Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine - Nori, Harsha; Lee, Yin Tat; Zhang, Sheng; Carignan, Dean; Edgar, Richard; Fusi, Nicolo; King, Nicholas; Larson, Jonathan; Li, Yuanzhi; Liu, Weishung; Luo, Renqian; McKinney, Scott Mayer; et al. (2023)
Generalist foundation models such as GPT-4 have displayed surprising capabilities in a wide variety of domains and tasks. Yet, there is a prevalent assumption that they cannot match specialist capabilities without intensive training of models with specialty knowledge. For example, most explorations to date on medical competency benchmarks have leveraged domain specific training, as exemplified by efforts on BioGPT and Med-PaLM. We build on a prior study of the specialist capabilities of GPT-4 on medical challenge benchmarks in the absence of special training. In distinction to the intentional use of simple prompting to highlight the model’s out of-the-box capabilities, we perform a systematic exploration of prompt engineering to boost performance. We find that prompting innovation can unlock deeper specialist capabilities and show that GPT-4 easily tops prior leading results for medical question-answering datasets. The prompt engineering methods we explore are general purpose, and make no specific use of domain expertise, removing the need for expert-curated content. Our experimental design carefully controls for overfitting during the prompt engineering process. As a culmination of the study, we introduce Medprompt, based on a composition of several prompting strategies. Medprompt greatly enhances GPT-4’s performance and achieves state of the art results on all nine of the benchmark datasets in the MultiMedQA suite. The method outperforms state-of-the-art specialist models such as Med-PaLM 2 by a large margin with an order of magnitude fewer calls to the model. Steering GPT-4 with Medprompt achieves a 27% reduction in error rate on the MedQA dataset (USMLE exam) over the best methods to date achieved with specialist models, and surpasses a score of 90% for the first time. Moving beyond medical challenge problems, we show the power of Medprompt to generalize to other domains and provide evidence for the broad applicability of the approach via studies of the strategy on competency exams in electrical engineering, machine learning, philosophy, accounting, law, nursing, and clinical psychology.