GenAI4ELab/papercli-papers huggingface dataset:
How Much Do Large Language Models Know about Human Motion? A Case Study in 3D Avatar Control
Authors: Kunhang Li; Jason Naradowsky; Yansong Feng; Yusuke Miyao
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: We explore the human motion knowledge of Large Language Models (LLMs) through 3D avatar control. Given a motion instruction, we prompt LLMs to first generate a high-level movement plan with consecutive steps (**High-level Planning**), then specify body part positions in each step (**Low-level Planning**), which we linearly interpolate into avatar animations. Using 20 representative motion instructions that cover fundamental movements and balance body part usage, we conduct comprehensive evaluations, including human and automatic scoring of both high-level movement plans and generated animations, as well as automatic comparison with oracle positions in low-level planning. Our findings show that LLMs are strong at interpreting high-level body movements but struggle with precise body part positioning. While decomposing motion queries into atomic components improves planning, LLMs face challenges in multi-step movements involving high-degree-of-freedom body parts. Furthermore, LLMs provide reasonable approximations for general spatial descriptions, but fall short in handling precise spatial specifications. Notably, LLMs demonstrate promise in conceptualizing creative motions and distinguishing culturally specific motion patterns.
The Search for Conflicts of Interest: Open Information Extraction in Scientific Publications
Authors: Garima Gaur; Oana Balalau; Ioana Manolescu; Prajna Devi Upadhyay
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: A conflict of interest (COI) appears when a person or a company has two or more interests that may directly conflict. This happens, for instance, when a scientist whose research is funded by a company audits the same company. For transparency and to avoid undue influence, public repositories of relations of interest are increasingly recommended or mandated in various domains, and can be used to avoid COIs. In this work, we propose an LLM-based open information extraction (OpenIE) framework for extracting financial or other types of interesting relations from scientific text. We target scientific publications in which authors declare funding sources or collaborations in the acknowledgment section, in the metadata, or in the publication, following editors’ requirements. We introduce an extraction methodology and present a knowledge base (KB) with a comprehensive taxonomy of COI centric relations. Finally, we perform a comparative study of disclosures of two journals in the field of toxicology and pharmacology.
On Collaborating Small and Large Models For Few-shot Intent Detection
Authors: Peng Chen; Bang Wang
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Few-shot intent detection (FSID) targets the classification of user queries into in-scope intent categories or detecting them as out-of-scope, with only a few or even zero labeled examples per class. Existing PLM-based methods struggle in low-resource situations; while LLM-based methods face high inference cost and label interference. To harness their complementary strengths, we propose the FCSLM, a framework that collaborates a small prediction model with a large language model for the FSID task. During training, we leverage LLMs for data augmentation in self-supervised pretraining and supervised fine-tuning a task-specific prediction model. During inference, a multi-round reasoning process first applies the small prediction model to output candidate intents with uncertainty estimations, then invokes an LLM with enriched intent descriptions for refined prediction and OOS detection. Extensive experiments on three benchmark datasets demonstrate that our FCSLM outperforms strong competitors, achieving the new state-of-the-art performance in both intent classification and OOS detection. Our code is available at: https://github.com/hustchenpeng/FCSLM
A Survey on LLMs for Story Generation
Authors: Maria Teleki; Vedangi Bengali; Xiangjue Dong; Sai Tejas Janjur; Haoran Liu; Tian Liu; Cong Wang; Ting Liu; Yin Zhang; Frank Shipman; James Caverlee
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Methods for story generation with Large Language Models (LLMs) have come into the spotlight recently. We create a novel taxonomy of LLMs for story generation consisting of two major paradigms: (i) independent story generation by an LLM, and (ii) author-assistance for story generation – a collaborative approach with LLMs supporting human authors. We compare existing works based on their methodology, datasets, generated story types, evaluation methods, and LLM usage. With a comprehensive survey, we identify potential directions for future work
From Knowledge to Treatment: Large Language Model Assisted Biomedical Concept Representation for Drug Repurposing
Authors: Chengrui Xiang; Tengfei Ma; Xiangzheng Fu; Yiping Liu; Bosheng Song; Xiangxiang Zeng
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Drug repurposing plays a critical role in accelerating treatment discovery, especially for complex and rare diseases. Biomedical knowledge graphs (KGs), which encode rich clinical associations, have been widely adopted to support this task. However, existing methods largely overlook common-sense biomedical concept knowledge in real-world labs, such as mechanistic priors indicating that certain drugs are fundamentally incompatible with specific treatments. To address this gap, we propose LLaDR, a Large Language Model-assisted framework for Drug Repurposing, which improves the representation of biomedical concepts within KGs. Specifically, we extract semantically enriched treatment-related textual representations of biomedical entities from large language models (LLMs) and use them to fine-tune knowledge graph embedding (KGE) models. By injecting treatment-relevant knowledge into KGE, LLaDR largely improves the representation of biomedical concepts, enhancing semantic understanding of under-studied or complex indications. Experiments based on benchmarks demonstrate that LLaDR achieves state-of-the-art performance across different scenarios, with case studies on Alzheimer’s disease further confirming its robustness and effectiveness.
SKRAG: A Retrieval-Augmented Generation Framework Guided by Reasoning Skeletons over Knowledge Graphs
Authors: Xiaotong Xu; Yizhao Wang; Yunfei Liu; Shengyang Li
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: In specialized domains such as space science and utilization, question answering (QA) systems are required to perform complex multi-fact reasoning over sparse knowledge graphs (KGs). Existing KG-based retrieval-augmented generation (RAG) frameworks often face challenges such as inefficient subgraph retrieval, limited reasoning capabilities, and high computational costs. These issues limit their effectiveness in specialized domains. In this paper, we propose SKRAG, a novel Skeleton-guided RAG framework for knowledge graph question answering (KGQA). SKRAG leverages a lightweight language model enhanced with the Finite State Machine (FSM) constraint to produce structurally grounded reasoning skeletons, which guide accurate subgraph retrieval. The retrieved subgraph is then used to prompt a general large language model (LLM) for answer generation. We also introduce SSUQA, a KGQA dataset in the space science and utilization domain. Experiments show that SKRAG outperforms strong baselines on SSUQA and two general-domain benchmarks, demonstrating its adaptability and practical effectiveness.
A Generative Framework for Personalized Sticker Retrieval
Authors: Changjiang Zhou; Ruqing Zhang; Jiafeng Guo; Yu-An Liu; Fan Zhang; Ganyuan Luo; Xueqi Cheng
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Formulating information retrieval as a variant of generative modeling, specifically using autoregressive models to generate relevant identifiers for a given query, has recently attracted considerable attention. However, its application to personalized sticker retrieval remains largely unexplored and presents unique challenges: existing relevance-based generative retrieval methods typically lack personalization, leading to a mismatch between diverse user expectations and the retrieved results. To address this gap, we propose PEARL, a novel generative framework for personalized sticker retrieval, and make two key contributions: (i) To encode user-specific sticker preferences, we design a representation learning model to learn discriminative user representations. It is trained on three prediction tasks that leverage personal information and click history; and (ii) To generate stickers aligned with a user’s query intent, we propose a novel intent-aware learning objective that prioritizes stickers associated with higher-ranked intents. Empirical results from both offline evaluations and online tests demonstrate that PEARL significantly outperforms state-of-the-art methods.
Bridging Semantic and Modality Gaps in Zero-Shot Captioning via Retrieval from Synthetic Data
Authors: Zhiyue Liu; Wenkai Zhou
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Zero-shot image captioning, which aims to generate image descriptions without relying on annotated data, has recently attracted increasing research interest. Pre-trained text-to-image generation models enable the creation of synthetic pairs solely from text data, while existing methods fall short in mitigating the discrepancy caused by the inability of synthetic images to fully capture the semantics of the textual input, resulting in unreliable cross-modal correspondences. To address this, we propose a retrieval-based framework that leverages only existing synthetic image-text pairs as its search corpus to systematically bridge the gap when using synthetic data for captioning. For the semantic gap between a synthetic image and its input text, our framework retrieves supplementary visual features from similar synthetic examples and integrates them to refine the image embedding. Then, it extracts image-related textual descriptions to mitigate the modality gap during decoding. Moreover, we introduce a plug-and-play visual semantic module that detects visual entities, further facilitating the construction of semantic correspondences between images and text. Experimental results on benchmark datasets demonstrate that our method obtains state-of-the-art results.
Humor in Pixels: Benchmarking Large Multimodal Models Understanding of Online Comics
Authors: Yuriel Ryan; Rui Yang Tan; Kenny Tsu Wei Choo; Roy Ka-Wei Lee
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Understanding humor is a core aspect of social intelligence, yet it remains a significant challenge for Large Multimodal Models (LMMs). We introduce PixelHumor, a benchmark dataset of 2,800 annotated multi-panel comics designed to evaluate LMMs’ ability to interpret multimodal humor and recognize narrative sequences. Experiments with state-of-the-art LMMs reveal substantial gaps: for instance, top models achieve only 61% accuracy in panel sequencing, far below human performance. This underscores critical limitations in current models’ integration of visual and textual cues for coherent narrative and humor understanding. By providing a rigorous framework for evaluating multimodal contextual and narrative reasoning, PixelHumor aims to drive the development of LMMs that better engage in natural, socially aware interactions.
BiMediX2 : Bio-Medical EXpert LMM for Diverse Medical Modalities
Authors: Sahal Shaji Mullappilly; Mohammed Irfan Kurpath; Sara Pieri; Saeed Yahya Alseiari; Shanavas Cholakkal; Khaled M Aldahmani; Fahad Shahbaz Khan; Rao Muhammad Anwer; Salman Khan; Timothy Baldwin; Hisham Cholakkal
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: We introduce BiMediX2, a bilingual (Arabic-English) Bio-Medical EXpert Large Multimodal Model that supports text-based and image-based medical interactions. It enables multi-turn conversation in Arabic and English and supports diverse medical imaging modalities, including radiology, CT, and histology. To train BiMediX2, we curate BiMed-V, an extensive Arabic-English bilingual healthcare dataset consisting of 1.6M samples of diverse medical interactions. This dataset supports a range of medical Large Language Model (LLM) and Large Multimodal Model (LMM) tasks, including multi-turn medical conversations, report generation, and visual question answering (VQA). We also introduce BiMed-MBench, the first Arabic-English medical LMM evaluation benchmark, verified by medical experts. BiMediX2 demonstrates excellent performance across multiple medical LLM and LMM benchmarks, achieving state-of-the-art results compared to other open-sourced models. On BiMed-MBench, BiMediX2 outperforms existing methods by over 9% in English and more than 20% in Arabic evaluations. Additionally, it surpasses GPT-4 by approximately 9% in UPHILL factual accuracy evaluations and excels in various medical VQA, report generation, and report summarization tasks. Our trained models, instruction set, and source code are available at - https://github.com/mbzuai-oryx/BiMediX2
DeMAC: Enhancing Multi-Agent Coordination with Dynamic DAG and Manager-Player Feedback
Authors: Yuhan Liu; Cong Xu; Lu Liu; Yihua Wang; Feiyu Chen; Qi Jia; Yaqian Zhao; Zhichun Wang; Xiang Li
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Multi-agent systems (MAS) powered by large language models (LLMs) have shown potential in tackling multifaceted problems through advanced understanding and reasoning. However, they struggle to adapt to evolving task dependencies and to handle uncertainties, such as shifting priorities or unpredictable disruptions. These constraints undermine their ability to dynamically adjust long-term strategies and inter-agent collaboration. To address these challenges, we propose DeMAC, a Dynamic Environment-Aware Manager-Player Agents Coordination framework that enhances multi-agent coordination through long-term strategic planning. DeMAC uses a dynamically updated directed acyclic graph (DAG) and a Manager-Player Dual-Feedback mechanism to align strategic and operational decisions. Moreover, DeMAC enables agents to maintain collaboration and dynamically adapt to changing environmental conditions, outperforming traditional reinforcement learning and human-agent collaboration in the Overcooked simulation. Experimental results highlight DeMAC’s ability to tackle complex coordination tasks, demonstrating its potential to advance LLM-based MAS in dynamic, complex task dependency environments.
Coherence of Argumentative Dialogue Snippets: A New Method for Large Scale Evaluation with an Application to Inference Anchoring Theory
Authors: Paul Piwek; Jacopo Amidei; Svetlana Stoyanchev
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: This paper introduces a novel method for testing the components of theories of (dialogue) coherence through utterance substitution. The method is described and then applied to Inference Anchoring Theory (IAT) in a large scale experimental study with 933 dialogue snippets and 87 annotators. IAT has been used for substantial corpus annotation and practical applications. To address the aim of finding out if and to what extent two aspects of IAT – illocutionary acts and propositional relations – contribute to dialogue coherence, we designed an experiment for systematically comparing the coherence ratings for several variants of short debate snippets. The comparison is between original human-human debate snippets, snippets generated with an IAT-compliant algorithm and snippets produced with ablated versions of the algorithm. This allows us to systematically compare snippets that have identical underlying structures as well as IAT-deficient structures with each other. We found that propositional relations do impact on dialogue coherence (at a statistically highly significant level) whereas we found no such effect for illocutionary act expression. This result suggests that fine-grained inferential relations impact on dialogue coherence, complementing the higher-level coherence structures of, for instance, Rhetorical Structure Theory.
Angular Dispersion Accelerates k-Nearest Neighbors Machine Translation
Authors: Evgeniia Tokarchuk; Sergey Troshin; Vlad Niculae
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Augmenting neural machine translation with external memory at decoding time, in the form of k-nearest neighbors machine translation (k-NN MT), is a well-established strategy for increasing translation performance. k-NN MT retrieves a set of tokens that occurred in the most similar contexts recorded in a prepared data store, using hidden state representations of translation contexts as vector lookup keys. One of the main disadvantages of this method is the high computational cost and memory requirements. Since an exhaustive search is not feasible in large data stores practitioners commonly use approximate k-NN lookup, yet even such algorithms are a bottleneck. In contrast to research directions seeking to accelerate k-NN MT by reducing data store size or the number of lookup calls, we pursue an orthogonal direction based on the performance properties of approximate k-NN lookup data structures. In particular, we propose encouraging angular dispersion of the neural hidden representations of contexts. We show that improving dispersion leads to better balance in the retrieval data structures, accelerating retrieval and slightly improving translations.
Benchmarking Contextual and Paralinguistic Reasoning in Speech-LLMs: A Case Study with In-the-Wild Data
Authors: Qiongqiong Wang; Hardik Bhupendra Sailor; Tianchi Liu; Wenyu Zhang; Muhammad Huzaifah; Nattadaporn Lertcheva; Shuo Sun; Nancy F. Chen; Jinyang Wu; AiTi Aw
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Recent speech-LLMs have shown impressive performance in tasks like transcription and translation, yet they remain limited in understanding the paralinguistic aspects of speech crucial for social and emotional intelligence. We propose CP-Bench, a benchmark for evaluating speech-LLMs on contextual paralinguistic reasoning the integration of verbal content with non-verbal cues like emotion and prosody. The benchmark includes two curated question answering (QA) datasets requiring both linguistic and empathetic understanding. We evaluate state-of-the-art speech-LLMs from both open and closed-source models and perform a comprehensive analysis across different question types. The top two models were further analyzed under temperature tuning to understand its effect on this task. Our benchmark reveals a key gap in existing evaluations and offers insights into building more context-aware and emotionally intelligent speech-capable LLMs.
This is not a Disimprovement: Improving Negation Reasoning in Large Language Models via Prompt Engineering
Authors: Joshua Jose Dias Barreto; Abhik Jana
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Negation reasoning remains a challenge for large language models (LLMs), often causing incorrect interpretations of negated statements. In this study, we analyze various LLMs for their handling of negation and propose two genres of prompts (*Warning-based* and *Persona-based*), which improve overall absolute accuracy by up to 3.17% and distractor negation accuracy by up to 25.14% over most competitive baselines. Next, we assess the robustness of LLMs by reordering prompts while preserving meaning, observing instability linked to positional encoding schemes. Further, we introduce a negative token attention score (NTAS) to quantify attention to negation words. From the comprehensive analysis, we point out that within a specific LLM family, the performance of a model (measured using accuracy) correlates more with NTAS than with model size. The code is publicly available: [https://github.com/Joshua-Dias-Barreto/This-is-not-a-Disimprovement](https://github.com/Joshua-Dias-Barreto/This-is-not-a-Disimprovement)
Make Every Letter Count: Building Dialect Variation Dictionaries from Monolingual Corpora
Authors: Robert Litschko; Verena Blaschke; Diana Burkhardt; Barbara Plank; Diego Frassinelli
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Dialects exhibit a substantial degree of variation due to the lack of a standard orthography. At the same time, the ability of Large Language Models (LLMs) to process dialects remains largely understudied. To address this gap, we use Bavarian as a case study and investigate the lexical dialect understanding capability of LLMs by examining how well they recognize and translate dialectal terms across different parts-of-speech. To this end, we introduce DiaLemma, a novel annotation framework for creating dialect variation dictionaries from monolingual data only, and use it to compile a ground truth dataset consisting of 100K human-annotated German-Bavarian word pairs. We evaluate how well nine state-of-the-art LLMs can judge Bavarian terms as dialect translations, inflected variants, or unrelated forms of a given German lemma. Our results show that LLMs perform best on nouns and lexically similar word pairs, and struggle most in distinguishing between direct translations and inflected variants. Interestingly, providing additional context in the form of example usages improves the translation performance, but reduces their ability to recognize dialect variants. This study highlights the limitations of LLMs in dealing with orthographic dialect variation and emphasizes the need for future work on adapting LLMs to dialects.
SelfAug: Mitigating Catastrophic Forgetting in Retrieval-Augmented Generation via Distribution Self-Alignment
Authors: Yuqing Huang; Rongyang Zhang; Qimeng Wang; Chengqiang Lu; Yan Gao; Yiwu; Yao Hu; Xuyang Zhi; Guiquan Liu; Xin Li; Hao Wang; Enhong Chen
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Recent advancements in large language models (LLMs) have revolutionized natural language processing through their remarkable capabilities in understanding and executing diverse tasks. While supervised fine-tuning, particularly in Retrieval-Augmented Generation (RAG) scenarios, effectively enhances task-specific performance, it often leads to catastrophic forgetting, where models lose their previously acquired knowledge and general capabilities. Existing solutions either require access to general instruction data or face limitations in preserving the model’s original distribution. To overcome these limitations, we propose SelfAug, a self-distribution alignment method that aligns input sequence logits to preserve the model’s semantic distribution, thereby mitigating catastrophic forgetting and improving downstream performance. Extensive experiments demonstrate that SelfAug achieves a superior balance between downstream learning and general capability retention. Our comprehensive empirical analysis reveals a direct correlation between distribution shifts and the severity of catastrophic forgetting in RAG scenarios, highlighting how the absence of RAG capabilities in general instruction tuning leads to significant distribution shifts during fine-tuning. Our findings not only advance the understanding of catastrophic forgetting in RAG contexts but also provide a practical solution applicable across diverse fine-tuning scenarios.