@inproceedings{ramponi-etal-2025-arg2st,
title = "{ARG}2{ST} at {CQ}s-Gen 2025: Critical Questions Generation through {LLM}s and Usefulness-based Selection",
author = "Ramponi, Alan and
Genoni, Gaudenzia and
Tonelli, Sara",
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-1.29/",
doi = "10.18653/v1/2025.argmining-1.29",
pages = "301--313",
ISBN = "979-8-89176-258-9",
abstract = "Critical questions (CQs) generation for argumentative texts is a key task to promote critical thinking and counter misinformation. In this paper, we present a two-step approach for CQs generation that i) uses a large language model (LLM) for generating candidate CQs, and ii) leverages a fine-tuned classifier for ranking and selecting the top-k most useful CQs to present to the user. We show that such usefulness-based CQs selection consistently improves the performance over the standard application of LLMs. Our system was designed in the context of a shared task on CQs generation hosted at the 12th Workshop on Argument Mining, and represents a viable approach to encourage future developments on CQs generation. Our code is made available to the research community."
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<abstract>Critical questions (CQs) generation for argumentative texts is a key task to promote critical thinking and counter misinformation. In this paper, we present a two-step approach for CQs generation that i) uses a large language model (LLM) for generating candidate CQs, and ii) leverages a fine-tuned classifier for ranking and selecting the top-k most useful CQs to present to the user. We show that such usefulness-based CQs selection consistently improves the performance over the standard application of LLMs. Our system was designed in the context of a shared task on CQs generation hosted at the 12th Workshop on Argument Mining, and represents a viable approach to encourage future developments on CQs generation. Our code is made available to the research community.</abstract>
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%0 Conference Proceedings
%T ARG2ST at CQs-Gen 2025: Critical Questions Generation through LLMs and Usefulness-based Selection
%A Ramponi, Alan
%A Genoni, Gaudenzia
%A Tonelli, Sara
%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 ramponi-etal-2025-arg2st
%X Critical questions (CQs) generation for argumentative texts is a key task to promote critical thinking and counter misinformation. In this paper, we present a two-step approach for CQs generation that i) uses a large language model (LLM) for generating candidate CQs, and ii) leverages a fine-tuned classifier for ranking and selecting the top-k most useful CQs to present to the user. We show that such usefulness-based CQs selection consistently improves the performance over the standard application of LLMs. Our system was designed in the context of a shared task on CQs generation hosted at the 12th Workshop on Argument Mining, and represents a viable approach to encourage future developments on CQs generation. Our code is made available to the research community.
%R 10.18653/v1/2025.argmining-1.29
%U https://aclanthology.org/2025.argmining-1.29/
%U https://doi.org/10.18653/v1/2025.argmining-1.29
%P 301-313
Markdown (Informal)
[ARG2ST at CQs-Gen 2025: Critical Questions Generation through LLMs and Usefulness-based Selection](https://aclanthology.org/2025.argmining-1.29/) (Ramponi et al., ArgMining 2025)
ACL