HyperLoader: Integrating Hypernetwork-Based LoRA and Adapter Layers into Multi-Task Transformers for Sequence Labelling

• URL: http://arxiv.org/abs/2407.01411v3
• Date: Sun, 25 Aug 2024 09:39:23 GMT
• Title: HyperLoader: Integrating Hypernetwork-Based LoRA and Adapter Layers into Multi-Task Transformers for Sequence Labelling
• Authors: Jesus-German Ortiz-Barajas, Helena Gomez-Adorno, Thamar Solorio,
• Abstract summary: We present HyperLoader, a simple approach that combines different parameter-efficient fine-tuning methods in a multi-task setting. Our method combines the benefits of multi-task learning by capturing the structure of all tasks. We provide empirical evidence that HyperLoader outperforms previous approaches in most datasets.
• Score: 5.955463697605461
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present HyperLoader, a simple approach that combines different parameter-efficient fine-tuning methods in a multi-task setting. To achieve this goal, our model uses a hypernetwork to generate the weights of these modules based on the task, the transformer layer, and its position within this layer. Our method combines the benefits of multi-task learning by capturing the structure of all tasks while reducing the task interference problem by encapsulating the task-specific knowledge in the generated weights and the benefits of combining different parameter-efficient methods to outperform full-fine tuning. We provide empirical evidence that HyperLoader outperforms previous approaches in most datasets and obtains the best average performance across tasks in high-resource and low-resource scenarios.

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