Chris Wendler

Clément Dumas on X: "Excited to share our latest paper, accepted as a spotlight at the #ICML2024 mechanistic interpretability workshop! We find evidence that LLMs use language-agnostic representations of concepts 🧵↘️ https://t.co/dDS5iv199i" / X Excited to share our latest paper, accepted as a spotlight at the #ICML2024 mechanistic interpretability workshop! We find evidence that LLMs use language-agnostic representations of concepts 🧵↘️ https://t.co/dDS5iv199i

Our mech interp ICML workshop paper got accepted to ACL 2025 main! 🎉
In this updated version, we extended our results to several models and showed they can actually generate good definitions of mean concept representations across languages.🧵

Can we uncover the list of topics a language model is censored on?

Refused topics vary strongly among models. Claude-3.5 vs DeepSeek-R1 refusal patterns:

Language Models use Lookbacks to Track Beliefs How do language models (LMs) represent characters' beliefs, especially when those beliefs may differ from reality? This question lies at the heart of understanding the Theory of Mind (ToM) capabilitie...

I am really proud to share our work led by Nikhil Prakash and in collaboration with more mechanistic interpretability and Theory of Mind (ToM) researchers:
arxiv.org/abs/2505.14685
You can find a tweet here with nice animations:
x.com/nikhil07prak...

Check out Sheridan’s work on concept induction circuits -- the soft version of induction we were promised a while ago :)

During our multilingual concept patching experiments I have always been wondering whether it is those circuits doing the work. Finally, some evidence:

We also have a website sdxl-unbox.epfl.ch
and a paper arxiv.org/abs/2410.22366

Huge shoutout to Viacheslav Surkov who executed this project! This is what can happen when you keep pushing on your course project :P Really amazing!

If you go and play with this app you are guaranteed to find some fascinating quirk about SDXL turbo that no-one has ever seen before, which is why I love this work!

If you are someone who is great at designing user interfaces and want to build a better app or website with us, reach out to me via DM.

If you are someone just curious about deep learning and diffusion models, go play with the features. We have more than 2000 features per layer.

It should be pretty self explanatory to use this app. You type in the feature index, select the layer, the strength of the coefficient, you brush a mask where the feature should be activated and hit "apply"...

You an also do more "abstract" things like brushing the face with a "water"-texture feature...

But that's not the best part yet. My favorite layer is the "style" layer. It allows you to draw with textures without modifying the rest of the image much. E.g. this happens when you brush the face with the "giraffe texture feature".

Inspired by this I also made one where I tried to take the hole-feature from a "Trypophobia" image...

Let's see what happens if we turn on a feature that activates on the beard but in the detail layer... We noticed in our experiments that these features often latch onto the context of the generated image (and require relevant context to be effective). The result is wild!

There is also one that seems to have something to do with the beard. Turning it on shows that it probably is more than just a beard... maybe a "manliness" feature or something like that.

We can look for interesting features in the "explore" tab. E.g. in the "composition" block feature number 199 seems to have to do with that hat. Let's turn it on...

Let's start with the prompt "an image of a colorful model"

Unboxing SDXL with SAEs - a Hugging Face Space by surokpro2 Discover amazing ML apps made by the community

We built an app where you can explore these features and turn them on while generating an image. The app is here:
huggingface.co/spaces/surok...

In case you ever wondered what you could do if you had SAEs for intermediate results of diffusion models, we trained SDXL Turbo SAEs on 4 blocks for you. We noticed that they specialize into a "composition", a "detail", and a "style" block. And one that is hard to make sense of.

Apply to Akhil's lab, he is great!

Lots of work coming soon to @iclr-conf.bsky.social and @naaclmeeting.bsky.social in April/May! Come chat with us about new methods for interpreting and editing LLMs, multilingual concept representations, sentence processing mechanisms, and arithmetic reasoning. 🧵

And are more transferrable. I think with the current methods, we get the best interpretations by accumulating many different agreeing angles onto the same question. SAE's can be one of them, salience maps another one, patching another one, and so on. But we still need better tools.

Unboxing SDXL Turbo with SAEs Sparse Autoencoders (SAEs) find interpretable features in Stable Diffusion Turbo and enable fine-grained image editing.

I am also skeptical about SAEs and steering. I usually compare SAE/steering interventions to adversarial attacks (AAs). For AAs we know that they can yield arbitrary outputs via minimal perturbations of arbitrary intermediate states. Compared to those steering/SAE IMO use less optimization power.

Unboxing SDXL Turbo with SAEs Sparse Autoencoders (SAEs) find interpretable features in Stable Diffusion Turbo and enable fine-grained image editing.

What exactly this tells us about the mechanisms is an open question. Compared to editing the input, you can get very different but still interpretable effects, e.g., in our work we basically found features that can be turned into style brushes by SAEing up.0.1 in SDXL Turbo sdxl-unbox.epfl.ch

I did not read that paper with the random weights, but this is something I imagine impossible to do with random weights and thus probably not properly discussed in that work.

This is different angle that makes interpretations of SAE features testable. „Do they affect the remaining forward pass in a way consistent with my interpretation?“

So I agree that random NN features + learnt SAE is a powerful encoder and one has to be careful with the interpretation.

But Clement was talking about interventions that in a generative model modify its output also accordingly.

I think that it makes total sense that learning SAE features, which is not much different from computing clusters (at least for k=1), on some layer of a randomly initialised NN should pick up interesting features of your data. People used random features as basis for ML since the beginning.

But I will say that SAE features often (especially with expansion factors common for the ones used in LLMs) are not good for interventions.

Honestly, I think Clément made a great point here and it is hard to get what you mean with this response.

Seems like you are not giving useful inputs tbh.