Let’s extend our previous analogy from this https://eolais.cloud/index.php/2025/11/05/llm-nlp-fundamental/
- NLP is the entire field of “Automotive Engineering.”
- Neural Networks are the “Internal Combustion Engine” technology a foundational way to build powertrains.
- LLMs are a specific, highly advanced type of engine, like a “Turbocharged V8 Engine” that is built using internal combustion principles.
In technical terms: LLMs are a type of Neural Network, and Neural Networks are a key technique used in NLP.
Detailed Breakdown
What is a Neural Network (NN)?
A Neural Network is a computing system vaguely inspired by the biological neural networks in animal brains. It’s composed of interconnected layers of nodes (“neurons”) that process information.
Key Idea: A neural network learns to map inputs to outputs by adjusting the “weights” of the connections between its neurons based on the data it’s trained on.
Simple Analogy: Imagine teaching a child to recognize dogs. You show them many pictures (data). Their brain strengthens the connections for “furry,” “four legs,” “wet nose” and weakens connections for “has wheels,” “feathers.” A neural network does the same thing mathematically.
The Hierarchical Relationship
The relationship between these three concepts is best visualized as a set of nesting dolls or a pyramid:
NLP (The Field)
↓
Uses various techniques, including…
→ Machine Learning / Deep Learning
↓
Which primarily uses…
→ Neural Networks (The Architecture)
↓
A specific, powerful type of neural network is the…
→ Transformer Network (The Model Architecture)
↓
A very large Transformer trained on text is a…
→ Large Language Model (LLM) (The Specific Tool)
How They Fit Together: A Technical Evolution
- NLP uses Neural Networks:
For decades, NLP relied on simpler, rule-based systems. The field was revolutionized when researchers started using Neural Networks because they could learn complex patterns in language directly from data, instead of needing humans to write all the rules. - A Specific Neural Network Architecture for Language: The Transformer
In 2017, a groundbreaking paper from Google introduced the Transformer architecture. This was a specific design for a neural network that was exceptionally good at handling sequences (like sentences) by using a mechanism called “attention.” The “attention” mechanism allows the model to weigh the importance of different words in a sentence when processing it.- Think of it like this: When reading “The cat sat on the mat because it was tired,” a Transformer learns that “it” refers to “cat” and not “mat.” It pays attention to the right words.
- The Birth of the LLM:
Researchers discovered that if you take the Transformer architecture and train it on a massive scale (using huge amounts of text data and vast computing power), it develops a remarkable, general-purpose understanding of language. These scaled-up models became known as Large Language Models (LLMs) like GPT-4, PaLM, and Llama.
Summary of the Relationship
| Concept | Role in the Stack | Simple Analogy |
|---|---|---|
| Neural Network | The Foundational Architecture | The “Internal Combustion Engine” – a powerful way to build learning machines. |
| Transformer | A Specific Blueprint for a neural network that is exceptionally good with language. | The blueprint for a “V8 Engine” design. |
| LLM | A concrete instance of a Transformer model that has been built at a massive scale. | A specific, fully-built “Turbocharged V8 Engine.” |
| NLP | The Overall Field & Goal that uses all of the above as tools. | The field of “Automotive Engineering.” |
Key Takeaway
You can think of it as a specialization chain:
- General AI Technique: Neural Networks
- Specialized for Sequences: Transformer Architecture
- Scaled Up with Data: Large Language Model (LLM)
- Applied to a Field: Natural Language Processing (NLP)
So, when you interact with ChatGPT, you are using an LLM (like GPT-4), which is a type of Neural Network (specifically a Transformer), to solve problems in the field of NLP.
