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Generative grammar is a research tradition in linguistics that aims to explain the cognitive basis of language by formulating and testing explicit models of humans' subconscious grammatical knowledge. Generative linguists, or generativists (/ ˈdʒɛnərətɪvɪsts /), [ 1 ] tend to share certain working assumptions such as the competence ...
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.
Generative AI models can reflect and amplify any cultural bias present in the underlying data. For example, a language model might assume that doctors and judges are male, and that secretaries or nurses are female, if those biases are common in the training data. [107]
Transformational grammar. In linguistics, transformational grammar (TG) or transformational-generative grammar (TGG) is part of the theory of generative grammar, especially of natural languages. It considers grammar to be a system of rules that generate exactly those combinations of words that form grammatical sentences in a given language and ...
Principles and parameters is a framework within generative linguistics in which the syntax of a natural language is described in accordance with general principles (i.e. abstract rules or grammars) and specific parameters (i.e. markers, switches) that for particular languages are either turned on or off. For example, the position of heads in ...
A generative pre-trained transformer (GPT) is a type of large language model (LLM) [1][2][3] and a prominent framework for generative artificial intelligence. [4][5] It is an artificial neural network that is used in natural language processing by machines. [6] It is based on the transformer deep learning architecture, pre-trained on large data ...
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. [3][4][5]
Generative grammar promotes a modular view of the mind, considering language as an autonomous mind module. Thus, language is separated from mathematical logic to the extent that inference cannot explain language acquisition. [13] The generative conception of human cognition is also influential in cognitive psychology and computer science. [14]
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