REVIEW 5 cited by
Spam-T5: Benchmarking Large Language Models for Few-Shot Email Spam Detection
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
This paper investigates the effectiveness of large language models (LLMs) in email spam detection by comparing prominent models from three distinct families: BERT-like, Sentence Transformers, and Seq2Seq. Additionally, we examine well-established machine learning techniques for spam detection, such as Na\"ive Bayes and LightGBM, as baseline methods. We assess the performance of these models across four public datasets, utilizing different numbers of training samples (full training set and few-shot settings). Our findings reveal that, in the majority of cases, LLMs surpass the performance of the popular baseline techniques, particularly in few-shot scenarios. This adaptability renders LLMs uniquely suited to spam detection tasks, where labeled samples are limited in number and models require frequent updates. Additionally, we introduce Spam-T5, a Flan-T5 model that has been specifically adapted and fine-tuned for the purpose of detecting email spam. Our results demonstrate that Spam-T5 surpasses baseline models and other LLMs in the majority of scenarios, particularly when there are a limited number of training samples available. Our code is publicly available at https://github.com/jpmorganchase/emailspamdetection.
Forward citations
Cited by 5 Pith papers
-
PrefillOnly: An Inference Engine for Prefill-only Workloads in Large Language Model Applications
PrefillOnly serves single-token LLM requests with hybrid prefilling and JCT-aware scheduling, achieving up to 4x higher throughput on long-input workloads.
-
Measuring and Evaluating the Performance of Generative AI Models for Scam Detection
A new benchmark of 2,742 real scam messages shows top LLMs reach about 64-65% micro-F1 and generalize to an unseen 59,991-sample proprietary set better than a fine-tuned BERT.
-
Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques
A survey that maps LLM applications, vulnerabilities, and defenses across eight cybersecurity domains, but with significant citation and rigor problems.
-
Next-Generation Phishing: How LLM Agents Empower Cyber Attackers
LLM-rephrased phishing emails evade current email detectors more often than original ones, and training on LLM-generated variants partly restores detection.
-
Advacheck at GenAI Detection Task 1: AI Detection Powered by Domain-Aware Multi-Tasking
A multi-task transformer with extra domain-classification heads improved AI text detection and achieved the top rank in the GenAI Detection Task 1 competition.
Discussion (0). Continue with ORCID to comment.