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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
Assessing biomedical knowledge robustness in large language models by query-efficient sampling attacks—0
Distractor Generation in Multiple-Choice Tasks: A Survey of Methods, Datasets, and Evaluation—0
A Novel Multi-Stage Prompting Approach for Language Agnostic MCQ Generation using GPTCode0
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
BERT-based distractor generation for Swedish reading comprehension questions using a small-scale datasetCode0
Automatic Distractor Generation for Multiple Choice Questions in Standard Tests—0
Better Distractions: Transformer-based Distractor Generation and Multiple Choice Question Filtering—0
Knowledge-Driven Distractor Generation for Cloze-style Multiple Choice Questions—0
Co-Attention Hierarchical Network: Generating Coherent Long Distractors for Reading Comprehension—0
Good, Better, Best: Textual Distractors Generation for Multiple-Choice Visual Question Answering via Reinforcement Learning—0
Equipping Educational Applications with Domain Knowledge—0
Difficulty-aware Distractor Generation for Gap-Fill Items—0
Distractor Generation for Multiple Choice Questions Using Learning to RankCode0
Distractor Generation for Chinese Fill-in-the-blank Items—0
Multiple Choice Question Generation Utilizing An Ontology—0
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