LLMs for product classification in e-commerce: A zero-shot comparative study of GPT and claude models

Abstract

In the rapidly evolving e-commerce landscape, efficient and accurate product classification is essential for enhancing customer experience and streamlining operations. Traditional product classification methods, which depend heavily on labeled data and manual effort, struggle with scalability and adaptability to diverse product categories. This study explores the transformative potential of large language models (LLMs) for zero-shot product classification in e-commerce, addressing the challenge of automating product categorization without prior labeled training data. We evaluate the performance of four state-of-the-art LLMs — GPT-4o, GPT-4o mini, Claude 3.5 Sonnet, and Claude 3.5 Haiku — on a diverse dataset of 248 product categories, each containing 20 samples, structured into 8 subsets. Each model performs zero-shot classification, assigning products to predefined categories without prior exposure. Our findings reveal significant variations in classification accuracy across models, with certain LLMs demonstrating superior scalability and adaptability for real-world e-commerce applications. Based on these insights, we developed an API software to integrate the top-performing models into e-commerce systems, enhancing automation and efficiency. This study underscores the transformative role of LLMs in revolutionizing e-commerce workflows and recommends their adoption for scalable, intelligent product classification.