Multilingual Model

Multilingual models aim to process and generate text across multiple languages, overcoming limitations of monolingual approaches and expanding access to natural language processing (NLP) for low-resource languages. Current research focuses on improving the performance of these models, particularly for low-resource languages, using architectures like transformer-based models (e.g., BERT, mT5) and exploring techniques such as instruction tuning, knowledge distillation, and targeted multilingual adaptation. This work is significant because it addresses biases inherent in predominantly English-centric models and enables broader access to NLP tools and applications across diverse linguistic communities.