التفاصيل البيبلوغرافية
| العنوان: |
Prompt Engineering or Fine-Tuning? A Case Study on Phishing Detection with Large Language Models. |
| المؤلفون: |
Trad, Fouad, Chehab, Ali |
| المصدر: |
Machine Learning & Knowledge Extraction; Mar2024, Vol. 6 Issue 1, p367-384, 18p |
| مصطلحات موضوعية: |
LANGUAGE models, UNIFORM Resource Locators, PHISHING, MACHINE learning, ENGINEERING |
| مستخلص: |
Large Language Models (LLMs) are reshaping the landscape of Machine Learning (ML) application development. The emergence of versatile LLMs capable of undertaking a wide array of tasks has reduced the necessity for intensive human involvement in training and maintaining ML models. Despite these advancements, a pivotal question emerges: can these generalized models negate the need for task-specific models? This study addresses this question by comparing the effectiveness of LLMs in detecting phishing URLs when utilized with prompt-engineering techniques versus when fine-tuned. Notably, we explore multiple prompt-engineering strategies for phishing URL detection and apply them to two chat models, GPT-3.5-turbo and Claude 2. In this context, the maximum result achieved was an F1-score of 92.74% by using a test set of 1000 samples. Following this, we fine-tune a range of base LLMs, including GPT-2, Bloom, Baby LLaMA, and DistilGPT-2—all primarily developed for text generation—exclusively for phishing URL detection. The fine-tuning approach culminated in a peak performance, achieving an F1-score of 97.29% and an AUC of 99.56% on the same test set, thereby outperforming existing state-of-the-art methods. These results highlight that while LLMs harnessed through prompt engineering can expedite application development processes, achieving a decent performance, they are not as effective as dedicated, task-specific LLMs. [ABSTRACT FROM AUTHOR] |
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