UAHV: Uncertainty-Aware Hierarchical Detection for Hallucination Detection in Large Language Models

To mitigate hallucinations in large language models (LLMs), recent methods have increasingly adopted fine-grained decomposition or repeated reasoning to improve factual verification. However, they often apply predefined verification procedures across samples, overlooking the fact that factual conflicts can differ in scope, evidence requirements, and decision uncertainty. This mismatch may lead to either missed conflicts or erroneous factual judgments. To address this limitation, we propose Uncertainty-Aware Hierarchical Verification (UAHV), an adaptive framework that adjusts verification according to sample-specific factual states. UAHV introduces two complementary mechanisms: Hierarchical Factual Verification (HFV) progressively routes samples from global semantic screening to local verification and selectively invokes arbitration for uncertain cases, while Type-Aware Asymmetric Verification (TAV) applies different evidence-matching strategies to evidence-sensitive and semantically flexible facts. Experiments on three datasets and four LLM backbones show that UAHV achieves the best F1 score in 11 of 12 settings, with an average F1 of 76.12%, outperforming the strongest baseline by 6.27 percentage points.