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

Excerpt

arXiv:2605.18648v2 Announce Type: replace-cross Abstract: Central to human-aligned AI is understanding the benefits of human-elicited labels over synthetic alternatives. While human soft-labels improve calibration by capturing uncertainty, prior studies conflate these benefits with the implicit correction of mislabeled data (mode shifts), obscuring true effects of soft-labels. We present a controlled audit of soft-label learning across MNIST and a synthetic variant, re-annotating subsets to extr