Abstract
Large Language Models (LLMs) have revolutionised natural language generation. But their statistical and auto-regressive nature makes them unreliable. It has become clear to the research community that in order to produce reliably correct answers, LLMs need to be enriched in some way with ‘world models’ reflecting the semantics of the domains being queried. We here propose a simple workflow to address this problem through a neuro-symbolic interaction protocol with the LLM treated as a blackbox. Answers given by an LLM are checked against accepted knowledge provided by a domain ontology. The approach aims to combine conflict detection with explanation extraction and formal repairs presented to the LLM in the form of specific artificial speech acts. The goal is to build constraining, incremental prompts that improve repeatability and veracity in the LLM’s output.