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Standard fine-tuning of large pre-trained language models (PLMs) fordownstream tasks requires updating hundreds of millions to billions ofparameters, and storing a large copy of the PLM weights for every taskresulting in increased cost for storing, sharing and serving the models. Toaddress this, parameter-efficient fine-tuning (PEFT) techniques were introducedwhere small trainable components are injected in the PLM and updated duringfine-tuning. We propose AdaMix as a general PEFT method that tunes a mixture ofadaptation modules -- given the underlying PEFT method of choice -- introducedin each Transformer layer while keeping most of the PLM weights frozen. Forinstance, AdaMix can leverage a mixture of adapters like Houlsby or a mixtureof low rank decomposition matrices like LoRA to improve downstream taskperformance over the corresponding PEFT methods for fully supervised andfew-shot NLU and NLG tasks. Further, we design AdaMix such that it matches thesame computational cost and the number of tunable parameters as the underlyingPEFT method. By only tuning 0.1-0.2% of PLM parameters, we show that AdaMixoutperforms SOTA parameter-efficient fine-tuning and full model fine-tuning forboth NLU and NLG tasks.

AdaMix: Mixture-of-Adaptations for Parameter-efficient Model Tuning