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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
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
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