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

arXiv: https://arxiv.org/abs/2401.07098 · HTML: https://mlsanity.com/paper/2401.07098/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 27 references that are in the map.
- First posted on arXiv 13 Jan 2024.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [RACE: Large-scale ReAding Comprehension Dataset From Examinations](https://mlsanity.com/paper/1704.04683/) (2017)
- [Reasoning with Language Model Prompting: A Survey](https://mlsanity.com/paper/2212.09597/) (2022)
- [Towards a Better Metric for Evaluating Question Generation Systems](https://mlsanity.com/paper/1808.10192/) (2018)
- [Prompted Opinion Summarization with GPT-3.5](https://mlsanity.com/paper/2211.15914/) (2022)
- [GermanQuAD and GermanDPR: Improving Non-English Question Answering and Passage Retrieval](https://mlsanity.com/paper/2104.12741/) (2021)
- [MSP: Multi-Stage Prompting for Making Pre-trained Language Models Better Translators](https://mlsanity.com/paper/2110.06609/) (2021)
- [Generative Language Models for Paragraph-Level Question Generation](https://mlsanity.com/paper/2210.03992/) (2022)
- [Cross-Lingual Training for Automatic Question Generation](https://mlsanity.com/paper/1906.02525/) (2019)
- [EduQG: A Multi-format Multiple Choice Dataset for the Educational Domain](https://mlsanity.com/paper/2210.06104/) (2022)
- [Leaf: Multiple-Choice Question Generation](https://mlsanity.com/paper/2201.09012/) (2022)

## Built on by (most cited first, 19 of 19)

- [Distractor Generation in Multiple-Choice Tasks: A Survey of Methods, Datasets, and Evaluation](https://mlsanity.com/paper/2402.01512/) (2024)
- [Applicability of Large Language Models and Generative Models for Legal Case Judgement Summarization](https://mlsanity.com/paper/2407.12848/) (2024)
- [Exploring the Capabilities of Prompted Large Language Models in Educational and Assessment Applications](https://mlsanity.com/paper/2405.11579/) (2024)
- [How Effective is GPT-4 Turbo in Generating School-Level Questions from Textbooks Based on Bloom's Revised Taxonomy?](https://mlsanity.com/paper/2406.15211/) (2024)
- [Code Generation and Algorithmic Problem Solving Using Llama 3.1 405B](https://mlsanity.com/paper/2409.19027/) (2024)
- [The Future of Learning in the Age of Generative AI: Automated Question Generation and Assessment with Large Language Models](https://mlsanity.com/paper/2410.09576/) (2024)
- [MIRROR: A Novel Approach for the Automated Evaluation of Open-Ended Question Generation](https://mlsanity.com/paper/2410.12893/) (2024)
- [Human-Centric eXplainable AI in Education](https://mlsanity.com/paper/2410.19822/) (2024)
- [Prompt Engineering Using GPT for Word-Level Code-Mixed Language Identification in Low-Resource Dravidian Languages](https://mlsanity.com/paper/2411.04025/) (2024)
- [Enhancing Student Learning with LLM-Generated Retrieval Practice Questions: An Empirical Study in Data Science Courses](https://mlsanity.com/paper/2507.05629/) (2025)
- [Self-hosted Lecture-to-Quiz: Local LLM MCQ Generation with Deterministic Quality Control](https://mlsanity.com/paper/2603.08729/) (2026)
- [Leveraging In-Context Learning and Retrieval-Augmented Generation for Automatic Question Generation in Educational Domains](https://mlsanity.com/paper/2501.17397/) (2025)
- [D-GEN: Automatic Distractor Generation and Evaluation for Reliable Assessment of Generative Model](https://mlsanity.com/paper/2504.13439/) (2025)
- [Conditional Multi-Stage Failure Recovery for Embodied Agents](https://mlsanity.com/paper/2507.06016/) (2025)
- [Difficulty-Controllable Multiple-Choice Question Generation Using Large Language Models and Direct Preference Optimization](https://mlsanity.com/paper/2510.19265/) (2025)
- [Cancer-Answer: Empowering Cancer Care with Advanced Large Language Models](https://mlsanity.com/paper/2411.06946/) (2024)
- [CryptoLLM: Unleashing the Power of Prompted LLMs for SmartQnA and Classification of Crypto Posts](https://mlsanity.com/paper/2411.07917/) (2024)
- [Harnessing Structured Knowledge: A Concept Map-Based Approach for High-Quality Multiple Choice Question Generation with Effective Distractors](https://mlsanity.com/paper/2505.02850/) (2025)
- [BengaliMCQ: Automatic Generation and Answer Prediction of Academic Multiple-Choice Questions in a Low-Resource Language](https://mlsanity.com/paper/2608.15547/) (2026)

## Abstract (arXiv)

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.