Austria

Long-context understanding is crucial for many NLP applications, yet transformers struggle with efficiency due to the quadratic complexity of self-attention. Sparse attention methods alleviate this cost but often impose static, predefined masks, failing to capture heterogeneous attention patterns. This results in suboptimal token interactions, limiting adaptability and retrieval accuracy in long-sequence tasks. This work introduces a dynamic sparse attention mechanism that assigns adaptive masks at the attention-map level, preserving heterogeneous patterns across layers and heads. Unlike existing approaches, our method eliminates the need for fine-tuning and predefined mask structures while maintaining computational efficiency. By learning context-aware attention structures, it achieves high alignment with full-attention models, ensuring minimal performance degradation while reducing memory and compute overhead. This approach provides a scalable alternative to full attention, enabling the practical deployment of large-scale Large Langue Models (LLMs) without sacrificing retrieval performance.

ACL 2025

DAM: Dynamic Attention Mask for Long-Context Large Language Model Inference Acceleration

efficient ai

large language models

sparse attention

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, three amazing keynote events and two new initiatives of invited talks: Spotlight Talks for Young Rising Stars (STIRS) and The Next Big Idea Talks. Posters (including Findings of ACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop will have an oral session and a poster session as part of Poster Session 1. The program also features eight Tutorials and 28 Workshops. 

 
We wish you a wonderful conference! 
[**The ACL 2022 Organizing Committee**](https://www.2022.aclweb.org/organisers)
 
[**Conference Handbook**](https://drive.google.com/file/d/1_BUCMfhMVrjG9E2e71aHdHeE28KSje0l/view?usp=sharing) 
[**Mini Handbook**](https://drive.google.com/file/d/1qlBKl0wzmlVF1oCeMQl3BahLd9nLP5Ce/view?usp=sharing) 
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ACL 2022

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

poster

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.* 
[Read more](https://drive.google.com/file/d/1GI_hvOpjswAuYdUTromfeDiPpCcqidwg/view?usp=sharing)

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Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

With the advent of LLMs and variants, a flurry of research has emerged, analyzing the performance of such models across an array of tasks. While most studies focus on evaluating the capabilities of state-of-the-art (SoTA) Vision Language Models (VLMs) through task accuracy (e.g., visual question answering, grounding), our work explores the related but complementary aspect of consistency – the ability of a VLM to produce semantically similar or identical responses to semantically similar queries. We note that consistency is a fundamental prerequisite (necessary but not sufficient condition) for robustness and trust in VLMs. Armed with this perspective, we propose the MM-Rbenchmark, which allows us to analyze performance, in terms of consistency and accuracy, of SoTA VLMs on three tasks: Question Rephrasing, Image Restyling, and Context Reasoning. Our analysis reveals that consistency does not always align with accuracy, indicating that models with higher accuracy are not necessarily more consistent, and vice versa. Furthermore, we propose a simple yet effective mitigation strategy in the form of an adapter module trained to minimize inconsistency across prompts. With our proposed strategy, we are able to achieve absolute improvements of 5.7% and 12.5%, on average on widely used VLMs such as BLIP-2 and LLaVa 1.5M in terms of consistency over their existing counterparts.

MM-R3: On (In-)Consistency of Vision-Language Models (VLMs)

Understanding how feature representations evolve across layers in large language models (LLMs) is crucial for enhancing their interpretability and robustness. Recent research has explored this by identifying critical layers associated with specific functions or behaviors in LLMs. However, prior work has primarily identified critical layers through data-dependent analyses of fine-tuned models, limiting their applicability to post-hoc scenarios. In contrast, we propose a data-oblivious method to pinpoint intrinsic critical layers in pre-fine-tuned LLMs by analyzing representation dynamics using Centered Kernel Alignment (CKA). We demonstrate that layers exhibiting significant changes in CKA correspond to those undergoing the most substantial modifications during fine-tuning, and we show that this phenomenon remains consistent across tasks for a given architecture. Furthermore, our novel spectral analysis reveals that shifts in layer representations are driven by changes in their top principal components, which govern semantic transformations from rationales to conclusions. Notably, freezing these layers during fine-tuning reduces adversarial attack success rates by 40%, enabling efficient, minimally invasive defenses. Our findings suggest that critical layers are an intrinsic property of pre-fine-tuned models, providing a unified framework for understanding LLM layer dynamics and facilitating resource-efficient adaptation strategies.

Spectral Insights into Data-Oblivious Critical Layers in Large Language Models

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.

AdaV: Adaptive Text-visual Redirection for Vision-Language Models

As the scale of large language models (LLMs) grows and natural language tasks become increasingly diverse, Parameter-Efficient Fine-Tuning (PEFT) has become the standard paradigm for fine-tuning LLMs. Among PEFT methods, LoRA is widely adopted for not introducing additional inference overhead. However, existing LoRA’s shared parameter space paradigm introduces parameter interference, leading to a gap in generalization performance for specific tasks compared to full fine-tuning. To address this issue, we propose a parameter-separated low-rank adapter, called Subspace Low-Rank Adaptation (SuLoRA). The core idea of SuLoRA is to account for task differences by decomposing LoRA’s parameter matrix into multiple independent subspaces and assigning them differentially to distinct tasks. This prevents interference across tasks and enhances the effectiveness of low-rank adaptation. Additionally, SuLoRA achieves higher rank expansion by freezing the A matrix, further improving generalization capability. We conduct extensive experiments on various NLP tasks, demonstrating that SuLoRA significantly outperforms LoRA in trainable parameter efficiency and overall model performance. Furthermore, we validate SuLoRA’s effectiveness in domain generalization and multi-modal tasks, showcasing its strong generalization ability.

SuLoRA: Subspace Low-Rank Adaptation for Parameter-Efficient Fine-Tuning

Hierarchical Merging is a technique commonly used to summarize very long texts (>100K tokens) by breaking down the input into smaller sections, summarizing those sections individually, and then merging or combining those summaries into a final coherent summary. Although it helps address the limitations of large language models (LLMs) with fixed input length constraints, the recursive merging process can amplify LLM hallucinations, increasing the risk of factual inaccuracies. In this paper, we seek to mitigate hallucinations by enriching hierarchical merging with context from the source document. Specifically, we propose different approaches to contextual augmentation ranging from *replacing* intermediate summaries with relevant input context, to *refining* them while using the context as supporting evidence, and *aligning* them implicitly (via citations) to the input. Experimental results on datasets representing legal and narrative domains show that contextual augmentation consistently outperforms zero-shot and hierarchical merging baselines for the Llama 3.1 model family. Our analysis further reveals that refinement methods tend to perform best when paired with extractive summarization for identifying relevant input.

Context-Aware Hierarchical Merging for Long Document Summarization

Detecting rumors on social media has become a crucial issue. Propagation structure-based methods have recently attracted increasing attention. When the propagation structure is represented by the dynamic graph, temporal information is considered. However, existing rumor detection models using dynamic graph typically focus only on coarse-grained temporal information and ignore the fine-grained temporal dynamics within individual snapshots and across snapshots. In this paper, we propose a novel Fine-Grained Dynamic Graph Neural Network (FGDGNN) model, which can incorporate the fine-grained temporal information of dynamic propagation graph in the intra-snapshot and dynamic embedding update mechanism in the inter-snapshots into a unified framework for rumor detection. Specifically, we firstly construct the edge-weighed propagation graph and the edge-aware graph isomorphism network is proposed. To get fine-grained temporal representations across snapshots, we propose an embedding transformation layer to update node embedding. Finally, we integrate the temporal information in the inter-snapshots at the graph level to enhance the effectiveness of the proposed model. Extensive experiments conducted on three public real-world datasets demonstrate that our FGDGNN model achieves significant improvements compared with the state-of-the-art baselines.

FGDGNN: Fine-Grained Dynamic Graph Neural Network for Rumor Detection on Social Media

Information retrieval has evolved from traditional sparse and dense retrieval methods to approaches driven by large language models (LLMs). Recent techniques, such as Generation-Augmented Retrieval (GAR) and Generative Document Retrieval (GDR), leverage LLMs to enhance retrieval but face key challenges: GAR’s generated content may not always align with the target document corpus, while GDR limits the generative capacity of LLMs by constraining outputs to predefined document identifiers. To address these issues, we propose Context-Aware Generation-Augmented Retrieval (CA-GAR), which enhances LLMs by integrating corpus information into their generation process. CA-GAR optimizes token selection by incorporating relevant document information and leverages a Distribution Alignment Strategy to extract corpus information using a lexicon-based approach. Experimental evaluations on seven tasks from the BEIR benchmark and four non-English languages from Mr.TyDi demonstrate that CA-GAR outperforms existing methods.

CA-GAR: Context-Aware Alignment of LLM Generation for Document Retrieval

Recent success in large multimodal models (LMMs) has sparked promising applications of agents capable of autonomously completing complex web tasks. While open-source LMM agents have made significant advances in offline evaluation benchmarks, their performance still falls substantially short of human-level capabilities in more realistic online settings. A key bottleneck is the lack of diverse and large-scale trajectory-level datasets across various domains, which are expensive to collect. In this paper, we address this challenge by developing a scalable recipe to synthesize the largest and most diverse trajectory-level dataset to date, containing over 94K successful multimodal web trajectories, spanning 49K unique URLs, 720K screenshots, and 33M web elements. In particular, we leverage extensive web exploration and refinement to obtain diverse task intents. The average cost is 28 cents per successful trajectory, making it affordable to a wide range of users in the community. Leveraging this dataset, we train Explorer, a multimodal web agent, and demonstrate strong performance on both offline and online web agent benchmarks such as Mind2Web-Live, Multimodal-Mind2Web, and MiniWob++. Additionally, our experiments highlight data scaling as a key driver for improving web agent capabilities. We hope this study makes state-of-the-art LMM-based agent research at a larger scale more accessible.

Explorer: Scaling Exploration-driven Web Trajectory Synthesis for Multimodal Web Agents

In Retrieval-Augmented Generation (RAG) and agent-based frameworks, the "Chain of Models" approach is widely used, where multiple specialized models work sequentially on distinct sub-tasks. This approach is effective but increases resource demands as each model must be deployed separately. Recent advancements attempt to address this by applying prompt tuning, which allows a shared base model to adapt to multiple tasks with minimal parameter changes. However, a key challenge remains: intermediate outputs, passed between models as plain text, require recomputation of hidden states (i.e., Key and Value (KV) states in Transformers) during inference. In this paper, we introduce FTHSS, a novel prompt-tuning method that enables models to share KV hidden states, eliminating redundant forward passes and reducing KV cache storage. By modifying input and attention masks during training, FTHSS allows models to effectively utilize KV hidden states from prior models in both single- and multi-round scenarios. Empirical results on four tasks show that FTHSS matches the performance of traditional model chains while improving inference efficiency.

Streamlining the Collaborative Chain of Models into A Single Forward Pass in Generation-Based Tasks

Large Language Models (LLMs) have shown remarkable capabilities across various tasks, but their deployment in high-stake domains requires consistent performance across multiple interaction rounds. This paper introduces a comprehensive framework for evaluating and improving LLM response consistency, making three key contributions. First, we propose a novel Position-Weighted Consistency (PWC) score that captures both the importance of early-stage stability and recovery patterns in multi-turn interactions. Second, we present a carefully curated benchmark dataset spanning diverse domains and difficulty levels, specifically designed to evaluate LLM consistency under various challenging follow-up scenarios. Third, we introduce Confidence-Aware Response Generation (CARG), a framework that significantly improves response stability by incorporating model confidence signals into the generation process. Empirical results demonstrate that CARG significantly improves response stability without sacrificing accuracy, underscoring its potential for reliable LLM deployment in critical applications.

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MM-R3: On (In-)Consistency of Vision-Language Models (VLMs)

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