What Emily Bender meant by "stochastic parrots"

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• Hacker News
• I think this is the most measured take I've seen from Bender, and I think it summarizes her only compelling point well (technologies should be referred to specifically rather than generally as AI, and that referring to everything as AI is not useful and helps hype the technology in a way that benefits those selling it).

  In her previous interviews, I've found her assertion that LLMs aren't useful and will never be good at anything totally uncompelling. Also laughed at this quote as she's been pretty harsh IMO on "the people who like the systems".

  > it’s all about trying to make vivid to people who aren’t in the business of building language technology what these systems actually do, which is not the same thing as insulting the systems or insulting the people who like the systems.

  by petergs
• Bender's paper had this to say about stochastic parrots:

  "Contrary to how it may seem when we observe its output, 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."

  This was not even a correct criticism in 2021. She is right that, at the time, the pretraining -- where it learns to predict missing words in pre-existing corpuses of text -- is basically a stochastic parrot.

  But nowhere in her paper does the term "reinforcement" come up. At the time, this was done mainly through RLHF (reinforcement learning from human feedback) - after the initial training is done, you then tune the model's responses based on human grading. Humans imbue their own meanings into the parameter weights through their judgment.

  At this point, they aren't really stochastic parrots anymore, because parameter weights have been shaped beyond the text corpus. It's not purely probabilistic in the sense of using the probabilities of the underlying text sequences. (It still is probabilistic in its output, but that is a pointless claim, because all events in the universe are also probabilistic; it is not enough to merely claim that probability is involved in some way in the outputs.)

  RLHF was already in use prior to the paper, and was written about by Christiano in 2017 "Deep reinforcement learning from human preferences," so it's surprising that Bender apparently didn't know about this well-known paper.

  RLHF was also, of course, a precursor to a more advanced form of parameter shaping - reinforcement learning with verified rewards, or RLVF, which has driven a lot of the gains in verifiable domains lately. That was not done in 2021 when she wrote the paper. But if you knew about RLHF -- and knew how Alpha Zero worked, with training neural nets on game rollouts -- you could squint and see that it might be useful for language models.

  So after being proven to not only having a limited understanding of the field at the time, but also not being able to forecast the field, she's now walking back what she meant by "stochastic parrot," I assume because she believes readers will not read what she wrote. But despite the protests, her original claim was that it is a parrot because the text has no meaning -- a direct quote from the paper, which only really makes sense if training stops at the pretrain.

  by mediaman
• Even if pre-training was the only training step, it still wouldn't necessarily follow that the only thing the model is doing is stitching words together probabilistically, unless you expand the definition of "probabilistically" to the point that it becomes meaningless. This kind of thinking assumes that design of the training process and the "design" of the artifact that training produces must be similar.
  by emtel
• She discusses it more in this debate (?) discussion (?): https://www.youtube.com/watch?v=MwfSCCo6jXs . I found it interesting but ultimately thought much less of her and her argument afterwards.
  by losvedir
• If your field (NLP/computational lingustics) has evaporated the next best thing to do is hit the pundit circuit.
  by a34729t
• No you see, she says right there in here "Frequently Unasked Questions" (lol) that she is in fact not at all bitter about her field being effectively solved by someone else, thank you very much! Her preferred approach is in fact alive and well, more important than ever and still very very interesting. One wonders if her PhD students feel the same.

  The authors of the paper make some good points (in it and elsewhere), but I've seen precisely nothing that suggests they aren't awful people who resort to playing the victim card, inciting cancel mobs and in general behaving like the worst stereotypes of "leftist academia" in response to any criticism.

  by kilpikaarna
• Personally, I've always read that paper as a political criticism of industry and industrialized research and capitalism. After decades in academic (and industrialized research) I've learned that smart people can write convincing takedowns of things they hate- and those takedowns, due to being well written, often punch above their weight in terms of impact on the community.

  I think this paper would have been best split off from the conjoined criticism of environmental effects (which could have been its own paper, but not one published by Google, since their leadership's fundamental beliefs disagree with the paper's environmental impact premise. And the remaining part on text models could have been a bit more focused on the technical issues associated with statistical text processing and meaning, rather than criticism of the power structure that is loosely associated with the current AI push.

  by dekhn
• I paid a bit of attention to this paper and the phrase 'stochastic parrots' when it came out and i thought this was worth saying and doing at that time. their suggestions about financial and environmental costs are worth studying, their concern about carefully evaluating datasets to feed to the model rather than feeding the entire internet is fully justified. so - to everyone saying this was a bad paper; if you have actually read the paper then please list a few criticisms. all i have seen is "oh this wasn't that good of a paper" or "can't believe how bad this paper was".
  by dwa3592
• It is a good blog, not a good paper.
  by matusp
• My criticism centers on the part of the paper they chose for their title, the “stochastic parrot” metaphor. 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.

  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.

  by daishi55
• The authors were wrong about their core thesis and are now lying about it. That's the only criticism needed. They said, quote:

  > 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.

  by mike_hearn
• The contention that there is no grounding because the training data is linguistic and thus can only reference a world model is disproven in "This sentence has five words"- there's real, grounded information about what "five" means within that sentence. While that's a trivial counterexample, I don't know that it's an obvious one (I didn't come up with it myself).

  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.)

  by ToValueFunfetti
• Those costs have to be compared to the way things are currently done without AI.

  They never are. Ever.

  by CamperBob2
• My main criticism of the paper is that it says LLMs work "haphazardly", using probabilistic information. That is a hypothesis, but it is stated as a known fact, a fundamental limitation.

  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.

  by azakai
• Her language consistently defines LLMs in negative terms like “synthetic text extruder” but she claims she’s not trying to denigrate it. What’s missing for me are similar terms from her about how humans create sentences and thoughts. Judging by the state of the internet humans are quite capable of making shit up to argue their point (see latest Fox News apology). She talks about sycophantic AI but give me a car battery and some cables and I can train a sycophantic human (no I can’t but there are people who can). She’s pretty much a walking counter argument for her own claims.
  by lowbloodsugar
• Bender's linked May 12, 2026 post "Frequently Unasked Questions", https://medium.com/@emilymenonbender/stochastic-parrots-freq... , was a better read.
  by troelsSteegin
• > when OpenAI imposed ChatGPT on the world...

  OpenAI offered ChatGPT to the world. A large, monied cross-section of the world had yet to throw its capital behind the Large Language Model technology that made the ChatBot possible. While it is fair to see AI development now as a global imposition, OpenAI did not have the agency as a 2022 startup to impose on the scale we see now.

  by waffletower
• > A large, monied cross-section of the world

  I asked Mistral, and it guestimated that Altman, Thiel, Musk, and Hoffman had like $20.3B together when they founded it. Sound to me that the founding of OpenAI was exactly the point when the monied world threw its dollars behind AI.

  by delis-thumbs-7e
• I think "imposed" is a pretty fair word. LLMs already affect everyone's lives whether you use them or not and their footprint is still growing. It's like I don't have to buy a car, but cars are absolutely imposed on everyone anyway.
  by tootie
• I agree with a lot of her points but that word really is revealing of her thoughts about OpenAI.
  by magicmicah85
• > With the octopus thought experiment, I initially had told the story in terms of a dolphin, because dolphins clearly are intelligent animals. My co-author on that paper, Alexander Koller, said it should be an octopus, because first of all, the environment that octopuses live in is much more distinct from where people live. It makes the metaphor more vivid, that the octopus is just feeling these pulses in the cable and has no way to look at what the people are looking at.

  On a completely tangential sidenote, octopusses are actually very very intelligent: https://www.nhm.ac.uk/discover/octopuses-keep-surprising-us-...

  by delis-thumbs-7e
• The continued use of animal metaphors is doing them a great disservice. Esp as we learn more about animal cognition, on first look, it smacks of human exceptionalism that has littered the historic scientific consensus.

  Now if they had said, "Imagine your average American ..." (/s)

  by genxy
• Also, last time I checked, the environment where octopuses live is actually the exact same environment where dolphins live?
  by rob74
• It's such a tragedy that they're also extremely solitary animals and die shortly after reproducing the first (and only) time.

  Almost all other particularly intelligent animals seem to be gregarious, and it's easy to conclude that a social lifestyle tends to select for more intelligence, a sophisticated theory of mind, and so on (I like to think that that's exactly what was responsible for a runaway intelligence explosion in humans). But in the case of cephalopods, there's something else that has been applying selection pressure towards exceptional intelligence.

  by Sharlin
• Here is what Jeff Dean said about the firing at the time: https://docs.google.com/document/d/1f2kYWDXwhzYnq8ebVtuk9CqQ...
  by ayhanfuat
• You can always count on management to tell it like it is /s
  by guelo
• > resignation

  I appeciate short letters like this that get straight to the point...

  by f33d5173
• She was probably repeating behaviors she learned in academia. These kinds of extremely toxic "don't just apologize for disagreeing with me but also give me the name of every person involved for collective punishment" is a classic move for academic tyrants throwing weight around. The understanding there is that they will then move to cut off every named person from power or access to academic resources. Google did the right thing by protecting their people.

  I have watched it happen multiple times that someone from academia joins a research group at a large corporation and finds out to their chagrin that they can't just overtly bully colleagues as easily without tenure.

  edit: And it looks like Mitchell immediately left an angry comment about being called a "linguist" despite the article never doing so. Starting to notice a pattern of confrontation, ego, and escalation among some of these people.

  by viccis
• > in part because Google fired two of the authors, Timnit Gebru

  I remember being angry about this situation when I first saw it on social media, until I read the details: This person submitted a list of demands to her employer and said that if they weren’t met, she quit. Google wasn’t going to meet her demands so they considered it acceptance of her resignation. There has been a movement trying to debate whether it was a firing or resignation ever since.

  The original paper they published gets recirculated every year or two as some landmark history of AI safety, but as other commenters have noted it wasn’t really a great paper nor was it groundbreaking at the time. If not for the controversy surrounding the resignation/firing (depending on your POV), I don’t think it would have been notable.

  by Aurornis
• Pressuring an employee to add unethical behavior or specific religious practices to their job description is constructive termination.

  I'd say what's under debate is whether uncritical LLM adoption is mainly unethical or mainly religious.

  by myhf