Decoding the Diversity: A Review of the Indic AI Research Landscape
Authors: name: Sankalp KJ, name: Vinija Jain, name: Sreyoshi Bhaduri, name: Tamoghna Roy, name: Aman Chadha

This review paper provides a comprehensive overview of large language model
(LLM) research directions within Indic languages. Indic languages are those
spoken in the Indian subcontinent, including India, Pakistan, Bangladesh, Sri
Lanka, Nepal, and Bhutan, among others. These languages have a rich cultural
and linguistic heritage and are spoken by over 1.5 billion people worldwide.
With the tremendous market potential and growing demand for natural language
processing (NLP) based applications in diverse languages, generative
applications for Indic languages pose unique challenges and opportunities for
research. Our paper deep dives into the recent advancements in Indic generative
modeling, contributing with a taxonomy of research directions, tabulating 84
recent publications. Research directions surveyed in this paper include LLM
development, fine-tuning existing LLMs, development of corpora, benchmarking
and evaluation, as well as publications around specific techniques, tools, and
applications. We found that researchers across the publications emphasize the
challenges associated with limited data availability, lack of standardization,
and the peculiar linguistic complexities of Indic languages. This work aims to
serve as a valuable resource for researchers and practitioners working in the
field of NLP, particularly those focused on Indic languages, and contributes to
the development of more accurate and efficient LLM applications for these
languages.