[2601.05905] Illusions of Confidence? Diagnosing LLM Truthfulness via Neighborhood Consistency

Computer Science > Computation and Language arXiv:2601.05905 (cs) [Submitted on 9 Jan 2026 (v1), last revised 7 Apr 2026 (this version, v2)] Title:Illusions of Confidence? Diagnosing LLM Truthfulness via Neighborhood Consistency Authors:Haoming Xu, Ningyuan Zhao, Yunzhi Yao, Weihong Xu, Hongru Wang, Xinle Deng, Shumin Deng, Jeff Z. Pan, Huajun Chen, Ningyu Zhang View a PDF of the paper titled Illusions of Confidence? Diagnosing LLM Truthfulness via Neighborhood Consistency, by Haoming Xu and 9 other authors View PDF HTML (experimental) 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 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 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 Structure-Aware Training (SAT), which ...