@inproceedings{cheng-etal-2025-domain,
title = "On Domain-Adaptive Post-Training for Multimodal Large Language Models",
author = "Cheng, Daixuan and
Huang, Shaohan and
Zhu, Ziyu and
Zhang, Xintong and
Zhao, Wayne Xin and
Luan, Zhongzhi and
Dai, Bo and
Zhang, Zhenliang",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.17/",
doi = "10.18653/v1/2025.findings-emnlp.17",
pages = "274--296",
ISBN = "979-8-89176-335-7",
abstract = "Adapting general multimodal large language models (MLLMs) to specific domains, such as scientific and industrial fields, is highly significant in promoting their practical applications. This paper systematically investigates domain adaptation of MLLMs via post-training, focusing on data synthesis, training pipeline, and task evaluation. (1) \textbf{Data Synthesis}: Using only open-source models, we develop a generate-then-filter pipeline that curates diverse visual instruction tasks based on domain-specific image-caption pairs. The resulting data surpass the data synthesized by manual rules or strong closed-source models in enhancing domain-specific performance. (2) \textbf{Training Pipeline}: Unlike general MLLMs that typically adopt a two-stage training paradigm, we find that a single-stage approach is more effective for domain adaptation. (3) \textbf{Task Evaluation}: We conduct extensive experiments in high-impact domains such as biomedicine, food, and remote sensing, by post-training a variety of MLLMs and then evaluating MLLM performance on various domain-specific tasks. Finally, we fully open-source our models, code, and data to encourage future research in this area."
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<abstract>Adapting general multimodal large language models (MLLMs) to specific domains, such as scientific and industrial fields, is highly significant in promoting their practical applications. This paper systematically investigates domain adaptation of MLLMs via post-training, focusing on data synthesis, training pipeline, and task evaluation. (1) Data Synthesis: Using only open-source models, we develop a generate-then-filter pipeline that curates diverse visual instruction tasks based on domain-specific image-caption pairs. The resulting data surpass the data synthesized by manual rules or strong closed-source models in enhancing domain-specific performance. (2) Training Pipeline: Unlike general MLLMs that typically adopt a two-stage training paradigm, we find that a single-stage approach is more effective for domain adaptation. (3) Task Evaluation: We conduct extensive experiments in high-impact domains such as biomedicine, food, and remote sensing, by post-training a variety of MLLMs and then evaluating MLLM performance on various domain-specific tasks. Finally, we fully open-source our models, code, and data to encourage future research in this area.</abstract>
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%0 Conference Proceedings
%T On Domain-Adaptive Post-Training for Multimodal Large Language Models
%A Cheng, Daixuan
%A Huang, Shaohan
%A Zhu, Ziyu
%A Zhang, Xintong
%A Zhao, Wayne Xin
%A Luan, Zhongzhi
%A Dai, Bo
%A Zhang, Zhenliang
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F cheng-etal-2025-domain
%X Adapting general multimodal large language models (MLLMs) to specific domains, such as scientific and industrial fields, is highly significant in promoting their practical applications. This paper systematically investigates domain adaptation of MLLMs via post-training, focusing on data synthesis, training pipeline, and task evaluation. (1) Data Synthesis: Using only open-source models, we develop a generate-then-filter pipeline that curates diverse visual instruction tasks based on domain-specific image-caption pairs. The resulting data surpass the data synthesized by manual rules or strong closed-source models in enhancing domain-specific performance. (2) Training Pipeline: Unlike general MLLMs that typically adopt a two-stage training paradigm, we find that a single-stage approach is more effective for domain adaptation. (3) Task Evaluation: We conduct extensive experiments in high-impact domains such as biomedicine, food, and remote sensing, by post-training a variety of MLLMs and then evaluating MLLM performance on various domain-specific tasks. Finally, we fully open-source our models, code, and data to encourage future research in this area.
%R 10.18653/v1/2025.findings-emnlp.17
%U https://aclanthology.org/2025.findings-emnlp.17/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.17
%P 274-296
Markdown (Informal)
[On Domain-Adaptive Post-Training for Multimodal Large Language Models](https://aclanthology.org/2025.findings-emnlp.17/) (Cheng et al., Findings 2025)
ACL
- Daixuan Cheng, Shaohan Huang, Ziyu Zhu, Xintong Zhang, Wayne Xin Zhao, Zhongzhi Luan, Bo Dai, and Zhenliang Zhang. 2025. On Domain-Adaptive Post-Training for Multimodal Large Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 274–296, Suzhou, China. Association for Computational Linguistics.