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It might be worth pointing out ‘ICA with Reconstruction Cost for Efficient Overcomplete Feature Learning’ (@NeurIPS 2011) which argues that the reconstruction cost |WW^Tx - x| can be used as a form of soft orthonormalization loss.
https://proceedings.neurips.cc/paper/2011/file/233509073ed3432027d48b1a83f5fbd2-Paper.pdf
Cool post! I often find myself confused/unable to guess why people I don't know are excited about SAEs (there seem to be a few vaguely conflicting reasons), and this was a very clear description of your agenda.
I'm a little confused by this point:
> The reconstruction loss trains the SAE features to approximate what the network does, thus optimizing for mathematical description accuracy
It's not clear to me that framing reconstruction loss as 'approximating what the network does' is the correct framing of this loss. In my mind, the reconstruction loss is more of a non-degeneracy control to encourage almost-orthogonality between features; In toy settings, SAEs are able to recover ground truth directions while still having sub-perfect reconstruction loss, and it seems very plausible that we should be able to use this (e.g. maybe through gradient-based attribution) without having to optimise heavily for reconstruction loss, which might degrade scalability (which seems very important for this agenda) and monosemanticity compared to currently-unexplored alternatives.
Edit: As per @Logan Riggs’s comment, I seem to have misunderstood what was being meant by ‘loss recovered’, so this comment is not relevant.
Cool post! However, it feels a little early to conclude that
> Conceptually, loss recovered seems a worse metric than KL divergence.
In toy settings (i.e. trying to apply SAEs to a standard sparse coding setting where we know the ground truth factors, like in https://www.lesswrong.com/posts/z6QQJbtpkEAX3Aojj/interim-research-report-taking-features-out-of-superposition/ ), SAEs do not acheive zero reconstruction loss even when they recover the ground truth overbasis with high mean max cosine similarity (and the situation is even worse when noise is present). It's never seemed that obvious to me that we should be aiming to have SAE reconstruction loss go to zero as we train better SAEs, as we could plausibly still use the basis that the SAEs extract, without having to plug the SAE into a 'production' system for mech interp (in which case, we would want good reconstructions).
Note: My take does not neccesarily represent the takes of my coauthors (Hoagy, Logan, Lee, Robert) etc etc. Or it might, but they may frame it differently. Take this as strictly my take.
My take is that the goal isn't strictly to get maximum expressive power under the assumptions detailed in Toy Models of Superposition; for instance, Anthropic found that FISTA-based dictionaries didn't work as well as sparse autoencoders, even though they are better in that they can achive lower reconstruction loss at the same level of sparsity. We might find that the sparsity-monosemanticity link breaks down at higher levels of autoencoder expressivity, although this needs to be rigourously tested.
To answer your question: I think Hoagy thinks that tied weights are more similar to how an MLP might use features during a forward pass, which would involve extracting the feature through a simple dot-product. I'm not sure I buy this, as having untied weights is equivalent to allowing the model to express simple linear computations like 'feature A activation = dot product along feature A direction - dot product along feature B direction', which could be a form of denoising if A and B were mutually exclusive but non-orthogonal features.
Yep! We are planning to do exactly that for (at least) the models we focus on in the paper (Pythia-70m + Pythia-410m), and probably also GPT2 small. We are also working on cleaning up our codebase (https://github.com/HoagyC/sparse_coding) and implementing some easy dictionary training solutions.
Hi David, co-author of the 'Sparse Autoencoders Find Highly Interpretable Directions in Language Models' paper here,
I think this might be of interest to you:
We are currently in the process of re-framing section 4 of the paper to focus more on model steering & activation editing; in line with what you hypothesise, we find that editing a small number of relevant features on e.g. the IOI task can steer the model from its predictions on one token to its predictions on a counterfactual token.
Really awesome work, although I felt a bit frustrated that more of the stuff about independence etc wasn't in e.g. an appendix or something. When is part 2(/3) scheduled for?
I'm slightly confused as to why red-teaming via activation additions should be preferred over e.g. RAT; it seems to be possible that RAT better/more robustly models out-of-test-distribution-but-still-in-deployment-distribution activations than directly adding some steering vector. Cool work though!