It is true that LLMs often behave haphazardly, and do rely on statistics. But plenty of research has shown them behaving in methodical ways too. There are findings going both ways!
Granted, many of the strongest contradictory results appeared after the Stochastic Parrots paper, so it isn't like they were ignoring the literature at the time. But they did make a very strong claim, and in the half-decade since, a lot of evidence has come out against it.
The point of the paper, in fact, is that language models are getting "too big", and another approach is needed to make progress, so they were certainly predicting things about later models.
With that said, they talked about "pure" language models, so it is fair to say that they didn't talk about, say, LLMs that are multimodal or that have tool use, which are advances that happened after their paper.
We’ve know that since 1943 when McCulloch-Pitts came up with the first “artificial neuron” definition. And since LLMs are a descendant technology — our assumption should be they’re reasoning in some internal learned logic.
This is what the evidence supports — eg, the “stochastic parrot” crowd never can explain transfer learning. Whereas for the internal reasoning crowd that is easy: removing your top level judgments from a theory still leaves you with useful terms for describing a new theory — eg, removing your judgments about “which animal is this?” but preserving the underlying structure for representing an image in your new judgments, “is this cancer?”
There’s 80 years of reason to think DNNs reason and zero support other than “sTaTs R mAgIc!” to support the stochastic parrot interpretation.
Ignorance isn’t argument.
They never are. Ever.
And even when they are: they sure seem to bet against Moore's Law or just the general tendency for things to get better/efficient over time.
It's frankly remarkable how capable the models have become that we can run locally now on a decent laptop.
The same thing happened with image generation. I've had arguments with people that image generators are killing the environment, but I can do it in 20-30 seconds on my GPU. No one bats an eyelash when I play 20-30 minutes or even hours of a video game on my GPU, but the images are burning down the planet.
It's slightly maddening.
It's not a criticism of the paper itself, but multimodal models came shortly after and provide grounding that is more of the sort the paper is getting at, and it didn't seem like anybody updated on that at all. If multimodal models were still stochastic parrots by the original argument, humans would have to be as well; we don't have any way to ground anything beneath sense data and evolution can't have programmed some innate grounding into us because it didn't either. But (and maybe this is my own misperception) nobody threw in the towel at that point.
I confess I never read the original paper until now, opting to absorb by osmosis instead, and I was quite surprised that they don't really make a deeper case than that. After just a few paragraphs about how they can't be grounded because humans don't express their thoughts directly, it lurches into a page about how they can be biased by training. And they certainly can be, but that has little to say about their stochastic nature- humans are biased as a rule with no exception. (For the record, I only read the Stochastic Parrots section before this reply.)
It's not really a bad paper, but I don't see why it ever carried the esteem it did. Hating on it is like hating on Taylor Swift- she's fine, yes, but for her level of success, one is inclined to question every dumb lyric where others get a pass. (Apologies to Swift fans, substitute a successful artist you don't care for here.)
did you think this through?
imagine the sentence was "This sentence has four words", now extrapolate that to all the shit that can exist in a dataset and train a model on that dataset - do you know what will happen? - go ahead and think it through.
"This sentence has five words" is going to appear far more often than "This sentence has four words". This is the entire premise of LLMs working at all, stochastic parrots or otherwise.
>>"This sentence has five words" is going to appear far more often than "This sentence has four words".
it's not about this at all. your point is about data quality. you need to take a step back. the point is that if you trained a language model just on this data set which has sentences akin to "this sentence has two words" - the model is going to learn that. this shows that the language modeling itself doesn't truly provide an understanding of the real world. you can train a language model with the most advanced technology on shitty data and the model will start providing shitty outputs confidently - the model will never say "hey there is something wrong with the data i am trained on". thats what 'understanding of the world' meant in that paper.
I don't know how or if I will notice. That's the biology, chemistry and physics of the brain that I don't know. I hope someone is looking into it. But this does not mean in any way that LLMs are similar to our brains!!!!!! WE DON'T KNOW HOW OUR BRAINS WORK. So going back to language modeling - language models were stochastic in nature when that paper was written, they still are albeit we are trying to make them as deterministic as possible.
But the paper argues a much stronger point than "LLMs are not similar to human brains": it claims LLMs cannot engage with meaning because they model human language instead of sensory input or human thought.
I also don't think human brains are particularly deterministic.
>If multimodal models were still stochastic parrots by the original argument, humans would have to be as well; we don't have any way to ground anything beneath sense data
Animals don't passively learn from their perceptions, don't have a separation between training and inference, and don't have a prompt-response execution model. Besides its fundamental biology, the grounding an animal brain has is that when it outputs a motor signal it receives some feedback as to the effects of a signal of that strength. A bird learns to fly because the grounding truth of aerodynamics and gravity consistently respond in a specific way to the flapping of its wings. It doesn't learn by passively replaying thousands of hours of somatosensory recordings of flights.
A multimodal model doesn't have the capacity to do much with a prompt. It has no head to turn to look at an image from a slightly different angle to attempt to gleam more information, doesn't have the capacity to interact with the real thing the image represents in any way, and even if it requests another angle and is given it, it lacks the capacity to learn that new information permanently. A multimodal model knows about images of pipes and facts about pipes, but doesn't know pipes; it doesn't have literally first-hand experience with them.
>evolution can't have programmed some innate grounding into us because it didn't either.
What do you mean? Of course genetics programs ground truths. For example, "forward is the way your face points when your neck is relaxed" and "if you can feel it, then it's part of your body".
That's not what I said. What I said was that it's physics that provides the ground truth.
>you could, at least in theory, falsify the entire experience of the bird
It wouldn't be a bird anymore, but a dysfunctional cyborg with false perceptions.
>There is nothing in a bird's brain that directly percieves reality.
Yes, of course there is. Animal sensory organs do not produce false information, nor do they provide the brain an interpretation of what they perceive. The brain may fail to distinguish hallucinations from reality, but those are processes internal to the brain. What it gets from the body is raw physical measurements, and what it sends out is raw motor commands.
A multimodal model doesn't have the same direct access to reality, it just has collections of words and images. It has no capacity to determine the reality of a photograph of a sunset or a CGI render of a dragon. The word "real" is itself meaningless to it; they're both real in that they appear in its training corpus. It lacks the capacity to investigate these stimuli in any way, and can just learn to associate different stimuli in arbitrary ways that appear to make sense to us, but nothing else.
The criticism in the paper is of the architecture of LLMs, isn't it? The paper contends
"""Text generated by an LM is not grounded in communicative intent, any model of the world, or any model of the reader’s state of mind. [...] an LM is a system for haphazardly stitching together sequences of linguistic forms it has observed in its vast training data, according to probabilistic information about how they combine, but without any reference to meaning: a stochastic parrot"""
They're saying that the model cannot learn anything about reality irrespective of training data. Your point is an interesting one, but I think it's distinct. To your point though, this is unfalsifiable from the perspective of the "bird". I can't prove that I'm not a dysfunctional cyborg with false perceptions, which makes me wonder if that's a meaningful distinction.
>Animal sensory organs do not produce false information,
I happen to be in possession of some of these and I think this needs a "usually, under ordinary conditions". (Nitpicky and not critical to my point, but I liked the beginning of this sentence too much to edit it out)
>nor do they provide the brain an interpretation of what they perceive
Whereas this I'd argue is not true at all. My cones interact with wave-particle photons at particular wavelengths. I can't even conceptualize wave-particle duality (though some humans can), but "red" and "blue" are the bread and butter of my visual consciousness. These correspond to firings of my sensory neurons much more than they correspond to anything in reality. If that's not interpretation, what is it/what is interpretation?
>The word "real" is itself meaningless to it; they're both real in that they appear in its training corpus.
I expect we agree that I can show a multimodal model a real and a CGI picture and it can tell me which is which. I can take a CGI dragon to GPT 5 and say "look what I found in my backyard" and it will say "Yeah right". Are you saying this is something only possible thanks to RLHF or other modern techniques? That may be the case, unsure how to test that without access to pretraining-only models. Or would you say my experiment is faulty here and doesn't get to your underlying claim?
On the flipside, I could show some meh drawings of fairies to Arthur Conan Doyle, and he'd say "Whoa, this changes everything". I consider him to be one of the great rational minds of history, but he was unable to pass your test here. (In fairness, he was in his 60s and his senses may have dulled, though his belief in spiritualism at large dates to his prime).
Appreciate the conversation!
It doesn't matter if you are one. If you were an AI researcher and encountered a model that saw things for what they really are you would deem it to be malfunctioning and discard it. Any functional model will always be at least one degree further removed from a raw sensory experience that agrees with yours, than you. Its perception will be invariably filtered through the lens of labeled human output.
>I happen to be in possession of some of these and I think this needs a "usually, under ordinary conditions".
I won't dwell much on what you mean, since you said it's unimportant, but it takes a lot for sensory organs to malfunction, and even when they do, they produce corrupted, not false, information; black blotches, not pink elephants. I assume you were thinking of alcohol or something; drugs that affect perception affect the brain, not the other organs.
An interesting edge case is stuff like entoptic phenomena, but those aren't false perceptions; they're, if you will, hypertrue perceptions (something so true, we would rather not see it).
>but "red" and "blue" are the bread and butter of my visual consciousness. These correspond to firings of my sensory neurons much more than they correspond to anything in reality. If that's not interpretation, what is it/what is interpretation?
"Red" and "blue" are your brain's interpretation of the signals it receives from the eye. Notice how "red" and "blue" are fully abstract words, decoupled from anything physical, whereas if I were to refer to the actual encoding of visual information that passes through the optic nerve, I'd have no choice but to reference physical processes (probably talk about voltage and action potentials; I honestly don't know how the optic nerve works). That's because a retinal cell is a simple transducer. The eye doesn't interpret, it merely converts and encodes. Interpretation is a higher level operation.
You cannot compare the simplicity of the mechanism of a whole eye to the indescribable complexity that is between the sentence "roses are red and violets are blue" written on a book, and the raw perception of red roses and blue violets. But a model will only ever be exposed to that distant interpretation, not to roseness or redness.
>I expect we agree that I can show a multimodal model a real and a CGI picture and it can tell me which is which.
Of course, but obviously that's not something it knows inherently. There's nothing about the image intrinsically that says it's fake; someone has to label it such that the model can associate it with unreality (according to our own parameters). That's not how an animal works. An animal assumes what its senses perceive is real and can distinguish its own thoughts from its sensory input.
>On the flipside, I could show some meh drawings of fairies to Arthur Conan Doyle, and he'd say "Whoa, this changes everything". I consider him to be one of the great rational minds of history, but he was unable to pass your test here.
That's a slight equivocation. He would not have mistaken the drawings of fairies for raw perceptions of fairies, he would have simply been swayed by the rhetorical strength of the testimony implicit in the drawing (and perhaps an explicit one that accompanied it). If you want to make a true parallel to a multimodal model you'd have to compare a CGI fairy and a photograph of someone holding a drawing of a fairy. The CGI is as raw to the model as the signals passing through your optic nerves right now, but the drawing is one level of abstraction further away.
> What it gets from the body is raw physical measurements
No, sensory organs like eyes do a lot of processing ("interpreation"). They certainly don't send "raw physical measurements" to the brain.
I'd argue this isn't true today, but that the loop for incorporation is long (ie, the next training or finetuning run).
>A multimodal model knows about images of pipes and facts about pipes, but doesn't know pipes; it doesn't have literally first-hand experience with them.
Wouldn't this mean that any human who hasn't seen a pipe in person or interacted with it, similarly doesn't "know" a pipe? Most of us haven't interacted with the vast majority of "things" in the world, yet we're still able to build a model and abstractions for them such that we can reason about them, right?
> LMs are not performing natural language understanding (NLU), and only have success in tasks that can be approached by manipulating linguistic form
... which is presented as unarguable fact, yet is untrue. It was obviously wrong at the time it was written and it's been proven wrong in many ways since. Worse is that they're still at it. In the article she's saying:
> Q: What are the most common misconceptions about the “stochastic parrots” metaphor? Bender: I think one of the biggest ones is, “Bender says AI is a stochastic parrot.”
Her name is on a paper titled "On the danger of stochastic parrots". It has a section titled "Stochastic parrots" and in section 6.1 it says:
> An LM is a system for haphazardly stitching together sequences of linguistic forms it has observed in its vast training data, according to probabilistic information about how they combine, but without any reference to meaning: a stochastic parrot.
She did say that, as clear as day. Now she's trying to rewrite history. Ugly behavior.
If you train it on lots of working code, then it's useful for coding. If you trained it primarily on non-working code it would produce nonsense.
The claim is odd in another way: you can train a person on non-working code and they'll produce nonsense. That doesn't mean people are just stitching together words they've previously seen.
And, not to insult you, but it's quite obviously wrong. As a mental model it fails to explain basic capabilities. How can an LLM follow elaborate instructions? How can it respond appropriately to user input, when the user input doesn't match any previously seen text? Hell - how does it even balance parentheses? There is no way to explain any of this without conceding that the LLM has semantic understanding. It knows that this comes after that, but "this" and "that" can be at an arbitrary level of abstraction.
Sure - they generate text "like" text they've seen before. That "like" does a ton of heavy lifting.
Look, some folks are more impassioned about this stuff than I am. Maybe that's a good thing. But LLMs do in fact just try to predict the next token, using a very big training set. They're very impressive (at tasks the training set prepares them for). But that's how they work.
The issue in this discussion is that "predict the next token" is a problematically reductive description of what's going on. It's like saying compilers are programs that emit bytes or that humans are mammals that make sounds. It's not strictly false but it's not capturing the depth of what's happening either.
A simple way to see this is to ask: predicting the next token of what? The obvious answer - predicting the next token that would be found in the training set - isn't correct. If that's what it were doing then it would yield no prediction or random predictions for any prefix not found in that training set, but it isn't what happens. We see generalization and reasoning. They can answer questions never asked before. And once post-training kicks in the question of what it's predicting becomes even harder. It becomes more like predicting what this specific AI assistant would say next, which is a circular definition.
Ok. Now. I think you're adding something to the description above. Maybe what you're describing is something "emergent," or maybe it's basically just word vectors that were built in on purpose. You may be adding something correct, or something incorrect. Fine.
But it's not reasonable to say that the "reductive" description above is a "lie". It's not. It's more like a recipe. If you look at correct instructions for making steak, and you call the author a "liar" then you are missing something important.
"interpolate" in what vector space, pray tell? What does "interpolate" even mean, when I prompt it "write me a story about a sentient banana in the style of Hemingway and oh make it a commentary on class consciousness"? You can't assemble such a thing by cutting and pasting pieces of other text. That kind of "interpolation" has to happen at the semantic level - ipso facto, there is a semantic level.
Not to mention that no, they don't predict the next token in the training set. Give any LLM the first paragraph of any Wikipedia article - almost certainly in the training set, and uniquely so - and it won't predict the next word correctly, a lot of the time. But it will predict a word that is grammatically correct, stylistically apropos, and most likely factually correct. So what's it really doing, hm?
LLMs aren't even large enough to contain their training data - not even remotely close. It can't "stitch together things it saw" because it doesn't remember them. It only remembers the ideas used to construct them. The learned abstraction is the entire point of the exercise. LLMs would be useless if they were overfit the way you say they are.
you are grossly negligent of LLMs are created. I would highly recommend reading about post training, RLHF, alignment etc. First pass of training is literally "predict the next token". That's it. The first pass is also known as pre training. There's a shit ton of work (instructions, tool use, and reasoning etc) that's done afterwards because the pre-trained is model is useless. if you have some free time, I'd recommend doing this course - https://www.deeplearning.ai/courses/post-training-of-llms
> What are the most common misconceptions about the “stochastic parrots” metaphor?
Bender: I think one of the biggest ones is, “Bender says AI is a stochastic parrot.”
But in the paper itself we see her say exactly that, several times. What she's trying to do now is wordsmith out of it by claiming that in her world LLMs are totally unrelated to AI, so when she said LLMs are stochastic parrots she wasn't making a claim about AI.
Nobody else defines AI to exclude LLMs, nor did they at the time, and it wasn't the core of the argument she made either. But then she admits that the paper does "generalize towards AI" at the end. So... whatever.
It's morally important to reject this stuff. When academics play word games it devalues all institutional output.
>> > LMs are not performing natural language understanding (NLU), and only have success in tasks that can be approached by manipulating linguistic form
did you read the paper carefully??? this line is directly cited from another paper (pay attention - it's from july 2020) . the full line is - LMs are not performing natural language understanding (NLU), and only have success in tasks that can be approached by manipulating linguistic form [ 14 ]
the paper its cited from is titled - Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data
>>>She did say that, as clear as day. Now she's trying to rewrite history. Ugly behavior.
did you read the article after that line or was there a shortage of attention span??
It's her own paper, she's citing herself. And the full sentence is "As we discuss in §5, LMs are not performing..." so she's actually just teeing up the same claim she's made previously for further discussion. Why are you trying to claim I'm misrepresenting her words?
> did you read the article after that line or was there a shortage of attention span??
I did. It's more of the same, so there's nothing to say about it.
And secondarily, and maybe only partially the authors’ fault, is the enormous tidal wave of morons that this paper minted who plague us with their misunderstandings to this day.
>>And my criticism is that if you observe Claude code with opus 4.8 working through an entirely novel problem that nobody has ever worked on before and which certainly wasn’t in its training data, the choice to even metaphorically call them stochastic parrots turned out to be egregiously wrong.
First of all, for your own benefit - Claude code stopped showing the real reasoning. It only shows a summarized version of it now. So don't ever ask someone to observe the model. Second, do you know when this paper came out? do you know when opus 4.8 came out??? how do you know what is novel? did opus 4.8 tell you it was novel? how do you know no one has worked on it before?
For example, note my use of the phrase “turned out” and then consider whether your main point about the release date of plus 4.8 vs the paper makes any sense at all?
> how do you know what is novel?
Because I work on novel ASICs that were created by my company and have never been used or seen outside of my company?