Go to ACL ARR 2025 February homepageFrom Black Box to Controller: Steering LM Behavior via Sparse Autoencoder Activations
Controlling the behavior of the language model (LM) during inference—such as adjusting toxicity, sentiment tendency, and degree of politeness—is crucial for natural language processing. In this work, we introduce NeuroSteer, a plug-and-play framework that facilitates the adjustment of the LM behavior without domain-specific training. NeuroSteer leverages a Sparse AutoEncoder (SAE) as an output controller, activating SAE neurons linked to target behaviors, extracting the corresponding feature residuals, and adding them to the model's hidden states to directly influence the generation process. This feature-space intervention amplifies the weight of target features in the latent representations, enabling precise control over the model's output distribution. NeuroSteer effectively alters the LM's stance, sentiment, toxicity, and politeness during inference, achieving SOTA performance across four datasets while maintaining a balance between generation quality and behavioral adjustments. Unlike fine-tuning, NeuroSteer enables fast domain adaptation by calculating activations on hundreds of examples in seconds, without the need for retraining. Furthermore, our work not only provides a possible task adaptation solution, but layer-wise interventions also provide deeper insights into the model's mechanisms, shedding light on how concepts are represented in the LM and how combining feature vectors influences behavior.