Beyond Linear Steering: Unified Multi-Attribute Control for Language Models

1. 1Train a single non-linear multi-label classifier on hidden activations
2. 2Compute interference between attributes to improve control
3. 3Apply K-Steering to inference time to control multiple attributes simultaneously
4. 4Evaluate and refine the approach through experimentation and analysis

ML researchers and engineers working on language models can benefit from this approach as it allows for more flexible and effective control of model behavior, enabling them to improve model performance and adaptability

💡 Non-linear multi-label classification can effectively control multiple behavioral attributes in LLMs

💡 Introducing K-Steering: a unified approach for controlling multiple attributes in large language models