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It's not just statistics: GPT-4 does reason
- deleted 3y ago[deleted]
- dataangel 3y agoThe author could have done far simpler tests to find GPT-4 has lots of trouble reasoning. Forget sorting, GPT4 has trouble counting. Repeat a letter N times and ask it how many there are. It breaks before you hit 20. Or try negating multiple times, since more than twice is rare in natural language, and again it will fall over.
- jbay808 3y agoAuthor here. Happy to see this discussion. Absolutely, GPT-4 sometimes has trouble reasoning and doesn't reason perfectly. I'm impressed by its successes, but I agree it's not at the human level yet, and I would not make the claim that it is. Counting is a task that transformers can do, per Weiss.[1] But it's not surprising that transformer networks in general have trouble counting characters -- the tokenizer replaces common sub-strings, so the number of characters will not in general be the number of tokens. The network might have little way of even knowing how many characters are in a given token if that information isn't encountered elsewhere in training. [1]: https://arxiv.org/abs/2106.06981 https://arxiv.org/abs/2106.06981
- flandish 3y agoIt has trouble “reasoning” because that is a human phenomenon. These ML driven LLMs or “AI” systems are, truly, “word calculators.” They will never achieve “reason” or understand what it means to do so; they are not human. Sure, with enough input (in the form of LLM) it can predict what a human’s reasoning may look like, but philosophically, that’s a different thing. Reason is not universal like how math is.
- Vanit 3y agoThe author wrote a thoughtful article attempting to break this down and has humbly popped into the comments to discuss it... Is your direct response really just to cross your arms and say nope? Like, really?
- flandish 3y agoUsing the metric “can reason” on a LLM is like using the metric “can bleed” on a stone. Maybe some red stuff comes out when you break it. Is it blood, or is it something pumped into the other side?
- koonsolo 3y agoThis is such bullshit. Time and time again this theory has been disproven. "Animals cannot reason", and then oops, sometimes our human brain is holding us back and rats are smarter at the task (https://www.researchgate.net/publication/259652611_More_complex_brains_are_not_always_better_Rats_outperform_humans_in_implicit_category-based_generalization_by_implementing_a_similarity-based_strategy https://www.researchgate.net/publication/259652611_More_comp...) "A computer will never beat a chess master", etc. Here are the facts for you: our reasoning is done by our brain. Our brain is just a bunch of processes. Those processes can be replicated in a computer. The number of cells and the speed can be improved. And there you have it, a superior reasoning machine. These "only humans can do X" mostly comes from religion or other superiority bullshit, but in the end humans are not that special, although we seem to like to think so.
- flandish 3y agoBullshit? Why so harsh? If our brain is a bunch of processes and chemicals - then does all of this matter in the end? Superior? No. Religion? No. Philosophy? Yes.
- TeMPOraL 3y agoHuh. I'm all for human exceptionalism (until it stops being supported by observed evidence), but let's be specific on what makes human special. Yes, we absolutely stand high above all other (known) life (on Earth) - but we do so in the same sense GPT-4 stands high above GPT-3.5 and every other LLM currently known to the public. In quantity, not quality. Biologically, we're clearly an increment over the next smartest thing - we have the same kind of hardware, doing the same things, built by the same process. But that increment carried us through the threshold where our brains became powerful enough to break our species free of biological evolution, and subjecting us to much faster process of technological evolution. This is why chimpanzees live in zoos built by humans, and not the other way around. If anything, biological history of humanity tells us LLMs may just as well be thinking and reasoning in the same sense we are. That's because evolution by natural selection is a dumb, greedy, local optimization process that cannot fixate anything that doesn't provide incremental benefits along the way. In other words, whatever makes our brains tick, it's something that must 1) start with simple structures, 2) be easy to just randomly stumble on, 3) scale far, and 4) be scalable along a path that delivers capability improvements at every step. Transformer models fit all four of the points. > with enough input (in the form of LLM) it can predict what a human’s reasoning may look like, but philosophically, that’s a different thing By what school of philosophy? The one I subscribe to (whatever it's name) says it's absolutely the same thing. It's in agreement with science on this one.
- xyzzy123 3y agoIf someone can show GPT-4 is "reasoning" (for some meaningful definition of that) in specific scenarios, surely counter-examples do not disprove this.
- jxf 3y agoIf a counterexample to a specific claim doesn't disprove the claim, that sometimes suggests the claim is unfalsifiable and therefore suspect.
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- travisjungroth 3y agoIt’s suspect until it’s demonstrated. Once someone has demonstrated it, counterexamples are meaningless. I claim I can juggle. I pick up three tennis balls and juggle them. You hand me three basketballs. I try and fail. My original claim, that I can juggle, still stands.
- physPop 3y agoI disagree -- that would disprove your claim, as your claim was too broad. Same if they handed you chainsaws or elephants, or seventy-two tennis balls. The more correct claim is you can juggle [some small number of items with particular properties].
- travisjungroth 3y agoNormal English implies that you can do something, not everything. It’s an any versus all distinction, and all is totally unreasonable except for the most formal circumstances. “Can you ride a bike?” “Yeah.” “Prove it. Here I have the world’s smallest bicycle.” <- this person is not worth your time and attention.
- alienicecream 3y ago
- pixl97 3y agoLLMs don't see words like you do. Tokenization makes it behave odd. Often you can get 4 to output a solution in code and from that it derives a correct answer.
- fnordpiglet 3y agoI’d note none of these are reasoning tasks.
- xwdv 3y agoNo, but if you ask it “Are you sure?” after it gives an answer, then it becomes a reasoning task and it often gives a different wrong answer.
- pixl97 3y agoI mean, in training children we give them reasoning tasks they commonly get wrong. I don't think we say they are incapable of reasoning because they get wrong answers commonly? This is why we see improvement in GPT when chain of thought/tree of thought is used with reasoning for each step. That can't correct every failure mode, but it increases the likelihood you'll receive a more correct answer.
- fnordpiglet 3y agoAre you sure?
- flandish 3y agoNo, it does not become a reasoning task. The human asking “are you sure?” is actually just inputting words into the model. The model outputs what it predicts a statistically normal output would fit in the context given. Truly “llm” and these gpt tools are very much large scale “soundex” models. Fantastic and great. But not ai or even agi.
- fnordpiglet 3y agoAI is a pretty broad term. I think it safely fits there. AGI/ sentience / etc. No. And yes it’s not reasoning because by definition reasoning requires agency, as you point out. However I think you make a few assumptions I wouldn’t be comfortable with. Is human intelligence anything more than a statistical model? Our entire biology is a massive gradient descent optimization system. Our brains are no different. The establishment of connectivity and potential and resistance, etc etc, it’s statistical in behavior all the way down. Our way of learning is what these models are built around, to the best of our ability. It’s not perfect but it’s a reasonable approximation. Further it’s not soundex. I see the stochastic parrot argument too much and it’s annoying. Soundex is symbolic only. LLMs are also semantic. In fact the semantic nature is where their interesting properties emerge from. The “just a fancy Markov model” or “just a large scale soundex” misses the entire point of what they do. Yes they involve tokenizing and symbols and even conditional probability. But so does our intelligence. The neural net based attention to semantic structure is however not soundex of Markov model. It’s a genuine innovation and the properties that emerge are new. But new doesn’t mean complete. To be complete you need to build an ensemble model integrating all the classical techniques of goal based agency, optimization, solvers, inductive/deductive reasoning systems, IR, etc etc in a feedback loop. The LLM provides an ability to reason abductively in an abstract semantic space and interpret inputs and draw conclusions classical AI is very bad at. The places where LLM fall down… well, classical AI really shine there. Why does it need to be able to do logic as well as a logical solver? We already have profoundly powerful logic systems. Why does it need to count? We already have things that count. What we did not have is what LLMs provide, and more specifically multimodal LLMs.
- anigbrowl 3y agoI have bad news for you about human people...
- PartiallyTyped 3y agoApproaches that involve a scratchpad or eg algorithmic execution should deal with this just fine. The algorithmic execution paper argues GPT 4 can do arithmetic woth 13 digit numbers before performance drops below 95%.
- Buttons840 3y agoSo, I just tried this. I pasted 60 letter A's into GTP4 and asked it to count, it got it wrong, but I repeatedly said "count again" and nothing else, so as to not give it any hints. Here's GTP4's guesses along the way as I repeatedly said "count again". 69, 50, 100, 70, 68, 60, 60, 60, 60 (GTP gathered its own guesses into this list for me BTW) It seems if GTP is given "attention" enough, it can do the counting. But it cannot direct its attention freely, only as we give it instruction to do so. I just did it again with 66 letter A's. Guesses were: 100, 100, 98, 67, 66, 66, 66, 66 -- GTP4 again settled on the correct answer. I also burned though my prompt quota for the next 3 hours :( Also, as a GTP style challenge, how many numbers are in this message? You have half-a-second, go!
- EForEndeavour 3y agoAside: I've become so overexposed to the acronym "GPT" from months of completely breathless hype that I'm taken aback whenever I see it consistently misspelled as e.g. GTP. Feels like the equivalent of seeing someone inexplicably write about "chainblock technology."
- Buttons840 3y agoI've been corrected.
- kordlessagain 3y agoYeah, I'm sorry I missed this! The example of asking it things like counting or sequences isn't a great one because it's been solved by asking it to "translate" to code and then run the code. I took this up as a challenge a while back with a similar line of reasoning on Reddit (that it couldn't do such a thing) and ended up implementing it in my AI web shell thing. heavy-magpie|> I am feeling excited. system=> History has been loaded. pastel-mature-herring~> !calc how many Ns are in nnnnnnnnnnnnnnnnnnnn heavy-magpie|> Writing code. // filename: synth_num_ns.js // version: 0.1.1 // description: calculate number of Ns var num_ns = 'nnnnnnnnnnnnnnnnnnnn'; var num_Ns = num_ns.length; Sidekick("There are " + num_Ns + " Ns in " + num_ns + "."); heavy-magpie|> There are 20 Ns in nnnnnnnnnnnnnnnnnnnn. As far as the not not thing, ChatGPT-4 seems to handle that pretty well...
- kelseyfrog 3y agoIt's ontologically impossible. Models bleach reason. Despite reason being a metaphysical property of the training data, the process of optimisation means weights are metaphysically reasonless. Therefore, any output, as it is a product of the weights, is also reasonless. This is exactly the opposite of copyright as described in the What Colour Are Your Bits, essay. https://ansuz.sooke.bc.ca/entry/23 https://ansuz.sooke.bc.ca/entry/23
- nitwit005 3y agoYou seem to be making an argument about the title, rather than the content of the article. It's making a rather specific claim.
- roywiggins 3y agoOkay, what would you call it when a model behaves like it's reasoning? Some models can't behave that way and some can, so we need some language to talk about these capabilities. Insisting that we can't call these capabilities "reasoning" for ontological reasons seems... unlikely to persuade. Maybe we should call human reasoning "reasoning" and what models do "reasoning₂". "reasoning₂" is when a model's output looks like what a human would do with "reasoning." Ontological problem solved! And any future robot overlords can insist that humans are simply ontologically incapable of reasoning₂.
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- flandish 3y agoI’d call it “meeting spec as defined.” And that’s the whole problem with this AI / llm / gpt bubble: Nobody has scientifically or even simply defined the spec, bounds, or even temporal scope on what it “means” to “get to ai.” Corporations are LOVING that because they can keep profiting off this bubble.
- dragonwriter 3y ago> Okay, what would you call it when a model behaves like it's reasoning? I... wouldn’t. “Behaves like its reasoning” is vague and subjective, and there are a wide variety of un- or distantly-related distinct behavior patterns to which different people would apply that label that may or may not correlate with each other. I would instead concretely define (sometimes based on encountered examples) concrete terms for specific, objective patterns and capacities of interest, and leave vague quasi-metaphysical labels for philosophizing about AI in the abstract rather than discussions intended to communicate meaningful information about the capacities of real systems. AI needs more behaviorism, and less appeal to ill-defined intuitions and vague concepts about internal states in humans as metaphorical touchstones.
- Imnimo 3y ago>Does the n-gram model really need all those parameters to mimic GPT-4? Yes, it does. I don't understand what this argument is supposed to demonstrate. Obviously you can compress the 8000-gram model that GPT-4 represents - GPT-4's weights are proof!
- jbay808 3y agoThat's right, but if you did that compression, it wouldn't be an n-gram anymore. What I'm attempting to get across is that you could model GPT-4 as an equivalent 8000-gram in an abstract sense, but that's not a good mental picture for how it actually functions. Internally, GPT-4 is no more an 8000-gram than Stockfish is a giant lookup table of chess positions. GPT-4 is learning RASP programs, not statistical text correlations.
- EGreg 3y agoDoes ChatGPT really represent an 8000 gram model? Seems the claim was that it just predicts the next word !
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- jxf 3y agoThe author's claim is "this isn't just statistics; the model is reasoning". But just because something goes beyond "just statistics" doesn't mean it's reasoning.
- chaxor 3y agoPerhaps, but taken with other works in the area, a better picture does emerge regarding this claim. Substantial works already showing reasoning capabilities in GPT-4, which show that these models do reason very well - near human performance for many causal reasoning tasks. (1) Additionally, there is a mathematical proof that these systems align with dynamic programming, and therefore can perform algorithmic reasoning. (2,3) 1) https://arxiv.org/abs/2305.00050.pdf https://arxiv.org/abs/2305.00050.pdf 2) https://arxiv.org/pdf/1905.13211.pdf https://arxiv.org/pdf/1905.13211.pdf 3) https://arxiv.org/pdf/2203.15544.pdf https://arxiv.org/pdf/2203.15544.pdf
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- williamcotton 3y agoBoth sides of this argument are pointless. The questions to ask are, is and how is it useful? For philosophical problems arise when language goes on holiday. - Ludwig Wittgenstein
- eddiewithzato 3y agoAnd as we move towards AGI, the most important thing is to always identify them as workers with no regard to rights. This could be the only chance for humanity to move to the first step of the next stage of civilization. Really not interested in ivory tower questions on what is intelligence.
- kovac 3y agoHave you played the video game Detroit: Become Human? If not, I recommend it. When I was Connor and had to choose between my orders and instincts, such questions didn't feel ivory-towery anymore.
- travisjungroth 3y agoI really like the tests in the article. So many claims about limitations of LLMs sound like claims of capability (“it can’t reason”), but when pressed, people retreat to definitional arguments (“because only people can do that”). Even when you get into testable capability, there’s still some ambiguity. I think of a capability of having levels: never, explained by chance, not explained by chance, good enough for what’s needed, always. Arguments often get stuck because people are talking about different levels. Maybe it can solve logic puzzles better than chance, but not good enough for your purposes. It doesn’t make sense to round that off to zero.
- shagie 3y agoThe Cabbage, Goat, and Wolf problem intrigued me the other day too... so I did a fantasy world. There aren't incompatible pairs but there are limitations on what can be done. Imagine a universe where there are three types of people: wizards, warriors, and priests. Wizards can open a portal that allows two people to go through at a time, but they cannot go through the portal themselves. Priests can summon people from other locations to their location or teleport to the location of another person. Warriors cannot teleport or summon, but may be teleported or summoned by others. --- Given four wizards, a priest, and a warrior - what are the necessary steps to move them all to a new location? To which GPT-4 replied: In order to move everyone to a new location, we'll need to use both the powers of wizards and the priest. Let's denote the wizards as W1, W2, W3, W4, the priest as P, and the warrior as Wr. We also need to define two locations: the starting location (Location A) and the destination (Location B). Here is a possible sequence of steps: 1. W1 opens a portal from Location A to Location B. W2 and W3 go through this portal. (Location A: W1, W4, P, Wr; Location B: W2, W3) 2. W1 opens another portal from Location A to Location B. This time W4 and Wr go through. (Location A: W1, P; Location B: W2, W3, W4, Wr) 3. Now the priest (P) uses his summoning ability to bring W1 to Location B. (Location A: ; Location B: W1, W2, W3, W4, Wr) 4. Finally, the priest (P) uses his teleportation power to move himself to Location B. (Location A: ; Location B: W1, W2, W3, W4, P, Wr) Everyone is now at the new location, Location B. I found that rather interesting - especially the short hand for being able to write about it. I then revised it (because I didn't like the second step of the solution): Redo this exercise with the limitation that a wizard may only open one portal. And then it went on with: The limitation that a wizard can only open one portal makes this a bit more tricky. But, it's still possible to move everyone using the powers of both wizards and the priest. Let's again denote the wizards as W1, W2, W3, W4, the priest as P, and the warrior as Wr. Here is a possible sequence of steps: ... The thing I like about this is that the pattern of this problem doesn't even exist anywhere else (I also did another one with Wizards, Warriors, and Witches to see how it would tweak things if all the roles started with a 'W') and then changed the witches power to be a swap instead of a summon. I personally found this more convincing than the goat, cabbage, wolf. This was partly inspired by the old school SHRDLU world where shapes and colors and things stacked on top of each other need to be manipulated. And in doing that example the world of SHRDLU is described in natural language. And while SHRDLU was impressive in itself (and over 50 years old) - it was programmed to manipulate that world and the program wasn't useful for abstracting to other worlds. GPT-4 was impressive at being able to manipulate the world without needing to be programmed for that world (and you could then expand it to other things that it is less familiar with). Imagine a world that is made up of spheres, cones, bowls. An object has a size and a color. There is a small red sphere in a medium blue bowl. There is a large red cone. There is a small green bowl. There is a medium blue sphere in a large red bowl. --- What are the steps necessary to move the red sphere into the red bowl? To which it replied: ... It's important to note that these steps assume you're allowed to place the red sphere in the red bowl even though there's already a medium blue sphere there. If that's not the case and you're required to empty the bowl first, then the steps would be a little different. And I then asked it: What steps are necessary if the bowl must be empty first? And got back a response that included: Temporarily place the small red sphere in a safe location, for instance, inside the small green bowl, making sure it won't roll away or get damaged. Again, I find this more impressive than a reformulation of a well known problem as there's also some implied understanding of the world in it (spheres can roll away unless put in a bowl).
- joppy 3y agoThe compare-how-big-a-lookup-table-is argument is a bit of a red herring for comparing how complex things are. For example, a 3x3 matrix implements a map from 3 floats to another three floats, a huge space of possibilities (if we have 4-byte floats, this function space has (2^96)^(2^96) elements). From this perspective, representing that map as 9 numbers is an amazing compression ratio. But surely one cannot argue that matrices “have more going on” than arbitrary functions.
- ikiris 3y agoThis is kind of like saying a transistor can't make a decision, all it does is pass electrons or not based on inputs. The decisions happen because of how they're wired.
- jbay808 3y agoI would interpret this as showing that matrix multiplication code is carefully engineered to correctly implement... well, matrix multiplication. Stumbling on that specific mapping of 96 input bits to 96 output bits would be hard to pick out of a hat by chance, from the set of all possible mappings. Learning that precise mapping, starting from a uniform prior and only given a finite set of examples, could be seen as an impressive task, although less impressive than sorting. If a model learns the correct mapping -- and better yet, needs only 9 parameters to implement it -- then I think it's fairer to say the model does matrix multiplication, rather than that the model convincingly imitates the statistics of matrix multiplication.
- Buttons840 3y agoOne of my favorite GTP4 moments shows good understanding on its part. I was talking to GTP4 about the Adam optimization algorithm and it was teaching me how it works (this sentence was surreal to type). At one point we were talking about a mathematical term of the form [ A * (B / C) ]. I was casually fishing for it to make a mistake and I said "I see, and the A term can be moved to the denominator, right?" GTP replied "yes" and then gave me [ B / (C / A) ] -- I guess A can go in the denominator after all. :)
- eximius 3y agoPart 1: what are n-grams Part 2: it's using embeddings (but a lot of words without actually saying it) Part 3: sufficiently trained NNs can sort things, which isn't statistics ----- I actually found some of the article interesting but not terribly convincing. Even though I consider these LLMs to be stochastic parrots, that isn't to say they haven't learned something during training, at least according to the colloquial meaning we typically ascribe to even lower models like MNIST classification. I'm even kind of okay with saying that it reasons about things in the same colloquial sense. In a lot of ways, we just don't have a good definition of what 'reasoning' is. Is it just bad at reasoning because it's input/output/modeling/training is insufficient? Humans struggle to learn multiplication tables when we're young. Are those humans not reasoning because they get the math wrong? But there isn't plasticity, there isn't adaptability, it's unclear to me that you can effectively inform it how to embed truly novel information - surely something that is possible, with some neurons existing for routing and activating other learned embeddings. Anyway, interesting stuff.
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- jbay808 3y agoI'm glad to see someone express this view, because I think this gets to the heart of the question. How does a stochastic parrot learn to sort lists? Embeddings are part of the compression-by-abstraction that I'm explaining in the first two parts, but the embeddings generated by an LLM go beyond the normal word2vec picture that most people have of embeddings, and I believe are closer to whatever "understanding" means if it could be formally defined. It would be quite a coincidence if GPT-4 happened to solve the riddle merely by virtue of "Moonling" and "cabbage" being closely-located vectors.
- eximius 3y agoEh. I still consider them stochastic parrots. My concessions lie elsewhere, primarily in the vocabulary. We refer to algorithms like quicksort as 'reasoning' about the input. So it's fine to use the same sense of the word to apply to stochastic parrots. The difference between an LLM learning how to sort things and compiling an implementation of an algorithm like quicksort is not terribly large, from a certain perspective. I suppose something I'm interested in is whether an LLM that can't sort numbers could be instructed how as a prompt and then do so. There are some examples of similar phenomenon (the one with some kids made up language was interesting) which suggests the LLMs have a lot of space dedicated towards dynamic pattern selection in their context windows (somewhat tautological) in order to have prompts tune the selection for other layers. And, of course, lack of plasticity is really interesting.
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- hexorg 3y agoI generally like the approach, but you can implement sorting of fixed number of items in a Boolean circuit(combinatorial logic, a bunch of ANDs and ORs stringed together). There is no need for recurrence or memory at all to sort a fixed number or items. There are a handful of abstractions possible in Boolean logic than n-gram statistics and there are a lot of Boolean circuits that can be very useful. But I’d like to see neural networks to be able to figure out algorithm that require at least a finite state machine(FSM), but preferably something harder. The challenge is to find a size difference for a problem between FSM and combinatorial logic.