This observed decrease in accuracies compared to Experiment 2 could be due to the larger and more diverse dataset. While this approach enhances generalisability, it can also lead to a slight decrease in performance on any single test set. The exposure to a broader range of examples increased the likelihood of generating errors. Adding additional examples from sources other than PubChem may have caused a data distribution shift . This shift could have introduced training examples that differ significantly from those in the test dataset, potentially leading to reduced model performance on the original test set.