It's not a problem, because the point at which we are in the logarithmic curve is the only thing that matters. No one in their right mind ever expected anything linear, because that would imply that creating a perfect oracle is possible.
More compute hasn't been the driving factor of the last developments, the driving factor has been distillation and synthetic data. Since we've seen massive success with that, I really struggle to understand why people continue to doomsay the transformer. I hear these same arguments year after year and people never learn.
I'm going to simply address what I think are your main points here.
There is nowhere that an LLM stores all possible outputs. Causality can trivially be represented by sampling by including the ordering of events, which you also implicitly did for LLMs.
The coin is an arbitrary distinction, you are never just modeling a coin, just as an LLM is never just modeling a word. You are also modeling an environment, and that model would capture whatever you used to influence the coin toss.
You are fundamentally misunderstanding probability and randomness, and then using that misunderstanding to arbitrarily imply simplicity in the system you want to diminish, while failing to apply the same reasoning to any other.
If you are indeed an AI researcher, which I highly doubt without you providing actual credentials, then you would know that you are being imprecise and using that imprecision to sneak in unfounded assumptions.
Again, you're stretching definitions into meaninglessness. The way you are using "sampling" and "distribution" here applies to any system processing any information. Yes, humans as well.
I can trivially define the entirety of all nerve impulses reaching and exiting your brain as a "distribution" in your usage of the term. And then all possible actions and experiences are just "sampling" that "distribution" as well. But that definition is meaningless.
They're not sampling from prior conversations. The model constructs abstracted representations of the domain-specific reasoning traces. Then it applies these reasoning traces in various combinations to solve unseen problems.
If you want to call that sampling, then you might as well call everything sampling.
My point isn't that the model falls for gender stereotypes, but that it falls for thinking that it needs to solve the unmodified riddle.
Humans fail at the original because they expect doctors to be male and miss crucial information because of that assumption. The model fails at the modification because it assumes that it is the unmodified riddle and misses crucial information because of that assumption.
In both cases, the trick is to subvert assumptions. To provoke the human or LLM into taking a reasoning shortcut that leads them astray.
You can construct arbitrary situations like this one, and the LLM will get it unless you deliberately try to confuse it by basing it on a well known variation with a different answer.
I mean, genuinely, do you believe that LLMs don't understand grammar? Have you ever interacted with one? Why not test that theory outside of adversarial examples that humans fall for as well?
Recognizing that it is a riddle isn't impressive, true. But the duration of its reasoning is irrelevant, since the riddle works on misdirection. As I keep saying here, give someone uninitiated the 7 wives with 7 bags going (or not) to St Ives riddle and you'll see them reasoning for quite some time before they give you a wrong answer.
If you are tricked about the nature of the problem at the outset, then all reasoning does is drive you further in the wrong direction, making you solve the wrong problem.
The trick with the 7 wives and 7 bags and so on is that no long reasoning is required. You just have to notice one part of the question that invalidates the rest and not shortcut to doing arithmetic because it looks like an arithmetic problem. There are dozens of trick questions like this and they don't test understanding, they exploit your tendency to predict intent.
But sure, we could ask more questions and that's what we should do. And if we do that with LLMs we can quickly see that when we leave the basin of the memorized answer by rephrasing the problem, the model solves it. And we would also see that we can ask billions of questions to the model, and the model understands us just fine.
No, that's not a conclusion we can draw, because there is nothing much more to do than memorize the answer to this specific trick question. That's why it's a trick question, it goes against expectations and therefore the generalized intuitions you have about the domain.
We can see that it doesn't memorize much at all by simply asking other questions that do require subtle understanding and generalization.
You could ask the model to walk you through an imaginary environment, describing your actions. Or you could simply talk to it, quickly noticing that for any longer conversation it becomes impossibly unlikely to be found in the training data.
It hasn't read that riddle because it is a modified version. The model would in fact solve this trivially if it _didn't_ see the original in its training. That's the entire trick.
It literally is a riddle, just as the original one was, because it tries to use your expectations of the world against you. The entire point of the original, which a lot of people fell for, was to expose expectations of gender roles leading to a supposed contradiction that didn't exist.
You are now asking a modified question to a model that has seen the unmodified one millions of times. The model has an expectation of the answer, and the modified riddle uses that expectation to trick the model into seeing the question as something it isn't.
That's it. You can transform the problem into a slightly different variant and the model will trivially solve it.
No, it's necessary to either know that it's a trick question or to have a feeling that it is based on context. The entire point of a question like that is to trick your understanding.
You're tricking the model because it has seen this specific trick question a million times and shortcuts to its memorized solution. Ask it literally any other question, it can be as subtle as you want it to be, and the model will pick up on the intent. As long as you don't try to mislead it.
I mean, I don't even get how anyone thinks this means literally anything. I can trick people who have never heard of the trick with the 7 wives and 7 bags and so on. That doesn't mean they didn't understand, they simply did what literally any human does, make predictions based on similar questions.
No, you're not. Are you genuinely trying to suggest that LLMs, which can:
- Construct arbitrary text that isn't just grammatically but semantically coherent
- Derive intent, subtle intent, from user queries and responses
- Emulate endless different personalities and their reactions to endless stimuli
- Describe in detail the statics and dynamics of the world, including sight, smell, touch and sound
do not have a model of the external world? What do you think a "corpus" means in this context? How is the "corpus" of sensory and evolutionary data that makes you up in any way different?
LLMs are excellent common sense reasoners, and they generalize just fine. Why exactly do you think they get things _subtly_ wrong? Make up API syntax that looks sensible but isn't actually implemented? In order to make these guesses they need to have generalized, they need an understanding of the structure underlying naming, such that they can produce _sensible_ output even if they lack the hard facts.
A weakness of the current models in some domains considered useful, yes - but not a fundamental limitation of the architecture. I see no consensus on the latter whatsoever.
The ARC challenge tests spatial reasoning, something we humans are obviously quite good at, given 4 billion years of evolutionary optimization. But as I said, there is no "general reasoning", it's all domain dependent. A child does better at the spatial problems in ARC given that it has that previously mentioned evolutionary advantage, but just as we don't worship calculators as superior intelligences because they can multiply 10^9 digit numbers in milliseconds, we shouldn't draw fundamental conclusions from humans doing well at a problem that they are in many ways built to solve. If the failures of previous predictions - those that considered Chess or Go as unmistakable signals of true general reasoning - have taught us anything, it's that general reasoning simply does not exist.
The bet of current labs is synthetic data in pre-training, or slight changes of natural data that induces more generalization pressure for multi-step transformations on state in various domains. The goal is to change the data so models learn these transformations more readily and develop good heuristics for them, so not the non-continuous patching that you suggest.
But yes, the next generation of models will probably reveal much more about where we're headed.
First of all, I would urge you to stop arbitrarily using negative words to make an argument. Saying that LLMs are "blathering" is equivalent to saying you and I are "smacking meat onto plastic to communicate" - it's completely empty of any meaning. This "vibes based arguing" is common in these discussions and a massive waste of time.
Now, I don't really understand what you mean by "almost impossible to maintain long-term context/attention". I'm writing fiction in my spare time, LLMs do very well on this by my testing, even subtle and complex simulations of environments, including keeping track of multiple "off-screen" dynamics like a pot boiling over.
There is nothing "1-dimensional" about the context, unless you mean that it is directional in time, which any human thought is as well, of course. As I said in my original reply, each token is represented by a multidimensional embedding, and even that is abstracted away by the time inference reaches the later layers. The word "citrus" isn't just a word for the LLM, just as it isn't just a word for you. Its internal representation retrieves all the contextual understanding that is related to it. Properties, associated feelings, usage - every relevant abstract concept is considered. And these concepts interact which every embedding of every other token in the input in a learned way, and with the position they have relative to each other. And then when an output is generated from that dynamic, said output influences the dynamic in a way that is just as multidimensional.
The model can maintain context as rich as it wants, and it can built upon that context in whatever way it wants as well. The problem is that in some domains, it didn't get enough training time to build robust transformation rules, leading it to draw false conclusions.
You should reflect on why you are only able to provide vague and under defined, often incorrect, arguments here. You're drawing distinctions that don't really exist and trying to hide that by appealing to false intuitions.
> The reasoning weakness... it's a fundamental architecturally-based limitation...
You have provided no evidence or reasoning for that conclusion. The river crossing puzzle is exactly what I had in mind when talking about specific domains. It is a common trick question with little to no variation and LLMs have overfit on that specific form of the problem. Translate it to any other version - say transferring potatoes from one pot to the next, or even a mathematical description of sets being modified - and the models do just fine. This is like tricking a human with the "As I was going to Saint Ives" question, exploiting their expectation of having to do arithmetic because it looks superficially like a math problem, and then concluding that they are fundamentally unable to reason.
> People like Demis Hassabis (CEO of DeepMind) acknowledge the weakness too.
What weakness? That current LLMs aren't as good as humans when reasoning over certain domains? I don't follow him personally but I doubt he would have the confidence to make any claims about fundamental inabilities of the transformer architecture. And even if he did, I could name you a couple of CEOs of AI labs with better models that would disagree, or even Turing award laureates. This is by no means a consensus stance in the expert community.
You seem to repeatedly insist that hidden computation is a distinction of any relevance whatsoever.
First of all, your understanding of the architecture itself is mistaken. A transformer can iterate endlessly because each token it produces allows it a forward pass, and each of these tokens is postpended to its input in the next inference. That's the autoregressive in autoregressive transformer, and the entire reason why it was proposed for arbitrary seq2seq transduction.
This means you get layers * tokens iterations, where tokens is up to two million, and is in practice unlimited due to the LLM being able to summarize and select from that. Parallelism is irrelevant, since the transformer is sequential in the output of tokens. A transformer can iterate endlessly, it simply has to output enough tokens.
And no, the throughput isn't limited either, since each token gets translated into a high-dimensional internal representation, that in turn is influenced by each other token in the model input. Models can encode whatever they want not just by choosing a token, but by choosing an arbitrary pattern of tokens encoding arbitrary latent-space interactions.
Secondly, internal thoughts are irrelevant, because something being "internal" is an arbitrary distinction without impact. If I trained an LLM to prepend and postpend <internal_thought> to some part of its output, and then simply didn't show that part, then the LLM wouldn't magically become human. This is something many models do even today, in fact.
Similarly, if I were to take a human and modify their brain to only be able to iterate using pen and paper, or by speaking out loud, then I wouldn't magically make them into something non-human. And I would definitely not reduce their capacity for reasoning in any way whatsoever. There are people with aphantasia working in the arts, there are people without an internal monologue working as authors - how "internal" something is can be trivially changed with no influence on either the architecture or the capabilities of that architecture.
Reasoning itself isn't some unified process, neither is it infinite iteration. It requires specific understanding about the domain being reasoned over, especially understanding of which transformation rules are applicable to produce desired states, where the judgement about which states are desirable has to be learned itself. LLMs can reason today, they're just not as good at it than humans are in some domains.