Definition
Plain language
Whether a model's confidence actually matches how often it's right — a well-calibrated model is sure when it should be sure and unsure when it shouldn't.
As stated in the literature
The degree to which a model's predicted probabilities match empirical outcome frequencies, distinct from raw accuracy; instruction tuning and RLHF often degrade it, yielding confident-but-wrong outputs, and conformal methods restore it as a controllable target rate.
Also called: calibrated, well-calibrated, miscalibration, calibration loss
Why it matters: Without it a model can sound completely confident while being wrong, which is dangerous when people trust its answers for medical, legal, or financial decisions.
For example, a well-calibrated weather model that says '70% chance of rain' should actually see rain on about 70 out of 100 such days.
Heard on the show
“The authors say the weights are calibration constants, not derived quantities.”Episode 245 — Fifteen Models Ran Football Clubs for Twenty Years, and Size Didn't Decide It