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Graph Retrieval-Augmented Generation: A Survey

A survey on GraphRAG methodologies for retrieval-augmented generation.

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Graph Retrieval-Augmented Generation: A Survey

By Boci Peng, Yun Zhu, Yongchao Liu, Xiaohe Bo, Haizhou Shi, Chuntao Hong, Yan Zhang, Siliang TangarXiv (Cornell University)
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The paper provides a comprehensive overview of GraphRAG, a framework that leverages structural information in databases to improve the accuracy and context-awareness of large language models. It formalizes the GraphRAG workflow and outlines core technologies and training methods.

The survey also examines downstream tasks, application domains, evaluation methodologies, and industrial use cases.

Abstract

The paper provides a comprehensive overview of GraphRAG, a framework that leverages structural information in databases to improve the accuracy and context-awareness of large language models. It formalizes the GraphRAG workflow and outlines core technologies and training methods. The survey also examines downstream tasks, application domains, evaluation methodologies, and industrial use cases.

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graph-retrieval-augmented-generationsurveygraphrag-methodologieslarge-language-modelsstructural-information-databasesKnowledge GraphsLarge Language ModelsRetrieval & RAGSemantic Interoperability
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Graph Retrieval-Augmented Generation: A Survey | Aramai