Question

What is a transformer and how does it work?

Answer 1

A transformer is a deep learning model architecture that was introduced in the paper "Attention is All You Need" by Vaswani et al. in 2017. It revolutionized the field of natural language processing and achieved state-of-the-art performance on various tasks, such as machine translation, text generation, and language understanding.

The key idea behind the transformer is the concept of self-attention mechanism. Traditional sequence-to-sequence models, like recurrent neural networks (RNNs) and long short-term memory networks (LSTMs), process input sequences one element at a time and sequentially update their hidden states. While they work well for short sequences, they suffer from limitations in handling long-range dependencies.

The transformer, on the other hand, utilizes self-attention to process all elements of the input sequence simultaneously, allowing it to capture dependencies between any pair of elements in the sequence. This parallelism significantly speeds up training and enables better modeling of long-range dependencies.

Here's a high-level overview of how a transformer works:

Input Encoding: The input sequence (e.g., a sentence) is first tokenized into individual tokens, and each token is embedded into a dense vector representation. These embeddings can be learned during training or use pre-trained word embeddings like Word2Vec or GloVe.

Positional Encoding: Since transformers don't have inherent positional information like RNNs, positional encodings are added to the input embeddings. These encodings convey the position of each token in the sequence and help the model understand the sequential order.

Self-Attention Mechanism: The core component of the transformer is the self-attention mechanism. It computes the importance (attention weight) that each element (token) in the input sequence should give to all other elements. The attention weights are calculated based on similarity scores between token representations.

Multi-Head Attention: To capture different types of information, the self-attention mechanism is extended to multiple "attention heads." Each attention head learns different aspects of the relationships between tokens.

Feed-Forward Neural Networks: After obtaining the attention-weighted representations, they are passed through fully connected feed-forward neural networks to capture non-linear relationships within the data.

Layer Norm and Residual Connections: Layer normalization and residual connections are applied to stabilize and accelerate training.

Encoder and Decoder Stacks: The transformer architecture consists of encoder and decoder stacks. The encoder processes the input sequence, while the decoder generates the output sequence during tasks like machine translation.

Decoding: In sequence-to-sequence tasks like machine translation, the decoder uses an autoregressive process, where it predicts one token at a time based on previously generated tokens until an end token is generated.

By employing self-attention and parallel computation, transformers have shown remarkable performance on various natural language processing tasks and have become the backbone of many state-of-the-art language models like BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer).