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

• URL: http://arxiv.org/abs/2309.05918v3
• Date: Thu, 14 Sep 2023 12:58:39 GMT
• Title: Stochastic LLMs do not Understand Language: Towards Symbolic, Explainable and Ontologically Based LLMs
• Authors: Walid S. Saba
• Abstract summary: We argue that the relative success of data-driven large language models (LLMs) is not a reflection on the symbolic vs. subsymbolic debate. We suggest in this paper applying the effective bottom-up strategy in a symbolic setting resulting in symbolic, explainable, and ontologically grounded language models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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