TransformerLens in Practice: Reading Model Circuits with Activation Patching
Using TransformerLens to directly manipulate model activations, we trace which layers and heads causally produce the answer. A hands-on guide to activation patching.
TransformerLens in Practice: Reading Model Circuits with Activation Patching
In the previous post, we treated Lens as a window into the model's intermediate thoughts.
But "reading" alone cannot answer the most important question:
Does the model actually *use* this information?
Just because a hidden state at some layer contains "Paris" does not mean that layer causally contributes to the final answer. Information can be present but unused. A layer might hold the right answer in its representation, yet the model might arrive at its output through entirely different pathways.
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