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.
Papers
On the Economics of Multilingual Few-shot Learning: Modeling the Cost-Performance Trade-offs of Machine Translated and Manual Data
Kabir Ahuja, Monojit Choudhury, Sandipan Dandapat
Multi Task Learning For Zero Shot Performance Prediction of Multilingual Models
Kabir Ahuja, Shanu Kumar, Sandipan Dandapat, Monojit Choudhury