大型语言模型生成的教育问题有多大用处？
How Useful are Educational Questions Generated by Large Language Models?
AI总结：
本文通过教师人工评估验证了结合可控文本生成与问题分类法所生成的教育问题质量高且课堂可用，证明其在教育中的广泛应用前景。
摘要：
大型语言模型的可控文本生成（CTG）具有巨大潜力，能够同时变革教师与学生的教育方式。具体而言，高质量且多样化的问题生成可以大幅减轻教师负担，并提升其教育内容的质量。该领域近期的工作在生成方面取得了进展，但未能证明真实教师会认为生成的问题在课堂环境中足够有用；或者相反，这些问题是否存在错误和/或教学上无益的内容。我们开展了一项由教师参与的人工评估，以评估结合可控文本生成与问题分类法（Bloom分类法和一个难度分类法）所产生输出的质量和有用性。结果表明，生成的问题质量高且足够有用，显示出它们在课堂环境中广泛应用的潜力。
英文摘要：
Controllable text generation (CTG) by large language models has a huge potential to transform education for teachers and students alike. Specifically, high quality and diverse question generation can dramatically reduce the load on teachers and improve the quality of their educational content. Recent work in this domain has made progress with generation, but fails to show that real teachers judge the generated questions as sufficiently useful for the classroom setting; or if instead the questions have errors and/or pedagogically unhelpful content. We conduct a human evaluation with teachers to assess the quality and usefulness of outputs from combining CTG and question taxonomies (Bloom's and a difficulty taxonomy). The results demonstrate that the questions generated are high quality and sufficiently useful, showing their promise for widespread use in the classroom setting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
