arXiv:2606.24172v1 Announce Type: cross Abstract: More than a billion people communicate in Indic languages, yet the natural language processing infrastructure serving them remains fragmented and underdeveloped. The cause is structural: the field organizes its tools and benchmarks around individual languages or small subsets of genealogical language families, building separate analyzers, parsers, and datasets for each language and starting over for the next. This overlooks a deep regularity. Through more than two millennia of convergence around Sanskrit, Indic languages came to share a morphos
The challenge of fragmented NLP infrastructure for Indic languages is now attracting foundational research, driven by the increasing global relevance of these populations and an awareness of the inherent inefficiencies in current approaches.
This research outlines a pathway to significantly accelerate AI development and accessibility for a billion people, potentially unlocking new markets and fostering digital inclusion on a massive scale.
The shift from language-specific NLP models to a unified, pan-Indic framework based on shared linguistic roots will streamline development, reduce redundant efforts, and improve model performance for these languages.
- · Indic language speakers
- · AI developers in South Asia
- · Multinational tech companies expanding into South Asia
- · Linguistics researchers
- · Fragmented NLP tool providers
- · Language-specific data collection efforts
Improved NLP tools for Indic languages will lead to better translation, voice assistants, and information access for large populations.
Enhanced digital literacy and participation among Indic language speakers could drive economic growth and social development in the region.
The success of a pan-Indic approach might inspire similar unified linguistic models for other language families globally, further accelerating AI development.
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Read at arXiv cs.AI