@inproceedings{luo-etal-2025-mmevol,
title = "{MME}vol: Empowering Multimodal Large Language Models with Evol-Instruct",
author = "Luo, Run and
Zhang, Haonan and
Chen, Longze and
Lin, Ting-En and
Liu, Xiong and
Wu, Yuchuan and
Yang, Min and
Li, Yongbin and
Wang, Minzheng and
Zeng, Pengpeng and
Gao, Lianli and
Shen, Heng Tao and
Li, Yunshui and
Alinejad-Rokny, Hamid and
Xia, Xiaobo and
Song, Jingkuan and
Huang, Fei",
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://aclanthology.org/2025.findings-acl.1009/",
doi = "10.18653/v1/2025.findings-acl.1009",
pages = "19655--19682",
ISBN = "979-8-89176-256-5",
abstract = "The development of Multimodal Large Language Models (MLLMs) has seen significant progress, driven by increasing demands across various fields (e.g., multimodal agents, embodied intelligence). While model-driven approaches aim to enhance MLLM capabilities through diverse architectures, their performance gains have become increasingly marginal. In contrast, data-driven methods, which scale up image-text instruction datasets, have proven more effective but face challenges related to limited data diversity and complexity. The absence of high-quality instruction data remains a major bottleneck in MLLM development. To address this issue, we propose , a novel multimodal instruction data evolution framework. This framework iteratively enhances data quality through a refined combination of fine-grained perception, cognitive reasoning, and interaction evolution, generating a more complex and diverse image-text instruction dataset that significantly improves MLLM capabilities. Starting with an initial dataset, SEED-163K, we employ to systematically expand instruction diversity, extend visual reasoning steps to improve cognitive abilities, and extract fine-grained visual details to enhance understanding and robustness. To rigorously evaluate our approach, we conduct extensive qualitative analysis and quantitative experiments across 13 vision-language tasks. Compared to baseline models trained on the original seed dataset, our method achieves an average accuracy improvement of 3.1 percentage points. Moreover, our approach attains state-of-the-art (SOTA) performance in nine tasks while using significantly less data than existing state-of-the-art models."
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<abstract>The development of Multimodal Large Language Models (MLLMs) has seen significant progress, driven by increasing demands across various fields (e.g., multimodal agents, embodied intelligence). While model-driven approaches aim to enhance MLLM capabilities through diverse architectures, their performance gains have become increasingly marginal. In contrast, data-driven methods, which scale up image-text instruction datasets, have proven more effective but face challenges related to limited data diversity and complexity. The absence of high-quality instruction data remains a major bottleneck in MLLM development. To address this issue, we propose , a novel multimodal instruction data evolution framework. This framework iteratively enhances data quality through a refined combination of fine-grained perception, cognitive reasoning, and interaction evolution, generating a more complex and diverse image-text instruction dataset that significantly improves MLLM capabilities. Starting with an initial dataset, SEED-163K, we employ to systematically expand instruction diversity, extend visual reasoning steps to improve cognitive abilities, and extract fine-grained visual details to enhance understanding and robustness. To rigorously evaluate our approach, we conduct extensive qualitative analysis and quantitative experiments across 13 vision-language tasks. Compared to baseline models trained on the original seed dataset, our method achieves an average accuracy improvement of 3.1 percentage points. Moreover, our approach attains state-of-the-art (SOTA) performance in nine tasks while using significantly less data than existing state-of-the-art models.</abstract>
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%0 Conference Proceedings
%T MMEvol: Empowering Multimodal Large Language Models with Evol-Instruct
%A Luo, Run
%A Zhang, Haonan
%A Chen, Longze
%A Lin, Ting-En
%A Liu, Xiong
%A Wu, Yuchuan
%A Yang, Min
%A Li, Yongbin
%A Wang, Minzheng
%A Zeng, Pengpeng
%A Gao, Lianli
%A Shen, Heng Tao
%A Li, Yunshui
%A Alinejad-Rokny, Hamid
%A Xia, Xiaobo
%A Song, Jingkuan
%A Huang, Fei
%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 luo-etal-2025-mmevol
%X The development of Multimodal Large Language Models (MLLMs) has seen significant progress, driven by increasing demands across various fields (e.g., multimodal agents, embodied intelligence). While model-driven approaches aim to enhance MLLM capabilities through diverse architectures, their performance gains have become increasingly marginal. In contrast, data-driven methods, which scale up image-text instruction datasets, have proven more effective but face challenges related to limited data diversity and complexity. The absence of high-quality instruction data remains a major bottleneck in MLLM development. To address this issue, we propose , a novel multimodal instruction data evolution framework. This framework iteratively enhances data quality through a refined combination of fine-grained perception, cognitive reasoning, and interaction evolution, generating a more complex and diverse image-text instruction dataset that significantly improves MLLM capabilities. Starting with an initial dataset, SEED-163K, we employ to systematically expand instruction diversity, extend visual reasoning steps to improve cognitive abilities, and extract fine-grained visual details to enhance understanding and robustness. To rigorously evaluate our approach, we conduct extensive qualitative analysis and quantitative experiments across 13 vision-language tasks. Compared to baseline models trained on the original seed dataset, our method achieves an average accuracy improvement of 3.1 percentage points. Moreover, our approach attains state-of-the-art (SOTA) performance in nine tasks while using significantly less data than existing state-of-the-art models.
%R 10.18653/v1/2025.findings-acl.1009
%U https://aclanthology.org/2025.findings-acl.1009/
%U https://doi.org/10.18653/v1/2025.findings-acl.1009
%P 19655-19682
Markdown (Informal)
[MMEvol: Empowering Multimodal Large Language Models with Evol-Instruct](https://aclanthology.org/2025.findings-acl.1009/) (Luo et al., Findings 2025)
ACL
- Run Luo, Haonan Zhang, Longze Chen, Ting-En Lin, Xiong Liu, Yuchuan Wu, Min Yang, Yongbin Li, Minzheng Wang, Pengpeng Zeng, Lianli Gao, Heng Tao Shen, Yunshui Li, Hamid Alinejad-Rokny, Xiaobo Xia, Jingkuan Song, and Fei Huang. 2025. MMEvol: Empowering Multimodal Large Language Models with Evol-Instruct. In Findings of the Association for Computational Linguistics: ACL 2025, pages 19655–19682, Vienna, Austria. Association for Computational Linguistics.