Fig. illustrates the performance provided by each descriptor set (All, Random-10, 20, Boruta, GPT-4) generated by the respective algorithms during a 5-fold cross-validation task (see Table and Table 3 for actual values). Overall, the combination of GPT-4 and Ridge (MAE = 0.0229) offered the best regression performance, followed by Lasso (0.0236). RMSE followed a similar trend with MAE, indicating that outliers did not affect results significantly. The next best performance was achieved under the condition of using all variables with Lasso (0.0272). In GPT -4's descriptor set, only one parameter (MaxAbsPartialCharge) had a coefficient reduced to zero by Lasso regression, indicating that GPT-4 selected instrumental variables for defining the refractive index from both physical chemistry and statistical perspectives (Fig. ). These results imply that if a crucial variable selection is conducted in advance by applying physical chemistry knowledge, the performance of the regression model can be improved.