Modelling Large-Scale Group Decision-Making Through Grouping with Large Language Models González Quesada, Juan Carlos Trillo Vílchez, José Ramón Porcel Gallego, Carlos Gustavo Pérez Gálvez, Ignacio Javier Cabrerizo Lorite, Francisco Javier large language model large-scale method group decision-making method 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. 2025-10-07T08:29:14Z 2025-10-07T08:29:14Z 2025-08-25 journal article González-Quesada, J.C.; Trillo, J.R.; Porcel, C.; Pérez, I.J.; Cabrerizo, F.J. Modelling Large-Scale Group Decision-Making Through Grouping with Large Language Models. Future Internet 2025, 17, 381. https://doi.org/10.3390/fi17090381 https://hdl.handle.net/10481/106856 10.3390/fi17090381 eng http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional MDPI