Mix, Don't Tune: Bilingual Pre-Training Outperforms Hyperparameter Search in Data-Constrained Settings
For most languages of the world, language model pre-training operates in a data-constrained regime where models must repeat their training data many times, degrading generalization. Two remedies exist: aggressive hyperparameter tuning such as high weight decay, and mixing in data from a high-resource auxiliary language to directly aid the low-resource target. While hyperparameter tuning regularizes the model by...
Se muestra el contenido original; la traduccion localizada aun no esta disponible.
Qué ocurrió
For most languages of the world, language model pre-training operates in a data-constrained regime where models must repeat their training data many times, degrading generalization. Two remedies exist: aggressive hyperparameter tuning such as high weight decay, and mixing in data from a high-resource auxiliary language to directly aid the low-resource target. While hyperparameter tuning regularizes the model by...
Por que importa
The development may change operating conditions or market expectations around AI. Further confirmation and measurable outcomes matter.
Entidades afectadas
Ver evidencia
1 articulos · 1 informe original · 1 independientes
- Apple Machine Learning ResearchFuente primaria · Respalda · EN · 100%Mix, Don't Tune: Bilingual Pre-Training Outperforms Hyperparameter Search in Data-Constrained Settings ↗
Afirmaciones
- Mix, Don't Tune: Bilingual Pre-Training Outperforms Hyperparameter Search in Data-Constrained Settings Observado
Conflictos
No se detectaron conflictos importantes en la evidencia disponible.
Cronología
- Primera publicación
Movimiento del mercado posterior al evento
La reacción del mercado aún no está disponible para este activo y periodo.