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arXiv · 2407.21772

ShieldGemma: Generative AI Content Moderation Based on Gemma

Wenjun Zeng·Yuchi Liu·Ryan Mullins·Ludovic Peran·Joe Fernandez·Hamza Harkous·Karthik Narasimhan·Drew Proud·Piyush Kumar·Bhaktipriya Radharapu·Olivia Sturman·Oscar Wahltinez
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Abstract

We present ShieldGemma, a comprehensive suite of LLM-based safety content moderation models built upon Gemma2. These models provide robust, state-of-the-art predictions of safety risks across key harm types (sexually explicit, dangerous content, harassment, hate speech) in both user input and LLM-generated output. By evaluating on both public and internal benchmarks, we demonstrate superior performance compared to existing models, such as Llama Guard (+10.8\% AU-PRC on public benchmarks) and WildCard (+4.3\%). Additionally, we present a novel LLM-based data curation pipeline, adaptable to a variety of safety-related tasks and beyond. We have shown strong generalization performance for model trained mainly on synthetic data. By releasing ShieldGemma, we provide a valuable resource to the research community, advancing LLM safety and enabling the creation of more effective content moderation solutions for developers.

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Wenjun Zeng, Yuchi Liu, Ryan Mullins, Ludovic Peran, Joe Fernandez, Hamza Harkous, Karthik Narasimhan, Drew Proud, Piyush Kumar, Bhaktipriya Radharapu, Olivia Sturman, Oscar Wahltinez. 2024-08-04. ShieldGemma: Generative AI Content Moderation Based on Gemma. https://arxiv.org/abs/2407.21772

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