title: StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via
    Inference-time Hybrid Information Structurization
summary: Retrieval-augmented generation (RAG) is a key means to effectively enhance
  large language models (LLMs) in many knowledge-based tasks. However, existing
  RAG methods struggle with knowledge-intensive reasoning tasks, because useful
  information required to these tasks are badly scattered. This characteristic
  makes it difficult for existing RAG methods to accurately identify key
  information and perform global reasoning with such noisy augmentation. In this
  paper, motivated by the cognitive theories that humans convert raw information
  into various structured knowledge when tackling knowledge-intensive reasoning,
  we proposes a new framework, StructRAG, which can identify the optimal
  structure type for the task at hand, reconstruct original documents into this
  structured format, and infer answers based on the resulting structure.
  Extensive experiments across various knowledge-intensive tasks show that
  StructRAG achieves state-of-the-art performance, particularly excelling in
  challenging scenarios, demonstrating its potential as an effective solution for
  enhancing LLMs in complex real-world applications.
paper_authors._id: 670c8c2a7603a5cfa432bfb9
paper_authors.name: Zhuoqun Li
paper_authors.status: claimed_verified
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paper_authors.fullname: Li Zhuoqun
paper_authors.type: user
paper_authors.user: lzq2021
paper_authors._id: 670c8c2a7603a5cfa432bfba
paper_authors.name: Xuanang Chen
paper_authors._id: 670c8c2a7603a5cfa432bfbb
paper_authors.name: Haiyang Yu
paper_authors._id: 670c8c2a7603a5cfa432bfbc
paper_authors.name: Hongyu Lin
paper_authors._id: 670c8c2a7603a5cfa432bfbd
paper_authors.name: Yaojie Lu
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paper_authors.fullname: Yaojie Lu
paper_authors.type: user
paper_authors.user: luyaojie
paper_authors._id: 670c8c2a7603a5cfa432bfbe
paper_authors.name: Qiaoyu Tang
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paper_authors.avatarUrl: /avatars/e8794ba19bdd965615bd9ead7df77d7d.svg
paper_authors.fullname: Qiaoyu Tang
paper_authors.type: user
paper_authors.user: TangQiaoYu
paper_authors._id: 670c8c2a7603a5cfa432bfbf
paper_authors.name: Fei Huang
paper_authors._id: 670c8c2a7603a5cfa432bfc0
paper_authors.name: Xianpei Han
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-14T10:47:48.178Z
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: 670c8c2a7603a5cfa432bfc1
paper_authors.name: Le Sun
paper_authors._id: 670c8c2a7603a5cfa432bfc2
paper_authors.name: Yongbin Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-14T10:47:46.291Z
paper_authors._id: 66641b2fd8e1e34bc621e688
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/66641b2fd8e1e34bc621e688/csPETwnx2zCIHSWi9uAi-.png
paper_authors.fullname: Yongbin Li
paper_authors.type: user
paper_authors.user: Yongbin-Li
paper_title: StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via
    Inference-time Hybrid Information Structurization
paper_summary: Retrieval-augmented generation (RAG) is a key means to effectively enhance
  large language models (LLMs) in many knowledge-based tasks. However, existing
  RAG methods struggle with knowledge-intensive reasoning tasks, because useful
  information required to these tasks are badly scattered. This characteristic
  makes it difficult for existing RAG methods to accurately identify key
  information and perform global reasoning with such noisy augmentation. In this
  paper, motivated by the cognitive theories that humans convert raw information
  into various structured knowledge when tackling knowledge-intensive reasoning,
  we proposes a new framework, StructRAG, which can identify the optimal
  structure type for the task at hand, reconstruct original documents into this
  structured format, and infer answers based on the resulting structure.
  Extensive experiments across various knowledge-intensive tasks show that
  StructRAG achieves state-of-the-art performance, particularly excelling in
  challenging scenarios, demonstrating its potential as an effective solution for
  enhancing LLMs in complex real-world applications.
paper_ai_summary: StructRAG enhances LLMs by identifying optimal structure types, reconstructing documents into these structures, and inferring answers for knowledge-intensive reasoning tasks, achieving state-of-the-art performance.
paper_ai_keywords: Retrieval-augmented generation RAG large language models LLMs knowledge-intensive reasoning structured knowledge StructRAG document reconstruction