Understanding Transformers: Inside Large Language Models Explained

Introduction to Transformers and Language Models

Large language models (LLMs) like GPT have revolutionized natural language processing (NLP), enabling machines to generate human-like text. But how do they actually work under the hood? This blog post demystifies the internal mechanics of transformer models, explaining key concepts in an accessible way for software engineers and AI enthusiasts alike.

We will explore the evolution of language models, the challenges they address, and the core components that make transformers so effective. By the end, you’ll understand tokenization, embeddings, attention, multi-head attention, and the training and inference process of LLMs.

Why Do We Need Transformers?

The Evolution of Language Models

Before transformers, language models faced several challenges:

Rule-based Systems: Early NLP systems used handcrafted grammar and rules but failed to capture the nuanced complexity of language.
Statistical Models: Later models learned word patterns from large corpora but mostly memorized text instead of understanding it.
Recurrent Neural Networks (RNNs): Introduced memory and sequential processing but struggled with long-term dependencies due to limited memory.
Long Short-Term Memory Networks (LSTMs): Improved on RNNs by selectively remembering important information via gating mechanisms but were still sequential and slow.

The Key Problems Transformers Solve

Transformers were introduced to overcome three major limitations:

Long Context Understanding: Previous models could forget crucial information from earlier in a sentence.
Parallel Processing: RNNs and LSTMs process words sequentially, limiting speed. Transformers process all words simultaneously.
Meaningful Word Relationships: Words often depend on others far away in a sentence, and understanding these relationships is essential.

Transformers enable parallel computation while preserving context and word relationships, making them ideal for large-scale language modeling.

How Do Transformers Understand Language?

Step 1: Tokenization – Breaking Down Text

Humans intuitively understand words, but machines require numerical representation. Tokenization is the process of splitting text into meaningful units called tokens.

Word-Level Tokenization: Splits text into whole words, but struggles with new or complex words.
Subword Tokenization: Breaks words into smaller parts (prefixes, suffixes). For example, “unhappiest” can be split into “un,” “happy,” and “est.” This allows the model to generalize better.
Character or Byte-Level Tokenization: Uses individual characters or bytes as tokens, enabling even finer granularity.

Each token is assigned a unique token ID from a vocabulary (e.g., GPT-3 uses around 50,000 tokens). Tokenization also includes spaces as tokens to preserve sentence structure.

Step 2: Word Embeddings – Translating Tokens into Vectors

Tokens by themselves are just numbers. To enable meaning, each token ID is mapped to a high-dimensional vector called a word embedding.

Embeddings serve as a translation layer converting discrete tokens into continuous numerical vectors.
Initially, embeddings are randomly initialized.
During training, embeddings are refined so that semantically similar words have similar vectors. For example, “cat” and “dog” embeddings cluster closer than “cat” and “apple.”
Embeddings capture rich semantic and syntactic information, enabling the model to reason about words mathematically.

Step 3: Positional Embeddings – Encoding Word Order

Transformers process tokens in parallel, so they need to know the position of each token to understand word order.

Positional embeddings are vectors representing the position of each token in a sequence.
These are added to the word embeddings so that the combined vector encodes both meaning and position.
Early models used sine and cosine functions to generate positional embeddings, while recent models use learned or rotational positional embeddings for efficiency.

The Transformer Architecture: Attention and Beyond

Core Components in a Transformer Block

A transformer consists of layers combining:

Self-Attention Mechanism: Allows each token to “attend” or relate to every other token in the sequence.
Multi-Layer Perceptron (MLP): Processes the information gathered from attention to refine understanding.
Layer Normalization: Ensures numerical stability by keeping values within sensible bounds.
Residual Connections: Pass information forward while adding new context.

Understanding Self-Attention: Query, Key, and Value

Self-attention is the heart of the transformer, enabling the model to weigh the importance of each word relative to others.

Each token’s embedding is transformed into three vectors: Query (Q), Key (K), and Value (V) using learned weight matrices.
The Query vector represents the current token’s “question” about the other tokens.
The Key vectors represent the “profiles” of all tokens.
Attention scores are computed by taking the dot product of the Query with all Keys, scaled and normalized via softmax to produce attention weights (probabilities).
These weights are used to compute a weighted sum of the Value vectors, producing a context-aware representation of each token.

Multi-Head Attention: Diverse Perspectives

Instead of a single attention mechanism, transformers use multi-head attention, where multiple attention heads operate in parallel.

Each head specializes in capturing different aspects of the input (e.g., color, size, ownership in a sentence).
The outputs of all heads are concatenated and transformed back into the original embedding space.
More heads generally mean the model can capture richer relationships and context.

The Role of MLP and Layer Normalization

MLP: Self-Study for Word Understanding

After attention aggregates information from the sequence, the MLP refines this knowledge by:

Projecting input vectors into a higher-dimensional space.
Applying nonlinear transformations to extract complex features.
Compressing the output back to original dimensions.

This process helps the model solidify its understanding of word roles and relationships.

Layer Normalization: Quality Control

Layer normalization ensures the model’s numerical stability by:

Preventing values from becoming too large or too small.
Maintaining consistent scales across layers.
Ensuring the model trains effectively and outputs meaningful results.

From Input to Output: The Transformer Workflow

Input Sentence: Tokenized into subwords or words.
Embedding: Each token is mapped to a word embedding vector.
Positional Encoding: Positional embeddings are added to word embeddings.
Transformer Layers: Multiple blocks of multi-head attention and MLP process the sequence.
Linear Transformation: Outputs are mapped back to vocabulary size.
Softmax Layer: Converts output scores to probabilities for next token prediction.
Next Token Prediction: Model selects the most probable next token (or samples based on temperature for creativity).

Training and Inference of Transformers

Training Phase: Learning from the Whole Sentence

The model is given entire sentences during training.
It predicts the next token for each position and compares it to the actual token.
A loss function calculates the error, providing negative feedback for wrong predictions.
Using backpropagation, the model updates all weights (embeddings, attention weights, MLP weights) to minimize errors.

Inference Phase: Generating Text Step-by-Step

During inference, the model receives only the initial tokens.
It predicts the next token, appends it to the input, and repeats.
The model does not see the full sentence but relies on context passed through previous predictions.
This autoregressive process generates coherent text sequences.

Advanced Concepts and Current Trends

Scale Effect: Bigger Models, Better Performance

Increasing model size (more parameters, heads, layers) improves performance.
Larger models capture more nuanced knowledge and generate higher-quality outputs.
However, training huge models requires massive compute resources.

Chain of Thought and Reasoning

Recent models generate intermediate reasoning steps before final answers.
This mimics human thinking and improves accuracy, especially in complex tasks like math.
Reinforcement learning techniques with verifiable rewards help train models for better reasoning.

Mixture of Experts (MoE)

MoE models have multiple “expert” attention heads specialized in different domains.
A router dynamically activates only relevant experts per input, saving computation.
This allows extremely large models (hundreds of billions of parameters) to operate efficiently.

Latent Attention

Compresses query and key matrices into a latent space for faster computation.
Enables handling longer contexts with reduced computational cost.

Summary: The Transformer as a Contextual Machine

Transformers can be viewed as contextual machines or “highways” where information flows through layers, progressively enriched by attention and MLP blocks. Each step adds local and global context to the input tokens, enabling the model to understand language intricately.

Despite the complexity, at their core, transformers are built on matrix multiplications and vector operations, making them accessible to engineers familiar with linear algebra and deep learning frameworks.

Understanding these internal workings empowers developers and researchers to innovate in NLP and build more intelligent AI systems.

Frequently Asked Questions (FAQ)

1. Is the math behind transformers complicated?

No, at a high level, transformers rely on matrix multiplication and simple functions like softmax. Complex training algorithms like backpropagation involve calculus but are handled by deep learning libraries.

2. Why do transformers use subword tokenization?

Subword tokenization balances vocabulary size and flexibility. It helps models understand prefixes, suffixes, and new words by breaking words into common sub-parts.

3. How does the model remember context in long sentences?

Self-attention mechanisms allow the model to relate every token with every other token in a sequence, capturing long-range dependencies without forgetting.

4. Can smaller models learn from bigger models?

Yes, through knowledge distillation, smaller “student” models learn to mimic larger “teacher” models by training on their outputs, enabling efficient deployment.

Conclusion

Transformers have fundamentally changed how machines understand and generate language. From tokenization and embeddings to multi-head self-attention and MLP layers, each component plays a crucial role in creating powerful language models like GPT.

By grasping these concepts, software engineers and AI practitioners can better harness transformer models, contribute to AI advancements, and develop applications that push the boundaries of language understanding.

Explore these ideas further, experiment with transformer architectures, and unlock the potential of next-generation AI!