mlsanityData to 7 Oct 2026

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

arXiv 2004.09853 ↗First posted 21 Apr 2020cs.CL cs.AIcited by 24

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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.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Apr 2020.

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Citing papers per year: 2021: 1; 2022: 5; 2023: 2; 2024: 8; 2025: 7; 2026: 1.

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  1. 2023Can Knowledge Graphs Reduce Hallucinations in LLMs? : A Surveycited by 92
  2. 2023A Comparative Study of AI-Generated (GPT-4) and Human-crafted MCQs in Programming Educationcited by 34
  3. 2024Distractor Generation in Multiple-Choice Tasks: A Survey of Methods, Datasets, and Evaluationcited by 25
  4. 2022SODAPOP: Open-Ended Discovery of Social Biases in Social Commonsense Reasoning Modelscited by 22
  5. 2021A Survey of Natural Language Generationcited by 13
  6. 2022Can Pre-trained Language Models Interpret Similes as Smart as Human?cited by 13
  7. 2024Unsupervised Distractor Generation via Large Language Model Distilling and Counterfactual Contrastive Decodingcited by 11
  8. 2024Enhancing Distractor Generation for Multiple-Choice Questions with Retrieval Augmented Pretraining and Knowledge Graph Integrationcited by 11
  9. 2022QAScore -- An Unsupervised Unreferenced Metric for the Question Generation Evaluationcited by 9
  10. 2022Evaluating the Knowledge Dependency of Questionscited by 9
  11. 2025Generating Plausible Distractors for Multiple-Choice Questions via Student Choice Predictioncited by 8
  12. 2025LookAlike: Consistent Distractor Generation in Math MCQscited by 7
12 more
  1. 2024DisGeM: Distractor Generation for Multiple Choice Questions with Span Maskingcited by 5
  2. 2024CDGP: Automatic Cloze Distractor Generation based on Pre-trained Language Modelcited by 3
  3. 2022Unified Question Generation with Continual Lifelong Learningcited by 2
  4. 2024Controlling Cloze-test Question Item Difficulty with PLM-based Surrogate Models for IRT Assessmentcited by 1
  5. 2025D-GEN: Automatic Distractor Generation and Evaluation for Reliable Assessment of Generative Modelcited by 1
  6. 2024Unsupervised multiple choices question answering via universal corpuscited by 0
  7. 2024Constructing Cloze Questions Generativelycited by 0
  8. 2025Tell Me Who Your Students Are: GPT Can Generate Valid Multiple-Choice Questions When Students' (Mis)Understanding Is Hintedcited by 0
  9. 2025DualReward: A Dynamic Reinforcement Learning Framework for Cloze Tests Distractor Generationcited by 0
  10. 2025FarsiMCQGen: a Persian Multiple-choice Question Generation Frameworkcited by 0
  11. 2025Difficulty-Controllable Cloze Question Distractor Generationcited by 0
  12. 2026Open Datasets in Learning Analytics: Trends, Challenges, and Best PRACTICEcited by 0