In the context of Large Language Models (LLMs), “Transformers” refer to a type of neural network architecture designed to handle sequential data, particularly text. They stand out for their ability to process entire sequences of data in parallel and their use of attention mechanisms, which allow them to efficiently handle long-range dependencies in text. This makes Transformers particularly effective for tasks like language translation, text generation, and understanding context, underpinning models like GPT3.
See Also: Transformer Model