Large language models have transformed how we interact with technology, powering everything from chatbots to content generation tools. But what exactly are these models, and how do they work? This guide breaks down the fundamentals of large language models in a way that’s accessible whether you’re a curious beginner or looking to deepen your technical understanding.
What Are Large Language Models?
At their core, large language models (LLMs) are artificial intelligence systems trained to understand and generate human language. These models are called “large” because they contain billions—sometimes trillions—of parameters, which are the adjustable values that determine how the model processes information.
Think of an LLM as a sophisticated pattern-recognition system that has read vast amounts of text from the internet, books, and other sources. Through this exposure, it learns the statistical relationships between words, phrases, and concepts. When you ask an LLM a question or give it a prompt, it predicts the most likely sequence of words that should come next based on patterns it learned during training.
Key characteristics that define LLMs:
- Scale: They contain billions of parameters (GPT-3 has 175 billion, for example)
- Training data: They’re trained on massive datasets containing hundreds of gigabytes or terabytes of text
- Versatility: They can handle multiple tasks without task-specific training
- Emergent abilities: At sufficient scale, they develop capabilities that weren’t explicitly programmed
The Architecture Behind Large Language Models
Most modern LLMs are built on the transformer architecture, a breakthrough introduced in 2017 by researchers at Google. This architecture revolutionized natural language processing by introducing a mechanism called “attention” that allows models to weigh the importance of different words in relation to each other.
The Transformer Architecture Explained
The transformer architecture consists of two main components: encoders and decoders. However, many popular LLMs like GPT (Generative Pre-trained Transformer) use only the decoder portion, which is optimized for generating text.
Transformer Layer Flow
The attention mechanism is what makes transformers special. Traditional neural networks processed words sequentially, but attention allows the model to look at all words in a sentence simultaneously and understand how they relate to each other. When processing the word “bank,” for example, the attention mechanism helps the model determine whether it refers to a financial institution or a riverbank by examining surrounding words.
The attention mechanism works through three steps:
- Query, Key, and Value vectors: Each word is transformed into three different representations that help compute attention
- Attention scores: The model calculates how much focus each word should receive when processing another word
- Weighted combination: Words are combined based on their attention scores to create context-aware representations
Layers Upon Layers
Large language models don’t use just one transformer layer—they stack dozens or even hundreds of them. GPT-3, for instance, has 96 layers. Each layer refines the understanding of the input text, building increasingly abstract representations. Early layers might capture simple patterns like grammar and syntax, while deeper layers understand complex semantic relationships and reasoning patterns.
This depth is crucial for the model’s ability to handle complex tasks. The layered structure allows the model to build hierarchical representations of language, similar to how our brains process information at multiple levels of abstraction.
The Training Process: How LLMs Learn
Training a large language model is a massive undertaking that requires substantial computational resources and carefully curated datasets. The process typically involves two main phases: pre-training and fine-tuning.
Pre-Training: Learning Language Fundamentals
During pre-training, the model learns to predict the next word in a sentence by processing billions of examples from its training data. This is called self-supervised learning because the model creates its own training signals from unlabeled text—it doesn’t need humans to manually annotate what each piece of text means.
The model sees a partial sentence like “The cat sat on the…” and learns to predict that “mat” or “chair” are likely next words, while “telescope” or “democracy” are unlikely. Through billions of these predictions, the model builds a statistical understanding of language structure, facts about the world, and reasoning patterns.
What happens during pre-training:
- The model processes massive text datasets, often containing hundreds of billions of words
- Parameters are adjusted using backpropagation to minimize prediction errors
- Training can take weeks or months on thousands of specialized GPUs or TPUs
- The process costs millions of dollars for the largest models
Fine-Tuning: Specializing for Specific Tasks
After pre-training, models can be fine-tuned for specific applications. This involves training the model on a smaller, task-specific dataset. For example, a pre-trained model might be fine-tuned on medical literature to become better at answering health-related questions, or on code repositories to improve programming assistance.
Fine-tuning is much faster and cheaper than pre-training because the model already understands language fundamentals. It’s adjusting its existing knowledge rather than learning from scratch. Many modern LLMs also undergo instruction tuning and reinforcement learning from human feedback (RLHF) to make them better at following instructions and producing helpful, harmless responses.
How LLMs Generate Text: The Inference Process
When you interact with a large language model, you’re experiencing the inference phase—where the trained model generates responses to your prompts. This process is fascinating in its elegance despite the model’s complexity.
The model doesn’t generate entire responses at once. Instead, it generates one token (roughly equivalent to a word or word piece) at a time. After generating each token, that token becomes part of the input for predicting the next token. This continues until the model produces a special “end” token or reaches a maximum length.
Text Generation Process
Temperature and Sampling Strategies
The model doesn’t always choose the most probable next word—that would make outputs predictable and repetitive. Instead, it uses sampling strategies controlled by parameters like temperature. A higher temperature makes the model more creative and random, while a lower temperature makes it more focused and deterministic.
Other sampling techniques include top-k sampling (choosing from the k most likely tokens) and nucleus sampling (choosing from the smallest set of tokens whose cumulative probability exceeds a threshold). These techniques balance coherence with creativity, allowing models to generate diverse yet sensible outputs.
Understanding Model Parameters and Scale
The term “parameters” appears frequently when discussing LLMs, but what does it really mean? Parameters are the learned weights in the neural network that determine how input information is transformed into output predictions. Every connection between neurons in the network has an associated weight, and these weights are what the model adjusts during training.
The number of parameters is often used as a proxy for model capability, though it’s not the only factor. Larger models generally have more capacity to store knowledge and recognize complex patterns, but they also require more computational resources to train and run.
Parameter counts of notable LLMs:
- GPT-2: 1.5 billion parameters
- GPT-3: 175 billion parameters
- GPT-4: Estimated to be over 1 trillion parameters (exact number not publicly disclosed)
- LLaMA 2 70B: 70 billion parameters
However, bigger isn’t always better for every use case. Smaller models can be more efficient, cheaper to run, and easier to fine-tune for specific tasks. The trend in recent years has been toward finding the optimal balance between model size and performance through techniques like efficient training methods and model compression.
Limitations and Considerations
While large language models are powerful, they have important limitations that users should understand. LLMs don’t truly “understand” language the way humans do—they recognize and manipulate patterns based on statistical correlations in their training data.
Key limitations include:
- Hallucination: Models can generate plausible-sounding but factually incorrect information with confidence
- Knowledge cutoff: Models only know information from their training data and can’t access real-time information without additional tools
- Bias: Training data contains human biases, which models can perpetuate or amplify
- Context window limitations: Models can only process a limited amount of text at once (though this is improving with newer architectures)
- Lack of true reasoning: Despite impressive performance, models don’t reason the way humans do and can fail at tasks requiring logical deduction
These limitations don’t diminish the utility of LLMs, but they inform how we should use them. They’re best viewed as powerful tools that augment human capabilities rather than replacements for human judgment and expertise.
Conclusion
Large language models represent one of the most significant advances in artificial intelligence, built on the foundation of transformer architecture and trained through exposure to vast amounts of text data. Their ability to generate coherent, contextually appropriate text across diverse domains stems from billions of parameters working together to recognize and replicate patterns in language.
Understanding the basics—from transformer architecture and attention mechanisms to the training process and text generation—provides crucial insight into both the capabilities and limitations of these systems. As LLMs continue to evolve and integrate into more aspects of our daily lives, this foundational knowledge becomes increasingly valuable for anyone looking to work with or simply understand this transformative technology.