Year: 2025
Type: article
Abstract: In recent years, anomaly detection in digital environments has become a critical research area due to issues such as spam messages and fake news, which can lead to privacy breaches, social disruption, and undermined information reliability. Traditional anomaly detection models often require specific training for each task, resulting in significant time and resource consumption and limited flexibility. This study explores the use of Prompt Engineering with Transformer-based Large Language Models (LLMs) to address these challenges more efficiently. By comparing techniques such as Zero-shot, Few-shot, Chain-of-Thought (CoT), Self-Consistency (SC), and Tree-of-Thought (ToT) prompting, the study identifies CoT and SC as particularly effective, achieving up to 0.96 accuracy in spam detection without the need for task-specific training. However, ToT exhibited limitations due to biases and misinterpretation. The findings emphasize the importance of selecting appropriate prompting strategies to optimize LLM performance across various tasks, highlighting the potential of Prompt Engineering to reduce costs and improve the adaptability of anomaly detection systems. Future research is needed to explore the broader applicability and scalability of these methods. Additionally, this study includes a survey of Prompt Engineering techniques applicable to anomaly detection, examining strategies such as Self-Refine and Retrieval-Augmented Generation to further enhance detection accuracy and adaptability.
Source: Applied Sciences
DOI: 10.3390/app151910384
Authors: Sungjune Park, Daeseon Choi
Institution: Soongsil University
Language: English
Topic: Spam and Phishing Detection
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Award: G7690800436
OpenAlex ID: W4414517929