Data-Free Knowledge Distillation (DFKD) plays a vital role in compressing the model when original training data is unavailable. Previous works for DFKD in NLP mainly focus on distilling encoder-only structures like BERT on classification tasks, which overlook the notable progress of generative language modeling. In this work, we propose a novel DFKD framework, namely DFKD-T$^{3}$, where the pretrained generative language model can also serve as a controllable data generator for model compression. This novel framework DFKD-T$^{3}$ leads to an end-to-end learnable text-to-text framework to transform the general domain corpus to compression-friendly task data, targeting to improve both the \textit{specificity} and \textit{diversity}. Extensive experiments show that our method can boost the distillation performance in various downstream tasks such as sentiment analysis, linguistic acceptability, and information extraction. Furthermore, we show that the generated texts can be directly used for distilling other language models and outperform the SOTA methods, making our method more appealing in a general DFKD setting. Our code is available at https://gitee.com/mindspore/models/tree/master/research/nlp/DFKD\_T3.

提出了一种新的DFKD框架，即DFKD-T^3，该框架将预训练的生成语言模型作为可控数据生成器用于模型压缩，实现了端到端可学习的文本转换框架，通过改善特异性和多样性提高了蒸馏性能，并在情感分析、语言可接受性和信息提取等各种下游任务中展示出卓越的性能，此外，生成的文本可直接用于蒸馏其他语言模型，超过了SOTA方法，在一般的DFKD场景中更具吸引力。

无数据蒸馏：通过文本到文本迁移的语言模型