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.

通过引入采样中的随机性，可以增加对话生成中的多样性。然而，这种采样方法可能会削弱对话生成中的真实性。为了在不依赖可疑的随机性的情况下推进创造力，并在源指向的范式中细致地协调真实性和多样性，提出了一种名为DoGe的新方法。DoGe可以根据模型的真实置信度动态地在内部参数知识和外部源知识之间进行切换。在三个广泛使用的数据集上进行的大量实验表明，DoGe不仅可以增强回答的多样性，而且可以保持真实性，并且明显优于其他各种解码策略基线。

一种融合真实性和多样性的基于知识的对话生成解码方法