This research introduces an amortized model-based evaluation approach that significantly reduces the cost and time needed to evaluate language models across multiple capabilities.

• Uses trained evaluator models that learn from expert judgments
• Enables efficient assessment of LLMs across diverse benchmarks
• Provides reliable signals for both development and deployment phases
• Maintains evaluation quality while reducing computational requirements

For the medical domain, this advancement means more thorough safety testing of AI diagnostic capabilities without prohibitive costs, enabling better validation before clinical implementation.

Reliable and Efficient Amortized Model-based Evaluation