Page 13 - ChatGPT Prompts Book: Precision Prompts, Role Prompting, Training & AI Writing Techniques for Mortals

For context, the earliest language models were primarily
statistical, relying on frequency counts of words and word

sequences in a large corpus (a database containing text) to
estimate probabilities. While these models helped to lay the
groundwork for understanding language, they were limited
in their ability to capture the complex relationships and
context that define human communication.

With the advent of advanced technology and methods,
including GPU chips for computer processing, neural

networks, and deep learning, the next generation of
language models evolved. These models, built on recurrent
neural networks (RNNs) and long short-term memory
(LSTM) networks, were better equipped to handle the
sequential nature of language, allowing for improved

predictions and more sophisticated text generation.
OpenAI's GPT model represents a significant advancement

in the evolution of language models. The first GPT model
was released in 2018, setting the stage for more powerful
and versatile language models. GPT-2, released in 2019,
subsequently built upon the success of its predecessor,
demonstrating even greater capabilities in generating

coherent and contextually relevant text. However, despite
its impressive performance, GPT-2 faced numerous
limitations, including the generation of nonsensical or

inaccurate responses and difficulties with handling longer
text sequences.
The release of GPT-3 in 2020 marked a major breakthrough
for large language models, showcasing unprecedented

performance and versatility. The success of GPT-3 paved the
way for the popular ChatGPT application and GPT-4, which
is a multimodal model that can accept both image and text

inputs to produce outputs. 1
Despite being less capable than humans in certain real-
world scenarios, GPT-4 has achieved human-level

performance on various professional and academic