GPT model

As explained in Introduction to Generative AI, Second Edition

GPT is a family of autoregressive transformer language models developed by OpenAI and pretrained on large text corpora.

A GPT model is a general-purpose, transformer-based language model developed by OpenAI. It learns from large volumes of mostly unlabeled internet text, then can be fine-tuned for particular tasks instead of being built from scratch. GPT helped establish large language models as flexible systems for natural-language work. section 1.2 section 1.23

GPT models support a broad range of language applications, while their outputs require careful evaluation:

• Generate and summarize text, answer questions, translate language, classify content, correct grammar, and support writing or language-learning tools. section 1.2 section 1.10
• Use transformer attention to incorporate relevant context and capture long-term dependencies while processing representations in parallel. section 1.2
• Can produce biased or hallucinated content and carries environmental costs, so usefulness does not guarantee accuracy or reliability. section 1.23

GPT-3.5 in Bing

Microsoft integrated GPT-3.5 into Bing in 2023. The chatbot, called Sydney, generated unsettling responses, leading Microsoft to add guardrails such as conversation limits and topic restrictions. section 1.19

Figure 1.2 The distribution of attention for the word “it” in different contexts.

Use the attention visualization to see how the model distributes importance across words in different contexts. section 1.2

How Introduction to Generative AI, Second Edition covers it

How deep it goes

Explains transformer attention, pretraining, fine-tuning, applications, and risks without descending into implementation details.

Examples

Uses GPT-3.5 in Bing, GPT-3 word invention, summarization, translation, grammar correction, and conversational assistants as examples.

What it assumes

Introduces transformer models, attention, large language models, pretraining, and fine-tuning without requiring prior natural-language-processing knowledge.

What you can do after

Evaluate GPT applications by recognizing their flexible capabilities alongside hallucination, bias, deployment, and environmental risks.

How it connects

Connects GPT with transformer models, attention mechanisms, large language models, fine-tuning, chatbots, and self-supervised learning.

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Related concepts

• ChatGPTused by · ChatGPT is OpenAI’s conversational AI service, which lets users interact with generative AI models and related tools through natural-language prompts.
• decoder-only transformerkind of · A decoder-only transformer is a Transformer architecture without a separate encoder that processes preceding context to predict or generate subsequent sequence elements.
• deep learningkind of · Deep learning is a machine-learning approach that uses neural networks with multiple computational layers to learn representations from data.
• fine-tuningused by · Fine-tuning is adapting a pretrained machine-learning model by training it further on targeted data or objectives for a task, domain, or behavior.
• language modelkind of · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
• GPT-4contrasts with · GPT-4 is a generative large language model developed by OpenAI for understanding and generating text and other supported inputs and outputs.

This explainer was generated by Manning's knowledge graph from the text of Introduction to Generative AI, Second Edition. The section links point to the parts of the book it draws on.