GenAI4ELab/papercli-papers huggingface dataset:
SimpleDoc: Multi‐Modal Document Understanding with Dual‐Cue Page Retrieval and Iterative Refinement
Authors: Chelsi Jain; Yiran Wu; Yifan Zeng; Jiale Liu; Shengyu Dai; Zhenwen Shao; Qingyun Wu; Huazheng Wang
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Document Visual Question Answering (DocVQA) is a practical yet challenging task, which is to ask questions based on documents while referring to multiple pages and different modalities of information, e.g., images and tables. To handle multi-modality, recent methods follow a similar Retrieval Augmented Generation (RAG) pipeline, but utilize Visual Language Models (VLMs) based embedding model to embed and retrieve relevant pages as images, and generate answers with VLMs that can accept an image as input. In this paper, we introduce SimpleDoc, a lightweight yet powerful retrieval - augmented framework for DocVQA. It boosts evidence page gathering by first retrieving candidates through embedding similarity and then filtering and re-ranking these candidates based on page summaries. A single VLM-based reasoner agent repeatedly invokes this dual-cue retriever, iteratively pulling fresh pages into a working memory until the question is confidently answered. SimpleDoc outperforms previous baselines by 3.2% on average on 4 DocVQA datasets with much fewer pages retrieved. Our code is available at https://github.com/ag2ai/SimpleDoc.
VLP: Vision-Language Preference Learning for Embodied Manipulation
Authors: Runze Liu; Chenjia Bai; Jiafei Lyu; Shengjie Sun; Yali Du; Xiu Li
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Reward engineering is one of the key challenges in Reinforcement Learning (RL). Preference-based RL effectively addresses this issue by learning from human feedback. However, it is both time-consuming and expensive to collect human preference labels. In this paper, we propose a novel Vision-Language Preference learning framework, named VLP, which learns a vision-language preference model to provide feedback for embodied manipulation tasks. To achieve this, we define three types of language-conditioned preferences and construct a vision-language preference dataset, which contains versatile implicit preference orders. The model learns to extract language-related features, and then serves as a predictor in various downstream tasks. The policy can be learned according to the annotated labels via reward learning or direct policy optimization. Extensive empirical results on simulated embodied manipulation tasks demonstrate that our method provides accurate preferences and generalizes to unseen tasks and unseen language instructions, outperforming the baselines by a large margin and shifting the burden from continuous, per-task human annotation to one-time, per-domain data collection.
QG-CoC: Question-Guided Chain-of-Captions for Large Multimodal Models
Authors: Kuei-Chun Kao; Hsu Tzu-Yin; Yunqi Hong; Ruochen Wang; Cho-Jui Hsieh
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Recently, Multimodal Large Language Models (MLLMs) encounter two key issues in multi-image contexts: (1) a lack of fine-grained perception across disparate images, and (2) a diminished capability to effectively reason over and synthesize information from multiple visual inputs. However, while various prompting methods aim to describe visual content, many existing studies focus primarily on single-image settings or specific, constrained scenarios. This leaves a critical gap in understanding and addressing how MLLMs tackle more general and complex multi-image reasoning tasks. Thus, we first extensively investigate how current prompting methods perceive fine-grained visual details and process visual information when dealing with multiple images. Our findings reveal that existing prompting methods fall short in attending to needed clues and seamlessly integrating perception and reasoning. Inspired by the findings, we propose a new zero-shot prompting method, Question-Guided Chain-of-Captions (QG-CoC), a generalized prompting approach that effectively handles problems with an arbitrary number of images. We evaluate our method on various open-source and closed-source MLLMs for multi-image and single-image benchmarks. Experimental results indicate that QG-CoC demonstrates competitive performance across tasks and exhibits robust improvements in the challenging scenarios where existing prompting methods fail.
EGOILLUSION: Benchmarking Hallucinations in Egocentric Video Understanding
Authors: Ashish Seth; Utkarsh Tyagi; Ramaneswaran Selvakumar; Nishit Anand; Sonal Kumar; Sreyan Ghosh; Ramani Duraiswami; Chirag Agarwal; Dinesh Manocha
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Multimodal Large Language Models (MLLMs) have demonstrated remarkable performance in complex multimodal tasks. While MLLMs excel at visual perception and reasoning in third-person and egocentric videos, they are prone to hallucinations, generating coherent yet inaccurate responses. We present EGOILLUSION, a first benchmark to evaluate MLLM hallucinations in egocentric videos. EGOILLUSION comprises 1,400 videos paired with 8,000 human-annotated open and closed-ended questions designed to trigger hallucinations in both visual and auditory cues in egocentric videos. Evaluations across ten MLLMs reveal significant challenges, including powerful models like GPT-4o and Gemini, achieving only 59% accuracy. EGOILLUSION lays the foundation in developing robust benchmarks to evaluate the effectiveness of MLLMs and spurs the development of better egocentric MLLMs with reduced hallucination rates. Our benchmark will be open-sourced for reproducibility
MULTIVOX: A Benchmark for Evaluating Voice Assistants for Multimodal Interactions
Authors: Ramaneswaran Selvakumar; Ashish Seth; Nishit Anand; Utkarsh Tyagi; Sonal Kumar; Sreyan Ghosh; Dinesh Manocha
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: The rapid progress of Large Language Models (LLMs) has empowered omni models to act as voice assistants capable of understanding spoken dialogues. These models can process multimodal inputs beyond text, such as speech and visual data, enabling more context-aware interactions. However, current benchmarks fall short in comprehensively evaluating how well these models generate context-aware responses, particularly when it comes to implicitly understanding fine-grained speech characteristics, such as pitch, emotion, timbre, and volume or the environmental acoustic context such as background sounds. Additionally, they inadequately assess the ability of models to align paralinguistic cues with complementary visual signals to inform their responses. To address these gaps, we introduce MultiVox, the first omni voice assistant benchmark designed to evaluate the ability of voice assistants to integrate spoken and visual cues including paralinguistic speech features for truly multimodal understanding. Specifically, MultiVox includes 1000 human-annotated and recorded speech dialogues that encompass diverse paralinguistic features and a range of visual cues such as images and videos. Our evaluation on 10 state-of-the-art models reveals that, although humans excel at these tasks, current open-source models consistently struggle to produce contextually grounded responses.
Do All Autoregressive Transformers Remember Facts the Same Way? A Cross-Architecture Analysis of Recall Mechanisms
Authors: Minyeong Choe; Haehyun Cho; Changho Seo; Hyunil Kim
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Understanding how Transformer-based language models store and retrieve factual associations is critical for improving interpretability and enabling targeted model editing. Prior work, primarily on GPT-style models, has identified MLP modules in early layers as key contributors to factual recall. However, it remains unclear whether these findings generalize across different autoregressive architectures. To address this, we conduct a comprehensive evaluation of factual recall across several models—including GPT, LLaMA, Qwen, and DeepSeek—analyzing where and how factual information is encoded and accessed. Consequently, we find that Qwen-based models behave differently from previous patterns: attention modules in the earliest layers contribute more to factual recall than MLP modules. Our findings suggest that even within the autoregressive Transformer family, architectural variations can lead to fundamentally different mechanisms of factual recall.
Probing Narrative Morals: A New Character-Focused MFT Framework for Use with Large Language Models
Authors: Luca Mitran; Sophie Wu; Andrew Piper
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Moral Foundations Theory (MFT) provides a framework for categorizing different forms of moral reasoning, but its application to computational narrative analysis remains limited. We propose a novel character-centric method to quantify moral foundations in storytelling, using large language models (LLMs) and a novel Moral Foundations Character Action Questionnaire (MFCAQ) to evaluate the moral foundations supported by the behaviour of characters in stories. We validate our approach against human annotations and then apply it to a study of 2,697 folktales from 55 countries. Our findings reveal: (1) broad distribution of moral foundations across cultures, (2) significant cross-cultural consistency with some key regional differences, and (3) a more balanced distribution of positive and negative moral content than suggested by prior work. This work connects MFT and computational narrative analysis, demonstrating LLMs’ potential for scalable moral reasoning in narratives.
Probing and Boosting Large Language Models Capabilities via Attention Heads
Authors: Dezhi Zhao; Xin Liu; Xiaocheng Feng; Hui Wang; Bing Qin
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Understanding the internal origins of capabilities in large language models (LLMs) is crucial for interpretability and efficient adaptation. However, the emergence of specific capabilities remains poorly understood, as most existing approaches rely on external signals (e.g., performance shifts or gradient similarities) with limited structural grounding. To address these issues, this paper proposes a lightweight and highly interpretable approach that links LLM capabilities to internal components by identifying correspondences at the level of attention heads. Specifically, we first define five fundamental capabilities, namely Mathematical Reasoning, Reading Comprehension, Commonsense Reasoning, Scientific Reasoning, and Professional Expertise, and employ probing techniques to detect the attention heads most predictive of each, thereby establishing capability–head mappings. For targeted instruction tuning, complex tasks are decomposed into these fundamental capabilities, and training data are selected accordingly. Experiments on LLaMA3.1-8B and Qwen2.5-7B show over 70% discrimination accuracy in identifying capabilities. On MMLU and BBH, our method improves accuracy by 1 to 1.5 points over the gradient-based method LESS and by 5 to 6 points over other intermediate-state baselines.
A Survey of Link Prediction in N-ary Knowledge Graphs
Authors: Jiyao Wei; Saiping Guan; Da Li; Zhongni Hou; Miao Su; Yucan Guo; Xiaolong Jin; Jiafeng Guo; Xueqi Cheng
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: N-ary Knowledge Graphs (NKGs) are a specialized type of knowledge graph designed to efficiently represent complex real-world facts. Unlike traditional knowledge graphs, where a fact typically involves two entities, NKGs can capture n-ary facts containing more than two entities. Link prediction in NKGs aims to predict missing elements within these n-ary facts, which is essential for completing NKGs and improving the performance of downstream applications. This task has recently gained significant attention. In this paper, we present the first comprehensive survey of link prediction in NKGs, providing an overview of the field, systematically categorizing existing methods, and analyzing their performance and application scenarios. We also outline promising directions for future research.
Multi-Frequency Contrastive Decoding: Alleviating Hallucinations for Large Vision-Language Models
Authors: Bingqian Liu; Fu Zhang; Guoqing Chen; Jingwei Cheng
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Large visual-language models (LVLMs) have demonstrated remarkable performance in visual-language tasks. However, object hallucination remains a significant challenge for LVLMs. Existing studies attribute object hallucinations in LVLMs mainly to linguistic priors and data biases. We further explore the causes of object hallucinations from the perspective of frequency domain and reveal that insufficient frequency information in images amplifies these linguistic priors, increasing the likelihood of hallucinations. To mitigate this issue, we propose the Multi-Frequency Contrastive Decoding (MFCD) method, a simple yet trainingfree approach that removes the hallucination distribution in the original output distribution, which arises from LVLMs neglecting the high-frequency information or low-frequency information in the image input. Without compromising the general capabilities of LVLMs, the proposed MFCD effectively mitigates the object hallucinations in LVLMs. Our experiments demonstrate that MFCD significantly mitigates object hallucination across diverse large-scale vision-language models, without requiring additional training or external tools. In addition, MFCD can be applied to various LVLMs without modifying model architecture or requiring additional training, demonstrating its generality and robustness. Codes are available at https://github.com/liubq-dev/mfcd.
ORPP: Self-Optimizing Role-playing Prompts to Enhance Language Model Capabilities
Authors: Yifan Duan; Yihong Tang; Kehai Chen; Liqiang Nie; Min Zhang
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: High-quality prompts are crucial for eliciting outstanding performance from large language models (LLMs) on complex tasks. Existing research has explored model-driven strategies for prompt optimization. However, these methods often suffer from high computational overhead or require strong optimization capabilities from the model itself, which limits their broad applicability.To address these challenges, we propose ORPP, a framework that enhances model performance by optimizing and generating role-playing prompts. The core idea of ORPP is to confine the prompt search space to role-playing scenarios, thereby fully activating the model’s intrinsic capabilities through carefully crafted, high-quality role-playing prompts. Specifically, ORPP first performs iterative optimization on a small subset of training samples to generate high-quality role-playing prompts. Then, leveraging the model’s few-shot learning capability, it transfers the optimization experience to efficiently generate suitable prompts for the remaining samples.Our experimental results show that ORPP not only matches but in most cases surpasses existing mainstream prompt optimization methods in terms of performance. Notably, ORPP suggests great “plug-and-play” capability. In most cases, it can be integrated with various other prompt methods and further enhance their effectiveness.
BrailleLLM: Braille Instruction Tuning with Large Language Models for Braille Domain Tasks
Authors: Tianyuan Huang; Zepeng Zhu; Hangdi Xing; Zirui Shao; Zhi Yu; Chaoxiong Yang; Jiaxian He; Xiaozhong Liu; Jiajun Bu
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Braille plays a vital role in education and information accessibility for visually impaired individuals. However, Braille information processing faces challenges such as data scarcity and ambiguities in mixed-text contexts. We construct English and Chinese Braille Mixed Datasets (EBMD/CBMD) with mathematical formulas to support diverse Braille domain research, and propose a syntax tree-based augmentation method tailored for Braille data. To address the underperformance of traditional fine-tuning methods in braille-related tasks, we investigate Braille Knowledge-Based Fine-Tuning (BKFT), which reduces the learning difficulty of Braille contextual features. BrailleLLM employs BKFT via instruction tuning to achieve unified Braille translation, formula-to-Braille conversion, and mixed-text translation. Experiments demonstrate that BKFT achieves significant performance improvements over conventional fine-tuning in Braille translation scenarios. Our open-sourced datasets and methodologies establish a foundation for low-resource multilingual Braille research.
MAviS: A Multimodal Conversational Assistant For Avian Species
Authors: Yevheniia Kryklyvets; Mohammed Irfan Kurpath; Sahal Shaji Mullappilly; Jinxing Zhou; Fahad Shahbaz Khan; Rao Muhammad Anwer; Salman Khan; Hisham Cholakkal
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Fine-grained understanding and species-specific, multimodal question answering are vital for advancing biodiversity conservation and ecological monitoring. However, existing multimodal large language models (MM-LLMs) face challenges when it comes to specialized topics like avian species, making it harder to provide accurate and contextually relevant information in these areas. To address this limitation, we introduce the **MAviS-Dataset**, a large-scale multimodal avian species dataset that integrates image, audio, and text modalities for over 1,000 bird species, comprising both pretraining and instruction-tuning subsets enriched with structured question–answer pairs. Building on the MAviS-Dataset, we introduce **MAviS-Chat**, a multimodal LLM that supports audio, vision, and text designed for fine-grained species understanding, multimodal question answering, and scene-specific description generation. Finally, for quantitative evaluation, we present **MAviS-Bench**, a benchmark of over 25,000 Q&A pairs designed to assess avian species-specific perceptual and reasoning abilities across modalities. Experimental results show that MAviS-Chat outperforms the baseline MiniCPM-o-2.6 by a large margin, achieving state-of-the-art open-source results and demonstrating the effectiveness of our instruction-tuned MAviS-Dataset. Our findings highlight the necessity of domain-adaptive MM-LLMs for ecological applications. Our code, training data, evaluation benchmark, and models are available at https://github.com/yevheniia-uv/MAviS.
Refining Text Generation for Realistic Conversational Recommendation via Direct Preference Optimization
Authors: Manato Tajiri; Michimasa Inaba
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Conversational Recommender Systems (CRSs) aim to elicit user preferences via natural dialogue to provide suitable item recommendations. However, current CRSs often deviate from realistic human interactions by rapidly recommending items in brief sessions. This work addresses this gap by leveraging Large Language Models (LLMs) to generate dialogue summaries from dialogue history and item recommendation information from item description. This approach enables the extraction of both explicit user statements and implicit preferences inferred from the dialogue context. We introduce a method using Direct Preference Optimization (DPO) to ensure dialogue summary and item recommendation information are rich in information crucial for effective recommendations. Experiments on two public datasets validate our method’s effectiveness in fostering more natural and realistic conversational recommendation processes. Our implementation is publicly available at: https://github.com/UEC-InabaLab/Refining-LLM-Text
Large Language Models Threaten Language’s Epistemic and Communicative Foundations
Authors: Shashank Srivastava
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Large language models are reshaping the norms of human communication, sometimes decou- pling words from genuine human thought. This transformation is deep, and undermines norms historically tied to authorship of text. We draw from linguistic philosophy and AI ethics to detail how large-scale text genera- tion can induce semantic drift, erode account- ability, and obfuscate intent and authorship. Our work here introduces hybrid authorship graphs (modeling humans, LLMs, and texts in a provenance network), epistemic doppel- gängers (LLM-generated texts that are indis- tinguishable from human-authored texts), and authorship entropy. We explore mechanisms such as “proof-of-interaction” authorship veri- fication and educational reforms to restore con- fidence in language. LLMs’ benefits (broader access, increased fluency, automation, etc.) are undeniable, but the upheavals they introduce to the linguistic landscape demand reckoning.
Detecting Knowledge Boundary of Vision Large Language Models by Sampling-Based Inference
Authors: Zhuo Chen; Xinyu Wang; Yong Jiang; Zhen Zhang; Xinyu Geng; Pengjun Xie; Fei Huang; Kewei Tu
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Despite the advancements made in Vision Large Language Models (VLLMs), like text Large Language Models (LLMs), they have limitations in addressing questions that require real-time information or are knowledge-intensive. Indiscriminately adopting Retrieval Augmented Generation (RAG) techniques is an effective yet expensive way to enable models to answer queries beyond their knowledge scopes. To mitigate the dependence on retrieval and simultaneously maintain, or even improve, the performance benefits provided by retrieval, we propose a method to detect the knowledge boundary of VLLMs, allowing for more efficient use of techniques like RAG. Specifically, we propose a method with two variants that fine-tune a VLLM on an automatically constructed dataset for boundary identification. Experimental results on various types of Visual Question Answering datasets show that our method successfully depicts a VLLM’s knowledge boundary, based on which we are able to reduce indiscriminate retrieval while maintaining or improving the performance. In addition, we show that the knowledge boundary identified by our method for one VLLM can be used as a surrogate boundary for other VLLMs. Code will be released at https://github.com/Chord-Chen-30/VLLM-KnowledgeBoundary