Establishing best practices in large language model research: an application to repeat prompting
Robert J. Gallo, Michael Baiocchi, Thomas Savage, Jonathan H. Chen · Journal of the American Medical Informatics Association · 2024
OBJECTIVES: We aimed to demonstrate the importance of establishing best practices in large language model research, using repeat prompting as an illustrative example. MATERIALS AND METHODS: Using data from a prior study investigating potential model bias in peer review of medical abstracts, we compared methods that ignore correlation in model outputs from repeated prompting with a random effects method that accounts for this correlation. RESULTS: High correlation within groups was found when repeatedly prompting the model, with intraclass correlation coefficient of 0.69. Ignoring the inherent correlation in the data led to over 100-fold inflation of effective sample size. After appropriately accounting for this issue, the authors' results reverse from a small but highly significant finding to no evidence of model bias. DISCUSSION: The establishment of best practices for LLM research is urgently needed, as demonstrated in this case where accounting for repeat prompting in analyses was critical for accurate study conclusions.