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Adapter-tuning is a paradigm that transfers a pretrained language model todownstream tasks by adding and tuning a small number of new parameters.Previously proposed adapter architectures are all feed-forward neural networks.In this paper, we investigate the effectiveness of using tiny-attention --i.e., attention with extremely small per-head dimensionality -- as adapters.Our tiny-attention adapter learns to modify the hidden states at each positiondirectly conditioned on the hidden states at all the other positions, which ismissed by the previously proposed adapters. Moreover, we view its multipleattention heads as a mixture of experts and propose to average their weightsduring deployment, which further reduces its inference computation cost. On theGLUE benchmark, our tiny-attention adapter outperforms the otherparameter-efficient transfer learning methods as well as full fine-tuning whileonly updating 0.05% of the parameters. On the FewGLUE benchmark, itsperformance is comparable to that of GPT-3 and PET.

Tiny-Attention Adapter: Contexts Are More Important Than the Number of Parameters