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ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems

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arxiv 2311.09476 v2 pith:DK5FBX4Y submitted 2023-11-16 cs.CL cs.AIcs.IR

ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems

Jon Saad-Falcon , Omar Khattab , Christopher Potts , Matei Zaharia This is my paper
classification cs.CL cs.AIcs.IR
keywords aressystemsevaluationacrossannotationsanswerautomatedevaluating
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Cited by 22 Pith papers

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