HopRAG: Multi-Hop Reasoning for Logic-Aware Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2502.12442v1
• Date: Tue, 18 Feb 2025 02:24:42 GMT
• Title: HopRAG: Multi-Hop Reasoning for Logic-Aware Retrieval-Augmented Generation
• Authors: Hao Liu, Zhengren Wang, Xi Chen, Zhiyu Li, Feiyu Xiong, Qinhan Yu, Wentao Zhang,
• Abstract summary: We propose HopRAG, a novel RAG framework that augments retrieval with logical reasoning.<n>During indexing, HopRAG constructs a passage graph, with text chunks as vertices and logical connections established via LLM-generated pseudo-queries as edges.<n>During retrieval, it employs a retrieve-reason-prune mechanism, starting with lexically or semantically similar passages.<n>Experiments demonstrate HopRAG's superiority, achieving 76.78% higher answer accuracy and 65.07% improved retrieval F1 score compared to conventional methods.
• Score: 28.69822159828129
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval-Augmented Generation (RAG) systems often struggle with imperfect retrieval, as traditional retrievers focus on lexical or semantic similarity rather than logical relevance. To address this, we propose HopRAG, a novel RAG framework that augments retrieval with logical reasoning through graph-structured knowledge exploration. During indexing, HopRAG constructs a passage graph, with text chunks as vertices and logical connections established via LLM-generated pseudo-queries as edges. During retrieval, it employs a retrieve-reason-prune mechanism: starting with lexically or semantically similar passages, the system explores multi-hop neighbors guided by pseudo-queries and LLM reasoning to identify truly relevant ones. Extensive experiments demonstrate HopRAG's superiority, achieving 76.78\% higher answer accuracy and 65.07\% improved retrieval F1 score compared to conventional methods. The repository is available at https://github.com/LIU-Hao-2002/HopRAG.

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