Using Combinatorial Testing for Prompt Engineering of LLMs in Medicine

Large Language Models (LLMs) like GPT-4o are of growing interest. Interfaces such as ChatGPT invite an ever-growing number of people to ask questions, including health advice, which brings in additional risks for harm. It is well known that tools based on LLMs tend to hallucinate or deliver different answers for the same or similar questions. In both cases, the outcome might be wrong or incomplete, possibly leading to safety issues. In this paper, we investigate the outcome of ChatGPT when we ask similar questions in the medical domain. In particular, we suggest using combinatorial testing to generate variants of questions aimed at identifying wrong or misleading answers. In detail, we discuss the general framework and its parts and present a proof-of-concept utilizing a medical query and ChatGPT.