Large Language Models (LLMs) have demonstrated remarkable performance across
diverse tasks and exhibited impressive reasoning abilities by applying
zero-shot Chain-of-Thought (CoT) prompting. However, due to the evolving nature
of sentence prefixes during the pre-training phase, existing zero-shot CoT
prompting methods that employ identical CoT prompting across all task instances
may not be optimal. In this paper, we introduce a novel zero-shot prompting
method that leverages evolutionary algorithms to generate diverse promptings
for LLMs dynamically. Our approach involves initializing two CoT promptings,
performing evolutionary operations based on LLMs to create a varied set, and
utilizing the LLMs to select a suitable CoT prompting for a given problem.
Additionally, a rewriting operation, guided by the selected CoT prompting,
enhances the understanding of the LLMs about the problem. Extensive experiments
conducted across ten reasoning datasets demonstrate the superior performance of
our proposed method compared to current zero-shot CoT prompting methods on
GPT-3.5-turbo and GPT-4. Moreover, in-depth analytical experiments underscore
the adaptability and effectiveness of our method in various reasoning tasks.

大语言模型通过应用无需预先训练的思维链式促进方法展示了卓越的表现，并展示了出色的推理能力。本文介绍了一种新的零样本促进方法，利用进化算法为大语言模型动态生成多样的促进方式，通过选择适合的促进方式提高模型的理解能力。详细实验结果表明，相较于当前的零样本推理方法，在 GPT-3.5-turbo 和 GPT-4 上，我们的方法表现出更优越的性能。此外，深入的分析实验突显了本方法在各种推理任务中的适应性和有效性。