Integrating Language Models with Symbolic Formulas for First-Order Logic Reasoning

Performing logical reasoning based on prior knowledge is a crucial human cognitive ability and has been a long-standing objective in the field of artificial intelligence. Large language models based on transformer architecture have been a common approach for logical reasoning over text. However, the current language models often struggle to learn semantic information from logical expressions, resulting in underwhelming performance on logical reasoning tasks. In this paper, we propose a novel method to convert first-order logic (FOL) expressions to the form of a graph and integrate it with embeddings from language models to enhance their reasoning ability. The proposed method is designed to learn directly from FOL formulas and is able to generalize to any scenarios involving logical expressions. Experimental results demonstrate that the proposed method enhances the model's ability of learning logical semantic representations, and thus it brings a significant improvement on the performance of complex reasoning tasks. The code is available at https://github.com/FOL-GNN.