@inproceedings{shen-etal-2025-gear,
title = "{G}e{AR}: Graph-enhanced Agent for Retrieval-augmented Generation",
author = "Shen, Zhili and
Diao, Chenxin and
Vougiouklis, Pavlos and
Merita, Pascual and
Piramanayagam, Shriram and
Chen, Enting and
Graux, Damien and
Melo, Andre and
Lai, Ruofei and
Jiang, Zeren and
Li, Zhongyang and
Qi, Ye and
Ren, Yang and
Tu, Dandan and
Pan, Jeff Z.",
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.624/",
doi = "10.18653/v1/2025.findings-acl.624",
pages = "12049--12072",
ISBN = "979-8-89176-256-5",
abstract = "Retrieval-augmented Generation (RAG) relies on effective retrieval capabilities, yet traditional sparse and dense retrievers inherently struggle with multi-hop retrieval scenarios. In this paper, we introduce $\text{G\small{E}\normalsize{AR}}$, a system that advances RAG performance through two key innovations: (i) an efficient graph expansion mechanism that augments any conventional base retriever, such as BM25, and (ii) an agent framework that incorporates the resulting graph-based retrieval into a multi-step retrieval framework. Our evaluation demonstrates $\text{G\small{E}\normalsize{AR}}${`}s superior retrieval capabilities across three multi-hop question answering datasets. Notably, our system achieves state-of-the-art results with improvements exceeding 10{\%} on the challenging MuSiQue dataset, while consuming fewer tokens and requiring fewer iterations than existing multi-step retrieval systems. The project page is available at https://gear-rag.github.io."
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<abstract>Retrieval-augmented Generation (RAG) relies on effective retrieval capabilities, yet traditional sparse and dense retrievers inherently struggle with multi-hop retrieval scenarios. In this paper, we introduce \textGEʼnormalsizeAR, a system that advances RAG performance through two key innovations: (i) an efficient graph expansion mechanism that augments any conventional base retriever, such as BM25, and (ii) an agent framework that incorporates the resulting graph-based retrieval into a multi-step retrieval framework. Our evaluation demonstrates \textGEʼnormalsizeAR‘s superior retrieval capabilities across three multi-hop question answering datasets. Notably, our system achieves state-of-the-art results with improvements exceeding 10% on the challenging MuSiQue dataset, while consuming fewer tokens and requiring fewer iterations than existing multi-step retrieval systems. The project page is available at https://gear-rag.github.io.</abstract>
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%0 Conference Proceedings
%T GeAR: Graph-enhanced Agent for Retrieval-augmented Generation
%A Shen, Zhili
%A Diao, Chenxin
%A Vougiouklis, Pavlos
%A Merita, Pascual
%A Piramanayagam, Shriram
%A Chen, Enting
%A Graux, Damien
%A Melo, Andre
%A Lai, Ruofei
%A Jiang, Zeren
%A Li, Zhongyang
%A Qi, Ye
%A Ren, Yang
%A Tu, Dandan
%A Pan, Jeff Z.
%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 shen-etal-2025-gear
%X Retrieval-augmented Generation (RAG) relies on effective retrieval capabilities, yet traditional sparse and dense retrievers inherently struggle with multi-hop retrieval scenarios. In this paper, we introduce \textGEʼnormalsizeAR, a system that advances RAG performance through two key innovations: (i) an efficient graph expansion mechanism that augments any conventional base retriever, such as BM25, and (ii) an agent framework that incorporates the resulting graph-based retrieval into a multi-step retrieval framework. Our evaluation demonstrates \textGEʼnormalsizeAR‘s superior retrieval capabilities across three multi-hop question answering datasets. Notably, our system achieves state-of-the-art results with improvements exceeding 10% on the challenging MuSiQue dataset, while consuming fewer tokens and requiring fewer iterations than existing multi-step retrieval systems. The project page is available at https://gear-rag.github.io.
%R 10.18653/v1/2025.findings-acl.624
%U https://aclanthology.org/2025.findings-acl.624/
%U https://doi.org/10.18653/v1/2025.findings-acl.624
%P 12049-12072
Markdown (Informal)
[GeAR: Graph-enhanced Agent for Retrieval-augmented Generation](https://aclanthology.org/2025.findings-acl.624/) (Shen et al., Findings 2025)
ACL
- Zhili Shen, Chenxin Diao, Pavlos Vougiouklis, Pascual Merita, Shriram Piramanayagam, Enting Chen, Damien Graux, Andre Melo, Ruofei Lai, Zeren Jiang, Zhongyang Li, Ye Qi, Yang Ren, Dandan Tu, and Jeff Z. Pan. 2025. GeAR: Graph-enhanced Agent for Retrieval-augmented Generation. In Findings of the Association for Computational Linguistics: ACL 2025, pages 12049–12072, Vienna, Austria. Association for Computational Linguistics.