A P\={a}ninian Foundation for Indic Language Processing

The article proposes a P\={a}ninian framework for natural language processing (NLP) in Indic languages, highlighting the shared morphosyntactic architecture derived from P\={a}nini's grammar, the Ast\={a}dhy\={a}y. It introduces a four-part benchmark suite aimed at improving the accuracy, data efficiency, and transferability of NLP systems for these languages by consolidating disparate resources into a unified framework. This approach could enhance model interpretability by examining whether neural models inherently capture P\={a}nini's linguistic categories, which is crucial for practitioners developing robust AI applications in this domain.