LLMs Explained: How Machines Understand and Generate Language
A beginner’s guide to how Large Language Models work, their power, and their limitations.
Introduction
Large Language Models (LLMs) have quickly become one of the most widely discussed innovations in artificial intelligence. Regardless of one’s background in engineering or AI, it’s likely that they have already interacted with an LLM whether through generating code, simplifying legal documents, or interpreting medical reports filled with technical jargon.
These models are increasingly being used to support tasks across domains, offering powerful assistance in writing, research, communication, and more. Yet, a fundamental question remains: Can we trust LLMs?
To begin answering that, we must first understand how LLMs process language and how they manage to represent and respond with such an extensive breadth of knowledge.
What is Large Language Model?
At its core, a Large Language Model (LLM) is an artificial intelligence system trained on an enormous collection of text data; often referred to as a “language corpus.” The word large here is no exaggeration, these models are trained on datasets comprising hundreds of billions (sometimes even trillions) of words gathered from books, websites, articles, and other written sources.
How do LLMs work?
Every Large Language Model (LLM) has its own vocabulary, a set of tokens it understands. These tokens aren’t always full words. For example, a simple word like “unhappiness” might be broken into smaller parts like “un”, “happy”, and “ness”. If a word isn’t in the model’s vocabulary, it gets replaced with a special placeholder like <unk> (unknown).
The first step in processing any input is turning tokens into numbers, specifically vectors. This is done by the embedding matrix, which assigns each token a unique vector that tries to capture its meaning and context.
Once token embeddings are ready, they pass through two main building blocks: attention layers and feed-forward layers (a type of neural network called a multilayer perceptron).
The attention layers figure out which words in the sentence matter most for generating the next word. Think of it as deciding how much each word “pays attention” to the others.
The feed-forward layers then transform that information to help the model make predictions.
These two blocks (attention and feed-forward) are stacked repeatedly, often dozens or even hundreds of times, so the model can learn deeper patterns at each level.
Finally, at the end of this stack, a component called the language modeling head takes the processed information and predicts the next token - completing the loop.
Why are LLMs so powerful?
LLMs are trained using a simple but powerful method: predicting the next word. During training, the model is given a partial sentence and asked to predict the most likely next word. Once predicted, the word is added to the sequence, and the model predicts the next one and so on. This process is repeated billions of times, enabling the model to learn patterns, grammar, facts, and even stylistic nuances of language.
This method of learning is known as autoregressive modeling, meaning the model uses all previous tokens (words or subwords) to generate the next one. Over time, this allows it to generate remarkably coherent and contextually appropriate text as if it “understands” what it’s saying.
Additionally, LLMs are context-aware. Unlike earlier models that could only look at a few words at a time, modern LLMs like GPT-4 can consider hundreds or thousands of previous words (tokens) when generating a response. This allows them to maintain context over long passages, follow instructions, and even simulate reasoning.
Limitations and Challenges
Despite their impressive capabilities, LLMs have important limitations.
No real understanding: LLMs do not truly understand language. They generate text based on patterns, not comprehension.
Hallucinations: They can confidently produce incorrect or misleading information, especially when asked about facts or uncommon topics.
Bias and fairness: Since LLMs learn from internet data, they may reflect harmful biases or stereotypes present in the training material. For example, generative models may sometimes assume that doctors are male and nurses are female.
Resource intensity: Training and running large models requires significant computing power, energy, and memory.
These challenges make it important to use LLMs carefully, especially in critical fields like healthcare, law, or education.
Conclusion
LLMs are transforming how we interact with technology, making language-based tasks faster and more accessible. While their capabilities are remarkable, understanding their inner workings and limitations is key to using them wisely and responsibly.