This is a research report about my attempt to extract interpretable features from a transformer MLP by distilling it into a larger student MLP, while encouraging sparsity by applying an L1 penalty to the activations, as depicted in Figure 1. I investigate the features learned by the distilled MLP, compare them to those found by an autoencoder, and discuss limitations of this approach.

See the code here: https://github.com/slavachalnev/ft_exp. A large part of both the training and analysis code is adapted from Neel Nanda's SAE replication.

Figure 1. Sparse distillation training setup.

TLDR

I find that a subset of the distilled MLP's neurons act as 'neuron simulators', mimicking the activations of the original MLP, while the remaining features are sparse and somewhat monosemantic. Unfortunately, the distilled model is not as good as an autoencoder in terms of activation monosemanticity and reconstruction error.

Introduction

The hidden layer neurons in transformer multilayer perceptrons (MLPs) are polysemantic, making them hard to interpret. The neuron activations are an intermediate state in the computation of the MLP's function. I wish to extract interpretable features out of this intermediate state to learn about what the MLP is doing.

One method for extracting features out of transformer MLPs is to train a sparse autoencoder on the activations. However, the autoencoder may learn features of the activation distribution which are not relevant to the function a particular MLP is performing. I explore an alternative method which, if successful, would allow us to find interpretable features that are directly useful for computing the function of a specific MLP.

The idea is to 'refactor' the MLP such that it retains its original function but with sparse activations in the hidden layer. To do this, I distill the original MLP into a new MLP, while imposing a sparsity constraint. My reasoning is that if two MLPs have the same input-output behaviour, then they are likely 'doing the same thing' and using the same features.

To compensate for the loss of expressiveness due to sparsity, the distilled MLP has an expanded hidden layer, containing more neurons than the original MLP. Importantly, the new MLP is trained to duplicate the original MLP's behaviour, without learning new or unrelated features.

Training Setup

The model I distill and investigate ...

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