FACT-GPT: Fact-Checking Augmentation via Claim Matching with LLMs
This provides an automated solution for fact-checkers to efficiently identify social media content related to debunked claims, though it is incremental as it focuses on one stage of fact-checking.
The paper tackles misinformation by introducing FACT-GPT, a system that uses specialized LLMs trained on synthetic data to automate claim matching in fact-checking, achieving accuracy comparable to larger models and human judgment.
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.