AI RESEARCH
In-depth Analysis of Graph-based RAG in a Unified Framework
arXiv CS.CL
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ArXi:2503.04338v2 Announce Type: replace-cross 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.