AutoRAG-HP: Automatic Online Hyper-Parameter Tuning for Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2406.19251v1
• Date: Thu, 27 Jun 2024 15:18:21 GMT
• Title: AutoRAG-HP: Automatic Online Hyper-Parameter Tuning for Retrieval-Augmented Generation
• Authors: Jia Fu, Xiaoting Qin, Fangkai Yang, Lu Wang, Jue Zhang, Qingwei Lin, Yubo Chen, Dongmei Zhang, Saravan Rajmohan, Qi Zhang,
• Abstract summary: Recent advancements in Large Language Models have transformed ML/AI development. Recent advancements in Large Language Models have transformed AutoML principles for the Retrieval-Augmented Generation (RAG) systems.
• Score: 37.456499537121886
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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