  Natural Language Inference Generation task is to generate a text hypothesis
given a text premise and a logical relation between the two. This task can be
used in data augmentation and controllable text generation in practice. In this
paper, we propose language models with prompt and dynamic demonstration
(LM-PDD) to tackle this problem in few-shot settings. Our framework outperforms
standard fine-tuned models with low resource, achieving an average 8% absolute
improvement on SNLI and MNLI datasets, and the results on 13 natural language
classification tasks also show that our dynamic demonstration method has good
generalizability.
