Understanding User Intent in Code-Mixed Sexual and Reproductive Health Queries in Urban India: Hierarchical Classification Approach Using Large Language Models

Hierarchical classification revealed persistent gaps in how LLMs handle code-mixed queries. Proprietary models performed best, but Sarvam-M shows that open-weight Indic systems can achieve performance near state-of-the-art models when supported by robust training data, cultural adaptation, and appropriate scale. These findings highlight the potential of localized, culturally aligned models to advance linguistically inclusive artificial intelligence tools and expand equitable access to SRH information in underserved populations globally.