The importance of tokenization in NLP-based research. Notably, we show that overall cosine distances between samples of a trained model and progressively randomly initiated models is minimal. This indicates that model interpretation in these cases is dependent on an aspect other than the learned model parameters. We analyse the variation with respect to the amount of carbon atoms in Figure , which shows that consistency is dependent on the amount of carbon atoms. This indicates that tokenization is what is measured mostly by these interpretation methods. In fact, we find this aligns with the previous research of Zafar et al. who found that interpretation of untrained models is not random in NLP-based models.