An Ultra-Fine Entity Typing Method Based on Ensemble of Large Language Models - Ferreira, Carlos Alberto F.; Ferreira, Anderson A. (Anais do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe); 2024: Anais do XII Symposium on Knowledge Discovery, Mining and Learning, 2024)
Ultra-fine Entity Typing (UFET) is an evolution of Named Entity Recognition (NER) that proposes the classification of entities at an ultra-fine level, based on a set of free-form phrases that adequately and comprehensively describe them. Increasing the number of labels while maintaining performance remains a challenge, and most current models are capable of classifying entities into a limited set of types. The proven efficiency of Large Language Models (LLMs) in improving performance in Natural Language Processing tasks presents an opportunity to enhance UFET results. In this sense, this paper proposes an ensemble method of fine-tuned public LLMs aiming to maximize the efficiency of entity classification at the ultra-fine level and expand the set of labels. Experiments show the effectiveness of the proposed method, which outperformed baselines metrics in almost all scenarios and improved the set of types.