In the news
Mix, Don't Tune: Bilingual Pre-Training Outperforms Hyperparameter Search in Data-Constrained Settings
Apple Machine Learning Research · Published · 3 min read
In 30 seconds
- What happened
- Apple researchers found that mixing high-resource language data during pre-training outperforms hyperparameter tuning for low-resource language models across multiple scales.
- Why it matters
- Engineers building language models for under-resourced languages should consider this when choosing between tuning hyperparameters or augmenting training data with auxiliary languages.
- Watch out
- The study used Arabic and English; results may not generalize equally to all language pairs or domains with different resource disparities.
- language model
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