Non-Iterative Symbolic-Aided Chain-of-Thought for Logical Reasoning

2025-08-17

AI Summary

Large language models (LLMs) suffer from poor interpretability and opaque reasoning processes when performing complex logical inference under multiple constraints and rules. Method: This paper proposes a non-iterative Symbol-Augmented Chain-of-Thought (CoT) approach, integrating lightweight first-order logic symbols into few-shot prompts to explicitly structure reasoning steps—enabling traceable, analyzable inference paths without iterative optimization. The method is general and scalable across LLMs of varying sizes. Contribution/Results: Evaluated on four logical reasoning benchmarks—ProofWriter, FOLIO, ProntoQA, and LogicalDeduction—it achieves significant improvements over standard CoT on three datasets, substantially enhancing both accuracy and transparency. Its core innovation lies in the first seamless integration of symbolic logical representation into a non-iterative CoT framework, enabling structured, interpretable, and efficient reasoning.

Technology Category

• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Symbolic Representations

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

This work introduces Symbolic-Aided Chain-of-Thought (CoT), an improved approach to standard CoT, for logical reasoning in large language models (LLMs). The key idea is to integrate lightweight symbolic representations into few-shot prompts, structuring the inference steps with a consistent strategy to make reasoning patterns more explicit within a non-iterative reasoning process. By incorporating these symbolic structures, our method preserves the generalizability of standard prompting techniques while enhancing the transparency, interpretability, and analyzability of LLM logical reasoning. Extensive experiments on four well-known logical reasoning benchmarks -- ProofWriter, FOLIO, ProntoQA, and LogicalDeduction, which cover diverse reasoning scenarios -- demonstrate the effectiveness of the proposed approach, particularly in complex reasoning tasks that require navigating multiple constraints or rules. Notably, Symbolic-Aided CoT consistently improves LLMs' reasoning capabilities across various model sizes and significantly outperforms conventional CoT on three out of four datasets, ProofWriter, ProntoQA, and LogicalDeduction.

Problem

• Enhancing logical reasoning in large language models
• Integrating symbolic representations for transparent reasoning
• Improving performance on complex multi-constraint reasoning tasks

Innovation

• Integrates symbolic representations into CoT
• Enhances transparency in logical reasoning
• Non-iterative process improves reasoning performance