Multi-level bootstrapping models annotator variance using large rater-ID datasets to find optimal tradeoffs between number of items N and ratings per item K for statistically significant AI evaluations.
Toward benchmarking group explanations: Evaluating the effect of aggregation strategies versus explanation
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Improving Reproducibility in Evaluation through Multi-Level Annotator Modeling
Multi-level bootstrapping models annotator variance using large rater-ID datasets to find optimal tradeoffs between number of items N and ratings per item K for statistically significant AI evaluations.