Improving Automated Distractor Generation for Math Multiple-choice Questions with Overgenerate-and-rank
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929342055972864 |
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| author | Scarlatos, Alexander Feng, Wanyong Smith, Digory Woodhead, Simon Lan, Andrew |
| author_facet | Scarlatos, Alexander Feng, Wanyong Smith, Digory Woodhead, Simon Lan, Andrew |
| contents | 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 model to predict how likely distractors are to be selected by real students. Experimental results on a real-world dataset and human evaluation with math teachers show that our ranking model increases alignment with human-authored distractors, although human-authored ones are still preferred over generated ones. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_05144 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Improving Automated Distractor Generation for Math Multiple-choice Questions with Overgenerate-and-rank Scarlatos, Alexander Feng, Wanyong Smith, Digory Woodhead, Simon Lan, Andrew Computers and Society Machine Learning 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 model to predict how likely distractors are to be selected by real students. Experimental results on a real-world dataset and human evaluation with math teachers show that our ranking model increases alignment with human-authored distractors, although human-authored ones are still preferred over generated ones. |
| title | Improving Automated Distractor Generation for Math Multiple-choice Questions with Overgenerate-and-rank |
| topic | Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2405.05144 |