LLMs use transfer learning by applying knowledge gained during pretraining on large, diverse datasets to perform specific tasks through fine-tuning. During pretraining, the model learns general language structures, such as grammar, syntax, and word relationships, by predicting masked tokens or the next word in massive text corpora. This equips the model with broad linguistic capabilities.
Fine-tuning adapts the pretrained model to a specific use case using a smaller, task-focused dataset. For example, a general LLM can be fine-tuned on legal documents to specialize in legal text analysis or on medical records for healthcare applications. This step refines the model’s knowledge to suit domain-specific requirements while retaining its general understanding of language.
Transfer learning significantly reduces the resources and time required for training, as it eliminates the need to start from scratch. It also allows LLMs to perform well on tasks with limited labeled data, making them versatile tools for a wide range of applications, from sentiment analysis to code generation.