A statistical framework decomposes human annotation outcomes into four interpretable variation sources and extends classical measurement-error models to handle both shared and individualized notions of truth.
Don’t waste a single annotation: Improving single-label classifiers through soft labels.arXiv preprint arXiv:2311.05265
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From Ground Truth to Measurement: A Statistical Framework for Human Labeling
A statistical framework decomposes human annotation outcomes into four interpretable variation sources and extends classical measurement-error models to handle both shared and individualized notions of truth.