Controllable Text Generation
The field of techniques and methods for steering language model outputs toward specific content types, styles, or topics. Fundamental connection to adversarial-attacks where jailbreaking attempts to control models toward unsafe content generation, and prompt-engineering which achieves controlled generation through input design.
Connection to Adversarial Attacks
lilian-weng establishes a crucial conceptual link between controllable text generation and adversarial attacks on LLMs. From this perspective, jailbreaking is essentially a form of controllable generation - specifically controlling the model to output unsafe or undesired content that bypasses alignment training.
This connection highlights that:
- Adversarial attacks exploit the same mechanisms used for legitimate content control
- Safety measures must account for the full spectrum of controllable generation techniques
- Research in controllable generation directly informs both attack and defense strategies
Technical Approaches
Controllable generation encompasses multiple methodologies:
- prompt-engineering for steering through input design
- Fine-tuning for task-specific control
- Activation steering through internal model representations
- Constrained generation through output filtering
Alignment Implications
The overlap between controllable generation and adversarial attacks creates fundamental challenges for Safety Alignment. Techniques that enable beneficial control can potentially be subverted for harmful purposes, requiring careful balance between model capability and safety constraints.
See also
- adversarial-attacks
- prompt-engineering
- llm-security
- Safety Alignment
- Model Steering