arXiv:2602.14862v2 Announce Type: replace-cross Abstract: 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. For classification, we show that increasing the temperature i
This research provides a foundational theoretical understanding of temperature scaling, a widely used technique in AI, particularly relevant as LLMs become more prevalent and their calibration critical.
Improved theoretical understanding of temperature scaling allows for more robust, reliable, and predictable AI models, impacting everything from safety to performance in critical applications.
The rigorous analysis helps move the application of temperature scaling from empirical tuning to principled methodology, potentially refining AI development and deployment practices.
- · AI researchers
- · AI developers
- · LLM providers
- · Machine learning platforms
- · Developers relying solely on ad-hoc empirical tuning
Better understanding of AI model uncertainty and calibration accuracy.
More reliable and trustworthy AI systems, possibly leading to broader adoption in sensitive domains.
Standardization of temperature scaling practices across different AI model architectures and applications.
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Read at arXiv cs.LG