Kavli Affiliate: Long Zhang
| First 5 Authors: Yu Chen, Yu Chen, , ,
| Summary:
Fine-tuning large pre-trained models for downstream tasks has become a
fundamental approach in natural language processing. Fully fine-tuning all
model parameters is computationally expensive and memory-intensive, especially
in resource-constrained environments. Existing parameter-efficient fine-tuning
methods reduce the number of trainable parameters but typically overlook the
varying sensitivity of different model layers and the importance of training
data. In this work, we propose TsqLoRA, a novel method that integrates
data-quality-driven selection with sensitivity-aware low-rank adaptation,
consisted of two main components: a quality-aware sampling mechanism for
selecting the most informative training data, and a dynamic rank allocation
module that adjusts the rank of each layer based on its sensitivity to
parameter updates. The experimental results demonstrate that TsqLoRA improves
fine-tuning efficiency while maintaining or even improving performance on a
variety of NLP tasks. Our code will be available at
https://github.com/Benjamin-Ricky/TsqLoRA.
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