Using LLMs as judges for evaluating outputs is promising, but it requires careful prompt engineering to ensure accurate grading of responses.

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

It's important to evaluate prompts by testing them in real scenarios, as this can reveal flaws in the prompt design that need addressing.

There are three types of evaluators: deterministic code-based evaluations, LLM-as-judge techniques for more complex assessments, and human annotation for real-time feedback.

Over time, we learned that certain questions we asked were indicators of whether a cleaning professional would be a good or bad performer on the platform. This insight came from data collection and analyzing responses.

The Frontier Red Team evaluates the helpful only model to assess potential risks and ensure that safeguards are effective against harmful outputs.

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

We are considering implementing a rating system for papers to help users distinguish between high-quality and low-quality research.

The demo system we recently put out is performing much better in terms of user interactions and robustness compared to previous versions.