Attribute-based Controlled Text Generation (CTG) refers to generating
sentences that satisfy desirable attributes (e.g., emotions and topics).
Existing works often utilize fine-tuning or resort to extra attribute
classifiers, yet suffer from storage and inference time increases. To address
these concerns, we explore attribute-based CTG in a prompt-based manner. In
short, the proposed Tailor represents each attribute as a pre-trained
continuous vector (i.e., single-attribute prompt) and guides the generation of
a fixed PLM switch to a pre-specified attribute. We experimentally find that
these prompts can be simply concatenated as a whole to multi-attribute CTG
without any re-training, yet raises problems of fluency decrease and position
sensitivity. To this end, Tailor provides a multi-attribute prompt mask and a
re-indexing position-ids sequence to bridge the gap between the training (one
prompt for each task) and testing stage (concatenating more than one prompt).
To further enhance such single-attribute prompt combinations, Tailor also
introduces a trainable prompt connector, which can be concatenated with any two
single-attribute prompts to multi-attribute text generation. Experiments on 11
attribute-specific generation tasks demonstrate strong performances of Tailor
on both single-attribute and multi-attribute CTG, with 0.08\% training
parameters of a GPT-2.

Tailor 模型通过预训练连续向量来指导固定的 PLM 切换到特定的属性，同时提供多属性提示掩码和重新索引 ID 序列，以支持单属性提示的组合并实现文本的多属性生成，这在 11 个属性特定的生成任务上表现出很强的性能。