Building a Llama2-finetuned LLM for Odia Language Utilizing Domain
  Knowledge Instruction Set
Guneet Singh Kohli Shantipriya Parida Sambit Sekhar Samirit Saha Nipun B Nair Parul Agarwal Sonal Khosla Kusumlata Patiyal Debasish Dhal
Building LLMs for languages other than English is in great demand due to the
unavailability and performance of multilingual LLMs, such as understanding the
local context. The problem is critical for low-resource languages due to the
need for instruction sets. In a multilingual country like India, there is a
need for LLMs supporting Indic languages to provide generative AI and LLM-based
technologies and services to its citizens.
  This paper presents our approach of i) generating a large Odia instruction
set, including domain knowledge data suitable for LLM fine-tuning, and ii)
building a Llama2-finetuned model tailored for enhanced performance in the Odia
domain. The proposed work will help researchers build an instruction set and
LLM, particularly for Indic languages. We will release the model and
instruction set for the public for research and noncommercial purposes.