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

Food safety is a critical global concern, with millions of people affected by foodborne illnesses each year [1], [2], [3]. Rapid and accurate detection of food hazards is essential to prevent health risks and ensure consumer protection. However, the vast amount of textual data available in scientific literature, reports, and regulatory documents makes it challenging to efficiently classify and assess food-related risks [4], [5]. With the rapid advancement of Artificial Intelligence (AI) [6], [7], particularly in the field of Natural Language Processing (NLP) a specialized subfield of AI dedicated to understanding, interpreting, and processing human language, we are now able to extract valuable insights from textual data with unprecedented efficiency [8], [9]. NLP has revolutionized automation across a wide range of applications, including text translation, grammar correction, information classification, text summarization, and question-answering [10], [11], [12], [13], [14].