Temperature Scaling Fails Under Human Label Distributions, Study Finds

Jul 16, 2026

A new preprint demonstrates that temperature scaling, a widely used model calibration technique, systematically misrepresents model reliability when ground-truth labels are soft or distributional, such as those from crowd-sourced human annotations. Evaluating nine model configurations on the CIFAR-10H and ChaosNLI datasets, the study finds that temperature scaling calibrated on hard labels consistently underperforms an oracle calibrated on soft labels, with calibration gaps notably larger in language tasks (mean 0.079) than in vision tasks (mean 0.003). The results hold across model scales and with an alternative calibration method, multiclass isotonic regression.

Why it matters: The findings highlight that standard calibration protocols relying on majority-vote labels can give a misleading sense of model reliability in real-world scenarios with inherent label ambiguity, posing risks for safety-critical AI deployments.

Full story at: arXiv Machine Learning ↗