@inproceedings{nguyen-etal-2025-serval,
    title = "{SERVAL}: Surprisingly Effective Zero-Shot Visual Document Retrieval Powered by Large Vision and Language Models",
    author = "Nguyen, Thong  and
      Lei, Yibin  and
      Ju, Jia-Huei  and
      Yates, Andrew",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1568/",
    pages = "30795--30810",
    ISBN = "979-8-89176-332-6",
    abstract = "Visual Document Retrieval (VDR) typically operates as text-to-image retrieval using specialized bi-encoders trained to directly embed document images. We revisit a zero-shot generate-and-encode pipeline: a vision{--}language model first produces a detailed textual description of each document image, which is then embedded by a standard text encoder. On the ViDoRe-v2 benchmark, the method reaches 63.4{\%} nDCG@5, surpassing the strongest specialised multi-vector visual document encoder, and it scales similarly on MIRACL-VISION with broader multilingual coverage. Analysis shows that modern vision{--}language models capture complex textual and visual cues with sufficient granularity to act as a reusable semantic proxy. By off-loading modality alignment to pretrained vision{--}language models, our approach removes the need for computationally intensive text-image contrastive training and establishes a strong zero-shot baseline for future VDR systems."
}