Master the science of understanding how transformer-based language models actually work internally, from attention patterns to emergent behaviors and circuit-level analysis.
Head-Specific Function Discovery: Different attention heads in transformer models often specialize in specific types of relationships or computational tasks. Some heads focus on syntactic relationships, others on semantic associations, and still others on positional information.
Attention Pattern Analysis: By visualizing and analyzing attention patterns across different inputs and contexts, researchers can understand how models build representations of relationships between tokens and concepts.
Multi-Layer Attention Interactions: Advanced interpretability work examines how attention patterns in earlier layers influence and compose with attention patterns in later layers to build increasingly sophisticated representations.
Information Flow Tracking: The residual stream in transformers serves as a shared workspace where different layers read from and write to. Understanding this information flow is crucial for comprehending model behavior.
Representation Evolution: Tracking how token representations change as they pass through successive layers reveals the gradual refinement and sophistication of the model's understanding.
Interference and Composition: Analyzing how different layers' contributions to the residual stream interact, sometimes constructively and sometimes destructively, provides insights into model limitations and capabilities.
Neuron Specialization: Individual neurons in MLP layers often develop specialized responses to particular features or concepts, though these specializations can be distributed and overlapping.
Feature Combination: MLP layers excel at combining features identified in earlier processing stages, creating more complex and abstract representations through non-linear transformations.
Memorization vs. Generalization: Understanding how MLP layers balance memorization of training data with development of generalizable algorithms is crucial for understanding model behavior.