The world of Transformers

The number of large language models has rapidly risen in various applications in the past year. Models like OpenAI's ChatGPT (opens in a new tab), Anthropic's Claude (opens in a new tab), and Mistral's Mixtral (opens in a new tab) are now used at the core of products and by hobbyists alike to accomplish interesting tasks. This work focuses on the transformer architecture, central to the current AI and deep learning push toward building the next generation of intelligent applications.

The transformer architecture, introduced in the paper "Attention is All You Need (opens in a new tab)," was the first designed to take advantage of the parallelization capabilities of GPUs, which had become the standard for neural network training. It tackles the challenge of handling long-range dependencies in sequential data, particularly in language, by using a self-attention mechanism that allows it to consider all positions in the input sequence simultaneously.

This capability has made transformers highly effective in various NLP tasks, including machine translation, text classification, and speech recognition. While initially developed for machine translation, transformers have proven highly beneficial for language modeling, which is their primary use case today.

The Transformer Architecture

The Transformer's fundamental innovation lies in replacing the recurrent layers with a self-attention mechanism, enabling the model to directly capture relationships between all words in a sentence, regardless of their position. By eliminating recurrence, the Transformer relies solely on the attention mechanism to establish global dependencies between input and output.

The attention mechanism allows the model to learn which items in the input sequence are most relevant to each other. It operates similarly to a soft dictionary lookup. For each item in the sequence (referred to as a query), the attention mechanism examines all other items (called keys) and calculates a similarity score between the query and each key. These similarity scores are then used to compute a weighted average of the values associated with each key, allowing the model to integrate information across the sequence when encoding each element selectively.

The Transformer architecture builds upon this core attention mechanism by repeatedly applying attention in multiple layers. Each layer refines and enriches the representation of the input sequence. The complete Transformer model comprises two main components: an encoder, which uses attention to create a rich representation of the input, and a decoder, which employs attention over both the encoded input and the outputs generated thus far to produce the target output one-element at a time.

By stacking attention in this layered manner and utilizing separate encoder and decoder mechanisms, the Transformer can learn highly expressive sequence representations that apply to various problems.

One of the most remarkable aspects of Transformers is their flexibility and adaptability. The same basic architecture can be applied to various tasks, including language understanding (e.g., sentiment analysis, named entity recognition), language generation (e.g., translation, summarization), and tasks involving other data modalities, such as image classification or speech recognition. This versatility has sparked a surge of research exploring the application of Transformers to new problems.

The Transformer's ability to parallelize computation and capture long-range dependencies has made it the backbone of recent breakthroughs in large language models, such as BERT, GPT-3, and PaLM. These models, pre-trained on massive amounts of text data, exhibit impressive performance on complex language tasks.

In summary, the Transformer has emerged as a highly influential model due to its novel self-attention mechanism, enabling more efficient and parallelizable computation, capturing long-range dependencies, and flexibility in sequence modeling tasks across various domains. By replacing recurrent layers with attention and leveraging a layered architecture, Transformers have pushed the boundaries of what is possible in machine learning, particularly in natural language processing.

The subsequent sections will delve deeper into the technical details of the Transformer architecture, providing a comprehensive understanding of how these powerful models operate under the hood.