It has always been an important yet challenging problem to control language
models to avoid generating texts with undesirable attributes, such as toxic
language and unnatural repetition. We introduce Click for controllable text
generation, which needs no modification to the model architecture and
facilitates out-of-the-box use of trained models. It employs a contrastive loss
on sequence likelihood, which fundamentally decreases the generation
probability of negative samples (i.e., generations with undesirable
attributes). It also adopts a novel likelihood ranking-based strategy to
construct contrastive samples from model generations. On the tasks of language
detoxification, sentiment steering, and repetition reduction, we show that
Click outperforms strong baselines of controllable text generation and
demonstrate the superiority of Click's sample construction strategy.

本文提出了一种名为 “Click” 的可控文本生成方法，不需要修改模型架构，利用序列似然的对比损失和新颖的似然排名策略，能够在有毒语言、情感倾向和文本重复等方面相比其他控制型生成模型有更好的性能。