@inproceedings{gunther-etal-2025-jina,
    title = "jina-embeddings-v4: Universal Embeddings for Multimodal Multilingual Retrieval",
    author = {G{\"u}nther, Michael  and
      Sturua, Saba  and
      Akram, Mohammad Kalim  and
      Mohr, Isabelle  and
      Ungureanu, Andrei  and
      Wang, Bo  and
      Eslami, Sedigheh  and
      Martens, Scott  and
      Werk, Maximilian  and
      Wang, Nan  and
      Xiao, Han},
    editor = "Adelani, David Ifeoluwa  and
      Arnett, Catherine  and
      Ataman, Duygu  and
      Chang, Tyler A.  and
      Gonen, Hila  and
      Raja, Rahul  and
      Schmidt, Fabian  and
      Stap, David  and
      Wang, Jiayi",
    booktitle = "Proceedings of the 5th Workshop on Multilingual Representation Learning (MRL 2025)",
    month = nov,
    year = "2025",
    address = "Suzhuo, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.mrl-main.36/",
    pages = "531--550",
    ISBN = "979-8-89176-345-6",
    abstract = "We introduce jina-embeddings-v4, a 3.8 billion parameter embedding model that unifies text and image representations, with a novel architecture supporting both single-vector and multi-vector embeddings. It achieves high performance on both single-modal and cross-modal retrieval tasks, and is particularly strong in processing visually rich content such as tables, charts, diagrams, and mixed-media formats that incorporate both image and textual information. We also introduce JVDR, a novel benchmark for visually rich document retrieval that includes more diverse materials and query types than previous efforts. We use JVDR to show that jina-embeddings-v4 greatly improves on state-of-the-art performance for these kinds of tasks."
}