CTRL: A Conditional Transformer Language Model for Controllable Generation

Understand

Large-scale language models show promising text generation capabilities, but users cannot easily control particular aspects of the generated text.

• We release CTRL, a 1.63 billion-parameter conditional transformer language model, trained to condition on control codes that govern style, content, and task-specific behavior.
• Control codes were derived from structure that naturally co-occurs with raw text, preserving the advantages of unsupervised learning while providing more explicit control over text generation.
• These codes also allow CTRL to predict which parts of the training data are most likely given a sequence.