元认知增强的少样本提示与正强化
Metacognition-Enhanced Few-Shot Prompting With Positive Reinforcement
AI总结：
提出元认知增强的少样本提示方法，结合正强化反馈，引导大语言模型反思思维过程以更好学习演示示例，在真实数据集上分类准确率和宏F1优于传统方法。
摘要：
少样本提示通过在输入中提供少量演示示例，激发了大语言模型的显著能力。然而，传统方法一次性向大语言模型提供所有演示输入-输出对，可能无法有效引导大语言模型学习特定的输入-输出映射关系。本文受元认知在学生学习中调节和支持作用的启发，提出了一种新颖的元认知增强少样本提示方法，该方法引导大语言模型反思其思维过程，以全面学习给定的演示示例。此外，考虑到正强化能够提高学生的学习动机，我们将正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来促进大语言模型的少样本学习。在两个真实世界数据集上的实验结果表明，我们提出的元认知增强少样本提示与正强化方法在分类准确率和宏F1分数上均优于传统少样本提示。
英文摘要：
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.
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。
