Transformers vs LLMs: Pre-training and Fine-Tuning
- Generative AI
This lesson explains how the original Transformer architecture relates to today's large language models, and why LLMs, though built on the same self attention mechanism, are much larger and trained on far more data. It covers why pre-training on unlabeled text gives a model general language understanding, and how fine-tuning then adapts that model to a task like sentiment analysis or entity recognition using a smaller, labeled dataset. It closes with real examples: BERT for sentiment analysis, GPT-3 as a chatbot, RoBERTa for entity recognition, and the open source h2ogpt project's task specific variants.
Key takeaways
- How the Transformer architecture and large language models differ in size, training data, and the breadth of tasks each handles
- Why pre-training on large amounts of unlabeled text gives a model general language understanding before any task specific training
- What the four steps of fine-tuning are: gathering task specific data, choosing a pretrained model, setting a goal, and adjusting parameters
- How BERT, GPT-3, and RoBERTa have each been fine-tuned for a different downstream task, from sentiment analysis to entity recognition
- Why fine-tuning on a smaller, labeled dataset is faster and cheaper than training a model from scratch for each new task
Start owning your AI