BERT and GPT models are both based on transformer architectures and are trained on large amounts of text data. However, they differ in some important ways:
- Training Data: BERT is trained on a combination of masked language modeling and next sentence prediction tasks, while GPT models are trained on an unsupervised language modeling task.
- Bidirectionality: BERT is a bidirectional model, meaning it can take into account both the left and right context of a given word when generating its representation. GPT models are unidirectional and can only consider the left context.
- Fine-tuning: BERT is often fine-tuned on specific downstream tasks, such as natural language understanding (NLU) or natural language generation (NLG), while GPT models are usually fine-tuned for generative language tasks.
Both BERT and GPT models are state-of-the-art language models, and the choice of which one to use depends on the specific task and the available resources.