Meanings and Vectors: How Language Models Work

If you’ve ever used Excel, you’re already halfway to understanding how language models like GPT or DeepSeek work. These models are like hyper-advanced Excel spreadsheets: they turn words into numbers, spot patterns, and generate human-like text. In this guide, we’ll break it down using simple analogies—no math, no jargon.

1. Tokenization: Breaking Text into Rows

Imagine typing the sentence “I love cats.” into an Excel sheet. To analyze it, you’d split the sentence into separate cells—one for each word or punctuation mark.

Language models do something similar through a process called tokenization, which breaks text into bite-sized pieces called tokens.

Row	Token
1	I
2	love
3	cats
4	.

2. Embedding: Turning Words into “Meaning Arrows”

Next, we need to turn these tokens into something a computer understands: numbers. But these aren’t random numbers—they’re vectors, secret codes that capture each word’s meaning.

2.1 What’s a Vector? Think of a Compass!

In physics, a vector is like a direction on a compass: it tells you which way and how far to go (e.g., “walk five steps north”).

In language models, vectors point through meaning space instead of physical space.

Magnitude: How “strong” or distinct a word’s meaning is.
Direction: How a word relates to others. (For example, “king” and “queen” point in similar directions because they share concepts like royalty.)

2.2 Example: Excel for Meanings

Imagine assigning meaning arrows to each token. In Excel terms, each row (token) gets a list of numbers—coordinates in meaning space.

Token	Dimension 1	Dimension 2	Dimension 3
I	0.12	-0.34	0.56
love	0.45	0.67	-0.78
cats	-0.23	0.89	0.12
.	0.34	-0.56	0.78

2.3 Real-World Models: Hundreds (or Thousands!) of Dimensions

While we’re using just three dimensions to keep things simple, real models like BERT or GPT-3 use vectors with hundreds or thousands of dimensions.

Model	Vector Dimensions	What It Means
BERT Base	768	Balances detail and efficiency
GPT-3	12,288 (in layers)	Captures subtle distinctions
Tiny Models	128	Good for simple tasks on phones

These high-dimensional vectors allow the model to capture incredibly fine-grained meanings.
For example, “apple” could mean fruit, tech company, or color red, and the model can distinguish which one you mean.

3. Synonyms, Antonyms, and Meaning Closeness: The Secret Language of Vectors

How does the model know “happy” and “joyful” are synonyms, while “happy” and “sad” are opposites? It’s all in the vectors.

3.1 Synonyms: Neighbors in Meaning Space

Think of two towns on a map: Happyville and Joyburg.
If they’re close together, they’re probably similar.
Synonyms have vectors that point in nearly the same direction.

Token	Dimension 1	Dimension 2
happy	0.8	-0.3
joyful	0.7	-0.2

3.2 Antonyms: Opposite Directions

Antonyms like “happy” and “sad” aren’t just far apart—they often point in opposite directions.

Token	Dimension 1	Dimension 2
happy	0.8	-0.3
sad	-0.7	0.4

3.3 Related Words: Distant Cousins

Words like “dog” and “bone” aren’t synonyms or antonyms, but they’re related by context.
Their vectors aren’t close or opposite—they share some meaning coordinates.

Token	Dimension 1	Dimension 2
dog	0.2	0.9
bone	0.4	0.8

4. Clustering: How Words Find Their Families

As training progresses, words with similar meanings cluster together in meaning space.

4.1 Example: Clustering in Action

Let’s say we start with these tokens:

Token	Dimension 1	Dimension 2	Dimension 3
cat	0.1	-0.2	0.3
dog	0.4	0.5	-0.6
pet	-0.7	0.8	0.9

After training, the vectors might shift closer together:

Token	Dimension 1	Dimension 2	Dimension 3
cat	0.15	-0.18	0.28
dog	0.16	-0.17	0.29
pet	0.14	-0.16	0.27

4.2 What Clustering Teaches the Model

Synonyms: Words like “cat” and “kitten” have vectors that are nearly identical.
Related Concepts: “Cat” and “pet” are close, but not identical.
Unrelated Words: “Cat” and “spaceship” are far apart.

4.3 Why Clustering Happens

The model is trained to predict the next word or fill in the blank.
If it sees “I love my ___,” it knows “cat” and “dog” are likely because their vectors cluster in similar contexts.

5. Attention Layers: Connecting the Dots

Once words are vectors, the model needs to connect them.
This is where attention layers come in.
They help the model focus on which words relate to each other in a sentence.

In “I love cats”, the model might focus on “I” and “love” to understand the emotion.
It links “love” and “cats” to identify the object of affection.

Think of attention layers as Excel’s “highlight cells with patterns” feature.

6. Training: Learning from Trial and Error

At first, the vectors are random.
But during training, the model adjusts them—like a student learning from mistakes.

If it predicts “I love dogs” instead of “cats”, it tweaks the vectors.
If it sees “cats” and “kittens” in similar sentences, their vectors move closer together.

The more it trains, the better it gets at mapping meaning.

7. Generating Text: From Numbers Back to Words

Once trained, the model generates text by:

Converting your input (e.g., “I love”) into vectors.
Using attention layers to spot patterns (e.g., “I love” often leads to pets or hobbies).
Picking the most likely next word (e.g., “cats”) and repeating the process.

Conclusion: Language Models Are Digital Storytellers

Think of language models as Excel workbooks that can talk. They:

Break sentences into rows (tokens),
Assign meaning arrows (vectors),
And connect the dots through attention layers.

The magic happens during training, as the model turns randomness into understanding—one number at a time.

Whether you’re a writer, teacher, or just curious, this analogy demystifies the “black box” of AI. Next time you chat with GPT, imagine it shuffling Excel rows and fine-tuning those compass arrows behind the scenes!