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When does label smoothing help? InAdvances in Neural Information Processing Systems, volume 32, pages 4696–4705 · 1973 · arXiv 2511.14117

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Metric-Dependent Annotation Saturation for Learning from Label Distributions

cs.CL · 2026-05-28 · unverdicted · novelty 6.0

Annotation saturation in learning from label distributions for NLI is metric-dependent, with KL divergence saturating at lower annotator counts (~10) than entropy correlation (20-50), and soft labels providing superior item-specific signal compared to smoothing.

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  • Metric-Dependent Annotation Saturation for Learning from Label Distributions cs.CL · 2026-05-28 · unverdicted · none · ref 3

    Annotation saturation in learning from label distributions for NLI is metric-dependent, with KL divergence saturating at lower annotator counts (~10) than entropy correlation (20-50), and soft labels providing superior item-specific signal compared to smoothing.