We've discussed how Large Language Models (LLMs) predict text based on preceding words (context) and how they represent words using tokens and embeddings. But how does a model effectively use that context, especially when relevant information might be many words away? Simply looking at the last few words isn't always enough to understand complex sentences or paragraphs.

This is where a specific type of model structure, known as the Transformer architecture, comes into play. Introduced in a 2017 paper titled "Attention Is All You Need" by researchers at Google, the Transformer has become the foundation for many of the most capable LLMs developed since.

The Problem with Sequential Processing

Older approaches to language modeling often processed text word by word in strict sequence. Imagine reading a long paragraph one word at a time and trying to remember the very first sentence perfectly by the time you reach the end. It's difficult! These sequential models could struggle to connect words that were far apart but semantically related. For instance, understanding which noun a pronoun refers to might be hard if the noun appeared much earlier in the text.

The Core Idea: Attention

The Transformer architecture introduced a powerful mechanism called attention, specifically self-attention. Instead of processing words strictly one after another, the attention mechanism allows the model to weigh the importance of all words in the input sequence when considering any single word.

Think of it like this: when you read the sentence, "The cat, which chased the mouse, quickly climbed up the tall tree," to understand the word "up," your brain naturally pays attention not just to "climbed" right before it, but also connects it back to "cat" and "tree" to get the full picture. The attention mechanism lets the model do something similar computationally. It learns to identify which other words in the input provide the most useful context for understanding the current word or predicting the next one.

This allows Transformers to effectively handle long-range dependencies – relationships between words that are far apart in the text. It helps the model understand nuances, resolve pronoun references, and grasp the overall context much better than earlier architectures.

A Simplified View of the Structure

While the full Transformer architecture involves several components, we can simplify it into two main parts for a high-level understanding:

Encoder: This part reads the input text. Using the self-attention mechanism, it processes all the input words simultaneously (or rather, in a way that considers all words) and builds rich representations (embeddings) for each word that incorporate context from the entire input sequence.
Decoder: This part generates the output text, one token at a time. It also uses self-attention to consider the words it has already generated. Critically, it also pays attention to the contextualized representations created by the encoder. This ensures the output is relevant to the input prompt and maintains coherence as it generates more text.

A simplified flow showing input processing by the Encoder and output generation by the Decoder, highlighting the flow of contextual information.

Positional Information

You might wonder: if the model looks at all words somewhat simultaneously using attention, how does it know the original order of the words? This is handled using positional encodings. Essentially, extra information representing the position of each word (first, second, third, etc.) is added to the word's embedding. This ensures that the model has information about the sequence order, even while using attention to weigh word importance regardless of position.

Why Transformers Excel

The Transformer architecture brought significant advantages:

Handling Context: Its attention mechanism is excellent at capturing relationships between words, even those far apart in the text.
Parallelization: Many computations within the Transformer, especially in the attention layers, can be performed in parallel. This makes training these large models much more efficient on hardware like GPUs (Graphics Processing Units) and TPUs (Tensor Processing Units) compared to strictly sequential models.

This ability to effectively process context and be trained efficiently on massive datasets is a primary reason why Transformer-based LLMs have become so powerful. They require a vast amount of training data and have a huge number of model parameters ( $P$ ) precisely because they need to learn these complex attention patterns across all the nuances of human language. This architecture provides the capacity to learn those patterns effectively.

Understanding the details of attention calculations or the exact layering within encoders and decoders requires more advanced study. For now, the key takeaway is that the Transformer architecture, through its attention mechanism, allows LLMs to intelligently consider the relevance of different parts of the input text when processing information and generating output. This is fundamental to how they understand prompts and produce coherent, contextually relevant responses.