Large Language Models: Scaling Laws and Emergent Properties

Re:Large Language Models: Scaling Laws and Emergent Properties by woolion on Friday April 21, 2023 @06:27AM (#35651841)

Well I've tried asking for lists of words that satisfy certain simple constraints, like a theme and a given number of syllables. Almost no word satisfied the constraint, despite my many tries. So if you can have a conversation that goes "give me a list of 3 syllables word", you get a list and then you ask for each word "how many syllables is that word?" and get answers like 2, 4, 5, sometimes 3. You can ask the question again and still get the same words with wrong numbers of syllables. Despite a very obvious logical implication of facts here.

This sounds quite similar to Bing famously explaining to a user that 2022 is in the future while we are in 2023.

It's something you would expect maybe a child to do (it reminds of "Patrick Star's Wallet" meme), but to me that clearly fails the notion of intelligence in a very fundamental way. What would be the counter-argument?

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Re:Large Language Models: Scaling Laws and Emergent Properties by og_kalu on Friday April 21, 2023 @07:52AM (#35652395)

Language Models don't see words. They see tokens. So asking them to perform minute operations on letters/syllables/words etc don't work well. Doesn't really have anything to do with Intelligence.

Sentences look something like this to model, [754 76 4752 67 456]

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Re:Large Language Models: Scaling Laws and Emergent Properties by woolion on Friday April 21, 2023 @10:04AM (#35653935)

Here you have two predicates on words P: Sigma* -> N and T: Sigma* -> N. The question is give a list of words w such that for all w, P(w) and T(w) are both true. It has access to the correct values for P(w) and T(w), so there is a simple algorithm that can produce all words, you just enumerate them each one and verify that the word satisfies both predicates. An intelligent agent should be able to understand this without any understanding of the underlying domain.

If I try to reformulate your answer, this is like words are a different domain than what GPT has been trained on (which is tokens), and so it is not unexpected that GPT cannot generalize to this other domain.

Is that a correct interpretation of your statement?

In my view failing to see the similarity in the relationship between class objects is a sign of a lack of 'intelligence', whatever that may be in a very precise sense.

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Re:Large Language Models: Scaling Laws and Emergent Properties by og_kalu on Friday April 21, 2023 @12:34PM (#35656251)

>In my view failing to see the similarity in the relationship between class objects is a sign of a lack of 'intelligence', whatever that may be in a very precise sense.

Ok but not all relationship similarities transfer across representations like that.

It's like you give an instruction in English, "Find a word that rhymes with ingenious" to a french man. The french man sees, "Trouver un mot qui rime avec ingénieux". Guess what ? His response to "Trouver un mot qui rime avec ingénieux" would not fulfill the request in english that it might in French.

The french man is not unintelligent to be unable to fulfill your request.

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