A Comprehensive Evaluation Framework for Retrieval-Augmented Generation Systems: CoFE-RAG

Kernkonzepte

CoFE-RAG is a novel evaluation framework designed to comprehensively assess all stages of Retrieval-Augmented Generation (RAG) systems, addressing limitations of existing methods by incorporating diverse data scenarios and multi-granularity keywords for enhanced analysis.

Zusammenfassung

This research paper introduces CoFE-RAG, a novel framework for evaluating Retrieval-Augmented Generation (RAG) systems.

Research Objective: The study aims to address the limitations of existing RAG evaluation methods, which often lack data diversity, struggle to pinpoint specific pipeline issues, and rely heavily on unstable retrieval evaluations based on golden chunks.

Methodology: CoFE-RAG divides the RAG process into four stages: chunking, retrieval, reranking, and generation. It introduces multi-granularity keywords (coarse-grained and fine-grained) to assess retrieved context, moving away from golden chunk annotations. The researchers also created a diverse benchmark dataset encompassing various document formats and query types (factual, analytical, comparative, and tutorial).

Key Findings: Experiments using CoFE-RAG revealed that existing retrieval models excel in handling factual queries but struggle with more complex types. Similarly, current LLMs show limitations in effectively leveraging retrieved context for accurate and reliable responses. Notably, the bge-large model demonstrated superior retrieval performance, while bge-reranker-large excelled in reranking. GPT-4 outperformed other LLMs in generation tasks.

Main Conclusions: CoFE-RAG provides a more robust and comprehensive evaluation framework for RAG systems, offering insights into their capabilities and limitations across diverse data scenarios. The research highlights the need for further advancements, particularly in handling complex queries and diverse knowledge sources.

Significance: This research significantly contributes to the field of RAG evaluation by providing a more reliable and insightful framework. The proposed dataset and multi-granularity keyword approach address critical limitations in existing methods, paving the way for developing more effective and robust RAG systems.

Limitations and Future Research: While CoFE-RAG offers a significant advancement, the authors acknowledge the potential for expanding the dataset and exploring alternative evaluation metrics. Future research could also focus on developing more sophisticated retrieval and reranking models capable of handling complex queries and diverse data sources effectively.

Statistiken

The majority of the documents used were created in recent years, with a considerable portion dating from 2024, surpassing the knowledge cutoff range of many widely used LLMs.
The overall acceptance rate for synthetic data after manual review is 60.2%.
Factual queries account for the largest proportion (47.4%) of the dataset, while tutorial queries have the smallest proportion (8.6%).
The bge-large model achieved the highest Recall and Accuracy across all query types in retrieval tasks.
GPT-4 achieved the best results across various LLMs in generation tasks, significantly outperforming other models.
Using a chunk size of 512 tokens yielded better retrieval, reranking, and generation performance compared to smaller chunk sizes.

Zitate

Wichtige Erkenntnisse aus

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

by Jintao Liu, ... um arxiv.org 10-17-2024

https://arxiv.org/pdf/2410.12248.pdf