IIT Hyderabad Professor Believes Indic Data is All You Need

While fine-tuning with Indic language tokens on top of existing English models is a viable approach, building foundational models from scratch offers several advantages, and that is what BharatGPT is aiming to do. “Existing models may not adequately represent the Indian cultural and linguistic diversity, which can lead to biases and limitations in their applicability,” said Professor Maunendra Sankar Desarkar from IIT Hyderabad, who is also a core team member of the BharatGPT initiative. “Moreover, fine-tuning may not fully address the unique linguistic challenges posed by Indic languages,” he added. He further said that by building foundational models tailored to the Indian context, we can ensure greater inclusivity and effectiveness across diverse linguistic communities, which would deliver AI in the best possible way in India.  “We're sourcing data from various repositories available on the web, including digitised books and datasets,” Desarkar added. He said