FACT-GPT: Fact-Checking Augmentation via Claim Matching with LLMs
Eun Cheol Choi Emilio Ferrara
Our society is facing rampant misinformation harming public health and trust.
To address the societal challenge, we introduce FACT-GPT, a system leveraging
Large Language Models (LLMs) to automate the claim matching stage of
fact-checking. FACT-GPT, trained on a synthetic dataset, identifies social
media content that aligns with, contradicts, or is irrelevant to previously
debunked claims. Our evaluation shows that our specialized LLMs can match the
accuracy of larger models in identifying related claims, closely mirroring
human judgment. This research provides an automated solution for efficient
claim matching, demonstrates the potential of LLMs in supporting fact-checkers,
and offers valuable resources for further research in the field.