# Generating Distractors for Reading Comprehension Questions from Real Examinations

arXiv: https://arxiv.org/abs/1809.02768 · HTML: https://mlsanity.com/paper/1809.02768/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2018.

## Built on by (most cited first, 24 of 24)

- [A Comparative Study of AI-Generated (GPT-4) and Human-crafted MCQs in Programming Education](https://mlsanity.com/paper/2312.03173/) (2023)
- [Distractor Generation in Multiple-Choice Tasks: A Survey of Methods, Datasets, and Evaluation](https://mlsanity.com/paper/2402.01512/) (2024)
- [Automated Distractor and Feedback Generation for Math Multiple-choice Questions via In-context Learning](https://mlsanity.com/paper/2308.03234/) (2023)
- [Distractor generation for multiple-choice questions with predictive prompting and large language models](https://mlsanity.com/paper/2307.16338/) (2023)
- [Exploring Automated Distractor Generation for Math Multiple-choice Questions via Large Language Models](https://mlsanity.com/paper/2404.02124/) (2024)
- [A Survey of Natural Language Generation](https://mlsanity.com/paper/2112.11739/) (2021)
- [EduQG: A Multi-format Multiple Choice Dataset for the Educational Domain](https://mlsanity.com/paper/2210.06104/) (2022)
- [Unsupervised Distractor Generation via Large Language Model Distilling and Counterfactual Contrastive Decoding](https://mlsanity.com/paper/2406.01306/) (2024)
- [BiRdQA: A Bilingual Dataset for Question Answering on Tricky Riddles](https://mlsanity.com/paper/2109.11087/) (2021)
- [Leaf: Multiple-Choice Question Generation](https://mlsanity.com/paper/2201.09012/) (2022)
- [Learning to Reuse Distractors to support Multiple Choice Question Generation in Education](https://mlsanity.com/paper/2210.13964/) (2022)
- [DiVERT: Distractor Generation with Variational Errors Represented as Text for Math Multiple-choice Questions](https://mlsanity.com/paper/2406.19356/) (2024)
- [Asking Questions Like Educational Experts: Automatically Generating Question-Answer Pairs on Real-World Examination Data](https://mlsanity.com/paper/2109.05179/) (2021)
- [Assessing Distractors in Multiple-Choice Tests](https://mlsanity.com/paper/2311.04554/) (2023)
- [Survey of Natural Language Processing for Education: Taxonomy, Systematic Review, and Future Trends](https://mlsanity.com/paper/2401.07518/) (2024)
- [An overview of artificial intelligence in computer-assisted language learning](https://mlsanity.com/paper/2505.02032/) (2025)
- [DISTO: Evaluating Textual Distractors for Multi-Choice Questions using Negative Sampling based Approach](https://mlsanity.com/paper/2304.04881/) (2023)
- [An Information-Theoretic Approach to Analyze NLP Classification Tasks](https://mlsanity.com/paper/2402.00978/) (2024)
- [Controlling Cloze-test Question Item Difficulty with PLM-based Surrogate Models for IRT Assessment](https://mlsanity.com/paper/2403.01456/) (2024)
- [EMBRACE: Evaluation and Modifications for Boosting RACE](https://mlsanity.com/paper/2305.08433/) (2023)
- [DGRC: An Effective Fine-tuning Framework for Distractor Generation in Chinese Multi-choice Reading Comprehension](https://mlsanity.com/paper/2405.19139/) (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)
- [From Model to Classroom: Evaluating Generated MCQs for Portuguese with Narrative and Difficulty Concerns](https://mlsanity.com/paper/2506.15598/) (2025)
- [FarsiMCQGen: a Persian Multiple-choice Question Generation Framework](https://mlsanity.com/paper/2510.15134/) (2025)

## Abstract (arXiv)

We investigate the task of distractor generation for multiple choice reading comprehension questions from examinations. In contrast to all previous works, we do not aim at preparing words or short phrases distractors, instead, we endeavor to generate longer and semantic-rich distractors which are closer to distractors in real reading comprehension from examinations. Taking a reading comprehension article, a pair of question and its correct option as input, our goal is to generate several distractors which are somehow related to the answer, consistent with the semantic context of the question and have some trace in the article. We propose a hierarchical encoder-decoder framework with static and dynamic attention mechanisms to tackle this task. Specifically, the dynamic attention can combine sentence-level and word-level attention varying at each recurrent time step to generate a more readable sequence. The static attention is to modulate the dynamic attention not to focus on question irrelevant sentences or sentences which contribute to the correct option. Our proposed framework outperforms several strong baselines on the first prepared distractor generation dataset of real reading comprehension questions. For human evaluation, compared with those distractors generated by baselines, our generated distractors are more functional to confuse the annotators.