[2024 Best AI Paper] Graph Retrieval-Augmented Generation: A Survey
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Title: Graph Retrieval-Augmented Generation: A Survey
Authors: Boci Peng, Yun Zhu, Yongchao Liu, Xiaohe Bo, Haizhou Shi, Chuntao Hong, Yan Zhang, Siliang Tang
Abstract:
Recently, Retrieval-Augmented Generation (RAG) has achieved remarkable
success in addressing the challenges of Large Language Models (LLMs) without
necessitating retraining. By referencing an external knowledge base, RAG
refines LLM outputs, effectively mitigating issues such as ``hallucination'',
lack of domain-specific knowledge, and outdated information. However, the
complex structure of relationships among different entities in databases
presents challenges for RAG systems. In response, GraphRAG leverages structural
information across entities to enable more precise and comprehensive retrieval,
capturing relational knowledge and facilitating more accurate, context-aware
responses. Given the novelty and potential of GraphRAG, a systematic review of
current technologies is imperative. This paper provides the first comprehensive
overview of GraphRAG methodologies. We formalize the GraphRAG workflow,
encompassing Graph-Based Indexing, Graph-Guided Retrieval, and Graph-Enhanced
Generation. We then outline the core technologies and training methods at each
stage. Additionally, we examine downstream tasks, application domains,
evaluation methodologies, and industrial use cases of GraphRAG. Finally, we
explore future research directions to inspire further inquiries and advance
progress in the field. In order to track recent progress in this field, we set
up a repository at \url{https://github.com/pengboci/GraphRAG-...}.