GenAI4ELab/papercli-papers huggingface dataset:
Unveiling the Lack of LVLM Robustness to Fundamental Visual Variations: Why and Path Forward
Authors: Zhiyuan Fan; Yumeng Wang; Sandeep Polisetty; Yi R. Fung
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Large Vision Language Models (LVLMs) have shown impressive performance on various vision-language tasks. However, while objects in natural scenes inevitably exhibit visual variations in position, scale, orientation, and context due to changes in viewpoint and environment, the robustness of LVLMs to these fundamental visual variations remains largely unexplored. To address this gap, we introduce V²R-Bench, a comprehensive benchmark framework for evaluating Visual Variation Robustness of LVLMs, which encompasses automated evaluation dataset generation and principled metrics for thorough robustness assessment. Through extensive evaluation of 13 LVLMs, we reveal a surprising vulnerability to visual variations, affecting even advanced models that excel at complex vision-language tasks yet significantly underperform on simple tasks like object recognition. Interestingly, these models exhibit a distinct visual position bias that contradicts theories of effective receptive fields and demonstrate a human-like visual acuity threshold. To identify the source of these vulnerabilities, we propose a systematic framework for component-level analysis, featuring a novel visualization approach for aligned visual features. Results show that these vulnerabilities stem from error accumulation in the pipeline architecture and inadequate multimodal alignment. Complementary experiments with synthetic data further demonstrate that these limitations are fundamentally architectural challenges, underscoring the need for architectural innovations in future LVLM designs.
DYNTEXT: Semantic-Aware Dynamic Text Sanitization for Privacy-Preserving LLM Inference
Authors: Juhua Zhang; Zhiliang Tian; Minghang Zhu; Yiping Song; Taishu Sheng; Siyi Yang; Qiunan Du; Xinwang Liu; Minlie Huang; Dongsheng Li
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: LLMs face privacy risks when handling sensitive data. To ensure privacy, researchers use differential privacy (DP) to provide protection by adding noise during LLM training. However, users may be hesitant to share complete data with LLMs. Researchers follow local DP to sanitize the text on the user side and feed non-sensitive text to LLMs. The sanitization usually uses a fixed non-sensitive token list or a fixed noise distribution, which induces the risk of being attacked or semantic distortion. We argue that the token’s protection level should be adaptively adjusted according to its semantic-based information to balance the privacy-utility trade-off. In this paper, we propose DYNTEXT, an LDP-based Dynamic Text sanitization for privacy-preserving LLM inference, which dynamically constructs semantic-aware adjacency lists of sensitive tokens to sample non-sensitive tokens for perturbation. Specifically, DYNTEXT first develops a semantic-based density modeling under DP to extract each token’s density information. We propose token-level smoothing sensitivity by combining the idea of global sensitivity (GS) and local sensitivity (LS), which dynamically adjusts the noise scale to avoid excessive noise in GS and privacy leakage in LS. Then, we dynamically construct an adjacency list for each sensitive token based on its semantic density information. Finally, we apply the replacement mechanism to sample non-sensitive, semantically similar tokens from the adjacency list to replace sensitive tokens. Experiments show that DYNTEXT excels strong baselines on three datasets.
InImageTrans: Multimodal LLM-based Text Image Machine Translation
Authors: Fei Zuo; Kehai Chen; Yu Zhang; Zhengshan Xue; Min Zhang
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Multimodal large language models (MLLMs) have shown remarkable capabilities across various downstream tasks. However, when MLLMs are transferred to the text image machine translation (TiMT) task, preliminary experiments reveal that MLLMs suffer from serious repetition and omission hallucinations. To alleviate these issues, this paper first designs an efficient MLLM named InImageTrans for TiMT and then proposes a simple and effective method named multi-conditional direct preference optimization (mcDPO) for advancing the TiMT. Particularly, the proposed mcDPO not only guides the MLLM in rejecting repetition output by creating text output preference pairs automatically, but also guides the MLLM in paying more attention to text information in images by creating image input preference pairs. Furthermore, we build a high-quality benchmark called MCiT for comprehensively evaluating the TiMT capabilities of InImageTrans. Experimental results show that the proposed method significantly outperforms existing open-source MLLMs on MCiT.
FRAME: Boosting LLMs with A Four-Quadrant Multi-Stage Pretraining Strategy
Authors: Xuemiao Zhang; Feiyu Duan; Xu Liangyu; Yongwei Zhou; Sirui Wang; Rongxiang Weng; Jingang Wang; Xunliang Cai
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Large language models (LLMs) have significantly advanced human language understanding and generation, with pretraining data quality and organization being crucial to their performance. Multi-stage pretraining is a promising approach, but existing methods often lack quantitative criteria for data partitioning and instead rely on intuitive heuristics. In this paper, we propose the novel Four-quadRAnt Multi-stage prEtraining strategy (FRAME), guided by the established principle of organizing the pretraining process into four stages to achieve significant loss reductions four times. This principle is grounded in two key findings: first, training on high Perplexity (PPL) data followed by low PPL data, and second, training on low PPL difference (PD) data followed by high PD data, both causing the loss to drop significantly twice and performance enhancements. By partitioning data into four quadrants and strategically organizing them, FRAME achieves a remarkable 16.8% average improvement over random across MMLU and CMMLU for the 3B model, effectively boosting LLM performance.
When Large Language Models Meet Speech: A Survey on Integration Approaches
Authors: Zhengdong Yang; Shuichiro Shimizu; Yahan Yu; Chenhui Chu
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Recent advancements in large language models (LLMs) have spurred interest in expanding their application beyond text-based tasks. A large number of studies have explored integrating other modalities with LLMs, notably speech modality, which is naturally related to text. This paper surveys the integration of speech with LLMs, categorizing the methodologies into three primary approaches: text-based, latent-representation-based, and audio-token-based integration. We also demonstrate how these methods are applied across various speech-related applications and highlight the challenges in this field to offer inspiration for future research.
KE-MHISTO: Towards a Multilingual Historical Knowledge Extraction Benchmark for Addressing the Long-Tail Problem
Authors: Arianna Graciotti; Leonardo Piano; Nicolas Lazzari; Enrico Daga; Rocco Tripodi; Valentina Presutti; Livio Pompianu
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Large Language Models (LLMs) face significant challenges when queried about long-tail knowledge, i.e., information that is rarely encountered during their training process. These difficulties arise due to the inherent sparsity of such data. Furthermore, LLMs often lack the ability to verify or ground their responses in authoritative sources, which can lead to plausible yet inaccurate outputs when addressing infrequent subject matter. Our work aims to investigate these phenomena by introducing KE-MHISTO, a multilingual benchmark for Entity Linking and Question Answering in the domain of historical music knowledge, available in both Italian and English. We demonstrate that KE-MHISTO provides significantly broader coverage of long-tail knowledge compared to existing alternatives. Moreover, it poses substantial challenges for state-of-the-art models. Our experiments reveal that smaller, multilingual models can achieve performance comparable to significantly larger counterparts, highlighting the potential of efficient, language-aware approaches for long-tail knowledge extraction. KE-MHISTO is available at: https://github.com/polifonia-project/KE-MHISTO.
TailorKV: A Hybrid Framework for Long-Context Inference via Tailored KV Cache Optimization
Authors: Dingyu Yao; Bowen Shen; Zheng Lin; Wei Liu; Jian Luan; Bin Wang; Weiping Wang
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: The Key-Value (KV) cache in generative large language models (LLMs) introduces substantial memory overhead. Existing works mitigate this burden by offloading or compressing the KV cache. However, loading the entire cache incurs significant latency due to PCIe bandwidth bottlenecks in CPU-GPU communication, while aggressive compression causes notable performance degradation. We identify that certain layers in the LLM need to maintain global information and are unsuitable for selective loading. In contrast, other layers primarily focus on a few tokens with dominant activations that potentially incur substantial quantization error. This observation leads to a key insight that loading dominant tokens and quantizing all tokens can complement each other. Building on this insight, we propose a hybrid compression method, TailorKV, which seamlessly integrates quantization and offloading. TailorKV develops an inference framework along with a hardware-friendly implementation that leverages these complementary characteristics. Extensive long-context evaluations exhibit that TailorKV achieves nearly lossless performance under aggressive compression settings, outperforming the state-of-the-art. Particularly, the Llama-3.1-8B with 128k context can be served within a single RTX 3090 GPU, reaching 82 ms per token during decoding.
The Elephant in the Room: Exploring the Role of Neutral Words in Language Model Group-Agnostic Debiasing
Authors: Xinwei Guo; Jiashi Gao; Junlei Zhou; Jiaxin Zhang; Guanhua Chen; Xiangyu Zhao; Quanying Liu; Haiyan Wu; Xin Yao; Xuetao Wei
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Large Language Models (LLMs) are increasingly integrated into our daily lives, raising significant ethical concerns, especially about perpetuating stereotypes.While group-specific debiasing methods have made progress, they often fail to address multiple biases simultaneously. In contrast, group-agnostic debiasing has the potential to mitigate a variety of biases at once, but remains underexplored.In this work, we investigate the role of neutral words—the group-agnostic component—in enhancing the group-agnostic debiasing process. We first reveal that neutral words are essential for preserving semantic modeling, and we propose 𝜖-DPCE, a method that incorporates a neutral word semantics-based loss function to effectively alleviate the deterioration of the Language Modeling Score (LMS) during the debiasing process. Furthermore, by introducing the SCM-Projection method, we demonstrate that SCM-based debiasing eliminates stereotypes by indirectly disrupting the association between attribute and neutral words in the Stereotype Content Model (SCM) space. Our experiments show that neutral words, which often embed multi-group stereotypical objects, play a key role in contributing to the group-agnostic nature of SCM-based debiasing.
LLMs Can Achieve High-quality Simultaneous Machine Translation as Efficiently as Offline
Authors: Biao Fu; Minpeng Liao; Kai Fan; Chengxi Li; Liang Zhang; Yidong Chen; Xiaodong Shi
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: When the complete source sentence is provided, Large Language Models (LLMs) perform excellently in offline machine translation even with a simple prompt “Translate the following sentence from [src lang] into [tgt lang]:”. However, in many real scenarios, the source tokens arrive in a streaming manner and simultaneous machine translation (SiMT) is required, then the efficiency and performance of decoder-only LLMs are significantly limited by their auto-regressive nature. To enable LLMs to achieve high-quality SiMT as efficiently as offline translation, we propose a novel paradigm that includes constructing supervised fine-tuning (SFT) data for SiMT, along with new training and inference strategies. To replicate the token input/output stream in SiMT, the source and target tokens are rearranged into an interleaved sequence, separated by special tokens according to varying latency requirements. This enables powerful LLMs to learn read and write operations adaptively, based on varying latency prompts, while still maintaining efficient auto-regressive decoding. Experimental results show that, even with limited SFT data, our approach achieves state-of-the-art performance across various SiMT benchmarks and different evaluation metrics, and preserves the original capabilities of offline translation. Moreover, our approach generalizes well to document-level SiMT setting without requiring specific fine-tuning, even beyond the offline translation model.
Beyond Completion: A Foundation Model for General Knowledge Graph Reasoning
Authors: Yin Hua; Zhiqiang Liu; Mingyang Chen; Zheng Fang; Chi Man Wong; Lingxiao Li; Chi Man Vong; Huajun Chen; Wen Zhang
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: In natural language processing (NLP) and computer vision (CV), the successful application of foundation models across diverse tasks has demonstrated their remarkable potential. However, despite the rich structural and textual information embedded in knowledge graphs (KGs), existing research of foundation model for KG has primarily focused on their structural aspects, with most efforts restricted to in-KG tasks (e.g., knowledge graph completion, KGC). This limitation has hindered progress in addressing more challenging out-of-KG tasks. In this paper, we introduce MERRY, a foundation model for general knowledge graph reasoning, and investigate its performance across two task categories: in-KG reasoning tasks (e.g., KGC) and out-of-KG tasks (e.g., KG question answering, KGQA). We not only utilize the structural information, but also the textual information in KGs. Specifically, we propose a multi-perspective Conditional Message Passing (CMP) encoding architecture to bridge the gap between textual and structural modalities, enabling their seamless integration. Additionally, we introduce a dynamic residual fusion module to selectively retain relevant textual information and a flexible edge scoring mechanism to adapt to diverse downstream tasks. Comprehensive evaluations on 28 datasets demonstrate that MERRY outperforms existing baselines in most scenarios, showcasing strong reasoning capabilities within KGs and excellent generalization to out-of-KG tasks such as KGQA.
Generative Error Correction for Emotion-aware Speech-to-text Translation
Authors: Zhengdong Yang; Sheng Li; Chenhui Chu
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: This paper explores emotion-aware speech-to-text translation (ST) using generative error correction (GER) by large language models (LLMs). Despite recent advancements in ST, the impact of the emotional content has been overlooked. First, we enhance the translation of emotional speech by adopting the GER paradigm: Finetuned an LLM to generate the translation based on the decoded N-best hypotheses. Moreover, we combine the emotion and sentiment labels into the LLM finetuning process to enable the model to consider the emotion content. In addition, we project the ST model’s latent representation into the LLM embedding space to further improve emotion recognition and translation. Experiments on an English-Chinese dataset show the effectiveness of the combination of GER, emotion and sentiment labels, and the projector for emotion-aware ST. Our code is available at https://github.com/N-Orien/EmoST.
SynapticRAG: Enhancing Temporal Memory Retrieval in Large Language Models through Synaptic Mechanisms
Authors: Yuki Hou; Haruki Tamoto; Qinghua Zhao; Homei Miyashita
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Existing retrieval methods in Large Language Models show degradation in accuracy when handling temporally distributed conversations, primarily due to their reliance on simple similarity-based retrieval. Unlike existing memory retrieval methods that rely solely on semantic similarity, we propose SynapticRAG, which uniquely combines temporal association triggers with biologically-inspired synaptic propagation mechanisms. Our approach uses temporal association triggers and synaptic-like stimulus propagation to identify relevant dialogue histories. A dynamic leaky integrate-and-fire mechanism then selects the most contextually appropriate memories. Experiments on four datasets of English, Chinese and Japanese show that compared to state-of-the-art memory retrieval methods, SynapticRAG achieves consistent improvements across multiple metrics up to 14.66% points. This work bridges the gap between cognitive science and language model development, providing a new framework for memory management in conversational systems.
Localizing and Mitigating Errors in Long-form Question Answering
Authors: Rachneet Singh Sachdeva; Yixiao Song; Mohit Iyyer; Iryna Gurevych
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Long-form question answering (LFQA) aims to provide thorough and in-depth answers to complex questions, enhancing comprehension. However, such detailed responses are prone to hallucinations and factual inconsistencies, challenging their faithful evaluation. This work introduces HaluQuestQA, the first hallucination dataset with localized error annotations for human-written and model-generated LFQA answers. HaluQuestQA comprises 698 QA pairs with 1.8k span-level error annotations for five different error types by expert annotators, along with preference judgments. Using our collected data, we thoroughly analyze the shortcomings of long-form answers and find that they lack comprehensiveness and provide unhelpful references. We train an automatic feedback model on this dataset that predicts error spans with incomplete information and provides associated explanations. Finally, we propose a prompt-based approach, Error-Informed Refinement, that uses signals from the learned feedback model to refine generated answers, which we show reduces errors and improves the quality of the answers across multiple models. Furthermore, humans find the answers generated by our approach comprehensive and highly prefer them (84%) over the baseline answers.
EMGLLM: Data-to-Text Alignment for Electromyogram Diagnosis Generation with Medical Numerical Data Encoding
Authors: Zefei Long; Zhenbiao Cao; Wei Chen; Zhongyu Wei
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Electromyography (EMG) tables are crucial for diagnosing muscle and nerve disorders, and advancing the automation of EMG diagnostics is significant for improving medical efficiency. EMG tables contain extensive continuous numerical data, which current Large Language Models (LLMs) often struggle to interpret effectively. To address this issue, we propose EMGLLM, a data-to-text model specifically designed for medical examination tables. EMGLLM employs the EMG Alignment Encoder to simulate the process that doctors compare test values with reference values, aligning the data into word embeddings that reflect health degree. Additionally, we construct ETM, a dataset comprising 17,250 real cases and their corresponding diagnostic results, to support medical data-to-text tasks. Experimental results on ETM demonstrate that EMGLLM outperforms various baseline models in understanding EMG tables and generating high-quality diagnoses, which represents an effective paradigm for automatic diagnosis generation from medical examination table.
LLMVoX: Autoregressive Streaming Text-to-Speech Model for Any LLM
Authors: Sambal Shikhar; Mohammed Irfan Kurpath; Sahal Shaji Mullappilly; Jean Lahoud; Fahad Shahbaz Khan; Rao Muhammad Anwer; Salman Khan; Hisham Cholakkal
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Recent advancements in speech-to-speech dialogue systems leverage LLMs for multimodal interactions, yet they remain hindered by fine-tuning requirements, high computational overhead, and text-speech misalignment. Existing speech-enabled LLMs often degrade conversational quality by modifying the LLM, thereby compromising its linguistic capabilities. In contrast, we propose LLMVoX, a lightweight 30M-parameter, LLM-agnostic, autoregressive streaming TTS system that generates high-quality speech with low latency, while fully preserving the capabilities of the base LLM. Our approach achieves a significantly lower Word Error Rate compared to speech-enabled LLMs, while operating at comparable latency. By decoupling speech synthesis from LLM processing via a multi-queue token streaming system, LLMVoX enables seamless, infinite-length dialogues. Its plug-and-play design also facilitates extension to various tasks with different backbones. Furthermore, LLMVoX generalizes to new languages with minimal dataset adaptation, attaining a low Character Error Rate on an Arabic speech task. Evaluations demonstrate that LLMVoX matches or surpasses existing speech-enabled LLMs in both speech quality and latency, while maintaining the original linguistic strengths of the LLM. Additionally, we have integrated LLMVoX with a Vision-Language Model to create an omni-model with speech, text, and vision capabilities, without requiring additional multimodal training.
Act2P: LLM-Driven Online Dialogue Act Classification for Power Analysis
Authors: Wenbo Zhang; Yuhan Wang
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: In team communication, dialogue acts play a crucial role in helping team members understand each other’s intentions and revealing the roles and communication patterns within interactions. Although existing studies have focused on using Dialogue Act classification to capture the speaker’s intentions, few have explored the underlying power dynamics reflected by these dialogue acts. To this end, we present an online Dialogue Act Classification and Dynamic Power Analysis framework—Act2P, which is based on large language model. The framework combines the zero-shot learning capability of LLMs and introduces an online feedback classification method that allows for online classification with iterative feedback to previous stages, achieving efficient and accurate classification without the labeled data. Additionally, we also propose the PowerRank algorithm, which quantifies power dynamics through a graph-based structure. Through comparative experiments with existing methods, we demonstrate the significant superiority of Act2P in online scenarios and successfully visualize dialogue power in online, clearly presenting the distribution and dynamic transfer of power. This framework provides new scientific insights and practical tools for optimizing team collaboration.