  Pretrained language models can be effectively stimulated by textual prompts
or demonstrations, especially in low-data scenarios. Recent works have focused
on automatically searching discrete or continuous prompts or optimized
verbalizers, yet studies for the demonstration are still limited. Concretely,
the demonstration examples are crucial for an excellent final performance of
prompt-tuning. In this paper, we propose a novel pluggable, extensible, and
efficient approach named contrastive demonstration tuning, which is free of
demonstration sampling. Furthermore, the proposed approach can be: (i) Plugged
into any previous prompt-tuning approaches; (ii) Extended to widespread
classification tasks with a large number of categories. Experimental results on
16 datasets illustrate that our method integrated with previous approaches
LM-BFF and P-tuning can yield better performance. Code is available in
https://github.com/zjunlp/PromptKG/tree/main/research/Demo-Tuning.
