  We propose a new method to measure the task-specific accuracy of
Retrieval-Augmented Large Language Models (RAG). Evaluation is performed by
scoring the RAG on an automatically-generated synthetic exam composed of
multiple choice questions based on the corpus of documents associated with the
task. Our method is an automated, cost-efficient, interpretable, and robust
strategy to select the optimal components for a RAG system. We leverage Item
Response Theory (IRT) to estimate the quality of an exam and its
informativeness on task-specific accuracy. IRT also provides a natural way to
iteratively improve the exam by eliminating the exam questions that are not
sufficiently informative about a model's ability. We demonstrate our approach
on four new open-ended Question-Answering tasks based on Arxiv abstracts,
StackExchange questions, AWS DevOps troubleshooting guides, and SEC filings. In
addition, our experiments reveal more general insights into factors impacting
RAG performance like size, retrieval mechanism, prompting and fine-tuning. Most
notably, our findings show that choosing the right retrieval algorithms often
leads to bigger performance gains than simply using a larger language model.
