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challengethegods t1_jact6ez wrote

well, the context window is not as limiting as people seem to think. That's basically the range of text it can handle in a single instant - for example if someone asked you a trick question, and the predictable false answer pops into your head immediately - that's what a single call to an LLM is. Once people figure out how to recursively call the LLM inside of a larger system that's keeping track of longterm memory/goals/tools/modalities/etc it will suddenly be a lot smarter, and using that kind of system can have even GPT-3 write entire books.

The problem is, the overarching system has to also be AI and sophisticated enough to compliment the LLM in order to breach into a range where the recursive calls are coherent, and context window is eaten very quickly by reminding it of relevant things, to a point where writing 1 more sentence/line might take the entire context window just to have all the relevant information, or even an additional pass afterwards to check the extra line against another entire block of text... which basically summarizes to say that 8k context window is not 2x as good as 4k context window... it's much higher, because all of the reminders are a flat subtraction.

realworld layman example:
suppose you have $3900/month in costs, and revenue $4000/month =
$100/month you can spend on "something".
now increase to revenue to $8000/month,
suddenly you have 41x as much to spend

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RabidHexley t1_jad8r8t wrote

> for example if someone asked you a trick question, and the predictable false answer pops into your head immediately - that's what a single call to an LLM is

Yep. This is the biggest issue with current consumer LLM implementations. We basically force the AI to word-vomit the first thing it thinks of. It's very good at getting things right in spite of that, but if it gets it wrong the system has no recourse. Coming to a correct conclusion, well-reasoned response, or even just coming to the conclusion that we don't know something requires multiple passes.

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