Sequential recommender systems, which leverage historical interactions to deliver targeted recommendations, have been significantly advanced by large language models (LLMs). However, LLM-based generative sequential recommendation often faces two key challenges: the lack of collaborative knowledge and the limited controllability over the generated content. In this paper, we propose a simple Bi-Tuning framework with collaborative information for controllable Large Language Model-based Sequential Recommendation (Laser). Specifically, Bi-Tuning works through incorporating learnable virtual tokens at both the prefix and suffix of the input text, where the prefix tokens enable the adaptation of LLMs with collaborative information, while the suffix token transforms the LLM output into item/user embeddings for similarity comparison, thereby facilitating controllable recommendations. Furthermore, we introduce an MoE-based querying transformer that selectively activates experts to extract relevant information from varying collaborative signals of frozen ID-based recommenders into the prefix, coupled with a multi-task loss function incorporating the MoE load-balancing objective. Finally, a two-phase training strategy is employed to progressively obtain high-quality item and user embeddings through the learnable suffix. Experiments on real-world datasets show that Laser effectively adapts LLMs for sequential recommendation, outperforming state-of-the-art baselines.

Large Language Models(LLMs) have brought significant transformations to various aspects of human life and productivity. However, the heavy reliance on vast amounts of data in developing these models has resulted in a notable disadvantage for low-resource languages, such as Nuosu and others, which lack large datasets. Moreover, many LLMs exhibit significant performance discrepancies between high-and lowresource languages, thereby restricting equitable access to technological advances for all linguistic communities. To address these challenges, this paper propose a low-resource multilingual large language model, termed VEEF-Multi-LLM, constructed through effective vocabulary expansion and parameter-efficient fine-tuning. We introduce a series of innovative methods to address challenges in low-resource languages. First, we adopt Byte-level Byte-Pair Encoding to expand the vocabulary for broader multilingual support. We separate input and output embedding weights to boost performance, and apply RoPE for long-context handling, as well as RMSNorm for efficient training. To generate high-quality supervised fine-tuning (SFT) data, we use self-training and selective translation, and refine the resulting dataset with the assistance of native speakers to ensure cultural and linguistic accuracy. Our model, VEEF-Multi-LLM-8B, is trained on 600 billion tokens across 50 natural and 16 programming languages. Experimental results show that the model excels in multilingual instruction-following tasks, particularly in translation, outperforming competing models in benchmarks such as XCOPA and XStoryCloze. Although it lags slightly behind English-centric models in some tasks (e.g., m-MMLU), it prioritizes safety, reliability, and inclusivity, making it valuable for diverse linguistic communities. We open-source our models on GitHub and Huggingface.

Existing video LLMs typically excel at capturing the overall description of a video but lack the ability to demonstrate an understanding of temporal dynamics and a fine-grained grasp of localized content within the video. In this paper, we propose a Time-Perception Enhanced Video Grounding via Boundary Perception and Temporal Reasoning aimed at mitigating LLMs’ difficulties in understanding the discrepancies between video and text temporality. Specifically, to address the inherent biases in current datasets, we design a series of boundary-perception tasks to enable LLMs to capture accurate video temporality. To tackle LLMs’ insufficient understanding of temporal information, we develop specialized tasks for boundary perception and temporal relationship reasoning to deepen LLMs’ perception of video temporality. Our experimental results show significant improvements across three datasets: ActivityNet, Charades, and DiDeMo (achieving up to 11.2% improvement on R@0.3), demonstrating the effectiveness of our proposed temporal awareness-enhanced data construction method.

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.

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.

Model editing aims to correct errors and outdated knowledge in the Large language models (LLMs) with minimal cost. Prior research has proposed a variety of datasets to assess the effectiveness of these model editing methods. However, most existing datasets only require models to output short phrases or sentences, overlooks the widespread existence of document level tasks in the real world, raising doubts about their practical usability. Aimed at addressing this limitation and promoting the application of model editing in real-world scenarios, we propose the task of document-level model editing. To tackle such challenges and enhance model capabilities in practical settings, we introduce DocMEdit, a dataset focused on document-level model editing, characterized by document-level inputs and outputs, extrapolative, and multiple facts within a single edit. We propose a series of evaluation metrics and experiments. The results show that the difficulties in document-level model editing pose challenges for existing model editing methods.

While large language models (LLMs) demonstrate impressive capabilities, their reliance on parametric knowledge often leads to factual inaccuracies. Retrieval-Augmented Generation (RAG) mitigates this by leveraging external documents, yet existing approaches treat retrieved passages as isolated chunks, ignoring valuable structure that is crucial for document organization. Motivated by this gap, we propose Retrieve-DocumentRoute-Read (RDR2), a novel framework that explicitly incorporates structural information throughout the RAG process. RDR2 employs an LLM-based router to dynamically navigate document structure trees, jointly evaluating content relevance and hierarchical relationships to assemble optimal evidence. Our key innovation lies in formulating document routing as a trainable task, with automatic action curation and structure-aware passage selection inspired by human reading strategies. Through comprehensive evaluation on five challenging datasets, RDR2 achieves state-of-the-art performance, demonstrating that explicit structural awareness significantly enhances RAG systems’ ability to acquire and utilize knowledge, particularly in complex scenarios requiring multi-document synthesis.

Due to the unidirectional masking mechanism, Decoder-Only models propagate information from left to right. LVLMs (Large Vision-Language Models) follow the same architecture, with visual information gradually integrated into semantic representations during forward propagation. Through systematic analysis, we observe that over 80% of the visual information is absorbed into the semantic representations. However, the model’s attention still predominantly focuses on the visual representations. This misalignment between the attention distribution and the actual information flow undermines the model’s visual understanding ability and contributes to hallucinations.To address this issue, we enhance the model’s visual understanding by leveraging the core information embedded in semantic representations. Specifically, we identify attention heads that focus on core semantic representations based on their attention distributions. Then, through a two-stage optimization paradigm, we propagate the advantages of these attention heads across the entire model, aligning the attention distribution with the actual information flow.We evaluate our method on three image captioning benchmarks using five different LVLMs,demonstrating its effectiveness in significantly reducing hallucinations. Further experiments reveal a trade-off between reduced hallucinations and richer details. Notably, our method allows for manual adjustment of the model’s conservativeness, enabling flexible control to meet diverse real-world requirements.

This paper presents our system for Subtask 10 of Entity Framing, which focuses on assigning one or more hierarchical roles to named entities in news articles. Our approach iteratively refines prompts and utilizes the Entity-Centric Chain of Thought to complete the task. Specifically, to minimize ambiguity in label definitions, we use the model’s predictions as supervisory signals, iteratively refining the category definitions. Furthermore, to minimize the interference of irrelevant information during inference, we incorporate entity-related information into the CoT framework, allowing the model to focus more effectively on entity-centric reasoning. Our system achieved the highest ranking on the leaderboard in the Russian main role classification and the second in English, with an accuracy of 0.8645 and 0.9362, respectively. We discuss the impact of several components of our multilingual classification approach, highlighting their effectiveness.

Large Language Models (LLMs) demonstrate significant value in domain-specific applications, benefiting from their fundamental capabilities. Nevertheless, it is still unclear which fundamental capabilities contribute to success in specific domains. Moreover, the existing benchmark-based evaluation cannot effectively reflect the performance of real-world applications. In this survey, we review recent advances of LLMs in domain applications, aiming to summarize the fundamental capabilities and their collaboration. Furthermore, we establish connections between fundamental capabilities and specific domains, evaluating the varying importance of different capabilities. Based on our findings, we propose a reliable strategy for domains to choose more robust backbone LLMs for real-world applications.

“焦虑、抑郁已成为人们常见的心理障碍,适度的疏导对于缓解人们精神、心理压力具有重要意义。然而由于病耻感等原因,很多人得不到及时的疏导和治疗。随着人工智能的发展,大语言模型(LLMs)优越的知识融会贯通能力和思维链能力,使得其成为心理疏导的有效工具。然而,现有少量面向心理健康咨询的大语言模型通常针对英文、中文等资源丰富的语种,而对于低资源语言,LLMs在心理咨询领域的应用尚缺少研究。本文以藏语作为低资源语言的代表,研究藏语心理咨询数据集的构建和藏语心理健康大语言模型的构建方法。首先,通过收集现有高质量的中文心理咨询对话数据,并对数据进行处理,生成心理健康多轮对话数据集;其次,构建汉藏翻译工具将其翻译成藏语多轮对话数据,并结合多种机制对数据进行筛选、过滤生成高质量藏语心理健康多轮对话数据;基于构造的数据,采用现有通用大语言模型Baichuan2和LLaMA2模型进行指令调优训练,形成藏语心理健康大语言模型,并将开源用于科学研究。最后通过实验验证了本文发布的藏语心理健康多轮对话数据集以及藏语心理健康咨询大语言模型的有效性。”

“语步结构是学术论文中的文本语篇单位,在语步分析、论文写作等方面具有重要价值。尽管关于学术论文的语步研究非常丰富,但语步标注数据资源仍然相对较少。本研究开发构建了一个英语科技论文摘要语步结构标注语料库,目前已标注近3.4万个语步结构,涵盖了自然语言处理、计算机视觉、通信工程、机械工程等学科领域,同时进行了标注数据统计和分析。语料库构建的第一阶段依靠人工标注形成高质量语料,在第二阶段也是主要阶段,采用了基于BERT的自动识别与标注模型,在保证标注质量的同时能够提升标注速度,扩大标注规模。本研究基于构建的语料库开展了不同学科领域摘要语步结构识别实验,对比了我们的模型与ChatGPT和Claude3等大语言模型的识别效果。结果显示我们的模型在各类语步识别上的F1指标均优于大语言模型,表明了模型的有效性。该语料库目前可公开获取使用,能够为科技论文信息抽取、英语写作智能批改等自然语言处理相关任务和学术用途英语等外语教学与研究等提供必要的数据资源,同时也能有效推动外语教育数字化转型。”

While LLMs have made notable advancements in natural language processing, they continue to struggle with processing extensive text. Memory mechanisms offer a flexible solution for managing long contexts, utilizing techniques such as compression, summarization, and structuring to facilitate nuanced and efficient handling of large volumes of text. However, existing techniques face challenges with static knowledge integration, leading to insufficient adaptation to task-specific needs and missing multi-segmentation relationships, which hinders the dynamic reorganization and logical combination of relevant segments during the response process. To address these issues, we introduce a novel strategy, Question then Reflection Memory Mechanism (QRMeM), which incorporates a dual-structured memory pool. This pool synergizes static textual content with structured graph guidance, fostering a reflective trial-and-error approach for navigating and identifying relevant segments. Our evaluation across multiple-choice questions (MCQ) and multi-document question answering (Multi-doc QA) benchmarks showcases QRMeM’s enhanced performance compared to existing approaches.

Move structures have been studied in English for Specific Purposes (ESP) and English for Academic Purposes (EAP) for decades. However, there are few move annotation corpora for Research Article (RA) abstracts. In this paper, we introduce RAAMove, a comprehensive multi-domain corpus dedicated to the annotation of move structures in RA abstracts. The primary objective of RAAMove is to facilitate move analysis and automatic move identification. This paper provides a thorough discussion of the corpus construction process, including the scheme, data collection, annotation guidelines, and annotation procedures. The corpus is constructed through two stages: initially, expert annotators manually annotate high-quality data; subsequently, based on the human-annotated data, a BERT-based model is employed for automatic annotation with the help of experts’ modification. The result is a large-scale and high-quality corpus comprising 33,988 annotated instances. We also conduct preliminary move identification experiments using the BERT-based model to verify the effectiveness of the proposed corpus and model. The annotated corpus is available for academic research purposes and can serve as essential resources for move analysis, English language teaching and writing, as well as move/discourse-related tasks in Natural Language Processing (NLP).

Event extraction aims to recognize pre-defined event triggers and arguments from texts, which suffer from the lack of high-quality annotations. In most NLP applications, involving a large scale of synthetic training data is a practical and effective approach to alleviate the problem of data scarcity. However, when applying to the task of event extraction, recent data augmentation methods often neglect the problem of grammatical incorrectness, structure misalignment, and semantic drifting, leading to unsatisfactory performances. In order to solve these problems, we propose a denoised structure-to-text augmentation framework for event extraction (DAEE), which generates additional training data through the knowledge-based structure-to-text generation model and selects the effective subset from the generated data iteratively with a deep reinforcement learning agent. Experimental results on several datasets demonstrate that the proposed method generates more diverse text representations for event extraction and achieves comparable results with the state-of-the-art.

Multi-head self-attention recently attracts enormous interest owing to its specialized functions, significant parallelizable computation, and flexible extensibility. However, very recent empirical studies show that some self-attention heads make little contribution and can be pruned as redundant heads. This work takes a novel perspective of identifying and then vitalizing redundant heads. We propose a redundant head enlivening (RHE) method to precisely identify redundant heads, and then vitalize their potential by learning syntactic relations and prior knowledge in the text without sacrificing the roles of important heads. Two novel syntax-enhanced attention (SEA) mechanisms: a dependency mask bias and a relative local-phrasal position bias, are introduced to revise self-attention distributions for syntactic enhancement in machine translation. The importance of individual heads is dynamically evaluated during the redundant heads identification, on which we apply SEA to vitalize redundant heads while maintaining the strength of important heads. Experimental results on widely adopted WMT14 and WMT16 English to German and English to Czech language machine translation validate the RHE effectiveness.

面向司法领域的藏汉机器翻译面临严重的数据稀疏问题。本文将从两个方面展录研究:第一,相比于通用领域,司法领域的藏语要有更严谨的逻辑表达和更多的专业术语。然而,目前藏语资源在司法领域内缺乏对应的语料,稀缺专业术语词以及句法结构。第二,藏语的特殊词汇表达方式和特定句法结构使得通用语料构建方法难以构建藏汉平行语料库。为此,本文提出仺种针对司法领域藏汉平行语料的轻量级构建方法。首先,我们采取人工标注获取一个中等规模的司法领域藏汉专业术语表作为先验知识库,以避免领域越界而产生的语料逻辑表达问题和领域术语缺失问题;其次,我们从全国的地方法庭官网采集实例语料数据,例如裁判文书。我们优先寻找藏文实例数据,其次是汉语,以避免后续构造藏语句子而丢失特殊的词汇表达和句式结构。我们基于以上原则采集藏汉语料构建高质量的藏汉平行语料库,具体方法包括:爬虫获取语料,规则断章对齐检测,语句边界识别,语料库自动清洗。朂终,我们构建了16万级规模的藏汉司法领域语料库,并通过多种翻译模型和交叉实验验证了构建的语料库的高质量特点和鲁棒性。另外,此语料库会弚源以便于相关研究人员用于科研工作。

We introduce a new task of modeling the role and function for on-line resource citations in scientific literature. By categorizing the on-line resources and analyzing the purpose of resource citations in scientific texts, it can greatly help resource search and recommendation systems to better understand and manage the scientific resources. For this novel task, we are the first to create an annotation scheme, which models the different granularity of information from a hierarchical perspective. And we construct a dataset SciRes, which includes 3,088 manually annotated resource contexts. In this paper, we propose a possible solution by using a multi-task framework to build the scientific resource classifier (SciResCLF) for jointly recognizing the role and function types. Then we use the classification results to help a scientific resource recommendation (SciResREC) task. Experiments show that our model achieves the best results on both the classification task and the recommendation task. The SciRes dataset is released for future research.

Relation Extraction suffers from dramatical performance decrease when training a model on one genre and directly applying it to a new genre, due to the distinct feature distributions. Previous studies address this problem by discovering a shared space across genres using manually crafted features, which requires great human effort. To effectively automate this process, we design a genre-separation network, which applies two encoders, one genre-independent and one genre-shared, to explicitly extract genre-specific and genre-agnostic features. Then we train a relation classifier using the genre-agnostic features on the source genre and directly apply to the target genre. Experiment results on three distinct genres of the ACE dataset show that our approach achieves up to 6.1% absolute F1-score gain compared to previous methods. By incorporating a set of external linguistic features, our approach outperforms the state-of-the-art by 1.7% absolute F1 gain. We make all programs of our model publicly available for research purpose