Dynamically Scaled Activation Steering

來源原標題

Dynamically Scaled Activation Steering

原始連結

https://machinelearning.apple.com/research/dynamically-scaled-activation-steering

摘要原文

research area Human-Computer Interaction , research area Methods and Algorithms Transactions on Machine Learning Research (TMLR) content type paper published September 2026 Dynamically Scaled Activation Steering Authors Alex Ferrando de las Morenas†, Xavier Suau Cuadros, Jordi Gonzàlez Sabaté†, Pau Rodríguez Lopez View publication Copy Bibtex Activation steering has emerged as a powerful method for guiding the behavior of generative models towards desired outcomes such as toxicity mitigation. However, most existing methods apply interventions uniformly across all inputs, degrading model performance when steering is unnecessary. We introduce Dynamically Scaled Activation Steering (DSAS), a method-agnostic steering framework that decouples when to steer from how to steer. DSAS adaptively modulates the strength of existing steering transformations across layers and inputs, intervening strongly only when undesired behavior is detected. At generation time, DSAS computes context-dependent scaling factors that selectively adjust the strength of any steering method. We also show how DSAS can be jointly optimized end-to-end together with the steering function. When combined with existing steering methods, DSAS consistently improves the Pareto front with respect to steering alone, achieving a better trade-off between toxicity mitigation and utility preservation. We further demonstrate DSAS’s generality by applying it to a text-to-image diffusion model, showing how adaptive steering allows the modulation of specific concepts. Finally, DSAS introduces minimal computational overhead while improving interpretability, pinpointing which tokens require steering and by how much. The code will be available in Github. † Centre de Visió per Computador Related readings and updates. ExpertLens: Activation Steering Features Are Highly Interpretable November 7, 2025 research area Methods and Algorithms , research area Speech and Natural Language Processing Workshop at NeurIPS This paper was accepted at the Workshop on Unifying Representations in Neural Models (UniReps) at NeurIPS 2025. Activation steering methods in large language models (LLMs) have emerged as an effective way to perform targeted updates to enhance generated language without requiring large amounts of adaptation data. We ask whether the features discovered by activation steering methods are interpretable. We identify neurons responsible for specific… Read more STEER: Semantic Turn Extension-Expansion Recognition for Voice Assistants November 8, 2023 research area Speech and Natural Language Processing conference EMNLP *Equal Contributors In the context of a voice assistant system, steering refers to the phenomenon in which a user issues a follow-up command attempting to direct or clarify a previous turn. We propose STEER, a steering detection model that predicts whether a follow-up turn is a user’s attempt to steer the previous command. Constructing a training dataset for steering use cases poses challenges due to the cold-start problem. To overcome this, we… Read more Discover opportunities in Machine Learning. Our research in machine learning breaks new ground every day. Work with us