自我调节学习理论下的LLM自我纠错路径构建研究

Research on Constructing Self-Correction Paths for Large Language Models under the Theory of Self-Regulated Learning

通过对ChatGPT等大语言模型的发展及其存在问题的梳理,如AI“幻觉”,探讨了自我调节学习(Self-Regulated Learning,SRL)理论与大语言模型(LLM)自我纠错技术在国际中文教育中的应用。对基于人类反馈的强化学习(RLHF)作为优化模型交互表现的方法进行分析,指出其依赖人类指导和自我调节能力不足的问题,回顾自我调节学习理论的发展历程,讨论了该理论在智慧学习环境中的应用前景。以SRL理论为核心,提出了基于SRL的LLM自我纠错新技术框架路径,讨论了LLM自我纠错路径在国际中文教育中的应用,包括自我监督与对比学习、元认知分析、对学习者的个性化纠错与辅导等方面。通过将SRL理论与LLM自我纠错技术相结合,为LLM自我纠错提供理论框架指导,促进ChatGPT深度融入国际中文教育。

This paper explores the application of Self-Regulated Learning(SRL)theory and Large Language Model(LLM)self-correction techniques in international Chinese education.It reviews the development and existing issues of large language models such as ChatGPT,including AI“hallucinations.”The paper analyzes Reinforcement Learning from Human Feedback(RLHF)as a method to optimize model interaction performance,highlighting its reliance on human guidance and insufficient self-regulation capabilities.It traces the development of SRL theory and discusses its application prospects in intelligent learning environments.Centered on SRL theory,the paper proposes a new framework for LLM self-correction based on SRL,discussing its application in international Chinese education,including self-supervision and contrastive learning,metacognitive analysis,and personalized error correction and tutoring for learners.By integrating SRL theory with LLM self-correction techniques,this paper provides a theoretical framework to guide LLM self-correction,promoting the deep integration of ChatGPT into international Chinese education.