Symbolization methods in large language models (LLMs) have been shown effective to improve LLMs' reasoning ability. However, most of these approaches hinge on mapping natural languages to formal languages (e.g., Python, SQL) that are more syntactically complete and free of ambiguity. Although effective, they depart from the natural language itself and deviate from the habits of human thinking, and instead cater more to the execution mindset of computers. In contrast, we hope to simplify natural language by starting from the concept of symbols in linguistics itself, so that LLMs can learn the common formulation and general solution of reasoning problems wrapped in different natural semantics. From this consideration, we propose \textbf{Meta-Reasoning}, which allows LLMs to automatically accomplish semantic-symbol deconstruction, i.e., semantic resolution, to maximally reduce different questions of certain reasoning tasks to similar natural language representation, thus gaining the ability to learn by analogy and facilitating data-efficient in-context learning. Our experiments show that the Meta-Reasoning paradigm saliently enhances LLMs' reasoning performance with fewer demonstrations. They can learn not only reasoning chains but also general solutions to certain types of tasks. In particular, for symbolic reasoning tasks, such as 7-step Tracking Shuffled Objects, GPT-3 (text-davinci-002) achieves over 99% accuracy with only one Meta-Reasoning demonstration, outperforming all current LLMs with the standard chain-of-thought prompting.

翻译：大语言模型中的符号化方法已被证明能有效提升其推理能力。然而，这些方法大多依赖将自然语言映射至语法更完整、无歧义的正式语言（如Python、SQL）。尽管有效，但这偏离了自然语言本身及人类思维习惯，反而更适应计算机的执行逻辑。与此不同，我们希望通过语言学中的符号概念来简化自然语言，使大语言模型能学习到蕴含在不同自然语义中推理问题的通用形式化与一般解法。基于此，我们提出**元推理**（Meta-Reasoning），使大语言模型能自动完成语义—符号解构（即语义解析），将特定推理任务的不同问题最大程度地简化为相似的自然语言表征，从而获得类比学习能力，并促进数据高效的情境学习。实验表明，元推理范式能显著增强大语言模型在更少示例下的推理性能——模型不仅习得推理链，还掌握某些任务类型的通用解法。特别地，在符号推理任务（如7步追踪打乱物体）中，GPT-3（text-davinci-002）仅需一个元推理示例即可达到99%以上的准确率，超越了当前所有采用标准思维链提示的大语言模型。