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A large language model (LLM) is a type of computational model designed for natural language processing tasks such as language generation. As language models, LLMs acquire these abilities by learning statistical relationships from vast amounts of text during a self-supervised and semi-supervised training process. [1]
It is based on the transformer deep learning architecture, pre-trained on large data sets of unlabeled text, and able to generate novel human-like content. [2] [3] As of 2023, most LLMs had these characteristics [7] and are sometimes referred to broadly as GPTs. [8] The first GPT was introduced in 2018 by OpenAI. [9]
The model architecture remains largely unchanged from that of LLaMA-1 models, but 40% more data was used to train the foundational models. [25] The accompanying preprint [25] also mentions a model with 34B parameters that might be released in the future upon satisfying safety targets. Llama 2 includes foundation models and models fine-tuned for ...
A standard Transformer architecture, showing on the left an encoder, and on the right a decoder. Note: it uses the pre-LN convention, which is different from the post-LN convention used in the original 2017 Transformer. A transformer is a deep learning architecture developed by researchers at Google and based on the multi-head attention ...
Similar to Gopher in terms of cost, Chinchilla has 70B parameters and four times as much data. [ 3 ] Chinchilla has an average accuracy of 67.5% on the Measuring Massive Multitask Language Understanding (MMLU) benchmark, which is 7% higher than Gopher's performance. Chinchilla was still in the testing phase as of January 12, 2023.
GPT-J or GPT-J-6B is an open-source large language model (LLM) developed by EleutherAI in 2021. [1] As the name suggests, it is a generative pre-trained transformer model designed to produce human-like text that continues from a prompt. The optional "6B" in the name refers to the fact that it has 6 billion parameters.
BLOOM (language model) BigScience Large Open-science Open-access Multilingual Language Model (BLOOM) [1][2] is a 176-billion-parameter transformer -based autoregressive large language model (LLM). The model, as well as the code base and the data used to train it, are distributed under free licences. [3] BLOOM was trained on approximately 366 ...
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.
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