@inproceedings{tang-etal-2025-unco,
    title = "{U}n{C}o: Uncertainty-Driven Collaborative Framework of Large and Small Models for Grounded Multimodal {NER}",
    author = "Tang, Jielong  and
      Yang, Yang  and
      Yu, Jianxing  and
      Wang, Zhen-Xing  and
      Liang, Haoyuan  and
      Yao, Liang  and
      Yin, Jian",
    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.388/",
    pages = "7644--7662",
    ISBN = "979-8-89176-332-6",
    abstract = {Grounded Multimodal Named Entity Recognition (GMNER) is a new information extraction task. It requires models to extract named entities and ground them to real-world visual objects. Previous methods, relying on domain-specific fine-tuning, struggle with unseen multimodal entities due to limited knowledge and generalization. Recently, multimodal large language models (MLLMs) have demonstrated strong open-set abilities. However, their performance is hindered by the lack of in-domain knowledge due to costly training for GMNER datasets. To address these limitations, we propose **UnCo**, a two-stage Uncertainty-driven Collaborative framework that leverages the complementary strengths of small fine-tuned models and MLLMs. Specifically, **in stage one**, we equip the small model with a unified uncertainty estimation (UE) for multimodal entities. This enables the small model to express $\textit{"I do not know"}$ when recognizing unseen entities beyond its capabilities. Predictions with high uncertainty are then filtered and delegated to the MLLM. **In stage two**, an Uncertainty-aware Hierarchical Correction mechanism guides the MLLM to refine uncertain predictions using its open-domain knowledge. Ultimately, UnCo effectively retains the in-domain knowledge of small models while utilizing the capabilities of MLLMs to handle unseen samples. Extensive experiments demonstrate UnCo{'}s effectiveness on two GMNER benchmarks.}
}