Zero-Shot Learning and Key Points Are All You Need for Automated Fact-Checking
<|reference_start|>Zero-Shot Learning and Key Points Are All You Need for Automated Fact-Checking: 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 Zero-Shot Learning and Key Points (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.<|reference_end|>
@article{mohammadkhani2024zero-shot,
    title={Zero-Shot Learning and Key Points Are All You Need for Automated
  Fact-Checking},
    author={Mohammad Ghiasvand Mohammadkhani, Ali Ghiasvand Mohammadkhani, Hamid
  Beigy},
    journal={arXiv preprint arXiv:2408.08400},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.08400},
    primaryClass={cs.CL}
}