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This article addresses critical post-hoc calibration techniques—Platt scaling, isotonic regression, and temperature scaling—for large language models to align confidence scores with actual accuracy. It highlights the widespread miscalibration issue in LLMs across reasoning and code generation tasks while noting that standard metrics like Expected Calibration Error may need supplementation for a complete reliability assessment.

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A Deep Dive into Calibration of Language Models: Platt Scaling, Isotonic Regression, Temperature Scaling

KDnuggetsJune 5, 20266 min read

Discover three post-hoc methods for closing the gap between confidence and accuracy.

A model that says it is 90% confident should be right 90% of the time. When that relationship breaks down, you get a miscalibration problem. The model's scores stop telling you anything useful about reliability. For large language models (LLMs), miscalibration is widespread. A 2024 NAACL survey found that confidence scores diverge from actual correctness rates across factual QA, code generation, and reasoning tasks. Another study on biomedical models found mean calibration scores ranging from only 23.9% to 46.6% across all tested models. The gap is consistent. The standard solution in classical machine learning is post-hoc recalibration: fit a simple function on a held-out validation set to map raw confidence…

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