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

TL;DR

This paper introduces a neuro-symbolic framework called Explanation-Refiner that combines Large Language Models and Theorem Provers to verify, formalize, and improve natural language explanations for natural language inference tasks.

Contribution

It presents a novel neuro-symbolic approach that integrates LLMs with TPs for explanation verification, formalization, and refinement in NLI, addressing limitations of crowd-sourced datasets.

Findings

Effective verification of explanations using formal guarantees

Automatic refinement improves explanation quality across domains

Framework evaluates and enhances LLM reasoning capabilities

Abstract

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. In turn, the TP is employed to provide formal guarantees on the logical validity of the explanations and to generate feedback for subsequent improvements. We demonstrate how Explanation-Refiner can be jointly used to evaluate explanatory reasoning, autoformalisation, and error correction mechanisms of state-of-the-art LLMs as well as to automatically enhance the quality of explanations of variable complexity in different domains.