The stellar performance of enormous language models (LLMs) resembling ChatGPT has shocked the world. The breakthrough was made by the invention of the Transformer architecture, which is surprisingly easy and scalable. It continues to be built of deep learning neural networks. The principal addition is the so-called “attention” mechanism that contextualizes each word token. Furthermore, its unprecedented parallelisms endow LLMs with massive scalability and, due to this fact, impressive accuracy after training over billions of parameters.
The simplicity that the Transformer architecture has demonstrated is, actually, comparable to the Turing machine. The difference is that the Turing machine controls what the machine can do at each step. The Transformer, nevertheless, is sort of a magic black box, learning from massive input data through parameter optimizations. Researchers and scientists are still intensely interested by discovering its potential and any theoretical implications for studying the human mind.
In this text, we are going to first discuss the 4 principal features of the Transformer architecture: word embedding, attention mechanism, single-word prediction, and generalization capabilities resembling multi-modal extension and transferred learning. The intention is to deal with why the architecture is so effective as a substitute of the best way to construct it (for which readers can find many…
