Meta-Learning Adaptation Impact on Few-Shot Anomaly Detection Accuracy of Llama-3.1-8B vs. Llama-3.1-70B in Out-of-Domain Text

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 goal: How does meta-learning adaptation affect few-shot anomaly detection accuracy of Llama-3.1-8B versus Llama-3.1-70B when evaluated on out-of-domain text classification tasks?

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