Leveraging Few-Shot Data Augmentation and Waterfall Prompting for
  Response Generation
Lea Krause Selene Báez Santamaría Michiel van der Meer Urja Khurana
This paper discusses our approaches for task-oriented conversational
modelling using subjective knowledge, with a particular emphasis on response
generation. Our methodology was shaped by an extensive data analysis that
evaluated key factors such as response length, sentiment, and dialogue acts
present in the provided dataset. We used few-shot learning to augment the data
with newly generated subjective knowledge items and present three approaches
for DSTC11: (1) task-specific model exploration, (2) incorporation of the most
frequent question into all generated responses, and (3) a waterfall prompting
technique using a combination of both GPT-3 and ChatGPT.