Adversarial Training for LLMs' Safety Robustness

Overview

Explore techniques for improving the safety robustness of Large Language Models through adversarial training methods with researcher Gauthier Gidel from IVADO-Mila.

Syllabus

• Introduction to Adversarial Training
Definition and importance of adversarial training
Overview of safety robustness in Large Language Models (LLMs)
Introduction to researcher Gauthier Gidel and IVADO-Mila
• Fundamentals of Large Language Models (LLMs)
Architecture and operation of LLMs
Limitations and vulnerabilities of LLMs
• Understanding Adversarial Attacks
Types of adversarial attacks on LLMs
Case studies of adversarial attacks on LLMs
• Adversarial Training Techniques
Basic adversarial training methods
Advanced techniques for LLM adversarial training
• Improving Safety Robustness in LLMs
Strategies for enhancing model robustness
Metrics for evaluating robustness
• Practical Implementation of Adversarial Training
Setting up experiments for adversarial training
Tools and libraries for implementing adversarial training
• Case Studies and Applications
Real-world applications of adversarially trained LLMs
Analysis of case studies demonstrating enhanced robustness
• Challenges and Future Directions
Current challenges in adversarial training for LLMs
Future research directions and opportunities
• Wrap-up and Discussion
Key takeaways from the course
Open Q&A session
• Additional Resources
Recommended reading and resources for further exploration

Subjects

Computer Science