From Weights to Activations: Is Steering the Next Frontier of Adaptation?
Simon Ostermann German Research Center for AI and Universität des Saarlandes, Daniil Gurgurov, Tanja Baeumel, Michael A. Hedderich Ludwig-Maximilians-Universität München, Sebastian Lapuschkin Technological University Dublin and Fraunhofer HHI, Wojciech Samek TU Berlin and Fraunhofer HHI, Vera Schmitt Technische Universität Berlin
Post-training adaptation of large language models is commonly achieved through parameter updates or input based methods such as fine-tuning, parameter-efficient adaptation, and prompting. In parallel, a growing body of work modifies internal activations at inference time to influence model behavior, an approach known as steering. Despite increasing use, steering is rarely analyzed within the same conceptual framework as established adaptation methods.In this work, we argue that steering should be regarded as a form of model adaptation. We introduce a set of functional criteria for adaptation methods and use them to compare steering approaches with classical alternatives. This analysis positions steering as a distinct adaptation paradigm based on targeted interventions in activation space, enabling local and reversible behavioral change without parameter updates. The resulting framing clarifies how steering relates to existing methods, motivating a unified taxonomy for model adaptation.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表