Auto-RAG：面向大型语言模型的自主检索增强生成
Auto-RAG: Autonomous Retrieval-Augmented Generation for Large Language Models
AI总结：
针对现有迭代检索方法的不足，本文提出Auto-RAG模型，以LLM决策能力为核心，通过与检索器多轮对话自主规划检索与优化查询，在六个基准测试中表现优异，还能自主调整迭代次数并增强可解释性。
摘要：
迭代检索指模型在生成过程中持续查询检索器以增强所获知识的相关性，从而提升检索增强生成（RAG）性能的过程。现有工作通常采用少样本提示或人工构建规则实现迭代检索，这会引入额外推理开销，且忽视了大型语言模型（LLMs）卓越的推理能力。本文提出Auto-RAG，一种以LLM强大决策能力为核心的自主迭代检索模型。Auto-RAG与检索器进行多轮对话，系统规划检索并优化查询以获取有价值知识，该过程持续至收集到足够外部信息后再向用户呈现结果。为此，我们开发了一种在迭代检索中自主合成基于推理的决策指令的方法，并微调了最新开源LLMs。实验结果表明，Auto-RAG能与检索器自主迭代交互，有效利用LLMs卓越的推理和决策能力，在六个基准测试中表现优异。进一步分析显示，Auto-RAG可根据问题难度和检索知识的效用自主调整迭代次数，无需任何人工干预。此外，Auto-RAG以自然语言表达迭代检索过程，在增强可解释性的同时为用户提供更直观的体验。
英文摘要：
Iterative retrieval refers to the process in which the model continuously queries the retriever during generation to enhance the relevance of the retrieved knowledge, thereby improving the performance of Retrieval-Augmented Generation (RAG). Existing work typically employs few-shot prompting or manually constructed rules to implement iterative retrieval. This introduces additional inference overhead and overlooks the remarkable reasoning capabilities of Large Language Models (LLMs). In this paper, we introduce Auto-RAG, an autonomous iterative retrieval model centered on the LLM's powerful decision-making capabilities. Auto-RAG engages in multi-turn dialogues with the retriever, systematically planning retrievals and refining queries to acquire valuable knowledge. This process continues until sufficient external information is gathered, at which point the results are presented to the user. To this end, we develop a method for autonomously synthesizing reasoning-based decision-making instructions in iterative retrieval and fine-tuned the latest open-source LLMs. The experimental results indicate that Auto-RAG is capable of autonomous iterative interaction with the retriever, effectively leveraging the remarkable reasoning and decision-making abilities of LLMs, which lead to outstanding performance across six benchmarks. Further analysis reveals that Auto-RAG can autonomously adjust the number of iterations based on the difficulty of the questions and the utility of the retrieved knowledge, without requiring any human intervention. Moreover, Auto-RAG expresses the iterative retrieval process in natural language, enhancing interpretability while providing users with a more intuitive experience\footnote{Code is available at \url{https://github.com/ictnlp/Auto-RAG}.
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。
