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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 1–41 of 41 papers

TitleStatusHype
Generating Distractors for Reading Comprehension Questions from Real ExaminationsCode1
A BERT-based Distractor Generation Scheme with Multi-tasking and Negative Answer Training Strategies.Code1
Quiz-Style Question Generation for News StoriesCode1
ZmBART: An Unsupervised Cross-lingual Transfer Framework for Language GenerationCode1
BRAINTEASER: Lateral Thinking Puzzles for Large Language ModelsCode1
EduQG: A Multi-format Multiple Choice Dataset for the Educational DomainCode1
CDGP: Automatic Cloze Distractor Generation based on Pre-trained Language ModelCode1
A BERT-based Distractor Generation Scheme with Multi-tasking and Negative Answer Training StrategiesCode1
Distractor Generation for Chinese Fill-in-the-blank Items—0
Automatic Distractor Generation for Multiple Choice Questions in Standard Tests—0
Better Distractions: Transformer-based Distractor Generation and Multiple Choice Question Filtering—0
Co-Attention Hierarchical Network: Generating Coherent Long Distractors for Reading Comprehension—0
D-GEN: Automatic Distractor Generation and Evaluation for Reliable Assessment of Generative Model—0
DGRC: An Effective Fine-tuning Framework for Distractor Generation in Chinese Multi-choice Reading Comprehension—0
Difficulty-aware Distractor Generation for Gap-Fill Items—0
DISTO: Evaluating Textual Distractors for Multi-Choice Questions using Negative Sampling based Approach—0
Improving Automated Distractor Generation for Math Multiple-choice Questions with Overgenerate-and-rank—0
ISSR: Iterative Selection with Self-Review for Vocabulary Test Distractor Generation—0
Knowledge-Driven Distractor Generation for Cloze-style Multiple Choice Questions—0
LookAlike: Consistent Distractor Generation in Math MCQs—0
Lost in the Passage: Passage-level In-context Learning Does Not Necessarily Need a "Passage"—0
Multiple Choice Question Generation Utilizing An Ontology—0
The Imitation Game for Educational AI—0
Unsupervised Distractor Generation via Large Language Model Distilling and Counterfactual Contrastive Decoding—0
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
Do LLMs Make Mistakes Like Students? Exploring Natural Alignment between Language Models and Human Error Patterns—0
Enhancing Distractor Generation for Multiple-Choice Questions with Retrieval Augmented Pretraining and Knowledge Graph Integration—0
Equipping Educational Applications with Domain Knowledge—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
Good, Better, Best: Textual Distractors Generation for Multiple-Choice Visual Question Answering via Reinforcement Learning—0
Distractor Generation for Multiple Choice Questions Using Learning to RankCode0
Distractor generation for multiple-choice questions with predictive prompting and large language modelsCode0
Exploring Automated Distractor Generation for Math Multiple-choice Questions via Large Language ModelsCode0
DiVERT: Distractor Generation with Variational Errors Represented as Text for Math Multiple-choice QuestionsCode0
Chain-of-Exemplar: Enhancing Distractor Generation for Multimodal Educational Question GenerationCode0
DisGeM: Distractor Generation for Multiple Choice Questions with Span MaskingCode0
Wrong Answers Can Also Be Useful: PlausibleQA -- A Large-Scale QA Dataset with Answer Plausibility ScoresCode0
A Novel Multi-Stage Prompting Approach for Language Agnostic MCQ Generation using GPTCode0
BERT-based distractor generation for Swedish reading comprehension questions using a small-scale datasetCode0
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