𝒴-Tuning: An Efficient Tuning Paradigm for Large-Scale Pre-Trained Models via Label Representation Learning
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With the 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. Although some parameter-efficient tuning paradigms have been proposed to address this problem, they still require large resources to compute the gradients in the training phase. In this paper, we propose 𝒴-Tuning, an efficient yet effective paradigm to adapt frozen large-scale PTMs to specific downstream tasks. 𝒴-tuning learns dense representations for labels 𝒴 defined in a given task and aligns them to fixed feature representation. Without tuning the features of input text and model parameters, 𝒴-tuning is both parameter-efficient and training-efficient. For DeBERTa_XXL with 1.6 billion parameters, 𝒴-tuning achieves performance more than 96% of full fine-tuning on GLUE Benchmark with only 2% tunable parameters and much fewer training costs.
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