  The increasing prevalence of Large Language Models (LMs) in critical
applications highlights the need for controlled language generation strategies
that are not only computationally efficient but that also enjoy performance
guarantees. To achieve this, we use a common model of concept semantics as
linearly represented in an LM's latent space. In particular, we take the view
that natural language generation traces a trajectory in this continuous
semantic space, realized by the language model's hidden activations. This view
permits a control-theoretic treatment of text generation in latent space, in
which we propose a lightweight, gradient-free intervention that dynamically
steers trajectories away from regions corresponding to undesired meanings.
Crucially, we show that this intervention, which we compute in closed form, is
guaranteed (in probability) to steer the output into the allowed region.
Finally, we demonstrate on a toxicity avoidance objective that the intervention
steers language away from undesired content while maintaining text quality.
