Neural-symbolic methods have shown their effectiveness in enhancing the reasoning abilities of large language models (LLMs). However, existing methods primarily rely on mapping natural languages to more syntactically complete formal languages (e.g., Python and SQL). Those approaches necessitate that reasoning tasks be convertible into programs, which cater more to the computer execution mindset and deviate from human reasoning habits. To expand the real-world applicability and flexibility of symbolic methods, we propose Meta-Reasoning from the scope of linguistics itself. This method empowers LLMs to deconstruct questions and effectively capture more generalized knowledge autonomously. We find that Meta-Reasoning achieves improved in-context learning efficiency, reasoning accuracy, and output stability in six arithmetic and symbolic reasoning tasks. In particular, when applied to symbolic reasoning tasks such as Tracking Shuffled Objects, GPT-3 (text-davinci-002) surpasses the few-shot Chain-of-Thought prompting approach (+37.7%), with 99% accuracy after a single demonstration of Meta-Reasoning.

翻译：神经符号方法已被证明能有效增强大型语言模型的推理能力。然而现有方法主要依赖将自然语言映射为语法更完备的形式语言（如Python和SQL），这类方法要求推理任务可转化为程序，更迎合计算机执行思维，偏离了人类推理习惯。为拓展符号方法的现实适用性与灵活性，我们从语言学本体出发提出元推理方法。该方法赋予大型语言模型自主解构问题、更高效捕获泛化知识的能力。实验表明，元推理在六项算术与符号推理任务中提升了上下文学习效率、推理准确率及输出稳定性。值得注意的是，在追踪混淆物体等符号推理任务中，GPT-3（text-davinci-002）仅需单次元推理示范即可达到99%的准确率，超越少样本思维链提示方法（+37.7%）。