Detection of Vehicle Purchases in Various Invoices using Large-Scale Language Models - Heisler, Gabriel V.; Beckhauser, William J.; Santos, Vitória S.; Fileto, Renato (Anais da Escola Regional de Aprendizado de Máquina e Inteligência Artificial da Região Sul (ERAMIA-RS); 2025: Anais da I Escola Regional de Aprendizado de Máquina e Inteligência Artificial da Região Sul, 2025)
The precise identification of products in invoice item descriptions is crucial for applications such as auditing and fraud detection. However, the free-text descriptions of these items are often short, diverse, and inconsistent with other data fields. It makes product identification challenging and compromises the performance of existing solutions. In this work we compare the performance and computational costs of language models (LLMs) in the task of detecting vehicle descriptions in an invoice dataset from public purchases. Experimental results reveal that some state-of-the-art LLMs can reach high performance, even in noisy scenarios, and lightweight models yield competitive performance with lower computational costs.