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.
An evaluation of Naive Bayesian anti-spam filtering
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
It has recently been argued that a Naive Bayesian classifier can be used to filter unsolicited bulk e-mail ("spam"). We conduct a thorough evaluation of this proposal on a corpus that we make publicly available, contributing towards standard benchmarks. At the same time we investigate the effect of attribute-set size, training-corpus size, lemmatization, and stop-lists on the filter's performance, issues that had not been previously explored. After introducing appropriate cost-sensitive evaluation measures, we reach the conclusion that additional safety nets are needed for the Naive Bayesian anti-spam filter to be viable in practice.
fields
cs.CL 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
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.