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Comprehensive Benchmarking of Entropy and Margin Based Scoring Metrics for Data Selection
While data selection methods have been studied extensively in active learning, data pruning, and data augmentation settings, there is little evidence for the efficacy of these methods in industry scale settings, particularly in low-resource languages.
Influence Scores at Scale for Efficient Language Data Sampling
Modern ML systems ingest data aggregated from diverse sources, such as synthetic, human-annotated, and live customer traffic.
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