STAR: Constraint LoRA with Dynamic Active Learning for Data-Efficient
  Fine-Tuning of Large Language Models
Linhai Zhang Jialong Wu Deyu Zhou Guoqiang Xu
Though Large Language Models (LLMs) have demonstrated the powerful
capabilities of few-shot learning through prompting methods, supervised
training is still necessary for complex reasoning tasks. Because of their
extensive parameters and memory consumption, both Parameter-Efficient
Fine-Tuning (PEFT) methods and Memory-Efficient Fine-Tuning methods have been
proposed for LLMs. Nevertheless, the issue of large annotated data consumption,
the aim of Data-Efficient Fine-Tuning, remains unexplored. One obvious way is
to combine the PEFT method with active learning. However, the experimental
results show that such a combination is not trivial and yields inferior
results. Through probe experiments, such observation might be explained by two
main reasons: uncertainty gap and poor model calibration. Therefore, in this
paper, we propose a novel approach to effectively integrate uncertainty-based
active learning and LoRA. Specifically, for the uncertainty gap, we introduce a
dynamic uncertainty measurement that combines the uncertainty of the base model
and the uncertainty of the full model during the iteration of active learning.
For poor model calibration, we incorporate the regularization method during
LoRA training to keep the model from being over-confident, and the Monte-Carlo
dropout mechanism is employed to enhance the uncertainty estimation.
Experimental results show that the proposed approach outperforms existing
baseline models on three complex reasoning tasks.