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In recent years, language models based onneural networks have revolutionized the way computers understand and generate natural language. Among the most important areGPT (Generative Pre-trained Transformer) andBERT (Bidirectional Encoder Representations from Transformers).
Both are based on theTransformer architecture, but are designed with different objectives and operating modes.
In this article we will see:
- What are GPT and BERT
- How they work
- Practical examples of use
- The main differences between the two models
1. What is a language model?
A language model is an artificial intelligence system trained to:
- understand text,
- predict words,
- generate coherent sentences,
- answer questions, or classify content.
These models learn by analyzing huge amounts of text and identifying statistical patterns in the language: which words tend to appear together, in what order, and with what meaning.
2. The Transformer Architecture (In Brief)
Both GPT and BERT are based on Transformers, an architecture introduced in 2017.
The core of the Transformer is the self-attention mechanism, which allows the model to:
- evaluate the importance of each word relative to the others,
- understand the context of a word within a sentence.
Example:
The bank is near the river
The model understands that “bank” refers to the riverbank and not to a financial institution, thanks to the context.
Further reading
If you'd like to learn more, you can read the article The Role of Transformers in Artificial Intelligence
3. GPT: Generative Pre-trained Transformer
What is GPT
GPT is a family of models designed primarily to generate text.
Its main goal is to predict the next word given a sequence of previous words.
In other words, GPT reads the text from left to right (autoregressively).
How GPT Works
- Pre-training
The model is trained on large amounts of text (books, articles, websites) to learn the structure of the language. - Sequential Prediction
Given an incomplete sentence, GPT predicts the most likely next word, and then the next, and so on. go.
Practical Example
Input:
The weather today is very
Possible Output:
Beautiful, and the sun is shining brightly.
GPT is very effective in:
- Conversational chatbots,
- summaries,
- translation,
- code generation.
Strengths of GPT:
- Excellent fluidity and coherence of the generated text and the ability to maintain a narrative style.
4. BERT: Bidirectional Encoder Representations from Transformers
What is BERT
BERT is a model designed primarily to understand text, not to generate it.
Its key feature is that it reads text bidirectionally, simultaneously analyzing the context to the left and right of each word.
How BERT Works
- Masked Language Model (MLM)
During training, some words are masked, and the model must guess which ones they are using the full context. - Deep Context Understanding
This allows BERT to capture very fine semantic nuances.
Example practical
I left my keys on the [MASK]
BERT uses the entire sentence to figure out that the missing word might be:
“table”, “desk”, “shelf”
Typical BERT applications:
- sentiment analysis,
- search engines,
- text classification,
- entity recognition (names, places, dates),
- document-based question answering.
BERT's strengths:
- Extremely accurate semantic understanding.
5. Main differences between GPT and BERT
| Feature | GPT | BERT |
|---|---|---|
| Reading direction | Left → Right | Bidirectional |
| Main objective | Text generation | Text understanding |
| Model type | Decoder | Encoder |
| Prediction | Next Word | Masquerade Words |
| Ideal for | Writing, Chatbots | Analysis, Research |
6. A comparative example
The doctor advised the patient to quit smoking because…
GPT will continue the sentence:
…smoking seriously damages your health.
BERT is better suited to answering questions like:
- Is the text positive or negative?
- Who is the main subject?
- Why does the doctor give this advice?
7. Are GPT and BERT alternatives or complements?
They are not direct competitors, but complementary:
- GPT is ideal when you need to produce language
- BERT is perfect when you need to understand language
Many modern systems combine models of both types to achieve better results.
8. Beyond GPT and BERT: Other Important Language Models
Although GPT and BERT are among the most well-known and widely used models, they are not the only approaches. Over time, other models have been developed that attempt to combine, improve, or specialize their features.
8.1 T5 (Text-To-Text Transfer Transformer)
T5 transforms any NLP task into a text → text problem.
In short: A flexible, generalist model that uses encoders and decoders.
8.2 RoBERTa
RoBERTa is an optimized version of BERT, trained better and longer.
In short: An enhanced BERT.
8.3 ALBERT
ALBERT reduces the number of parameters while maintaining good performance.
In short: A more efficient version of BERT.
8.4 Encoder-Decoder Models (e.g., BART)
BART combines a bidirectional encoder and an autoregressive decoder.
In Brief: It combines the strengths of GPT and BERT.
8.5 Recent Open-Source Models (e.g., LLaMA)
These models aim for high performance and greater accessibility.
In Brief: Powerful and customizable models.
9. Conclusion
GPT and BERT are two fundamental pillars of natural language processing, but they are part of a much larger ecosystem.
UnderstandThese model families allow you to choose the most suitable technology, better interpret the results, and gain a comprehensive view of the modern NLP landscape.
Don't forget → LLM
In common parlance, the term LLM (Large Language Model) is often used to refer to large-scale generative models like GPT.
BERT is also a large language model, but it is designed primarily for text understanding, not generation. For this reason, in modern discussions it is often distinguished from generative LLMs, even though they share the same architectural foundations.
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