Transformers and Large Language Models: The Architecture Powering AI
Large Language Models are built on a neural network architecture called transformers. Introduced in 2017 through the paper "Attention Is All You Need" by Vaswani et al., transformers have become the backbone of natural language processing (NLP) and other AI advancements.. But what makes transformers so effective? In this post, we’ll break down what transformers are and how they work.
What Are Transformers?
Transformers are a type of neural network designed to handle sequential data like text. Unlike earlier models such as recurrent neural networks (RNNs) or long short-term memory networks (LSTMs), transformers process entire sequences simultaneously. This parallel processing enables faster computation and improved scalability.
The core innovation of transformers is the attention mechanism, which allows the model to focus on the most relevant parts of the input sequence, capturing long-range dependencies crucial for language understanding and generation.
Key Components of a Transformer
Encoder-Decoder Structure:
The architecture is divided into two main parts:
The encoder, which processes the input sequence to create a representation of its meaning.
The decoder, which uses this representation to generate the output sequence.
In LLMs like GPT-4, the focus is primarily on the decoder, as their main task is text generation.
Self-Attention Mechanism:
This mechanism analyses how each word in a sequence relates to every other word. For example, in the sentence "The book on the table belongs to her," the model determines that "book" relates closely to "table" and "belongs," creating a richer understanding of context.
Positional Encoding:
Transformers don’t inherently process text in a fixed order, so they add positional encodings to track word order. These encodings ensure the sequence's structure is retained.
Feedforward Neural Networks:
Each attention layer is followed by a feedforward network that further refines the processed information.
Multi-Head Attention:
Instead of a single attention calculation, transformers use multiple “heads” to focus on different aspects of the input simultaneously. This allows the model to capture diverse relationships within the data.
How Transformers Work
Here’s how transformers process input:
The input text is tokenised into smaller units, such as words or subwords.
Positional encodings are added to preserve the order of tokens.
The encoder uses self-attention to create contextual representations of the input sequence.
The decoder takes these representations and generates an output sequence, one token at a time, while considering the relationships between all tokens in the context.
For example, in machine translation, the encoder might process the sentence "She is reading a book" in English, and the decoder will generate the equivalent in French: "Elle lit un livre."
Why Transformers Are Crucial for LLMs
Transformers enable LLMs to:
Scale Effectively: Their parallel processing allows them to handle vast amounts of training data efficiently.
Understand Complex Contexts: Attention mechanisms let transformers capture nuanced relationships between words, phrases, and sentences.
Generalise Across Tasks: Once trained, transformers can be fine-tuned for various NLP applications, such as summarisation, translation, and question answering.
Transformers are the backbone of modern LLMs, powering their ability to process and generate text with remarkable accuracy. Their architecture—rooted in attention, scalability, and efficiency—has transformed the field of AI. Understanding transformers sheds light on the technology behind the text-based systems driving innovation in academia, research, and beyond.
