Generative Large Language Models in Automated Fact-Checking: A Survey
- URL: http://arxiv.org/abs/2407.02351v1
- Date: Tue, 2 Jul 2024 15:16:46 GMT
- Title: Generative Large Language Models in Automated Fact-Checking: A Survey
- Authors: Ivan Vykopal, Matúš Pikuliak, Simon Ostermann, Marián Šimko,
- Abstract summary: The dissemination of false information across online platforms poses a serious societal challenge.
The growing volume of false information requires automated methods.
Large language models (LLMs) offer promising opportunities to assist fact-checkers.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The dissemination of false information across online platforms poses a serious societal challenge, necessitating robust measures for information verification. While manual fact-checking efforts are still instrumental, the growing volume of false information requires automated methods. Large language models (LLMs) offer promising opportunities to assist fact-checkers, leveraging LLM's extensive knowledge and robust reasoning capabilities. In this survey paper, we investigate the utilization of generative LLMs in the realm of fact-checking, illustrating various approaches that have been employed and techniques for prompting or fine-tuning LLMs. By providing an overview of existing approaches, this survey aims to improve the understanding of utilizing LLMs in fact-checking and to facilitate further progress in LLMs' involvement in this process.
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