An Assessment of Human vs. Model Uncertainty in Soft-Label Learning and Calibration
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 extract human uncertainty. By decoupling soft-label supervision from underlying label mode shifts, we show that while human soft-labels do provide accuracy gains, their larger value lies in acting as a regularizer that improves model calibration on difficult ...
| Authors | Maja Pavlovic, Silviu Paun, Massimo Poesio |
|---|---|
| Primary category | cs.LG |
| Published | 18 May 2026 |
| Last updated at source | 5 October 2026 |
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