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}.

翻译：迭代检索是指模型在生成过程中持续查询检索器以增强检索知识的相关性，从而提升检索增强生成性能的过程。现有工作通常采用少量示例提示或人工构建的规则来实现迭代检索。这种方法会引入额外的推理开销，且忽视了大语言模型卓越的推理能力。本文提出Auto-RAG，一种以大语言模型强大决策能力为核心的自主迭代检索模型。Auto-RAG通过与检索器进行多轮对话，系统规划检索过程并优化查询以获取有价值的知识。该过程将持续进行直至收集到充足的外部信息，最终将结果呈现给用户。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，并对最新的开源大语言模型进行了微调。实验结果表明，Auto-RAG能够与检索器进行自主迭代交互，有效利用大语言模型卓越的推理与决策能力，在六个基准测试中均取得优异性能。进一步分析表明，Auto-RAG能根据问题难度与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使用自然语言表达迭代检索过程，在提升可解释性的同时为用户提供更直观的体验\footnote{代码发布于 \url{https://github.com/ictnlp/Auto-RAG}。