@inproceedings{zhang-etal-2025-vision,
    title = "Vision-aided Unsupervised Constituency Parsing with Multi-{MLLM} Debating",
    author = "Zhang, Dong  and
      Tian, Haiyan  and
      Sun, Qingying  and
      Li, Shoushan",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/transition-to-people-yaml/2025.findings-acl.353/",
    doi = "10.18653/v1/2025.findings-acl.353",
    pages = "6800--6810",
    ISBN = "979-8-89176-256-5",
    abstract = "This paper presents a novel framework for vision-aided unsupervised constituency parsing (VUCP), leveraging multimodal large language models (MLLMs) pre-trained on diverse image-text or video-text data. Unlike previous methods requiring explicit cross-modal alignment, our approach eliminates this need by using pre-trained models like Qwen-VL and VideoLLaVA, which seamlessly handle multimodal inputs. We introduce two multi-agent debating mechanisms{---}consensus-driven (CD) and round-driven (RD){---}to enable cooperation between models with complementary strengths. Extensive experiments demonstrate that our approach achieves state-of-the-art performance on both image-text and video-text datasets for VUCP, improving robustness and accuracy."
}