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Controlling Language and Diffusion Models by Transporting Activations
Large generative models are becoming increasingly capable and more widely deployed to power production applications, but getting these…
For example, a user might want to adjust the tone of text without altering its content, change the style of generated images while maintaining their context, or to ensure sensitive topics are handled with care, without compromising overall coherence. Linear-AcT learns a transport map from the source to the target set, resulting in a controllable intervention that can remove a negated concept (like "pink elephant") that otherwise would have been included in the generated model, as shown in figure 7. While common approaches like RLHF and instruction fine-tuning have been effective in improving the alignment of LLM output with users’ expectations, these methods are resource intensive, and become impractical as models grow in complexity.
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