LLMs as Knowledge Workers

Abstract

Generative AI has a problem with trust. Knowledge-based AI does not, but faces the knowledge bottleneck: the cost of manually building the lexicons and ontologies that enable reasoning, explanation, and targeted correction. We present a neurosymbolic approach toward overcoming this bottleneck. This approach uses a symbolic semantic language interpreter and static knowledge resources (ontology, lexicon, and a corpus of 1,780 validated text meaning representations) of OntoAgent, a cognitive architecture, in coordination with a language model guided by narrative descriptions of decision-making algorithms that encode principles from decades of knowledge acquisition research. The acquisition process: a) is triggered when the semantic analyzer encounters knowledge gaps while constructing meaning representations; b) proposes new lexicon entries and ontological concepts; and c) automatically validates them against ontological constraints. We report the results of an initial experiment using this approach as the first step toward establishing what an LLM can achieve when boot-strapped from deep knowledge resources and integrated with a knowledge-based analyzer. This work describes a step in an ongoing R&D program whose core objective is to demonstrate that, just as GPUs enabled neural methods to scale, the use of language models as knowledge acquisition tools will enable knowledge-based AI to scale.