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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
Automatic Distractor Generation for Multiple Choice Questions in Standard Tests—0
Better Distractions: Transformer-based Distractor Generation and Multiple Choice Question Filtering—0
Co-Attention Hierarchical Network: Generating Coherent Long Distractors for Reading Comprehension—0
D-GEN: Automatic Distractor Generation and Evaluation for Reliable Assessment of Generative Model—0
DGRC: An Effective Fine-tuning Framework for Distractor Generation in Chinese Multi-choice Reading Comprehension—0
Difficulty-aware Distractor Generation for Gap-Fill Items—0
DISTO: Evaluating Textual Distractors for Multi-Choice Questions using Negative Sampling based Approach—0
Distractor Generation for Chinese Fill-in-the-blank Items—0
Distractor Generation in Multiple-Choice Tasks: A Survey of Methods, Datasets, and Evaluation—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
Improving Automated Distractor Generation for Math Multiple-choice Questions with Overgenerate-and-rank—0
ISSR: Iterative Selection with Self-Review for Vocabulary Test Distractor Generation—0
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