Few-shot prompting elicits the remarkable abilities of large language models by equipping them with a few demonstration examples in the input. However, the traditional method of providing large language models with all demonstration input-output pairs at once may not effectively guide large language models to learn the specific input-output mapping relationship. In this paper, inspired by the regulatory and supportive role of metacognition in students' learning, we propose a novel metacognition-enhanced few-shot prompting, which guides large language models to reflect on their thought processes to comprehensively learn the given demonstration examples. Furthermore, considering that positive reinforcement can improve students' learning motivation, we introduce positive reinforcement into our metacognition-enhanced few-shot prompting to promote the few-shot learning of large language models by providing response-based positive feedback. The experimental results on two real-world datasets show that our metacognition-enhanced few-shot prompting with positive reinforcement surpasses traditional few-shot prompting in classification accuracy and macro F1.

翻译：小样本提示通过向大语言模型输入少量示范示例激发其卓越能力。然而，传统方法一次性向大语言模型提供所有输入-输出对的示范示例，可能无法有效引导模型学习特定的映射关系。受元认知对学生学习的调节与支持作用启发，本文提出一种新颖的元认知增强小样本提示方法，引导大语言模型反思其思维过程以全面学习给定的示范示例。此外，鉴于正向激励能提升学生的学习动机，我们将其引入元认知增强小样本提示中，通过提供基于回答的正向反馈促进大语言模型的小样本学习。在两个真实数据集上的实验结果表明，我们提出的融合正向激励的元认知增强小样本提示在分类准确率和宏观F1值上均优于传统小样本提示。