Comparison of Machine Learning Algorithms and Large Language Models for Product Categorization

This study explores the efficacy of traditional machine learning algorithms and Large Language Models (LLMs) in automating product categorization for online e-commerce platforms. By comparing these methodologies, we assess their performance in classifying a diverse range of product listings. Our findings indicate that for this context, LLMs offer similar performance in understanding and categorizing complex textual data to traditional machine learning techniques, suggesting that use of LLMs in this context may be unnecessary, and that the trade-off ultimately comes down to the operational costs and resource consumption of each model. This work contributes to the field by providing insights into the capabilities and limitations of current text categorization techniques in the context of rapidly expanding online marketplaces.