@inproceedings{tang-etal-2026-unidata,
title = "{U}ni{D}ata: Universal Multimodal Instruction Generation Pipeline",
author = "Tang, Jiaqi and
Wu, Yi-Feng and
Zhang, Yuting and
LU, Hao and
Fu, Bowen and
Chen, Qing-Guo and
Xu, Xiaogang and
Hu, Yuwei and
Lu, Shiyin and
Wei, Wei and
Zhang, Lei and
Xu, Zhao and
Luo, Weihua and
Chen, Qifeng and
Chen, Ying-Cong",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1218/",
pages = "25793--25813",
ISBN = "979-8-89176-462-0",
abstract = "Multimodal Large Language Models (MLLMs) are increasingly being applied in a wider range of real-world scenarios. However, due to the substantial labor cost, creating high-quality multimodal instruction datasets for MLLMs remains a significant challenge. Although some methods propose to generate instruction data, they often face limitations in modality support and struggle with generating multi-round instructions. To address these problems, we introduce UniData, a universal instruction generation pipeline, to transform simple user requirements into multi-round, multimodal instructions. Specifically, UniData first expands user requirements into multiple diverse events. Using these events, UniData then integrates an any-to-any large model for multimodal instruction generation. Finally, UniData enhances data quality by correcting irrelevant and redundant inference flow, leveraging correlations between instruction rounds. To train this pipeline, we also build UniDataset, a dataset comprising 20,000 entries across nine modalities for improved multimodal generation. Our experiments demonstrate that UniData achieves SOTA performance in data quality and can also enhance the understanding and generation capabilities of other multimodal models."
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<abstract>Multimodal Large Language Models (MLLMs) are increasingly being applied in a wider range of real-world scenarios. However, due to the substantial labor cost, creating high-quality multimodal instruction datasets for MLLMs remains a significant challenge. Although some methods propose to generate instruction data, they often face limitations in modality support and struggle with generating multi-round instructions. To address these problems, we introduce UniData, a universal instruction generation pipeline, to transform simple user requirements into multi-round, multimodal instructions. Specifically, UniData first expands user requirements into multiple diverse events. Using these events, UniData then integrates an any-to-any large model for multimodal instruction generation. Finally, UniData enhances data quality by correcting irrelevant and redundant inference flow, leveraging correlations between instruction rounds. To train this pipeline, we also build UniDataset, a dataset comprising 20,000 entries across nine modalities for improved multimodal generation. Our experiments demonstrate that UniData achieves SOTA performance in data quality and can also enhance the understanding and generation capabilities of other multimodal models.</abstract>
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%0 Conference Proceedings
%T UniData: Universal Multimodal Instruction Generation Pipeline
%A Tang, Jiaqi
%A Wu, Yi-Feng
%A Zhang, Yuting
%A LU, Hao
%A Fu, Bowen
%A Chen, Qing-Guo
%A Xu, Xiaogang
%A Hu, Yuwei
%A Lu, Shiyin
%A Wei, Wei
%A Zhang, Lei
%A Xu, Zhao
%A Luo, Weihua
%A Chen, Qifeng
%A Chen, Ying-Cong
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F tang-etal-2026-unidata
%X Multimodal Large Language Models (MLLMs) are increasingly being applied in a wider range of real-world scenarios. However, due to the substantial labor cost, creating high-quality multimodal instruction datasets for MLLMs remains a significant challenge. Although some methods propose to generate instruction data, they often face limitations in modality support and struggle with generating multi-round instructions. To address these problems, we introduce UniData, a universal instruction generation pipeline, to transform simple user requirements into multi-round, multimodal instructions. Specifically, UniData first expands user requirements into multiple diverse events. Using these events, UniData then integrates an any-to-any large model for multimodal instruction generation. Finally, UniData enhances data quality by correcting irrelevant and redundant inference flow, leveraging correlations between instruction rounds. To train this pipeline, we also build UniDataset, a dataset comprising 20,000 entries across nine modalities for improved multimodal generation. Our experiments demonstrate that UniData achieves SOTA performance in data quality and can also enhance the understanding and generation capabilities of other multimodal models.
%U https://aclanthology.org/2026.findings-emnlp.1218/
%P 25793-25813
Markdown (Informal)
[UniData: Universal Multimodal Instruction Generation Pipeline](https://aclanthology.org/2026.findings-emnlp.1218/) (Tang et al., Findings 2026)
ACL
- Jiaqi Tang, Yi-Feng Wu, Yuting Zhang, Hao LU, Bowen Fu, Qing-Guo Chen, Xiaogang Xu, Yuwei Hu, Shiyin Lu, Wei Wei, Lei Zhang, Zhao Xu, Weihua Luo, Qifeng Chen, and Ying-Cong Chen. 2026. UniData: Universal Multimodal Instruction Generation Pipeline. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 25793–25813, Budapest, Hungary. Association for Computational Linguistics.