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Humans are notoriously bad at probability as well, and since LLMs are trained data from humans, it kinda makes sense.
by Drakim 2y ago
Humans are notoriously bad at probability as well, and since LLMs are trained data from humans, it kinda makes sense.
- unshavedyak 2y agoI assume somewhat related to this, but humans are also terrible at "random". ~See~ Related 37[1]. The more we advance on LLMs the more i am convinced i'm an LLM. :s [1]: https://www.youtube.com/watch?v=d6iQrh2TK98 https://www.youtube.com/watch?v=d6iQrh2TK98
- brigadier132 2y agoWe are more than LLMs, we have a pretty terrible CPU too. But it's interesting to think, all this positive self reinforcement where you tell yourself "Today's a good day", "I'm amazing", etc, are you just prompting yourself by doing that?
- throwuxiytayq 2y agoKinda, yes. You can do the opposite too (see: negative self-talk).
- zeroonetwothree 2y agoHumans are bad at generating random data yes but that video isn’t exactly convincing proof of it.
- unshavedyak 2y agoOh i didn't mean it (or anything i said) to be proof.
- brigadier132 2y agoIs it because humans are bad at probability that LLMs are bad at probability or is it something inherent in this kind of statistical inference technique? If you trained an LLM on trillions of random numbers will it become an effective random number generator?
- simonw 2y agoIn this case being "bad at randomness" isn't because it was trained on text from humans who are bad at randomness, it's because asking a computer system that doesn't have the ability to directly execute a random number generator to produce a random number is never going to be reliable.
- brigadier132 2y agoMy question was about the scenario if it was trained on this kind of query with good data. It would be interesting to see if it could generalize at all. I'm pretty certain if you trained it specifically on "Generate a random number from 0 to 100" and actually give it a random number from 0 to 100 and give it billions of such examples it would be pretty effective at generating a number from 0 to 100. Wouldn't each token have equal weighted probability of appearing?
- jdiff 2y agoSorta, not really. Neural networks are deterministic in the wrong ways. If you feed them the same input, you'll get the same output. Any variation comes from varying the input or randomly varying your choice from the output. And if you're randomly picking from a list of even probabilities, you're just doing all the heavy lifting of picking a random number yourself, with a bunch of kinda pointless singing and dancing beforehand.
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