  In the field of Artificial (General) Intelligence (AI), the several recent
advancements in Natural language processing (NLP) activities relying on Large
Language Models (LLMs) have come to encourage the adoption of LLMs as
scientific models of language. While the terminology employed for the
characterization of LLMs favors their embracing as such, it is not clear that
they are in a place to offer insights into the target system they seek to
represent. After identifying the most important theoretical and empirical risks
brought about by the adoption of scientific models that lack transparency, we
discuss LLMs relating them to every scientific model's fundamental components:
the object, the medium, the meaning and the user. We conclude that, at their
current stage of development, LLMs hardly offer any explanations for language,
and then we provide an outlook for more informative future research directions
on this topic.
