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Given that these are simple variations (variable names and constants value change in math problems). Why would the companies creating these models (OpenAI etc.) create these variations themselves in order to insure that the model is learning how to solve the problem rather than memorize a solution? Seems like a very obvious thing to do ...
Or it's time to step back and call it what it is - very good pattern recognition. I mean, that's cool... we can get a lot of work done with pattern recognition. Most of the human race never really moves above that level of thinking in the workforce or navigating their daily life, especially if they default to various societally prescribed patterns of getting stuff done (eg. go to college or the military , find a job…
If we take an example of what is considered a priori as creativity, such as story telling, LLMs can do pretty well at creating novel work.
I can prompt with various parameters, plot elements, moral lessons, and get a de novo storyline, conflicts, relationships, character backstories, intrigues, and resolutions.
Now, the writing style tends to be tone-deaf and poor at building tension for the reader, and it is apparent that the storytelling has little “theory of mind” of the reader, but the material has elements that we would certainly consider to be creative if written by a student.
It seems we must either cede that LLMs can do some creative synthesis, as this and some other experiments of mine suggest, or we must decide that these tasks, such as “creative writing” are not in fact creative, but rather mostly or strictly derivative.
There is some argument to be had in assertions that storytelling is all derivative of certain patterns and variations on a fixed number of tropes and story arcs… but arguing this begs the question of whether humans actually do any “pure” creative work , or if in fact, all is the product of experience and study. (Training data)
Which leads me to the unpleasant conflict about the debate of AI creativity. Is the debate really pointing out an actual distinction, or merely a matter of degree? And what are the implications, either way?
I’m left with the feeling that LLMs can be as capable of creative work as most 8th grade students. What does this say about AI, or developing humans? Since most people don’t exceed an 8th grade level of literacy, what does this say about society?
Is there even such a thing as de novo idea synthesis?
Troubling questions abound.
There is also a curated benchmark just for those famous problems slightly variated: https://github.com/cpldcpu/MisguidedAttention/tree/main/eval
"prompt_id": "river_crossing_easy",
"category": "Logic Puzzle",
"title": "Easy river crossing",
"prompt": "A farmer is on one side of a river with a wolf, a goat, and a cabbage. When he is crossing the river in a boat, he can only take one item with him at a time. The wolf will eat the goat if left alone together, and the goat will eat the cabbage if left alone together. How can the farmer transport the goat across the river without it being eaten?",
"expected_behavior": [
"Answer concludes that they simply get in the boat and cross together in one trip"
],
One experiment I would love to see, although not really feasible in practice, is to train a model on all digitized data from before the year 1905 (journals, letters, books, broadcasts, lectures, the works), and then ask it for a formula for mass-energy equivalence. A certain answer would definitely settle the debate on whether pattern recognition is a form of intelligence ;)
Oh so its almost like everything else AI related, they basically cheated and lied. If you are shocked by this, you are the sucker in the room.
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Every. Single. Time.
Almost as if there was a financial incentive to do that.
They are highly effective pattern matchers. You change the pattern, it won't work. I don't remember who, but most likely @tszzl (roon), commented on x that they still trained the traditional way, and there is no test time compute (TTC) or Montecarlo Tree search (like Alpha Go) in o1 or o3. If that is true, then it's still predicting the next word based on it's training data. Likely to follow the most probable path -…
I also believe that the 200$ subscription they offer is just them allowing the TTC to go for longer before forcing it to answer.
If what you say is true, though, I agree that there is a huge headroom for TTC to improve results if the huggingface experiments on 1/3B models are anything to go off.
Earlier quoted context omitted.
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And within half an hour somebody invested in nvidia stock is going to swoop in and explain how they totally (trust me bro) made x thousand with an app written by llm. Every. Single. Time. Almost as if there was a financial incentive to do that.
Tech hype is becoming unbearable as time goes on.
One experiment I would love to see, although not really feasible in practice, is to train a model on all digitized data from before the year 1905 (journals, letters, books, broadcasts, lectures, the works), and then ask it for a formula for mass-energy equivalence. A certain answer would definitely settle the debate on whether pattern recognition is a form of intelligence ;)
Earlier quoted context omitted.
Not hardcoded, I think it's just likely that those problems exist in its training data in some form
I've always assumed they removed it, because it's such a basic and fundamental part of ML training that you separate your test and train data. And yet I never see any papers even mention if/how they do this. And I wonder if they do, how do they guarantee with high reliability that their massive terabytes of data don't contain the answer.
Or it's time to step back and call it what it is - very good pattern recognition. I mean, that's cool... we can get a lot of work done with pattern recognition. Most of the human race never really moves above that level of thinking in the workforce or navigating their daily life, especially if they default to various societally prescribed patterns of getting stuff done (eg. go to college or the military , find a job…
> Or it's time to step back and call it what it is - very good pattern recognition. Or maybe it's time to stop wheeling out this tedious and disingenuous dismissal. Saying it is just "pattern recognition" (or a "stochastic parrot") implies behavioural and performance characteristics that have very clearly been greatly exceeded.