5 ms·
Because by definition LLMs are permutation machines, not creativity machines. (My premise, which you may disagree with, is that creativity/imagination/artistry
by dvt 5mo ago
Because by definition LLMs are permutation machines, not creativity machines. (My premise, which you may disagree with, is that creativity/imagination/artistry is not merely permutation.)
- KoolKat23 5mo agoIt pretty much is, otherwise it is randomness or entropy.
- nh23423fefe 5mo agogod of the gaps
- iwontberude 5mo agonon overlapping magisteria
- fnordpiglet 5mo agoI prefer to think of it as they’re interpolation machines not extrapolation machines. They can project within the space they’re trained in, and what they produce may not be in their training corpus, but it must be implied by it. I don’t know if this is sufficient to make them too weak to create original “ideas” of this sort, but I think it is sufficient to make them incapable of original thought vs a very complex to evaluate expected thought.
- drdeca 5mo agoPeople keep saying this, but if you try to interpret this at all literally, it just doesn’t work. Like, it’s phrased like it should have a precise meaning, right? Like, people even mention convex hulls when talking about it. But if you actually try to take a convex hull of, some encoding of sentences as vectors? It isn’t true. The outputs are not in the convex hull of the training data. I guess it’s supposed to be a metaphor and not literal, but in that case it’s confusing. Especially seeing as there are contexts in machine learning where literal interpolation vs literal extrapolation, is relevant. So, please, find a better way to say it than saying that “it can only interpolate”?
- Muromec 5mo agoIf it's all just points in the multidimensional space, why would the thing be restricted to some operations and not others. I'm not buying the argument
- drdeca 5mo agoSorry, I don't understand what you mean. Are you agreeing or disagreeing with me? If it can only interpolate in a literal sense, that means that it only produces good outputs on convex combinations of inputs that appear in the training set. That's what interpolation means. But, if you take the embedding vectors of sentences/prompts, and then take the convex hull of these, it is not typical for new sentences not in the training set to have its embedding vectors be in the convex hull of these.
- fnordpiglet 5mo agoI’m not sure I follow your end to end reasoning. In an n dimensional space interpolation along and within the convex hull is pretty much what they’re doing. How can it possibly not be? How would it interpolate a point that’s not within its vector space? Yes, it’s very complex with non linear transformations and a very high dimensionality, and residuals and other features create more complexity in the shape of the hull. But an LLM can not infer a concept to which it has no information channel. That’s clearly nonsense. The fact that they do bounded, learned, nonlinear compositional generalizations over a representational space induced by training -is by nature interpolation- not extrapolation. I’m sorry, but I believe their immense power has you confusing math with magic.
- drdeca 4mo agoA convex hull is a different thing than the linear span. It is smaller. And, my point is that the inputs it is often fed are not in the convex hull of the inputs in the training data. When the input space is very high dimensional, this is a common outcome. I’m not denying that the outputs are causally downstream from the training data. Of course it is. I’m saying that the inference time inputs aren’t in the convex hull of the training time inputs. This isn’t about saying that the output isn’t because of the training data. Of course it is. But when you have very high dimensional input space, then even with many inputs in the training data, it is still common for inference time inputs to not be in the convex hull of the train time inputs. This has nothing to do with the complexities of how the models work after the initial embedding of the tokens as vectors. It’s just about the inputs that appear during training, and the inputs that appear at inference time. > But an LLM can not infer a concept to which it has no information channel. Of course! And nothing I said implies otherwise. Really, the point I’m making doesn’t even depend on what the model outputs! If I took a best fit line from 1 parameter to a 1D output, and then provided that linear model an output that was outside the range of inputs the best fit line was obtained from, that would not be interpolation, it would be extrapolation. It is similar here, except instead of the input being outside the convex hull due to being further away, it is outside the convex hull due to, like, the shape of the convex hull of training inputs just doesn’t include the point in question.
- lajamerr 5mo agoLLMs by themselves are not able to but you are missing a piece here. LLMs are prompted by humans and the right query may make it think/behave in a way to create a novel solution. Then there's a third factor now with Agentic AI system loops with LLMs. Where it can research, try, experiment in its own loop that's tied to the real world for feedback. Agentic + LLM + Initial Human Prompter by definition can have it experiment outside of its domain of expertise. So that's extending the "LLM can't create novel ideas" but I don't think anyone can disagree the three elements above are enough ingredients for an AI to come up with novel ideas.
- awesome_dude 5mo agoYou're proving the GP's argument - LLMs aren't creative you say as much, it's the driving that is the creative force
- Barbing 5mo agoIf that’s a requirement, aren’t LLMs driven by pretraining which was human driven? Who decides at which the last point it’s OK to provide text to the model in order to be able to describe it as creative? (non-rhetorical)
- lajamerr 5mo agoYou can tell an agentic system. "Go and find a novel area of math that has unresolved answers and solve it mathematically with verified properties in LEAN. Verify before you start working on a problem that no one has solved this area of math" That's not creative prompt. That's a driving prompt to get it to start its engine. You could do that nowadays and while it may spend $1,000 to $100,000 worth of tokens. It will create something humans haven't done before as long as you set it up with all its tool calls/permissions.
- awesome_dude 5mo agoLet me know when the Fields medal arrives in the mail. It won't because even though it looks clever to you, people who /do/ understand math and LLMs understand that LLMs /are/ regurgitating Why does your LLM need you to tell it to look in the first place? Why isn't just telling us all the answers to unsolved conjectures known and unknown? Why isn't the LLM just telling us all the answers to all the problems we are facing? Why isn't the LLM telling us, step by step with zero error, how to build the machine that can answer the ultimate question?
- lukol 5mo agoThis "new math" might be a recombination of things that we already know - or an obvious pattern that emerges if you take a look at things from a far enough distance - or something that can be brute-forced into existence. All things LLMs are perfectly capable of. In the end, creativity has always been a combination of chance and the application of known patterns in new contexts.
- dvt 5mo ago> This "new math" might be a recombination of things that we already know If you know anything about the invention of new math (analytic geometry, Calculus, etc.), you'd know how untrue this is. In fact, Calculus was extremely hand-wavy and without rigorous underpinnings until the mid 1800s. Again: more art than science.
- baq 5mo agoAnd yet nowadays you can restate all of it using just combinations of sets of sets and some logic operators.
- jfyi 5mo agoNewton and Leibniz were "hand-waving"? If anything, they were fighting an uphill battle against the perception of hand-waving by their contemporaries.
- dvt 5mo ago> Newton and Leibniz were "hand-waving"? Yes, and it's pretty common knowledge that Calculus was (finally) formalized by Weierstrass in the early 19th century, having spent almost two centuries in mathematical limbo. Calculus was intuitive, solved a great class of problems, but its roots were very much (ironically) vibes-based. This isn't unique to Newton or Leibniz, Euler did all kinds of "illegal" things (like playing with divergent series, treating differentials as actual quantities, etc.) which worked out and solved problems, but were also not formalized until much later.
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- satvikpendem 5mo agoWhat is creativity if not permutation? A brain has some model of the world and recombines concepts to create new concepts.
- d3ffa 5mo ago[flagged]