Establishing best practices in large language model research: an application to repeat prompting.

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.