  Demonstration learning aims to guide the prompt prediction via providing
answered demonstrations in the few shot settings. Despite achieving promising
results, existing work only concatenates the answered examples as
demonstrations to the prompt template (including the raw context) without any
additional operation, neglecting the prompt-demonstration dependencies.
Besides, prior research found that randomly replacing the labels of
demonstrations marginally hurts performance, illustrating that the model could
not properly learn the knowledge brought by the demonstrations. Inspired by the
human learning process, in this paper, we introduce Imitation DEMOnstration
Learning (Imitation-Demo) to strengthen demonstration learning via explicitly
imitating human review behaviour, which includes: (1) contrastive learning
mechanism to concentrate on the similar demonstrations. (2) demonstration-label
re-prediction method to consolidate known knowledge. Experiment results show
that our proposed method achieves state-of-the-art performance on 11 out of 14
classification corpora. Further studies also prove that Imitation-Demo
strengthen the association between prompt and demonstrations, which could
provide the basis for exploring how demonstration learning works.
