Fine-Tuning Pre-Trained Large Language Models is a domain-specific optimization method within computational linguistics and machine learning that adjusts weights in frozen pre-trained parameters to align model outputs with downstream task distributions using low-rank factorization or full-parameter updates. The core principle relies on the Transfer Learning theorem, which posits that high-capacity models trained on general corpora can adapt to narrow tasks by minimizing a supervised loss function $\mathcal{L}(\theta')$ while constraining catastrophic forgetting through regularization techniques such as LoRA (Low-Rank Adaptation) or parameter-efficient fine-tuning strategies. This mechanism operates within the subfield of neural architecture optimization, distinguishing itself from pre-training via its focus on task-specific convergence rather than broad knowledge acquisition, ensuring formal equivalence between source and target domain representations in a high-dimensional embedding space.
The core principle described is parameter-efficient fine-tuning (PEFT), a mechanism enabling domain specialization of pre-trained large language models by updating only subsets of parameters rather t…