@inproceedings{xu-etal-2026-illusions,
    title = "Illusions of Confidence? Diagnosing {LLM} Truthfulness via Neighborhood Consistency",
    author = "Xu, Haoming  and
      Zhao, Ningyuan  and
      Yao, Yunzhi  and
      Xu, Weihong  and
      Wang, Hongru  and
      Deng, Xinle  and
      Deng, Shumin  and
      Pan, Jeff Z.  and
      Chen, Huajun  and
      Zhang, Ningyu",
    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.203/",
    pages = "4432--4457",
    ISBN = "979-8-89176-390-6",
    abstract = "As Large Language Models (LLMs) are increasingly deployed in real-world settings, correctness alone is insufficient. Reliable deployment requires maintaining truthful beliefs under contextual perturbations. Existing evaluations largely rely on point-wise confidence like Self-Consistency, which can mask brittle belief. We show that even facts answered with perfect self-consistency can rapidly collapse under mild contextual interference. To address this gap, we propose \textbf{Neighbor-Consistency Belief (NCB)}, a structural measure of belief robustness that evaluates response coherence across a conceptual neighborhood. To validate the efficiency of NCB, we introduce a new \textbf{cognitive stress-testing protocol} that probes outputs stability under contextual interference. Experiments across multiple LLMs show that the performance of high-NCB data is relatively more resistant to interference. Finally, we present \textbf{Structure-Aware Training (SAT)}, which optimizes context-invariant belief structure and reduces long-tail knowledge brittleness by approximately \textbf{30{\%}}."
}