Papers › RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework

RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework

2 Aug 2024arXiv:2408.01262archive 2025-07-28

Kunlun Zhu, Yifan Luo, Dingling Xu, Yukun Yan, Zhenghao Liu, Shi Yu, Ruobing Wang, Shuo Wang, Yishan Li, Nan Zhang, Xu Han, Zhiyuan Liu, Maosong Sun

Retrieval-Augmented Generation (RAG) is a powerful approach that enables large language models (LLMs) to incorporate external knowledge. However, evaluating the effectiveness of RAG systems in specialized scenarios remains challenging due to the high costs of data construction and the lack of suitable evaluation metrics. This paper introduces RAGEval, a framework designed to assess RAG systems across diverse scenarios by generating high-quality documents, questions, answers, and references through a schema-based pipeline. With a focus on factual accuracy, we propose three novel metrics: Completeness, Hallucination, and Irrelevance to evaluate LLM generated responses rigorously. Experimental results show that RAGEval outperforms zero-shot and one-shot methods in terms of clarity, safety, conformity, and richness of generated samples. Furthermore, the use of LLMs for scoring the proposed metrics demonstrates a high level of consistency with human evaluations. RAGEval establishes a new paradigm for evaluating RAG systems in real-world applications. The code and dataset are released at https://github.com/OpenBMB/RAGEval.

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Tasks

Benchmarking Dataset Generation General Knowledge Hallucination Question Answering RAG Retrieval Retrieval-augmented Generation

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Methods

Adam Attention Attention Dropout BART BERT BPE Dense Connections Dropout Focus Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention RAG Residual Connection Softmax Weight Decay WordPiece

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