mlsanityData to 7 Oct 2026

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

arXiv 2401.07098 ↗First posted 13 Jan 2024cs.CLcited by 19

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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.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 18 of its 27 references that are in the map. First posted on arXiv 13 Jan 2024.

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18 of 27 references.

References · 18

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  4. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  5. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  6. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  7. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,115
  8. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,769
  9. 2017RACE: Large-scale ReAding Comprehension Dataset From Examinationscited by 851
  10. 2022Reasoning with Language Model Prompting: A Surveycited by 251
  11. 2018Towards a Better Metric for Evaluating Question Generation Systemscited by 52
  12. 2022Prompted Opinion Summarization with GPT-3.5cited by 44
6 more
  1. 2021GermanQuAD and GermanDPR: Improving Non-English Question Answering and Passage Retrievalcited by 39
  2. 2021MSP: Multi-Stage Prompting for Making Pre-trained Language Models Better Translatorscited by 35
  3. 2022Generative Language Models for Paragraph-Level Question Generationcited by 28
  4. 2019Cross-Lingual Training for Automatic Question Generationcited by 23
  5. 2022EduQG: A Multi-format Multiple Choice Dataset for the Educational Domaincited by 11
  6. 2022Leaf: Multiple-Choice Question Generationcited by 9

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 11; 2025: 6; 2026: 2.

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  1. 2024Distractor Generation in Multiple-Choice Tasks: A Survey of Methods, Datasets, and Evaluationcited by 25
  2. 2024Applicability of Large Language Models and Generative Models for Legal Case Judgement Summarizationcited by 23
  3. 2024Exploring the Capabilities of Prompted Large Language Models in Educational and Assessment Applicationscited by 12
  4. 2024How Effective is GPT-4 Turbo in Generating School-Level Questions from Textbooks Based on Bloom's Revised Taxonomy?cited by 8
  5. 2024Code Generation and Algorithmic Problem Solving Using Llama 3.1 405Bcited by 7
  6. 2024The Future of Learning in the Age of Generative AI: Automated Question Generation and Assessment with Large Language Modelscited by 7
  7. 2024MIRROR: A Novel Approach for the Automated Evaluation of Open-Ended Question Generationcited by 6
  8. 2024Human-Centric eXplainable AI in Educationcited by 3
  9. 2024Prompt Engineering Using GPT for Word-Level Code-Mixed Language Identification in Low-Resource Dravidian Languagescited by 2
  10. 2025Enhancing Student Learning with LLM-Generated Retrieval Practice Questions: An Empirical Study in Data Science Coursescited by 2
  11. 2026Self-hosted Lecture-to-Quiz: Local LLM MCQ Generation with Deterministic Quality Controlcited by 2
  12. 2025Leveraging In-Context Learning and Retrieval-Augmented Generation for Automatic Question Generation in Educational Domainscited by 1
7 more
  1. 2025D-GEN: Automatic Distractor Generation and Evaluation for Reliable Assessment of Generative Modelcited by 1
  2. 2025Conditional Multi-Stage Failure Recovery for Embodied Agentscited by 1
  3. 2025Difficulty-Controllable Multiple-Choice Question Generation Using Large Language Models and Direct Preference Optimizationcited by 1
  4. 2024Cancer-Answer: Empowering Cancer Care with Advanced Large Language Modelscited by 0
  5. 2024CryptoLLM: Unleashing the Power of Prompted LLMs for SmartQnA and Classification of Crypto Postscited by 0
  6. 2025Harnessing Structured Knowledge: A Concept Map-Based Approach for High-Quality Multiple Choice Question Generation with Effective Distractorscited by 0
  7. 2026BengaliMCQ: Automatic Generation and Answer Prediction of Academic Multiple-Choice Questions in a Low-Resource Languagecited by 0

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  1. 2025Prompting Strategies for Language Model-Based Item Generation in K-12 Education: Bridging the Gap Between Small and Large Language Models
  2. 2024Structured Chain-of-Thought Prompting for Few-Shot Generation of Content-Grounded QA Conversations
  3. 2026ReasonLab: A Controlled and Auditable Evaluation of Prompting Techniques for Multiple-Choice QA
  4. 2026Cognitively Diverse Multiple-Choice Question Generation: A Hybrid Multi-Agent Framework with Large Language Models
  5. 2024MCQG-SRefine: Multiple Choice Question Generation and Evaluation with Iterative Self-Critique, Correction, and Comparison Feedback
  6. 2026Evaluating Prompting Strategies for Chart Question Answering with Large Language Models
  7. 2024Rethinking Generative Large Language Model Evaluation for Semantic Comprehension
  8. 2023Semi-Structured Chain-of-Thought: Integrating Multiple Sources of Knowledge for Improved Language Model Reasoning