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This post is part of the work done at Conjecture.

TLDR

If we take the SVD of the weight matrices of the OV circuit and of MLP layers of GPT models, and project them to token embedding space, we notice this results in highly interpretable semantic clusters. This means that the network learns to align the principal directions of each MLP weight matrix or attention head to read from or write to semantically interpretable directions in the residual stream.

We can use this to both improve our understanding of transformer language models and edit their representations. We use this finding to design both a natural language query locator, where you can write a set of natural language concepts and find all weight directions in the network which correspond to it, and also to edit the network's representations by deleting specific singular vectors, which results in relatively large effects on the logits related to the semantics of that vector and relatively small effects on semantically different clusters

Introduction

Trying to understand the internal representations of language models, and of deep neural networks in general, has been the primary focus of the field of mechanistic interpretability, with clear applications to AI alignment. If we can understand the internal dimensions along which language models store and manipulate representations, then we can get a much better grasp on their behaviour and ultimately may be able to both make provable statements about bounds on their behaviour, as well as make precise edits to the network to prevent or enhance desired behaviours.

Interpretability, however, is a young field where we still do not yet fully understand what the basic units of the networks' representations are. While analyzing and investigating individual neurons has led to some impressive results, especially in convolutional vision models, a key issue has always been the polysemanticity of neurons. A single neuron might not just represent a single 'feature' but some linear combination of features in superposition. This effect has been studied in toy models where it is argued that neural networks resort to superposition when required to represent many more features ...

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