Scalable Educational Question Generation with Pre-trained Language Models

被引:24
作者
Bulathwela, Sahan [1 ]
Muse, Hamze [1 ]
Yilmaz, Emine [1 ]
机构
[1] UCL, Ctr Artificial Intelligence, London, England
来源
ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2023 | 2023年 / 13916卷
基金
英国工程与自然科学研究理事会;
关键词
D O I
10.1007/978-3-031-36272-9_27
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The automatic generation of educational questions will play a key role in scaling online education, enabling self-assessment at scale when a global population is manoeuvring their personalised learning journeys. We develop EduQG, a novel educational question generation model built by adapting a large language model. Our extensive experiments demonstrate that EduQG can produce superior educational questions by further pre-training and fine-tuning a pre-trained language model on the scientific text and science question data.
引用
收藏
页码:327 / 339
页数:13
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