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was_der_Fall_ist t1_jduk3s8 wrote

Reply to comment by astrange in [D]GPT-4 might be able to tell you if it hallucinated by Cool_Abbreviations_9

They’re also not realizing that even if the goal is to produce the most probable/useful next word, that doesn’t preclude the neural network from doing other complicated operations in order to figure out the most probable/useful word.

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bpooqd t1_jdun73m wrote

I suspect those people believe that gpt4 is actually a markov chain.

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IDe- t1_jdv5f5b wrote

I mean it is a (higher order) Markov chain.

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sineiraetstudio t1_jdws5js wrote

All higher-order markov chains can be modeled as a first-order markov chain by squashing states together.

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light24bulbs t1_jduwgqt wrote

Yeah, like it's actually using a huge amount of brain power to figure out what the next word is. Just because that's how it works doesn't mean it's not intelligent.

If you want to be really good at figuring out what the next word is you have to be really smart

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bartvanh t1_jdyd6om wrote

Ugh, yes it's so frustrating to see people not realizing this bit all the time. And also kind of painful to imagine that (presumably - correct me if I'm wrong) all those internal "thoughts" are probably discarded after each word, only to be painstakingly reconstructed almost identically for predicting the next word.

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was_der_Fall_ist t1_je3ng6m wrote

Maybe that’s part of the benefit of using looped internal monologue/action systems. By having them iteratively store thoughts and otherwise in their context window, they no longer have to use the weights of the neural network to “re-think” every thought each time they predict a token. They could think more effectively by using their computation to do other operations that take the internal thoughts and actions as their basis.

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ntaylor- t1_je11vt1 wrote

Fairly sure the "final" gpt4 model is still using a generate function that predicts one token at a time. Just the training was good and complicated via RLHF. After training it's not doing any "complicated operations".

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was_der_Fall_ist t1_je15397 wrote

You don’t think the neural network, going through hundreds of billions of parameters each time it calculates the next token, is doing anything complicated?

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ntaylor- t1_je5qtl2 wrote

Nope. It's the same as all neural networks using transformer architecture. Just a big old series of matrix multiplications with some non linear transformations at end of the day

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was_der_Fall_ist t1_je6lfl9 wrote

Why are matrix multiplications mutually exclusive with complicated operations?

A computer just goes through a big series of 0s and 1s, yet through layers of abstraction they accomplish amazing things far more complicated than a naive person would think 0s and 1s could represent and do. Why not the same for a massive neural network trained via gradient descent to maximize a goal by means of matrix multiplication?

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