Empirical Analysis of Efficient Fine-Tuning Methods for Large Pre-Trained Language Models
This work addresses the challenge of fine-tuning large language models efficiently for NLP practitioners, offering incremental insights into parameter-efficient methods.
This paper empirically compares efficient fine-tuning methods BitFit and adapter modules to full fine-tuning on GLUE benchmark datasets, finding that BitFit matches full fine-tuning performance with only bias terms trained and shows stability with 30% of data, while adapters are inconsistent.
Fine-tuning large pre-trained language models for downstream tasks remains a critical challenge in natural language processing. This paper presents an empirical analysis comparing two efficient fine-tuning methods - BitFit and adapter modules - to standard full model fine-tuning. Experiments conducted on GLUE benchmark datasets (MRPC, COLA, STS-B) reveal several key insights. The BitFit approach, which trains only bias terms and task heads, matches full fine-tuning performance across varying amounts of training data and time constraints. It demonstrates remarkable stability even with only 30\% of data, outperforming full fine-tuning at intermediate data levels. Adapter modules exhibit high variability, with inconsistent gains over default models. The findings indicate BitFit offers an attractive balance between performance and parameter efficiency. Our work provides valuable perspectives on model tuning, emphasizing robustness and highlighting BitFit as a promising alternative for resource-constrained or streaming task settings. The analysis offers actionable guidelines for efficient adaptation of large pre-trained models, while illustrating open challenges in stabilizing techniques like adapter modules.