Nice work! I'm especially impressed by the [word] and [word] example: This cannot be read-off the embeddings, thus the model must be actually computing and storing this feature somewhere! I think this is exciting since the things we care about (deception etc.) are also definitely not included in the embeddings. I think you could make a similar case for Title Case and Beginning & End of First Sentence but those examples look less clear, e.g. the Title Case could be mostly stored in "embedding of uppercase word that is usually lowercase".

Thank you for making the early write-up! I'm not entirely certain I completely understand what you're doing, could I give you my understanding and ask you to fill the gaps / correct me if you have the time? No worries if not, I realize this is a quick & early write-up!

Setup:

As previously you run Pythia on a bunch of data (is this the same data for all of your examples?) and save its activations.
Then you take the residual stream activations (from which layer?) and train an autoencoder (like Lee, Dan & beren here) with a single hidden layer (w/ ReLU), larger than the residual stream width (how large?), trained with an L1-regularization on the hidden activations. This L1-regularization penalizes multiple hidden activations activating at once and therefore encourages encoding single features as single neurons in the autoencoder.

Results:

You found a bunch of features corresponding to a word or combined words (= words with similar meaning?). This would be the embedding stored as a features (makes sense).

But then you also find e.g. a "German Feature", a neuron in the autoencoder that mostly activates when the current token is clearly part of a German word. When you show Uniform examples you show randomly selected dataset examples? Or randomly selected samples where the autoencoder neuron is activated beyond some threshold?

When you show Logit lens you show how strong the embedding(?) or residual stream(?) at a token projects into the read-direction of that particular autoencoder neuron?

In Ablated Text you show how much the autoencoder neuron activation changes (change at what position?) when ablating the embedding(?) / residual stream(?) at a certain position (same or different from the one where you measure the autoencoder neuron activation?). Does ablating refer to setting some activations at that position to zero, or to running the model without that word?

Note on my use of the word neuron: To distinguish residual stream features from autoencoder activations, I use neuron to refer to the hidden activation of the autoencoder (subject to an activation function) while I use feature to refer to (a direction of) residual stream activations.

[word] and [word]
can be thought of as "the previous token is ' and'."

It might just be one of a family of linear features or ?? aspect of some other representation ?? corresponding to what the previous token is, to be used for at least induction head.

Maybe the reason you found ' and' first is because ' and' is an especially frequent word. If you train on the normal document distribution, you'll find the most frequent features first.