Apple Research Finds Bilingual Data Mixing Beats Hyperparameter Tuning in Low-Resource Pretraining
Apple researchers compared two remedies for data-constrained language model pretraining: aggressive hyperparameter tuning, such as high weight decay, and mixing in data from a high-resource auxiliary language. They report that bilingual pre-training outperforms hyperparameter search in these settings, where models must repeat training data many times, degrading generalization.
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