# Distractor generation for multiple-choice questions with predictive prompting and large language models

arXiv: https://arxiv.org/abs/2307.16338 · HTML: https://mlsanity.com/paper/2307.16338/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 39 references that are in the map.
- First posted on arXiv 30 Jul 2023.

## Builds on (most cited first, 11 of 11)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [Generating Distractors for Reading Comprehension Questions from Real Examinations](https://mlsanity.com/paper/1809.02768/) (2018)
- [A BERT-based Distractor Generation Scheme with Multi-tasking and Negative Answer Training Strategies](https://mlsanity.com/paper/2010.05384/) (2020)
- [Enhancing Large Language Models Against Inductive Instructions with Dual-critique Prompting](https://mlsanity.com/paper/2305.13733/) (2023)
- [Co-Attention Hierarchical Network: Generating Coherent Long Distractors for Reading Comprehension](https://mlsanity.com/paper/1911.08648/) (2019)
- [BERT-based distractor generation for Swedish reading comprehension questions using a small-scale dataset](https://mlsanity.com/paper/2108.03973/) (2021)
- [Learning to Reuse Distractors to support Multiple Choice Question Generation in Education](https://mlsanity.com/paper/2210.13964/) (2022)
- [Learning from Partially Annotated Data: Example-aware Creation of Gap-filling Exercises for Language Learning](https://mlsanity.com/paper/2306.01584/) (2023)

## Built on by (most cited first, 15 of 15)

- [MicroVQA: A Multimodal Reasoning Benchmark for Microscopy-Based Scientific Research](https://mlsanity.com/paper/2503.13399/) (2025)
- [Unsupervised Distractor Generation via Large Language Model Distilling and Counterfactual Contrastive Decoding](https://mlsanity.com/paper/2406.01306/) (2024)
- [An Automatic Question Usability Evaluation Toolkit](https://mlsanity.com/paper/2405.20529/) (2024)
- [Do LLMs Make Mistakes Like Students? Exploring Natural Alignment between Language Models and Human Error Patterns](https://mlsanity.com/paper/2502.15140/) (2025)
- [Benchmarking the Pedagogical Knowledge of Large Language Models](https://mlsanity.com/paper/2506.18710/) (2025)
- [Survey of Natural Language Processing for Education: Taxonomy, Systematic Review, and Future Trends](https://mlsanity.com/paper/2401.07518/) (2024)
- [Tailoring Diagnostic Modeling to Individual Learners: Personalized Distractor Generation via MCTS-Guided Reasoning Reconstruction](https://mlsanity.com/paper/2508.11184/) (2025)
- [Enhancing Student Learning with LLM-Generated Retrieval Practice Questions: An Empirical Study in Data Science Courses](https://mlsanity.com/paper/2507.05629/) (2025)
- [A ChatGPT-based approach for questions generation in higher education](https://mlsanity.com/paper/2507.21174/) (2025)
- [Cognitively Diverse Multiple-Choice Question Generation: A Hybrid Multi-Agent Framework with Large Language Models](https://mlsanity.com/paper/2602.03704/) (2026)
- [DGRC: An Effective Fine-tuning Framework for Distractor Generation in Chinese Multi-choice Reading Comprehension](https://mlsanity.com/paper/2405.19139/) (2024)
- [From Model to Classroom: Evaluating Generated MCQs for Portuguese with Narrative and Difficulty Concerns](https://mlsanity.com/paper/2506.15598/) (2025)
- [Corpus Prevalence of Multiple-Choice Question Options](https://mlsanity.com/paper/2602.17377/) (2026)
- [Beyond Fine-Tuning: In-Context Learning and Chain-of-Thought for Reasoned Distractor Generation](https://mlsanity.com/paper/2604.17574/) (2026)
- [The Architect, the Adversary, and the Judge: Closed-Loop Generation of Standards-Aligned Assessment Items at Scale](https://mlsanity.com/paper/2609.26087/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) such as ChatGPT have demonstrated remarkable performance across various tasks and have garnered significant attention from both researchers and practitioners. However, in an educational context, we still observe a performance gap in generating distractors -- i.e., plausible yet incorrect answers -- with LLMs for multiple-choice questions (MCQs). In this study, we propose a strategy for guiding LLMs such as ChatGPT, in generating relevant distractors by prompting them with question items automatically retrieved from a question bank as well-chosen in-context examples. We evaluate our LLM-based solutions using a quantitative assessment on an existing test set, as well as through quality annotations by human experts, i.e., teachers. We found that on average 53% of the generated distractors presented to the teachers were rated as high-quality, i.e., suitable for immediate use as is, outperforming the state-of-the-art model. We also show the gains of our approach 1 in generating high-quality distractors by comparing it with a zero-shot ChatGPT and a few-shot ChatGPT prompted with static examples.