EIVEN: Efficient Implicit Attribute Value Extraction using Multimodal LLM
This addresses the challenge of implicit attribute extraction for e-commerce retailers to enhance user experience and efficiency, representing a novel method for a known bottleneck.
The paper tackles the problem of extracting implicit product attribute values from multimodal e-commerce data by introducing EIVEN, a framework that uses multimodal LLMs and a Learning-by-Comparison technique, achieving significant performance improvements with less labeled data.
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.