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I got the highest score on ARC-AGI again swapping Python for English

jeremyberman.substack.com

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Re: I got the highest score on ARC-AGI again swapping Python for English

#81

Earlier quoted context omitted.

> are they really learning to reason, or are they just learning to pattern match to steer generation in the direction of problem-specific reasoning steps that they had been trained on? Are you sure there's a real difference? Do you have a definition of "reasoning" that excludes this?

It seems readily apparent there is a difference given their inability to do tasks we would otherwise reasonably describe as achievable via basic reasoning on the same facts.

I agree LLMs have many differences in abilities relative to humans. I'm not sure what this implies for their ability to reason though. I'm not even sure what examples about their bad reasoning can prove about the presence or absence of any kind of "reasoning", which is why I keep asking for definitions to remove the ambiguity. If examples of bad reasoning sufficed, then this would prove that humans can't reason either, which is silly.

A rigourous definition of "reasoning" is challenging though, which is why people consistently can't provide a general one that's satisfactory when I ask, and this is why I'm skeptical that pattern matching isn't a big part of it. Arguments that LLMs are "just pattern matching" are thus not persuasive arguments that they are not "reasoning" at some cruder level.

Maybe humans are just higher order pattern matchers and LLMs are only first or second-order pattern matchers. Maybe first-order pattern matching shouldn't count as "reasoning", but should second-order? Third-order? Is there evidence or some proof that LLMs couldn't be trained to be higher order pattern matchers, even in principle?

None of the arguments or evidence I've seen about LLMs and reasoning is rigourous or persuasive on these questions.

Re: I got the highest score on ARC-AGI again swapping Python for English

#82

Earlier quoted context omitted.

"Not understanding or reasoning" is anthropocentric cope. There is very little practical difference between "understanding" and "reasoning" implemented in human mind and that implemented in LLMs. One notable difference, however, is that LLMs disproportionately suck at spatial reasoning . Which shouldn't be surprising, considering that their training datasets are almost entirely text. The ultimate wordcel makes for a…

> There is very little practical difference between "understanding" and "reasoning" implemented in human mind and that implemented in LLMs. Source?

There is no source and arguing this is dumb because no one knows what reasoning or understanding is. No one.

So all we have is "Does it swim like a duck, look like a duck, quack like a duck?"

Re: I got the highest score on ARC-AGI again swapping Python for English

#83
post #5

I've been testing LLMs on Sokoban-like puzzles (in the style of ARC-AGI-3) and they are completely awful at them. It really highlights how poor their memory is. They can't remember abstract concepts or rules between steps, even if they discover them themselves. They can only be presented with lossy text descriptions of such things which they have to re-read and re-interpret at every step. LLMs are completely helpless…

I really think that the problem is with tokenizing vision.

Any kind of visually based reasoning and they become dumb as rocks. It feels similar to having a person play sokoban but blindfolded and only with text prompts. The same issue cropped up with playing pokemon. Like the image gets translated to text, and then the model works on that.

I'm no expert on transformers, but it just feels like there is some kind of limit that prevents the models from "thinking" visually.

Re: I got the highest score on ARC-AGI again swapping Python for English

#84
post #68

Earlier quoted context omitted.

> are they really learning to reason, or are they just learning to pattern match to steer generation in the direction of problem-specific reasoning steps that they had been trained on? Are you sure there's a real difference? Do you have a definition of "reasoning" that excludes this?

It's trivial to demonstrate that LLMs are pattern matching rather than reasoning. A good way is to provide modified riddles-that-aren't. As an example: > Prompt: A man working at some white collar job gets an interview scheduled with an MBA candidate. The man says "I can't interview this candidate, he's my son." How is this possible? > ChatGPT: Because the interviewer is the candidate’s mother. (The riddle plays on t…

People make the same sort of mistakes.

Re: I got the highest score on ARC-AGI again swapping Python for English

#85

Earlier quoted context omitted.

> There is very little practical difference between "understanding" and "reasoning" implemented in human mind and that implemented in LLMs. Source?

There is no source and arguing this is dumb because no one knows what reasoning or understanding is. No one. So all we have is "Does it swim like a duck, look like a duck, quack like a duck?"

I’m sympathetic to your point, but this isn’t quite fair. The field of psychology does exist.

Re: I got the highest score on ARC-AGI again swapping Python for English

#86
post #79

> LLMs have "dead reasoning zones" — areas in their weights where logic doesn't work. Humans have dead knowledge zones (things we don't know), but not dead reasoning zones. Religion often is, as "the Lord's ways are inscrutable"

And people have started seeing LLM's as a quasi-religion.

Re: I got the highest score on ARC-AGI again swapping Python for English

#87
post #68

Earlier quoted context omitted.

It's trivial to demonstrate that LLMs are pattern matching rather than reasoning. A good way is to provide modified riddles-that-aren't. As an example: > Prompt: A man working at some white collar job gets an interview scheduled with an MBA candidate. The man says "I can't interview this candidate, he's my son." How is this possible? > ChatGPT: Because the interviewer is the candidate’s mother. (The riddle plays on t…

> It's trivial to demonstrate that LLMs are pattern matching rather than reasoning. Again, this is just asserting the premise that reasoning cannot include pattern matching, but this has never been justified. What is your definition for "reasoning"? > This is clearly pattern matching and overfitting to the "doctor riddle" and a good demonstration of how there's no actual reasoning going on. Not really, no. "Bad reaso…

If your assertion is that you can't prove reasoning isn't just pattern matching, then I counter by saying you can't prove reasoning isn't just chaining a large number of IF/THEN/ELSE logic statements and therefore computers have been generally intelligent since ~1960.

Re: I got the highest score on ARC-AGI again swapping Python for English

#88

Earlier quoted context omitted.

The primary source is: measured LLM performance on once-human-exclusive tasks - such as high end natural language processing or commonsense reasoning. Those things were once thought to require a human mind - clearly, not anymore. Human commonsense knowledge can be both captured and applied by a learning algorithm trained on nothing but a boatload of text. But another important source is: loads and loads of mech inter…

I haven't seen LLMs perform common sense reasoning. Feel free to share some links. Your post reads like anthropomorphized nonsense.

What? Do you even know what "commonsense reasoning" means?

Re: I got the highest score on ARC-AGI again swapping Python for English

#89
post #68

Earlier quoted context omitted.

It's trivial to demonstrate that LLMs are pattern matching rather than reasoning. A good way is to provide modified riddles-that-aren't. As an example: > Prompt: A man working at some white collar job gets an interview scheduled with an MBA candidate. The man says "I can't interview this candidate, he's my son." How is this possible? > ChatGPT: Because the interviewer is the candidate’s mother. (The riddle plays on t…

People make the same sort of mistakes.

Please explain how this is relevant to the topic at hand. Thanks!
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