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That is not an example of a LLM being capable of abstract reasoning. Changing the question from "What is the capital of United States?" which is easily answerab

That is not an example of a LLM being capable of abstract reasoning. Changing the question from "What is the capital of United States?" which is easily answerable to something completely abstract and "not in the training model" doesn't change that LLM's are just very advanced text prediction, and always will be. The nature of their design means they are incapable of AGI.

jdietrich 2y ago
The question I gave is a literal textbook example of abstract reasoning. LLMs are just very advanced text prediction, but they are also provably capable of abstract reasoning. If you think that those statements are contradictory, I would encourage you to read up on the Bayesian hypotheses in cognitive science - it is highly plausible that our brains are also just very advanced prediction models.
- nsagent 2y ago
  You're quite right that LLMs can seemingly do some abstract reasoning problems, but I would not say they aren't in the training data. Sure, the exact form using the made up word gronk might not be in the training data, but the general form of that reasoning problem definitely exists, quite frequently in fact.
  - jdietrich 2y ago
    Yes, but the general form of the problem tells you nothing about the answer to any specific case. To perform any better than chance, the model has to actually reason through the problem.
  - cgag 2y ago
    Have you seen this? ``` You will be given a name of an object (such as Car, Chair, Elephant) and a letter in the alphabet. Your goal is to first produce a 1-line description of how that object can be combined with the letter in an image (for example, for an elephant and the letter J, the trunk of the elephant can have a J shape, and for the letter A and a house, the house can have an A shape with the upper triangle of the A being the roof). Following the short description, please create SVG code to produce this (in the SVG use shapes like ellipses, triangles etc and polygons but try to defer from using quadratic curves). ``` ``` Round 5: A car and the letter E. Description: The car has an E shape on its front bumper, with the horizontal lines of the E being lights and the vertical line being the license plate. ``` Image generated here: https://imgur.com/a/Ia4Q2h3 https://imgur.com/a/Ia4Q2h3 How does it "just" predict the letter E could be used in such a way to draw a car? How does it just text predict working SVG code that draws the car made out of basic shapes and the letter E? I don't know how anyone could suggest there are no conceptual models embedded in there.
- smolder 2y ago
  Pleasure and pain, along with subtler emotions that regulate our behavior, aren't things that arise from word prediction, or even from understanding the world, I don't think. So to say human brains are just prediction models seems like a mischaracterization.
brookst 2y ago
That’s a tautology that seems just as applicable to humans.
roenxi 2y ago
> LLM's are just very advanced text prediction, and always will be How do you predict the next word in answering an abstract logic question without being capable of abstract reasoning, though? In some sense it probably is possible, but this is a gaping flaw in your argument. A sufficiently advanced text prediction process has to encompass the process of abstract reasoning. The text prediction problem is necessarily a superset of the abstract reasoning problem. Ie, in the limit text prediction is fundamentally harder than abstract reasoning.

jdietrich 2y ago

The question I gave is a literal textbook example of abstract reasoning. LLMs are just very advanced text prediction, but they are also provably capable of abstract reasoning. If you think that those statements are contradictory, I would encourage you to read up on the Bayesian hypotheses in cognitive science - it is highly plausible that our brains are also just very advanced prediction models.

nsagent 2y ago
You're quite right that LLMs can seemingly do some abstract reasoning problems, but I would not say they aren't in the training data. Sure, the exact form using the made up word gronk might not be in the training data, but the general form of that reasoning problem definitely exists, quite frequently in fact.
- jdietrich 2y ago
  Yes, but the general form of the problem tells you nothing about the answer to any specific case. To perform any better than chance, the model has to actually reason through the problem.
- cgag 2y ago
  Have you seen this? ``` You will be given a name of an object (such as Car, Chair, Elephant) and a letter in the alphabet. Your goal is to first produce a 1-line description of how that object can be combined with the letter in an image (for example, for an elephant and the letter J, the trunk of the elephant can have a J shape, and for the letter A and a house, the house can have an A shape with the upper triangle of the A being the roof). Following the short description, please create SVG code to produce this (in the SVG use shapes like ellipses, triangles etc and polygons but try to defer from using quadratic curves). ``` ``` Round 5: A car and the letter E. Description: The car has an E shape on its front bumper, with the horizontal lines of the E being lights and the vertical line being the license plate. ``` Image generated here: https://imgur.com/a/Ia4Q2h3 https://imgur.com/a/Ia4Q2h3 How does it "just" predict the letter E could be used in such a way to draw a car? How does it just text predict working SVG code that draws the car made out of basic shapes and the letter E? I don't know how anyone could suggest there are no conceptual models embedded in there.
smolder 2y ago
Pleasure and pain, along with subtler emotions that regulate our behavior, aren't things that arise from word prediction, or even from understanding the world, I don't think. So to say human brains are just prediction models seems like a mischaracterization.

brookst 2y ago

That’s a tautology that seems just as applicable to humans.

roenxi 2y ago

> LLM's are just very advanced text prediction, and always will be How do you predict the next word in answering an abstract logic question without being capable of abstract reasoning, though? In some sense it probably is possible, but this is a gaping flaw in your argument. A sufficiently advanced text prediction process has to encompass the process of abstract reasoning. The text prediction problem is necessarily a superset of the abstract reasoning problem. Ie, in the limit text prediction is fundamentally harder than abstract reasoning.