GenAI4ELab/papercli-papers huggingface dataset:
ISACL: Internal State Analyzer for Copyrighted Training Data Leakage
Authors: Guangwei Zhang; Qisheng Su; Jiateng Liu; Cheng Qian; Yanzhou Pan; Yanjie Fu; Denghui Zhang
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP) but pose risks of inadvertently exposing copyrighted or proprietary data, especially when such data is used for training but not intended for distribution. Traditional methods address these leaks only after content is generated, which can lead to the exposure of sensitive information. This study introduces a proactive approach: examining LLMs’ internal states before text generation to detect potential leaks. By using a curated dataset of copyrighted materials, we trained a neural network classifier to identify risks, allowing for early intervention by stopping the generation process or altering outputs to prevent disclosure. Integrated with a Retrieval-Augmented Generation (RAG) system, this framework ensures adherence to copyright and licensing requirements while enhancing data privacy and ethical standards. Our results show that analyzing internal states effectively mitigates the risk of copyrighted data leakage, offering a scalable solution that fits smoothly into AI workflows, ensuring compliance with copyright regulations while maintaining high-quality text generation. Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP) but pose risks of inadvertently exposing copyrighted or proprietary data, especially when such data is used for training but not intended for distribution. Traditional methods address these leaks only after content is generated, which can lead to the exposure of sensitive information. This study introduces a proactive approach: examining LLMs’ internal states before text generation to detect potential leaks. By using a curated dataset of copyrighted materials, we trained a neural network classifier to identify risks, allowing for early intervention by stopping the generation process or altering outputs to prevent disclosure. Integrated with a Retrieval-Augmented Generation (RAG) system, this framework ensures adherence to copyright and licensing requirements while enhancing data privacy and ethical standards. Our results show that analyzing internal states effectively mitigates the risk of copyrighted data leakage, offering a scalable solution that fits smoothly into AI workflows, ensuring compliance with copyright regulations while maintaining high-quality text generation. Our code can be found here: (https://anonymous.4open.science/r/Internal-states-leakage-9D6E).
Steering LVLMs via Sparse Autoencoder for Hallucination Mitigation
Authors: Zhenglin Hua; Jinghan He; Zijun Yao; Tianxu Han; Haiyun Guo; Yuheng Jia; Junfeng Fang
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Large vision-language models (LVLMs) have achieved remarkable performance on multimodal tasks. However, they still suffer from hallucinations, generating text inconsistent with visual input, posing significant risks in real-world applications. Existing approaches to address this issue focus on incorporating external knowledge bases, alignment training, or decoding strategies, all of which require substantial computational cost and time. Recent works try to explore more efficient alternatives by adjusting LVLMs’ internal representations. Although promising, these methods may cause hallucinations to be insufficiently suppressed or lead to excessive interventions that negatively affect normal semantics. In this work, we leverage sparse autoencoders (SAEs) to identify semantic directions closely associated with faithfulness or hallucination, extracting more precise and disentangled hallucination-related representations. Our analysis demonstrates that interventions along the identified faithful direction can mitigate hallucinations, while those along the hallucinatory direction can exacerbate them. Building on these insights, we propose **S**teering LVLMs via **S**AE **L**atent Directions (SSL), a plug-and-play method based on SAE-derived latent directions to mitigate hallucinations in LVLMs. Extensive experiments demonstrate that SSL significantly outperforms existing decoding approaches in mitigating hallucinations, while maintaining transferability across different model architectures with negligible additional time overhead. The code is available at [https://github.com/huazhenglin2003/SSL](https://github.com/huazhenglin2003/SSL).
On the Perception Bottleneck of VLMs for Chart Understanding
Authors: Junteng Liu; Weihao Zeng; Xiwen Zhang; Yijun Wang; Zifei Shan; Junxian He
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Chart understanding requires models to effectively analyze and reason about numerical data, textual elements, and complex visual components. Our observations reveal that the perception capabilities of existing large vision-language models (LVLMs) constitute a critical bottleneck in this process. In this study, we delve into this perception bottleneck by decomposing it into two components: the vision encoder bottleneck, where the visual representation may fail to encapsulate the correct information, and the extraction bottleneck, where the language model struggles to extract the necessary information from the provided visual representations. Through comprehensive experiments, we find that (1) the information embedded within visual representations is substantially richer than what is typically captured by linear extractors, such as the widely used retrieval accuracy metric; (2) While instruction tuning effectively enhances the extraction capability of LVLMs, the vision encoder remains a critical bottleneck, demanding focused attention and improvement. Therefore, we further enhance the visual encoder to mitigate the vision encoder bottleneck under a contrastive learning framework. Empirical results demonstrate that our approach significantly mitigates the perception bottleneck and improves the ability of LVLMs to comprehend charts.
Self-Guided Function Calling in Large Language Models via Stepwise Experience Recall
Authors: Sijia Cui; Aiyao He; Shuai Xu; Hongming Zhang; Yanna Wang; Qingyang Zhang; Yajing Wang; Bo Xu
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Function calling enables large language models (LLMs) to interact with external systems by leveraging tools and APIs. When faced with multi-step tool usage, LLMs still struggle with tool selection, parameter generation, and tool-chain planning. Existing methods typically rely on manually designing task-specific demonstrations, or retrieving from a curated library. These approaches demand substantial expert effort and prompt engineering becomes increasingly complex and inefficient as tool diversity and task difficulty scale. To address these challenges, we propose a self-guided method, Stepwise ExperiencE Recall (SEER), which performs fine-grained, stepwise retrieval from a continually updated experience pool. Instead of relying on static or manually curated library, SEER incrementally augments the experience pool with past successful trajectories, enabling continuous expansion of the pool and improved model performance over time. Evaluated on the ToolQA benchmark, SEER achieves an average improvement of 6.1% on easy and 4.7% on hard questions. We further test SEER on 𝜏-bench, which includes two real-world domains. Powered by Qwen2.5-7B and Qwen2.5-72B models, SEER demonstrates substantial accuracy gains of 7.44% and 23.38%, respectively.
Multilingual Generative Retrieval via Cross-lingual Semantic Compression
Authors: Yuxin Huang; Simeng Wu; Ran Song; Yan Xiang; Yantuan Xian; Shengxiang Gao; Zhengtao Yu
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Generative Information Retrieval is an emerging retrieval paradigm that exhibits remarkable performance in monolingual scenarios. However, applying these methods to multilingual retrieval still encounters two primary challenges, cross-lingual identifier misalignment and identifier inflation. To address these limitations, we propose Multilingual Generative Retrieval via Cross-lingual Semantic Compression (MGR-CSC), a novel framework that unifies semantically equivalent multilingual keywords into shared atoms to align semantics and compresses the identifier space, and we propose a dynamic multi-step constrained decoding strategy during retrieval. MGR-CSC improves cross-lingual alignment by assigning consistent identifiers and enhances decoding efficiency by reducing redundancy. Experiments demonstrate that MGR-CSC achieves outstanding retrieval accuracy, improving by 6.83% on mMarco100k and 4.77% on mNQ320k, while reducing document identifiers length by 74.51% and 78.2%, respectively. We publicly release our dataset and code at https://github.com/simengggg/MGR-CSC
Towards Multi-Document Question Answering in Scientific Literature: Pipeline, Dataset, and Evaluation
Authors: Hui Huang; Julien Velcin; Yacine Kessaci
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Question-Answering (QA) systems are vital for rapidly accessing and comprehending information in academic literature.However, some academic questions require synthesizing information across multiple documents. While several prior resources consider multi-document QA, they often do not strictly enforce cross-document synthesis or exploit the explicit inter-paper structure that links sources.To address this, we introduce a pipeline methodology for constructing a Multi-Document Academic QA (MDA-QA) dataset. By both detecting communities based on citation networks and leveraging Large Language Models (LLMs), we were able to form thematically coherent communities and generate QA pairs related to multi-document content automatically.We further develop an automated filtering mechanism to ensure multi-document dependence.Our resulting dataset consists of 6,804 QA pairs and serves as a benchmark for evaluating multi-document retrieval and QA systems.Our experimental results highlight that standard lexical and embedding-based retrieval methods struggle to locate all relevant documents, indicating a persistent gap in multi-document reasoning. We release our dataset and source code for the community.
Multilingual Knowledge Graph Completion via Efficient Multilingual Knowledge Sharing
Authors: Cunli Mao; Xiaofei Gao; Ran Song; Shizhu He; Shengxiang Gao; Kang Liu; Zhengtao Yu
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Large language models (LLMs) based Multilingual Knowledge Graph Completion (MKGC) aim to predict missing facts by leveraging LLMs’ multilingual understanding capabilities, improving the completeness of multilingual knowledge graphs (KGs).However, existing MKGC research underutilizes the multilingual capabilities of LLMs and ignores the shareability of cross-lingual knowledge.In this paper, we propose a novel MKGC framework that leverages multilingual shared knowledge to significantly enhance performance through two components: Knowledge-level Grouped Mixture of Experts (KL-GMoE) and Iterative Entity Reranking (IER).KL-GMoE efficiently models shared knowledge, while IER significantly enhances its utilization.To evaluate our framework, we constructed a mKG dataset containing 5 languages and conducted comprehensive comparative experiments with existing state-of-the-art (SOTA) MKGC method.The experimental results demonstrate that our framework achieves improvements of 5.47%, 3.27%, and 1.01% in the Hits@1, Hits@3, and Hits@10 metrics, respectively, compared with SOTA MKGC method.Further experimental analysis revealed the properties of knowledge sharing in settings of unseen and unbalanced languages.We have released the dataset and code for our work on https://github.com/gaoxiaofei07/KL-GMoE.
Mitigating Attention Localization in Small Scale: Self-Attention Refinement via One-step Belief Propagation
Authors: Nakyung Lee; Yeongoon Kim; Minhae Oh; Suhwan Kim; Jin Woo Koo; Hyewon Jo; Jungwoo Lee
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Transformer-based self-attention mechanism serves as the core of modern language models, yet it often suffers from *localization*, where attentions collapse onto a limited subset of tokens and fail to capture long-range dependencies. To address this issue, we propose **Self-Attention One-step Belief Propagation (SAOBP)**, a refinement framework that injects *multi-hop* relationships through a belief propagation process. To interpret and quantify these interactions, we introduce **Global Token Dependency (GTD)** that captures the relative contribution of multi-hop connections within the attention graph. Empirical results indicate that SAOBP helps prevent entropy collapse in deeper layers and adaptively maintains GTD at task-appropriate levels, thereby supporting improvements in model performance. Importantly, we observe competitive gains in small-scale models, highlighting its potential for improving inference quality in resource-constrained scenarios.
Imagination and Contemplation: A Balanced Framework for Semantic-Augmented Multimodal Machine Translation
Authors: Zhuang Yu; Shiliang Sun; Jing Zhao; Tengfei Song; Hao Yang
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Multimodal Machine Translation (MMT) enhances textual translation through auxiliary inputs such as images, which is particularly effective in resolving linguistic ambiguities. However, visual information often introduces redundancy or noise, potentially impairing translation quality. To address this challenge, we propose a balanced semantic-augmented framework that integrates “Imagination“ and “Contemplation“ in multimodal understanding. Specifically, we first generate synthetic images from the source text and align them with the authentic images via an optimal transport (OT) loss to enhance visual-semantic consistency. A CLIP-based similarity gating mechanism is introduced to adaptively fuse visual features from both authentic and synthetic images during visual representation learning. To strengthen semantic grounding, a neural machine translation (NMT) branch is incorporated as a regularization signal, and a Kullback-Leibler (KL) divergence is applied between MMT and NMT outputs to mitigate modality mismatch. Furthermore, an image-text contrastive (ITC) loss aligns the final translations with image representations, reinforcing multimodal coherence. Experiments on multiple translation datasets with a diverse set of language pairs demonstrate that our framework outperforms existing baselines, particularly in cases with visually ambiguous or weakly correlated content.
NeLLCom-Lex: A Neural-agent Framework to Study the Interplay between Lexical Systems and Language Use
Authors: Yuqing Zhang; Ecesu Ürker; Tessa Verhoef; Gemma Boleda; Arianna Bisazza
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Lexical semantic change has primarily been investigated with observational and experimental methods; however, observational methods (corpus analysis, distributional semantic modeling) cannot get at causal mechanisms, and experimental paradigms with humans are hard to apply to semantic change due to the extended diachronic processes involved. This work introduces NeLLCom-Lex, a neural-agent framework designed to simulate semantic change by first grounding agents in a real lexical system (e.g. English) and then systematically manipulating their communicative needs. Using a well-established color naming task, we simulate the evolution of a lexical system within a single generation, and study which factors lead agents to: (i) develop human-like naming behavior and lexicons, and (ii) change their behavior and lexicons according to their communicative needs. Our experiments with different supervised and reinforcement learning pipelines show that neural agents trained to ‘speak’ an existing language can reproduce human-like patterns in color naming to a remarkable extent, supporting the further use of NeLLCom-Lex to elucidate the mechanisms of semantic change.
RLMEval: Evaluating Research-Level Neural Theorem Proving
Authors: Auguste Poiroux; Antoine Bosselut; Viktor Kunčak
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Despite impressive results on curated benchmarks, the practical impact of large language models (LLMs) on research-level neural theorem proving and proof autoformalization is still limited. We introduce RLMEval, an evaluation suite for these tasks, focusing on research-level mathematics from real-world Lean formalization projects. RLMEval targets the evaluation of neural theorem proving and proof autoformalization on challenging research-level theorems by leveraging real Lean Blueprint formalization projects. Our evaluation of state-of-the-art models on RLMEval, comprising 613 theorems from 6 Lean projects, reveals a significant gap: progress on existing benchmarks does not readily translate to these more realistic settings, with the best model achieving only a 10.3% pass rate. RLMEval provides a new, challenging benchmark designed to guide and accelerate progress in automated reasoning for formal mathematics.
KaeDe: Progressive Generation of Logical Forms via Knowledge-Aware Question Decomposition for Improved KBQA
Authors: Ranran Bu; Jian Cao; Jianqi Gao; Shiyou Qian; Hongming Cai
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Knowledge base question answering (KBQA) refers to the task of answering natural language questions using large-scale structured knowledge bases (KBs). Existing semantic parsing-based (SP-based) methods achieve superior performance by directly converting questions into structured logical form (LF) queries using fine-tuned large language models (LLMs). However, these methods face the key challenge of difficulty in directly generating LFs for complex graph structures, which often leads to non-executable LFs that negatively impact overall KBQA performance. To address this challenge, we propose KaeDe, a novel generate-then-retrieve method for KBQA. This approach integrates knowledge-aware question decomposition and subsequent progressive LF generation within the generation phase, followed by an unsupervised retrieval phase. Specifically, the original question is decomposed into simplified, topic entity-centric sub-questions and explanations within the KB context. Path-level LFs are derived from these intermediate expressions and then combined into a comprehensive graph-level LF. Finally, the LF is refined through unsupervised entity and relation retrieval. Experimental results demonstrate that our method achieves state-of-the-art (SOTA) performance on WebQuestionSP (WebQSP) and ComplexWebQuestions (CWQ) benchmarks, particularly with fewer model parameters.
Where Fact Ends and Fairness Begins: Redefining AI Bias Evaluation through Cognitive Biases
Authors: Jen-tse Huang; Yuhang Yan; Linqi Liu; Yixin Wan; Wenxuan Wang; Kai-Wei Chang; Michael R. Lyu
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Recent failures such as Google Gemini generating people of color in Nazi-era uniforms illustrate how AI outputs can be factually plausible yet socially harmful. AI models are increasingly evaluated for “fairness,” yet existing benchmarks often conflate two fundamentally different dimensions: factual correctness and normative fairness. A model may generate responses that are factually accurate but socially unfair, or conversely, appear fair while distorting factual reality. We argue that identifying the boundary between fact and fair is essential for meaningful fairness evaluation. We introduce Fact-or-Fair, a benchmark with (i) objective queries aligned with descriptive, fact-based judgments, and (ii) subjective queries aligned with normative, fairness-based judgments. Our queries are constructed from 19 statistics and are grounded in cognitive psychology, drawing on representativeness bias, attribution bias, and ingroup–outgroup bias to explain why models often misalign fact and fairness. Experiments across ten frontier models reveal different levels of fact-fair trade-offs. By reframing fairness evaluation, we provide both a new theoretical lens and a practical benchmark to advance the responsible model assessments. Our test suite is publicly available at https://github.com/uclanlp/Fact-or-Fair.
Equal Truth: Rumor Detection with Invariant Group Fairness
Authors: Junyi Chen; Mengjia Wu; Qian Liu; Jing Sun; Ying Ding; Yi Zhang
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Due to the widespread dissemination of rumors on social media platforms, detecting rumors has been a long-standing concern for various communities. However, existing rumor detection methods rarely consider the fairness issues inherent in the model, which can lead to biased predictions across different stakeholder groups (e.g., domains and originating platforms of the detected content), also undermining their detection effectiveness. In this work, we propose a two-step framework to address this issue. First, we perform unsupervised partitioning to dynamically identify potential unfair data patterns without requiring sensitive attribute annotations. Then, we apply invariant learning to these partitions to extract fair and informative feature representations that enhance rumor detection. Extensive experiments show that our method outperforms strong baselines regarding detection and fairness performance, and also demonstrate robust performance on out-of-distribution samples. Further empirical results indicate that our learned features remain informative and fair across stakeholder groups and can correct errors when applied to existing baselines.
STEAM: A Semantic-Level Knowledge Editing Framework for Large Language Models
Authors: Geunyeong Jeong; Juoh Sun; Seonghee Lee; Harksoo Kim
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Large Language Models store extensive factual knowledge acquired during large-scale pre-training. However, this knowledge is inherently static, reflecting only the state of the world at the time of training. Knowledge editing has emerged as a promising solution for updating outdated or incorrect facts without full retraining. However, most existing locate-and-edit methods primarily focus on token-level likelihood optimization without addressing semantic coherence. Our analysis reveals that such edited knowledge is often encoded as isolated residual streams in the model’s latent space, distinct from pre-existing knowledge and bypassing natural reasoning process. To address this, we propose STEAM, a semantic-level knowledge editing framework that enhances integration of updated knowledge into the model’s knowledge structure. STEAM first identifies target representations as semantic anchors for the updated factual association, then guides the internal representation of the edited fact towards these anchors through an alignment loss during optimization. Experimental results demonstrate that STEAM improves model’s ability to reason with edited knowledge and enhances semantic coherence, underscoring the importance of latent-space alignment for reliable and coherent knowledge editing. The code is available at https://github.com/GY-Jeong/STEAM.