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A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models

Survey on Graph-based Retrieval-Augmented Generation (GraphRAG) for customizing large language models.

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A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models

By Qinggang Zhang, Shengyuan Chen, Yuanchen Bei, Zheng Yuan, Huachi Zhou, Zijin Hong, Hao Chen, Xiao, YilinarXiv (Cornell University)
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This survey presents a systematic analysis of GraphRAG, a paradigm that addresses traditional RAG limitations through graph-structured knowledge representation and efficient retrieval techniques.

It examines current implementations across various professional domains and identifies key technical challenges and research directions. The survey aims to revolutionize domain-specific LLM applications by seamlessly integrating external knowledge bases.

Abstract

This survey presents a systematic analysis of GraphRAG, a paradigm that addresses traditional RAG limitations through graph-structured knowledge representation and efficient retrieval techniques. It examines current implementations across various professional domains and identifies key technical challenges and research directions. The survey aims to revolutionize domain-specific LLM applications by seamlessly integrating external knowledge bases.

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graph-based retrieval-augmented generationcustomized large language modelsdomain-specific expertiseexternal knowledge basesknowledge integrationsystem efficiency bottlenecksKnowledge GraphsLarge Language ModelsRetrieval & RAGSemantic Interoperability
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