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
Membership Inference and Privacy Attacks
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- Membership Inference Attacks Against Machine Learning Models, Reza Shokri, Marco Stronati, Congzheng Song, Vitaly Shmatikov, 2017 IEEE Symposium on Security and Privacy (SP) (IEEE) DOI: 10.1109/SP.2017.37 - This foundational paper introduces the concept and methodology of membership inference attacks against machine learning models.
- Quantifying Memorization Across Neural Language Models, Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramer, Chiyuan Zhang, 2022 arXiv preprint arXiv:2202.07646 DOI: 10.48550/arXiv.2202.07646 - This research investigates the extent and mechanisms of memorization in neural language models, a key factor in membership inference vulnerability.
- Deep Learning with Differential Privacy, Martín Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, Li Zhang, 2016 Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (Association for Computing Machinery) DOI: 10.1145/2976749.2978318 - This paper introduces DP-SGD, a widely adopted method for training deep learning models with differential privacy guarantees, offering protection against membership inference.