@inproceedings{putta-etal-2025-claimcheck,
    title = "{C}laim{C}heck: Automatic Fact-Checking of Textual Claims using Web Evidence",
    author = "Putta, Akshith Reddy  and
      Devasier, Jacob  and
      Li, Chengkai",
    editor = "Shi, Weijia  and
      Yu, Wenhao  and
      Asai, Akari  and
      Jiang, Meng  and
      Durrett, Greg  and
      Hajishirzi, Hannaneh  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 4th International Workshop on Knowledge-Augmented Methods for Natural Language Processing",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.knowledgenlp-1.26/",
    pages = "303--316",
    ISBN = "979-8-89176-229-9",
    abstract = "We introduce ClaimCheck, an efficient fact-checking system that verifies textual claims using smaller, open-source large language models. ClaimCheck integrates two fact-checking strategies, claim-matching and novel claim processing. Claim-matching uses related fact-checks from trusted organizations to fact-check a claim. Novel claim processing breaks down fact-checking into manageable subtasks{---}generating targeted questions, retrieving Web evidence, extracting answers, and synthesizing verdicts. Evaluation on the AVeriTeC benchmark demonstrates 62.6{\%} verdict prediction accuracy, with claim-matching providing a 2.8{\%} improvement. ClaimCheck approaches the performance of state-of-the-art systems while requiring significantly fewer computational resources, demonstrating the effectiveness of using small language models for fact-checking tasks. Furthermore, our code is publicly available to help make automated fact-checking more accessible."
}