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This paper presents a comprehensive analysis of graph-based Retrieval-Augmented Generation (RAG) methods within a unified framework, highlighting their effectiveness in enhancing large language models (LLMs) by integrating external knowledge. The authors systematically compare various graph-based RAG approaches across multiple question-answering datasets, identifying new variants that outperform existing state-of-the-art methods. Additionally, the study outlines promising research opportunities based on the

Author

yjin276

Language

EN