INDRA (INDian Regional Adapter): Optimized Generative AI Model for Multi-Lingual Platforms in the Context of India's Linguistic Diversity - Yadao, Shivani; Vyas, Harshita (Ingeniería Solidaria, 2025)
Introduction: India’s multilingual diversity poses significant challenges in natural language processing. INDRA introduces a unified generative AI framework optimized for multiple Indic languages.Problem: Existing multilingual models underperform in handling low-resource Indic languages. There is a need for a more effective and scalable NLP architecture tailored to India’s linguistic landscape.Objective: The study aims to develop and evaluate INDRA, a novel architecture that enhances multilingual NLP performance, especially for underrepresented Indic languages.Methodology: INDRA integrates a shared encoder-decoder with language family-specific adapters, typological features, and hierarchical attention. It is benchmarked against mBART, IndicTrans2, MuRIL, and mT5 using standard NLP metrics.Results: The experimental evaluation shows that INDRA outperforms all baseline models in accuracy, F1-score, BLEU, TER, and chrF++, particularly for low-resource languages.Conclusion: INDRA proves to be an effective and efficient solution for multilingual NLP in India, offering impro-ved performance and scalability.Originality: The architecture’s novel use of hierarchical attention and language-specific components tailored for Indic languages marks a significant innovation over existing models.Limitations: The study focuses on textual datasets and does not yet address speech or multimodal processing within Indic languages.
Keywords: Indian Languages; Generative AI; INDRA; NLP; Multilingual translation