Summary of Controlling Language and Diffusion Models by Transporting Activations, By Pau Rodriguez et al.
Controlling Language and Diffusion Models by Transporting Activations
by Pau Rodriguez, Arno Blaas, Michal Klein, Luca Zappella, Nicholas Apostoloff, Marco Cuturi, Xavier Suau
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a framework called Activation Transport (AcT) to steer the generation of large generative models, addressing concerns about reliability, safety, and potential misuse. The approach generalizes previous works by controlling model activations using optimal transport theory, providing fine-grained control with minimal computational overhead. AcT is modality-agnostic and effective in various tasks, including mitigating toxicity in language models, inducing arbitrary concepts, increasing truthfulness, and enabling style control in text-to-image models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a way to control big computer models that make things like fake images or text. These models are getting better at doing things on their own, but that’s also making people worried about what they might do. The new method, called Activation Transport (AcT), helps us guide these models so we can get the results we want and avoid bad things happening. |
