Human soft-labels improve AI model calibration and uncertainty alignment

A new research paper explores the benefits of human-elicited soft-labels in AI training, particularly for model calibration and capturing uncertainty. The study, conducted on the MNIST dataset and a synthetic variant, decouples the effects of soft-labels from potential mislabeling corrections. Findings indicate that human soft-labels improve accuracy and act as a regularizer, leading to more stable training and better performance on difficult samples. Models trained with human soft-labels demonstrated an alignment with human uncertainty, unlike those trained on synthetic labels. AI

IMPACT Enhances understanding of human-AI alignment by demonstrating how human-provided uncertainty data can improve model stability and calibration.

RANK_REASON Research paper published on arXiv detailing findings on AI model uncertainty and calibration. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• alphaXiv
• arXiv
• CatalyzeX
• DagsHub
• Gotit.pub
• Hugging Face
• IArxiv
• Maja Pavlovic
• MNIST database
• ScienceCast

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