XRAG: eXamining the Core -- Benchmarking Foundational Components in Advanced Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2412.15529v2
• Date: Tue, 24 Dec 2024 10:32:13 GMT
• Title: XRAG: eXamining the Core -- Benchmarking Foundational Components in Advanced Retrieval-Augmented Generation
• Authors: Qianren Mao, Yangyifei Luo, Jinlong Zhang, Hanwen Hao, Zhilong Cao, Xiaolong Wang, Xiao Guan, Zhenting Huang, Weifeng Jiang, Shuyu Guo, Zhentao Han, Qili Zhang, Siyuan Tao, Yujie Liu, Junnan Liu, Zhixing Tan, Jie Sun, Bo Li, Xudong Liu, Richong Zhang, Jianxin Li,
• Abstract summary: Retrieval-augmented generation (RAG) synergizes the retrieval of pertinent data with the generative capabilities of Large Language Models (LLMs) We introduce XRAG, an open-source, modular that facilitates exhaustive evaluation of the performance of foundational components of advanced RAG modules.
• Score: 37.78210992036775
• License:
• Abstract: Retrieval-augmented generation (RAG) synergizes the retrieval of pertinent data with the generative capabilities of Large Language Models (LLMs), ensuring that the generated output is not only contextually relevant but also accurate and current. We introduce XRAG, an open-source, modular codebase that facilitates exhaustive evaluation of the performance of foundational components of advanced RAG modules. These components are systematically categorized into four core phases: pre-retrieval, retrieval, post-retrieval, and generation. We systematically analyse them across reconfigured datasets, providing a comprehensive benchmark for their effectiveness. As the complexity of RAG systems continues to escalate, we underscore the critical need to identify potential failure points in RAG systems. We formulate a suite of experimental methodologies and diagnostic testing protocols to dissect the failure points inherent in RAG engineering. Subsequently, we proffer bespoke solutions aimed at bolstering the overall performance of these modules. Our work thoroughly evaluates the performance of advanced core components in RAG systems, providing insights into optimizations for prevalent failure points.

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