Current Evaluation Methods are a Bottleneck in Automatic Question Generation

Guher Gorgun; Okan Bulut

Current Evaluation Methods are a Bottleneck in Automatic Question Generation

Guher Gorgun, Okan Bulut

Proceedings of the 2024 AAAI Conference on Artificial Intelligence, PMLR 257:3-8, 2024.

Abstract

This study provides a comprehensive review of frequently used evaluation methods for assessing the quality of automatic question generation (AQG) systems based on computational linguistics techniques and large language models. As we present a comprehensive overview of the current state of evaluation methods, we discuss the advantages and limitations of each method. Furthermore, we elucidate the next steps for the full integration of automatic question generation systems in educational settings to achieve effective personalization and adaptation.

Cite this Paper

BibTeX


@InProceedings{pmlr-v257-gorgun24a,
  title = 	 {Current Evaluation Methods are a Bottleneck in Automatic Question Generation},
  author =       {Gorgun, Guher and Bulut, Okan},
  booktitle = 	 {Proceedings of the 2024 AAAI Conference on Artificial Intelligence},
  pages = 	 {3--8},
  year = 	 {2024},
  editor = 	 {Ananda, Muktha and Malick, Debshila Basu and Burstein, Jill and Liu, Lydia T. and Liu, Zitao and Sharpnack, James and Wang, Zichao and Wang, Serena},
  volume = 	 {257},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {26--27 Feb},
  publisher =    {PMLR},
  pdf = 	 {https://raw.githubusercontent.com/mlresearch/v257/main/assets/gorgun24a/gorgun24a.pdf},
  url = 	 {https://proceedings.mlr.press/v257/gorgun24a.html},
  abstract = 	 {This study provides a comprehensive review of frequently used evaluation methods for assessing the quality of automatic question generation (AQG) systems based on computational linguistics techniques and large language models. As we present a comprehensive overview of the current state of evaluation methods, we discuss the advantages and limitations of each method. Furthermore, we elucidate the next steps for the full integration of automatic question generation systems in educational settings to achieve effective personalization and adaptation.}
}

Endnote

%0 Conference Paper
%T Current Evaluation Methods are a Bottleneck in Automatic Question Generation
%A Guher Gorgun
%A Okan Bulut
%B Proceedings of the 2024 AAAI Conference on Artificial Intelligence
%C Proceedings of Machine Learning Research
%D 2024
%E Muktha Ananda
%E Debshila Basu Malick
%E Jill Burstein
%E Lydia T. Liu
%E Zitao Liu
%E James Sharpnack
%E Zichao Wang
%E Serena Wang	
%F pmlr-v257-gorgun24a
%I PMLR
%P 3--8
%U https://proceedings.mlr.press/v257/gorgun24a.html
%V 257
%X This study provides a comprehensive review of frequently used evaluation methods for assessing the quality of automatic question generation (AQG) systems based on computational linguistics techniques and large language models. As we present a comprehensive overview of the current state of evaluation methods, we discuss the advantages and limitations of each method. Furthermore, we elucidate the next steps for the full integration of automatic question generation systems in educational settings to achieve effective personalization and adaptation.

APA


Gorgun, G. & Bulut, O.. (2024). Current Evaluation Methods are a Bottleneck in Automatic Question Generation. Proceedings of the 2024 AAAI Conference on Artificial Intelligence, in Proceedings of Machine Learning Research 257:3-8 Available from https://proceedings.mlr.press/v257/gorgun24a.html.

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