Generative Large Language Models in Automated Fact-Checking: A Survey

• URL: http://arxiv.org/abs/2407.02351v2
• Date: Wed, 30 Oct 2024 07:57:46 GMT
• Title: Generative Large Language Models in Automated Fact-Checking: A Survey
• Authors: Ivan Vykopal, Matúš Pikuliak, Simon Ostermann, Marián Šimko,
• Abstract summary: Large Language Models (LLMs) offer promising opportunities to support fact-checkers with their vast knowledge and advanced reasoning capabilities. This survey explores the application of generative LLMs in fact-checking, highlighting various approaches and techniques for prompting or fine-tuning these models.
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• Abstract: The dissemination of false information on online platforms presents a serious societal challenge. While manual fact-checking remains crucial, Large Language Models (LLMs) offer promising opportunities to support fact-checkers with their vast knowledge and advanced reasoning capabilities. This survey explores the application of generative LLMs in fact-checking, highlighting various approaches and techniques for prompting or fine-tuning these models. By providing an overview of existing methods and their limitations, the survey aims to enhance the understanding of how LLMs can be used in fact-checking and to facilitate further progress in their integration into the fact-checking process.

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