@inproceedings{fihey-etal-2026-enhancing,
    title = "Enhancing Two Steps Textual Anomaly Detection through Anisotropy Mitigation",
    author = "Fihey, Pierre  and
      Labeau, Matthieu  and
      Mozharovskyi, Pavlo",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1312/",
    pages = "28442--28464",
    ISBN = "979-8-89176-390-6",
    abstract = "Anomaly detection aims at distinguishing between \textit{in-distribution} samples, which belong to the same distribution as the training set, and \textit{out-of-distribution} samples, which lie outside of it. In textual anomaly detection, recent approaches routinely apply anomaly detection algorithms directly to embeddings extracted from pre-trained embedding models (\textit{two-stage approaches}). However, the geometric properties of pre-trained embeddings can hinder the effectiveness of detection algorithms, which often rely on distance-based measures. In this work, we first highlight the relevance of similarity-trained models for textual anomaly detection. Beyond being trained to capture semantic similarities, these models also exhibit geometric properties that appear better suited to detection algorithms. We further demonstrate that, besides model choice, a simple post-processing step can significantly improve anomaly detection by adapting embeddings to the assumptions made by classical detection algorithms. The bulk of our experiments is done on a reformulation of the classification tasks from the MTEB benchmark into anomaly detection tasks."
}