Explanation-augmented LoRA fine-tuning lets small LLMs detect phishing emails with accuracy and F1 around 0.94 to 0.96 on the SpamAssassin test set, while transferring to unseen datasets.
Boosting Trees for Anti-Spam Email Filtering
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abstract
This paper describes a set of comparative experiments for the problem of automatically filtering unwanted electronic mail messages. Several variants of the AdaBoost algorithm with confidence-rated predictions [Schapire & Singer, 99] have been applied, which differ in the complexity of the base learners considered. Two main conclusions can be drawn from our experiments: a) The boosting-based methods clearly outperform the baseline learning algorithms (Naive Bayes and Induction of Decision Trees) on the PU1 corpus, achieving very high levels of the F1 measure; b) Increasing the complexity of the base learners allows to obtain better ``high-precision'' classifiers, which is a very important issue when misclassification costs are considered.
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Improving Phishing Email Detection Performance of Small Large Language Models
Explanation-augmented LoRA fine-tuning lets small LLMs detect phishing emails with accuracy and F1 around 0.94 to 0.96 on the SpamAssassin test set, while transferring to unseen datasets.