@inproceedings{jiang-etal-2026-flare,
    title = "{FLARE}: Task-Agnostic Embedding Model Evaluation via Normalizing Flows",
    author = "Jiang, Jingzhou  and
      Tang, Yixuan  and
      Yang, Yi  and
      Tam, Kar Yan",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1957/",
    pages = "39271--39294",
    ISBN = "979-8-89176-395-1",
    abstract = "Despite the widespread adoption of text embedding models, selecting the optimal model for a specific target corpus remains challenging due to the lack of task-specific labels. While task-agnostic evaluation offers a promising solution by relying on unlabeled data, existing approaches based on kernel estimators or Gaussian mixtures fail to model high-dimensional distributions effectively, resulting in unstable rankings. To address this limitation, we propose FLARE (Flow-based Label-free Assessment of Representation Embeddings), which employs normalizing flows to estimate information sufficiency in high-dimensional spaces. By learning invertible transformations, flows enable exact density estimation while mitigating the instability inherent in distance-based methods. We provide theoretical guarantees showing that our estimation error depends on the data{'}s intrinsic structure rather than its raw dimensionality. Experiments across 11 datasets demonstrate that FLARE achieves a strong Spearman{'}s {\ensuremath{\rho}} (up to 0.90) with supervised benchmarks, remaining robust even for high-dimensional embeddings (d {\ensuremath{\geq}} 3,584)."
}