Wiki Contributions


Thanks for posting this! I've had a lot of conversations with people lately about OthelloGPT and I think it's been useful for creating consensus about what we expect sparse autoencoders to recover in language models. 

Maybe I missed it but:

  • What is the performance of the model when the SAE output is used in place of the activations?
  • What is the L0? You say 12% of features active so I assume that means 122 features are active.This seems plausibly like it could be too dense (though it's hard to say, I don't have strong intuitions here). It would be preferable to have a sweep where you have varying L0's, but similar explained variance. The sparsity is important since that's where the interpretability is coming from.  One thing worth plotting might be the feature activation density of your SAE features as compares to the feature activation density of the probes (on a feature density histogram). I predict you will have features that are too sparse to match your probe directions 1:1 (apologies if you address this and I missed this). 
  • In particular, can you point to predictions (maybe in the early game) where your model is effectively perfect and where it is also perfect with the SAE output in place of the activations at some layer? I think this is important to quantify as I don't think we have a good understanding of the relationship between explained variance of the SAE and model performance and so it's not clear what counts as a "good enough" SAE. 

I think a number of people expected SAEs trained on OthelloGPT to recover directions which aligned with the mine/their probe directions, though my personal opinion was that besides "this square is a legal move", it isn't clear that we should expect features to act as classifiers over the board state in the same way that probes do. 

This reflects several intuitions:

  1. At a high level, you don't get to pick the ontology. SAEs are exciting because they are unsupervised and can show us results we didn't expect. On simple toy models, they do recover true features, and with those maybe we know the "true ontology" on some level. I think it's a stretch to extend the same reasoning to OthelloGPT just because information salient to us is linearly probe-able. 
  2. Just because information is linearly probeable, doesn't mean it should be recovered by sparse autoencoders. To expect this, we'd have to have stronger priors over the underlying algorithm used by OthelloGPT. Sure, it must us representations which enable it to make predictions up to the quality it predicts, but there's likely a large space of concepts it could represent. For example, information could be represented by the model in a local or semi-local code or deep in superposition. Since the SAE is trying to detect representations in the model, our beliefs about the underlying algorithm should inform our expectations of what it should recover, and since we don't have a good description of the circuits in OthelloGPT, we should be more uncertain about what the SAE should find. 
  3. Separately, it's clear that sparse autoencoders should be biased toward local codes over semi-local / compositional codes due to the L1 sparsity penalty on activations. This means that even if we were sure that the model represented information in a particular way, it seems likely the SAE would create representations for variables like (A and B) and (A and B') in place of A even if the model represents A. However, the exciting thing about this intuition is it makes a very testable prediction about combinations of features likely combining to be effective classifiers over the board state. I'd be very excited to see an attempt to train neuron-in-a-haystack style sparse probes over SAE features in OthelloGPT for this reason.

Some other feedback:

  • Positive: I think this post was really well written and while I haven't read it in more detail, I'm a huge fan of how much detail you provided and think this is great. 
  • Positive: I think this is a great candidate for study and I'm very interested in getting "gold-standard" results on SAEs for OthelloGPT. When Andy and I trained them, we found they could train in about 10 minutes making them a plausible candidate for regular / consistent methods benchmarking. Fast iteration is valuable. 
  • Negative: I found your bolded claims in the introduction jarring. In particular "This demonstrates that current techniques for sparse autoencoders may fail to find a large majority of the interesting, interpretable features in a language model". I think this is overclaiming in the sense that OthelloGPT is not toy-enough, nor do we understand it well enough to know that SAEs have failed here, so much as they aren't recovering what you expect. Moreover, I think it would best to hold-off on proposing solutions here (in the sense that trying to map directly from your results to the viability of the technique encourages us to think about arguments for / against SAEs rather than asking, what do SAEs actually recover, how do neural networks actually work and what's the relationship between the two).
  • Negative: I'm quite concerned that tieing the encoder / decoder weights and not having a decoder output bias results in worse SAEs. I've found the decoder bias initialization to have a big effect on performance (sometimes) and so by extension whether or not it's there seems likely to matter. Would be interested to see you follow up on this. 

Oh, and maybe you saw this already but an academic group put out this related work:  I don't think they quantify the proportion of probe directions they recover, but they do indicate recovery of all types of features that been previously probed for. Likely worth a read if you haven't seen it. 

Cool paper. I think the semantic similarity result is particularly interesting.

As I understand it you've got a circuit  that wants to calculate something like Sim(A,B), where A and B might have many "senses" aka: features but the Sim might not be a linear function of each of thes Sims across all senses/features. 

So for example, there are senses in which "Berkeley" and "California" are geographically related, and there might be a few other senses in which they are semantically related but probably none that really matter for copy suppression. For this reason wouldn't expect the tokens of each of to have cosine similarity that is predictive of the copy suppression score.  This would only happen for really "mono-semantic tokens" that have only one sense (maybe you could test that). 

Moreover, there are also tokens which you might want to ignore when doing copy suppression (speculatively). Eg: very common words or punctuations (the/and/etc). 

I'd be interested if you have use something like SAE's to decompose the tokens into the underlying feature/s present at different intensities in each of these tokens (or the activations prior to the key/query projections). Follow up experiments could attempt to determine whether copy suppression could be better understood when the semantic subspaces are known. Some things that might be cool here:
- Show that some features are mapped to the null space of keys/queries in copy suppression heads indicating semantic senses / features that are ignored by copy suppression. Maybe multiple anti-induction heads compose (within or between layers) so that if one maps a feature to the null space, another doesn't (or some linear combination) or via a more complicated function of sets of features being used to inform suppression. 
- Similarly, show that the OV circuit is suppressing the same features/features you think are being used to determine semantic similarity. If there's some asymmetry here, that could be interesting as it would correspond to "I calculate A and B as similar by their similarity in the *california axis* but I suppress predictions of any token that has the feature for anywhere on the West Coast*).

I'm particularly excited about this because it might represent a really good way to show how knowing features informs the quality of mechanistic explanations. 

I'm a little confused by this question. What are you proposing? 

Lots of thoughts. This is somewhat stream of consciousness as I happen to be short on time this week, but feel free to follow up again in the future:

  • Anthropic tested their SAEs on a model with random weights here and found that the results look noticeably different in some respects to SAEs trained on real models "The resulting features are here, and contain many single-token features (such as "span", "file", ".", and "nature") and some other features firing on seemingly arbitrary subsets of different broadly recognizable contexts (such as LaTeX or code)." I think further experiments like this which identify classes of features which are highly non-trivial, don't occur in SAEs trained on random models (or random models with a W_E / W_U from a real model) or which can be related to interpretable circuity would help. 
  • I should not that, to the extent that SAEs could be capturing structure in the data, the model might want to capture structure in the data too, so it's not super clear to me what you would observe that would distinguish SAEs capturing structure in the data which the model itself doesn't utilise. Working this out seems important. 
  • Furthermore, the embedding space of LLM's is highly structured already and since we lack good metrics, it's hard to say how precisely SAE's capture "marginal" structure over existing methods. So quantifying what we mean by structure seems important too. 
  • The specific claim that SAEs learn features which are combinations of true underlying features is a reasonable one given the L1 penalty, but I think it's far from obvious how we should think about this in practice. 
  • I'm pretty excited about deliberate attempts to understand where SAEs might be misleading or not capturing information well (eg: here or here). It seems like there are lots of technical questions that are slightly more low level that help us build up to this.  

So in summary: I'm a bit confused about what we mean here and think there are various technical threads to follow up on. Knowing which actually resolve this requires we try to define our terms here more thoroughly. 

Thanks for asking:

  1. Currently we load SAEs into my codebase here. How hard this is will depend on how different your SAE architecture/forward pass is from what I currently support. We're planning to support users / do this ourselves for the first n users and once we can, we'll automate the process. So feel free to link us to huggingface or a public wandb artifact. 
  2.  We run the SAEs over random samples from the same dataset on which the model was trained (with activations drawn from forward passes of the same length). Callum's SAE vis codebase has a demo where you can see how this works. 
  3. Since we're doing this manually, the delay will depend on the complexity on handling the SAEs and things like whether they're trained on a new model (not GPT2 small) and how busy we are with other people's SAEs or other features. We'll try our best and keep you in the loop. Ballpark is 1 -2 weeks not months. Possibly days (especially if the SAEs are very similar to those we are hosting already). We expect this to be much faster in the future. 

We've made the form in part to help us estimate the time / effort required to support SAEs of different kinds (eg: if we get lots of people who all have SAEs for the same model or with the same methodological variation, we can jump on that). 

It helps a little but I feel like we're operating at too high a level of abstraction. 

with the mech interp people where they think we can identify values or other high-level concepts like deception simply by looking at the model's linear representations bottom-up, where I think that'll be a highly non-trivial problem.


I'm not sure anyone I know in mech interp is claiming this is a non-trivial problem. 

biological and artificial neural-networks are based upon the same fundamental principles


I'm confused by this statement. Do we know this? Do we have enough of an understanding of either to say this? Don't get me wrong, there's some level on which I totally buy this. However, I'm just highly uncertain about what is really being claimed here. 

Depending on model size I'm fairly confident we can train SAEs and see if they can find relevant features (feel free to dm me about this).

I think we got similar-ish results. @Andy Arditi  was going to comment here to share them shortly. 

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