Understanding How ChatGPT Works: A Step-by-Step Guide ChatGPT, developed by OpenAI, is a sophisticated language model capable of generating human-like responses to various queries. Understanding its architecture and functionality provides insight into how it processes and generates text. 1. Input Processing: Tokenization and Embedding When ChatGPT receives a sentence, it first performs tokenization, breaking the input into individual units called tokens. These tokens can be words or subwords. Each token is then converted into a numerical vector through a process called embedding, which captures semantic information in a high-dimensional space. Example: For the input: “Write a strategy for treating otitis in a young adult,” the tokenization might yield tokens like “Write,” “a,” “strategy,” etc. Each of these tokens is then mapped to a corresponding vector in the embedding space. 2. Decoder-Only Architecture: Contextual Understanding and Response Generation Unlike traditional transformer models that utilize an encoder-decoder architecture, ChatGPT employs a decoder-only structure. This design allows the model to handle both understanding the input and generating responses within a single framework. The model uses self-attention mechanisms to capture relationships between tokens, enabling it to understand context and generate coherent outputs. Key Points: Self-Attention: Allows the model to weigh the importance of…
Brief OverView of How ChatGPT Works? – Day 68
