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.

本文提出了 Instance-wise Prompt Tuning （IPT）方法，该方法是基于 Prompt Learning 的新型范式，利用输入数据实例注入知识来生成更富有信息量和具体的上下文信息，并在多个任务和资源设置中显着优于任务为基础的 Prompt Learning 方法，达到仅有 0.5％-1.5％调整参数时的基准微调性能。