Distractor Generation
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Benchmarks
RACE
Most implemented
BRAINTEASER: Lateral Thinking Puzzles for Large Language Models
Distractor generation for multiple-choice questions with predictive prompting and large language models
Quiz-Style Question Generation for News Stories
Generating Distractors for Reading Comprehension Questions from Real Examinations
Papers
Beyond Fine-Tuning: In-Context Learning and Chain-of-Thought for Reasoned Distractor Generation
Distractor generation (DG) remains a labor-intensive task that still significantly depends on domain experts. The task focuses on generating plausible yet incorrect options, known as distractors, for multiple-choice ques…
Distractor GenerationContrastive LearningSemantic RetrievalCan LLMs Model Incorrect Student Reasoning? A Case Study on Distractor Generation
Modeling student misconceptions in a realistic manner is critical for AI in education. In this work, we examine how large language models (LLMs) reason about misconceptions when generating distractor answers for multiple…
Distractor GenerationArabicDialectHub: A Cross-Dialectal Arabic Learning Resource and Platform
We present ArabicDialectHub, a cross-dialectal Arabic learning resource comprising 552 phrases across six varieties (Moroccan Darija, Lebanese, Syrian, Emirati, Saudi, and MSA) and an interactive web platform. Phrases we…
Distractor GenerationDifficulty-Controllable Cloze Question Distractor Generation
Multiple-choice cloze questions are commonly used to assess linguistic proficiency and comprehension. However, generating high-quality distractors remains challenging, as existing methods often lack adaptability and cont…
Distractor GenerationData AugmentationTailoring Diagnostic Modeling to Individual Learners: Personalized Distractor Generation via MCTS-Guided Reasoning Reconstruction
Distractors-incorrect yet plausible answer choices in multiple-choice questions (MCQs)-are vital in educational assessments, as they help identify student misconceptions by presenting potential reasoning errors. Current …
Distractor GenerationDualReward: A Dynamic Reinforcement Learning Framework for Cloze Tests Distractor Generation
This paper introduces DualReward, a novel reinforcement learning framework for automatic distractor generation in cloze tests. Unlike conventional approaches that rely primarily on supervised learning or static generativ…
Reinforcement LearningDistractor GenerationCloze Test