Click: Controllable Text Generation with Sequence Likelihood Contrastive Learning

Understand

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.