Self-Checker: Plug-and-Play Modules for Fact-Checking with Large Language Models

Miaoran Li, Baolin Peng, Michel Galley, Jianfeng Gao, Zhu Zhang

Abstract
Fact-checking is an essential task in NLP that is commonly utilized to validate the factual accuracy of a piece of text. Previous approaches mainly involve the resource-intensive process of fine-tuning pre-trained language models on specific datasets. In addition, there is a notable gap in datasets that focus on fact-checking texts generated by large language models (LLMs). In this paper, we introduce Self-Checker, a plug-and-play framework that harnesses LLMs for efficient and rapid fact-checking in a few-shot manner. We also present the BingCheck dataset, specifically designed for fact-checking texts generated by LLMs. Empirical results demonstrate the potential of Self-Checker in the use of LLMs for fact-checking. Compared to state-of-the-art fine-tuned models, there is still significant room for improvement, indicating that adopting LLMs could be a promising direction for future fact-checking research.
Anthology ID:
2024.findings-naacl.12
Volume:
Findings of the Association for Computational Linguistics: NAACL 2024
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
163–181
Language:
URL:
https://aclanthology.org/2024.findings-naacl.12
DOI:
10.18653/v1/2024.findings-naacl.12
Bibkey:
Cite (ACL):
Miaoran Li, Baolin Peng, Michel Galley, Jianfeng Gao, and Zhu Zhang. 2024. Self-Checker: Plug-and-Play Modules for Fact-Checking with Large Language Models. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 163–181, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Self-Checker: Plug-and-Play Modules for Fact-Checking with Large Language Models (Li et al., Findings 2024)
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https://preview.aclanthology.org/add_acl24_videos/2024.findings-naacl.12.pdf
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