A Comparative Study of Neurosymbolic AI Approaches to Interpretable Logical Reasoning

TL;DR

This paper compares neurosymbolic AI approaches for logical reasoning, finding that hybrid models combining neural networks with symbolic solvers are more promising for general reasoning due to better interpretability and retention of LLM capabilities.

Contribution

It introduces a comparative analysis of integrative and hybrid neurosymbolic approaches, highlighting the advantages of hybrid models for general logical reasoning.

Findings

Hybrid approach offers more interpretable reasoning chains.

Hybrid models retain LLM capabilities and advantages.

Hybrid approach is more promising for domain-agnostic reasoning.

Abstract

General logical reasoning, defined as the ability to reason deductively on domain-agnostic tasks, continues to be a challenge for large language models (LLMs). Current LLMs fail to reason deterministically and are not interpretable. As such, there has been a recent surge in interest in neurosymbolic AI, which attempts to incorporate logic into neural networks. We first identify two main neurosymbolic approaches to improving logical reasoning: (i) the integrative approach comprising models where symbolic reasoning is contained within the neural network, and (ii) the hybrid approach comprising models where a symbolic solver, separate from the neural network, performs symbolic reasoning. Both contain AI systems with promising results on domain-specific logical reasoning benchmarks. However, their performance on domain-agnostic benchmarks is understudied. To the best of our knowledge, there has not been a comparison of the contrasting approaches that answers the following question: Which approach is more promising for developing general logical reasoning? To analyze their potential, the following best-in-class domain-agnostic models are introduced: Logic Neural Network (LNN), which uses the integrative approach, and LLM-Symbolic Solver (LLM-SS), which uses the hybrid approach. Using both models as case studies and representatives of each approach, our analysis demonstrates that the hybrid approach is more promising for developing general logical reasoning because (i) its reasoning chain is more interpretable, and (ii) it retains the capabilities and advantages of existing LLMs. To support future works using the hybrid approach, we propose a generalizable framework based on LLM-SS that is modular by design, model-agnostic, domain-agnostic, and requires little to no human input.