---
title: "RAGVUE — paper by Murugaraj et al. (2026)"
description: "Assessing retrieval-augmented generation (RAG) pipelines is difficult because a bad answer may come from poor retrieval… 30 references."
authors: ["Keerthana Murugaraj", "Salima Lamsiyah", "Martin Theobald"]
year: 2026
venue: "Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 3: System Demonstrations)"
source: "https://aclanthology.org/2026.eacl-demo.35/"
doi: "10.18653/v1/2026.eacl-demo.35"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/murugaraj-2026-ragvue-diagnostic-view-explainable-automated/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/murugaraj-2026-ragvue-diagnostic-view-explainable-automated/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/16/) / RAGVUE: A Diagnostic View for Explainable and Automated Evaluation of Retrieval-Augmented Generation

# RAGVUE: A Diagnostic View for Explainable and Automated Evaluation of Retrieval-Augmented Generation

**Authors:** Keerthana Murugaraj, [Salima Lamsiyah](https://md.research.elamur.ai/free/en/authors/salima-lamsiyah/) and [Martin Theobald](https://md.research.elamur.ai/free/en/authors/martin-theobald/) · **Year:** 2026 · **Venue:** [Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 3: System Demonstrations)](https://md.research.elamur.ai/free/en/venues/eacl/) ([EACL 2026](https://md.research.elamur.ai/free/en/venues/eacl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.eacl-demo.35/) · [PDF](https://aclanthology.org/2026.eacl-demo.35.pdf) · [DOI 10.18653/v1/2026.eacl-demo.35](https://doi.org/10.18653/v1/2026.eacl-demo.35) · [OpenAlex](https://openalex.org/W7140078728)

**Citations:** 1 citations (OpenAlex, 2026-10-14)

## Summary
Assessing retrieval-augmented generation (RAG) pipelines is difficult because a bad answer may come from poor retrieval, weak generation, or claims not backed by the retrieved evidence. Popular evaluators tend to merge these into one number and apply lenient grounding checks. RAGVUE is a reference-free evaluation framework that uses LLM judges to score a pipeline along separate axes. These cover retrieval, answer quality, and grounding plus judge stability, and every score comes with a structured explanation. Its faithfulness check is deliberately strict. Each answer is broken into atomic claims, and claims about entities or dates count as supported only when they match the context exactly. A calibration score measures how much different LLM judges agree. Users can pick metrics manually or let an agentic orchestrator choose and aggregate them. The tool is available as a Python API, a CLI, and a local Streamlit app. On a multi-hop benchmark derived from StrategyQA, RAGVUE exposed fine-grained failures that RAGAS overlooked. This makes it useful for debugging RAG systems step by step.

## Key points
- The framework has seven metrics in three groups. Retrieval Relevance and Retrieval Coverage cover retrieval. Clarity, Answer Relevance and Answer Completeness cover answer quality. Strict Faithfulness and judge calibration cover grounding and stability.
- Retrieval Relevance is a precision-style metric. An LLM scores each chunk, and a chunk counts as relevant only at τ = 0.7 or above (highly useful evidence).
- Coverage and Completeness both rely on one shared set of atomic aspects taken from the question. Neither needs reference answers or reference contexts.
- Strict Faithfulness uses a single LLM call to place every claim in one of three categories: backed by the context, partly hallucinated, or entirely hallucinated. This differs from RAGAS's two-step semantic-inference pipeline.
- The paper appeared at EACL 2026 (System Demonstrations), and the code is at github.com/KeerthanaMurugaraj/RAGVue. The tool reports diagnostics but does not automatically fix the pipeline.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [University of Luxembourg](https://md.research.elamur.ai/free/en/universities/university-of-luxembourg/) — University, Luxembourg, Luxembourg · 28 works · 337 citations

📚 Shelves (5): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 17,450 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 17,296 works · [🗄️ Information Systems](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) · 1,730 works

🧭 Topics (1): [🔍 Information retrieval & search](https://md.research.elamur.ai/free/en/topics/information-retrieval/) · 552 works

## Cite

Cite as: Keerthana Murugaraj, Salima Lamsiyah and Martin Theobald. 2026. RAGVUE: A Diagnostic View for Explainable and Automated Evaluation of Retrieval-Augmented Generation. Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 3: System Demonstrations).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{murugaraj2026ragvue,
  title = {{RAGVUE: A Diagnostic View for Explainable and Automated Evaluation of Retrieval-Augmented Generation}},
  author = {Keerthana Murugaraj, Salima Lamsiyah and Martin Theobald},
  booktitle = {Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 3: System Demonstrations)},
  year = {2026},
  doi = {10.18653/v1/2026.eacl-demo.35},
  url = {https://doi.org/10.18653/v1/2026.eacl-demo.35}
}
```

- [← previous RAGTUM at SemEval-2026 Task 8: Contextual Query Rewriting and Dense Retrieval for Multi-Turn RAG](https://md.research.elamur.ai/free/en/papers/wigger-2026-ragtum-semeval-2026-task-8/)
- [next → Ramsa: A Large Sociolinguistically Rich Emirati Arabic Speech Corpus for ASR and TTS](https://md.research.elamur.ai/free/en/papers/sabbagh-2026-ramsa-large-sociolinguistically-rich-emirati/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/16/)

## References

30 references, 13 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. María Andrea Cruz Blandón, Jayasimha Talur, Bruno Charron, Dong Liu, Saab Mansour, and Marcello Federico. 2025. MEMERAG: A multilingual end-to-end meta-evaluation benchmark for retrieval augmented generation. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics… [find](https://scholar.google.com/scholar?q=Mar%C3%ADa+Andrea+Cruz+Bland%C3%B3n%2C+Jayasimha+Talur%2C+Bruno+Charron%2C+Dong+Liu%2C+Saab+Mansour%2C+and+Marcello+Federico%2E+2025%2E+MEMERAG%3A+A+multilingual+end%2Dto%2Dend+meta%2Devaluation+benchmark+for+retrieval+augmented+gen)
2. Shahul Es, Jithin James, Luis Espinosa Anke, and Steven Schockaert. 2024. [Ragas: Automated evaluation of retrieval augmented generation](https://md.research.elamur.ai/free/en/papers/es-2023-ragas-automated-evaluation-retrieval-augmented/). In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, pages 150–158.
3. Aoran Gan, Hao Yu, Kai Zhang, Qi Liu, Wenyu Yan, Zhenya Huang, Shiwei Tong, and Guoping Hu. 2025. Retrieval augmented generation evaluation in the era of large language models: A comprehensive survey. arXiv preprint arXiv:2504.14891. [find](https://scholar.google.com/scholar?q=Aoran+Gan%2C+Hao+Yu%2C+Kai+Zhang%2C+Qi+Liu%2C+Wenyu+Yan%2C+Zhenya+Huang%2C+Shiwei+Tong%2C+and+Guoping+Hu%2E+2025%2E+Retrieval+augmented+generation+evaluation+in+the+era+of+large+language+models%3A+A+comprehensive+survey%2E)
4. Mor Geva, Daniel Khashabi, Elad Segal, Tushar Khot, Dan Roth, and Jonathan Berant. 2021. Did aristotle use a laptop? a question answering benchmark with implicit reasoning strategies. Transactions of the Association for Computational Linguistics, 9:346– 361. [find](https://scholar.google.com/scholar?q=Mor+Geva%2C+Daniel+Khashabi%2C+Elad+Segal%2C+Tushar+Khot%2C+Dan+Roth%2C+and+Jonathan+Berant%2E+2021%2E+Did+aristotle+use+a+laptop%3F+a+question+answering+benchmark+with+implicit+reasoning+strategies%2E+Transactions+of+)
5. Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang. 2020. [Retrieval augmented language model pre-training](https://md.research.elamur.ai/free/en/papers/guu-2020-realm-retrieval-augmented-language-model/). In International conference on machine learning, pages 3929–3938. PMLR.
6. Gautier Izacard, Patrick Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni, Timo Schick, Jane Dwivedi-Yu, Armand Joulin, Sebastian Riedel, and Edouard Grave. 2023. [Atlas: few-shot learning with retrieval augmented language models](https://md.research.elamur.ai/free/en/papers/izacard-2022-atlas-few-shot-learning-retrieval/). J. Mach. Learn. Res., 24(1).
7. Yannis Katsis, Sara Rosenthal, Kshitij Fadnis, Chulaka Gunasekara, Young-Suk Lee, Lucian Popa, Vraj Shah, Huaiyu Zhu, Danish Contractor, and Marina Danilevsky. 2025. mtrag: A multi-turn conversational benchmark for evaluating retrieval-augmented generation systems. Transactions of the Association fo… [find](https://scholar.google.com/scholar?q=Yannis+Katsis%2C+Sara+Rosenthal%2C+Kshitij+Fadnis%2C+Chulaka+Gunasekara%2C+Young%2DSuk+Lee%2C+Lucian+Popa%2C+Vraj+Shah%2C+Huaiyu+Zhu%2C+Danish+Contractor%2C+and+Marina+Danilevsky%2E+2025%2E+mtrag%3A+A+multi%2Dturn+conversational)
8. Omar Khattab, Christopher Potts, and Matei Zaharia. 2021. [Relevance-guided supervision for openqa with colbert](https://md.research.elamur.ai/free/en/papers/khattab-2021-relevance-guided-supervision-openqa-colbert/). Transactions of the association for computational linguistics, 9:929–944.
9. Tom Kocmi and Christian Federmann. 2023. Large language models are state-of-the-art evaluators of translation quality. In Proceedings of the 24th Annual Conference of the European Association for Machine Translation, pages 193–203, Tampere, Finland. European Association for Machine Translation. [find](https://scholar.google.com/scholar?q=Tom+Kocmi+and+Christian+Federmann%2E+2023%2E+Large+language+models+are+state%2Dof%2Dthe%2Dart+evaluators+of+translation+quality%2E+In+Proceedings+of+the+24th+Annual+Conference+of+the+European+Association+for+Mach)
10. Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, and 1 others. 2020. [Retrieval-augmented generation for knowledge-intensive nlp tasks](https://md.research.elamur.ai/free/en/papers/lewis-2020-retrieval-augmented-generation-knowledge-intensive/). Advances in neural information processing systems, 33:9459–…
11. Kun Li, Yunxiang Li, Tianhua Zhang, Hongyin Luo, Xixin Wu, James R. Glass, and Helen M. Meng. 2025. RAG-zeval: Enhancing RAG responses evaluator through end-to-end reasoning and ranking-based reinforcement learning. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Proce… [find](https://scholar.google.com/scholar?q=Kun+Li%2C+Yunxiang+Li%2C+Tianhua+Zhang%2C+Hongyin+Luo%2C+Xixin+Wu%2C+James+R%2E+Glass%2C+and+Helen+M%2E+Meng%2E+2025%2E+RAG%2Dzeval%3A+Enhancing+RAG+responses+evaluator+through+end%2Dto%2Dend+reasoning+and+ranking%2Dbased+reinforc)
12. Shuliang Liu, Xinze Li, Zhenghao Liu, Yukun Yan, Cheng Yang, Zheni Zeng, Zhiyuan Liu, Maosong Sun, and Ge Yu. 2025. Judge as a judge: Improving the evaluation of retrieval-augmented generation through the judge-consistency of large language models. In Findings of the Association for Computational Li… [find](https://scholar.google.com/scholar?q=Shuliang+Liu%2C+Xinze+Li%2C+Zhenghao+Liu%2C+Yukun+Yan%2C+Cheng+Yang%2C+Zheni+Zeng%2C+Zhiyuan+Liu%2C+Maosong+Sun%2C+and+Ge+Yu%2E+2025%2E+Judge+as+a+judge%3A+Improving+the+evaluation+of+retrieval%2Daugmented+generation+through)
13. Yang Liu, Dan Iter, Yichong Xu, Shuohang Wang, Ruochen Xu, and Chenguang Zhu. 2023. [G-eval: NLG evaluation using gpt-4 with better human alignment](https://md.research.elamur.ai/free/en/papers/liu-2023-g-eval-nlg-evaluation-gpt/). In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 2511–2522, Singapore. Association for Computational Lin…
14. Yuxuan Liu, Tianchi Yang, Shaohan Huang, Zihan Zhang, Haizhen Huang, Furu Wei, Weiwei Deng, Feng Sun, and Qi Zhang. 2024. [Calibrating LLM-based evaluator](https://md.research.elamur.ai/free/en/papers/liu-2024-calibrating-llm-based-evaluator/). In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pag…
15. Potsawee Manakul, Adian Liusie, and Mark Gales. 2023. SelfCheckGPT: Zero-resource black-box hallucination detection for generative large language models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 9004–9017, Singapore. Association for Computation… [find](https://scholar.google.com/scholar?q=Potsawee+Manakul%2C+Adian+Liusie%2C+and+Mark+Gales%2E+2023%2E+SelfCheckGPT%3A+Zero%2Dresource+black%2Dbox+hallucination+detection+for+generative+large+language+models%2E+In+Proceedings+of+the+2023+Conference+on+Empir)
16. Sewon Min, Kalpesh Krishna, Xinxi Lyu, Mike Lewis, Wen-tau Yih, Pang Koh, Mohit Iyyer, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2023. [FActScore: Fine-grained atomic evaluation of factual precision in long form text generation](https://md.research.elamur.ai/free/en/papers/min-2023-factscore-fine-grained-atomic-evaluation/). In Proceedings of the 2023 Conference on Empirical Methods in Natural L…
17. Cheng Niu, Yuanhao Wu, Juno Zhu, Siliang Xu, Kashun Shum, Randy Zhong, Juntong Song, and Tong Zhang. 2024. [Ragtruth: A hallucination corpus for developing trustworthy retrieval-augmented language models](https://md.research.elamur.ai/free/en/papers/niu-2024-ragtruth-hallucination-corpus-developing-trustworthy/). In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volu…
18. Jie Ouyang, Tingyue Pan, Mingyue Cheng, Ruiran Yan, Yucong Luo, Jiaying Lin, and Qi Liu. 2025. HoH: A dynamic benchmark for evaluating the impact of outdated information on retrieval-augmented generation. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Vol… [find](https://scholar.google.com/scholar?q=Jie+Ouyang%2C+Tingyue+Pan%2C+Mingyue+Cheng%2C+Ruiran+Yan%2C+Yucong+Luo%2C+Jiaying+Lin%2C+and+Qi+Liu%2E+2025%2E+HoH%3A+A+dynamic+benchmark+for+evaluating+the+impact+of+outdated+information+on+retrieval%2Daugmented+generat)
19. Arjun Panickssery, Samuel R. Bowman, and Shi Feng. 2024. Llm evaluators recognize and favor their own generations. In Proceedings of the 38th International Conference on Neural Information Processing Systems, NIPS ’24, Red Hook, NY, USA. Curran Associates Inc. [find](https://scholar.google.com/scholar?q=Arjun+Panickssery%2C+Samuel+R%2E+Bowman%2C+and+Shi+Feng%2E+2024%2E+Llm+evaluators+recognize+and+favor+their+own+generations%2E+In+Proceedings+of+the+38th+International+Conference+on+Neural+Information+Processing+)
20. Xiangyu Peng, Prafulla Kumar Choubey, Caiming Xiong, and Chien-Sheng Wu. 2025. Unanswerability evaluation for retrieval augmented generation. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8452–8472, Vienna, Austria. Associat… [find](https://scholar.google.com/scholar?q=Xiangyu+Peng%2C+Prafulla+Kumar+Choubey%2C+Caiming+Xiong%2C+and+Chien%2DSheng+Wu%2E+2025%2E+Unanswerability+evaluation+for+retrieval+augmented+generation%2E+In+Proceedings+of+the+63rd+Annual+Meeting+of+the+Associati)
21. Sara Rosenthal, Avirup Sil, Radu Florian, and Salim Roukos. 2025. [CLAPnq: Cohesive long-form answers from passages in natural questions for RAG systems](https://md.research.elamur.ai/free/en/papers/rosenthal-2025-clapnq-cohesive-long-form-answers/). Transactions of the Association for Computational Linguistics, 13:53–72.
22. Jon Saad-Falcon, Omar Khattab, Christopher Potts, and Matei Zaharia. 2024. ARES: An automated evaluation framework for retrieval-augmented generation systems. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technolo… [find](https://scholar.google.com/scholar?q=Jon+Saad%2DFalcon%2C+Omar+Khattab%2C+Christopher+Potts%2C+and+Matei+Zaharia%2E+2024%2E+ARES%3A+An+automated+evaluation+framework+for+retrieval%2Daugmented+generation+systems%2E+In+Proceedings+of+the+2024+Conference+of+)
23. Kayla Schroeder and Zach Wood-Doughty. 2024. Can you trust llm judgments? reliability of llm-as-a-judge. arXiv preprint arXiv:2412.12509. — [arXiv:2412.12509](https://arxiv.org/abs/2412.12509) [find](https://scholar.google.com/scholar?q=Kayla+Schroeder+and+Zach+Wood%2DDoughty%2E+2024%2E+Can+you+trust+llm+judgments%3F+reliability+of+llm%2Das%2Da%2Djudge%2E+arXiv+preprint+arXiv%3A2412%2E12509%2E+%E2%80%94+%5BarXiv%3A2412%2E12509%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2412%2E12509%29)
24. Maojia Song, Shang Hong Sim, Rishabh Bhardwaj, Hai Leong Chieu, Navonil Majumder, and Soujanya Poria. 2024. Measuring and enhancing trustworthiness of llms in rag through grounded attributions and learning to refuse. arXiv preprint arXiv:2409.11242. — [arXiv:2409.11242](https://arxiv.org/abs/2409.11242) [find](https://scholar.google.com/scholar?q=Maojia+Song%2C+Shang+Hong+Sim%2C+Rishabh+Bhardwaj%2C+Hai+Leong+Chieu%2C+Navonil+Majumder%2C+and+Soujanya+Poria%2E+2024%2E+Measuring+and+enhancing+trustworthiness+of+llms+in+rag+through+grounded+attributions+and+lea)
25. Ionut Teodor Sorodoc, Leonardo F. R. Ribeiro, Rexhina Blloshmi, Christopher Davis, and Adrià de Gispert. 2025. GaRAGe: A benchmark with grounding annotations for RAG evaluation. In Findings of the Association for Computational Linguistics: ACL 2025, pages 17030–17049, Vienna, Austria. Association fo… [find](https://scholar.google.com/scholar?q=Ionut+Teodor+Sorodoc%2C+Leonardo+F%2E+R%2E+Ribeiro%2C+Rexhina+Blloshmi%2C+Christopher+Davis%2C+and+Adri%C3%A0+de+Gispert%2E+2025%2E+GaRAGe%3A+A+benchmark+with+grounding+annotations+for+RAG+evaluation%2E+In+Findings+of+the+Ass)
26. Jiaan Wang, Yunlong Liang, Fandong Meng, Zengkui Sun, Haoxiang Shi, Zhixu Li, Jinan Xu, Jianfeng Qu, and Jie Zhou. 2023. [Is ChatGPT a good NLG evaluator? a preliminary study](https://md.research.elamur.ai/free/en/papers/wang-2023-chatgpt-good-nlg-evaluator-preliminary/). In Proceedings of the 4th New Frontiers in Summarization Workshop, pages 1–11, Singapore. Association for Computational Lingu…
27. Hao Yu, Aoran Gan, Kai Zhang, Shiwei Tong, Qi Liu, and Zhaofeng Liu. 2024. Evaluation of retrieval-augmented generation: A survey. In CCF Conference on Big Data, pages 102–120. Springer. [find](https://scholar.google.com/scholar?q=Hao+Yu%2C+Aoran+Gan%2C+Kai+Zhang%2C+Shiwei+Tong%2C+Qi+Liu%2C+and+Zhaofeng+Liu%2E+2024%2E+Evaluation+of+retrieval%2Daugmented+generation%3A+A+survey%2E+In+CCF+Conference+on+Big+Data%2C+pages+102%E2%80%93120%2E+Springer%2E)
28. Zhirui Zeng, Jiamou Liu, Meng-Fen Chiang, Jialing He, and Zijian Zhang. 2025. S-RAG: A novel audit framework for detecting unauthorized use of personal data in RAG systems. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 10375… [find](https://scholar.google.com/scholar?q=Zhirui+Zeng%2C+Jiamou+Liu%2C+Meng%2DFen+Chiang%2C+Jialing+He%2C+and+Zijian+Zhang%2E+2025%2E+S%2DRAG%3A+A+novel+audit+framework+for+detecting+unauthorized+use+of+personal+data+in+RAG+systems%2E+In+Proceedings+of+the+63rd+)
29. Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P. Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. 2023. [Judging llm-as-a-judge with mt-bench and chatbot arena](https://md.research.elamur.ai/free/en/papers/zheng-2023-judging-llm-judge-mt-bench/). In Proceedings of the 37th International Conference on Neu…
30. 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](https://md.research.elamur.ai/free/en/papers/zhu-2025-rageval-scenario-specific-rag-evaluation/). In Proceedings of the 63rd Annual Meeting of the Association for Com…

*Updated 31 October 2026*