@inproceedings{mohammadkhani-etal-2024-zero,
    title = "Zero-Shot Learning and Key Points Are All You Need for Automated Fact-Checking",
    author = "Mohammadkhani, Mohammad Ghiasvand  and
      Mohammadkhani, Ali Ghiasvand  and
      Beigy, Hamid",
    editor = "Schlichtkrull, Michael  and
      Chen, Yulong  and
      Whitehouse, Chenxi  and
      Deng, Zhenyun  and
      Akhtar, Mubashara  and
      Aly, Rami  and
      Guo, Zhijiang  and
      Christodoulopoulos, Christos  and
      Cocarascu, Oana  and
      Mittal, Arpit  and
      Thorne, James  and
      Vlachos, Andreas",
    booktitle = "Proceedings of the Seventh Fact Extraction and VERification Workshop (FEVER)",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Add-Cong-Liu-Florida-Atlantic-University-author-id/2024.fever-1.9/",
    doi = "10.18653/v1/2024.fever-1.9",
    pages = "86--90",
    abstract = "Automated fact-checking is an important task because determining the accurate status of a proposed claim within the vast amount of information available online is a critical challenge. This challenge requires robust evaluation to prevent the spread of false information. Modern large language models (LLMs) have demonstrated high capability in performing a diverse range of Natural Language Processing (NLP) tasks. By utilizing proper prompting strategies, their versatility{---}due to their understanding of large context sizes and zero-shot learning ability{---}enables them to simulate human problem-solving intuition and move towards being an alternative to humans for solving problems. In this work, we introduce a straightforward framework based on {\_}**Z**ero-**S**hot **L**earning{\_} and {\_}**Ke**y **P**oints{\_} (ZSL-KeP) for automated fact-checking, which despite its simplicity, performed well on the AVeriTeC shared task dataset by robustly improving the baseline and achieving 10th place."
}