Knowledge Graph-Guided Retrieval Augmented Generation
FOS: Computer and information sciences
Computer Science - Computation and Language
Artificial Intelligence (cs.AI)
Computer Science - Artificial Intelligence
Computation and Language (cs.CL)
DOI:
10.48550/arxiv.2502.06864
Publication Date:
2025-01-01
AUTHORS (5)
ABSTRACT
Retrieval-augmented generation (RAG) has emerged as a promising technology for addressing hallucination issues in the responses generated by large language models (LLMs). Existing studies on RAG primarily focus on applying semantic-based approaches to retrieve isolated relevant chunks, which ignore their intrinsic relationships. In this paper, we propose a novel Knowledge Graph-Guided Retrieval Augmented Generation (KG$^2$RAG) framework that utilizes knowledge graphs (KGs) to provide fact-level relationships between chunks, improving the diversity and coherence of the retrieved results. Specifically, after performing a semantic-based retrieval to provide seed chunks, KG$^2$RAG employs a KG-guided chunk expansion process and a KG-based chunk organization process to deliver relevant and important knowledge in well-organized paragraphs. Extensive experiments conducted on the HotpotQA dataset and its variants demonstrate the advantages of KG$^2$RAG compared to existing RAG-based approaches, in terms of both response quality and retrieval quality.<br/>Accepted in the 2025 Annual Conference of the Nations of the Americas Chapter of the ACL (NAACL 2025)<br/>
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