United States

Accurate product categorization in e-commerce is critical for delivering a satisfactory online shopping experience to customers. With the vast number of available products and the numerous potential categories, it becomes crucial to develop a classification system capable of assigning products to their correct categories with high accuracy. We present a dual-expert classification system that utilizes the power of large language models (LLMs). This framework integrates domain-specific knowledge and pre-trained LLM’s general knowledge through effective model fine-tuning and prompting techniques. First, the fine-tuned domain-specific expert recommends top K candidate categories for a given input product. Then, the more general LLM-based expert, through prompting techniques, analyzes the nuanced differences between candidate categories and selects the most suitable target category. We introduce a new in-context learning approach that utilizes LLM self-generated summarization to provide clearer instructions and enhance its performance. Experiments on e-commerce datasets demonstrate the effectiveness of our LLM-based Dual-Expert classification system.

EMNLP 2024

E-Commerce Product Categorization with LLM-based Dual-Expert Classification Paradigm

dual-expert

categoriztion

generative artificial intelligence

genai

large language model

e-commerce

classification

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Vocabulary Transfer for Biomedical Texts: Add Tokens if You Can Not Add Data

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