Master the science of understanding how transformer-based language models actually work internally, from attention patterns to emergent behaviors and circuit-level analysis.
Hypothesis-Driven Investigation: Approaching interpretability research with clear, testable hypotheses rather than purely exploratory analysis to ensure meaningful and actionable insights.
Multi-Method Validation: Using multiple complementary techniques to validate interpretability claims and avoid over-reliance on any single method or perspective.
Quantitative Validation: Developing quantitative metrics for evaluating the quality and completeness of mechanistic explanations.
Cherry-Picking Avoidance: Ensuring that interpretability claims are based on systematic analysis rather than selective presentation of favorable examples.
Correlation vs. Causation: Distinguishing between correlational patterns and genuine causal relationships in model behavior and internal representations.
Scale Sensitivity: Understanding how interpretability findings may change as models scale in size and capability, avoiding over-generalization from smaller models.
Transparency Standards: Establishing clear standards for reporting interpretability research, including limitations, uncertainties, and potential misinterpretations.
Dual-Use Awareness: Understanding that interpretability techniques could potentially be misused for adversarial purposes or to exploit model vulnerabilities.
Accessibility and Communication: Making interpretability research accessible to diverse stakeholders, including policymakers, practitioners, and the general public.