Our two phenomenal interns, Alireza Mousavi-Hosseini and Stephen Zhang @syz.bsky.social have been cooking some really cool work with Michal Klein and me over the summer.

Relying on optimal transport couplings (to pick noise and data pairs) should, in principle, be helpful to guide flow matching

🧵

Our work on fine-grained control of LLMs and diffusion models via Activation Transport will be presented @iclr_conf as spotlight✨Check out our new blog post machinelearning.apple.com/research/tra...

Què és l’aprenentatge profund ?

La @marionamec.bsky.social de @neurofregides.bsky.social ens ho explica en motiu del Deep Learning Barcelona Symposium 2024 (@dlbcn.ai), aquest dijous 19 de desembre.

#deeplearning #ciencia #català #barcelona

www.youtube.com/shorts/R4u_Z...

Evaluating your LLM uncertainties with Rougle-L will show clear winners... except that they aren't actually good. We find that Rouge-L spuriously favors some methods over others. 🧵1/4

📄 openreview.net/forum?id=jGt...
NeurIPS: Sunday, East Exhibition Hall A, Safe Gen AI workshop

Kudos to all co-authors 👏 Arno Blaas, Michal Klein, Luca Zappella, Nicholas Apostoloff, Marco Cuturi, and Xavier Suau.

Extra 👏 to Xavi for making this so great! Like a friend would say, he's the Rolls-Royce of the co-authors, and he should be regarded the first author too!

Summary:
🤝 Unifying activation steering w/ OT.
✨ Linear-AcT preserves distributions w/ interpretable ([0, 1]) strength.
💪 Robust: models/layers/modalities
💬 LLMs: toxicity mitigation, truthfulness and concept induction,
🌄 T2I: style induction and concept negation.
🚀 Negligible cost!

8/9 T2I models tend to generate negated concepts 😮

In the image, StableDiffusion XL prompted with: “2 tier cake with multicolored stars attached to it and no {white bear, pink elephant, gorilla} can be seen.”

✨Linear-AcT makes the negated concept disappear✨

7/9 And here we induce Cyberpunk 🤖 for the same prompt!

6/9 Amazingly, we can condition Text-to-Image (T2I) Diffusion with the same exact method we used for LLMs! 🤯

In this example, we induce a specific style (Art Nouveau 🎨), which we can accurately control with our λ parameter.

5/9 With Linear-AcT, we achieve great results in LLM 👿 toxicity mitigation and 👩🏼‍⚖️ truthfulness induction.

And the best result is always obtained at λ=1, as opposed to vector-based steering methods!

4/9 Linear-AcT preserves target distributions, with interpretable strength λ 🌈

🍰 All we need is two small sets of sentences {a},{b} from source and target distributions to estimate the Optimal Transport (OT) map 🚚

🚀 We linearize the map for speed/memory, thus ⭐Linear-AcT⭐

3/9 An activation has a different output distributions per behavior, eg. 🦠 toxic (source) and 😊 non-toxic (target). i) Vector-based AS moves activations OOD 🤯, with catastrophic consequences 💥 harming model utility. ii) The strength λ is unbounded and non-interpretable 🤨!

2/9 🤓 Activation Steering (AS) is a fast and cheap alternative for alignment/control.

Most AS techniques perform a vector addition such as a* = a + λv, where v is some estimated vector and λ the conditioning strength. How v is estimated differs for each method.

1/9 🤔 How do we currently align/control generative models?
- Pre-prompting
- Fine-tuning
- RLHF
However, these techniques can be slow/expensive! 🐢

Thrilled to share the latest work from our team at
@Apple
where we achieve interpretable and fine-grained control of LLMs and Diffusion models via Activation Transport 🔥

📄 arxiv.org/abs/2410.23054
🛠️ github.com/apple/ml-act

0/9 🧵

Thank you to the @neuripsconf.bsky.social for this recognition of the Generative Adversarial Nets paper published ten years ago with @ian-goodfellow.bsky.social, Jean Pouget-Abadie, @memimo.bsky.social, Bing Xu, David Warde-Farley, Sherjil Ozair and Aaron Courville.
blog.neurips.cc/2024/11/27/a...

Apple will be a platinum sponsor of the Deep Learning Barcelona Symposim 2024. This is the first time that Apple sponsors the event. #DLBCN

Bring stats to LM evals!!

open.substack.com/pub/desiriva...

Watching Frieren can’t stop thinking that demons are evil LLMs 😅