Pre-trained Language Models - Fine-tuning Strategies: Investigating fine-tuning strategies for pre-trained language models to adapt them to specific NLP tasks with minimal labelled data

Abstract

Pre-trained language models have revolutionised natural language processing (NLP) by learning rich representations of language from vast amounts of text data. Fine-tuning these models on task-specific data has been shown to achieve state-of-the-art performance across various NLP tasks. However, fine-tuning strategies can significantly impact the performance and efficiency of these models, especially when labelled data is limited. This paper reviews and compares different fine-tuning strategies for pre-trained language models, focusing on techniques that enhance performance with minimal labeled data. We analyze strategies such as gradual unfreezing, adapter modules, and distillation, highlighting their strengths and limitations. Furthermore, we discuss the impact of data augmentation and domain adaptation on fine-tuning. Through a series of experiments on benchmark datasets, we demonstrate the effectiveness of these strategies and provide insights into their optimal usage.