VoCoT: Unleashing Visually Grounded Multi-Step Reasoning in Large Multi-Modal Models

Zejun Li, Ruipu Luo, Jiwen Zhang, Minghui Qiu, Xuanjing Huang, Zhongyu Wei


Anthology ID:
2025.naacl-long.192
Volume:
Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
Month:
April
Year:
2025
Address:
Albuquerque, New Mexico
Editors:
Luis Chiruzzo, Alan Ritter, Lu Wang
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3769–3798
Language:
URL:
https://preview.aclanthology.org/fix-sig-urls/2025.naacl-long.192/
DOI:
Bibkey:
Cite (ACL):
Zejun Li, Ruipu Luo, Jiwen Zhang, Minghui Qiu, Xuanjing Huang, and Zhongyu Wei. 2025. VoCoT: Unleashing Visually Grounded Multi-Step Reasoning in Large Multi-Modal Models. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 3769–3798, Albuquerque, New Mexico. Association for Computational Linguistics.
Cite (Informal):
VoCoT: Unleashing Visually Grounded Multi-Step Reasoning in Large Multi-Modal Models (Li et al., NAACL 2025)
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PDF:
https://preview.aclanthology.org/fix-sig-urls/2025.naacl-long.192.pdf