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Multiple-choice questions (MCQs) offer the most promising avenue for skillevaluation in the era of virtual education and job recruiting, wheretraditional performance-based alternatives such as projects and essays havebecome less viable, and grading resources are constrained. The automatedgeneration of MCQs would allow assessment creation at scale. Recent advances innatural language processing have given rise to many complex question generationmethods. However, the few methods that produce deployable results in specificdomains require a large amount of domain-specific training data that can bevery costly to acquire. Our work provides an initial foray into MCQ generationunder high data-acquisition cost scenarios by strategically emphasizingparaphrasing the question context (compared to the task). In addition tomaintaining semantic similarity between the question-answer pairs, ourpipeline, which we call AGenT Zero, consists of only pre-trained models andrequires no fine-tuning, minimizing data acquisition costs for questiongeneration. AGenT Zero successfully outperforms other pre-trained methods influency and semantic similarity. Additionally, with some small changes, ourassessment pipeline can be generalized to a broader question and answer space,including short answer or fill in the blank questions.

AGenT Zero: Zero-shot Automatic Multiple-Choice Question Generation for Skill Assessments