nlphuji/DOVE

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Declared licence: cdla-permissive-2.0, from the tagEvidence: README.md at b552dd9
Licence files: No licence file at the top level of the repositoryEvidence: file list at b552dd9
Declared sources: None listed in the card metadata
Access gated on Hugging Face: Yes

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Citation text from the card

@inproceedings{habba-etal-2025-dove,
    title = "{DOVE}: A Large-Scale Multi-Dimensional Predictions Dataset Towards Meaningful {LLM} Evaluation",
    author = "Habba, Eliya  and
      Arviv, Ofir  and
      Itzhak, Itay  and
      Perlitz, Yotam  and
      Bandel, Elron  and
      Choshen, Leshem  and
      Shmueli-Scheuer, Michal  and
      Stanovsky, Gabriel",
    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://aclanthology.org/2025.findings-acl.611/",
    doi = "10.18653/v1/2025.findings-acl.611",
    pages = "11744--11763",
    ISBN = "979-8-89176-256-5",
    abstract = "Recent work found that LLMs are sensitive to a wide range of arbitrary prompt dimensions, including the type of delimiters, answer enumerators, instruction wording, and more. This throws into question popular single-prompt evaluation practices. We present DOVE (Dataset Of Variation Evaluation) a large-scale dataset containing prompt perturbations of various evaluation benchmarks. In contrast to previous work, we examine LLM sensitivity from an holistic perspective, and assess the joint effects of perturbations along various dimensions, resulting in thousands of perturbations per instance. We evaluate se

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dataset_licence({ "id": "nlphuji/DOVE" })
signal: "unclear"
stated_licence: "cdla-permissive-2.0"
sources: 0