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.

翻译：近年来，预训练模型已广泛应用于自然语言理解、计算机视觉和自然语言生成等各个领域。然而，这些语言生成模型的性能在很大程度上依赖于模型规模和数据集大小。虽然大型模型在某些方面表现优异，但它们无法学习时效性知识，且重新学习相对困难。本文提出EvoText，一种新颖的训练方法，可在整个训练过程中无需额外数据集（尽管预训练需要先验数据集）的情况下，提升任意自然语言生成模型的性能。EvoText采用两个模型：$G$（文本生成模型）和$D$（用于判定$G$生成数据是否合法的模型）。初始阶段，微调后的$D$模型作为知识库；随后，将$G$生成的文本输入$D$以判定其合法性；最后，基于$D$的输出对$G$进行微调。EvoText通过基于先验知识的自我升级过程，使模型能够学习时效性知识。当需要学习新知识时，只需微调$D$模型。该方法适用于所有Transformer类的自回归语言建模。应用EvoText后，八个模型在七项自然语言处理任务中均实现了稳定的性能提升，且无需改变模型结构。