@inproceedings{feng-etal-2024-exploring,
title = "Exploring Automated Distractor Generation for Math Multiple-choice Questions via Large Language Models",
author = "Feng, Wanyong and
Lee, Jaewook and
McNichols, Hunter and
Scarlatos, Alexander and
Smith, Digory and
Woodhead, Simon and
Ornelas, Nancy and
Lan, Andrew",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2024",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-naacl.193",
pages = "3067--3082",
abstract = "Multiple-choice questions (MCQs) are ubiquitous in almost all levels of education since they are easy to administer, grade, and are a reliable format in assessments and practices. One of the most important aspects of MCQs is the distractors, i.e., incorrect options that are designed to target common errors or misconceptions among real students. To date, the task of crafting high-quality distractors largely remains a labor and time-intensive process for teachers and learning content designers, which has limited scalability. In this work, we study the task of automated distractor generation in the domain of math MCQs and explore a wide variety of large language model (LLM)-based approaches, from in-context learning to fine-tuning. We conduct extensive experiments using a real-world math MCQ dataset and find that although LLMs can generate some mathematically valid distractors, they are less adept at anticipating common errors or misconceptions among real students.",
}
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<abstract>Multiple-choice questions (MCQs) are ubiquitous in almost all levels of education since they are easy to administer, grade, and are a reliable format in assessments and practices. One of the most important aspects of MCQs is the distractors, i.e., incorrect options that are designed to target common errors or misconceptions among real students. To date, the task of crafting high-quality distractors largely remains a labor and time-intensive process for teachers and learning content designers, which has limited scalability. In this work, we study the task of automated distractor generation in the domain of math MCQs and explore a wide variety of large language model (LLM)-based approaches, from in-context learning to fine-tuning. We conduct extensive experiments using a real-world math MCQ dataset and find that although LLMs can generate some mathematically valid distractors, they are less adept at anticipating common errors or misconceptions among real students.</abstract>
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%0 Conference Proceedings
%T Exploring Automated Distractor Generation for Math Multiple-choice Questions via Large Language Models
%A Feng, Wanyong
%A Lee, Jaewook
%A McNichols, Hunter
%A Scarlatos, Alexander
%A Smith, Digory
%A Woodhead, Simon
%A Ornelas, Nancy
%A Lan, Andrew
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F feng-etal-2024-exploring
%X Multiple-choice questions (MCQs) are ubiquitous in almost all levels of education since they are easy to administer, grade, and are a reliable format in assessments and practices. One of the most important aspects of MCQs is the distractors, i.e., incorrect options that are designed to target common errors or misconceptions among real students. To date, the task of crafting high-quality distractors largely remains a labor and time-intensive process for teachers and learning content designers, which has limited scalability. In this work, we study the task of automated distractor generation in the domain of math MCQs and explore a wide variety of large language model (LLM)-based approaches, from in-context learning to fine-tuning. We conduct extensive experiments using a real-world math MCQ dataset and find that although LLMs can generate some mathematically valid distractors, they are less adept at anticipating common errors or misconceptions among real students.
%U https://aclanthology.org/2024.findings-naacl.193
%P 3067-3082
Markdown (Informal)
[Exploring Automated Distractor Generation for Math Multiple-choice Questions via Large Language Models](https://aclanthology.org/2024.findings-naacl.193) (Feng et al., Findings 2024)
ACL
- Wanyong Feng, Jaewook Lee, Hunter McNichols, Alexander Scarlatos, Digory Smith, Simon Woodhead, Nancy Ornelas, and Andrew Lan. 2024. Exploring Automated Distractor Generation for Math Multiple-choice Questions via Large Language Models. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 3067–3082, Mexico City, Mexico. Association for Computational Linguistics.