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Distractor Generation

Given a passage, a question, and an answer phrase, the goal of distractor generation (DG) is to generate context-related wrong options (i.e., distractor) for multiple-choice questions (MCQ).

Papers

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Showing 26–41 of 41 papers

TitleStatusHype
Distractor Generation in Multiple-Choice Tasks: A Survey of Methods, Datasets, and Evaluation—0
Do LLMs Make Mistakes Like Students? Exploring Natural Alignment between Language Models and Human Error Patterns—0
Enhancing Distractor Generation for Multiple-Choice Questions with Retrieval Augmented Pretraining and Knowledge Graph Integration—0
Equipping Educational Applications with Domain Knowledge—0
Examining Multilingual Embedding Models Cross-Lingually Through LLM-Generated Adversarial Examples—0
Generating Plausible Distractors for Multiple-Choice Questions via Student Choice Prediction—0
Good, Better, Best: Textual Distractors Generation for Multiple-Choice Visual Question Answering via Reinforcement Learning—0
Distractor Generation for Multiple Choice Questions Using Learning to RankCode0
Distractor generation for multiple-choice questions with predictive prompting and large language modelsCode0
Exploring Automated Distractor Generation for Math Multiple-choice Questions via Large Language ModelsCode0
DiVERT: Distractor Generation with Variational Errors Represented as Text for Math Multiple-choice QuestionsCode0
Chain-of-Exemplar: Enhancing Distractor Generation for Multimodal Educational Question GenerationCode0
DisGeM: Distractor Generation for Multiple Choice Questions with Span MaskingCode0
Wrong Answers Can Also Be Useful: PlausibleQA -- A Large-Scale QA Dataset with Answer Plausibility ScoresCode0
A Novel Multi-Stage Prompting Approach for Language Agnostic MCQ Generation using GPTCode0
BERT-based distractor generation for Swedish reading comprehension questions using a small-scale datasetCode0
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