Oddballness: universal anomaly detection with language models

Filip Gralinski; Ryszard Staruch; Krzysztof Jurkiewicz

COLING 2025main1 citations

Oddballness: universal anomaly detection with language models

Filip Gralinski, Ryszard Staruch, Krzysztof Jurkiewicz

Abstract

We present a new method to detect anomalies in texts (in general: in sequences of any data), using language models, in a totally unsupervised manner. The method considers probabilities (likelihoods) generated by a language model, but instead of focusing on low-likelihood tokens, it considers a new metric defined in this paper: oddballness. Oddballness measures how “strange” a given token is according to the language model. We demonstrate in grammatical error detection tasks (a specific case of text anomaly detection) that oddballness is better than just considering low-likelihood events, if a totally unsupervised setup is assumed.

BibTeX

@inproceedings{gralinski-etal-2025-oddballness,
    title = "Oddballness: universal anomaly detection with language models",
    author = "Gralinski, Filip  and
      Staruch, Ryszard  and
      Jurkiewicz, Krzysztof",
    editor = "Rambow, Owen  and
      Wanner, Leo  and
      Apidianaki, Marianna  and
      Al-Khalifa, Hend  and
      Eugenio, Barbara Di  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.coling-main.183/",
    pages = "2683--2689"
}