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

• URL: http://arxiv.org/abs/2405.01379v2
• Date: Wed, 8 May 2024 03:05:04 GMT
• Title: Verification and Refinement of Natural Language Explanations through LLM-Symbolic Theorem Proving
• Authors: Xin Quan, Marco Valentino, Louise A. Dennis, André Freitas,
• Abstract summary: This paper investigates the verification and refinement of natural language explanations through the integration of Large Language Models (LLMs) and Theorem Provers (TPs) We present a neuro-symbolic framework, named Explanation-Refiner, that augments a TP with LLMs to generate and formalise explanatory sentences. In turn, the TP is employed to provide formal guarantees on the logical validity of the explanations and to generate feedback for subsequent improvements.
• Score: 13.485604499678262
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Natural language explanations have become a proxy for evaluating explainable 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 augments a TP 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 human-annotated explanations of variable complexity in different domains.

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