@inproceedings{xie-etal-2025-fire,
title = "{FIRE}: Fact-checking with Iterative Retrieval and Verification",
author = "Xie, Zhuohan and
Xing, Rui and
Wang, Yuxia and
Geng, Jiahui and
Iqbal, Hasan and
Sahnan, Dhruv and
Gurevych, Iryna and
Nakov, Preslav",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-naacl.158/",
doi = "10.18653/v1/2025.findings-naacl.158",
pages = "2901--2914",
ISBN = "979-8-89176-195-7",
abstract = "Fact-checking long-form text is challenging, and it is therefore common practice to break it down into multiple atomic claims. The typical approach to fact-checking these atomic claims involves retrieving a fixed number of pieces of evidence, followed by a verification step. However, this method is usually not cost-effective, as it underutilizes the verification model{'}s internal knowledge of the claim and fails to replicate the iterative reasoning process in human search strategies. To address these limitations, we propose FIRE, a novel agent-based framework that integrates evidence retrieval and claim verification in an iterative manner. Specifically, FIRE employs a unified mechanism to decide whether to provide a final answer or generate a subsequent search query, based on its confidence in the current judgment. We compare FIRE with other strong fact-checking frameworks and find that it achieves slightly better performance while reducing large language model (LLM) costs by an average of 7.6 times and search costs by 16.5 times. These results indicate that FIRE holds promise for application in large-scale fact-checking operations."
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<abstract>Fact-checking long-form text is challenging, and it is therefore common practice to break it down into multiple atomic claims. The typical approach to fact-checking these atomic claims involves retrieving a fixed number of pieces of evidence, followed by a verification step. However, this method is usually not cost-effective, as it underutilizes the verification model’s internal knowledge of the claim and fails to replicate the iterative reasoning process in human search strategies. To address these limitations, we propose FIRE, a novel agent-based framework that integrates evidence retrieval and claim verification in an iterative manner. Specifically, FIRE employs a unified mechanism to decide whether to provide a final answer or generate a subsequent search query, based on its confidence in the current judgment. We compare FIRE with other strong fact-checking frameworks and find that it achieves slightly better performance while reducing large language model (LLM) costs by an average of 7.6 times and search costs by 16.5 times. These results indicate that FIRE holds promise for application in large-scale fact-checking operations.</abstract>
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%0 Conference Proceedings
%T FIRE: Fact-checking with Iterative Retrieval and Verification
%A Xie, Zhuohan
%A Xing, Rui
%A Wang, Yuxia
%A Geng, Jiahui
%A Iqbal, Hasan
%A Sahnan, Dhruv
%A Gurevych, Iryna
%A Nakov, Preslav
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F xie-etal-2025-fire
%X Fact-checking long-form text is challenging, and it is therefore common practice to break it down into multiple atomic claims. The typical approach to fact-checking these atomic claims involves retrieving a fixed number of pieces of evidence, followed by a verification step. However, this method is usually not cost-effective, as it underutilizes the verification model’s internal knowledge of the claim and fails to replicate the iterative reasoning process in human search strategies. To address these limitations, we propose FIRE, a novel agent-based framework that integrates evidence retrieval and claim verification in an iterative manner. Specifically, FIRE employs a unified mechanism to decide whether to provide a final answer or generate a subsequent search query, based on its confidence in the current judgment. We compare FIRE with other strong fact-checking frameworks and find that it achieves slightly better performance while reducing large language model (LLM) costs by an average of 7.6 times and search costs by 16.5 times. These results indicate that FIRE holds promise for application in large-scale fact-checking operations.
%R 10.18653/v1/2025.findings-naacl.158
%U https://aclanthology.org/2025.findings-naacl.158/
%U https://doi.org/10.18653/v1/2025.findings-naacl.158
%P 2901-2914
Markdown (Informal)
[FIRE: Fact-checking with Iterative Retrieval and Verification](https://aclanthology.org/2025.findings-naacl.158/) (Xie et al., Findings 2025)
ACL
- Zhuohan Xie, Rui Xing, Yuxia Wang, Jiahui Geng, Hasan Iqbal, Dhruv Sahnan, Iryna Gurevych, and Preslav Nakov. 2025. FIRE: Fact-checking with Iterative Retrieval and Verification. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 2901–2914, Albuquerque, New Mexico. Association for Computational Linguistics.