Automated Claim Matching with Large Language Models: Empowering Fact-Checkers in the Fight Against Misinformation

• URL: http://arxiv.org/abs/2310.09223v1
• Date: Fri, 13 Oct 2023 16:21:07 GMT
• Title: Automated Claim Matching with Large Language Models: Empowering Fact-Checkers in the Fight Against Misinformation
• Authors: Eun Cheol Choi and Emilio Ferrara
• Abstract summary: FACT-GPT is a framework designed to automate the claim matching phase of fact-checking using Large Language Models. This framework identifies new social media content that either supports or contradicts claims previously debunked by fact-checkers. We evaluated FACT-GPT on an extensive dataset of social media content related to public health.
• Score: 11.323961700172175
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In today's digital era, the rapid spread of misinformation poses threats to public well-being and societal trust. As online misinformation proliferates, manual verification by fact checkers becomes increasingly challenging. We introduce FACT-GPT (Fact-checking Augmentation with Claim matching Task-oriented Generative Pre-trained Transformer), a framework designed to automate the claim matching phase of fact-checking using Large Language Models (LLMs). This framework identifies new social media content that either supports or contradicts claims previously debunked by fact-checkers. Our approach employs GPT-4 to generate a labeled dataset consisting of simulated social media posts. This data set serves as a training ground for fine-tuning more specialized LLMs. We evaluated FACT-GPT on an extensive dataset of social media content related to public health. The results indicate that our fine-tuned LLMs rival the performance of larger pre-trained LLMs in claim matching tasks, aligning closely with human annotations. This study achieves three key milestones: it provides an automated framework for enhanced fact-checking; demonstrates the potential of LLMs to complement human expertise; offers public resources, including datasets and models, to further research and applications in the fact-checking domain.

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