SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 21–30 of 41 papers

TitleStatusHype
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
Do LLMs Make Mistakes Like Students? Exploring Natural Alignment between Language Models and Human Error Patterns—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
Show:102550
← PrevPage 3 of 5Next →

No leaderboard results yet.