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

Nigel Doering, Cyril Gorlla, Trevor Tuttle, Adhvaith Vijay

TL;DR

This paper investigates efficient fine-tuning for large pre-trained language models by empirically comparing BitFit and Adapter modules against full fine-tuning on GLUE tasks MRPC, COLA, STS-B. It shows BitFit achieves comparable performance to full fine-tuning with significantly fewer trainable parameters and displays robustness with limited data, while Adapter modules exhibit unstable gains. The study provides practical guidance for resource-constrained deployment and streaming adaptation, and highlights stability challenges in adapter-based approaches. The findings contribute to understanding when minimal-parameter updates suffice and emphasize BitFit as a robust, data-efficient alternative.

Abstract

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.

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

TL;DR

This paper investigates efficient fine-tuning for large pre-trained language models by empirically comparing BitFit and Adapter modules against full fine-tuning on GLUE tasks MRPC, COLA, STS-B. It shows BitFit achieves comparable performance to full fine-tuning with significantly fewer trainable parameters and displays robustness with limited data, while Adapter modules exhibit unstable gains. The study provides practical guidance for resource-constrained deployment and streaming adaptation, and highlights stability challenges in adapter-based approaches. The findings contribute to understanding when minimal-parameter updates suffice and emphasize BitFit as a robust, data-efficient alternative.

Abstract

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.
Paper Structure (10 sections, 9 equations, 4 figures)

This paper contains 10 sections, 9 equations, 4 figures.

Table of Contents

  1. Abstract
  2. Introduction
  3. Related Works
  4. Datasets
  5. Methods
  6. Adapter Modules
  7. BitFit
  8. Experimental Design
  9. Results
  10. Discussion

Figures (4)

  • Figure 1: Sequential Analysis of Fine-Tuning Techniques on COLA Dataset. From left to right, the graphs show model performance for 30%, 50%, 70%, and 100% of the training data.
  • Figure 2: Sequential Analysis of Fine-Tuning Techniques on MRPC Dataset. From left to right, the graphs show model performance for 30%, 50%, 70%, and 100% of the training data.
  • Figure 3: Sequential Analysis of Fine-Tuning Techniques on STS-B Dataset. From left to right, the graphs show model performance for 30%, 50%, 70%, and 100% of the training data.
  • Figure 4: From left to right, the graphs show model performance on 100% of COLA, MRPC, STS-B Training Data