Summary of Shieldgemma: Generative Ai Content Moderation Based on Gemma, by Wenjun Zeng and Yuchi Liu and Ryan Mullins and Ludovic Peran and Joe Fernandez and Hamza Harkous and Karthik Narasimhan and Drew Proud and Piyush Kumar and Bhaktipriya Radharapu and Olivia Sturman and Oscar Wahltinez
ShieldGemma: Generative AI Content Moderation Based on Gemma
by Wenjun Zeng, Yuchi Liu, Ryan Mullins, Ludovic Peran, Joe Fernandez, Hamza Harkous, Karthik Narasimhan, Drew Proud, Piyush Kumar, Bhaktipriya Radharapu, Olivia Sturman, Oscar Wahltinez
First submitted to arxiv on: 31 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents ShieldGemma, a suite of language models designed for comprehensive safety content moderation. Built upon Gemma2, these models predict safety risks across various harm types, including sexually explicit content, dangerous material, harassment, and hate speech, in both user input and language model-generated output. Evaluations on public and internal benchmarks demonstrate superior performance compared to existing models like Llama Guard (+10.8% AU-PRC) and WildCard (+4.3%). The paper also introduces a novel data curation pipeline for safety-related tasks and beyond. Trained mainly on synthetic data, ShieldGemma shows strong generalization performance. This research provides a valuable resource for the community, advancing language model safety and enabling more effective content moderation solutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates new tools to help keep online communities safe. They built a special set of computer programs that can identify different kinds of harmful content, like explicit images or mean messages. These programs are really good at predicting when something is dangerous or inappropriate. The researchers tested their program against others and found it did better on most tasks. They also developed a new way to collect data for these types of tasks. By sharing their tools with other experts, they hope to make the internet safer and help developers create better ways to keep people safe online. |
Keywords
* Artificial intelligence * Generalization * Language model * Llama * Synthetic data
