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

TL;DR

XRAG presents an open-source, modular benchmark framework to systematically evaluate foundational components of advanced retrieval-augmented generation (RAG) systems across pre-retrieval, retrieval, post-retrieval, and generation. It couples a unified dataset and corpus format with a three-pronged evaluation suite (ConR, ConG, CogL) and a rich set of metrics to quantify both retrieval quality and generative fidelity, including semantic evaluations via GPT-$4$ Turbo. Through extensive experiments, XRAG demonstrates that advanced retrieval strategies (e.g., reranking, fusion retrieval) can substantially improve performance, while highlighting that golden-context evaluation does not always translate to optimal QA performance and that longer queries may help but more contexts do not linearly boost results. The framework aims to standardize RAG benchmarking, diagnose failure modes, and guide practical improvements, making it a valuable tool for researchers and developers building robust, scalable RAG systems.

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.

Figures (7)

• Figure 1: Schematic overview of the XRAG framework.
• Figure 2: The golden contextual distribution of the corpora across three datasets providing the quantity and length of annotated contexts. This distribution aids in analyzing the contextual structure, enabling a clearer understanding of how contexts vary in detail for each dataset.
• Figure 3: Comparison of retrieval and question-answering generation effects as query length varies. Experiments conducted on the HQA dataset (Test).
• Figure 4: Ridge plots of the impact of varying numbers of retrieved contexts on Q&A performance in the HQA dataset (Test). The chart shows maximum and minimum values.
• Figure 5: The typology of questions encompassed by HQA (HotpotQA), DQA (DropQA), and NQA (NaturalQA) is delineated through a heuristic extraction process. This process initiates at interrogative words or the prepositions that precede them. Blocks with low saturation denote suffixes that are too infrequent to be displayed individually.