# Zero-Shot Spam Email Classification Using Pre-trained Large Language Models

arXiv: https://arxiv.org/abs/2405.15936 · HTML: https://mlsanity.com/paper/2405.15936/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 32 references that are in the map.
- First posted on arXiv 24 May 2024.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm](https://mlsanity.com/paper/2102.07350/) (2021)
- [A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4](https://mlsanity.com/paper/2310.12321/) (2023)
- [ChatSpamDetector: Leveraging Large Language Models for Effective Phishing Email Detection](https://mlsanity.com/paper/2402.18093/) (2024)
- [Generating Phishing Attacks using ChatGPT](https://mlsanity.com/paper/2305.05133/) (2023)
- [Spam-T5: Benchmarking Large Language Models for Few-Shot Email Spam Detection](https://mlsanity.com/paper/2304.01238/) (2023)
- [Detecting Phishing Sites Using ChatGPT](https://mlsanity.com/paper/2306.05816/) (2023)
- [Evaluating the Performance of ChatGPT for Spam Email Detection](https://mlsanity.com/paper/2402.15537/) (2024)
- [Using BERT Encoding to Tackle the Mad-lib Attack in SMS Spam Detection](https://mlsanity.com/paper/2107.06400/) (2021)

## Built on by (most cited first, 2 of 2)

- [Phishing Email Detection Using Large Language Models](https://mlsanity.com/paper/2512.10104/) (2025)
- [AURA: Adaptive Uncertainty-Routed Analysis for Email Threat Detection](https://mlsanity.com/paper/2609.19873/) (2026)

## Abstract (arXiv)

This paper investigates the application of pre-trained large language models (LLMs) for spam email classification using zero-shot prompting. We evaluate the performance of both open-source (Flan-T5) and proprietary LLMs (ChatGPT, GPT-4) on the well-known SpamAssassin dataset. Two classification approaches are explored: (1) truncated raw content from email subject and body, and (2) classification based on summaries generated by ChatGPT. Our empirical analysis, leveraging the entire dataset for evaluation without further training, reveals promising results. Flan-T5 achieves a 90% F1-score on the truncated content approach, while GPT-4 reaches a 95% F1-score using summaries. While these initial findings on a single dataset suggest the potential for classification pipelines of LLM-based subtasks (e.g., summarisation and classification), further validation on diverse datasets is necessary. The high operational costs of proprietary models, coupled with the general inference costs of LLMs, could significantly hinder real-world deployment for spam filtering.