By training language models to output leaderboards based on binary preferences, we can convert the evaluation problem into a learning problem, allowing us to assess model performance across different contexts.

We've developed a 'prompt to leaderboard' system that identifies which AI models are best suited for specific prompts, even if those prompts have never been seen before.

Our focus is on optimizing for reasoning and behavior of models rather than just their scores on traditional benchmarks like MMLU.

In the context of AI evaluation, benchmarks are like supervised learning, while LMArena operates more like reinforcement learning, allowing for more dynamic and real-world testing.

When creating benchmarks for AI models, it's crucial to consider whether to prioritize distinguishing high-quality outputs or providing a continuous improvement path.

As AI evolves, we need to shift from static benchmarks to real-time evaluation systems that can adapt to user feedback.

We have developed an internal evaluation system to measure our models, as relying on external benchmarks often leads to data contamination and unreliable results.

Initially, it's more important to have a continuous improvement path for models rather than just focusing on top-tier performance.

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