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This paper proposes CORD, a post-fit adapter for post-hoc calibration that repairs probability vectors to preserve original top-1 predictions while maintaining calibration quality, achieving zero TPCR and improved metrics on datasets like CIFAR and ImageNet.
This paper introduces GA-AMLS, a rare-event Monte Carlo method adapted to language model activation spaces, and SPB Loss, a proper scoring rule for asymmetric penalties, demonstrating improved estimation of rare harmful outputs.