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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. The largest and most capable LLMs are generative pretrained transformers (GPTs).
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. This page lists notable large language models.
seq2seq – Family of machine learning approaches; Perceiver – Variant of Transformer designed for multimodal data; Vision transformer – Machine learning model for vision processing; Large language model – Type of machine learning model; BERT (language model) – Series of language models developed by Google AI
Unlike previous models, BERT is a deeply bidirectional, unsupervised language representation, pre-trained using only a plain text corpus. Context-free models such as word2vec or GloVe generate a single word embedding representation for each word in the vocabulary, whereas BERT takes into account the context for each occurrence of a given word ...
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.
These models differ from an encoder-decoder NMT system in a number of ways: [35]: 1 Generative language models are not trained on the translation task, let alone on a parallel dataset. Instead, they are trained on a language modeling objective, such as predicting the next word in a sequence drawn from a large dataset of text.
For example, GPT-3, and its precursor GPT-2, [11] are auto-regressive neural language models that contain billions of parameters, BigGAN [12] and VQ-VAE [13] which are used for image generation that can have hundreds of millions of parameters, and Jukebox is a very large generative model for musical audio that contains billions of parameters. [14]
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.