Comparing Prompt-based LLMs, Fine-Tuning, and Classical Models for Legal Text Classification in Portuguese - Santos, Willgnner Ferreira; Galvão Filho, Arlindo Rodrigues; Oliveira, Sávio Salvarino Teles de; Presa, João Paulo Cavalcante (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), 2025)
This study compares fine-tuned Transformer models, LLMs with prompting, and traditional models in Portuguese legal text classification. A new Portuguese dataset was introduced for empirical evaluation. Four input formats were tested in prompting, complete text, summaries, centroids, and descriptions. Summaries achieved effective performance with reduced token usage. Similarly, KNN proved competitive in resource-limited scenarios. Input format and model capacity affected performance. The study discusses trade-offs between efficiency and interpretability. Guidelines are proposed for choosing effective strategies in legal NLP tasks.