The flexibility of our approach allows us to quickly adapt to new threats by simply rewriting the constitution to include new harmful categories.

Constitutional classifiers use a natural language set of rules to specify harmful and harmless topics, guiding the model's responses accordingly.

Constitutional classifiers are a promising development in AI monitoring, demonstrating that smaller models can effectively check larger ones.

The classifiers can detect attempts to circumvent the system, which is crucial for maintaining security.

With the constitutional classifiers, we achieved thousands of hours of robustness to Red Teaming, significantly improving our defenses against jailbreaks.

Classifiers are decoupled from the text generation model, allowing for easier updates without unintended consequences in other domains, which gives customers a lot of assurance that there are no major changes happening in general to the model.

The classifiers approach aims to improve user experience for non-dangerous tasks while effectively blocking harmful content.

Having a system that's super flexible is great because threat modeling is really hard.

Rapid response monitoring for new jailbreaks allows for quick adaptation and retraining of classifiers to improve safety.