The paper introduces a novel framework called Symbolic Chain-of-Thought (SymbCoT), which integrates symbolic expressions and logical rules with Chain-of-Thought (CoT) prompting to enhance the logical reasoning capabilities of large language models (LLMs). SymbCoT is designed to address the limitations of traditional CoT methods, which struggle with tasks that heavily rely on symbolic expressions and rigid deduction rules. The framework consists of four main modules: Translator, Planner, Solver, and Verifier. The Translator converts natural language into symbolic format, the Planner breaks down the problem into smaller sub-problems, the Solver applies logical rules to derive answers, and the Verifier ensures the accuracy and reliability of the reasoning process. Experimental results on five standard datasets with First-Order Logic (FOL) and Constraint Optimization (CO) symbolic expressions show that SymbCoT significantly outperforms existing CoT methods, demonstrating improved robustness, human-understandable explanations, and better utilization of information. The paper also discusses the benefits of combining symbolic and natural language expressions, the impact of different LLMs, and the faithfulness of the reasoning process.The paper introduces a novel framework called Symbolic Chain-of-Thought (SymbCoT), which integrates symbolic expressions and logical rules with Chain-of-Thought (CoT) prompting to enhance the logical reasoning capabilities of large language models (LLMs). SymbCoT is designed to address the limitations of traditional CoT methods, which struggle with tasks that heavily rely on symbolic expressions and rigid deduction rules. The framework consists of four main modules: Translator, Planner, Solver, and Verifier. The Translator converts natural language into symbolic format, the Planner breaks down the problem into smaller sub-problems, the Solver applies logical rules to derive answers, and the Verifier ensures the accuracy and reliability of the reasoning process. Experimental results on five standard datasets with First-Order Logic (FOL) and Constraint Optimization (CO) symbolic expressions show that SymbCoT significantly outperforms existing CoT methods, demonstrating improved robustness, human-understandable explanations, and better utilization of information. The paper also discusses the benefits of combining symbolic and natural language expressions, the impact of different LLMs, and the faithfulness of the reasoning process.