LLavaGuard: VLM-based Safeguards for Vision Dataset Curation and Safety Assessment
<|reference_start|>LLavaGuard: VLM-based Safeguards for Vision Dataset Curation and Safety Assessment: 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.<|reference_end|>
@article{helff2024llavaguard:,
    title={LLavaGuard: VLM-based Safeguards for Vision Dataset Curation and Safety
  Assessment},
    author={Lukas Helff, Felix Friedrich, Manuel Brack, Kristian Kersting, Patrick
  Schramowski},
    journal={arXiv preprint arXiv:2406.05113},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.05113},
    primaryClass={cs.CV cs.AI cs.LG}
}