Temperature Scaling Simplifies LLM Calibration, but Platt and Isotonic Offer Trade‑offs
The article explains three post‑hoc calibration methods—temperature scaling (simple, effective), Platt scaling (fast, data‑efficient but coarse), and isotonic regression (most flexible and accurate)—and the challenges of adapting them to large language models, whose massive output spaces and limited token‑probability access complicate standard approaches. It also covers calibration metrics such as Expected Calibration Error and reliability diagrams.