Retrieval-Augmented Generation with Hierarchical Knowledge

• URL: http://arxiv.org/abs/2503.10150v1
• Date: Thu, 13 Mar 2025 08:22:31 GMT
• Title: Retrieval-Augmented Generation with Hierarchical Knowledge
• Authors: Haoyu Huang, Yongfeng Huang, Junjie Yang, Zhenyu Pan, Yongqiang Chen, Kaili Ma, Hongzhi Chen, James Cheng,
• Abstract summary: Graph-based Retrieval-Augmented Generation (RAG) methods have significantly enhanced the performance of large language models (LLMs) in domain-specific tasks.<n>Existing RAG methods do not adequately utilize the naturally inherent hierarchical knowledge in human cognition.<n>We introduce a new RAG approach, called HiRAG, which utilizes hierarchical knowledge to enhance the semantic understanding and structure capturing capabilities of RAG systems.
• Score: 38.500133410610495
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph-based Retrieval-Augmented Generation (RAG) methods have significantly enhanced the performance of large language models (LLMs) in domain-specific tasks. However, existing RAG methods do not adequately utilize the naturally inherent hierarchical knowledge in human cognition, which limits the capabilities of RAG systems. In this paper, we introduce a new RAG approach, called HiRAG, which utilizes hierarchical knowledge to enhance the semantic understanding and structure capturing capabilities of RAG systems in the indexing and retrieval processes. Our extensive experiments demonstrate that HiRAG achieves significant performance improvements over the state-of-the-art baseline methods. The code of our proposed method is available at \href{https://github.com/hhy-huang/HiRAG}{https://github.com/hhy-huang/HiRAG}.

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