Overview of the Critical Questions Generation Shared Task

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. 1

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Publication Details

Published
2025-01-01
DOI
https://doi.org/10.18653/v1/2025.argmining-1.23
Primary Topic
Educational Games and Gamification
Type
article
Field-Weighted Citation Impact
0.00

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article

Overview of the Critical Questions Generation Shared Task

Serena Villata, Blanca Calvo Figueras, Elena Cabrio, Rodrigo Agerri et al.
Educational Games and Gamification
article

Overview of the Critical Questions Generation Shared Task

Serena Villata, Blanca Calvo Figueras, Elena Cabrio, Rodrigo Agerri, Jaione Bengoetxea, Maite Heredia
article en

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. 1

European Commission, Agence Nationale de la Recherche, Euskal Herriko Unibertsitatea, Agencia Estatal de Investigación
Openalex Percentile: Top 79%
Educational Games and Gamification
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