An increasing amount of research in Natural Language Inference (NLI) focuses
on the application and evaluation of Large Language Models (LLMs) and their
reasoning capabilities. Despite their success, however, LLMs are still prone to
factual errors and inconsistencies in their explanations, offering limited
control and interpretability for inference in complex domains. In this paper,
we focus on ethical NLI, investigating how hybrid neuro-symbolic techniques can
enhance the logical validity and alignment of ethical explanations produced by
LLMs. Specifically, we present an abductive-deductive framework named
Logic-Explainer, which integrates LLMs with an external backward-chaining
solver to refine step-wise natural language explanations and jointly verify
their correctness, reduce incompleteness and minimise redundancy. An extensive
empirical analysis demonstrates that Logic-Explainer can improve explanations
generated via in-context learning methods and Chain-of-Thought (CoT) on
challenging ethical NLI tasks, while, at the same time, producing formal proofs
describing and supporting models' reasoning. As ethical NLI requires
commonsense reasoning to identify underlying moral violations, our results
suggest the effectiveness of neuro-symbolic methods for multi-step NLI more
broadly, opening new opportunities to enhance the logical consistency,
reliability, and alignment of LLMs.

本研究探讨如何利用混合神经符号技术来增强大型语言模型在伦理自然语言推理中的逻辑有效性和一致性，通过整合外部的向后求解器，改进逐步自然语言解释的过程，验证解释的正确性，减少不完整性和冗余性，并生成支持模型推理的形式证明，从而提高在多步伦理自然语言推理任务中解释的质量。