@inproceedings{deng-etal-2025-silver,
title = "A Silver Bullet or a Compromise for Full Attention? A Comprehensive Study of Gist Token-based Context Compression",
author = "Deng, Chenlong and
Zhang, Zhisong and
Mao, Kelong and
Li, Shuaiyi and
Huang, Xinting and
Yu, Dong and
Dou, Zhicheng",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.241/",
doi = "10.18653/v1/2025.acl-long.241",
pages = "4861--4879",
ISBN = "979-8-89176-251-0",
abstract = "In this work, we provide an empirical investigation of gist-based context compression methods to improve context processing in large language models. We focus on two key questions: (1) How well can these methods replace full attention models? and (2) What potential failure patterns arise due to compression? Through extensive experiments, we show that while gist-based compression can achieve only slight performance loss on tasks like retrieval-augmented generation and long-document QA, it faces challenges in tasks like synthetic recall. Furthermore, we identify three key failure patterns: lost by the boundary, lost if surprise, and lost along the way. To mitigate these issues, we propose two effective strategies: fine-grained autoencoding, which enhances the reconstruction of original token information, and segment-wise token importance estimation, which adjusts optimization based on token dependencies. Our work provides valuable insights into the understanding of gist token-based context compression and offers practical strategies for improving compression capabilities."
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%0 Conference Proceedings
%T A Silver Bullet or a Compromise for Full Attention? A Comprehensive Study of Gist Token-based Context Compression
%A Deng, Chenlong
%A Zhang, Zhisong
%A Mao, Kelong
%A Li, Shuaiyi
%A Huang, Xinting
%A Yu, Dong
%A Dou, Zhicheng
%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 deng-etal-2025-silver
%X In this work, we provide an empirical investigation of gist-based context compression methods to improve context processing in large language models. We focus on two key questions: (1) How well can these methods replace full attention models? and (2) What potential failure patterns arise due to compression? Through extensive experiments, we show that while gist-based compression can achieve only slight performance loss on tasks like retrieval-augmented generation and long-document QA, it faces challenges in tasks like synthetic recall. Furthermore, we identify three key failure patterns: lost by the boundary, lost if surprise, and lost along the way. To mitigate these issues, we propose two effective strategies: fine-grained autoencoding, which enhances the reconstruction of original token information, and segment-wise token importance estimation, which adjusts optimization based on token dependencies. Our work provides valuable insights into the understanding of gist token-based context compression and offers practical strategies for improving compression capabilities.
%R 10.18653/v1/2025.acl-long.241
%U https://aclanthology.org/2025.acl-long.241/
%U https://doi.org/10.18653/v1/2025.acl-long.241
%P 4861-4879
Markdown (Informal)
[A Silver Bullet or a Compromise for Full Attention? A Comprehensive Study of Gist Token-based Context Compression](https://aclanthology.org/2025.acl-long.241/) (Deng et al., ACL 2025)
ACL