Calibrating the full set of LLM judges with labeled data halves calibration error versus top-5 accuracy selection on RewardBench2 and outperforms on four benchmarks.
CLEAR: Calibrated learning for epistemic and aleatoric risk.arXiv preprint arXiv:2507.08150
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The book curates and presents proofs of important existing results in conformal prediction in a unified pedagogical format with illustrations.
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Calibrate, Don't Curate: Label-Efficient Estimation from Noisy LLM Judges
Calibrating the full set of LLM judges with labeled data halves calibration error versus top-5 accuracy selection on RewardBench2 and outperforms on four benchmarks.
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Theoretical Foundations of Conformal Prediction
The book curates and presents proofs of important existing results in conformal prediction in a unified pedagogical format with illustrations.