  Evaluating retrieval-augmented generation (RAG) systems traditionally relies
on hand annotations for input queries, passages to retrieve, and responses to
generate. We introduce ARES, an Automated RAG Evaluation System, for evaluating
RAG systems along the dimensions of context relevance, answer faithfulness, and
answer relevance. By creating its own synthetic training data, ARES finetunes
lightweight LM judges to assess the quality of individual RAG components. To
mitigate potential prediction errors, ARES utilizes a small set of
human-annotated datapoints for prediction-powered inference (PPI). Across eight
different knowledge-intensive tasks in KILT, SuperGLUE, and AIS, ARES
accurately evaluates RAG systems while using only a few hundred human
annotations during evaluation. Furthermore, ARES judges remain effective across
domain shifts, proving accurate even after changing the type of queries and/or
documents used in the evaluated RAG systems. We make our code and datasets
publicly available on Github.
