Improving Automated Distractor Generation for Math Multiple-choice Questions with Overgenerate-and-rank

arXiv:2405.05144v1 Announce Type: cross
Abstract: Multiple-choice questions (MCQs) are commonly used across all levels of math education since they can be deployed and graded at a large scale. A critical component of MCQs is the distractors, i.e., incorrect answers crafted to reflect student errors or misconceptions. Automatically generating them in math MCQs, e.g., with large language models, has been challenging. In this work, we propose a novel method to enhance the quality of generated distractors through overgenerate-and-rank, training a ranking …

abstract arxiv automated cs.cy cs.lg education errors improving math multiple questions scale them type