Grounding external knowledge can enhance the factuality of responses in dialogue generation. However, excessive emphasis on it might result in the lack of engaging and diverse expressions. Through the introduction of randomness in sampling, current approaches can increase the diversity. Nevertheless, such sampling method could undermine the factuality in dialogue generation. In this study, to discover a solution for advancing creativity without relying on questionable randomness and to subtly reconcile the factuality and diversity within the source-grounded paradigm, a novel method named DoGe is proposed. DoGe can dynamically alternate between the utilization of internal parameter knowledge and external source knowledge based on the model's factual confidence. Extensive experiments on three widely-used datasets show that DoGe can not only enhance response diversity but also maintain factuality, and it significantly surpasses other various decoding strategy baselines.

翻译：在对话生成中引入外部知识可以增强回复的事实性。然而，过度强调知识可能导致表达缺乏吸引力和多样性。现有方法通过引入随机性采样来提升多样性，但这种方式可能损害对话生成的事实性。本研究旨在探索一种不依赖随机性、同时能巧妙协调源知识驱动范式中事实性与多样性的解决方案，提出了一种名为DoGe的新方法。DoGe能够根据模型的事实置信度，动态切换内部参数知识与外部源知识的使用。在三个广泛使用的数据集上进行的大量实验表明，DoGe不仅能提升回复的多样性，还能保持事实性，并且显著超越了其他多种解码策略基线。