Feb. 9, 2024, 5:47 a.m. | Feihu Jin Yifan Liu Ying Tan

cs.CL updates on arXiv.org arxiv.org

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 …

algorithms cs.cl diverse evolutionary algorithms instances language language models large language large language models llms nature performance pre-training prompting reasoning tasks thought training zero-shot

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