Building a Llam2 Finetuned LLM for Low-Resource Odia Language

What is it about?

The demand for building LLMs for languages other than English is growing due to the limitations of multilingual LLMs in understanding local contexts. This issue is particularly critical for low-resource languages that lack comprehensive instruction sets. In a multilingual country like India, there is a pressing need for LLMs that support Indic languages to provide generative AI and LLM-based technologies and services to its citizens. This work addresses this need by i) generating a large Odia instruction set that includes domain-specific data suitable for LLM fine-tuning, and ii) developing a Llama2-finetuned model designed for enhanced performance in the Odia domain. This initiative aims to assist researchers in building instruction sets and LLMs, especially for Indic languages.

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Photo by Paul Lequay on Unsplash

Photo by Paul Lequay on Unsplash

Why is it important?

This work emphasizes the critical role of domain knowledge in developing Large Language Models (LLMs) that can accurately understand and respond to local contexts, cultures, and customs. By integrating specific domain knowledge, LLMs become more adept at interpreting nuanced information, making them significantly more effective for use in particular regions and languages.

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