  Conformal prediction is a statistical framework that generates prediction
sets containing ground-truth labels with a desired coverage guarantee. The
predicted probabilities produced by machine learning models are generally
miscalibrated, leading to large prediction sets in conformal prediction. To
address this issue, we propose a novel algorithm named $\textit{Sorted Adaptive
Prediction Sets}$ (SAPS), which discards all the probability values except for
the maximum softmax probability. The key idea behind SAPS is to minimize the
dependence of the non-conformity score on the probability values while
retaining the uncertainty information. In this manner, SAPS can produce compact
prediction sets and communicate instance-wise uncertainty. Extensive
experiments validate that SAPS not only lessens the prediction sets but also
broadly enhances the conditional coverage rate of prediction sets.
