As the development and deployment of large language models (LLMs) accelerates, evaluating model outputs has become increasingly important. The established method of evaluating responses typically involves recruiting and training human evaluators, having them evaluate the model responses, and then auditing the quality of the evaluations. Unfortunately, this process does not