BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer) are both transformer-based models but differ in architecture, training objectives, and applications. BERT is designed for bidirectional context understanding, processing text by considering both the preceding and following words. This makes it highly effective for tasks requiring in-depth comprehension, such as question answering and sentiment analysis. It is pre-trained using a masked language model objective, where random words are masked, and the model predicts them based on surrounding context.
GPT, in contrast, is unidirectional and generates text sequentially, predicting the next word based on preceding words. It excels in generative tasks like text completion, creative writing, and chatbots. GPT is pre-trained using a causal language model objective, where it learns to predict the next token in a sequence.
In summary, BERT is optimized for understanding and analyzing existing text (e.g., classification, NER), while GPT focuses on generating coherent and contextually relevant text. Both models have evolved with newer versions like BERT-large and GPT-4, further pushing the boundaries of NLP capabilities.