用查询性能预测提升智能检索生成的搜索效率
Am I on the Right Track? What Can Predicted Query Performance Tell Us about the Search Behaviour of Agentic RAG
- 通过预测查询质量判断是否该检索
- 高质量查询可缩短推理过程并提升答案准确率
- 适合研究智能问答系统与自适应检索的开发者
智能检索增强生成（Agentic RAG）是一种新范式，其中推理模型自主决定何时调用检索器获取外部信息。尽管此类模型如Search-R1和R1-Searcher已实现搜索决策自动化，但其生成的查询行为及检索器的作用仍缺乏研究。本研究首次探讨在Search-R1与R1-Searcher中应用查询性能预测（QPP）的可行性。结果表明，使用高效检索器可在更短推理时间内获得更高答案质量；且生成查询的QPP估计值与最终答案质量呈正相关。该工作为实现基于QPP的自适应检索迈出了关键一步，使模型能判断检索结果是否有效。
原文摘要 · Abstract (English)
Agentic Retrieval-Augmented Generation (RAG) is a new paradigm where the reasoning model decides when to invoke a retriever (as a "tool") when answering a question. This paradigm, exemplified by recent research works such as Search-R1, enables the model to decide when to search and obtain external information. However, the queries generated by such Agentic RAG models and the role of the retriever in obtaining high-quality answers remain understudied. To this end, this initial study examines the applicability of query performance prediction (QPP) within the recent Agentic RAG models Search-R1 and R1-Searcher. We find that applying effective retrievers can achieve higher answer quality within a shorter reasoning process. Moreover, the QPP estimates of the generated queries, used as an approximation of their retrieval quality, are positively correlated with the quality of the final answer. Ultimately, our work is a step towards adaptive retrieval within Agentic RAG, where QPP is used to inform the model if the retrieved results are likely to be useful.
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