Existing pre-trained large language models have shown unparalleled generative capabilities. However, they are not controllable. In this paper, we propose MEGATRON-CNTRL, a novel framework that uses large-scale language models and adds control to text generation by incorporating an external knowledge base. Our framework consists of a keyword predictor, a knowledge retriever, a contextual knowledge ranker, and a conditional text generator. As we do not have access to ground-truth supervision for the knowledge ranker, we make use of weak supervision from sentence embedding. The empirical results show that our model generates more fluent, consistent, and coherent stories with less repetition and higher diversity compared to prior work on the ROC story dataset. We showcase the controllability of our model by replacing the keywords used to generate stories and re-running the generation process. Human evaluation results show that 77.5% of these stories are successfully controlled by the new keywords. Furthermore, by scaling our model from 124 million to 8.3 billion parameters we demonstrate that larger models improve both the quality of generation (from 74.5% to 93.0% for consistency) and controllability (from 77.5% to 91.5%).

本文介绍了一个新的框架MEGATRON-CNTRL，通过结合外部知识库，利用大规模语言模型实现生成文本的控制，并由关键字预测器，知识检索器，上下文知识排名器和条件文本生成器四个部分组成。实验结果表明，相较于之前的ROC story数据集，该模型生成更加流畅、一致、连贯且重复率低，差异性高的故事，使用新关键词进行控制后，人类评价结果表明77.5%的故事已经成功受到了控制。同时，由124million经验推广到8.3billion再次展示了大规模模型提高生成质量和控制性能的过程。

MEGATRON-CNTRL：基于大规模语言模型的可控外部知识故事生成