Controlling Language and Diffusion Models by Transporting Activations
This addresses safety and misuse concerns in generative AI by providing a general, efficient method for controlling model outputs, though it builds incrementally on prior activation-steering works.
The paper tackles the problem of controlling large generative models for reliability and safety by introducing Activation Transport (AcT), a framework that steers model activations using optimal transport theory, enabling fine-grained control over concepts and behaviors in language and diffusion models with negligible computational overhead.
The increasing capabilities of large generative models and their ever more widespread deployment have raised concerns about their reliability, safety, and potential misuse. To address these issues, recent works have proposed to control model generation by steering model activations in order to effectively induce or prevent the emergence of concepts or behaviors in the generated output. In this paper we introduce Activation Transport (AcT), a general framework to steer activations guided by optimal transport theory that generalizes many previous activation-steering works. AcT is modality-agnostic and provides fine-grained control over the model behavior with negligible computational overhead, while minimally impacting model abilities. We experimentally show the effectiveness and versatility of our approach by addressing key challenges in large language models (LLMs) and text-to-image diffusion models (T2Is). For LLMs, we show that AcT can effectively mitigate toxicity, induce arbitrary concepts, and increase their truthfulness. In T2Is, we show how AcT enables fine-grained style control and concept negation.