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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. Wikipedia : Using neural network language models on Wikipedia

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

    Experienced editors may ask an LLM to improve the grammar, flow, or tone of pre-existing article text. Rather than taking the output and pasting it directly into Wikipedia, you must compare the LLM's suggestions with the original text, and thoroughly review each change for correctness, accuracy, and neutrality. Summarizing a reliable source.

  4. The Knowledge: How to Rebuild Our World from Scratch

    en.wikipedia.org/wiki/The_Knowledge:_How_to...

    The UK paperback was released by Vintage on 5 March 2015 while the US paperback, retitled The Knowledge: How to Rebuild Civilization in the Aftermath of a Cataclysm, was published on 10 March 2015 by Penguin Books. The book is written as a quick-start guide to restarting civilization following a global catastrophe.

  5. AlexNet - Wikipedia

    en.wikipedia.org/wiki/AlexNet

    If one freezes the rest of the model and only finetune the last layer, one can obtain another vision model at cost much less than training one from scratch. AlexNet block diagram AlexNet is a convolutional neural network (CNN) architecture, designed by Alex Krizhevsky in collaboration with Ilya Sutskever and Geoffrey Hinton , who was Krizhevsky ...

  6. Retrieval-augmented generation - Wikipedia

    en.wikipedia.org/wiki/Retrieval-augmented_generation

    Retrieval-augmented generation (RAG) is a technique that grants generative artificial intelligence models information retrieval capabilities. It modifies interactions with a large language model (LLM) so that the model responds to user queries with reference to a specified set of documents, using this information to augment information drawn from its own vast, static training data.

  7. GPT-2 - Wikipedia

    en.wikipedia.org/wiki/GPT-2

    570 GB plaintext, 300 billion tokens of CommonCrawl, WebText, English Wikipedia, and two books corpora (Books1 and Books2). GPT-2 was to be followed by the 175-billion-parameter GPT-3 , [ 39 ] revealed to the public in 2020 [ 40 ] (whose source code has never been made available).

  8. Mamba (deep learning architecture) - Wikipedia

    en.wikipedia.org/wiki/Mamba_(deep_learning...

    Mamba LLM represents a significant potential shift in large language model architecture, offering faster, more efficient, and scalable models [citation needed]. Applications include language translation, content generation, long-form text analysis, audio, and speech processing [citation needed

  9. T5 (language model) - Wikipedia

    en.wikipedia.org/wiki/T5_(language_model)

    T5 (Text-to-Text Transfer Transformer) is a series of large language models developed by Google AI introduced in 2019. [1] [2] Like the original Transformer model, [3] T5 models are encoder-decoder Transformers, where the encoder processes the input text, and the decoder generates the output text.