Generative Large Language Models in Automated Fact-Checking: A Survey
<|reference_start|>Generative Large Language Models in Automated Fact-Checking: A Survey: 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.<|reference_end|>
@article{vykopal2024generative,
    title={Generative Large Language Models in Automated Fact-Checking: A Survey},
    author={Ivan Vykopal, Mat'uv{s} Pikuliak, Simon Ostermann, Mari'an
  v{S}imko},
    journal={arXiv preprint arXiv:2407.02351},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.02351},
    primaryClass={cs.CL}
}