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 factchecking 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-theart fine-tuned models, there is still significant room for improvement, indicating that adopting LLMs could be a promising direction for future fact-checking research.