培训Optimus Prime，医学博士：通过OpenAI的gpt2 Transformer模型微调生成医疗认证项目
Training Optimus Prime, M.D.: Generating Medical Certification Items by Fine-Tuning OpenAI's gpt2 Transformer Model
摘要：
本文介绍了新的结果的应用程序使用基于transformer的语言模型自动生成项目（AIG），一个领域的认证测试领域的持续兴趣，以及在教育测量和心理测试。OpenAI的gpt 2预训练的345 M参数语言模型使用PubMed文章的公共领域文本挖掘集进行重新训练，随后用于生成项目词干（案例插图）以及多项选择项目的干扰项建议。这个案例研究展示了promise，并生成了可供人工项目编写者用作创作输入的草案文本。使用现有项目库对最近的Transformer模型（如Grover、TransformerXL）进行的未来实验预计将进一步改善结果，并促进评估材料的开发。
英文摘要：
This article describes new results of an application using transformer-based language models to automated item generation (AIG), an area of ongoing interest in the domain of certification testing as well as in educational measurement and psychological testing. OpenAI's gpt2 pre-trained 345M parameter language model was retrained using the public domain text mining set of PubMed articles and subsequently used to generate item stems (case vignettes) as well as distractor proposals for multiple-choice items. This case study shows promise and produces draft text that can be used by human item writers as input for authoring. Future experiments with more recent transformer models (such as Grover, TransformerXL) using existing item pools are expected to improve results further and to facilitate the development of assessment materials.
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。
