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Advanced LLM Alignment and Safety Techniques

Chapter 1: Foundations of LLM Alignment

Defining Alignment in Large Language Models

The Alignment Problem: Objectives and Challenges

Instruction Following and Fine-tuning Review

Measuring Alignment: Initial Metrics and Limitations

The Concept of Inner and Outer Alignment

Specification Gaming and Reward Hacking

Chapter 2: Reinforcement Learning from Human Feedback (RLHF)

The RLHF Pipeline: Components and Workflow

Preference Data Collection and Annotation

Reward Model Training: Architectures and Loss Functions

Challenges in Reward Modeling

Policy Optimization with PPO

PPO Implementation Considerations

Analyzing RLHF Performance and Stability

Limitations and Extensions of RLHF

Hands-on Practical: Implementing Core RLHF Components

Chapter 3: Advanced Alignment Algorithms

Constitutional AI: Principles and Implementation

Reinforcement Learning from AI Feedback (RLAIF)

Direct Preference Optimization (DPO)

Contrastive Methods for Alignment

Iterated Amplification and Debate

Comparative Analysis of Alignment Techniques

Practice: Implementing a DPO Loss Function

Chapter 4: Evaluating LLM Safety and Alignment

Defining Dimensions of Safety: Harmlessness, Honesty, Helpfulness

Automated Evaluation Benchmarks (HELM, TruthfulQA)

Human Evaluation Protocols for Safety

Red Teaming Methodologies for LLMs

Quantifying Bias and Fairness in LLMs

Evaluating Robustness to Distributional Shifts

Challenges in Scalable and Reliable Evaluation

Hands-on Practical: Applying Safety Benchmarks

Chapter 5: Adversarial Attacks and Defenses

Taxonomy of Adversarial Attacks on LLMs

Jailbreaking Techniques and Examples

Prompt Injection Attacks

Data Poisoning Attacks during Training/Fine-tuning

Membership Inference and Privacy Attacks

Adversarial Training for LLM Robustness

Input Sanitization and Output Filtering Defenses

Formal Verification Approaches (Limitations and Potential)

Practice: Crafting and Defending Against Basic Jailbreaks

Chapter 6: Interpretability and Monitoring for Safety

The Role of Interpretability in AI Safety

Feature Attribution Methods for LLMs

Neuron and Circuit Analysis Techniques

Concept Probing and Representation Analysis

Model Editing for Safety Corrections

Monitoring LLMs in Production for Safety Issues

Anomaly Detection in LLM Behavior

Hands-on Practical: Applying Attribution to Analyze Outputs

Chapter 7: Building Safer LLM Systems

System-Level Safety Architectures

Implementing Safety Guardrails

Content Moderation Integration

Managing Context and Memory for Safety

Safe Deployment and Rollout Strategies

Incident Response for LLM Safety Failures

Documentation and Transparency in Safety Measures

Practice: Designing a Guardrail Specification

Advanced LLM Alignment and Safety Techniques

Prerequisites: Strong foundation in Machine Learning, Deep Learning, and Natural Language Processing. Experience with training or fine-tuning language models. Proficiency in Python and standard ML frameworks (e.g., PyTorch, TensorFlow).

Level: Advanced

What You'll Learn

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.

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