Autorag-hp: Automatic Online Hyper-parameter Tuning For Retrieval-augmented Generation

Fu Jia, Qin Xiaoting, Yang Fangkai, Wang Lu, Zhang Jue, Lin Qingwei, Chen Yubo, Zhang Dongmei, Rajmohan Saravan, Zhang Qi. Arxiv 2024

[Paper] [Code]    
Efficiency And Optimization Fine Tuning Has Code Prompting RAG Tools

Recent advancements in Large Language Models have transformed ML/AI development, necessitating a reevaluation of AutoML principles for the Retrieval-Augmented Generation (RAG) systems. To address the challenges of hyper-parameter optimization and online adaptation in RAG, we propose the AutoRAG-HP framework, which formulates the hyper-parameter tuning as an online multi-armed bandit (MAB) problem and introduces a novel two-level Hierarchical MAB (Hier-MAB) method for efficient exploration of large search spaces. We conduct extensive experiments on tuning hyper-parameters, such as top-k retrieved documents, prompt compression ratio, and embedding methods, using the ALCE-ASQA and Natural Questions datasets. Our evaluation from jointly optimization all three hyper-parameters demonstrate that MAB-based online learning methods can achieve Recall@5 \(\approx 0.8\) for scenarios with prominent gradients in search space, using only \(\sim20%\) of the LLM API calls required by the Grid Search approach. Additionally, the proposed Hier-MAB approach outperforms other baselines in more challenging optimization scenarios. The code will be made available at https://aka.ms/autorag.

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