Cross Lingual Ability

Cross-lingual ability in language models focuses on enabling these models to understand and generate text across multiple languages, overcoming limitations imposed by training data imbalances. Current research investigates how architectural choices, such as multilingual BERT and XLM-R, and training methodologies, including instruction tuning and translation-following demonstrations, impact cross-lingual performance, with a particular emphasis on understanding the role of linguistic features like compositionality and the dynamics of multilingual pretraining. These advancements are significant for improving multilingual natural language processing applications and furthering our understanding of how language models represent and process linguistic information across different languages.