Implement sophisticated techniques to align Large Language Models (LLMs) with intended behaviors and ensure their operational safety. This course covers advanced methodologies including Reinforcement Learning from Human Feedback (RLHF), adversarial robustness strategies, and rigorous evaluation protocols for building reliable AI systems. Suitable for engineers and researchers seeking to manage the complexities of modern LLM development.
Prerequisites Python, Deep Learning, LLMs
Level:
RLHF Implementation
Implement and critically analyze Reinforcement Learning from Human Feedback pipelines for LLM alignment.
Advanced Alignment Methods
Understand and apply alignment techniques beyond RLHF, such as Constitutional AI and Direct Preference Optimization.
LLM Evaluation
Evaluate LLM alignment and safety using sophisticated metrics, benchmarks, and red teaming strategies.
Adversarial Robustness
Identify vulnerabilities in LLMs and implement defensive measures against adversarial attacks like jailbreaking and prompt injection.
Safety Mechanisms
Design and integrate safety mechanisms like guardrails and content filters into LLM deployment pipelines.
Interpretability for Safety
Apply interpretability techniques to understand and address safety-critical model behaviors.