Bridging Neural and Symbolic Reasoning: A Dual-System Framework for Interpretable Question Answering

Abstract

Large Language Models (LLMs), such as the GPT series, have achieved remarkable performance in question answering through large-scale pretraining. However, LLMs often lack transparency in their reasoning processes and struggle with hallucination. To overcome these challenges, we propose Dual-NeSy, a Dual-system framework that integrates Neural networks with Symbolic logic for interpretable question answering. Specifically, Dual-NeSy leverages fast, heuristic reasoning for knowledge composition (System 1) and utilizes structured, multi-step symbolic reasoning (System 2) for more deliberate, logical verification. This combined approach enhances both the interpretability and accuracy of reasoning. Our approach outperforms previous methods in commonsense reasoning and reading comprehension tasks, achieving state-of-the-art results on three benchmark datasets: QASC, WorldTree, and WikiHop.