International Journal For Multidisciplinary Research

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A Comprehensive Theoretical and Empirical Framework for Fine-Tuning the CoRover's BharatGPT Transformer for Indic Languages

Author(s) Ankush Sabharwal, Vikas Tripathi, Onkar Nath
Country India
Abstract The widespread adoption of transformer-based models in natural language processing (NLP) has led to significant breakthroughs in numerous languages. However, models like BharatGPT (from CoRover.a) - though robust for high-resource languages - require specialized adaptation to effectively handle the rich morphological and syntactic diversity of Indic languages. In this paper, we propose a comprehensive framework for fine-tuning the BharatGPT transformer to support Indic languages. Our approach integrates tailored data preprocessing, script-specific embedding enhancements, and rigorous convergence analysis. We derive key theoretical properties of the fine-tuning algorithm, including a convergence theorem under Lipschitz continuity and bounded gradient variance assumptions, and we validate our approach with empirical evaluations using standard metrics such as perplexity, BLEU, and F1 score. The results demonstrate significant improvements across several Indic languages, thereby underscoring the effectiveness of our methodology.
Keywords BharatGPT, CoRover, Fine Tuning, LLM, Indic Languages, AI,
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 1, January-February 2025
Published On 2025-02-15
Cite This A Comprehensive Theoretical and Empirical Framework for Fine-Tuning the CoRover's BharatGPT Transformer for Indic Languages - Ankush Sabharwal, Vikas Tripathi, Onkar Nath - IJFMR Volume 7, Issue 1, January-February 2025. DOI 10.36948/ijfmr.2025.v07i01.37188
DOI https://doi.org/10.36948/ijfmr.2025.v07i01.37188
Short DOI https://doi.org/g847z5
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