Human Review for Post-Training Improvement of Low-Resource Language Performance in Large Language Models
Delta-Marie Lewis, Brian DeRenzi, Amos Misomali, Themba Nyirenda, Everlisto Phiri, Lyness Chifisi, Charles Makwenda, Neal B. Lesh · 2024
Large language models (LLMs) have significantly improved natural language processing, holding the potential to support health workers and their clients directly. Unfortunately, there is a substantial and variable drop in performance for low-resource languages. This paper presents an exploratory case study in Malawi, aiming to enhance the performance of LLMs in Chichewa through innovative prompt engineering techniques. By focusing on practical evaluations over traditional metrics, we assess the subjective utility of LLM outputs, prioritizing end-user satisfaction. Our findings suggest that tailored prompt engineering may improve LLM utility in underserved linguistic contexts, offering a promising avenue to bridge the language inclusivity gap in digital health interventions.