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Exploring the Landscape of Large Language Models In Medical Question Answering: Observations and Open Questions

11 October 2023
Andrew M. Bean
Karolina Korgul
"Felix H. Krones
Robert McCraith
Adam Mahdi
    LM&MAELM
ArXiv (abs)PDFHTML
Abstract

Large Language Models (LLMs) have shown promise in medical question answering by achieving passing scores in standardised exams and have been suggested as tools for supporting healthcare workers. Deploying LLMs into such a high-risk context requires a clear understanding of the limitations of these models. With the rapid development and release of new LLMs, it is especially valuable to identify patterns which exist across models and may, therefore, continue to appear in newer versions. In this paper, we evaluate a wide range of popular LLMs on their knowledge of medical questions in order to better understand their properties as a group. From this comparison, we provide preliminary observations and raise open questions for further research.

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