Fine-tuning refers to the process of adapting a pre-trained language model to a specific downstream task or domain by further training it on task-specific data. This approach leverages the knowledge and representations learned by the pre-trained model during its initial training on large-scale corpora, allowing for more efficient and effective learning on task-specific datasets. Fine-tuning enables language models to achieve state-of-the-art performance across a wide range of language tasks, including text classification, sentiment analysis, and machine translation.
