Pre-trained models have been used in many fields in recent years, ranging from natural language understanding to computer vision and natural language generation. However, the performance of these natural language generation models is overly dependent on the scale of the model and the size of the dataset. 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 called Auto-Learning. This 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 or not. 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. The results are good in existing experimental tasks, including more grammatical text generation and better performance on some text comprehension tasks.

翻译：近年来，预训练模型已广泛应用于自然语言理解、计算机视觉及自然语言生成等领域。然而，这类自然语言生成模型的性能过度依赖于模型规模和数据集大小。虽然大规模语言模型在某些方面表现优异，但无法学习最新知识，且重新学习成本较高。本文提出一种名为自学习的新型对抗式学习方法，无需额外数据集即可提升任意自然语言生成模型的性能。该方法包含两个模型：$G$为文本生成模型，$D$用于检测$G$生成数据的合法性。首先，将微调后的$D$模型作为过程启动前的脑知识库；然后以$G$生成的文本作为$D$的输入，判断文本合法性；最后根据$D$的输出对$G$进行微调。这种对抗过程类似于大脑通过先验知识实现自我升级。当系统需要学习新知识时，只需微调$D$模型即可。本方法适用于所有Transformer类自回归语言模型。在现有实验任务中取得良好效果，包括生成更规范的语法文本，以及在部分文本理解任务中表现更优。