Distractor Generation
13 papers with code • 1 benchmarks • 2 datasets
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).
RACE
SciQ
Most implemented papers
A Novel Multi-Stage Prompting Approach for Language Agnostic MCQ Generation using GPT
We introduce a multi-stage prompting approach (MSP) for the generation of multiple choice questions (MCQs), harnessing the capabilities of GPT models such as text-davinci-003 and GPT-4, renowned for their excellence across various NLP tasks.
CDGP: Automatic Cloze Distractor Generation based on Pre-trained Language Model
Manually designing cloze test consumes enormous time and efforts.
Exploring Automated Distractor Generation for Math Multiple-choice Questions via Large Language Models
umass-ml4ed/prompt_distractor_generation_naacl
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Multiple-choice questions (MCQs) are ubiquitous in almost all levels of education since they are easy to administer, grade, and are a reliable format in assessments and practices.
