DAC: A Dynamic Attention-aware Approach for Task-Agnostic Prompt Compression
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866911059422478336 |
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| author | Zhao, Yi Li, Zuchao Zhao, Hai Qi, Baoyuan Liu, Guoming |
| author_facet | Zhao, Yi Li, Zuchao Zhao, Hai Qi, Baoyuan Liu, Guoming |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_11942 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DAC: A Dynamic Attention-aware Approach for Task-Agnostic Prompt Compression Zhao, Yi Li, Zuchao Zhao, Hai Qi, Baoyuan Liu, Guoming Computation and Language 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. |
| title | DAC: A Dynamic Attention-aware Approach for Task-Agnostic Prompt Compression |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2507.11942 |