Reducing Retraining by Recycling Parameter-Efficient Prompts

TL;DR

This paper explores methods to adapt task-specific prompts from one large language model to another without retraining, significantly reducing the need for costly prompt re-tuning when models are updated.

Contribution

It introduces prompt recycling techniques that transform prompts for new models without supervised data or additional training, achieving high success rates.

Findings

Recycling prompts is feasible with up to 88.9% success rate.

Recycled prompts can outperform baseline prompts.

Significant performance gaps remain, indicating room for improvement.

Abstract

Parameter-efficient methods are able to use a single frozen pre-trained large language model (LLM) to perform many tasks by learning task-specific soft prompts that modulate model behavior when concatenated to the input text. However, these learned prompts are tightly coupled to a given frozen model -- if the model is updated, corresponding new prompts need to be obtained. In this work, we propose and investigate several approaches to "Prompt Recycling'" where a prompt trained on a source model is transformed to work with the new target model. Our methods do not rely on supervised pairs of prompts, task-specific data, or training updates with the target model, which would be just as costly as re-tuning prompts with the target model from scratch. We show that recycling between models is possible (our best settings are able to successfully recycle $88.9\%$ of prompts, producing a prompt that out-performs baselines), but significant performance headroom remains, requiring improved recycling techniques.