Dunstan Matekenya

Bridging the Language Gap in Text-to-SQL: Adapting LLMs for Chichewa in a Low-Resource Setting

The adaptation of LLMs for Text-to-SQL generation in Chichewa, a low-resource Bantu language spoken by over 12 million people in Malawi and neighboring regions, is investigated and parameter-efficient fine-tuning (QLoRA) is applied to selected models and the combined effect of QLoRA fine-tuning with retrieval-augmented prompting is evaluated.