Parameter-Efficient Fine-Tuning with Layer Pruning on Free-Text Sequence-to-Sequence Modeling

• URL: http://arxiv.org/abs/2305.08285v3
• Date: Fri, 19 May 2023 01:29:08 GMT
• Title: Parameter-Efficient Fine-Tuning with Layer Pruning on Free-Text Sequence-to-Sequence Modeling
• Authors: Yunqi Zhu and Xuebing Yang and Yuanyuan Wu and Wensheng Zhang
• Abstract summary: We propose a framework that integrates LoRA and structured layer pruning. Our framework can reduce 50% of GPU memory usage and speed up 100% of the training phase.
• Score: 5.601559340796398
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The increasing size of language models raises great research interests in parameter-efficient fine-tuning such as LoRA that freezes the pre-trained model, and injects small-scale trainable parameters for multiple downstream tasks (e.g., summarization, question answering and translation). To further enhance the efficiency of fine-tuning, we propose a framework that integrates LoRA and structured layer pruning. The integrated framework is validated on two created deidentified medical report summarization datasets based on MIMIC-IV-Note and two public medical dialogue datasets. By tuning 0.6% parameters of the original model and pruning over 30% Transformer-layers, our framework can reduce 50% of GPU memory usage and speed up 100% of the training phase, while preserving over 92% generation qualities on free-text sequence-to-sequence tasks.

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