Common ChatGPT terms explained – Geeky Gadgets

It allows to use the system more accurately, effectively and knowledgeably. For those looking to get started building applications connected to the OpenAI ChatGPT model, it is essential to understand terms such as “fine tuning”, “parameters”, “training epoch” or “loss function”, as these concepts are key to modifying and optimizing the model.

They provide insights into how the model learns and generates responses, which can guide choices about training and deployment. Meanwhile, users benefit from understanding terms like “summoned“,”Codeordeduction,” as they help clarify the operation of the model, leading to better use and management of expectations. Knowing these terms is a stepping stone to mastering the technology and exploring its vast potential.

You may also be interested to know that today OpenAI has released a new update for ChatGPT that brings a wealth of new features for developers and users to enjoy.

ChatGPT terminology

1. Generative Pre-Training Transformer (GPT): This refers to the foundational structure of the AI ​​model developed by OpenAI. It is an adapter-based language model that is trained on a large set of text data. The term “pre-training” refers to the first stage of training, in which the model learns to predict the next word in a sentence.
2. ChatGPT: This is a kind of GPT template, fine-tuned specifically for generating conversational responses. The model was further trained on a dataset containing dialogue format to improve its ability to participate in the conversation.
3. fine tuning: After the initial pre-training phase, the GPT model undergoes fine tuning. This process involves training the model on a more specific task (such as generating ChatGPT conversation responses), usually with a smaller data set and a specific task.
4. ChatGPT Proxy: This term can refer to an instance of the ChatGPT (like me) model that generates responses in a chat or chat-like setup.
5. Language model: A type of model that predicts the next word or letter in a sequence. These models are the core of many natural language processing tasks, from machine translation to automatic summarization.
6. Transformer Engineering: This is the basic structure for forms like GPT. It revolutionized the field of natural language processing with its ability to handle long-term dependencies in text. The name “transducer” comes from the model’s use of “attention mechanisms,” which help it “convert” inputs into outputs.
7. Code: In paradigms of languages, a token usually denotes a word or a character. However, in models such as GPT, the token is a bit more flexible and can represent a whole word, part of a word, or a single character, depending on the language and encoding strategy selected.
8. summoned: Input given to a form like ChatGPT, which it uses to generate a response. For example, in this conversation, each of your questions or statements to ChatGPT is a prompt.
9. response or generation: The text that the ChatGPT model produces in response to a prompt.
10. Eduction: The process of using a trained model to make predictions. For ChatGPT, inference generates responses to prompts.
11. Model parameters: These are the components of the model that are learned during the training process. They define how the model converts inputs into outputs. For GPT models, these include the weights and biases of the neural network.
12. Training era: Epoch is a complete pass through the entire training dataset. Typically, models like ChatGPT go through multiple epochs during training.
13. Education rate: This is a hyperparameter that controls how much the model parameters are updated in response to the estimated error each time the model weights are updated. It affects the speed and quality of learning.
14. Processing and installation: These terms describe potential problems with machine learning models. Overfitting occurs when the model learns the training data so well, that it performs poorly on unseen data because it is too specialized. Misfitting is the opposite problem, in which the model fails to learn significant patterns in the training data, resulting in poor performance.
15. Settlement: Methods used to prevent overfitting by discouraging model parameters from becoming too complex. Common methods include L1 and L2 regularization.
16. job loss: A measure of how well a model performs in its task. For ChatGPT, the loss function measures how well the model predicts the next word in a sequence. During training, the goal is to reduce the loss function.
17. reverse reproduction: Basic algorithm for performing gradient descent on neural networks. Computes the gradient of the loss function with respect to the parameters of the model and uses this to update the parameters.
18. Neural network layer: A component of a neural network that performs a specific transformation on its input. GPT models are deep learning models, which means they contain many layers of neural network layers stacked on top of each other.
19. Activation function: A mathematical function used in the neural network layer that helps determine the output of the network. Common activation functions include ReLU, sigmoid, and tanh functions.
20. Sequence length/context window: Refers to the maximum length of a sequence a form can handle in a single batch, due to the fixed-length nature of adapter models such as GPT. For GPT-3, the maximum length of a sequence is 2048 tokens.

To learn more about using ChatGPT, go to the official OpenAI documentation which provides everything you need to know to get started as quickly as possible.

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