How do LLMs use transfer learning?

Large language models (LLMs) use transfer learning by first training on broad datasets to learn general language patterns, then adapting that knowledge to specialized tasks through fine-tuning. This two-step approach avoids training models from scratch for every new application. The initial pre-training phase involves exposing the model to massive amounts of text (e.g., books, articles, or web content) to build foundational skills like grammar, context understanding, and basic reasoning. Once this general-purpose base exists, developers can repurpose it for specific use cases by retraining subsets of the model on smaller, task-specific datasets.

For example, BERT’s pre-training uses masked language modeling (predicting missing words in sentences), which teaches it relationships between words. A developer could then fine-tune BERT for sentiment analysis by adding a classification layer and training it on labeled movie reviews. Similarly, GPT-3’s base model, pre-trained to predict the next word in text, can be adapted for code generation by fine-tuning on programming languages. Crucially, only a fraction of the model’s parameters—often just the final layers—are adjusted during fine-tuning. This preserves the general linguistic knowledge in earlier layers while specializing the output for the target task. Tools like Hugging Face’s Transformers library simplify this process by providing pre-trained models and APIs to modify specific components.

This approach benefits developers by reducing computational costs and data requirements. Training a 175B-parameter model like GPT-3 from scratch is impractical for most teams, but fine-tuning it on a task with 10,000 examples might take hours on a single GPU. It also enables specialization in domains with limited labeled data: a medical chatbot can be built by fine-tuning on clinical notes instead of requiring millions of labeled patient interactions. By decoupling general language understanding from task-specific adaptation, transfer learning makes LLMs versatile tools that balance broad capabilities with practical deployability.