Learning a Better Initialization for Soft Prompts via Meta-Learning

• URL: http://arxiv.org/abs/2205.12471v1
• Date: Wed, 25 May 2022 03:50:23 GMT
• Title: Learning a Better Initialization for Soft Prompts via Meta-Learning
• Authors: Yukun Huang, Kun Qian, Zhou Yu
• Abstract summary: We propose MetaPT (Meta-learned Prompt Tuning) to improve prompt tuning. We introduce the structure by first clustering pre-training data into different auxiliary tasks. We use these tasks to pre-train prompts with a meta-learning algorithm.
• Score: 58.53984967461313
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Prompt tuning (PT) is an effective approach to adapting pre-trained language models to downstream tasks. Without a good initialization, prompt tuning doesn't perform well under few-shot settings. So pre-trained prompt tuning (PPT) is proposed to initialize prompts by leveraging pre-training data. We propose MetaPT (Meta-learned Prompt Tuning) to further improve PPT's initialization by considering latent structure within the pre-training data. Specifically, we introduce the structure by first clustering pre-training data into different auxiliary tasks with unsupervised methods. Then we use these tasks to pre-train prompts with a meta-learning algorithm. Such a process can make prompts learn a better initialization by discovering commonalities among these auxiliary tasks. We evaluate our method on seven downstream tasks. Our MetaPT achieves better and more stable performance than the state-of-the-art method.

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