LLavaGuard: VLM-based Safeguards for Vision Dataset Curation and Safety Assessment

• URL: http://arxiv.org/abs/2406.05113v1
• Date: Fri, 7 Jun 2024 17:44:32 GMT
• Title: LLavaGuard: VLM-based Safeguards for Vision Dataset Curation and Safety Assessment
• Authors: Lukas Helff, Felix Friedrich, Manuel Brack, Kristian Kersting, Patrick Schramowski,
• Abstract summary: We introduce LlavaGuard, a family of VLM-based safeguard models. LlavaGuard offers a versatile framework for evaluating the safety compliance of visual content. Our experiments highlight the capabilities of LlavaGuard in complex and real-world applications.
• Score: 26.148022772521493
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce LlavaGuard, a family of VLM-based safeguard models, offering a versatile framework for evaluating the safety compliance of visual content. Specifically, we designed LlavaGuard for dataset annotation and generative model safeguarding. To this end, we collected and annotated a high-quality visual dataset incorporating a broad safety taxonomy, which we use to tune VLMs on context-aware safety risks. As a key innovation, LlavaGuard's new responses contain comprehensive information, including a safety rating, the violated safety categories, and an in-depth rationale. Further, our introduced customizable taxonomy categories enable the context-specific alignment of LlavaGuard to various scenarios. Our experiments highlight the capabilities of LlavaGuard in complex and real-world applications. We provide checkpoints ranging from 7B to 34B parameters demonstrating state-of-the-art performance, with even the smallest models outperforming baselines like GPT-4. We make our dataset and model weights publicly available and invite further research to address the diverse needs of communities and contexts.

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