Evaluating Fine-Tuned and Base Language Models in Maternal and Vaccination Healthcare for African Settings
Background: Large language models (LLMs) can improve healthcare information delivery in low-resource settings but may produce inaccurate or culturally inappropriate advice. This study evaluated domain-specific fine-tuning for maternal health and vaccination in Nigeria. Objective: To compare HelpMum's MamaBot-Llama and Vax-Llama with Meta's Llama-3.1-8B-Instruct for accuracy, safety, clarity, contextual appropriateness, and trustworthiness. Methods: We evaluated 200 healthcare questions, 100 each for maternal health and vaccination, across five subdomains per domain. MamaBot-Llama and Vax-Llama were fine-tuned using Low-Rank Adaptation on over 36,000 maternal health and 9,000 vaccination q...
| Authors | Abdulquddus Ajibade, Oluwaseun Odunsi, Iyinoluwa Animasaun et al. |
|---|---|
| Primary category | cs.CL |
| Published | 17 August 2026 |
| Last updated at source | 17 August 2026 |
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