Enhancing Phishing Email Detection with Context-Augmented Open Large Language Models

Abstract

Large Language Models offer a promising approach for improving phishing detection through advanced natural language processing. This paper evaluates the effectiveness of context-augmented open LLMs in identifying phishing emails. An approach was developed that combines the methods of Few-Shot Learning and Retrieval-Augmented Generation (RAG) to remarkably improve the performance of LLMs in this area. On this basis, it has been shown that the presented approach can significantly improve the recognition rate even for smaller models.