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For many years, sequence modelling and generation was done by using plain recurrent neural networks (RNNs). A well-cited early example was the Elman network (1990). In theory, the information from one token can propagate arbitrarily far down the sequence, but in practice the vanishing-gradient problem leaves the model's state at the end of a long sentence without precise, extractable ...
A foundation model, also known as large AI model, is a machine learning or deep learning model that is trained on a broad dataset so it can be applied across a wide range of use cases. [1] Generative AI applications like Large Language Models are often foundation models.
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.
The big model is used as a "Teacher" model to impart its knowledge and power to smaller ‘Student’ models — a process widely used in the field of generative AI.
BERT (language model) Bidirectional encoder representations from transformers (BERT) is a language model introduced in October 2018 by researchers at Google. [ 1 ][ 2 ] It learns to represent text as a sequence of vectors using self-supervised learning. It uses the encoder-only transformer architecture.
Performance of AI models on various benchmarks from 1998 to 2024. In machine learning, a neural scaling law is an empirical scaling law that describes how neural network performance changes as key factors are scaled up or down. These factors typically include the number of parameters, training dataset size, [ 1 ][ 2 ] and training cost.
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.
t. e. Mamba is a deep learning architecture focused on sequence modeling. It was developed by researchers from Carnegie Mellon University and Princeton University to address some limitations of transformer models, especially in processing long sequences. It is based on the Structured State Space sequence (S4) model. [1][2][3]
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