AGenT Zero：Zero-shot自动生成用于技能评估的多项选择题
AGenT Zero: Zero-shot Automatic Multiple-Choice Question Generation for Skill Assessments
摘要：
在虚拟教育和招聘时代，多项选择题（MCQ）为技能评估提供了最有前途的途径，在这个时代，传统的基于绩效的替代方案（如项目和论文）变得不那么可行，评分资源也受到限制。MCQ的自动生成将允许大规模创建评估。自然语言处理的最新进展产生了许多复杂问题生成方法。然而，在特定领域中产生可部署结果的少数方法需要大量的特定于领域的训练数据，这可能是非常昂贵的获取。我们的工作提供了一个初步的进军MCQ生成下的高数据采集成本的情况下，战略性地强调释义的问题上下文（相比的任务）。除了保持问答对之间的语义相似性外，我们称之为AGenT Zero的管道仅由预先训练的模型组成，不需要微调，从而最大限度地减少了问题生成的数据采集成本。AGenT Zero在流畅性和语义相似性方面成功地优于其他预先训练的方法。此外，通过一些小的变化，我们的评估管道可以推广到更广泛的问题和答案空间，包括简短的答案或填写空白的问题。
英文摘要：
Multiple-choice questions (MCQs) offer the most promising avenue for skill evaluation in the era of virtual education and job recruiting, where traditional performance-based alternatives such as projects and essays have become less viable, and grading resources are constrained. The automated generation of MCQs would allow assessment creation at scale. Recent advances in natural language processing have given rise to many complex question generation methods. However, the few methods that produce deployable results in specific domains require a large amount of domain-specific training data that can be very costly to acquire. Our work provides an initial foray into MCQ generation under high data-acquisition cost scenarios by strategically emphasizing paraphrasing the question context (compared to the task). In addition to maintaining semantic similarity between the question-answer pairs, our pipeline, which we call AGenT Zero, consists of only pre-trained models and requires no fine-tuning, minimizing data acquisition costs for question generation. AGenT Zero successfully outperforms other pre-trained methods in fluency and semantic similarity. Additionally, with some small changes, our assessment pipeline can be generalized to a broader question and answer space, including short answer or fill in the blank questions.
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。
