it has already been trained on a huge data set before being released to the public.
Users who wish to further train the gpt engine - to specialize in certain tasks, such as writing reports for their unique organization - can use techniques to customize llms.
Transformer
Transformers are a type of neural network architecture presented in a 2017 paper titled "Attention is all you need" by Vaswani et al. Before transformers, models such as oman mobile phone number recurrent neural networks (rnn) and long-term memory networks (lstm) were commonly used to process text sequences.
The rnn and lstm networks read text sequentially, just like a human would. But the transformative architecture is able to process and evaluate each word in a sentence at the same time, allowing it to rate some words as more relevant, even if they are in the middle or at the end of a sentence. This is what is known as the self-attention mechanism.
Take the phrase: "the mouse couldn't fit in the cage because it was too big."
A transformer might rate the word "mouse" as more important than "cage," and correctly identify that "it" in the sentence refers to the mouse.
But a model like an rnn could interpret "it" to be the cage, since it was the most recently processed noun.
The "transformative" aspect allows chatgpt to better understand context and produce more intelligent responses than its predecessors.
Training process
chatgpt is trained using a two-step process: pretraining and tuning.
Previous training
First, the AI model is exposed to a large amount of text data: books, web pages, and other files.
During pre-training, the model learns to predict the next word in a sentence, which helps it understand language patterns. Essentially, you build a statistical understanding of the language, allowing you to generate coherent-sounding texts.
Tuning
After pre-training, the model is refined with more specific data sets. Conversation data sets are included in chatgpt.
A key part of this step is reinforcement learning from human feedback (rlhf), in which human instructors classify the model's responses. This feedback loop helps chatgpt improve its ability to generate appropriate, useful, and contextually accurate responses.
