Calibrated Preference Learning: The Case of Label Ranking
This paper formalizes calibration for probabilistic label ranking, developing a hierarchy of notions covering full, sub-, and top-k rankings. It proves full-rank calibration implies others but not conversely. Empirical results show popular label ranking models are poorly calibrated, and calibration correlates with RLHF reward model accuracy beyond top-1.
[2605.30447] Calibrated Preference Learning: The Case of Label Ranking
[Submitted on 28 May 2026]
Title:Calibrated Preference Learning: The Case of Label Ranking
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Abstract:Calibration, the alignment of predicted probabilities with true outcome frequencies, is essential for reliable decision-making. While extensively studied for classification and regression, calibration has not been formally addressed for probabilistic label ranking, where the goal is to predict a distribution over orderings of a label set. Naively treating rankings as classes ignores their structure and fails to capture important modalities such as pairwise and top-k predictions. We formalize calibration for label ranking and develop a hierarchy of notions covering full rankings, sub-rankings, and top-k rankings. We prove that full-rank calibration implies the others but not conversely, and sub-ranking and top-k calibration are incomparable. Empirically, we find popular label ranking models are often poorly calibrated, with substantial differences between sub-ranking and top-k metrics. Applying our framework to RLHF reward models, we find that calibration correlates strongly but not perfectly with benchmark accuracy, suggesting it captures a meaningful quality dimension beyond top-1 accuracy. These findings motivate future work on understanding the downstream effects of miscalibration and developing methods to correct it.
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
Cite as: arXiv:2605.30447 [cs.LG]
(or arXiv:2605.30447v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.30447
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Santo Thies [view email] [v1] Thu, 28 May 2026 18:18:21 UTC (3,152 KB)
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