  The increasingly Large Language Models (LLMs) demonstrate stronger language
understanding and generation capabilities, while the memory demand and
computation cost of fine-tuning LLMs on downstream tasks are non-negligible.
Besides, fine-tuning generally requires a certain amount of data from
individual tasks whilst data collection cost is another issue to consider in
real-world applications. In this work, we focus on Parameter-Efficient
Fine-Tuning (PEFT) methods for few-shot Natural Language Generation (NLG),
which freeze most parameters in LLMs and tune a small subset of parameters in
few-shot cases so that memory footprint, training cost, and labeling cost are
reduced while maintaining or even improving the performance. We propose a
Scaled Prompt-Tuning (SPT) method which surpasses conventional PT with better
performance and generalization ability but without an obvious increase in
training cost. Further study on intermediate SPT suggests the superior
transferability of SPT in few-shot scenarios, providing a recipe for
data-deficient and computation-limited circumstances. Moreover, a comprehensive
comparison of existing PEFT methods reveals that certain approaches exhibiting
decent performance with modest training cost such as Prefix-Tuning in prior
study could struggle in few-shot NLG tasks, especially on challenging datasets.
