Interpretability and Monitoring for Safety
Ensuring Large Language Models operate safely requires more than initial alignment; it necessitates understanding the reasons behind their outputs and continuously monitoring their behavior in operation. This chapter moves from training-time alignment to post-hoc analysis and ongoing vigilance.
You will examine techniques for interpreting model internals, including feature attribution methods and approaches for analyzing neuron or circuit functions relevant to safety. We will cover strategies for monitoring deployed LLMs for emergent issues, detecting behavioral anomalies using statistical methods, and techniques for model editing aimed at correcting specific safety concerns. The objective is to provide practical methods for verifying and maintaining safety throughout the model's operational lifespan.
Sections
6.1 The Role of Interpretability in AI Safety
6.2 Feature Attribution Methods for LLMs
6.3 Neuron and Circuit Analysis Techniques
6.4 Concept Probing and Representation Analysis
6.5 Model Editing for Safety Corrections
6.6 Monitoring LLMs in Production for Safety Issues
6.7 Anomaly Detection in LLM Behavior
6.8 Hands-on Practical: Applying Attribution to Analyze Outputs
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