@inproceedings{zhao-etal-2025-omnialign,
title = "{O}mni{A}lign-{V}: Towards Enhanced Alignment of {MLLM}s with Human Preference",
author = "Zhao, Xiangyu and
Ding, Shengyuan and
Zhang, Zicheng and
Huang, Haian and
Cao, Maosong and
Wang, Weiyun and
Wang, Jiaqi and
Fang, Xinyu and
Wang, Wenhai and
Zhai, Guangtao and
Duan, Haodong and
Yang, Hua and
Chen, Kai",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.906/",
doi = "10.18653/v1/2025.acl-long.906",
pages = "18490--18515",
ISBN = "979-8-89176-251-0",
abstract = "Recent advancements in open-source multi-modal large language models (MLLMs) have primarily focused on enhancing foundational capabilities, leaving a significant gap in human preference alignment. This paper introduces OmniAlign-V, a comprehensive dataset of 200K high-quality training samples featuring diverse images, complex questions, and varied response formats to improve MLLMs' alignment with human preferences. We also present MM-AlignBench, a human-annotated benchmark specifically designed to evaluate MLLMs' alignment with human values. Experimental results show that finetuning MLLMs with OmniAlign-V, using Supervised Fine-Tuning (SFT) or Direct Preference Optimization (DPO), significantly enhances human preference alignment while maintaining or enhancing performance on standard VQA benchmarks, preserving their fundamental capabilities."
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<abstract>Recent advancements in open-source multi-modal large language models (MLLMs) have primarily focused on enhancing foundational capabilities, leaving a significant gap in human preference alignment. This paper introduces OmniAlign-V, a comprehensive dataset of 200K high-quality training samples featuring diverse images, complex questions, and varied response formats to improve MLLMs’ alignment with human preferences. We also present MM-AlignBench, a human-annotated benchmark specifically designed to evaluate MLLMs’ alignment with human values. Experimental results show that finetuning MLLMs with OmniAlign-V, using Supervised Fine-Tuning (SFT) or Direct Preference Optimization (DPO), significantly enhances human preference alignment while maintaining or enhancing performance on standard VQA benchmarks, preserving their fundamental capabilities.</abstract>
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%0 Conference Proceedings
%T OmniAlign-V: Towards Enhanced Alignment of MLLMs with Human Preference
%A Zhao, Xiangyu
%A Ding, Shengyuan
%A Zhang, Zicheng
%A Huang, Haian
%A Cao, Maosong
%A Wang, Weiyun
%A Wang, Jiaqi
%A Fang, Xinyu
%A Wang, Wenhai
%A Zhai, Guangtao
%A Duan, Haodong
%A Yang, Hua
%A Chen, Kai
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F zhao-etal-2025-omnialign
%X Recent advancements in open-source multi-modal large language models (MLLMs) have primarily focused on enhancing foundational capabilities, leaving a significant gap in human preference alignment. This paper introduces OmniAlign-V, a comprehensive dataset of 200K high-quality training samples featuring diverse images, complex questions, and varied response formats to improve MLLMs’ alignment with human preferences. We also present MM-AlignBench, a human-annotated benchmark specifically designed to evaluate MLLMs’ alignment with human values. Experimental results show that finetuning MLLMs with OmniAlign-V, using Supervised Fine-Tuning (SFT) or Direct Preference Optimization (DPO), significantly enhances human preference alignment while maintaining or enhancing performance on standard VQA benchmarks, preserving their fundamental capabilities.
%R 10.18653/v1/2025.acl-long.906
%U https://aclanthology.org/2025.acl-long.906/
%U https://doi.org/10.18653/v1/2025.acl-long.906
%P 18490-18515
Markdown (Informal)
[OmniAlign-V: Towards Enhanced Alignment of MLLMs with Human Preference](https://aclanthology.org/2025.acl-long.906/) (Zhao et al., ACL 2025)
ACL
- Xiangyu Zhao, Shengyuan Ding, Zicheng Zhang, Haian Huang, Maosong Cao, Weiyun Wang, Jiaqi Wang, Xinyu Fang, Wenhai Wang, Guangtao Zhai, Haodong Duan, Hua Yang, and Kai Chen. 2025. OmniAlign-V: Towards Enhanced Alignment of MLLMs with Human Preference. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 18490–18515, Vienna, Austria. Association for Computational Linguistics.