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Overcoming the "Impracticality" of RAG: Proposing a Real-World Benchmark and Multi-Dimensional Diagnostic Framework

Kenichirou Narita, Siqi Peng, Taku Fukui, Moyuru Yamada, Satoshi Munakata, Satoru Takahashi

Abstract

Performance evaluation of Retrieval-Augmented Generation (RAG) systems within enterprise environments is governed by multi-dimensional and composite factors extending far beyond simple final accuracy checks. These factors include reasoning complexity, retrieval difficulty, the diverse structure of documents, and stringent requirements for operational explainability. Existing academic benchmarks fail to systematically diagnose these interlocking challenges, resulting in a critical gap where models achieving high performance scores fail to meet the expected reliability in practical deployment. To bridge this discrepancy, this research proposes a multi-dimensional diagnostic framework by defining a four-axis difficulty taxonomy and integrating it into an enterprise RAG benchmark to diagnose potential system weaknesses.

Overcoming the "Impracticality" of RAG: Proposing a Real-World Benchmark and Multi-Dimensional Diagnostic Framework

Abstract

Performance evaluation of Retrieval-Augmented Generation (RAG) systems within enterprise environments is governed by multi-dimensional and composite factors extending far beyond simple final accuracy checks. These factors include reasoning complexity, retrieval difficulty, the diverse structure of documents, and stringent requirements for operational explainability. Existing academic benchmarks fail to systematically diagnose these interlocking challenges, resulting in a critical gap where models achieving high performance scores fail to meet the expected reliability in practical deployment. To bridge this discrepancy, this research proposes a multi-dimensional diagnostic framework by defining a four-axis difficulty taxonomy and integrating it into an enterprise RAG benchmark to diagnose potential system weaknesses.

Paper Structure

This paper contains 20 sections, 2 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Diagnostic Capability
  4. Real-world Complexity
  5. Evidence Traceability
  6. Problems and Challenges
  7. Taxonomy of Enterprise RAG Difficulty
  8. Dimension 1: Reasoning Complexity
  9. Dimension 2: Retrieval Difficulty
  10. Dimension 3: Source Structure & Modality
  11. Dimension 4: Explainability Requirement
  12. Proposed Benchmark Dataset
  13. Experiment
  14. Diagnostic Metrics
  15. Results
  16. ...and 5 more sections

Figures (2)

  • Figure 1: An overview of evaluation framework of our benchmark dataset. Each test query is tagged with difficulty labels based on our original taxonomy of RAG difficulty, which help provide a more comprehensive evaluation of the RAG system.
  • Figure 2: Examples of Evidence Chunk. (Source: Ministry of Internal Affairs and Communications (MIC), Japan. (2024). Information and Communications in Japan White Paper 2024 (English Version). p.133. (https://www.soumu.go.jp/johotsusintokei/whitepaper/eng/WP2024/2024-index.html)).