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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 1–25 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
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
Enhancing Distractor Generation for Multiple-Choice Questions with Retrieval Augmented Pretraining and Knowledge Graph Integration—0
Unsupervised Distractor Generation via Large Language Model Distilling and Counterfactual Contrastive Decoding—0
DGRC: An Effective Fine-tuning Framework for Distractor Generation in Chinese Multi-choice Reading Comprehension—0
Improving Automated Distractor Generation for Math Multiple-choice Questions with Overgenerate-and-rank—0
Exploring Automated Distractor Generation for Math Multiple-choice Questions via Large Language ModelsCode0
CDGP: Automatic Cloze Distractor Generation based on Pre-trained Language ModelCode1
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
BRAINTEASER: Lateral Thinking Puzzles for Large Language ModelsCode1
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
EduQG: A Multi-format Multiple Choice Dataset for the Educational DomainCode1
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