genre on these patterns. Using the framework of eRST, we propose a graded definition of DM polysemy, and conduct correlation and regression analyses to examine whether polysemous DMs are accompanied by more numerous and diverse non-DM signals. Our findings reveal that while polysemous DMs do co-occur with more diverse non-DMs, the total number of co-occurring signals does not necessarily increase. Moreover, genre plays a significant role in shaping DM-signal interactions.

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Enhancing the Automatic Classification of Metadiscourse in Low-Proficiency Learners’ Spoken and Written English Texts Using XLNet
Wenwen Guan | Marijn Alta | Jelke Bloem

This study aims to enhance the automatic identification and classification of metadiscourse markers in English texts, evaluating various large language models for the purpose. Metadiscourse is a commonly used rhetorical strategy in both written and spoken language to guide addressees through discourse. Due to its linguistic complexity and dependency on the context, automated metadiscourse classification is challenging. With a hypothesis that LLMs may handle complicated tasks more effectively than supervised machine learning approaches, we tune and evaluate seven encoder language models on the task using a dataset totalling 575,541 tokens and annotated with 24 labels. The results show a clear improvement over supervised machine learning approaches as well as an untuned Llama3.3-70B-Instruct baseline, with XLNet-large achieving an accuracy and F1-score of 0.91 and 0.93, respectively. However, four less frequent categories record F-scores below 0.5, highlighting the need for more balanced data representation.

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Entity Tracking in Small Language Models: An Attention-Based Study of Parameter-Efficient Fine-Tuning
Sungho Jeon | Michael Strube

The ability to track entities is fundamental for language understanding, yet the internal mechanisms governing this capability in Small Language Models (SLMs) are poorly understood. Previous studies often rely on indirect probing or complex interpretability methods, leaving a gap for lightweight diagnostics that connect model behavior to performance. To bridge this gap, we introduce a framework to analyze entity tracking by measuring the attention flow between entity and non-entity tokens within SLMs. We apply this to analyze models both before and after Parameter-Efficient Fine-Tuning (PEFT). Our analysis reveals two key findings. First, SLMs’ attentional strategies vary significantly with text type, but entities consistently receive a high degree of focus. Second, we show that PEFT – specifically QLoRA – dramatically improves classification performance on entity-centric tasks by increasing the model’s attentional focus on entity-related tokens. Our work provides direct evidence for how PEFT can refine a model’s internal mechanisms and establishes attention analysis as a valuable, lightweight diagnostic tool for interpreting and improving SLMs.

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Stance Detection on Nigerian 2023 Election Tweets Using BERT: A Low-Resource Transformer-Based Approach
Mahmoud Ahmad | Habeebah Kakudi

This paper investigates stance detection on Nigerian 2023 election tweets by comparing transformer-based and classical machine learning models. A balanced dataset of 2,100 annotated tweets was constructed, and BERT-base-uncased was fine-tuned to classify stances into Favor, Neutral, and Against. The model achieved 98.1% accuracy on an 80/20 split and an F1-score of 96.9% under 5-fold cross-validation. Baseline models such as Naïve Bayes, Logistic Regression, Random Forest, and SVM were also evaluated, with SVM achieving 97.6% F1. While classical methods remain competitive on curated datasets, BERT proved more robust in handling noisy, sarcastic, and ambiguous text, making it better suited for real-world applications in low-resource African NLP contexts.

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Code-switching in Context: Investigating the Role of Discourse Topic in Bilingual Speech Production
Debasmita Bhattacharya | Anxin Yi | Siying Ding | Julia Hirschberg

Code-switching (CSW) in speech is motivated by conversational factors across levels of linguistic analysis. While we know much about why speakers code-switch, there remains great scope for exploring how CSW occurs in speech, particularly within the discourse-level linguistic context. We build on prior work by asking: how are patterns of CSW influenced by different conversational contexts spanning Academic, Cultural, Personal, and Professional discourse topics? To answer this, we annotate a Mandarin-English spontaneous speech corpus, and analyze its discourse topics alongside various aspects of CSW production. We show that discourse topics interact significantly with utterance-level CSW, resulting in distinctive patterns of CSW presence, richness, language direction, and syntax that are uniquely associated with different contexts. Our work is the first to take such a context-sensitive approach to studying CSW, contributing to a broader understanding of the discourse topics that motivate speakers to code-switch in diverse ways.

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“Otherwise” in Context: Exploring Discourse Functions with Language Models
Guifu Liu | Bonnie Webber | Hannah Rohde

Discourse adverbials are key features of discourse coherence, but their function is often ambiguous. In this work, we investigate how the discourse function of otherwise varies in different contexts. We revise the function set in Rohde et al. (2018b) to account for a new meaning we have encountered. In turn, we create the “otherwise” corpus, a dataset of naturally occurring passages annotated for discourse functions, and identify lexical signals that make a function available with a corpus study. We define continuation acceptability, a metric based on surprisal to probe language models for what they take the function of otherwise to be in a given context. Our experiments show that one can improve function inference by focusing solely on tokens up to and including the head verb of the continuation (i.e., otherwise clause) that have the most varied surprisal across function-disambiguating discourse markers. Lastly, we observe that some of these tokens confirm lexical signals we found in our earlier corpus study, which provides some promising evidence to motivate future pragmatic studies in language models

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On the Role of Context for Discourse Relation Classification in Scientific Writing
Stephen Wan | Wei Liu | Michael Strube

With the increasing use of generative Artificial Intelligence (AI) methods to support science workflows, we are interested in the use of discourse-level information to find supporting evidence for AI generated scientific claims. A first step towards this objective is to examine the task of inferring discourse structure in scientific writing.In this work, we present a preliminary investigation of pretrained language model (PLM) and Large Language Model (LLM) approaches for Discourse Relation Classification (DRC), focusing on scientific publications, an under-studied genre for this task. We examine how context can help with the DRC task, with our experiments showing that context, as defined by discourse structure, is generally helpful. We also present an analysis of which scientific discourse relation types might benefit most from context.

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Zero-Shot Belief: A Hard Problem for LLMs
John Murzaku | Owen Rambow

We present two LLM-based approaches to zero-shot source-and-target belief prediction on FactBank: a unified system that identifies events, sources, and belief labels in a single pass, and a hybrid approach that uses a fine-tuned DeBERTa tagger for event detection. We show that multiple open-sourced, closed-source, and reasoning-based LLMs struggle with the task. We then argue that careful source normalization is crucial and provide a few-shot normalization method that improves alignment between predicted and gold-standard sources. Using the hybrid approach, we achieve new state-of-the-art results on FactBank and offer a detailed error analysis. Our approach is then tested on the Italian belief corpus ModaFact. Although we fall short of prior fine-tuned baselines, our zero-shot methods substantially narrow the gap, emphasizing the promise of hybrid pipelines for belief prediction beyond English. We conclude that integrated event tagging, careful prompting, and robust source normalization all jointly enable effective zero-shot belief models.

Understanding and interpreting culturally specific language remains a significant challenge for multilingual natural language processing (NLP) systems, particularly for less-resourced languages. To address this problem, this paper introduces PRONE, a novel dataset of 2,830 Nepali proverbs, and evaluates the performance of various language models (LMs) in two tasks: (i) identifying the correct meaning of a proverb from multiple choices, and (ii) categorizing proverbs into predefined thematic categories. The models, including both open-source and proprietary, were tested in zero-shot and few-shot settings with prompts in English and Nepali. While models like GPT-4o demonstrated promising results and achieved the highest performance among LMs, they still fall short of human-level accuracy in understanding and categorizing culturally nuanced content, highlighting the need for more inclusive NLP.

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Measuring Sexism in US Elections: A Comparative Analysis of X Discourse from 2020 to 2024
Anna Fuchs | Elisa Noltenius | Caroline Weinzierl | Bolei Ma | Anna-Carolina Haensch

Sexism continues to influence political campaigns, affecting public perceptions of candidates in a variety of ways. This paper examines sexist content on the social media platform X during the 2020 and 2024 US election campaigns, focusing on both male and female candidates. Two approaches, single-step and two-step categorization, were employed to classify tweets into different sexism categories. By comparing these approaches against a human-annotated subsample, we found that the single-step approach outperformed the two-step approach. Our analysis further reveals that sexist content increased over time, particularly between the 2020 and 2024 elections, indicating that female candidates face a greater volume of sexist tweets compared to their male counterparts. Compared to human annotations, GPT-4 struggled with detecting sexism, reaching an accuracy of about 51%. Given both the low agreement among the human annotators and the obtained accuracy of the model, our study emphasizes the challenges in detecting complex social phenomena such as sexism.

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Discourse Relation Recognition with Language Models Under Different Data Availability
Shuhaib Mehri | Chuyuan Li | Giuseppe Carenini

Large Language Models (LLMs) have demonstrated remarkable performance across various NLP tasks, yet they continue to face challenges in discourse relation recognition (DRR). Current state-of-the-art methods for DRR primarily rely on smaller pre-trained language models (PLMs). In this study, we conduct a comprehensive analysis of different approaches using both PLMs and LLMs, evaluating their effectiveness for DRR at multiple granularities and under different data availability settings. Our findings indicate that no single approach consistently outperforms the others, and we offer a general comparison framework to guide the selection of the most appropriate model based on specific DRR requirements and data conditions.

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EmbiText: Embracing Ambiguity by Annotation, Recognition and Generation of Pronominal Reference with Event-Entity Ambiguity
Amna Sheikh | Christian Hardmeier

Consider the example “The bird sang the nursery rhyme beautifully. It made everyone in the room smile”. The pronoun ‘it’ here refers either to the bird or to the event of singing. This example is inherently ambiguous. It cannot be meaningfully disambiguated as an event or entity reference, as both readings result in the same text meaning. This study introduces a new dataset EMBITEXT to preserve ambiguity in the language by navigating through the ambiguity surrounding the pronominal reference to the entity or event. Oftentimes, ambiguity does not necessarily need to be resolved but is modelled carefully. Furthermore, this study explores the capacity of LLMs (Llama, Mistral, Gemini, Claude AI) to embrace ambiguity in generating text that exhibit referential ambiguity via an In-Context learning approach. To evaluate of the dataset, RoBERTa was finetuned on this data to model ambiguity while simultaneously distinguishing between entity or event references. Results demonstrate EmbiText’s capacity to advance the ongoing NLP research by modelling linguistic ambiguity in computational environments instead of fully disambiguating it, thereby retaining diverse interpretations where resolution may alter meaning.

Understanding the strategies that make expert-led explanations effective is a core challenge in didactics and a key goal for explainable AI. To study this computationally, we introduce ReWIRED, a large corpus of explanatory dialogues annotated by education experts with fine-grained, span-level teaching acts across five levels of explainee knowledge. We use this resource to assess the capabilities of modern language models, finding that while few-shot LLMs struggle to label these acts, fine-tuning is a highly effective methodology. Moving beyond structural annotation, we propose and validate a suite of didactic quality metrics. We demonstrate that a prompt-based evaluation using an LLM as a “judge” is required to capture how the functional quality of an explanation aligns with the learner’s expertise – a nuance missed by simpler static metrics. Together, our dataset, modeling insights, and evaluation framework provide a comprehensive methodology to bridge pedagogical principles with computational discourse analysis.

This study addresses the fundamental task of discourse unit detection – the critical initial step in discourse parsing. We analyze how various discourse frameworks conceptualize and structure discourse units, with a focus on their underlying taxonomies and theoretical assumptions. While approaches to discourse segmentation vary considerably, the extent to which these conceptual divergences influence practical implementations remains insufficiently studied. To address this gap, we investigate similarities and differences in segmentation across several English datasets, segmented and annotated according to distinct discourse frameworks, using a simple, rule-based heuristics. We evaluate the effectiveness of rules with respect to gold-standard segmentation, while also checking variability and cross-framework generalizability. Additionally, we conduct a manual comparison of a sample of rule-based segmentation outputs against benchmark segmentation, identifying points of convergence and divergence.Our findings indicate that discourse frameworks align strongly at the level of segmentation: particular clauses consistently serve as the primary boundaries of discourse units. Discrepancies arise mainly in the treatment of other structures, such as adpositional phrases, appositions, interjections, and parenthesised text segments, which are inconsistently marked as separate discourse units across formalisms.

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Bridging Discourse Treebanks with a Unified Rhetorical Structure Parser
Elena Chistova

We introduce UniRST, the first unified RST-style discourse parser capable of handling 18 treebanks in 11 languages without modifying their relation inventories. To overcome inventory incompatibilities, we propose and evaluate two training strategies: Multi-Head, which assigns separate relation classification layer per inventory, and Masked-Union, which enables shared parameter training through selective label masking. We first benchmark mono-treebank parsing with a simple yet effective augmentation technique for low-resource settings. We then train a unified model and show that (1) the parameter efficient Masked-Union approach is also the strongest, and (2) UniRST outperforms 16 of 18 mono‐treebank baselines, demonstrating the advantages of a single-model, multilingual end-to-end discourse parsing across diverse resources.

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Corpus-Oriented Stance Target Extraction
Benjamin Steel | Derek Ruths

Understanding public discourse through the frame of stance detection requires effective extraction of issues of discussion, or stance targets. Yet current approaches to stance target extraction are limited, only focusing on a single document to single stance target mapping. We propose a broader view of stance target extraction, which we call corpus-oriented stance target extraction. This approach considers that documents have multiple stance targets, those stance targets are hierarchical in nature, and document stance targets should not be considered in isolation of other documents in a corpus. We develop a formalization and metrics for this task, propose a new method to address this task, and show its improvement over previous methods using supervised and unsupervised metrics, and human evaluation tasks. Finally, we demonstrate its utility in a case study, showcasing its ability to aid in reliably surfacing key issues of discussion in large-scale corpuses.

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Information-Theoretic and Prompt-Based Evaluation of Discourse Connective Edits in Instructional Text Revisions
Berfin Aktas | Michael Roth

We present a dataset of text revisions involving the deletion or replacement of discourse connectives. Manual annotation of a replacement subset reveals that only 19% of edits were judged either necessary or should be left unchanged, with the rest appearing optional. Surprisal metrics from GPT-2 token probabilities and prompt-based predictions from GPT-4.1 correlate with these judgments, particularly in such clear cases.

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bib (full) Proceedings of the 4th Shared Task on Discourse Relation Parsing and Treebanking (DISRPT 2025)

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Proceedings of the 4th Shared Task on Discourse Relation Parsing and Treebanking (DISRPT 2025)
Chloé Braud | Yang Janet Liu | Philippe Muller | Amir Zeldes | Chuyuan Li

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The DISRPT 2025 Shared Task on Elementary Discourse Unit Segmentation, Connective Detection, and Relation Classification
Chloé Braud | Amir Zeldes | Chuyuan Li | Yang Janet Liu | Philippe Muller

In 2025, we held the fourth iteration of the DISRPT Shared Task (Discourse Relation Parsing and Treebanking) dedicated to discourse parsing across formalisms. Following the success of the 2019, 2021, and 2023 tasks on Elementary Discourse Unit Segmentation, Connective Detection, and Relation Classification, this iteration added 13 new datasets, including three new languages (Czech, Polish, Nigerian Pidgin) and two new frameworks: the ISO framework and Enhanced Rhetorical Structure Theory, in addition to the previously included frameworks: RST, SDRT, DEP, and PDTB. In this paper, we review the data included in DISRPT 2025, which covers 39 datasets across 16 languages, survey and compare submitted systems, and report on system performance on each task for both treebanked and plain-tokenized versions of the data. The best systems obtain a mean accuracy of 71.19% for relation classification, a mean F1 of 91.57 (Treebanked Track) and 87.38 (Plain Track) for segmentation, and a mean F1 of 81.53 (Treebanked Track) and 79.92 (Plain Track) for connective identification. The data and trained models of several participants can be found at https://huggingface.co/multilingual-discourse-hub.

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DisCuT and DiscReT: MELODI at DISRPT 2025 Multilingual discourse segmentation, connective tagging and relation classification
Robin Pujol | Firmin Rousseau | Philippe Muller | Chloé Braud

This paper presents the results obtained by the MELODI team for the three tasks proposed within the DISRPT 2025 shared task on discourse: segmentation, connective identification, and relation classification. The competition involves corpora in various languages, in several underlying frameworks, and datasets are given with or without sentence segmentation. This year, for the ranked, closed track, the campaign adds as a constraint to train only one model for each task, with an upper bound on the size of the model (no more than 4B parameters).An additional open track authorizes any size of, possibly non public, models that will not be reproduced by the organizers and thus not ranked.We compared several fine-tuning approaches either based on encoder-only transformer-based models, or auto-regressive generative ones. To be able to train one model on the variety of corpora, we explored various ways of combining data – by framework, language or language groups, with different sequential orderings –, and the addition of features to guide the model. For the closed track, our final submitted system is based on XLM-RoBERTa large for relation identification, and on InfoXLM for segmentation and connective identification. Our experiments demonstrate that building a single, multilingual model does not necessarily degrade the performance compared to language-specific systems, with at best 64.06% for relation identification, 90.19% for segmentation and 81.15% for connective identification (on average on the development sets), results that are similar or higher that the ones obtained in previous campaigns.We also found that a generative approach could give even higher results on relation identification, with at best 64.65% on the dev sets.

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CLaC at DISRPT 2025: Hierarchical Adapters for Cross-Framework Multi-lingual Discourse Relation Classification
Nawar Turk | Daniele Comitogianni | Leila Kosseim

We present our submission to Task 3 (Discourse Relation Classification) of the DISRPT 2025 shared task. Task 3 introduces a unified set of 17 discourse relation labels across 39 corpora in 16 languages and six discourse frameworks, posing significant multilingual and cross‐formalism challenges. We first benchmark the task by fine‐tuning multilingual BERT‐based models (mBERT, XLM‐RoBERTa‐Base, and XLM‐RoBERTa‐Large) with two argument‐ordering strategies and progressive unfreezing ratios to establish strong baselines. We then evaluate prompt‐based large language models (namely Claude Opus 4.0) in zero‐shot and few‐shot settings to understand how LLMs respond to the newly proposed unified labels. Finally, we introduce HiDAC, a Hierarchical Dual‐Adapter Contrastive learning model. Results show that while larger transformer models achieve higher accuracy, the improvements are modest, and that unfreezing the top 75% of encoder layers yields performance comparable to full fine‐tuning while training far fewer parameters. Prompt‐based models lag significantly behind fine‐tuned transformers, and HiDAC achieves the highest overall accuracy (67.5%) while remaining more parameter‐efficient than full fine‐tuning.

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DeDisCo at the DISRPT 2025 Shared Task: A System for Discourse Relation Classification
Zhuoxuan Ju | Jingni Wu | Abhishek Purushothama | Amir Zeldes

This paper presents DeDisCo, Georgetown University’s entry in the DISRPT 2025 shared task on discourse relation classification. We test two approaches, using an mt5-based encoder and a decoder based approach using the openly available Qwen model. We also experiment on training with augmented dataset for low-resource languages using matched data translated automatically from English, as well as using some additional linguistic features inspired by entries in previous editions of the Shared Task. Our system achieves a macro-accuracy score of 71.28, and we provide some interpretation and error analysis for our results.

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HITS at DISRPT 2025: Discourse Segmentation, Connective Detection, and Relation Classification
Souvik Banerjee | Yi Fan | Michael Strube

This paper describes the submission of the HITS team to the DISRPT 2025 shared task. The shared task includes three sub-tasks: (1) discourse unit segmentation across formalisms, (2) cross-lingual discourse connective identification, and (3) cross-formalism discourse relation classification. This paper presents our strategies for the DISRPT 2025 Shared Task. In Task 1, our approach involves fine-tuning through multilingual joint training on linguistically motivated language groups. We incorporated two key techniques to improve model performance: a weighted loss function to address the task’s significant class imbalance and Fast Gradient Method (FGM) adversarial training to boost the model’s robustness. In task 2, our approach involves building an ensemble of three encoder models whose embeddings are smartly fused together with a multi-head attention layer. We also add Part-Of-Speech tags and dependency relations present in the training file as linguistic features. A CRF layer is added after the classification layer to account for dependencies between adjacent labels. To account for label imbalance, we use focal loss and label smoothing. This ensures our model is robust and flexible enough to handle different languages. In task 3, we use two-stage fine-tuning framework designed to transfer the nuanced reasoning capabilities of a very large “teacher” model to a compact “student” model so that the smaller model can learn complex discourse relationships. The fine-tuning process follows a curriculum learning framework. In such a framework the model learns to perform increasingly harder tasks. In our case, the model first learns to look at the discourse units and then predict the label followed by looking at Chain-Of-Thought reasoning for harder examples. This way it can learn to internalise such reasoning and increase prediction accuracy on the harder samples.

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SeCoRel: Multilingual Discourse Analysis in DISRPT 2025
Sobha Lalitha Devi | Pattabhi Rk Rao | Vijay Sundar Ram

The work presented here describes our participation in DISRPT 2025 shared task in three tasks, Task1: Discourse Unit Segmentation across Formalisms, Task 2: Discourse Connective Identification across Languages and Task 3: Discourse Relation Classification across Formalisms. We have fine-tuned XLM-RoBERTa, a language model to address these three tasks. We have come up with one single multilingual language model for each task. Our system handles data in both the formats .conllu and .tok and different discourse formalisms. We have obtained encouraging results. The performance on test data in the three tasks is similar to the results obtained for the development data.

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bib (full) Proceedings of The 10th Workshop on Financial Technology and Natural Language Processing

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LLM as a Guide: an Approach for Unsupervised Economic Relation Discovery in Administrative Documents
Thomas Sebbag | Solen Quiniou | Emmanuel Morin

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Zero-Shot Extraction of Stock Relationship Graphs with LLMs
Hao Zhou | Luis Felipe Costa Sperb | Tiejun Ma

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StockGenChaR: A Study on the Evaluation of Large Vision-Language Models on Stock Chart Captioning
Le Qiu | Emmanuele Chersoni

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Towards Efficient FinBERT via Quantization and Coreset for Financial Sentiment Analysis
Avinash Kumar Sharma | Aisha Hamad Hassan | Tushar Shinde

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LAVA: Logic-Aware Validation and Augmentation Framework for Large-Scale Financial Document Auditing
Ruoqi Shu | Xuhui Wang | Isaac Wang | Yanming Mai | Bo Wan

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Do Companies Reveal Their Own Fraud? - A Novel Data Set for Fraud Detection Based on 10-K Reports
Moustafa Amin | Matthias A

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Synthesizing Behaviorally-Grounded Reasoning Chains: A Data-Generation Framework for Personal Finance LLMs
Akhil Theerthala

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Detecting Evasive Answers in Financial Q&A: A Psychological Discourse Taxonomy and Lightweight Baselines
Khaled Al Nuaimi | Gautier Marti | Alexis Marchal | Andreas Henschel

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Enhancing Post Earnings Announcement Drift Measurement with Large Language Models
Samuel Hadlock | Jesse Roberts | Joohun Lee

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Natural Language Inference as a Judge: Detecting Factuality and Causality Issues in Language Model Self-Reasoning for Financial Analysis
Yilin Wu | Han Yuan | Li Zhang | Zheng Ma

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A Self-Improving Method for Generating Descriptions of Financial Data Quality Grading Using LLMs
Yang Zhao | Yohei Ikawa | Bishwaranjan Bhattacharjee

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Beyond Summaries: Multi-Agent Generation of Investment Reports with Text, Tables, and Charts
Weijie Yang | Junbo Peng | Weijie Yang

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From Earnings Calls to Investment Reports: Evaluating Role-based Multi-Agent LLM Systems
Ranjan Satapathy | Raphael Liew | Joyjit Chattorj | Erik Cambria | Rick Goh

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Structured Adversarial Synthesis: A Multi-Agent Framework for Generating Persuasive Financial Analysis from Earnings Call Transcripts
Saisab Sadhu | Biswajit Patra | Tannay Basu

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Meta Prompting for Analyst Report Generation: Turning Earnings Calls into Investment Guidance
Pulkit Chatwal | Mann Bajpai | Priyanshu Deswal | Harish Pratap Singh | Santosh Kumar Mishra

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LangKG at the FinNLP 2025 - Earnings2Insights: Task-Adaptive LLMs To Generate Human-Persuasive Investment Reports
Shivika Prasanna | Hui Su

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Agentic LLMs for Analyst-Style Financial Insights: An LLM Pipeline for Persuasive Financial Analysis
Gaurangi Sinha | Rajarajeswari Palacharla | Manoj Balaji Jagadeeshan

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Multi-Agent Collaboration for Investment Guidance: Earnings2Insights Report Generation
Mingrui Tan | Yang Liu | Kun Gao | Fei Gao | Yuting Song

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Digital Tongues: Internet Language, Collective Identity, and Implications for Human-Computer Interaction
Zi-Xiang Lin

Nowadays, internet languages, including emojis, memes, hashtags, and slang, have become vital in constructing online communities’ collective identities. However, all these forms of internet language can sometimes disempower people from other generations or cultures. This position paper presents an argument explaining how online forms of communication create social belonging for specific groups at the expense of users, and especially elderly people, due to interpretation hurdles. The present study aims to evaluate the relationship between the internet language and online collective identity, highlighting how patterns in internet language can inform human- computer interaction (HCI) by revealing how users’ express identity, inclusion, and exclusion online.

Online spaces provide individuals with the opportunity to engage in discussions on important topics and make collective decisions, regardless of their geographic location or time zone. However, without adequate support and careful design, such discussions often suffer from a lack of structure and civility in the exchange of opinions. Artificial intelligence (AI) offers a promising avenue for helping both participants and organizers in managing large-scale online participation processes. This paper introduces an extension of adhocracy+, a large-scale open-source participation platform. Our extension features two AI-supported debate modules designed to improve discussion quality and foster participant interaction.In a large-scale user study we examined the effects and usability of both modules. We report our findings in this paper. The extended platform is available at https://github.com/mabehrendt/discuss2.0.

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User-Centric Design Paradigms for Trust and Control in Human-LLM-Interactions: A Survey
Milena Belosevic

As LLMs become widespread, trust in their behavior becomes increasingly important. For NLP research, it is crucial to ensure that not only AI designers and developers, but also end users, are enabled to control the properties of trustworthy LLMs, such as transparency, privacy, or accuracy. However, involving end users in this process remains a practical challenge. Based on a design-centered survey of methods developed in recent papers from HCI and NLP venues, this paper proposes seven design paradigms that can be integrated in NLP research to enhance end-user control over the trustworthiness of LLMs. We discuss design gaps and challenges of applying these paradigms in NLP and propose future research directions.

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TripleCheck: Transparent Post-Hoc Verification of Biomedical Claims in AI-Generated Answers
Ana Valeria González | Sidsel Boldsen | Roland Hangelbroek

Retrieval Augmented Generation (RAG) has advanced Question Answering (QA) by connecting Large Language Models (LLMs) to external knowledge. However, these systems can still produce answers that are unsupported, lack clear traceability, or misattribute information — a critical issue in the biomedical domain where accuracy, trust and control are essential. We introduce TripleCheck, a post-hoc framework that breaks down an LLM’s answer into factual triples and checks each against both the retrieved context and a biomedical knowledge graph. By highlighting which statements are supported, traceable, or correctly attributed, TripleCheck enables users to spot gaps, unsupported claims, and misattributions, prompting more careful follow up. We present the TripleCheck framework, evaluate it on the SciFact benchmark, analyze its limitations, and share preliminary expert feedback. Results show that TripleCheck provides nuanced insight, potentially supporting greater trust and safer AI adoption in biomedical applications.

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Rethinking Search: A Study of University Students’ Perspectives on Using LLMs and Traditional Search Engines in Academic Problem Solving
Md. Faiyaz Abdullah Sayeedi | Md. Sadman Haque | Zobaer Ibn Razzaque | Robiul Awoul Robin | Sabila Nawshin

With the increasing integration of Artificial Intelligence (AI) in academic problem solving, university students frequently alternate between traditional search engines like Google and large language models (LLMs) for information retrieval. This study explores students’ perceptions of both tools, emphasizing usability, efficiency, and their integration into academic workflows. Employing a mixed-methods approach, we surveyed 109 students from diverse disciplines and conducted in-depth interviews with 12 participants. Quantitative analyses, including ANOVA and chi-square tests, were used to assess differences in efficiency, satisfaction, and tool preference. Qualitative insights revealed that students commonly switch between GPT and Google: using Google for credible, multi-source information and GPT for summarization, explanation, and drafting. While neither tool proved sufficient on its own, there was a strong demand for a hybrid solution. In response, we developed a prototype, a chatbot embedded within the search interface, that combines GPT’s conversational capabilities with Google’s reliability to enhance academic research and reduce cognitive load.

Accessing government welfare schemes in India remains difficult for emergent users—individuals with limited literacy, digital familiarity, or language support. This paper compares two mobile platforms that deliver the same scheme-related information but differ in interaction modality: myScheme, a government-built, form-based Android application, and Prabodhini, a voice-based conversational prototype powered by generative AI and Retrieval-Augmented Generation (RAG). Through a task-based comparative study with 15 low-income participants, we examine usability, task completion time, and user preference. Drawing on theories such as the Gulf of Execution and Zipf’s Law of Least Effort, we show that Prabodhini’s conversational design and support for natural language input better align with emergent users’ mental models and practices. Our findings highlight the value of multimodal, voice-first NLP systems for improving trust, access, and inclusion in public digital services. We discuss implications for designing accessible language technologies for marginalised populations.

Voice-controlled interfaces can support older adults in clinical contexts – with chatbots being a prime example – but reliable Automatic Speech Recognition (ASR) for underrepresented groups remains a bottleneck. This study evaluates state-of-the-art ASR models on language use of older Dutch adults, who interacted with the Welzijn.AI chatbot designed for geriatric contexts. We benchmark generic multilingual ASR models, and models fine-tuned for Dutch spoken by older adults, while also considering processing speed. Our results show that generic multilingual models outperform fine-tuned models, which suggests recent ASR models can generalise well out of the box to real-world datasets. Moreover, our results indicate that truncating generic models is helpful in balancing the accuracy-speed trade-off. Nonetheless, we also find inputs which cause a high word error rate and place them in context.

The advancement of mobile GUI agents has opened new opportunities for automating tasks on mobile devices. Training these agents requires large-scale high-quality data, which is prohibitively expensive when relying on human labor. Given the vast population of global mobile phone users, if automated data collection from them becomes feasible, the resulting data volume and the subsequently trained mobile agents could reach unprecedented levels. Nevertheless, two major challenges arise: (1) extracting user instructions without human intervention and (2) utilizing distributed user data while preserving privacy.To tackle these challenges, we propose MobileA3gent, a collaborative framework that trains mobile GUI Agents using decentralized self-sourced data from diverse users. The framework comprises two components, each targeting a specific challenge: (1) Auto-Annotation, which enables the automatic collection of high-quality datasets during users’ routine phone usage with minimal cost. (2) FedVLM-A, which enhances federated VLM training under non-IID distributions by incorporating adapted global aggregation based on both episode-level and step-level variability. Extensive experiments prove that MobileA3gent achieves superior performance over traditional approaches at only 1% of the cost, highlighting its potential for real-world applications. Our code is publicly available at: https://anonymous.4open.science/r/MobileA3gent-Anonymous.

This paper investigates the effectiveness of TikTok’s enforcement mechanisms for limiting the exposure of harmful content to youth accounts. We collect over 7000 videos, classify them as harmful vs not-harmful, and then simulate interactions using age-specific sockpuppet accounts through both passive and active engagement strategies. We also evaluate the performance of large language (LLMs) and vision-language models (VLMs) in detecting harmful content, identifying key challenges in precision and scalability. Preliminary results show minimal differences in content exposure between adult and youth accounts, raising concerns about the platform’s age-based moderation. These findings suggest that the platform needs to strengthen youth safety measures and improve transparency in content moderation.

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Predictive Modeling of Human Developers’ Evaluative Judgment of Generated Code as a Decision Process
Sergey Kovalchuk | Yanyu Li | Dmitriy Fedrushkov

The paper presents early results in the development of an approach to predictive modeling of human developer perceiving of code generated in question-answering scenarios with Large Language Model (LLM) applications. The study is focused on building a model for the description and prediction of human implicit behavior during evaluative judgment of generated code through evaluation of its consistency, correctness, and usefulness as subjective perceiving characteristics. We used Markov Decision Process (MDP) as a basic framework to describe the human developer and his/her perceiving. We consider two approaches (regression-based and classification-based) to identify MDP parameters so it can be used as an “artificial” developer for human-centered code evaluation. An experimental evaluation of the proposed approach was performed with survey data previously collected for several code generation LLMs in a question-answering scenario. The results show overall good performance of the proposed model in acceptance rate prediction (accuracy 0.82) and give promising perspectives for further development and application.

Large language models (LLMs) are increasingly embedded in development pipelines and the daily workflows of AI practitioners. However, their effectiveness depends on access to high-quality datasets that are sufficiently large, diverse, and contextually relevant. Existing datasets often fall short of these requirements, prompting the use of synthetic data (SD) generation. A critical step in this process is the creation of human seed examples, which guide the generation of SD tailored to specific tasks. We propose a participatory methodology for seed example generation, involving multidisciplinary teams in structured workshops to co-create examples aligned with Responsible AI principles. In a pilot study with a Responsible AI team, we facilitated hands-on activities to produce seed examples and evaluated the resulting data across three dimensions: diversity, sensibility, and relevance. Our findings suggest that participatory approaches can enhance the representativeness and contextual fidelity of synthetic datasets. We provide a reproducible framework to support NLP practitioners in generating high-quality seed data for LLM development and deployment

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How Well Can AI Models Generate Human Eye Movements During Reading?
Ivan Stebakov | Ilya Pershin

Eye movement analysis has become an essential tool for studying cognitive processes in reading, serving both psycholinguistic research and natural language processing applications aimed at enhancing language model performance. However, the scarcity of eye-tracking data and its limited generalizability constrain data-driven approaches. Synthetic scanpath generation offers a potential solution to these limitations. While recent advances in scanpath generation show promise, current literature lacks systematic evaluation frameworks that comprehensively assess models’ ability to reproduce natural reading gaze patterns. Existing studies often focus on isolated metrics rather than holistic evaluation of cognitive plausibility. This study presents a systematic evaluation of contemporary scanpath generation models, assessing their capacity to replicate natural reading behavior through comprehensive scanpath analysis. We demonstrate that while synthetic scanpath models successfully reproduce basic gaze patterns, significant limitations persist in capturing part-of-speech dependent gaze features and reading behaviors. Our cross-dataset comparison reveals performance degradation in three key areas: generalization across text genres, processing of long sentences, and reproduction of psycholinguistic effects. These findings underscore the need for more robust evaluation protocols and model architectures that better account for psycholinguistic complexity. Through detailed analysis of fixation sequences, durations, and reading patterns, we identify concrete pathways for developing more cognitively plausible scanpath generation models.

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Re:Member: Emotional Question Generation from Personal Memories
Zackary Rackauckas | Nobuaki Minematsu | Julia Hirschberg

We present Re:Member, a system that explores how emotionally expressive, memory-grounded interaction can support more engaging second language (L2) learning. By drawing on users’ personal videos and generating stylized spoken questions in the target language, Re:Member is designed to encourage affective recall and conversational engagement. The system aligns emotional tone with visual context, using expressive speech styles such as whispers or late-night tones to evoke specific moods. It combines WhisperX-based transcript alignment, 3-frame visual sampling, and Style-BERT-VITS2 for emotional synthesis within a modular generation pipeline. Designed as a stylized interaction probe, Re:Member highlights the role of affect and personal media in learner-centered educational technologies.

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Word Clouds as Common Voices: LLM-Assisted Visualization of Participant-Weighted Themes in Qualitative Interviews
Joseph T Colonel | Baihan Lin

Word clouds are a common way to summarize qualitative interviews, yet traditional frequency-based methods often fail in conversational contexts: they surface filler words, ignore paraphrase, and fragment semantically related ideas. This limits their usefulness in early-stage analysis, when researchers need fast, interpretable overviews of what participant actually said. We introduce ThemeClouds, an open-source visualization tool that uses large language models (LLMs) to generate thematic, participant-weighted word clouds from dialogue transcripts. The system prompts an LLM to identify concept-level themes across a corpus and then counts how many unique participants mention each topic, yielding a visualization grounded in breadth of mention rather than raw term frequency. Researchers can customize prompts and visualization parameters, providing transparency and control. Using interviews from a user study comparing five recording-device configurations (31 participants; 155 transcripts, Whisper ASR), our approach surfaces more actionable device concerns than frequency clouds and topic-modeling baselines (e.g., LDA, BERTopic). We discuss design trade-offs for integrating LLM assistance into qualitative workflows, implications for interpretability and researcher agency, and opportunities for interactive analyses such as per-condition contrasts (“diff clouds”).

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Time Is Effort: Estimating Human Post-Editing Time for Grammar Error Correction Tool Evaluation
Ankit Vadehra | Bill Johnson | Gene Saunders | Pascal Poupart

Text editing can involve several iterations of revision. Incorporating an efficient Grammar Error Correction (GEC) tool in the initial correction round can significantly impact further human editing effort and final text quality. This raises an interesting question to quantify GEC Tool usability: How much effort can the GEC Tool save users? We present the first large-scale dataset of post-editing (PE) time annotations and corrections for two English GEC test datasets (BEA19 and CoNLL14). We introduce Post-Editing Effort in Time (PEET) for GEC Tools as a human-focused evaluation scorer to rank any GEC Tool by estimating PE time-to-correct. Using our dataset, we quantify the amount of time saved by GEC Tools in text editing. Analyzing the edit type indicated that determining whether a sentence needs correction and edits like paraphrasing and punctuation changes had the greatest impact on PE time. Finally, comparison with human rankings shows that PEET correlates well with technical effort judgment, providing a new human-centric direction for evaluating GEC tool usability. We release our dataset and code at : https://github.com/ankitvad/PEET_Scorer.

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Hybrid Intelligence for Logical Fallacy Detection
Mariia Kutepova | Khalid Al Khatib

This study investigates the impact of Hybrid Intelligence (HI) on improving the detection of logical fallacies, addressing the pressing challenge of misinformation prevalent across communication platforms. Employing a between-subjects experimental design, the research compares the performance of two groups: one relying exclusively on human judgment and another supported by an AI assistant. Participants evaluated a series of statements, with the AI-assisted group utilizing a custom ChatGPT-based chatbot that provided real-time hints and clarifications. The findings reveal a significant improvement in fallacy detection with AI support, increasing from an F1-score of 0.76 in the human-only group to 0.90 in the AI-assisted group. Despite this enhancement, both groups struggled to accurately identify non-fallacious statements, highlighting the need to further refine how AI assistance is leveraged.

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Cognitive Feedback: Decoding Human Feedback from Cognitive Signals
Yuto Harada | Yohei Oseki

Alignment methods such as Direct Preference Optimization (DPO) have played a crucial role in enhancing the performance of large language models. However, conventional approaches typically require creating large amounts of explicit preference labels, which is costly, time-consuming, and demands sustained human attention.In this work, we propose Cognitive Preference Optimization (CPO), a novel alignment method that infers preferences from electroencephalography (EEG) signals recorded while annotators simply read text, eliminating the need for explicit labeling. To our knowledge, this is the first empirical investigation of EEG-based feedback as an alternative to conventional human annotations for aligning language models.Experiments on controlled sentiment generation show that CPO achieves performance comparable to explicit human feedback, suggesting that brain-signal-derived preferences can provide a viable, lower-burden pathway for language model alignment.

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Culturally-Aware Conversations: A Framework & Benchmark for LLMs
Shreya Havaldar | Young Min Cho | Sunny Rai | Lyle Ungar

Existing benchmarks that measure cultural adaptation in LLMs are misaligned with the actual challenges these models face when interacting with users from diverse cultural backgrounds. In this work, we introduce the first framework and benchmark designed to evaluate LLMs in realistic, multicultural conversational settings. Grounded in sociocultural theory, our framework formalizes how linguistic style — a key element of cultural communication — is shaped by situational, relational, and cultural context. We construct a benchmark dataset based on this framework, annotated by culturally diverse raters, and propose a new set of desiderata for cross-cultural evaluation in NLP: conversational framing, stylistic sensitivity, and subjective correctness. We evaluate today’s top LLMs on our benchmark and show that these models struggle with cultural adaptation in a conversational setting.

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From Regulation to Interaction: Expert Views on Aligning Explainable AI with the EU AI Act
Mahdi Dhaini | Lukas Ondrus | Gjergji Kasneci

Explainable AI (XAI) aims to support people who interact with high-stakes AI-driven decisions, and the EU AI Act mandates that users must be able to interpret system outputs appropriately. Although the Act requires users to interpret outputs and mandates human oversight, it offers no technical guidance for implementing explainability, leaving interpretability methods opaque to non-experts and compliance obligations unclear. To address these gaps, we interviewed eight experts to explore (1) how explainability is defined and perceived under the Act, (2) the practical and regulatory obstacles to XAI implementation, and (3) recommended solutions and future directions. Our findings reveal that experts view explainability as context- and audience-dependent, face challenges from regulatory vagueness and technical trade-offs, and advocate for domain-specific rules, hybrid methods, and user-centered explanations. These insights provide a basis for a potential framework to align XAI methods—particularly for AI and Natural Language Processing (NLP) systems—with regulatory requirements, and suggest actionable steps for policymakers and practitioners

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From Noise to Nuance: Enriching Subjective Data Annotation through Qualitative Analysis
Ruyuan Wan | Haonan Wang | Ting-Hao Kenneth Huang | Jie Gao

Subjective data annotation (SDA) plays an important role in many NLP tasks, including sentiment analysis, toxicity detection, and bias identification. Conventional SDA often treats annotator disagreement as noise, overlooking its potential to reveal deeper insights. In contrast, qualitative data analysis (QDA) explicitly engages with diverse positionalities and treats disagreement as a meaningful source of knowledge. In this position paper, we argue that human annotators are a key source of valuable interpretive insights into subjective data beyond surface-level descriptions. Through a comparative analysis of SDA and QDA methodologies, we examine similarities and differences in task nature (e.g., human’s role, analysis content, cost, and completion conditions) and practice (annotation schema, annotation workflow, annotator selection, and evaluation). Based on this comparison, we propose five practical recommendations for enabling SDA to capture richer insights. We demonstrate these recommendations in a reinforcement learning from human feedback (RLHF) case study and envision that our interdisciplinary perspective will offer new directions for the field.

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A Survey of LLM-Based Applications in Programming Education: Balancing Automation and Human Oversight
Griffin Pitts | Anurata Prabha Hridi | Arun Balajiee Lekshmi Narayanan

Novice programmers benefit from timely, personalized support that addresses individual learning gaps, yet the availability of instructors and teaching assistants is inherently limited. Large language models (LLMs) present opportunities to scale such support, though their effectiveness depends on how well technical capabilities are aligned with pedagogical goals. This survey synthesizes recent work on LLM applications in programming education across three focal areas: formative code feedback, assessment, and knowledge modeling. We identify recurring design patterns in how these tools are applied and find that interventions are most effective when educator expertise complements model output through human-in-the-loop oversight, scaffolding, and evaluation. Fully automated approaches are often constrained in capturing the pedagogical nuances of programming education, although human-in-the-loop designs and course-specific adaptation offer promising directions for future improvement. Future research should focus on improving transparency, strengthening alignment with pedagogy, and developing systems that flexibly adapt to the needs of varied learning contexts.

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Toward Human-Centered Readability Evaluation
Bahar İlgen | Georges Hattab

Text simplification is essential for making public health information accessible to diverse populations, including those with limited health literacy. However, commonly used evaluation metrics in Natural Language Processing (NLP)—such as BLEU, FKGL, and SARI—mainly capture surface-level features and fail to account for human-centered qualities like clarity, trustworthiness, tone, cultural relevance, and actionability. This limitation is particularly critical in high-stakes health contexts, where communication must be not only simple but also usable, respectful, and trustworthy. To address this gap, we propose the Human-Centered Readability Score (HCRS), a five-dimensional evaluation framework grounded in Human-Computer Interaction (HCI) and health communication research. HCRS integrates automatic measures with structured human feedback to capture the relational and contextual aspects of readability. We outline the framework, discuss its integration into participatory evaluation workflows, and present a protocol for empirical validation. This work aims to advance the evaluation of health text simplification beyond surface metrics, enabling NLP systems that align more closely with diverse users’ needs, expectations, and lived experiences.

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Exploring Gender Differences in Emoji Usage: Implications for Human-Computer Interaction
Zi-Xiang Lin

This study discusses the emojis employment that compensate for the absence of supralinguistic emotive cues in digital communication. Analyzing gender relations (Male-to-Male, Male-to-Female, Female-to-Male, Female-to-Female) as a social influence factor in emoji use, the research explores the use of anger-related emojis and their dual functions as emotion signals and intensifiers. Findings reveal women use more intense emojis toward men and less severe ones toward women, a pattern not observed in men when emphasizing emotions. Hence, the study contributes to the conceptual application of emotional expression via emojis within digital media, raising sentiments on gender variances and improving emotional intelligence in artificial intelligence systems to yield a more accurate human feeling interpretation.

In contemporary workplaces, meetings are essential for exchanging ideas and ensuring team alignment but often face challenges such as time consumption, scheduling conflicts, and inefficient participation. Recent advancements in Large Language Models (LLMs) have demonstrated their strong capabilities in natural language generation and reasoning, prompting the question- can LLMs effectively delegate participants in meetings? To explore this, we develop a prototype LLM-powered meeting delegate system and create a comprehensive benchmark using real meeting transcripts. Our evaluation shows GPT-4/4o balance active and cautious engagement, Gemini 1.5 Pro leans cautious, and Gemini 1.5 Flash and Llama3-8B/70B are more active. About 60% of responses capture at least one key point from the ground truth. Challenges remain in reducing irrelevant or repetitive content and handling transcription errors in real-world settings. We further validate the system through practical deployment and collect feedback. Our results highlight both the promise and limitations of LLMs as meeting delegates, providing insights for their real-world application in reducing meeting burden

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Exploring Gender Differences in Emoji Usage: Implications for Human-Computer Interaction
Arunima Maitra | Dorothea French | Katharina von der Wense

Large language models (LLMs) have revolutionized natural language generation across various applications. Although LLMs are highly capable in many domains, they sometimes produce responses that lack coherence or fail to align with conversational norms such as turn-taking, or providing relevant acknowledgments. Conversational LLMs are widely used, but evaluation often misses pragmatic aspects of dialogue. In this paper, we evaluate how LLM-generated dialogue compares to human conversation through the lens of dialogue acts, the functional building blocks of interaction. Using the Switchboard Dialogue Act (SwDA) corpus, we prompt two widely used open-source models, Llama 2 and Mistral, to generate responses under varying context lengths. We then automatically annotate the dialogue acts of both model and human responses with a BERT classifier and compare their distributions. Our experimental findings reveal that the distribution of dialogue acts generated by these models differs significantly from the distribution of dialogue acts in human conversation, indicating an area for improvement. Perplexity analysis further highlights that certain dialogue acts like Acknowledge (Backchannel) are harder for models to predict. While preliminary, this study demonstrates the value of dialogue act analysis as a diagnostic tool for human-LLM interaction, highlighting both current limitations and directions for improvement.

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bib (full) Proceedings of The 3rd Workshop on Mathematical Natural Language Processing (MathNLP 2025)

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Proceedings of The 3rd Workshop on Mathematical Natural Language Processing (MathNLP 2025)
Marco Valentino | Deborah Ferreira | Mokanarangan Thayaparan | Leonardo Ranaldi | Andre Freitas

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Syntactic Blind Spots: How Misalignment Leads to LLMs’ Mathematical Errors
Dane A Williamson | Yangfeng Ji | Matthew B. Dwyer

Large Language Models (LLMs) demonstrate strong mathematical problem-solving abilities but frequently fail on problems that deviate syntactically from their training distribution. We identify a systematic failure mode, syntactic blind spots, in which models misapply familiar reasoning strategies to problems that are semantically straightforward but phrased in unfamiliar ways. These errors are not due to gaps in mathematical competence, but rather reflect a brittle coupling between surface form and internal representation. To test this, we rephrase incorrectly answered questions using syntactic templates drawn from correct examples. These rephrasings, which preserve semantics while reducing structural complexity, often lead to correct answers. We quantify syntactic complexity using a metric based on Dependency Locality Theory (DLT), and show that higher DLT scores are associated with increased failure rates across multiple datasets. Our findings suggest that many reasoning errors stem from structural misalignment rather than conceptual difficulty, and that syntax-aware interventions can reveal and mitigate these inductive failures.

Large language models (LLMs) have recently demonstrated remarkable success in mathematical reasoning. Despite progress in methods like chain-of-thought prompting and self-consistency sampling, these advances often focus on final correctness without ensuring that the underlying reasoning process is coherent and reliable. This paper introduces Step-KTO, a training framework that combines process-level and outcome-level binary feedback to guide LLMs toward more trustworthy reasoning trajectories. By providing binary evaluations for both the intermediate reasoning steps and the final answer, Step-KTO encourages the model to adhere to logical progressions rather than relying on superficial shortcuts. Our experiments on challenging mathematical benchmarks show that Step-KTO significantly improves both final answer accuracy and the quality of intermediate reasoning steps. For example, on the MATH-500 dataset, Step-KTO achieves a notable improvement in Pass@1 accuracy over strong baselines. These results highlight the promise of integrating stepwise process feedback into LLM training, paving the way toward more interpretable and dependable reasoning capabilities.

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BloomWise: Enhancing Problem-Solving capabilities of Large Language Models using Bloom’s-Taxonomy-Inspired Prompts
Maria-Eleni Zoumpoulidi | Georgios Paraskevopoulos | Alexandros Potamianos

Despite the remarkable capabilities of large language models (LLMs) across a range of tasks, mathematical reasoning remains a challenging frontier. Motivated by the observation that humans learn more effectively when prompted not what to think but how to think, we introduce BloomWise, a cognitively-inspired prompting technique designed to enhance LLMs’ performance on mathematical problem solving while making their solutions more explainable. BloomWise encourages LLMs to generate solutions - in the form of explanations - by progressing through a sequence of cognitive operations-from basic (e.g., remembering) to more advanced reasoning skills (e.g., evaluating) - mirroring how humans build understanding. The process iterates through these levels, halting early if a convergence criterion is met: specifically, if two or more consecutive levels yield the same answer, the solution from the earliest such level is output; otherwise, the process continues until all levels are completed. Through extensive experiments across five popular math reasoning datasets, we demonstrate the effectiveness of BloomWise. We also present comprehensive ablation studies to analyze the strengths of each component within our system.

Recent studies indicate that LLM-based Multi-Agent Systems (MAS) encounter scalability challenges in complex mathematical problem-solving or coding tasks, exhibiting issues such as inconsistent role adherence and ineffective inter-agent communication. Moreover, the performance advantages of LLM-based MAS over a single agent employing test-time scaling methods (e.g., majority voting) remain marginal. This raises a critical question: Can LLM-based MAS scale effectively to achieve performance comparable to standalone LLMs or even Large Reasoning Models (LRMs) under optimal test-time compute?In this paper, we conduct a preliminary investigation into the scalability of LLM-based MAS for scientific code generation. We propose a simple yet scalable two-player framework based on iterative critic-in-the-loop refinement. Our experiments demonstrate that a minimalist actor-critic framework based on DeepSeek-V3 can outperform DeepSeek-R1 under equivalent computational budgets. Surprisingly, more complex frameworks fail to yield significant gains. These findings corroborate recent insights into multi-agent system limitations and highlight the importance of scalable workflows for advancing scientific code generation.

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FIRMA: Bidirectional Formal-Informal Mathematical Language Alignment with Proof-Theoretic Grounding
Maryam Fatima

While large language models excel at gener- ating plausible mathematical text, they often produce subtly incorrect formal translations that violate proof-theoretic constraints. We present FIRMA (Formal-Informal Reasoning in Mathematical Alignment), a bidirectional translation system between formal and informal mathematical language that leverages proof-theoretic interpretability hierarchies and specialized architectural components for proof preservation. Unlike existing approaches that treat this as pure sequence-to-sequence translation, FIRMA introduces a hierarchical architecture with complexity-aware routing, proof-preserving attention mechanisms, and multi-objective training that balances formal correctness with natural readability. Through progressive complexity training on curated datasets from Lean 4 and formal mathematics repositories, we evaluate FIRMA on 200 translation samples across complexity levels and compare against two baseline systems. Our analysis shows statistically significant improvements of 277.8% over BFS-Prover- V1-7B and 6307.5% over REAL-Prover on overall translation quality metrics. Ablation studies on 50 samples demonstrate that each architectural component contributes substan- tially to performance, with removal of any component resulting in 83-85% performance degradation. We release our code at https://github.com/smfatima3/FIRMA

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CHECK-MAT: Probing the Mathematical Reasoning and Rubric-Alignment of Vision-Language Models on Handwritten Solutions
Ruslan Khrulev

The application of contemporary NLP models for inference over mathematical text remains a critical and under-explored area. While Vision-Language Models (VLMs) have shown promise, a significant gap exists in their ability to perform nuanced, rubric-based assessment of handwritten mathematical arguments, a task requiring the joint interpretation of visual, textual, and symbolic modalities. This paper directly addresses the need for robust evaluation tasks in this domain. This paper introduces CHECK-MAT, a new benchmark and methodology for the automated, rubric-based assessment of handwritten mathematical solutions using Vision-Language Models (VLMs). Composed of 122 real-world solutions from a high-stakes national exam, CHECK-MAT evaluates the capacity of VLMs to emulate expert graders by identifying logical flaws and applying detailed grading rubrics. Our systematic evaluation of seven state-of-the-art VLMs serves as a direct instance of probing the mathematical understanding of state-of-the-art models. We reveal key limitations in their ability to parse complex notation and align with human grading rubrics, which we frame as a challenge in understanding the linguistic analysis of mathematical discourse. Our work contributes a robust benchmark to the NLP community and offers critical insights for developing models with more sophisticated mathematical reasoning capabilities. You can find code in https://github.com/Karifannaa/Auto-check-EGE-math.

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RoMath: A Mathematical Reasoning Benchmark in Romanian
Adrian Cosma | Ana-Maria Bucur | Emilian Radoi

Mathematics has long been conveyed through natural language, primarily for human understanding. With the rise of mechanized mathematics and proof assistants, there is a growing need to understand informal mathematical text, yet most existing benchmarks focus solely on English, overlooking other languages. This paper introduces RoMath, a Romanian mathematical reasoning benchmark suite comprising three subsets: Baccalaureate, Competitions and Synthetic, which cover a range of mathematical domains and difficulty levels, aiming to improve non-English language models and promote multilingual AI development. By focusing on Romanian, a low-resource language with unique linguistic features, RoMath addresses the limitations of Anglo-centric models and emphasizes the need for dedicated resources beyond simple automatic translation. We benchmark several open-weight language models, highlighting the importance of creating resources for underrepresented languages. The code and datasets are available for research purposes.

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Into The Limits of Logic: Alignment Methods for Formal Logical Reasoning
Francisco Fernando Lopez-Ponce | Gemma Bel-Enguix

We implement Large Language Model Alignment algorithms to formal logic reasoning tasks involving natural-language (NL) to first-order logic (FOL) translation, formal logic inference, and premise retranslation. These methodologies were implemented using task-specific preference datasets created based on the FOLIO datasets and LLM generations. Alignment was based on DPO, this algorithm was implemented and tested on off-the-shelf and pre-aligned models, showing promising results for higher quality NL-FOL parsing, as well as general alignment strategies. In addition, we introduce a new similarity metric (LogicSim) between LLM-generated responses and gold standard values, that measures logic-relevant information such as premise count and overlap between answers and expands evaluation of NL-FOL translation pipelines. Our results show that LLMs still struggle with logical inference, however alignment benefits semantic parsing and retranslation of results from formal logic to natural language.

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Formula-Text Cross-Retrieval: A Benchmarking Study of Dense Embedding Methods for Mathematical Information Retrieval
Zichao Li

Mathematical information retrieval requires understanding the complex relationship between natural language and formulae. This paper presents a benchmarking study on Formula-Text Cross-Retrieval, comparing a sparse baseline (BM25), off-the-shelf dense embeddings (OpenAI, BGE), and a fine-tuned dual-encoder model. Our model, trained with a contrastive objective on the ARQAR dataset, significantly outperforms all baselines, achieving state-of-the-art results. Ablation studies confirm the importance of linearization, a shared-weight architecture, and the Multiple Negatives Ranking loss. The work provides a strong foundation for mathematical NLP applications.

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BanglaMATH : A Bangla benchmark dataset for testing LLM mathematical reasoning at grades 6, 7, and 8
Tabia Tanzin Prama | Christopher M. Danforth | Peter Dodds

Large Language Models (LLMs) have tremendous potential to play a key role in supporting mathematical reasoning, with growing use in education and AI research. However, most existing benchmarks are limited to English, creating a significant gap for low-resource languages. For example, Bangla is spoken by nearly 250 million people who would collectively benefit from LLMs capable of native fluency. To address this, we present BanglaMATH, a dataset of 1.7k Bangla math word problems across topics such as Arithmetic, Algebra, Geometry, and Logical Reasoning, sourced from Bangla elementary school workbooks and annotated with details like grade level and number of reasoning steps. We have designed BanglaMATH to evaluate the mathematical capabilities of both commercial and open-source LLMs in Bangla, and we find that Gemini 2.5 Flash and DeepSeek V3 are the only models to achieve strong performance, with ≥ 80% accuracy across three elementary school grades. Furthermore, we assess the robustness and language bias of these top-performing LLMs by augmenting the original problems with distracting information, and translating the problems into English. We show that both LLMs fail to maintain robustness and exhibit significant performance bias in Bangla. Our study underlines current limitations of LLMs in handling arithmetic and mathematical reasoning in low-resource languages, and highlights the need for further research on multilingual and equitable mathematical understanding.

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Logically Constrained Decoding
Franklin Ma | Alan J. Hu

Constrained decoding is a state-of-the-art technique for restrictingthe output of an Large Language Model (LLM) to obey syntactic rules,e.g., a regular expression or context-free grammar.In this paper, we propose a method for extending constrained decodingbeyond syntactic constraints, to enforcing formal, logical constraintsthat reflect some world model being reasoned about.We demonstrate proof-of-concept implementations for the game of chess,and for propositional resolution proofs:we constrain the LLM’s decoding such that the LLM is free to outputwhatever tokens it wants, as long as it does not make illegalmoves (chess) or unsound proof steps (resolution).We believe this technique holds promise for improving LLMs’ generationof precise, formal reasoning, as is particularly necessary formathematics.

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Modeling Tactics as Operators: Effect-Grounded Representations for Lean Theorem Proving
Elisaveta Samoylov | Soroush Vosoughi

Interactive theorem provers (ITPs) such as Lean expose proof construction as a sequence of tactics applied to proof states. Existing machine learning approaches typically treat tactics either as surface tokens or as labels conditioned on the current state, eliding their operator-like semantics. This paper introduces a representation learning framework in which tactics are characterized by the changes they induce on proof states. Using a stepwise Lean proof corpus, we construct delta contexts—token-level additions/removals and typed structural edits—and train simple distributional models (𝛥-SGNS and CBOW-𝛥) to learn tactic embeddings grounded in these state transitions. Experiments on tactic retrieval and operator-style analogy tests show that 𝛥-supervision yields more interpretable and generalizable embeddings than surface-only baselines. Our findings suggest that capturing the semantics of tactics requires modeling their state-transformational effects, rather than relying on distributional co-occurrence alone.

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UniMath-CoT: A Unified Framework for Multimodal Mathematical Reasoning with Re-Inference Affirmation
Zhixiang Lu | Mian Zhou | Angelos Stefanidis | Jionglong Su

Large Language Models (LLMs) have achieved considerable success in text-based mathematical reasoning, yet their potential remains underexplored in the multimodal mathematics domain where joint text and image understanding is imperative. A key bottleneck hindering progress is the scarcity of high-quality, genuinely multimodal benchmarks. To address this gap, we construct a unified benchmark by consolidating and curating three public multimodal mathematics datasets. We subsequently propose the UniMath-CoT framework, which establishes a robust performance baseline by combining Chain-of-Thought (CoT) principles with efficient Supervised Fine-Tuning (SFT) based on Low-Rank Adaptation (LoRA). Furthermore, to bolster the model’s reasoning robustness, we introduce an innovative verification mechanism, AARI (Answer Affirmation by Re-Inference), which leverages a specialized re-inference protocol to have the model self-scrutinize and validate its initial conclusions. Our comprehensive experiments show that this integrated strategy substantially boosts performance, surpassing a wide range of open-source models and markedly closing the gap with leading proprietary systems.

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An in-depth human study of the mathematical reasoning abilities in Large Language Models
Carolina Dias-Alexiou | Edison Marrese-Taylor | Yutaka Matsuo

We study the generalization capabilities of large language models (LLM) through the lens of mathematical reasoning, asking if these models can recognize that two structures are the same even when they do not share the same nomenclature. We propose a human study to evaluate if LLMs reproduce proofs that they have most likely seen during training, but when the symbols do not match the ones seen. To test this in a controlled scenario, we look at proofs in propositional calculus, foundational for other logic systems, semantically complete and widely discussed online. We replace the implication operator (→) with an unrelated, arbitrary symbol (♠) and ask experts to evaluate how the output of a selection of LLMs changes in terms of compliance, correctness, extensiveness and coherence. Our results show that nearly all our tested models produce lower quality proofs in this test, in particular open-weights models, suggesting the abilities of these LLMs to reason in this context have important limitations.

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Synthetic Proofs with Tool-Integrated Reasoning: Contrastive Alignment for LLM Mathematics with Lean
Mark Obozov | Michael Diskin | Aleksandr Beznosikov | Alexander Gasnikov | Serguei Barannikov

Modern mathematical reasoning benchmarks primarily focus on answer finding rather than proof verification, creating a gap in evaluating the proving capabilities of large language models (LLMs). We present a methodology for generating diverse mathematical proof tasks using formal tools. Our approach combines Lean-based synthetic problem generation with a Tool-Integrated Reasoning (TiR) framework for partial (sampling-based) proof validation, and it uses contrastive preference optimization to align the model’s proof outputs. Experiments on the Qwen-2.5 family of models demonstrate meaningful improvements in mathematical reasoning, particularly for smaller models. Our aligned models achieve up to a 57% higher success rate than baselines on the MiniF2F benchmark (across 0.5B, 1.5B, and 7B parameter models). These results highlight the potential of synthetic data and integrated validation for advancing LLM-based mathematical reasoning.

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bib (full) Proceedings of the 5th Workshop on Multilingual Representation Learning (MRL 2025)

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No Language Data Left Behind: A Cross-Cultural Study of CJK Language Datasets in the Hugging Face Ecosystem
Dasol Choi | Woomyoung Park | Youngsook Song

Recent advances in Natural Language Processing (NLP) have underscored the crucial role of high-quality datasets in building large language models (LLMs). However, while extensive resources and analyses exist for English, the landscape for East Asian languages, particularly Chinese, Japanese, and Korean (CJK), remains fragmented and underexplored, despite these languages serving over 1.6 billion speakers. To address this gap, we investigate the HuggingFace ecosystem from a cross-linguistic perspective, focusing on how cultural norms, research environments, and institutional practices shape dataset availability and quality. Drawing on more than 3,300 datasets, we employ quantitative and qualitative methods to examine how these factors drive distinct creation and curation patterns across Chinese, Japanese, and Korean NLP communities. Our findings highlight the large-scale and often institution-driven nature of Chinese datasets, grassroots community-led development in Korean NLP, and an entertainment and subculture-focused emphasis on Japanese collections. By uncovering these patterns, we reveal practical strategies for enhancing dataset documentation, licensing clarity, and cross-lingual resource sharing, guiding more effective and culturally attuned LLM development in East Asia. We conclude by discussing best practices for future dataset curation and collaboration, aiming to strengthen resource development across all three languages.

Natural Language Inference (NLI) is a fundamental task in natural language processing. While NLI has developed many sub-directions such as sentence-level NLI, document-level NLI and cross-lingual NLI, Cross-Document Cross-Lingual NLI (CDCL-NLI) remains largely unexplored. In this paper, we propose a novel paradigm: CDCL-NLI, which extends traditional NLI capabilities to multi-document, multilingual scenarios. To support this task, we construct a high-quality CDCL-NLI dataset including 25,410 instances and spanning 26 languages. To address the limitations of previous methods on CDCL-NLI task, we further propose an innovative method that integrates RST-enhanced graph fusion with interpretability-aware prediction. Our approach leverages RST (Rhetorical Structure Theory) within heterogeneous graph neural networks for cross-document context modeling, and employs a structure-aware semantic alignment based on lexical chains for cross-lingual understanding. For NLI interpretability, we develop an EDU (Elementary Discourse Unit)-level attribution framework that produces extractive explanations. Extensive experiments demonstrate our approach”s superior performance, achieving significant improvements over both conventional NLI models as well as large language models. Our work sheds light on the study of NLI and will bring research interest on cross-document cross-lingual context understanding, hallucination elimination and interpretability inference. Our dataset and code are available at https://anonymous.4open.science/r/CDCL-NLI-637E/ for peer review.

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Universal Patterns of Grammatical Gender in Multilingual Large Language Models
Andrea Schröter | Ali Basirat

Grammatical gender is a fundamental linguistic feature that varies across languages, and its cross-linguistic correspondence has been a central question in disciplines such as cognitive science and linguistic typology. This study takes an information-theoretic approach to investigate the extent to which variational usable information about grammatical gender encoded by a large language model generalizes across languages belonging to different language families. Using mBERT as a case study, we analyze how grammatical gender is encoded and transferred across languages based on the usable information of the intermediate representations. The empirical results provide evidence that gender mechanisms are driven by abstract semantic features largely shared across languages, and that the information becomes more accessible at the higher layers of the language model.

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Cross-lingual Transfer Dynamics in BLOOMZ: Insights into Multilingual Generalization
Sabyasachi Samantaray | Preethi Jyothi

Multilingual large language models have emerged as a promising solution for resource-constrained settings, with significant efforts aimed towards improving multilingual capabilities of English-centric pretrained models. However, the broader cross-lingual implications of fine-tuning interventions remain understudied. This work examines instruction tuning (IT) over the BLOOMZ model for Question Answering (QA) in low-resource settings, with special emphasis on transfer dynamics across several languages. Our findings reveal two critical insights: first, IT on the target language can negatively impact its own performance in constrained short-span generation tasks due to overgeneration tendencies; second, in QA tasks, IT appears to suppress performance in some interfering languages, thereby enhancing capabilities in some target Indic languages by extbfmore than doubling QA performance. These results highlight important trade-offs in multilingual LLM adaptation and enhance our understanding of cross-lingual transfer mechanisms.

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CoCo-CoLa: Evaluating and Improving Language Adherence in Multilingual LLMs
Elnaz Rahmati | Alireza Salkhordeh Ziabari | Morteza Dehghani

Multilingual Large Language Models (LLMs) develop cross-lingual abilities despite being trained on limited parallel data. However, they often struggle to generate responses in the intended language, favoring high-resource languages such as English. In this work, we introduce CoCo-CoLa (Correct Concept - Correct Language), a novel metric to evaluate language adherence in multilingual LLMs. Using fine-tuning experiments on a closed-book QA task across seven languages, we analyze how training in one language affects others’ performance. Our findings reveal that multilingual models share task knowledge across languages but exhibit biases in the selection of output language. We identify language-specific layers, showing that final layers play a crucial role in determining output language. Accordingly, we propose a partial training strategy that selectively fine-tunes key layers, improving language adherence while reducing computational cost. Our method achieves comparable or superior performance to full fine-tuning, particularly for low-resource languages, offering a more efficient multilingual adaptation.

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Understand, Solve and Translate: Bridging the Multilingual Mathematical Reasoning Gap
Hyunwoo Ko | Guijin Son | Dasol Choi

Large language models (LLMs) demonstrate exceptional performance on complex reasoning tasks. However, despite their strong reasoning capabilities in high-resource languages (e.g., English and Chinese), a significant performance gap persists in other languages. To investigate this gap in Korean, we introduce HRM8K, a benchmark comprising 8,011 English-Korean parallel bilingual math problems. Through systematic analysis of model behaviors, we identify a key finding: these performance disparities stem primarily from difficulties in comprehending non-English inputs, rather than limitations in reasoning capabilities. Based on these findings, we propose UST(Understand, Solve, and Translate), a method that strategically uses English as an anchor for reasoning and solution generation. By fine-tuning the model on 130k synthetically generated data points, method achieves a 10.91% improvement on the HRM8K benchmark and reduces the multilingual performance gap from 11.6%% to 0.7%%. Additionally, we show that improvements from method generalize effectively to different Korean domains, demonstrating that capabilities acquired from machine-verifiable content can be generalized to other areas. We publicly release the benchmark, training dataset, and models.

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Unlocking LLM Safeguards for Low-Resource Languages via Reasoning and Alignment with Minimal Training Data
Zhuowei Chen | Bowei Zhang | Nankai Lin | Tian Hou | Lianxi Wang

Recent advances in LLMs have enhanced AI capabilities, but also increased the risk posed by malicious requests, highlighting the need for effective LLM safeguards to detect such queries. Existing approaches largely rely on classifier-based methods that lack interpretability and perform poorly on low-resource languages. To address these limitations, we propose ConsistentGuard, a novel reasoning-based multilingual safeguard, which enhances explainability via reasoning and boosts knowledge transfer between languages through alignment. With only 1,000 training samples, our method demonstrates superior performance on three datasets across six languages, outperforming larger models trained with significantly more data, and exhibits strong interpretability and generalization ability. We also contribute a multilingual benchmark extension and release our code to support future research.

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Meta-Pretraining for Zero-Shot Cross-Lingual Named Entity Recognition in Low-Resource Philippine Languages
David Demitri Africa | Suchir Salhan | Yuval Weiss | Paula Buttery | Richard Diehl Martinez

Named-entity recognition (NER) in low-resource languages is usually tackled by finetuning very large multilingual LMs, an option that is often infeasible in memory- or latency-constrained settings. We ask whether small decoder LMs can be pretrained so that they adapt quickly and transfer zero-shot to languages unseen during pretraining. To this end we replace part of the autoregressive objective with first-order model-agnostic meta-learning (MAML). Tagalog and Cebuano are typologically similar yet structurally different in their actor/non-actor voice systems, and hence serve as a challenging test-bed. Across four model sizes (11 M – 570 M) MAML lifts zero-shot micro-F1 by 2–6 pp under head-only tuning and 1–3 pp after full tuning, while cutting convergence time by up to 8%. Gains are largest for single-token person entities that co-occur with Tagalog case particles si/ni, highlighting the importance of surface anchors.

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Extended Abstract for “Linguistic Universals”: Emergent Shared Features in Independent Monolingual Language Models via Sparse Autoencoders
Ej Zhou | Suchir Salhan

Do independently trained monolingual language models converge on shared linguistic principles? To explore this question, we propose to analyze a suite of models trained separately on single languages but with identical architectures and budgets. We train sparse autoencoders (SAEs) on model activations to obtain interpretable latent features, then align them across languages using activation correlations. We do pairwise analyses to see if feature spaces show non-trivial convergence, and we identify universal features that consistently emerge across diverse models. Positive results will provide evidence that certain high-level regularities in language are rediscovered independently in machine learning systems.

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The Unreasonable Effectiveness of Model Merging for Cross-Lingual Transfer in LLMs
Lucas Bandarkar | Nanyun Peng

Large language models (LLMs) still struggle across tasks outside of high-resource languages. In this work, we investigate cross-lingual transfer to lower-resource languages where task-specific post-training data is scarce. Building on prior work, we first validate that the subsets of model parameters that matter most for mathematical reasoning and multilingual capabilities are distinctly non-overlapping. To exploit this implicit separability between task and target language parameterization, we develop and analyze numerous modular frameworks to improve the composition of the two during fine-tuning. These methods generally employ freezing parameters or post hoc model merging to assign math and language improvement to different key parts of the LLM. In the absence of in-language math data, we demonstrate that the modular approaches successfully improve upon baselines across three languages, four models, and two fine-tuning paradigms (full and LoRA). Furthermore, we identify the most consistently successful modular method to be fine-tuning separate language and math experts and model merging via Layer-Swapping, somewhat surprisingly. We offer possible explanations for this result via recent works on the linearity of task vectors. We further explain this by empirically showing that reverting less useful fine-tuning updates after training often outperforms freezing them from the start.

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Reassessing Speech Translation for Low-Resource Languages: Do LLMs Redefine the State-of-the-Art Against Cascaded Models?
Jonah Dauvet | Min Ma | Jessica Ojo | David Ifeoluwa Adelani

Automatic speech translation (AST) promotes seamless communication among speakers of different languages. While current state-of-the-art models excel with high-resource languages, their performance on low-resource languages (LRLs) is not well-established. We investigate this by evaluating state-of-the-art models on 10 LRLs with varying data amounts (10-30+ hours). Through six finetuning strategies and experimenting with three main AST paradigms, we observe that: (1) The latest Large Language Models (LLMs) may struggle with LRLs. (2) Comprehensive experiments suggest that for LRLs, more AST finetuning data is not always beneficial. (3) Our 2-Stage with ASR corrector finetuning recipe can substantially improve AST performance on LRLs, achieving up to a 5.8x BLEU score boost on translating related languages to English, while on par with the best monolingual finetuning in BLEU score when translating the target language to English. (4) We share effective engineering practices, including how to effectively adapt AST models to unseen languages.

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Quality-Aware Translation Tagging in Multilingual RAG system
Hoyeon Moon | Byeolhee Kim | Nikhil Verma

Multilingual Retrieval-Augmented Generation (mRAG) often retrieves English documents and translates them into the query language for low-resource settings. However, poor translation quality degrades response generation performance. Existing approaches either assume sufficient translation quality or utilize the rewriting method, which introduces factual distortion and hallucinations. To mitigate these problems, we propose Quality-Aware Translation Tagging in mRAG (QTT-RAG), which explicitly evaluates translation quality along three dimensions-semantic equivalence, grammatical accuracy, and naturalness&fluency-and attach these scores as metadata without altering the original content. We evaluate QTT-RAG against CrossRAG and DKM-RAG as baselines in two open-domain QA benchmarks (XORQA, MKQA) using six instruction-tuned LLMs ranging from 2.4B to 14B parameters, covering two low-resource languages (Korean and Finnish) and one high-resource language (Chinese). QTT-RAG outperforms the baselines by preserving factual integrity while enabling generator models to make informed decisions based on translation reliability. This approach allows for effective usage of cross-lingual documents in low-resource settings with limited native language documents, offering a practical and robust solution across multilingual domains.

Existing multilingual neural machine translation (MNMT) approaches mainly focus on improving models with the encoder-decoder architecture to translate multiple languages. However, decoder-only architecture has been explored less in MNMT due to its underperformance when trained on parallel data solely. In this work, we attribute the issue of the decoder-only architecture to its lack of language transfer capability. Specifically, the decoder-only architecture is insufficient in encoding source tokens with the target language features. We propose dividing the decoding process into two stages so that target tokens are explicitly excluded in the first stage to implicitly boost the transfer capability across languages. Additionally, we impose contrastive learning on translation instructions, resulting in improved performance in zero-shot translation. We conduct experiments on TED-19 and OPUS-100 datasets, considering both training from scratch and fine-tuning scenarios.results show that, compared to the encoder-decoder architecture, our methods not only perform competitively in supervised translations but also achieve improvements of up to 3.39 BLEU, 6.99 chrF++, 3.22 BERTScore, and 4.81 COMET in zero-shot translations. We release our codes at https://github.com/zhiqu22/PhasedDecoder.

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How Can We Relate Language Modeling to Morphology?
Wessel Poelman | Thomas Bauwens | Miryam de Lhoneux

The extent to which individual language characteristics influence tokenization and language modeling is an open question. Differences in morphological systems have been suggested as both unimportant and crucial to consider (e.g., Cotterell et al., 2018; Park et al., 2021, Arnett & Bergen, 2025). We argue this conflicting evidence is due to confounding factors in experimental setups, making it hard to compare results and draw conclusions. We identify confounding factors in analyses trying to answer the question of whether, and how, morphology relates to language modeling. Next, we introduce token bigram metrics as an intrinsic way to predict the difficulty of causal language modeling, and find that they are gradient proxies for morphological complexity that do not require expert annotation. Ultimately, we outline necessities to reliably answer whether, and how, morphology relates to language modeling.

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On the Consistency of Multilingual Context Utilization in Retrieval-Augmented Generation
Jirui Qi | Raquel Fernández | Arianna Bisazza

Retrieval-augmented generation (RAG) with large language models (LLMs) has demonstrated strong performance in multilingual question-answering (QA) tasks by leveraging relevant passages retrieved from corpora. In multilingual RAG (mRAG), the retrieved passages can be written in languages other than that of the query entered by the user, making it challenging for LLMs to effectively utilize the provided information. Recent research suggests that retrieving passages from multilingual corpora can improve RAG performance, particularly for low-resource languages. However, the extent to which LLMs can leverage different kinds of multilingual contexts to generate accurate answers, independently from retrieval quality, remains understudied. In this paper, we conduct an extensive assessment of LLMs’ ability to (i) make consistent use of a relevant passage regardless of its language, (ii) respond in the expected language, and (iii) focus on the relevant passage even when multiple ‘distracting passages’ in different languages are provided in the context. Our experiments with four LLMs across three QA datasets covering 48 languages reveal a surprising ability of LLMs to extract relevant information from passages in a different language than the query, but a much weaker ability to produce a full answer in the correct language. Our analysis, based on both accuracy and feature attribution techniques, further shows that distracting passages negatively impact answer quality regardless of their language. However, distractors in the query language exert a slightly stronger influence. Taken together, our findings deepen the understanding of how LLMs utilize context in mRAG systems, providing directions for future improvements. All codes and data are released at https://github.com/Betswish/mRAG-Context-Consistency.

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CLIRudit: Cross-Lingual Information Retrieval of Scientific Documents
Francisco Valentini | Diego Kozlowski | Vincent Lariviere

Cross-lingual information retrieval (CLIR) helps users find documents in languages different from their queries. This is especially important in academic search, where key research is often published in non-English languages. We present CLIRudit, a novel English-French academic retrieval dataset built from Érudit, a Canadian publishing platform. Using multilingual metadata, we pair English author-written keywords as queries with non-English abstracts as target documents, a method that can be applied to other languages and repositories. We benchmark various first-stage sparse and dense retrievers, with and without machine translation. We find that dense embeddings without translation perform nearly as well as systems using machine translation, that translating documents is generally more effective than translating queries, and that sparse retrievers with document translation remain competitive while offering greater efficiency. Along with releasing the first English-French academic retrieval dataset, we provide a reproducible benchmarking method to improve access to non-English scholarly content.

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TenseLoC: Tense Localization and Control in a Multilingual LLM
Ariun-Erdene Tumurchuluun | Yusser Al Ghussin | David Mareček | Josef Van Genabith | Koel Dutta Chowdhury

Multilingual language models excel across languages, yet how they internally encode grammatical tense remains largely unclear. We investigate how decoder-only transformers represent, transfer, and control tense across eight typologically diverse languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai. We construct a synthetic tense-annotated dataset and combine probing, causal analysis, feature disentanglement, and model steering to LLaMA-3.1 8B. We show that tense emerges as a distinct signal from early layers and transfers most strongly within the same language family. Causal tracing reveals that attention outputs around layer 16 consistently carry cross-lingually transferable tense information. Leveraging sparse autoencoders in this subspace, we isolate and steer English tense-related features, improving target-tense prediction accuracy by up to 11%% in a downstream cloze task.

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Reversible Disentanglement of Meaning and Language Representations from Multilingual Sentence Encoders
Keita Fukushima | Tomoyuki Kajiwara | Takashi Ninomiya

We propose an unsupervised method to disentangle sentence embeddings from multilingual sentence encoders into language-specific and language-agnostic representations. Such language-agnostic representations distilled by our method can estimate cross-lingual semantic sentence similarity by cosine similarity. Previous studies have trained individual extractors to distill each language-specific and -agnostic representation. This approach suffers from missing information resulting in the original sentence embedding not being fully reconstructed from both language-specific and -agnostic representations; this leads to performance degradation in estimating cross-lingual sentence similarity. We only train the extractor for language-agnostic representations and treat language-specific representations as differences from the original sentence embedding; in this way, there is no missing information. Experimental results for both tasks, quality estimation of machine translation and cross-lingual sentence similarity estimation, show that our proposed method outperforms existing unsupervised methods.

Developing a high-performing large language models (LLMs) for low-resource languages such as Urdu, present several challenges. These challenges include the scarcity of high-quality datasets, multilingual inconsistencies, and safety concerns. Existing multilingual LLMs often address these issues by translating large volumes of available data. However, such translations often lack quality and cultural nuance while also incurring significant costs for data curation and training. To address these issues, we propose Alif-1.0-8B-Instruct, a multilingual Urdu-English model, that tackles these challenges with a unique approach. We train the model on a high-quality, multilingual synthetic dataset (Urdu-Instruct), developed using a modified self-instruct technique. By using unique prompts and seed values for each task along with a global task pool, this dataset incorporates Urdu-native chain-of-thought based reasoning, bilingual translation, cultural relevance, and ethical safety alignments. This technique significantly enhances the comprehension of Alif-1.0-8B-Instruct model for Urdu-specific tasks. As a result, Alif-1.0-8B-Instruct, built upon the pretrained Llama-3.1-8B, demonstrates superior performance compared to Llama-3.1-8B-Instruct for Urdu specific-tasks. It also outperformed leading multilingual LLMs, including Mistral-7B-Instruct-v0.3, Qwen-2.5-7B-Instruct, and Cohere-Aya-Expanse-8B, all within a training budget of under $100. Our results demonstrate that high-performance and low-resource language LLMs can be developed efficiently and culturally aligned using our modified self-instruct approach.

The effectiveness of Large Language Models (LLMs) depends heavily on the availability of high-quality post-training data, particularly instruction-tuning and preference-based examples. Existing open-source datasets, however, often lack multilingual coverage, cultural grounding, and suffer from task diversity gaps that are especially pronounced for Indian languages. We introduce a human-in-the-loop pipeline that combines translations with synthetic expansion to produce reliable and diverse Indic post-training data. Using this pipeline, we curate two datasets: Pragyaan-IT (22.5K) and Pragyaan-Align (100K) across 10 Indian languages covering 13 broad and 56 sub-categories, leveraging 57 diverse datasets. Our dataset protocol incorporates several often-overlooked dimensions and emphasize task diversity, multi-turn dialogue, instruction fidelity, safety alignment, and preservation of cultural nuance, providing a foundation for more inclusive and effective multilingual LLMs.

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SOI Matters: Analyzing Multi-Setting Training Dynamics in Pretrained Language Models via Subsets of Interest
Shayan Vassef | Amirhossein Dabiriaghdam | Mohammadreza Bakhtiari | Yadollah Yaghoobzadeh

This work investigates the impact of multi-task, multi-lingual, and multi-source learning approaches on the robustness and performance of pretrained language models. To enhance this analysis, we introduce Subsets of Interest (SOI), a novel categorization framework that identifies six distinct learning behavior patterns during training, including forgettable examples, unlearned examples, and always correct examples. Through SOI transition heatmaps and dataset cartography visualization, we analyze how examples shift between these categories when transitioning from single-setting to multi-setting configurations. We perform comprehensive experiments across three parallel comparisons: multi-task vs. single-task learning using English tasks (entailment, paraphrase, sentiment), multi-source vs. single-source learning using sentiment analysis datasets, and multi-lingual vs. single-lingual learning using intent classification in French, English, and Persian. Our results demonstrate that multi-source learning consistently improves out-of-distribution performance by up to 7%, while multi-task learning shows mixed results with notable gains in similar task combinations. We further introduce a two-stage fine-tuning approach where the second stage leverages SOI-based subset selection to achieve additional performance improvements. These findings provide new insights into training dynamics and offer practical approaches for optimizing multi-setting language model performance.

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When Scripts Diverge: Strengthening Low-Resource Neural Machine Translation Through Phonetic Cross-Lingual Transfer
Ammon Shurtz | Christian Richardson | Stephen D. Richardson

Multilingual Neural Machine Translation (MNMT) models enhance translation quality for low-resource languages by exploiting cross-lingual similarities during training—a process known as knowledge transfer. This transfer is particularly effective between languages that share lexical or structural features, often enabled by a common orthography. However, languages with strong phonetic and lexical similarities but distinct writing systems experience limited benefits, as the absence of a shared orthography hinders knowledge transfer. To address this limitation, we propose an approach based on phonetic information that enhances token-level alignment across scripts by leveraging transliterations. We systematically evaluate several phonetic transcription techniques and strategies for incorporating phonetic information into NMT models. Our results show that using a shared encoder to process orthographic and phonetic inputs separately consistently yields the best performance for Khmer, Thai, and Lao in both directions with English, and that our custom Cognate-Aware Transliteration (CAT) method consistently improves translation quality over the baseline.

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Conditions for Catastrophic Forgetting in Multilingual Translation
Danni Liu | Jan Niehues

Fine-tuning multilingual foundation models on specific languages often induces catastrophic forgetting, degrading performance on languages unseen in fine-tuning. While this phenomenon is widely-documented, the literature presents fragmented results about when forgetting occurs. To address this ambiguity, we conduct a systematic empirical study using machine translation as a testbed to identify the conditions that trigger catastrophic forgetting in multilingual fine-tuning. Through controlled experiments across different model architectures, data scales, and fine-tuning approaches, we reveal that the relative scale between model and data size is a primary determinant of forgetting. Moreover, we demonstrate that a model’s instruction-following ability is more critical for retaining multilingual knowledge than its architecture. Contrary to assumptions, parameter-efficient fine-tuning offers no clear advantage over full fine-tuning in mitigating forgetting. Lastly, we show that cross-lingual alignment can mitigate forgetting while also facilitating positive transfer to unseen target languages.

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Monolingual Adapter Networks for Efficient Cross-Lingual Alignment
Pulkit Arya

Multilingual alignment for low-resource languages is a challenge for embedding models. The scarcity of parallel datasets in addition to rich morphological diversity in languages adds to the complexity of training multilingual embedding models. To aid in the development of multilingual models for under-represented languages such as Sanskrit, we introduce GitaDB: a collection of 640 Sanskrit verses translated in 5 Indic languages and English. We benchmarked various state-of-the-art embedding models on our dataset in different bilingual and cross-lingual semantic retrieval tasks of increasing complexity and found a steep degradation in retrieval scores. We found a wide margin in the retrieval performance between English and Sanskrit targets. To bridge this gap, we introduce Monolingual Adapter Networks: a parameter-efficient method to bolster cross-lingual alignment of embedding models without the need for parallel corpora or full finetuning.

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Culturally-Nuanced Story Generation for Reasoning in Low-Resource Languages: The Case of Javanese and Sundanese
Salsabila Zahirah Pranida | Rifo Ahmad Genadi | Fajri Koto

Culturally grounded commonsense reasoning is underexplored in low-resource languages due to scarce data and costly native annotation. We test whether large language models (LLMs) can generate culturally nuanced narratives for such settings. Focusing on Javanese and Sundanese, we compare three data creation strategies: (1) LLM-assisted stories prompted with cultural cues, (2) machine translation from Indonesian benchmarks, and (3) native-written stories. Human evaluation finds LLM stories match natives on cultural fidelity but lag in coherence and correctness. We fine-tune models on each dataset and evaluate on a human-authored test set for classification and generation. LLM-generated data yields higher downstream performance than machine-translated and Indonesian human-authored training data. We release a high-quality benchmark of culturally grounded commonsense stories in Javanese and Sundanese to support future work.

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Zero-Shot Cross-Lingual Transfer using Prefix-Based Adaptation
Snegha A | Sayambhu Sen | Piyush Singh Pasi | Abhishek Singhania | Preethi Jyothi

With the release of new large language models (LLMs) like Llama and Mistral, zero-shot cross-lingual transfer has become increasingly feasible due to their multilingual pretraining and strong generalization capabilities. However, adapting these decoder-only LLMs to new tasks across languages remains challenging. While parameter-efficient fine-tuning (PeFT) techniques like Low-Rank Adaptation (LoRA) are widely used, prefix-based techniques such as soft prompt tuning, prefix tuning, and Llama Adapter are less explored, especially for zero-shot transfer in decoder-only models. We present a comprehensive study of three prefix-based methods for zero-shot cross-lingual transfer from English to 35+ high- and low-resource languages. Our analysis further explores transfer across linguistic families and scripts, as well as the impact of scaling model sizes from 1B to 24B. With Llama 3.1 8B, prefix methods outperform LoRA-baselines by up to 6% on the Belebele benchmark. Similar improvements were observed with Mistral v0.3 7B as well. Despite using only 1.23M learning parameters with prefix tuning, we achieve consistent improvements across diverse benchmarks. These findings highlight the potential of prefix-based techniques as an effective and scalable alternative to LoRA, particularly in low-resource multilingual settings.

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Exploring the Role of Transliteration in In-Context Learning for Low-resource Languages Written in Non-Latin Scripts
Chunlan Ma | Yihong Liu | Haotian Ye | Hinrich Schuetze

Decoder-only large language models (LLMs) excel in high-resource languages across various tasks through few-shot or even zero-shot in-context learning (ICL). However, their performance often does not transfer well to low-resource languages, especially those written in non-Latin scripts. Inspired by recent work that leverages transliteration in encoder-only models, we investigate whether transliteration is also effective in improving LLMs’ performance for low-resource languages written in non-Latin scripts. To this end, we propose three prompt templates, where the target-language text is represented in (1) its original script, (2) Latin script transliteration, or (3) combined. We apply these methods to several representative LLMs of different sizes on various tasks including text classification and sequential labeling. Our findings show that the effectiveness of transliteration varies by task type and model size. For instance, all models benefit from transliterations for sequential labeling (with increases of up to 25%%).

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Type and Complexity Signals in Multilingual Question Representations
Robin Kokot | Wessel Poelman

This work investigates how a multilingual transformer model represents morphosyntactic properties of questions. We introduce the Question Type and Complexity (QTC) dataset with sentences across seven languages, annotated with type information and complexity metrics including dependency length, tree depth, and lexical density. Our evaluation extends probing methods to regression labels with selectivity controls to quantify gains in generalizability. We compare layer-wise probes on frozen Glot500-m (Imani et al., 2023) representations against subword TF-IDF baselines, and a fine-tuned model. Results show that statistical features classify questions well in explicitly marked languages and structural complexity prediction, while neural probes lead on individual metrics. We use these results to evaluate when contextual representations outperform statistical baselines and whether parameter updates reduce availability of pre-trained linguistic information.

We introduce Entropy2Vec, a novel framework for deriving cross-lingual language representations by leveraging the entropy of monolingual language models. Unlike traditional typological inventories that suffer from feature sparsity and static snapshots, Entropy2Vec uses the inherent uncertainty in language models to capture typological relationships between languages. By training a language model on a single language, we hypothesize that the entropy of its predictions reflects its structural similarity to other languages: Low entropy indicates high similarity, while high entropy suggests greater divergence. This approach yields dense, non-sparse language embeddings that are adaptable to different timeframes and free from missing values. Empirical evaluations demonstrate that Entropy2Vec embeddings align with established typological categories and achieved competitive performance in downstream multilingual NLP tasks, such as those addressed by the LinguAlchemy framework.

Large Language Models (LLMs) have demonstrated remarkable generalization capabilities across tasks and languages, revolutionizing natural language processing. This paper investigates the naturally emerging representation alignment in LLMs, particularly in the middle layers, and its implications for disentangling language-specific and language-agnostic information. We empirically confirm the existence of this alignment, analyze its behavior in comparison to explicitly designed alignment models, and demonstrate its potential for language-specific manipulation without semantic degradation. Building on these findings, we propose Inference-Time Language Control (ITLC), a novel method that leverages latent injection to enable precise cross-lingual language control and mitigate language confusion in LLMs. Our experiments highlight ITLC’s strong cross-lingual control capabilities while preserving semantic integrity in target languages. Furthermore, we demonstrate its effectiveness in alleviating the cross-lingual language confusion problem, which persists even in current large-scale LLMs, leading to inconsistent language generation. This work advances our understanding of representation alignment in LLMs and introduces a practical solution for enhancing their cross-lingual performance.

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Relevant for the Right Reasons? Investigating Lexical Biases in Zero-Shot and Instruction-Tuned Rerankers
Yuchen Mao | Barbara Plank | Robert Litschko

Large Language Models (LLMs) show strong potential for reranking documents in information retrieval (IR), but training with monolingual data often leads to monolingual overfitting and lexical bias, limiting generalization in cross-lingual IR (CLIR). To overcome these issues, we investigate instruction-tuning LLaMA-3.1-8B-Instruct on English and multilingual code-switched data, and evaluate on mMARCO and XQuAD-R. Results show that instruction-tuning on code-switched data substantially improves CLIR performance, while monolingual tuning remains more effective for monolingual reranking. We introduce a novel measure to analyze the relationship between lexical overlap and reranking performance, showing that the two factors are correlated. We finally conduct a causal analysis using counterfactual examples, where we evaluate whether rewriting passages that share overlapping keywords with the query causes models to change their relevance predictions. Overall, we find that code-switching serves as an effective and lightweight strategy to improve cross-lingual generalization in LLM-based re-ranking, while our analyses show that lexical overlap remains a major factor that can mislead reranking models.

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Cross-Lingual Knowledge Augmentation for Mitigating Generic Overgeneralization in Multilingual Language Models
Sello Ralethe | Jan Buys

Generic statements like “birds fly” or “lions have manes” express generalizations about kinds that allow exceptions, yet language models tend to overgeneralize them to universal claims. While previous work showed that ASCENT KB could reduce this effect in English by 30-40%, the effectiveness of broader knowledge sources and the cross-lingual nature of this phenomenon remain unexplored. We investigate generic overgeneralization across English and four South African languages (isiZulu, isiXhosa, Sepedi, SeSotho), comparing the impact of ConceptNet and DBpedia against the previously used ASCENT KB. Our experiments show that ConceptNet reduces overgeneralization by 45-52%% for minority characteristic generics, while DBpedia achieves 48-58%% for majority characteristics, with combined knowledge bases reaching 67%% reduction. These improvements are consistent across all languages, though Nguni languages show higher baseline overgeneralization than Sotho-Tswana languages, potentially suggesting that morphological features may influence this semantic bias. Our findings demonstrate that commonsense and encyclopedic knowledge provide complementary benefits for multilingual semantic understanding, offering insights for developing NLP systems that capture nuanced semantics in low-resource languages.

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What if I ask in alia lingua? Measuring Functional Similarity Across Languages
Debangan Mishra | Arihant Rastogi | Agyeya Singh Negi | Shashwat Goel | Ponnurangam Kumaraguru

How similar are model outputs across languages? In this work, we study this question using a recently proposed model similarity metric—𝜅_p—applied to 20 languages and 47 subjects in GlobalMMLU. Our analysis reveals that a model’s responses become increasingly consistent across languages as its size and capability grow. Interestingly, models exhibit greater cross-lingual consistency within themselves than agreement with other models prompted in the same language. These results highlight not only the value of 𝜅_p as a practical tool for evaluating multilingual reliability, but also its potential to guide the development of more consistent multilingual systems.

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Multilingual Learning Strategies in Multilingual Large Language Models
Ali Basirat

Despite the effective performance of multilingual large language models (LLMs), the mechanisms underlying their multilingual capabilities remain unclear. This study examines the intermediate representations of multilingual LLMs to determine if these models utilize human-like second language acquisition strategies: coordinate, sub-coordinate, or compound learning. Our investigations into the discriminative and generative aspects of these models indicate that coordinate learning is the dominant mechanism, with decoder-only models progressively developing distinct feature spaces for each language, while encoder-only models exhibit a mixture of coordinate and compound learning in their middle layers. We find little evidence for sub-coordinate learning. Moreover, the role of training data coverage in shaping multilingual representations is reflected in the fact that languages present in a model’s training data consistently exhibit stronger separation than those absent from it.

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Sub-1B Language Models for Low-Resource Languages: Training Strategies and Insights for Basque
Gorka Urbizu | Ander Corral | Xabier Saralegi | Iñaki San Vicente

This work investigates the effectiveness of small autoregressive language models (SLMs) with up to one billion parameters (sub-1B) for natural language processing (NLP) tasks in low-resource languages, focusing on Basque. We analyze optimal training strategies by comparing training from scratch and continual pre-training using state-of-the-art SLM architectures. Our analysis considers factors such as model size and the extent of Basque presence in the pre-training corpus. To assess linguistic capabilities, models are evaluated on 12 NLP tasks using the Harness framework. We also conduct a manual evaluation of fine-tuned models on three downstream natural language generation (NLG) tasks: question answering (QA), summarization, and machine translation (MT). Our findings indicate that continual pre-training on a multilingual SLM substantially enhances linguistic performance compared to training from scratch, particularly in low-resource language settings where available corpora typically contain fewer than one billion words. Additionally, the presence of Basque during the pre-training and larger model sizes contribute positively to performance in NLG tasks.

We introduce jina-embeddings-v4, a 3.8 billion parameter embedding model that unifies text and image representations, with a novel architecture supporting both single-vector and multi-vector embeddings. It achieves high performance on both single-modal and cross-modal retrieval tasks, and is particularly strong in processing visually rich content such as tables, charts, diagrams, and mixed-media formats that incorporate both image and textual information. We also introduce JVDR, a novel benchmark for visually rich document retrieval that includes more diverse materials and query types than previous efforts. We use JVDR to show that jina-embeddings-v4 greatly improves on state-of-the-art performance for these kinds of tasks.

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RoBiologyDataChoiceQA: A Romanian Dataset for improving Biology understanding of Large Language Models
Dragos-Dumitru Ghinea | Adela-Nicoleta Corbeanu | Marius-Adrian Dumitran

In recent years, large language models (LLMs) have demonstrated significant potential across various natural language processing (NLP) tasks. However, their performance in domainspecific applications and non-English languages remains less explored. This study introduces a novel Romanian-language dataset 1 for multiple-choice biology questions, carefully curated to assess LLM comprehension and reasoning capabilities in scientific contexts. Containing approximately 14,000 questions, the dataset provides a comprehensive resource for evaluating and improving LLM performance in biology. We benchmark several popular LLMs, analyzing their accuracy, reasoning patterns, and ability to understand domain-specific terminology and linguistic nuances. Additionally, we perform comprehensive experiments to evaluate the impact of prompt engineering, fine-tuning, and other optimization techniques on model performance. Our findings highlight both the strengths and limitations of current LLMs in handling specialized knowledge tasks in lowresource languages, offering valuable insights for future research and development.

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Mind the (Language) Gap: Towards Probing Numerical and Cross-Lingual Limits of LVLMs
Somraj Gautam | Abhirama Subramanyam Penamakuri | Abhishek Bhandari | Gaurav Harit

We introduce MMCRICBENCH-3K, a benchmark for Visual Question Answering (VQA) on cricket scorecards, designed to evaluate large vision-language models (LVLMs) on complex numerical and cross-lingual reasoning over semi-structured tabular images. MMCRICBENCH-3K comprises 1,463 synthetically generated scorecard images from ODI, T20, and Test formats, accompanied by 1,500 English QA pairs. It includes two subsets: MMCRICBENCH-E-1.5K, featuring English scorecards, and MMCRICBENCH-H1.5K, containing visually similar Hindi scorecards, with all questions and answers kept in English to enable controlled cross-script evaluation. The task demands reasoning over structured numerical data, multi-image context, and implicit domain knowledge. Empirical results show that even state-of-the-art LVLMs, such as GPT-4o and Qwen2.5VL, struggle on the English subset despite it being their primary training language and exhibit a further drop in performance on the Hindi subset. This reveals key limitations in structure-aware visual text understanding, numerical reasoning, and cross-lingual generalization. The dataset is publicly available via Hugging Face at https://huggingface.co/ datasets/DIALab/MMCricBench, to promote LVLM research in this direction.

Evaluating text generation capabilities of large language models (LLMs) is challenging, particularly for low-resource languages where methods for direct assessment are scarce. We propose MUG-Eval, a novel framework that evaluates LLMs’ multilingual generation capabilities by transforming existing benchmarks into conversational tasks and measuring the LLMs’ accuracies on those tasks. We specifically designed these conversational tasks to require effective communication in the target language. Then, we simply use task success rate as a proxy for successful conversation generation. Our approach offers two key advantages: it is independent of language-specific NLP tools or annotated datasets, which are limited for most languages, and it does not rely on LLMs-as-judges, whose evaluation quality degrades outside a few high-resource languages. We evaluate 8 LLMs across 30 languages spanning high, mid, and low-resource categories, and we find that MUG-Eval correlates strongly with established benchmarks (r > 0.75) while enabling standardized comparisons across languages and models. Our framework provides a robust and resource-efficient solution for evaluating multilingual generation that can be extended to thousands of languages.

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Scaling, Simplification, and Adaptation: Lessons from Pretraining on Machine-Translated Text
Dan John Velasco | Matthew Theodore Roque

Most languages lack sufficient data for largescale monolingual pretraining, creating a “data wall.” Multilingual pretraining helps but is limited by language imbalance and the “curse of multilinguality.” An alternative is to translate high-resource text with machine translation (MT), which raises three questions: (1) How does MT-derived data scale with model capacity? (2) Can source-side transformations (e.g., simplifying English with an LLM) improve generalization to native text? (3) How well do models pretrained on MT-derived data adapt when continually trained on limited native text? We investigate these questions by translating English into Indonesian and Tamil—two typologically distant, lowerresource languages—and pretraining GPT-2 models (124M–774M) on native or MT-derived corpora from raw and LLM-simplified English. We evaluate cross-entropy loss on native text, along with accuracy on syntactic probes and downstream tasks. Our results show that (1) MT-pretrained models benefit from scaling; (2) source-side simplification harms generalization to native text; and (3) adapting MT-pretrained models on native text often yields better performance than native-only models, even with less native data. However, tasks requiring cultural nuance (e.g., toxicity detection) demand more exposure to native data.

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A Federated Approach to Few-Shot Hate Speech Detection for Marginalized Communities
Haotian Ye | Axel Wisiorek | Antonis Maronikolakis | Özge Alaçam | Hinrich Schütze

Despite substantial efforts, detecting and preventing hate speech online remains an understudied task for marginalized communities, particularly in the Global South, which includes developing societies with increasing internet penetration. In this paper, we aim to provide marginalized communities in societies where the dominant language is low-resource with a privacy-preserving tool to protect themselves from online hate speech by filtering offensive content in their native languages. Our contributions are twofold: 1) we release REACT (REsponsive hate speech datasets Across ConTexts), a collection of high-quality, culturespecific hate speech detection datasets comprising multiple target groups and low-resource languages, curated by experienced data collectors; 2) we propose a few-shot hate speech detection approach based on federated learning (FL), a privacy-preserving method for collaboratively training a central model that exhibits robustness when tackling different target groups and languages. By keeping training local to user devices, we ensure data privacy while leveraging the collective learning benefits of FL. We experiment with both multilingual and monolingual pre-trained representation spaces as backbones to examine the interaction between FL and different model representations. Furthermore, we explore personalized client models tailored to specific target groups and evaluate their performance. Our findings indicate the overall effectiveness of FL across different target groups, and point to personalization as a promising direction.

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Training of LLM-Based List-Wise Multilingual Reranker
Hao Yu | David Ifeoluwa Adelani

Multilingual retrieval-augmented generation (MRAG) systems heavily rely on robust Information Retrieval (IR). Reranking as a key component optimizes the initially retrieved document set to present the most pertinent information to the generative model, addressing context limitations and minimizing hallucinations. We propose an approach that trains Large Language Models (LLMs) as multilingual listwise rerankers through supervised fine-tuning (SFT) on a diverse mixture of multilingual and extended English ranking examples, and enhancing reasoning capabilities through Direct Preference Optimization (DPO) from translated task-specific reasoning processes. Experiments demonstrate that the approach improves accuracy@5 by 20-30% across all six high- mediumand low-resource languages compared to the BM25. The posted training 1B models achieve comparable performance to 7B baseline models while enabling faster inference. Finally, we investigate the effectiveness of different reasoning strategies in DPO with crosslingual and monolingual thinking processes.

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bib (full) Proceedings of The 5th New Frontiers in Summarization Workshop

Large language models (LLMs) such as GPT-4, Claude and LLaMA are routinely used to evaluate long-form text generated by language models. We study the ability of these models to identify low quality texts, an increasingly rare subset of output which is of great interest to pinpoint during development. We present experiments with a panel of LLM judges, and crowd-sourced approximations of reference judgments. Pinpointing sub-par outputs is a difficult task for both models and crowdworkers, with models doing overall better. Moreover, unlike findings in prior work on factoid question answering, panels of cheaper models do not agree as well with high quality developer judgments of low quality as panels of frontier models. We present qualitative and quantitative analysis of the relative strengths of models in the panel, gleaning insights why they yield better results over a single model.

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Hierarchical Attention Adapter for Abstractive Dialogue Summarization
Raymond Li | Chuyuan Li | Gabriel Murray | Giuseppe Carenini

Dialogue summarization is still a very challenging task even for large language models (LLMs). On the one hand, some previous approaches have pre-trained language models specifically for dialogue understanding and summarization, but they have been limited to relatively small models. On the other hand, other works have tried to directly exploit the dialogue semantics and discourse structures in their modeling effort, but by construction, they require access to those structures, which is in itself a largely unsolved problem. In this paper, we synergistically combine these two ideas in an approach that can be seamlessly integrated into the decoder-only architecture adopted by the most state-of-the-art LLMs. In particular, our novel solution leverages the parameter-efficient fine-tuning (PEFT) paradigm to model the hierarchical structure of dialogues, where input sequences are naturally segmented into dialogue turns, and then fine-tune the model for abstractive summarization. From experiments on two datasets, we find that Hierarchical Attention Adapter outperforms all baseline adapter methods on SummScreen, where our approach can also be combined with LoRA to achieve the best performance on SamSum.

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CS-Sum: A Benchmark for Code-Switching Dialogue Summarization and the Limits of Large Language Models
Sathya Krishnan Suresh | Tanmay Surana | Lim Zhi Hao | Eng Siong Chng

Code-switching (CS) poses a significant challenge for Large Language Models (LLMs), yet its comprehensibility remains underexplored in LLMs. We introduce CS-Sum, to evaluate the comprehensibility of CS by the LLMs through CS dialogue to English summarization. CS-Sum is the first benchmark for CS dialogue summarization across Mandarin-English (EN-ZH), Tamil-English (EN-TA), and Malay-English (EN-MS), with 900-1300 human-annotated dialogues per language pair. Evaluating ten LLMs, including open and closed-source models, we analyze performance across few-shot, translate-summarize, and fine-tuning (LoRA, QLoRA on synthetic data) approaches. Our findings show that though the scores on automated metrics are high, LLMs make subtle mistakes that alter the complete meaning of the dialogue. To this end, we introduce 3 most common type of errors that LLMs make when handling CS input. Error rates vary across CS pairs and LLMs, with some LLMs showing more frequent errors on certain language pairs, underscoring the need for specialized training on code-switched data.

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Beyond Paraphrasing: Analyzing Summarization Abstractiveness and Reasoning
Nathan Zeweniuk | Ori Ernst | Jackie CK Cheung

While there have been many studies analyzing the ability of LLMs to solve problems through reasoning, their application of reasoning in summarization remains largely unexamined. This study explores whether reasoning is essential to summarization by investigating three questions: (1) Do humans frequently use reasoning to generate new summary content? (2) Do summarization models exhibit the same reasoning patterns as humans? (3) Should summarization models integrate more complex reasoning abilities? Our findings reveal that while human summaries often contain reasoning-based information, system-generated summaries rarely contain this same information. This suggests that models struggle to effectively apply reasoning, even when it could improve summary quality. We advocate for the development of models that incorporate deeper reasoning and abstractiveness, and we release our annotated data to support future research.

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Improving Aspect-Based Summarization via Contrastive Learning with Anchored Negative Examples
Elizabeth Palmieri | Yangfeng Ji

Text summarization helps users manage information overload, but traditional methods can be cumbersome when seeking specific details within a document. Aspect-based text summarization addresses this by using a query to guide which information should be summarized. However, distinguishing relevant from irrelevant information for a given aspect remains challenging in LLM-based summarization models. In this work, we propose utilizing contrastive learning to encourage LLMs to focus on aspect-related signals during training. We further design two variants of the learning algorithm, aspect-anchored and summary-anchored, corresponding to the strategies used in constructing negative examples. Evaluation with two representative LLM families (Llama 2 and Pythia) and two benchmark datasets (AnyAspect and CovidET) demonstrates the proposed methods’ strong performance compared to their supervised fine-tuning and zero-shot counterparts, highlighting contrastive learning as a promising direction for aspect-based text summarization.

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REFER: Mitigating Bias in Opinion Summarisation via Frequency Framed Prompting
Nannan Huang | Haytham M. Fayek | Xiuzhen Zhang

Individuals express diverse opinions, a fair summary should represent these viewpoints comprehensively.Previous research on fairness in opinion summarisation using large language models (LLMs) relied on hyperparameter tuning or providing ground truth distributional information in prompts. However, these methods face practical limitations: end-users rarely modify default model parameters, and accurate distributional information is often unavailable. Building upon cognitive science research demonstrating that frequency-based representations reduce systematic biases in human statistical reasoning by making reference classes explicit and reducing cognitive load, this study investigates whether frequency framed prompting (REFER) can similarly enhance fairness in LLM opinion summarisation. Through systematic experimentation with different prompting frameworks, we adapted techniques known to improve human reasoning to elicit more effective information processing in language models compared to abstract probabilistic representations. Our results demonstrate that REFER enhances fairness in language models when summarising opinions. This effect is particularly pronounced in larger language models and using stronger reasoning instructions.

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DACP: Domain-Adaptive Continual Pre-Training of Large Language Models for Phone Conversation Summarization
Xue-Yong Fu | Elena Khasanova | Md Tahmid Rahman Laskar | Harsh Saini | Shashi Bhushan Tn

Large language models (LLMs) have achieved impressive performance in text summarization, yet their performance often falls short when applied to specialized domains that differ from their original pre-training distribution. While fine-tuning can improve summarization quality, it typically relies on costly and scarce high-quality labeled data. In this work, we explore continual pre-training as a scalable, self-supervised approach to adapt LLMs for downstream summarization tasks, particularly in the context of noisy real-world conversation transcripts. We conduct extensive experiments using large-scale, unlabeled business conversation data to investigate whether continual pre-training enhances model capabilities in conversational summarization. Our results demonstrate that continual pre-training yields substantial gains in both in-domain and out-of-domain summarization benchmarks, while maintaining strong generalization and robustness. We also analyze the effects of data selection strategies, providing practical guidelines for applying continual pre-training in summarization-focused industrial applications.

Topic models represent topics as ranked term lists, which are often hard to interpret in scientific domains. We explore Topic Description for Scientific Corpora, an approach to generating structured summaries for topic-specific document sets. We propose and investigate two LLM-based pipelines: Selective Context Summarisation (SCS), which uses maximum marginal relevance to select representative documents; and Compressed Context Summarisation (CCS), a hierarchical approach that compresses document sets through iterative summarisation. We evaluate both methods using SUPERT and multi-model LLM-as-a-Judge across three topic modeling backbones and three scientific corpora. Our preliminary results suggest that SCS tends to outperform CCS in quality and robustness, while CCS shows potential advantages on larger topics. Our findings highlight interesting trade-offs between selective and compressed strategies for topic-level summarisation in scientific domains. We release code and data for two of the three datasets.

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HalluTree: Explainable Multi-Hop Hallucination Detection for Abstractive Summarization
Daniel Orshansky | Oskar Oomen | Naaisha Agarwal | Ryan Lagasse

Black-box verifiers for abstractive summaries often struggle with complex claims that require multi-hop reasoning, and they typically provide a single verdict without an interpretable rationale. As a result, it becomes difficult to understand or audit their failures. We address this with HalluTree, a framework that models verification as an interpretable claim tree. HalluTree first decomposes summaries into subclaims, classifying each into two types – extractive (directly verifiable against evidence) or inferential (requiring reasoning) – which follow distinct verification paths. Extractive claims are robustly verified against evidence using an ensemble of lightweight NLI models. Crucially, inferential claims trigger a process that generates a natural program – an explicit reasoning chain that integrates supporting evidence and logical steps – which is then executed to determine the claim’s validity. Evaluation on the LLM-AggreFact benchmark demonstrates HalluTree’s effectiveness: it achieves performance competitive with top-tier black-box models, including Bespoke-MiniCheck, while providing transparent and auditable reasoning programs for every inferential judgment. This combination of competitive accuracy and high interpretability offers a significant advance over opaque, single-classification verifiers. We will publically release code, data, prompts, and other artifacts upon acceptance.

Recent advances in test-time scaling have shown promising results in improving large language model performance through strategic computation allocation during inference. While this approach has demonstrated strong improvements in reasoning tasks, its application to natural language generation tasks, particularly summarization, remains unexplored.Among all of the generation tasks, multi-document summarization (MDS) presents unique challenges by requiring models to extract and synthesize essential information across multiple lengthy documents. Unlike reasoning tasks, MDS demands a more complicated approach to prompt design and ensemble methods, as no single “best-overall” prompt can satisfy diverse summarization requirements. The inherent diversity in summarization needs necessitates exploring how different prompting strategies can be systematically combined to improve performance.We propose a novel framework that harnesses prompt diversity to enhance MDS performance. Our approach generates multiple candidate summaries using carefully designed prompt variations, then ensemble them through sophisticated aggregation methods to produce refined summaries. This prompt diversity enables models to capture different aspects and perspectives of the source documents, leading to more comprehensive and higher-quality summaries. To evaluate our method effectively, we also introduce two new LLM-based metrics: the Preference Alignment Score (PAS) and LLM Atom-Content-Unit score (LLM-ACU), which assess summary quality while addressing the positional bias inherent in automatic evaluations performed by LLMs.Our experiments demonstrate that leveraging prompt diversity significantly enhances summary quality, while also revealing the practical scaling boundaries for MDS tasks.

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Bridging Multimodal and Video Summarization: A Unified Survey
Haopeng Zhang

Multimodal summarization (MMS) and video summarization (VS) have traditionally evolved in separate communities—natural language processing (NLP) and computer vision (CV), respectively. MMS focuses on generating textual summaries from inputs such as text, images, or audio, while VS emphasizes selecting key visual content. With the recent rise of vision-language models (VLMs), these once-disparate tasks are converging under a unified framework that integrates visual and linguistic understanding.In this survey, we provide a unified perspective that bridges MMS and VS. We formalize the task landscape, review key datasets and evaluation metrics, and categorize major modeling approaches into new taxonomy. In addition, we highlight core challenges and outline future directions toward building general-purpose multimodal summarization systems. By synthesizing insights from both NLP and CV communities, this survey aims to establish a coherent foundation for advancing this rapidly evolving field.

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AdvSumm: Adversarial Training for Bias Mitigation in Text Summarization
Mukur Gupta | Nikhil Reddy Varimalla | Nicholas Deas | Melanie Subbiah | Kathleen McKeown

Large Language Models (LLMs) have achieved impressive performance in text summarization and are increasingly deployed in real-world applications. However, these systems often inherit associative and framing biases from pre-training data, leading to inappropriate or unfair outputs in downstream tasks. In this work, we present AdvSumm (Adversarial Summarization), a domain-agnostic training framework designed to mitigate bias in text summarization through improved generalization. Inspired by adversarial robustness, AdvSumm introduces a novel Perturber component that applies gradient-guided perturbations at the embedding level of Sequence-to-Sequence models, enhancing the model’s robustness to input variations. We empirically demonstrate that AdvSumm effectively reduces different types of bias in summarization—specifically, name-nationality bias and political framing bias—without compromising summarization quality. Compared to standard transformers and data augmentation techniques like back-translation, AdvSumm achieves stronger bias mitigation performance across benchmark datasets.

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NSF-SciFy: Mining the NSF Awards Database for Scientific Claims
Delip Rao | Weiqiu You | Eric Wong | Chris Callison-Burch

We introduce NSF-SciFy, a comprehensive dataset of scientific claims and investigation proposals extracted from National Science Foundation award abstracts. While previous scientific claim verification datasets have been limited in size and scope, NSF-SciFy represents a significant advance with an estimated 2.8 million claims from 400,000 abstracts spanning all science and mathematics disciplines. We present two focused subsets: NSF-SciFy-MatSci with 114,000 claims from materials science awards, and NSF-SciFy-20K with 135,000 claims across five NSF directorates. Using zero-shot prompting, we develop a scalable approach for joint extraction of scientific claims and investigation proposals. We demonstrate the dataset’s utility through three downstream tasks: non-technical abstract generation, claim extraction, and investigation proposal extraction. Fine-tuning language models on our dataset yields substantial improvements, with relative gains often exceeding 100%, particularly for claim and proposal extraction tasks. Our error analysis reveals that extracted claims exhibit high precision but lower recall, suggesting opportunities for further methodological refinement. NSF-SciFy enables new research directions in large-scale claim verification, scientific discovery tracking, and meta-scientific analysis.

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QA-prompting: Improving Summarization with Large Language Models using Question-Answering
Neelabh Sinha

Language Models (LMs) have revolutionized natural language processing, enabling high-quality text generation through prompting and in-context learning. However, models often struggle with long-context summarization due to positional biases, leading to suboptimal extraction of critical information. There are techniques to improve this with fine-tuning, pipelining, or using complex techniques, which have their own challenges. To solve these challenges, we propose QA-prompting - a simple prompting method for summarization that utilizes question-answering as an intermediate step prior to summary generation. Our method extracts key information and enriches the context of text to mitigate positional biases and improve summarization in a single LM call per task without requiring fine-tuning or pipelining. Experiments on multiple datasets belonging to different domains using ten state-of-the-art pre-trained models demonstrate that QA-prompting outperforms baseline and other state-of-the-art methods, achieving up to 29% improvement in ROUGE scores. This provides an effective and scalable solution for summarization and highlights the importance of domain-specific question selection for optimal performance.

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bib (full) Proceedings of the Natural Legal Language Processing Workshop 2025

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Tracing Definitions: Lessons from Alliance Contracts in the Biopharmaceutical Industry
Maximilian Kreutner | Doerte Leusmann | Florian Lemmerich | Carolin Haeussler

Definitions in alliance contracts play a critical role in shaping agreements, yet they can also lead to costly misunderstandings. This is exemplified by the multimillion-dollar AstraZeneca-Euopean Commission (EC) dispute, where the interpretation of ‘best reasonable effort’ became the focal point of contention. In this interdisciplinary study, we leverage natural language processing (NLP) to systematically analyze patterns in the definitions included in alliance contracts. More specifically, we categorize the content of definitions into topics, identify common terms versus outliers that are semantically dissimilar and infrequently used, and track how definitions evolve over time. Analyzing a dataset of 380,131 definitions from 12,468 alliance contracts in the biopharmaceutical industry, we distinguish that definitions span legal, technological, and social topics, with social terms showing the highest dissimilarity across contracts. Using dynamic topic modeling, we explore how the content of definitions has shifted over two decades (2000–2020) and identify prevalent trends suggesting that contractual definitions reflect broader economic contexts. Notably, our results reveal that the AstraZeneca-EC dispute arose from an outlier—a highly unusual definition—that could have been flagged using NLP. Overall, these findings highlight the potential of data-driven approaches to uncover patterns in alliance contracts.

AI agents are increasingly used in consumer-facing applications to assist with tasks such as product search, negotiation, and transaction execution. In this paper, we investigate a future setting where both consumers and merchants authorize AI agents to automate the negotiations and transactions in consumer settings. We aim to address two questions: (1) Do different LLM agents exhibit varying performances when making deals on behalf of their users? (2) What are the potential risks when we use AI agents to fully automate negotiations and deal-making in consumer settings? We designed an experimental framework to evaluate AI agents’ capabilities and performance in real-world negotiation and transaction scenarios, and experimented with a range of open-source and closed-source LLMs. Our analysis reveals that deal-making with LLM agents in consumer settings is an inherently imbalanced game: different AI agents have large disparities in obtaining the best deals for their users. Furthermore, we found that LLMs’ behavioral anomaly might lead to financial loss when deployed in real-world decision-making scenarios, such as overspending or making unreasonable deals. Our findings highlight that while automation can enhance transactional efficiency, it also poses nontrivial risks to consumer markets. Users should be careful when delegating business decisions to LLM agents.

Retrieval-Augmented Generation (RAG) is a promising approach to mitigate hallucinations in Large Language Models (LLMs) for legal applications, but its reliability is critically dependent on the accuracy of the retrieval step. This is particularly challenging in the legal domain, where large databases of structurally similar documents often cause retrieval systems to fail. In this paper, we address this challenge by first identifying and quantifying a critical failure mode we term Document-Level Retrieval Mismatch (DRM), where the retriever selects information from entirely incorrect source documents. To mitigate DRM, we investigate a simple and computationally efficient technique which we refer to as Summary-Augmented Chunking (SAC). This method enhances each text chunk with a document-level synthetic summary, thereby injecting crucial global context that would otherwise be lost during a standard chunking process. Our experiments on a diverse set of legal information retrieval tasks show that SAC greatly reduces DRM and, consequently, also improves text-level retrieval precision and recall. Interestingly, we find that a generic summarization strategy outperforms an approach that incorporates legal expert domain knowledge to target specific legal elements. Our work provides evidence that this practical, scalable, and easily integrable technique enhances the reliability of RAG systems when applied to large-scale legal document datasets.

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Translating Tax Law to Code with LLMs: A Benchmark and Evaluation Framework
Gabriele Lorenzo | Aldo Pietromatera | Nils Holzenberger

Catala is a domain-specific programming language for tax law, meant to facilitate the translation of legal text into executable computer code, thanks to a syntax close to that of legal language and reasoning. Legal statutes paired with their Catala translation have been published online periodically, but manual translation remains labor-intensive. In this work, we develop a benchmark for the evaluation of Catala code generation from legal text, including a training set to fine-tune Large Language Models. To assess the quality of the generated code, we introduce an evaluation framework extending current metrics for code generation. Our experiments with few-shot learning, as well as fine-tuned models, suggest the feasibility of automating legal code generation, and contrast with prior attempts to translate legal language into a formal representation.

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Beyond the Haystack: Sensitivity to Context in Legal Reference Recall
Eric Xia | Karthik Srikumar | Keshav Karthik | Advaith Renjith | Ashwinee Panda

Reference retrieval is critical for many applications in the legal domain, for instance in determining which case texts support a particular claim. However, existing benchmarking methods do not rigorously enable evaluation of recall capabilities in previously unseen contexts. We develop an evaluation framework from U.S. court opinions which ensures models have no prior knowledge of case results or context. Applying our framework, we identify an consistent gap across models and tasks between traditional needle-in-a-haystack retrieval and actual performance in legal recall. Our work shows that standard needle-in-a-haystack benchmarks consistently overestimate recall performance in the legal domain. By isolating the causes of performance degradation to contextual informativity rather than distributional differences, our findings highlight the need for specialized testing in reference-critical applications, and establish an evaluation framework for improving retrieval across informativity levels.

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Machine Unlearning of Personally Identifiable Information in Large Language Models
Dan Parii | Thomas van Osch | Chang Sun

Pretrained LLMs are trained on massive web-scale datasets, which often contain personally identifiable information (PII), raising serious legal and ethical concerns. A key research challenge is how to effectively unlearn PII without degrading the model’s utility or leaving implicit knowledge that can be exploited.This study proposes UnlearnPII, a benchmark designed to evaluate the effectiveness of PII unlearning methods, addressing limitations in existing metrics that overlook implicit knowledge and assess all tokens equally. Our benchmark focuses on detecting PII leakage, testing model robustness through obfuscated prompts and jailbreak attacks over different domains, while measuring utility and retention quality.To advance practical solutions, we propose a new PII unlearning method - PERMU_tok. By applying token-level noise, we achieve 1) simplified integration into existing workflows, 2) improved retention and output quality, while maintaining unlearning effectiveness. The code is open-source and publicly available.

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Modeling Motivated Reasoning in Law: Evaluating Strategic Role Conditioning in LLM Summarization
Eunjung Cho | Alexander Miserlis Hoyle | Yoan Hermstrüwer

Large Language Models (LLMs) are increasingly used to generate user-tailored summaries, adapting outputs to specific stakeholders. In legal contexts, this raises important questions about motivated reasoning — how models strategically frame information to align with a stakeholder’s position within the legal system. Building on theories of legal realism and recent trends in legal practice, we investigate how LLMs respond to prompts conditioned on different legal roles (e.g., judges, prosecutors, attorneys) when summarizing judicial decisions. We introduce an evaluation framework grounded in legal fact and reasoning inclusion, also considering favorability towards stakeholders. Our results show that even when prompts include balancing instructions, models exhibit selective inclusion patterns that reflect role-consistent perspectives. These findings raise broader concerns about how similar alignment may emerge as LLMs begin to infer user roles from prior interactions or context, even without explicit role instructions. Our results underscore the need for role-aware evaluation of LLM summarization behavior in high-stakes legal settings.

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Label-Free Distinctiveness: Building a Continuous Trademark Scale via Synthetic Anchors
Huihui Xu | Kevin D. Ashley

Trademark law protects distinctive marks that are able to identify and distinguish goods or services. The Abercrombie spectrum classifies marks from generic to fanciful based on distinctiveness. The Abercrombie spectrum employs hard buckets while the real world ofbranding rarely falls into neat bins: marks often hover at the blurry border between “descriptive” and “suggestive” for example. Byrequiring trademark examiners or researchers to pick one of the five buckets, one loses useful information where the lines get blurry. Sohard boundaries obscure valuable gradations of meaning. In this work, we explore creating a continuous ruler of distinctiveness asa complementary diagnostic tool to the original buckets. The result is a label-free ladder, where every mark, real or synthetic, gets a real-valued score. These continuous scores reveal subtle distinctions among marks and provide interpretable visualizations that help practitioners understand where a mark falls relative to established anchors. Testing with 95 expert-classified trademark examples achieves a Spearman’s ρ = 0.718 and Pearson’s r = 0.724 against human labels, while offering intuitive visualizations on the continuous spectrum. Ademo can be found at https://distinctiveness-ruler-demo.streamlit.app/.

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Copyright Infringement by Large Language Models in the EU: Misalignment, Safeguards, and the Path Forward
Noah Scharrenberg | Chang Sun

This position paper argues that European copyright law has struggled to keep pace with the development of large language models (LLMs), possibly creating a fundamental epistemic misalignment: copyright compliance relies on qualitative, context-dependent standards, while LLM development is governed by quantitative, proactive metrics. This gap means that technical safeguards, by themselves, may be insufficient to reliably demonstrate legal compliance. We identify several practical limitations in the existing EU legal frameworks, including ambiguous “lawful access” rules, fragmented opt-outs, and vague disclosure duties. We then discuss technical measures such as provenance-first data governance, machine unlearning for post-hoc removal, and synthetic data generation, showing their promise but also their limits.Finally, we propose a path forward grounded in legal-technical co-design, suggesting directions for standardising machine-readable opt-outs, disclosure templates, clarifying core legal terms, and developing legally-informed benchmarks and evidence standards. We conclude that such an integrated framework is essential to make compliance auditable, thus protected creators’ rights while enabling responsible AI innovation at scale.

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Grounded Answers from Multi-Passage Regulations: Learning-to-Rank for Regulatory RAG
Tuba Gokhan | Ted Briscoe

Regulatory compliance questions often require aggregating evidence from multiple, interrelated sections of long, complex documents. To support question-answering (QA) in this setting, we introduce ObliQA-MP, a dataset for multi-passage regulatory QA, extending the earlier ObliQA benchmark (CITATION), and improve evidence quality with an LLM–based validation step that filters out ~20% of passages missed by prior natural language inference (NLI) based filtering. Our benchmarks show a notable performance drop from single- to multi-passage retrieval, underscoring the challenges of semantic overlap and structural complexity in regulatory texts. To address this, we propose a feature-based learning-to-rank (LTR) framework that integrates lexical, semantic, and graph-derived information, achieving consistent gains over dense and hybrid baselines. We further add a lightweight score-based filter to trim noisy tails and an obligation-centric prompting technique. On ObliQA-MP, LTR improves retrieval (Recall@10/MAP@10/nDCG@10) over dense, hybrid, and fusion baselines. Our generation approach, based on domain-specific filtering plus prompting, achieves strong scores using the RePAS metric (CITATION) on ObliQA-MP, producing faithful, citation-grounded answers. Together, ObliQA-MP and our validation and RAG systems offer a stronger benchmark and a practical recipe for grounded, citation-controlled QA in regulatory domains.

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NyayGraph: A Knowledge Graph Enhanced Approach for Legal Statute Identification in Indian Law using Large Language Models
Siddharth Shukla | Tanuj Tyagi | Abhay Singh Bisht | Ashish Sharma | Basant Agarwal

One of the first steps in the judicial processis finding the applicable statutes/laws basedon the facts of the current situation. Manu-ally searching through multiple legislation andlaws to find the relevant statutes can be time-consuming, making the Legal Statute Identi-fication (LSI) task important for reducing theworkload, helping improve the efficiency ofthe judicial system. To address this gap, wepresent a novel knowledge graph-enhanced ap-proach for Legal Statute Identification (LSI) inIndian legal documents using Large LanguageModels, incorporating structural relationshipsfrom the Indian Penal Code (IPC) the main leg-islation codifying criminal laws in India. Onthe IL-TUR benchmark, explicit KG inferencesignificantly enhances recall without sacrific-ing competitive precision. Augmenting LLMprompts with KG context, though, merely en-hances coverage at the expense of precision,underscoring the importance of good rerank-ing techniques. This research provides the firstcomplete IPC knowledge graph and shows thatorganized legal relations richly augment statuteretrieval, subject to being integrated into lan-guage models in a judicious way. Our code anddata are publicly available at Github. (https://github.com/SiddharthShukla48/NyayGraph)

The rise of influencer marketing has blurred boundaries between organic content and sponsored content, making the enforcement of legal rules relating to transparency challenging. Effective regulation requires applying legal knowledge with a clear purpose and reason, yet current detection methods of undisclosed sponsored content generally lack legal grounding or operate as opaque “black boxes.” Using 1,143 Instagram posts, we compare gpt-5-nano and gemini-2.5-flash-lite under three prompting strategies with controlled levels of legal knowledge provided. Both models perform strongly in classifying content as sponsored or not (F1 up to 0.93), with Gemini favouring recall (0.93) and GPT favouring precision (0.95), though performance drops by over 10 points on ambiguous cases. We further develop a taxonomy of reasoning errors, showing frequent citation omissions (28.57%), unclear references (20.71%), and hidden ads exhibiting the highest miscue rate (28.57%). While adding regulatory text to the prompt improves explanation quality, it does not consistently improve detection accuracy. The contribution of this paper is threefold. First, it makes a novel addition to regulatory compliance technology by providing a taxonomy of common errors in LLM-generated legal reasoning to evaluate whether automated moderation is not only accurate but also legally robust, thereby advancing the transparent detection of influencer marketing content. Second, it features an original dataset of LLM explanations annotated by two students who were trained in influencer marketing law. Third, it combines quantitative and qualitative evaluation strategies for LLM explanations and critically reflects on how these findings can support advertising regulatory bodies in automating moderation processes on a solid legal foundation.

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Validate Your Authority: Benchmarking LLMs on Multi-Label Precedent Treatment Classification
M. Mikail Demir | M Abdullah Canbaz

Automating the classification of negative treatment in legal precedent is a critical yet nuanced NLP task where misclassification carries significant risk. To address the shortcomings of standard accuracy, this paper introduces a more robust evaluation framework. We benchmark modern Large Language Models on a new, expert-annotated dataset of 239 real-world legal citations and propose a novel Average Severity Error metric to better measure the practical impact of classification errors. Our experiments reveal a performance split: Google’s Gemini 2.5 Flash achieved the highest accuracy on a high-level classification task (79.1%), while OpenAI’s GPT-5-mini was the top performer on the more complex fine-grained schema (67.7%). This work establishes a crucial baseline, provides a new context-rich dataset, and introduces an evaluation metric tailored to the demands of this complex legal reasoning task.

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Labor Lex: A New Portuguese Corpus and Pipeline for Information Extraction in Brazilian Legal Texts
Pedro Vitor Quinta de Castro | Nádia Félix Felipe Da Silva

Relation Extraction (RE) is a challenging Natural Language Processing task that involves identifying named entities from text and classifying the relationships between them. When applied to a specific domain, the task acquires a new layer of complexity, handling the lexicon and context particular to the domain in question. In this work, this task is applied to the Legal domain, specifically targeting Brazilian Labor Law. Architectures based on Deep Learning, with word representations derived from Transformer Language Models (LM), have shown state-of-the-art performance for the RE task. Recent works on this task handle Named Entity Recognition (NER) and RE either as a single joint model or as a pipelined approach. In this work, we introduce Labor Lex, a newly constructed corpus based on public documents from Brazilian Labor Courts. We also present a pipeline of models trained on it. Different experiments are conducted for each task, comparing supervised training using LMs and In-Context Learning (ICL) with Large Language Models (LLM), and verifying and analyzing the results for each one. For the NER task, the best achieved result was 89.97% F1-Score, and for the RE task, the best result was 82.38% F1-Score. The best results for both tasks were obtained using the supervised training approach.

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Evaluating the Role of Verifiers in Test-Time Scaling for Legal Reasoning Tasks
Davide Romano | Jonathan Richard Schwarz | Daniele Giofrè

Test-time scaling (TTS) techniques can improve the performance of large language models (LLMs) at the expense of additional computation and latency. While TTS has proven effective in formal domains such as mathematics and programming (Snell et al., 2024; Chen et al., 2024), its value in argumentative domains such as law remains underexplored. We present an empirical study of verifier-based TTS methods for legal multiple-choice QA (MCQA) across five benchmarks. Using a family of 7 reward models, we evaluate both outcome-level (Best-of-N) and process-level (tree search) verification under realistic low-N budgets. Our analysis systematically investigates how verifier utility is affected by key properties such as domain specialization, model size, and supervision type (process-supervised PRMs vs. outcome-only ORMs), even when applied across different roles.

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Domain Adapted Text Summarization with Self-Generated Guidelines
Andrianos Michail | Bartosz Rudnikowicz | Pavlos Fragkogiannis | Cristina Kadar

Text summarization systems face significant adaptation costs when deployed across diverse domains, requiring expensive few-shot learning or manual prompt engineering. We propose a cost-effective domain adaptation framework that generates reusable summarization guidelines using only two reference summaries and three LLM inferences. Our approach works by having the model compare its own generated summaries against domain specific reference summaries in a one time preparation step that derives concise natural language guidelines that capture the summarization patterns of the target domain. These guidelines are then appended to the summarization prompt to adapt the LLM to the target domain at a minimal cost. We evaluate our method across diverse model sizes on three distinct summarization domains: Lawsuits, ArXiv papers, and Patents. Automatic metrics show that guideline-based adaptation achieves comparable or superior performance compared to in-context learning and zero-shot baselines. An LLM preference evaluation using the latest models shows that summaries generated using such guidelines are superior to the zero-shot or in-context learning summarization prompts. Our method enables efficient domain adaptation of text summarizer LLMs with a minimal resource overhead, making specialized summarization particularly accessible for agentic systems that require to process heterogeneous texts in enterprise environments.

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PILOT-Bench: A Benchmark for Legal Reasoning in the Patent Domain with IRAC-Aligned Classification Tasks
Yehoon Jang | Chaewon Lee | Hyun-seok Min | Sungchul Choi

The Patent Trial and Appeal Board (PTAB) of the USPTO adjudicates thousands of *ex parte* appeals each year, requiring the integration of technical understanding and legal reasoning. While large language models (LLMs) are increasingly applied in patent and legal practice, their use has remained limited to lightweight tasks, with no established means of systematically evaluating their capacity for structured legal reasoning in the patent domain. To address this gap, we introduce **PILOT-Bench** (**P**atent **I**nva**L**idati**O**n **T**rial Benchmark), a dataset and benchmark that aligns PTAB decisions with USPTO patent data at the case level and formalizes three IRAC-aligned classification tasks: Issue Type, Board Authorities, and Subdecision. We evaluate a diverse set of commercial and open-source LLMs and conduct analyses across multiple perspectives, including input-variation settings, model families, and error tendencies. Notably, on the Issue Type task, commercial models consistently exceed 0.75 in Exact Match, whereas the strongest open-source model (Qwen-8B) achieves performance around 0.56, highlighting the substantial gap in reasoning capabilities. PILOT-Bench establishes a foundation for the systematic evaluation of patent-domain legal reasoning and points toward future directions for improving LLMs through dataset design and model alignment. All data, code, and benchmark resources are available at https://github.com/TeamLab/pilot-bench.

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Efficient Prompt Optimisation for Legal Text Classification with Proxy Prompt Evaluator
Hyunji Lee | Kevin Chenhao Li | Matthias Grabmair | Shanshan Xu

Prompt optimization aims to systematically refine prompts to enhance a language model’s performance on specific tasks. Fairness detection in Terms of Service (ToS) clauses is a challenging legal NLP task that demands carefully crafted prompts to ensure reliable results. However, existing prompt optimization methods are often computationally expensive due to inefficient search strategies and costly prompt candidate scoring. In this paper, we propose a framework that combines Monte Carlo Tree Search (MCTS) with a proxy prompt evaluator to more effectively explore the prompt space while reducing evaluation costs. Experiments demonstrate that our approach achieves higher classification accuracy and efficiency than baseline methods under a constrained computation budget.

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ContractEval: Benchmarking LLMs for Clause-Level Legal Risk Identification in Commercial Contracts
Shuang Liu | Zelong Li | Ruoyun Ma | Haiyan Zhao | Mengnan Du

The potential of large language models (LLMs) in contract legal risk analysis remains underexplored. In response, this paper introduces ContractEval, the first benchmark to thoroughly evaluate whether open-source LLMs could match proprietary LLMs in identifying clause-level legal risks in commercial contracts. Using the Contract Understanding Atticus Dataset (CUAD), we assess 4 proprietary and 15 open-source LLMs. Our results highlight five key findings: (1) Proprietary models outperform open-source models in both correctness and output effectiveness. (2) Larger open-source models generally perform better, though the improvement slows down as models get bigger. (3) Reasoning (“thinking”) mode improves output effectiveness but reduces correctness, likely due to over-complicating simpler tasks. (4) Open-source models generate “no related clause” responses more frequently even when relevant clauses are present. (5) Model quantization speed up inference but at the cost of performance drop, showing the tradeoff between efficiency and accuracy. These findings suggest that while most LLMs perform at a level comparable to junior legal assistants, open-source models require targeted fine-tuning to ensure correctness and effectiveness in high-stakes legal settings. ContractEval offers a solid benchmark to guide future development of legal-domain LLMs.

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Contemporary LLMs struggle with extracting formal legal arguments
Lena Held | Ivan Habernal

Legal Argument Mining (LAM) is a complex challenge for humans and language models alike. This paper explores the application of Large Language Models (LLMs) in LAM, focusing on the identification of fine-grained argument types within judgment texts. We compare the performance of Flan-T5 and Llama 3 models against a baseline RoBERTa model to study if the advantages of magnitude-bigger LLMs can be leveraged for this task. Our study investigates the effectiveness of fine-tuning and prompting strategies in enhancing the models’ ability to discern nuanced argument types. Despite employing state-of-the-art techniques, our findings indicate that neither fine-tuning nor prompting could surpass the performance of a domain-pre-trained encoder-only model. This highlights the challenges and limitations in adapting general-purpose large language models to the specialized domain of legal argumentation. The insights gained from this research contribute to the ongoing discourse on optimizing NLP models for complex, domain-specific tasks. Our code and data for reproducibility are available at https://github.com/trusthlt/legal-argument-spans.

The Retrieval-Augmented Generation (RAG) systems’ performance on Thai legal question answering is still limited, especially for questions requiring extensive, complex legal reasoning. To address these limitations, we introduce a resource-efficient approach that aligns Large Language Models (LLMs) for improved citation accuracy and response quality using Group-Relative Policy Optimization (GRPO). Our proposed method leverages BGE-M3 embeddings as a cost-efficient semantic-similarity reward, significantly reducing computational expenses up to 2.5x compared to an LLM-based reward model. Experiments on the NitiBench benchmark demonstrate substantial improvements: GRPO achieves up to 90% citation-F1 gains relative to the base model and a 31% increase in joint quality metrics over instruction tuning. Crucially, our approach provides a practical and effective solution for enhancing legal LLMs in resource-constrained environments.

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Not ready for the bench: LLM legal interpretation is unstable and uncalibrated to human judgments
Abhishek Purushothama | Junghyun Min | Brandon Waldon | Nathan Schneider

Legal interpretation frequently involves assessing how a legal text, as understood by an ‘ordinary’ speaker of the language, applies to the set of facts characterizing a legal dispute. Recent scholarship has proposed that legal practitioners add large language models (LLMs) to their interpretive toolkit. This work offers an empirical argument against LLM-assisted interpretation as recently practiced by legal scholars and federal judges. Our investigation in English shows that models do not provide stable interpretive judgments and are susceptible to subtle variations in the prompt. While instruction tuning slightly improves model calibration to human judgments, even the best-calibrated LLMs remain weak predictors of human native speakers’ judgments.

Evaluating large language model (LLM) outputs in the legal domain presents unique challenges due to the complex and nuanced nature of legal analysis. Current evaluation approaches either depend on reference data, which is costly to produce, or use standardized assessment methods, both of which have significant limitations for legal applications.Although LLM-as-a-Judge has emerged as a promising evaluation technique, its reliability and effectiveness in legal contexts depend heavily on evaluation processes unique to the legal industry and how trustworthy the evaluation appears to the human legal expert. This is where existing evaluation methods currently fail and exhibit considerable variability.This paper aims to close the gap: a) we break down lengthy responses into “Legal Data Points” (LDPs) — self-contained units of information — and introduce a novel, reference-free evaluation methodology that reflects how lawyers evaluate legal answers; b) we demonstrate that our method outperforms a variety of baselines on both our proprietary dataset and an open-source dataset (LegalBench); c) we show how our method correlates more closely with human expert evaluations and helps improve inter-annotator agreement; and finally d) we open source our Legal Data Points for a subset of LegalBench used in our experiments, allowing the research community to replicate our results and advance research in this vital area of LLM evaluation on legal question-answering.

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A Framework to Retrieve Relevant Laws for Will Execution
Md Asiful Islam | Alice Saebom Kwak | Derek Bambauer | Clayton T Morrison | Mihai Surdeanu

Wills must comply with jurisdiction-specific statutory provisions to be valid, but retrieving the relevant laws for execution, validation, and probate remains labor-intensive and error-prone. Prior legal information retrieval (LIR) research has addressed contracts, criminal law, and judicial decisions, but wills and probate law remain largely unexplored, with no prior work on retrieving statutes for will validity assessment. We propose a legal information retrieval framework that combines lexical and semantic retrieval in a hybrid pipeline with large language model (LLM) reasoning to retrieve the most relevant provisions for a will statement. Evaluations on annotated will-statement datasets from the U.S. states of Tennessee and Idaho using six LLMs show that our hybrid framework consistently outperforms zero-shot baselines. Notably, when paired with our hybrid retrieval pipeline, GPT-5-mini achieves the largest relative accuracy gains, improving by 41.09 points on the Tennessee and 48.68 points on the Idaho test set. We observed similarly strong improvements across all models and datasets.

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CourtNav: Voice-Guided, Anchor-Accurate Navigation of Long Legal Documents in Courtrooms
Sai Khadloya | Kush Juvekar | Arghya Bhattacharya | Utkarsh Saxena

Judicial work depends on close reading of longrecords, charge sheets, pleadings, annexures,orders, often spanning hundreds of pages. Withlimited staff support, exhaustive reading duringhearings is impractical. We present CourtNav,a voice-guided, anchor-first navigator for legalPDFs that maps a judge’s spoken command(e.g., “go to paragraph 23”, “highlight the contradiction in the cross-examination”) directlyto a highlighted paragraph in seconds. CourtNav transcribes the command, classifies intentwith a grammar-first, LLM-backed router, retrieves over a layout-aware hybrid index, andauto-scrolls the viewer to the cited span whilehighlighting it and close alternates. By design, the interface shows only grounded pas-sages, never free text, keeping evidence verifiable and auditable. This need is acute in India, where judgments and cross-examinations notoriously long.In a pilot on representative charge sheets, pleadings, and orders, median time-to-relevance drops from 3–5 minutes (manual navigation) to 10–15 seconds;with quick visual verification included, 30–45seconds. Under fixed time budgets, thisnavigation-first design increases the breadth ofthe record actually consulted while preservingcontrol and transparency

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Are LLMs Court-Ready? Evaluating Frontier Models on Indian Legal Reasoning
Kush Juvekar | Arghya Bhattacharya | Sai Khadloya | Utkarsh Saxena

Large language models (LLMs) are moving into legal workflows, yet we lack a jurisdiction-grounded way to gauge their basic competence in thereof. We use India’s public legal examinations as a transparent proxy. Our multi-year benchmark assembles objective screens from top national and state exams and evaluates open and frontier LLMs under real world exam conditions. To probe beyond MCQs, we also include a lawyer-graded, paired-blinded study of long-form answers from the Supreme Court’s Advocate-on-Record exam. This is, to our knowledge, the first exam-grounded, India-specific yardstick for LLM court-readiness released with datasets and protocols. Our work shows that while frontier systems consistently clear historical cutoffs and often match or exceed recent top-scorer bands on objective exams, none surpasses the human topper on long-form reasoning. Grader notes converge on three reliability failure modes—procedural/format compliance, authority/citation discipline, and forum-appropriate voice/structure. These findings delineate where LLMs can assist (checks, cross-statute consistency, statute and precedent lookups) and where human leadership remains essential: forum-specific drafting and filing, procedural and relief strategy, reconciling authorities and exceptions, and ethical, accountable judgment.

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LegalSim: Multi-Agent Simulation of Legal Systems for Discovering Procedural Exploits
Sanket Badhe

We present LegalSim, a modular multi-agent simulation of adversarial legal proceedings that explores how AI systems can exploit procedural weaknesses in codified rules. Plaintiff and defendant agents choose from a constrained action space (for example, discovery requests, motions, meet-and-confer, sanctions) governed by a JSON rules engine, while a stochastic judge model with calibrated grant rates, cost allocations, and sanction tendencies resolves outcomes. We compare four policies: PPO, a contextual bandit with an LLM, a direct LLM policy, and a hand-crafted heuristic; Instead of optimizing binary case outcomes, agents are trained and evaluated using effective win rate and a composite exploit score that combines opponent-cost inflation, calendar pressure, settlement pressure at low merit, and a rule-compliance margin. Across configurable regimes (e.g., bankruptcy stays, inter partes review, tax procedures) and heterogeneous judges, we observe emergent “exploit chains”, such as cost-inflating discovery sequences and calendar-pressure tactics that remain procedurally valid yet systemically harmful. Evaluation via cross-play and Bradley-Terry ratings shows, PPO wins more often, the bandit is the most consistently competitive across opponents, the LLM trails them, and the heuristic is weakest. The results are stable in judge settings, and the simulation reveals emergent exploit chains, motivating red-teaming of legal rule systems in addition to model-level testing.

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Linking Transparency and Accountability: Analysing The Connection Between TikTok’s Terms of Service and Moderation Decisions
Leonard Eßer | Gerasimos Spanakis

The European Commission’s Digital Services Act (DSA) mandates that Very Large Online Platforms (VLOPs), like TikTok, provide Statements of Reason (SoRs) to justify their content moderation decisions in an attempt to enhance transparency and accountability for these platforms. However, we can often notice a gap between these automated decisions and the platform’s written policies. This leaves users unable to understand the specific rule they have violated. This paper addresses this gap by developing and evaluating a pipeline to link TikTok’s SoRs from the DSA transparency database to the most relevant clause from TikTok’s policy documents. We test multiple methods to perform the linking task and evaluate performance using a wide range of retrieval methods and metrics.We develop and deliver a gold-standard dataset where a team of legal research assistants annotated 100 SoRs based on four criteria: clarity, understanding, presence of unclear terms and level of detail, each rated on a 1–4 scale. In addition, a binary rating is assigned for redress clarity. Moreover, annotators determined the best link to the relevant TikTok policy clauses. Results show that both TikTok’s SoRs and policy clauses are often extremely broad, granting TikTok more freedom to decide how to apply the clauses, making it even less transparent for users. We also provide a demo that, for each SoR, provides a ranking of the most relevant clauses from TikTok’s written policies, a tool that can be useful for users, regulators and researchers to better understand content moderation decisions, assess compliance with transparency requirements, and support further analysis of platform accountability.

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Risks and Limits of Automatic Consolidation of Statutes
Max Prior | Adrian Hof | Niklas Wais | Matthias Grabmair

As in many countries of the Civil Law tradition, consolidated versions of statutes - statutes with added amendments - are difficult to obtain reliably and promptly in Germany. This gap has prompted interest in using large language models (LLMs) to ‘synthesize’ current and historical versions from amendments. Our paper experiments with an LLM-based consolidation framework and a dataset of 908 amendment–law pairs drawn from 140 Federal Law Gazette documents across four major codes. While automated metrics show high textual similarity (93-99%) for single-step and multi-step amendment chains, only 50.3% of exact matches (single-step) and 20.51% (multi-step) could be achieved; our expert assessment reveals that non-trivial errors persist and that even small divergences can carry legal significance. We therefore argue that any public or private deployment must treat outputs as drafts subject to rigorous human verification.

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GReX: A Graph Neural Network-Based Rerank-then-Expand Method for Detecting Conflicts Among Legal Articles in Korean Criminal Law
Seonho An | Young-Yik Rhim | Min-Soo Kim

As social systems become more complex, legal articles have grown increasingly intricate, making it harder for humans to identify potential conflicts among them, particularly when drafting new laws or applying existing ones. Despite its importance, no method has been proposed to detect such conflicts. We introduce a new legal NLP task, Legal Article Conflict Detection (LACD), which aims to identify conflicting articles within a given body of law. To address this task, we propose GReX, a novel graph neural network-based retrieval method. Experimental results show that GReX significantly outperforms existing methods, achieving improvements of 44.8% in nDCG@50, 32.8% in Recall@50, and 39.8% in Retrieval F1@50. Our codes are in github.com/asmath472/LACD-public.

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GuRE:Generative Query REwriter for Legal Passage Retrieval
Daehui Kim | Deokhyung Kang | Jonghwi Kim | Sangwon Ryu | Gary Lee

Legal Passage Retrieval (LPR) systems are crucial as they help practitioners save time when drafting legal arguments. However, it remains an underexplored avenue. One primary reason is the significant vocabulary mismatch between the query and the target passage. To address this, we propose a simple yet effective method, the Generative query REwriter (GuRE). We leverage the generative capabilities of Large Language Models (LLMs) by training the LLM for query rewriting. "Rewritten queries" help retrievers to retrieve target passages by mitigating vocabulary mismatch. Experimental results show that GuRE significantly improves performance in a retriever-agnostic manner, outperforming all baseline methods. Further analysis reveals that different training objectives lead to distinct retrieval behaviors, making GuRE more suitable than direct retriever fine-tuning for real-world applications. Codes are avaiable at github.com/daehuikim/GuRE.

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Extract-Explain-Abstract: A Rhetorical Role-Driven Domain-Specific Summarisation Framework for Indian Legal Documents
Veer Chheda | Aaditya Uday Ghaisas | Avantika Sankhe | Dr. Narendra Shekokar

Legal documents are characterized by theirlength, intricacy, and dense use of jargon, making efficacious summarisation both paramountand challenging. Existing zero-shot methodologies in small language models struggle tosimplify this jargon and are prone to punts andhallucinations with longer prompts. This paperintroduces the Rhetorical Role-based Extract-Explain-Abstract (EEA) Framework, a novelthree-stage methodology for summarisation ofIndian legal documents in low-resource settings. The approach begins by segmenting legaltexts using rhetorical roles, such as facts, issues and arguments, through a domain-specificphrase corpus and extraction based on TF-IDF.In the explanation stage, the segmented output is enriched with logical connections to ensure coherence and legal fidelity. The final abstraction phase condenses these interlinked segments into cogent, high-level summaries thatpreserve critical legal reasoning. Experimentson Indian legal datasets show that the EEAframework typically outperforms in ROUGE,BERTScore, Flesch Reading Ease, Age of Acquisition, SummaC and human evaluations. Wealso employ InLegalBERTScore as a metric tocapture domain specific semantics of Indianlegal documents.

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bib (full) Proceedings of the The 4th Workshop on Perspectivist Approaches to NLP

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Proceedings of the The 4th Workshop on Perspectivist Approaches to NLP
Gavin Abercrombie | Valerio Basile | Simona Frenda | Sara Tonelli | Shiran Dudy

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A Disaggregated Dataset on English Offensiveness Containing Spans
Pia Pachinger | Janis Goldzycher | Anna M. Planitzer | Julia Neidhardt | Allan Hanbury

Toxicity labels at sub-document granularity and disaggregated labels lead to more nuanced and personalized toxicity classification and facilitate analysis. We re-annotate a subset of 1983 posts of the Jigsaw Toxic Comment Classification Challenge and provide disaggregated toxicity labels and spans that identify inappropriate language and targets of toxic statements. Manual analysis shows that five annotations per instance effectively capture meaningful disagreement patterns and allow for finer distinctions between genuine disagreement and that arising from annotation error or inconsistency. Our main findings are: (1) Disagreement often stems from divergent interpretations of edge-case toxicity (2) Disagreement is especially high in cases of toxic statements involving non-human targets (3) Disagreement on whether a passage consists of inappropriate language occurs not only on inherently questionable terms, but also on words that may be inappropriate in specific contexts while remaining acceptable in others (4) Transformer-based models effectively learn from aggregated data that reduces false negative classifications by being more sensitive towards minority opinions for posts to be toxic. We publish the new annotations under the CC BY 4.0 license.

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CINEMETRIC: A Framework for Multi-Perspective Evaluation of Conversational Agents using Human-AI Collaboration
Vahid Sadiri Javadi | Zain Ul Abedin | Lucie Flek

Despite advances in conversational systems, the evaluation of such systems remains a challenging problem. Current evaluation paradigms often rely on costly homogeneous human annotators or oversimplified automated metrics, leading to a critical gap in socially aligned conversational agents, where pluralistic values (i.e., acknowledging diverse human experiences) are essential to reflect the inherently subjective and contextual nature of dialogue quality. In this paper, we propose CINEMETRIC, a novel framework that operationalizes pluralistic alignment by leveraging the perspectivist capacities of large language models. Our approach introduces a mechanism where LLMs simulate a diverse set of evaluators, each with distinct personas constructed by matching real human annotators to movie characters based on both demographic profiles and annotation behaviors. These role-played characters independently assess subjective tasks, offering a scalable and human-aligned alternative to traditional evaluation. Empirical results show that our approach consistently outperforms baseline methods, including LLM as a Judge and as a Personalized Judge, across multiple LLMs, showing high and consistent agreement with human ground truth. CINEMETRIC improves accuracy by up to 20% and reduces mean absolute error in toxicity prediction, demonstrating its effectiveness in capturing human-like perspectives.

Irony is a subjective and pragmatically complex phenomenon, often conveyed through rhetorical figures and interpreted differently across individuals. In this study, we adopt a perspectivist approach, accounting for the socio-demographic background of annotators, to investigate whether specific rhetorical strategies promote a shared perception of irony within demographic groups, and whether Large Language Models (LLMs) reflect specific perspectives. Focusing on the Italian subset of the perspectivist MultiPICo dataset, we manually annotate rhetorical figures in ironic replies using a linguistically grounded taxonomy. The annotation is carried out by expert annotators balanced by generation and gender, enabling us to analyze inter-group agreement and polarization. Our results show that some rhetorical figures lead to higher levels of agreement, suggesting that certain rhetorical strategies are more effective in promoting a shared perception of irony. We fine-tune multilingual LLMs for rhetorical figure classification, and evaluate whether their outputs align with different demographic perspectives. Results reveal that models show varying degrees of alignment with specific groups, reflecting potential perspectivist behavior in model predictions. These findings highlight the role of rhetorical figures in structuring irony perception and underscore the importance of socio-demographics in both annotation and model evaluation.

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From Disagreement to Understanding: The Case for Ambiguity Detection in NLI
Chathuri Jayaweera | Bonnie J. Dorr

This position paper argues that annotation disagreement in Natural Language Inference (NLI) is not mere noise but often reflects meaningful variation, especially when triggered by ambiguity in the premise or hypothesis. While underspecified guidelines and annotator behavior contribute to variation, content-based ambiguity provides a process-independent signal of divergent human perspectives. We call for a shift toward ambiguity-aware NLI that first identifies ambiguous input pairs, classifies their types, and only then proceeds to inference. To support this shift, we present a framework that incorporates ambiguity detection and classification prior to inference. We also introduce a unified taxonomy that synthesizes existing taxonomies, illustrates key subtypes with examples, and motivates targeted detection methods that better align models with human interpretation. Although current resources lack datasets explicitly annotated for ambiguity and subtypes, this gap presents an opportunity: by developing new annotated resources and exploring unsupervised approaches to ambiguity detection, we enable more robust, explainable, and human-aligned NLI systems.

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Balancing Quality and Variation: Spam Filtering Distorts Data Label Distributions
Eve Fleisig | Matthias Orlikowski | Philipp Cimiano | Dan Klein

For datasets to accurately represent diverse opinions in a population, they must preserve variation in data labels while filtering out spam or low-quality responses. How can we balance annotator reliability and representation? We empirically evaluate how a range of heuristics for annotator filtering affect the preservation of variation on subjective tasks. We find that these methods, designed for contexts in which variation from a single ground-truth label is considered noise, often remove annotators who disagree instead of spam annotators, introducing suboptimal tradeoffs between accuracy and label diversity. We find that conservative settings for annotator removal (<5%) are best, after which all tested methods increase the mean absolute error from the true average label. We analyze performance on synthetic spam to observe that these methods often assume spam annotators are less random than real spammers tend to be: most spammers are distributionally indistinguishable from real annotators, and the minority that are distinguishable tend to give fixed answers, not random ones. Thus, tasks requiring the preservation of variation reverse the intuition of existing spam filtering methods: spammers tend to be less random than non-spammers, so metrics that assume variation is spam fare worse. These results highlight the need for spam removal methods that account for label diversity.

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Consistency is Key: Disentangling Label Variation in Natural Language Processing with Intra-Annotator Agreement
Gavin Abercrombie | Tanvi Dinkar | Amanda Cercas Curry | Verena Rieser | Dirk Hovy

We commonly use agreement measures to assess the utility of judgements made by human annotators in Natural Language Processing (NLP) tasks. While inter-annotator agreement is frequently used as an indication of label reliability by measuring consistency between annotators, we argue for the additional use of intra-annotator agreement to measure label stability (and annotator consistency) over time. However, in a systematic review, we find that the latter is rarely reported in this field. Calculating these measures can act as important quality control and could provide insights into why annotators disagree. We conduct exploratory annotation experiments to investigate the relationships between these measures and perceptions of subjectivity and ambiguity in text items, finding that annotators provide inconsistent responses around 25% of the time across four different NLP tasks.

Access to high-quality labeled data remains a limiting factor in applied supervised learning. Active learning (AL), a popular approach to optimizing the use of limited annotation budgets in training ML models, often relies on at least one of several simplifying assumptions, which rarely hold in practice when acknowledging human label variation (HLV). Label variation (LV), i.e., differing labels for the same instance, is common, especially in natural language processing. Yet annotation frameworks often still rest on the assumption of a single ground truth, overlooking HLV, i.e., the occurrence of plausible differences in annotations, as an informative signal. In this paper, we examine foundational assumptions about truth and label nature, highlighting the need to decompose observed LV into signal (e.g., HLV) and noise (e.g., annotation error). We survey how the AL and (H)LV communities have addressed—or neglected—these distinctions and propose a conceptual framework for incorporating HLV throughout the AL loop, including instance selection, annotator choice, and label representation. We further discuss the integration of large language models (LLM) as annotators. Our work aims to lay a conceptual foundation for (H)LV-aware active learning, better reflecting the complexities of real-world annotation.

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Weak Ensemble Learning from Multiple Annotators for Subjective Text Classification
Ziyi Huang | N. R. Abeynayake | Xia Cui

With the rise of online platforms, moderating harmful or offensive user-generated content has become increasingly critical. As manual moderation is infeasible at scale, machine learning models are widely used to support this process. However, subjective tasks, such as offensive language detection, often suffer from annotator disagreement, resulting in noisy supervision that hinders training and evaluation. We propose Weak Ensemble Learning (WEL), a novel framework that explicitly models annotator disagreement by constructing and aggregating weak predictors derived from diverse annotator perspectives. WEL enables robust learning from subjective and inconsistent labels without requiring annotator metadata. Experiments on four benchmark datasets show that WEL outperforms strong baselines across multiple metrics, demonstrating its effectiveness and flexibility across domains and annotation conditions.

Models trained on crowdsourced annotations may not reflect population views, if those who work as annotators do not represent the broader population. In this paper, we propose PAIR: Population-Aligned Instance Replication, a post-processing method that adjusts training data to better reflect target population characteristics without collecting additional annotations. Using simulation studies on offensive language and hate speech detection with varying annotator compositions, we show that non-representative pools degrade model calibration while leaving accuracy largely unchanged. PAIR corrects these calibration problems by replicating annotations from underrepresented annotator groups to match population proportions. We conclude with recommendations for improving the representativity of training data and model performance.

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Hypernetworks for Perspectivist Adaptation
Daniil Ignatev | Denis Paperno | Massimo Poesio

The task of perspective-aware classification introduces a bottleneck in terms of parametric efficiency that did not get enough recognition in existing studies. In this article, we aim to address this issue by applying an existing architecture, the hypernetwork+adapters combination, to perspectivist classification. Ultimately, we arrive at a solution that can compete with specialized models in adopting user perspectives on hate speech and toxicity detection, while also making use of considerably fewer parameters. Our solution is architecture-agnostic and can be applied to a wide range of base models out of the box.

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SAGE: Steering Dialog Generation with Future-Aware State-Action Augmentation
Yizhe Zhang | Navdeep Jaitly

Recent advances in large language models have enabled impressive task-oriented applications, yet building emotionally intelligent chatbots for natural, strategic conversations remains challenging. Current approaches often assume a single “ground truth” for emotional responses, overlooking the subjectivity of human emotion. We present a novel perspectivist approach, SAGE, that models multiple perspectives in dialogue generation using latent variables. At its core is the State-Action Chain (SAC), which augments standard fine-tuning with latent variables capturing diverse emotional states and conversational strategies between turns, in a future-looking manner. During inference, these variables are generated before each response, enabling multi-perspective control while preserving natural interactions. We also introduce a self-improvement pipeline combining dialogue tree search, LLM-based reward modeling, and targeted fine-tuning to optimize conversational trajectories. Experiments show improved LLM-based judgments while maintaining strong general LLM performance. The discrete latent variables further enable search-based strategies and open avenues for state-level reinforcement learning in dialogue systems, where learning can occur at the state level rather than the token level.

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Calibration as a Proxy for Fairness and Efficiency in a Perspectivist Ensemble Approach to Irony Detection
Samuel B. Jesus | Guilherme Dal Bianco | Wanderlei Junior | Valerio Basile | Marcos André Gonçalves

Identifying subjective phenomena, such as irony in language, poses unique challenges, as these tasks involve subjective interpretation shaped by both cultural and individual perspectives. Unlike conventional models that rely on aggregated annotations, perspectivist approaches aim to capture the diversity of viewpoints by leveraging the knowledge of specific annotator groups, promoting fairness and representativeness. However, such models often incur substantial computational costs, particularly when fine-tuning large-scale pre-trained language models. We also observe that the fine-tuning process can negatively impact fairness, producing certain perspective models that are underrepresented and have limited influence on the outcome. To address these, we explore two complementary strategies: (i) the adoption of traditional machine learning algorithms—such as Support Vector Machines, Random Forests, and XGBoost—as lightweight alternatives; and (ii) the application of calibration techniques to reduce imbalances in inference generation across perspectives. Our results demonstrate up to 12× faster processing with no statistically significant drop in accuracy. Notably, calibration significantly enhances fairness, reducing inter-group bias and leading to more balanced predictions across diverse social perspectives.

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Non-directive corpus annotation to reveal individual perspectives with underspecified guidelines: the case of mental workload
Iuliia Arsenteva | Caroline Dubois | Philippe Le Goff | Sylvie Plantin | Ludovic Tanguy

This paper investigates personal perceptions of mental workload through an innovative, non-directive corpus annotation method, allowing individuals of diverse profiles to define their own dimensions of annotation based on their personal perception. It contrasts with traditional approaches guided by explicit objectives and strict guidelines. Mental workload, a multifaceted concept in psychology, is characterized through various academic definitions and models. Our research, aligned with the principles of the perspectivist approach, aims to examine the degree to which individuals share a common understanding of this concept when reading the same texts. It seeks to compare the corpus produced by this non-directive annotation method. The participants, mainly employees of a large French enterprise and some academic experts on mental workload, were given the freedom to propose labels and annotate a set of texts. The experimental protocol revealed notable similarities in labels, segments, and overall annotation behavior, despite the absence of predefined guidelines. These findings suggest that individuals, given the freedom, tend to develop overlapping representations of mental workload. Furthermore, they demonstrate how non-directive annotation can uncover shared and diverse perceptions of complex concepts like mental workload, contributing to a richer understanding of how such perceptions are constructed across different individuals.

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BoN Appetit Team at LeWiDi-2025: Best-of-N Test-time Scaling Can Not Stomach Annotation Disagreements (Yet)
Tomas Ruiz | Siyao Peng | Barbara Plank | Carsten Schwemmer

Test-time scaling is a family of techniques to improve LLM outputs at inference time by performing extra computation. To the best of our knowledge, test-time scaling has been limited to domains with verifiably correct answers, like mathematics and coding. We transfer test-time scaling to the LeWiDi-2025 tasks to evaluate annotation disagreements. We experiment with three test-time scaling methods: two benchmark algorithms (Model Averaging and Majority Voting), and a Best-of-N (BoN) sampling method. The two benchmark methods improve LLM performance consistently on the LeWiDi tasks, but the BoN method does not. Our experiments suggest that the BoN method does not currently transfer from mathematics to LeWiDi tasks, and we analyze potential reasons for this gap.

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DeMeVa at LeWiDi-2025: Modeling Perspectives with In-Context Learning and Label Distribution Learning
Daniil Ignatev | Nan Li | Hugh Mee Wong | Anh Dang | Shane Kaszefski Yaschuk

This system paper presents the DeMeVa team’s approaches to the third edition of the Learning with Disagreements shared task (LeWiDi 2025; Leonardelli et al., 2025). We explore two directions: in-context learning (ICL) with large language models, where we compare example sampling strategies; and label distribution learning (LDL) methods with RoBERTa (Liu et al., 2019b), where we evaluate several fine-tuning methods. Our contributions are twofold: (1) we show that ICL can effectively predict annotator-specific annotations (perspectivist annotations), and that aggregating these predictions into soft labels yields competitive performance; and (2) we argue that LDL methods are promising for soft label predictions and merit further exploration by the perspectivist community.

Many researchers have reached the conclusion that ai models should be trained to be aware of the possibility of variation and disagreement in human judgments, and evaluated as per their ability to recognize such variation. The LeWiDi series of shared tasks on Learning With Disagreements was established to promote this approach to training and evaluating ai models, by making suitable datasets more accessible and by developing evaluation methods. The third edition of the task builds on this goal by extending the LeWiDi benchmark to four datasets spanning paraphrase identification, irony detection, sarcasm detection, and natural language inference, with labeling schemes that include not only categorical judgments as in previous editions, but ordinal judgments as well. Another novelty is that we adopt two complementary paradigms to evaluate disagreement-aware systems: the soft-label approach, in which models predict population-level distributions of judgments, and the perspectivist approach, in which models predict the interpretations of individual annotators. Crucially, we moved beyond standard metrics such as cross-entropy, and tested new evaluation metrics for the two paradigms. The task attracted diverse participation, and the results provide insights into the strengths and limitations of methods to modeling variation. Together, these contributions strengthen LeWiDi as a framework and provide new resources, benchmarks, and findings to support the development of disagreement-aware technologies.

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LPI-RIT at LeWiDi-2025: Improving Distributional Predictions via Metadata and Loss Reweighting with DisCo
Mandira Sawkar | Samay U. Shetty | Deepak Pandita | Tharindu Cyril Weerasooriya | Christopher M. Homan

The Learning With Disagreements (LeWiDi) 2025 shared task aims to model annotator disagreement through soft label distribution prediction and perspectivist evaluation, which focuses on modeling individual annotators. We adapt DisCo (Distribution from Context), a neural architecture that jointly models item-level and annotator-level label distributions, and present detailed analysis and improvements. In this paper, we extend DisCo by introducing annotator metadata embeddings, enhancing input representations, and multi-objective training losses to capture disagreement patterns better. Through extensive experiments, we demonstrate substantial improvements in both soft and perspectivist evaluation metrics across three datasets. We also conduct in-depth calibration and error analyses that reveal when and why disagreement-aware modeling improves. Our findings show that disagreement can be better captured by conditioning on annotator demographics and by optimizing directly for distributional metrics, yielding consistent improvements across datasets.

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McMaster at LeWiDi-2025: Demographic-Aware RoBERTa
Mandira Sawkar | Samay U. Shetty | Deepak Pandita | Tharindu Cyril Weerasooriya | Christopher M. Homan

We present our submission to the Learning With Disagreements (LeWiDi) 2025 shared task. Our team implemented a variety of BERT-based models that encode annotator meta-data in combination with text to predict soft-label distributions and individual annotator labels. We show across four tasks that a combination of demographic factors leads to improved performance, however through ablations across all demographic variables we find that in some cases, a single variable performs best. Our approach placed 4th in the overall competition.

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NLP-ResTeam at LeWiDi-2025:Performance Shifts in Perspective Aware Models based on Evaluation Metrics
Olufunke O. Sarumi | Charles Welch | Daniel Braun

Recent works in Natural Language Processing have focused on developing methods to model annotator perspectives within subjective datasets, aiming to capture opinion diversity. This has led to the development of various approaches that learn from disaggregated labels, leading to the question of what factors most influence the performance of these models. While dataset characteristics are a critical factor, the choice of evaluation metric is equally crucial, especially given the fluid and evolving concept of perspectivism. A model considered state-of-the-art under one evaluation scheme may not maintain its top-tier status when assessed with a different set of metrics, highlighting a potential challenge between model performance and the evaluation framework. This paper presents a performance analysis of annotator modeling approaches using the evaluation metrics of the 2025 Learning With Disagreement (LeWiDi) shared task and additional metrics. We evaluate five annotator-aware models under the same configurations. Our findings demonstrate a significant metric-induced shift in model rankings. Across four datasets, no single annotator modeling approach consistently outperformed others using a single metric, revealing that the “best” model is highly dependent on the chosen evaluation metric. This study systematically shows that evaluation metrics are not agnostic in the context of perspectivist model assessment.

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Opt-ICL at LeWiDi-2025: Maximizing In-Context Signal from Rater Examples via Meta-Learning
Taylor Sorensen | Yejin Choi

Many natural language processing (NLP) tasks involve subjectivity, ambiguity, or legitimate disagreement between annotators. In this paper, we outline our system for modeling human variation. Our system leverages language models’ (LLMs) in-context learning abilities, along with a two-step meta-learning training procedure for 1) post-training on many datasets requiring in-context learning and 2) specializing the model via in-context meta-learning to the particular data distribution of interest. We also evaluate the performance of our system submission to the Learning With Disagreements (LeWiDi) competition, where it was the overall winner on both tasks. Additionally, we perform an ablation study to measure the importance of each system component. We find that including rater examples in-context is crucial for our system’s performance, dataset-specific fine-tuning is helpful on the larger datasets, post-training on other in-context datasets is helpful on one of the competition datasets, and that performance improves with model scale.

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PromotionGo at LeWiDi-2025: Enhancing Multilingual Irony Detection with Data-Augmented Ensembles and L1 Loss
Ziyi Huang | N. R. Abeynayake | Xia Cui

This paper presents our system for the Learning with Disagreements (LeWiDi-2025) shared task (Leonardelli et al., 2025), which targets the challenges of interpretative variation in multilingual irony detection. We introduce a unified framework that models annotator disagreement through soft-label prediction, multilingual adaptation and robustness-oriented training. Our approach integrates tailored data augmentation strategies (i.e., lexical swaps, prompt-based reformulation and back-translation) with an ensemble learning scheme to enhance sensitivity to contextual and cultural nuances. To better align predictions with human-annotated probability distributions, we compare multiple loss functions, including cross-entropy, Kullback—Leibler divergence and L1 loss, the latter showing the strongest compatibility with the Average Manhattan Distance evaluation metric. Comprehensive ablation studies reveal that data augmentation and ensemble learning consistently improve performance across languages, with their combination delivering the largest gains. The results demonstrate the effectiveness of combining augmentation diversity, metric-compatible optimisation and ensemble aggregation for tackling interpretative variation in multilingual irony detection.

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twinhter at LeWiDi-2025: Integrating Annotator Perspectives into BERT for Learning with Disagreements
Nguyen Huu Dang Nguyen | Dang Van Thin

Annotator-provided information during labeling can reflect differences in how texts are understood and interpreted, though such variation may also arise from inconsistencies or errors. To make use of this information, we build a BERT-based model that integrates annotator perspectives and evaluate it on four datasets from the third edition of the Learning With Disagreements (LeWiDi) shared task. For each original data point, we create a new (text, annotator) pair, optionally modifying the text to reflect the annotator’s perspective when additional information is available. The text and annotator features are embedded separately and concatenated before classification, enabling the model to capture individual interpretations of the same input. Our model achieves first place on both tasks for the Par and VariErrNLI datasets. More broadly, it performs very well on datasets where annotators provide rich information and the number of annotators is relatively small, while still maintaining competitive results on datasets with limited annotator information and a larger annotator pool.

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Uncertain (Mis)Takes at LeWiDi-2025: Modeling Human Label Variation With Semantic Entropy
Ieva Raminta Staliūnaitė | Andreas Vlachos

The VariErrNLI task requires detecting the degree to which each Natural Language Inference (NLI) label is acceptable to a group of annotators. This paper presents an approach to VariErrNLI which incorporates measures of uncertainty, namely Semantic Entropy (SE), to model human label variation. Our method is based on the assumption that if two labels are plausible alternatives, then their explanations must be non-contradictory. We measure SE over Large Language Model (LLM)-generated explanations for a given NLI label, which represents the model uncertainty over the semantic space of possible explanations for that label. The system employs SE scores combined with an encoding of the inputs and generated explanations, and reaches a 0.31 Manhattan distance score on the test set, ranking joint first in the soft evaluation of VariErrNLI.

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bib (full) Proceedings of the First Workshop on Tailoring AI: Exploring Active and Passive LLM Personalization (PALS 2025)

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Proceedings of the First Workshop on Tailoring AI: Exploring Active and Passive LLM Personalization (PALS 2025)
Andrew Silva | Katherine Metcalf | Lucie Charlotte Magister | Clemencia Siro | Maartje Ter Hoeve

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bib (full) Proceedings of the 14th Joint Conference on Lexical and Computational Semantics (*SEM 2025)

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Proceedings of the 14th Joint Conference on Lexical and Computational Semantics (*SEM 2025)
Lea Frermann | Mark Stevenson

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ChengyuSTS: An Intrinsic Perspective on Mandarin Idiom Representation
Le Qiu | Emmanuele Chersoni | Aline Villavicencio

Chengyu, or four-character idioms, are ubiquitous in both spoken and written Chinese. Despite their importance, chengyu are often underexplored in NLP tasks, and existing evaluation frameworks are primarily based on extrinsic measures. In this paper, we introduce an intrinsic evaluation task for Chinese idiomatic understanding: idiomatic semantic textual similarity (iSTS), which evaluates how well models can capture the semantic similarity of sentences containing idioms. To this purpose, we present a curated dataset: ChengyuSTS. Our experiments show that current pre-trained sentence Transformer models generally fail to capture the idiomaticity of chengyu in a zero-shot setting. We then show results of fine-tuned models using the SimCSE contrastive learning framework, which demonstrate promising results for handling idiomatic expressions. We also presented the results of DeepSeek for reference

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Bridging Information Gaps with Comprehensive Answers: Improving the Diversity and Informativeness of Follow-Up Questions
Zhe Liu | Taekyu Kang | Haoyu Wang | Seyed Hossein Alavi | Vered Shwartz

Generating diverse follow-up questions that uncover missing information remains challenging for conversational agents, particularly when they run on small, locally hosted models. To tackle this problem, we develop an information-gap–driven pipeline that contrasts the initial answer with an LLM-generated comprehensive answer, identifies the information gaps, and formulates gap-bridging follow-up questions. Applying the pipeline, we augment an existing dataset–FollowupQG–tenfold. Experiments show that models fine-tuned on the augmented dataset achieve significantly higher informativeness and diversity than variations trained on the original dataset. These findings indicate that our pipeline, which mirrors the human cognitive process of information seeking, provides an efficient distillation channel from state-of-the-art LLMs to smaller models, enabling resource-constrained conversational systems to generate more diverse and informative follow-up questions.

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Injecting Frame Semantics into Large Language Models via Prompt-Based Fine-Tuning
Shahid Iqbal Rai | Danilo Croce | Roberto Basili

Large Language Models (LLMs) have demonstrated remarkable generalization across diverse NLP tasks, yet they often produce outputs lacking semantic coherence due to insufficient grounding in structured linguistic knowledge. This paper proposes a novel method for injecting Frame Semantics into a pretrained LLaMA model using Low-Rank Adaptation (LoRA). Leveraging FrameNet (a rich resource of over 1,000 semantic frames) we construct a training corpus comprising structured triples of frame definitions, frame elements, and lexical units. Our method encodes these examples into the model via LoRA adapters and evaluates performance using zero-shot prompting for textual entailment and semantic role labeling (SRL) over Framenet. Experimental results show that our adapted frame-aware LLM substantially outperforms the baseline across closed, open-ended, and multiple-choice prompts. Moreover, we observe significant improvements in SRL accuracy, demonstrating the efficacy of combining frame-semantic theory with parameter-efficient pretraining.

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From Complex Word Identification to Substitution: Instruction-Tuned Language Models for Lexical Simplification
Tonghui Han | Xinru Zhang | Yaxin Bi | Maurice D. Mulvenna | Dongqiang Yang

Lexical-level sentence simplification is essential for improving text accessibility, yet traditional methods often struggle to dynamically identify complex terms and generate contextually appropriate substitutions, resulting in limited generalization. While prompt-based approaches with large language models (LLMs) have shown strong performance and adaptability, they often lack interpretability and are prone to hallucinating. This study proposes a fine-tuning approach for mid-sized LLMs to emulate the lexical simplification pipeline. We transform complex word identification datasets into an instruction–response format to support instruction tuning. Experimental results show that our method substantially enhances complex word identification accuracy with reduced hallucinations while achieving competitive performance on lexical simplification benchmarks. Furthermore, we find that integrating fine-tuning with prompt engineering reduces dependency on manual prompt optimization, leading to a more efficient simplification framework.

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Semantic Prosody in Machine Translation: the English-Chinese Case of Passive Structures
Xinyue Ma | Pol Pastells | Mariona Taulé Delor | Mireia Farrús

Semantic prosody is a collocational meaning formed through the co-occurrence of a linguistic unit and a consistent series of collocates, which should be treated separately from semantic meaning. Since words that are literal translation of each other may have different semantic prosody, more attention should be paid to this linguistic property in order to generate accurate translation. However, current machine translation models cannot handle this problem. To bridge the gap, we propose an approach to teach machine translation models about semantic prosody of a specific structure. We focus on Chinese BEI passives and create a dataset of English-Chinese sentence pairs with the purpose of demonstrating the negative semantic prosody of BEI passives. Then we fine-tune OPUS-MT, NLLB-600M and mBART50-mmt models with our dataset for the English-Chinese translation task. Our results show that fine-tuned MT models perform better on using BEI passives for translating unfavourable content and avoid using it for neutral and favourable content. Also, in NLLB-600M, which is a multilingual model, this knowledge of semantic prosody can be transferred from English-Chinese translation to other language pairs, such as Spanish-Chinese.

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Semantic Differentiation in Speech Emotion Recognition: Insights from Descriptive and Expressive Speech Roles
Rongchen Guo | Vincent Francoeur | Isar Nejadgholi | Sylvain Gagnon | Miodrag Bolic

Speech Emotion Recognition (SER) is essential for improving human-computer interaction, yet its accuracy remains constrained by the complexity of emotional nuances in speech. In this study, we distinguish between descriptive\ semantics, which represents the contextual content of speech, and expressive\ semantics, which reflects the speaker’s emotional state. After watching emotionally charged movie segments, we recorded audio clips of participants describing their experiences, along with the intended emotion tags for each clip, participants’ self-rated emotional responses, and their valence/arousal scores. Through experiments we show that descriptive semantics align with intended emotions, while expressive semantics correlate with evoked emotions. Our findings inform SER applications in human-AI interaction and pave the way for more context-aware AI systems.

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Generalizability of Media Frames: Corpus creation and analysis across countries
Agnese Daffara | Sourabh Dattawad | Sebastian Padó | Tanise Ceron

Frames capture aspects of an issue that are emphasized in a debate by interlocutors and can help us understand how political language conveys different perspectives and ultimately shapes people’s opinions. The Media Frame Corpus (MFC) is the most commonly used framework with categories and detailed guidelines for operationalizing frames. It is, however, focused on a few salient U.S. news issues, making it unclear how well these frames can capture news issues in other cultural contexts. To explore this, we introduce FrameNews-PT, a dataset of Brazilian Portuguese news articles covering political and economic news and annotate it within the MFC framework.Through several annotation rounds, we evaluate the extent to which MFC frames generalize to the Brazilian debate issues. We further evaluate how fine-tuned and zero-shot models perform on out-of-domain data.Results show that the 15 MFC frames remain broadly applicable with minor revisions of the guidelines. However, some MFC frames are rarely used, and novel news issues are analyzed using general ‘fallback’ frames. We conclude that cross-cultural frame use requires careful consideration.

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Cross-Lingual Extractive Question Answering with Unanswerable Questions
Yuval Gorodissky | Elior Sulem | Dan Roth

Cross-lingual Extractive Question Answering (EQA) extends standard EQA by requiring models to find answers in passages written in languages different from the questions. The Generalized Cross-Lingual Transfer (G-XLT) task evaluates models’ zero-shot ability to transfer question answering capabilities across languages using only English training data. While previous research has primarily focused on scenarios where answers are always present, real-world applications often encounter situations where no answer exists within the given context. This paper introduces an enhanced G-XLT task definition that explicitly handles unanswerable questions, bridging a critical gap in current research. To address this challenge, we present two new datasets: miXQuAD and MLQA-IDK, which address both answerable and unanswerable questions and respectively cover 12 and 7 language pairs. Our study evaluates state-of-the-art large language models using fine-tuning, parameter-efficient techniques, and in-context learning approaches, revealing interesting trade-offs between a smaller fine-tuned model’s performance on answerable questions versus a larger in-context learning model’s capability on unanswerable questions. We also examine language similarity patterns based on model performance, finding alignments with known language families.

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Evaluating Compositional Generalisation in VLMs and Diffusion Models
Beth Pearson | Bilal Boulbarss | Michael Wray | Martha Lewis

A fundamental aspect of the semantics of natural language is that novel meanings can be formed from the composition of previously known parts.Vision-language models (VLMs) have made significant progress in recent years, however, there is evidence that they are unable to perform this kind of composition. For example, given an image of a red cube and a blue cylinder, a VLM such as CLIP is likely to incorrectly label the image as a red cylinder or a blue cube, indicating it represents the image as a ‘bag-of-words’ and fails to capture compositional semantics. Diffusion models have recently gained significant attention for their impressive generative abilities, and zero-shot classifiers based on diffusion models have been shown to perform competitively with CLIP in certain compositional tasks. We explore whether the generative Diffusion Classifier has improved compositional generalisation abilities compared to discriminative models. We assess three models—Diffusion Classifier, CLIP, and ViLT—on their ability to bind objects with attributes and relations in both zero-shot learning (ZSL) and generalised zero-shot learning (GZSL) settings. Our results show that the Diffusion Classifier and ViLT perform well at concept binding tasks, but that all models struggle significantly with the relational GZSL task, underscoring the broader challenges VLMs face with relational reasoning. Analysis of CLIP embeddings suggests that the difficulty may stem from overly similar representations of relational concepts such as left and right. Code and dataset are available at [link redacted for anonymity].

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On the Distinctive Co-occurrence Characteristics of Antonymy
Zhihan Cao | Hiroaki Yamada | Takenobu Tokunaga

Antonymy has long received particular attention in lexical semantics.Previous studies have shown that antonym pairs frequently co-occur in text, across genres and parts of speech, more often than would be expected by chance. However, whether this co-occurrence pattern is distinctive of antonymy remains unclear, due to a lack of comparison with other semantic relations. This work fills the gap by comparing antonymy with three other relations across parts of speech using robust co-occurrence metrics. We find that antonymy is distinctive in three respects: antonym pairs co-occur with high strength, in a preferred linear order, and within short spans. All results are available online.

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Evaluating Textual and Visual Semantic Neighborhoods of Abstract and Concrete Concepts
Sven Naber | Diego Frassinelli | Sabine Schulte Im Walde

This paper presents a systematic evaluation of nearest neighbors in a range of semantic spaces across textual and visual modalities.Focusing on the abstractness-concreteness continuum, we define an overlap measure to compare concepts differing in their linguistic vs. perceptual nature, and indeed find that alignment is primarily determined by modality and concreteness: Models from the same modality show stronger alignment than cross-modal models, and spaces of concrete concepts show stronger alignment than those of abstract ones.

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AmbiStory: A Challenging Dataset of Lexically Ambiguous Short Stories
Janosch Gehring | Michael Roth

Word sense disambiguation is the task of selecting a word’s applicable word sense in a given context. However, ambiguous texts may lack the information necessary to disambiguate words completely, resulting in multiple word senses with varying degrees of plausibility. We design a dataset around this premise: Our samples consist of 4–5 sentence short stories, where the sentence with the word to be disambiguated is itself ambiguous and surrounding sentences only contain indirect clues towards the more plausible word sense. We collect annotations from humans who rate the plausibility of a given word sense on a scale from 1–5. In total, our dataset contains 19,701 human word sense annotations on 1,899 stories. We investigate the performance of large language models on our data and find that many poorly correlate with human judgments. We also find that fine-tuning on our data can increase performance.

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WiC Evaluation in Galician and Spanish: Effects of Dataset Quality and Composition
Marta Vázquez Abuín | Marcos Garcia

This work explores the impact of dataset quality and composition on Word-in-Context performance for Galician and Spanish. We assess existing datasets, validate their test sets, and create new manually constructed evaluation data. Across five experiments with controlled variations in training and test data, we find that while the validation of test data tends to yield better model performance, evaluations on manually created datasets suggest that contextual embeddings are not sufficient on their own to reliably capture word meaning variation. Regarding training data, our results suggest that performance is influenced not only by size and human validation but also by deeper factors related to the semantic properties of the datasets. All new resources will be freely released.

We ask whether contemporary LLMs are able to perform natural language inference (NLI) tasks on mathematical texts. We call this the Math NLI problem. We construct a corpus of Math NLI pairs whose premises are from extant mathematical text and whose hypotheses and gold labels were provided by people with experience in both research-level mathematics and also in the NLI field. We also investigate the quality of corpora using the same premises but whose hypotheses are provided by LLMs themselves. We not only investigate the performance but also the inter-group consistency of the diverse group of LLMs. We have both positive and negative findings. Among our positive findings: in some settings, using a majority vote of LLMs is approximately equivalent to using human-labeled data in the Math NLI area. On the negative side: LLMs still struggle with mathematical language. They occasionally fail at even basic inferences. Current models are not as prone to hypothesis-only “inference” in our data the way the previous generation had been. In addition to our findings, we also provide our corpora as data to support future work on Math NLI.

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All Entities are Not Created Equal: Examining the Long Tail for Ultra-Fine Entity Typing
Advait Deshmukh | Ashwin Umadi | Dananjay Srinivas | Maria Leonor Pacheco

Due to their capacity to acquire world knowledge from large corpora, pre-trained language models (PLMs) are extensively used in ultra-fine entity typing tasks where the space of labels is extremely large. In this work, we explore the limitations of the knowledge acquired by PLMs by proposing a novel heuristic to approximate the pre-training distribution of entities when the pre-training data is unknown. Then, we systematically demonstrate that entity-typing approaches that rely solely on the parametric knowledge of PLMs struggle significantly with entities at the long tail of the pre-training distribution, and that knowledge-infused approaches can account for some of these shortcomings. Our findings suggest that we need to go beyond PLMs to produce solutions that perform well for infrequent entities.

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When Does Meaning Backfire? Investigating the Role of AMRs in NLI
Junghyun Min | Xiulin Yang | Shira Wein

Natural Language Inference (NLI) relies heavily on adequately parsing the semantic content of the premise and hypothesis.In this work, we investigate whether adding semantic information in the form of an Abstract Meaning Representation (AMR) helps pretrained language models better generalize in NLI. Our experiments integrating AMR into NLI in both fine-tuning and prompting settings show that the presence of AMR in fine-tuning hinders model generalization while prompting with AMR leads to slight gains in GPT-4o.However, an ablation study reveals that the improvement comes from amplifying surface-level differences rather than aiding semantic reasoning. This amplification can mislead models to predict non-entailment even when the core meaning is preserved.

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Explanations explained. Influence of Free-text Explanations on LLMs and the Role of Implicit Knowledge
Andrea Zaninello | Roberto Dessi | Malvina Nissim | Bernardo Magnini

In this work, we investigate the relationship between the quality of explanations produced by different models and the amount of implicit knowledge the are able to provide beyond the input. We approximate explanation quality via accuracy on a downstream task with a standardized pipeline (GEISER) and study its correlation with three different association measures, each capturing different aspects of implicitness, defined as a combination of relevance and novelty. We conduct experiments with three SOTA LLMs on four tasks involving implicit knowledge, with explanations either confirming or contradicting the correct label. Our results demonstrate that providing quality explanations consistently improves the accuracy of LLM predictions, even when the models are not explicitly trained to take explanations as input, and underline the correlation between implicit content delivered by the explanation and its effectiveness.

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Latent Traits and Cross-Task Transfer: Deconstructing Dataset Interactions in LLM Fine-tuning
Shambhavi Krishna | Haw-Shiuan Chang | Taesung Lee

Large language models are increasingly deployed across diverse applications. This often includes tasks LLMs have not encountered during training.This implies that enumerating and obtaining the high-quality training data for all tasks is infeasible. Thus, we often need to rely on transfer learning using datasets with different characteristics, and anticipate out-of-distribution requests.Motivated by this practical need, we propose an analysis framework, building a transfer learning matrix and dimensionality reduction, to dissect these cross-task interactions.We train and analyze 10 models to identify latent abilities (e.g., Reasoning, Sentiment Classification, NLU, Arithmetic)and discover the side effects of the transfer learning.Our findings reveal that performance improvements often defy explanations based on surface-level dataset similarity or source data quality. Instead, hidden statistical factors of the source dataset, such as class distribution and generation length proclivities, alongside specific linguistic features, are actually more influential.This work offers insights into the complex dynamics of transfer learning, paving the way for more predictable and effective LLM adaptation.

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LLMs as annotators of argumentation
Anna Lindahl

Annotated data is essential for most NLP tasks, but creating it can be time-consuming and challenging. Argumentation annotation is especially complex, often resulting in moderate human agreement. While large language models (LLMs) have excelled in increasingly complex tasks, their application to argumentation annotation has been limited. This paper investigates how well GPT-4o and Claude can annotate three types of argumentation in Swedish data compared to human annotators. Using full annotation guidelines, we evaluate the models on argumentation schemes, argumentative spans, and attitude annotation. Both models perform similarly to humans across all tasks, with Claude showing better human agreement than GPT-4o. Agreement between models is higher than human agreement in argumentation scheme and span annotation.

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If We May De-Presuppose: Robustly Verifying Claims through Presupposition-Free Question Decomposition
Shubhashis Roy Dipta | Francis Ferraro

Prior work has shown that presupposition in generated questions can introduce unverified assumptions, leading to inconsistencies in claim verification. Additionally, prompt sensitivity remains a significant challenge for large language models (LLMs), resulting in performance variance as high as **3–6%**. While recent advancements have reduced this gap, our study demonstrates that prompt sensitivity remains a persistent issue. To address this, we propose a structured and robust claim verification framework that reasons through presupposition-free, decomposed questions. Extensive experiments across multiple prompts, datasets, and LLMs reveal that even state-of-the-art models remain susceptible to prompt variance and presupposition. Our method consistently mitigates these issues, achieving up to a **2–5%** improvement.

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Modeling Language Learning in Corrective Feedback Interactions
Juan Luis Castro-Garcia | Parisa Kordjamshidi

To study computational models for language acquisition, we propose an interactive computational framework that utilizes a miniature language acquisition dataset in a controlled environment. In this framework, a neural learner model interacts with a teacher model that provides corrective feedback. Within this framework, we investigate various corrective feedback strategies, specifically focusing on reformulations and their effect on the learner model during their interactions. We design experimental settings to evaluate the learner’s production of syntactically and semantically correct linguistic utterances and perception of concepts and word-meaning associations.These results offer insights into the effectiveness of different feedback strategies in language acquisition using artificial neural networks. The outcome of this research is establishing a framework with a dataset for the systematic evaluation of various aspects of language acquisition in a controlled environment.

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Relation-Aware Prompting Makes Large Language Models Effective Zero-shot Relation Extractors
Mahdi Rahimi | Razvan-Gabriel Dumitru | Mihai Surdeanu

While supervised relation extraction (RE) models have considerably advanced the state-of-the-art, they often perform poorly in low-resource settings. Zero-shot RE is vital when annotations are not available either due to costs or time constraints. As a result, zero-shot RE has garnered interest in the research community. With the advent of large language models (LLMs) many approaches have been proposed for prompting LLMs for RE, but these methods often either rely on an accompanying small language model (e.g., for finetuning on synthetic data generated by LLMs) or require complex post-prompt processing. In this paper, we propose an effective prompt-based method that does not require any additional resources. Instead, we use an LLM to perform a two-step process. In the first step, we perform a targeted summarization of the text with respect to the underlying relation, reduce the applicable label space, and synthesize examples. Then, we combine the products of these processes with other elements into a final prompt. We evaluate our approach with various LLMs on four real-world RE datasets. Our evaluation shows that our method outperforms the previous state-of-the-art zero-shot methods by a large margin. This work can also be considered as a new strong baseline for zero-shot RE that is compatible with any LLM.

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Enhancing Readability-Controlled Text Modification with Readability Assessment and Target Span Prediction
Liu Fengkai | John Sie Yuen Lee

Readability-controlled text modification aims to rewrite an input text so that it reaches a target level of difficulty. This task is closely related to automatic readability assessment (ARA) since, depending on the difficulty level of the input text, it may need to be simplified or complexified. Most previous research in LLM-based text modification has focused on zero-shot prompting, without further input from ARA or guidance on text spans that most likely require revision. This paper shows that ARA models for texts and sentences, as well as predictions of text spans that should be edited, can enhance performance in readability-controlled text modification.

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TAG–EQA: Text–And–Graph for Event Question Answering via Structured Prompting Strategies
Maithili Sanjay Kadam | Francis Ferraro

Large language models (LLMs) excel at general language tasks but often struggle with event-based questions—especially those requiring causal or temporal reasoning. We introduce TAG-EQA (Text-And-Graph for Event Question Answering), a prompting framework that injects causal event graphs into LLM inputs by converting structured relations into natural-language statements. TAG-EQA spans nine prompting configurations, combining three strategies (zero-shot, few-shot, chain-of-thought) with three input modalities (text-only, graph-only, text+graph), enabling a systematic analysis of when and how structured knowledge aids inference. On the TORQUESTRA benchmark, TAG-EQA improves accuracy by ~5% on average over text-only baselines, with gains up to ~12% in zero-shot settings and ~18% when graph-augmented CoT prompting is effective. While performance varies by model and configuration, our findings show that causal graphs can enhance event reasoning in LLMs without fine-tuning, offering a flexible way to encode structure in prompt-based QA.

Recent vision–language models excel at large-scale image–text alignment but often neglect the compositional structure of language, leading to failures on tasks that hinge on word order and predicate–argument structure. We introduce DisCoCLIP, a multimodal encoder that combines a frozen CLIP vision transformer with a novel tensor network text encoder that explicitly encodes syntactic structure. Sentences are parsed with a Combinatory Categorial Grammar parser to yield distributional word tensors whose contractions mirror the sentence’s grammatical derivation. To keep the model efficient, high-order tensors are factorized with tensor decompositions, reducing parameter count from tens of millions to under one million. Trained end-to-end with a self-supervised contrastive loss, DisCoCLIP markedly improves sensitivity to verb semantics and word order: it raises CLIP’s SVO-Probes verb accuracy from 77.6% to 82.4%, boosts ARO attribution and relation scores by over 9% and 4%, and achieves 93.7% on a newly introduced SVO-Swap benchmark. These results demonstrate that embedding explicit linguistic structure via tensor networks yields interpretable, parameter-efficient representations that substantially improve compositional reasoning in vision–language tasks.

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HSGM: Hierarchical Segment-Graph Memory for Scalable Long-Text Semantics
Dong Liu | Yanxuan Yu

Semantic parsing of long documents remains challenging due to quadratic growth in pairwise composition and memory requirements. We introduce Hierarchical Segment-Graph Memory (HSGM), a novel framework that decomposes an input of length N into M meaningful segments, constructs Local Semantic Graphs on each segment, and extracts compact summary nodes to form a Global Graph Memory. HSGM supports incremental updates—only newly arrived segments incur local graph construction and summary-node integration—while Hierarchical Query Processing locates relevant segments via top-K retrieval over summary nodes and then performs fine-grained reasoning within their local graphs.Theoretically, HSGM reduces worst-case complexity from O(N²) to O\bigl(N\,k + (N/k)²\bigr),with segment size k ≪ N, and we derive Frobenius-norm bounds on the approximation error introduced by node summarization and sparsification thresholds. Empirically, on three benchmarks—long-document AMR parsing, segment-level semantic role labeling (OntoNotes), and legal event extraction—HSGM achieves 2–4× inference speedup, >60% reduction in peak memory, and ≥95% of baseline accuracy. Our approach unlocks scalable, accurate semantic modeling for ultra-long texts, enabling real-time and resource-constrained NLP applications.

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Knowledge Editing Induces Underconfidence in Language Models
Ryo Hasegawa | Yusuke Sakai | Hidetaka Kamigaito | Taro Watanabe

As language models continue to scale, the demand for knowledge editing, a retraining-free knowledge update method, has increased. However, since knowledge editing directly alters token prediction probabilities acquired during pretraining, the probabilities may diverge from the empirical distribution. In this study, we analyze the impact of knowledge editing to compare the alignment between token prediction probabilities and task accuracy by calculating confidence calibration before and after knowledge editing. Our results reveal that, for tasks requiring semantic understanding, the range of increase in token prediction probabilities tends to be smaller than that of accuracy improvement, suggesting that knowledge editing methods lead to less confidence in prediction.

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How Do Large Language Models Evaluate Lexical Complexity?
Abdelhak Kelious | Mathieu Constant | Christophe Coeur

In this work, we explore the prediction of lexical complexity by combining supervised approaches and the use of large language models (LLMs). We first evaluate the impact of different prompting strategies (zero-shot, one-shot, and chain-of-thought) on the quality of the predictions, comparing the results with human annotations from the CompLex 2.0 corpus. Our results indicate that LLMs, and in particular gpt-4o, benefit from explicit instructions to better approximate human judgments, although some discrepancies remain. Moreover, a calibration approach to better align LLMs predictions and human judgements based on few manually annotated data appears as a promising solution to improve the reliability of the annotations in a supervised scenario.

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SAG: Enhancing Domain-Specific Information Retrieval with Semantic-Augmented Graphs
Carol-Luca Gasan | Vasile Pais

Retrieval-Augmented Generation (RAG) systems rely on high-quality embeddings to retrieve relevant context for large language models. This paper introduces the Semantic-Augmented Graph (SAG), a new architecture that improves domain-specific embeddings by capturing hierarchical semantic relationships between text segments. Inspired by human information processing, SAG organizes content from general to specific concepts using a graph-based structure. By combining static embeddings with dynamic semantic graphs, it generates context-aware representations that reflect both lexical and conceptual links. Experiments on text similarity and domain-specific question answering show that SAG consistently outperforms standard embedding methods within RAG pipelines.

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Cross-Domain Persuasion Detection with Argumentative Features
Bagyasree Sudharsan | Maria Leonor Pacheco

The main challenge in cross-domain persuasion detection lies in the vast differences in vocabulary observed across different outlets and contexts. Superficially, an argument made on social media will look nothing like an opinion presented in the Supreme Court, but the latent factors that make an argument persuasive are common across all settings. Regardless of domain, persuasive arguments tend to use sound reasoning and present solid evidence, build on the credibility and authority of the source, or appeal to the emotions and beliefs of the audience. In this paper, we show that simply encoding the different argumentative components and their semantic types can significantly improve a language model’s ability to detect persuasion across vastly different domains.

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Hallucinated Span Detection with Multi-View Attention Features
Yuya Ogasa | Yuki Arase

This study addresses the problem of hallucinated span detection in the outputs of large language models. It has received less attention than output-level hallucination detection despite its practical importance. Prior work has shown that attentions often exhibit irregular patterns when hallucinations occur. Motivated by these findings, we extract features from the attention matrix that provide complementary views capturing (a) whether certain tokens are influential or ignored, (b) whether attention is biased toward specific subsets, and (c) whether a token is generated referring to a narrow or broad context, in the generation. These features are input to a Transformer-based classifier to conduct sequential labelling to identify hallucinated spans. Experimental results indicate that the proposed method outperforms strong baselines on hallucinated span detection with longer input contexts, such as data-to-text and summarisation tasks.

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AdvERSEM: Adversarial Robustness Testing and Training of LLM-based Groundedness Evaluators via Semantic Structure Manipulation
Kaustubh Dhole | Ramraj Chandradevan | Eugene Agichtein

Evaluating outputs from large language models (LLMs) presents significant challenges, especially as hallucinations and adversarial manipulations are often difficult to detect. Existing evaluation methods lack robustness against subtle yet intentional linguistic alterations, necessitating novel techniques for reliably assessing model-generated content. Training accurate and robust groundedness evaluators is key for mitigating hallucinations and ensuring the alignment of model or human-generated claims to real-world evidence. However, as we show, many models, while optimizing for accuracy, lack robustness to subtle variations of claims, making them unsuitable and brittle in real-world settings where adversaries employ purposeful and deceitful tactics like hedging to deceive readers, which go beyond surface-level variations. To address this problem, we propose AdvERSem, a controllable adversarial approach to manipulating LLM output via Abstract Meaning Representations (AMR) to generate attack claims of multiple fine-grained types, followed by automatic verification of the correct label. By systematically manipulating a unique linguistic facet AdvERSem provides an interpretable testbed for gauging robustness as well as useful training data. We demonstrate that utilizing these AMR manipulations during training across multiple fact verification datasets helps improve the accuracy and robustness of groundedness evaluation while also minimizing the requirement of costly annotated data. To encourage further systematic evaluation, we release AdvERSem-Test, a manually verified groundedness test-bed.

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Connecting Concept Layers and Rationales to Enhance Language Model Interpretability
Thomas Bailleux | Tanmoy Mukherjee | Pierre Marquis | Zied Bouraoui

With the introduction of large language models, NLP has undergone a paradigm shift where these models now serve as the backbone of most developed systems. However, while highly effective, they remain opaque and difficult to interpret, which limits their adoption in critical applications that require transparency and trust. Two major approaches aim to address this: rationale extraction, which highlights input spans that justify predictions, and concept bottleneck models, which make decisions through human-interpretable concepts. Yet each has limitations. Crucially, current models lack a unified framework that connects where a model looks (rationales) with why it makes a decision (concepts). We introduce CLARITY, a model that first selects key input spans, maps them to interpretable concepts, and then predicts using only those concepts. This design supports faithful, multi-level explanations and allows users to intervene at both the rationale and concept levels. CLARITY, achieves competitive accuracy while offering improved transparency and controllability.

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Towards Evaluation of Language Models with Skill Dimensions: A Case Study on Narrative Question Answering
Emil Kalbaliyev | Kairit Sirts

Large language models have demonstrated varying levels of competence across a range of reasoning tasks, but coarse-grained evaluations often do not reflect their specific strengths and weaknesses, particularly in complex tasks such as Narrative Question Answering. In this paper, we advocate for a multi-dimensional skill-based evaluation that assesses models across distinct core skill dimensions. Our proposed skill-focused evaluation framework offers a granular and more realistic measure of model performance, revealing targeted areas for improvement and guiding future development. Experiments on Narrative Question Answering demonstrate that dimension-level analysis captures the multifaceted nature of the task and informs more effective model evaluation.

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Potentially Problematic Word Usages and How to Detect Them: A Survey
Aina Garí Soler | Matthieu Labeau | Chloé Clavel

We introduce and explore the concept of potentially problematic word usages (PPWUs): word occurrences that are likely to cause communication breakdowns of a semantic nature. While much research has been devoted to lexical complexity, ambiguity, vagueness and related issues, no work has attempted to fully capture the intricate nature of PPWUs. We review linguistic factors, datasets and metrics that can be helpful for PPWU detection. We also discuss challenges to their study, such as their complexity and subjectivity, and highlight the need for future work on this phenomenon.

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bib (full) Proceedings of the Fourth Workshop on Text Simplification, Accessibility and Readability (TSAR 2025)

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Template-Based Text-to-Image Alignment for Language Accessibility A Study on Visualizing Text Simplifications
Belkiss Souayed | Sarah Ebling | Yingqiang Gao

Individuals with intellectual disabilities often have difficulties in comprehending complex texts. While many text-to-image models prioritize photorealism over cognitive accessibility it is not clear how visual illustrations relate to text simplifications TS generated from them. This paper presents a structured vision language model VLM prompting framework for generating cognitively accessible images from simplified texts. We designed five prompt templates i.e. Basic Object Focus Contextual Scene Educational Layout Multi-Level Detail and Grid Layout each following distinct spatial arrangements while adhering to accessibility constraints such as object count limits spatial separation and content restrictions. Using 400 sentence-level TS pairs from four established text simplification datasets OneStopEnglish SimPA Wikipedia ASSET we conducted a two-phase evaluation Phase 1 assessed template effectiveness with CLIP similarity scores and Phase 2 involved expert annotation of generated images across ten visual styles by four accessibility specialists. Results show that the Basic Object Focus template achieved the highest semantic alignment indicating that visual minimalism enhances accessibility. Expert evaluation further identified Retro style as the most accessible and Wikipedia as the most effective text source. Inter-annotator agreement varied across dimensions with Text Simplicity showing strong reliability and Image Quality proving more subjective. Overall our framework offers practical guidelines for accessible content creation and underscores the importance of structured prompting in AI-generated visual accessibility tools.

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Document-level Simplification and Illustration Generation Multimodal Coherence
Yuhang Liu | Mo Zhang | Zhaoyi Cheng | Sarah Ebling

We present a novel method for document-level text simplification and automatic illustration generation aimed at enhancing information accessibility for individuals with cognitive impairments. While prior research has primarily focused on sentence- or paragraph-level simplification and text-to-image generation for narrative contexts this work addresses the unique challenges of simplifying long-form documents and generating semantically aligned visuals. The pipeline consists of three stages (1) discourse-aware segmentation using large language models (2) visually grounded description generation via abstraction and (3) controlled image synthesis using state-of-the-art diffusion models including DALLE 3 and FLUX1-dev. We further incorporate stylistic constraints to ensure visual coherence and we conduct a human evaluation measuring comprehension semantic alignment and visual clarity. Experimental results demonstrate that our method effectively combines simplified text and visual content with generated illustrations enhancing textual accessibility.

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Medical Text Simplification From Jargon Detection to Jargon-Aware Prompting
Taiki Papandreou | Jan Bakker | Jaap Kamps

Jargon identification is critical for improving the accessibility of biomedical texts yet models are often evaluated on isolated datasets leaving open questions about generalization. After reproducing MedReadMes jargon detection results and extending evaluation to the PLABA dataset we find that transfer learning across datasets yields only modest gains largely due to divergent annotation objectives. Through manual re-annotation we show that aligning labeling schemes improves cross-dataset performance. Building on these findings we evaluate several jargon-aware prompting strategies for LLM-based medical text simplification. Explicitly highlighting jargon in prompts does not consistently improve simplification quality. When gains occur they often trade off against readability and are model-dependent. Human evaluation indicates that simple prompting can be as effective as more complex jargon-aware instructions. We release code to facilitate further research https//anonymous.4open.science/r/tsar-anonymous-2D66F/README.md

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Readability Reconsidered A Cross-Dataset Analysis of Reference-Free Metrics
Catarina Belem | Parker Glenn | Alfy Samuel | Anoop Kumar | Daben Liu

Automatic readability assessment plays a key role in ensuring effective communication between humans and language models. Despite significant progress the field is hindered by inconsistent definitions of readability and measurements that rely on surface-level text properties. In this work we investigate the factors shaping human perceptions of readability through the analysis of 1.2k judgments finding that beyond surface-level cues information content and topic strongly shape text comprehensibility. Furthermore we evaluate 15 popular readability metrics across 5 datasets contrasting them with 5 more nuanced model-based metrics. Our results show that four model-based metrics consistently place among the top 4 in rank correlations with human judgments while the best performing traditional metric achieves an average rank of 7.8. These findings highlight a mismatch between current readability metrics and human perceptions pointing to model-based approaches as a more promising direction.

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Evaluating Health Question Answering Under Readability-Controlled Style Perturbations
Md Mushfiqur Rahman | Kevin Lybarger

Patients often ask semantically similar medical questions in linguistically diverse ways that vary in readability tone and background knowledge. A robust question answering QA system should both provide semantically consistent answers across stylistic differences and adapt its response style to match the users input however existing QA evaluations rarely test this capability creating critical gaps in QA evaluation that undermine accessibility and health literacy. We introduce SPQA an evaluation framework and benchmark that applies controlled stylistic perturbations to consumer health questions while preserving semantic intent then measures how model answers change across correctness completeness coherence fluency and linguistic adaptability using a human-validated LLM-based judge. The style axes include reading level formality and patient background knowledge all perturbations are grounded in human annotations to ensure fidelity and alignment with human judgments. Our contributions include a readability-aware evaluation methodology a style-diverse benchmark with human-grounded perturbations and an automated evaluation pipeline validated against expert judgments. Evaluation results across multiple health QA models indicate that stylistic perturbations lead to measurable performance degradation even when semantic intent is preserved during perturbation. The largest performance drops occur in answer correctness and completeness while models also show limited ability to adapt their style to match the input. These findings underscore the risk of inequitable information delivery and highlight the need for accessibility-aware QA evaluation.

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A Multi-Agent Framework with Diagnostic Feedback for Iterative Plain Language Summary Generation from Cochrane Medical Abstracts
Felipe Arias Russi | Carolina Salazar Lara | Ruben Manrique

Plain Language Summaries PLS improve health literacy and enable informed healthcare decisions but writing them requires domain expertise and is time-consuming. Automated methods often prioritize efficiency over comprehension and medical documents unique simplification requirements challenge generic solutions. We present a multi-agent system for generating PLS using Cochrane PLS as proof of concept. The system uses specialized agents for information extraction writing diagnosis and evaluation integrating a medical glossary and statistical analyzer to guide revisions. We evaluated three architectural configurations on 100 Cochrane abstracts using six LLMs both proprietary and open-source. Results reveal model-dependent trade-offs between factuality and readability with the multi-agent approach showing improvements for smaller models and providing operational advantages in control and interpretability.

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Efficient On-Device Text Simplification for Firefox with Synthetic Data Fine-Tuning
Pablo Romero | Zihao Li | Matthew Shardlow

This work presents a system for on-device text simplification that enables users to process sensitive text without relying on cloud-based services. Through the use of quantization techniques and a novel approach to controllable text simplification we reduce model size by up to 75 percent with minimal performance degradation. Our models demonstrate efficient state-of-the-art results using a synthetic dataset of 2909 examples outperforming prior work trained on 300K examples. This efficiency stems from (1) a single control token strategy that precisely targets specific reading levels (2) a contrastive training approach that enriches model understanding through exposure to multiple simplification levels and (3) individual models that dedicate full parameter capacity to specific reading level transformations. Our best models achieve up to 82.18 BLEU at the Advanced level and 46.12 SARI at the Elementary level on standard benchmarks with performance preserved even after aggressive quantization. This work is implemented as a collaboration with the Mozilla AI team to process text entirely locally ensuring sensitive information never leaves the users device. We have a demonstration video https//youtu.be/TzmaxnARMzg and a web demo available at https//pablorom2004.github.io/Simplification-Web-Demo

This paper presents the findings of the first Shared Task on Readability-Controlled Text Simplification at TSAR 2025. The task required systems to simplify English texts to specific target readability levels of the Common European Framework of Reference for Languages (CEFR). We received 48 submissions from 20 participating teams, with approaches predominantly based on large language models (LLMs), which included iterative refinement, multi-agent setups, and LLM-as-a-judge pipelines. For this shared task, we developed a new dataset of pedagogical texts and evaluated submissions using a weighted combination of semantic similarity and CEFR-level accuracy. The results of the participating teams demonstrate that while LLMs can perform substantially well on this task, dependable and controlled simplification often requires complex, multi-iterative processes. Our findings also suggest that the capabilities of current systems are beginning to saturate existing automatic evaluation metrics, underscoring the need for reevaluation and practicality.

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OneNRC@TSAR2025 Shared Task Small Models for Readability Controlled Text Simplification
Sowmya Vajjala

In this system description paper, we describe the team OneNRC’s experiments on readability controlled text simplification, focused on using smaller, quantized language models (<20B). We compare these with one large proprietary model and show that the smaller models offer comparable or even better results in some experimental settings. The approach primarily comprises of prompt optimization, agentic workflow, and tool calling. The best results were achieved while using a CEFR proficiency classifier as a verification tool for the language model agent. In terms of comparison with other systems, our submission that used a quantized Gemma3:12B model that ran on a laptop achieved a rank of 9.88 among the submitted systems as per the AUTORANK framework used by the organizers. We hope these results will lead into further exploration on the usefulness of smaller models for text simplification.

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GRIPF at TSAR 2025 Shared Task Towards controlled CEFR level simplification with the help of inter-model interactions
David Alfter | Sebastian Gombert

In this contribution to the CEFR level simplification TSAR 2025 Shared Task, we propose two systems, EZ-SCALAR and SAGA, that implement two differing approaches to prompting LLMs for proficiency-adapted simplification. Our results place us in the middle of the participating teams, and reveal that using external lexical resources to guide simplification improves overall results.

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ITU NLP at TSAR 2025 Shared Task A Three-Stage Prompting Approach for CEFR-Oriented Text Simplification
Kutay Arda Dinç | Fatih Bektaş | Gülşen Eryiğit

Automatic Text Simplification (TS) makes complex texts more accessible but often lacks control over target readability levels. We propose a lightweight, prompt-based approach to English TS that explicitly aligns outputs with CEFR proficiency standards. Our method employs a three-stage pipeline, guided by rule-informed prompts inspired by expert strategies. In the TSAR 2025 Shared Task, our system achieved competitive performance, with stronger results at B1 level and challenges at A2 level due to over-simplification. These findings highlight the promise of prompt-based CEFR-oriented simplification and the need for more flexible constraint design.

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STARLING at TSAR 2025 Shared Task Leveraging Alternative Generations for Readability Level Adjustment in Text Simplification
Piotr Przybyła

Readability adjustment is crucial in text simplification, as it allows to generate language appropriate to the needs of a particular group of readers. Here we present a method for simplifying a text fragment that aims for a given CEFR level, e.g. A2 or B1. The proposed approach combines prompted large language model with sentence-level adjustment of difficulty level. The work is evaluated within the framework of TSAR 2025 shared task, showing a trade-off between precise readability adjustment and faithful meaning preservation.

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taskGen at TSAR 2025 Shared Task Exploring prompt strategies with linguistic knowledge
Juan Cruz Oviedo | Elisabet Comelles Pujadas | Laura Alonso Alemany | Jordi Atserias Batalla

TaskGen ranked as 6th best team in the TSAR 2025 shared task for English text adaptation to a target CEFR level. Our experiments consisted of prompting a Llama-3.1-8B-Instruct model with linguistic descriptors of the target level, examples of adaptations and multi-step approaches. Our best run, 13th in the overall ranking, applied an ensemble strategy using a voting mechanism to find the most adequate among 10 texts, each produced by a different prompting strategy.

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EasyJon at TSAR 2025 Shared Task Evaluation of Automated Text Simplification with LLM-as-a-Judge
Paul-Gerhard Barbu | Adrianna Lipska-Dieck | Lena Lindner

This paper presents an approach to automated text simplification for CEFR A2 and B1 levels using large language models and prompt engineering. We evaluate seven models across three prompting strategies short, descriptive, and descriptive with examples. A two-round evaluation system using LLM-as-a-Judge and traditional metrics for text simplification determines optimal model-prompt combinations for final submissions. Results demonstrate that descriptive prompts consistently outperform other strategies across all models, achieving 46-65% of first-place rankings. Qwen3 shows superior performance for A2-level simplification, while B1-level results are more balanced across models. The LLM-as-a-Judge evaluation method shows strong alignment with traditional metrics while providing enhanced explainability.

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HULAT-UC3M at TSAR 2025 Shared Task A Prompt-Based Approach using Lightweight Language Models for Readability-Controlled Text Simplification
Jesus M. Sanchez-Gomez | Lourdes Moreno | Paloma Martínez | Marco Antonio Sanchez-Escudero

This paper describes the participation of the HULAT-UC3M team in the TSAR 2025 Shared Task on Readability-Controlled Text Simplification. Our approach uses open and lightweight Large Language Models (LLMs) with different sizes, together with two strategies for prompt engineering. The proposed system has been tested on the trial data provided, and evaluated using the official metrics CEFR Compliance, Meaning Preservation, and Similarity to References. LLaMA 3 8B model with reinforced prompts was selected as our final proposal for submission, and ranking fourteenth according to the overall metric. Finally, we discuss the main challenges that we identified in developing our approach for this task.

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UoL-UPF at TSAR 2025 Shared Task A Generate-and-Select Approach for Readability-Controlled Text Simplification
Akio Hayakawa | Nouran Khallaf | Horacio Saggion | Serge Sharoff

The TSAR 2025 Shared Task on Readability-Controlled Text Simplification focuses on simplifying English paragraphs written at an advanced level (B2 or higher) and rewriting them to target CEFR levels (A2 or B1). The challenge is to reduce linguistic complexity without sacrificing coherence or meaning. We developed three complementary approaches based on large language models (LLMs). The first approach (Run 1) generates a diverse set of paragraph-level simplifications. It then applies filters to enforce CEFR alignment, preserve meaning, and encourage diversity, and finally selects the candidates with the lowest perceived risk. The second (Run 2) performs simplification at the sentence level, combining structured prompting, coreference resolution, and explainable AI techniques to highlight influential phrases, with candidate selection guided by automatic and LLM-based judges. The third hybrid approach (Run 3) integrates both strategies by pooling paragraph- and sentence-level simplifications, and subsequently applying the identical filtering and selection architecture used in Run 1. In the official TSAR evaluation, the hybrid system ranked 2nd overall, while its component systems also achieved competitive results.

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Uniandes at TSAR 2025 Shared Task Multi-Agent CEFR Text Simplification with Automated Quality Assessment and Iterative Refinement
Felipe Arias Russi | Kevin Cohen Solano | Ruben Manrique

We present an agent-based system for the TSAR 2025 Shared Task on Readability-Controlled Text Simplification, which requires simplifying English paragraphs from B2+ levels to target A2 or B1 levels while preserving meaning. Our approach employs specialized agents for keyword extraction, text generation, and evaluation, coordinated through an iterative refinement loop. The system integrates a CEFR vocabulary classifier, pretrained evaluation models, and few-shot learning from trial data. Through iterative feedback between the evaluator and writer agents, our system automatically refines outputs until they meet both readability and semantic preservation constraints. This architecture achieved 4th position among participating teams, showing the effectiveness of combining specialized LLMs with automated quality control strategies for text simplification.

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EhiMeNLP at TSAR 2025 Shared Task Candidate Generation via Iterative Simplification and Reranking by Readability and Semantic Similarity
Rina Miyata | Koki Horiguchi | Risa Kondo | Yuki Fujiwara | Tomoyuki Kajiwara

We introduce the EhiMeNLP submission, which won the TSAR 2025 Shared Task on Readability-Controlled Text Simplification. Our system employed a two-step strategy of candidate generation and reranking. For candidate generation, we simplified the given text into more readable versions by combining multiple large language models with prompts. Then, for reranking, we selected the best candidate by readability-based filtering and ranking based on semantic similarity to the original text.

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OUNLP at TSAR 2025 Shared Task Multi-Round Text Simplifier via Code Generation
Cuong Huynh | Jie Cao

This paper describes the system submission of our team OUNLP to the TSAR-2025 shared task on readability-controlled text simplification. Based on the analysis of prompt-based text simplification methods, we discovered that simplification performance is highly related to the gap between the source CEFR level and the target CEFR level. Inspired by this finding, we propose two multi-round simplification methods generated via GPT-4o rule-based simplification (MRS-Rule) and jointly rule-based LLM simplification (MRS-Joint). Our submitted systems ranked 7th out of 20 teams. Later improvements with MRS-Joint show that taking the LLM simplified candidates as the starting point could further boost multi-round simplification performance.

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HIT-YOU at TSAR 2025 Shared Task Leveraging Similarity-Based Few-Shot Prompting, Round-Trip Translation, and Self-Refinement for Readability-Controlled Text Simplification
Mao Shimada | Kexin Bian | Zhidong Ling | Mamoru Komachi

We describe our submission to the TSAR 2025 shared task on readability-controlled text simplification, which evaluates systems on their ability to adjust linguistic complexity to specified CEFR levels while preserving meaning and coherence. We explored two complementary frameworks leveraging the shared task CEFR classifier as feedback. The first is an ensemble approach generating diverse candidates using multiple LLMs under zero-shot prompting with level-specific instructions and vocabulary lists, one-shot prompting, and round-trip translation. Candidates were filtered by predicted CEFR level before an LLM judge selected the final output. The second framework is a self-refinement loop, where a single candidate is iteratively revised with classifier feedback until matching the target level or reaching a maximum number of iterations. This study is among the first to apply round-trip translation and iterative self-refinement to controlled simplification, broadening the toolkit for adapting linguistic complexity.

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SQUREL at TSAR 2025 Shared Task CEFR-Controlled Text Simplification with Prompting and Reinforcement Fine-Tuning
Daria Sokova | Anastasiia Bezobrazova | Constantin Orasan

This paper summarises the submissions of our team to the TSAR 2025 Shared Task on Readability-Controlled Text Simplification, which aims to create text simplifications balancing reduced linguistic complexity, meaning preservation, and fluency while meeting predefined target readability levels. We tested two different methods for CEFR-controlled simplification a conservative lexical pipeline relying on prompting LLMs to simplify sentences, and a setup employing reinforcement fine-tuning.

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Archaeology at TSAR 2025 Shared Task Teaching Small Models to do CEFR Simplifications
Rares-Alexandru Roscan | Sergiu Nisioi

Large language models (LLMs) have demonstrated strong performance in text simplification tasks, but their high computational cost and proprietary nature often limit practical use, especially in education. We explore open-source LLMs for CEFR-level text simplification. By reducing model size and computational requirements, our approach enables greater accessibility and deployment in educational environments. Our results show some of the lowest error rates in producing CEFR-compliant texts at TSAR 2025, using models with 8 billion and 1 billion parameters. Such approaches have the potential to democratize NLP technologies for real-world applications.

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HOPE at TSAR 2025 Shared Task Balancing Control and Complexity in Readability-Controlled Text Simplification
Sujal Maharjan | Astha Shrestha

This paper describes our submissions to the TSAR 2025 Shared Task on Readability-Controlled Text Simplification. We present a comparative study of three architectures a rule-based baseline, a heuristic-driven expert system, and a zero-shot generative T5 pipeline with a semantic guardrail. Our analysis shows a trade-off between the controllability of rule-based systems and the fluency of generative models. In this zero-shot setting, simpler, confined systems achieved superior meaning preservation scores compared to the more powerful but less predictable generative model. We present a diagnostic failure analysis on system outputs, illustrating how different architectures result in distinct error patterns such as under-simplification, information loss via heuristics, and semantic drift.

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Know-AI at TSAR 2025 Shared Task Difficulty-aware Text Simplification System
Yiheng Wu | Anisia Katinskaia | Jue Hou | Roman Yangarber

Text simplification is an active research topic with applications in multiple domains. In a simplification pipeline, assessment of text difficulty plays a crucial role as a quality control mechanism it acts as a critic and guides models to generate text at the difficulty level required by the user. This paper presents our Difficulty-aware Text Simplification System. We evaluate our pipeline using the TSAR shared task dataset and discuss challenges in constructing corpora for training models to assess text difficulty.

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bib (full) Proceedings of the 2nd Workshop on Uncertainty-Aware NLP (UncertaiNLP 2025)

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Proceedings of the 2nd Workshop on Uncertainty-Aware NLP (UncertaiNLP 2025)
Noidea Noidea

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Uncertainty-driven Partial Diacritization for Arabic Text
Humaid Ali Alblooshi | Artem Shelmanov | Hanan Aldarmaki

We present an uncertainty-based approach to Partial Diacritization (PD) for Arabic text. We evaluate three uncertainty metrics for this task: Softmax Response, BALD via MC-dropout, and Mahalanobis Distance. We further introduce a lightweight Confident Error Regularizer to improve model calibration. Our preliminary exploration illustrates possible ways to use uncertainty estimation for selectively retaining or discarding diacritics in Arabic text with an analysis of performance in terms of correlation with diacritic error rates. For instance, the model can be used to detect words with high diacritic error rates which tend to have higher uncertainty scores at inference time. On the Tashkeela dataset, the method maintains low Diacritic Error Rate while reducing the amount of visible diacritics on the text by up to 50% with thresholding-based retention.

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Phases of Uncertainty: Confidence–Calibration Dynamics in Language Model Training
Aneesh Durai

Autoregressive language models achieve strong performance across a wide range of natural language processing (NLP) tasks, yet their uncertainty estimates remain poorly understood, particularly during training. Prior work has primarily evaluated calibration and out-of-distribution (OOD) robustness at the final checkpoint, overlooking the dynamics that unfold earlier. We introduce a phase-based framework for tracking uncertainty metrics—including expected calibration error (ECE) and Kullback–Leibler (KL) divergence—across distinct stages of training. Using GPT-2 models trained across multiple random seeds, we find that uncertainty dynamics follow a consistent set of phases: models begin conservative and relatively well calibrated, but later phases introduce a paradoxical decoupling where confidence increases even as calibration worsens, especially under distribution shift. This paradox implies that the final checkpoint is not always the most reliable for deployment and motivates phase-aware strategies such as dynamic checkpoint selection or targeted calibration. Our findings highlight that uncertainty should be understood as a training-dependent property rather than a static one, opening new directions for scaling this framework to larger models, tasks, and distribution shift scenarios.

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Beyond Human Judgment: A Bayesian Evaluation of LLMs’ Moral Values Understanding
Maciej Skorski | Alina Landowska

How do Large Language Models understand moral dimensions compared to humans?This first comprehensive large-scale Bayesian evaluation of leading language models provides the answer. In contrast to prior approaches based on deterministic ground truth (obtained via majority or inclusion consensus), we obtain the labels by modelling annotators’ disagreement to capture both aleatoric uncertainty (inherent human disagreement) and epistemic uncertainty (model domain sensitivity).We evaluated Claude Sonnet 4, DeepSeek-V3, and Llama 4 Maverick across 250K+ annotations from nearly 700 annotators in 100K+ texts spanning social networks, news, and discussion forums. Our GPU-optimized Bayesian framework processed 1M+ model queries, revealing that AI models generally rank among the top 25% of annotators in terms of balanced accuracy, substantially better than average humans.Importantly, we find that AI produces far fewer false negatives than humans, highlighting their more sensitive moral detection capabilities.

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Do Large Language Models Know When Not to Answer in Medical QA?
Sravanthi Machcha | Sushrita Yerra | Sharmin Sultana | Hong Yu | Zonghai Yao

Uncertainty awareness is essential for large language models (LLMs), particularly in safety-critical domains such as medicine where erroneous or hallucinatory outputs can cause harm. Yet most evaluations remain centered on accuracy, offering limited insight into model confidence and its relation to abstention. In this work, we present preliminary experiments that combine conformal prediction with abstention-augmented and perturbed variants of medical QA datasets. Our early results suggest a positive link between uncertainty estimates and abstention decisions, with this effect amplified under higher difficulty and adversarial perturbations. These findings highlight abstention as a practical handle for probing model reliability in medical QA.

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The Geometry of Creative Variability: How Credal Sets Expose Calibration Gaps in Language Models
Esteban Garces Arias | Julian Rodemann | Christian Heumann

Understanding uncertainty in large language models remains a fundamental challenge, particularly in creative tasks where multiple valid outputs exist. We present a geometric framework using credal sets—convex hulls of probability distributions—to quantify and decompose uncertainty in neural text generation, calibrated against human creative variation. Analyzing 500 creative writing prompts from the dataset with 10 unique human continuations each, we evaluate four language models across five decoding strategies, generating 100,000 stories. Our credal set analysis reveals substantial gaps in capturing human creative variation, with the best model-human calibration reaching only 0.434 (Gemma-2B with temperature 0.7). We decompose total uncertainty into epistemic and aleatoric components, finding that the choice of decoding strategy contributes 39.4% to 72.0% of total epistemic uncertainty. Model scale shows weak correlation with calibration quality and no significant difference exists between base and instruction-tuned models in calibration quality. Our geometric framework provides actionable insights for improving generation systems for human-AI creative alignment. We release our complete experimental framework at https://github.com/EstebanGarces/uncertainHuman.

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Certain but not Probable? Differentiating Certainty from Probability in LLM Token Outputs for Probabilistic Scenarios
Autumn Toney | Ryan Wails

Reliable uncertainty quantification (UQ) is essential for ensuring trustworthy downstream use of large language models, especially when they are deployed in decision-support and other knowledge-intensive applications. Model certainty can be estimated from token logits, with derived probability and entropy values offering insight into performance on the prompt task. However, this approach may be inadequate for probabilistic scenarios, where the probabilities of token outputs are expected to align with the theoretical probabilities of the possible outcomes. We investigate the relationship between token certainty and alignment with theoretical probability distributions in well-defined probabilistic scenarios. Using GPT-4.1 and DeepSeek-Chat, we evaluate model responses to ten prompts involving probability (e.g., roll a six-sided die), both with and without explicit probability cues in the prompt (e.g., roll a fair six-sided die). We measure two dimensions: (1) response validity with respect to scenario constraints, and (2) alignment between token-level output probabilities and theoretical probabilities. Our results indicate that, while both models achieve perfect in-domain response accuracy across all prompt scenarios, their token-level probability and entropy values consistently diverge from the corresponding theoretical distributions.

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The Benefits of Being Uncertain: Perplexity as a Signal for Naturalness in Multilingual Machine Translation
Timothy Pistotti | Michael J. Witbrock | Dr Padriac Amato Tahua O’Leary | Jason Brown

Model-internal uncertainty metrics like perplexity potentially offer low-cost signals for Machine Translation Quality Estimation (TQE). This paper analyses perplexity in the No Language Left Behind (NLLB) multilingual model. We quantify a significant model-human perplexity gap, where the model is consistently more confident in its own, often literal, machine-generated translation than in diverse, high-quality human versions. We then demonstrate that the utility of perplexity as a TQE signal is highly context-dependent, being strongest for low-resource pairs. Finally, we present an illustrative case study where a flawed translation is refined by providing potentially useful information in a targeted prompt, simulating a knowledge-based repair. We show that as the translation’s quality and naturalness improve (a +0.15 COMET score increase), its perplexity also increases, challenging the simple assumption that lower perplexity indicates higher quality and motivating a more nuanced view of uncertainty as signalling a text’s departure from rigid translationese.

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Asking a Language Model for Diverse Responses
Sergey Troshin | Irina Saparina | Antske Fokkens | Vlad Niculae

Large language models increasingly rely on explicit reasoning chains and can produce multiple plausible responses for a given context. We study the candidate sampler that produces the set of plausible responses contrasting the ancestral (parallel) sampling against two alternatives: enumeration, which asks the model to produce n candidates in one pass, and iterative sampling, which proposes candidates sequentially while conditioning on the currently generated response set. Under matched budgets, we compare these samplers on quality, lexical and computation flow diversity, and efficiency. Our empirical results demonstrate that enumeration and iterative strategies result in higher diversity at comparable quality. Our findings highlight the potential of simple non-independent sampling strategies to improve response diversity without sacrificing generation quality.

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Learning to vary: Teaching LMs to reproduce human linguistic variability in next-word prediction
Tobias Groot | Salo Lacunes | Evgenia Ilia

Natural language generation (NLG) tasks are often subject to inherent variability; e.g. predicting the next word given a context has multiple valid responses, evident when asking multiple humans to complete the task. While having language models (LMs) that are aligned pluralistically, so that they are able to reproduce well the inherent diversity in perspectives of an entire population of interest is clearly beneficial, Ilia and Aziz (2024) show that LMs do not reproduce this type of linguistic variability well. They speculate this inability might stem from the lack of consistent training of LMs with data reflecting this type of inherent variability. As such, we investigate whether training LMs on multiple plausible word continuations per context can improve their ability to reproduce human linguistic variability for next-word prediction. We employ fine-tuning techniques for pre-trained and instruction-tuned models; and demonstrate their potential when fine-tuning GPT-2 and Mistral-7B-IT, using Provo Corpus. Our evaluation, which measures divergence among empirically estimated human and model next-word distributions across contexts before and after fine-tuning, shows that our multi-label fine-tuning improves the LMs’ ability to reproduce linguistic variability; both for contexts that admit higher and lower variability.

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HALLUCINOGEN: Benchmarking Hallucination in Implicit Reasoning within Large Vision Language Models
Ashish Seth | Dinesh Manocha | Chirag Agarwal

Large Vision-Language Models (LVLMs) have demonstrated remarkable performance in complex multimodal tasks. However, these models still suffer from hallucinations, particularly when required to implicitly recognize or infer diverse visual entities from images for complex vision-language tasks. To address this challenge, we propose HALLUCINOGEN, a novel visual question answering (VQA) benchmark that employs contextual reasoning prompts as hallucination attacks to evaluate the extent of hallucination in state-of-the-art LVLMs. Our benchmark provides a comprehensive study of the implicit reasoning capabilities of these models by first categorizing visual entities based on the ease of recognition in an image as either salient (prominent, visibly recognizable objects such as a car) or latent entities (such as identifying a disease from a chest X-ray), which are not readily visible and require domain knowledge or contextual reasoning for accurate inference. Next, we design hallucination attacks for both types of entities to assess hallucinations in LVLMs while performing various vision-language tasks, such as locating or reasoning about specific entities within an image, where models must perform implicit reasoning by verifying the existence of the queried entity within the image before generating responses. Finally, our extensive evaluations of eleven LVLMs, including powerful open-source models (like LLaMA-3.2 and DeepSeek-V2), commercial models like Gemini, and two hallucination mitigation strategies across multiple datasets, demonstrate that current LVLMs remain susceptible to hallucination attacks.

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Uncertainty in Semantic Language Modeling with PIXELS
Stefania Radu | Marco Zullich | Matias Valdenegro-Toro

Pixel-based language models aim to solve the vocabulary bottleneck problem in language modeling, but the challenge of uncertainty quantification remains open. The novelty of this work consists of analysing uncertainty and confidence in pixel-based language models across 18 languages and 7 scripts, all part of 3 semantically challenging tasks. This is achieved through several methods such as Monte Carlo Dropout, Transformer Attention, and Ensemble Learning. The results suggest that pixel-based models underestimate uncertainty when reconstructing patches. The uncertainty is also influenced by the script, with Latin languages displaying lower uncertainty. The findings on ensemble learning show better performance when applying hyperparameter tuning during the named entity recognition and question-answering tasks across 16 languages.

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Confidence Calibration in Large Language Model-Based Entity Matching
Iris Kamsteeg | Juan Cardenas-Cartagena | Floris van Beers | Tsegaye Misikir Tashu | Matias Valdenegro-Toro

This research aims to explore the intersection of Large Language Models and confidence calibration in Entity Matching. To this end, we perform an empirical study to compare baseline RoBERTa confidences for an Entity Matching task against confidences that are calibrated using Temperature Scaling, Monte Carlo Dropout and Ensembles. We use the Abt-Buy, DBLP-ACM, iTunes-Amazon and Company datasets. The findings indicate that the proposed modified RoBERTa model exhibits a slight overconfidence, with Expected Calibration Error scores ranging from 0.0043 to 0.0552 across datasets. We find that this overconfidence can be mitigated using Temperature Scaling, reducing Expected Calibration Error scores by up to 23.83%.

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Consensus or Conflict? Fine-Grained Evaluation of Conflicting Answers in Question-Answering
Eviatar Nachshoni | Arie Cattan | Shmuel Amar | Ori Shapira | Ido Dagan

Large Language Models (LLMs) have demonstrated strong performance in question answering (QA) tasks. However, Multi-Answer Question Answering (MAQA), where a question may have several valid answers, remains challenging. Traditional QA settings often assume consistency across evidences, but MAQA can involve conflicting answers. Constructing datasets that reflect such conflicts is costly and labor-intensive, while existing benchmarks often rely on synthetic data, restrict the task to yes/no questions, or apply unverified automated annotation. To advance research in this area, we extend the conflict-aware MAQA setting to require models not only to identify all valid answers, but also to detect specific conflicting answer pairs, if any. To support this task, we introduce a novel cost-effective methodology for leveraging fact-checking datasets to construct NATCONFQA, a new benchmark for realistic, conflict-aware MAQA, enriched with detailed conflict labels, for all answer pairs. We evaluate eight high-end LLMs on NATCONFQA, revealing their fragility in handling various types of conflicts and the flawed strategies they employ to resolve them.

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Demystify Verbosity Compensation Behavior of Large Language Models
Yusen Zhang | Sarkar Snigdha Sarathi Das | Rui Zhang

Recent work has revealed Large Language Models (LLMs) often exhibit undesirable behaviors, such as hallucination and toxicity, limiting their reliability and broader adoption. In this paper, we discover an understudied type of undesirable behavior of LLMs, which we term Verbosity Compensation (VC). VC is similar to the hesitation behavior of humans under uncertainty, compensating with excessive words such as repeating questions, introducing ambiguity, or providing excessive enumeration. We present the first work that analyzes Verbosity Compensation, explores its causes, and proposes a simple mitigating approach. Our experiments on five datasets of knowledge and reasoning-based QA tasks with 14 LLMs, reveal three conclusions. 1) A pervasive presence of VC across all models and all datasets. 2) The large performance gap between verbose and concise responses. We also demonstrate that this difference does not naturally diminish as LLM capability increases. 3) Higher uncertainty exhibited by VC responses across all five datasets, suggesting a strong connection between verbosity and model uncertainty. We propose a simple yet effective cascade algorithm that replaces the verbose responses with the other model-generated responses, alleviating the VC of the Mistral model from 63.81% to 16.16% on the Qasper dataset.

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On the Role of Unobserved Sequences on Sample-based Uncertainty Quantification for LLMs
Lucie Kunitomo-Jacquin | Edison Marrese-Taylor | Ken Fukuda

Quantifying uncertainty in large language models (LLMs) is important for safety-critical applications because it helps spot incorrect answers, known as hallucinations. One major trend of uncertainty quantification methods is based on estimating the entropy of the distribution of the LLM’s potential output sequences. This estimation is based on a set of output sequences and associated probabilities obtained by querying the LLM several times. In this paper, we advocate and experimentally and show that the probability of unobserved sequences plays a crucial role, and we recommend future research to integrate it to enhance such LLM uncertainty quantification methods.

We propose a method for confidence estimation in retrieval-augmented generation (RAG) systems that aligns closely with the correctness of large language model (LLM) outputs. Confidence estimation is especially critical in high-stakes domains such as finance and healthcare, where the cost of an incorrect answer outweighs that of not answering the question. Our approach extends prior uncertainty quantification methods by leveraging raw feed-forward network (FFN) activations as auto-regressive signals, avoiding the information loss inherent in token logits and probabilities after projection and softmax normalization. We model confidence prediction as a sequence classification task, and regularize training with a Huber loss term to improve robustness against noisy supervision. Applied in a real-world financial industry customer-support setting with complex knowledge bases, our method outperforms strong baselines and maintains high accuracy under strict latency constraints. Experiments on Llama 3.1 8B model show that using activations from only the 16th layer preserves accuracy while reducing response latency. Our results demonstrate that activation-based confidence modeling offers a scalable, architecture-aware path toward trustworthy RAG deployment.

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Amortized Bayesian Meta-Learning for Low-Rank Adaptation of Large Language Models
Liyi Zhang | Jake C. Snell | Thomas L. Griffiths

Fine-tuning large language models (LLMs) with low-rank adaptaion (LoRA) is a cost-effective way to incorporate information from a specific dataset. However, it is often unclear how well the fine-tuned LLM will generalize, i.e., how well it will perform on unseen datasets. Methods have been proposed to improve generalization by optimizing with in-context prompts, or by using meta-learning to fine-tune LLMs. However, these methods are expensive in memory and computation, requiring either long-context prompts or saving copies of parameters and using second-order gradient updates. To address these challenges, we propose Amortized Bayesian Meta-Learning for LoRA (ABMLL). This method builds on amortized Bayesian meta-learning for smaller models, adapting this approach to LLMs while maintaining its computational efficiency. We reframe task-specific and global parameters in the context of LoRA and use a set of new hyperparameters to balance reconstruction accuracy and the fidelity of task-specific parameters to the global ones. ABMLL provides effective generalization and scales to large models such as Llama3-8B. Furthermore, as a result of using a Bayesian framework, ABMLL provides improved uncertainty quantification. We test ABMLL on Unified-QA and Crossfit datasets and find that it outperforms existing methods on these benchmarks in terms of both accuracy and expected calibration error.

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Towards Trustworthy Summarization of Cardiovascular Articles: A Factuality-and-Uncertainty-Aware Biomedical LLM Approach
Eleni Partalidou | Tatiana Passali | Chrysoula Zerva | Grigorios Tsoumakas | Sophia Ananiadou

While large, biomedical documents with complex terminology are in need of being understood more easily and efficiently, summarizing this kind of content can be problematic, as Large Language Models (LLMs) aren’t always trustworthy. Considering the importance of comprehending Cardiovascular Diseases, we study in depth the ability of different state-of-the-art biomedical LLMs to generate factual and certain summaries in this topic, and examine which generation choices can influence their trustworthiness. To that end, besides using factuality metrics, we employ techniques for token-level uncertainty estimation, an area that has received little attention from the scientific community. Our results reveal dissimilarities between LLMs and generation methods, and highlight connections between factuality and uncertainty metrics, thereby laying the groundwork for further investigation in the area.

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Causal Understanding by LLMs: The Role of Uncertainty
Oscar William Lithgow-Serrano | Vani Kanjirangat | Alessandro Antonucci

Recent papers show LLMs achieve near-random accuracy in causal relation classification, raising questions about whether such failures arise from limited pretraining exposure or deeper representational gaps. We investigate this under uncertainty-based evaluation, testing whether pretraining exposure to causal examples improves causal understanding using >18K PubMed sentences—half from The Pile corpus, half post-2024—across seven models (Pythia-1.4B/7B/12B, GPT-J-6B, Dolly-7B/12B, Qwen-7B). We analyze model behavior through: (i) causal classification, where the model identifies causal relationships in text, and (ii) verbatim memorization probing, where we assess whether the model prefers previously seen causal statements over their paraphrases. Models perform four-way classification (direct/conditional/correlational/no-relationship) and select between originals and their generated paraphrases. Results show almost identical accuracy on seen/unseen sentences (p>0.05), no memorization bias (24.8% original selection), output distribution over the possible options almost flat — with entropic values near the maximum (1.35/1.39), confirming random guessing. Instruction-tuned models show severe miscalibration (Qwen: >95% confidence, 32.8% accuracy, ECE=0.49). Conditional relations induce highest entropy (+11% vs direct). These findings suggest that failures in causal understanding arise from the lack of structured causal representation, rather than insufficient exposure to causal examples during pretraining.

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It Depends: Resolving Referential Ambiguity in Minimal Contexts with Commonsense Knowledge
Lukas Ellinger | Georg Groh

Ambiguous words or underspecified references require interlocutors to resolve them, often by relying on shared context and commonsense knowledge. Therefore, we systematically investigate whether Large Language Models (LLMs) can leverage commonsense to resolve referential ambiguity in multi-turn conversations and analyze their behavior when ambiguity persists. Further, we study how requests for simplified language affect this capacity. Using a novel multilingual evaluation dataset, we test DeepSeek v3, GPT-4o, Qwen3-32B, GPT-4o-mini, and Llama-3.1-8B via LLM-as-Judge and human annotations. Our findings indicate that current LLMs struggle to resolve ambiguity effectively: they tend to commit to a single interpretation or cover all possible references, rather than hedging or seeking clarification. This limitation becomes more pronounced under simplification prompts, which drastically reduce the use of commonsense reasoning and diverse response strategies. Fine-tuning Llama-3.1-8B with Direct Preference Optimization substantially improves ambiguity resolution across all request types. These results underscore the need for advanced fine-tuning to improve LLMs’ handling of ambiguity and to ensure robust performance across diverse communication styles.

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Read Your Own Mind: Reasoning Helps Surface Self-Confidence Signals in LLMs
Jakub Podolak | Rajeev Verma

We study the source of uncertainty in DeepSeek R1-32B by analyzing its self-reported verbal confidence on question answering (QA) tasks. In the default answer-then-confidence setting, the model is regularly over-confident, whereas semantic entropy - obtained by sampling many responses - remains reliable. We hypothesize that this is because of semantic entropy’s larger test-time compute, which lets us explore the model’s predictive distribution. We show that granting DeepSeek the budget to explore its distribution by forcing a long chain-of-thought before the final answer greatly improves its verbal score effectiveness, even on simple fact-retrieval questions that normally require no reasoning. Our analysis concludes that reliable uncertainty estimation requires explicit exploration of the generative space, and self-reported confidence is trustworthy only after such exploration.

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Calibrating Language Models for Neural Ranking under Noisy Supervision with Relaxed Labels
Arnab Sharma | Daniel Vollmers | Axel-Cyrille Ngonga Ngomo

In recent years, we have seen an increased usage of neural ranking models in the information retrieval domain. Although language model-based rankers have shown significant progress in performing ranking tasks, little to no work has addressed the issue of fine-tuning them in the presence of label noise in the training data. In a general learning setting, training models in the presence of noisy labeled data is studied extensively. To this end, confidence calibration approaches have shown significant promise; however, their usage in training neural ranking models is relatively less studied. In this work, we address this gap by adapting and analyzing regularization-based calibration approaches to reduce the effect of label noise in ranking tasks. Specifically, we study label relaxation in neural ranking models. We demonstrate the effectiveness of this approach by performing extensive evaluations comparing the label relaxation approach to standard loss functions. Additionally, we analyze the calibration error associated with the loss functions.After evaluating on five different noise levels, two different ranking models, and four diverse ranking datasets, the results suggest that label relaxation can improve the performance of the ranking models under noisy labels. Furthermore, we find that label relaxation reduces calibration error, although it suggests a better metric to be used for neural ranking models.

Large Language Models (LLMs) suffer significant performance degradation in multi-turn conversations when information is presented incrementally. Given that multi-turn conversations characterize everyday interactions with LLMs, this degradation poses a severe challenge to real world usability. We hypothesize that abrupt increases in model uncertainty signal misalignment in multi-turn LLM interactions, and we exploit this insight to dynamically realign conversational context. We introduce ERGO (Entropy-guided Resetting for Generation Optimization), which continuously quantifies internal uncertainty via Shannon entropy over next token distributions and triggers adaptive prompt consolidation when a sharp spike in entropy is detected. By treating uncertainty as a first class signal rather than a nuisance to eliminate, ERGO embraces variability in language and modeling, representing and responding to uncertainty. In multi-turn tasks with incrementally revealed instructions, ERGO yields a 56.6% average performance gain over standard baselines, increases aptitude (peak performance capability) by 24.7%, and decreases unreliability (variability in performance) by 35.3%, demonstrating that uncertainty aware interventions can improve both accuracy and reliability in conversational AI.

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Towards Open-Ended Discovery for Low-Resource NLP
Bonaventure F. P. Dossou | Henri Aïdasso

Natural language processing (NLP) for low-resource languages remains fundamentally constrained by the lack of textual corpora, standardized orthographies, and scalable annotation pipelines. While recent advances in large language models have improved cross-lingual transfer, they remain inaccessible to underrepresented communities due to their reliance on massive, pre-collected data and centralized infrastructure. In this position paper, we argue for a paradigm shift toward open-ended, interactive language discovery, where AI systems learn new languages dynamically through dialogue rather than static datasets. We contend that the future of language technology, particularly for low-resource and under-documented languages, must move beyond static data collection pipelines toward interactive, uncertainty-driven discovery, where learning emerges dynamically from human-machine collaboration instead of being limited to pre-existing datasets. We propose a framework grounded in joint human-machine uncertainty, combining epistemic uncertainty from the model with hesitation cues and confidence signals from human speakers to guide interaction, query selection, and memory retention. This paper is a call to action: we advocate a rethinking of how AI engages with human knowledge in under-documented languages, moving from extractive data collection toward participatory, co-adaptive learning processes that respect and empower communities while discovering and preserving the world’s linguistic diversity. This vision aligns with principles of human-centered AI and participatory design, emphasizing interactive, cooperative model building between AI systems and speakers.

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Can Vision-Language Models Infer Speaker’s Ignorance? The Role of Visual and Linguistic Cues
Ye-eun Cho | Yunho Maeng

This study investigates whether vision-language models (VLMs) can perform pragmatic inference, focusing on ignorance implicatures, utterances that imply the speaker’s lack of precise knowledge. To test this, we systematically manipulated contextual cues: the visually depicted situation (visual cue) and QUD-based linguistic prompts (linguistic cue). When only visual cues were provided, three state-of-the-art VLMs (GPT-4o, Gemini 1.5 Pro, and Claude 3.5 sonnet) produced interpretations largely based on the lexical meaning of the modified numerals. When linguistic cues were added to enhance contextual informativeness, Claude exhibited more human-like inference by integrating both types of contextual cues. In contrast, GPT and Gemini favored precise, literal interpretations. Although the influence of contextual cues increased, they treated each contextual cue independently and aligned them with semantic features rather than engaging in context-driven reasoning. These findings suggest that although the models differ in how they handle contextual cues, Claude’s ability to combine multiple cues may signal emerging pragmatic competence in multimodal models.

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DeLTa: A Decoding Strategy based on Logit Trajectory Prediction Improves Factuality and Reasoning Ability
Yunzhen He | Yusuke Takase | Hidetoshi Shimodaira

Large Language Models (LLMs) are increasingly being used in real-world applications. However, concerns about the reliability of the content they generate persist, as it frequently deviates from factual correctness or exhibits deficiencies in logical reasoning. This paper proposes a novel decoding strategy aimed at enhancing both factual accuracy and inferential reasoning without requiring any modifications to the architecture or pre-trained parameters of LLMs. Our approach adjusts next-token probabilities by analyzing the trajectory of logits from lower to higher layers in Transformers and applying linear regression. We find that this Decoding by Logit Trajectory-based approach (DeLTa) effectively reinforces factuality and reasoning while mitigating incorrect generation. Experiments on TruthfulQA demonstrate that DeLTa attains up to a 4.9% improvement over the baseline. Furthermore, it enhances performance by up to 8.1% on StrategyQA and 7.3% on GSM8K, both of which demand strong reasoning capabilities.

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Investigating Factuality in Long-Form Text Generation: The Roles of Self-Known and Self-Unknownan
Lifu Tu | Rui Meng | Shafiq Joty | Yingbo Zhou | Semih Yavuz

Large language models (LLMs) have demonstrated strong capabilities in text understanding and generation. However, they often lack factuality, producing a mixture of true and false in- formation, especially in long-form generation. In this work, we investigates the factuality of long-form text generation across various large language models (LLMs), including GPT-4, Gemini-1.5-Pro, Claude-3-Opus, Llama-3-70B, and Mistral. Our analysis reveals that factuality tend to decline in later sentences of the generated text, accompanied by a rise in the number of unsupported claims. Furthermore, we explore the effectiveness of different evaluation settings to assess whether LLMs can accurately judge the correctness of their own outputs: Self- Known (the percentage of supported atomic claims, decomposed from LLM outputs, that the corresponding LLMs judge as correct) and Self-Unknown (the percentage of unsupported atomic claims that the corresponding LLMs judge as incorrect). The results indicate that even advanced models fail to achieve perfect Self-Known scores, while their Self-Unknown scores remain notably above zero, reflecting ongoing uncertainty in their self-assessments. Moreover, we find a correlation between higher Self-Known scores and improved factuality, while higher Self-Unknown scores are associated with lower factuality. Even without sig nificant changes in the models’ self-judgment (Self-Known and Self-Unknown), the number of unsupported claims can increases, likely as an artifact of long-form generation. Additional Retrieval-Augmented Generation (RAG) experiments also show the limitations of current LLMs in long-form generation, and provide the more research is needed to improve factuality in long-form text generation.

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bib (full) Proceedings of the 9th Widening NLP Workshop

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Seeing Symbols, Missing Cultures: Probing Vision-Language Models’ Reasoning on Fire Imagery and Cultural Meaning
Haorui Yu | Yang Zhao | Yijia Chu | Qiufeng Yi

Vision-Language Models (VLMs) often appearculturally competent but rely on superficial pat.tern matching rather than genuine cultural understanding. We introduce a diagnostic framework to probe VLM reasoning on fire-themedcultural imagery through both classification andexplanation analysis. Testing multiple modelson Western festivals, non-Western traditions.and emergency scenes reveals systematic biases: models correctly identify prominent Western festivals but struggle with underrepresentedcultural events, frequently offering vague labelsor dangerously misclassifying emergencies ascelebrations. These failures expose the risksof symbolic shortcuts and highlight the needfor cultural evaluation beyond accuracy metrics to ensure interpretable and fair multimodalsystems.

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GPT4AMR: Does LLM-based Paraphrasing Improve AMR-to-text Generation Fluency?
Jiyuan Ji | Shira Wein

Abstract Meaning Representation (AMR) is a graph-based semantic representation that has been incorporated into numerous downstream tasks, in particular due to substantial efforts developing text-to-AMR parsing and AMR-to-text generation models. However, there still exists a large gap between fluent, natural sentences and texts generated from AMR-to-text generation models. Prompt-based Large Language Models (LLMs), on the other hand, have demonstrated an outstanding ability to produce fluent text in a variety of languages and domains. In this paper, we investigate the extent to which LLMs can improve the AMR-to-text generated output fluency post-hoc via prompt engineering. We conduct automatic and human evaluations of the results, and ultimately have mixed findings: LLM-generated paraphrases generally do not exhibit improvement in automatic evaluation, but outperform baseline texts according to our human evaluation. Thus, we provide a detailed error analysis of our results to investigate the complex nature of generating highly fluent text from semantic representations.

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Probing Gender Bias in Multilingual LLMs: A Case Study of Stereotypes in Persian
Ghazal Kalhor | Behnam Bahrak

Multilingual Large Language Models (LLMs) are increasingly used worldwide, making it essential to ensure they are free from gender bias to prevent representational harm. While prior studies have examined such biases in high-resource languages, low-resource languages remain understudied. In this paper, we propose a template-based probing methodology, validated against real-world data, to uncover gender stereotypes in LLMs. As part of this framework, we introduce the Domain-Specific Gender Skew Index (DS-GSI), a metric that quantifies deviations from gender parity. We evaluate four prominent models, GPT-4o mini, DeepSeek R1, Gemini 2.0 Flash, and Qwen QwQ 32B, across four semantic domains, focusing on Persian, a low-resource language with distinct linguistic features. Our results show that all models exhibit gender stereotypes, with greater disparities in Persian than in English across all domains. Among these, sports reflect the most rigid gender biases. This study underscores the need for inclusive NLP practices and provides a framework for assessing bias in other low-resource languages.

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Whose Palestine Is It? A Topic Modelling Approach to National Framing in Academic Research
Maida Aizaz | Taegyoon Kim | Lanu Kim

In this study, we investigate how author affiliation shapes academic discourse, proposing it as an effective proxy for author perspective in understanding what topics are studied, how nations are framed, and whose realities are prioritised. Using Palestine as a case study, we apply BERTopic and Structural Topic Modelling (STM) to 29,536 English-language academic articles collected from the OpenAlex database. We find that domestic authors focus on practical, local issues like healthcare, education, and the environment, while foreign authors emphasise legal, historical, and geopolitical discussions. These differences, in our interpretation, reflect lived proximity to war and crisis. We also note that while BERTopic captures greater lexical nuance, STM enables covariate-aware comparisons, offering deeper insight into how affiliation correlates with thematic emphasis. We propose extending this framework to other underrepresented countries, including a future study focused on Gaza post-October 7.

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Fine-tuning XLM-RoBERTa for Named Entity Recognition in Kurmanji Kurdish
Hossein Hassani

Named Entity Recognition (NER) is the information extraction task of identifying predefined named entities such as person names, location names, organization names and more. High-resource languages have made significant progress in NER tasks. However, low-resource languages such as Kurmanji Kurdish have not seen the same advancements, due to these languages having less available data online. This research aims to close this gap by developing an NER system via fine-tuning XLM-RoBERTa on a manually annotated dataset for Kurmanji. The dataset used for fine-tuning consists of 7,919 annotated sentences, which were manually annotated by three native Kurmanji speakers. The classes labeled in the dataset are Person (PER), Organization (ORG), and Location (LOC). A web-based application has also been developed using Streamlit to make the model more accessible. The model achieved an F1 score of 0.8735, precision of 0.8668, and recall of 0.8803, demonstrating the effectiveness of fine-tuning transformer-based models for NER tasks in low-resource languages. This work establishes a methodology that can be applied to other low-resource languages and Kurdish varieties.

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Human-AI Moral Judgment Congruence on Real-World Scenarios: A Cross-Lingual Analysis
Nan Li | Bo Kang | Tijl De Bie

As Large Language Models (LLMs) are deployed in every aspect of our lives, understanding how they reason about moral issues becomes critical for AI safety. We investigate this using a dataset we curated from Reddit’s r/AmItheAsshole, comprising real-world moral dilemmas with crowd-sourced verdicts. Through experiments on five state-of-the-art LLMs across 847 posts, we find a significant and systematic divergence where LLMs are more lenient than humans. Moreover, we find that translating the posts into another language changes LLMs’ verdicts, indicating their judgments lack cross-lingual stability.

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Transfer learning for dependency parsing of Vedic Sanskrit
Abhiram Vinjamuri | Weiwei Sun

This paper focuses on data-driven dependency parsing for Vedic Sanskrit. We propose and evaluate a transfer learning approach that benefits from syntactic analysis of typologically related languages, including Ancient Greek and Latin, and a descendant language - Classical Sanskrit. Experiments on the Vedic TreeBank demonstrate the effectiveness of cross-lingual transfer, demonstrating improvements from the biaffine baseline as well as outperforming the current state of the art benchmark, the deep contextualised self-training algorithm, across a wide range of experimental setups.

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Debiasing Large Language Models in Thai Political Stance Detection via Counterfactual Calibration
Kasidit Sermsri | Teerapong Panboonyuen

Political stance detection in low-resource and culturally complex settings poses a critical challenge for large language models (LLMs). In the Thai political landscape—rich with indirect expressions, polarized figures, and sentiment-stance entanglement—LLMs often exhibit systematic biases, including sentiment leakage and entity favoritism. These biases not only compromise model fairness but also degrade predictive reliability in real-world applications. We introduce ThaiFACTUAL, a lightweight, model-agnostic calibration framework that mitigates political bias without fine-tuning LLMs. ThaiFACTUAL combines counterfactual data augmentation with rationale-based supervision to disentangle sentiment from stance and neutralize political preferences. We curate and release the first high-quality Thai political stance dataset with stance, sentiment, rationale, and bias markers across diverse political entities and events. Our results show that ThaiFACTUAL substantially reduces spurious correlations, improves zero-shot generalization, and enhances fairness across multiple LLMs. This work underscores the need for culturally grounded bias mitigation and offers a scalable blueprint for debiasing LLMs in politically sensitive, underrepresented languages.

Large language models (LLMs) have significantly advanced automated code generation and debugging, facilitating powerful multi-agent coding frameworks. However, deploying these sophisticated models on resource-constrained edge devices remains challenging due to high computational demands, limited adaptability, and significant privacy risks associated with cloud-based processing. Motivated by these constraints, we propose Edge Code Cloak Coder (ECCC), a novel edge-cloud hybrid framework integrating lightweight quantized LLM with robust AST-based anonymization and edge-side privacy validation. ECCC enables high-performance, privacy-preserving LLM capabilities on consumer GPUs, anonymizing user code before securely delegating abstracted tasks to cloud LLMs. Experimental evaluations demonstrate that ECCC achieves competitive correctness (within 4–5pp of the GPT-4-based frameworks) and a perfect privacy score of 10/10, effectively balancing functionality and security for sensitive and proprietary code applications.

As AI advances, aligning it with diverse human and societal values grows critical. But how do we define these values and measure AI’s adherence to them? We present ValueCompass, a framework grounded in psychological theories, to assess human-AI alignment. Applying it to five diverse LLMs and 112 humans from seven countries across four scenarios—collaborative writing, education, public sectors, and healthcare—we uncover key misalignments. For example, humans prioritize national security, while LLMs often reject it. Values also shift across contexts, demanding scenario-specific alignment strategies. This work advances AI design by mapping how systems can better reflect societal ethics.

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ASR Under Noise: Exploring Robustness for Sundanese and Javanese
Salsabila Zahirah Pranida | Rifo Ahmad Genadi | Muhammad Cendekia Airlangga | Shady Shehata

We investigate the robustness of Whisper-based automatic speech recognition (ASR) models for two major Indonesian regional languages: Javanese and Sundanese. While recent work has demonstrated strong ASR performance under clean conditions, their effectiveness in noisy environments remains unclear. To address this, we experiment with multiple training strategies, including synthetic noise augmentation and SpecAugment, and evaluate performance across a range of signal-to-noise ratios (SNRs). Our results show that noise-aware training substantially improves robustness, particularly for larger Whisper models. A detailed error analysis further reveals language-specific challenges, highlighting avenues for future improvements.

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A Simple Data Augmentation Strategy for Text-in-Image Scientific VQA
Belal Shoer | Yova Kementchedjhieva

Scientific visual question answering poses significant challenges for vision-language models due to the complexity of scientific figures and their multimodal context. Traditional approaches treat the figure and accompanying text (e.g., questions and answer options) as separate inputs. EXAMS-V introduced a new paradigm by embedding both visual and textual content into a single image. However, even state-of-the-art proprietary models perform poorly on this setup in zero-shot settings, underscoring the need for task-specific fine-tuning. To address the scarcity of training data in this “text-in-image” format, we synthesize a new dataset by converting existing separate image-text pairs into unified images. Fine-tuning a small multilingual multimodal model on a mix of our synthetic data and EXAMS-V yields notable gains across 13 languages, demonstrating strong average improvements and cross-lingual transfer.

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Hybrid Fact-Checking that Integrates Knowledge Graphs, Large Language Models, and Search-Based Retrieval Agents Improves Interpretable Claim Verification
Shaghayeghkolli | Richard Rosenbaum | Timo Cavelius | Lasse Strothe | Andrii Lata | Jana Diesner

Large language models (LLMs) excel in generating fluent utterances but can lack reliable grounding in verified information. At the same time, knowledge-graph-based fact-checkers deliver precise and interpretable evidence, yet suffer from limited coverage or latency. By integrating LLMs with knowledge graphs and real-time search agents, we introduce a hybrid fact-checking approach that leverages the individual strengths of each component. Our system comprises three autonomous steps: 1) a Knowledge Graph (KG) Retrieval for rapid one‐hop lookups in DBpedia, 2) an LM-based classification guided by a task-specific labeling prompt, producing outputs with internal rule-based logic, and 3) a Web Search Agent invoked only when KG coverage is insufficient. Our pipeline achieves an F1 score of 0.93 on the FEVER benchmark on the Supported/Refuted split without task‐specific fine‐tuning. To address Not enough information cases, we conduct a targeted reannotation study showing that our approach frequently uncovers valid evidence for claims originally labeled as Not Enough Information (NEI), as confirmed by both expert annotators and LLM reviewers. With this paper, we present a modular, open-source fact-checking pipeline with fallback strategies and generalization across datasets.

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Insights from a Disaggregated Analysis of Kinds of Biases in a Multicultural Dataset
Guido Ivetta | Hernán Maina | Luciana Benotti

Warning: This paper contains explicit statements of offensive stereotypes which may be upsetting.Stereotypes vary across cultural contexts, making it essential to understand how language models encode social biases. MultiLingualCrowsPairs is a dataset of culturally adapted stereotypical and anti-stereotypical sentence pairs across nine languages. While prior work has primarily reported average fairness metrics on masked language models, this paper analyzes social biases in generative models by disaggregating results across specific bias types.We find that although most languages show an overall preference for stereotypical sentences, this masks substantial variation across different types of bias, such as gender, religion, and socioeconomic status. Our findings underscore that relying solely on aggregated metrics can obscure important patterns, and that fine-grained, bias-specific analysis is critical for meaningful fairness evaluation.

As Large Language Models (LLMs) gain mainstream public usage, understanding how users interact with them becomes increasingly important. Limited variety in training data raises concerns about LLM reliability across different language inputs. To explore this, we test several LLMs using functionally equivalent prompts expressed in different English sublanguages. We frame this analysis using Question-Answer (QA) pairs, which allow us to detect and evaluate appropriate and anomalous model behavior. We contribute a cross-LLM testing method and a new QA dataset translated into AAVE and WAPE variants. Early results reveal a notable drop in accuracy for one sublanguage relative to the baseline.

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Amharic News Topic Classification: Dataset and Transformer-Based Model Benchmarks
Dagnachew Mekonnen Marilign | Eyob Nigussie Alemu

News classification is a downstream task in Natural Language Processing (NLP) that involves the automatic categorization of news articles into predefined thematic categories. Although notable advancements have been made for high-resource languages, low-resource languages such as Amharic continue to encounter significant challenges, largely due to the scarcity of annotated corpora and the limited availability of language-specific, state-of-the-art model adaptations. To address these limitations, this study significantly expands an existing Amharic news dataset, increasing its size from 50,000 to 144,000 articles, thus enriching the linguistic and topical diversity available for the model training and evaluation. Using this expanded dataset, we systematically evaluated the performance of five transformer-based models: mBERT, XLM-R, DistilBERT, AfriBERTa, and AfroXLM in the context of Amharic news classification. Among these, AfriBERTa and XLM-R achieved the highest F1-scores of 90.25% and 90.11%, respectively, establishing a new performance baseline for the task. These findings underscore the efficacy of advanced multilingual and Africa-centric transformer architectures when applied to under-resourced languages, and further emphasize the critical importance of large-scale, high-quality datasets in enabling robust model generalization. This study offers a robust empirical foundation for advancing NLP research in low-resource languages, which remain underrepresented in current NLP resources and methodologies.

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Is this Chatbot Trying to Sell Something? Towards Oversight of Chatbot Sales Tactics
Simrat Deol | Jack Luigi Henry Contro | Martim Brandao

This research investigates the detection of covert sales tactics in human-chatbot interactions with a focus on the classification of solicited and unsolicited product recommendations. A custom dataset of 630 conversations was generated using a Large Language Model (LLM) to simulate chatbot-user interactions in various contexts, such as when interacting with users from different age groups, recommending different types of products and using different types of sales tactics. We then employ various approaches, including BiLSTM-based classification with sentence and word-level embeddings, as well as zero-shot, few-shot and CoT classification on large state-of-the-art LLMs. Our results show that few-shot GPT4 (86.44%) is the most accurate model on our dataset, followed by our compact SBERT+BiLSTM model (78.63%) - despite its small size.Our work demonstrates the feasibility of implementing oversight algorithms for monitoring chatbot conversations for undesired practices and that such monitoring could potentially be implemented locally on-device to mitigate privacy concerns. This research thus lays the groundwork for the development of auditing and oversight methods for virtual assistants such as chatbots, allowing consumer protection agencies to monitor the ethical use of conversational AI.

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Sarc7: Evaluating Sarcasm Detection and Generation with Seven Types and Emotion-Informed Techniques
Lang Xiong | Raina Gao | Alyssa Jeong

Sarcasm is a complex linguistic and pragmatic phenomenon where expressions convey meanings that contrast with their literal interpretations, requiring sensitivity to the speaker’s intent and context. Misinterpreting sarcasm in collaborative human–AI settings can lead to under- or overreliance on LLM outputs, with consequences ranging from breakdowns in communication to critical safety failures. We introduce Sarc7, a benchmark for fine-grained sarcasm evaluation based on the MUStARD dataset, annotated with seven pragmatically defined sarcasm types: self-deprecating, brooding, deadpan, polite, obnoxious, raging, and manic. These categories are adapted from prior linguistic work and used to create a structured dataset suitable for LLM evaluation. For classification, we evaluate multiple prompting strategies—zero-shot, few-shot, chain-of-thought (CoT), and a novel emotion-based technique—across five major LLMs. Emotion-based prompting yields the highest macro-averaged F1 score of 0.3664 (Gemini 2.5), outperforming CoT for several models and demonstrating its effectiveness in sarcasm type recognition. For sarcasm generation, we design structured prompts using fixed values across four sarcasm-relevant dimensions: incongruity, shock value, context dependency, and emotion. Using Claude 3.5 Sonnet, this approach produces more subtype-aligned outputs, with human evaluators preferring emotion-based generations 38.46% more often than zero-shot baselines. Sarc7 offers a foundation for evaluating nuanced sarcasm understanding and controllable generation in LLMs, pushing beyond binary classification toward interpretable, emotion-informed language modeling.

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Emotionally Aware or Tone-Deaf? Evaluating Emotional Alignment in LLM-Based Conversational Recommendation Systems
Darshna Parmar | Pramit Mazumdar

Recent advances in Large Language Models (LLMs) have enhanced the fluency and coherence of Conversational Recommendation Systems (CRSs), yet emotional intelligence remains a critical gap. In this study, we systematically evaluate the emotional behavior of six state-of-the-art LLMs in CRS settings using the ReDial and INSPIRED datasets. We propose an emotion-aware evaluation framework incorporating metrics such as Emotion Alignment, Emotion Flatness, and per-emotion F1-scores. Our analysis shows that most models frequently default to emotionally flat or mismatched responses, often misaligning with user affect (e.g., joy misread as neutral). We further examine patterns of emotional misalignment and their impact on user-centric qualities such as personalization, justification, and satisfaction. Through qualitative analysis, we demonstrate that emotionally aligned responses enhance user experience, while misalignments lead to loss of trust and relevance. This work highlights the need for emotion-aware design in CRS and provides actionable insights for improving affective sensitivity in LLM-generated recommendations.

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MULBERE: Multilingual Jailbreak Robustness Using Targeted Latent Adversarial Training
Anastasia Dunca | Maanas Kumar Sharma | Olivia Munoz | Victor Rosales

Jailbreaking, the phenomenon where specific prompts cause LLMs to assist with harmful requests, remains a critical challenge in NLP, particularly in non-English and lower-resourced languages. To address this, we introduce MULBERE, a method that extends the method of Targeted Latent Adversarial Training (T-LAT) to a multilingual context. We first create and share a multilingual jailbreak dataset spanning high-, medium-, and low-resource languages, and then fine-tune LLaMA-2-7b-chat with interleaved T-LAT for jailbreak robustness and chat examples for model performance. Our evaluations show that MULBERE reduces average multilingual jailbreak success rates by 75% compared to the base LLaMA safety training and 71% compared to English-only T-LAT while maintaining or improving standard LLM performance.

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Investigating Motivated Inference in Large Language Models
Nutchanon Yongsatianchot | Stacy Marsella

Our desires often influence our beliefs and expectations. Humans tend to think good things are more likely to happen than they actually are, while believing bad things are less likely. This tendency has been referred to as wishful thinking in research on coping strategies. With large language models (LLMs) increasingly being considered as computational models of human cognition, we investigate whether they can simulate this distinctly human bias. We conducted two systematic experiments across multiple LLMs, manipulating outcome desirability and information uncertainty across multiple scenarios including probability games, natural disasters, and sports events. Our experiments revealed limited wishful thinking in LLMs. In Experiment 1, only two models showed the bias, and only in sports-related scenarios when role-playing characters. Models exhibited no wishful thinking in mathematical contexts. Experiment 2 found that explicit prompting about emotional states (being hopeful) was necessary to elicit wishful thinking in logical domains. These findings reveal a significant gap between human cognitive biases and LLMs’ default behavior patterns, suggesting that current models require explicit guidance to simulate wishful thinking influences on belief formation.

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Large Language Models as Detectors or Instigators of Hate Speech in Low-resource Ethiopian Languages
Nuhu Ibrahim | Felicity Mulford | Riza Batista-Navarro

We introduce a multilingual benchmark for evaluating large language models (LLMs) on hate speech detection and generation in low-resource Ethiopian languages: Afaan Oromo, Amharic and Tigrigna, and English (both monolingual and code-mixed). Using a balanced and expert-annotated dataset, we assess five state-of-the-art LLM families across both tasks. Our results show that while LLMs perform well on English detection, their performance on low-resource languages is significantly weaker, revealing that increasing model size alone does not ensure multilingual robustness. More critically, we find that all models, including closed and open-source variants, can be prompted to generate profiled hate speech with minimal resistance. These findings underscore the dual risk of exclusion and exploitation: LLMs fail to protect low-resource communities while enabling scalable harm against them. We make our evaluation framework available to facilitate future research on multilingual model safety and ethical robustness.

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Brown Like Chocolate: How Vision-Language Models Associate Skin Tone with Food Colors
Nutchanon Yongsatianchot | Pachaya Sailamul

We investigate how Vision-Language Models (VLMs) leverage visual features when making analogical comparisons about people. Using synthetic images of individuals varying in skin tone and nationality, we prompt GPT and Gemini models to make analogical associations with desserts and drinks. Results reveal that VLMs systematically associate darker-skinned individuals with brown-colored food items, with GPT showing stronger associations than Gemini. These patterns are amplified in Thai versus English prompts, suggesting language-dependent encoding of visual stereotypes. The associations persist across manipulation checks including position swapping and clothing changes, though presenting individuals alone yields divergent language-specific patterns. This work reveals concerning associations in VLMs’ visual reasoning that vary by language, with important implications for multilingual deployment.

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Improving BGE-M3 Multilingual Dense Embeddings for Nigerian Low Resource Languages
Abdulmatin Omotoso | Habeeb Shopeju | Adejumobi Monjolaoluwa Joshua | Shiloh Oni

Multilingual dense embedding models such as Multilingual E5, LaBSE, and BGE-M3 have shown promising results on diverse benchmarks for information retrieval in low-resource languages. But their result on low resource languages is not up to par with other high resource languages. This work improves the performance of BGE-M3 through contrastive fine-tuning; the model was selected because of its superior performance over other multilingual embedding models across MIRACL, MTEB, and SEB benchmarks. To fine-tune this model, we curated a comprehensive dataset comprising Yorùbá (32.9k rows), Igbo (18k rows) and Hausa (85k rows) from mainly news sources. We further augmented our multilingual dataset with English queries and mapped it to each of the Yoruba, Igbo, and Hausa documents, enabling cross-lingual semantic training. We evaluate on two settings: the Wura test set and the MIRACL benchmark. On Wura, the fine-tuned BGE-M3 raises mean reciprocal rank (MRR) to 0.9201 for Yorùbá, 0.8638 for Igbo, 0.9230 for Hausa, and 0.8617 for English queries matched to local documents, surpassing the BGE-M3 baselines of 0.7846, 0.7566, 0.8575, and 0.7377, respectively. On MIRACL (Yorùbá subset), the fine-tuned model attains 0.5996 MRR, slightly surpassing base BGE-M3 (0.5952) and outperforming ML-E5-large (0.5632) and LaBSE (0.4468).

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Challenges in Processing Chinese Texts Across Genres and Eras
Minghao Zheng | Sarah Moeller

Pre-trained Chinese Natural Language Processing (NLP) tools show reduced performance when analyzing poetry compared to prose. This study investigates the discrepancies between tools trained on either Classical or Modern Chinese prose when handling Classical Chinese prose and Classical Chinese poetry. Three experiments reveal error patterns that indicate the weaker performance on Classical Chinese poemsis due to challenges identifying word boundaries. Specifically, tools trained on Classical prose struggle recognizing word boundaries within Classical poetic structures and tools trained on Modern prose have difficulty with word segmentation in both Classical Chinese genres. These findings provide valuable insights into the limitations of current NLP tools for studying Classical Chinese literature.

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The Gemma Sutras: Fine-Tuning Gemma 3 for Sanskrit Sandhi Splitting
Samarth P | Sanjay Balaji Mahalingam

Sandhi, the phonological merging of morphemes, is a central feature of Sanskrit grammar. While Sandhi formation is well-defined by Pāṇini’s Aṣṭādhyāyī, the reverse task—Sandhi splitting—is substantially more complex due to inherent ambiguity and context-sensitive transformations. Accurate splitting is a critical precursor to tokenization in Sanskrit, which lacks explicit word boundaries and presents densely fused compounds. In this work, we present a data-driven approach, fine-tuning the Gemma-3 4B large language model on a dataset of over 49,000 training and 2,000 test examples of compound words and their morpheme-level decompositions. Leveraging the Unsloth framework with low-rank adaptation (LoRA) and 4-bit quantization, we train the model to predict these splits. Our work yields a scalable, Sandhi-aware system designed to enhance modern NLP pipelines for classical Sanskrit, demonstrating an effective application of LLMs to this linguistic challenge.

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Evaluation Sheet for Deep Research: A Use Case for Academic Survey Writing
Israel Abebe Azime | Tadesse Destaw Belay | Atnafu Lambebo Tonja

Large Language Models (LLMs) powered with argentic capabilities are able to do knowledge-intensive tasks without human involvement. A prime example of this tool is Deep research with the capability to browse the web, extract information and generate multi-page reports.In this work, we introduce an evaluation sheet that can be used for assessing the capability of Deep Research tools. In addition, we selected academic survey writing as a use case task and evaluated output reports based on the evaluation sheet we introduced. Our findings show the need to have carefully crafted evaluation standards. The evaluation done on OpenAI‘s Deep Search and Google’s Deep Search in generating an academic survey showed the huge gap between search engines and standalone Deep Research tools, as well as the shortcomings in representing the targeted area.

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Reference-Guided Verdict: LLMs-as-Judges in Automatic Evaluation of Free-Form QA
Sher Badshah | Hassan Sajjad

The emergence of Large Language Models (LLMs) as chat assistants capable of generating human-like conversations has amplified the need for robust evaluation methods, particularly for open-ended tasks. Conventional metrics such as EM and F1, while useful, are inadequate for capturing the full semantics and contextual depth of such generative outputs. We propose a reference-guided verdict method that automates the evaluation process by leveraging multiple LLMs as judges. Through experiments on free-form question-answering tasks, we demonstrate that combining multiple models improves the reliability and accuracy of evaluations, especially in tasks where a single model may struggle. The results indicate a strong correlation with human evaluations, establishing the proposed method as a reliable alternative to traditional metrics.

Large language models (LLMs) are increasingly integrated into our daily lives and personalized. However, LLM personalization might also increase unintended side effects. Recent work suggests that persona prompting can lead models to falsely refuse user requests. However, no work has fully quantified the extent of this issue. To address this gap, we measure the impact of 15 sociodemographic personas (based on gender, race, religion, and disability) on false refusal. To control for other factors, we also test 16 different models, 3 tasks (Natural Language Inference, politeness, and offensiveness classification), and nine prompt paraphrases. We propose a Monte Carlo-based method to quantify this issue in a sample-efficient manner. Our results show that as models become more capable, personas impact the refusal rate less. However, we find that the choice of model significantly influence false refusals, especially in sensitive content tasks. The impact of certain sociodemographic personas further increases the false refusal effect in some models, which suggests that there are underlying biases in the alignment strategies or safety mechanisms.

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Proceedings of the Tenth Conference on Machine Translation
Barry Haddow | Tom Kocmi | Philipp Koehn | Christof Monz

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An Empirical Analysis of Machine Translation for Expanding Multilingual Benchmarks
Sara Rajaee | Rochelle Choenni | Ekaterina Shutova | Christof Monz

The rapid advancement of large language models (LLMs) has introduced new challenges in their evaluation, particularly for multilingual settings. The limited evaluation data are more pronounced in low-resource languages due to the scarcity of professional annotators, hindering fair progress across languages. In this work, we systematically investigate the viability of using machine translation (MT) as a proxy for evaluation in scenarios where human-annotated test sets are unavailable. Leveraging a state-of-the-art translation model, we translate datasets from four tasks into 198 languages and employ these translations to assess the quality and robustness of MT-based multilingual evaluation under different setups. We analyze task-specific error patterns, identifying when MT-based evaluation is reliable and when it produces misleading results. Our translated benchmark reveals that current language selections in multilingual datasets tend to overestimate LLM performance on low-resource languages. We conclude that although machine translation is not yet a fully reliable method for evaluating multilingual models, overlooking its potential means missing a valuable opportunity to track progress in non-English languages.

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Cross-lingual Human-Preference Alignment for Neural Machine Translation with Direct Quality Optimization
Kaden Uhlig | Joern Wuebker | Raphael Reinauer | John Denero

Reinforcement Learning from Human Feedback (RLHF) and derivative techniques like Direct Preference Optimization (DPO) are task-alignment algorithms used to repurpose general, foundational models for specific tasks. We show that applying task-alignment to neural machine translation (NMT) addresses an existing task–data mismatch in NMT, leading to improvements across all languages of a multilingual model, even when task-alignment is only applied to a subset of those languages. We do so by introducing Direct Quality Optimization (DQO), a variant of DPO leveraging a pre-trained translation quality estimation model as a proxy for human preferences, and verify the improvements with both automatic metrics and through human evaluation.

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Audio-Based Crowd-Sourced Evaluation of Machine Translation Quality
Sami Haq | Sheila Castilho | Yvette Graham

Machine Translation (MT) has achieved remarkable performance, with growing interest in speech translation and multimodal approaches. However, despite these advancements, MT quality assessment remains largely text-centric, typically relying on human experts who read and compare texts. Since many real-world MT applications (e.g., Google Translate Voice Mode, iFLYTEK Translator) involve translation being spoken rather printed or read, a more natural way to assess translation quality would be through speech as opposed text-only evaluations. This study compares text-only and audio-based evaluations of 10 MT systems from the WMT General MT Shared Task, using crowd-sourced judgments collected via Amazon Mechanical Turk. We additionally, performed statistical significance testing and self-replication experiments to test reliability and consistency of audio-based approach. Crowd-sourced assessments based on audio yield rankings largely consistent with text-only evaluations but, in some cases, identify significant differences between translation systems. We attribute this to speech’s richer, more natural modality and propose incorporating speech-based assessments into future MT evaluation frameworks.

We present a comprehensive study on meaningfully evaluating sign language utterances in the form of human skeletal poses. The study covers keypoint distance-based, embedding-based, and back-translation-based metrics. We show tradeoffs between different metrics in different scenarios through (1) automatic meta-evaluation of sign-level retrieval, and (2) a human correlation study of text-to-pose translation across different sign languages. Our findings, along with the open-source pose-evaluation toolkit, provide a practical and reproducible approach for developing and evaluating sign language translation or generation systems.

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Context Is Ubiquitous, but Rarely Changes Judgments: Revisiting Document-Level MT Evaluation
Ahrii Kim

As sentence-level performance in modern Machine Translation (MT) has plateaued, reliable document-level evaluation is increasingly needed. While the recent FALCON framework with pragmatic features offers a promising direction, its reliability and reproducibility are unclear. We address this gap through human evaluation, analyzing sources of low inter-annotator agreement and identifying key factors. Based on these findings, we introduce H-FALCON, a Human-centered refinement of FALCON. Our experiments show that, even with limited annotator consensus, FALCON achieves correlations comparable to or better than standard sentence-level protocols.Furthermore, we find that contextual information is inherent in all sentences, challenging the view that only some require it. This suggests that prior estimates such as “n% of sentences require context” may stem from methodological artifacts. At the same time, we show that while context is pervasive, not all of it directly influences human judgment.

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GIIFT: Graph-guided Inductive Image-free Multimodal Machine Translation
Jiafeng Xiong | Yuting Zhao

Multimodal Machine Translation (MMT) has demonstrated the significant help of visual information in machine translation. However, existing MMT methods face challenges in leveraging the modality gap by enforcing rigid visual-linguistic alignment whilst being confined to inference within their trained multimodal domains. In this work, we construct novel multimodal scene graphs to preserve and integrate modality-specific information and introduce GIIFT, a two-stage Graph-guided Inductive Image-Free MMT framework that uses a cross-modal Graph Attention Network adapter to learn multimodal knowledge in a unified fused space and inductively generalize it to broader image-free translation domains. Experimental results on the Multi30K dataset of English-to-French and English-to-German tasks demonstrate that our GIIFT surpasses existing approaches and achieves the state-of-the-art, even without images during inference. Results on the WMT benchmark show significant improvements over the image-free translation baselines, demonstrating the strength of GIIFT towards inductive image-free inference.

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Specification-Aware Machine Translation and Evaluation for Purpose Alignment
Yoko Kayano | Saku Sugawara

In professional settings, translation is guided by communicative goals and client needs, often formalized as specifications.While existing evaluation frameworks acknowledge the importance of such specifications, these specifications are often treated only implicitly in machine translation (MT) research.Drawing on translation studies, we provide a theoretical rationale for why specifications matter in professional translation, as well as a practical guide to implementing specification-aware MT and evaluation.Building on this foundation, we apply our framework to the translation of investor relations texts from 33 publicly listed companies.In our experiment, we compare five translation types, including official human translations and prompt-based outputs from large language models (LLMs), using expert error analysis, user preference rankings, and an automatic metric. The results show that LLM translations guided by specifications consistently outperformed official human translations in human evaluations, highlighting a gap between perceived and expected quality.These findings demonstrate that integrating specifications into MT workflows, with human oversight, can improve translation quality in ways aligned with professional practice.

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OpenWHO: A Document-Level Parallel Corpus for Health Translation in Low-Resource Languages
Raphael Merx | Hanna Suominen | Trevor Cohn | Ekaterina Vylomova

Health machine translation (MT) is a high-stakes domain characterised by widespread deployment and domain-specific vocabulary. However, there is a lack of MT evaluation datasets for low-resource languages in the health domain. To address this gap, we introduce OpenWHO, a document-level parallel corpus of 2,978 documents and 26,824 sentences from the World Health Organization’s e-learning platform. Sourced from expert-authored, professionally translated materials shielded from web-crawling, OpenWHO spans a diverse range of over 20 languages, of which nine are low-resource. Leveraging this new resource, we evaluate modern large language models (LLMs) against traditional MT models. Our findings reveal that LLMs consistently outperform traditional MT models, with Gemini 2.5 Flash achieving a +4.79 ChrF point improvement over NLLB-54B on our low-resource test set. Further, we investigate how LLM context utilisation affects accuracy, finding that the benefits of document-level translation are most pronounced in specialised domains like health. We release the OpenWHO corpus to encourage further research into low-resource MT in the health domain.

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Factors Affecting Translation Quality in In-context Learning for Multilingual Medical Domain
Jonathan Mutal | Raphael Rubino | Pierrette Bouillon

Multilingual machine translation in the medical domain presents critical challenges due to limited parallel data, domain-specific terminology, and the high stakes associated with translation accuracy. In this paper, we explore the potential of in-context learning (ICL) with general-purpose large language models (LLMs) as an alternative to fine-tuning. Focusing on the medical domain and low-resource languages, we evaluate an instruction-tuned LLM on a translation task across 16 languages. We address four research questions centered on prompt design, examining the impact of the number of examples, the domain and register of examples, and the example selection strategy. Our results show that prompting with one to three examples from the same register and domain as the test input leads to the largest improvements in translation quality, as measured by automatic metrics, while translation quality gains plateau with an increased number of examples. Furthermore, we find that example selection methods - lexical and embedding based - do not yield significant benefits over random selection if the register of selected examples does not match that of the test input.

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Character-Aware English-to-Japanese Translation of Fictional Dialogue Using Speaker Embeddings and Back-Translation
Ayuna Nagato | Takuya Matsuzaki

In Japanese, the form of utterances often reflect speaker-specific character traits, such as gender and personality, through the choise of linguistic elements including personal pronouns and sentence-final particles. However, such elements are not always available in English and a character’s traits are often not directly expressed in English utterances, which can lead to character-inconsistent translations of English novels into Japanese. To address this, we propose a character-aware translation framework that incorporates speaker embeddings. We first train a speaker embedding model by masking the expressions in Japanese utterances that manifest the speaker’s traits and learning to predict them. The resulting embeddings are then injected into a machine translation model. Experimental results show that our proposed method outperforms conventional fine-tuning in preserving speaker-specific character traits in translations.

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DTW-Align: Bridging the Modality Gap in End-to-End Speech Translation with Dynamic Time Warping Alignment
Abderrahmane Issam | Yusuf Can Semerci | Jan Scholtes | Gerasimos Spanakis

End-to-End Speech Translation (E2E-ST) is the task of translating source speech directly into target text bypassing the intermediate transcription step. The representation discrepancy between the speech and text modalities has motivated research on what is known as bridging the modality gap. State-of-the-art methods addressed this by aligning speech and text representations on the word or token level. Unfortunately, this requires an alignment tool that is not available for all languages. Although this issue has been addressed by aligning speech and text embeddings using nearest-neighbor similarity search, it does not lead to accurate alignments. In this work, we adapt Dynamic Time Warping (DTW) for aligning speech and text embeddings during training. Our experiments demonstrate the effectiveness of our method in bridging the modality gap in E2E-ST. Compared to previous work, our method produces more accurate alignments and achieves comparable E2E-ST results while being significantly faster. Furthermore, our method outperforms previous work in low resource settings on 5 out of 6 language directions.

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Targeted Source Text Editing for Machine Translation: Exploiting Quality Estimators and Large Language Models
Hyuga Koretaka | Atsushi Fujita | Tomoyuki Kajiwara

To improve the translation quality of “black-box” machine translation (MT) systems,we focus on the automatic editing of source texts to be translated.In addition to the use of a large language model (LLM) to implement robust and accurate editing,we investigate the usefulness of targeted editing, i.e., instructing the LLM with a text span to be edited.Our method determines such source text spans using a span-level quality estimator, which identifies actual translation errors caused by the MT system of interest, and a word aligner, which identifies alignments between the tokens in the source text and translation hypothesis.Our empirical experiments with eight MT systems and ten test datasets for four translation directionsconfirmed the efficacy of our method in improving translation quality.Through analyses, we identified several characteristics of our method andthat the segment-level quality estimator is a vital component of our method.

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Self-Retrieval from Distant Contexts for Document-Level Machine Translation
Ziqian Peng | Rachel Bawden | François Yvon

Document-level machine translation is a challenging task, as it requires modeling both short-range and long-range dependencies to maintain the coherence and cohesion of the generated translation. However, these dependencies are sparse, and most context-augmented translation systems resort to two equally unsatisfactory options: either to include maximally long contexts, hoping that the useful dependencies are not lost in the noise; or to use limited local contexts, at the risk of missing relevant information. In this work, we study a self-retrieval-augmented machine translation framework (Self-RAMT), aimed at informing translation decisions with informative local and global contexts dynamically extracted from the source and target texts. We examine the effectiveness of this method using three large language models, considering three criteria for context selection. We carry out experiments on TED talks as well as parallel scientific articles, considering three translation directions. Our results show that integrating distant contexts with Self-RAMT improves translation quality as measured by reference-based scores and consistency metrics.

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Using Encipherment to Isolate Conditions for the Successful Fine-tuning of Massively Multilingual Translation Models
Carter Louchheim | Denis Sotnichenko | Yukina Yamaguchi | Mark Hopkins

When fine-tuning massively multilingual translation models for low-resource languages, practitioners often include auxiliary languages to improve performance, but factors determining successful auxiliary language selection remain unclear. This paper investigates whether syntactic similarity or lexical overlap is more important for effective multilingual fine-tuning. We use encipherment to create controlled experimental conditions that disentangle these confounded factors, generating novel languages with identical syntax but no lexical overlap, and conversely, languages that preserve lexical overlap. Through extensive NLLB-200 fine-tuning experiments across Europarl and AmericasNLP datasets, we demonstrate that lexical overlap is the dominant factor. Syntactically identical auxiliary languages provide negligible benefits ( 1.0 ChrF), while languages with significant lexical overlap provide substantial improvements ( 5.0 ChrF), with effectiveness strongly correlated to KL-divergence between token distributions (r = -0.47, p .001). Our findings provide clear guidance: when selecting auxiliary languages for multilingual fine-tuning, prioritize lexical overlap over syntactic similarity.

Multilingual toxicity detection remains a significant challenge due to the scarcity of training data and resources for many languages. While prior work has leveraged the translate-test paradigm to support cross-lingual transfer across a range of classification tasks, the utility of translation in supporting toxicity detection at scale remains unclear.In this work, we conduct a comprehensive comparison of translation-based and language-specific/multilingual classification pipelines.We find that translation-based pipelines consistently outperform out-of-distribution classifiers in 81.3% of cases (13 of 16 languages), with translation benefits strongly correlated with both the resource level of the target language and the quality of the machine translation (MT) system.Our analysis reveals that traditional classifiers continue to outperform LLM-based judgment methods, with this advantage being particularly pronounced for low-resource languages, where translate-classify methods dominate translate-judge approaches in 6 out of 7 cases.We show that MT-specific fine-tuning on LLMs yields lower refusal rates compared to standard instruction-tuned models, but it can negatively impact toxicity detection accuracy for low-resource languages.These findings offer actionable guidance for practitioners developing scalable multilingual content moderation systems.

We investigate the tradeoff between adequacy and fluency in machine translation. We show the severity of this tradeoff at the evaluation level and analyze where popular metrics fall within it. Essentially, current metrics generally lean toward adequacy, meaning that their scores correlate more strongly with the adequacy of translations than with fluency. More importantly, we find that this tradeoff also persists at the meta-evaluation level, and that the standard WMT meta-evaluation favors adequacy-oriented metrics over fluency-oriented ones. We show that this bias is partially attributed to the composition of the systems included in the meta-evaluation datasets. To control this bias, we propose a method that synthesizes translation systems in meta-evaluation. Our findings highlight the importance of understanding this tradeoff in meta-evaluation and its impact on metric rankings.

Existing document-level machine translation resources are only available for a handful of languages, mostly high-resourced ones. To facilitate the training and evaluation of document-level translation and, more broadly, long-context modeling for global communities, we create DocHPLT, the largest publicly available document-level translation dataset to date. It contains 124 million aligned document pairs across 50 languages paired with English, comprising 4.26 billion sentences. By adding pivoted alignments, practitioners can obtain 2500 additional pairs not involving English. Unlike previous reconstruction-based approaches that piece together documents from sentence-level data, we modify an existing web extraction pipeline to preserve complete document integrity from the source, retaining all content, including unaligned portions. After our preliminary experiments identify the optimal training context strategy for document-level translation, we demonstrate that LLMs fine-tuned on DocHPLT substantially outperform off-the-shelf instruction-tuned baselines, with particularly dramatic improvements for under-resourced languages. We open-source the dataset under a permissive license, providing essential infrastructure for advancing multilingual document-level translation.

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SONAR-SLT: Multilingual Sign Language Translation via Language-Agnostic Sentence Embedding Supervision
Yasser Hamidullah | Shakib Yazdani | Cennet Oguz | Josef Van Genabith | Cristina España-Bonet

Sign language translation (SLT) is typically trained with text in a single spoken language, which limits scalability and cross-language generalization. Earlier approaches have replaced gloss supervision with text-based sentence embeddings, but up to now, these remain tied to a specific language and modality. In contrast, here we employ language-agnostic, multimodal embeddings trained on text and speech from multiple languages to supervise SLT, enabling direct multilingual translation. To address data scarcity, we propose a coupled augmentation method that combines multilingual target augmentations (i.e. translations into many languages) with video-level perturbations, improving model robustness. Experiments show consistent BLEURT gains over text-only sentence embedding supervision, with larger improvements in low-resource settings. Our results demonstrate that language-agnostic embedding supervision, combined with coupled augmentation, provides a scalable and semantically robust alternative to traditional SLT training.

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GAMBIT+: A Challenge Set for Evaluating Gender Bias in Machine Translation Quality Estimation Metrics
George Filandrianos | Orfeas Menis Mastromichalakis | Wafaa Mohammed | Giuseppe Attanasio | Chrysoula Zerva

Gender bias in machine translation (MT) systems has been extensively documented, but bias in automatic quality estimation (QE) metrics remains comparatively underexplored. Existing studies suggest that QE metrics can also exhibit gender bias, yet most analyses are limited by small datasets, narrow occupational coverage, and restricted language variety. To address this gap, we introduce a large-scale challenge set specifically designed to probe the behavior of QE metrics when evaluating translations containing gender-ambiguous occupational terms. Building on the GAMBIT corpus of English texts with gender-ambiguous occupations, we extend coverage to three source languages that are genderless or natural-gendered, and eleven target languages with grammatical gender, resulting in 33 source–target language pairs. Each source text is paired with two target versions differing only in the grammatical gender of the occupational term(s) (masculine vs. feminine), with all dependent grammatical elements adjusted accordingly. An unbiased QE metric should assign equal or near-equal scores to both versions. The dataset’s scale, breadth, and fully parallel design, where the same set of texts is aligned across all languages, enables fine-grained bias analysis by occupation and systematic comparisons across languages.

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Implementing and Evaluating Multi-source Retrieval-Augmented Translation
Tommi Nieminen | Jörg Tiedemann | Sami Virpioja

In recent years, neural machine translation (NMT) systems have been integrated with external databases with the aim of improving machine translation (MT) quality and enforcing domain-specific terminology and other conventions in the MT output. Most of the work in incorporating external knowledge with NMT has concentrated on integrating a single source of information, usually either a terminology database or a translation memory. However, in real-life translation scenarios, all relevant knowledge sources should be used in parallel. In this article, we evaluate different methods of integrating external knowledge from multiple sources in a single NMT system. In addition to training single models trained to utilize multiple kinds of information, we also ensemble models that have been trained to utilize a single type of information. We evaluate our models against state-of-the-art LLMs using an extensive purpose-built English to Finnish test suite.

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A Cross-Lingual Perspective on Neural Machine Translation Difficulty
Esther Ploeger | Johannes Bjerva | Jörg Tiedemann | Robert Oestling

Intuitively, machine translation (MT) between closely related languages, such as Swedish and Danish, is easier than MT between more distant pairs, such as Finnish and Danish. Yet, the notions of ‘closely related’ languages and ‘easier’ translation have so far remained underspecified. Moreover, in the context of neural MT, this assumption was almost exclusively evaluated in scenarios where English was either the source or target language, leaving a broader cross-lingual view unexplored. In this work, we present a controlled study of language similarity and neural MT difficulty for 56 European translation directions. We test a range of language similarity metrics, some of which are reasonable predictors of MT difficulty. On a text-level, we reassess previously introduced indicators of MT difficulty, and find that they are not well-suited to our domain, or neural MT more generally. Ultimately, we hope that this work inspires further cross-lingual investigations of neural MT difficulty

This paper presents the results of the General Machine Translation Task organized as part of the 2025 Conference on Machine Translation (WMT). Participants were invited to build systems for any of 30 language pairs. For half of these pairs, we conducted a human evaluation on test sets spanning four to five different domains.We evaluated 60 systems in total: 36 submitted by participants and 24 for which we collected translations from large language models (LLMs) and popular online translation providers.This year, we focused on creating challenging test sets by developing a difficulty sampling technique and using more complex source data. We evaluated system outputs with professional annotators using the Error Span Annotation (ESA) protocol, except for two language pairs, for which we used Multidimensional Quality Metrics (MQM) instead.We continued the trend of increasingly moving towards document-level translation, providing the source texts as whole documents containing multiple paragraphs.

The WMT25 Multilingual Instruction Shared Task (MIST) introduces a benchmark to evaluate large language models (LLMs) across 30 languages. The benchmark covers five types of problems: machine translation, linguistic reasoning, open-ended generation, cross-lingual summarization, and LLM-as-a-judge.We provide automatic evaluation and collect human annotations, which highlight the limitations of automatic evaluation and allow further research into metric meta-evaluation. We run on our benchmark a diverse set of open- and closed-weight LLMs, providing a broad assessment of the multilingual capabilities of current LLMs. Results highlight substantial variation across sub-tasks and languages, revealing persistent challenges in reasoning, cross-lingual generation, and evaluation reliability. This work establishes a standardized framework for measuring future progress in multilingual LLM development.

The WMT25 Shared Task on Automated Translation Evaluation Systems evaluates metrics and quality estimation systems that assess the quality of language translation systems. This task unifies and consolidates the separate WMT shared tasks on Machine Translation Evaluation Metrics and Quality Estimation from previous years. Our primary goal is to encourage the development and assessment of new state-of-the-art translation quality evaluation systems. The shared task this year consisted of three subtasks: (1) segment-level quality score prediction, (2) span-level translation error annotation, and (3) quality-informed segment-level error correction. The evaluation data for the shared task were provided by the General MT shared task and were complemented by “challenge sets” from both the organizers and participants. Task 1 results indicate the strong performance of large LLMs at the system level, whilereference-based baseline metrics outperform LLMs at the segment level. Task 2 results indicate that accurate error detection and balancing precision and recall are persistent challenges. Task 3 results show that minimal editing is challenging even when informed by quality indicators. Robustness across the broad diversity of languages remains a major challenge across all three subtasks.

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Findings of the WMT 2025 Shared Task on Model Compression: Early Insights on Compressing LLMs for Machine Translation
Marco Gaido | Roman Grundkiewicz | Thamme Gowda | Matteo Negri

We present the results of the first edition of the Model Compression shared task, organized as part of the 10th Conference on Machine Translation (WMT25). The task challenged participants to compress Large Language Models (LLMs) toward enabling practical deployment in resource-constrained scenarios, while minimizing loss in translation performance. In this edition, participants could choose to compete in either a constrained track, which required compressing a specific model (Aya Expanse 8B) evaluated on a limited set of language pairs (Czech→German, Japanese→Chinese, and English→Arabic), or an unconstrained track, which placed no restrictions on the model and allowed submissions for any of the 15 language directions covered by the General MT task (GenMT). We received 12 submissions from three teams, all in the constrained track. They proposed different compression solutions and covered various language combinations: all targeted Czech→German, while one covered all the language pairs. Evaluation was conducted separately for each language, measuring translation quality using COMET and MetricX, model size, and inference speed on an Nvidia A100 GPU.

We present the results of the WMT 2025 shared task of the Open Language Data Initiative. Participants were invited to contribute to the massively multilingual open datasets (FLORES+, MT Seed, WMT24++) or create new such resources. We accepted 8 submissions, including 7 extensions or revisions of the existing datasets and one submission with a new parallel training dataset, SMOL.

We present the findings of the WMT 2025 Shared Task LLMs with Limited Resources for Slavic Languages. This shared task focuses on training LLMs using limited data and compute resources for three Slavic languages: Upper Sorbian (hsb), Lower Sorbian (dsb), and Ukrainian (uk), with the objective to develop and improve LLMs for these languages. We consider two tasks which are to be evaluated jointly: Machine Translation (MT) and Multiple-Choice Question Answering (QA). In total, three teams participated in this shared task, with submissions from all three teams for the Sorbian languages and one submission for Ukrainian. All submissions led to an improvement compared to the baseline Qwen2.5-3B model through varying fine-tuning strategies. We note, however, that training purely on MT degrades original QA capabilities. We also report further analyses on the submissions, including MT evaluation using advanced neural metrics for Ukrainian, as well as manual annotation and comparison to the current Sorbian machine translator.

Efforts towards better machine translation (MT) for Creole languages have historically been isolated, due to Creole languages’ geographic and linguistic diversity. However, most speakers of Creole languages stand to benefit from improved MT for low-resource languages. To galvanize collaboration for Creole MT across the NLP community, we introduce the First Shared Task for Creole Language Machine Translation at WMT25. This Shared Task consists of two systems tracks and one data track, for which we received submissions from five participating teams. Participants experimented with a wide variety of systems development techniques. Our evaluation campaign gave rise to improvements in MT performance in several languages, and particularly large improvements in new testing genres, though some participants found that reusing subsets of pretraining data for specialized post-training did not yield significant improvements. Our campaign also yielded new test sets for Mauritian Creole and a vast expansion of public training data for two Creole languages of Latin America.

This study proposes the results of the lowresource Indic language translation task organized in collaboration with the Tenth Conference on Machine Translation (WMT) 2025. In this workshop, participants were required to build and develop machine translation models for the seven language pairs, which were categorized into two categories. Category 1 is moderate training data available in languages i.e English–Assamese, English–Mizo, English-Khasi, English–Manipuri and English– Nyishi. Category 2 has very limited training data available in languages, i.e English–Bodo and English–Kokborok. This task leverages the enriched IndicNE-corp1.0 dataset, which consists of an extensive collection of parallel and monilingual corpora for north eastern Indic languages. The participant results were evaluated using automatic machine translation metrics, including BLEU, TER, ROUGE-L, ChrF, and METEOR. Along with those metrics, this year’s work also includes Cosine similarity for evaluation, which captures the semantic representation of the sentence to measure the performance and accuracy of the models. This work aims to promote innovation and advancements in low-resource Indic languages.

The WMT25 Terminology Translation Task releases new resources in high-stakes domains and investigates the capabilities of translation systems to accurately and consistently translate specialized terms. This year, we feature new domain and language coverage over previous editions, introducing two distinct tracks: (1) sentence-level translation in the information technology domain for English→German, English→Russian, and English→Spanish, and (2) document-level translation in the finance domain for English↔Traditional Chinese with a document-level one-to-many dictionary. Participants are challenged to translate texts under three modes: no terminology, proper terminology, and random terminology, allowing for a causal analysis of terminology utility. Evaluation combines overall quality, terminology accuracy, and terminology consistency. This shared task attracted broad participation, with 13 teams submitting 20 systems in Track 1 and 4 teams participating in Track 2. The results show that providing proper terminology consistently boosts both overall translation quality and term accuracy, whereas reliance on random terminology yields smaller gains. Despite the near-saturation of sentence-level benchmarks, document-level finance translation still fallsshort, indicating an urgent need for long-form evaluation and more robust metrics tailored to professional domains.

We present Midheind’s system contribution to two tasks at WMT25 – Tenth Conference on Machine Translation: The General Machine Translation Task and the WMT25 Terminology Shared Task. Erlendur is a multilingual LLM-based translation system that employs a multi-stage pipeline approach, with enhancements especially for translations from English to Icelandic. We address translation quality and grammatical accuracy challenges in current LLMs through a hybrid prompt-based approach that can benefit lower-resource language pairs. In a preparatory step, the LLM analyzes the source text and extracts key terms for lookup in an English-Icelandic dictionary. The findings of the analysis and the retrieved dictionary results are then incorporated into the translation prompt. When provided with a custom glossary, the system identifies relevant terms from the glossary and incorporates them into the translation, to ensure consistency in terminology. For longer inputs, the system maintains translation consistency by providing contextual information from preceding text chunks. Lastly, Icelandic target texts are passed through our custom-developed seq2seq language correction model (Ingólfsdóttir et al., 2023), where grammatical errors are corrected. Using this hybrid method, Erlendur delivers high-quality translations, without fine-tuning. Erlendur ranked 3rd-4th overall in the General Machine Translation Task for English-Icelandic translations, achieving the highest rank amongst all systems submitted by WMT25 participants (Kocmi et al., 2025a). Notably, in the WMT25 Terminology Shared Task, Erlendur placed 3rd in Track 1 and took first place in the more demanding Track 2 (Semenov et al., 2025).

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A Preliminary Study of AI Agent Model in Machine Translation
Ahrii Kim

We present IR_Multi-agentMT, our submission to the WMT25 General Shared Task. The system adopts an AI-agent paradigm implemented through a multi-agent workflow, Prompt Chaining, in combination with RUBRIC-MQM, an automatic MQM-based error annotation metric. Our primary configuration follows the Translate–Postedit–Proofread paradigm, where each stage progressively enhances translation quality. We conduct a preliminary study to investigate (i) the impact of initial translation quality and (ii) the effect of enforcing explicit responses from the Postedit Agent. Our findings highlight the importance of both factors in shaping the overall performance of multi-agent translation systems.

This paper presents the Marco-MT-Algharb system, our submission to the WMT2025 General Machine Translation Shared Task from Alibaba International Digital Commerce (AIDC). Built on a large language model (LLM) foundation, the system’s strong performance stems from novel quality-aware training and decoding techniques: (1) a two-step supervised fine-tuning (SFT) process incorporating data distillation, (2) a two-step reinforcement learning (RL) framework for preference alignment, and (3) a hybrid decoding strategy that integrates word alignment with Minimum Bayes Risk (MBR) re-ranking to improve faithfulness. These approaches jointly ensure high accuracy and robustness across diverse languages and domains. In the official human evaluation, our system secured five first‐place finishes, one second, and four third‐place results in the constrained category across the 13 directions we participated in. Notably, for the English-Chinese, our results surpassed all open/closed‐source systems.

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Evaluation of QWEN-3 for English to Ukrainian Translation
Cristian Grozea | Oleg Verbitsky

We report the results of evaluating Qwen3 for the English-to-Ukrainian language pair of the general MT task of WMT 2025.In addition to the quantitative evaluation, we performed a qualitative evaluation, in collaboration with a native Ukrainian speaker - therefore we present an example-heavy analysis of the typical failures the LLMs still do when translating natural language, particularly into Ukrainian.We report also on the practicalities of using LLMs, such as on the difficulties of making them follow instructions, on ways to exploit the increased “smartness” of the reasoning models while simultaneously avoiding the reasoning part improperly interfering with the chain in which the LLM is just one element.

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SYSTRAN @ WMT 2025 General Translation Task
Dakun Zhang | Yara Khater | Ramzi Rahli | Anna Rebollo | Josep Crego

We present an English-to-Japanese translationsystem built upon the EuroLLM-9B (Martinset al., 2025) model. The training process involvestwo main stages: continue pretraining(CPT) and supervised fine-tuning (SFT). Afterboth stages, we further tuned the model using adevelopment set to optimize performance. Fortraining data, we employed both basic filteringtechniques and high-quality filtering strategiesto ensure data cleanness. Additionally, we classifyboth the training data and development datainto four different domains and we train andfine-tune with domain specific prompts duringsystem training. Finally, we applied MinimumBayes Risk (MBR) decoding and paragraph-levelreranking for post-processing to enhancetranslation quality.

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Shy-hunyuan-MT at WMT25 General Machine Translation Shared Task
Mao Zheng | Zheng Li | Yang Du | Bingxin Qu | Mingyang Song

In this paper, we present our submission to the WMT25 shared task on machine translation, for which we propose Synergy-enhanced policy optimization framework, named Shy. This novel two-phase training framework synergistically combines knowledge distillation and fusion via reinforcement learning.In the first phase, we introduce a multi-stage training framework that harnesses the complementary strengths of multiple state-of-the-art large language models to generate diverse, high-quality translation candidates. These candidates serve as pseudo-references to guide the supervised fine-tuning of our model, Hunyuan-7B, effectively distilling the collective knowledge of multiple expert systems into a single efficient model.In the second phase, we further refine the distilled model through Group Relative Policy Optimization, a reinforcement learning technique that employs a composite reward function. By calculating reward from multiple perspectives, our model ensures better alignment with human preferences and evaluation metrics.Extensive experiments across multiple language pairs demonstrate that our model Shy-hunyuan-MT yields substantial improvements in translation quality compared to baseline approaches. Notably, our framework achieves competitive performance comparable to that of state-of-the-art systems while maintaining computational efficiency through knowledge distillation and strategic ensemble.

In this paper, we present the SalamandraTA family of models, an improved iteration of Salamandra LLMs (Gonzalez-Agirre et al., 2025) specifically trained to achieve strong performance in translation-related tasks for 38 European languages. SalamandraTA comes in two scales: 2B and 7B parameters. For both versions, we applied the same training recipe with a first step of continual pre-training on parallel data, and a second step of supervised fine-tuning on high-quality instructions.The BSC submission to the WMT25 General Machine Translation shared task is based on the 7B variant of SalamandraTA. We first extended the model vocabulary to support the additional non-European languages included in the task. This was followed by a second phase of continual pretraining and supervised fine-tuning, carefully designed to optimize performance across all translation directions for this year’s shared task. For decoding, we employed two quality-aware strategies: Minimum Bayes Risk Decoding and Translation Reranking using Comet and Comet-kiwi.We publicly release both the 2B and 7B versions of SalamandraTA, along with the newer SalamandraTA-v2 model, on Hugging Face.

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Instruction-Tuned English to Bhojpuri Neural Machine Translation Using Contrastive Preference Optimization
Kshetrimayum Boynao Singh | Deepak Kumar | Asif Ekbal

This paper presents an English to Bhojpuri machine translation (MT) system developed for the WMT25 General MT Shared Task. Given the low-resource nature of Bhojpuri, we adopt a two-stage training pipeline: unsupervised pretraining followed by supervised fine-tuning. During pretraining, we use a 300,000-sentence corpus comprising 70% Bhojpuri monolingual data and 30% English data to establish language grounding. The fine-tuning stage utilizes 29,749 bilingual English to Bhojpuri sentence pairs (including training, validation, and test sets). To adapt the system to instruction-following scenarios, we apply a novel optimization strategy: Contrastive Preference Optimization (CPO). This technique enables the model to capture fine-grained translation preferences and maintain semantic fidelity in instruction-tuned settings. We evaluate our system across multiple metrics, demonstrating moderate performance in low-resource MT tasks, particularly in diverse domains such as literary, news, social, and speech.

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SH at WMT25 General Machine Translation Task
Hayate Shiroma

I participated in the unconstrained track of the English-to-Japanese translation task at the WMT 2025 General Machine Translation Task.My submission leverages several large language models, all of which are trained with supervised fine-tuning, and some further optimized via preference learning.To enhance translation quality, I introduce an automatic post-editing model and perform automatic post-editing.In addition, I select the best translation from multiple candidates using Minimum Bayes Risk (MBR) decoding.For MBR decoding, I use COMET-22 and LaBSE-based cosine similarity as evaluation metrics.

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Simple Test Time Scaling for Machine Translation: Kaze-MT at the WMT25 General Translation Task
Shaomu Tan

This paper describes the Kaze-MT submission to the WMT25 General Machine Translation task (Japanese–Chinese). Our system deliberately adopts a minimalist Test-Time Scaling (TTS) pipeline with three stages—Sampling, Scoring, and Selection—while avoiding any task-specific fine-tuning, in-context exemplars, or bespoke decoding heuristics. In the sampling stage, we use the zero-shot Qwen2.5-72B-Instruct model to generate 512 candidate translations under a fixed temperature schedule designed to encourage lexical and syntactic diversity without sacrificing fluency. In the scoring stage, each candidate is evaluated by multiple reference-free quality estimation (QE) models—KIWI-22, MetricX-24 Hybrid-XXL, and Remedy-24-9B. The selection stage aggregates metric-specific rankings and chooses the candidate with the lowest mean rank, which we found more stable than averaging raw scores across heterogeneous ranges. We submit to both constrained and unconstrained tracks with minimal configuration changes. According to official preliminary results, our submissions are competitive on automatic metrics; in human evaluation, Kaze-MT falls within the 8–13 cluster, delivering performance comparable to CommandA-WMT and DeepSeek-V3 and outperforming other large LLM baselines such as Mistral-Medium and other extensively tuned MT systems.

This paper presents the submission of NTTSU for the constrained track of the English–Japanese and Japanese–Chinese at the WMT2025 general translation task.For each translation direction, we build translation models from a large language model by combining continual pretraining, supervised fine-tuning, and preference optimization based on the translation quality and adequacy.We finally generate translations via context-aware MBR decoding to maximize translation quality and document-level consistency.

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A* Decoding for Machine Translation in LLMs - SRPOL Participation in WMT2025
Adam Dobrowolski | Paweł Przewłocki | Paweł Przybysz | Marcin Szymański | Dawid Siwicki

SRPOL team submission to WMT2025 introduces innovative approach using A* (A-star) algorithm of decoding in EuroLLM which gives diverse set of translation hypotheses. Subsequent reranking by Comet-QE and NLLB chooses the best of the diversed hypotheses which gives significant improvement of translation quality. The A* algorithm can be applied to decoding in any LLMs or other translation models. The experiment shows that by using free, openly accessible MT models you can achieve translation quality of the best online translators and LLMs using just a PC under your desk.

This paper presents the open-system submission by the In2x research team for the WMT25 General Machine Translation Shared Task. Our submission focuses on Japanese-related translation tasks, aiming to explore a generalizable paradigm for extending large language models (LLMs) to other languages. This paradigm encompasses aspects such as data construction methods and reward model design. The ultimate goal is to enable large language model systems to achieve exceptional performance in low-resource or less commonly spoken languages.

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CUNI at WMT25 General Translation Task
Josef Jon | Miroslav Hrabal | Martin Popel | Ondřej Bojar

This paper describes the CUNI submissions to the WMT25 General Translation task, namely for the English to Czech, English to Serbian, Czech to German and Czech to Ukrainian language pairs. We worked in multiple teams, each with a different approach, spanning from traditional, smaller Transformer NMT models trained on both sentence and document level, to fine-tuning LLMs using LoRA and CPO. We show that these methods are effective in improving automatic MT evaluation scores compared to the base pretrained models.

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UvA-MT’s Participation in the WMT25 General Translation Shared Task
Di Wu | Yan Meng | Maya Konstantinovna Nachesa | Seth Aycock | Christof Monz

This paper presents UvA-MT’s submission to the WMT 2025 shared task on general machine translation, competing in the unconstrained track across all 16 translation directions. Unusually, this year we use only WMT25’s blind test set (source sentences only) to generate synthetic data for LLM training, and translations are produced using pure beam search for submission. Overall, our approach can be seen as a special variant of data distillation, motivated by two key considerations: (1) perfect domain alignment, where the training and test domains are distributionally identical; and (2) the strong teacher model, GPT-4o-mini, offers high-quality outputs as both a reliable reference and a fallback in case of mere memorization.Interestingly, the outputs of the resulting model, trained on Gemma3-12B using Best-of-N (BoN) outputs from GPT-4o-mini, outperform both original BoN outputs from GPT-4o-mini and Gemma3-12B in some high-resource languages across various metrics. We attribute this to a successful model ensemble, where the student model (Gemma3-12B) retains the strengths of the teacher (GPT-4o-mini) while implicitly avoiding their flaws.

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AMI at WMT25 General Translation Task: How Low Can We Go? Finetuning Lightweight Llama Models for Low Resource Machine Translation
Atli Jasonarson | Steinthor Steingrimsson

We present the submission of the Árni Magnússon Institute’s team for the WMT25 General translation task. We focus on the English-Icelandic translation direction. We pre-train Llama 3.2 3B on 10B tokens of English and Icelandic texts and fine-tune on parallel corpora. Multiple translation hypotheses are produced first by the fine-tuned model, and then more hypotheses are added by that same model further tuned using contrastive preference optimization. The hypotheses are then post-processed using a grammar correction model and post-processing rules before the final translation is selected using minimum Bayes risk decoding. We found that while it is possible to generate translations of decent quality based on a lightweight model with simple approaches such as the ones we apply, our models are quite far behind the best participating systems and it would probably take somewhat larger models to reach competitive levels.

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KIKIS at WMT 2025 General Translation Task
Koichi Iwakawa | Keito Kudo | Subaru Kimura | Takumi Ito | Jun Suzuki

We participated in the constrained English–Japanese track of the WMT 2025 General Machine Translation Task.Our system collected the outputs produced by multiple subsystems, each of which consisted of LLM-based translation and reranking models configured differently (e.g., prompting strategies and context sizes), and reranked those outputs.Each subsystem generated multiple segment-level candidates and iteratively selected the most probable one to construct the document translation.We then reranked the document-level outputs from all subsystems to obtain the final translation.For reranking, we adopted a text-based LLM reranking approach with a reasoning model to take long contexts into account.Additionally, we built a bilingual dictionary on the fly from parallel corpora to make the system more robust to rare words.

Large Language Models have shown impressive multilingual capabilities, where translation is one among many tasks. Google Translate’s submission to the 2025 WMT evaluation tries to research how these models behave when pushing their translation performance to the limit. Starting with the strong Gemma 3 model, we carry out supervised fine tuning on high quality, synthetically generated parallel data. Afterwards we perform an additional reinforcement learning step, with reward models based on translation metrics to push the translation capabilities even further. Controlling the combination of reward models, including reference-based and quality estimation metrics, we found that the behaviour of the model could be tailored towards a more literal or more creative translation style. Our two submissions correspond to those two models. We chose the more creative system as our primary submission, targetting a human preference for better sounding, more naturally flowing text, although at the risk of losing on the accuracy of the translation. It is an open question to find the sweet spot between these two dimensions, which certainly will depend on the specific domain to handle and user preferences.

This paper presents the submission from Dalian University of Technology (DLUT) and Global Tone Communication Technology Co., Ltd. (GTCOM) to the WMT25 General Machine Translation Task. Amidst the paradigm shift from specialized encoder-decoder models to general-purpose Large Language Models (LLMs), this work conducts a systematic comparison of both approaches across five language pairs. For traditional Neural Machine Translation (NMT), we build strong baselines using deep Transformer architectures enhanced with data augmentation. For the LLM paradigm, we explore zero-shot performance and two distinct supervised fine-tuning (SFT) strategies: direct translation and translation refinement. Our key findings reveal a significant discrepancy between lexical and semantic evaluation metrics: while strong NMT systems remain competitive in BLEU scores, fine-tuned LLMs demonstrate marked superiority in semantic fidelity as measured by COMET. Furthermore, we find that fine-tuning LLMs for direct translation is more effective than for refinement, suggesting that teaching the core task directly is preferable to correcting baseline outputs.

This paper describes Yandex submission to the WMT25 General Machine Translation task. We participate in English-to-Russian translation direction and propose a purely LLM-based translation model. Our training procedure comprises a training pipeline of several stages built upon YandexGPT, an in-house general-purpose LLM. In particular, firstly, we employ continual pretraining (post-pretrain) for MT task for initial adaptation to multilinguality and translation. Subsequently, we use SFT on parallel document-level corpus in the form of P-Tuning. Following SFT, we propose a novel alignment scheme of two stages, the first one being a curriculum learning with difficulty schedule and a second one - training the model for tag preservation and error correction with human post-edits as training samples. Our model achieves results comparable to human reference translations on multiple domains.

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IRB-MT at WMT25 Translation Task: A Simple Agentic System Using an Off-the-Shelf LLM
Ivan Grubišić | Damir Korencic

Large Language Models (LLMs) have been demonstrated to achieve state-of-art results on machine translation. LLM-based translation systems usually rely on model adaptation and fine-tuning, requiring datasets and compute. The goal of our team’s participation in the “General Machine Translation” and “Multilingual” tasks of WMT25 was to evaluate the translation effectiveness of a resource-efficient solution consisting of a smaller off-the-shelf LLM coupled with a self-refine agentic workflow. Our approach requires a high-quality multilingual LLM capable of instruction following. We select Gemma3-12B among several candidates using the pretrained translation metric MetricX-24 and a small development dataset. WMT25 automatic evaluations place our solution in the mid tier of all WMT25 systems, and also demonstrate that it can perform competitively for approximately 16% of language pairs.

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Improving Low-Resource Japanese Translation with Fine-Tuning and Backtranslation for the WMT 25 General Translation Task
Felipe Fujita | Hideyuki Takada

In this paper, we explore the effectiveness of combining fine-tuning and backtranslation on a small Japanese corpus for neural machine translation. Starting from a baseline English→Japanese model (COMET = 0.460), we first apply backtranslation (BT) using synthetic data generated from monolingual Japanese corpora, yielding a modest increase (COMET = 0.468). Next, we fine-tune (FT) the model on a genuine small parallel dataset drawn from diverse Japanese news and literary corpora, achieving a substantial jump to COMET = 0.589 when using Mistral 7B. Finally, we integrate both backtranslation and fine-tuning—first augmenting the small dataset with BT generated examples, then adapting via FT—which further boosts performance to COMET = 0.597. These results demonstrate that, even with limited training data, the synergistic use of backtranslation and targeted fine-tuning on Japanese corpora can significantly enhance translation quality, outperforming each technique in isolation. This approach offers a lightweight yet powerful strategy for improving low-resource language pairs.

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Multi-agentMT: Deploying AI Agent in the WMT25 Shared Task
Ahrii Kim

We present Multi-agentMT, our system for the WMT25 General Shared Task. The model adopts Prompt Chaining, a multi-agent workflow combined with Rubric-MQM, an automatic MQM-based error annotation metric. Our primary submission follows a Translate–Postedit–Proofread pipeline, in which error positions are explicitly marked and iteratively refined. Results suggest that a semi-autonomous agent scheme for machine translation is feasible with a smaller, earlier-generation model in low-resource settings, achieving comparable quality at roughly half the cost of larger systems.

This work describes Laniqo’s submission to the constrained track of the WMT25 General MT Task. We participated in 11 translation directions. Our approach combines several techniques: fine-tuning the EuroLLM-9B-Instruct model using Contrastive Preference Optimization on a synthetic dataset, applying Retrieval-Augmented Translation with human-translated data, implementing Quality-Aware Decoding, and performing postprocessing of translations with a rule-based algorithm. We analyze the contribution of each method and report improvements at every stage of our pipeline.

We present Command A Translate, an LLMbased machine translation model built off Cohere’s Command A. It reaches state-of-the-art machine translation quality via direct preference optimization. Our meticulously designed data preparation pipeline emphasizes robust quality control and a novel difficulty filtering – a key innovation that distinguishes Command A Translate. Furthermore, we extend our model and participate at WMT with a system (CommandA-WMT) that uses two models and post-editing steps of step-by-step reasoning and limited Minimum Bayes Risk decoding.

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GENDER1PERSON: Test Suite for Estimating Gender Bias of First-person Singular Forms
Maja Popović | Ekaterina Lapshinova-Koltunski

The gender1person test suite is designed to measure gender bias in translating singular first-person forms from English into two Slavic languages, Russian and Serbian. The test suite consists of 1,000 Amazon product reviews, uniformly distributed over 10 different product categories. Bias is measured through a gender score ranging from -100 (all reviews are feminine) to 100 (all reviews are masculine). The test suite shows that the majority of the systems participating in the WMT-2025 task for these two target languages prefer the masculine writer’s gender. There is no single system which is biased towards the feminine variant. Furthermore, for each language pair, there are seven systems that are considered balanced, having the gender scores between -10 and 10.Finally, the analysis of different products showed that the choice of the writer’s gender depends to a large extent on the product. Moreover, it is demonstrated that even the systems with overall balanced scores are actually biased, but in different ways for different product categories.

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Evaluation of LLM for English to Hindi Legal Domain Machine Translation Systems
Kshetrimayum Boynao Singh | Deepak Kumar | Asif Ekbal

The study critically examines various Machine Translation systems, particularly focusing on Large Language Models, using the WMT25 Legal Domain Test Suite for translating English into Hindi. It utilizes a dataset of 5,000 sentences designed to capture the complexity of legal texts, based on word frequency ranges from 5 to 54. Each frequency range contains 100 sentences, collectively forming a corpus that spans from simple legal terms to intricate legal provisions. Six metrics were used to evaluate the performance of the system: BLEU, METEOR, TER, CHRF++, BERTScore and COMET. The findings reveal diverse capabilities and limitations of LLM architectures in handling complex legal texts. Notably, Gemini-2.5-Pro, Claude-4 and ONLINE-B topped the performance charts in terms fo human evaluation, showcasing the potential of LLMs for nuanced trans- lation. Despite these advances, the study identified areas for further research, especially in improving robustness, reliability, and explainability for use in critical legal contexts. The study also supports the WMT25 subtask focused on evaluating weaknesses of large language models (LLMs). The dataset and related resources are publicly available at https://github.com/helloboyn/WMT25-TS.

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RoCS-MT v2 at WMT 2025: Robust Challenge Set for Machine Translation
Rachel Bawden | Benoît Sagot

RoCS-MT (Robust Challenge Set for Machine Translation) was initially proposed at the test suites track of WMT 2023. Designed to challenge MT systems’ translation performance on user-generated content (UGC), it contains examples sourced from English Reddit, with manually normalised versions, aligned labelled annotation spans and reference translations in five languages. In this article, we describe version 2 of RoCS-MT in the context of the 2025 WMT test suites track. This new version contains several improvements on the initial version including (i) minor corrections of normalisation, (ii) corrections to reference translations and addition of alternative references to accommodate for different possible genders (e.g. of speakers) and (iii) a redesign and re-annotation of normalisation spans for further analysis of different non-standard UGC phenomena. We describe these changes and provide results and preliminary analysis of the MT submissions to the 2025 general translation task.

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Automated Evaluation for Terminology Translation Related to the EEA Agreement
Selma Dis Hauksdottir | Steinthor Steingrimsson

This paper presents a submission to the WMT25 test suite subtask, focusing on terminology in official documents related to the EEA Agreement. The test suite evaluates the accuracy of MT systems in translating terminology for the English-Icelandic translation direction when applied to the EEA documents. We focus on the use of terminology in four domains of the agreement; science, technology, economics, and society. We find that the manual evaluation confirms that our test suite can be helpful in selecting the best MT system for working with these domains. Surprisingly, an online system that does not achieve very high scores on the general translation task, according to preliminary results, is most adept at translating the terminology our test suite evaluates.

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Up to Par? MT Systems Take a Shot at Sports Terminology
Einar Sigurdsson | Magnús Magnússon | Atli Jasonarson | Steinthor Steingrimsson

We present a submission to the WMT25 test suite subtask, focusing on the capabilities of MT systems to translate sports-related language. Although many sports attract extensive media attention and feature a rich, polysemous language, often shaped by active neologism and community-driven translations, the sports domain has received relatively little focus in MT research. In English-Icelandic automatic translations, sports-specific vocabulary often appears to be mistranslated. Our test suite is designed to test whether this observation holds merit. We evaluate 34 systems, both automatically and manually, and find that sports language poses challenges to a varying degree for all the systems.

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Fine-Grained Evaluation of English-Russian MT in 2025: Linguistic Challenges Mirroring Human Translator Training
Shushen Manakhimova | Maria Kunilovskaya | Ekaterina Lapshinova-Koltunski | Eleftherios Avramidis

We analyze how English–Russian machine translation (MT) systems submitted to WMT25 perform on linguistically challenging translation tasks, similar to problems used in university professional translator training. We assessed the ten top-performing systems using a fine-grained test suite containing 465 manually devised test items, which cover 55 lexical, grammatical, and discourse phenomena, in 13 categories. By applying pass/fail rules with human adjudication and micro/macro aggregates, we observe three performance tiers. Compared with the official WMT25 ranking, our ranking broadly aligns but reveals notable shifts.Our findings show that in 2025, even top-performing MT systems still struggle with translation problems that require deep understanding and rephrasing, much like human novices do. The best systems exhibit creativity and can be very good at handling such challenges, often producing more natural translations rather than producing word-for-word renditions. However, persistent structural and lexical problems remain: literal word order carry-overs, misused verb forms, and rigid phrase translations were common, mirroring errors typically seen in beginner translator assignments.

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Tagged Span Annotation for Detecting Translation Errors in Reasoning LLMs
Taemin Yeom | Yonghyun Ryu | Yoonjung Choi | Jinyeong Bak

We present the AIP team’s submission to the WMT 2025 Unified MT Evaluation SharedTask, focusing on the span-level error detection subtask. Our system emphasizes response format design to better harness the capabilities of OpenAI’s o3, the state-of-the-art reasoning LLM. To this end, we introduce Tagged SpanAnnotation (TSA), an annotation scheme designed to more accurately extract span-level information from the LLM. On our refined version of WMT24 ESA dataset, our reference-free method achieves an F1 score of approximately 27 for character-level label prediction, outperforming the reference-based XCOMET-XXL at approximately 17.

Automated metrics for machine translation attempt to replicate human judgment. Unlike humans, who often assess a translation in the context of multiple alternatives, these metrics typically consider only the source sentence and a single translation. This discrepancy in the evaluation setup may negatively impact the performance of automated metrics. We propose two automated metrics that incorporate additional information beyond the single translation. COMET-polycand uses alternative translations of the same source sentence to compare and contrast with the translation at hand, thereby providing a more informed assessment of its quality. COMET-polyic, inspired by retrieval-based in-context learning, takes in translations of similar source texts along with their human-labeled quality scores to guide the evaluation. We find that including a single additional translation in COMET-polycand improves the segment-level metric performance (0.079 to 0.118 Kendall’s tau-b correlation), with further gains when more translations are added. Incorporating retrieved examples in COMET-polyic yields similar improvements (0.079 to 0.116 Kendall’s tau-b correlation). We release our models publicly.

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Long-context Reference-based MT Quality Estimation
Sami Haq | Chinonso Osuji | Sheila Castilho | Brian Davis | Thiago Castro Ferreira

In this paper, we present our submission to the Tenth Conference on Machine Translation (WMT25) Shared Task on Automated Translation Quality Evaluation. Our systems are built upon the COMET framework and trained to predict segment-level ESA scores using augmented long-context data. To construct long-context training examples, we concatenate multiple in-domain sentences and compute a weighted average of their scores. We further integrate human judgment datasets MQM, SQM, and DA) through score normalisation and train multilingual models on the source, hypothesis, and reference translations. Experimental results demonstrate that incorporating long-context information yields higher correlations with human judgments compared to models trained exclusively on short segments.

This paper presents the evaluation of submissions to the WMT 2025 Metrics Shared Task on the SSA-MTE challenge set, a large-scale benchmark for machine translation evaluation (MTE) in Sub-Saharan African languages. The SSA-MTE test sets contains over 12,768 human-annotated adequacy scores across 11 language pairs sourced from English, French, and Portuguese, spanning 6 commercial and open-source MT systems. Results show that correlations with human judgments remain generally low, with most systems falling below the 0.4 Spearman threshold for medium-level agreement. Performance varies widely across language pairs, with most correlations under 0.4; in some extremely low-resource cases, such as Portuguese–Emakhuwa, correlations drop to around 0.1, underscoring the difficulty of evaluating MT for very low-resource African languages. These findings highlight the urgent need for more research on robust, generalizable MT evaluation methods tailored for African languages.

This paper describes Nvidia-Nemo’s WMT 2025 Metrics Shared Task submission. We investigated two strategies for extending Machine Translation (MT) evaluation to unsegmented documents: 1) first segmenting into sentences and then applying regression-based metrics and 2) directly utilizing the long-context capabilities of LLMs. The base comparison of the segmentation-based and LLM-based metrics on the WMT 2023-24 evaluation sets indicated that the former performs more robustly across language pairs.Thus we sought to improve the LLM-based approach by incorporating relative evaluation - this setting jointly evaluates all candidate translations at once and relative to each other, rather than evaluating each separately. Our experiments using the open-source Qwen3 LLM show that relative evaluation improves score correlations with human judgment, but only if the task is structured as a 2-stage evaluate-then-refine problem.

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GEMBA V2: Ten Judgments Are Better Than One
Marcin Junczys-Dowmunt

We introduce GEMBA-MQM V2, an MQM-inspired, reference-free LLM evaluation metric for the WMT25 Metrics Shared Task (Subtask 1). Building on GEMBA/GEMBA-MQM, we prompt GPT-4.1-mini to produce structured MQM error annotations per segment. We map annotations to scores with 25/5/1 severity weights (minor punctuation = 0.1). To reduce stochastic variance, each segment is scored ten times and aggregated with a reciprocal-rank weighted average (RRWA) after removing outliers beyond 2𝜎. On the WMT24 MQM test sets, GEMBA-MQM V2 ranks first by average correlation, with strong results across languages and evaluation levels; WMT23 results show comparable performance.

This paper describes the joint effort of Phrase a.s. and Charles University’sInstitute of Formal and Applied Linguistics (CUNI/UFAL) on the WMT25Automated Translation Quality Evaluation Systems Shared Task. Both teamsparticipated both in a collaborative and competitive manner, i.e. they eachsubmitted a system of their own as well as a contrastive joint system ensemble.In Task~1, we show that such an ensembling—if chosen in a clever way—canlead to a performance boost. We present the analysis of various kinds ofsystems comprising both “traditional” NN-based approach, as well as differentflavours of LLMs—off-the-shelf commercial models, their fine-tuned versions,but also in-house, custom-trained alternative models. In Tasks~2 and~3 we showPhrase’s approach to tackling the tasks via various GPT models: Error SpanAnnotation via the complete MQM solution using non-reasoning models (includingfine-tuned versions) in Task~2, and using reasoning models in Task~3.

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MSLC25: Metric Performance on Low-Quality Machine Translation, Empty Strings, and Language Variants
Rebecca Knowles | Samuel Larkin | Chi-Kiu Lo

In this challenge set, we examine how automatic metrics for machine translation perform on a wide variety of machine translation output, covering a wider range of quality than the WMT submissions. We also explore metric results on specific types of corner cases, such as empty strings, wrong- or mixed-language text, and more. We primarily focus on Japanese–Chinese data, with some work on English and Czech.

In this paper, we present our submissions to the unified WMT25 Translation Evaluation Shared Task. For the Quality Score Prediction subtask, we create a new generation of MetricX with improvements in the input format and the training protocol, while for the Error Span Detection subtask we develop a new model, GemSpanEval, trained to predict error spans along with their severities and categories. Both systems are based on the state-of-the-art multilingual open-weights model Gemma 3, fine-tuned on publicly available WMT data. We demonstrate that MetricX-25, adapting Gemma 3 to an encoder-only architecture with a regression head on top, can be trained to effectively predict both MQM and ESA quality scores, and significantly outperforms its predecessor. Our decoder-only GemSpanEval model, on the other hand, we show to be competitive in error span detection with xCOMET, a strong encoder-only sequence-tagging baseline. With error span detection formulated as a generative task, we instruct the model to also output the context for each predicted error span, thus ensuring that error spans are identified unambiguously.

This paper presents the submissions of Huawei Translate Services Center (HW-TSC) to the WMT 2025 Segment-level quality score prediction Task. We participate in 16 language pairs. For the prediction of translation quality scores for long multi-sentence text units, we propose an automatic evaluation framework based on alignment algorithms. Our approach integrates sentence segmentation tools and dynamic programming to construct sentence-level alignments between source and translated texts, then adapts sentence-level evaluation models to document-level assessment via sliding-window aggregation. Our submissions achieved competitive results in the final evaluations of all language pairs we participated in.

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UvA-MT at WMT25 Evaluation Task: LLM Uncertainty as a Proxy for Translation Quality
Di Wu | Christof Monz

This year, we focus exclusively on using the uncertainty quantification as a proxy for translation quality. While this has traditionally been regarded as a form of unsupervised quality estimation, such signals have been overlooked in the design of the current metric models—we show their value in the context of LLMs. More specifically, in contrast to conventional unsupervised QE methods, we apply recent calibration technology to adjust translation likelihoods to better align with quality signals, and we use the single resulting model to participate in both the general translation and QE tracks at WMT25.Our offline experiments show some advantages: 1) uncertainty signals extracted from LLMs, like Tower or Gemma-3, provide accurate quality predictions; and 2) calibration technology further improves this QE performance, sometimes even surpassing certain metric models that were trained with human annotations, such as CometKiwi. We therefore argue that uncertainty quantification (confidence), especially from LLMs, can serve as a strong and complementary signal for the metric design, particularly when human-annotated data are lacking. However, we also identify limitations, such as its tendency to assign disproportionately higher scores to hypotheses generated by the model itself.

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Submission for WMT25 Task 3
Govardhan Padmanabhan

The paper presents two approaches submitted to the WMT 2025 Automated Translation Quality Evaluation Systems Task 3 - Quality Estimation (QE)-informed Segment-level Error Correction. While jointly training QE systems with Automatic Post-Editing (APE) has shown improved performance for both tasks, APE systems are still known to overcorrect the output of Machine Translation (MT), leading to a degradation in performance. We investigate a simple training-free approach - QE-informed Retranslation, and compare it with another within the same training-free paradigm. Our winning approach selects the highest-quality translation from multiple candidates generated by different LLMs. The second approach, more akin to APE, instructs an LLM to replace error substrings as specified in the provided QE explanation(s). A conditional heuristic was employed to minimise the number of edits, with the aim of maximising the Gain-to-Edit ratio. The two proposed approaches achieved a ∆COMET scoreof 0.0201 and −0.0108, respectively, leading the first approach to achieve the winning position on the subtask leaderboard.

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RankedCOMET: Elevating a 2022 Baseline to a Top-5 Finish in the WMT 2025 QE Task
Sujal Maharjan | Astha Shrestha

This paper presents rankedCOMET, a lightweight per-language-pair calibration applied to the publicly available Unbabel/wmt22-comet-da model that yields a competitive Quality Estimation (QE) system for the WMT 2025 shared task. This approach transforms raw model outputs into per-language average ranks and min–max normalizes those ranks to [0,1], maintaining intra-language ordering while generating consistent numeric ranges across language pairs. Applied to 742,740 test segments and submitted to Codabench, this unsupervised post-processing enhanced the aggregated Pearson correlation on the preliminary snapshot and led to a 5th-place finish. We provide detailed pseudocode, ablations (including a negative ensemble attempt), and a reproducible analysis pipeline providing Pearson, Spearman, and Kendall correlations with bootstrap confidence intervals.

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Quality-Informed Segment-Level Error Correction Using Natural Language Explanations from xTower and Large Language Models
Prashant Sharma

This paper describes our submission to the WMT25 Automated Translation Quality Evaluation Systems Task 3 - QE-informed Segment-level Error Correction. We propose a two-step approach for Automatic Post-Editing (APE) that leverages natural language explanations of translation errors. Our method first utilises the xTower model to generate a descriptive explanation of the errors present in a machine-translated segment, given the source text, the machine translation, and quality estimation annotations. This explanation is then provided as a prompt to a powerful Large Language Model, Gemini 1.5 Pro, which generates the final, corrected translation. This approach is inspired by recent work in edit-based APE and aims to improve the interpretability and performance of APE systems. We Evaluated across six language pairs (EN→ZH, EN→CS, EN→IS, EN→JA, EN→RU, EN→UK), our approach demonstrates promising results, especially in cases requiring fine-grained edits.

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TASER: Translation Assessment via Systematic Evaluation and Reasoning
Monishwaran Maheswaran | Marco Carini | Christian Federmann | Tony Diaz

We introduce TASER (Translation Assessment via Systematic Evaluation and Reasoning), a metric that uses Large Reasoning Models (LRMs) for automated translation quality assessment. TASER harnesses the explicit reasoning capabilities of LRMs to conduct systematic, step-by-step evaluation of translation quality. We evaluate TASER on the WMT24 Metrics Shared Task across both reference-based and reference-free scenarios, demonstrating state-of-the-art performance. In system-level evaluation, TASER achieves the highest soft pairwise accuracy in both reference-based and reference-free settings, outperforming all existing metrics. At the segment level, TASER maintains competitive performance with our reference-free variant ranking as the top-performing metric among all reference-free approaches. Our experiments reveal that structured prompting templates yield superior results with LRMs compared to the open-ended approaches that proved optimal for traditional LLMs. We evaluate o3, a large reasoning model from OpenAI, with varying reasoning efforts, providing insights into the relationship between reasoning depth and evaluation quality. The explicit reasoning process in LRMs offers interpretability and visibility, addressing a key limitation of existing automated metrics. Our results demonstrate that Large Reasoning Models show a measurable advancement in translation quality assessment, combining improved accuracy with transparent evaluation across diverse language pairs.

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Vicomtech@WMT 2025: Evolutionary Model Compression for Machine Translation
David Ponce | Harritxu Gete | Thierry Etchegoyhen

We describe Vicomtech’s participation in the WMT 2025 Shared Task on Model Compression. We addressed all three language pairs of the constrained task, namely Czech to German, English to Arabic and Japanese to Chinese, using the Aya Expanse 8B model as our base model. Our approach centers on GeLaCo, an evolutionary method for LLM compression via layer collapse operations, which efficiently explores the compression solution space through population-based search and a module-wise similarity fitness function that captures attention, feed-forward, and hidden state representations. We systematically evaluated compression at three different ratios (0.25, 0.50, and 0.75) and applied targeted post-training techniques to recover performance through fine-tuning and knowledge distillation over translation instructions. Additionally, we explored quantization techniques to achieve further model size reduction. Our experimental results demonstrate that the combination of evolutionary layer compression, targeted post-training, and quantization can achieve substantial model size reduction while maintaining competitive translation quality across all language pairs.

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Iterative Layer Pruning for Efficient Translation Inference
Yasmin Moslem | Muhammad Hazim Al Farouq | John Kelleher

Large language models (LLMs) have transformed many areas of natural language processing, including machine translation. However, efficient deployment of LLMs remains challenging due to their intensive computational requirements. In this paper, we address this challenge and present our submissions to the Model Compression track at the Conference on Machine Translation (WMT 2025). In our experiments, we investigate iterative layer pruning guided by layer importance analysis. We evaluate this method using the Aya-Expanse-8B model for translation from Czech to German, and from English to Egyptian Arabic. Our approach achieves substantial reductions in model size and inference time, while maintaining the translation quality of the baseline models.

The Romansh language, spoken in Switzerland, has limited resources for machine translation evaluation. In this paper, we present a benchmark for six varieties of Romansh: Rumantsch Grischun, a supra-regional variety, and five regional varieties: Sursilvan, Sutsilvan, Surmiran, Puter, and Vallader. Our reference translations were created by human translators based on the WMT24++ benchmark, which ensures parallelism with more than 55 other languages. An automatic evaluation of existing MT systems and LLMs shows that translation out of Romansh into German is handled relatively well for all the varieties, but translation into Romansh is still challenging.

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A French Version of the OLDI Seed Corpus
Malik Marmonier | Benoît Sagot | Rachel Bawden

We present the first French partition of the OLDI Seed Corpus, our submission to the WMT 2025 Open Language Data Initiative (OLDI) shared task. We detail its creation process, which involved using multiple machine translation systems and a custom-built interface for post-editing by qualified native speakers. We also highlight the unique translation challenges presented by the source data, which combines highly technical, encyclopedic terminology with the stylistic irregularities characteristic of user-generated content taken from Wikipedia. This French corpus is not an end in itself, but is intended as a crucial pivot resource to facilitate the collection of parallel corpora for the under-resourced regional languages of France.

Recent advances in neural machine translation (NMT) have opened new possibilities for developing translation systems also for smaller, so-called low-resource, languages. The rise of large language models (LLMs) has further revolutionized machine translation by enabling more flexible and context-aware generation. However, many challenges remain for low-resource languages, and the availability of high-quality, validated test data is essential to support meaningful development, evaluation, and comparison of translation systems. In this work, we present an extension of the FLORES+ dataset for two Ladin variants, Val Badia and Gherdëina, as a submission to the Open Language Data Initiative Shared Task 2025. To complement existing resources, we additionally release two parallel datasets for Gherdëina–Val Badia and Gherdëina–Italian. We validate these datasets by evaluating state-of-the-art LLMs and NMT systems on this test data, both with and without leveraging the newly released parallel data for fine-tuning and prompting. The results highlight the considerable potential for improving translation quality in Ladin, while also underscoring the need for further research and resource development, for which this contribution provides a basis.

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Correcting the Tamazight Portions of FLORES+ and OLDI Seed Datasets
Alp Oktem | Mohamed Aymane Farhi | Brahim Essaidi | Naceur Jabouja | Farida Boudichat

We present the manual correction of the Tamazight portions of the FLORES+ and OLDI Seed datasets to improve the quality of open machine translation resources for the language. These widely used reference corpora contained numerous issues, including mistranslations, orthographic inconsistencies, overuse of loanwords, and non-standard transliterations. Overall, 36% of FLORES+ and 40% of Seed sentences were corrected by expert linguists, with average token divergence of 19% and 25% among changed items. Evaluation of multiple MT systems, including NLLB models and commercial LLM services, showed consistent gains in automated evaluation metrics when using the corrected data. Fine-tuning NLLB-600M on the revised Seed corpus yielded improvements of +6.05 chrF (en→zgh) and +2.32 (zgh→en), outperforming larger parameter models and LLM providers in en→zgh direction.

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Filling the Gap for Uzbek: Creating Translation Resources for Southern Uzbek
Mukhammadsaid Mamasaidov | Azizullah Aral | Abror Shopulatov | Mironshoh Inomjonov

Southern Uzbek (uzs) is a Turkic language variety spoken by around 5 million people in Afghanistan and differs significantly from Northern Uzbek (uzn) in phonology, lexicon, and orthography. Despite the large number of speakers, Southern Uzbek is underrepresented in natural language processing. We present new resources for Southern Uzbek machine translation, including a 997-sentence FLORES+ dev set, 39,994 parallel sentences from dictionary, literary, and web sources, and a fine-tuned NLLB-200 model (lutfiy). We also propose a post-processing method for restoring Arabic-script half-space characters, which improves handling of morphological boundaries. All datasets, models, and tools are released publicly to support future work on Southern Uzbek and other low-resource languages.

We present a Kyrgyz language seed dataset as part of our contribution to the WMT25 Open Language Data Initiative (OLDI) shared task. This paper details the process of collecting and curating English–Kyrgyz translations, highlighting the main challenges encountered in translating into a morphologically rich, low-resource language. We demonstrate the quality of the dataset through fine-tuning experiments, showing consistent improvements in machine translation performance across multiple models. Comparisons with bilingual and MNMT Kyrgyz-English baselines reveal that, for some models, our dataset enables performance surpassing pretrained baselines in both English–Kyrgyz and Kyrgyz–English translation directions. These results validate the dataset’s utility and suggest that it can serve as a valuable resource for the Kyrgyz MT community and other related low-resource languages.

We open-source SMOL(Set of Maximal Over-all Leverage), a suite of training data to un-lock machine translation for low-resource languages (LRLs). SMOL has been translated into123 under-resourced languages (125 language pairs), including many for which there exist no previous public resources, for a total of 6.1M translated tokens. SMOL comprises two sub-datasets, each carefully chosen for maximum impact given its size: SMOLSENT, a set of sentences chosen for broad unique token coverage, and SMOLDOC, a document-level source focusing on a broad topic coverage. They join the already released GATITOS for a trifecta of paragraph, sentence, and token-level content. We demonstrate that using SMOL to prompt or fine-tune Large Language Models yields robust chrF improvements. In addition to translation, we provide factuality ratings and rationales for all documents in SMOLDOC, yielding the first factuality datasets for most of these languages.

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Improved Norwegian Bokmål Translations for FLORES
Petter Mæhlum | Anders Næss Evensen | Yves Scherrer

FLORES+ is a collection of parallel datasets obtained by translation from originally English source texts. FLORES+ contains Norwegian translations for the two official written variants of Norwegian: Norwegian Bokmål and Norwegian Nynorsk. However, the earliest Bokmål version contained non-native-like mistakes, and even after a later revision, the dataset contained grammatical and lexical errors. This paper aims at correcting unambiguous mistakes, and thus creating a new version of the Bokmål dataset. At the same time, we provide a translation into Radical Bokmål, a sub-variety of Norwegian which is closer to Nynorsk in some aspects, while still being within the official norms for Bokmål. We discuss existing errors and differences in the various translations and the corrections that we provide.

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NRC Systems for the WMT2025-LRSL Shared Task
Samuel Larkin | Chi-Kiu Lo | Rebecca Knowles

We describe the NRC team systems for the WMT25 Shared Tasks on Large Language Models (LLMs) with Limited Resources for Slavic Languages. We participate in the Lower Sorbian and Upper Sorbian Machine Translation and Question Answering tasks. On the machine translation tasks, our primary focus, our systems rank first according to the automatic MT evaluation metric (chrF). Our systems underperform on the QA tasks.

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TartuNLP at WMT25 LLMs with Limited Resources for Slavic Languages Shared Task
Taido Purason | Mark Fishel

This paper describes the TartuNLP submission to the Upper Sorbian (hsb) and Lower Sorbian (dsb) tracks of the WMT25 LLMs with Limited Resources for Slavic Languages shared task, which jointly targets machine translation (MT) and question answering (QA). We develop a single multilingual model based on Qwen2.5-3B-Instruct by continuing pretraining on Sorbian monolingual and parallel data together with general instruction datasets, combining language acquisition and instruction-following in a single step. The resulting model delivers substantial improvements over the baseline Qwen2.5-3B-Instruct model and also achieves the highest ranking for both tasks in the hsb and dsb shared task tracks.

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JGU Mainz’s Submission to the WMT25 Shared Task on LLMs with Limited Resources for Slavic Languages: MT and QA
Hossain Shaikh Saadi | Minh Duc Bui | Mario Sanz-Guerrero | Katharina Von Der Wense

This paper presents the JGU Mainz submission to the WMT25 Shared Task on LLMs with Limited Resources for Slavic Languages: Machine Translation and Question Answering, focusing on Ukrainian, Upper Sorbian, and Lower Sorbian. For each language, we jointly fine-tune a Qwen2.5-3B-Instruct model for both tasks with parameter-efficient finetuning. Our pipeline integrates additional translation and multiple-choice question answering (QA) data. For Ukrainian QA, we further use retrieval-augmented generation. We also apply ensembling for QA in Upper and Lower Sorbian. Experiments show that our models outperform the baseline on both tasks.

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Krey-All WMT 2025 CreoleMT System Description: Language Agnostic Strategies for Low-Resource Translation
Ananya Ayasi

The paper is a submission to the WMT 2025 Creole MT Task, aimed at improving translation between Seychellois Creole and English by using data from other closely related Creole languages. The approach treats all Creoles as variations of the same language, allowing the model to share knowledge across them. This strategy led to better translation quality than using separate language settings. The work highlights that related languages can effectively support each other in low-resource settings, while also noting challenges such as data imbalance, differences in language domains, and the absence of human evaluation.

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EdinHelsOW WMT 2025 CreoleMT System Description: Improving Lusophone Creole Translation through Data Augmentation, Model Merging and LLM Post-editing
Jacqueline Rowe | Ona De Gibert | Mateusz Klimaszewski | Coleman Haley | Alexandra Birch | Yves Scherrer

In this work, we present our submissions to the unconstrained track of the System subtask of the WMT 2025 Creole Language Translation Shared Task. Of the 52 Creole languages included in the task, we focus on translation between English and seven Lusophone Creoles. Our approach leverages known strategies for low-resource machine translation, including back-translation and distillation of data, fine-tuning pre-trained multilingual models, and post-editing with large language models and lexicons. We also demonstrate that adding high-quality parallel Portuguese data in training, initialising Creole embeddings with Portuguese embedding weights, and strategically merging best checkpoints of different fine-tuned models all produce considerable gains in performance in certain translation directions. Our best models outperform the baselines on the Task test set for eight out of fourteen translation directions. When evaluated on a more diverse test set, they surpass the baselines in all but one direction.

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KozKreolMRU WMT 2025 CreoleMT System Description: Koz Kreol: Multi-Stage Training for English–Mauritian Creole MT
Yush Rajcoomar

Mauritian Creole (Kreol Morisyen), spoken by approximately 1.5 million people worldwide, faces significant challenges in digital language technology due to limited computational resources. This paper presents “Koz Kreol”, a comprehensive approach to English–Mauritian Creole machine translation using a three-stage training methodology: monolingual pretraining, parallel data training, and LoRA fine-tuning. We achieve state-of-the-art results with a 28.82 BLEU score for EN→MFE translation, representing a 74% improvement over ChatGPT-4o. Our work addresses critical data scarcity through the use of existing datasets, synthetic data generation, and community-sourced translations. The methodology provides a replicable framework for other low-resource Creole languages while supporting digital inclusion and cultural preservation for the Mauritian community. This paper consists of both a systems and data subtask submission as part of a Creole MT Shared Task.

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JHU WMT 2025 CreoleMT System Description: Data for Belizean Kriol and French Guianese Creole MT
Nathaniel Robinson

This document details the Johns Hopkins University’s submission to the 2025 WMT Shared Task for Creole Language Translation. We submitted exclusively to the data subtask, contributing machine translation bitext corpora for Belizean Kriol with English translations, and French Guianese Creole with French translations. These datasets contain 5,530 and 1,671 parallel lines of text, respectively, thus amounting to an 2,300% increase in publicly available lines of bitext for Belizean Creole with English, and an 370% such increase for French Guianese Creole with French. Experiments demonstrate genre-dependent improvements on our proposed test sets when the relevant state-of-the-art model is fine-tuned on our proposed train sets, with improvements across genres of up to 33.3 chrF++.

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WMT 2025 CreoleMT Systems Description : Martinican Creole and French
Ludovic Mompelat

This paper describes our submissions to the constrained subtask of the WMT25 Creole Machine Translation shared task. We participated with a bidirectional Martinican Creole – French system. Our work explores training-time strategies tailored for low-resource MT, including LoRA fine-tuning, curriculum sampling, gradual unfreezing, and multitask learning. We report competitive results against the baseline on both translation directions.

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JU-NLP: Improving Low-Resource Indic Translation System with Efficient LoRA-Based Adaptation
Priyobroto Acharya | Haranath Mondal | Dipanjan Saha | Dipankar Das | Sivaji Bandyopadhyay

Low-resource Indic languages such as Assamese, Manipuri, Mizo, and Bodo face persistent challenges in NMT due to limited parallel data, diverse scripts, and complex morphology. We address these issues in the WMT $2025$ shared task by introducing a unified multilingual NMT framework that combines rigorous language-specific preprocessing with parameter-efficient adaptation of large-scale models. Our pipeline integrates the NLLB-$200$ and IndicTrans$2$ architectures, fine-tuned using LoRA and DoRA, reducing trainable parameters by over 90% without degrading translation quality. A comprehensive preprocessing suite, including Unicode normalization, semantic filtering, transliteration, and noise reduction, ensures high-quality inputs, while script-aware post-processing mitigates evaluation bias from orthographic mismatches. Experiments across English-Indic directions demonstrate that NLLB-$200$ achieves superior results for Assamese, Manipuri, and Mizo, whereas IndicTrans$2$ excels in English-Bodo. Evaluated using BLEU, chrF, METEOR, ROUGE-L, and TER, our approach yields consistent improvements over baselines, underscoring the effectiveness of combining efficient fine-tuning with linguistically informed preprocessing for low-resource Indic MT.

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An Attention-Based Neural Translation System for English to Bodo
Subhash Wary | Birhang Borgoyary | Akher Ahmed | Mohanji Sah | Apurbalal Senapati

Bodo is a resource scarce, the indigenous language belongs to the Tibeto-Burman family. It is mainly spoken in the north-east region of India. It has both linguistic and cultural significance in the region. Only a limited number of resources and tools are available in this language. This paper presents a study of neural machine translation for the English-Bodo language pair. The system is developed on a relatively small parallel corpus provided by the Low-Resource Indic Language Translation as a part of WMT-2025. The system is evaluated by the WMT-2025 organizers with the evaluation matrices like BLUE, METEOR, ROUGE-L, chrF and TER. The result is not promising but it will help for the further improvement. The result is not encouraging, but it provides a foundation for further improvement.

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Tackling Low-Resource NMT with Instruction-Tuned LLaMA: A Study on Kokborok and Bodo
Deepak Kumar | Kshetrimayum Boynao Singh | Asif Ekbal

This paper presents a new neural machine translation (NMT) system aimed at low-resource language pairs: English to Kokborok and English to Bodo. The framework leverages the LLaMA3-8B-Instruct model along with LoRA-based parameter-efficient fine-tuning. For translating into Kokborok, the model undergoes an initial continued pre-training phase on a dataset containing 75,000 Kokborok and 25,000 English monolingual sentences, followed by instruction-tuning. This tuning uses a reformulated version of WMT25 dataset, adapted to the Alpaca format to support instructional goals. In the Bodo translation, the model is pre-trained on a more extensive dataset of 350,000 Bodo and 125,000 English sentences, using a similar instruction-tuning approach. LoRA adapters are used to modify the large LLaMA3 model for these low-resource settings. Testing with the WMT25 test dataset reveals modest translation results, highlighting the difficulties in translating for low-resource languages. Translating English to Bodo, the model achieved a BLEU score of 4.38, a TER of 92.5, and a chrF score of 35.4. For English to Kokborok, it yielded scores of 5.59 in chrF, 105.4 in TER, and 0.17 in BLEU. These results underscore the intricacies of the task and highlight the critical need for further data collection, domain-specific adaptations, and improvements in model design to better support underrepresented languages.

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DELAB-IIITM WMT25: Enhancing Low-Resource Machine Translation for Manipuri and Assamese
Dingku Oinam | Navanath Saharia

This paper describe DELAB-IIITM’s submission system for the WMT25 machine translation shared task. We participated in two sub-task of the Indic Translation Task, en↔as and en↔mn i.e. Assamese (Indo Aryan language) and Manipuri (Tibeto Burman language) with a total of six translation directions, including mn→en, mn←en, en→as, en←as, mn→as, mn←as. Our fine tuning process aims to leverages the pretrained multilingual NLLB-200 model, a machine translation model developed by Meta AI as part of the No Language Left Behind (NLLB) project, through two main development, Synthetic parallel corpus creation and Strategic Fine-tuning. The Fine-tuning process involves strict data cleaning protocols, Adafactor optimizer with low learning rate(2e-5), 2 training epochs, train-test data splits to prevent overfitting, and Seq2SeqTrainer framework. The official test data was used to generate the target language with our fine-tuned model. Experimental results show that our method improves the BLEU scores for translation of these two language pairs. These findings confirm that back-translation remains challenging, largely due to morphological complexity and limited data availability.

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Transformers: Leveraging OpenNMT and Transfer Learning for Low-Resource Indian Language Translation
Bhagyashree Wagh | Harish Bapat | Neha Gupta | Saurabh Salunkhe

This paper describes our submission to the WMT 2025 (Pakray et al, 2025) Shared Task on Low-Resource Machine Translation for Indic languages. This task is an extension of the efforts which was originally initiated in WMT 2023 (Pal et al., 2023), and further continued to WMT 2024 (Pakray et al, 2024), received significant participation from the global community. We address English ↔ {Assamese, Bodo, Manipuri} translation, leveraging Hindi and Bengali as high-resource bridge languages. Our approach employs Transformer-based Neural Machine Translation (NMT) models, initialized through multilingual pre-training on high-resource Indic languages, followed by fine-tuning on limited parallel data for the target low-resource languages. The pre-training stage provides a strong multilingual representation space, while fine-tuning enables adaptation to specific linguistic characteristics of the target languages. We also apply consistent preprocessing, including tokenization, true casing, and subword segmentation (Sennrich et al., 2016) with Byte-Pair Encoding (BPE), to handle the morphological complexity of Indic languages. Evaluation on the shared task test sets demonstrates that pre-training followed by fine-tuning yields notable improvements over models trained solely on the target language data.

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RBG-AI: Benefits of Multilingual Language Models for Low-Resource Languages
Barathi Ganesh Hb | Michal Ptaszynski

This paper investigates how multilingual language models benefit low-resource languages through our submission to the WMT 2025 Low-Resource Indic Language Translation shared task. We explore whether languages from related families can effectively support translation for low-resource languages that were absent or underrepresented during model training. Using a quantized multilingual pretrained foundation model, we examine zero-shot translation capabilities and cross-lingual transfer effects across three language families: Tibeto-Burman, Indo-Aryan, and Austroasiatic. Our findings demonstrate that multilingual models failed to leverage linguistic similarities, particularly evidenced within the Tibeto-Burman family. The study provides insights into the practical feasibility of zero-shot translation for low-resource language settings and the role of language family relationships in multilingual model performance.

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ANVITA : A Multi-pronged Approach for Enhancing Machine Translation of Extremely Low-Resource Indian Languages
Sivabhavani J | Daneshwari Kankanwadi | Abhinav Mishra | Biswajit Paul

India has a rich diverse linguistic landscape including 22 official languages and 122 major languages. Most of these 122 languages fall into low, extremely low resource categories and pose significant challenges in building robust machine translation system. This paper presents ANVITA Indic LR machine translation system submitted to WMT 2025 shared task on Low-Resource Indic Language Translation covering three extremely low-resource Indian languages Nyshi, Khasi, and Kokborok. A transfer learning based strategy is adopted and selected suitable public pretrained models (NLLB, ByT5), considering aspects such as language, script, tokenization and fine-tuned with the organizer provided dataset. Further, to tackle low-resource language menace better, the pretrained models are enriched with new vocabulary for improved representation of these three languages and selectively augmented data with related-language corpora, supplied by the organizer. The contrastive submissions however made use of supplementary corpora sourced from the web, generated synthetically, and drawn from proprietary data. On the WMT 2025 official test set, ANVITA achieved BLEU score of 2.41-11.59 with 2.2K to 60K corpora and 6.99-19.43 BLUE scores with augmented corpora. Overall ANVITA ranked first for {Nyishi, Kokborok}↔English and second for Khasi↔English across evaluation metrics including BLUE, METEOR, ROUGE-L, chrF and TER.

Low-resource translation for Indic languages poses significant challenges due to limited parallel corpora and linguistic diversity. In this work, we describe our participation in the WMT 2025 shared task for four Indic languages-Khasi, Mizo, Assamese, which is categorized into Category 1 and Bodo in Cate- gory 2. For our PRIMARY submission, we fine- tuned the distilled NLLB-200 model on bidi- rectional English↔Khasi and English↔Mizo data, and employed the IndicTrans2 model family for Assamese and Bodo translation. Our CONTRASTIVE submission augments training with external corpora from PMIN- DIA and Google SMOL to further enrich low- resource data coverage. Both systems lever- age Low-Rank Adaptation (LoRA) within a parameter-efficient fine-tuning framework, en- abling lightweight adapter training atop frozen pretrained weights. The translation pipeline was developed using the Hugging Face Trans- formers and PEFT libraries, augmented with bespoke preprocessing modules that append both language and domain identifiers to each instance. We evaluated our approach on par- allel corpora spanning multiple domains- ar- ticle based, newswire, scientific, and biblical texts as provided by the WMT25 dataset, under conditions of severe data scarcity. Fine-tuning lightweight LoRA adapters on targeted parallel corpora yields marked improvements in evalua- tion metrics, confirming their effectiveness for cross-domain adaptation in low-resource Indic languages.

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A Preliminary Exploration of Phrase-Based SMT and Multi-BPE Segmentations through Concatenated Tokenised Corpora for Low-Resource Indian Languages
Saumitra Yadav | Manish Shrivastava

This paper describes our methodology and findings in building Machine Translation (MT) systems for submission to the WMT 2025 Shared Task on Low-Resource Indic Language Translation. Our primary aim was to evaluate the effectiveness of a phrase-based Statistical Machine Translation (SMT) system combined with a less common subword segmentation strategy for languages with very limited parallel data. We applied multiple Byte Pair Encoding (BPE) merge operations to the parallel corpora and concatenated the outputs to improve vocabulary coverage. We built systems for the English–Nyishi, English–Khasi, and English–Assamese language pairs. Although the approach showed potential as a data augmentation method, its performance in BLEU scores was not competitive with other shared task systems. This paper outlines our system architecture, data processing pipeline, and evaluation results, and provides an analysis of the challenges, positioning our work as an exploratory benchmark for future research in this area.

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AkibaNLP-TUT: Injecting Language-Specific Word-Level Noise for Low-Resource Language Translation
Shoki Hamada | Tomoyosi Akiba | Hajime Tsukada

In this paper, we describes our system for the WMT 2025 Low-Resource Indic Language Translation Shared Task.The language directions addressed are Assamese↔English and Manipuri→English.We propose a method to improve translation performance from low-resource languages (LRLs) to English by injecting Language-specific word-level noise into the parallel corpus of a closely related high-resource language (HRL).In the proposed method, word replacements are performed based on edit distance, using vocabulary and frequency information extracted from an LRL monolingual corpus.Experiments conducted on Assamese and Manipuri show that, in the absence of LRL parallel data, the proposed method outperforms both the w/o noise setting and existing approaches. Furthermore, we confirmed that increasing the size of the monolingual corpus used for noise injection leads to improved translation performance.

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BVSLP: Machine Translation Using Linguistic Embellishments for IndicMT Shared Task 2025
Nisheeth Joshi | Palak Arora | Anju Krishnia | Riya Lonchenpa | Mhasilenuo Vizo

This paper describes our submission to the Indic MT 2025 shared task, where we trained machine translation systems for five low-resource language pairs: English–Manipuri, Manipuri–English, English–Bodo, English–Assamese, and Assamese–English. To address the challenge of out-of-vocabulary errors, we introduced a Named Entity Translation module that automatically identified named entities and either translated or transliterated them into the target language. The augmented corpus produced by this module was used to fine-tune a Transformer-based neural machine translation system. Our approach, termed HEMANT (Highly Efficient Machine-Assisted Natural Translation), demonstrated consistent improvements, particularly in reducing named entity errors and improving fluency for Assamese–English and Manipuri–English. Official shared task evaluation results show that the system achieved competitive performance across all five language pairs, underscoring the effectiveness of linguistically informed preprocessing for low-resource Indic MT.

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TranssionMT’s Submission to the Indic MT Shared Task in WMT 2025
Zebiao Zhou | Hui Li | Xiangxun Zhu | Kangzhen Liu

This study addresses the low-resource Indian lan- 002guage translation task (English Assamese, English Ma- 003nipuri) at WMT 2025, proposing a cross-iterative back- 004translation and data augmentation approach based on 005dual pre-trained models to enhance translation perfor- 006mance in low-resource scenarios. The research method- 007ology primarily encompasses four aspects: (1) Utilizing 008open-source pre-trained models IndicTrans2_1B and 009NLLB_3.3B, fine-tuning them on official bilingual data, 010followed by alternating back-translation and incremen- 011tal training to generate high-quality pseudo-parallel cor- 012pora and optimize model parameters through multiple 013iterations; (2) Employing the open-source semantic sim- 014ilarity model (all-mpnet-base-v2) to filter monolingual 015sentences with low semantic similarity to the test set 016from open-source corpora such as NLLB and BPCC, 017thereby improving the relevance of monolingual data 018to the task; (3) Cleaning the training data, including 019removing URL and HTML format content, eliminating 020untranslated sentences in back-translation, standardiz- 021ing symbol formats, and normalizing capitalization of 022the first letter; (4) During the model inference phase, 023combining the outputs generated by the fine-tuned In- 024dicTrans2_1B and NLLB3.3B

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Laniqo at WMT25 Terminology Translation Task: A Multi-Objective Reranking Strategy for Terminology-Aware Translation via Pareto-Optimal Decoding
Kamil Guttmann | Adrian Charkiewicz | Zofia Rostek | Mikołaj Pokrywka | Artur Nowakowski

This paper describes the Laniqo system submitted to the WMT25 Terminology Translation Task. Our approach uses a Large Language Model fine-tuned on parallel data augmented with source-side terminology constraints. To select the final translation from a set of generated candidates, we introduce Pareto-Optimal Decoding - a multi-objective reranking strategy. This method balances translation quality with term accuracy by leveraging several quality estimation metrics alongside Term Success Rate (TSR). Our system achieves TSR greater than 0.99 across all language pairs on the Shared Task testset, demonstrating the effectiveness of the proposed approach.

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Fine-tuning NMT Models and LLMs for Specialised EN-ES Translation Using Aligned Corpora, Glossaries, and Synthetic Data: MULTITAN at WMT25 Terminology Shared Task
Lichao Zhu | Maria Zimina-Poirot | Stephane Patin | Cristian Valdez

This paper describes our participation in the WMT25 Terminology Shared Task, specifically Track 1 (Spanish to English) focused on translation within the Information Technology (IT) domain. The shared task challenges participants to improve machine translation systems by effectively incorporating terminology constraints to ensure accurate and consistent translation of specialised technical terms. We experimented with several approaches to tackle terminology and lexical constraints with both NMT systems and LLMs with a small amount of training data and a glossary. Experimental results demonstrate that systems behave differently with and without glossary. The NMT system seems rather limited in adapting to special lexicon and resizing embeddings, which is the opposite of the case with LLMs preferring structured instructions. Through this participation, our objective is to improve terminology accuracy and overall translation quality, highlight the potential of specialised terminology-aware translation models for technical domains, and explore possibilities of fine-tuning of LLMs and NMT models with domain and lexical constraints.

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Contextual Selection of Pseudo-terminology Constraints for Terminology-aware Neural Machine Translation in the IT Domain
Benjamin Pong

This system paper describes the development of a Neural Machine Translation system that is adapted to the Information Technology (IT) domain, and is able to translate specialized IT-related terminologies. Despite the popularity of incorporating terminology constraints at training time to develop terminology-aware Neural Machine Translation engines, one of the main issues is: In the absence of terminology references for training, and with the proliferation of source-target alignments, how does one select word alignments as pseudo-terminology constraints? The system in this work uses the encoder’s final hidden states as proxies for terminologies, and selects word alignments with the highest norm as pseudo-terminology constraints for inline annotation at run-time. It compares this context-based approach against a conventional statistical approach, where terminology-constraints are selected based on a low-frequency threshold. The systems were evaluated for general translation quality and Terminology Success Rates, with results that validate the effectiveness of the contextual approach.

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IRB-MT at WMT25 Terminology Translation Task: Metric-guided Multi-agent Approach
Ivan Grubišić | Damir Korencic

Terminology-aware machine translation (MT) is needed in case of specialized domains such as science and law. Large Language Models (LLMs) have raised the level of state-of-art performance on the task of MT, but the problem is not completely solved, especially for use-cases requiring precise terminology translations. We participate in the WMT25 Terminology Translation Task with an LLM-based multi-agent system coupled with a custom terminology-aware translation quality metric for the selection of the final translation. We use a number of smaller open-weights LLMs embedded in an agentic “translation revision” workflow, and we do not rely on data- and compute-intensive fine-tuning of models. Our evaluations show that the system achieves very good results in terms of both MetricX-24 and a custom TSR metric designed to measure the adherence to predefined term mappings.

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Terminology-Constrained Translation from Monolingual Data Using GRPO
Javier Garcia Gilabert | Carlos Escolano | Xixian Liao | Maite Melero

Terminology consistency is essential for high-quality machine translation, especially in domain-specific and professional contexts, where accurate term translation directly impacts usability. This paper presents the submission from the BSC team to the WMT25 Terminology-Aware Translation Task. We propose the use of GRPO (Group Relative Policy Optimization) to adapt translation models using monolingual data only, without requiring parallel corpora. Our reward function jointly optimizes for terminology adherence and overall translation quality, leveraging quality-estimation metrics. Experimental results demonstrate that our method consistently improves terminology translation across three language directions—English to Spanish, German, and Russian—by up to +0.36 Tₚ points across all evaluated models.

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It Takes Two: A Dual Stage Approach for Terminology-Aware Translation
Akshat Jaswal

This paper introduces DuTerm, a novel two-stage architecture for terminology-constrained machine translation. Our system combines a terminology-aware NMT model, adapted via fine-tuning on large-scale synthetic data, with a prompt-based LLM for post-editing. The LLM stage refines NMT output and enforces terminology adherence. We evaluate DuTerm on English-to German, English-to-Spanish, and English-to-Russian for the WMT 2025 Terminology Shared Task. We demonstrate that flexible, context-driven terminology handling by the LLM consistently yields higher quality translations than strict constraint enforcement. Our results highlight a critical trade-off, revealing that an LLM’s intrinsic knowledge often provides a stronger basis for high-quality translation than rigid, externally imposed constraints.

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bib (full) Proceedings of the 5th Wordplay: When Language meets Games Workshop (Wordplay 2025)

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Proceedings of the 5th Wordplay: When Language meets Games Workshop (Wordplay 2025)
Eric Yuan | Hiromi Wakaki | Lara Martin | Marc-Alexandre Côté