Control LLM Behavior with Activation Steering
Control LLM Behavior with Activation Steering Prompting controls a language model through its input tokens. Fine-tuning changes model parameters. Activation steering offers a third option: change selected internal activations while the model runs, without rewriting its weights. That makes activation steering useful for experiments where you want to test whether an internal direction is connected to a behavior, or apply a lightweight behavior shift during generation. It also creates new engineering questions. A steering vector can help at one layer and damage output at another. A strength that works on short prompts can become excessive on different inputs. A behavioral shift can also come with losses in fluency or task accuracy.