Papers with text anomaly detection
Oddballness: universal anomaly detection with language models (2025.coling-main)
Copied to clipboard
| Challenge: | a new method to detect anomalies in texts uses a metric called oddballness . the method considers probabilities generated by a language model but not low-likelihood tokens . |
| Approach: | They propose a method to detect anomalies in texts using unsupervised language models . they define oddballness as a function that measures how strange a given token is . |
| Outcome: | The proposed method is better than state-of-the-art models for grammatical error detection tasks. |
Text Anomaly Detection with Simplified Isolation Kernel (2025.findings-emnlp)
Copied to clipboard
| Challenge: | High-dimensional dense embeddings extracted by large language models pose memory requirements and high computation time. |
| Approach: | They propose a method that maps high-dimensional dense embeddings to lower-dimensional sparse representations while preserving crucial anomaly characteristics. |
| Outcome: | The proposed method achieves better detection performance than 11 SOTA anomaly detection algorithms while maintaining computational efficiency and low memory cost. |