  Prompt Learning has recently gained great popularity in bridging the gap
between pretraining tasks and various downstream tasks. It freezes Pretrained
Language Models (PLMs) and only tunes a few task-related parameters (prompts)
for downstream tasks, greatly reducing the cost of tuning giant models. The key
enabler of this is the idea of querying PLMs with task-specific knowledge
implicated in prompts. This paper reveals a major limitation of existing
methods that the indiscriminate prompts for all input data in a task ignore the
intrinsic knowledge from input data, resulting in sub-optimal performance. We
introduce Instance-wise Prompt Tuning (IPT), the first prompt learning paradigm
that injects knowledge from the input data instances to the prompts, thereby
providing PLMs with richer and more concrete context information. We devise a
series of strategies to produce instance-wise prompts, addressing various
concerns like model quality and cost-efficiency. Across multiple tasks and
resource settings, IPT significantly outperforms task-based prompt learning
methods, and achieves comparable performance to conventional finetuning with
only 0.5% - 1.5% of tuned parameters.
