Technical Overview of ChatGPT

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Technical Overview of ChatGPT

Now, a very common confusion among many of us about ChatGPT is probably it’s been thought of as an advanced version of commonly used Chatbots. Although it may look somewhat like a chatbot and how it functions, in reality it is far different from a chatbot from a technical standpoint. ChatGPT is a generative model, meaning it can generate novel responses rather than just selecting a predefined response from a list. This allows ChatGPT to have more natural and varied responses and makes it more suitable for use in open-ended conversations. GPT stands for “Generative Pre-training Transformer,” a transformer neural network architecture that is trained using a large dataset of human conversation to generate human-like responses to user input. Now, ChatGPT is optimized for dialogue generation and has been trained on a dataset of chat logs, documents, research papers etc. from various online platforms. So, in a way the intelligence that ChatGPT possessed is from the conversation that you had anytime in any of the social media or messaging platforms.

From a technical angle, ChatGPT is based on the transformer architecture which uses self-attention mechanisms to process input sequences with a large number of parameters (on the order of billions) and is trained using a variant of the Masked Language Modeling (MLM) objective. During training, the model is presented with a sequence of tokens and is asked to predict the next token in the sequence, with some of the tokens in the input sequence masked out. This forces the model to use context from the unmasked tokens to make a prediction for the masked token, which helps it learn general-purpose language representation.