  Retrieval-augmented generation integrates the capabilities of large language
models with relevant information retrieved from an extensive corpus, yet
encounters challenges when confronted with real-world noisy data. One recent
solution is to train a filter module to find relevant content but only achieve
suboptimal noise compression. In this paper, we propose to introduce the
information bottleneck theory into retrieval-augmented generation. Our approach
involves the filtration of noise by simultaneously maximizing the mutual
information between compression and ground output, while minimizing the mutual
information between compression and retrieved passage. In addition, we derive
the formula of information bottleneck to facilitate its application in novel
comprehensive evaluations, the selection of supervised fine-tuning data, and
the construction of reinforcement learning rewards. Experimental results
demonstrate that our approach achieves significant improvements across various
question answering datasets, not only in terms of the correctness of answer
generation but also in the conciseness with $2.5\%$ compression rate.
