mlsanityData to 7 Oct 2026

Adapting Vision-Language Models for E-commerce Understanding at Scale

arXiv 2602.11733 ↗First posted 12 Feb 2026cs.CV cs.AIcited by 0

On the map: Multimodal LLMs / vision-language models → Document understanding and multimodal RAG

Abstract (arXiv)

E-commerce product understanding demands by nature, strong multimodal comprehension from text, images, and structured attributes. General-purpose Vision-Language Models (VLMs) enable generalizable multimodal latent modelling, yet there is no documented, well-known strategy for adapting them to the attribute-centric, multi-image, and noisy nature of e-commerce data, without sacrificing general performance. In this work, we show through a large-scale experimental study, how targeted adaptation of general VLMs can substantially improve e-commerce performance while preserving broad multimodal capabilities. Furthermore, we propose a novel extensive evaluation suite covering deep product understanding, strict instruction following, and dynamic attribute extraction.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Document understanding and multimodal RAG, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 34 of its 54 references that are in the map. First posted on arXiv 12 Feb 2026.

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34 of 54 references.

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  1. 2023PUMGPT: A Large Vision-Language Model for Product Understanding18 Aug

Other references · 33

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21 more
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