  Thanks to the advanced improvement of large pre-trained language models,
prompt-based fine-tuning is shown to be effective on a variety of downstream
tasks. Though many prompting methods have been investigated, it remains unknown
which type of prompts are the most effective among three types of prompts
(i.e., human-designed prompts, schema prompts and null prompts). In this work,
we empirically compare the three types of prompts under both few-shot and
fully-supervised settings. Our experimental results show that schema prompts
are the most effective in general. Besides, the performance gaps tend to
diminish when the scale of training data grows large.
