@inproceedings{masry-etal-2025-colmate,
    title = "{C}ol{M}ate: Contrastive Late Interaction and Masked Text for Multimodal Document Retrieval",
    author = "Masry, Ahmed  and
      Thakkar, Megh  and
      Bechard, Patrice  and
      Madhusudhan, Sathwik Tejaswi  and
      Awal, Rabiul  and
      Mishra, Shambhavi  and
      Suresh, Akshay Kalkunte  and
      Daruru, Srivatsava  and
      Hoque, Enamul  and
      Gella, Spandana  and
      Scholak, Torsten  and
      Rajeswar, Sai",
    editor = "Potdar, Saloni  and
      Rojas-Barahona, Lina  and
      Montella, Sebastien",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = nov,
    year = "2025",
    address = "Suzhou (China)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-industry.145/",
    pages = "2071--2080",
    ISBN = "979-8-89176-333-3",
    abstract = "Retrieval-augmented generation has proven practical when models require specialized knowledge or access to the latest data. However, existing methods for multimodal document retrieval often replicate techniques developed for text-only retrieval, whether in how they encode documents, define training objectives, or compute similarity scores. To address these limitations, we present ColMate, a document retrieval model that bridges the gap between multimodal representation learning and document retrieval. ColMate utilizes a novel OCR-based pretraining objective, a self-supervised masked contrastive learning objective, and a late interaction scoring mechanism more relevant to multimodal document structures and visual characteristics. ColMate obtains 3.61{\%} improvements over existing retrieval models on the ViDoRe V2 benchmark, demonstrating stronger generalization to out-of-domain benchmarks."
}