Efficient Fine-Tuning Strategies for Domain-Specific Large Language Models: A Comprehensive Study of Parameter-Efficient Methods

Large Language Models (LLMs) such as GPT-3, GPT-4, BERT, and T5 have transformed the field of natural language processing (NLP) by demonstrating robust zero-shot and few-shot generalization capabilities across a variety of tasks. Nevertheless, their performance diminishes in specialized fields like finance, healthcare, and law, where domain-specific language, structured reasoning methods, and implicit constraints are not adequately represented in large-scale pretraining datasets. Although full fine-tuning allows for substantial adaptation to specific domains, it is not scalable for contemporary LLMs due to excessive computational demands, high memory requirements, and the operational complexity of managing numerous domain-specific model checkpoints. This paper offers a detailed empirical investigation into parameter-efficient fine-tuning (PEFT) techniques for domain adaptation. We assess these techniques across NLP benchmarks in finance, healthcare, and law using three representative transformer models: GPT-2 XL, BERT-Large, and T5- Base. Our experiments indicate that LoRA consistently achieves results within 1%–2% of those obtained from full fine-tuning while modifying less than 2% of model parameters, resulting in a reduction of peak GPU memory utilization by up to 65%, training duration by 4–5 times, and checkpoint storage requirements by more than 50 times. Adapter-based approaches provide modularity but come with added inference latency, whereas BitFit offers remarkable parameter efficiency but incurs a greater drop in performance. We also examine convergence patterns, sensitivity to hyperparameters, and generalization across domains, showing that the majority of domain-specific improvements stem from modifying attention projections, and that moderate LoRA ranks (16–32) capture most of the performance advantages. In scenarios with limited data, PEFT techniques equal or slightly surpass full fine-tuning, indicating implicit regularization effects from restricted parameter updates. PEFT facilitates multi- domain deployment through lightweight and interchangeable adaptation components, greatly simplifying version control, rollback, and ongoing updates in live systems. Our results establish parameter-efficient fine-tuning as a feasible, scalable, and deployment-friendly method for systems utilizing domain-specialized LLMs.