An Assessment of Human vs. Model Uncertainty in Soft-Label Learning and Calibration

In three linesControlled study comparing human vs synthetic soft-labels on MNIST. Human soft-labels improve model calibration and alignment with human uncertainty, beyond mere correction of mislabeled data. Shows primary value lies in regularization and stable convergence across training runs.

Summary generated by Claude — human-verified