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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.
LLMs do not follow Wikipedia's policies on verifiability and reliable sourcing. LLMs sometimes exclude citations altogether or cite sources that don't meet Wikipedia's reliability standards (including citing Wikipedia as a source). In some case, they hallucinate citations of non-existent references by making up titles, authors, and URLs.
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.
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.
A language model is a model of natural language. [1] Language models are useful for a variety of tasks, including speech recognition, [2] machine translation, [3] natural language generation (generating more human-like text), optical character recognition, route optimization, [4] handwriting recognition, [5] grammar induction, [6] and information retrieval.
Wikipedia:Large language models, a draft proposal for a Wikipedia guideline on the use of language models; Wikipedia:Artificial intelligence, an essay about the use of artificial intelligence on Wikipedia and Wikimedia projects; Initial version of Artwork title, a surviving article developed from raw LLM output (before this page had been developed)
Vicuna LLM is an omnibus Large Language Model used in AI research. [1] Its methodology is to enable the public at large to contrast and compare the accuracy of LLMs "in the wild" (an example of citizen science) and to vote on their output; a question-and-answer chat format is used.
This page was last edited on 28 January 2025, at 06:42 (UTC).; Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; additional terms may apply.