Rank-weighted adaptive sampling of which model to score next recovers top-model rankings from WMT human evaluation data with less budget than uniform evaluation.
In Proceedings of the 30th International Conference on Machine Learning, pages 1238–1246, Atlanta, Georgia, USA
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Dynamically Allocating Evaluation Effort for Model Ranking
Rank-weighted adaptive sampling of which model to score next recovers top-model rankings from WMT human evaluation data with less budget than uniform evaluation.