Konkani LLM: Multi-Script Instruction Tuning and Evaluation for a Low-Resource Indian Language
Large Language Models (LLMs) consistently under perform in low-resource linguistic contexts such as Konkani. This performance deficit stems from acute training data scarcity compounded by high script diversity across Devanagari, Romi and Kannada orthographies. To address this gap, we introduce Konkani-Instruct-100k, a comprehensive synthetic instruction-tuning dataset generated through Gemini 3. We establish rigorous baseline benchmarks by evaluating leading open-weights architectures including Llama 3.1, Qwen2.5 and Gemma 3 alongside proprietary closed-source models. Our primary contribution involves the development of Konkani LLM, a series of fine-tuned models optimized for regional nua...
| Authors | Reuben Chagas Fernandes, Gaurang S. Patkar |
|---|---|
| Primary category | cs.CL |
| Published | 7 March 2026 |
| Last updated at source | 7 March 2026 |
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