  In our opinion the exuberance surrounding the relative success of data-driven
large language models (LLMs) is slightly misguided and for several reasons (i)
LLMs cannot be relied upon for factual information since for LLMs all ingested
text (factual or non-factual) was created equal; (ii) due to their subsymbolic
na-ture, whatever 'knowledge' these models acquire about language will always
be buried in billions of microfeatures (weights), none of which is meaningful
on its own; and (iii) LLMs will often fail to make the correct inferences in
several linguistic contexts (e.g., nominal compounds, copredication, quantifier
scope ambi-guities, intensional contexts. Since we believe the relative success
of data-driven large language models (LLMs) is not a reflection on the symbolic
vs. subsymbol-ic debate but a reflection on applying the successful strategy of
a bottom-up reverse engineering of language at scale, we suggest in this paper
applying the effective bottom-up strategy in a symbolic setting resulting in
symbolic, explainable, and ontologically grounded language models.
