DiFA: Dual Evidence Fusion and Aggregation for Token-Level Text Anomaly Detection
A Dual-evidence framework with adaptive Fusion and Aggregation (DiFA) for token-level anomaly detection, which derives anomaly scores from form-structural and semantic views to capture visible structural abnormality and contextual inconsistency, thereby providing complementary evidence for identifying diverse anomalies...
Yan-Yu Qian, Peng-Cheng Weng, Yue Tan et al.
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