@inproceedings{tang-etal-2025-perception,
title = "Perception Compressor: A Training-Free Prompt Compression Framework in Long Context Scenarios",
author = "Tang, Jiwei and
Xu, Jin and
Lu, Tingwei and
Zhang, Zhicheng and
YimingZhao, YimingZhao and
LinHai, LinHai and
Zheng, Hai-Tao",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-naacl.229/",
doi = "10.18653/v1/2025.findings-naacl.229",
pages = "4093--4108",
ISBN = "979-8-89176-195-7",
abstract = "Large language models (LLMs) demonstrate exceptional capabilities in various scenarios. However, they suffer from much redundant information and are sensitive to the position of key information in long context scenarios. To address these challenges, we present Perception Compressor, a training-free prompt compression framework. It includes a perception retriever that leverages guiding questions and instruction to retrieve the most relevant demonstrations, a dual-slope ratio allocator to dynamically allocate compression ratios and open-book ratios, and a semi-guided iterative compression that retains key information at the token level while removing tokens that distract the LLM. We conduct extensive experiments on long context benchmarks, i.e., NaturalQuestions, LongBench, and MuSiQue. Experiment results show that Perception Compressor outperforms existing methods by a large margin, achieving state-of-the-art performance."
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<abstract>Large language models (LLMs) demonstrate exceptional capabilities in various scenarios. However, they suffer from much redundant information and are sensitive to the position of key information in long context scenarios. To address these challenges, we present Perception Compressor, a training-free prompt compression framework. It includes a perception retriever that leverages guiding questions and instruction to retrieve the most relevant demonstrations, a dual-slope ratio allocator to dynamically allocate compression ratios and open-book ratios, and a semi-guided iterative compression that retains key information at the token level while removing tokens that distract the LLM. We conduct extensive experiments on long context benchmarks, i.e., NaturalQuestions, LongBench, and MuSiQue. Experiment results show that Perception Compressor outperforms existing methods by a large margin, achieving state-of-the-art performance.</abstract>
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%0 Conference Proceedings
%T Perception Compressor: A Training-Free Prompt Compression Framework in Long Context Scenarios
%A Tang, Jiwei
%A Xu, Jin
%A Lu, Tingwei
%A Zhang, Zhicheng
%A YimingZhao, YimingZhao
%A LinHai, LinHai
%A Zheng, Hai-Tao
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F tang-etal-2025-perception
%X Large language models (LLMs) demonstrate exceptional capabilities in various scenarios. However, they suffer from much redundant information and are sensitive to the position of key information in long context scenarios. To address these challenges, we present Perception Compressor, a training-free prompt compression framework. It includes a perception retriever that leverages guiding questions and instruction to retrieve the most relevant demonstrations, a dual-slope ratio allocator to dynamically allocate compression ratios and open-book ratios, and a semi-guided iterative compression that retains key information at the token level while removing tokens that distract the LLM. We conduct extensive experiments on long context benchmarks, i.e., NaturalQuestions, LongBench, and MuSiQue. Experiment results show that Perception Compressor outperforms existing methods by a large margin, achieving state-of-the-art performance.
%R 10.18653/v1/2025.findings-naacl.229
%U https://aclanthology.org/2025.findings-naacl.229/
%U https://doi.org/10.18653/v1/2025.findings-naacl.229
%P 4093-4108
Markdown (Informal)
[Perception Compressor: A Training-Free Prompt Compression Framework in Long Context Scenarios](https://aclanthology.org/2025.findings-naacl.229/) (Tang et al., Findings 2025)
ACL