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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
Wrong Answers Can Also Be Useful: PlausibleQA -- A Large-Scale QA Dataset with Answer Plausibility ScoresCode0
The Imitation Game for Educational AI—0
Do LLMs Make Mistakes Like Students? Exploring Natural Alignment between Language Models and Human Error Patterns—0
Lost in the Passage: Passage-level In-context Learning Does Not Necessarily Need a "Passage"—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
ISSR: Iterative Selection with Self-Review for Vocabulary Test Distractor Generation—0
DisGeM: Distractor Generation for Multiple Choice Questions with Span MaskingCode0
Chain-of-Exemplar: Enhancing Distractor Generation for Multimodal Educational Question GenerationCode0
DiVERT: Distractor Generation with Variational Errors Represented as Text for Math Multiple-choice QuestionsCode0
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