@inproceedings{zhu-etal-2025-rageval,
title = "{RAGE}val: Scenario Specific {RAG} Evaluation Dataset Generation Framework",
author = "Zhu, Kunlun and
Luo, Yifan and
Xu, Dingling and
Yan, Yukun and
Liu, Zhenghao and
Yu, Shi and
Wang, Ruobing and
Wang, Shuo and
Li, Yishan and
Zhang, Nan and
Han, Xu and
Liu, Zhiyuan and
Sun, Maosong",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.418/",
doi = "10.18653/v1/2025.acl-long.418",
pages = "8520--8544",
ISBN = "979-8-89176-251-0",
abstract = "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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<abstract>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.</abstract>
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%0 Conference Proceedings
%T RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework
%A Zhu, Kunlun
%A Luo, Yifan
%A Xu, Dingling
%A Yan, Yukun
%A Liu, Zhenghao
%A Yu, Shi
%A Wang, Ruobing
%A Wang, Shuo
%A Li, Yishan
%A Zhang, Nan
%A Han, Xu
%A Liu, Zhiyuan
%A Sun, Maosong
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F zhu-etal-2025-rageval
%X 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.
%R 10.18653/v1/2025.acl-long.418
%U https://aclanthology.org/2025.acl-long.418/
%U https://doi.org/10.18653/v1/2025.acl-long.418
%P 8520-8544
Markdown (Informal)
[RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework](https://aclanthology.org/2025.acl-long.418/) (Zhu et al., ACL 2025)
ACL
- Kunlun Zhu, Yifan Luo, Dingling Xu, Yukun Yan, Zhenghao Liu, Shi Yu, Ruobing Wang, Shuo Wang, Yishan Li, Nan Zhang, Xu Han, Zhiyuan Liu, and Maosong Sun. 2025. RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8520–8544, Vienna, Austria. Association for Computational Linguistics.