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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 21–30 of 41 papers

TitleStatusHype
A Novel Multi-Stage Prompting Approach for Language Agnostic MCQ Generation using GPTCode0
BRAINTEASER: Lateral Thinking Puzzles for Large Language ModelsCode1
Distractor generation for multiple-choice questions with predictive prompting and large language modelsCode0
DISTO: Evaluating Textual Distractors for Multi-Choice Questions using Negative Sampling based Approach—0
EduQG: A Multi-format Multiple Choice Dataset for the Educational DomainCode1
BERT-based distractor generation for Swedish reading comprehension questions using a small-scale datasetCode0
ZmBART: An Unsupervised Cross-lingual Transfer Framework for Language GenerationCode1
Quiz-Style Question Generation for News StoriesCode1
Automatic Distractor Generation for Multiple Choice Questions in Standard Tests—0
A BERT-based Distractor Generation Scheme with Multi-tasking and Negative Answer Training Strategies.Code1
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