@inproceedings{geng-etal-2025-vscbench,
title = "{VSCB}ench: Bridging the Gap in Vision-Language Model Safety Calibration",
author = "Geng, Jiahui and
Li, Qing and
Chen, Zongxiong and
Wang, Yuxia and
Zhu, Derui and
Xie, Zhuohan and
Lyu, Chenyang and
Chen, Xiuying and
Nakov, Preslav and
Karray, Fakhri",
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.158/",
doi = "10.18653/v1/2025.findings-acl.158",
pages = "3047--3059",
ISBN = "979-8-89176-256-5",
abstract = "The rapid advancement of vision-language models (VLMs) has brought a lot of attention to their safety alignment. However, existing methods have primarily focused on model undersafety, where the model responds to hazardous queries, while neglecting oversafety, where the model refuses to answer safe queries. In this paper, we introduce the concept of safety calibration, which systematically addresses both undersafety and oversafety. Specifically, we present VSCBench, a novel dataset of 3,600 image-text pairs that are visually or textually similar but differ in terms of safety, which is designed to evaluate safety calibration across image-centric and text-centric scenarios. Based on our benchmark, we evaluate safety calibration across eleven widely used VLMs. Our extensive experiments revealed major issues with both undersafety and oversafety. We further investigated four approaches to improve the model{'}s safety calibration. We found that even though some methods effectively calibrated the models' safety problems, these methods also lead to the degradation of models' utility. This trade-off underscores the urgent need for advanced calibration methods, and our benchmark provides a valuable tool for evaluating future approaches."
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<abstract>The rapid advancement of vision-language models (VLMs) has brought a lot of attention to their safety alignment. However, existing methods have primarily focused on model undersafety, where the model responds to hazardous queries, while neglecting oversafety, where the model refuses to answer safe queries. In this paper, we introduce the concept of safety calibration, which systematically addresses both undersafety and oversafety. Specifically, we present VSCBench, a novel dataset of 3,600 image-text pairs that are visually or textually similar but differ in terms of safety, which is designed to evaluate safety calibration across image-centric and text-centric scenarios. Based on our benchmark, we evaluate safety calibration across eleven widely used VLMs. Our extensive experiments revealed major issues with both undersafety and oversafety. We further investigated four approaches to improve the model’s safety calibration. We found that even though some methods effectively calibrated the models’ safety problems, these methods also lead to the degradation of models’ utility. This trade-off underscores the urgent need for advanced calibration methods, and our benchmark provides a valuable tool for evaluating future approaches.</abstract>
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%0 Conference Proceedings
%T VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration
%A Geng, Jiahui
%A Li, Qing
%A Chen, Zongxiong
%A Wang, Yuxia
%A Zhu, Derui
%A Xie, Zhuohan
%A Lyu, Chenyang
%A Chen, Xiuying
%A Nakov, Preslav
%A Karray, Fakhri
%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 geng-etal-2025-vscbench
%X The rapid advancement of vision-language models (VLMs) has brought a lot of attention to their safety alignment. However, existing methods have primarily focused on model undersafety, where the model responds to hazardous queries, while neglecting oversafety, where the model refuses to answer safe queries. In this paper, we introduce the concept of safety calibration, which systematically addresses both undersafety and oversafety. Specifically, we present VSCBench, a novel dataset of 3,600 image-text pairs that are visually or textually similar but differ in terms of safety, which is designed to evaluate safety calibration across image-centric and text-centric scenarios. Based on our benchmark, we evaluate safety calibration across eleven widely used VLMs. Our extensive experiments revealed major issues with both undersafety and oversafety. We further investigated four approaches to improve the model’s safety calibration. We found that even though some methods effectively calibrated the models’ safety problems, these methods also lead to the degradation of models’ utility. This trade-off underscores the urgent need for advanced calibration methods, and our benchmark provides a valuable tool for evaluating future approaches.
%R 10.18653/v1/2025.findings-acl.158
%U https://aclanthology.org/2025.findings-acl.158/
%U https://doi.org/10.18653/v1/2025.findings-acl.158
%P 3047-3059
Markdown (Informal)
[VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration](https://aclanthology.org/2025.findings-acl.158/) (Geng et al., Findings 2025)
ACL
- Jiahui Geng, Qing Li, Zongxiong Chen, Yuxia Wang, Derui Zhu, Zhuohan Xie, Chenyang Lyu, Xiuying Chen, Preslav Nakov, and Fakhri Karray. 2025. VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration. In Findings of the Association for Computational Linguistics: ACL 2025, pages 3047–3059, Vienna, Austria. Association for Computational Linguistics.