Transformer - Page 3

A transformer is a neural network architecture that uses attention to model relationships within a sequence or across multiple data types. Unlike recurrent networks that process elements step by step, transformers can analyze many positions in parallel during training. Self-attention lets each token weigh the relevance of other tokens, while additional layers transform those contextual representations. The architecture underpins modern language models and is also used for images, audio, video, biology, and robotics. Transformers scale effectively but can require substantial data, memory, and computation. Their outputs still reflect training limitations, and long context does not guarantee correct reasoning or recall. Model size, attention design, data quality, and evaluation remain central to performance.