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Fine-tuning large pre-trained language models for downstream tasks remains acritical challenge in natural language processing. This paper presents anempirical analysis comparing two efficient fine-tuning methods - BitFit andadapter modules - to standard full model fine-tuning. Experiments conducted onGLUE benchmark datasets (MRPC, COLA, STS-B) reveal several key insights. TheBitFit approach, which trains only bias terms and task heads, matches fullfine-tuning performance across varying amounts of training data and timeconstraints. It demonstrates remarkable stability even with only 30\% of data,outperforming full fine-tuning at intermediate data levels. Adapter modulesexhibit high variability, with inconsistent gains over default models. Thefindings indicate BitFit offers an attractive balance between performance andparameter efficiency. Our work provides valuable perspectives on model tuning,emphasizing robustness and highlighting BitFit as a promising alternative forresource-constrained or streaming task settings. The analysis offers actionableguidelines for efficient adaptation of large pre-trained models, whileillustrating open challenges in stabilizing techniques like adapter modules.

Empirical Analysis of Efficient Fine-Tuning Methods for Large Pre-Trained Language Models