RAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generation

Abstract: Despite Retrieval-Augmented Generation (RAG) showing promising capability in leveraging external knowledge, a comprehensive evaluation of RAG systems is still challenging due to the modular nature of RAG, evaluation of long-form responses and reliability of measurements. In this paper, we propose a fine-grained evaluation framework, RAGChecker, that incorporates a suite of diagnostic metrics for both the retrieval and generation modules. Meta evaluation verifies that RAGChecker has significantly better correlations with human judgments than other evaluation metrics. Using RAGChecker, we evaluate 8 RAG systems and conduct an in-depth analysis of their performance, revealing insightful patterns and trade-offs in the design choices of RAG architectures. The metrics of RAGChecker can guide researchers and practitioners in developing more effective RAG systems. This work has been open sourced at https://github.com/amazon-science/RAGChecker.

• The paper presents a novel claim-level evaluation framework that diagnoses both retriever and generator components in RAG systems.
• The methodology integrates semantic-based metrics to provide granular insights into faithfulness, noise sensitivity, and hallucinations.
• The experiments reveal that improved retriever quality and larger generator models significantly boost precision, recall, and overall performance.

A Detailed Analysis of "RAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generation"

Introduction

The introduction of the "RAGChecker" framework addresses the increasing complexity in evaluating retrieval-augmented generation (RAG) systems effectively. These systems, which enhance LLMs by incorporating external knowledge bases, have found application in various fields. RAG systems face significant evaluation challenges due to their modular nature consisting of retrievers and generators. RAGChecker offers a sophisticated diagnostic framework to evaluate each component and their interactions thoroughly.

Modular Challenges and Metric Limitations

Evaluating RAG systems with accuracy presents substantial difficulties, especially given their modular configuration. Existing metrics often rely on rule-based or coarse-grained evaluations that cannot capture the full scope necessary for effective diagnostics:

• Retriever Metrics like recall@k and MRR hinge on annotated chunks, neglecting broader semantic scopes.
• Generator Metrics using BLEU, ROUGE, or BERTScore perform well on concise answers but miss nuances in lengthy responses.

The significant challenge lies in seamlessly integrating semantic-based metrics that can reconcile the intricacies of both retrieval and generation processes.

Figure 1: Illustration of the proposed metrics in RagChecker. The upper Venn diagram represents potential model response errors relative to the ground truth, with evaluation metrics presented below.

RagChecker Framework Structure

RagChecker introduces a claim-level entailment-based evaluation. It processes queries along with retrieved contexts, responses, and ground-truth answers to deliver comprehensive metrics that evaluate RAG systems wholly and at module levels:

1. Overall Metrics: Provide a system-wide perspective through claim precision and recall, crucial for assessing a system's capacity to generate complete and accurate responses.
2. Retriever Metrics: These evaluate how many relevant chunks are retrieved (context precision) and how many claims from ground truth are covered (claim recall).
3. Generator Metrics: Analyze the generator's effectiveness by measuring faithfulness to the context, noise sensitivity, hallucination, and the reliance on external knowledge versus self-knowledge.

Experimental Insights

Experiments with eight state-of-the-art RAG systems on a curated benchmark revealed several pivotal insights:

• Retriever Importance: Better retrievers like E5-Mistral paired with strong generators significantly improve precision, recall, and F1 scores.
• Generator Model Size: Larger models generally exhibit enhanced performance across all metrics due to better handling of retrieval complexities and context utilization.
• Faithfulness and Context Utilization: More informative context generally increases faithfulness and decreases hallucination but increases sensitivity to noise.

Diagnosis of RAG System Settings

Adjusting parameters like the number of chunks, chunk size, and generation prompts demonstrates the flexibility and diagnostic power of RagChecker:

• Increasing Context Amount: Leads to better faithfulness but also raises noise sensitivity.
• Prompt Requirements: Explicit prompts improve faithfulness and context utilization but highlight the complexity in optimizing all aspects simultaneously.

Conclusion

RagChecker elevates the standard for RAG system evaluation, equipping researchers and developers with actionable insights into system behavior. Future research could focus on refining retriever diagnostics and expanding RagChecker's applicability across diverse modalities and languages for more comprehensive RAG system analysis.