By measuring how much each language helps other languages learn during training, you can predict model performance more accurately and find better language mixture ratios than methods that ignore cross-lingual transfer effects.
This paper treats multilingual language model training as a cooperative game where each language contributes to overall performance. It uses game theory to measure how much each language helps others learn (cross-lingual transfer), then uses these insights to predict the best mix of languages for training data.