ARG2ST at CQs-Gen 2025: Critical Questions Generation through LLMs and Usefulness-based Selection
Year: 2025
Type: conference-paper
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.1
Authors: Alan Ramponi, Gaudenzia Genoni, Sara Tonelli
Institutions: Fondazione Bruno Kessler, University of Trento
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4412889283