Introduction to Large Language Models

History of Large Language Models

The History of Large Language Models

In the 1950s, the first machine translation systems were developed, which were based on rule-based approaches.
In the 1990s, statistical machine translation models began to be used more widely, which were more efficient and accurate than rule-based systems.
In the early 2000s, researchers began to explore the use of neural networks for language modeling.
Recurrent neural network language models (RNN-LMs) were introduced in 2003 by Mikolov et al.
In 2014, the transformer architecture was introduced by Vaswani et al., which led to the development of large pre-trained language models like BERT and GPT-2.

Applications of Large Language Models

Large language models are being used in a variety of applications, including:

Text generation
Machine translation
Sentiment analysis
Question answering

Ethical Implications

There are concerns about the ethical implications of large language models, such as their potential to perpetuate biases and their impact on human jobs.

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Types of Language Models

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