REVIEW 1 cited by
Oddballness: universal anomaly detection with language models
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original 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.
Forward citations
Cited by 1 Pith paper
-
Detecting Spelling and Grammatical Anomalies in Russian Poetry Texts
A new Russian poetry dataset and benchmark show that supervised classifiers trained on synthetic distortions detect grammatical defects (F0.5=0.86) while unsupervised and zero-shot methods perform near random.
Discussion (0). Continue with ORCID to comment.