StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via
  Inference-time Hybrid Information Structurization
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.
670c8c2a7603a5cfa432bfb9
Zhuoqun Li
claimed_verified
2024-10-14T12:16:05.106Z
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Li Zhuoqun
user
lzq2021
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Xuanang Chen
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Haiyang Yu
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Hongyu Lin
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Yaojie Lu
claimed_verified
2025-01-08T13:23:40.077Z
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Yaojie Lu
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luyaojie
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Qiaoyu Tang
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2024-10-18T08:25:31.329Z
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Qiaoyu Tang
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TangQiaoYu
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Fei Huang
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Xianpei Han
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2024-10-14T10:47:48.178Z
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Xianpei Han
user
xphan
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Le Sun
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Yongbin Li
claimed_verified
2024-10-14T10:47:46.291Z
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Yongbin Li
user
Yongbin-Li
StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via
  Inference-time Hybrid Information Structurization
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.
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.
Retrieval-augmented generation RAG large language models LLMs knowledge-intensive reasoning structured knowledge StructRAG document reconstruction