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.

本文提出了元推理（Meta-Reasoning）的概念，通过符号解构，最大限度地将某些推理任务的不同问题转化为类似的自然语言表达式，从而使得大语言模型 (LLMs) 能够通过类比学习和促进数据有效的上下文学习，从而显著地提高推理能力，GPT-3 (text-davinci-002) 在使用一个元推理演示的情况下可以达到超过 99％的准确率。