  In recent years, pretrained models have been widely used in various fields,
including natural language understanding, computer vision, and natural language
generation. However, the performance of these language generation models is
highly dependent on the model size and the dataset size. While larger models
excel in some aspects, they cannot learn up-to-date knowledge and are
relatively difficult to relearn. In this paper, we introduce EvoText, a novel
training method that enhances the performance of any natural language
generation model without requiring additional datasets during the entire
training process (although a prior dataset is necessary for pretraining).
EvoText employs two models: $G$, a text generation model, and $D$, a model that
can determine whether the data generated by $G$ is legitimate. Initially, the
fine-tuned $D$ model serves as the knowledge base. The text generated by $G$ is
then input to $D$ to determine whether it is legitimate. Finally, $G$ is
fine-tuned based on $D$'s output. EvoText enables the model to learn up-to-date
knowledge through a self-escalation process that builds on a priori knowledge.
When EvoText needs to learn something new, it simply fine-tunes the $D$ model.
Our approach applies to autoregressive language modeling for all Transformer
classes. With EvoText, eight models achieved stable improvements in seven
natural language processing tasks without any changes to the model structure.
