Summary of Decoding the Diversity: a Review Of the Indic Ai Research Landscape, by Sankalp Kj et al.
Decoding the Diversity: A Review of the Indic AI Research Landscape
by Sankalp KJ, Vinija Jain, Sreyoshi Bhaduri, Tamoghna Roy, Aman Chadha
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The review paper provides an overview of large language model (LLM) research directions within Indic languages, including India, Pakistan, Bangladesh, Sri Lanka, Nepal, and Bhután. The paper reviews recent advancements in Indic generative modeling, contributing a taxonomy of research directions and tabulating 84 recent publications. The surveyed research directions include LLM development, fine-tuning existing LLMs, developing corpora, benchmarking and evaluation, as well as publications around specific techniques, tools, and applications. The paper highlights the challenges associated with limited data availability, lack of standardization, and peculiar linguistic complexities of Indic languages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The review paper looks at how to improve language models for Indian languages like Hindi, Urdu, Bengali, and others. These languages are spoken by over 1.5 billion people worldwide and are important for things like natural language processing (NLP) applications. The paper talks about the challenges of developing language models for these languages, including having limited data and not enough standardization. |
Keywords
» Artificial intelligence » Fine tuning » Large language model » Natural language processing » Nlp
