Apollo Research (London).
My main research interests are mechanistic interpretability and inner alignment.
Lee Sharkey
Karma: 1,710
Paper: Open Problems in Mechanistic Interpretability
seems great for mechanistic anomaly detection! very intuitive to map ADP to surprise accounting (I was vaguely trying to get at a method like ADP here)
Agree! I’d be excited by work that uses APD for MAD, or even just work that applies APD to Boolean circuit networks. We did consider using them as a toy model at various points, but ultimately opted to go for other toy models instead.
(btw typo: *APD)
IMO most exciting mech-interp research since SAEs, great work
I think so too! (assuming it can be made more robust and scaled, which I think it can)
And thanks! :)
We’re aware of model diffing work like this, but I wasn’t aware of this particular paper.
It’s probably an edge case: They do happen both to be in weight space and to be suggestive of weight space linearity. Indeed, our work was informed by various observations from a range of areas that suggest weight space linearity (some listed here).
On the other hand, our work focused on decomposing a given network’s parameters. But the line of work you linked above seems more in pursuit of model editing and understanding the difference between two similar models, rather than decomposing a particular model’s weights.
all in all, whether it deserved to be in the related work section is unclear to me. Seems plausible either way. The related work section was already pretty long, but it maybe deserves a section on weight space linearity, though probably not one on model diffing imo.
Attribution-based parameter decomposition
It would be interesting to meditate in the question “What kind of training procedure could you use to get a meta-SAE directly?” And I think answering this relies in part on mathematical specification of what you want.
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.
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.
Figure 3 in this paper (AtP*, Kramar et al.) illustrates the point nicely: https://arxiv.org/abs/2403.00745