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Transformer-based pre-trained models with millions of parameters requirelarge storage. Recent approaches tackle this shortcoming by training adapters,but these approaches still require a relatively large number of parameters. Inthis study, AdapterBias, a surprisingly simple yet effective adapterarchitecture, is proposed. AdapterBias adds a token-dependent shift to thehidden output of transformer layers to adapt to downstream tasks with only avector and a linear layer. Extensive experiments are conducted to demonstratethe effectiveness of AdapterBias. The experiments show that our proposed methodcan dramatically reduce the trainable parameters compared to the previous workswith a minimal decrease in task performances compared with fine-tunedpre-trained models. We further find that AdapterBias automatically learns toassign more significant representation shifts to the tokens related to the taskin consideration.

AdapterBias: Parameter-efficient Token-dependent Representation Shift for Adapters in NLP Tasks