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    <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.</abstract>
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%0 Conference Proceedings
%T Vision-aided Unsupervised Constituency Parsing with Multi-MLLM Debating
%A Zhang, Dong
%A Tian, Haiyan
%A Sun, Qingying
%A Li, Shoushan
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F zhang-etal-2025-vision
%X 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.
%R 10.18653/v1/2025.findings-acl.353
%U https://aclanthology.org/2025.findings-acl.353/
%U https://doi.org/10.18653/v1/2025.findings-acl.353
%P 6800-6810

Markdown (Informal)

[Vision-aided Unsupervised Constituency Parsing with Multi-MLLM Debating](https://aclanthology.org/2025.findings-acl.353/) (Zhang et al., Findings 2025)

• Vision-aided Unsupervised Constituency Parsing with Multi-MLLM Debating (Zhang et al., Findings 2025)

ACL

• Dong Zhang, Haiyan Tian, Qingying Sun, and Shoushan Li. 2025. Vision-aided Unsupervised Constituency Parsing with Multi-MLLM Debating. In Findings of the Association for Computational Linguistics: ACL 2025, pages 6800–6810, Vienna, Austria. Association for Computational Linguistics.