Apollo Research (London).
My main research interests are mechanistic interpretability and inner alignment.
Lee Sharkey
Karma: 1,532
Showing SAE Latents Are Not Atomic Using Meta-SAEs
Interpretability as Compression: Reconsidering SAE Explanations of Neural Activations with MDL-SAEs
So I believe I had in mind “active means [is achieved deliberately through the agent’s actions]”.
I think your distinction makes sense. And if I ever end up updating this article I would consider incorporating it. However, I think the reason I didn’t make this distinction at the time is because the difference is pretty subtle.
The mechanisms I labelled as “strictly active” are the kind of strategy that it would be extremely improbable to implement successfully without some sort of coherent internal representations to help orchestrate the actions required to do it. This is true even if they’ve been selected for passively.
So I’d argue that they all need to be implemented actively (if they’re to have a reasonable probability of success) but may be selected for passively or actively. I’m curious if you agree with this? If not, then I may have missed your argument.
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For the convenience of other readers of this thread, the kind of strategies I labelled as strictly active are:
1.1.3. Environmental artifacts that corrupt its future self: A misaligned AI may leave behind artifacts that serve as adversarial examples or poisoned data that corrupt its future self. (Strictly active)
1.1.4. Deleting (and relearning) dangerous representations: When not under surveillance, an AI might be able to delete dangerous representations so that it looks safe when surveillance resumes. It might also be able to schedule when it relearns those representations. (Strictly active)
1.1.5. Weights poisoning: Instead of providing manipulated inputs, the AI might manipulate its own weights so that a small subset of normal inputs behave like adversarial examples. (Strictly active)
1.1.6. Pre-committing to following potentially dangerous instructions: Hubinger: ”… if the model has the ability to read and write to some sort of state … it could write out a pre-commitment to follow the hard-coded policy and then just have a policy of always following any pre-commitments it finds such that it never thinks about the deception that led it to that policy in the first place.” (Strictly active)
1.1.7. Hard-coded neural shortcuts: Hubinger again: “… it could hack its own gradient to produce a new model which will still exhibit the same deceptive behavior, but without doing any of the detectable deceptive cognition. … not because it’s trying to act deceptive, but just because it’s a hard-coded part of its algorithm” (Strictly active)
1.1.8. Following cached thoughts: Nanda: “[An AI might develop] the instinct to follow cached thoughts without thinking about them, and in occasional moments of deception to generate a lot of cached thoughts that will lead to misaligned actions, but with no deceptive cognition in the moment.” (Strictly active)
1.1.9. Instrumental incapability: A misaligned AI that’s able to modify its own parameters could damage capabilities that serve some goals (e.g. aligned goals) more than its misaligned goals. Generalizes instrumental clumsiness. (Strictly active)
1.1.10. Instrumental clumsiness: An AI might hack its future self so that it is more likely to make certain kinds of mistakes while leaving other capabilities intact. (Strictly active)
1.3.3. Interpretability illusions: By manipulating the dataset shown to the tool, an AI can control its interpretations. (Strictly active)
1.4.1. Adversarial attack against networks in the interpretation tool: The AI could adversarially attack networks in the tool to manipulate the tool’s outputs. (Strictly active)
2.4.1. Manipulating, mind-hacking, or incapacitating the interpreter: An AI might be able to manipulate, directly control, or incapacitate humans using specific kinds of outputs. (Strictly active)
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Extremely glad to see this! The Guez et al. model has long struck me as one of the best instances of a mesaoptimizer and it was a real shame that it was closed source. Looking forward to the interp findings!
Both of these seem like interesting directions (I had parameters in mind, but params and activations are too closely linked to ignore one or the other). And I don’t have a super clear idea but something like representational similarity analysis between SwitchSAEs and regular SAEs could be interesting. This is just one possibility of many though. I haven’t thought about it for long enough to be able to list many more, but it feels like a direction with low hanging fruit for sure. For papers, here’s a good place to start for RSA: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3730178/
Great work! Very excited to see work in this direction (In fact, I didn’t know you were working on this, so I’d expressed enthusiasm for MoE SAEs in our recent list of project ideas published just a few days ago!)
Comments:
I’d love to see some geometric analysis of the router. Is it just approximately a down-projection from the encoder features learned by a dense SAE trained on the same activations?
Consider integrating with SAELens.
Following Fedus et al., we route to a single expert SAE. It is possible that selecting several experts will improve performance. The computational cost will scale with the number of experts chosen.
If there are some very common features in particular layers (e.g. an ‘attend to BOS’ feature), then restricting one expert to be active at a time will potentially force SAEs to learn common features in every expert.
Thanks! Fixed now
A List of 45+ Mech Interp Project Ideas from Apollo Research’s Interpretability Team
Decomposing the QK circuit with Bilinear Sparse Dictionary Learning
I’ve no great resources in mind for this. Off the top of my head, a few examples of ideas that are common in comp neuro that might have bought mech interp some time if more people in mech interp were familiar with them when they were needed:
- Polysemanticity/distributed representations/mixed selectivity
- Sparse coding
- Representational geometry
I am not sure about what future mech interp needs will be, so it’s hard to predict which ideas or methods that are common in neuro will be useful (topological data analysis? Dynamical systems?). But it just seems pretty likely that a field that tackles such a similar problem will continue to be a useful source of intuitions and methods. I’d love if someone were to write a review or post on the interplay between the parallel fields of comp neuro and mech interp. It might help flag places where there ought to be more interplay.
I’ll add a strong plus one to this and a note for emphasis:
Representational geometry is already a long-standing theme in computational neuroscience (e.g. Kriegeskorte et al., 2013)
Overall I think mech interp practitioners would do well to pay more attention to ideas and methods in computational neuroscience. I think mech interp as a field has a habit of overlooking some hard-won lessons learned by that community.
Apollo Research 1-year update
Identifying Functionally Important Features with End-to-End Sparse Dictionary Learning
I’m pretty sure that there’s at least one other MATS group (unrelated to us) currently working on this, although I’m not certain about any of the details. Hopefully they release their research soon!
There’s recent work published on this here by Chris Mathwin, Dennis Akar, and me. The gated attention block is a kind of transcoder adapted for attention blocks.
Nice work by the way! I think this is a promising direction.
Note also the similar, but substantially different, use of the term transcoder here, whose problems were pointed out to me by Lucius. Addressing those problems helped to motivate our interest in the kind of transcoders that you’ve trained in your work!
Trying to summarize my current understanding of what you’re saying:
Yes all four sound right to me.
To avoid any confusion, I’d just add an emphasis that the descriptions are mathematical, as opposed semantic.I’d guess you have intuitions that the “short description length” framing is philosophically the right one, and I probably don’t quite share those and feel more confused how to best think about “short descriptions” if we don’t just allow arbitrary Turing machines (basically because deciding what allowable “parts” or mathematical objects are seems to be doing a lot of work). Not sure how feasible converging on this is in this format (though I’m happy to keep trying a bit more in case you’re excited to explain).
I too am keen to converge on a format in terms of Turing machines or Kolmogorov complexity or something else more formal. But I don’t feel very well placed to do that, unfortunately, since thinking in those terms isn’t very natural to me yet.
Hm I think of the (network, dataset) as scaling multiplicatively with size of network and size of dataset. In the thread with Erik above, I touched a little bit on why:
“SAEs (or decompiled networks that use SAEs as the building block) are supposed to approximate the original network behaviour. So SAEs are mathematical descriptions of the network, but not of the (network, dataset). What’s a mathematical description of the (network, dataset), then? It’s just what you get when you pass the dataset through the network; this datum interacts with this weight to produce this activation, that datum interacts with this weight to produce that activation, and so on. A mathematical description of the (network, dataset) in terms of SAEs are: this datum activates dictionary features xyz (where xyz is just indices and has no semantic info), that datum activates dictionary features abc, and so on.”And spiritually, we only need to understand behavior on the training dataset to understand everything that SGD has taught the model.
Yes, I roughly agree with the spirit of this.
Is there some formal-ish definition of “explanation of (network, dataset)” and “mathematical description length of an explanation” such that you think SAEs are especially short explanations? I still don’t think I have whatever intuition you’re describing, and I feel like the issue is that I don’t know how you’re measuring description length and what class of “explanations” you’re considering.
I’ll register that I prefer using ‘description’ instead of ‘explanation’ in most places. The reason is that ‘explanation’ invokes a notion of understanding, which requires both a mathematical description and a semantic description. So I regret using the word explanation in the comment above (although not completely wrong to use it—but it did risk confusion). I’ll edit to replace it with ‘description’ and strikethrough ‘explanation’.
“explanation of (network, dataset)”: I’m afraid I don’t have a great formalish definition beyond just pointing at the intuitive notion. But formalizing what an explanation is seems like a high bar. If it’s helpful, a mathematical description is just a statement of what the network is in terms of particular kinds of mathematical objects.
“mathematical description length of an explanation”: (Note: Mathematical descriptions are of networks, not of explanations.) It’s just the set of objects used to describe the network. Maybe helpful to think in terms of maps between different descriptions: E.g. there is a many-to-one map between a description of a neural network in terms of polytopes and in terms of neurons. There are ~exponentially many more polytopes. Hence the mathematical description of the network in terms of individual polytopes is much larger.
Focusing instead on what an “explanation” is: would you say the network itself is an “explanation of (network, dataset)” and just has high description length?
I would not. So:
If not, then the thing I don’t understand is more about what an explanation is and why SAEs are one, rather than how you measure description length.
I think that the confusion might again be from using ‘explanation’ rather than description.
SAEs (or decompiled networks that use SAEs as the building block) are supposed to approximate the original network behaviour. So SAEs are mathematical descriptions of the network, but not of the (network, dataset). What’s a mathematical description of the (network, dataset), then? It’s just what you get when you pass the dataset through the network; this datum interacts with this weight to produce this activation, that datum interacts with this weight to produce that activation, and so on. A mathematical description of the (network, dataset) in terms of SAEs are: this datum activates dictionary features xyz (where xyz is just indices and has no semantic info), that datum activates dictionary features abc, and so on.
Lmk if that’s any clearer.
Gated Attention Blocks: Preliminary Progress toward Removing Attention Head Superposition
Thanks Aidan!
I’m not sure I follow this bit:
In my mind, the reconstruction loss is more of a non-degeneracy control to encourage almost-orthogonality between features.
I don’t currently see why reconstruction would encourage features to be different directions from each other in any way unless paired with an L_{0<p<1}. And I specifically don’t mean L1, because in toy data settings with recon+L1, you can end up with features pointing in exactly the same direction.
At Apollo we’re currently working on something that we think will achieve this. Hopefully will have an idea and a few early results (toy models only) to share soon.