A Silver Bullet or a Compromise for Full Attention? A Comprehensive Study of Gist Token-based Context Compression
Chenlong Deng, Zhisong Zhang, Kelong Mao, Shuaiyi Li, Xinting Huang, Dong Yu, Zhicheng Dou
2024-12-27
Summary
This paper talks about gist token-based context compression methods, which aim to improve how large language models handle long pieces of text by simplifying the information they process.
What's the problem?
Large language models often struggle with long contexts because they need to remember and process a lot of information at once. Traditional methods, like full attention, can be resource-intensive and slow, making it hard for these models to perform well in tasks that require understanding lengthy texts. Additionally, compressing this information can lead to loss of important details, which can affect performance.
What's the solution?
The authors investigate how gist token-based compression can help solve these issues. They explore two main questions: how well these methods can replace full attention models and what problems might arise from compressing the information. Through their experiments, they find that while gist-based methods work well for many tasks, they also face challenges in specific situations. To improve performance, they propose two strategies: fine-grained autoencoding, which helps reconstruct original information better, and segment-wise token importance estimation, which focuses on the most important tokens based on their context.
Why it matters?
This research is important because it provides new insights into how we can make large language models more efficient when dealing with long texts. By improving context compression techniques, we can enhance the models' ability to understand and generate text accurately, which is crucial for many applications like chatbots, translation services, and content generation.
Abstract
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.