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Too Sure to Be Safe: Model Calibration for Reliable Log Anomaly Detection
Online log anomaly detection is critical for maintaining the reliability of large-scale computing systems. Although recent language model-based log anomaly detectors achieve strong detection performance, their confidence estimates remain poorly calibrated. We show that these detectors frequently ass
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