transformer-lens-interpretability

Mechanistic interpretability reverse-engineers algorithms within transformer models by isolating attention patterns and neuron activations. TransformerLens enables this analysis via HookPoints and activation caching to understand how models process information and make predictions.

Activation patching identifies the causal impact of specific activations on model outputs. You can perform causal tracing experiments on GPT-2 using TransformerLens by caching activations, patching them via HookPoints, and measuring the resulting output changes.

Yes, PyTorch and TransformerLens are required dependencies for circuit analysis. The deterministic analysis of transformer architectures relies on the Torch backend to run model inference and manipulate internal activations.

You can inspect and manipulate attention heads using TransformerLens HookPoints. By caching activations during model inference, you can analyze attention patterns and neuron activations to reverse-engineer the specific circuits responsible for predictions.

The best way to study transformer predictions is mechanistic interpretability using TransformerLens. You can isolate specific attention heads and neuron activations through activation patching and circuit analysis to trace the prediction's causal pathway.