Summary of Cybermetric: a Benchmark Dataset Based on Retrieval-augmented Generation For Evaluating Llms in Cybersecurity Knowledge, by Norbert Tihanyi et al.
CyberMetric: A Benchmark Dataset based on Retrieval-Augmented Generation for Evaluating LLMs in Cybersecurity Knowledge
by Norbert Tihanyi, Mohamed Amine Ferrag, Ridhi Jain, Tamas Bisztray, Merouane Debbah
First submitted to arxiv on: 12 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Cryptography and Security (cs.CR)
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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 paper presents a set of benchmark datasets, CyberMetric-80 to CyberMetric-10000, designed to test the general knowledge of Large Language Models (LLMs) in cybersecurity. The datasets consist of multiple-choice Q&A questions generated using GPT-3.5 and Retrieval-Augmented Generation (RAG), which drew from various sources including NIST standards, research papers, books, RFCs, and publications. Human experts validated the questions and solutions to ensure accuracy and relevance. The paper evaluates 25 state-of-the-art LLM models on these datasets, finding that GPT-4o, GPT-4-turbo, Mixtral-8x7B-Instruct, Falcon-180B-Chat, and GEMINI-pro 1.0 were the top-performing models. Interestingly, the best-performing LLMs were more accurate than humans on CyberMetric-80, although highly experienced human experts still outperformed smaller models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a set of questions to test how well computers know about cybersecurity. It makes a special collection of questions and answers to see which computer languages can answer them correctly. These questions come from many different sources like books, papers, and official documents. People checked the questions to make sure they’re correct and relevant. The researchers tested 25 good computer language models on these questions and found that some are much better than others at answering them correctly. |
Keywords
* Artificial intelligence * Gemini * Gpt * Rag * Retrieval augmented generation
