Build 10x products in minutes by chatting with AI - beyond just a prototype.
What is the difference between Transformers and LLMs?
What's the difference between LLM and GPT?
Is BERT an LLM?
This blog clearly explains the terms "Transformer" and "LLM" for developers navigating the world of modern language models. It clarifies their distinctions and illustrates their collaborative functionality within Natural Language Processing tasks.
Do terms like "Transformer" and "LLM" confuse you?
Many developers face this when learning about modern language models. Understanding llm vs transformer is key for NLP tasks.
This blog will clear up the confusion. You’ll learn their differences and how they work together. We'll explain when each one matters. This breakdown helps you make informed choices without jargon.
Continue reading for a clear explanation.
The Transformer architecture is a neural network model introduced in the 2017 paper “Attention is All You Need.” It was designed to overcome the limitations of recurrent neural networks and convolutional architectures for natural language processing tasks.
Self-Attention Mechanism: Every word in the input sequence can attend to every other word, regardless of position.
Positional Encoding: Since transformers don't process data sequentially, they need positional encoding to maintain order.
Parallel Processing: Unlike recurrent neural networks, transformers handle sequences simultaneously.
Transformers consist of:
Encoder blocks that read and encode the input text
Decoder blocks that generate the output sequence
Layers of multi-head self-attention, feed-forward networks, layer normalization, and residual connections
A large language model is an AI system trained on vast amounts of text data using architectures like the Transformer. Examples include GPT (Generative Pre-Training Transformer), BERT (Bidirectional Encoder Representations), and T5 (Text-To-Text Transfer Transformer).
Built using transformer architecture
Trained on amounts of text data ranging from billions to trillions of tokens
Uses self-attention layers to capture dependencies between words
Capable of generating human-like text, question answering, sentiment analysis, and code generation
| Component | Description |
|---|---|
| Model Parameters | LLMs often have billions of parameters, allowing them to understand nuanced patterns |
| Pre Trained | These models are initially trained on generic text data and then optionally fine tuned |
| Decoder Only Models | Like GPT, these generate output tokens based only on prior input tokens |
| Bidirectional Models | Like BERT, which use bidirectional encoder representations to understand context |
| Aspect | Transformer | Large Language Model (LLM) |
|---|---|---|
| Definition | A deep learning architecture | A specific AI model trained on text using transformers |
| Purpose | A general-purpose structure for processing input sequence | Specialized for generating text, understanding context, etc. |
| Training | Not a model by itself; needs a defined task | Trained on vast amounts of text data |
| Applications | Used in vision transformers, speech recognition tasks, machine translation | Used for text summarization, question answering, named entity recognition |
| Examples | Encoder-Decoder models like T5 | GPT, BERT, Claude, LLaMA |
Transformers are the blueprint; LLMs are the product. The self-attention mechanism enables LLMs to handle long-range dependencies in language.
In the sentence: “The trophy doesn’t fit in the suitcase because it is too small,”
A large language model understands that “it” refers to “suitcase” through self-attention across the input sequence.
| Task | Role |
|---|---|
| Text Generation | Generative pre trained models like GPT generate coherent and human like text |
| Sentiment Analysis | BERT evaluates tone across the input text |
| Question Answering | LLMs trained with encoder representations from transformers locate and return answers from context |
| Named Entity Recognition | Identifies proper nouns, places, and entities |
| Text Summarization | Reduces content length while retaining meaning |
Like Conformer models, transformer-based models process speech recognition tasks better than recurrent neural networks by capturing long-term dependencies. They improve accuracy in language processing of spoken commands.
The vision transformer adapts transformer architecture for object detection, image classification, and input data segmentation, a major shift from convolutional methods.
| Feature | Transformer | LLM |
|---|---|---|
| Training Data | Requires definition | Uses amounts of text data from books, websites |
| Fine Tuning | Done per task | LLMs can be fine tuned for specific tasks |
| Computational Resources | Moderate | Very high due to large model parameters |
| Training Goals | Encode/decode efficiently | Generate coherent, context-aware human like text |
| Extensive Training | Required only in LLMs | Transformers need less unless integrated into LLMs |
False. LLMs use transformer architecture as their core.
Not always. Only models like BERT use bidirectional encoder representations.
LLMs model probability distributions over words based on training data, not actual comprehension.
Computational Resources: LLMs require massive infrastructure to train and deploy.
Next Word Prediction: They rely on learning the next word in context rather than meaning.
Vanishing Gradient Problem: Solved better in transformers than in recurrent neural networks .
Understanding the difference between a transformer and a large language model helps choose the right tools for natural language processing tasks, computer vision, or speech recognition. Transformers are the foundation; LLMs are the application layer that generates human-like text, answers questions, and assists in language processing at scale. As AI evolves, expect transformer-based models to remain at the center of progress across multiple tasks in both text and vision.
