What is Parameter, Token, and Context in Machine Learning

1. Introduction

Machine learning (ML) models, especially those involved in natural language processing (NLP) and large language models (LLMs) such as GPT, BERT, or Gemini, rely on three fundamental concepts: parameters, tokens, and context.

These three ideas describe how:

A model learns and stores knowledge (parameters),
How it reads and processes text (tokens),
And how it understands meaning and continuity (context).

To understand how modern AI systems operate — how they read, remember, and respond intelligently — one must clearly understand these three foundational elements. This tutorial aims to explain each term in depth, compare them with traditional ML concepts, and finally show how they interact together inside modern deep learning systems.

2. Parameters in Machine Learning

2.1 Concept Overview

A parameter is an internal variable that defines the model’s behavior. Parameters are not provided by the user; they are learned by the model through exposure to data during the training process.

They represent the knowledge the model has gained from its training data. Once training is complete, these parameters are fixed (unless fine-tuned later), and they guide the model’s responses or predictions.

In simple words:

Parameters are the “internal settings” that determine how the model transforms inputs into outputs.

2.2 Parameters in Traditional ML Models

In classical machine learning algorithms like Linear Regression, Logistic Regression, or Support Vector Machines, the number of parameters is small and explicitly defined.

For example, consider a linear regression model:

y=w₁x₁+w₂x₂+⋯+w_nx_n+b

Here:

w₁,w₂,…,w_n are weights (parameters).
b is the bias (also a parameter).

The model learns these parameters by minimizing a loss function, such as mean squared error, using techniques like gradient descent.

Parameters in this context have a very direct interpretation — they represent how much influence each input variable (feature) has on the output.

2.3 Parameters in Neural Networks

In a neural network, parameters take the form of weights and biases across multiple layers.

For example:

Each connection between neurons has a weight.
Each neuron has an associated bias term.

If you have a layer with 512 neurons connected to another layer of 512 neurons, that alone means:

512×512=262,144

weights (plus 512 biases).

Modern neural networks may have millions or billions of such parameters.

For example:

ResNet-50 (a computer vision model): ~25 million parameters.
BERT-base (language model): ~110 million parameters.
GPT-4 or GPT-5 (large language models): estimated hundreds of billions to trillions of parameters.

2.4 What Parameters Actually Do

Each parameter controls how strongly the model activates certain neurons or detects specific features. During training, these parameters are adjusted gradually to minimize the model’s prediction error.

For instance:

In an image model, some parameters learn to detect edges, colors, or textures.
In a text model, parameters learn word relationships, grammar, meaning, tone, and reasoning patterns.

This learning happens through backpropagation, which calculates how much each parameter contributed to the overall error and then updates it accordingly.

2.5 Parameters vs Hyperparameters

A common source of confusion is the difference between parameters and hyperparameters:

Type	Learned by Model?	Example	Description
Parameter	Yes	Weights, biases	Learned from training data
Hyperparameter	No	Learning rate, number of layers, batch size	Set manually before training

Hyperparameters control how the training process works, while parameters are the actual internal values learned during that process.

2.6 Importance of Parameters

Knowledge Representation: Parameters store everything the model has learned about patterns and relationships in data.
Model Capacity: More parameters increase the model’s ability to capture complex patterns.
Trade-off: Too many parameters can cause overfitting (the model memorizes training data). Too few can cause underfitting (it fails to capture patterns).
Generalization: Good parameter optimization helps the model perform well on unseen data, not just training samples.

3. Tokens in Machine Learning

3.1 What is a Token?

A token is the smallest unit of data that a model processes as an input or output. In NLP, this usually means a piece of text, such as a word, part of a word, or even a single character.

Models do not understand text in its raw form (letters or words). Instead, text is tokenized, which means it’s split into pieces (tokens) and converted into numerical IDs before being fed into the model.

3.2 Tokenization Process

Tokenization is the first step in preparing textual data for model processing. The model uses a tokenizer — an algorithm that maps words, subwords, or symbols into tokens.

Example sentence:

“Machine learning is powerful.”

Depending on the tokenizer type:

Tokenization Type	Tokens Generated
Word-level	[“Machine”, “learning”, “is”, “powerful”, “.”]
Subword-level (BPE)	[“Machine”, “learn”, “ing”, “is”, “powerful”, “.”]
Character-level	[“M”, “a”, “c”, “h”, “i”, “n”, “e”, …]

Each token is assigned a token ID (a number). The model converts these IDs into embeddings — high-dimensional vectors that represent the semantic meaning of the tokens.

3.3 Token Embeddings

Each token is represented by a vector of real numbers, often in hundreds or thousands of dimensions.
For example:

“learning” → [0.312, -0.582, 0.041, 0.904, …]

This embedding captures:

Semantic meaning (similar words have similar vectors)
Contextual relationships (meanings change with usage)
Syntactic roles (verbs, nouns, etc.)

In LLMs like GPT, these embeddings are learned as part of the training process, so the model develops an understanding of how words and phrases relate to one another.

3.4 Token Limits in LLMs

Every model has a context window that defines how many tokens it can handle at once.
For example:

GPT-3 → ~4,096 tokens
GPT-4 → up to 128,000 tokens
Claude 3 Opus → around 200,000 tokens

This limit includes both:

Input tokens (the user’s message or text prompt)
Output tokens (the model’s generated response)

Once the token limit is reached, the oldest tokens are usually dropped or truncated to make room for new ones, which can cause the model to “forget” earlier parts of the conversation.

3.5 Importance of Tokens

Foundation of Input: Models cannot process text directly; tokenization bridges the gap between text and numeric computation.
Efficient Representation: Subword tokenization helps handle rare or unknown words (e.g., “bioinformatics” becomes “bio”, “informatics”).
Performance Impact: Longer token sequences increase memory and computation costs.
Precision in Output: Fine-grained tokens allow the model to generate language with detailed control over spelling, phrasing, and grammar.

4. Context in Machine Learning and NLP

4.1 Concept Overview

Context is the surrounding information that gives meaning to the current input or token.
It’s what allows the model to interpret words, phrases, or data correctly depending on the situation.

For example:

The word “bank” could mean a financial institution or a riverbank.
The model uses context — the surrounding words — to determine which meaning applies.

In essence:

Context provides memory and understanding beyond isolated tokens.

4.2 Context in Traditional Machine Learning

In classical ML (like regression or decision trees), “context” might refer to feature dependencies — how one variable influences another. However, it’s much simpler because traditional models usually treat each input independently.

In contrast, modern sequence models (RNNs, Transformers, LSTMs) treat data as ordered, meaning each input depends on the preceding tokens — this dependency is the “context.”

4.3 Context in Large Language Models

In LLMs like GPT, context is the entire sequence of tokens that the model has seen in the current session (conversation or text input).
The model does not have long-term memory; instead, it relies on the context window to “remember” what has been said so far.

So, when you chat with a model:

Each new user message + model reply is added to the conversation history.
That entire history (up to the token limit) becomes the context for generating the next output.

4.4 Why Context is Crucial

Disambiguation: It helps the model resolve ambiguous words (e.g., “bat” could mean animal or sports gear).
Continuity: It allows coherent conversation flow across multiple turns.
Relevance: The model uses context to generate outputs that are relevant to the ongoing discussion.
Reasoning: For complex tasks like summarization, reasoning, or translation, the model must use context to maintain logical consistency.

4.5 Context Window and Memory Trade-off

A context window defines how much text the model can process at once.
If a model has a 128k-token window, it can “see” roughly 300 pages of text at a time.

However:

Once the limit is reached, older tokens fall out of scope.
The model can no longer “remember” them.
This is why long conversations may lose continuity.

Recent research introduces methods like Retrieval-Augmented Generation (RAG) or external memory modules, which allow the model to fetch context dynamically from external sources beyond the token window.

5. How Parameters, Tokens, and Context Work Together

Imagine you’re having a conversation with a language model.

You type your message → it’s tokenized.
The model processes those tokens through billions of parameters (learned weights).
It interprets meaning and relationships using the current context window (previous messages and current input).
Finally, it predicts the most likely next token, repeating this step until a full response is generated.

These three components work in unison:

Concept	Description	Analogy
Parameter	Learned internal values (weights and biases) that represent model knowledge	The brain’s “synapses” storing experience
Token	The input and output units of text, converted to numeric form	The words or letters being read
Context	The surrounding tokens that give meaning to each new token	The sentence or conversation memory

6. Summary

Term	Definition	Role
Parameter	Internal numeric weights learned during model training	Stores knowledge and defines behavior
Token	The smallest unit of data (word, subword, or symbol) used as input/output	Represents text in numeric form
Context	The surrounding tokens or information used to interpret meaning	Enables understanding, continuity, and relevance

Together:

Parameters are the model’s memory.
Tokens are the language it reads and writes.
Context is the understanding of how those tokens fit together over time.