Papers Distractor Generation
“Distractor Generation” 태그가 달린 논문 47편 · 필터 해제
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 TestLookAlike: Consistent Distractor Generation in Math MCQs
Large language models (LLMs) are increasingly used to generate distractors for multiple-choice questions (MCQs), especially in domains like math education. However, existing approaches are limited in ensuring that the ge…
Distractor GenerationMathMultiple-choiceD-GEN: Automatic Distractor Generation and Evaluation for Reliable Assessment of Generative Model
Evaluating generative models with open-ended generation is challenging due to inconsistencies in response formats. Multiple-choice (MC) evaluation mitigates this issue, but generating high-quality distractors is time-con…
Distractor GenerationMultiple-choiceWrong Answers Can Also Be Useful: PlausibleQA -- A Large-Scale QA Dataset with Answer Plausibility Scores
Large Language Models (LLMs) are revolutionizing information retrieval, with chatbots becoming an important source for answering user queries. As by their design, LLMs prioritize generating correct answers, the value of …
Distractor GenerationInformation RetrievalMultiple-choiceMultiple Choice Question Answering (MCQA)+1Do LLMs Make Mistakes Like Students? Exploring Natural Alignment between Language Models and Human Error Patterns
Large Language Models (LLMs) have demonstrated remarkable capabilities in various educational tasks, yet their alignment with human learning patterns, particularly in predicting which incorrect options students are most …
Distractor GenerationMultiple-choiceThe Imitation Game for Educational AI
As artificial intelligence systems become increasingly prevalent in education, a fundamental challenge emerges: how can we verify if an AI truly understands how students think and reason? Traditional evaluation methods l…
Distractor GenerationMisconceptionsLost in the Passage: Passage-level In-context Learning Does Not Necessarily Need a "Passage"
By simply incorporating demonstrations into the context, in-context learning (ICL) enables large language models (LLMs) to yield awesome performance on many tasks. In this paper, we focus on passage-level long-context IC…
Distractor GenerationIn-Context LearningExamining Multilingual Embedding Models Cross-Lingually Through LLM-Generated Adversarial Examples
The evaluation of cross-lingual semantic search capabilities of models is often limited to existing datasets from tasks such as information retrieval and semantic textual similarity. To allow for domain-specific evaluati…
Distractor GenerationInformation RetrievalLanguage ModelingLanguage Modelling+4Generating Plausible Distractors for Multiple-Choice Questions via Student Choice Prediction
In designing multiple-choice questions (MCQs) in education, creating plausible distractors is crucial for identifying students' misconceptions and gaps in knowledge and accurately assessing their understanding. However, …
Distractor GenerationMisconceptionsMultiple-choiceISSR: Iterative Selection with Self-Review for Vocabulary Test Distractor Generation
Vocabulary acquisition is essential to second language learning, as it underpins all core language skills. Accurate vocabulary assessment is particularly important in standardized exams, where test items evaluate learner…
Distractor GenerationvalidDisGeM: Distractor Generation for Multiple Choice Questions with Span Masking
Recent advancements in Natural Language Processing (NLP) have impacted numerous sub-fields such as natural language generation, natural language inference, question answering, and more. However, in the field of question …
Distractor GenerationMultiple-choiceNatural Language InferenceQuestion Answering+3Chain-of-Exemplar: Enhancing Distractor Generation for Multimodal Educational Question Generation
Multiple-choice questions (MCQs) are important in enhancing concept learning and student engagement for educational purposes. Despite the multimodal nature of educational content, current methods focus mainly on text-bas…
Distractor GenerationMultiple-choiceQuestion GenerationQuestion-Generation+1DiVERT: Distractor Generation with Variational Errors Represented as Text for Math Multiple-choice Questions
High-quality distractors are crucial to both the assessment and pedagogical value of multiple-choice questions (MCQs), where manually crafting ones that anticipate knowledge deficiencies or misconceptions among real stud…
Distractor GenerationMathMisconceptionsMultiple-choiceEnhancing Distractor Generation for Multiple-Choice Questions with Retrieval Augmented Pretraining and Knowledge Graph Integration
In this paper, we tackle the task of distractor generation (DG) for multiple-choice questions. Our study introduces two key designs. First, we propose \textit{retrieval augmented pretraining}, which involves refining the…
BenchmarkingDistractor GenerationKnowledge GraphsLanguage Modeling+3Unsupervised Distractor Generation via Large Language Model Distilling and Counterfactual Contrastive Decoding
Within the context of reading comprehension, the task of Distractor Generation (DG) aims to generate several incorrect options to confuse readers. Traditional supervised methods for DG rely heavily on expensive human-ann…
counterfactualDistractor GenerationLanguage ModelingLanguage Modelling+2