Large Language Models (LLMs) have achieved remarkable success in various natural language processing tasks. However, their ability to perform tasks requiring formal representation and reasoning remains limited. This paper explores the integration of Meta Inverse Entailment (MIE) with LLMs to enhance their reasoning capabilities. In our experiments, we examine a hybrid GPT-MIE model on a simplified natural language grammar. The results suggest that the accuracy of GPT is significantly improved when it incorporates the grammar learned using MIE. This hybrid approach demonstrates the potential of combining LLMs’ linguistic proficiency with MIE’s rigorous formalism, leading to better performance in tasks demanding logical representation and reasoning.