Towards Token-Level Text Anomaly Detection

• URL: http://arxiv.org/abs/2601.13644v1
• Date: Tue, 20 Jan 2026 06:27:09 GMT
• Title: Towards Token-Level Text Anomaly Detection
• Authors: Yang Cao, Bicheng Yu, Sikun Yang, Ming Liu, Yujiu Yang,
• Abstract summary: We introduce token-level anomaly detection, a novel paradigm that enables fine-grained localization of anomalies within text.<n>We formally define text anomalies at both document and token-levels, and propose a unified detection framework that operates across multiple levels.
• Score: 48.821180044375176
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Despite significant progress in text anomaly detection for web applications such as spam filtering and fake news detection, existing methods are fundamentally limited to document-level analysis, unable to identify which specific parts of a text are anomalous. We introduce token-level anomaly detection, a novel paradigm that enables fine-grained localization of anomalies within text. We formally define text anomalies at both document and token-levels, and propose a unified detection framework that operates across multiple levels. To facilitate research in this direction, we collect and annotate three benchmark datasets spanning spam, reviews and grammar errors with token-level labels. Experimental results demonstrate that our framework get better performance than other 6 baselines, opening new possibilities for precise anomaly localization in text. All the codes and data are publicly available on https://github.com/charles-cao/TokenCore.

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