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

Jon Saad-Falcon, Omar Khattab, Christopher Potts, Matei Zaharia


Abstract
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.
Anthology ID:
2024.naacl-long.20
Volume:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
338–354
Language:
URL:
https://aclanthology.org/2024.naacl-long.20
DOI:
Bibkey:
Cite (ACL):
Jon Saad-Falcon, Omar Khattab, Christopher Potts, and Matei Zaharia. 2024. ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 338–354, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems (Saad-Falcon et al., NAACL 2024)
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PDF:
https://aclanthology.org/2024.naacl-long.20.pdf
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 2024.naacl-long.20.copyright.pdf