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arXiv · 2502.12442

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

Hao Liu·Zhengren Wang·Xi Chen·Zhiyu Li·Feiyu Xiong·Qinhan Yu·Wentao Zhang
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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 \textbf{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 \textit{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. Experiments on multiple multi-hop benchmarks demonstrate that HopRAG's \textit{retrieve-reason-prune} mechanism can expand the retrieval scope based on logical connections and improve final answer quality.

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Hao Liu, Zhengren Wang, Xi Chen, Zhiyu Li, Feiyu Xiong, Qinhan Yu, Wentao Zhang. 2025-02-18. HopRAG: Multi-Hop Reasoning for Logic-Aware Retrieval-Augmented Generation. https://arxiv.org/abs/2502.12442

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