GPT (Generative Pre-trained Transformer) focuses on generating text by predicting the next token in a sequence, making it highly effective for tasks like writing, summarization, and question answering. It is a decoder-only model, meaning it processes inputs and generates outputs in a unidirectional manner, considering only past tokens when predicting the next one.
In contrast, models like BERT (Bidirectional Encoder Representations from Transformers) are encoder-only and designed for understanding text. BERT processes tokens bidirectionally, analyzing the entire sentence or context at once, making it well-suited for tasks like classification and sentiment analysis. Models like T5 and BART combine encoder and decoder components, allowing them to perform both understanding and generation tasks efficiently.
The choice between GPT and other LLMs depends on the use case. GPT excels at open-ended tasks requiring coherent text generation, while BERT and hybrid models are better for tasks demanding deep comprehension or bidirectional context understanding.