What Are Large Language Models in Machine Learning?

What is an LLM (Large Language Model)?

Before understanding what a Large Language Model (LLM) is, we first need to understand what a model means in machine learning.

What is a Model?

A model is a construct or a defined function that takes an input and generates an output (or predicts one).

Example of a mathematical model:

F(x, y) = x² + 7xy + 21

Example in programming logic:

Evaluate X
If X > 40:
   Return(X)
Else:
   Return(X - 10)

Both examples take inputs and produce outputs. In machine learning, models can be of various types, including:

Linear regression models
Neural network models
Decision tree models

What is a Language Model?

A language model is a machine learning model designed to predict or generate words in response to given text inputs.

Daily life example: Autocomplete on your phone or email.
If you type:

“Let’s party on _____”

The model might predict:

Friday (~90% probability)
Weekend (~100% probability)
Monday (~10% probability)

Language models have evolved from predicting a single word to generating entire sentences, paragraphs, or even full documents.

How Are Models Trained?

Machine Learning (ML) is the process of training a model to make predictions and generate outputs.

Without ML, programmers would need to write complex, rule-based code for every possible prediction. With ML, massive datasets—containing billions of tokens and gigabytes of text—are used to train models so they learn correlations and patterns on their own.

The core formula of machine learning is:

ML Algorithm + Training Data = ML Model

So, What Makes an LLM “Large”?

The “L” in LLM stands for Large. In this context, large refers to:

The size of the dataset used for training
The number of parameters in the model (often in the billions)
The computational power required for training

For example, BERT (Bidirectional Encoder Representations from Transformers), developed by Google, is considered a large model due to its massive training dataset and parameter count.

Key Takeaways

A model is a system that turns inputs into outputs.
A language model predicts or generates human language.
Machine learning trains these models using algorithms and data.
LLMs are large-scale language models capable of complex text generation, translation, summarization, and more.

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