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arXiv · 2208.05577

Reducing Retraining by Recycling Parameter-Efficient Prompts

Brian Lester·Joshua Yurtsever·Siamak Shakeri·Noah Constant
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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.

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Brian Lester, Joshua Yurtsever, Siamak Shakeri, Noah Constant. 2022-08-10. Reducing Retraining by Recycling Parameter-Efficient Prompts. https://arxiv.org/abs/2208.05577

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