@inproceedings{deverna-etal-2026-large,
    title = "Large Language Models Require Curated Context for Reliable Political Fact-Checking{---}{E}ven with Reasoning and Web Search",
    author = "DeVerna, Matthew R.  and
      Yang, Kai-Cheng  and
      Yan, Harry Yaojun  and
      Menczer, Filippo",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1467/",
    pages = "29338--29360",
    ISBN = "979-8-89176-395-1",
    abstract = "Large language models (LLMs) have raised hopes for automated end-to-end fact-checking, but prior studies report mixed results. As mainstream chatbots increasingly ship with reasoning capabilities and web search tools{---}and millions of users already rely on them for verification{---}rigorous evaluation is urgent. We evaluate 15 recent LLMs from OpenAI, Google, Meta, and DeepSeek on more than 6,000 claims fact-checked by PolitiFact, comparing standard models with reasoning- and web-search variants. Standard models perform poorly, reasoning offers minimal benefits, and web search provides only moderate gains, despite fact-checks being available on the web. In contrast, a curated RAG system using PolitiFact summaries improved macro F1 by 233{\%} on average across model variants. These findings suggest that giving models access to curated high-quality context is a promising path for automated fact-checking."
}