零样本思维链推理在大语言模型中由进化算法引导
Zero-Shot Chain-of-Thought Reasoning Guided by Evolutionary Algorithms in Large Language Models
AI总结：
本文提出一种基于进化算法动态生成零样本思维链提示的方法，通过初始化、进化操作和选择重写，在十个数据集上优于现有方法，提升了大语言模型的推理能力。
摘要：
大语言模型（LLMs）在各类任务中展现了卓越的性能，并通过应用零样本思维链（CoT）提示表现出令人印象深刻的推理能力。然而，由于预训练阶段句子前缀的演化特性，现有的零样本CoT提示方法在所有任务实例中使用相同的CoT提示可能并非最优。在本文中，我们介绍了一种新颖的零样本提示方法，利用进化算法为LLMs动态生成多样化的提示。我们的方法包括初始化两个CoT提示，基于LLMs执行进化操作以创建多样化的提示集，并利用LLMs为给定问题选择合适的CoT提示。此外，由所选CoT提示引导的重写操作增强了LLMs对问题的理解。在十个推理数据集上进行的广泛实验表明，与当前在GPT-3.5-turbo和GPT-4上的零样本CoT提示方法相比，我们提出的方法性能更优。此外，深入的分析实验强调了我们的方法在各种推理任务中的适应性和有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
