Can LLMs Reason with Rules? Logic Scaffolding for Stress-Testing and Improving LLMs

TL;DR

This paper investigates the reasoning capabilities of large language models by constructing a logic rule base, revealing their limitations and proposing an inference engine to improve their logical reasoning in various tasks.

Contribution

The paper introduces ULogic, a comprehensive inferential rule base for testing and enhancing LLMs' logical reasoning, and demonstrates its effectiveness in improving reasoning tasks.

Findings

LLMs show significant gaps in understanding complex and compositional rules.

The inference engine can generate accurate, complex, and abstract conclusions.

Using the rule base improves LLMs' performance on reasoning tasks.

Abstract

Large language models (LLMs) have achieved impressive human-like performance across various reasoning tasks. However, their mastery of underlying inferential rules still falls short of human capabilities. To investigate this, we propose a logic scaffolding inferential rule generation framework, to construct an inferential rule base, ULogic, comprising both primitive and compositional rules across five domains. Our analysis of GPT-series models over a rule subset reveals significant gaps in LLMs' logic understanding compared to human performance, especially in compositional and structural complex rules with certain bias patterns. We further distill these rules into a smaller-scale inference engine for flexible rule generation and enhancing downstream reasoning. Through a multi-judger evaluation, our inference engine proves effective in generating accurate, complex and abstract conclusions and premises, and improve various commonsense reasoning tasks. Overall, our work sheds light on LLMs' limitations in grasping inferential rule and suggests ways to enhance their logical reasoning abilities~\footnote{Code and data are available at \url{https://github.com/SiyuanWangw/ULogic}.}.