What can we take away from the ‘stochastic parrot’ saga?

41 点作者 Philpax大约 1 个月前

22 条评论

parpfish大约 1 个月前

My take on stochastic parrots is the similar to the authors concluding section.This debate isn’t about the computations underlying cognition, it’s about wanting to feel special.The contention that “it’s a stochastic parrot” usually implied “it’s merely a stochastic parrot, and we know that we must be so much more than that, so obviously this thing falls short”.But… there never was any compelling proof that we’re anything more than stochastic parrots. Moreover, those folks would say that any explanation of cognition falls short because they can always move the goal posts to make sure that humans are special.

评论 #43676848 未加载

评论 #43679481 未加载

评论 #43677482 未加载

评论 #43678945 未加载

评论 #43678226 未加载

评论 #43676824 未加载

评论 #43676853 未加载

评论 #43678651 未加载

评论 #43678336 未加载

Tossrock大约 1 个月前

My favorite part of the "stochastic parrot" discourse was all the people repeating it without truly understanding what they were talking about.

评论 #43676726 未加载

_heimdall大约 1 个月前

This argument isn't particularly compelling in my opinion.I don't actually like the stochastic parrot argument either to be fair.I feel like the author is ignoring the various knobs (randomization factors may be a better term) applied to the models during inference that are tuned specifically to make the output more believable or appealing.Turn the knobs too far and the output is unintelligible garbage. Don't then them far enough and the output feels very robotic or mathematical, its obvious that the output isn't human. The other risk of not turning the knobs far enough would be copyright infringement, but I don't know if that happens often in practice.Claiming that LLMs aren't stochastic parrots without dealing with the fact that we forced randomization factors into the mix misses a huge potential argument that they are just cleverly disguised stochastic parrots.

评论 #43677181 未加载

评论 #43679395 未加载

评论 #43677099 未加载

nopinsight大约 1 个月前

For the skeptics: Scoring just 10% or so in Math-Perturb-Hard below the original MATH Level 5 (hardest) dataset seems in line with or actually better than most people would do.Does that mean most people are merely parrots too?<a href="https://math-perturb.github.io/" rel="nofollow">https://math-perturb.github.io/</a><a href="https://arxiv.org/abs/2502.06453" rel="nofollow">https://arxiv.org/abs/2502.06453</a>Leaderboard: <a href="https://math-perturb.github.io/#leaderboard" rel="nofollow">https://math-perturb.github.io/#leaderboard</a>Anyone who continues to use the parrot metaphor should support it with evidence at least as strong as the “On the Biology of a Large Language Model” research by Anthropic which the article refers to:<a href="https://transformer-circuits.pub/2025/attribution-graphs/biology.html" rel="nofollow">https://transformer-circuits.pub/2025/attribution-graphs/bio...</a>

评论 #43676935 未加载

int_19h大约 1 个月前

The whole "debate" around LMs being stochastic parrots is strictly a philosophical one, because the argument hinges on a very specific definition of intelligence. Thought experiments such as Chinese room make this abundantly clear.

评论 #43677648 未加载

评论 #43676809 未加载

anothernewdude大约 1 个月前

> If a language model was just a stochastic parrot, when we looked inside to see what was going on, we’d basically find a lookup tableI disagree right away. There are more sophisticated probability models than lookup tables.> It'd be running a search for the most similar pattern in its training data and copying this.Also untrue. Sophisticated probability models combine probabilities based on combining all the bits of context, and by fuzzing similar tokens together via compressing (i.e. you don't care what particular token is used, just that a similar one is used.)They're parrots, just better parrots than this person can conceive of.

treetalker大约 1 个月前

> The parrot is dead. Don’t be the shopkeeper.Continuing the metaphor, we never wanted to work in a pet shop in the first place. We wanted to be … lumberjacks! Floating down the mighty rivers of British Columbia! With our best girls by our side!

devmor大约 1 个月前

I am getting fairly tired of seeing articles about LLMs that claim “[insert criticism] was wrong” but offer nothing other than the opinion of the author’s interpretation of a collection of other people’s writings with limited veracity.

skybrian大约 1 个月前

There’s still a lot to learn about how LLM’s do things. They could be doing it in either a deep or a shallow way (parroting information) depending on the task. It’s not something to be settled once and for all.So what’s “dead?” Overconfidently assuming you can know how an LLM does something without actually investigating it.

agentultra大约 1 个月前

The conclusion goes into that glassy-eyed realm of, “what if we’re no better than the algorithm?”Problem is, we don’t even know what makes us think. So you can jump to any conclusion and nobody could really tell if you’re wrong.We do know how transformers and layers work. They’re algorithms that crunch numbers. A great deal of numbers. And we can use the training set to generate plausible outputs given some input. Yes, stochastic parrot is a reduction of all the technical sophistication in LLMs. But it’s not entirely baseless. At the end of the day it is copying what’s in the training data. In a very clever way.However, resist the temptation to believe we understand human brains and human thought. And resist the temptation to anthropomorphize algorithms. It’s data and patterns.

jrmg大约 1 个月前

For a while, some people dismissed language models as “stochastic parrots”. They said models could just memorise statistical patterns, which they would regurgitate back to users.…The problem with this theory, is that, alas, it isn’t true.If a language model was just a stochastic parrot, when we looked inside to see what was going on, we’d basically find a lookup table. … But it doesn’t look like this.But does that matter? My understanding is that, if you don’t inject randomness (“heat”) into a model while it’s running, it will always produce the same output for the same input. In effect, a lookup table. The fancy stuff happening inside that the article describes is, in effect, [de]compression of the lookup table.Of course, maybe that’s all human intelligence is too (the whole ‘free will is an illusion in a deterministic universe’ argument is all about this) - but just because the internals are fancy and complicated doesn’t mean it’s not a lookup table.

评论 #43677903 未加载

alganet大约 1 个月前

"ESSE É UM ESPERTO", or, "this is a smart one", in portuguese.So far, LLM models have not demonstrated grasp on dual language phonetic jokes and false cognates.Humans learn a second language very quickly, and false cognates that work on phonetics are the first steps in doing so, doesn't require a genius to understand.I am yet to see an LLM that can demonstrate that. They can translate it, or repeat known false cognates, but can't come up with new ones on the spot.If they do acquire that, we will come up with another creative example of what humans can do that machines can't.

评论 #43676894 未加载

hulitu大约 1 个月前

> The Parrot Is DeadThe page says "Something has gone terribly wrong :(".He's not dead, he's resting.

cadamsdotcom大约 1 个月前

Why the existential crisis?LLMs are stochastic parrots and so are humans - but humans still get to be special. Humans are more stochastic as we act on far more input than a several-thousand token prompt.

getnormality大约 1 个月前

"Stochastic parrot" is a deepity, an ambiguous phrase that blends a defensible but trivial meaning with a more profound but false meaning.It's true, and trivial, that all next word predictors are stochastic and are designed to generate output based on information from their training data.The claim that this generation merely "parrots" the training data is more significant, but obviously false if you interact with these models at all.

petermcneeley大约 1 个月前

And yet... <a href="https://uwaterloo.ca/news/media/qa-experts-why-chatgpt-struggles-math" rel="nofollow">https://uwaterloo.ca/news/media/qa-experts-why-chatgpt-strug...</a>

lukasb大约 1 个月前

Given LLMs' OOD performance the parrot metaphor still looks good to me

评论 #43676882 未加载

derbOac大约 1 个月前

This struck me as a strawman argument against the "stochastic parrot" interpretation. I really disagree with this premise in particular: "if a language model was just a stochastic parrot, when we looked inside to see what was going on, we’d basically find a lookup table." I'm not sure how the latter follows from the former at all.As someone else pointed out, I think there's deep philosophical issues about intelligence and consciousness underlying all this and I'm not sure it can be resolved this way. In some sense, we all might be stochastic parrots — or rather, I don't think the problem can be waved away without deeper and more sophisticated treatments on the topic.

NooneAtAll3大约 1 个月前

my personal anecdote about stochastic parrot arguments is that the argument itself became so repetitive that its defenders sound as parrots...

kerkeslager大约 1 个月前

> This kind of circuitry—to plan forwards and back—was learned by the model without explicit instruction; it just emerged from trying to predict the next word in other poems.This author has no idea what's going on.The AI didn't just start trying to predict the next word in other poems, it was explicitly instructed to do so. It then sucked in a bunch of poems and parroted them out.And... the author drastically over-represents its success with a likely cherry-picked example. When I gave Claude lines to rhyme with, it gave me back "flicker" to rhyme with "killer" and "function" to rhyme with "destruction". Of the 10 rhymes I tried, only two actually matched two syllables ("later/creator" and "working"/"shirking")I'm not sure how many iterations the author had to run to find a truly unusual rhyme like "rabbit/grab it", but it pretty obviously is selection bias.And...I actually agree with the other poster who says that part of this stochastic parrot argument is about humans wanting to feel special. Exceptionalism runs deep: we want to believe our group (be it our nation, our species, etc.) are better than other groups. It's often wrong: I don't think we're particularly unique in a lot of aspects--it's sort of a combination of things that makes us special if we are at all.AI are obviously stochastic parrots if you know how they work. The research is largely public and unless there's something going on in non-public research, they're all just varieties of stochastic parroting.But, these systems were designed in part off of how the human brain works. I do no think it's in evidence at all that humans aren't stochastic parrots. The problem is that we don't have a clear definition of what it means to understand something that's clearly distinct from being a stochastic parrot. At a certain level of complexity of stochastic parroting, a stochastic parrot is likely indistinguishable from someone who truly understands concepts.I think ultimately, the big challenge for AI isn't that it is a stochastic parrot (and it is a stochastic parrot)--I think a sufficiently complex and sufficiently trained stochastic parrot can probably be just as intelligent as a human.I think the bigger challenge is simply that entire classes of data simply have not been made available to AI, and can't be made available with current technology. Sensory data. The kind of data a baby gets from doing something and seeing what happens. Real-time experimentation. I think a big part of why humans are still ahead of AI is that we have a lot of implicit training we haven't been able to articulate, let alone pass on to AI.

zeofig大约 1 个月前

I'm so glad We have all Decided this Together and we can now Enjoy the Koolaid

pyfon大约 1 个月前

Dead parrot is a Monty Python reference. Also where the Python language get's its name.