Summary of Quito-x: a New Perspective on Context Compression From the Information Bottleneck Theory, by Yihang Wang et al.
QUITO-X: A New Perspective on Context Compression from the Information Bottleneck Theory
by Yihang Wang, Xu Huang, Bowen Tian, Yueyang Su, Lei Yu, Huaming Liao, Yixing Fan, Jiafeng Guo, Xueqi Cheng
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract describes a study that addresses the challenge of compressing long contexts in generative language models (LLMs) for industrial applications. The issue arises when LLMs are used for complex tasks, where excessive context leads to high costs and inference delays. To overcome this, researchers introduce information bottleneck theory (IB), which models the problem and provides a novel perspective on context compression. A cross-attention-based approach is proposed to approximate mutual information in IB, allowing for flexible alternatives in different scenarios. Experimental results on four datasets show that the method achieves a 25% increase in compression rate compared to state-of-the-art methods while maintaining question answering performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study aims to help generative language models (LLMs) work better with long contexts. Right now, LLMs struggle when they have too much information to consider. This makes them slow and expensive to use. The researchers found a new way to think about this problem using something called information bottleneck theory. They also created a special method that helps LLMs focus on the most important parts of the context. By doing so, LLMs can learn more quickly and make better decisions. |
Keywords
» Artificial intelligence » Cross attention » Inference » Question answering
