Open-World Text Classification by Combining Weak Models and Large Language Models - Zitei, Daniel P.; Sakiyama, Kenzo M.; Marcacini, Ricardo M. (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2024: Anais do XXI Encontro Nacional de Inteligência Artificial e Computacional, 2024)
Open-world classification presents significant challenges in text classification. Large Language Models (LLMs) have made advances in addressing these challenges by leveraging their contextual understanding to improve classification accuracy without requiring knowledge of the entire label space. However, current LLM-based approaches still encounter limitations, such as context size constraints and computational scalability. To overcome these issues, we draw inspiration from strategies like Retrieval-Augmented Generation (RAG) to adapt LLMs more effectively to open-world classification problems. Our proposed approach combines a Weak Classifier (WM) Model with LLMs. In this case, we use the WM to filter and identify the top-k most probable classes, and then use a LLM to make the final classification decision.