Brazilian Consumer Protection Code: a methodology for a dataset to Question-Answer (QA) Models - Athaydes, Aline; Bulcao, Lucas Krug; Sacramento, Caio; Mane, Babacar; Claro, Daniela Barreiro; Souza, Marlo; Pita, Robespierre (Anais do Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana (STIL); 2024: Anais do XV Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana, 2024)
This work introduces the methodology for building a new dataset based onthe Brazilian Consumer Protection Code (CDC), focusing on question-answer (QA) models. The dataset collected legal data, including CDC articles, legal summaries, and court rulings from the Superior Court of Justice (STJ). Automated data extraction techniques using Python were employed, and advanced language models such as Llama3-8b-8192, Gemma2-9b-it, and GPT-4o-mini were used to generate question-answer (QA) structures. This work presents our methodology for creating such a dataset to be used by language models for training in the legal domain, particularly in the CDC domain.