Researchers from Apple Machine Learning Research and the Technical University of Denmark have found that mixing data from a high-resource language, like English, with a low-resource target language, such as Arabic, significantly improves model performance. This bilingual pre-training approach proved more effective than aggressive hyperparameter tuning, especially as model size increased. The study, involving approximately 1000 pre-training runs across various model scales, indicated that data mixing provides benefits equivalent to 2-3 times the unique target data for validation loss and 2-13 times for downstream task accuracy. AI
IMPACT This research suggests that data mixing is a more effective strategy than hyperparameter tuning for improving language models in data-constrained environments, potentially leading to more efficient model development.
RANK_REASON The cluster contains a research paper detailing a new method for pre-training language models. [lever_c_demoted from research: ic=1 ai=1.0]
Read on Apple Machine Learning Research →
- Anastasiia Sedova
- Apple Inc.
- Arabic
- English
- International Conference on Learning Representations
- International Conference on Machine Learning
- Jes Frellsen
- Louis Béthune
- Natalie Schluter
- Paul Jeha
- Pierre Ablin
- Skyler Seto
- Technical University of Denmark
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