Using a more capable classifier after serving a response can help flag potentially harmful outputs and involve human reviewers for the most dangerous responses.

Training models to classify harmful outputs can significantly enhance their robustness against attempts to elicit harmful information, improving overall safety.

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

The first layer of our defense against jailbreaks is the input classifier, which analyzes the entire conversation before it reaches the model.

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

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

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.

Control evaluations must be rerun with each new model to ensure that monitoring systems remain effective against potential threats.

Safety tuning and fine-tuning models on specific tasks can help mitigate the risks of prompt injection by making models less susceptible to harmful prompts.