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
Evaluating Robustness to Distributional Shifts
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- Holistic Evaluation of Language Models, Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, Benjamin Newman, Binhang Yuan, Bobby Yan, Ce Zhang, Christian Cosgrove, Christopher D. Manning, Christopher Ré, Diana Acosta-Navas, Drew A. Hudson, Eric Zelikman, Esin Durmus, Faisal Ladhak, Frieda Rong, Hongyu Ren, Huaxiu Yao, Jue Wang, Keshav Santhanam, Laurel Orr, Lucia Zheng, Mert Yuksekgonul, Mirac Suzgun, Nathan Kim, Neel Guha, Niladri Chatterji, Omar Khattab, Peter Henderson, Qian Huang, Ryan Chi, Sang Michael Xie, Shibani Santurkar, Surya Ganguli, Tatsunori Hashimoto, Thomas Icard, Tianyi Zhang, Vishrav Chaudhary, William Wang, Xuechen Li, Yifan Mai, Yuhui Zhang, Yuta Koreeda, 2023 Transactions on Machine Learning Research DOI: 10.48550/arXiv.2211.09110 - This paper introduces a comprehensive framework for evaluating language models across various scenarios, including how they perform under distribution shifts.
- A Survey on Distributional Robustness in Deep Learning, Hossein Esmaeili, Sina Khoshangosht, Mehdi Sajjadi, and Hamid R. Tizhoosh, 2022 Journal of Artificial Intelligence Research, Vol. 75 (JAIR) DOI: 10.1613/jair.1.13506 - Provides a recent overview of distributional robustness techniques and aspects in deep learning, covering theoretical and practical facets of various shifts.
- Red Teaming Language Models to Reduce Harms: Methods, Limitations, and Ethical Considerations, Deep Ganguli, Liane Lovitt, Jackson Kernion, Amanda Askell, Yuntao Bai, Saurav Kadavath, Ben Mann, Ethan Perez, Nicholas Schiefer, Kamal Ndousse, Andy Jones, Sam Bowman, Anna Chen, Tom Conerly, Nova DasSarma, Dawn Drain, Nelson Elhage, Sheer El-Showk, Stanislav Fort, Zac Hatfield-Dodds, Tom Henighan, Danny Hernandez, Tristan Hume, Josh Jacobson, Scott Johnston, Shauna Kravec, Catherine Olsson, Sam Ringer, Eli Tran-Johnson, Dario Amodei, Tom Brown, Nicholas Joseph, Sam McCandlish, Chris Olah, Jared Kaplan, Jack Clark, 2022 arXiv preprint arXiv:2209.07858 DOI: 10.48550/arXiv.2209.07858 - Describes red teaming as a method for identifying and mitigating risks in LLMs by searching for prompts that cause harmful outputs, simulating stress tests under out-of-distribution scenarios.