An Analysis of Hyper-Parameter Optimization Methods for Retrieval Augmented Generation

• URL: http://arxiv.org/abs/2505.03452v2
• Date: Tue, 10 Jun 2025 09:56:20 GMT
• Title: An Analysis of Hyper-Parameter Optimization Methods for Retrieval Augmented Generation
• Authors: Matan Orbach, Ohad Eytan, Benjamin Sznajder, Ariel Gera, Odellia Boni, Yoav Kantor, Gal Bloch, Omri Levy, Hadas Abraham, Nitzan Barzilay, Eyal Shnarch, Michael E. Factor, Shila Ofek-Koifman, Paula Ta-Shma, Assaf Toledo,
• Abstract summary: We present a comprehensive study involving 5 HPO algorithms over 5 datasets from diverse domains.<n>Our study explores the largest HPO search space considered to date, with three evaluation metrics as optimization targets.<n>Analysis of the results shows that RAG HPO can be done efficiently, either greedily or with random search, and that it significantly boosts RAG performance for all datasets.
• Score: 6.98773220458697
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Finding the optimal Retrieval-Augmented Generation (RAG) configuration for a given use case can be complex and expensive. Motivated by this challenge, frameworks for RAG hyper-parameter optimization (HPO) have recently emerged, yet their effectiveness has not been rigorously benchmarked. To address this gap, we present a comprehensive study involving 5 HPO algorithms over 5 datasets from diverse domains, including a new one collected for this work on real-world product documentation. Our study explores the largest HPO search space considered to date, with three evaluation metrics as optimization targets. Analysis of the results shows that RAG HPO can be done efficiently, either greedily or with random search, and that it significantly boosts RAG performance for all datasets. For greedy HPO approaches, we show that optimizing model selection first is preferable to the prevalent practice of optimizing according to RAG pipeline order.

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