In e-commerce, accurately extracting product attribute values from multimodal
data is crucial for improving user experience and operational efficiency of
retailers. However, previous approaches to multimodal attribute value
extraction often struggle with implicit attribute values embedded in images or
text, rely heavily on extensive labeled data, and can easily confuse similar
attribute values. To address these issues, we introduce EIVEN, a data- and
parameter-efficient generative framework that pioneers the use of multimodal
LLM for implicit attribute value extraction. EIVEN leverages the rich inherent
knowledge of a pre-trained LLM and vision encoder to reduce reliance on labeled
data. We also introduce a novel Learning-by-Comparison technique to reduce
model confusion by enforcing attribute value comparison and difference
identification. Additionally, we construct initial open-source datasets for
multimodal implicit attribute value extraction. Our extensive experiments
reveal that EIVEN significantly outperforms existing methods in extracting
implicit attribute values while requiring less labeled data.

在电子商务中，准确从多模态数据中提取产品属性值对于提高用户体验和零售商的运营效率至关重要。为了解决这些问题，我们引入了 EIVEN，一个数据和参数高效的生成框架，首次使用多模态 LLM 来进行隐含属性值提取，并通过对比学习技术降低模型混淆，构建了多模态隐含属性值提取的初始开源数据集。经过广泛实验，我们发现 EIVEN 在提取隐含属性值方面明显优于现有方法，并且需要更少的标记数据。