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Dialog models can be greatly strengthened through grounding on variousexternal information, but grounded dialog corpora are usually not naturallyaccessible. In this work, we focus on the few-shot learning for grounded dialoggeneration (GDG). We first propose a simple prompting method for GDG tasks,where different constructs of model input, such as the grounding source and theconversation context, are distinguished through continuous or discrete prompts.On three typical GDG tasks, we empirically demonstrate and analyze in-depth theeffectiveness of our method. We then conduct extensive experiments tothoroughly investigate how our prompting method works with differentpre-trained models. We show that prompted language models perform superiorly toconversational models, and further analyze various factors that influence theeffects of prompting. Overall, our work introduces a prompt-based perspectiveto the few-shot learning for GDG tasks, and provides valuable findings andinsights for future research.

Exploring Prompt-based Few-shot Learning for Grounded Dialog Generation