Transformers and LLMs: The Revolution in Language Processing

Transformers: A Pillar of Modern Language Processing

Transformers have revolutionized natural language processing (NLP), replacing traditional methods based on RNNs and LSTMs. Their ability to process words in parallel allows for more efficient and scalable language modeling, forming the foundation of advanced models such as GPT and Gemini.

In this article, we explore how Transformers work, starting with text representation, then discussing self-attention, multi-head attention, and the complete Transformer block. These components come together to enable efficient language generation.

The Architecture of Transformers in AI Models

Modern AI models, such as GPT, Claude, and Gemini, rely on Transformer architectures to handle large-scale language processing tasks. These models require vast text datasets for training to learn the necessary linguistic patterns. GPT models (GPT-4, GPT-5) use Transformers solely in decoder mode, meaning a stack of decoder layers with masked self-attention. Claude (Anthropic) and Gemini (Google) also utilize similar stacks of Transformers, which they modify through custom transformations.

Google's Gemma models, for example, use the Transformer design described in the paper "Attention Is All You Need" to generate text sequentially, one token at a time. This approach allows for greater flexibility and adaptability in text generation.

Part 1: From Human Reading to Machine Reading

The first step in how Transformers operate involves converting text into a numerical form. This process begins with tokenization and embeddings, which transform words into distinct tokens and then into vector representations. Positional encodings help the model understand the order of words in a sentence.

Step 1: Text Tokenization

Language models cannot directly process raw text. Neural networks require numerical inputs, and tokenization allows for converting a string of text into separate elements with numerical identifiers.

Why Raw Text is Unintelligible for LLMs

Raw text is a string of characters, and language contains an infinite number of possible forms. A word-index matching system is impossible, as it could not cover all linguistic variations. For example, the sentence "Transformers have changed natural language processing" must be converted into a sequence of tokens before being processed by the model.

How Tokenization Works

Tokenization segments the text into smaller linguistic components, such as words, subwords, and characters. Each token receives a unique numerical identifier, essential for training and inference. Tokens can represent entire words, subwords, or even individual characters and punctuation marks.

Types of Tokens Used in LLMs

Different tokenization strategies exist, such as Byte-Pair Encoding (BPE), WordPiece, and Unigram. These methods keep common words as single tokens while breaking down uncommon words into essential components. For example, the word "transformers" remains whole while "unbelievability" is broken down into "un", "believ", "ability". This approach allows models to handle new or uncommon terms using known word components.

Step 2: Token Embeddings

Tokens are then converted into embedding vectors, representing the meanings of words through dense numerical vectors. Each token is mapped to a vector in a high-dimensional space, allowing the model to understand semantic relationships.

An embedding is a numerical vector representation of a token. You can think of it as each token having coordinates in a high-dimensional space. For example, the word "cat" will be mapped to a vector that exists in 768 dimensions. The model acquires these embeddings through its training process. Tokens with equivalent meanings produce vectors that show their relationship to one another. The words "Hello" and "Hi" have close embedding values, while "Hello" and "Goodbye" show a significant distance between their respective embeddings.

Semantic Meaning in Vector Space

Embeddings capture meaning, allowing us to evaluate relationships through vector similarity measures. The vectors for "cat" and "dog" show greater proximity than those for "cat" and "table" due to their stronger semantic relationship. The model discovers word similarity during the initial stage of its processing. The embedding of a token starts as a basic sense that lacks context, as it only shows the specific meaning of the word. The system first learns the basic meanings of words through its attention system, which provides context later.

Step 3: Positional Encoding

Transformers require explicit sequence information, as they process all tokens simultaneously. Positional encoding provides this information, allowing the model to understand the order of words.

Why Transformers Need Positional Information

Transformers perform their calculations by processing all tokens simultaneously, which differs from RNNs that require sequential processing. The system's ability to handle tasks simultaneously leads to fast performance, but this design choice prevents the system from understanding the order of events. The Transformer treats the input as a collection of words until we provide positional information for the embeddings. The model receives information about the order of words through positional encoding.