On the other hand, GPT-4, having been trained on a vast amount of scientific data, can make feature selections that consider the context of physical chemistry and the hierarchy of the parameters. We can call this a knowledge-oriented feature selection. We performed prompts to infer their causal relationships to confirm whether GPT-4 truly understands the hierarchical relationships between variables (Fig. , see Supplementary Information for the actual conversation). The model postulated that the refractive index is a function of a molecule's energy, HOMO, LUMO, dipole moment, and volume. It also suggested that these parameters are functions of lower-order variables such as the number of valence electrons and molecular weight. There were some instances where the model made incorrect physical-chemical interpretations, such as boiling point being an independent variable for the number of valence electrons. The cause seems to be either that GPT-4's inferential ability is still insufficient, that it lacks sufficient chemical knowledge, or possibly both. Regardless, the relationships between variables were generally captured correctly. Combining this with data-oriented methods is expected to achieve higher regression performance.