While the recent Chain-of-Thought (CoT) technique enhances the reasoning
ability of large language models (LLMs) with the theory of mind, it might still
struggle in handling logical reasoning that relies much on symbolic expressions
and rigid deducing rules. To strengthen the logical reasoning capability of
LLMs, we propose a novel Symbolic Chain-of-Thought, namely SymbCoT, a fully
LLM-based framework that integrates symbolic expressions and logic rules with
CoT prompting. Technically, building upon an LLM, SymbCoT 1) first translates
the natural language context into the symbolic format, and then 2) derives a
step-by-step plan to solve the problem with symbolic logical rules, 3) followed
by a verifier to check the translation and reasoning chain. Via thorough
evaluations on 5 standard datasets with both First-Order Logic and Constraint
Optimization symbolic expressions, SymbCoT shows striking improvements over the
CoT method consistently, meanwhile refreshing the current state-of-the-art
performances. We further demonstrate that our system advances in more faithful,
flexible, and explainable logical reasoning. To our knowledge, this is the
first to combine symbolic expressions and rules into CoT for logical reasoning
with LLMs. Code is open at this https URL

提出了一种基于大型语言模型的新型符号化思维链 (SymbCoT) 框架，通过将符号表达和逻辑规则与语言模型集成，以增强其逻辑推理能力，并在 5 个标准数据集上通过深入评估展示了显著的性能提升。