Furthermore, we used a limited amount of synthetic data for this first case study. Notably, it has been shown for the BERT model that the size of the finetuning dataset can impact performance drastically , which we did not take advantage of. The corresponding data generation methods could have been exploited further to create more synthetic training data. This would provide further insights about the change in model performance with training samples. Combining data from multiple generation methods is also a promising avenue towards further performance improvements.