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Natural language generation (NLG) is a software process that produces natural language output. A widely-cited survey of NLG methods describes NLG as "the subfield of artificial intelligence and computational linguistics that is concerned with the construction of computer systems than can produce understandable texts in English or other human languages from some underlying non-linguistic ...
They said that GPT-4 could also read, analyze or generate up to 25,000 words of text, and write code in all major programming languages. [195] Observers reported that the iteration of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based iteration, with the caveat that GPT-4 retained some of the problems with earlier revisions. [196]
t. e. Generative Pre-trained Transformer 2 ( GPT-2) is a large language model by OpenAI and the second in their foundational series of GPT models. GPT-2 was pre-trained on a dataset of 8 million web pages. [2] It was partially released in February 2019, followed by full release of the 1.5-billion-parameter model on November 5, 2019.
History of machine translation. Machine translation is a sub-field of computational linguistics that investigates the use of software to translate text or speech from one natural language to another. In the 1950s, machine translation became a reality in research, although references to the subject can be found as early as the 17th century.
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In artificial intelligence, researchers can induce the evolution of language in multi-agent systems when sufficiently capable AI agents have an incentive to cooperate on a task and the ability to exchange a set of symbols capable of serving as tokens in a generated language. Such languages can be evolved starting from a natural (human) language ...
Word2vec was created, patented, and published in 2013 by a team of researchers led by Mikolov at Google over two papers. [1] [2] Other researchers helped analyse and explain the algorithm. [4] Embedding vectors created using the Word2vec algorithm have some advantages compared to earlier algorithms [1] [ further explanation needed ] such as ...
Data sets include BookCorpus, Wikipedia, and others (see List of text corpora). Code. In addition to natural language text, large language models can be trained on programming language text, allowing them to generate source code for new computer programs. Examples include OpenAI Codex. Images
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