@inproceedings{fang-etal-2025-kirag,
title = "{K}i{RAG}: Knowledge-Driven Iterative Retriever for Enhancing Retrieval-Augmented Generation",
author = "Fang, Jinyuan and
Meng, Zaiqiao and
MacDonald, Craig",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.929/",
doi = "10.18653/v1/2025.acl-long.929",
pages = "18969--18985",
ISBN = "979-8-89176-251-0",
abstract = "Iterative retrieval-augmented generation (iRAG) models offer an effective approach for multihop question answering (QA). However, their retrieval processes face two key challenges: (1) they can be disrupted by irrelevant documents or factually inaccurate chain-of-thoughts; (2) their retrievers are not designed to dynamically adapt to the evolving information needs in multi-step reasoning, making it difficult to identify and retrieve the missing information required at each iterative step. Therefore, we propose KiRAG, which uses a knowledge-driven iterative retriever model to enhance the retrieval process of iRAG. Specifically, KiRAG decomposes documents into knowledge triples and performs iterative retrieval with these triples to enable a factually reliable retrieval process. Moreover, KiRAG integrates reasoning into the retrieval process to dynamically identify and retrieve knowledge that bridges information gaps, effectively adapting to the evolving information needs. Empirical results show that KiRAG significantly outperforms existing iRAG models, with an average improvement of 9.40{\%} in R@3 and 5.14{\%} in F1 on multi-hop QA datasets."
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<abstract>Iterative retrieval-augmented generation (iRAG) models offer an effective approach for multihop question answering (QA). However, their retrieval processes face two key challenges: (1) they can be disrupted by irrelevant documents or factually inaccurate chain-of-thoughts; (2) their retrievers are not designed to dynamically adapt to the evolving information needs in multi-step reasoning, making it difficult to identify and retrieve the missing information required at each iterative step. Therefore, we propose KiRAG, which uses a knowledge-driven iterative retriever model to enhance the retrieval process of iRAG. Specifically, KiRAG decomposes documents into knowledge triples and performs iterative retrieval with these triples to enable a factually reliable retrieval process. Moreover, KiRAG integrates reasoning into the retrieval process to dynamically identify and retrieve knowledge that bridges information gaps, effectively adapting to the evolving information needs. Empirical results show that KiRAG significantly outperforms existing iRAG models, with an average improvement of 9.40% in R@3 and 5.14% in F1 on multi-hop QA datasets.</abstract>
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%0 Conference Proceedings
%T KiRAG: Knowledge-Driven Iterative Retriever for Enhancing Retrieval-Augmented Generation
%A Fang, Jinyuan
%A Meng, Zaiqiao
%A MacDonald, Craig
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F fang-etal-2025-kirag
%X Iterative retrieval-augmented generation (iRAG) models offer an effective approach for multihop question answering (QA). However, their retrieval processes face two key challenges: (1) they can be disrupted by irrelevant documents or factually inaccurate chain-of-thoughts; (2) their retrievers are not designed to dynamically adapt to the evolving information needs in multi-step reasoning, making it difficult to identify and retrieve the missing information required at each iterative step. Therefore, we propose KiRAG, which uses a knowledge-driven iterative retriever model to enhance the retrieval process of iRAG. Specifically, KiRAG decomposes documents into knowledge triples and performs iterative retrieval with these triples to enable a factually reliable retrieval process. Moreover, KiRAG integrates reasoning into the retrieval process to dynamically identify and retrieve knowledge that bridges information gaps, effectively adapting to the evolving information needs. Empirical results show that KiRAG significantly outperforms existing iRAG models, with an average improvement of 9.40% in R@3 and 5.14% in F1 on multi-hop QA datasets.
%R 10.18653/v1/2025.acl-long.929
%U https://aclanthology.org/2025.acl-long.929/
%U https://doi.org/10.18653/v1/2025.acl-long.929
%P 18969-18985
Markdown (Informal)
[KiRAG: Knowledge-Driven Iterative Retriever for Enhancing Retrieval-Augmented Generation](https://aclanthology.org/2025.acl-long.929/) (Fang et al., ACL 2025)
ACL