What is GPT?
GPT (Generative Pre-trained Transformer) is a family of large language models developed by OpenAI that uses the Transformer architecture with self-attention mechanisms to generate human-like text by predicting the next token in a sequence, pre-trained on massive text corpora and fine-tuned for various downstream tasks.
Quick Facts
| Full Name | Generative Pre-trained Transformer |
|---|---|
| Created | 2018 (GPT-1 released by OpenAI) |
| Specification | Official Specification |
How It Works
GPT models represent a paradigm shift in natural language processing, combining unsupervised pre-training on large text datasets with supervised fine-tuning. The evolution from GPT-1 (2018, 117M parameters) to GPT-2 (2019, 1.5B parameters), GPT-3 (2020, 175B parameters), and GPT-4 (2023, multimodal) demonstrates rapid scaling and capability improvements. GPT models use autoregressive language modeling, predicting each token based on all previous tokens. The architecture leverages multi-head self-attention and feed-forward neural networks, enabling the model to capture long-range dependencies and contextual relationships in text. GPT-4 introduced multimodal capabilities, accepting both text and image inputs. GPT-4o (2024) introduced optimized multimodal capabilities with native audio and vision processing. The o1 series (2024) represents a new paradigm of reasoning models that use inference-time compute for complex problem-solving through extended chain-of-thought.
Key Characteristics
- Pre-trained on massive text corpora using unsupervised learning before task-specific fine-tuning
- Autoregressive generation: predicts the next token based on all preceding tokens
- Transformer decoder architecture with multi-head self-attention mechanisms
- Exhibits emergent abilities at scale: in-context learning, chain-of-thought reasoning, instruction following
- Supports few-shot and zero-shot learning without explicit fine-tuning
- Multimodal capabilities in GPT-4: processes both text and image inputs
Common Use Cases
- Conversational AI: ChatGPT for customer support, virtual assistants, and interactive dialogue
- Content generation: article writing, creative writing, marketing copy, and email drafting
- Code generation and assistance: GitHub Copilot, code completion, debugging, and explanation
- Language translation and summarization: multilingual text processing and document summarization
- Education and tutoring: personalized learning, question answering, and concept explanation
Example
Loading code...Frequently Asked Questions
What does GPT stand for?
GPT stands for Generative Pre-trained Transformer. 'Generative' refers to its ability to generate text, 'Pre-trained' means it's trained on large text datasets before fine-tuning, and 'Transformer' is the neural network architecture it uses.
What is the difference between GPT-3 and GPT-4?
GPT-4 is significantly more capable than GPT-3. Key differences include multimodal capabilities (accepting images as input), improved reasoning and accuracy, better at following complex instructions, larger context window, and reduced hallucinations. GPT-4 is estimated to have over 1 trillion parameters.
How does GPT generate text?
GPT uses autoregressive generation - it predicts the next token based on all previous tokens. During inference, it generates text one token at a time, each prediction considering the full context. The process uses probability distributions and sampling strategies like temperature and top-p.
What is ChatGPT vs GPT?
GPT is the underlying language model, while ChatGPT is a conversational interface built on top of GPT (specifically GPT-3.5 or GPT-4). ChatGPT is fine-tuned using RLHF (Reinforcement Learning from Human Feedback) to be more helpful, harmless, and honest in dialogue.
How to use GPT API in Python?
Use the OpenAI Python library: install with 'pip install openai', create a client with your API key, then call client.chat.completions.create() with model name, messages array, and optional parameters like temperature and max_tokens.