@inproceedings{murugaraj-etal-2026-ragvue,
    title = "{RAGVUE}: A Diagnostic View for Explainable and Automated Evaluation of Retrieval-Augmented Generation",
    author = "Murugaraj, Keerthana  and
      Lamsiyah, Salima  and
      Theobald, Martin",
    editor = "Croce, Danilo  and
      Leidner, Jochen  and
      Moosavi, Nafise Sadat",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
    month = mar,
    year = "2026",
    address = "Rabat, Marocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-demo.35/",
    pages = "512--526",
    ISBN = "979-8-89176-382-1",
    abstract = "Evaluating Retrieval-Augmented Generation(RAG) systems remains a challenging task: existingmetrics often collapse heterogeneous behaviorsinto single scores and provide little insightinto whether errors arise from retrieval,reasoning, or grounding. In this paper, we introduceRAGVUE, a diagnostic and explainableframework for automated, reference-freeevaluation of RAG pipelines. RAGVUE decomposesRAG behavior into retrieval quality,answer relevance and completeness, strictclaim-level faithfulness, and judge calibration.Each metric includes a structured explanation,making the evaluation process transparent. Ourframework supports both manual metric selectionand fully automated agentic evaluation. Italso provides a Python API, CLI, and a localStreamlit interface for interactive usage. Incomparative experiments, RAGVUE surfacesfine-grained failures that existing tools suchas RAGAS often overlook. We showcase thefull RAGVUE workflow and illustrate how itcan be integrated into research pipelines andpractical RAG development. The source codeand detailed instructions on usage are publiclyavailable on Github."
}