@inproceedings{li-etal-2026-beyond-output,
    title = "Beyond Output Confidence: Epistemic-Aware Hallucination Detection with Answer-Level Signals",
    author = "Li, Jieran  and
      Hu, Xiuyuan  and
      Zhao, Yang  and
      Sun, Dongbiao  and
      Zhang, Hao",
    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.674/",
    pages = "13796--13806",
    ISBN = "979-8-89176-395-1",
    abstract = "Despite their strong generative capabilities, large language models frequently exhibit hallucinations, particularly due to outside-boundary confidence where incorrect assertions are produced with high statistical certainty. Existing approaches commonly use output probability as a proxy for truthfulness; however, this signal is confounded by epistemic uncertainty and cannot reliably distinguish genuine uncertainty from fabricated content. We argue that effective hallucination detection requires integrating surface-level confidence with signals that reflect the model{'}s underlying epistemic state. To this end, we propose Answer-level Intrinsic Cognition (AIC), a model-agnostic metric that captures epistemic boundary deviations by measuring answer-level stability across multiple stochastic forward passes. By coupling AIC with conventional output uncertainty, we derive a composite metric that disentangles within-boundary uncertainty from outside-boundary confidence. Across three public question-answering benchmarks and diverse model scales, the two-dimensional score consistently outperforms strong uncertainty-only baselines, with larger gains on adversarially constructed hallucination sets. The code is available at: https://github.com/HXYfighter/AIC-ACL2026."
}