Summary of Autorag-hp: Automatic Online Hyper-parameter Tuning For Retrieval-augmented Generation, by Jia Fu et al.
AutoRAG-HP: Automatic Online Hyper-Parameter Tuning for Retrieval-Augmented Generation
by Jia Fu, Xiaoting Qin, Fangkai Yang, Lu Wang, Jue Zhang, Qingwei Lin, Yubo Chen, Dongmei Zhang, Saravan Rajmohan, Qi Zhang
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes the AutoRAG-HP framework, a novel approach to hyper-parameter optimization for Retrieval-Augmented Generation (RAG) systems. The framework formulates hyper-parameter tuning as an online multi-armed bandit problem and introduces a Hierarchical MAB method for efficient exploration of large search spaces. The authors conduct experiments on three key hyper-parameters using the ALCE-ASQA and Natural Questions datasets, demonstrating that MAB-based online learning methods can achieve high recall rates with reduced API calls compared to Grid Search. Additionally, the proposed Hier-MAB approach outperforms other baselines in challenging optimization scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to make language models work together more effectively. It’s like finding the right recipe for a cake – you need to adjust different ingredients to get the perfect mix. The authors came up with a new way to do this, called AutoRAG-HP, which is really good at finding the right settings. They tested it on two big datasets and showed that it can work much faster than other methods while still getting great results. |
Keywords
» Artificial intelligence » Grid search » Online learning » Optimization » Rag » Recall » Retrieval augmented generation
