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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).
It is a good idea, if you are producing a large amount of text, to use a search engine for snippets, on the off-chance that the model has coincidentally duplicated previously-published material. Apart from the a possibility that saving an LLM output may cause verbatim non-free content to be carried over to the article, these models can produce ...
the article about bibliographic databases for information about databases giving bibliographic information about finding books and journal articles. Note that "free" or "subscription" can refer both to the availability of the database or of the journal articles included. This has been indicated as precisely as possible in the lists below.
A word n-gram language model is a purely statistical model of language. It has been superseded by recurrent neural network–based models, which have been superseded by large language models. [12] It is based on an assumption that the probability of the next word in a sequence depends only on a fixed size window of previous words.
It is notable for its dramatic improvement over previous state-of-the-art models, and as an early example of a large language model. As of 2020, BERT is a ubiquitous baseline in natural language processing (NLP) experiments. [3] BERT is trained by masked token prediction and next sentence prediction.
The authors continue to maintain their concerns about the dangers of chatbots based on large language models, such as GPT-4. [ 15 ] Stochastic parrot is now a neologism used by AI skeptics to refer to machines' lack of understanding of the meaning of their outputs and is sometimes interpreted as a "slur against AI". [ 6 ]
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.
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 ]