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A recent family of techniques, dubbed lightweight fine-tuning methods,facilitates parameter-efficient transfer learning by updating only a small setof additional parameters while keeping the parameters of the pretrainedlanguage model frozen. While proven to be an effective method, there are noexisting studies on if and how such knowledge of the downstream fine-tuningapproach should affect the pretraining stage. In this work, we show that takingthe ultimate choice of fine-tuning method into consideration boosts theperformance of parameter-efficient fine-tuning. By relying onoptimization-based meta-learning using MAML with certain modifications for ourdistinct purpose, we prime the pretrained model specifically forparameter-efficient fine-tuning, resulting in gains of up to 1.7 points oncross-lingual NER fine-tuning. Our ablation settings and analyses furtherreveal that the tweaks we introduce in MAML are crucial for the attained gains.

Know Where You're Going: Meta-Learning for Parameter-Efficient Fine-Tuning