@inproceedings{alhazmi-etal-2024-distractor,
title = "Distractor Generation in Multiple-Choice Tasks: A Survey of Methods, Datasets, and Evaluation",
author = "Alhazmi, Elaf and
Sheng, Quan and
Zhang, Wei Emma and
Zaib, Munazza and
Alhazmi, Ahoud",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.799",
pages = "14437--14458",
abstract = "The distractor generation task focuses on generating incorrect but plausible options for objective questions such as fill-in-the-blank and multiple-choice questions. This task is widely utilized in educational settings across various domains and subjects. The effectiveness of these questions in assessments relies on the quality of the distractors, as they challenge examinees to select the correct answer from a set of misleading options. The evolution of artificial intelligence (AI) has transitioned the task from traditional methods to the use of neural networks and pre-trained language models. This shift has established new benchmarks and expanded the use of advanced deep learning methods in generating distractors. This survey explores distractor generation tasks, datasets, methods, and current evaluation metrics for English objective questions, covering both text-based and multi-modal domains. It also evaluates existing AI models and benchmarks and discusses potential future research directions.",
}
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<abstract>The distractor generation task focuses on generating incorrect but plausible options for objective questions such as fill-in-the-blank and multiple-choice questions. This task is widely utilized in educational settings across various domains and subjects. The effectiveness of these questions in assessments relies on the quality of the distractors, as they challenge examinees to select the correct answer from a set of misleading options. The evolution of artificial intelligence (AI) has transitioned the task from traditional methods to the use of neural networks and pre-trained language models. This shift has established new benchmarks and expanded the use of advanced deep learning methods in generating distractors. This survey explores distractor generation tasks, datasets, methods, and current evaluation metrics for English objective questions, covering both text-based and multi-modal domains. It also evaluates existing AI models and benchmarks and discusses potential future research directions.</abstract>
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%0 Conference Proceedings
%T Distractor Generation in Multiple-Choice Tasks: A Survey of Methods, Datasets, and Evaluation
%A Alhazmi, Elaf
%A Sheng, Quan
%A Zhang, Wei Emma
%A Zaib, Munazza
%A Alhazmi, Ahoud
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F alhazmi-etal-2024-distractor
%X The distractor generation task focuses on generating incorrect but plausible options for objective questions such as fill-in-the-blank and multiple-choice questions. This task is widely utilized in educational settings across various domains and subjects. The effectiveness of these questions in assessments relies on the quality of the distractors, as they challenge examinees to select the correct answer from a set of misleading options. The evolution of artificial intelligence (AI) has transitioned the task from traditional methods to the use of neural networks and pre-trained language models. This shift has established new benchmarks and expanded the use of advanced deep learning methods in generating distractors. This survey explores distractor generation tasks, datasets, methods, and current evaluation metrics for English objective questions, covering both text-based and multi-modal domains. It also evaluates existing AI models and benchmarks and discusses potential future research directions.
%U https://aclanthology.org/2024.emnlp-main.799
%P 14437-14458
Markdown (Informal)
[Distractor Generation in Multiple-Choice Tasks: A Survey of Methods, Datasets, and Evaluation](https://aclanthology.org/2024.emnlp-main.799) (Alhazmi et al., EMNLP 2024)
ACL
- Elaf Alhazmi, Quan Sheng, Wei Emma Zhang, Munazza Zaib, and Ahoud Alhazmi. 2024. Distractor Generation in Multiple-Choice Tasks: A Survey of Methods, Datasets, and Evaluation. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 14437–14458, Miami, Florida, USA. Association for Computational Linguistics.