Today, Large Language Models (LLMs) are at the forefront of the most extensive progress in artificial intelligence (AI). These models do an almost uncanny job of not just understanding or generating natural human language but also manipulating it at a high level. But how exactly do they work? What allows a computer to write a poem, describe complex subjects or create computer code? Let’s look under the hood of LLMs, from the fundamentals of machine learning all the way up to the deep neural networks that make them hum.
Understanding Large Language Models (LLMs)
At their core, LLMs are a type of AI designed to process and generate human language. They’re trained on vast datasets—often comprising billions of words—so they can learn the statistical relationships between words, sentences, and paragraphs. This allows them to produce coherent, contextually relevant text when given a prompt.
LLMs and Natural Language Processing (NLP)
Large language models are a category of a type of artificial intelligence called natural language processing (NLP) that works to teach machines how to find meaning in and generate human language. NLP fuses linguistics with computer science and AI to build systems that can read, speak and even “understand” natural language.
LLMs are the most recent iteration of the NLP evolution, with unmatched fluency and versatility driven by their scale and training methods.
The Role of Machine Learning in LLMs
Machine learning (ML) is the foundation of LLMs. It refers to algorithms that learn from data to make predictions or decisions without being explicitly programmed. In the case of LLMs, ML is used to recognize patterns in massive text datasets.
Rather than learning language through grammar rules, an LLM learns by example. It sees millions or billions of sentence structures, word combinations, and meanings. Over time, it builds an internal model of how language works.
Deep Learning and Neural Networks
LLMs rely on a specific type of machine learning called deep learning. Deep learning uses artificial neural networks inspired by the human brain. These networks are composed of layers of interconnected “neurons” that process information.
When training an LLM, input text passes through multiple layers of a neural network. Each layer extracts more complex features from the data. The deeper the network, the more sophisticated the model becomes at interpreting language.
Language Model Training – How It Works
Training a language model involves feeding it enormous amounts of text data. This data may include books, articles, websites, code repositories, and other text sources. The model learns by predicting the next word in a sentence over and over again.
For example, if the training sentence is “The dog chased the…,” the model might learn that “cat,” “ball,” or “rabbit” are likely the next words. Each time it guesses correctly, its internal weights are updated to reinforce that learning. This prediction process happens billions of times during training.
The training requires powerful hardware—typically graphics processing units (GPUs) or tensor processing units (TPUs)—and can take days or even weeks to complete.
Fine-Tuning and Specialization
After initial training, LLMs can be fine-tuned for specific tasks. Fine-tuning involves continuing the training process on a smaller, targeted dataset. For instance, a model trained broadly on internet text can be fine-tuned to specialize in legal, medical, or technical language.
This tuning phase helps align the model with specific goals, such as customer service, coding assistance, or scientific research.
Text Generation and Contextual Understanding
Once trained, an LLM can generate human-like text from scratch. Given a prompt like “Write a story about a robot on Mars,” the model uses probability to choose the next word, then the next, and so on, until a full response is created.
What sets modern LLMs apart is their ability to understand context. They don’t just predict the next word based on the last two or three—they consider the entire prompt and sometimes even longer conversation history. This is enabled by transformer architecture, which allows models to weigh different parts of a sentence or paragraph for meaning.
Transformers and Self-Attention Mechanism
Transformers are the backbone of most modern LLMs. They use a technique called self-attention to determine which parts of a sentence or input are most relevant.
For instance, in the sentence “The cat that chased the mouse was fast,” a transformer can understand that “was fast” refers to “the cat,” not “the mouse.” This contextual sensitivity is a major reason LLMs produce coherent and accurate responses.
Limitations and Challenges of LLMs
While impressive, large language models are not without flaws. One of their biggest challenges is hallucination—producing incorrect or fictional information that sounds plausible. Since they generate text based on probability, they don’t verify facts or cross-check sources.
Moreover, the output of an LLM is only as reliable as the data it was trained on. If the training data includes biased, outdated, or incorrect content, the model may reproduce those errors.
Another concern is data privacy. LLMs can inadvertently memorize and regurgitate sensitive information seen during training if not properly filtered.