# Advancing Email Spam Detection: Leveraging Zero-Shot Learning and Large Language Models

arXiv: https://arxiv.org/abs/2505.02362 · HTML: https://mlsanity.com/paper/2505.02362/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 13 references that are in the map.
- First posted on arXiv 5 May 2025.

## Builds on (most cited first, 5 of 5)

- [Semantics-aware BERT for Language Understanding](https://mlsanity.com/paper/1909.02209/) (2019)
- [Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review](https://mlsanity.com/paper/2402.10350/) (2024)
- [ChatSpamDetector: Leveraging Large Language Models for Effective Phishing Email Detection](https://mlsanity.com/paper/2402.18093/) (2024)
- [Evaluating the Performance of ChatGPT for Spam Email Detection](https://mlsanity.com/paper/2402.15537/) (2024)
- [SpamDam: Towards Privacy-Preserving and Adversary-Resistant SMS Spam Detection](https://mlsanity.com/paper/2404.09481/) (2024)

## Built on by (most cited first, 1 of 1)

- [Ensemble of Unsupervised Deep Learning for Clustering Imbalanced Tabular Data](https://mlsanity.com/paper/2608.00346/) (2026)

## Abstract (arXiv)

Email spam detection is a critical task in modern communication systems, essential for maintaining productivity, security, and user experience. Traditional machine learning and deep learning approaches, while effective in static settings, face significant limitations in adapting to evolving spam tactics, addressing class imbalance, and managing data scarcity. These challenges necessitate innovative approaches that reduce dependency on extensive labeled datasets and frequent retraining. This study investigates the effectiveness of Zero-Shot Learning using FLAN-T5, combined with advanced Natural Language Processing (NLP) techniques such as BERT for email spam detection. By employing BERT to preprocess and extract critical information from email content, and FLAN-T5 to classify emails in a Zero-Shot framework, the proposed approach aims to address the limitations of traditional spam detection systems. The integration of FLAN-T5 and BERT enables robust spam detection without relying on extensive labeled datasets or frequent retraining, making it highly adaptable to unseen spam patterns and adversarial environments. This research highlights the potential of leveraging zero-shot learning and NLPs for scalable and efficient spam detection, providing insights into their capability to address the dynamic and challenging nature of spam detection tasks.