Self-Control of LLM Behaviors by Compressing Suffix Gradient into Prefix Controller

We propose $SelfControl$, a novel method utilizing suffix gradients to control the behavior of large language models (LLMs) without explicit human annotations. Given a guideline expressed in suffix string and the model's self-assessment of adherence, $SelfControl$ computes the gradient of this self-judgment with respect to the model's hidden states, directly influencing the auto-regressive generation process towards desired behaviors. To enhance efficiency, we introduce $SelfControl_{Prefix}$, a compact module that encapsulates the learned representations from suffix gradients into a Prefix Controller, facilitating inference-time control for various LLM behaviors. Our experiments demonstrate $SelfControl$'s efficacy across multiple domains, including emotional modulation, ensuring harmlessness, and enhancing complex reasoning. Especially, $SelfControl_{Prefix}$ enables a plug-and-play control and jointly control multiple attributes, improving model outputs without altering model parameters or increasing inference-time costs.