My vibe from this post is something like "we're making on stuff that could be helpful so there's stuff to work on!" and this is a vibe I like. However, I suspect that for people who might not be as excited about these approaches, you're likely not touching on important cruxes (eg: do these approaches really scale? Are some agendas capabilities enhancing? Will these solve deceptive alignment or just corrigible alignment?)
I also think that if the goal is to actually make progress and not to maximize the number of people making progress or who feel like they're making progress, then engaging with those cruxes is important before people invest substantive energy (ie: beyond upskilling). However as a directional update for people who are otherwise pretty cynical, this seems like a good update.
Really exciting! I added a version of AVEC to my interpretability tool for gridworld agents and am keen to explore it more. I really like that the injection coefficient has a scalar and this had enabled me to do what I can "an injection coefficient scan". The procedure I'm using looks like this:
So far, my results seem very interesting and possibly quite useful. It's possible this method is impractical in LLMs but I think it might be fine as well. Will dm some example figures. I also want to investigate using a continuous injection coefficient in activation patching is similarly useful since it seems like it might be. I am very excited to see if this makes my analyses easier! Great work!
We would love to see more ideas & hypotheses on why the model might be doing this, as well as attempts to test this! We mainly wrote-up this post because both Alex and I independently noticed this and weren't aware of this previously, so we wanted to make a reference post.
Happy to provide! I think I'm pretty interested in testing this/working on this in the future. Currently a bit tied up but I think (as Alex hints at) there could be some big implications for interpretability here.TLDR: Documenting existing circuits is good but explaining what relationship circuits have to each other within the model, such as by understanding how the model allocated limited resources such as residual stream and weights between different learnable circuit seems important.
The general topic I think we are getting at is something like "circuit economics". The thing I'm trying to gesture at is that while circuits might deliver value in distinct ways (such as reducing loss on different inputs, activating on distinct patterns), they share capacity in weights (see polysemantic and capacity in neural networks) and I guess "bandwidth" (getting penalized for interfering signals in activations). There are a few reasons why I think this feels like economics which include: scarce resources, value chains (features composed of other features) and competition (if a circuit is predicting something well with one heuristic, maybe there will be smaller gradient updates to encourage another circuit learning a different heuristic to emerge). So to tie this back to your post and Alex's comment "which seems like it would cut away exponentially many virtual heads? That would be awfully convenient for interpretability.". I think that what interpretability has recently dealt with in elucidating specific circuits is something like "micro-interpretability" and is akin to microeconomics. However this post seems to show a larger trend ie "macro-interpretability" which would possibly affect which of such circuits are possible/likely to be in the final model. I'll elaborate briefly on the off chance this seems like it might be a useful analogy/framing to motivate further work.
This is very speculative "theory" if you can call it that, but I guess I feel this would be "big if true". I also make no claims about this being super original or actually that useful in practice but it does feel intuition generating. I think this is totally the kind of thing people might have worked on sooner but it's likely been historically hard to measure the kinds of things that might be relevant. What your post shows is that between the transformer circuits framework and TransformerLens we are able to somewhat quickly take a bunch of interesting measurements relatively quickly which may provide more traction on this than previously possible.
Second pass through this post which solidly nerd-sniped me!
A quick summary of my understand of the post: (intentionally being very reductive though I understand the post may make more subtle points).
Thanks for writing this up! Looking forward to subsequent post/details :) PS: Is there are non-trivial relationship between this post and tuned lens/logit lens? https://arxiv.org/pdf/2303.08112.pdf Seems possible.
Thank you for letting me know about your work on procgen with MI. It sounds like you're making progress, particularly I'd be interested in your visualisation techniques (how do they compare to what was done in Understanding RL Vision?) and the reproduction of the cheese-maze policies (is this tricky? Do you think a DT could be well-calibrated on this problem?). Some questions that might be useful to discuss more:
Glad to hear your progress is going well! I'll be in the Bay Area for EAG if anyone from the team would like to chat.
Hey Adam, thanks for running Refine and writing this up.
Out of curiosity, do you (or anyone else) know if there are statistics for previous SERI-MATS cohorts/other programs designed to generate conceptual alignment researchers?