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

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

The output classifier monitors the model's responses in real-time and can block dangerous outputs that violate our safety values.

The techniques used to bypass classifiers often involve encoding mechanisms that obscure harmful outputs, making them less detectable.

The classifiers project was evidence that we really care about solving safety problems and finding empirical solutions rather than just doing research that looks good.

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

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.