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

2024-12-20

AI Summary

To address the challenges of diagnosing performance bottlenecks in Retrieval-Augmented Generation (RAG) systems and the lack of fine-grained evaluation criteria for component-level optimization, this paper introduces XRAG—the first modular, open-source framework and diagnostic evaluation paradigm targeting RAG’s foundational components. Methodologically, we decouple RAG into four sequential stages: pre-retrieval, retrieval, post-retrieval, and generation; and design a cross-dataset reconfiguration protocol, component-level performance profiling techniques, and a failure attribution methodology. Our key contributions are: (1) a reusable, fine-grained benchmarking suite; (2) systematic identification of prevalent failure modes across stages for mainstream RAG components; and (3) targeted optimization strategies. Experiments demonstrate significant improvements in end-to-end accuracy and robustness. XRAG establishes a new paradigm for interpretable RAG optimization and practical deployment.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• RAG Optimization
• Information Retrieval
• Content Generation

Innovation

• XRAG
• RAG Optimization
• Performance Evaluation