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Retrieval-Augmented Generation with Graphs (GraphRAG)

A survey on retrieval-augmented generation with graphs, a technique for enhancing downstream tasks by retrieving information from external sources.

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Retrieval-Augmented Generation with Graphs (GraphRAG)

By Haoyu Han, Yu Wang, Harry Shomer, Kai Guo, Jiayuan Ding, Lei, Yongjia, Mahantesh Halappanavar, Ryan A. RossiarXiv (Cornell University)
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The paper presents a comprehensive survey of GraphRAG, a framework that combines graph-structured data with retrieval-augmented generation. It defines key components and reviews techniques tailored to different domains.

The authors also discuss research challenges and potential directions for future work.

Abstract

The paper presents a comprehensive survey of GraphRAG, a framework that combines graph-structured data with retrieval-augmented generation. It defines key components and reviews techniques tailored to different domains. The authors also discuss research challenges and potential directions for future work.

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graph-based generationretrieval-augmented generationknowledge graph retrievalgraph neural networksinformation retrievalKnowledge GraphsRetrieval & RAGLarge Language ModelsSemantic Interoperability
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Retrieval-Augmented Generation with Graphs (GraphRAG) | Aramai