Exposing the inner machinery: how token predictions, transformers, and human feedback shape ChatGPT's behavior.
ChatGPT is a pattern-based system driven by tokens and math, not meaning or ideas.
Training uses gradient descent on vast text to tune billions of parameters for prediction.
Attention in transformers lets the model weigh parts of text to capture long-range relations.
Humans shape responses via RLHF and safety layers to curb harmful or unreliable outputs.
Context windows, retrieval, and tool use ground outputs and extend capabilities.
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