Reducing Retraining By Recycling Parameter-efficient Prompts
Lester Brian, Yurtsever Joshua, Shakeri Siamak, Constant Noah. Arxiv 2022
[Paper]
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.
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