Xinze Li, Dingwen Zhang, Zhenghao Liu, Yukun Yan, Shuo Wang, Yu Gu, Ge Yu, Maosong Sun · 2026 · arXiv
DOI: 10.48550/arXiv.2610.29792 · open-access PDF
RAG(Retrieval-Augmented Generation)通过获取外部知识来提高大语言模型(LLMs)的性能。最近,一些RAG方法将推理和获取相互关联,使LLM能够逐步生成子查询并获取外部知识。然而,这些方法可能会遭受多余或无效的子查询和不够多样化的查询探索。此外,由于推理和获取的密切相互关联性质,它仍然不清楚如何演变的推理过程会影响查询的意图。
Machine translation (Meta M2M100); the English original follows.
Retrieval-Augmented Generation (RAG) improves the performance of Large Language Models (LLMs) by retrieving external knowledge. Recently, some RAG methods interleave reasoning and retrieval, enabling LLMs to progressively generate sub-queries and retrieve external knowledge. However, these methods may suffer from redundant or ineffective sub-queries and insufficiently diverse query exploration. Moreover, due to the tightly interleaved nature of reasoning and retrieval, it remains unclear how the evolving reasoning process influences query intent. To characterize the evolution of query intent d
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