This post was written as part of the work done at Conjecture.

You can try input swap graphs in a collab notebook or explore the library to replicate the results.

Thanks to Beren Millidge and Eric Winsor for useful discussions throughout this project. Thanks to Beren for feedback on a draft of this post.

Activation and path patching are techniques employed to manipulate neural network internals. One approach, used in ROME, which we'll refer to as corrupted patching, involves setting the output of certain parts of the network to a corrupted value. Another method used in the work on indirect object identification, referred to as input swap patching, involves assigning these parts the values they would produce on a different input sample, while the rest of the model operates normally. These experimental techniques have proven effective in gaining structural insights into the information flow within neural networks, providing a better understanding of which components are involved in a given behavior. These structural analyses can be straightforwardly automated using algorithms like Automatic Circuit DisCovery.

An illustration of input swap patching on two sentences from the IOI dataset. In red are the activations on the source sequence, in green on the target sequence, and in yellow are "chimera activation" that appears neither on the target nor source sequences.

Patching experiments also enable the discovery of the semantics of components, i.e. the abstract variables a component encodes. They have been used to discover the "a vs an" neuron in gpt2, the token and position signal in S-inhibition heads in the IOI circuit, or Othello board representations. In all these cases, researchers were not only able to say 'this component matters', but also 'it roughly plays this role'. However, these additional results come at a cost: they often required a lot of manual effort to design alternative datasets and come up with an intuition for the likely role of the component in advance.

In this post, I introduce input swap graphs (swap graphs in short), a new tool to systematically analyze the results of input swap patching experiments and recover semantic information with as little human effort as possible.

In the following, I present

A concise overview of swap graphs, their motivation, and practical application to Name Movers within the IOI circuit.
A simple theoretical f

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