Oddballness: universal anomaly detection with language models
Computation and Language
2024-09-06 v1
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 introduced 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.
Cite
@article{arxiv.2409.03046,
title = {Oddballness: universal anomaly detection with language models},
author = {Filip Graliński and Ryszard Staruch and Krzysztof Jurkiewicz},
journal= {arXiv preprint arXiv:2409.03046},
year = {2024}
}