GenAI4ELab/papercli-papers huggingface dataset:
Shadow-Activated Backdoor Attacks on Multimodal Large Language Models
Authors: Ziyi Yin; Muchao Ye; Yuanpu Cao; Jiaqi Wang; Aofei Chang; Han Liu; Jinghui Chen; Ting Wang; Fenglong Ma
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: This paper delves into a novel backdoor attack scenario, aiming to uncover potential security risks associated with Multimodal Large Language Models (MLLMs) during multi-round open-ended conversations with users. In the practical use of MLLMs, users have full control over the interaction process with the model, such as using their own collected photos and posing arbitrary open-ended questions. Traditional backdoor attacks that rely on adding external triggers are less applicable. To this end, we introduce a new shadow-activated backdoor attacking paradigm in this paper, wherein attacks implicitly inject malicious content into the responses of MLLMs when the responses explicitly relate to the shadowed object, i.e., without any triggers. To facilitate the shadow-activated backdoor attack, we present a novel framework named BadMLLM to achieve the desired behaviors by constructing a poisoned dataset using GPT-4 Vision and implementing an attention-regularized tuning strategy to address the semantic discontinuity between the original response and the inserted promotion. Extensive experimental results conducted on five MLLMs, three objects, and two types of promotion slogans have demonstrated impressive performance in achieving both efficacy and utility goals, thereby highlighting the significant potential risks concealed within MLLMs.
Why Vision Language Models Struggle with Visual Arithmetic? Towards Enhanced Chart and Geometry Understanding
Authors: Kung-Hsiang Huang; Can Qin; Haoyi Qiu; Philippe Laban; Shafiq Joty; Caiming Xiong; Chien-Sheng Wu
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Vision Language Models (VLMs) have achieved remarkable progress in multimodal tasks, yet they often struggle with visual arithmetic, seemingly simple capabilities like object counting or length comparison, which are essential for relevant complex tasks like chart understanding and geometric reasoning. In this work, we first investigate the root causes of this deficiency through a suite of probing tasks focusing on basic visual arithmetic. Our analysis reveals that while pre-trained vision encoders typically capture sufficient information, the text decoder often fails to decode it correctly for arithmetic reasoning. To address this, we propose CogAlign, a novel post-training strategy inspired by Piaget’s theory of cognitive development. CogAlign trains VLMs to recognize invariant properties under visual transformations. We demonstrate that this approach significantly improves the performance of three diverse VLMs on our proposed probing tasks. Furthermore, CogAlign enhances performance by an average of 4.6% on CHOCOLATE and 2.9% on MATH-VISION, outperforming or matching supervised fine-tuning methods while requiring only 60% less training data. These results highlight the effectiveness and generalizability of CogAlign in improving fundamental visual arithmetic capabilities and their transfer to downstream tasks.
K-order Ranking Preference Optimization for Large Language Models
Authors: Shihao Cai; Chongming Gao; Yang Zhang; Wentao Shi; Jizhi Zhang; Keqin Bao; Qifan Wang; Fuli Feng
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: To adapt large language models (LLMs) to ranking tasks, existing list-wise methods, represented by list-wise Direct Preference Optimization (DPO), focus on optimizing partial-order or full-order list ranking consistency for LLMs to enhance their ranking abilities.However, we argue that optimizing top-K ranking consistency could be more appropriate for real-world applications. There are two main reasons: (1) users are typically concerned with only the top-K results, making top-K ranking more important, and (2) tail items often lack precise feedback, making top-K ranking more reliable. Based on this, we propose K-order Ranking Preference Optimization (KPO) by extending the DPO’s Plackett-Luce model to accommodate top-K rankings. Additionally, recognizing that the number of important items can vary across queries, we extend KPO to dynamically determine appropriate K for different samples and introduce a curriculum learning strategy to boost training efficiency. Extensive experiments demonstrate the effectiveness of KPO, highlighting its high sample efficiency and robustness to noise. The code is available at https://github.com/Lanyu0303/KPO.
Spectral Insights into Data-Oblivious Critical Layers in Large Language Models
Authors: Xuyuan Liu; Lei Hsiung; Yaoqing Yang; Yujun Yan
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Understanding how feature representations evolve across layers in large language models (LLMs) is key to improving their interpretability and robustness. While recent studies have identified critical layers linked to specific functions or behaviors, these efforts typically rely on data-dependent analyses of fine-tuned models, limiting their use to post-hoc settings. In contrast, we introduce a data-oblivious approach to identify intrinsic critical layers in pre-fine-tuned LLMs by analyzing representation dynamics via Centered Kernel Alignment (CKA). We show that layers with significant shifts in representation space are also those most affected during fine-tuning—a pattern that holds consistently across tasks for a given model. Our spectral analysis further reveals that these shifts are driven by changes in the top principal components, which encode semantic transitions from rationales to conclusions.We further apply these findings to two practical scenarios: efficient domain adaptation, where fine-tuning critical layers leads to greater loss reduction compared to non-critical layers; and backdoor defense, where freezing them reduces attack success rates by up to 40%.
SynFix: Dependency-Aware Program Repair via RelationGraph Analysis
Authors: Xunzhu Tang; Jiechao Gao; Jin Xu; Tiezhu Sun; Yewei Song; Saad Ezzini; Wendkûuni C. Ouédraogo; Jacques Klein; Tegawendé F. Bissyandé
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Recent advancements in large language models (LLMs) have significantly improved software development automation, including bug localization, code synthesis, program repair, and test generation. However, most prior work on program repair focuses on isolated elements, such as classes or functions, neglecting their interdependencies, which limits repair accuracy. We present SynFix, a RelationGraph-based approach that integrates LLMs with structural search and synchronization techniques for coordinated program repair across codebases. SynFix constructs a RelationGraph to capture relationships among classes, functions, variables, and their interactions (e.g., imports, inheritance, dependencies). Each RelationGraph node includes detailed code descriptions to help LLMs understand root causes and retrieve relevant contexts. By analyzing one-hop nodes in the RelationGraph, SynFixensures repairs account for dependent updates across components. Patch validation is conducted using regression tests from the SWE-bench benchmark suite. Evaluated on SWE-bench datasets, SynFix resolves 52.33% of issues in SWE-bench-lite (300 GitHub issues), 55.8% in SWE-bench-verified (500 issues), and 29.86% in SWE-bench-full (2,294 issues), outperforming baselines such as Swe-Agent, Agentless and AutoCodeRover. The codebase is available at https://anonymous.4open.science/r/AutoFix-EC86/.
EXIT: Context-Aware Extractive Compression for Enhancing Retrieval-Augmented Generation
Authors: Taeho Hwang; Sukmin Cho; Soyeong Jeong; Hoyun Song; SeungYoon Han; Jong C. Park
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: We introduce EXIT, an extractive context compression framework that enhances both the effectiveness and efficiency of retrieval-augmented generation (RAG) in question answering (QA). Current RAG systems often struggle when retrieval models fail to rank the most relevant documents, leading to the inclusion of more context at the expense of latency and accuracy. While abstractive compression methods can drastically reduce token counts, their token-by-token generation process significantly increases end-to-end latency. Conversely, existing extractive methods reduce the latency but rely on independent, non-adaptive sentence selection, failing to fully utilize contextual information. EXIT addresses these limitations by classifying sentences from retrieved documents—while preserving their contextual dependencies—enabling parallelizable, context-aware extraction that adapts to query complexity and retrieval quality. Our evaluations on both single-hop and multi-hop QA tasks show that EXIT consistently surpasses existing compression methods and even uncompressed baselines in QA accuracy, while also delivering substantial reductions in inference time and token count. By improving both effectiveness and efficiency, EXIT provides a promising direction for developing scalable, high-quality QA solutions in RAG pipelines. Our code is available at https://github.com/ThisIsHwang/EXIT.
Re-TASK: Revisiting LLM Tasks from Capability, Skill, and Knowledge Perspectives
Authors: Zhihu Wang; Shiwan Zhao; Yu Wang; Heyuan Huang; Sitao Xie; Yubo Zhang; Jiaxin Shi; Zhixing Wang; Hongyan Li; Junchi Yan
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: The Chain-of-Thought (CoT) paradigm has become a pivotal method for solving complex problems with large language models (LLMs). However, its application to domain-specific tasks remains challenging, as LLMs often fail to decompose tasks accurately or execute subtasks effectively. This paper introduces the Re-TASK framework, a novel theoretical model that Revisits LLM Tasks from cApability, Skill, and Knowledge perspectives, drawing on the principles of Bloom’s Taxonomy and Knowledge Space Theory. While CoT provides a workflow-centric perspective on tasks, Re-TASK introduces a Chain-of-Learning (CoL) paradigm that highlights task dependencies on specific capability items, further broken down into their constituent knowledge and skill components. To address CoT failures, we propose a Re-TASK prompting strategy, which strengthens task-relevant capabilities through targeted knowledge injection and skill adaptation. Experiments across diverse domains demonstrate the effectiveness of Re-TASK. In particular, we achieve improvements of 45.00% on Yi-1.5-9B and 24.50% on Llama3-Chinese-8B for legal tasks. These results highlight the potential of Re-TASK to significantly enhance LLM performance and its applicability in specialized domains. We release our code and data at https://github.com/Uylee/Re-TASK.
Unlearning Backdoor Attacks for LLMs with Weak-to-Strong Knowledge Distillation
Authors: Shuai Zhao; Xiaobao Wu; Cong-Duy T Nguyen; Yanhao Jia; Meihuizi Jia; Feng Yichao; Anh Tuan Luu
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Parameter-efficient fine-tuning (PEFT) can bridge the gap between large language models (LLMs) and downstream tasks. However, PEFT has been proven vulnerable to malicious attacks. Research indicates that poisoned LLMs, even after PEFT, retain the capability to activate internalized backdoors when input samples contain predefined triggers. In this paper, we introduce a novel weak-to-strong unlearning algorithm to defend against backdoor attacks based on feature alignment knowledge distillation, named W2SDefense. Specifically, we first train a small-scale language model through full-parameter fine-tuning to serve as the clean teacher model. Then, this teacher model guides the large-scale poisoned student model in unlearning the backdoor, leveraging PEFT. Theoretical analysis suggests that W2SDefense has the potential to enhance the student model’s ability to unlearn backdoor features, preventing the activation of the backdoor. We conduct comprehensive experiments on three state-of-the-art large language models and several different backdoor attack algorithms. Our empirical results demonstrate the outstanding performance of W2SDefense in defending against backdoor attacks without compromising model performance.
Packing Analysis: Packing Is More Appropriate for Large Models or Datasets in Supervised Fine-tuning
Authors: Shuhe Wang; Guoyin Wang; Yizhong Wang; Jiwei Li; Eduard Hovy; Chen Guo
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Packing, initially utilized in the pre-training phase, is an optimization technique designed to maximize hardware resource efficiency by combining different training sequences to fit the model’s maximum input length. Although it has demonstrated effectiveness during pre-training, there remains a lack of comprehensive analysis for the supervised fine-tuning (SFT) stage on the following points: (1) whether packing can effectively enhance training efficiency while maintaining performance, (2) the suitable size of the model and dataset for fine-tuning with the packing method, and (3) whether packing unrelated or related training samples might cause the model to either excessively disregard or over-rely on the context.In this paper, we perform extensive comparisons between SFT methods using padding and packing, covering SFT datasets ranging from 69K to 1.2M and models from 8B to 70B. This provides the first comprehensive analysis of the advantages and limitations of packing versus padding, as well as practical considerations for implementing packing in various training scenarios. Our analysis covers various benchmarks, including knowledge, reasoning, and coding, as well as GPT-based evaluations, time efficiency, and other fine-tuning parameters. We also open-source our code for fine-tuning and evaluation and provide checkpoints fine-tuned on datasets of different sizes, aiming to advance future research on packing methods.
Better Red Teaming via Searching with Large Language Model
Authors: Yongkang Chen; Chongyang Zhao; Jianwen Tian; Guiling Cao; Hu Li; Xiaohui Kuang
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: The safe deployment of large language models (LLMs) necessitates comprehensive safety evaluations through red teaming. However, existing methods face challenges in managing semantic intricacies and optimizing the efficiency of the search process. To overcome these limitations, we propose Better Red Teaming (BRT)—an innovative framework that reconceptualizes test case generation as a strategic planning problem, leveraging Monte Carlo Tree Search (MCTS). A notable advancement of our approach is the incorporation of LLMs as world models, enabling the prediction of state transitions and simulation of long-term outcomes throughout the search process. By jointly optimizing objectives related to conditional mutual information and diversity, we improve the world model’s capacity to follow actions while maintaining output diversity. Extensive experiments conducted across a range of LLM architectures demonstrate that BRT achieves state-of-the-art attack success rates without sacrificing computational efficiency.
AdaV: Adaptive Text-visual Redirection for Vision-Language Models
Authors: Jiayi Han; Liang Du; Yiwen Wu; Guanming Liang; Xiangguo Zhou; Weibo Zheng; Donghong Han; Zixun Sun
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: The success of Vision-Language Models (VLMs) often relies on high-resolution schemes that preserve image details, while these approaches also generate an excess of visual tokens, leading to a substantial decrease in model efficiency. A typical VLM includes a visual encoder, a text encoder, and an LLM. Recent studies suggest pruning visual tokens based on visual and textual priors to accelerate VLMs without additional training costs. However, these methods often overlook prompt semantics or suffer from biased self-attention in the LLM. Inspired by the efficient mechanisms of the human brain for multimodal understanding, we introduce AdaV, a novel training-free visual token pruning method. By emulating the neural pathways that preprocess visual and auditory information before the reasoning stage, we shift text-guided visual attention redirection to the pre-LLM stage, which reduces biased token pruning and enhances model robustness with a limited visual token budget. A Self-adaptive Cross-modality Attention Redirection (SCAR) module is further proposed that effectively merges and redirects visual attention with text-to-image attention. Extensive experiments on seven challenging benchmarks demonstrate that our AdaV achieves SOTA performance in training-free VLM acceleration and can be plug-and-play on various VLMs. We plan to open-source the code upon publication.
MegaAgent: A Large-Scale Autonomous LLM-based Multi-Agent System Without Predefined SOPs
Authors: Qian Wang; Tianyu Wang; Zhenheng Tang; Qinbin Li; Nuo Chen; Jingsheng Liang; Bingsheng He
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: LLM-based multi-agent systems (MAS) have shown promise in tackling complex tasks. However, existing solutions often suffer from limited agent coordination and heavy reliance on predefined Standard Operating Procedures (SOPs), which demand extensive human input. To address these limitations, we propose MegaAgent, a large-scale autonomous LLM-based multi-agent system. MegaAgent generates agents based on task complexity and enables dynamic task decomposition, parallel execution, efficient communication, and comprehensive system monitoring of agents. In evaluations, MegaAgent demonstrates exceptional performance, successfully developing a Gobang game within 800 seconds and scaling up to 590 agents in a national policy simulation to generate multi-domain policies. It significantly outperforms existing systems, such as MetaGPT, in both task completion efficiency and scalability. By eliminating the need for predefined SOPs, MegaAgent demonstrates exceptional scalability and autonomy, setting a foundation for advancing true autonomy in MAS.
Persona-judge: Personalized Alignment of Large Language Models via Token-level Self-judgment
Authors: Xiaotian Zhang; Ruizhe Chen; Yang Feng; Zuozhu Liu
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Aligning language models with human preferences presents significant challenges, particularly in achieving personalization without incurring excessive computational costs. Existing methods rely on reward signals and additional annotated data, limiting their scalability and adaptability to diverse human values. To address these challenges, we introduce Persona-judge, a novel discriminative paradigm that enables training-free personalized alignment with unseen preferences. Instead of optimizing policy parameters through external reward feedback, Persona-judge leverages the intrinsic preference judgment capabilities of the model. Specifically, a draft model generates candidate tokens conditioned on a given preference, while a judge model, embodying another preference, cross-validates the predicted tokens whether to be accepted. Experimental results demonstrate that Persona-judge, using the inherent preference evaluation mechanisms of the model, offers a scalable and computationally efficient solution to personalized alignment, paving the way for more adaptive customized alignment. Our code is available here.
A Self-Distillation Recipe for Neural Machine Translation
Authors: Hongfei Xu; Zhuofei Liang; Qiuhui Liu; Lingling Mu
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Self-distillation distills the deeper sub-networks to the shallower sub-networks without using an extra teacher model, and has been proven effective in improving the performance of a series of computer vision tasks. In this paper, we study the representation-based self-distillation methods for Neural Machine Translation (NMT) considering the efficiency issue with a large vocabulary. We present a rank-order augmented Pearson correlation loss and an iterative distillation method to prevent the discrepancy of predictions between the student and a stronger teacher from disturbing the training. To prevent the teacher from misleading the student’s learning, we utilize a warm-up strategy and present a gradient adaption method to scale down or zero the Knowledge Distillation (KD) gradients which are opposite to the translation. Experiments show that our method can lead to significant improvements over the strong Transformer baseline on low/middle/high-resource tasks, obtaining comparable performance to previous MT KD studies without pre-training a teacher. Deeper Transformer experiments show that our method can lead to comparable or better performance with fewer layers.
BlockPruner: Fine-grained Pruning for Large Language Models
Authors: Longguang Zhong; Fanqi Wan; Ruijun Chen; Xiaojun Quan; Liangzhi Li
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: With the rapid growth in the size and complexity of large language models (LLMs), the costs associated with their training and inference have escalated significantly. Research indicates that certain layers in LLMs harbor substantial redundancy, and pruning these layers has minimal impact on the overall performance. While various layer pruning methods have been developed based on this insight, they generally overlook the finer-grained redundancies within the layers themselves. In this paper, we delve deeper into the architecture of LLMs and demonstrate that finer-grained pruning can be achieved by targeting redundancies in multi-head attention (MHA) and multi-layer perceptron (MLP) blocks. We propose a novel, training-free structured pruning approach called BlockPruner. Unlike existing layer pruning methods, BlockPruner segments each Transformer layer into MHA and MLP blocks. It then assesses the importance of these blocks using perplexity measures and applies a heuristic search for iterative pruning. We applied BlockPruner to LLMs of various sizes and architectures and validated its performance across a wide range of downstream tasks. Experimental results show that BlockPruner achieves more granular and effective pruning compared to state-of-the-art baselines.
Evaluating Implicit Bias in Large Language Models by Attacking From a Psychometric Perspective
Authors: Yuchen Wen; Keping Bi; Wei Chen; Jiafeng Guo; Xueqi Cheng
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: As large language models (LLMs) become an important way of information access, there have been increasing concerns that LLMs may intensify the spread of unethical content, including implicit bias that hurts certain populations without explicit harmful words. In this paper, we conduct a rigorous evaluation of LLMs’ implicit bias towards certain demographics by attacking them from a psychometric perspective to elicit agreements to biased viewpoints. Inspired by psychometric principles in cognitive and social psychology, we propose three attack approaches, i.e., Disguise, Deception, and Teaching. Incorporating the corresponding attack instructions, we built two benchmarks: (1) a bilingual dataset with biased statements covering four bias types (2.7K instances) for extensive comparative analysis, and (2) BUMBLE, a larger benchmark spanning nine common bias types (12.7K instances) for comprehensive evaluation. Extensive evaluation of popular commercial and open-source LLMs shows that our methods can elicit LLMs’ inner bias more effectively than competitive baselines. Our attack methodology and benchmarks offer an effective means of assessing the ethical risks of LLMs, driving progress toward greater accountability in their development.