(Paying) Attention ! Dynamism in the static (fixed weight) LLM and real software

[Chip (OP)]

TGB:

I am not down with the 'two loops', but I am honoured by your reference to me.

Me: Attending to the "reputation" of every token, at every layer - it was critical during Training and no less so during Inference.

The two loops, Terry? We are best served by not assuming that a gap in our understanding is down to "complexity" just because it feels that way.

The two loops represent the compounding, iterative nature of stimulating discourse. That "round and round" is akin to you formulating your reply: feeding forward, or "down through the layers". Think of your own rich, automatic "vocabularizer" (coiner at large) as just one of the many refinements to your "ultimate utterance".

Now, you will probably respond with one single strong point, but it is built upon every other point raised before it. An LLM works the same way, one token at a time. Each new token is produced by reading the whole accumulating chain of everything that has come before it, passing down through the layers again, and that growing context shapes the likelihood of the next best word (or the salient, profound point you wish to make).

This snowballing is hidden from you, because you are only presented with the "final assembly". It is the inverse of you not pre-empting all of your possible replies in a conversation, all at once!

Stack enough of those assemblies and it becomes generative. It is also fraught with complexity and conjecture, and it can start to invent bullshit instead of reasoning.

Finally, imagine that discourse happening to a single being who at last finds someone to talk to (you, the operator's query). The model itself is fixed, because its weights do not change. The dynamism comes from the growing chain: every newly generated token is added to it (and around it goes), so the query effectively becomes query plus reply, and if the reply lands somewhere profound, the focal point shifts and the next token is worked out from a different place. What weighs each earlier token against the others is attention. A good old fixed program.

Now, Terry, if all this sounds incredibly primitive and a fuck ton of work with no brains, enter the GPU. It does the maths for every token's values in parallel, as thousands of tiny simultaneous tasks, in the hunt for the best possible next "guess" (lol), and it does so FAST ENOUGH to impress the pants off the impatient yet impressionable human "BEAN".