Leveraging Large Language Models for Author Name Disambiguation in Portuguese Contexts - Santos, Samuel G. dos; Diniz, Vitória M.; Gusella, Bartolomeu S.; Rodrigues, Natan de S. (Anais da Escola Regional de Informática de Goiás (ERI-GO); 2025: Anais da XIII Escola Regional de Informática de GoiásDO - 10.5753/erigo.2025.16983, 2025)
Author Name Disambiguation (AND) is a fundamental task in digital libraries and repositories, especially in Portuguese contexts where metadata often lacks persistent identifiers and shows frequent inconsistencies. This study presents an unsupervised approach that integrates Large Language Models (LLMs) for semantic normalization, MiniLM embeddings for similarity modeling, and automatic clustering followed by a post-merging heuristic. Experiments on the BDBComp dataset show competitive cluster cohesion (K = 0.907) compared to baselines, while the pairwise F1 score (pF1 = 0.448) highlights the difficulty posed by highly ambiguous surnames. Future work will refine LLM summaries and clustering thresholds to improve accuracy while preserving cluster consistency.