Focused Prefix Tuning for Controllable Text Generation

抄録

In a controllable text generation dataset, unannotated attributes may provide irrelevant learning signals to models that use them for training, thereby degrading their performance. We propose focused prefix tuning(FPT) to mitigate this problem and enable control to focus on the desired attribute. Experimental results show that FPT can achieve better control accuracy and text fluency than baseline models in single-attribute control tasks. In multi-attribute control tasks, FPT achieves control accuracy comparable to that of the state-of-the-art approach while maintaining the flexibility to control new attributes without retraining existing models.