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Distractor Generation
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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).
논문
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D-GEN: Automatic Distractor Generation and Evaluation for Reliable Assessment of Generative Model
Wrong Answers Can Also Be Useful: PlausibleQA -- A Large-Scale QA Dataset with Answer Plausibility Scores
The Imitation Game for Educational AI
Do LLMs Make Mistakes Like Students? Exploring Natural Alignment between Language Models and Human Error Patterns
Lost in the Passage: Passage-level In-context Learning Does Not Necessarily Need a "Passage"
Examining Multilingual Embedding Models Cross-Lingually Through LLM-Generated Adversarial Examples
Generating Plausible Distractors for Multiple-Choice Questions via Student Choice Prediction
ISSR: Iterative Selection with Self-Review for Vocabulary Test Distractor Generation
DisGeM: Distractor Generation for Multiple Choice Questions with Span Masking
DiVERT: Distractor Generation with Variational Errors Represented as Text for Math Multiple-choice Questions
Enhancing Distractor Generation for Multiple-Choice Questions with Retrieval Augmented Pretraining and Knowledge Graph Integration
Unsupervised Distractor Generation via Large Language Model Distilling and Counterfactual Contrastive Decoding
DGRC: An Effective Fine-tuning Framework for Distractor Generation in Chinese Multi-choice Reading Comprehension
Improving Automated Distractor Generation for Math Multiple-choice Questions with Overgenerate-and-rank
Exploring Automated Distractor Generation for Math Multiple-choice Questions via Large Language Models
CDGP: Automatic Cloze Distractor Generation based on Pre-trained Language Model
Assessing biomedical knowledge robustness in large language models by query-efficient sampling attacks
Distractor Generation in Multiple-Choice Tasks: A Survey of Methods, Datasets, and Evaluation
A Novel Multi-Stage Prompting Approach for Language Agnostic MCQ Generation using GPT
BRAINTEASER: Lateral Thinking Puzzles for Large Language Models
Distractor generation for multiple-choice questions with predictive prompting and large language models
DISTO: Evaluating Textual Distractors for Multi-Choice Questions using Negative Sampling based Approach
EduQG: A Multi-format Multiple Choice Dataset for the Educational Domain
BERT-based distractor generation for Swedish reading comprehension questions using a small-scale dataset
ZmBART: An Unsupervised Cross-lingual Transfer Framework for Language Generation
Quiz-Style Question Generation for News Stories
Automatic Distractor Generation for Multiple Choice Questions in Standard Tests
Better Distractions: Transformer-based Distractor Generation and Multiple Choice Question Filtering
A BERT-based Distractor Generation Scheme with Multi-tasking and Negative Answer Training Strategies
Knowledge-Driven Distractor Generation for Cloze-style Multiple Choice Questions
Co-Attention Hierarchical Network: Generating Coherent Long Distractors for Reading Comprehension
Good, Better, Best: Textual Distractors Generation for Multiple-Choice Visual Question Answering via Reinforcement Learning
Generating Distractors for Reading Comprehension Questions from Real Examinations
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