AutoRAG-HP: Automatic Online Hyper-Parameter Tuning for Retrieval-Augmented Generation
<|reference_start|>AutoRAG-HP: Automatic Online Hyper-Parameter Tuning for Retrieval-Augmented Generation: 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.<|reference_end|>
@article{fu2024autorag-hp:,
    title={AutoRAG-HP: Automatic Online Hyper-Parameter Tuning for
  Retrieval-Augmented Generation},
    author={Jia Fu, Xiaoting Qin, Fangkai Yang, Lu Wang, Jue Zhang, Qingwei Lin,
  Yubo Chen, Dongmei Zhang, Saravan Rajmohan, Qi Zhang},
    journal={arXiv preprint arXiv:2406.19251},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.19251},
    primaryClass={cs.CL cs.AI}
}