Apollo Research (London).
My main research interests are mechanistic interpretability and inner alignment.
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.
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.
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.
(
For the convenience of other readers of this thread, the kind of strategies I labelled as strictly active are:
)
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:
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.
Thanks! Fixed now
Figure 3 in this paper (AtP*, Kramar et al.) illustrates the point nicely: https://arxiv.org/abs/2403.00745