Master the science of understanding how transformer-based language models actually work internally, from attention patterns to emergent behaviors and circuit-level analysis.
Knockouts and Ablations: Systematically removing or corrupting specific components helps identify their necessity for different types of model behavior and performance.
Activation Editing: Precisely modifying internal activations allows researchers to test hypotheses about what different components represent and how they influence model outputs.
Path Patching: This sophisticated technique involves selectively replacing activations along specific computational pathways to understand how information flows and transforms through the model.
Automated Circuit Identification: Advanced techniques can automatically identify recurring computational patterns and circuits across different model architectures and training conditions.
Circuit Completeness Verification: Once potential circuits are identified, rigorous testing ensures they capture the complete computational process rather than just correlated patterns.
Cross-Model Circuit Analysis: Comparing circuits across different models trained on similar tasks reveals universal computational strategies versus model-specific idiosyncrasies.
Capability Onset Analysis: Understanding how specific capabilities emerge during training, including the identification of critical points where qualitative changes in behavior occur.
Scaling Law Interpretability: Examining how interpretable circuits and mechanisms change as models scale in size, revealing which aspects of intelligence scale smoothly versus discontinuously.
Training Dynamics Interpretation: Analyzing how interpretable structures develop, strengthen, and sometimes disappear during the training process.