基于大语言模型提示调优的教育文本分类方法OA

Educational text classification is a core task in intelligent education,with wide applications in textbook content analysis,automatic exam-question categorization,and educational-resource recommendation.Compared with general texts such as news or reviews,educational texts are characterized by dense terminology and rich structured knowledge,which substantially increase the difficulty of semantic understanding and transfer learning.Under low-resource and small-sample conditions,mainstream approaches that rely on large-scale annotated data and supervised fine-tuning suffer from severe data scarcity.This paper proposes a text-classification method tailored for low-resource and small-sample scenarios,based on a prompt-tuning strategy that facilitates the transfer of prior knowledge from pre-trained language models(PLMs)and large language models(LLMs).This paper designs three types of prompt templates:discrete,continuous,and hybrid.It incorporates a bidirectional long short-term memory network(BiLSTM)to model dependencies among prompt tokens,thereby improving the model's task-specific semantic adaptation.Experiments on four representative benchmarks(EduBooks,AGNews,MELD,and IMDB)show that the proposed method outperforms eight strong baselines on Fl by 3.7％,3.3％,5.7％,and 1.1％,respectively.The results indicate that the proposed approach can efficiently and accurately classify diverse text types with strong generalizability,without fine-tuning the parameters of the pre-trained models.Abla-tion studies further validate the effectiveness of the parameter-freezing strategy.