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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 11–20 of 41 papers

TitleStatusHype
Chain-of-Exemplar: Enhancing Distractor Generation for Multimodal Educational Question GenerationCode0
DiVERT: Distractor Generation with Variational Errors Represented as Text for Math Multiple-choice QuestionsCode0
Enhancing Distractor Generation for Multiple-Choice Questions with Retrieval Augmented Pretraining and Knowledge Graph Integration—0
Unsupervised Distractor Generation via Large Language Model Distilling and Counterfactual Contrastive Decoding—0
DGRC: An Effective Fine-tuning Framework for Distractor Generation in Chinese Multi-choice Reading Comprehension—0
Improving Automated Distractor Generation for Math Multiple-choice Questions with Overgenerate-and-rank—0
Exploring Automated Distractor Generation for Math Multiple-choice Questions via Large Language ModelsCode0
CDGP: Automatic Cloze Distractor Generation based on Pre-trained Language ModelCode1
Assessing biomedical knowledge robustness in large language models by query-efficient sampling attacks—0
Distractor Generation in Multiple-Choice Tasks: A Survey of Methods, Datasets, and Evaluation—0
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