@misc{10481/106856,
year = {2025},
month = {8},
url = {https://hdl.handle.net/10481/106856},
abstract = {The growing ubiquity of digital platforms has enabled unprecedented participation in largescale group decision-making processes. Nevertheless, integrating subjective linguistically
expressed opinions into structured decision protocols remains a significant challenge. This
paper presents a novel framework that leverages the semantic and affective capabilities of
large language models to support large-scale group decision-making tasks by extracting
and quantifying experts’ communicative traits—specifically clarity and trust—from natural
language input. Based on these traits, participants are clustered into behavioural groups,
each of which is assigned a representative preference structure and a weight reflecting its
internal cohesion and communicative quality. A sentiment-informed consensus mechanism
then aggregates these group-level matrices to form a collective decision outcome. The
method enhances scalability and interpretability while preserving the richness of human
expression. The results suggest that incorporating behavioural dimensions into largescale group decision-making via large language models fosters fairer, more balanced,
and semantically grounded decisions, offering a promising avenue for next-generation
decision-support systems.},
organization = {MICIU/AEI/10.13039/501100011033 - ERDF/EU (PID2022-139297OB-I00)},
organization = {Regional Ministry of University, Research and Innovation and by the European
Union - Andalusia ERDF Program 2021-2027 (C-ING-165-UGR23)},
publisher = {MDPI},
keywords = {large language model},
keywords = {large-scale method},
keywords = {group decision-making method},
title = {Modelling Large-Scale Group Decision-Making Through Grouping with Large Language Models},
doi = {10.3390/fi17090381},
author = {González Quesada, Juan Carlos and Trillo Vílchez, José Ramón and Porcel Gallego, Carlos Gustavo and Pérez Gálvez, Ignacio Javier and Cabrerizo Lorite, Francisco Javier},
}