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.

“Collocate list and collocation network are two widely used representation methods of colloca-tions, but they have significant weaknesses in representing contextual information. To solve thisproblem, we propose a new representation method, namely the contextualized representation ofcollocate (CRC), which highlights the importance of the position of the collocates and pins acollocate as the interaction of two dimensions: association strength and co-occurrence position. With a full image of all the collocates surrounding the node word, CRC carries the contextualinformation and makes the representation more informative and intuitive. Through three casestudies, i.e., synonym distinction, image analysis, and efficiency in lexical use, we demonstratethe advantages of CRC in practical applications. CRC is also a new quantitative tool to measurelexical usage pattern similarities for corpus-based research. It can provide a new representationframework for language researchers and learners.”