An Analysis of Hyper-Parameter Optimization Methods for Retrieval Augmented Generation (2505.03452v2)

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.

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

The paper "An Analysis of Hyper-Parameter Optimization Methods for Retrieval Augmented Generation" presents a detailed paper on hyper-parameter optimization (HPO) techniques for Retrieval-Augmented Generation (RAG) systems. The authors conducted an extensive analysis by comparing five different HPO algorithms across five datasets from a range of domains, including a novel dataset drawn from real-world product documentation. This work builds upon the modular framework of RAG systems which leverages a retrieval component to supply generative models like LLMs with contextually relevant information, thus aiming to mitigate factual inaccuracies.

Core Contributions and Findings

1. Benchmarking RAG HPO Algorithms: The paper benchmarks the efficacy of several optimization strategies, including Tree-Structured Parzen Estimators (TPE), greedy optimization, and random search algorithms. The primary metrics used for evaluation were context correctness and answer correctness, both pivotal in determining the overall performance of RAG configurations.
2. Exploration of Search Space: The paper examines an extensive search space, comprising 162 distinct RAG configurations formed by optimizing five crucial parameters. They deploy both lexical and LLM-based metrics for thorough evaluation. The authors demonstrate that effective HPO can significantly enhance RAG performance, and propose that a greedy optimization strategy focusing on generative models first is superior to sequential optimization based on pipeline order.
3. Generative and Embedding Models Consideration: The granularity of search space is expanded by including generative model selection as a parameter, underscoring its critical role, which was demonstrated by the divergent results based on the selected objective metrics, LLMaaJ-AC and Lexical-AC.
4. Sampling Efficiency: The paper also explores the notion of sampled benchmarks, finding mixed results. Developing an effective sampling strategy for HPO remains an open challenge, suggesting that while sampling reduces computational cost, it must be carefully designed to avoid suboptimal model configuration.

Practical and Theoretical Implications

The successful application of automated HPO methods in enhancing RAG system performance across diversified datasets suggests potential theoretical extensions and practical applications. This research supports not only the optimization of RAG systems but provides a foundational framework for continuous improvements in machine learning models, particularly as datasets evolve or as new models become available. The introduction of an open-source RAG dataset specifically for enterprise product documentation further serves as a valuable resource for the community.

Future Directions

Future research could expand upon optimizing more complex RAG workflows, considering additional parameters like prompt tuning or the integration of multi-modal inputs. Furthermore, refining the sampling methodology in HPO processes could lead to significant advancements in efficiency and effectiveness. It opens the avenue for large-scale, dynamic data management in RAG systems, thereby facilitating adaptive and robust AI-driven solutions.

This paper contributes notably to the HPO landscape in RAG environments, highlighting the imperative for adaptive strategies that cater to evolving technologies and domain-specific demands.