@inproceedings{nulli-etal-2026-adapting,
    title = "Adapting Vision-Language Models for {E}-commerce Understanding at Scale",
    author = "Nulli, Matteo  and
      Vladimir, Orshulevich  and
      Bazazo, Tala  and
      Herold, Christian  and
      Kozielski, Michael  and
      Mazur, Marcin  and
      Tuzel, Szymon  and
      Snoek, Cees G. M.  and
      Hashemi, Seyyed Hadi  and
      Javed, Omar  and
      Versley, Yannick  and
      Khadivi, Shahram",
    editor = {Matusevych, Yevgen  and
      Eryi{\u{g}}it, G{\"u}l{\c{s}}en  and
      Aletras, Nikolaos},
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 5: Industry Track)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-industry.38/",
    pages = "496--512",
    ISBN = "979-8-89176-384-5",
    abstract = "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."
}