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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 1–10 of 41 papers

TitleStatusHype
LookAlike: Consistent Distractor Generation in Math MCQs—0
D-GEN: Automatic Distractor Generation and Evaluation for Reliable Assessment of Generative Model—0
Wrong Answers Can Also Be Useful: PlausibleQA -- A Large-Scale QA Dataset with Answer Plausibility ScoresCode0
Do LLMs Make Mistakes Like Students? Exploring Natural Alignment between Language Models and Human Error Patterns—0
The Imitation Game for Educational AI—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
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