@inproceedings{helm-etal-2025-statistical,
    title = "Statistical inference on black-box generative models in the data kernel perspective space",
    author = "Helm, Hayden  and
      Acharyya, Aranyak  and
      Park, Youngser  and
      Duderstadt, Brandon  and
      Priebe, Carey",
    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/mtsummit-25-ingestion/2025.findings-acl.204/",
    doi = "10.18653/v1/2025.findings-acl.204",
    pages = "3955--3970",
    ISBN = "979-8-89176-256-5",
    abstract = "Generative models are capable of producing human-expert level content across a variety of topics and domains. As the impact of generative models grows, it is necessary to develop statistical methods to understand collections of available models. These methods are particularly important in settings where the user may not have access to information related to a model{'}s pre-training data, weights, or other relevant model-level covariates. In this paper we extend recent results on representations of black-box generative models to model-level statistical inference tasks. We demonstrate that the model-level representations are effective for multiple inference tasks."
}