@inproceedings{sorodoc-etal-2025-garage,
    title = "{G}a{RAG}e: A Benchmark with Grounding Annotations for {RAG} Evaluation",
    author = "Sorodoc, Ionut Teodor  and
      Ribeiro, Leonardo F. R.  and
      Blloshmi, Rexhina  and
      Davis, Christopher  and
      de Gispert, Adri{\`a}",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/display_plenaries/2025.findings-acl.875/",
    pages = "17030--17049",
    ISBN = "979-8-89176-256-5",
    abstract = "We present GaRAGe, a large RAG benchmark with human-curated long-form answers and annotations of each grounding passage, allowing a fine-grained evaluation of whether LLMs can identify relevant grounding when generating RAG answers. Our benchmark contains 2366 questions of diverse complexity, dynamism, and topics, and includes over 35K annotated passages retrieved from both private document sets and the Web, to reflect real-world RAG use cases. This makes it an ideal test bed to evaluate an LLM{'}s ability to identify only the relevant information necessary to compose a response, or provide a deflective response when there is insufficient information. Evaluations of multiple state-of-the-art LLMs on GaRAGe show that the models tend to over-summarise rather than (a) ground their answers strictly on the annotated relevant passages (reaching at most a Relevance-Aware Factuality Score of 60{\%}), or (b) deflect when no relevant grounding is available (reaching at most 31{\%} true positive rate in deflections). The F$_{1}$ in attribution to relevant sources is at most 58.9{\%}, and we show that performance is particularly reduced when answering time-sensitive questions and when having to draw knowledge from sparser private grounding sources."
}