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：少样本提示通过向大语言模型的输入中提供少量示例，激发了其卓越能力。然而，传统方法将所有示例的输入-输出对一次性提供给模型，可能无法有效引导大语言模型学习具体的输入-输出映射关系。受元认知在学生学习中调节与支持作用的启发，本文提出了一种新颖的元认知增强少样本提示方法，通过引导大语言模型反思其思维过程，全面学习给定的演示示例。此外，考虑到正向强化能提升学生的学习动机，我们将正向强化引入元认知增强的少样本提示中，通过提供基于响应的正反馈促进大语言模型的少样本学习。在两个真实数据集上的实验结果表明，我们提出的元认知增强的正向强化少样本提示在分类准确率和宏F1值上均优于传统少样本提示方法。