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
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
Show:102550
← PrevPage 3 of 5Next →

No leaderboard results yet.