In this study, we demonstrated that the preselection of explanatory variables, a task historically performed by human scientists through the application of domain knowledge, can be delegated to large language models like GPT-4. Large language models can quickly and objectively verify and delegate such tasks. For example, we set a task to predict the refractive index from molecular structure using a small dataset of approximately 40 polymer records. Instead of providing an actual dataset, we asked GPT-4, which 'knows' physical chemistry, to select variables that could influence the refractive index.