When Small Wins Big: Classification Tasks Where Compact Models Outperform Original GPT-4[Quand les petits gagnent les grands : tâches de classification pour lesquelles les modèles compacts sont plus performants que les modèles originaux GPT-4]

This paper evaluates Large Language Models (LLMs) on financial text classification, comparing GPT-4 (1.76 trillion parameters) against FinBERT (110 million parameters) and FinDROBERTA (82.1 million parameters). We achieved a classification task on short financial sentences involving multiple divergent insights with both textual and numerical data. We developed a market-based large dataset that enabled us to fine-tune the models on a real-world ground truth. Utilizing a marketbased dataset for fine-tuning on extensive datasets, we achieved significant enhancements with Fin-BERT and FinDROBERTA over GPT-4. However, the use of a bagging majority classifier did not yield performance improvements, demonstrating that the principles of Condorcet's jury Theorem do not apply, suggesting a lack of independence among the models and similar behavior patterns across all evaluated models. Our results indicate that for complex sentiment classification, compact models match larger models, even with fine-tuning. The fine-tuned models are made available as opensource for additional research.