How many labelers do you have? A closer look at gold-standard labels

The paper examines the common practice of aggregating multiple labels per instance into a single ‘true’ label for supervised learning. By creating a theoretical model, the authors show that using the full, non‑aggregated label information can make it easier to train well‑calibrated models, though the benefits depend on the specific problem. They predict when non‑aggregated labels will improve learning and validate these predictions on real datasets.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.