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

TL;DR

This paper introduces XRAG, a comprehensive benchmark and diagnostic framework for evaluating and improving the core components of retrieval-augmented generation systems, enhancing their accuracy and robustness.

Contribution

We present XRAG, an open-source modular benchmark and diagnostic toolkit for systematically evaluating and optimizing RAG system components across multiple stages.

Findings

Identified key failure points in RAG components

Provided systematic evaluation protocols for RAG modules

Proposed targeted solutions to improve RAG performance

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.