\mathcal{Y}\text{-}Tuning: an efficient tuning paradigm for large-scale pre-trained models via label representation learning

Abstract

With current success of large-scale pre-trained models (PTMs), how efficiently adapting PTMs to downstream tasks has attracted tremendous attention, especially for PTMs with billions of parameters. Previous work focuses on designing parameter-efficient tuning paradigms but needs to save and compute the gradient of the whole computational graph. In this paper, we propose \mathcal{Y}-Tuning, an efficient yet effective paradigm to adapt frozen large-scale PTMs to specific downstream tasks. \mathcal{Y}-Tuning learns dense representations for labels \mathcal{Y} defined in a given task and aligns them to fixed feature representation. Without computing the gradients of text encoder at training phrase, \mathcal{Y}-Tuning is not only parameter-efficient but also training-efficient. Experimental results show that for {\mathrm{D}\mathrm{e}\mathrm{B}\mathrm{E}\mathrm{R}\mathrm{T}\mathrm{a}}_{\mathrm{X}\mathrm{X}\mathrm{L}} with 1.6 billion parameters, \mathcal{Y}-Tuning achieves performance more than 96\mathrm{\%} of full fine-tuning on GLUE Benchmark with only 2\mathrm{\%} tunable parameters and much fewer training costs.

Graphical abstract

Keywords

pre-trained model / lightweight fine-tuning paradigms / label representation

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