Zero-Shot Chain-of-Thought Reasoning Guided by Evolutionary Algorithms in Large Language Models

TL;DR

This paper introduces a novel zero-shot prompting method for large language models that uses evolutionary algorithms to generate diverse prompts, improving reasoning performance across multiple datasets.

Contribution

The paper proposes an evolutionary algorithm-based approach to dynamically generate and select optimal zero-shot prompts for LLMs, enhancing reasoning capabilities.

Findings

Outperforms existing zero-shot CoT prompting methods on GPT-3.5-turbo and GPT-4.

Demonstrates improved reasoning accuracy across ten datasets.

Shows adaptability and effectiveness in various reasoning tasks.

Abstract

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.