Summary of An Information Bottleneck Perspective For Effective Noise Filtering on Retrieval-augmented Generation, by Kun Zhu et al.

An Information Bottleneck Perspective for Effective Noise Filtering on Retrieval-Augmented Generation

by Kun Zhu, Xiaocheng Feng, Xiyuan Du, Yuxuan Gu, Weijiang Yu, Haotian Wang, Qianglong Chen, Zheng Chu, Jingchang Chen, Bing Qin

First submitted to arxiv on: 3 Jun 2024

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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 proposed approach integrates large language models with retrieved information from a corpus to improve retrieval-augmented generation. However, existing solutions face challenges when dealing with real-world noisy data. The authors introduce the information bottleneck theory to filter out noise by maximizing mutual information between compression and ground output while minimizing mutual information between compression and retrieved passage. This approach is evaluated on various question answering datasets, demonstrating significant improvements in answer generation correctness and conciseness with a 2.5% compression rate.
Low	GrooveSquid.com (original content)	Low Difficulty Summary The paper proposes a new way to improve the generation of answers by combining large language models with other sources of information. Currently, this process can be tricky when dealing with noisy or incorrect data. The authors suggest using a theory called the information bottleneck to help remove this noise. They show that their approach works well on different question-answering tasks and generates answers that are both correct and concise.