Zero-Shot Chain-of-Thought Reasoning Guided by Evolutionary Algorithms
  in Large Language Models
Feihu Jin Yifan Liu Ying Tan
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.