Google Research asks: how many annotators are enough for AI evaluations?
Building better AI benchmarks: How many raters are enough?
What happened
In the paper “Forest vs Tree: The (N, K) Trade-off in Reproducible ML Evaluation,” Google Research studies the tradeoff between the number of evaluation items and annotators per item. It finds that the commonly used one to five annotators per item is often insufficient; more than ten may be needed to capture variation in human opinion.
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