title: DeepRAG: Thinking to Retrieval Step by Step for Large Language Models
summary: Large Language Models (LLMs) have shown remarkable potential in reasoning
  while they still suffer from severe factual hallucinations due to timeliness,
  accuracy, and coverage of parametric knowledge. Meanwhile, integrating
  reasoning with retrieval-augmented generation (RAG) remains challenging due to
  ineffective task decomposition and redundant retrieval, which can introduce
  noise and degrade response quality. In this paper, we propose DeepRAG, a
  framework that models retrieval-augmented reasoning as a Markov Decision
  Process (MDP), enabling strategic and adaptive retrieval. By iteratively
  decomposing queries, DeepRAG dynamically determines whether to retrieve
  external knowledge or rely on parametric reasoning at each step. Experiments
  show that DeepRAG improves retrieval efficiency while improving answer accuracy
  by 21.99%, demonstrating its effectiveness in optimizing retrieval-augmented
  reasoning.
paper_authors._id: 67a1b4630e9634919de9bc52
paper_authors.name: Xinyan Guan
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paper_authors.fullname: Xinyan Guan
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paper_authors.name: Jiali Zeng
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paper_authors.user: zengjiali
paper_authors._id: 67a1b4630e9634919de9bc54
paper_authors.name: Fandong Meng
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paper_authors.fullname: Fandong Meng
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paper_authors._id: 67a1b4630e9634919de9bc55
paper_authors.name: Chunlei Xin
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paper_authors.fullname: Chunlei Xin
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paper_authors.user: meow77
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paper_authors.name: Yaojie Lu
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paper_authors.fullname: Yaojie Lu
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paper_authors.user: luyaojie
paper_authors._id: 67a1b4630e9634919de9bc57
paper_authors.name: Hongyu Lin
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paper_authors.avatarUrl: /avatars/178e9567c3111ab22717c3c0dd003a6a.svg
paper_authors.fullname: Hongyu  Lin
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paper_authors.user: sanmusunrise
paper_authors._id: 67a1b4630e9634919de9bc58
paper_authors.name: Xianpei Han
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-04T17:37:26.589Z
paper_authors._id: 65e99a77e71555ed193609cf
paper_authors.avatarUrl: /avatars/38ceb127883944677665da967d17dd18.svg
paper_authors.fullname: Xianpei Han
paper_authors.type: user
paper_authors.user: xphan
paper_authors._id: 67a1b4630e9634919de9bc59
paper_authors.name: Le Sun
paper_authors._id: 67a1b4630e9634919de9bc5a
paper_authors.name: Jie Zhou
paper_title: DeepRAG: Thinking to Retrieval Step by Step for Large Language Models
paper_summary: Large Language Models (LLMs) have shown remarkable potential in reasoning
  while they still suffer from severe factual hallucinations due to timeliness,
  accuracy, and coverage of parametric knowledge. Meanwhile, integrating
  reasoning with retrieval-augmented generation (RAG) remains challenging due to
  ineffective task decomposition and redundant retrieval, which can introduce
  noise and degrade response quality. In this paper, we propose DeepRAG, a
  framework that models retrieval-augmented reasoning as a Markov Decision
  Process (MDP), enabling strategic and adaptive retrieval. By iteratively
  decomposing queries, DeepRAG dynamically determines whether to retrieve
  external knowledge or rely on parametric reasoning at each step. Experiments
  show that DeepRAG improves retrieval efficiency while improving answer accuracy
  by 21.99%, demonstrating its effectiveness in optimizing retrieval-augmented
  reasoning.
paper_ai_summary: DeepRAG improves answer accuracy by 21.99% through strategic and adaptive retrieval, modeled as a Markov Decision Process for efficient reasoning-augmented generation.
paper_ai_keywords: Markov Decision Process retrieval-augmented reasoning retrieval-augmented generation parametric reasoning task decomposition strategic retrieval adaptive retrieval