CoFE-RAG: A Comprehensive Full-chain Evaluation Framework for Retrieval-Augmented Generation with Enhanced Data Diversity (2410.12248v1)

Abstract: Retrieval-Augmented Generation (RAG) aims to enhance LLMs to generate more accurate and reliable answers with the help of the retrieved context from external knowledge sources, thereby reducing the incidence of hallucinations. Despite the advancements, evaluating these systems remains a crucial research area due to the following issues: (1) Limited data diversity: The insufficient diversity of knowledge sources and query types constrains the applicability of RAG systems; (2) Obscure problems location: Existing evaluation methods have difficulty in locating the stage of the RAG pipeline where problems occur; (3) Unstable retrieval evaluation: These methods often fail to effectively assess retrieval performance, particularly when the chunking strategy changes. To tackle these challenges, we propose a Comprehensive Full-chain Evaluation (CoFE-RAG) framework to facilitate thorough evaluation across the entire RAG pipeline, including chunking, retrieval, reranking, and generation. To effectively evaluate the first three phases, we introduce multi-granularity keywords, including coarse-grained and fine-grained keywords, to assess the retrieved context instead of relying on the annotation of golden chunks. Moreover, we release a holistic benchmark dataset tailored for diverse data scenarios covering a wide range of document formats and query types. We demonstrate the utility of the CoFE-RAG framework by conducting experiments to evaluate each stage of RAG systems. Our evaluation method provides unique insights into the effectiveness of RAG systems in handling diverse data scenarios, offering a more nuanced understanding of their capabilities and limitations.

Comprehensive Evaluation Framework for RAG Systems: An Analysis of CoFE-RAG

The paper "CoFE-RAG: A Comprehensive Full-chain Evaluation Framework for Retrieval-Augmented Generation with Enhanced Data Diversity" introduces an evaluation framework aimed at assessing the full pipeline of Retrieval-Augmented Generation (RAG) systems. This work addresses specific challenges within RAG systems, focusing on the diversity of data sources, problem localization across various stages, and the instability of retrieval evaluations due to changes in chunking strategies.

Key Challenges in RAG Evaluation

The paper identifies three central issues plaguing current evaluation methods in RAG systems:

1. Limited Data Diversity: Existing evaluation frameworks predominantly utilize well-structured text from HTML sources, limiting their applicability to varied document formats like PDF and Excel. Moreover, they mainly process factual queries, disregarding the complex demands of analytical, comparative, and tutorial queries.
2. Obscure Problem Localization: Current methods typically observe only the final outputs, making it difficult to pinpoint where within the RAG pipeline the system falters, reducing interpretability and optimization efficiency.
3. Unstable Retrieval Evaluation: Dependency on "golden chunks" for evaluating retrieval models introduces labor-intensive annotations and instability upon modifying chunking strategies.

The CoFE-RAG Framework

To address these issues, the CoFE-RAG framework offers a comprehensive evaluation mechanism that spans the entire RAG pipeline: chunking, retrieval, reranking, and generation. The framework uses multi-granularity keywords instead of golden chunks, thereby facilitating the evaluation of varying chunking strategies without the need for exhaustive annotations.

• Coarse-grained Keywords: Serve as general indicators of relevance between queries and contexts.
• Fine-grained Keywords: Provide detailed references for assessing the retrieval and reranking stages, enhancing robustness against diverse data scenarios.

Dataset and Methodology

The authors present a dataset covering a broad spectrum of document formats, such as PDFs and spreadsheets, catering to diverse query types. This dataset is used to benchmark existing methods, offering insights into their operational strengths and weaknesses. The CoFE-RAG framework evaluates retrieval models using accuracy and recall metrics and assesses generation quality using BLEU, Rouge-L, and task-specific correctness metrics.

Experimental Insights

The research offers empirical analysis across multiple embedding and LLMs. Findings indicate that:

• Existing retrieval models show effectiveness primarily with factual queries, struggling with more dynamic analytical, comparative, and tutorial queries.
• Retraining models used in CoFE-RAG, although improving context relevance, are still limited by their dependency on initial retrieval quality.
• LLMs, like GPT-4, demonstrated superior performance, yet highlighted the need for richer retrieval strategies to leverage retrieved contexts effectively.

Implications and Future Directions

CoFE-RAG represents a significant step towards improving the evaluation of RAG systems by providing a more granular understanding of each stage's contribution to overall system performance. Its emphasis on diverse data handling and stage-specific evaluation offers promising implications for real-world applications of RAG systems. Future research might focus on enhancing reranking strategies and devising novel retrieval mechanisms that accommodate complex queries, further refining the robustness of RAG systems.

The release of the CoFE-RAG dataset and accompanying evaluation code marks an advance for researchers and practitioners aiming to develop and assess the next generation of RAG models, promoting improvements in both algorithmic performance and practical applicability.