Master the science of understanding how transformer-based language models actually work internally, from attention patterns to emergent behaviors and circuit-level analysis.
Activation Visualization Tools: Specialized software for visualizing high-dimensional activations, attention patterns, and information flow through complex neural architectures.
Circuit Analysis Platforms: Integrated environments that support systematic circuit discovery, validation, and analysis across different model architectures.
Intervention Frameworks: Tools that enable precise, controlled interventions on model internals for causal analysis and hypothesis testing.
Control Group Methodology: Rigorous experimental design that includes appropriate controls to distinguish genuine mechanistic understanding from spurious correlations.
Replication and Validation: Techniques for validating interpretability findings across different models, training conditions, and evaluation metrics.
Statistical Significance Testing: Appropriate statistical methods for evaluating the significance and reliability of interpretability claims.
Systematic Dataset Design: Creating datasets specifically designed to test particular interpretability hypotheses and reveal specific aspects of model behavior.
Behavioral Characterization: Comprehensive characterization of model behavior across diverse tasks and conditions to provide context for mechanistic findings.
Cross-Model Comparison: Methodologies for comparing interpretability findings across different architectures, sizes, and training paradigms.