New paper: Finetuning on narrow domains leaves traces behind. By looking at the difference in activations before and after finetuning, we can interpret what it was finetuned for. And so can our interpretability agent! 🧵

Can we interpret what happens in finetuning? Yes, if for a narrow domain! Narrow fine tuning leaves traces behind. By comparing activations before and after fine-tuning we can interpret these, even with an agent! We interpret subliminal learning, emergent misalignment, and more

Very cool initiative!

Causal Abstraction, the theory behind DAS, tests if a network realizes a given algorithm. We show (w/ @denissutter.bsky.social, T. Hofmann, @tpimentel.bsky.social ) that the theory collapses without the linear representation hypothesis—a problem we call the non-linear representation dilemma.

In this new paper, w/ @denissutter.bsky.social , @jkminder.bsky.social, and T.Hofmann, we study *causal abstraction*, a formal specification of when a deep neural network (DNN) implements an algorithm. This is the framework behind, e.g., distributed alignment search.

Paper: arxiv.org/abs/2507.08802

Mechanistic interpretability often relies on *interventions* to study how DNNs work. Are these interventions enough to guarantee the features we find are not spurious? No!⚠️ In our new paper, we show many mech int methods implicitly rely on the linear representation hypothesis🧵

With @butanium.bsky.social and @neelnanda.bsky.social we've just published a post on model diffing that extends our previous paper.
Rather than trying to reverse-engineer the full fine-tuned model, model diffing focuses on understanding what makes it different from its base model internally.

In our most recent work, we looked at how to best leverage crosscoders to identify representational differences between base and chat models. We find many cool things, e.g., a knowledge boundary, a detailed info and a humor/ joke detection latent.

background: the technique here is "model-diffing" introduced by @anthropic.com just 8 weeks ago and quickly replicated by others. this includes an open source @hf.co model release by @butanium.bsky.social and @jkminder.bsky.social which I'm using. transformer-circuits.pub/2024/crossco...

New @acm-cscw.bsky.social paper, new content moderation paradigm.

Post Guidance lets moderators prevent rule-breaking by triggering interventions as users write posts!

We implemented PG on Reddit and tested it in a massive field experiment (n=97k). It became a feature!

arxiv.org/abs/2411.16814

Can we understand and control how language models balance context and prior knowledge? Our latest paper shows it’s all about a 1D knob! 🎛️
arxiv.org/abs/2411.07404

Co-led with
@kevdududu.bsky.social - @niklasstoehr.bsky.social , Giovanni Monea, @wendlerc.bsky.social, Robert West & Ryan Cotterell.

In case you also wondered how to derive the maximal update parametrisation (muP) learning rate for ADAM. I did a short write up: tinyurl.com/mup-for-adam. Thanks Ilia Badanin and Eugene Golikov for your help on this.

If you’re interested in mechanistic interpretability, I just found this starter pack and wanted to boost it (thanks for creating it @butanium.bsky.social !). Excited to have a mech interp community on bluesky 🎉

go.bsky.app/LisK3CP

Hey, @bsky.app @support.bsky.team, is there a way for you to shorten the displayed usernames when trailed by “bsky.social”? If someone has some other domain name, then fine, show that, but if we're using the default domain, can we get rid of these lengthy string of characters?

Trying to bring ML/NLP/etal people from ETH Zürich together. Ping me to add you. 🙂
bsky.app/starter-pack...