SafeCoT: Improving VLM Safety with Minimal Reasoning

• URL: http://arxiv.org/abs/2506.08399v2
• Date: Wed, 11 Jun 2025 06:57:37 GMT
• Title: SafeCoT: Improving VLM Safety with Minimal Reasoning
• Authors: Jiachen Ma, Zhanhui Zhou, Chao Yang, Chaochao Lu,
• Abstract summary: We introduce SafeCoT, a lightweight, interpretable framework to improve refusal behavior in vision-language models.<n>We show that SafeCoT significantly reduces overrefusal and enhances generalization, even with limited training data.
• Score: 5.452721786714111
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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