@inproceedings{duenas-etal-2023-youve,
title = "You{'}ve Got a Friend in ... a Language Model? A Comparison of Explanations of Multiple-Choice Items of Reading Comprehension between {C}hat{GPT} and Humans",
author = "Duenas, George and
Jimenez, Sergio and
Mateus Ferro, Geral",
editor = {Kochmar, Ekaterina and
Burstein, Jill and
Horbach, Andrea and
Laarmann-Quante, Ronja and
Madnani, Nitin and
Tack, Ana{\"\i}s and
Yaneva, Victoria and
Yuan, Zheng and
Zesch, Torsten},
booktitle = "Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.bea-1.30",
doi = "10.18653/v1/2023.bea-1.30",
pages = "372--381",
abstract = "Creating high-quality multiple-choice items requires careful attention to several factors, including ensuring that there is only one correct option, that options are independent of each other, that there is no overlap between options, and that each option is plausible. This attention is reflected in the explanations provided by human item-writers for each option. This study aimed to compare the creation of explanations of multiple-choice item options for reading comprehension by ChatGPT with those created by humans. We used two context-dependent multiple-choice item sets created based on EvidenceCentered Design. Results indicate that ChatGPT is capable of producing explanations with different type of information that are comparable to those created by humans. So that humans could benefit from additional information given to enhance their explanations. We conclude that ChatGPT ability to generate explanations for multiple-choice item options in reading comprehension tests is comparable to that of humans.",
}
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<abstract>Creating high-quality multiple-choice items requires careful attention to several factors, including ensuring that there is only one correct option, that options are independent of each other, that there is no overlap between options, and that each option is plausible. This attention is reflected in the explanations provided by human item-writers for each option. This study aimed to compare the creation of explanations of multiple-choice item options for reading comprehension by ChatGPT with those created by humans. We used two context-dependent multiple-choice item sets created based on EvidenceCentered Design. Results indicate that ChatGPT is capable of producing explanations with different type of information that are comparable to those created by humans. So that humans could benefit from additional information given to enhance their explanations. We conclude that ChatGPT ability to generate explanations for multiple-choice item options in reading comprehension tests is comparable to that of humans.</abstract>
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%0 Conference Proceedings
%T You’ve Got a Friend in ... a Language Model? A Comparison of Explanations of Multiple-Choice Items of Reading Comprehension between ChatGPT and Humans
%A Duenas, George
%A Jimenez, Sergio
%A Mateus Ferro, Geral
%Y Kochmar, Ekaterina
%Y Burstein, Jill
%Y Horbach, Andrea
%Y Laarmann-Quante, Ronja
%Y Madnani, Nitin
%Y Tack, Anaïs
%Y Yaneva, Victoria
%Y Yuan, Zheng
%Y Zesch, Torsten
%S Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F duenas-etal-2023-youve
%X Creating high-quality multiple-choice items requires careful attention to several factors, including ensuring that there is only one correct option, that options are independent of each other, that there is no overlap between options, and that each option is plausible. This attention is reflected in the explanations provided by human item-writers for each option. This study aimed to compare the creation of explanations of multiple-choice item options for reading comprehension by ChatGPT with those created by humans. We used two context-dependent multiple-choice item sets created based on EvidenceCentered Design. Results indicate that ChatGPT is capable of producing explanations with different type of information that are comparable to those created by humans. So that humans could benefit from additional information given to enhance their explanations. We conclude that ChatGPT ability to generate explanations for multiple-choice item options in reading comprehension tests is comparable to that of humans.
%R 10.18653/v1/2023.bea-1.30
%U https://aclanthology.org/2023.bea-1.30
%U https://doi.org/10.18653/v1/2023.bea-1.30
%P 372-381
Markdown (Informal)
[You’ve Got a Friend in ... a Language Model? A Comparison of Explanations of Multiple-Choice Items of Reading Comprehension between ChatGPT and Humans](https://aclanthology.org/2023.bea-1.30) (Duenas et al., BEA 2023)
ACL