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  2. GPT-3 - Wikipedia

    en.wikipedia.org/wiki/GPT-3

    GPT-3 is capable of performing zero-shot and few-shot learning (including one-shot). [ 1 ] In June 2022, Almira Osmanovic Thunström wrote that GPT-3 was the primary author on an article on itself, that they had submitted it for publication, [ 24 ] and that it had been pre-published while waiting for completion of its review.

  3. Generative pre-trained transformer - Wikipedia

    en.wikipedia.org/wiki/Generative_pre-trained...

    Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.

  4. List of large language models - Wikipedia

    en.wikipedia.org/wiki/List_of_large_language_models

    The first of a series of free GPT-3 alternatives released by EleutherAI. GPT-Neo outperformed an equivalent-size GPT-3 model on some benchmarks, but was significantly worse than the largest GPT-3. [25] GPT-J: June 2021: EleutherAI: 6 [26] 825 GiB [24] 200 [27] Apache 2.0 GPT-3-style language model Megatron-Turing NLG: October 2021 [28 ...

  5. OpenAI - Wikipedia

    en.wikipedia.org/wiki/OpenAI

    First described in May 2020, Generative Pre-trained [a] Transformer 3 (GPT-3) is an unsupervised transformer language model and the successor to GPT-2. [ 176 ] [ 177 ] [ 178 ] OpenAI stated that the full version of GPT-3 contained 175 billion parameters , [ 178 ] two orders of magnitude larger than the 1.5 billion [ 179 ] in the full version of ...

  6. Prompt engineering - Wikipedia

    en.wikipedia.org/wiki/Prompt_engineering

    The model may output text that appears confident, though the underlying token predictions have low likelihood scores. Large language models like GPT-4 can have accurately calibrated likelihood scores in their token predictions, [45] and so the model output uncertainty can be directly estimated by reading out the token prediction likelihood ...

  7. Large language model - Wikipedia

    en.wikipedia.org/wiki/Large_language_model

    A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation. LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text. The largest and most capable LLMs are generative pretrained transformers (GPTs).

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  9. Generative artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Generative_artificial...

    Generative AI systems trained on words or word tokens include GPT-3, GPT-4, GPT-4o, LaMDA, LLaMA, BLOOM, Gemini and others (see List of large language models). They are capable of natural language processing, machine translation, and natural language generation and can be used as foundation models for other tasks. [62]