DayDreamer at CQs-Gen 2025: Generating Critical Questions through Argument Scheme Completion

Wendi Zhou, Ameer Saadat-Yazdi, Nadin Kökciyan


Abstract
Critical questions are essential resources to provoke critical thinking when encountering an argumentative text. We present our system for the Critical Questions Generation (CQs-Gen) Shared Task at ArgMining 2025. Our approach leverages large language models (LLMs) with chain-of-thought prompting to generate critical questions guided by Walton’s argumentation schemes. For each input intervention, we conversationally prompt LLMs to instantiate the corresponding argument scheme template to first obtain structured arguments, and then generate relevant critical questions. Following this, we rank all the available critical questions by prompting LLMs to select the top 3 most helpful questions based on the original intervention text. This combination of structured argumentation theory and step-by-step reasoning enables the generation of contextually relevant and diverse critical questions. Our pipeline achieves competitive performance in the final test set, showing its potential to foster critical thinking given argumentative text and detect missing or uninformed claims.
Anthology ID:
2025.argmining-1.27
Volume:
Proceedings of the 12th Argument mining Workshop
Month:
July
Year:
2025
Address:
Vienna, Austria
Editors:
Elena Chistova, Philipp Cimiano, Shohreh Haddadan, Gabriella Lapesa, Ramon Ruiz-Dolz
Venues:
ArgMining | WS
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Publisher:
Association for Computational Linguistics
Note:
Pages:
289–294
Language:
URL:
https://preview.aclanthology.org/landing_page/2025.argmining-1.27/
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Cite (ACL):
Wendi Zhou, Ameer Saadat-Yazdi, and Nadin Kökciyan. 2025. DayDreamer at CQs-Gen 2025: Generating Critical Questions through Argument Scheme Completion. In Proceedings of the 12th Argument mining Workshop, pages 289–294, Vienna, Austria. Association for Computational Linguistics.
Cite (Informal):
DayDreamer at CQs-Gen 2025: Generating Critical Questions through Argument Scheme Completion (Zhou et al., ArgMining 2025)
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https://preview.aclanthology.org/landing_page/2025.argmining-1.27.pdf