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Dynamic integration of taxonomy-specific knowledge graphs with Large Language Models for hierarchical product categorization in e-commerce
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Resumo(s)
This research explored techniques to improve Large Language Models performance for Hi erarchical Product Classification (HPC), including optimized fine-tuning, optimal prompting
techniques, taxonomy-specific Knowledge Graphs, leveraging Retrieval-Augmented Genera tion, and implementing LLM-based Entity Matching. Tested on benchmark datasets Icecat and
WDC-222, these methods significantly enhanced LLMs’ ability to solve HPC tasks across var ious scenarios. Results achieved a hierarchical F1-score (hF) of 0.921, surpassing traditional
DL benchmarks (0.85 hF). While not outperforming proprietary models like GPT, the proposed
approaches offer a cost-efficient and effective alternative for businesses, demonstrating strong
performance without reliance on expensive LLM solutions.
Descrição
Palavras-chave
Large Language Models Hierarchical classification E-Commerce In-context learning Fine tuning Prompt engineering Knowledge graphs Retrieval Augmented Generation Entity matching