Summary of Evaluating the Efficacy Of Large Language Models in Identifying Phishing Attempts, by Het Patel et al.
Evaluating the Efficacy of Large Language Models in Identifying Phishing Attempts
by Het Patel, Umair Rehman, Farkhund Iqbal
First submitted to arxiv on: 23 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 abstract discusses the ongoing threat of phishing attacks, which use social engineering tactics to trick individuals into revealing sensitive information. The paper analyzes the effectiveness of 15 Large Language Models (LLMs) in detecting such attempts. Specifically, it focuses on a set of “419 Scam” emails and evaluates how well the LLMs can detect phishing emails based on predefined criteria. The experiment found that ChatGPT 3.5, GPT-3.5-Turbo-Instruct, and ChatGPT were the most effective in detecting phishing emails. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Phishing is a big problem in today’s digital world. It’s when bad guys try to trick people into giving them important information. They might pretend to be someone trustworthy or make something look really urgent. To figure out how well computers can help detect these attacks, researchers looked at 15 special language models. They tested these models on fake emails that were designed to look like real phishing attempts. The results showed that three of the models – ChatGPT 3.5, GPT-3.5-Turbo-Instruct, and ChatGPT – did a great job of spotting the fake emails. |
Keywords
» Artificial intelligence » Gpt
