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0 pointsspuz3y ago0 comments

> In order to resolve this, ChatGPT's training corpus needs to contain a "correct answer" next to every unique permutation of every question.

This is not quite the right understanding of how ChatGPT works. It's not necessary to show ChatGPT an example of every possible permutation of an animal crossing puzzle in order for it to solve one it has never seen before. That's because the neural network is not a database of recorded word probabilities. It can instead represent the underlying logic of the puzzle, the relationships between different animals and using this abstract, pared down information, extrapolate the correct answer to the puzzle.

I see the failure in the example with the goat the lion and the cabbage as simply a matter of overfitting.

Edit: I see a lot of people saying "it doesn't understand logic; it's just predicting the next word."

I'm basing my understanding on this video:

https://youtu.be/viJt_DXTfwA

The claim is that it would be impossible to feed enough input into a system such that it could produce anything as useful as ChatGPT unless it was able to abstract the underlying logic from the information provided. If you consider the he number of permutations of the animal crossing puzzle this quickly becomes clear. In fact it would be impossible for ChatGPT to produce anything brand new without this capability.

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nebulousthree3y ago

I think what they mean by "resolve this" is "make it error-free". Your claim that "it isn't necessary to show every permutation for it to solve one it hasn't seen before" doesn't really contradict their point.

For puzzles whose entire permutation space is semantically similar enough, your claim is likely true. But for puzzles whose permutations can involve more "human" semantic manipulations, there is likely a much higher risk of failure.

spuzOP3y ago

Yes I think it depends on how you definite permutations for this puzzle. For example, if you limit your goal to training GPT to solve puzzles of the form where there only ever 3 distinct real animals, then my claim is that you wouldn't need to feed it examples of this puzzle with every single permutation of 3 different animals (assuming 10000 different animals that is already over 100bn permutations) before the neural network developed an internal logical model that can solve the puzzle as well as a human. It would only need a few descriptions of each animal plus a few examples of the puzzle to understand the logic.

If you mean to say that the permutations of the puzzle extend to changing the rules such as "if it's the Sabbath then reptiles can't travel" then sure it would require more representative examples and may never meet your standard of "error free" but I would also argue the same applies to humans when you present them a logic puzzle that is new to them.

thomastjeffery3y ago

> you wouldn't need to feed it examples of this puzzle with every single permutation

No, but you would need "enough"; whatever that number happens to be.

> It would only need a few descriptions of each animal plus a few examples of the puzzle to understand the logic.

That's the mistake.

GPT itself can't combine those two things. That work has to be done by the content of the already-written training corpus.

And the result is not the same as "understanding logic". It doesn't model the meaning of the puzzle: it models the structure of examples.

GPT can't distinguish the meaning of rules. It can only follow examples. It can't invent new strategies, it can only construct new collections of strategy parts; and it can only pick the parts that seem closest, and put those parts into a familiar order.

GPT doesn't play games, it plays plays.

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