Leveraging Few-Shot Data Augmentation and Waterfall Prompting for Response Generation
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 22 of its 25 references that are in the map. First posted on arXiv 2 Aug 2023.
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22 of 25 references.
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