EIVEN: Efficient Implicit Attribute Value Extraction using Multimodal LLM

Henry Zou, Gavin Yu, Ziwei Fan, Dan Bu, Han Liu, Peng Dai, Dongmei Jia, Cornelia Caragea

Abstract
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.
Anthology ID:
2024.naacl-industry.40
Volume:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 6: Industry Track)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Yi Yang, Aida Davani, Avi Sil, Anoop Kumar
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
453–463
Language:
URL:
https://aclanthology.org/2024.naacl-industry.40
DOI:
Bibkey:
Cite (ACL):
Henry Zou, Gavin Yu, Ziwei Fan, Dan Bu, Han Liu, Peng Dai, Dongmei Jia, and Cornelia Caragea. 2024. EIVEN: Efficient Implicit Attribute Value Extraction using Multimodal LLM. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 6: Industry Track), pages 453–463, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
EIVEN: Efficient Implicit Attribute Value Extraction using Multimodal LLM (Zou et al., NAACL 2024)
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