mlsanityData to 7 Oct 2026

Exploring Automated Distractor Generation for Math Multiple-choice Questions via Large Language Models

arXiv 2404.02124 ↗First posted 2 Apr 2024cs.CLcited by 15

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Abstract (arXiv)

Multiple-choice questions (MCQs) are ubiquitous in almost all levels of education since they are easy to administer, grade, and are a reliable format in assessments and practices. One of the most important aspects of MCQs is the distractors, i.e., incorrect options that are designed to target common errors or misconceptions among real students. To date, the task of crafting high-quality distractors largely remains a labor and time-intensive process for teachers and learning content designers, which has limited scalability. In this work, we study the task of automated distractor generation in the domain of math MCQs and explore a wide variety of large language model (LLM)-based approaches, from in-context learning to fine-tuning. We conduct extensive experiments using a real-world math MCQ dataset and find that although LLMs can generate some mathematically valid distractors, they are less adept at anticipating common errors or misconceptions among real students.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 13 of its 39 references that are in the map. First posted on arXiv 2 Apr 2024.

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13 of 39 references.

References · 13

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  3. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  4. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  5. 2017Deep reinforcement learning from human preferencescited by 3,846
  6. 2022Z-ICL: Zero-Shot In-Context Learning with Pseudo-Demonstrationscited by 70
  7. 2021Math Word Problem Generation with Mathematical Consistency and Problem Context Constraintscited by 27
  8. 2020Automatic Distractor Generation for Multiple Choice Questions in Standard Testscited by 26
  9. 2018Generating Distractors for Reading Comprehension Questions from Real Examinationscited by 24
  10. 2022Automatic Short Math Answer Grading via In-context Meta-learningcited by 18
  11. 2023Interpretable Math Word Problem Solution Generation Via Step-by-step Planningcited by 17
  12. 2021BERT-based distractor generation for Swedish reading comprehension questions using a small-scale datasetcited by 12
1 more
  1. 2021Math Operation Embeddings for Open-ended Solution Analysis and Feedbackcited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 5; 2026: 6.

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  1. 2025MicroVQA: A Multimodal Reasoning Benchmark for Microscopy-Based Scientific Researchcited by 30
  2. 2025Learning to Make MISTAKEs: Modeling Incorrect Student Thinking And Key Errorscited by 19
  3. 2024HumanVBench: Probing Human-Centric Video Understanding in MLLMs with Automatically Synthesized Benchmarkscited by 10
  4. 2024Math Multiple Choice Question Generation via Human-Large Language Model Collaborationcited by 9
  5. 2025Do LLMs Make Mistakes Like Students? Exploring Natural Alignment between Language Models and Human Error Patternscited by 8
  6. 2025The StudyChat Dataset: Analyzing Student Dialogues With ChatGPT in an Artificial Intelligence Coursecited by 7
  7. 2025From Text to Visuals: Using LLMs to Generate Math Diagrams with Vector Graphicscited by 5
  8. 2026FoundationalASSIST: An Educational Dataset for Foundational Knowledge Tracing and Pedagogical Grounding of LLMscited by 4
  9. 2024KCIF: Knowledge-Conditioned Instruction Followingcited by 2
  10. 2024EvalYaks: Instruction Tuning Datasets and LoRA Fine-tuned Models for Automated Scoring of CEFR B2 Speaking Assessment Transcriptscited by 1
  11. 2026Rethinking Multiple-Choice Questions for RLVR: Unlocking Potential via Distractor Designcited by 1
  12. 2026A self-evolving multi-role collaborative framework with fine-grained difficulty guidance for innovative mathematical problem generationcited by 0
3 more
  1. 2026Generating Multiple-Choice Knowledge Questions with Interpretable Difficulty Estimation using Knowledge Graphs and Large Language Modelscited by 0
  2. 2026MCQ Difficulty Prediction via Modeling Learner Heterogeneity Using Data-Driven Cognitive Profilingcited by 0
  3. 2026The Architect, the Adversary, and the Judge: Closed-Loop Generation of Standards-Aligned Assessment Items at Scalecited by 0