Benchmarking Open-Source Large Language Models, GPT-4 and Claude 2 on Multiple-Choice Questions in Nephrology
Sean Wu, Michael Koo, Lesley Blum, Andy Black, Liyo Kao, Zhe Fei, Fabien Scalzo, Ira Kurtz
Abstract
BACKGROUND In recent years, significant breakthroughs have been made in the field of natural language processing, particularly with the development of large language models (LLMs). LLMs have demonstrated remarkable capabilities on benchmarks related to general medical question answering, but there are fewer data about their performance in subspecialty fields and fewer studies still comparing the many available LLMs. These models have the potential to be used as a part of adaptive physician training, medical copilot applications, and digital patient interaction scenarios. The ability of LLMs to participate in medical training and patient care depends in part on their mastery of the knowledge content of specific medical fields.