A transformer is a neural network architecture designed to process sequential data, such as text, using a mechanism called attention. Introduced in the 2017 paper “Attention Is All You Need,” transformers avoid the sequential processing limitations of earlier models like RNNs and LSTMs by analyzing all parts of the input simultaneously. The core idea is the self-attention mechanism, which allows the model to weigh the importance of different words in a sentence relative to each other. For example, in the sentence “The cat sat on the mat,” the word “cat” might strongly relate to “sat” and “mat,” while “on” is less critical. This enables the model to capture long-range dependencies and context more effectively than sequential models.
Transformers consist of two main components: the encoder and the decoder. The encoder processes the input data (e.g., a sentence) and builds a representation that captures contextual relationships. The decoder uses this representation to generate output (e.g., a translated sentence). Both encoder and decoder layers include multi-head attention modules, which run multiple self-attention operations in parallel, and feed-forward networks. A key innovation is positional encoding, which injects information about the order of tokens (words or subwords) into the model since transformers lack inherent sequential processing. For instance, positional encodings might use sine and cosine functions to represent token positions numerically, allowing the model to understand word order without relying on recurrence.
Transformers have become foundational in natural language processing (NLP). For example, models like BERT use the encoder stack for tasks like text classification, while GPT uses the decoder stack for text generation. Unlike RNNs, transformers process all tokens in parallel during training, making them faster to train on GPUs. They also scale better with large datasets, enabling models with billions of parameters. A practical example is machine translation: the encoder converts an input sentence (e.g., English) into a contextualized representation, and the decoder generates the translated output (e.g., French) step by step, using attention to focus on relevant parts of the input. This architecture’s efficiency and flexibility have made it the standard for modern NLP systems.