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.

翻译：电子商务平台需要以属性-值对形式的结构化产品数据，以支持分面产品搜索或基于属性的产品比较等功能。然而，供应商通常提供非结构化的产品描述，因此需要从这些文本中抽取属性-值对。基于BERT的抽取方法需要大量特定任务的训练数据，并且难以处理未见过的属性值。本文探索使用大型语言模型作为一种更高效利用训练数据且更鲁棒的替代方案。我们为零样本和少样本场景提出了提示模板，并比较了基于文本和基于JSON的目标模式表示。实验表明，GPT-4在使用详细属性描述和示例的情况下取得了最高的平均F1分数（85%）。Llama-3-70B表现接近，提供了一个具有竞争力的开源替代方案。GPT-4的F1分数比最佳PLM基线高出5%。对GPT-3.5进行微调可将其性能提升至GPT-4的水平，但会降低模型对未见属性值的泛化能力。