Prompt tuning (PT) is a promising parameter-efficient method to utilize
extremely large pre-trained language models (PLMs), which can achieve
comparable performance to full-parameter fine-tuning by only tuning a few soft
prompts. However, PT requires much more training time than fine-tuning.
Intuitively, knowledge transfer can help to improve the efficiency. To explore
whether we can improve PT via prompt transfer, we empirically investigate the
transferability of soft prompts across different downstream tasks and PLMs in
this work. We find that (1) in zero-shot setting, trained soft prompts can
effectively transfer to similar tasks on the same PLM and also to other PLMs
with a cross-model projector trained on similar tasks; (2) when used as
initialization, trained soft prompts of similar tasks and projected prompts of
other PLMs can significantly accelerate training and also improve the
performance of PT. Moreover, to explore what decides prompt transferability, we
investigate various transferability indicators and find that the overlapping
rate of activated neurons strongly reflects the transferability, which suggests
how the prompts stimulate PLMs is essential. Our findings show that prompt
transfer is promising for improving PT, and further research shall focus more
on prompts' stimulation to PLMs. The source code can be obtained from
this https URL

本文考察了使用预训练的语言模型进行 Prompt tuning (PT) 的有效性，同时也研究了软提示在不同任务和不同模型之间的可迁移性，并发现神经元激活的重叠率是决定软提示可迁移性的重要指标。我们的发现表明，软提示迁移有望改善 PT，并建议进一步研究提示对模型的刺激方法