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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 31–40 of 41 papers

TitleStatusHype
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
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