Large Language Models (LLMs), which are also based on transformer architectures initially trained on vast amounts of natural language data, have been recognized for their disruptive effect in nearly every field. Although they are not explicitly trained to be knowledgeable in chemistry, they have the advantage of being adaptable and generalizable. Given their shared foundation with the aforementioned molecular design models, LLMs hold significant promise for advancing the field through their flexibility and broad applicability.