Cross Lingual Generalisation

Cross-lingual generalization focuses on enabling language models trained on one language (often English) to perform well on others, bridging the digital divide and expanding access to natural language processing (NLP) technologies. Current research investigates factors influencing this generalization, such as data imbalance during training (where some languages are significantly more represented than others), the minimal amount of multilingual data needed for effective transfer, and the fairness of these models across different languages. This work is crucial for developing truly multilingual NLP systems and ensuring equitable access to advanced language technologies across diverse linguistic communities, impacting fields ranging from machine translation to question answering.