Structuring Information from Initial Petitions Using LLMs: A Study in Brazilian Courts of Justice - Esashika, Rhedson; Figueiredo, Carlos M. S.; Melo, Tiago de (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), 2025)
This study proposes the use of Large Language Models to extract structured information from Brazilian legal petitions. We guided four models (from the Gemini and Gemma3 families) to generate structured JSON outputs, providing a comparative performance benchmark for this novel task. The main contribution is a validated workflow, where results show Gemini models significantly outperform Gemma in capturing complex semantic data. This work establishes a robust evaluation and an important methodological baseline for a critical task in Brazilian legal Natural Language Processing (NLP).