Leveraging Bayesian Optimization for Accelerating RAG Pipeline Optimization

Abstract

Retrieval-Augmented Generation (RAG) pipelines are widely used to enhance the performance of large language models by combining retrieval mechanisms with generative modeling. However, optimizing these pipelines is complicated and time-consuming, as it involves carefully selecting and tuning components such as retrievers, filters, and generators.

Bayesian Optimization (BO) is a probabilistic method designed to efficiently identify optimal solutions for complex optimization problems. This research will investigate the application of BO within RAG pipelines to automate and accelerate the optimization process. Specifically, we aim to explore how BO can be effectively utilized for both discrete module selection and continuous hyperparameter tuning within a unified framework.

The research will also evaluate how robust Bayesian Optimization is under different conditions, including variations in data quality and domain characteristics. The main goal of this research is to find the optimal configurations for RAG pipelines while reducing the optimization time. 

Research Questions

• R1: To what extent can Bayesian Optimization be used to choose between different RAG pipeline modules (e.g., retrievers, filters) and tune their hyperparameters?                                                                                                                                                                     
• R2: How do different resource constraints (e.g., evaluation budges, time, compute) affect the performance and stability of BO in RAG tuning?  
  
  
• R3: How sensitive is Bayesian Optimization to the size and quality of the evaluation dataset during RAG pipeline tuning? 

• R4: How consistent are the RAG pipeline configurations discovered by Bayesian Optimization across datasets from different domains or with different query characteristics? 
  
  

References:

1. Kim, D., Kim, B., Han, D., & Eibich, M. (2024, October 28). AutoRAG: Automated Framework for optimization of Retrieval Augmented Generation Pipeline. arXiv.org. https://arxiv.org/abs/2410.20878

2. Falkner, S., Klein, A., & Hutter, F. (2018, July 4). BOHB: Robust and efficient hyperparameter optimization at scale. arXiv.org. https://arxiv.org/abs/1807.01774

3. Liaw, R., Liang, E., Nishihara, R., Moritz, P., Gonzalez, J. E., & Stoica, I. (2018, July 13). Tune: a research platform for distributed model selection and training. arXiv.org. https://arxiv.org/abs/1807.05118

4. Hutter, F., Hoos, H. H., & Leyton-Brown, K. (2011). Sequential Model-Based optimization for general algorithm configuration. In Lecture notes in computer science (pp. 507–523). https://doi.org/10.1007/978-3-642-25566-3_40