Verification and Refinement of Natural Language Explanations through LLM-Symbolic Theorem Proving

Understand

Natural language explanations represent a proxy for evaluating explanation-based and multi-step Natural Language Inference (NLI) models.

• However, assessing the validity of explanations for NLI is challenging as it typically involves the crowd-sourcing of apposite datasets, a process that is time-consuming and prone to logical errors.
• To address existing limitations, this paper investigates the verification and refinement of natural language explanations through the integration of Large Language Models (LLMs) and Theorem Provers (TPs).
• Specifically, we present a neuro-symbolic framework, named Explanation-Refiner, that integrates TPs with LLMs to generate and formalise explanatory sentences and suggest potential inference strategies for NLI.