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arXiv · 2601.05905

Illusions of Confidence? Diagnosing LLM Truthfulness via Neighborhood Consistency

Haoming Xu·Ningyuan Zhao·Yunzhi Yao·Weihong Xu·Hongru Wang·Xinle Deng·Shumin Deng·Jeff Z. Pan·Huajun Chen·Ningyu Zhang
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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 optimizes context-invariant belief structure and reduces long-tail knowledge brittleness by approximately 30%. Code is available at https://github.com/zjunlp/belief.

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Haoming Xu, Ningyuan Zhao, Yunzhi Yao, Weihong Xu, Hongru Wang, Xinle Deng, Shumin Deng, Jeff Z. Pan, Huajun Chen, Ningyu Zhang. 2026-04-07. Illusions of Confidence? Diagnosing LLM Truthfulness via Neighborhood Consistency. https://arxiv.org/abs/2601.05905

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