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.

该综述论文提供了印度语系大型语言模型（LLM）研究方向的全面概述，包括LLM的发展、现有LLM的微调、语料库的开发、基准测试和评估以及围绕特定技术、工具和应用的出版物。该论文指出了印度语系的挑战，如数据有限、缺乏标准化和语言复杂性，旨在为从事NLP领域研究工作的人员提供有价值的资源，并为这些语言的更准确、高效的LLM应用的发展做出贡献。

揭示多样性：对印度AI研究领域的综述