Prototype-based HyperAdapter for Sample-Efficient Multi-task Tuning
This work addresses sample-efficient multi-task tuning for NLP applications, offering incremental improvements over existing PEFT methods.
The paper tackled the problem of parameter-efficient fine-tuning (PEFT) for multi-task learning by proposing Prototype-based HyperAdapter (PHA), which improves sample efficiency and outperforms baselines, especially in low-data regimes, as shown in experiments across various datasets.
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.