@inproceedings{calvo-figueras-etal-2025-overview,
title = "Overview of the Critical Questions Generation Shared Task",
author = "Calvo Figueras, Blanca and
Bengoetxea, Jaione and
Heredia, Maite and
Sviridova, Ekaterina and
Cabrio, Elena and
Villata, Serena and
Agerri, Rodrigo",
editor = "Chistova, Elena and
Cimiano, Philipp and
Haddadan, Shohreh and
Lapesa, Gabriella and
Ruiz-Dolz, Ramon",
booktitle = "Proceedings of the 12th Argument mining Workshop",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.argmining-1.23/",
doi = "10.18653/v1/2025.argmining-1.23",
pages = "243--257",
ISBN = "979-8-89176-258-9",
abstract = "The proliferation of AI technologies has reinforced the importance of developing critical thinking skills. We propose leveraging Large Language Models (LLMs) to facilitate the generation of critical questions: inquiries designed to identify fallacious or inadequately constructed arguments. This paper presents an overview of the first shared task on Critical Questions Generation (CQs-Gen). Thirteen teams investigated various methodologies for generating questions that critically assess arguments within the provided texts. The highest accuracy achieved was 67.6, indicating substantial room for improvement in this task. Moreover, three of the four top-performing teams incorporated argumentation scheme annotations to enhance their systems. Finally, while most participants employed open-weight models, the two highest-ranking teams relied on proprietary LLMs."
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<abstract>The proliferation of AI technologies has reinforced the importance of developing critical thinking skills. We propose leveraging Large Language Models (LLMs) to facilitate the generation of critical questions: inquiries designed to identify fallacious or inadequately constructed arguments. This paper presents an overview of the first shared task on Critical Questions Generation (CQs-Gen). Thirteen teams investigated various methodologies for generating questions that critically assess arguments within the provided texts. The highest accuracy achieved was 67.6, indicating substantial room for improvement in this task. Moreover, three of the four top-performing teams incorporated argumentation scheme annotations to enhance their systems. Finally, while most participants employed open-weight models, the two highest-ranking teams relied on proprietary LLMs.</abstract>
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%0 Conference Proceedings
%T Overview of the Critical Questions Generation Shared Task
%A Calvo Figueras, Blanca
%A Bengoetxea, Jaione
%A Heredia, Maite
%A Sviridova, Ekaterina
%A Cabrio, Elena
%A Villata, Serena
%A Agerri, Rodrigo
%Y Chistova, Elena
%Y Cimiano, Philipp
%Y Haddadan, Shohreh
%Y Lapesa, Gabriella
%Y Ruiz-Dolz, Ramon
%S Proceedings of the 12th Argument mining Workshop
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-258-9
%F calvo-figueras-etal-2025-overview
%X The proliferation of AI technologies has reinforced the importance of developing critical thinking skills. We propose leveraging Large Language Models (LLMs) to facilitate the generation of critical questions: inquiries designed to identify fallacious or inadequately constructed arguments. This paper presents an overview of the first shared task on Critical Questions Generation (CQs-Gen). Thirteen teams investigated various methodologies for generating questions that critically assess arguments within the provided texts. The highest accuracy achieved was 67.6, indicating substantial room for improvement in this task. Moreover, three of the four top-performing teams incorporated argumentation scheme annotations to enhance their systems. Finally, while most participants employed open-weight models, the two highest-ranking teams relied on proprietary LLMs.
%R 10.18653/v1/2025.argmining-1.23
%U https://aclanthology.org/2025.argmining-1.23/
%U https://doi.org/10.18653/v1/2025.argmining-1.23
%P 243-257
Markdown (Informal)
[Overview of the Critical Questions Generation Shared Task](https://aclanthology.org/2025.argmining-1.23/) (Calvo Figueras et al., ArgMining 2025)
ACL
- Blanca Calvo Figueras, Jaione Bengoetxea, Maite Heredia, Ekaterina Sviridova, Elena Cabrio, Serena Villata, and Rodrigo Agerri. 2025. Overview of the Critical Questions Generation Shared Task. In Proceedings of the 12th Argument mining Workshop, pages 243–257, Vienna, Austria. Association for Computational Linguistics.