ExtractGPT: Exploring the Potential of Large Language Models for Product Attribute Value Extraction

TL;DR

This paper investigates the use of large language models like GPT-4 and Llama-3-70B for extracting structured product attribute-value pairs from unstructured e-commerce descriptions, demonstrating high accuracy with minimal training.

Contribution

It introduces prompt-based methods for attribute extraction using LLMs, showing they outperform traditional BERT-based approaches in data efficiency and robustness.

Findings

GPT-4 achieves 85% F1-score with detailed prompts

Llama-3-70B performs competitively as an open-source alternative

Fine-tuning GPT-3.5 improves performance but reduces generalization

Abstract

E-commerce platforms require structured product data in the form of attribute-value pairs to offer features such as faceted product search or attribute-based product comparison. However, vendors often provide unstructured product descriptions, necessitating the extraction of attribute-value pairs from these texts. BERT-based extraction methods require large amounts of task-specific training data and struggle with unseen attribute values. This paper explores using large language models (LLMs) as a more training-data efficient and robust alternative. We propose prompt templates for zero-shot and few-shot scenarios, comparing textual and JSON-based target schema representations. Our experiments show that GPT-4 achieves the highest average F1-score of 85% using detailed attribute descriptions and demonstrations. Llama-3-70B performs nearly as well, offering a competitive open-source alternative. GPT-4 surpasses the best PLM baseline by 5% in F1-score. Fine-tuning GPT-3.5 increases the performance to the level of GPT-4 but reduces the model's ability to generalize to unseen attribute values.