Mix, Don't Tune: Bilingual Pre-Training Outperforms Hyperparameter Search in Data-Constrained Settings.

Apple researchers found that bilingual pre-training outperforms hyperparameter tuning when training data is scarce. The work addresses the data-constrained regime in which most of the world's languages must repeat training data many times, degrading generalization.

Why it matters: It suggests that pairing languages in pre-training could be a more effective strategy than tuning settings like weight decay for low-resource languages.

Written by AI from Apple Machine Learning Research's published text.

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