AI RESEARCH
The Well-Tempered Classifier: Some Elementary Properties of Temperature Scaling
arXiv CS.AI
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ArXi:2602.14862v2 Announce Type: replace-cross Temperature scaling is a simple method that allows to control the uncertainty of probabilistic models. It is mostly used in two contexts: improving the calibration of classifiers and tuning the stochasticity of large language models (LLMs). In both cases, temperature scaling is the most popular method for the job. Despite its popularity, a rigorous theoretical analysis of the properties of temperature scaling has remained elusive. We investigate here some of these properties.