@inproceedings{mi-etal-2026-logical,
    title = "Logical Consistency as a Bridge: Improving {LLM} Hallucination Detection via Label Constraint Modeling between Responses and Self-Judgments",
    author = "Mi, Hao  and
      Sheng, Qiang  and
      Wang, Shaofei  and
      Hu, Beizhe  and
      Sun, Yifan  and
      Wang, Zhengjia  and
      Zeng, Hengqi  and
      Li, Yang  and
      Wang, Danding  and
      Cao, Juan",
    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 1: Long 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-long.286/",
    pages = "6311--6330",
    ISBN = "979-8-89176-390-6",
    abstract = "Large Language Models (LLMs) are prone to factual hallucinations, risking their reliability in real-world applications. Existing hallucination detectors mainly extract micro-level intrinsic patterns for uncertainty quantification or elicit macro-level self-judgments through verbalized prompts. However, these methods address only a single facet of the hallucination, focusing either on implicit neural uncertainty or explicit symbolic reasoning, thereby treating these inherently coupled behaviors in isolation and failing to exploit their interdependence for a holistic view. In this paper, we propose LaaB (Logical Consistency-as-a-Bridge), a framework that bridges neural features and symbolic judgments for hallucination detection. LaaB introduces a ``meta-judgment'' process to map symbolic labels back into the feature space. By leveraging the inherent logical bridge where response and meta-judgment labels are either the same or opposite based on the self-judgment{'}s semantics, LaaB aligns and integrates dual-view signals via mutual learning and enhances the hallucination detection. Extensive experiments on 4 public datasets, across 4 LLMs, against 8 baselines demonstrate the superiority of LaaB."
}