READ: Recurrent Adaptation of Large Transformers

• URL: http://arxiv.org/abs/2305.15348v2
• Date: Thu, 03 Oct 2024 19:40:14 GMT
• Title: READ: Recurrent Adaptation of Large Transformers
• Authors: John Nguyen, Sid Wang, Ke Li, Carole-Jean Wu,
• Abstract summary: Fine-tuning large-scale Transformers becomes impractical as the model size and number of tasks increase. We introduce textbfREcurrent textbfADaption (READ) -- a lightweight and memory-efficient fine-tuning method.
• Score: 7.982905666062059
• License:
• Abstract: Fine-tuning large-scale Transformers has led to the explosion of many AI applications across Natural Language Processing and Computer Vision tasks. However, fine-tuning all pre-trained model parameters becomes impractical as the model size and number of tasks increase. Parameter-efficient transfer learning (PETL) methods aim to address these challenges. While effective in reducing the number of trainable parameters, PETL methods still require significant energy and computational resources to fine-tune. In this paper, we introduce \textbf{RE}current \textbf{AD}aption (READ) -- a lightweight and memory-efficient fine-tuning method -- to overcome the limitations of the current PETL approaches. Specifically, READ inserts a small RNN network alongside the backbone model so that the model does not have to back-propagate through the large backbone network. Through comprehensive empirical evaluation of the GLUE benchmark, we demonstrate READ can achieve a $56\%$ reduction in the training memory consumption and an $84\%$ reduction in the GPU energy usage while retraining high model quality compared to full-tuning. Additionally, the model size of READ does not grow with the backbone model size, making it a highly scalable solution for fine-tuning large Transformers.

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