@inproceedings{daohuan-etal-2026-factuality,
    title = "From Factuality to Meta-Factivity: A Cognitive Blueprint for Trustworthy {LLM}s",
    author = "Daohuan, Liu  and
      Lun, Xia  and
      Wang, Yuer  and
      Su, Jiaoyang  and
      Tang, Xuri",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-short.7/",
    pages = "62--69",
    ISBN = "979-8-89176-391-3",
    abstract = "Current research on Event Factuality Prediction (EFP) predominantly treats LLMs as passive classifiers, where high aggregate metrics often mask shortcut learning and unreliable reasoning. In this position paper, we argue for a focus shift from event factuality to meta-factivity. We introduce the Meta-Factivity Framework (MFF), a theoretical roadmap that moves evaluation beyond surface recognition to belief trajectory reasoning and epistemic regulation. By framing hallucination as a failure of meta-cognitive control, we advocate for a transition from measuring black-box accuracy to evaluating white-box cognition, laying the groundwork for a more rigorous benchmark for explainable self-governance."
}