@inproceedings{liu-etal-2025-hoprag,
    title = "{H}op{RAG}: Multi-Hop Reasoning for Logic-Aware Retrieval-Augmented Generation",
    author = "Liu, Hao  and
      Wang, Zhengren  and
      Chen, Xi  and
      Li, Zhiyu  and
      Xiong, Feiyu  and
      Yu, Qinhan  and
      Zhang, Wentao",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.findings-acl.97/",
    pages = "1897--1913",
    ISBN = "979-8-89176-256-5",
    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."
}