An Information Bottleneck Perspective for Effective Noise Filtering on Retrieval-Augmented Generation (2024)

Abstract: 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.

## introduction large language models represent a significant advancement in natural language understanding and generation, with the capability to process and produce human-like language at an unprecedented scale and complexity @xcite @xcite . nonetheless, large language models have several drawbacks, such as hallucination @xcite and lacking knowledge for specific domains or highly specialized queries @xcite . retrievalaugmented generation @xcite has gained attention for its ability to incorporate information from external knowledge sources during the inference stage. by combining retrieval-based methods with generative models, this approach can improve the relevance, coherence, and factual accuracy of text generation @xcite . retrieval-augmented generation also presents problems. on the one hand, the retriever's efficacy may be suboptimal in practical use @xcite @xcite . on the other hand, the internet data is often of low quality, with redundancy and noise. indeed, the retrieved content can be completely irrelevant to the query, leading to the model producing incorrect results @xcite . recent solutions to mitigate noise in retrieval evidence often involve the adoption of a filtering module @xcite @xcite . however, these methods encounter several issues: (1) the inability to ensure that the annotated filtering results can effectively support the generation model in accurately answering questions. (2) the difficulty in directing the filter to refrain from answering when confronted with retrieval evidence that does not support question resolution. (3) the lack of adaptation to the compression extent of the filtering results, impeding the achievement of an optimal solution in terms of cost performance. we observe that issues above originate from suboptimal objectives. as shown in figure 1a , the intersection between retrieved passages x and outputs y denotes the precise information in x which is useful for y . the noise filter extracts compression x from retrieved passages x, where the filter is optimized with log likelihood objective to output y . the noise filter trained with this objective can obtain a compression x containing the intersection x ∩ y , but is incapable of realizing its exact area, which means the filter cannot in principle eliminate the interference of noise for subsequent generation. therefore, we propose to utilize the information bottleneck theory @xcite to optimize the noise filter from a comprehensive perspective, via simultaneously maximizing the useful information while minimizing the noise, thus facilitating a precise delineation of the intersection @xmath0. furthermore, in cases (figure 1b ) where retrieval is not necessitated for content generation or exhibits limited efficacy, the information bottleneck objective enables noise filters to compress the retrieved passages into empty @xmath1. specifically, we consider information bottleneck as a principle for retrieval augmentation. we first theoretically derive the formula of information bottleneck for retrieval-augmented generation, which integrates large language models. then we introduce information bottleneck as a new comprehensive evaluation metric for noise filtering, assessing both conciseness and correctness of compressed contents. next we derive information bottleneck version of supervised fine-tuning and reinforcement learning objectives to train the noise filter. we conduct experiments on the open-domain question answering datasets: natural questions (nq) @xcite , triviaqa @xcite , and a more complex multi-hop hotpotqa @xcite . using llama2 as filtering and generation model, our approach is proved to be effective compared with strong baseline models, including rankgpt, longllm-lingua and lllama2 on all three datasets. we achieve a 2.5% significant compression rate and a 3.2 improvement of exact answer match at most. our paper presents three main innovations: information bottleneck the information bottleneck (ib) @xcite is a rather simplistic concept: when facing a task, one should attempt to accomplish it using minimal information. the information bottleneck theory characterizes learning as a delicate balance between data compression and information retention. when applied to specific tasks, the idea is to extract all the essential informative features for the task while discarding redundant information @xcite . it has been applied in the study of representation learning @xcite @xcite , deep learning @xcite @xcite , document clustering @xcite , speech recognition @xcite , summarization @xcite , etc. noise filtering retrieval-augmented generation usually concatenates retrieved passages with their queries as the input of language models. however, this can potentially exceed the context window limit, introduce extra noise and redundancy, and increase computing resource requirements, leading to a decrease in model performance. flare @xcite and self-rag @xcite are dedicated to training models to have the capability of actively retrieving and filtering retrieval content on their own. replug @xcite improves the retriever by the kl divergence between the retriever and the llm. post-processing techniques such as noise filtering can help alleviate these issues. @xcite focuses on re-ranking retrieved articles to filter out noise. some methods like selective context @xcite and llmlingua @xcite ) make use of small language models to measure prompt mutual information or perplexity, finding the highest-scoring elements. there are also some methods employ summarization techniques to design compressors @xcite . tcra-llm @xcite and longllmlingua @xcite ## methodology in this section, we first introduce the background of information bottleneck ( §3.1), and then convert the information bottleneck into forms for noise filters of retrieval augmented generation ( §3.2). next, we provide details of information bottleneck objectives for retrieval augmented generation ( §3.3). ## conclusion we apply the information bottleneck principle to noise filters in retrieval-augmented generation, balancing the trade-off between the conciseness and correctness. not only as an evaluation method for noise filtering, we also apply it to select supervised fine-tuning training data and provide reward for reinforcement learning. our two-stage optimization gradually approaches the oracle filtering objective. experimental results demonstrate that our filter significantly outperforms baseline methods and achieves an impressive compression rate.