GenAI4ELab/papercli-papers huggingface dataset:
Position IDs Matter: An Enhanced Position Layout for Efficient Context Compression in Large Language Models
Authors: Runsong Zhao; Xin Liu; Xinyu Liu; Pengcheng Huang; Chunyang Xiao; Tong Xiao; JingBo Zhu
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Using special tokens (e.g., gist, memory, or compressed tokens) to compress context information is a common practice for large language models (LLMs). However, existing approaches often neglect that position encodings inherently induce local inductive biases in models, causing the compression process to ignore holistic contextual dependencies. We propose **Enhanced Position Layout (EPL)**, a simple yet effective method that improves the context compression capability of LLMs by only adjusting position IDs, the numerical identifiers that specify token positions. EPL minimizes the distance between context tokens and their corresponding special tokens and at the same time maintains the sequence order in position IDs between context tokens, special tokens, and the subsequent tokens. Integrating EPL into our best performing context compression model results in 1.9 ROUGE-1 F1 improvement on out-of-domain question answering datasets in average. When extended to multimodal scenarios, EPL brings an average accuracy gain of 2.6 to vision compression LLMs.
Can Role Vectors Affect LLM Behaviour?
Authors: Daniele Potertì; Andrea Seveso; Fabio Mercorio
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: The influence of personas on Large Language Models (LLMs) has been widely studied, yet their direct impact on performance remains uncertain. This work explores a novel approach to guiding LLM behaviour through role vectors, an alternative to persona-based prompting. We construct 29 role vectors derived from model activations and evaluate their impact on benchmark performance across multiple domains. Our analysis investigates whether these vectors can effectively steer models toward domain-specific expertise. We measure two key interventions: (i) activation addition, which reinforces role-specific directions, and (ii) directional ablation, which removes them. Results on well-established benchmarks indicate that role vectors do, in fact, influence model behaviour, improving in-domain task performance while also yielding unexpected cross-domain gains.This, in turn, suggests that manipulating internal model representations has a greater impact on outcomes than persona-based prompting.
Semantic Component Analysis: Introducing Multi-Topic Distributions to Clustering-Based Topic Modeling
Authors: Florian Eichin; Carolin M. Schuster; Georg Groh; Michael A. Hedderich
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Topic modeling is a key method in text analysis, but existing approaches fail to efficiently scale to large datasets or are limited by assuming one topic per document. Overcoming these limitations, we introduce Semantic Component Analysis (SCA), a topic modeling technique that discovers multiple topics per sample by introducing a decomposition step to the clustering-based topic modeling framework. We evaluate SCA on Twitter datasets in English, Hausa and Chinese. There, it achieves competetive coherence and diversity compared to BERTopic, while uncovering at least double the topics and maintaining a noise rate close to zero. We also find that SCA outperforms the LLM-based TopicGPT in scenarios with similar compute budgets. SCA thus provides an effective and efficient approach for topic modeling of large datasets.
ThinkQE: Query Expansion via an Evolving Thinking Process
Authors: Yibin Lei; Tao Shen; Andrew Yates
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Effective query expansion for web search benefits from promoting both exploration and result diversity to capture multiple interpretations and facets of a query. While recent LLM-based methods have improved retrieval performance and demonstrate strong domain generalization without additional training, they often generate narrowly focused expansions that overlook these desiderata. We propose ThinkQE, a test-time query expansion framework addressing this limitation through two key components: a thinking-based expansion process that encourages deeper and comprehensive semantic exploration, and a corpus-interaction strategy that iteratively refines expansions using retrieval feedback from the corpus. Experiments on diverse web search benchmarks (DL19, DL20, and BRIGHT) show ThinkQE consistently outperforms prior approaches, including training-intensive dense retrievers and rerankers.
Hierarchical Reward Modeling for Fault Localization in Large Code Repositories
Authors: Jiwei Zhang; Jianxun Lian; Haiming Qin; Mingyang Zhou; KeZhong Lu; Rui Mao; Hao Liao
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Large Language Models (LLMs) exhibit significant potential in complex software engineering tasks, however, their fault localization capabilities within repository are constrained by inherent limitations in max context length. Although Test-Time Scaling (TTS) can generate multiple candidate solutions, traditional selection strategies often fail to identify the optimal one. To solve this problem, we introduces Hierarchical Localization Reward Model (HiLoRM), which specifically designed to evaluate and select the most accurate fault localization candidates (at file, function, and line levels) from the multiple sampled outputs of LLMs, thereby enhancing localization accuracy. Furthermore, we constructed the HiFL-44k dataset, comprising approximately 44,000 fault localization instances, to train HiLoRM. Experimental results demonstrate that on the SWE-Bench-Lite dataset, HiLoRM improves the final line-level localization recall by 12% compared to a baseline model that does not use a reward model. Concurrently, HiLoRM exhibits a strong capability to evaluate predictions from larger LLMs (e.g., 32B parameters) and demonstrates transferability and generalization potential when applied to other fault localization methods. This work provides an effective methodology and an accessible model to significantly improve the accuracy and reliability of LLMs for repository-level fault localization. Our codes and datasets are available at https://github.com/SZU-ZJW/HiFL-Method.
Layer Duplication in LLMs
Authors: Neo Eyal; Nachum Dershowitz; Kfir Bar
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: We investigate the effect of duplicating multihead self-attention layers in large language models (LLMs) across a range of language tasks, with and without fine-tuning. The results demonstrate that duplicating the initial layers once or twice often yields a significant performance boost. Attention analysis uncovered the underlying mechanisms driving the improvement when performing layer duplication. This method enhances LLM capabilities with or without additional training or labeled data.
Semantic-Aware Action Space Compression via LLM-DRL Synergy for Efficient Task-oriented Dialogue Policy Exploration
Authors: Yangyang Zhao; Ben Niu; Yuxuan Tan; Shihan Wang; Libo Qin
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: The flexibility of natural language significantly expands the action space in task-oriented dialogue systems, causing inefficient exploration and slow convergence in deep reinforcement learning (DRL)-based policy optimization. Pre-trained large language models (LLMs), with world knowledge and semantic understanding, offer promising solutions. To this end, we propose LLM-Guided DRL via Semantic-Aware Action Pruning (LLMSAP), a novel framework that synergizes pretrained LLMs with DRL. LLMSAP leverages the world knowledge and contextual understanding of LLMs to guide decision-making via an action feasibility assessment. Instead of requiring LLMs to directly generate optimal actions due to their limited precision in sequential decision tasks, LLMSAP employs a lightweight action pruning mechanism. Specifically, LLMs act as action filters, rapidly eliminating semantically implausible or low-potential actions from multi-turn dialogue context, allowing the DRL agent to focus exploration on a refined candidate subset. This two-stage framework (“prune-then-optimize”) avoids extensive LLM fine-tuning while preserving the decision-making precision of DRL. Experiments on multiple benchmarks verify the effectiveness of LLMSAP.
Linear Steerability in Language Models: When It Emerges and How It Evolves
Authors: Jianshu She; Xinyue Li; Eric P. Xing; Zhengzhong Liu; Qirong Ho
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Language models can be steered by modifying their internal representations to control concepts such as emotion, style, or truthfulness in generation. However, the conditions for an effective intervention remain unclear and are often validated through heuristics and trial-and-error. To fill this gap, we demonstrate that intervention efficacy, measured by linear steerability (i.e., the ability to adjust output via linear transformations of hidden states), emerges during intermediate stages of training. Moreover, even closely related concepts (e.g., anger and sadness) exhibit steerability emergence at distinct stages of training*.To better interpret the dynamics of steerability during training, we adapt existing intervention techniques into a unified framework, referred to as the “Intervention Detector” (ID), which is designed to reveal how linear steerability evolves over the course of training through hidden state and representation analysis. ID reveals that concepts become increasingly linearly separable in the hidden space as training progresses, which strongly correlates with the emergence of linear steerability. We further introduce ID-based metrics, such as heatmaps, entropy trends, and cosine similarity, to help interpret how linear steerability evolves throughout training. In addition, we apply ID across different model families to ensure the generality of our findings on steerability dynamics.
A Comprehensive Survey on Learning from Rewards for Large Language Models: Reward Models and Learning Strategies
Authors: Xiaobao Wu
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Recent developments in Large Language Models (LLMs) have shifted from pre-training scaling to post-training and test-time scaling. Across these developments, a key unified paradigm has arisen: Learning from Rewards, where reward signals act as the guiding stars to steer LLM behavior. It has underpinned a wide range of prevalent techniques, such as reinforcement learning (RLHF, RLAIF, DPO, and GRPO), reward-guided decoding, and post-hoc correction. Crucially, this paradigm enables the transition from passive learning from static data to active learning from dynamic feedback. This endows LLMs with aligned preferences and deep reasoning capabilities for diverse tasks. In this survey, we present a comprehensive overview of learning from rewards, from the perspective of reward models and learning strategies across training, inference, and post-inference stages. We further discuss the benchmarks for reward models and the primary applications. Finally we highlight the challenges and future directions.
InFact: Informativeness Alignment for Improved LLM Factuality
Authors: Roi Cohen; Russa Biswas; Gerard de Melo
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Factual completeness is a general term that captures how detailed and informative a factually correct text is. For instance, the factual sentence “Barack Obama was born in the United States” is factually correct, though less informative than the factual sentence “Barack Obama was born in Honolulu, Hawaii, United States”. Despite the known fact that LLMs tend to hallucinate and generate factually incorrect text, they might also tend to choose to generate factual text that is indeed factually correct and yet less informative than other, more informative choices. In this work, we tackle this problem by proposing an informativeness alignment mechanism. This mechanism takes advantage of recent factual informativeness benchmarks to propose an informativeness alignment objective. This objective prioritizes answers that are both correct and informative. We find that when training a model to maximize this objective or optimize its preference, we can improve not just informativeness but also factuality.
Large Language Model Agents in Finance: A Survey Bridging Research, Practice, and Real-World Deployment
Authors: Yifei Dong; Fengyi Wu; Kunlin Zhang; Yilong Dai; Sanjian Zhang; Wanghao Ye; Sihan Chen; Zhi-Qi Cheng
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Large language models (LLMs) are increasingly applied to finance, yet challenges remain in aligning their capabilities with real-world institutional demands. In this survey, we provide a systematic, dual-perspective review bridging financial practice and LLM research. From a practitioner-centric standpoint, we introduce a functional taxonomy covering five core financial domains—Data Analysis, Investment Research, Trading, Investment Management, and Risk Management—mapping each to representative tasks, datasets, and institutional constraints. From a research-focused perspective, we analyze key modeling challenges, including numerical reasoning limitations, prompt sensitivity, and lack of real-time adaptability. We comprehensively catalog over 30 financial benchmarks and 20 representative models, and compare them across modalities, tasks, and deployment limitations. Finally, we identify open challenges and outline emerging directions such as continual adaptation, coordination-aware multi-agent systems, and privacy-compliant deployment. We emphasize deeper researcher–practitioner collaboration and transparent model architectures as critical pathways to safer and more scalable AI adoption in finance.
Problem Solved? Information Extraction Design Space for Layout-Rich Documents using LLMs
Authors: Gaye Colakoglu; Gürkan Solmaz; Jonathan Fürst
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: This paper defines and explores the design space for information extraction (IE) from layout-rich documents using large language models (LLMs). The three core challenges of layout-aware IE with LLMs are 1) data structuring, 2) model engagement, and 3) output refinement. Our study investigates the sub-problems and methods within these core challenges, such as input representation, chunking, prompting, selection of LLMs, and multimodal models. It examines the effect of different design choices through LayIE-LLM, a new, open-source, layout-aware IE test suite, benchmarking against traditional, fine-tuned IE models. The results on two IE datasets show that LLMs require adjustment of the IE pipeline to achieve competitive performance: the optimized configuration found with LayIE-LLM achieves 13.3–37.5 F1 points more than a general-practice baseline configuration using the same LLM. To find a well-working configuration, we develop a one-factor-at-a-time (OFAT) method that achieves near-optimal results. Our method is only 0.8–1.8 points lower than the best full factorial exploration with a fraction (~2.8%) of the required computation. Overall, we demonstrate that, if well-configured, general-purpose LLMs match the performance of specialized models, providing a cost-effective, finetuning-free alternative. Our test-suite is available at https://github.com/gayecolakoglu/LayIE-LLM
Generation-Augmented Retrieval: Rethinking the Role of Large Language Models in Zero-Shot Relation Extraction
Authors: Zehan Li; Fu Zhang; Tianyue Peng; He Liu; Jingwei Cheng
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Recent advances in Relation Extraction (RE) emphasize Zero-Shot methodologies, aiming to recognize unseen relations between entities with no annotated data. Although Large Language Models (LLMs) have demonstrated outstanding performance in many NLP tasks, their performance in Zero-Shot RE (ZSRE) without entity type constraints still lags behind Small Language Models (SLMs). LLM-based ZSRE often involves manual interventions and significant computational overhead, especially when scaling to large-scale multi-choice data.To this end, we introduce RE-GAR-AD, which not only leverages the generative capability of LLMs but also utilizes their representational power without tuning LLMs. We redefine LLM-based ZSRE as a retrieval challenge, utilizing a Generation-Augmented Retrieval framework coupled with a retrieval Adjuster. Specifically, our approach guides LLMs through crafted prompts to distill sentence semantics and enrich relation labels. We encode sentences and relation labels using LLMs and match their embeddings in a triplet fashion. This retrieval technique significantly reduces token input requirements. Additionally, to further optimize embeddings, we propose a plug-in retrieval adjuster with only 2M parameters, which allows rapid fine-tuning without accessing LLMs’ parameters. Our LLM-based model demonstrates comparable performance on multiple benchmarks.
Following Occam’s Razor: Dynamic Combination of Structured Knowledge for Multi-Hop Question Answering using LLMs
Authors: Wei Chen; Zhi Zheng; Lili Zhao; Huijun Hou; Tong Xu
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Multi-hop question answering is a challenging task that requires capturing information from different positions in multiple documents. Recently, several methods propose to enhance Large Language Models (LLMs) by incorporating structured knowledge, aiming to grasp key information for solving this task. Despite certain achievements, they still face the following challenges: 1) The neglect of text-based reasoning capabilities. 2) Information redundancy between text and triples. 3) Information loss during structured knowledge extraction. To solve the above challenges, in this paper, we propose Dynamic Combination of Structured Knowledge (DCSK), a novel framework for integrating text-based and triple-based paradigms. Following Occam’s Razor, DCSK dynamically determine the necessity of structured knowledge by the designed multi-faceted evaluation, which systematically assess the correctness, clarity, and informativeness of text-based prediction. For questions that require structured knowledge, we develop an iterative fact refiner that screens for question-relevant triples, verifies their factual adequacy, and thereby effectively excludes irrelevant and redundant information. Furthermore, based on the verification, we construct an adaptive knowledge reasoner that dynamically adjusts the need for text supplementation, thus mitigating the information deficiency in selected triples. Extensive experiments on three MHQA datasets demonstrate the efficiency and effectiveness of DCSK.
Large Language Models as Reader for Bias Detection
Authors: Xuan Luo; Jing Li; Zhong Wenzhong; Geng Tu; Ruifeng Xu
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Detecting bias in media content is crucial for maintaining information integrity and promoting inclusivity. Traditional methods analyze text from the writer’s perspective, which analyzes textual features directly from the writer’s intent, leaving the reader’s perspective underexplored. This paper investigates whether Large Language Models (LLMs) can be leveraged as readers for bias detection by generating reader-perspective comments. Experiments are conducted on the BASIL (news bias) and BeyondGender (gender bias) datasets with LLMs Gemma-7B, Phi-3-3.8B, Llama3.1-8B, Llama3.1-70B, and GPT4. The results demonstrate the effectiveness of reader-perspective comments for open-source LLMs, achieving performance comparable to GPT4’s. The findings highlight the significance of emotion-related comments, which are generally more beneficial than value-related ones in bias detection. In addition, experiments on Llamas show that comment selection ensures consistent performance regardless of model sizes and comment combinations. This study is particularly beneficial for small-size open-source LLMs.
LOHRec: Leveraging Order and Hierarchy in Generative Sequential Recommendation
Authors: Jiawen Xie; Haiyang Wu; Deyi Ji; Yuekui Yang; Shaoping Ma
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: The sequential recommendation task involves predicting the items users will be interested in next based on their past interaction sequence. Recently, sequential recommender systems with generative retrieval have garnered significant attention. However, during training, these generative recommenders focus only on maximizing the prediction probability of the next target item in the temporal sequence, while neglecting awareness of diverse plausible potential items.Although introducing large language models (LLMs) with world knowledge and adding a set of auxiliary tasks that can link item identifiers to their real-world meanings can alleviate this issue, the high inference costs associated with these LLM-based recommenders make them challenging to deploy in practical scenarios. In this paper, we propose a novel learning framework, LOHRec, which leverages the order and hierarchy in generative recommendation using quantized identifiers to further explore the performance ceiling of lightweight generative recommenders. Under fair comparisons with approximate backbone parameter sizes, comprehensive experiments show that all variants of generative recommenders using our framework outperform strong prior baselines across multiple datasets. Furthermore, we empirically demonstrate that LOHRec can efficiently align lightweight generative recommenders with LLM recommendation preferences in low-resource scenarios, further demonstrating its practical utility. Our code repository is available at [https://github.com/xjw-nlp/LOHRec](https://github.com/xjw-nlp/LOHRec).
Biology-Instructions: A Dataset and Benchmark for Multi-Omics Sequence Understanding Capability of Large Language Models
Authors: Haonan He; Yuchen Ren; Yining Tang; Ziyang Xu; Junxian Li; Minghao Yang; Di Zhang; Yuan Dong; Tao Chen; Shufei Zhang; Yuqiang Li; Nanqing Dong; Wanli Ouyang; Dongzhan Zhou; Peng Ye
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Large language models (LLMs) have shown remarkable capabilities in general domains, but their application to multi-omics biology remains underexplored. To address this gap, we introduce Biology-Instructions, the first large-scale instruction-tuning dataset for multi-omics biological sequences, including DNA, RNA, proteins, and multi-molecules. This dataset bridges LLMs and complex biological sequence-related tasks, enhancing their versatility and reasoning while maintaining conversational fluency. We also highlight significant limitations of current state-of-the-art LLMs on multi-omics tasks without specialized training. To overcome this, we propose ChatMultiOmics, a strong baseline with a novel three-stage training pipeline, demonstrating superior biological understanding through Biology-Instructions. Both resources are publicly available, paving the way for better integration of LLMs in multi-omics analysis. The Biology-Instructions is publicly available at: https://github.com/hhnqqq/Biology-Instructions.