Interpretable LLM Guardrails via Sparse Representation Steering

Large language models (LLMs) exhibit impressive capabilities in generation tasks but are prone to producing harmful, misleading, or biased content, posing significant ethical and safety concerns. To mitigate such risks, representation engineering, which steer model behavior toward desired attributes by injecting carefully designed steering vectors into LLM's representations at inference time, has emerged as a promising alternative to fine-tuning approaches. However, due to the semantically entangled nature of LLM's representation, existing representation engineering methods still suffer from several limitations: limited fine-grained controllability, content quality degradation, and conflict in multi-attribute control. To overcome these challenges, we propose Sparse Representation Steering (SRS), a novel framework that achieves fine-grained and interpretable control over LLM behavior by first disentangling internal activations into a sparse, semantically meaningful representation space, and then selectively steering relevant dimensions. Specifically, SRS leverages a pretrained Sparse Autoencoder (SAE) to transform dense, entangled activation patterns into a sparse monosemantic feature space. To identify relevant features, SRS contrasts sparse activations from positive and negative prompt pairs and measures their bidirectional KL divergence to locate dimensions most associated with the target attribute. We conduct comprehensive experiments on Gemma-2 series model across three alignment dimensions, i.e., safety, fairness, and truthfulness, to evaluate the effectiveness of SRS. Results show that SRS consistently outperforms existing steering methods, which achieves significantly improved controllability across both single and multiple attribute settings, while preserving high linguistic quality and general ability.