GenAI4ELab/papercli-papers huggingface dataset:
Don’t Get Lost in the Trees: Streamlining LLM Reasoning by Overcoming Tree Search Exploration Pitfalls
Authors: Ante Wang; Linfeng Song; Ye Tian; Dian Yu; Haitao Mi; Xiangyu Duan; Zhaopeng Tu; Jinsong Su; Dong Yu
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Recent advancements in tree search algorithms guided by verifiers have significantly enhanced the reasoning capabilities of large language models (LLMs), but at the cost of increased computational resources. In this work, we identify two key challenges contributing to this inefficiency: over-exploration due to redundant states with semantically equivalent content, and under-exploration caused by high variance in verifier scoring leading to frequent trajectory switching. To address these issues, we propose FETCH – an e ffici ent tree sear ch framework, which is a flexible, plug-and-play system compatible with various tree search algorithms.Our framework mitigates over-exploration by merging semantically similar states using agglomerative clustering of text embeddings obtained from a fine-tuned SimCSE model. To tackle under-exploration, we enhance verifiers by incorporating temporal difference learning with adjusted 𝜆-returns during training to reduce variance, and employing a verifier ensemble to aggregate scores during inference. Experiments on GSM8K, GSM-Plus, and MATH datasets demonstrate that our methods significantly improve reasoning accuracy and computational efficiency across four different tree search algorithms, paving the way for more practical applications of LLM-based reasoning. The code is available at https://github.com/DeepLearnXMU/Fetch.
MEXMA: Token-level objectives improve sentence representations
Authors: João Maria Janeiro; Benjamin Piwowarski; Patrick Gallinari; Loic Barrault
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Cross-lingual sentence encoders (CLSE) create fixed-size sentence representations with aligned translations. Current pre-trained CLSE approaches use sentence-level objectives only. This can lead to loss of information, especially for tokens, which then degrades the sentence representation. We propose MEXMA, a novel approach that integrates both sentence-level and token-level objectives. The sentence representation in one language is used to predict masked tokens in another language, with both the sentence representation and *all tokens directly update the encoder*. We show that adding token-level objectives greatly improves the sentence representation quality across several tasks. Our approach outperforms current pre-trained cross-lingual sentence encoders on bitext mining as well as several downstream tasks. We also analyse the information encoded in our tokens, and how the sentence representation is built from them.
Uncertainty-Aware Iterative Preference Optimization for Enhanced LLM Reasoning
Authors: Lei Li; Hehuan Liu; Yaxin Zhou; ZhaoYang Gui; Xudong Weng; Yi Yuan; Zheng Wei; Zang Li
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Direct Preference Optimization (DPO) has recently emerged as an efficient and effective method for aligning large language models with human preferences. However, constructing high-quality preference datasets remains challenging, often necessitating expensive manual or powerful LM annotations. Additionally, standard DPO exhibits suboptimal performance in complex reasoning tasks, such as mathematical and code reasoning. In this paper, we introduce an approach to collect preference pairs through iterative sampling and execution feedback, tailored to the current learning state (e.g. well-learned, mis-learned, and unlearned) of the policy model. To alleviate the failures of DPO and improve its applicability in reasoning tasks, we propose , an iterative uncertainty-aware preference optimization method that achieves fine-grained preference control by assessing model confidence. We validate our approach across three reasoning tasks, incorporating five established reasoning datasets and one self-curated dataset. Our experimental results demonstrate an overall improvement of 3.6% over the standard DPO method and show the model exhibits promising generalizability.
AgentDropout: Dynamic Agent Elimination for Token-Efficient and High-Performance LLM-Based Multi-Agent Collaboration
Authors: Zhexuan Wang; Yutong Wang; Xuebo Liu; Liang Ding; Miao Zhang; Jie Liu; Min Zhang
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Multi-agent systems (MAS) based on large language models (LLMs) have demonstrated significant potential in collaborative problem-solving. However, they still face substantial challenges of low communication efficiency and suboptimal task performance, making the careful design of the agents’ communication topologies particularly important. Inspired by the management theory that roles in an efficient team are often dynamically adjusted, we propose AgentDropout, which identifies redundant agents and communication across different communication rounds by optimizing the adjacency matrices of the communication graphs and eliminates them to enhance both token efficiency and task performance. Compared to state-of-the-art methods, AgentDropout achieves an average reduction of 21.6% in prompt token consumption and 18.4% in completion token consumption, along with a performance improvement of 1.14 on the tasks. Furthermore, the extended experiments demonstrate that AgentDropout achieves notable domain transferability and structure robustness, revealing its reliability and effectiveness. We release our code at https://github.com/wangzx1219/AgentDropout.
Towards Dynamic Theory of Mind: Evaluating LLM Adaptation to Temporal Evolution of Human States
Authors: Yang Xiao; Jiashuo Wang; Qiancheng Xu; Changhe Song; Chunpu Xu; Yi Cheng; Wenjie Li; Pengfei Liu
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: As Large Language Models (LLMs) increasingly participate in human-AI interactions, evaluating their Theory of Mind (ToM) capabilities - particularly their ability to track dynamic mental states - becomes crucial. While existing benchmarks assess basic ToM abilities, they predominantly focus on static snapshots of mental states, overlooking the temporal evolution that characterizes real-world social interactions. We present **DynToM**, a novel benchmark specifically designed to evaluate LLMs’ ability to understand and track the temporal progression of mental states across interconnected scenarios. Through a systematic four-step framework, we generate 1,100 social contexts encompassing 5,500 scenarios and 78,100 questions, each validated for realism and quality. Our comprehensive evaluation of ten state-of-the-art LLMs reveals that their average performance underperforms humans by 44.7%, with performance degrading significantly when tracking and reasoning about the shift of mental states. This performance gap highlights fundamental limitations in current LLMs’ ability to model the dynamic nature of human mental states.
Marco-Bench-MIF: On Multilingual Instruction-Following Capability of Large Language
Authors: Bo Zeng; Chenyang Lyu; Sinuo Liu; Mingyan Zeng; Minghao Wu; Xuanfan Ni; Tianqi Shi; Yu Zhao; Yefeng Liu; Chenyu Zhu; Ruizhe Li; Jiahui Geng; Qing Li; Yu Tong; Longyue Wang; Weihua Luo; Kaifu Zhang
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Instruction-following capability has become a major ability to be evaluated for Large Language Models. However, existing datasets, such as IFEval, are either predominantly monolingual and centered on English or simply machine translated to other languages, limiting their applicability in multilingual contexts. In this paper, we present an carefully-curated extension of IFEval to a localized multilingual version named Marco-Bench-MIF, covering 30 languages with varying levels of localization. Our benchmark addresses linguistic constraints (e.g., modifying capitalization requirements for Chinese) and cultural references (e.g., substituting region-specific company names in prompts) via a hybrid pipeline combining translation with verification. Through comprehensive evaluation of 20+ LLMs on our Marco-Bench-MIF, we found that: (1) 25-35% accuracy gap between high/low-resource languages, (2) model scales largely impact performance by 45-60% yet persists script-specific challenges, and (3) machine-translated data underestimates accuracy by 7-22% versus localized data. Our analysis identifies challenges in multilingual instruction following, including keyword consistency preservation and compositional constraint adherence across languages. Our Marco-Bench-MIF will be made publicly available to the community.
Representation Bending for Large Language Model Safety
Authors: Ashkan Yousefpour; Taeheon Kim; Ryan Sungmo Kwon; Seungbeen Lee; Wonje Jeung; Seungju Han; Alvin Wan; Harrison Ngan; Youngjae Yu; Jonghyun Choi
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Large Language Models (LLMs) have emerged as powerful tools, but their inherent safety risks – ranging from harmful content generation to broader societal harms – pose significant challenges. These risks can be amplified by the recent adversarial attacks, fine-tuning vulnerabilities, and the increasing deployment of LLMs in high-stakes environments. Existing safety-enhancing techniques, such as fine-tuning with human feedback or adversarial training, are still vulnerable as they address specific threats and often fail to generalize across unseen attacks, or require manual system-level defenses. This paper introduces RepBend, a novel approach that fundamentally disrupts the representations underlying harmful behaviors in LLMs, offering a scalable solution to enhance (potentially inherent) safety. RepBend brings the idea of activation steering – simple vector arithmetic for steering model’s behavior during inference – to loss-based fine-tuning. Through extensive evaluation, RepBend achieves state-of-the-art performance, outperforming prior methods such as Circuit Breaker, RMU, and NPO, with up to 95% reduction in attack success rates across diverse jailbreak benchmarks, all with negligible reduction in model usability and general capabilities.
Analyzing LLMs’ Knowledge Boundary Cognition Across Languages Through the Lens of Internal Representations
Authors: Chenghao Xiao; Hou Pong Chan; Hao Zhang; Mahani Aljunied; Lidong Bing; Noura Al Moubayed; Yu Rong
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: While understanding the knowledge boundaries of LLMs is crucial to prevent hallucination, research on the knowledge boundaries of LLMs has predominantly focused on English. In this work, we present the first study to analyze how LLMs recognize knowledge boundaries across different languages by probing their internal representations when processing known and unknown questions in multiple languages. Our empirical studies reveal three key findings: 1) LLMs’ perceptions of knowledge boundaries are encoded in the middle to middle-upper layers across different languages. 2) Language differences in knowledge boundary perception follow a linear structure, which motivates our proposal of a training-free alignment method that effectively transfers knowledge boundary perception ability across languages, thereby helping reduce hallucination risk in low-resource languages; 3) Fine-tuning on bilingual question pair translation further enhances LLMs’ recognition of knowledge boundaries across languages. Given the absence of standard testbeds for cross-lingual knowledge boundary analysis, we construct a multilingual evaluation suite comprising three representative types of knowledge boundary data. Our code and datasets are publicly available at https://github.com/DAMO-NLP-SG/LLM-Multilingual-Knowledge-Boundaries.
Enhancing Retrieval-Augmented Generation via Evidence Tree Search
Authors: Hao Sun; Hengyi Cai; Yuchen Li; Xuanbo Fan; Xiaochi Wei; Shuaiqiang Wang; Yan Zhang; Dawei Yin
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Retrieval-Augmented Generation (RAG) is widely used to enhance Large Language Models (LLMs) by grounding responses in external knowledge. However, in real-world applications, retrievers often return lengthy documents with redundant or irrelevant content, confusing downstream readers. While evidence retrieval aims to address this by extracting key information, it faces critical challenges: (1) inability to model synergistic inter-dependencies among evidence sentences, (2) lack of supervision for evaluating multi-sentence evidence quality, and (3) computational inefficiency in navigating exponentially growing search spaces of candidate evidence sets. To tackle these challenges, we propose ETS (Evidence Tree Search), a novel framework that reformulates evidence retrieval as a dynamic tree expansion process. Our approach first constructs an evidence tree where each path represents a candidate evidence set, explicitly modeling inter-sentence dependencies through context-aware node selection. We then leverage Monte Carlo Tree Search (MCTS) to efficiently assess evidence quality and introduce an Early-Terminating Beam Search strategy to efficiently accelerate the model inference. Extensive experiments on five datasets demonstrate that ETS significantly outperforms existing methods across different readers. Our code and datasets will be released to facilitate future research.
HalluLens: LLM Hallucination Benchmark
Authors: Yejin Bang; Ziwei Ji; Alan Schelten; Anthony Hartshorn; Tara Fowler; Cheng Zhang; Nicola Cancedda; Pascale Fung
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Large language models (LLMs) often generate responses that deviate from user input or training data, a phenomenon known as “hallucination.” These hallucinations undermine user trust and hinder the adoption of generative AI systems. Addressing hallucinations is important for the advancement of LLMs. This paper introduces a comprehensive hallucination benchmark HalluLens, incorporating both extrinsic and intrinsic evaluation tasks, built upon a clear taxonomy of hallucination. A major challenge in benchmarking hallucinations is the lack of a unified framework due to inconsistent definitions and categorizations. We disentangle LLM hallucination from “factuality” and propose a taxonomy distinguishing extrinsic and intrinsic hallucinations to promote consistency and facilitate research. We emphasize extrinsic hallucinations – where generated content deviates from training data – as they become increasingly relevant with LLM advancements. However, no benchmark is solely dedicated to extrinsic hallucinations. To address this gap, HalluLens introduces three new extrinsic tasks with dynamic test set generation to mitigate data leakage and ensure robustness. We release codebase for extrinsic hallucination benchmark.
DEEPER Insight into Your User: Directed Persona Refinement for Dynamic Persona Modeling
Authors: Aili Chen; Chengyu Du; Jiangjie Chen; Jinghan Xu; Yikai Zhang; Siyu Yuan; Zulong Chen; Liangyue Li; Yanghua Xiao
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: To advance personalized applications such as recommendation systems and user behavior prediction, recent research increasingly adopts large language models (LLMs) for human-readable persona modeling. In dynamic real-world scenarios, effective persona modeling necessitates leveraging streaming behavior data to continually optimize user personas.However, existing methods—whether regenerating personas or incrementally extending them with new behaviors—often fail to achieve sustained improvements in persona quality or future behavior prediction accuracy. To address this, we propose DEEPER, a novel approach for dynamic persona modeling that enables continual persona optimization. Specifically, we enhance the model’s direction-search capability through an iterative reinforcement learning framework, allowing it to automatically identify effective update directions and optimize personas using discrepancies between user behaviors and model predictions.Extensive experiments on dynamic persona modeling involving 4,800 users across 10 domains highlight ’s superior persona optimization capabilities, delivering an impressive 32.2% average reduction in user behavior prediction error over four update rounds—outperforming the best baseline by a remarkable 22.92%.
Asclepius: A Spectrum Evaluation Benchmark for Medical Multi-Modal Large Language Models
Authors: Jie Liu; Wenxuan Wang; Su Yihang; Jingyuan Huang; Yudi Zhang; Cheng-Yi Li; Wenting Chen; Xiaohan Xing; Kao-Jung Chang; Linlin Shen; Michael R. Lyu
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: The significant breakthroughs of Medical Multi-Modal Large Language Models (Med-MLLMs) renovate modern healthcare with robust information synthesis and medical decision support. However, these models are often evaluated on benchmarks that are unsuitable for the Med-MLLMs due to the intricate nature of the real-world diagnostic frameworks, which encompass diverse medical specialties and involve complex clinical decisions. Thus, a clinically representative benchmark is highly desirable for credible Med-MLLMs evaluation. To this end, we introduce Asclepius, a novel Med-MLLM benchmark that comprehensively assesses Med-MLLMs in terms of: distinct medical specialties (cardiovascular, gastroenterology, etc.) and different diagnostic capacities (perception, disease analysis, etc.). Grounded in 3 proposed core principles, Asclepius ensures a comprehensive evaluation by encompassing 15 medical specialties, stratifying into 3 main categories and 8 sub-categories of clinical tasks, and exempting overlap with the existing VQA dataset. We further provide an in-depth analysis of 6 Med-MLLMs and compare them with 3 human specialists, providing insights into their competencies and limitations in various medical contexts. Our work not only advances the understanding of Med-MLLMs’ capabilities but also sets a precedent for future evaluations and the safe deployment of these models in clinical environments.
InstructPart: Task-Oriented Part Segmentation with Instruction Reasoning
Authors: Zifu Wan; Yaqi Xie; Ce Zhang; Zhiqiu Lin; Zihan Wang; Simon Stepputtis; Deva Ramanan; Katia P. Sycara
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Large multimodal foundation models, particularly in the domains of language and vision, have significantly advanced various tasks, including robotics, autonomous driving, information retrieval, and grounding. However, many of these models perceive objects as indivisible, overlooking the components that constitute them. Understanding these components and their associated affordances provides valuable insights into an object’s functionality, which is fundamental for performing a wide range of tasks. In this work, we introduce a novel real-world benchmark, InstructPart, comprising hand-labeled part segmentation annotations and task-oriented instructions to evaluate the performance of current models in understanding and executing part-level tasks within everyday contexts. Through our experiments, we demonstrate that task-oriented part segmentation remains a challenging problem, even for state-of-the-art Vision-Language Models (VLMs). In addition to our benchmark, we introduce a simple baseline that achieves a twofold performance improvement through fine-tuning with our dataset. With our dataset and benchmark, we aim to facilitate research on task-oriented part segmentation and enhance the applicability of VLMs across various domains, including robotics, virtual reality, information retrieval, and other related fields. Project website: https://zifuwan.github.io/InstructPart/.
GRaMPa: Subword Regularisation by Skewing Uniform Segmentation Distributions with an Efficient Path-counting Markov Model
Authors: Thomas Bauwens; David Kaczér; Miryam de Lhoneux
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Stochastically sampling word segmentations from a subword tokeniser, also called subword regularisation, is a known way to increase robustness of language models to out-of-distribution inputs, such as text containing spelling errors. Recent work has observed that usual augmentations that make popular deterministic subword tokenisers stochastic still cause only a handful of all possible segmentations to be sampled. It has been proposed to uniformly sample across these instead, through rejection sampling of paths in an unweighted segmentation graph. In this paper, we argue that uniformly random segmentation in turn skews the distributions of certain segmentational properties (e.g. token lengths and amount of tokens produced) away from uniformity, which still ends up hiding meaningfully diverse tokenisations. We propose an alternative uniform sampler using the same segmentation graph, but weighted by counting the paths through it. Our sampling algorithm, GRaMPa, provides hyperparameters allowing sampled tokenisations to skew towards fewer, longer tokens. Furthermore, GRaMPa is single-pass, guaranteeing significantly better computational complexity than previous approaches relying on rejection sampling. We show experimentally that language models trained with GRaMPa outperform existing regularising tokenisers in a data-scarce setting on token-level tasks such as dependency parsing, especially with spelling errors present.
Evaluating the Evaluation of Diversity in Commonsense Generation
Authors: Tianhui Zhang; Bei Peng; Danushka Bollegala
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: In commonsense generation, given a set of input concepts, a model must generate a response that is not only commonsense bearing, but also capturing multiple diverse viewpoints. Numerous evaluation metrics based on form- and content-level overlap have been proposed in prior work for evaluating the diversity of a commonsense generation model. However, it remains unclear as to which metrics are best suited for evaluating the diversity in commonsense generation. To address this gap, we conduct a systematic meta-evaluation of diversity metrics for commonsense generation. We find that form-based diversity metrics tend to consistently overestimate the diversity in sentence sets, where even randomly generated sentences are assigned overly high diversity scores. We then use an Large Language Model (LLM) to create a novel dataset annotated for the diversity of sentences generated for a commonsense generation task, and use it to conduct a meta-evaluation of the existing diversity evaluation metrics. Our experimental results show that content-based diversity evaluation metrics consistently outperform the form-based counterparts, showing high correlations with the LLM-based ratings. We recommend that future work on commonsense generation should use content-based metrics for evaluating the diversity of their outputs.