GenAI4ELab/papercli-papers huggingface dataset:
AskToAct: Enhancing LLMs Tool Use via Self-Correcting Clarification
Authors: Xuan Zhang; Yongliang Shen; Zhe Zheng; Linjuan Wu; Wenqi Zhang; Yuchen Yan; Qiuying Peng; Jun Wang; Weiming Lu
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: 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.
START: Self-taught Reasoner with Tools
Authors: Chengpeng Li; Mingfeng Xue; Zhenru Zhang; Jiaxi Yang; Beichen Zhang; Bowen Yu; Binyuan Hui; Junyang Lin; Xiang Wang; Dayiheng Liu
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: 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.
The Impact of Negated Text on Hallucination with Large Language Models
Authors: Jaehyung Seo; Hyeonseok Moon; Heuiseok Lim
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: 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.
A Probabilistic Inference Scaling Theory for LLM Self-Correction
Authors: Zhe Yang; Yichang Zhang; Yudong Wang; Ziyao Xu; Junyang Lin; Zhifang Sui
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: 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 tth round of self-correction is given by: Acct = Upp - 𝛼t(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.
MentalGLM Series: Explainable Large Language Models for Mental Health Analysis on Chinese Social Media
Authors: Wei Zhai; Nan Bai; Qing Zhao; Jianqiang Li; Fan Wang; Hongzhi Qi; Meng Jiang; Xiaoqin Wang; Bing Xiang Yang; Guanghui Fu
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: 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.
Knowledge-Aware Co-Reasoning for Multidisciplinary Collaboration
Authors: Xurui Li; Wanghaijiao; Kaisong Song; Rui Zhu; Haixu Tang
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: 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.
Astra: Efficient Transformer Architecture and Contrastive Dynamics Learning for Embodied Instruction Following
Authors: Yueen Ma; DaFeng Chi; Shiguang Wu; Yuecheng Liu; Yuzheng Zhuang; Irwin King
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: 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.
MAVL: A Multilingual Audio-Video Lyrics Dataset for Animated Song Translation
Authors: Woohyun Cho; Youngmin Kim; Sunghyun Lee; Youngjae Yu
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: 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.
MuTIS: Enhancing Reasoning Efficiency through Multi Turn Intervention Sampling in Reinforcement Learning
Authors: Wenshuo Zhao; Haoxing Zhai; Xinyu Qiu; Zhenting Qi; Shuhe Li; Linchao Zhu
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: 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.
PRIM: Towards Practical In-Image Multilingual Machine Translation
Authors: Yanzhi Tian; Zeming Liu; Zhengyang Liu; Chong Feng; Xin Li; Heyan Huang; Yuhang Guo
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: 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.
Mind the Inclusivity Gap: Multilingual Gender-Neutral Translation Evaluation with mGeNTE
Authors: Beatrice Savoldi; Giuseppe Attanasio; Eleonora Cupin; Eleni Gkovedarou; Janiça Hackenbuchner; Anne Lauscher; Matteo Negri; Andrea Piergentili; Manjinder Thind; Luisa Bentivogli
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: 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.
DiplomacyAgent: Do LLMs Balance Interests and Ethical Principles in International Events?
Authors: Jianxiang Peng; Ling Shi; Xinwei Wu; Hanwen Zhang; Fujiang Liu; Haocheng Lyu; Deyi Xiong
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: 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.
DisLoRA: Task-specific Low-Rank Adaptation via Orthogonal Basis from Singular Value Decomposition
Authors: She Yifei; Xinhao Wei; Yulong Wang
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: 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.
Unmasking Deceptive Visuals: Benchmarking Multimodal Large Language Models on Misleading Chart Question Answering
Authors: Zixin Chen; Sicheng Song; KaShun Shum; Yanna Lin; Rui Sheng; Weiqi Wang; Huamin Qu
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: 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.
Textual Aesthetics in Large Language Models
Authors: Lingjie Jiang; Shaohan Huang; Xun Wu; Furu Wei
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: 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.
Section-Level Simplification of Biomedical Abstracts
Authors: Jan Bakker; Jaap Kamps
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: 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.