When fine-tuned on domain-specific security corpora, how do Llama3 and Code Llama 7B compare in few-shot (5-15

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

When fine-tuned on domain-specific security corpora, how do Llama3 and Code Llama 7B compare in few-shot (5-15 shots) code generation accuracy for secure implementation patterns across programming languages?

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