SafeCoT: Improving VLM Safety with Minimal Reasoning
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866913889029980160 |
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| author | Ma, Jiachen Zhou, Zhanhui Yang, Chao Lu, Chaochao |
| author_facet | Ma, Jiachen Zhou, Zhanhui Yang, Chao Lu, Chaochao |
| contents | Ensuring safe and appropriate responses from vision-language models (VLMs) remains a critical challenge, particularly in high-risk or ambiguous scenarios. We introduce SafeCoT, a lightweight, interpretable framework that leverages rule-based chain-of-thought (CoT) supervision to improve refusal behavior in VLMs. Unlike prior methods that rely on large-scale safety annotations or complex modeling, SafeCoT uses minimal supervision to help models reason about safety risks and make context-aware refusals. Experiments across multiple benchmarks show that SafeCoT significantly reduces overrefusal and enhances generalization, even with limited training data. Our approach offers a scalable solution for aligning VLMs with safety-critical objectives. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_08399 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SafeCoT: Improving VLM Safety with Minimal Reasoning Ma, Jiachen Zhou, Zhanhui Yang, Chao Lu, Chaochao Artificial Intelligence Machine Learning Ensuring safe and appropriate responses from vision-language models (VLMs) remains a critical challenge, particularly in high-risk or ambiguous scenarios. We introduce SafeCoT, a lightweight, interpretable framework that leverages rule-based chain-of-thought (CoT) supervision to improve refusal behavior in VLMs. Unlike prior methods that rely on large-scale safety annotations or complex modeling, SafeCoT uses minimal supervision to help models reason about safety risks and make context-aware refusals. Experiments across multiple benchmarks show that SafeCoT significantly reduces overrefusal and enhances generalization, even with limited training data. Our approach offers a scalable solution for aligning VLMs with safety-critical objectives. |
| title | SafeCoT: Improving VLM Safety with Minimal Reasoning |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2506.08399 |