Product offers on e-commerce websites often consist of a textual product
title and a textual product description. In order to provide features such as
faceted product filtering or content-based product recommendation, the websites
need to extract attribute-value pairs from the unstructured product
descriptions. This paper explores the potential of using large language models
(LLMs), such as OpenAI's GPT-3.5 and GPT-4, to extract and normalize attribute
values from product titles and product descriptions. For our experiments, we
introduce the WDC Product Attribute-Value Extraction (WDC PAVE) dataset. WDC
PAVE consists of product offers from 87 websites that provide schema.org
annotations. The offers belong to five different categories, each featuring a
specific set of attributes. The dataset provides manually verified
attribute-value pairs in two forms: (i) directly extracted values and (ii)
normalized attribute values. The normalization of the attribute values requires
systems to perform the following types of operations: name expansion,
generalization, unit of measurement normalization, and string wrangling. Our
experiments demonstrate that GPT-4 outperforms PLM-based extraction methods by
10%, achieving an F1-Score of 91%. For the extraction and normalization of
product attribute values, GPT-4 achieves a similar performance to the
extraction scenario, while being particularly strong at string wrangling and
name expansion.

本文探讨使用大型语言模型（如 OpenAI 的 GPT-3.5 和 GPT-4）从产品标题和产品描述中提取和规范化属性值的潜力，并介绍了 WDC PAVE 数据集，通过实验展示 GPT-4 在产品属性值的提取和规范化上相对于 PLM 的方法获得了 91% 的 F1 得分，并且在字符串处理和名称扩展方面表现出色。