Perplexity allows to quantify the CLM confidence that a specific SMILES string could have belonged to the training data. Because the training objectives of the CLM are implicitly encoded in the fine-tuning data, the perplexity score allows one to assess whether the generated SMILES strings match the objectives. A SMILES string composed of probable characters (high pi values) exhibits low perplexity, whereas a string containing many unlikely characters (low pi values) exhibits high perplexity. Hence, low perplexity scores are desirable.