Multilingual Data

Multilingual data research focuses on developing natural language processing (NLP) models capable of handling multiple languages effectively, aiming to overcome the limitations of English-centric models and address performance disparities across languages. Current research emphasizes improving model architectures, such as incorporating language-specific modules or leveraging techniques like low-rank adaptation and self-distillation, to enhance multilingual capabilities and mitigate biases stemming from imbalanced data. This work is crucial for broadening NLP's accessibility and impact, enabling applications in diverse fields like finance, e-commerce, and healthcare, where multilingual data is prevalent and accurate analysis is essential.