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

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

Replacing harmful chemicals with benign terms can trick classifiers into not flagging dangerous content as suspicious.

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

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

We need mechanisms to detect and respond to jailbreaks that may still bypass our classifiers, including a bug bounty program and incident detection, while ensuring the classifiers are efficient, small, and support token-by-token streaming to reduce latency and provide immediate responses.

Prompt injection techniques can trick models into generating harmful content, and these techniques continue to evolve and pose security risks.

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 challenge of filtering out specific facts from AI models is compounded by the complexity of language and the potential for encoded language to evade detection.