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Related models and techniques are, among others, latent semantic indexing, independent component analysis, probabilistic latent semantic indexing, non-negative matrix factorization, and Gamma-Poisson distribution. The LDA model is highly modular and can therefore be easily extended. The main field of interest is modeling relations between topics.
The Phi series of small language models were trained on textbook-like data generated by large language models, for which data is only limited by amount of compute available. [ 21 ] Chinchilla optimality was defined as "optimal for training compute", whereas in actual production-quality models, there will be a lot of inference after training is ...
Performance of bigger models on various tasks, when plotted on a log-log scale, appears as a linear extrapolation of performance achieved by smaller models. However, this linearity may be punctuated by " break(s) " [ 97 ] in the scaling law, where the slope of the line changes abruptly, and where larger models acquire "emergent abilities".
Natural language processing (NLP) is a subfield of computer science and especially artificial intelligence.It is primarily concerned with providing computers with the ability to process data encoded in natural language and is thus closely related to information retrieval, knowledge representation and computational linguistics, a subfield of linguistics.
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 It has been superseded by recurrent neural network–based models, which have been superseded by large language models.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.
Foundation models are built by optimizing a training objective(s), which is a mathematical function that determines how model parameters are updated based on model predictions on training data. [34] Language models are often trained with a next-tokens prediction objective, which refers to the extent at which the model is able to predict the ...
Artificial intelligence (AI), in its broadest sense, is intelligence exhibited by machines, particularly computer systems.It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. [1]
Based on these RNN-based architectures, Baidu launched the "first large-scale NMT system" [23]: 144 in 2015, followed by Google Neural Machine Translation in 2016. [ 23 ] : 144 [ 24 ] From that year on, neural models also became the prevailing choice in the main machine translation conference Workshop on Statistical Machine Translation.