GPT stands for Generative Pre-trained Transformer. It is a family of language models developed by OpenAI that use deep learning techniques, specifically the Transformer architecture, to generate human-like text based on input prompts.
GPT models are trained in two main phases:
- Pre-training: Learning from vast amounts of publicly available text to predict the next word in a sentence.
- Fine-tuning (optional): Adapting the model to specific tasks or domains with additional targeted data and supervision.
Core Concepts
Transformer Architecture
The backbone of GPT, introduced in the paper “Attention is All You Need” (2017).
Key elements:
- Self-attention mechanism: Helps the model focus on different parts of input text when making predictions.
- Layered structure: Multiple attention layers stacked to build understanding from simple to complex patterns.
Unidirectional (Causal) Attention
GPT uses attention in a way that each word can only “see” previous words during training, ensuring natural left-to-right text generation.
Tokenization
Text is broken into smaller units called tokens, which are processed by the model.
Example: "ChatGPT" might be tokenized into "Chat", "G", "PT".
Typical Applications
- Text generation: Writing essays, articles, summaries.
- Conversation agents: Chatbots, customer support, virtual assistants.
- Code generation: Programming help (e.g., GitHub Copilot).
- Translation: Translating between languages.
- Search and retrieval: Semantic search, question answering.
Limitations
- Hallucination: May generate text that sounds correct but is factually wrong.
- Bias: Can inherit biases present in training data.
- Resource-intensive: Requires significant computational resources for training and serving.
- Lack of true reasoning or understanding: Mimics reasoning but does not “think” or “understand” like a human.