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This is utterly wrong. Predicting the next word requires a large sample of data made into a statistical model. It has nothing to do with "understanding", which
by IAmGraydon 11mo ago
This is utterly wrong. Predicting the next word requires a large sample of data made into a statistical model. It has nothing to do with "understanding", which implies it knows why rather than what.
- orionsbelt 11mo agoIlya Sustkever was on a podcast, saying to imagine a mystery novel where at the end it says “and the killer is: (name)”. Saying it’s just a statistical model generating the next most likely word, how can it do that in this case if it doesn’t have some understanding of all the clues, etc. A specific name is not statistically likely to appear
- shwaj 11mo agoCan current LLMs actually do that, though? What Ilya posed was a thought experiment: if it could do that, then we would say that it has understanding. But AFAIK that is beyond current capabilities.
- krackers 11mo agoSomeone should try it and create a new "mysterybench". Find all mystery novels written after LLM training cutoff, and see how many models unravel the mystery
- IAmGraydon 11mo agoIt can't do that without the answer to who did it being in the training data. I think the reason people keep falling for this illusion is that they can't really imagine how vast the training dataset is. In all cases where it appears to answer a question like the one you posed, it's regurgitating the answer from its training data in a way that creates an illusion of using logic to answer it.
- CamperBob2 11mo agoIt can't do that without the answer to who did it being in the training data. Try it. Write a simple original mystery story, and then ask a good model to solve it. This isn't your father's Chinese Room. It couldn't solve original brainteasers and puzzles if it were.
- dyauspitr 11mo agoThat’s not true, at all.
- IAmGraydon 11mo agoPlease…go on.
- stevenhuang 11mo agoYou sound more like a stochastic parrot than an LLM does at this point.
- squigz 11mo agoThis implies understanding of preceding tokens, no? GP was saying they have understanding of future tokens.
- nicpottier 11mo agoI once was chatting with an author of books (very much an amateur) and he said he enjoyed writing because he liked discovering where the story goes. IE, he starts and builds characters and creates scenarios for them and at some point the story kind of takes over, there is only one way a character can act based on what was previously written, but it wasn't preordained. That's why he liked it, it was a discovery to him. I'm not saying this is the right way to write a book but it is a way some people write at least! And one LLMs seem capable of doing. (though isn't a book outline pretty much the same as a coding plan and well within their wheelhouse?)
- astrange 11mo agoIf you're claiming a transformer model is a Markov chain, this is easily disprovable by, eg, asking the model why it isn't a Markov chain! But here is a really big one of those if you want it: https://arxiv.org/abs/2401.17377 https://arxiv.org/abs/2401.17377
- nl 11mo agoModern LLMs are post trained for tasks other than next word prediction. They still output words through (except for multi-modal LLMs) so that does involve next word generation.
- Workaccount2 11mo ago"Understanding" is just a trap to get wrapped up in. A word with no definition and no test to prove it. Whether or not the model are "understanding" is ultimately immaterial, as their ability to do things is all that matters.
- pinnochio 11mo agoIf they can't do things that require understanding, it's material, bub. And just because you have no understanding of what "understanding" means, doesn't mean nobody does.
- red75prime 11mo ago> doesn't mean nobody does If it's not a functional understating that allows to replicate functionality of understanding, is it the real understanding?
- dyauspitr 11mo agoThe line between understanding and “large sample of data made into a statistical model” is kind of fuzzy.