论文标题
代理零：零射击自动多项选择问题，用于技能评估
AGenT Zero: Zero-shot Automatic Multiple-Choice Question Generation for Skill Assessments
论文作者
论文摘要
在虚拟教育和招聘工作时代，多项选择问题（MCQ）为技能评估提供了最有希望的途径，在这种时代，传统的基于绩效的替代方案（例如项目和论文）变得不可行，并且对资源进行了限制。 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引起了许多复杂的问题产生方法。但是，在特定领域中产生可部署的少数方法需要大量特定领域的培训数据，而这些数据可能非常昂贵。我们的工作通过策略性地强调释义问题上下文（与任务相比），从而在高数据收购成本方案下对MCQ生成进行了初步攻击。除了保持问题解答对之间的语义相似性外，我们称零件的管道仅由预训练的模型组成，并且不需要微调，也不需要微调，从而最大程度地减少了问题生成的数据采集成本。代理零在流利和语义相似性方面成功地超过了其他预训练的方法。此外，随着一些微小的更改，我们的评估管道可以推广到更广泛的问题和回答空间，包括简短的答案或填写空白问题。
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.
