A Guide to Transformer Architecture

The Transformer architecture is a type of neural network designed for processing sequential data such as text. It was introduced by Vaswani et al. in 2017 and has since become the foundation for many large language models (LLMs) and other machine learning models. One key advantage of transformers over their predecessors, recurrent neural networks (RNNs) and long short-term memory (LSTM), is that they can process input sequences simultaneously in parallel, resulting in faster training and inference times. Additionally, the positional encoding mechanism within the transformer allows it to handle longer-range dependencies more effectively than RNNs or LSTMs. The Transformer architecture consists of an encoder and a decoder, each containing multiple layers that work together to convert input sequences into numerical representations and generate output tokens. Despite its many advantages, the transformer architecture still has some limitations, such as limited context length, large resource requirements, longer training times, and lack of transparency in internal reasoning. Ongoing research aims to address these shortcomings and further enhance the capabilities of transformers in AI applications.