Prototype-based HyperAdapter for Sample-Efficient Multi-task Tuning

• URL: http://arxiv.org/abs/2310.11670v3
• Date: Sat, 11 Nov 2023 15:30:30 GMT
• Title: Prototype-based HyperAdapter for Sample-Efficient Multi-task Tuning
• Authors: Hao Zhao, Jie Fu, Zhaofeng He
• Abstract summary: Prototype-based HyperAdapter (PHA) is a novel framework built on the adapter-tuning and hypernetwork. It introduces an instance-dense retriever and prototypical hypernetwork to generate conditional modules in a sample-efficient manner. We show that PHA strikes a better trade-off between trainable parameters, accuracy on stream tasks, and sample efficiency.
• Score: 30.251155072822055
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Parameter-efficient fine-tuning (PEFT) has shown its effectiveness in adapting the pre-trained language models to downstream tasks while only updating a small number of parameters. Despite the success, most existing methods independently adapt to each task without considering knowledge transfer between tasks and are limited to low-data regimes. To overcome this issue, we propose Prototype-based HyperAdapter (PHA), a novel framework built on the adapter-tuning and hypernetwork. It introduces an instance-dense retriever and a prototypical hypernetwork to generate the conditional modules in a sample-efficient manner. This leads to comparable performance improvements against existing PEFT methods on multi-task learning and few-shot transfer learning. More importantly, when the available data size gets smaller, our method outperforms other strong baselines by a large margin. Based on our extensive empirical experiments across various datasets, we demonstrate that PHA strikes a better trade-off between trainable parameters, accuracy on stream tasks, and sample efficiency.

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