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

Stochastic LLMs do not Understand Language: Towards Symbolic, Explainable and Ontologically Based LLMs