Different types of LLMs
LLMs are generally neural network architectures that are trained on a large corpus of text data. The term “large” refers to the size of these models in terms of the number of parameters and the scale of training data. Here are some examples of LLMs.
Transformer models
Transformer models have been at the forefront of the recent wave of LLMs. They are based on the “Transformer” architecture, which uses self-attention mechanisms to weigh the relevance of different words in the input when making predictions. Transformers are a type of neural network architecture introduced in the paper Attention is All You Need by Vaswani et al. One of their significant advantages, particularly for training LLMs, is their suitability for parallel computing.
In traditional RNN models, such as LSTM and GRU, the sequence of tokens (words, subwords, or characters in the text) must be processed sequentially. That’s because each token’...