TLDR Tech / Data
A Deep Dive into Calibration of Language Models: Platt Scaling, Isotonic Regression, Temperature Scaling
Temperature Scaling is the simplest for LLM calibration, Platt Scaling is data-efficient and fast but often too coarse, and Isotonic Regression is the most flexible and accurate when you have plenty of calibration data, though it risks overfitting on small sets. For best results with LLMs, evaluate using Expected Calibration Error (ECE), reliability diagrams, and Brier score.
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