A Silver Bullet or a Compromise for Full Attention? A Comprehensive
  Study of Gist Token-based Context Compression
Authors: name: Chenlong Deng, name: Zhisong Zhang, name: Kelong Mao, name: Shuaiyi Li, name: Xinting Huang, name: Dong Yu, name: Zhicheng Dou

In this work, we provide a thorough investigation of gist-based context
compression methods to improve long-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 near-lossless performance 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.