title: Decoding the Diversity: A Review of the Indic AI Research Landscape
summary: 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.
paper_authors._id: 666fc2679494795851c7e2d1
paper_authors.name: Sankalp KJ
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-17T16:28:48.049Z
paper_authors._id: 664e218ea10258b399d5c358
paper_authors.avatarUrl: /avatars/77f2ee0949560504e1643263fd7084da.svg
paper_authors.fullname: Sankalp KJ
paper_authors.type: user
paper_authors.user: SankalpKJ
paper_authors._id: 666fc2679494795851c7e2d2
paper_authors.name: Vinija Jain
paper_authors._id: 666fc2679494795851c7e2d3
paper_authors.name: Sreyoshi Bhaduri
paper_authors._id: 666fc2679494795851c7e2d4
paper_authors.name: Tamoghna Roy
paper_authors._id: 666fc2679494795851c7e2d5
paper_authors.name: Aman Chadha
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-17T07:31:44.640Z
paper_authors._id: 63a4754927f1f64ed7238dac
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63a4754927f1f64ed7238dac/aH-eJF-31g4vof9jv2gmI.jpeg
paper_authors.fullname: Aman Chadha
paper_authors.type: user
paper_authors.user: amanchadha
paper_title: Decoding the Diversity: A Review of the Indic AI Research Landscape
paper_summary: 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.
paper_ai_keywords: large language models (LLM) generative modeling fine-tuning corpora benchmarking evaluation technique tool application linguistics