This research introduces sparse activation steering as a more precise method to control and guide LLM behavior during inference, addressing a critical challenge in AI alignment.

• Leverages sparse representation spaces where features are less entangled than in traditional dense spaces
• Overcomes the superposition problem that limits interpretability in previous approaches
• Enables more targeted manipulation of specific behaviors without affecting unrelated capabilities
• Improves security and safety by providing more transparent and controllable AI alignment

This approach significantly advances our ability to make LLMs follow desired behaviors at test time while maintaining better explainability—a crucial development for responsible AI deployment in security-sensitive applications.

Steering Large Language Model Activations in Sparse Spaces