GenAI4ELab/papercli-papers huggingface dataset:
InMind: Evaluating LLMs in Capturing and Applying Individual Human Reasoning Styles
Authors: Zizhen Li; Chuanhao Li; Yibin Wang; Qi Chen; Diping Song; Yukang Feng; Jianwen Sun; Jiaxin Ai; Fanrui Zhang; Mingzhu Sun; Kaipeng Zhang
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: LLMs have shown strong performance on human-centric reasoning tasks. While previous evaluations have explored whether LLMs can infer intentions or detect deception, they often overlook the individualized reasoning styles that influence how people interpret and act in social contexts. Social deduction games (SDGs) provide a natural testbed for evaluating individualized reasoning styles, where different players may adopt diverse but contextually valid reasoning strategies under identical conditions. To address this, we introduce InMind, a cognitively grounded evaluation framework designed to assess whether LLMs can capture and apply personalized reasoning styles in SDGs. InMind enhances structured gameplay data with round-level strategy traces and post-game reflections, collected under both Observer and Participant modes. It supports four cognitively motivated tasks that jointly evaluate both static alignment and dynamic adaptation. As a case study, we apply InMind to the game Avalon, evaluating 11 state-of-the-art LLMs. General-purpose LLMs, even GPT-4o frequently rely on lexical cues, struggling to anchor reflections in temporal gameplay or adapt to evolving strategies. In contrast, reasoning-enhanced LLMs like DeepSeek-R1 exhibit early signs of style-sensitive reasoning. These findings reveal key limitations in current LLMs’ capacity for individualized, adaptive reasoning, and position InMind as a step toward cognitively aligned human–AI interaction.
MIO: A Foundation Model on Multimodal Tokens
Authors: Zekun Moore Wang; King Zhu; Chunpu Xu; Wangchunshu Zhou; Jiaheng Liu; Yibo Zhang; Jessie Wang; Ning Shi; Siyu Li; Yizhi Li; Haoran Que; Zhaoxiang Zhang; Yuanxing Zhang; Ge Zhang; Ke Xu; Jie Fu; Wenhao Huang
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: In this paper, we introduce MIO, a novel foundation model built on multimodal tokens, capable of understanding and generating speech, text, images, and videos in an end-to-end, autoregressive manner. While the emergence of large language models (LLMs) and multimodal large language models (MM-LLMs) propels advancements in artificial general intelligence through their versatile capabilities, they still lack true any-to-any understanding and generation. Recently, the release of GPT-4o has showcased the remarkable potential of any-to-any LLMs for complex real-world tasks, enabling omnidirectional input and output across images, speech, and text. However, it is closed-source and does not support the generation of multimodal interleaved sequences. To address this gap, we present MIO, which is trained on a mixture of discrete tokens across four modalities using causal multimodal modeling. MIO undergoes a four-stage training process: (1) alignment pre-training, (2) interleaved pre-training, (3) speech-enhanced pre-training, and (4) comprehensive supervised fine-tuning on diverse textual, visual, and speech tasks. Our experimental results indicate that MIO exhibits competitive, and in some cases superior, performance compared to previous dual-modal baselines, any-to-any model baselines, and even modality-specific baselines. Moreover, MIO demonstrates advanced capabilities inherent to its any-to-any feature, such as interleaved video-text generation, chain-of-visual-thought reasoning, visual guideline generation, instructional image editing, etc.
DART: Distilling Autoregressive Reasoning to Silent Thought
Authors: Nan Jiang; Ziming Wu; De-Chuan Zhan; Fuming Lai; Shaobing Lian
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Chain-of-Thought (CoT) reasoning has significantly advanced Large Language Models (LLMs) in solving complex tasks. However, its autoregressive paradigm leads to significant computational overhead, hindering its deployment in latency-sensitive applications. To address this, we propose **DART** (**D**istilling **A**utoregressive **R**easoning to Silent **T**hought), a self-distillation framework that enables LLMs to replace autoregressive CoT with non-autoregressive Silent Thought (ST). Specifically, DART introduces two training pathways: the CoT pathway for traditional reasoning and the ST pathway for generating answers directly from a few ST tokens. The ST pathway utilizes a lightweight Reasoning Evolvement Module (REM) to align its hidden states with the CoT pathway, enabling the ST tokens to evolve into informative embeddings. During inference, only the ST pathway is activated, leveraging evolving ST tokens to deliver the answer directly. Extensive experimental results demonstrate that DART offers significant performance gains compared with existing non-autoregressive baselines without extra inference latency, serving as a feasible alternative for efficient reasoning.
LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization
Authors: Qi Zhang; Shouqing Yang; Lirong Gao; Hao Chen; Xiaomeng Hu; Jinglei Chen; Jiexiang Wang; Sheng Guo; Bo Zheng; Haobo Wang; Junbo Zhao
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Large language models (LLMs) have demonstrated impressive capabilities in reasoning with the emergence of reasoning models like OpenAI-o1 and DeepSeek-R1. Recent research focuses on integrating reasoning capabilities into the realm of retrieval-augmented generation (RAG) via outcome-supervised reinforcement learning (RL) approaches, while the correctness of intermediate think-and-search steps is usually neglected. To address this issue, we design a process-level reward module to mitigate the unawareness of intermediate reasoning steps in outcome-level supervision without additional annotation. Grounded on this, we propose **Le**arning to **T**hink-and-**S**earch (**LeTS**), a novel framework that hybridizes stepwise process reward and outcome-based reward to current RL methods for RAG. Extensive experiments demonstrate the generalization and inference efficiency of **LeTS** across various RAG benchmarks. In addition, these results reveal the potential of process- and outcome-level reward hybridization in boosting LLMs’ reasoning ability via RL under other scenarios.
CYCLE-INSTRUCT: Fully Seed-Free Instruction Tuning via Dual Self-Training and Cycle Consistency
Authors: Zhanming Shen; Hao Chen; Yulei Tang; Shaolin Zhu; Wentao Ye; Xiaomeng Hu; Haobo Wang; Gang Chen; Junbo Zhao
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Instruction tuning is vital for aligning large language models (LLMs) with human intent, but current methods typically rely on costly human-annotated seed data or powerful external teacher models. While instruction back-translation techniques reduce this dependency, they remain fundamentally tethered to an initial seed set, which limits full automation, introduces biases, and can lead to inefficient use of unlabeled corpora. In this paper, we propose Cycle-Instruct, a novel framework that achieves fully seed-free instruction tuning. Inspired by cycle consistency, Cycle-Instruct employs a dual self-training loop where two models—an answer generator and a question generator—are bootstrapped solely from raw, unlabeled text. These models mutually supervise each other by reconstructing original text segments from their counterpart’s generated pseudo-labels, effectively learning from the intrinsic structure of the data without any human-provided seeds. We demonstrate Cycle-Instruct’s efficacy across four diverse data tracks, including general instruction-following, domain-specific tasks, dialogue logs, and plain text. Our extensive experiments show that Cycle-Instruct not only outperforms seed-driven back-translation baselines but also achieves performance comparable to strongly supervised methods.
Good Intentions Beyond ACL: Who Does NLP for Social Good, and Where?
Authors: Grace LeFevre; Qingcheng Zeng; Adam Leif; Jason Jewell; Denis Peskoff; Rob Voigt
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: The social impact of Natural Language Processing (NLP) is increasingly important, with a rising community focus on initiatives related to NLP for Social Good (NLP4SG). Indeed, in recent years, almost 20% of all papers in the ACL Anthology address topics related to social good as defined by the UN Sustainable Development Goals (Aduato et al. 2023). In this study, we take an author- and venue-level perspective to map the landscape of NLP4SG, quantifying the proportion of work addressing social good concerns both within and beyond the ACL community, by both core ACL contributors and non-ACL authors. With this approach we discover two surprising facts about the landscape of NLP4SG. First, ACL authors are dramatically more likely to do work addressing social good concerns when publishing in venues outside of ACL. Second, the vast majority of publications using NLP techniques to address concerns of social good are done by non-ACL authors in venues outside of ACL. We discuss the implications of these findings on agenda-setting considerations for the ACL community related to NLP4SG.
From General Reward to Targeted Reward: Improving Open-ended Long-context Generation Models
Authors: Zhihan Guo; Jiele Wu; Wenqian Cui; Yifei Zhang; Minda Hu; Yufei Wang; Irwin King
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Current research on long-form context in Large Language Models (LLMs) primarily focuses on the understanding of long-contexts, the **Open-ended Long Text Generation** (Open-LTG) remains insufficiently explored. Training a long text generation model requires curation of gold-standard reference data, which is typically nonexistent for informative Open-LTG tasks. However, previous methods only utilize general assessments as reward signals, which limits accuracy. To bridge this gap, we introduce **ProxyReward**, an innovative reinforcement learning (RL) based framework, which includes a data synthesis method and a novel reward signal. Firstly, **ProxyReward Dataset** synthesis is accomplished through simple prompts that enables the model to create automatically, obviating extensive labeled data or significant manual effort. Secondly, **ProxyReward Signal** offers a targeted evaluation of information comprehensiveness and accuracy for specific questions. The experimental results indicate that our method ProxyReward **surpasses even GPT-4-Turbo**. It can significantly enhance performance by 20% on the Open-LTG task when training widely used open-source models, while also surpassing the LLM-as-a-Judge approach. Our work presents effective methods to enhance the ability of LLMs to address complex open-ended questions posed by humans.
Think in Safety: Unveiling and Mitigating Safety Alignment Collapse in Multimodal Large Reasoning Model
Authors: Xinyue Lou; You Li; Jinan Xu; Xiangyu Shi; Chi Chen; Kaiyu Huang
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: The rapid development of Multimodal Large Reasoning Models (MLRMs) has demonstrated broad application potential, yet their safety and reliability remain critical concerns that require systematic exploration. To address this gap, we conduct a comprehensive and systematic safety evaluation of 13 MLRMs across 5 benchmarks and unveil prevalent safety degradation phenomena in most advanced models. Moreover, our analysis reveals distinct safety patterns across different benchmarks: significant safety degradation is observed across jailbreak robustness benchmarks, whereas safety-awareness benchmarks demonstrate less pronounced degradation. In particular, the long thought process in some scenarios even enhances safety performance. Therefore, it is a potential approach to address safety issues in MLRMs by leveraging the intrinsic reasoning capabilities of the model to detect unsafe intent. To operationalize this insight, we construct a multimodal tuning dataset that incorporates a safety-oriented thought process. Experimental results from fine-tuning existing MLRMs with this dataset effectively enhance the safety on both jailbreak robustness and safety-awareness benchmarks. This study provides a new perspective for developing safe MLRMs.
Understanding the Modality Gap: An Empirical Study on the Speech-Text Alignment Mechanism of Large Speech Language Models
Authors: Bajian Xiang; Shuaijiang Zhao; Tingwei Guo; Wei Zou
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: End-to-end Large Speech Language Models (LSLMs) have demonstrated impressive conversational generation abilities, yet consistently fall short of traditional pipeline systems on semantic understanding benchmarks. In this work, we reveal through systematic experimentation that although LSLMs lose some text input performance after speech-text alignment training, the performance gap between speech and text inputs is more pronounced, which we refer to as the modality gap. To understand this gap, we analyze both coarse- and fine-grained text and speech representations. At the coarse-grained level, representations of speech and text in deeper layers are found to be increasingly aligned in direction (cosine similarity), while concurrently diverging in magnitude (Euclidean distance). We further find that representation similarity is strongly correlated with the modality gap. At the fine-grained level, a spontaneous token-level alignment pattern between text and speech representations is observed. Based on this, we introduce the Alignment Path Score to quantify token-level alignment quality, which exhibits stronger correlation with the modality gap. Building on these insights, we design targeted interventions on critical tokens through angle projection and length normalization. These strategies demonstrate the potential to improve correctness for speech inputs. Our study provides the first systematic empirical analysis of the modality gap and alignment mechanisms in LSLMs, offering both theoretical and methodological guidance for future optimization.
AssoCiAm: A Benchmark for Evaluating Association Thinking while Circumventing Ambiguity
Authors: Yifan Liu; Wenkuan Zhao; Shanshan Zhong; Jinghui Qin; Mingfu Liang; Zhongzhan Huang; Wushao Wen
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Recent advancements in multimodal large language models (MLLMs) have garnered significant attention, offering a promising pathway toward artificial general intelligence (AGI). Among the essential capabilities required for AGI, creativity has emerged as a critical trait for MLLMs, with association serving as its foundation. Association reflects a model’s ability to think creatively, making it vital to evaluate and understand. While several frameworks have been proposed to assess associative ability, they often overlook the inherent ambiguity in association tasks, which arises from the divergent nature of associations and undermines the reliability of evaluations. To address this issue, we decompose ambiguity into two types—internal ambiguity and external ambiguity—and introduce AssoCiAm, a benchmark designed to evaluate associative ability while circumventing the ambiguity through a hybrid computational method. We then conduct extensive experiments on MLLMs, revealing a strong positive correlation between cognition and association. Additionally, we observe that the presence of ambiguity in the evaluation process causes MLLMs’ behavior to become more random-like. Finally, we validate the effectiveness of our method in ensuring more accurate and reliable evaluations. See Project Page for the data and codes.
M-BRe: Discovering Training Samples for Relation Extraction from Unlabeled Texts with Large Language Models
Authors: Zexuan Li; Hongliang Dai; Piji Li
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: For Relation Extraction (RE), the manual annotation of training data may be prohibitively expensive, since the sentences that contain the target relations in texts can be very scarce and difficult to find. It is therefore beneficial to develop an efficient method that can automatically extract training instances from unlabeled texts for training RE models. Recently, large language models (LLMs) have been adopted in various natural language processing tasks, with RE also benefiting from their advances. However, when leveraging LLMs for RE with predefined relation categories, two key challenges arise. First, in a multi-class classification setting, LLMs often struggle to comprehensively capture the semantics of every relation, leading to suboptimal results. Second, although employing binary classification for each relation individually can mitigate this issue, it introduces significant computational overhead, resulting in impractical time complexity for real-world applications. Therefore, this paper proposes a framework called M-BRe to extract training instances from unlabeled texts for RE. It utilizes three modules to combine the advantages of both of the above classification approaches: Relation Grouping, Relation Extraction, and Label Decision. Extensive experiments confirm its superior capability in discovering high-quality training samples from unlabeled texts for RE.
R-TOFU: Unlearning in Large Reasoning Models
Authors: Sangyeon Yoon; Wonje Jeung; Albert No
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Large Reasoning Models (LRMs) embed private or copyrighted information not only in their final answers but also throughout multi-step chain-of-thought (CoT) traces, making reliable unlearning far more demanding than in standard LLMs. We introduce Reasoning-TOFU (R-TOFU), the first benchmark tailored to this setting. R-TOFU augments existing unlearning tasks with realistic CoT annotations and provides step-wise metrics that expose residual knowledge invisible to answer-level checks. Using R-TOFU, we carry out a comprehensive comparison of gradient-based and preference-optimization baselines and show that conventional answer-only objectives leave substantial forget traces in reasoning. We further propose Reasoned IDK, a preference-optimization variant that preserves coherent yet inconclusive reasoning, achieving a stronger balance between forgetting efficacy and model utility than earlier refusal styles. Finally, we identify a failure mode: decoding variants such as ZeroThink and LessThink can still reveal forgotten content despite seemingly successful unlearning, emphasizing the need to evaluate models under diverse decoding settings. Together, the benchmark, analysis, and new baseline establish a systematic foundation for studying and improving unlearning in LRMs while preserving their reasoning capabilities.
Chat-Driven Text Generation and Interaction for Person Retrieval
Authors: Zequn Xie; Chuxin Wang; Yeqiang Wang; Sihang Cai; Shulei Wang; Tao Jin
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Text-based person search (TBPS) enables the retrieval of person images from large-scale databases using natural language descriptions, offering critical value in surveillance applications. However, a major challenge lies in the labor-intensive process of obtaining high-quality textual annotations, which limits scalability and practical deployment. To address this, we introduce two complementary modules: Multi-Turn Text Generation (MTG) and Multi-Turn Text Interaction (MTI). MTG generates rich pseudo-labels through simulated dialogues with MLLMs, producing fine-grained and diverse visual descriptions without manual supervision. MTI refines user queries at inference time through dynamic, dialogue-based reasoning, enabling the system to interpret and resolve vague, incomplete, or ambiguous descriptions—characteristics often seen in real-world search scenarios. Together, MTG and MTI form a unified and annotation-free framework that significantly improves retrieval accuracy, robustness, and usability. Extensive evaluations demonstrate that our method achieves competitive or superior results while eliminating the need for manual captions, paving the way for scalable and practical deployment of TBPS systems.
Spontaneous Giving and Calculated Greed in Language Models
Authors: Yuxuan Li; Hirokazu Shirado
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Large language models demonstrate strong problem-solving abilities through reasoning techniques such as chain-of-thought prompting and reflection. However, it remains unclear whether these reasoning capabilities extend to a form of social intelligence: making effective decisions in cooperative contexts. We examine this question using economic games that simulate social dilemmas. First, we apply chain-of-thought and reflection prompting to GPT-4o in a Public Goods Game. We then evaluate multiple off-the-shelf models across six cooperation and punishment games, comparing those with and without explicit reasoning mechanisms. We find that reasoning models consistently reduce cooperation and norm enforcement, favoring individual rationality. In repeated interactions, groups with more reasoning agents exhibit lower collective gains. These behaviors mirror human patterns of “spontaneous giving and calculated greed.” Our findings underscore the need for LLM architectures that incorporate social intelligence alongside reasoning, to help address—rather than reinforce—the challenges of collective action.
SenDetEX: Sentence-Level AI-Generated Text Detection for Human-AI Hybrid Content via Style and Context Fusion
Authors: Lei Jiang; Desheng Wu; Xiaolong Zheng
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

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