Creating high-quality evaluation datasets is essential for developing effective control measures, as benchmarks help optimize performance.

Our platform not only evaluates models but also helps partners select the best-performing models based on user feedback.

Our platform not only evaluates models but also helps partners select the best-performing models based on user feedback.

Evaluations consist of two parts: data and evaluators, which can vary depending on whether you're using ground truth or reference-free methods.

The 'prompt to leaderboard' approach teaches us that we can evaluate models by learning how they perform in various scenarios, rather than relying solely on static benchmarks.

As we gather more data and build a larger platform, we can create more granular and personalized evaluations of AI models.

We find that human feedback is often unreliable, so our self-evolving models are designed to learn from their own mistakes without needing human input.

The goal is to integrate subjective measurements into the model release pipeline, ensuring continuous evaluation post-release.

The goal is to integrate subjective measurements into the model release pipeline, ensuring continuous evaluation post-release.