EIVEN: Efficient Implicit Attribute Value Extraction using Multimodal
  LLM
Henry Peng Zou Gavin Heqing Yu Ziwei Fan Dan Bu Han Liu Peng Dai Dongmei Jia Cornelia Caragea
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.