# Knowledge-Driven Distractor Generation for Cloze-style Multiple Choice Questions

arXiv: https://arxiv.org/abs/2004.09853 · HTML: https://mlsanity.com/paper/2004.09853/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2020.

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

- [Can Knowledge Graphs Reduce Hallucinations in LLMs? : A Survey](https://mlsanity.com/paper/2311.07914/) (2023)
- [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)
- [SODAPOP: Open-Ended Discovery of Social Biases in Social Commonsense Reasoning Models](https://mlsanity.com/paper/2210.07269/) (2022)
- [A Survey of Natural Language Generation](https://mlsanity.com/paper/2112.11739/) (2021)
- [Can Pre-trained Language Models Interpret Similes as Smart as Human?](https://mlsanity.com/paper/2203.08452/) (2022)
- [Unsupervised Distractor Generation via Large Language Model Distilling and Counterfactual Contrastive Decoding](https://mlsanity.com/paper/2406.01306/) (2024)
- [Enhancing Distractor Generation for Multiple-Choice Questions with Retrieval Augmented Pretraining and Knowledge Graph Integration](https://mlsanity.com/paper/2406.13578/) (2024)
- [QAScore -- An Unsupervised Unreferenced Metric for the Question Generation Evaluation](https://mlsanity.com/paper/2210.04320/) (2022)
- [Evaluating the Knowledge Dependency of Questions](https://mlsanity.com/paper/2211.11902/) (2022)
- [Generating Plausible Distractors for Multiple-Choice Questions via Student Choice Prediction](https://mlsanity.com/paper/2501.13125/) (2025)
- [LookAlike: Consistent Distractor Generation in Math MCQs](https://mlsanity.com/paper/2505.01903/) (2025)
- [DisGeM: Distractor Generation for Multiple Choice Questions with Span Masking](https://mlsanity.com/paper/2409.18263/) (2024)
- [CDGP: Automatic Cloze Distractor Generation based on Pre-trained Language Model](https://mlsanity.com/paper/2403.10326/) (2024)
- [Unified Question Generation with Continual Lifelong Learning](https://mlsanity.com/paper/2201.09696/) (2022)
- [Controlling Cloze-test Question Item Difficulty with PLM-based Surrogate Models for IRT Assessment](https://mlsanity.com/paper/2403.01456/) (2024)
- [D-GEN: Automatic Distractor Generation and Evaluation for Reliable Assessment of Generative Model](https://mlsanity.com/paper/2504.13439/) (2025)
- [Unsupervised multiple choices question answering via universal corpus](https://mlsanity.com/paper/2402.17333/) (2024)
- [Constructing Cloze Questions Generatively](https://mlsanity.com/paper/2410.04266/) (2024)
- [Tell Me Who Your Students Are: GPT Can Generate Valid Multiple-Choice Questions When Students' (Mis)Understanding Is Hinted](https://mlsanity.com/paper/2505.05815/) (2025)
- [DualReward: A Dynamic Reinforcement Learning Framework for Cloze Tests Distractor Generation](https://mlsanity.com/paper/2507.11875/) (2025)
- [FarsiMCQGen: a Persian Multiple-choice Question Generation Framework](https://mlsanity.com/paper/2510.15134/) (2025)
- [Difficulty-Controllable Cloze Question Distractor Generation](https://mlsanity.com/paper/2511.01526/) (2025)
- [Open Datasets in Learning Analytics: Trends, Challenges, and Best PRACTICE](https://mlsanity.com/paper/2602.17314/) (2026)

## Abstract (arXiv)

In this paper, we propose a novel configurable framework to automatically generate distractive choices for open-domain cloze-style multiple-choice questions, which incorporates a general-purpose knowledge base to effectively create a small distractor candidate set, and a feature-rich learning-to-rank model to select distractors that are both plausible and reliable. Experimental results on datasets across four domains show that our framework yields distractors that are more plausible and reliable than previous methods. This dataset can also be used as a benchmark for distractor generation in the future.