  We argue that the relative success of large language models (LLMs) is not a
reflection on the symbolic vs. subsymbolic debate but a reflection on employing
a successful bottom-up strategy of a reverse engineering of language at scale.
However, and due to their subsymbolic nature whatever knowledge these systems
acquire about language will always be buried in millions of weights none of
which is meaningful on its own, rendering such systems utterly unexplainable.
Furthermore, and due to their stochastic nature, LLMs will often fail in making
the correct inferences in various linguistic contexts that require reasoning in
intensional, temporal, or modal contexts. To remedy these shortcomings we
suggest employing the same successful bottom-up strategy employed in LLMs but
in a symbolic setting, resulting in explainable, language-agnostic, and
ontologically grounded language models.
