Exam Question Generation Based on Large Language Models and Retrieval-Augmentation Techniques
Automatic question generation is an important task in intelligent education.Current question generation methods overly focus on textual details while neglecting assessment of knowledge points.This paper introduce a method that leverages large language models and retrieval-augmentation technology for generating exam questions.This approach uses precise instruction with few-shot in-context learning and textbook retrieval to enhance the capa-bilities of large language models,ensuring the generated questions match real-world requirements in style and diffi-culty.Tested on two types of questions,the method outperforms existing benchmarks in both automatic and manual evaluations,achieving 77.5％of human expert effectiveness and generating a higher proportion of high-quality ques-tions.
large language modelretrieval-augmentation techniquesquestion generationsmart education
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