What is the correlation between Llama3's cross-domain anomaly detection accuracy and the percentage of energy-specific tokens in its training data

Abstract

Abstract: Graph Anomaly Detection (GAD) has demonstrated great effectiveness in identifying unusual patterns within graph-structured data. However, while labeled anomalies are often scarce in emerging applications, existing supervised GAD approaches are either ineffective or not applicable when moved across graph domains due to distribution shifts and heterogeneous feature spaces. To address these challenges, we present GADT3, a novel test-time training framework for cross-domain GAD. GADT3 combines supervised and self-supervised learning during training while adapting to a new domain during test time

Research Question

What is the correlation between Llama3's cross-domain anomaly detection accuracy and the percentage of energy-specific tokens in its training data?

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