Train-Test Split Contamination Rate Effects on Few-Shot Anomaly Detection AVPR Scores Across 8B and 70B Language Models

Abstract

Abstract: We propose a meta learning framework for detecting anomalies in human language across diverse domains with limited labeled data. Anomalies in language ranging from spam and fake news to hate speech pose a major challenge due to their sparsity and variability. We treat anomaly detection as a few shot binary classification problem and leverage meta-learning to train models that generalize across tasks. Using datasets from domains such as SMS spam, COVID-19 fake news, and hate speech, we evaluate model generalization on unseen tasks with minimal labeled anomalies. Our method combines episodic tra

Research Question

How does the choice of train-test split contamination rate affect the final AVPR scores of few-shot anomaly detection across different language model sizes (8B vs. 70B)?

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