This research demonstrates how data augmentation using ChatGPT-4o-mini can significantly improve large language models' ability to detect food hazards and products.

• Training RoBERTa-base and Flan-T5-base models with augmented data improved key performance metrics including recall, F1 score, precision, and accuracy
• The approach addresses critical needs in food safety monitoring and public health protection
• Results show augmentation is an effective strategy to enhance model performance with limited initial training data

For the gastronomy sector, this research offers more reliable AI tools for identifying potential food hazards, ensuring safer food products, and streamlining quality control processes.

Data Augmentation to Improve Large Language Models in Food Hazard and Product Detection