Abstract
As pre-trained language models have gotten larger, there has been growing interest in parameter-efficient methods to apply these models to downstream tasks. Building on the PromptTuning approach of Lester et al. (2021), which learns task-specific soft prompts to condition a frozen language model to perform downstream 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 PromptTuning across many tasks. More importantly, SPoT either matches or outperforms ModelTuning, which fine-tunes the entire model on each individual task, across all model sizes while being more parameter-efficient (up to 27,000x fewer task-specific parameters). We further conduct a large-scale study on task transferability with 26 NLP tasks and 160 combinations of source-target tasks, and demonstrate that tasks can often benefit each other via prompt transfer. Finally, we propose a simple yet efficient retrieval approach that interprets task prompts as task embeddings to identify the similarity between tasks and predict the most transferable source tasks for a given novel target task.
Abstract (translated)
URL
https://arxiv.org/abs/2110.07904
