Research on Constructing Self-Correction Paths for Large Language Models under the Theory of Self-Regulated Learning
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 Learn-ing 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 dis-cusses 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 tu-toring 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 edu-cation.
self-regulated learninglarge language modelhuman-computer interactioninternational chinese edu-cationchatGPT
万方数据
维普
