There has been growing interest in parameter-efficient methods to apply
pre-trained language models to downstream tasks. Building on the Prompt Tuning
approach of Lester et al. (2021), which learns task-specific soft prompts to
condition a frozen pre-trained model to perform different tasks, we propose a
novel prompt-based transfer learning approach called SPoT: Soft Prompt
Transfer. SPoT first learns a prompt on one or more source tasks and then uses
it to initialize the prompt for a target task. We show that SPoT significantly
boosts the performance of Prompt Tuning across many tasks. More remarkably,
across all model sizes, SPoT matches or outperforms standard Model Tuning
(which fine-tunes all model parameters) on the SuperGLUE benchmark, while using
up to 27,000x fewer task-specific parameters. To understand where SPoT is most
effective, we conduct a large-scale study on task transferability with 26 NLP
tasks in 160 combinations, and demonstrate that many tasks can benefit each
other via prompt transfer. Finally, we propose an efficient retrieval approach
that interprets task prompts as task embeddings to identify similar tasks and
predict the most transferable source tasks for a novel target task.

本文介绍了一种使用少量参数进行预训练语言模型在下游任务中的应用实现，该方法名为 SPoT：Soft Prompt Transfer，通过学习源任务的 prompt 来初始化目标任务的 prompt，从而提升了 Prompt Tuning 的性能，并在语言模型大小上向 SuperGLUE 基准匹配或胜过标准模型调整，同时减少了多达 27,000 倍的任务特定参数，可以通过大规模的实验证明 prompt 的任务可传递性，最后还提出了一种高效的检索方法来识别相似的任务和预测最适合新目标任务的来源任务。