Ontology-Aware RAG for Improved Question-Answering in Cybersecurity Education
Project Overview
The document explores the role of generative AI in education, emphasizing its application in cybersecurity through the introduction of CyberRAG, an ontology-aware retrieval-augmented generation framework. This innovative approach aims to enhance question-answering capabilities in cybersecurity education by effectively managing uncertainty and ensuring the accuracy of AI-generated responses via validated knowledge bases and ontologies. While the integration of generative AI holds great potential for transforming educational practices, the document highlights significant challenges, such as the occurrence of hallucinations in AI responses and the necessity for domain-specific knowledge, which must be addressed to maximize the benefits of AI in learning environments. Overall, the findings suggest that while generative AI can significantly improve educational outcomes, careful implementation and ongoing refinement are crucial to mitigate risks and enhance reliability in educational contexts.
Key Applications
CyberRAG - Ontology-aware Retrieval-Augmented Generation framework
Context: Cybersecurity education for students learning complex cybersecurity concepts and tools
Implementation: Integrates a two-step approach using a knowledge base to retrieve validated cybersecurity documents and a knowledge graph ontology to validate AI-generated answers.
Outcomes: Delivers accurate, reliable responses aligned with domain knowledge, enhances cognitive engagement, and fosters interactive learning experiences.
Challenges: Issues of hallucination in AI outputs, limited domain-specific knowledge, and the need for validation against the knowledge graph ontology.
Implementation Barriers
Technical barrier
Hallucinations and inaccurate responses from large language models (LLMs) can undermine their effectiveness in educational contexts.
Proposed Solutions: Utilization of retrieval-augmented generation (RAG) methods and ontology-based validation approaches to enhance accuracy and reliability.
Operational barrier
The labor-intensive, costly nature of verifying AI-generated responses through reinforcement learning from human feedback (RLHF).
Proposed Solutions: Development of an automatic validation approach using domain-specific knowledge graphs to ensure accuracy without constant human oversight.
Project Team
Chengshuai Zhao
Researcher
Garima Agrawal
Researcher
Tharindu Kumarage
Researcher
Zhen Tan
Researcher
Yuli Deng
Researcher
Ying-Chih Chen
Researcher
Huan Liu
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Chengshuai Zhao, Garima Agrawal, Tharindu Kumarage, Zhen Tan, Yuli Deng, Ying-Chih Chen, Huan Liu
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18