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    <abstract>Task-agnostic prompt compression leverages the redundancy in natural language to reduce computational overhead and enhance information density within prompts, especially in long-context scenarios. Existing methods predominantly rely on information entropy as the metric to compress lexical units, aiming to achieve minimal information loss. However, these approaches overlook two critical aspects: (i) the importance of attention-critical tokens at the algorithmic level, and (ii) shifts in information entropy during the compression process. Motivated by these challenges, we propose a dynamic attention-aware approach for task-agnostic prompt compression (DAC). This approach effectively integrates entropy and attention information, dynamically sensing entropy shifts during compression to achieve fine-grained prompt compression. Extensive experiments across various domains, including LongBench, GSM8K, and BBH, show that DAC consistently yields robust and substantial improvements across a diverse range of tasks and LLMs, offering compelling evidence of its efficacy.</abstract>
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%0 Conference Proceedings
%T DAC: A Dynamic Attention-aware Approach for Task-Agnostic Prompt Compression
%A Zhao, Yi
%A Li, Zuchao
%A Zhao, Hai
%A Qi, Baoyuan
%A Guoming, Liu
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F zhao-etal-2025-dac
%X Task-agnostic prompt compression leverages the redundancy in natural language to reduce computational overhead and enhance information density within prompts, especially in long-context scenarios. Existing methods predominantly rely on information entropy as the metric to compress lexical units, aiming to achieve minimal information loss. However, these approaches overlook two critical aspects: (i) the importance of attention-critical tokens at the algorithmic level, and (ii) shifts in information entropy during the compression process. Motivated by these challenges, we propose a dynamic attention-aware approach for task-agnostic prompt compression (DAC). This approach effectively integrates entropy and attention information, dynamically sensing entropy shifts during compression to achieve fine-grained prompt compression. Extensive experiments across various domains, including LongBench, GSM8K, and BBH, show that DAC consistently yields robust and substantial improvements across a diverse range of tasks and LLMs, offering compelling evidence of its efficacy.
%R 10.18653/v1/2025.acl-long.952
%U https://aclanthology.org/2025.acl-long.952/
%U https://doi.org/10.18653/v1/2025.acl-long.952
%P 19395-19407

Markdown (Informal)

[DAC: A Dynamic Attention-aware Approach for Task-Agnostic Prompt Compression](https://aclanthology.org/2025.acl-long.952/) (Zhao et al., ACL 2025)

• DAC: A Dynamic Attention-aware Approach for Task-Agnostic Prompt Compression (Zhao et al., ACL 2025)

ACL

• Yi Zhao, Zuchao Li, Hai Zhao, Baoyuan Qi, and Liu Guoming. 2025. DAC: A Dynamic Attention-aware Approach for Task-Agnostic Prompt Compression. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 19395–19407, Vienna, Austria. Association for Computational Linguistics.