DeepRAG: Thinking to Retrieval Step by Step for Large Language Models
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.
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Xinyan Guan
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2025-02-04T09:39:08.849Z
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Xinyan Guan
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xinyan233333
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Jiali Zeng
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2025-02-04T17:36:59.186Z
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zeng
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zengjiali
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Fandong Meng
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2025-02-04T17:36:44.284Z
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Fandong Meng
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fandong
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Chunlei Xin
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2025-02-04T17:37:04.877Z
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Chunlei Xin
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meow77
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Yaojie Lu
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2025-02-04T17:37:11.297Z
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Yaojie Lu
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luyaojie
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Hongyu Lin
admin_assigned
2025-02-04T17:37:20.338Z
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Hongyu  Lin
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sanmusunrise
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Xianpei Han
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2025-02-04T17:37:26.589Z
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Xianpei Han
user
xphan
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Le Sun
67a1b4630e9634919de9bc5a
Jie Zhou
DeepRAG: Thinking to Retrieval Step by Step for Large Language Models
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.
DeepRAG improves answer accuracy by 21.99% through strategic and adaptive retrieval, modeled as a Markov Decision Process for efficient reasoning-augmented generation.
Markov Decision Process retrieval-augmented reasoning retrieval-augmented generation parametric reasoning task decomposition strategic retrieval adaptive retrieval