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The workflow of pretraining and fine-tuning has emerged as a popular paradigmfor solving various NLP and V&L (Vision-and-Language) downstream tasks. Withthe capacity of pretrained models growing rapidly, how to performparameter-efficient fine-tuning has become fairly important for quick transferlearning and deployment. In this paper, we design a novel unifiedparameter-efficient transfer learning framework that works effectively on bothpure language and V&L tasks. In particular, we use a shared hypernetwork thattakes trainable hyper-embeddings as input, and outputs weights for fine-tuningdifferent small modules in a pretrained language model, such as tuning theparameters inserted into multi-head attention blocks (i.e., prefix-tuning) andfeed-forward blocks (i.e., adapter-tuning). We define a set of embeddings(e.g., layer, block, task and visual embeddings) as the key components tocalculate hyper-embeddings, which thus can support both pure language and V&Ltasks. Our proposed framework adds fewer trainable parameters in multi-tasklearning while achieving superior performances and transfer ability compared tostate-of-the-art methods. Empirical results on the GLUE benchmark and multipleV&L tasks confirm the effectiveness of our framework on both textual and visualmodalities.

HyperPELT: Unified Parameter-Efficient Language Model Tuning for Both Language and Vision-and-Language Tasks