  We introduce a multi-stage prompting approach (MSP) for the generation of
multiple choice questions (MCQs), harnessing the capabilities of GPT models
such as text-davinci-003 and GPT-4, renowned for their excellence across
various NLP tasks. Our approach incorporates the innovative concept of
chain-of-thought prompting, a progressive technique in which the GPT model is
provided with a series of interconnected cues to guide the MCQ generation
process. Automated evaluations consistently demonstrate the superiority of our
proposed MSP method over the traditional single-stage prompting (SSP) baseline,
resulting in the production of high-quality distractors. Furthermore, the
one-shot MSP technique enhances automatic evaluation results, contributing to
improved distractor generation in multiple languages, including English,
German, Bengali, and Hindi. In human evaluations, questions generated using our
approach exhibit superior levels of grammaticality, answerability, and
difficulty, highlighting its efficacy in various languages.
