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 plausible student misconceptions is critical for AI in education. In this work, we examine how large language models (LLMs) reason about misconceptions when generating multiple-choice distractors, a task that re…
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