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

A Novel Multi-Stage Prompting Approach for Language Agnostic MCQ Generation using GPT