> And the only way to verify calibration is having ground truth data to validate on
replyI’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.
replyIn my experience, calibration is only ever in expectation, even for a classic classifier. Any given prediction is often still over- or under-confident.
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