LLMs知道它们需要什么：利用缺失信息引导框架赋能检索增强生成
LLMs Know What They Need: Leveraging a Missing Information Guided Framework to Empower Retrieval-Augmented Generation
AI总结：
针对RAG在复杂多跳查询中的推理与检索难题，提出缺失信息引导的MIGRES范式，利用缺失信息生成针对性查询并结合句子级重排序过滤，有效提升了RAG的整体效能。
摘要：
检索增强生成（Retrieval-Augmented Generation, RAG）通过向LLMs提供更新的相关知识，在缓解过时知识或幻觉方面展现出巨大价值。然而，RAG在理解复杂多跳查询和检索相关文档时仍面临诸多困难，这要求LLMs逐步进行推理和检索。受人类逐步搜索所需信息这一推理过程的启发，自然会产生一个疑问：LLMs能否注意到每个推理步骤中缺失的信息。在本研究中，我们首先通过实验验证了LLMs提取信息以及知晓缺失信息的能力。基于上述发现，我们提出了一种缺失信息引导的检索-提取-求解范式（Missing Information Guided Retrieve-Extraction-Solving paradigm, MIGRES），其中我们利用对缺失信息的识别来生成针对性查询，从而引导后续的知识检索。此外，我们设计了一种句子级重排序过滤方法，以滤除文档中的无关内容，并结合LLMs的信息提取能力，从清理后的文档中提取有用信息，进而增强RAG的整体效能。在多个公开数据集上进行的大量实验证明了所提出的MIGRES方法的优越性，分析实验也论证了我们所提模块的有效性。
英文摘要：
Retrieval-Augmented Generation (RAG) demonstrates great value in alleviating outdated knowledge or hallucination by supplying LLMs with updated and relevant knowledge. However, there are still several difficulties for RAG in understanding complex multi-hop query and retrieving relevant documents, which require LLMs to perform reasoning and retrieve step by step. Inspired by human's reasoning process in which they gradually search for the required information, it is natural to ask whether the LLMs could notice the missing information in each reasoning step. In this work, we first experimentally verified the ability of LLMs to extract information as well as to know the missing. Based on the above discovery, we propose a Missing Information Guided Retrieve-Extraction-Solving paradigm (MIGRES), where we leverage the identification of missing information to generate a targeted query that steers the subsequent knowledge retrieval. Besides, we design a sentence-level re-ranking filtering approach to filter the irrelevant content out from document, along with the information extraction capability of LLMs to extract useful information from cleaned-up documents, which in turn to bolster the overall efficacy of RAG. Extensive experiments conducted on multiple public datasets reveal the superiority of the proposed MIGRES method, and analytical experiments demonstrate the effectiveness of our proposed modules.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
