Dialog grounding enables conversational models to make full use of external information to establish multiple desired qualities, such as knowledgeable, engaging and empathetic. However, naturally grounded dialog corpora are usually not directly available, which puts forward requirements for the few-shot learning ability of conversational models. Motivated by recent advances in pre-trained language models and prompt-based learning, in this paper we explore prompt-based few-shot learning for grounded dialog generation (GDG). We first formulate the prompt construction for GDG tasks, based on which we then conduct comprehensive empirical analysis on two common types of prompting methods: template-based prompting and soft-prompting. We demonstrate the potential of prompt-based methods in few-shot learning for GDG and provide directions of improvement for future work.
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