@inproceedings{qu-etal-2025-self,
title = "Self-adaptive Dataset Construction for Real-World Multimodal Safety Scenarios",
author = "Qu, Jingen and
Li, Lijun and
Zhang, Bo and
Yan, Yichen and
Shao, Jing",
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.912/",
doi = "10.18653/v1/2025.findings-emnlp.912",
pages = "16805--16829",
ISBN = "979-8-89176-335-7",
abstract = "Multimodal large language models (MLLMs) are rapidly evolving, presenting increasingly complex safety challenges. However, current dataset construction methods, which are risk-oriented, fail to cover the growing complexity of real-world multimodal safety scenarios (RMS). And due to the lack of a unified evaluation metric, their overall effectiveness remains unproven. This paper introduces a novel image-oriented self-adaptive dataset construction method for RMS, which starts with images and end constructing paired text and guidance responses. Using the image-oriented method, we automatically generate an RMS dataset comprising 35,610 image{--}text pairs with guidance responses. Additionally, we introduce a standardized safety dataset evaluation metric: fine-tuning a safety judge model and evaluating its capabilities on other safety datasets. Extensive experiments on various tasks demonstrate the effectiveness of the proposed image-oriented pipeline. The results confirm the scalability and effectiveness of the image-oriented approach, offering a new perspective for the construction of real-world multimodal safety datasets."
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%0 Conference Proceedings
%T Self-adaptive Dataset Construction for Real-World Multimodal Safety Scenarios
%A Qu, Jingen
%A Li, Lijun
%A Zhang, Bo
%A Yan, Yichen
%A Shao, Jing
%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 qu-etal-2025-self
%X Multimodal large language models (MLLMs) are rapidly evolving, presenting increasingly complex safety challenges. However, current dataset construction methods, which are risk-oriented, fail to cover the growing complexity of real-world multimodal safety scenarios (RMS). And due to the lack of a unified evaluation metric, their overall effectiveness remains unproven. This paper introduces a novel image-oriented self-adaptive dataset construction method for RMS, which starts with images and end constructing paired text and guidance responses. Using the image-oriented method, we automatically generate an RMS dataset comprising 35,610 image–text pairs with guidance responses. Additionally, we introduce a standardized safety dataset evaluation metric: fine-tuning a safety judge model and evaluating its capabilities on other safety datasets. Extensive experiments on various tasks demonstrate the effectiveness of the proposed image-oriented pipeline. The results confirm the scalability and effectiveness of the image-oriented approach, offering a new perspective for the construction of real-world multimodal safety datasets.
%R 10.18653/v1/2025.findings-emnlp.912
%U https://aclanthology.org/2025.findings-emnlp.912/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.912
%P 16805-16829
Markdown (Informal)
[Self-adaptive Dataset Construction for Real-World Multimodal Safety Scenarios](https://aclanthology.org/2025.findings-emnlp.912/) (Qu et al., Findings 2025)
ACL