alt Hacker News> And the only way to verify calibration is having ground truth data to validate on
I’m not sure that’s true in real cases.
I would expect that in distribution tasks have quite consistent outcomes with variations on phrasing. I would expect that to be much less true when tasks are out of distribution. So could we randomly adjust phrasing and see how much the results change - looking less for truth but consistency?
A model can be consistently over- or under-confident for similar inputs. The only way to check if it's actually calibrated is to compare against some known ground truth. I don't see any way around that.