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  2. 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.

  3. List of large language models - Wikipedia

    en.wikipedia.org/wiki/List_of_large_language_models

    LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text. This page lists notable large language models. For the training cost column, 1 petaFLOP-day = 1 petaFLOP/sec × 1 day = 8.64E19 FLOP. Also, only the largest model's cost is written.

  4. Prompt engineering - Wikipedia

    en.wikipedia.org/wiki/Prompt_engineering

    For example, a prompt may include a few examples for a model to learn from, such as asking the model to complete "maison → house, chat → cat, chien →" (the expected response being dog), [23] an approach called few-shot learning. [24] In-context learning is an emergent ability [25] of large language models.

  5. Wikipedia:Large language models - Wikipedia

    en.wikipedia.org/wiki/Wikipedia:Large_language...

    LLMs are pattern completion programs: They generate text by outputting the words most likely to come after the previous ones. They learn these patterns from their training data, which includes a wide variety of content from the Internet and elsewhere, including works of fiction, low-effort forum posts, unstructured and low-quality content for ...

  6. Language model - Wikipedia

    en.wikipedia.org/wiki/Language_model

    A language model is a probabilistic model of a natural language. [1] In 1980, the first significant statistical language model was proposed, and during the decade IBM performed ‘Shannon-style’ experiments, in which potential sources for language modeling improvement were identified by observing and analyzing the performance of human subjects in predicting or correcting text.

  7. GPT-2 - Wikipedia

    en.wikipedia.org/wiki/GPT-2

    Since the transformer architecture enabled massive parallelization, GPT models could be trained on larger corpora than previous NLP (natural language processing) models.. While the GPT-1 model demonstrated that the approach was viable, GPT-2 would further explore the emergent properties of networks trained on extremely large corpo

  8. BERT (language model) - Wikipedia

    en.wikipedia.org/wiki/BERT_(language_model)

    Bidirectional encoder representations from transformers (BERT) is a language model introduced in October 2018 by researchers at Google. [1] [2] It learns to represent text as a sequence of vectors using self-supervised learning.

  9. Zero-shot learning - Wikipedia

    en.wikipedia.org/wiki/Zero-shot_learning

    The name is a play on words based on the earlier concept of one-shot learning, in which classification can be learned from only one, or a few, examples. Zero-shot methods generally work by associating observed and non-observed classes through some form of auxiliary information, which encodes observable distinguishing properties of objects. [1]