On Transferability of Prompt Tuning for Natural Language Processing

TL;DR

This paper investigates the transferability of prompt tuning in NLP, showing that soft prompts can effectively transfer across tasks and models, and that prompt stimulation patterns influence transfer success, enhancing efficiency.

Contribution

It empirically demonstrates prompt transferability across tasks and models, and identifies neuron activation overlap as a key factor influencing transfer success.

Findings

Soft prompts transfer effectively across similar tasks and models.

Using transferred prompts accelerates training and improves performance.

Neuron activation overlap correlates with transferability.

Abstract

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 https://github.com/thunlp/Prompt-Transferability.