@inproceedings{zhang-etal-2025-statecloud,
title = "{S}tate{C}loud at Critical Questions Generation: Prompt Engineering for Critical Question Generation",
author = "Zhang, Jinghui and
Yang, Dongming and
Lin, Binghuai",
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-1.24/",
doi = "10.18653/v1/2025.argmining-1.24",
pages = "258--264",
ISBN = "979-8-89176-258-9",
abstract = "This paper presents StateCloud{'}s submission to the Critical Questions Generation (CQs-Gen) shared task at the Argument Mining Workshop 2025. To generate high-quality critical questions from argumentative texts, we propose a framework that combines prompt engineering with few-shot learning to effectively guide generative models. Additionally, we ensemble outputs from diverse large language models (LLMs) to enhance accuracy. Notably, our approach achieved 3rd place in the competition, demonstrating the viability of prompt engineering strategies for argumentative tasks."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zhang-etal-2025-statecloud">
<titleInfo>
<title>StateCloud at Critical Questions Generation: Prompt Engineering for Critical Question Generation</title>
</titleInfo>
<name type="personal">
<namePart type="given">Jinghui</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Dongming</namePart>
<namePart type="family">Yang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Binghuai</namePart>
<namePart type="family">Lin</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 12th Argument mining Workshop</title>
</titleInfo>
<name type="personal">
<namePart type="given">Elena</namePart>
<namePart type="family">Chistova</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Philipp</namePart>
<namePart type="family">Cimiano</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Shohreh</namePart>
<namePart type="family">Haddadan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Gabriella</namePart>
<namePart type="family">Lapesa</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ramon</namePart>
<namePart type="family">Ruiz-Dolz</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Vienna, Austria</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-258-9</identifier>
</relatedItem>
<abstract>This paper presents StateCloud’s submission to the Critical Questions Generation (CQs-Gen) shared task at the Argument Mining Workshop 2025. To generate high-quality critical questions from argumentative texts, we propose a framework that combines prompt engineering with few-shot learning to effectively guide generative models. Additionally, we ensemble outputs from diverse large language models (LLMs) to enhance accuracy. Notably, our approach achieved 3rd place in the competition, demonstrating the viability of prompt engineering strategies for argumentative tasks.</abstract>
<identifier type="citekey">zhang-etal-2025-statecloud</identifier>
<identifier type="doi">10.18653/v1/2025.argmining-1.24</identifier>
<location>
<url>https://aclanthology.org/2025.argmining-1.24/</url>
</location>
<part>
<date>2025-07</date>
<extent unit="page">
<start>258</start>
<end>264</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T StateCloud at Critical Questions Generation: Prompt Engineering for Critical Question Generation
%A Zhang, Jinghui
%A Yang, Dongming
%A Lin, Binghuai
%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 zhang-etal-2025-statecloud
%X This paper presents StateCloud’s submission to the Critical Questions Generation (CQs-Gen) shared task at the Argument Mining Workshop 2025. To generate high-quality critical questions from argumentative texts, we propose a framework that combines prompt engineering with few-shot learning to effectively guide generative models. Additionally, we ensemble outputs from diverse large language models (LLMs) to enhance accuracy. Notably, our approach achieved 3rd place in the competition, demonstrating the viability of prompt engineering strategies for argumentative tasks.
%R 10.18653/v1/2025.argmining-1.24
%U https://aclanthology.org/2025.argmining-1.24/
%U https://doi.org/10.18653/v1/2025.argmining-1.24
%P 258-264
Markdown (Informal)
[StateCloud at Critical Questions Generation: Prompt Engineering for Critical Question Generation](https://aclanthology.org/2025.argmining-1.24/) (Zhang et al., ArgMining 2025)
ACL