论文标题
与科学文本进行预培训可以改善教育问题的产生
Pre-Training With Scientific Text Improves Educational Question Generation
论文作者
论文摘要
随着数字教育材料和可扩展的电子学习系统的繁荣，实现AI辅助的个性化学习的潜力飙升。在这种景观中，自动产生的教育问题将发挥关键作用，当全球人口正在操纵其个性化的学习旅程时，可以实现可扩展的自我评估。我们开发了Eduqg，这是一种通过适应大型语言模型来构建的新型教育问题生成模型。我们的最初实验表明，EduqG可以通过对科学文本进行预培训来提出较高的教育问题。
With the boom of digital educational materials and scalable e-learning systems, the potential for realising AI-assisted personalised learning has skyrocketed. In this landscape, the automatic generation of educational questions will play a key role, enabling scalable self-assessment 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 initial experiments demonstrate that EduQG can produce superior educational questions by pre-training on scientific text.
