How ChatGPT Works Technically
ChatGPT, introduced on November 30, 2022, quickly achieved a milestone of 100 million monthly active users within just two months, making it the fastest-growing application in history. At its core, ChatGPT relies on a Large Language Model (LLM), specifically the GPT-3.5 model, although it has the potential to incorporate newer versions like GPT-4 when available. A Large Language Model is a neural network-based model trained on vast amounts of text data to comprehend and generate human language. These models are characterized by their immense size and the number of parameters they contain, with GPT-3.5 boasting 175 billion parameters distributed across 96 neural network layers.
How text is understood by AI models is also important, so let’s look a little deeper at tokens. Tokens are fundamental to how ChatGPT processes language. Tokens are numerical representations of words or parts of words, chosen for their computational efficiency. GPT-3.5 was trained on a massive dataset containing 500 billion tokens, which equates to hundreds of billions of words. It learned to predict the next token in a sequence, enabling it to generate text that is grammatically correct and semantically aligned with the training data. However, without proper guidance, the model may produce untruthful, toxic, or harmful content.
To make ChatGPT safer and capable of chatbot-style question and answer interactions, the model undergoes a fine-tuning process called Reinforcement Training from Human Feedback (RLHF). This process can be likened to refining a skilled chef’s abilities to create more delicious dishes. Initially, the model is trained with a dataset of recipes and cooking techniques, but it may struggle with specific customer requests. Feedback from real users is collected to create a comparison dataset, where multiple model-generated responses are ranked based on quality. This forms the basis of a reward model, guiding the model to generate responses that align with user preferences. The model is then iteratively improved using Proximal Policy Optimization (PPO), enhancing its ability to satisfy user requests.
ChatGPT’s usage involves several components. It is context-aware, meaning it retains knowledge of the ongoing conversation by feeding the entire chat history to the model with each new input — a process known as conversational prompt injection. Additionally, ChatGPT incorporates primary prompt engineering, injecting instructions before and after the user’s prompt to guide the model’s conversational tone. These instructions are invisible to the user. Moreover, the generated responses are subject to moderation through an API to identify and block unsafe content, ensuring a safe and appropriate user experience.
In conclusion, ChatGPT’s technical architecture and operation rely on a Large Language Model, extensive training data, reinforcement learning, and a multi-faceted approach to contextual understanding and content moderation. This technology has immense potential and continues to evolve, reshaping the way we communicate and interact with AI-powered systems.
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