title: Controlling Language and Diffusion Models by Transporting Activations
summary: 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.
paper_authors._id: 672b447c1b5f7a5e97a026e8
paper_authors.name: Pau Rodriguez
paper_authors._id: 672b447c1b5f7a5e97a026e9
paper_authors.name: Arno Blaas
paper_authors._id: 672b447c1b5f7a5e97a026ea
paper_authors.name: Michal Klein
paper_authors._id: 672b447c1b5f7a5e97a026eb
paper_authors.name: Luca Zappella
paper_authors._id: 672b447c1b5f7a5e97a026ec
paper_authors.name: Nicholas Apostoloff
paper_authors._id: 672b447c1b5f7a5e97a026ed
paper_authors.name: Marco Cuturi
paper_authors._id: 672b447c1b5f7a5e97a026ee
paper_authors.name: Xavier Suau
paper_title: Controlling Language and Diffusion Models by Transporting Activations
paper_summary: 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.
paper_ai_keywords: Activation Transport (AcT) optimal transport theory activation-steering modality-agnostic computational overhead large language models (LLMs) text-to-image diffusion models (T2Is) toxicity optimization arbitrary concepts induction truthfulness fine-grained style control concept negation