How a language model is grown

The seven steps by which engineers produce an LLM, showing why nobody designs what ends up inside.

Eliezer Yudkowsky and Nate Soares's framework, from If Anyone Builds It, Everyone Dies, pp. 21 to 101.

Render the input as numbers
Turn a text fragment into a series of numbers.
Provide parameters
Acquire storage for billions of numbers, each slot a parameter.
Fill with random weights
Fill the slots with randomly chosen numbers called weights.
Choose the architecture
Set the rules that combine the input with the weights.
Read the output
Treat the output numbers as probabilities for the next letter or token.
Train by gradient descent
For every weight, work out how a tiny change would make the answer more correct, nudge it that way and repeat over trillions of words.
Generate text
Feed each predicted token back in to continue the text, then tune the model to answer as an assistant.

From chapter: PART I: NONHUMAN MINDS, pp. 21–101

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