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
Preference Data Collection and Annotation
Was this section helpful?
- Learning to summarize with human feedback, Nisan Stiennon, Long Ouyang, Jeff Wu, Daniel M. Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, Paul Christiano, 2020 NeurIPS 2020 DOI: 10.48550/arXiv.2009.01325 - This pioneering work introduced the core methodology of Reinforcement Learning from Human Feedback (RLHF) for training models on sequence generation tasks, specifically summarization. It details the initial approach to collecting human preference data through pairwise comparisons to train a reward model.
- Inter-Annotator Agreement, Eduard Hovy, Sabine L. L. Lohmann, 2022 Encyclopedia of Language and Linguistics (Springer, Cham) DOI: 10.1007/978-3-030-80275-9_38 - This reference provides a focused explanation of Inter-Annotator Agreement (IAA) methods and their importance for assessing the reliability and consistency of human annotations, a critical aspect of quality control in preference data collection. It is a chapter in the book 'Data Science and Human-in-the-Loop'.