Language Imbalance

Language imbalance, the uneven representation of languages in machine learning datasets, significantly impacts the performance and fairness of multilingual natural language processing (NLP) models. Current research focuses on mitigating this imbalance through techniques like data augmentation, improved sampling strategies during model training and tokenizer creation, and the development of algorithms that explicitly address the unequal distribution of linguistic resources. Addressing language imbalance is crucial for ensuring equitable access to NLP technologies and improving the performance of cross-lingual tasks, ultimately fostering inclusivity in AI.