  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.
