  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.
