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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
A Novel Multi-Stage Prompting Approach for Language Agnostic MCQ Generation using GPTCode0
DiVERT: Distractor Generation with Variational Errors Represented as Text for Math Multiple-choice QuestionsCode0
BERT-based distractor generation for Swedish reading comprehension questions using a small-scale datasetCode0
Distractor generation for multiple-choice questions with predictive prompting and large language modelsCode0
Distractor Generation for Multiple Choice Questions Using Learning to RankCode0
Chain-of-Exemplar: Enhancing Distractor Generation for Multimodal Educational Question GenerationCode0
Exploring Automated Distractor Generation for Math Multiple-choice Questions via Large Language ModelsCode0
DISTO: Evaluating Textual Distractors for Multi-Choice Questions using Negative Sampling based Approach—0
Difficulty-aware Distractor Generation for Gap-Fill Items—0
DGRC: An Effective Fine-tuning Framework for Distractor Generation in Chinese Multi-choice Reading Comprehension—0
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