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Math Multiple Choice Question Generation via Human-Large Language Model Collaboration
May 3, 2024, 4:14 a.m. | Jaewook Lee, Digory Smith, Simon Woodhead, Andrew Lan
cs.CL updates on arXiv.org arxiv.org
Abstract: Multiple choice questions (MCQs) are a popular method for evaluating students' knowledge due to their efficiency in administration and grading. Crafting high-quality math MCQs is a labor-intensive process that requires educators to formulate precise stems and plausible distractors. Recent advances in large language models (LLMs) have sparked interest in automating MCQ creation, but challenges persist in ensuring mathematical accuracy and addressing student errors. This paper introduces a prototype tool designed to facilitate collaboration between LLMs …
abstract administration advances arxiv collaboration cs.cl efficiency human knowledge labor language language model language models large language large language model large language models llms math multiple popular process quality question questions students type via
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