Pre-trained models have been used in many fields in recent years, ranging from natural language understanding to computer vision and natural language generation. Nowadays, the performance of these natural language generation models is overly dependent on the model's scale and the dataset's size. While the larger language model is excellent in some respects, it cannot learn up-to-date knowledge and is relatively difficult to relearn. In this paper, a new adversarial process learning method is called Auto-Learning, which can improve the performance of any natural language generation model without the help of additional datasets. Auto-Learning includes two models: $G$ is a text generation model, and $D$ can test whether the data generated by G is legitimate. Firstly, the fine-tuned $D$ model is used as the brain's knowledge base before the process. Then the text generated by the $G$ model is used as the input of $D$ to determine whether the text is legitimate. Finally, $G$ is fine-tuned according to the output of $D$. This adversarial process is like a self-escalation of the brain through some a priori knowledge. When this adversarial system wants to learn something new, simply fine-tune the $D$ model. Our approach applies to Autoregressive Language Modeling for all Transformer classes. Auto-Learning enables 8 models to achieve stable improvement in 10 natural language processing tasks without any change in structure.

翻译：近年来，预训练模型已被广泛应用于众多领域，从自然语言理解到计算机视觉和自然语言生成。当前，这些自然语言生成模型的性能过度依赖于模型规模和数据集的尺寸。尽管更大规模的语言模型在某些方面表现出色，但无法学习最新知识且重新学习相对困难。本文提出一种名为自动学习（Auto-Learning）的新型对抗过程学习方法，该方法无需借助额外数据集即可提升任何自然语言生成模型的性能。自动学习包含两个模型：$G$为文本生成模型，$D$能够检验$G$生成数据的合法性。首先，将微调后的$D$模型作为过程开始前的大脑知识库；随后，将$G$模型生成的文本作为$D$的输入，以判断文本是否合法；最后，根据$D$的输出对$G$进行微调。这一对抗过程类似于大脑利用先验知识实现的自我提升。当该对抗系统需要学习新知识时，仅需对$D$模型进行微调。我们的方法适用于所有Transformer类模型的自回归语言建模。自动学习使8个模型在10项自然语言处理任务中无需任何结构变更即可实现稳定性能提升。