arxiv preprint - Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine

In this episode we discuss Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine by Harsha Nori, Yin Tat Lee, Sheng Zhang, Dean Carignan, Richard Edgar, Nicolo Fusi, Nicholas King, Jonathan Larson, Yuanzhi Li, Weishung Liu, Renqian Luo, Scott Mayer McKinney, Robert Osazuwa Ness, Hoifung Poon, Tao Qin, Naoto Usuyama, Chris White, Eric Horvitz. The paper discusses enhancing the performance of GPT-4, a generalist language model, in medical question-answering tasks without domain-specific training. By innovatively engineering prompts, the researchers created Medprompt, which significantly outperformed specialized models, achieving state-of-the-art results on the MultiMedQA benchmark suite with fewer model calls. Moreover, Medprompt was also successful in generalizing its capabilities to other fields, demonstrating its broad applicability across various competency exams beyond medicine.