RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework
<|reference_start|>RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework: Retrieval-Augmented Generation (RAG) systems have demonstrated their advantages in alleviating the hallucination of Large Language Models (LLMs). Existing RAG benchmarks mainly focus on evaluating whether LLMs can correctly answer the general knowledge. However, they are unable to evaluate the effectiveness of the RAG system in dealing with the data from different vertical domains. This paper introduces RAGEval, a framework for automatically generating evaluation datasets to evaluate the knowledge usage ability of different LLMs in different scenarios. Specifically, RAGEval summarizes a schema from seed documents, applies the configurations to generate diverse documents, and constructs question-answering pairs according to both articles and configurations. We propose three novel metrics, Completeness, Hallucination, and Irrelevance, to carefully evaluate the responses generated by LLMs. By benchmarking RAG models in vertical domains, RAGEval has the ability to better evaluate the knowledge usage ability of LLMs, which avoids the confusion regarding the source of knowledge in answering question in existing QA datasets--whether it comes from parameterized memory or retrieval. The code and dataset will be released.<|reference_end|>
@article{zhu2024rageval:,
    title={RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework},
    author={Kunlun Zhu, Yifan Luo, Dingling Xu, Ruobing Wang, Shi Yu, Shuo Wang,
  Yukun Yan, Zhenghao Liu, Xu Han, Zhiyuan Liu, Maosong Sun},
    journal={arXiv preprint arXiv:2408.01262},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.01262},
    primaryClass={cs.CL cs.IR}
}