Transformer - Attention Is All You Need

Transformer-based models are a type of neural network architecture that uses self-attention mechanisms to process input data. They were introduced in the paper "Attention Is All You Need" by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin in 2017, and have since become a popular choice for many natural language processing tasks.

Architecture

The architecture of transformer-based models is based on the idea of self-attention, which allows the model to focus on different parts of the input data at different times. This is in contrast to previous methods, such as recurrent neural networks (RNNs), which process the input data sequentially and can only attend to a limited context at each step.

At a high level, the architecture of a transformer-based model consists of the following components:

1. An encoder, which takes the input data and converts it into a set of vectors that can be processed by the model.
2. A series of self-attention layers, which apply dot products to the vectors to allow the model to attend to different parts of the input.
3. A feed-forward neural network, which applies a series of linear transformations to the output of the self-attention layers.
4. A decoder, which takes the output of the feed-forward network and produces a prediction.

The encoder and decoder in a transformer-based model are similar to those used in RNNs, but instead of processing the input sequentially, they convert the input data into a set of vectors, which are then fed into the self-attention layers. These vectors are called "keys" and "values", and they are used to compute the dot products that allow the model to attend to different parts of the input.

The self-attention layers in a transformer-based model consist of three components: the query, the key, and the value. The query is a vector that is used to "query" the key-value pairs to determine which parts of the input should be attended to. The key and value are vectors that are used to compute the dot product between the query and the input data. This dot product is then used to weigh the different parts of the input data, which allows the model to attend to the most relevant parts of the input.

Once the self-attention layers have produced an output, it is passed through a feed-forward neural network, which applies a series of linear transformations to the data. This allows the model to learn more complex relationships between the input and output data. Finally, the output of the feed-forward network is passed through a decoder, which produces the final prediction.

There is an excellent blog named The Illustrated Transformer by Jay Alammar which explain the Transformer architecture in the simpliest way possible.

Why do transformer-based models work better than previous methods?

Transformer-based models have proven to be very effective for many natural language processing tasks, such as machine translation and language modeling, because they are able to capture long-term dependencies in the input data.

Previous methods, such as recurrent neural networks (RNNs), struggled to capture these long-term dependencies because they processed the input data in a sequential manner. This means that the output at each step was only dependent on the input at that step and the previous hidden state. In contrast, transformer-based models use self-attention mechanisms, which allow the model to look at the entire input sequence at once and weight the different parts of the input according to their importance. This makes it possible for the model to capture long-term dependencies and produce more accurate predictions.

Additionally, transformer-based models are highly parallelizable, which makes them much more efficient to train and run on modern hardware, such as GPUs and TPUs. This has allowed them to be applied to large-scale natural language processing tasks, such as machine translation, which require the ability to process a large amount of data quickly.

Various Transformer-based models

1. The BERT (Bidirectional Encoder Representations from Transformers) model, which was developed by Google and is a popular choice for many natural language understanding tasks.
2. The GPT (Generative Pre-trained Transformer) model, which was developed by OpenAI and is a popular choice for many natural language generation tasks.
3. The Transformer-XL model, which was developed by Google and is a variant of the original transformer model that is able to capture longer-term dependencies in the input data.
4. The XLNet model, which was developed by Google and is a variant of the Transformer-XL model that is able to capture even longer-term dependencies in the input data.
5. The RoBERTa (Robustly Optimized BERT) model, which was developed by Facebook and is a variant of the BERT model that is trained on a larger dataset and uses a different training objective.
6. The ALBERT (A Lite BERT) model, which was developed by Google and is a smaller and more efficient variant of the BERT model.
7. The T5 (Text-To-Text Transfer Transformer) model, which was developed by Google and is a multi-task model that can be fine-tuned for a wide range of natural language processing tasks.
8. The BART (Denoising Autoencoding Representations from Transformers) model, which was developed by Facebook and is a denoising autoencoder that is trained to reconstruct the input data from corrupted versions of it.
9. The ELECTRA (Efficiently Learning an Encoder that Classifies Tokens Accurately) model, which was developed by Google and is a generative model that is trained to produce high-quality text.

Author: Sadman Kabir Soumik