@inproceedings{zhao-etal-2025-realbench,
title = "{R}eal{B}ench: A {C}hinese Multi-image Understanding Benchmark Close to Real-world Scenarios",
author = "Zhao, Fei and
Lu, Chengqiang and
Shen, Yufan and
Wang, Qimeng and
Qian, Yicheng and
Zhang, Haoxin and
Gao, Yan and
Yiwu and
Hu, Yao and
Wu, Zhen and
Xing, Shangyu and
Dai, Xinyu",
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.1039/",
doi = "10.18653/v1/2025.findings-emnlp.1039",
pages = "19097--19115",
ISBN = "979-8-89176-335-7",
abstract = "While various multimodal multi-image evaluation datasets have been emerged, but these datasets are primarily based on English, and there has yet to be a Chinese multi-image dataset. To fill this gap, we introduce RealBench, the first Chinese multimodal multi-image dataset, which contains 9393 samples and 69910 images. RealBench distinguishes itself by incorporating real user-generated content, ensuring high relevance to real-world applications. Additionally, the dataset covers a wide variety of scenes, image resolutions, and image structures, further increasing the difficulty of multi-image understanding. Ultimately, we conduct a comprehensive evaluation of RealBench using 21 multimodal LLMs of different sizes, including closed-source models that support multi-image inputs as well as open-source visual and video models. The experimental results indicate that even the most powerful closed-source models still face challenges when handling multi-image Chinese scenarios. Moreover, there remains a noticeable performance gap of around 71.8{\%} on average between open-source visual/video models and closed-source models. These results show that RealBench provides an important research foundation for further exploring multi-image understanding capabilities in the Chinese context. Our datasets will be publicly available."
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%0 Conference Proceedings
%T RealBench: A Chinese Multi-image Understanding Benchmark Close to Real-world Scenarios
%A Zhao, Fei
%A Lu, Chengqiang
%A Shen, Yufan
%A Wang, Qimeng
%A Qian, Yicheng
%A Zhang, Haoxin
%A Gao, Yan
%A Hu, Yao
%A Wu, Zhen
%A Xing, Shangyu
%A Dai, Xinyu
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%A Yiwu
%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 zhao-etal-2025-realbench
%X While various multimodal multi-image evaluation datasets have been emerged, but these datasets are primarily based on English, and there has yet to be a Chinese multi-image dataset. To fill this gap, we introduce RealBench, the first Chinese multimodal multi-image dataset, which contains 9393 samples and 69910 images. RealBench distinguishes itself by incorporating real user-generated content, ensuring high relevance to real-world applications. Additionally, the dataset covers a wide variety of scenes, image resolutions, and image structures, further increasing the difficulty of multi-image understanding. Ultimately, we conduct a comprehensive evaluation of RealBench using 21 multimodal LLMs of different sizes, including closed-source models that support multi-image inputs as well as open-source visual and video models. The experimental results indicate that even the most powerful closed-source models still face challenges when handling multi-image Chinese scenarios. Moreover, there remains a noticeable performance gap of around 71.8% on average between open-source visual/video models and closed-source models. These results show that RealBench provides an important research foundation for further exploring multi-image understanding capabilities in the Chinese context. Our datasets will be publicly available.
%R 10.18653/v1/2025.findings-emnlp.1039
%U https://aclanthology.org/2025.findings-emnlp.1039/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.1039
%P 19097-19115
Markdown (Informal)
[RealBench: A Chinese Multi-image Understanding Benchmark Close to Real-world Scenarios](https://aclanthology.org/2025.findings-emnlp.1039/) (Zhao et al., Findings 2025)
ACL
- Fei Zhao, Chengqiang Lu, Yufan Shen, Qimeng Wang, Yicheng Qian, Haoxin Zhang, Yan Gao, Yiwu, Yao Hu, Zhen Wu, Shangyu Xing, and Xinyu Dai. 2025. RealBench: A Chinese Multi-image Understanding Benchmark Close to Real-world Scenarios. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 19097–19115, Suzhou, China. Association for Computational Linguistics.