Konkani LLM: Multi-Script Instruction Tuning and Evaluation for a Low-Resource Indian Language Reuben Chagas Fernandes <https://arxiv.org/search/cs?searchtype=author&query=Fernandes,+R+C>, Gaurang S. Patkar <https://arxiv.org/search/cs?searchtype=author&query=Patkar,+G+S>
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 nuances. Furthermore, we are developing the Multi-Script Konkani Benchmark to facilitate cross-script linguistic evaluation. In machine translation, Konkani LLM delivers consistent gains over the corresponding base models and is competitive with and in several settings surpasses proprietary baselines https://arxiv.org/abs/2603.23529 _/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/ _/ Frederick Noronha फ्रेडरिक नोरोन्या * فريدريك نورونيا _/ AUDIO https://archive.org/details/@fredericknoronha _/ http://goa1556.in +91-9822122436 784 Saligao Goa _/ Goanet :: 30 years of discussions. [email protected] _/ http://lists.goanet.org/pipermail/goanet-goanet.org/ _/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/_/ -- You received this message because you are subscribed to the Google Groups "Goa-Research-Net" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To view this discussion, visit https://groups.google.com/d/msgid/goa-research-net/CA%2Bmqab8n87oQUuD51v0UMO5e2-o7Kb-cehxiZz7q7JePCndJVA%40mail.gmail.com.
