GenAI4ELab/papercli-papers huggingface dataset:
Reinforced Query Reasoners for Reasoning-intensive Retrieval Tasks
Authors: Xubo Qin; Jun Bai; Jiaqi Li; Zixia Jia; Zilong Zheng
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Traditional information retrieval (IR) methods excel at textual and semantic matching but struggle in reasoning-intensive retrieval tasks that require multi-hop inference or complex semantic understanding between queries and documents. One promising solution is to explicitly rewrite or augment queries using large language models (LLMs) to elicit reasoning-relevant content prior to retrieval. However, the widespread use of large-scale LLMs like GPT-4 or LLaMA3-70B remains impractical due to their high inference cost and limited deployability in real-world systems. In this work, we introduce Reinforced Query Reasoner (RQR), a family of small-scale language models for query reasoning and rewriting in reasoning-intensive retrieval. Our approach frames query reformulation as a reinforcement learning problem and employs a novel semi-rule-based reward function. This enables smaller language models, e.g., Qwen2.5-7B-Instruct and Qwen2.5-1.5B-Instruct, to achieve reasoning performance rivaling large-scale LLMs without their prohibitive inference costs. Experiment results on BRIGHT benchmark show that, with BM25 as retrievers, both RQR-7B and RQR-1.5B models significantly outperform existing baselines, including prompt-based query reasoners and some latest dense retrievers trained for reasoning-intensive retrieval tasks, offering superior adaptability for real-world deployment. All code and dataset will be publicly released.
TokenSelect: Efficient Long-Context Inference and Length Extrapolation for LLMs via Dynamic Token-Level KV Cache Selection
Authors: Wei Wu; Zhuoshi Pan; Kun Fu; Chao Wang; Liyi Chen; Yunchu Bai; Tianfu Wang; Zheng Wang; Hui Xiong
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Rapid advances in Large Language Models (LLMs) have spurred demand for processing extended context sequences in contemporary applications. However, this progress faces two challenges: performance degradation due to sequence lengths out-of-distribution, and excessively long inference times caused by the quadratic computational complexity of attention. These issues limit LLMs in long-context scenarios. In this paper, we propose Dynamic Token-Level KV Cache Selection (*TokenSelect*), a training-free method for efficient and accurate long-context inference. *TokenSelect* builds upon the observation of non-contiguous attention sparsity, using QK dot products to measure per-head KV Cache criticality at token-level. By per-head soft voting mechanism, *TokenSelect* selectively involves a few critical KV cache tokens in attention calculation without sacrificing accuracy. To further accelerate *TokenSelect*, we design the Selection Cache based on observations of consecutive Query similarity and implemented the efficient Paged Dot Product Kernel, significantly reducing the selection overhead. A comprehensive evaluation of *TokenSelect* demonstrates up to 23.84× speedup in attention computation and up to 2.28× acceleration in end-to-end latency, while providing superior performance compared to state-of-the-art long-context inference methods.
MUSE: MCTS-Driven Red Teaming Framework for Enhanced Multi-Turn Dialogue Safety in Large Language Models
Authors: Siyu Yan; Long Zeng; Xuecheng Wu; Chengcheng Han; Kongcheng Zhang; Chong Peng; Xuezhi Cao; Xunliang Cai; Chenjuan Guo
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: As large language models (LLMs) become widely adopted, ensuring their alignment with human values is crucial to prevent jailbreaks where adversaries manipulate models to produce harmful content. While most defenses target single-turn attacks, real-world usage often involves multi-turn dialogues, exposing models to attacks that exploit conversational context to bypass safety measures. We introduce MUSE, a comprehensive framework tackling multi-turn jailbreaks from both attack and defense angles. For attacks, we propose MUSE-A, a method that uses frame semantics and heuristic tree search to explore diverse semantic trajectories. For defense, we present MUSE-D, a fine-grained safety alignment approach that intervenes early in dialogues to reduce vulnerabilities. Extensive experiments on various models show that MUSE effectively identifies and mitigates multi-turn vulnerabilities. Code is available at https://anonymous.4open.science/r/MUSE-75F7.
EnAnchored-X2X: English-Anchored Optimization for Many-to-Many Translation
Authors: Sen Yang; Yu Bao; Yu Lu; Jiajun Chen; Shujian Huang; Shanbo Cheng
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Large language models (LLMs) have demonstrated strong machine translation capabilities for English-centric language pairs but underperform in direct non-English (x2x) translation. This work addresses this limitation through a synthetic data generation framework that leverages models’ established English-to-x (en2x) capabilities. By extending English parallel corpora into omnidirectional datasets and developing an English-referenced quality evaluation proxy, we enable effective collection of high-quality x2x training data. Combined with preference-based optimization, our method achieves significant improvement across 72 x2x directions for widely used LLMs, while generalizing to enhance en2x performance. The results demonstrate that strategic exploitation of English-centric strengths can bootstrap comprehensive multilingual translation capabilities in LLMs.
“I’ve Decided to Leak”: Probing Internals Behind Prompt Leakage Intents
Authors: Jianshuo Dong; Yutong Zhang; Liu Yan; Zhenyu Zhong; Tao Wei; Ke Xu; Minlie Huang; Chao Zhang; Han Qiu
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Large language models (LLMs) exhibit prompt leakage vulnerabilities, where they may be coaxed into revealing system prompts embedded in LLM services, raising intellectual property and confidentiality concerns. An intriguing question arises: Do LLMs genuinely internalize prompt leakage intents in their hidden states before generating tokens? In this work, we use probing techniques to capture LLMs’ intent-related internal representations and confirm that the answer is yes. We start by comprehensively inducing prompt leakage behaviors across diverse system prompts, attack queries, and decoding methods. We develop a hybrid labeling pipeline, enabling the identification of broader prompt leakage behaviors beyond mere verbatim leaks. Our results show that a simple linear probe can predict prompt leakage risks from pre-generation hidden states without generating any tokens. Across all tested models, linear probes consistently achieve 90%+ AUROC, even when applied to new system prompts and attacks. Understanding the model internals behind prompt leakage drives practical applications, including intention-based detection of prompt leakage risks. Code is available at: https://github.com/jianshuod/Probing-leak-intents.
Nullspace Disentanglement for Red Teaming Language Models
Authors: Yi Han; Yuanxing Liu; Weinan Zhang; Ting Liu
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: With the widespread deployment of generative language models, concerns about safety issues have continuously grown. High-quality fine-tuning data generated from red teaming plays a crucial role in the model’s safety. Recently, automated red teaming approaches have been proposed to create test cases. However, these approaches, which rely on open-ended generation, encounter issues related to inefficiency and low attack success rates. In this work, we introduce a black-box approach that ingeniously exploits the unique properties of the nullspace to disentangle and regulate the crucial success information within test cases. Our study provides a brand-new perspective for automated red team research. Experimental results demonstrate that our approach outperforms baseline methods regarding the attack success rate. The generated test cases also excel in aspects of diversity and fluency.
Supervised Attention Mechanism for Low-quality Multimodal Data
Authors: Sijie Mai; Shiqin Han; Haifeng Hu
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: In practical applications, multimodal data are often of low quality, with noisy modalities and missing modalities being typical forms that severely hinder model performance, robustness, and applicability. However, current studies address these issues separately. To this end, we propose a framework for multimodal affective computing that jointly addresses missing and noisy modalities to enhance model robustness in low-quality data scenarios. Specifically, we view missing modality as a special case of noisy modality, and propose a supervised attention framework. In contrast to traditional attention mechanisms that rely on main task loss to update the parameters, we design supervisory signals for the learning of attention weights, ensuring that attention mechanisms can focus on discriminative information and suppress noisy information. We further propose a ranking-based optimization strategy to compare the relative importance of different interactions by adding a ranking constraint for attention weights, avoiding training noise caused by inaccurate absolute labels. The proposed model consistently outperforms state-of-the-art baselines on multiple datasets under the settings of complete modalities, missing modalities, and noisy modalities.
Reinforcement Learning for Large Language Models via Group Preference Reward Shaping
Authors: Huaisheng Zhu; Siyuan Xu; Hangfan Zhang; Teng Xiao; Zhimeng Guo; Shijie Zhou; Shuyue Hu; Vasant G. Honavar
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Large Language Models (LLMs) require alignment via reinforcement learning (RL) to effectively perform task-specific objectives, such as human preference alignment and enhanced reasoning. While Proximal Policy Optimization (PPO) is widely adopted, its computational overhead, stemming from additional value model requirements, limits applicability. Existing alternatives, like Group Relative Policy Optimization (GRPO), mitigate computational costs but remain sensitive to reward model quality. To address this, we introduce Group Preference Reward Shaping (GPRS), a novel method that leverages preference-based comparisons rather than precise numerical rewards. GPRS requires no extra model components and remains robust across varying reward model sizes and qualities. Extensive experiments demonstrate that GPRS consistently outperforms existing critic-model-free RL algorithms in Reinforcement Learning from Human Feedback (RLHF) and reasoning tasks, providing stable and good alignment performance.
zFLoRA: Zero-Latency Fused Low-Rank Adapters
Authors: Dhananjaya Gowda; Seoha Song; Harshith Goka; Junhyun Lee
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Large language models (LLMs) are increasingly deployed with task-specific adapters catering to multiple downstream applications. In such a scenario, the additional compute associated with these apparently insignificant number of adapter parameters (typically less than 1% of the base model) turns out to be disproportionately significant during inference time (up to 2.5x times that of the base model). In this paper, we propose a new zero-latency fused low-rank adapter (zFLoRA) that introduces zero or negligible latency overhead on top of the base model. Experimental results on LLMs of size 1B, 3B and 7B show that zFLoRA compares favorably against the popular supervised fine-tuning benchmarks including low-rank adapters (LoRA) as well as full fine-tuning (FFT). Experiments are conducted on 18 different tasks across three different categories namely commonsense reasoning, math reasoning and summary-dialogue. Latency measurements made on NPU (Samsung Galaxy S25+) as well as GPU (NVIDIA H100) platforms show that the proposed zFLoRA adapters introduce zero to negligible latency overhead.
PLAN-TUNING: Post-Training Language Models to Learn Step-by-Step Planning for Complex Problem Solving
Authors: Mihir Parmar; Palash Goyal; Xin Liu; Yiwen Song; Mingyang Ling; Chitta Baral; Hamid Palangi; Tomas Pfister
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Recently, decomposing complex problems into simple subtasks–a crucial part of human-like natural planning–to solve the given problem has significantly boosted the performance of large language models (LLMs). However, leveraging such planning structures during post-training to boost the performance of smaller open-source LLMs remains underexplored. Motivated by this, we introduce PLAN-TUNING, a unified post-training framework that (i) distills synthetic task decompositions (termed “planning trajectories”) from large-scale LLMs and (ii) fine-tunes smaller models via supervised and reinforcement-learning objectives designed to mimic these planning processes to improve complex reasoning. On GSM8k and the MATH benchmarks, plan-tuned models outperform strong baselines by an average ~7%. Furthermore, plan-tuned models show better generalization capabilities on out-of-domain datasets, with average ~10% and ~12% performance improvements on OlympiadBench and AIME 2024, respectively. Our detailed analysis demonstrates how planning trajectories improves complex reasoning capabilities, showing that PLAN-TUNING is an effective strategy for improving task-specific performance of smaller LLMs.
Semantic Inversion, Identical Replies: Revisiting Negation Blindness in Large Language Models
Authors: Jinsung Kim; Seonmin Koo; Heuiseok Lim
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Large language models (LLMs) often fail to capture semantic changes in queries due to negation, and generate incorrect responses. Negation frequently exists in the real world and is useful for understanding the opposite or absence of a statement, so it is an essential element in logical reasoning. Previous studies have explored LLMs’ ability to capture negations ‘separately’ from their ability to properly ground knowledge for positive queries. However, this perspective is limited in that it cannot clearly distinguish whether the cause of incorrect responses is the logical incoherence caused by negations or the lack of grounding ability for the given context. To address this issue, we focus on the phenomenon of the model failing to capture semantic contradictions in negated queries despite its accurate understanding of knowledge about positive queries. We term this phenomenon negation blindness on the query. We propose a verification framework that includes task design and measurement methods to verify this issue. In detail, we establish two criteria for systematic task design–i) ‘complexity’ and ii) ‘constrainedness’–and devise four verification tasks accordingly. Moreover, we analyze the results extensively and provide insights into problem alleviation feasibility through experiments on various approaches. Our code and resources can be found at https://www.github.com/jin62304/NegationBlindness.
AMACE: Automatic Multi-Agent Chart Evolution for Iteratively Tailored Chart Generation
Authors: Hyuk Namgoong; Jeesu Jung; Hyeonseok Kang; Yohan Lee; Sangkeun Jung
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Many statistical facts are conveyed through charts. While various methods have emerged for chart understanding, chart generation typically requires users to manually input code, intent, and other parameters to obtain the desired format on chart generation tools. Recently, the advent of image-generating Large Language Models has facilitated chart generation; however, even this process often requires users to provide numerous constraints for accurate results. In this paper, we propose a loop-based framework for automatically evolving charts in a multi-agent environment. Within this framework, three distinct agents—Chart Code Generator, Chart Replier, and Chart Quality Evaluator—collaborate for iterative, user-tailored chart generation using large language models. Our approach demonstrates an improvement of up to 29.97% in performance compared to first generation, while also reducing generation time by up to 86.9% compared to manual prompt-based methods, showcasing the effectiveness of this multi-agent collaboration in enhancing the quality and efficiency of chart generation.
ActionStudio: A Lightweight Framework for Data and Training of Large Action Models
Authors: Jianguo Zhang; Thai Hoang; Ming Zhu; Zuxin Liu; Shiyu Wang; Tulika Awalgaonkar; Akshara Prabhakar; Haolin Chen; Weiran Yao; Zhiwei Liu; Juntao Tan; Juan Carlos Niebles; Shelby Heinecke; Huan Wang; Silvio Savarese; Caiming Xiong
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Large Action models are essential for enabling autonomous agents to perform complex tasks. However, training such models remains challenging due to the diversity of agent environments and the complexity of noisy agentic data. Existing infrastructure offers limited support for scalable, agent-specific fine-tuning and standardized agent data processing. We introduce ActionStudio, a lightweight and extensible data and training framework designed for large action models. ActionStudio unifies diverse agent trajectories using our proposed Unified Format 2.0, supports a range of training workflows with optimized multi-node distributed setup, and integrates robust preprocessing and real-time verification tools. ActionStudio demonstrates up to 9× higher throughput compared to existing agentic training frameworks, and our trained models yield top performances across public and realistic agent benchmarks. To support the broader research community, we open-source the ActionStudio framework and release actionstudio-98k, a curated dataset of 98k high-quality trajectories.
Interpretation Meets Safety: A Survey on Interpretation Methods and Tools for Improving LLM Safety
Authors: Seongmin Lee; Aeree Cho; Grace C. Kim; ShengYun Peng; Mansi Phute; Duen Horng Chau
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: As large language models (LLMs) see wider real-world use, understanding and mitigating their unsafe behaviors is critical. Interpretation techniques can reveal causes of unsafe outputs and guide safety, but such connections with safety are often overlooked in prior surveys. We present the first survey that bridges this gap, introducing a unified framework that connects safety-focused interpretation methods, the safety enhancements they inform, and the tools that operationalize them. Our novel taxonomy, organized by LLM workflow stages, summarizes nearly 70 works at their intersections. We conclude with open challenges and future directions. This timely survey helps researchers and practitioners navigate key advancements for safer, more interpretable LLMs.
Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens
Authors: Sohee Kim; Soohyun Ryu; Joonhyung Park; Eunho Yang
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Large Vision-Language Models (LVLMs) generate contextually relevant responses by jointly interpreting visual and textual inputs. However, our finding reveals they often mistakenly perceive text inputs lacking visual evidence as being part of the image, leading to erroneous responses. In light of this finding, we probe whether LVLMs possess an internal capability to determine if textual concepts are grounded in the image, and discover a specific subset of Feed-Forward Network (FFN) neurons, termed Visual Absence-aware (VA) neurons, that consistently signal the visual absence through a distinctive activation pattern. Leveraging these patterns, we develop a detection module that systematically classifies whether an input token is visually grounded. Guided by its prediction, we propose a method to refine the outputs by reinterpreting question prompts or replacing the detected absent tokens during generation. Extensive experiments show that our method effectively mitigates the models’ tendency to falsely presume the visual presence of text input and its generality across various LVLMs.
Improving Task Diversity in Label Efficient Supervised Finetuning of LLMs
Authors: Abhinav Arabelly; Jagrut Nemade; Robert D Nowak; Jifan Zhang
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse domains, but developing high-performing models for specialized applications often requires substantial human annotation — a process that is time-consuming, labor-intensive, and expensive. In this paper, we address the label-efficient learning problem for supervised finetuning (SFT) by leveraging task-diversity as a fundamental principle for effective data selection. This is markedly different from existing methods based on the prompt-diversity. Our approach is based on two key observations: 1) task labels for different prompts are often readily available; 2) pre-trained models have significantly varying levels of confidence across tasks. We combine these facts to devise a simple yet effective sampling strategy: we select examples across tasks using an inverse confidence weighting strategy. This produces models comparable to or better than those trained with more complex sampling procedures, while being significantly easier to implement and less computationally intensive. Notably, our experimental results demonstrate that this method can achieve better accuracy than training on the complete dataset (a 4% increase in MMLU score). Across various annotation budgets and two instruction finetuning datasets, our algorithm consistently performs at or above the level of the best existing methods, while reducing annotation costs by up to 80%.
Look Beyond Feeling: Unveiling Latent Needs from Implicit Expressions for Proactive Emotional Support
Authors: Xing Fu; Haozhen Li; Bichen Wang; Hao Yang; Yanyan Zhao; Bing Qin
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: In recent years, Large Language Models (LLMs) have made significant progress in emotional support dialogue. However, there are two major challenges for LLM-based support systems. First, users may be hesitant to fully disclose their emotions at the outset. Second, direct probing or excessive questioning can induce discomfort or even resistance. To bridge this gap, we propose COCOON, a proactive emotional support framework that leverages principles of active listening to uncover implicit user needs. We design a multi-stage data curation pipeline and an annotation mechanism for support strategies. Based on this framework, we build COCOON-Llama3, a fine-tuned large language model, and evaluate it using both standard metrics and psychological scales. Experimental results indicate that our model more effectively elicits implicit emotional needs and delivers empathetic support compared to existing baselines, suggesting its utility for building more inclusive emotional support dialogue systems.