Transformer models are a type of deep learning model that are commonly used in NLP and other applications of generative AI. Transformers were introduced in a seminal paper by Vaswani and others in 2017. They have since become a key building block for many state-of-the-art NLP models.
At a high level, transformer models are designed to learn contextual relationships between words in a sentence or text sequence. They achieve this learning by using a mechanism called self-attention, which allows the model to weigh the importance of different words in a sequence based on their context. This method is in contrast to traditional recurrent neural network (RNN) models, which process input sequences sequentially and do not have a global view of the sequence.
A key advantage of transformer models is their ability to process input sequences in parallel, which makes them faster than RNNs for many NLP tasks. They have also been shown to be highly effective for a range of NLP tasks, including language modeling, text classification, question answering, and machine translation.
The success of transformer models has led to the development of large-scale, pretrained language models, referred to as generative pretraining transformers (GPTs), such as OpenAI's GPT series and Google's Bidirectional Encoder Representations from Transformers (BERT) model. These pretrained models can be fine-tuned for specific NLP tasks with relatively little additional training data, making them highly effective for a wide range of NLP applications.
Overall, transformer models have revolutionized the field of NLP and have become a key building block for many state-of-the-art generative AI models. Their ability to learn contextual relationships between words in a text sequence has offered new possibilities for language generation, text understanding, and other NLP tasks.