researchJan 27
ATLAS: Practical scaling laws for multilingual models
Researchers at Google propose ATLAS, a set of practical scaling laws for multilingual models. The study provides guidelines for efficiently training large models across many languages. You can use these laws to estimate optimal model size and training data for a given language set. The results show that multilingual models can match or exceed monolingual performance with sufficient scale and data.
Key takeaways
- ATLAS provides scaling laws for efficient multilingual model training
- Laws estimate optimal model size and training data for a language set
- Multilingual models can match monolingual performance with sufficient scale