Calibrated Preference Learning: The Case of Label Ranking

In three linesFormal study of calibration for probabilistic label ranking. Authors define a hierarchy of notions (full rankings, sub-rankings, top-k) and show popular models are poorly calibrated. Application to RLHF reward models reveals calibration and accuracy are not perfectly correlated.

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