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Training transformer-based architectures can be expensive, especially for long inputs. [92] Many methods have been developed to attempt to address the issue. In the image domain, Swin Transformer is an efficient architecture that performs attention inside shifting windows. [93]
The paper introduced a new deep learning architecture known as the transformer, based on the attention mechanism proposed in 2014 by Bahdanau et al. [4] It is considered a foundational [5] paper in modern artificial intelligence, as the transformer approach has become the main architecture of large language models like those based on GPT.
It is based on the transformer deep learning architecture, pre-trained on large data sets of unlabeled text, and able to generate novel human-like content. [2] [3] As of 2023, most LLMs had these characteristics [7] and are sometimes referred to broadly as GPTs. [8] The first GPT was introduced in 2018 by OpenAI. [9]
A vision transformer (ViT) is a transformer designed for computer vision. [1] A ViT decomposes an input image into a series of patches (rather than text into tokens ), serializes each patch into a vector, and maps it to a smaller dimension with a single matrix multiplication .
The Blackwell architecture is named after American mathematician David Blackwell who was known for his contributions to the mathematical fields of game theory, probability theory, information theory, and statistics. These areas have influenced or are implemented in transformer-based generative AI model designs or their training algorithms.
At the 2017 NeurIPS conference, Google researchers introduced the transformer architecture in their landmark paper "Attention Is All You Need". This paper's goal was to improve upon 2014 seq2seq technology, [10] and was based mainly on the attention mechanism developed by Bahdanau et al. in 2014. [11]
T5 (Text-to-Text Transfer Transformer) is a series of large language models developed by Google AI introduced in 2019. [ 1 ] [ 2 ] Like the original Transformer model, [ 3 ] T5 models are encoder-decoder Transformers , where the encoder processes the input text, and the decoder generates the output text.
Mamba [a] is a deep learning architecture focused on sequence modeling. It was developed by researchers from Carnegie Mellon University and Princeton University to address some limitations of transformer models, especially in processing long sequences. It is based on the Structured State Space sequence (S4) model. [2] [3] [4]