@inproceedings{chernogorskii-etal-2026-dragon,
    title = "{DRAGO}n: Designing {RAG} On Periodically Updated Corpus",
    author = "Chernogorskii, Fedor  and
      Averkiev, Sergei  and
      Kudraleeva, Liliya  and
      Martirosian, Zaven  and
      Tikhonova, Maria  and
      Malykh, Valentin  and
      Fenogenova, Alena",
    editor = "Baez Santamaria, Selene  and
      Somayajula, Sai Ashish  and
      Yamaguchi, Atsuki",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 4: Student Research Workshop)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-srw.48/",
    pages = "622--638",
    ISBN = "979-8-89176-383-8",
    abstract = "This paper introduces \textbf{DRAGOn}, method to design a RAG benchmark on a regularly updated corpus. It features recent reference datasets, a question generation framework, an automatic evaluation pipeline, and a public leaderboard.Specified reference datasets allow for uniform comparison of RAG systems, while newly generated dataset versions mitigate data leakage and ensure that all models are evaluated on unseen, comparable data.The pipeline for automatic question generation extracts the Knowledge Graph from the text corpus and produces multiple question-answer pairs utilizing modern LLM capabilities.A set of diverse LLM-as-Judge metrics is provided for a comprehensive model evaluation.We used Russian news outlets to form the datasets and demonstrate our methodology. We launch a public leaderboard to track the development of RAG systems and encourage community participation."
}