GPT
In plain wordsShort for 'generative pre-trained transformer', the family of OpenAI models behind ChatGPT. The letters are also used loosely for any similar chatbot model.
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OpenAI modelstrademark
A series of large language modelslarge language model, n.: A computer program trained on enormous amounts of text to predict the words that come next. At vast scale, that simple skill lets it chat, answer questions, translate, summarize and write code. developed by OpenAI, each a decoder-only transformerdecoder-only transformer, n.: The kind of transformer behind most chatbots: a single stack that reads text left to right and continues it, without a separate part that first encodes an input. pretrainedpretraining, n.: The first and usually the most expensive stage of building a modern AI model, in which it learns general patterns from enormous amounts of raw text, images or code before being specialized for particular uses. by next-token predictionnext-token prediction, n.: The simple task most AI chatbots were trained on: given some text, guess the next small piece of it. Repeating that guess, piece by piece, produces whole answers. on large text corpora: GPT (2018, 117 million parameters), GPT-2GPT-2, prop. n.: A 2019 OpenAI language model that wrote surprisingly fluent paragraphs. OpenAI at first withheld the full version, saying it could be misused for fake news, which made it famous. (2019, 1.5 billion), GPT-3GPT-3, prop. n.: A huge 2020 OpenAI language model that could perform new tasks simply from instructions or a few examples written into its prompt, without retraining. It made prompting a new way of programming. (2020, 175 billion) and GPT-4GPT-4, prop. n.: OpenAI's 2023 language model, a big step up from the one behind the first ChatGPT. It scored well on many professional and academic exams and could also read images. (2023, multimodal, size undisclosed), followed by GPT-4o (2024), GPT-4.1 and GPT-5 (2025) and later versions. GPT-3.5 models powered the original ChatGPTChatGPT, prop. n.: OpenAI's AI chatbot, released free to the public in November 2022, which answers questions, writes and converses in everyday language. Its sudden popularity began the current AI boom. (November 2022). Successive releases showed that scaling one basic recipe produced qualitatively new abilities, such as GPT-3's few-shot learningin-context learning, n.: A language model's ability to pick up a new task from instructions or examples placed in its input, on the spot, without any retraining — like a quick study learning a game's rules from one round..
The paper compares GPT-2 and GPT-3 checkpoints on the same probe.
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Genericinformal
Any generative pretrained transformertransformer, n.: The design behind today's chatbots and much of modern AI. It handles text by letting every word look at every other word and decide which of them matter for understanding it. language modellanguage model, n.: A system that has learned how likely different sequences of words are, so it can predict the next word in a text and, by doing that repeatedly, write text of its own. or chatbot built on one, whoever made it, as in 'a GPT for legal research'. Developers of other model families generally avoid the usage for their own products.
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OpenAI producttrademark
A custom version of ChatGPT configured with instructions, files and tools, a feature OpenAI introduced as 'GPTs' in November 2023 and distributed through its GPT Store from January 2024.
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Economics
General-purpose technology: in the sense set out by the economists Timothy Bresnahan and Manuel Trajtenberg (1995), a technology that becomes pervasive across the economy, keeps improving and spawns complementary innovations; electricity, the steam engine and computing are standard examples. The 2023 paper 'GPTs are GPTs' by Tyna Eloundou and colleagues argued that LLMs qualify.
Origin
OpenAI's June 2018 report 'Improving Language Understanding by Generative Pre-Training', by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever, introduced the model; the acronym was in use by October 2018, when Google's BERT paper compared against 'OpenAI GPT'. 'General purpose technology' in economics dates from Timothy Bresnahan and Manuel Trajtenberg (1995).
Usage note
The US Patent and Trademark Office refused OpenAI's application to register 'GPT' as merely descriptive (February 2024) and, in May 2025, as likely generic. Not every chatbot is a GPT model: Claude, Gemini and Llama are separate families from other developers.
Related
- ChatGPTChatGPT, prop. n.: OpenAI's AI chatbot, released free to the public in November 2022, which answers questions, writes and converses in everyday language. Its sudden popularity began the current AI boom.
- GPT-2GPT-2, prop. n.: A 2019 OpenAI language model that wrote surprisingly fluent paragraphs. OpenAI at first withheld the full version, saying it could be misused for fake news, which made it famous.
- GPT-3GPT-3, prop. n.: A huge 2020 OpenAI language model that could perform new tasks simply from instructions or a few examples written into its prompt, without retraining. It made prompting a new way of programming.
- GPT-4GPT-4, prop. n.: OpenAI's 2023 language model, a big step up from the one behind the first ChatGPT. It scored well on many professional and academic exams and could also read images.
- decoder-only transformerdecoder-only transformer, n.: The kind of transformer behind most chatbots: a single stack that reads text left to right and continues it, without a separate part that first encodes an input.
- large language modellarge language model, n.: A computer program trained on enormous amounts of text to predict the words that come next. At vast scale, that simple skill lets it chat, answer questions, translate, summarize and write code.
- BERTBERT, prop. n.: An influential 2018 Google language model that learned to understand text by filling in hidden words. It made search engines and many language tools much better at grasping meaning in context.
- BERTBERT, prop. n.: An influential 2018 Google language model that learned to understand text by filling in hidden words. It made search engines and many language tools much better at grasping meaning in context.
Sources
- Radford, A., Narasimhan, K., Salimans, T. & Sutskever, I. (2018). Improving Language Understanding by Generative Pre-Training. OpenAI technical report.
- Eloundou, T., Manning, S., Mishkin, P. & Rock, D. (2023). GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models. arXiv; Science 2024.
Mentioned in
OpenAIOpenAI, prop. n.: The American company that makes ChatGPT and the GPT models. It began in 2015 as a nonprofit research lab and is now a for-profit company overseen by a nonprofit foundation., transfer learningtransfer learning, n.: Reusing what a model learned on one task to get a head start on another — like a pianist picking up the organ faster than a beginner. It lets models learn new jobs from little data., transformertransformer, n.: The design behind today's chatbots and much of modern AI. It handles text by letting every word look at every other word and decide which of them matter for understanding it., Alec RadfordAlec Radford, prop. n.: An American AI researcher who led OpenAI's first GPT language models and its CLIP and Whisper systems, key steps toward today's chatbots and multimodal AI., Attention Is All You NeedAttention Is All You Need, prop. n.: The 2017 Google research paper that introduced the transformer, the neural-network design behind ChatGPT, Claude, Gemini and nearly every other modern AI language system., autoregressive modelautoregressive model, n.: A model that produces a sequence one piece at a time, each new piece depending on the pieces already produced — like writing a sentence word by word without going back., BERTBERT, prop. n.: An influential 2018 Google language model that learned to understand text by filling in hidden words. It made search engines and many language tools much better at grasping meaning in context., causal language modelcausal language model, n.: A language model that reads text only from left to right, predicting each word from the words before it. The models behind today's chatbots are of this kind., decoder-only transformerdecoder-only transformer, n.: The kind of transformer behind most chatbots: a single stack that reads text left to right and continues it, without a separate part that first encodes an input., GELUGELU, abbr.: A smooth version of the ReLU rule, used in models such as BERT and GPT: it keeps large positive values, removes large negative ones, and blends softly in between., GPT-2GPT-2, prop. n.: A 2019 OpenAI language model that wrote surprisingly fluent paragraphs. OpenAI at first withheld the full version, saying it could be misused for fake news, which made it famous., Ilya SutskeverIlya Sutskever, prop. n.: A Russian-born Israeli-Canadian computer scientist who co-built the AlexNet image recognizer, helped found OpenAI and lead its research until 2024, and now heads the startup Safe Superintelligence Inc., self-supervised learningself-supervised learning, n.: A way for AI to learn from huge amounts of unlabeled data by setting itself fill-in-the-blank puzzles, such as guessing a hidden word or a missing patch of an image., Ashish VaswaniAshish Vaswani, prop. n.: An Indian-born computer scientist listed first on 'Attention Is All You Need', the 2017 paper that introduced the transformer, the design behind today's language models., BookCorpusBookCorpus, prop. n.: A collection of about 11,000 free self-published novels gathered from the web in 2015, used to train the first GPT model and BERT despite problems with consent and duplicates found later., ELMoELMo, prop. n.: A 2018 model whose word representations changed with context, so 'bank' got different numbers in 'river bank' and 'bank loan'. It was a stepping stone between fixed word vectors and BERT., T5T5, prop. n.: A 2019 Google model that treats every language task — translating, summarizing, answering, classifying — as turning one piece of text into another, using one shared design.