That's technically even more conditional as the intervention (subtract the parallel component) also depends on the residual stream. But yes. I think it's reasonable to lump these together though, orthogonalisation also should be fairly non destructive unless the direction was present, while steering likely always has side effects

Note that this is conditional SAE steering - if the latent doesn't fire it's a no-op. So it's not that surprising that it's less damaging, a prompt is there on every input! It depends a lot on the performance of the encoder as a classifier though

When do you use escape?

It seems unlikely that openai is truly following the test the model plan? They keep eg putting new experimental versions onto lmsys, presumably mostly due to different post training, and it seems pretty expensive to be doing all the DC evals again on each new version (and I think it's pretty reasonable to assume that a bit of further post training hasn't made things much more dangerous)

I'm not super sure what I think of this project. I endorse the seed of the idea re "let's try to properly reverse engineer what representing facts in superposition looks like" and think this was a good idea ex ante. Ex post, I consider our results fairly negative, and have mostly confused that this kind of thing is cursed and we should pursue alternate approaches to interpretability (eg transcoders). I think this is a fairly useful insight! But also something I made from various other bits of data. Overall I think this was a fairly useful conclusion re updating away from ambitious mech interp and has had a positive impact on my future research, though it's harder to say if this impacted others (beyond the general sphere of people I mentor/manage)

I think the circuit analysis here is great, a decent case study of what high quality circuit analysis looks like, one of studies of factual recall I trust most (though I'm biased), and introduced some new tricks that I think are widely useful, like using probes to understand when information is introduced Vs signal boosted, and using mechanistic probes to interpret activations without needing training data. However, I largely haven't seen much work build on this, beyond a few scattered examples, which suggests it hasn't been too impactful. I also think this project took much longer than it should have, which is a bit sad.

Though, this did get discussed in a 3Blue1Brown video, which is the most important kind of impact!

I really like this paper (though, obviously, am extremely biased). I don't think it was groundbreaking, but I think it was an important contribution to mech interp, and one of my favourite papers that I've supervised.

Superposition seems like an important phenomena that affects our ability to understand language models. I think this paper was some of the first evidence that it actually happens in language models, and on what it actually looks like. Thinking about eg why neurons detecting compound words (eg blood pressure) were unusually easy to represent in superposition, while "this text is in French" merited dedicated neurons, helped significantly clarify my understanding of superposition beyond what was covered in Toy Models of Superposition (discussed in Appendix A). I also just like having case studies and examples of phenomena in language models to think about, and have found some of the neuron families in this paper helpful to keep in mind when reasoning about other weirdnesses in LLMs. I largely think the results in this paper have stood the test of time.

Sparse autoencoders have been one of the most important developments in mechanistic interpretability in the past year or so, and significantly shaped the research of the field (including my own work). I think this is in substantial part due to Towards Monosemanticity, between providing some rigorous preliminary evidence that the technique actually worked, a bunch of useful concepts like feature splitting, and practical advice for training these well. I think that understanding what concepts are represented in model activations is one of the most important problems in mech interp right now. Though highly imperfect, SAEs seem the best current bet we have here, and I expect whatever eventually works to look at least vaguely like an SAE.

I have various complaints and caveats about the paper (that I may elaborate on in a longer review in the discussion phase), and pessimisms about SAEs, but I think this work remains extremely impactful and significantly net positive on the field, and SAEs are a step in the right direction.

How would you evade their tools?

A tip for anyone on the ML job/PhD market - people will plausibly be quickly skimming your google scholar to get a sense of "how impressive is this person/what is their deal" read (I do this fairly often), so I recommend polishing your Google scholar if you have publications! It can make a big difference.

I have a lot of weird citable artefacts that confuse Google Scholar, so here's some tips I've picked up:

• First, make a google scholar profile if you don't already have one!
  • Verify the email (otherwise it doesn't show up properly in search)
• (Important!) If you are co-first author on a paper but not in the first position, indicate this by editing the names of all co-first authors to end in a *
  • You edit by logging in to the google account you made the profile with, going to your profile, clicking on the paper's name, and then editing the author's names
  • Co-first vs second author makes a big difference to how impressive a paper is, so you really want this to be clear!
• Edit the venue of your work to be the most impressive place it was published, and include any notable awards from the venue (eg spotlight, oral, paper awards, etc).
  • You can edit this by clicking on the paper name and editing the journal field.
  • If it was a workshop, make sure you include the word workshop (otherwise it can appear deceptive).
  • See my profile for examples.
• Hunt for lost citations: Often papers have weirdly formatted citations and Google scholar gets confused and thinks it was a different paper. You can often find these by clicking on the plus just below your profile picture then add articles, and then clicking through the pages for anything that you wrote. Add all these papers, and then use the merge function to combine them into one paper (with a combined citation count).
  • Merge lets you choose which of the merged artefacts gets displayed
  • Merge = return to the main page, click the tick box next to the paper titles, then clicking merge at the top
  • Similar advice applies if you have eg a blog post that was later turned into a paper, and have citations for both
  • Another merging hack, if you have a weird artefact on your google scholar (eg a blog post or library) and you don't like how Google scholar thinks it should be presented, you can manually add the citation in the format you like, and then merge this with the existing citation, and display your new one
• If you're putting citations on a CV, semantic scholar is typically better for numbers, as it updates more frequently than Google scholar. Though it's worse at picking up on the existence of non paper artefacts like a cited Github or blog post
• Make your affiliation/title up to date at the top

Do you know what topics within AI Safety you're interested in? Or are you unsure and so looking for something that lets you keep your options open?

+1 to the other comments, I think this is totally doable, especially if you can take time off work.

The hard part imo is letters of recommendation, especially if you don't have many people who've worked with you on research before. If you feel awkward about asking for letters of recommendation on short notice (which multiple people have asked me for in the past week, if it helps, so this is pretty normal), one thing that makes it lower effort for the letter writer is giving them a bunch of notes on specific things you did while working with them and what traits of your's this demonstrates or, even better, offering to write a rough first draft letter for them to edit (try not to give very similar letters to all your recommenders though!).

Thanks a lot for the post! It's really useful to have so many charities and a bit of context in the same place when thinking about my own donations. I found it hard to navigate a post with so many charities, so I put this into a spreadsheet that lets me sort and filter the categories - hopefully this is useful to others too! https://docs.google.com/spreadsheets/d/1WN3uaQYJefV4STPvhXautFy_cllqRENFHJ0Voll5RWA/edit?gid=0#gid=0

Cool project! Thanks for doing it and sharing, great to see more models with SAEs

interpretability research on proprietary LLMs that was quite popular this year and great research papers by Anthropic[1][2], OpenAI[3][4] and Google Deepmind

I run the Google DeepMind team, and just wanted to clarify that our work was not on proprietary closed weight models, but instead on Gemma 2, as were our open weight SAEs - Gemma 2 is about as open as llama imo. We try to use open models wherever possible for these general reasons of good scientific practice, ease of replicability, etc. Though we couldn't open source the data, and didn't go to the effort of open sourcing the code, so I don't think they can be considered true open source. OpenAI did most of their work on gpt2, and only did their large scale experiment on GPT4 I believe. All Anthropic work I'm aware of is on proprietary models, alas.

It's essentially training an SAE on the concatenation of the residual stream from the base model and the chat model. So, for each prompt, you run it through the base model to get a residual stream vector v_b, through the chat model to get a residual stream vector v_c, and then concatenate these to get a vector twice as long, and train an SAE on this (with some minor additional details that I'm not getting into)

This is somewhat similar to the approach of the ROME paper, which has been shown to not actually do fact editing, just inserting louder facts that drown out the old ones and maybe suppressing the old ones.

In general, the problem with optimising model behavior as a localisation technique is that you can't distinguish between something that truly edits the fact, and something which adds a new fact in another layer that cancels out the first fact and adds something new.

Agreed, chance of success when cold emailing busy people is low, and spamming them is bad. And there are alternate approaches that may work better, depending on the person and their setup - some Youtubers don't have a manager or employees, some do. I also think being able to begin an email with "Hi, I run the DeepMind mechanistic interpretability team" was quite helpful here.

The high level claim seems pretty true to me. Come to the GDM alignment team, it's great over here! It seems quite important to me that all AGI labs have good safety teams

Thanks for writing the post!

Huh, are there examples of right leaning stuff they stopped funding? That's new to me

+1. Concretely this means converting every probability p into p/(1-p), and then multiplying those (you can then convert back to probabilities)

Intuition pump: Person A says 0.1 and Person B says 0.9. This is symmetric, if we instead study the negation, they swap places, so any reasonable aggregation should give 0.5

Geometric mean does not, instead you get 0.3

Arithmetic gets 0.5, but is bad for the other reasons you noted

Geometric mean of odds is sqrt(1/9 * 9) = 1, which maps to a probability of 0.5, while also eg treating low probabilities fairly

Interesting thought! I expect there's systematic differences, though it's not quite obvious how. Your example seems pretty plausible to me. Meta SAEs are also more incentived to learn features which tend to split a lot, I think, as then they're useful for more predicting many latents. Though ones that don't split may be useful as they entirely explain a latent that's otherwise hard to explain.

Anyway, we haven't checked yet, but I expect many of the results in this post would look similar for eg sparse linear regression over a smaller SAEs decoder. Re why meta SAEs are interesting at all, they're much cheaper to train than a smaller SAE, and BatchTopK gives you more control over the L0 than you could easily get with sparse linear regression, which are some mild advantages, but you may have a small SAE lying around anyway. I see the interesting point of this post more as "SAE latents are not atomic, as shown by one method, but probably other methods would work well too"

What's wrong with twitter as an archival source? You can't edit tweets (technically you can edit top level tweets for up to an hour, but this creates a new URL and old links still show the original version). Seems fine to just aesthetically dislike twitter though

To me, this model predicts that sparse autoencoders should not find abstract features, because those are shards, and should not be localisable to a direction in activation space on a single token. Do you agree that this is implied?

If so, how do you square that with eg all the abstract features Anthropic found in Sonnet 3?

Thanks for making the correction!

I expect there's lots of new forms of capabilities elicitation for this kind of model, which their standard framework may not have captured, and which requires more time to iterate on

Thanks for the post!

sample five random users’ forecasts, score them, and then average

Are you sure this is how their bot works? I read this more as "sample five things from the LLM, and average those predictions". For Metaculus, the crowd is just given to you, right, so it seems crazy to sample users?

Yeah, fair point, disagreement retracted

I think this is important to define anyway! (and likely pretty obvious). This would create a lot more friction for someone to take on such a role though, or move out

But only a small fraction work on evaluations, so the increased cost is much smaller than you make out

Cool work! This is the outcome I expected, but I'm glad someone actually went and did it

Yeah, if I made an introduction it would ruin the spirit of it!

I don't see important differences between that and ce loss delta in the context Lucius is describing

This seems true to me, though finding the right scaling curve for models is typically quite hard so the conversion to effective compute is difficult. I typically use CE loss change, not loss recovered. I think we just don't know how to evaluate SAE quality.

My personal guess is that SAEs can be a useful interpretability tool despite making a big difference in effective compute, and we should think more in terms of useful they are for downstream tasks. But I agree this is a real phenomena, that is easy to overlook, and is bad.

These are LLM generated labels, there are no "real" labels (because they're expensive!). Especially in our demo, Neuronpedia made them with gpt 3.5 which is kinda dumb.

I mostly think they're much better than nothing, but shouldn't be trusted, and I'm glad our demo makes this apparent to people! I'm excited about work to improve autointerp, though unfortunately the easiest way is to use a better model, which gets expensive

I dislike clickbait when it's misleading, or takes a really long time to get to the point (esp if it's then underwhelming). I was fine with this post on that front.

Cold emailing Youtubers offering to chat about mechanistic interpretability turns out to be a way, way more effective strategy than I predicted! I'm super excited about that video (and it came out so well!). The video

This is a fair point. I think Newsom is a very visible and prominent target who has more risk here (I imagine people don't pay that much attention to individual California legislators), it's individually his fault if he doesn't veto, and he wants to be President and thus cares much more about national stuff. While the California legislators were probably annoyed at Pelosi butting into state business.

My model is basically just "Newsom likely doesn't want to piss off Big Tech or Pelosi, and the incentive to not veto doesn't seem that high, and so seems highly likely to veto, and 50% veto seems super low". My fair is, like, 80% veto I think?

I'm not that compelled by the base rates argument, because I think the level of controversy over the bill is atypically high, so it's quite out of distribution. Eg I think Pelosi denouncing it is very unusual for a state Bill and a pretty big deal

Thanks for taking the time to write this up, I found it a really helpful overview

I'd be interested! I would also love to see the full answer to why people care about SAEs

But they're not atomic! See eg the phenomena of feature splitting, and the fact that UMAP finds structure between semantically similar features

(In fairness, atoms are also not very atomic)

Thanks for writing this up Lewis! I'm very happy with this change, I think the term "SAE feature" is kinda sloppy and anti-conducive to clear thinking, and I hope the rest of the field adopts this too.

We are finding a bunch of insights about the internal features and circuits inside models that I believe to be true, and developing useful techniques like sparse autoencoders and activation patching that expand the space of what we can do. We're starting to see signs of life of actually doing things with mech interp, though it's early days. I think skepticism is reasonable, and we're still far from actually mattering for alignment, but I feel like the field is making real progress and is far from failed

My read is that the target audience is much more about explaining alignment concerns to a mainstream audience and that GDM takes them seriously (which I think is great!), than about providing non trivial details to a LessWrong etc audience

Seems unclear if that's their true beliefs or just the rhetoric they believed would work in DC.

The latter could be perfectly benign - eg you might think that labs need better cyber security to stop eg North Korea getting the weights, but this is also a good idea to stop China getting them, so you focus on the latter when talking to Nat sec people as a form of common ground

My (maybe wildly off) understanding from several such conversations is that people tend to say:

• We think that everyone is racing super hard already, so the marginal effect of pushing harder isn't that high
• Having great models is important to allow Anthropic to push on good policy and do great safety work
• We have an RSP and take it seriously, so think we're unlikely to directly do harm by making dangerous AI ourselves

China tends not to explicitly come up, though I'm not confident it's not a factor.

(to be clear, the above is my rough understanding from a range of conversations, but I expect there's a diversity of opinions and I may have misunderstood)

I think mech interp, debate and model organism work are notable for currently having no practical applications lol (I am keen to change this for mech interp!)

Yeah, this seems obviously true to me, and exactly how it should be.

work on capabilities at Anthropic because of the supposed inevitability of racing with China

I can't recall hearing this take from Anthropic people before

Thanks! I will separately say that I disagree with the statement regardless of whether you're treating my tweet as evidence