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Enhancing product categorization with retrieval augmented generation: a comparative study of architectures, techniques and strategies
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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
various scenarios. The paper investigates the enhancement of product categorization through
various RAG configurations such as NaiveRAG, AdvancedRAG, GraphRAG, and HybridRAG,
applied in both flat and hierarchical systems. 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 Multi-tired retrieval NaiveRAG AdvancedRAG GraphRAG HybridRAG