Automating Knowledge Acquisition for Content-Centric Cognitive Agents
  Using LLMs
Sanjay Oruganti Sergei Nirenburg Jesse English Marjorie McShane
The paper describes a system that uses large language model (LLM) technology
to support the automatic learning of new entries in an intelligent agent's
semantic lexicon. The process is bootstrapped by an existing non-toy lexicon
and a natural language generator that converts formal, ontologically-grounded
representations of meaning into natural language sentences. The learning method
involves a sequence of LLM requests and includes an automatic quality control
step. To date, this learning method has been applied to learning multiword
expressions whose meanings are equivalent to those of transitive verbs in the
agent's lexicon. The experiment demonstrates the benefits of a hybrid learning
architecture that integrates knowledge-based methods and resources with both
traditional data analytics and LLMs.