Controlling Language and Diffusion Models by Transporting Activations
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866915030510862336 |
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| author | Rodriguez, Pau Blaas, Arno Klein, Michal Zappella, Luca Apostoloff, Nicholas Cuturi, Marco Suau, Xavier |
| author_facet | Rodriguez, Pau Blaas, Arno Klein, Michal Zappella, Luca Apostoloff, Nicholas Cuturi, Marco Suau, Xavier |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_23054 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Controlling Language and Diffusion Models by Transporting Activations Rodriguez, Pau Blaas, Arno Klein, Michal Zappella, Luca Apostoloff, Nicholas Cuturi, Marco Suau, Xavier Machine Learning Artificial Intelligence Computation and Language Computer Vision and Pattern Recognition 68T07, 49Q22 I.2.6; I.2.7; I.4.8 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. |
| title | Controlling Language and Diffusion Models by Transporting Activations |
| topic | Machine Learning Artificial Intelligence Computation and Language Computer Vision and Pattern Recognition 68T07, 49Q22 I.2.6; I.2.7; I.4.8 |
| url | https://arxiv.org/abs/2410.23054 |