EXPLORING FINE-TUNING TECHNIQUES FOR LARGE LANGUAGE MODELS IN DOMAIN-SPECIFIC APPLICATIONS
Abstract
Domain-specific deployment of large language models (LLMs) requires more than transferring a general-purpose checkpoint to a small labelled dataset. A defensible adaptation pipeline must align the model, data, objective, evaluation protocol, and operational controls with the target domain. This paper presents a structured technical synthesis of full-parameter fine-tuning, continued domain- and task-adaptive pre-training, adapters, prompt and prefix tuning, low-rank adaptation (LoRA), and quantized LoRA (QLoRA). It replaces unsupported cross-domain score comparisons with an evidence-bounded framework that separates established findings from implementation guidance. The methodology covers corpus governance, leakage-resistant data splitting, trainable-parameter selection, regularized optimization, early stopping, calibration, robustness, fairness, privacy, latency, and cost evaluation. Evidence from biomedical, scientific, legal, and financial language modelling shows that domain-relevant pre-training and carefully controlled adaptation can improve specialised tasks, while parameter-efficient methods reduce storage and memory requirements. However, no method is uniformly best: the appropriate choice depends on model access, data volume, domain shift, compute, safety requirements, and expected maintenance frequency. The paper concludes with a reproducible decision framework and monitored deployment architecture for selecting, validating, and maintaining domain-adapted LLMs.
