GenAI4ELab/papercli-papers huggingface dataset:
Answering Narrative-Driven Recommendation Queries via a Retrieve–Rank Paradigm and the OCG-Agent
Authors: Yunxiao Shi; Haoning Shang; Xing Zi; Wujiang Xu; Yue Feng; Min Xu
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Narrative-driven recommendation queries are common in question-answering platforms, AI search engines, social forums, and some domain-specific vertical applications. Users typically submit free-form text requests for recommendations, e.g., “Any mind-bending thrillers like Shutter Island you’d recommend?” Such special queries have traditionally been addressed as generic QA task under the RAG paradigm. This work formally introduces narrative recommendation as a distinct task and contends that the RAG paradigm is inherently ill-suited for it, owing to information loss in LLMs when retrieving information from from multiple long and fragmented contexts, and limitations in ranking effectiveness. To overcome these limitations, we propose a novel retrieve-rank paradigm by theoretically demonstrating its superiority over RAG paradigm. Central to this new paradigm, we specially focus on the information retrieval stage and introduce Open-domain Candidate Generation (OCG)-Agent that generatively retrieves structurally adaptive and semantically aligned candidates, ensuring both extensive candidate coverage and high-quality information. We validate effectiveness of new paradigm and OCG-Agent’s retrieve mechanism under real-world datasets from Reddit and corporate education-consulting scenarios. Further extensive ablation studies confirming the rationality of each OCG-Agent component.
Direct Value Optimization: Improving Chain-of-Thought Reasoning in LLMs with Refined Values
Authors: Hongbo Zhang; Han Cui; Guangsheng Bao; Linyi Yang; Jun Wang; Yue Zhang
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: We introduce Direct Value Optimization (DVO), an innovative offline reinforcement learning framework for enhancing large language models in complex reasoning tasks. Unlike traditional methods relying on preference labels, DVO utilizes value signals at individual reasoning steps, optimizing models via a mean squared error loss. The key benefit of DVO lies in its fine-grained supervision, circumventing the need for labor-intensive human annotations. Target values within the DVO are estimated using either Monte Carlo Tree Search or an outcome value model. Our empirical analysis on 3 math reasoning, 4 commonsense reasoning, and 3 coding tasks shows that DVO consistently outperforms existing offline preference optimization techniques by a significant margin of 4% to 6%, and is competitive to online GRPO but with higher sample efficiency. These findings underscore the importance of value signals in advancing reasoning capabilities and highlight DVO as a superior methodology under scenarios lacking explicit human preference information.
Jailbreak-Tuning: Models Efficiently Learn Jailbreak Susceptibility
Authors: Brendan Murphy; Dillon Bowen; Shahrad Mohammadzadeh; Tom Tseng; Julius Broomfield; Adam Gleave; Kellin Pelrine
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: AI systems are rapidly advancing in capability, and frontier model developers broadly acknowledge the need for safeguards against serious misuse. However, this paper demonstrates that fine-tuning, whether via open weights or closed fine-tuning APIs, can produce helpful-only models with safeguards destroyed. In contrast to prior work which is blocked by modern moderation systems or achieved only partial removal of safeguards or degraded output quality, our jailbreak-tuning method teaches models to generate detailed, high-quality responses to arbitrary harmful requests. For example, OpenAI, Google, and Anthropic models will fully comply with requests for CBRN assistance, executing cyberattacks, and other criminal activity. We further show that backdoors can increase not only the stealth but also the severity of attacks. Stronger jailbreak prompts become even more effective in fine-tuning attacks, linking attacks and potentially defenses in the input and weight spaces. Not only are current models vulnerable, more recent ones also appear to be becoming even more vulnerable to these attacks, underscoring the urgent need for tamper-resistant safeguards. Until such safeguards are discovered, companies and policymakers should view the release of any fine-tunable model as simultaneously releasing its evil twin: equally capable as the original model, and usable for any malicious purpose within its capabilities.
Neural Topic Modeling via Contextual and Graph Information Fusion
Authors: Jiyuan Liu; Jiaxing Yan; Chunjiang Zhu; Xingyu Liu; Li Qing; Yanghui Rao
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Topic modeling is a powerful unsupervised tool for knowledge discovery. However, existing work struggles with generating limited-quality topics that are uninformative and incoherent, which hindering interpretable insights from managing textual data. In this paper, we improve the original variational autoencoder framework by incorporating contextual and graph information to address the above issues. First, the encoder utilizes topic fusion techniques to combine contextual and bag-of-words information well, and meanwhile exploits the constraints of topic alignment and topic sharpening to generate informative topics. Second, we develop a simple word co-occurrence graph information fusion strategy that efficiently increases topic coherence. On three benchmark datasets, our new framework generates more coherent and diverse topics compared to various baselines, and achieves strong performance on both automatic and manual evaluations.
CARE: A Disagreement Detection Framework with Concept Alignment and Reasoning Enhancement
Authors: Jiyuan Liu; Jielin Song; Yunhe Pang; Zhiyu Shen; Yanghui Rao
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Disagreement detection is a crucial task in natural language processing (NLP), particularly in analyzing online discussions and social media content. Large language models (LLMs) have demonstrated significant advancements across various NLP tasks. However, the performance of LLM in disagreement detection is limited by two issues: *conceptual gap* and *reasoning gap*. In this paper, we propose a novel two-stage framework, Concept Alignment and Reasoning Enhancement (CARE), to tackle the issues. The first stage, Concept Alignment, addresses the gap between expert and model by performing **sub-concept taxonomy extraction**, aligning the model’s comprehension with human experts. The second stage, Reasoning Enhancement, improves the model’s reasoning capabilities by introducing curriculum learning workflow, which includes **rationale to critique** and **counterfactual to detection** for reducing spurious association. Extensive experiments on disagreement detection task demonstrate the effectiveness of our framework, showing superior performance in zero-shot and supervised learning settings, both within and across domains.
Beyond Task-Oriented and Chitchat Dialogues: Proactive and Transition-Aware Conversational Agents
Authors: Yejin Yoon; Yuri Son; Namyoung So; Minseo Kim; Minsoo Cho; Chanhee Park; Seungshin Lee; Taeuk Kim
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Conversational agents have traditionally been developed for either task-oriented dialogue (TOD) or open-ended chitchat, with limited progress in unifying the two. Yet, real-world conversations naturally involve fluid transitions between these modes. To address this gap, we introduce TACT (TOD-And-Chitchat Transition), a dataset designed for transition-aware dialogue modeling that incorporates structurally diverse and integrated mode flows. TACT supports both user- and agent-driven mode switches, enabling robust modeling of complex conversational dynamics.To evaluate an agent’s ability to initiate and recover from mode transitions, we propose two new metrics—Switch and Recovery.Models trained on TACT outperform baselines in both intent detection and mode transition handling. Moreover, applying Direct Preference Optimization (DPO) to TACT-trained models yields additionalgains, achieving 75.74% joint mode-intent accuracy and a 70.1% win rate against GPT-4o in human evaluation.These results demonstrate that pairing structurally diverse data with DPO enhances response quality and transition control, paving the way for more proactive and transition-aware conversational agents.
LightThinker: Thinking Step-by-Step Compression
Authors: Jintian Zhang; Yuqi Zhu; Mengshu Sun; Yujie Luo; Shuofei Qiao; Lun Du; Da Zheng; Huajun Chen; Ningyu Zhang
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Large language models (LLMs) have shown remarkable performance in complex reasoning tasks, but their efficiency is hindered by the substantial memory and computational costs associated with generating lengthy tokens. In this paper, we propose LightThinker, a novel method that enables LLMs to dynamically compress intermediate thoughts during reasoning. Inspired by human cognitive processes, LightThinker compresses verbose thought steps into compact representations and discards the original reasoning chains, thereby significantly reducing the number of tokens stored in the context window.This is achieved by training the model on when and how to perform compression through data construction, mapping hidden states to condensed gist tokens, and creating specialized attention masks. Additionally, we introduce the Dependency (Dep) metric to quantify the degree of compression by measuring the reliance on historical tokens during generation. Extensive experiments on four datasets and two models show that LightThinker reduces peak memory usage and inference time, while maintaining competitive accuracy. Our work provides a new direction for improving the efficiency of LLMs in complex reasoning tasks without sacrificing performance.
How Is LLM Reasoning Distracted by Irrelevant Context? An Analysis Using a Controlled Benchmark
Authors: Minglai Yang; Ethan Huang; Liang Zhang; Mihai Surdeanu; William Yang Wang; Liangming Pan
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: We introduce Grade School Math with Distracting Context (GSM-DC), a synthetic benchmark to evaluate Large Language Models’ (LLMs) reasoning robustness against systematically controlled irrelevant context (IC). GSM-DC constructs symbolic reasoning graphs with precise distractor injections, enabling rigorous, reproducible evaluation. Our experiments demonstrate that LLMs are significantly sensitive to IC, affecting both reasoning path selection and arithmetic accuracy. Additionally, training models with strong distractors improves performance in both in-distribution and out-of-distribution scenarios. We further propose a stepwise tree search guided by a process reward model, which notably enhances robustness in out-of-distribution conditions.
Investigating Pedagogical Teacher and Student LLM Agents: Genetic Adaptation Meets Retrieval-Augmented Generation Across Learning Styles
Authors: Debdeep Sanyal; Agniva Maiti; Umakanta Maharana; Dhruv Kumar; Ankur Mali; C. Lee Giles; Murari Mandal
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Effective teaching necessitates adapting pedagogical strategies to the inherent diversity of students, encompassing variations in aptitude, learning styles, and personality, a critical challenge in education and teacher training. Large Language Models (LLMs) offer a powerful tool to simulate complex classroom dynamics, providing a controlled environment for exploring optimal teaching patterns. However, existing simulation frameworks often fall short by neglecting comprehensive student modeling beyond basic knowledge states and, more importantly, by lacking mechanisms for teachers to dynamically adapt their approach based on student feedback and collective performance. Addressing these limitations, we propose a simulation framework that integrates LLM-based diverse student agents with a self-evolving teacher agent. We use genetic algorithms to automatically tune and optimize the teacher’s pedagogical parameters based on simulated student performance, enabling the teacher agent to discover and refine teaching patterns tailored to specific class characteristics. Complementing this, we introduce Persona-RAG, a novel Retrieval-Augmented Generation method specifically designed for personalized knowledge retrieval in pedagogical contexts, allowing students to retrieve information as per their learning styles. We show how Persona-RAG remains competitive with standard RAG baselines in accurately retrieving relevant information while adding a touch of personalization for students. Crucially, we perform extensive experiments and highlight the different patterns learnt by the teacher agent while optimizing over classes with students of various learning styles. Our work presents a significant step towards creating adaptive educational technologies and improving teacher training through realistic, data-driven simulation.
GeoEdit: Geometric Knowledge Editing for Large Language Models
Authors: Yujie Feng; Li-Ming Zhan; Zexin Lu; Yongxin Xu; Xu Chu; Yasha Wang; Jiannong Cao; Philip S. Yu; Xiao-Ming Wu
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Regular updates are essential for maintaining up-to-date knowledge in large language models (LLMs). However, existing training-based model editing methods often struggle to effectively incorporate new knowledge while preserving unrelated general knowledge. To address this challenge, we propose a novel framework called Geometric Knowledge Editing (GeoEdit). GeoEdit utilizes the geometric relationships of parameter updates from fine-tuning to differentiate between neurons associated with new knowledge updates and those related to general knowledge perturbations. By employing a direction-aware knowledge identification method, we avoid updating neurons with directions approximately orthogonal to existing knowledge, thus preserving the model’s generalization ability. For the remaining neurons, we integrate both old and new knowledge for aligned directions and apply a “forget-then-learn” editing strategy for opposite directions. Additionally, we introduce an importance-guided task vector fusion technique that filters out redundant information and provides adaptive neuron-level weighting, further enhancing model editing performance. Extensive experiments on two publicly available datasets demonstrate the superiority of GeoEdit over existing state-of-the-art methods.
A Generative Pre-Trained Language Model for Channel Prediction in Wireless Communications Systems
Authors: Bo Lin; Huanming Zhang; Yuhua Jiang; Yucong Wang; Tengyu Zhang; Shaoqiang Yan; Hongyao Li; Yihong Liu; Feifei Gao
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Channel prediction can greatly reduce the pilot overhead and is a critical technology in the fifth-generation (5G) and the coming 6G wireless communications systems. Conventional model-based channel prediction methods suffer from limited accuracy due to imperfect temporal modeling, while existing AI-based methods suffer from limited generalization due to inadequate training strategies. Recently, large language models (LLMs) have demonstrated remarkable generalization and generation capabilities across diverse domains such as computer vision, quantitative economics, and bioinformatics, which motivates us to apply LLMs in channel prediction. In this paper, we formulate the ‘channel sentence’ based on channel correlation, where the channel is regarded as a ‘word’. Subsequently, we propose a generative pre-trained language model for channel prediction (CP-GPT). We collect 12M channel data according to the 3GPP 38.901 protocol and train CP-GPT based on the transformer decoder architecture. Moreover, we design two pre-training tasks based on the characteristics of wireless channels to enhance CP-GPT’s understanding of communications channels. We further propose a comprehensive benchmark to rigorously evaluate the capabilities of CP-GPT across multiple dimensions. The simulation results demonstrate that CP-GPT has successfully learned various channel characteristics and exhibits impressive capabilities across numerous downstream tasks.
AIMMerging: Adaptive Iterative Model Merging Using Training Trajectories for Language Model Continual Learning
Authors: Yujie Feng; Jian Li; Xiaoyu Dong; Pengfei Xu; Xiaohui Zhou; Yujia Zhang; Zexin Lu; Yasha Wang; Alan Zhao; Xu Chu; Xiao-Ming Wu
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Continual learning (CL) is essential for deploying large language models (LLMs) in dynamic real-world environments without the need for costly retraining. Recent model merging-based methods have attracted significant attention, but they still struggle to effectively manage the trade-off between learning new knowledge and preventing forgetting, a challenge largely stemming from suboptimal number of merges and merging frequency. In this paper, we introduce Adaptive Iterative Model Merging (AimMerging), a novel CL framework that utilizes learning and forgetting signals from the training trajectory to dynamically monitor the model’s training status. Guided by dynamic monitoring, the training trajectory-guided merge controller adaptively determines the timing and frequency of iterative fusion, while the rehearsal-based knowledge fusion module computes the merging weights and executes the fusion. Comprehensive experiments on three CL benchmarks with various model sizes (from 770M to 13B) demonstrate that AimMerging achieves significant performance improvements over existing state-of-the-art methods, with an average relative improvement of 80% and 59% on FWT and BWT, respectively. The source code is provided for reproducibility.
R-PRM: Reasoning-Driven Process Reward Modeling
Authors: Shuaijie She; Junxiao Liu; Yifeng Liu; Jiajun Chen; Xin Huang; Shujian Huang
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Process Reward Models (PRMs) have emerged as a promising solution to address the reasoning mistakes of large language models (LLMs). However, existing PRMs typically output evaluation scores directly, limiting both learning efficiency and evaluation accuracy. This limitation is further compounded by the scarcity of annotated data. To address these issues, we propose Reasoning-Driven Process Reward Modeling (R-PRM), which activates inherent reasoning to enhance process-level evaluation. First, we leverage stronger LLMs to generate seed data from limited annotations, effectively activating reasoning capabilities and enabling comprehensive step-by-step evaluation. Second, we explore self-improvement of our PRM through preference optimization, without requiring additional annotated data. Third, we introduce inference time scaling to fully harness our model’s reasoning potential. Extensive experiments demonstrate R-PRM’s effectiveness: on ProcessBench and PRMBench, it surpasses strong baselines by 13.9 and 8.5 F1 scores. When applied to guide mathematical reasoning, R-PRM achieves consistent accuracy improvements of over 8.6 points across six challenging datasets. Further analysis reveals that R-PRM exhibits more comprehensive evaluation and robust generalization, indicating its broader potential.
RLAE: Reinforcement Learning-Assisted Ensemble for LLMs
Authors: Yuqian Fu; Yuanheng Zhu; Jiajun Chai; Guojun Yin; Wei Lin; Qichao Zhang; Dongbin Zhao
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Ensembling large language models (LLMs) can effectively combine diverse strengths of different models, offering a promising approach to enhance performance across various tasks. However, existing methods typically rely on fixed weighting strategies that fail to adapt to the dynamic, context-dependent characteristics of LLM capabilities. In this work, we propose **R**einforcement **L**earning-**A**ssisted **E**nsemble for LLMs (RLAE), a novel framework that reformulates LLM ensemble through the lens of a Markov Decision Process (MDP). Our approach introduces a RL agent that dynamically adjusts ensemble weights by considering both input context and intermediate generation states, with the agent being trained using rewards that directly correspond to the quality of final outputs. We implement RLAE using both single-agent and multi-agent reinforcement learning algorithms (RLAE_PPO and RLAE_MAPPO ), demonstrating substantial improvements over conventional ensemble methods. Extensive evaluations on a diverse set of tasks show that RLAE outperforms existing approaches by up to 3.3\\% accuracy points, offering a more effective framework for LLM ensembling. Furthermore, our method exhibits superior generalization capabilities across different tasks without the need for retraining, while simultaneously achieving lower time latency. The source code is available at here.
Do Large Language Models Truly Grasp Addition? A Rule-Focused Diagnostic Using Two-Integer Arithmetic
Authors: Yang Yan; Yu Lu; Renjun Xu; Zhenzhong Lan
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Large language models (LLMs) achieve impressive results on advanced mathematics benchmarks but sometimes fail on basic arithmetic tasks, raising the question of whether they have truly grasped fundamental arithmetic rules or are merely relying on pattern matching. To unravel this issue, we systematically probe LLMs’ understanding of two-integer addition (0 to 264) by testing three crucial properties: commutativity (A+B=B+A), representation invariance via symbolic remapping (e.g., 7 ↦ Y), and consistent accuracy scaling with operand length. Our evaluation of 12 leading LLMs reveals a stark disconnect: while models achieve high numeric accuracy (73.8–99.8%), they systematically fail these diagnostics. Specifically, accuracy plummets to ≤ 7.5% with symbolic inputs, commutativity is violated in up to 20% of cases, and accuracy scaling is non-monotonic. Interventions further expose this pattern-matching reliance: explicitly providing rules degrades performance by 29.49%, while prompting for explanations before answering merely maintains baseline accuracy. These findings demonstrate that current LLMs address elementary addition via pattern matching, not robust rule induction, motivating new diagnostic benchmarks and innovations in model architecture and training to cultivate genuine mathematical reasoning. Our dataset and generating code are available at https://github.com/kuri-leo/llm-arithmetic-diagnostic.