Limits to scalable evaluation at the frontier: LLM as Judge won't beat twice the data

Understand

High quality annotations are increasingly a bottleneck in the explosively growing machine learning ecosystem.

• Scalable evaluation methods that avoid costly annotation have therefore become an important research ambition.
• Many hope to use strong existing models in lieu of costly labels to provide cheap model evaluations.
• Unfortunately, this method of using models as judges introduces biases, such as self-preferencing, that can distort model comparisons.