Summary of Llms Are One-shot Url Classifiers and Explainers, by Fariza Rashid et al.
LLMs are One-Shot URL Classifiers and Explainers
by Fariza Rashid, Nishavi Ranaweera, Ben Doyle, Suranga Seneviratne
First submitted to arxiv on: 22 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework uses Large Language Models (LLMs) to classify malicious URLs in one-shot learning setting. The approach leverages Chain-of-Thought (CoT) reasoning to predict whether a given URL is benign or phishing. The LLM-based framework is evaluated using three URL datasets and five state-of-the-art LLMs, with GPT-4 Turbo being the best-performing model. The study also examines the readability, coherence, and informativeness of the LLM explanations, finding that they align with those of supervised classifiers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special computers called Large Language Models to help figure out if a website is good or bad. It’s like having a super smart friend who can look at a URL and tell you right away if it’s safe or not. The team tested different models and found that one of them, GPT-4 Turbo, was the best at doing this task. They also looked at how well these computers could explain why they made certain decisions, and found that their explanations were clear and easy to understand. |
Keywords
* Artificial intelligence * Gpt * One shot * Supervised
