@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://aclanthology.org/2025.findings-acl.97/",
doi = "10.18653/v1/2025.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."
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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 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. Experiments on multiple multi-hop benchmarks demonstrate that HopRAG’s retrieve-reason-prune mechanism can expand the retrieval scope based on logical connections and improve final answer quality.</abstract>
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%0 Conference Proceedings
%T HopRAG: Multi-Hop Reasoning for Logic-Aware Retrieval-Augmented Generation
%A Liu, Hao
%A Wang, Zhengren
%A Chen, Xi
%A Li, Zhiyu
%A Xiong, Feiyu
%A Yu, Qinhan
%A Zhang, Wentao
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F liu-etal-2025-hoprag
%X 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. Experiments on multiple multi-hop benchmarks demonstrate that HopRAG’s retrieve-reason-prune mechanism can expand the retrieval scope based on logical connections and improve final answer quality.
%R 10.18653/v1/2025.findings-acl.97
%U https://aclanthology.org/2025.findings-acl.97/
%U https://doi.org/10.18653/v1/2025.findings-acl.97
%P 1897-1913
Markdown (Informal)
[HopRAG: Multi-Hop Reasoning for Logic-Aware Retrieval-Augmented Generation](https://aclanthology.org/2025.findings-acl.97/) (Liu et al., Findings 2025)
ACL