When Do Supervised UQ Ensembles Improve LLM Hallucination Detection? A Robustness Study
Uncertainty quantification (UQ) methods are widely used for hallucination detection in large language models (LLMs) in closed-book settings where ground-truth evidence is unavailable at inference time. Prior work has proposed combining UQ signals via learned ensembles, but empirical investigations into the robustness of these ensembles are limited. We study a supervised ensembling framework that trains a classifier over heterogeneous UQ-based scorer outputs on a small, domain-specific dataset of labeled LLM responses, then applies it to out-of-sample hallucination classification without retrieval, tools, or reference documents. Across four LLMs, nine datasets, and three generation regimes...
| Authors | Mohit Singh Chauhan, Vipin Gyanchandani, Dylan Bouchard |
|---|---|
| Primary category | cs.LG |
| Published | 25 August 2026 |
| Last updated at source | 8 September 2026 |
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