Transformers are a type of neural network architecture particularly effective for sequence-based tasks, such as language translation and text summarization. They rely on a mechanism called self-attention, which allows them to weigh the importance of different words in a sentence when constructing representations. Unlike traditional recurrent networks, Transformers process all input data simultaneously, leading to faster processing times and improved performance in large-scale tasks like language models compared to recurrent neural networks.