  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.
