In-depth Analysis of Graph-based RAG in a Unified Framework

• URL: http://arxiv.org/abs/2503.04338v1
• Date: Thu, 06 Mar 2025 11:34:49 GMT
• Title: In-depth Analysis of Graph-based RAG in a Unified Framework
• Authors: Yingli Zhou, Yaodong Su, Youran Sun, Shu Wang, Taotao Wang, Runyuan He, Yongwei Zhang, Sicong Liang, Xilin Liu, Yuchi Ma, Yixiang Fang,
• Abstract summary: Graph-based Retrieval-Augmented Generation (RAG) has proven effective in integrating external knowledge into large language models.<n>We first summarize a unified framework to incorporate all graph-based RAG methods from a high-level perspective.<n>We then extensively compare representative graph-based RAG methods over a range of questing-answering (QA) datasets.
• Score: 17.941941997783267
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph-based Retrieval-Augmented Generation (RAG) has proven effective in integrating external knowledge into large language models (LLMs), improving their factual accuracy, adaptability, interpretability, and trustworthiness. A number of graph-based RAG methods have been proposed in the literature. However, these methods have not been systematically and comprehensively compared under the same experimental settings. In this paper, we first summarize a unified framework to incorporate all graph-based RAG methods from a high-level perspective. We then extensively compare representative graph-based RAG methods over a range of questing-answering (QA) datasets -- from specific questions to abstract questions -- and examine the effectiveness of all methods, providing a thorough analysis of graph-based RAG approaches. As a byproduct of our experimental analysis, we are also able to identify new variants of the graph-based RAG methods over specific QA and abstract QA tasks respectively, by combining existing techniques, which outperform the state-of-the-art methods. Finally, based on these findings, we offer promising research opportunities. We believe that a deeper understanding of the behavior of existing methods can provide new valuable insights for future research.

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