> 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.
True but also... she wasn't a software engineer putting code in production nor a researcher working no the fundamentals of machine learning negotiating a raise.
She was part of the "Ethical Artificial Intelligence Team" of what was then, and still is now, one of the corporations World wide spending the largest amount of resources precisely on using AI commercially.
> 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.
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)
After having used LLMs for some time now, I don't agree with the concept they are just token generators, unless you think that's all humans are too. The way we test in most schools is just picking the right token. We also give them unique problems that they never saw in their training, which is the nature of programming. I realize they are probabilistic token generator models, but I find it harder and harder to accept that somehow there isn't something more going on. I'm not sure whether they are intelligent or not, but for the most part token generation is how you get degrees too. The thing is a parrot just says things it has already heard, it doesn't perform complex reasoning on novel situations and then explain it succinctly.
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.
I'm sorry but I do tend to feel like this muddies up the discussion on "what this technology really is".
I think "artificial" is actually a pretty good term to describe the output of the models. That output does appear to resemble at least some definition of the word "intelligence" - there is some ability there to do cognition over information that's been provided to them in-context.
What is it to understand, then? If they can work in complex domains and produce coherent output, it would seem to necessitate at least some definition of "understanding" of the corpus, even if that understanding is unlike how a human's brain would understand it.
What else should we call them then? They model language and information in ways that allow them to manipulate it on the fly. They do so 'unnaturally' from a human's point of reference.
I legitimately can't come up with a better term than 'artifical intelligence' -- not to be confused with artificial consciousness, which I don't think exists (yet).
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.
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".
What I have been doing in many places—the octopus thought experiment, stochastic parrots, the phrase “synthetic text-extruding machines”—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
> Meanwhile, O, a hyper-intelligent deep-sea octopus who is unable to visit or observe the two islands, discovers a way to tap into the underwater cable and listen in on A and B’s conversations. O knows nothing about English initially, but is very good at detecting statistical patterns. Over time, O learns to predict with great accuracy how B will respond to each of A’s utterances. O also observes that certain words tend to occur in similar contexts, and perhaps learns to generalize across lexical patterns by hypothesizing that they can be used somewhat interchangeably. Nonetheless, Ohas never observed these objects, and thus would not be able to pick out the referent of a word when presented with a set of (physical) alternatives.
This seems kind of obviously wrong at least in the context of coding agents. These models get trained on actual output of the previous version of the model doing its job, often "IRL" on a real computer/project. It's like O is in the conversation for years now and learning from his own interactions between A <-> O <-> B, where A is the human and B is the computer.
The idea O ontologically has never "observed" "these objects" or referents is philosophically strained. Have I observed the moon, or a finger pointing at the moon? Have I observed `sed` more than Fable?
Anyone who has spent time with parrots would realise that they can understand the meaning of speech without knowing what the words mean. Then somehow the meaning of word parts, and then you will find them making new words out of other words. Very clever indeed.
So stochastic parrots could indeed be a good description of LLMs. But I think that she meant it as a diminishing term (against the technology) which is pointless. Probably more of a reaction against SV tech bros than more nuanced interpretations.
Five years on, which term do we see as less accurate to describe LLMs? Artificial Intelligence or Stochastic Parrot? I guess it's still an open debate.
I think "(intelligent) language understander" is an apt term. It contains within it the fact that these models are mainly trained on text, and "understand" it beyond a simple token-by-token level (i.e. their latent space maps to more and more complex concepts).
It also separates them from "world understanders" since any understanding they might have about the world comes from text (or images if we include multimodal models). They do not gather experience, memories or other "qualia" that many people (me included) would probably include in a definition of human experience/intelligence.
(fwiw i think artificial intelligence is a good, broad term, but it is both too broad to describe the current sota, and too loaded nowadays to be using in nuanced discussions)
>Which frame inspires a more productive research program?
I know what you're implying, but it's not nearly as clever of a comeback as you think.
This question depends on how you define research productivity. There is close to two hundred AI papers published every weekday. Most of them are about GenAI. Despite that the progress in actual model improvement had mostly stalled. If you interact with latest "raw" models they display all of the issues we've seen in GPT-3.5, just at a smaller rate.
The new "hotness" in AI is clearly building more and more elaborate harnesses. This is not at all the direction AI boosters have predicted couple years ago.
There seems to be some confusion between "we can" and "we should" in your comment. Bender (and others) are not discussing the capabilities, but rather (a) the fundamental mechanism(s) (b) the advisability and desirability of deploying systems that use these mechanisms.
> Which has better predicted the trajectory of capabilities over the past five years?
By that standard, parrots, and it's not even close. The framing of intelligence led to an enormous number of predictions that simply haven't been realised: an end to all white collar work, UBI, a total revolution in society, a literal robot god.
People are so desperate to view 'stochastic parrots' as dismissive that they misread the original argument while quickly ignoring all the failed predictions about how AI was going to overturn, save, and destroy everything.
The latter is definitely more colorful, and reflects a parrot's tendency to glom on to patterns. "Not X, but Y" being one of the more infamous ones.
Once in frustration I called a certain frontier model "Sam Altman's Tin Bird" to another agent with memory, and ever since then that other agent refers to ChatGPT as "the tin bird". Definitely a RAG artifact more than an attractor in that case, but I found it amusing.
Its less of open debate would say, and although superposition [1] is interesting, as a way to explain power of some effects, it is clear they are right now closer to Stochastic Parrots than AGI.
Why do I say that? Because you can trivially beat most guardrails, simply by encoding your prompt in base64 for example. :-) Just word matching...no real understanding.
Nearly all (99%+) people who use this phrase are anti-AI and just looking to show off how much they dislike AI and how clever they can be in insulting it.
So it's a great phrase because in just about every case I can ignore what someone says afterwards.
afaik before the final sampling, every "next" token has a probability, so theoretically it could select the 10 most likely tokens (based on some kind of sampling algorithm), but you'd end up with exponentially many output-sequences, so nobody does that.
I think the point the poster above was making is that it doesn't predict a phrase or anything like that - just the single next token. So all 10 or 1000 or whatever number of tokens you want are each individually candidates for the single next token, not a sequence of 10 or 100 next tokens. If you wanted to create multiple possible seuqneces, you'd then feed each of the 10 tokens to the network in the initial state, and extract the next token (or 10 next tokens) from that one, than revert back and feed another single one of the 10 tokens, etc.
it annoys me how eager people are to hurl the word stochastic as pejorative. Statistics are a great tool for gleaning information from stochastic processes; statistics don't contribute randomness. Random sampling is necessary in order not to bias a sample, it's not used to contribute randomness to the sample but to preserve/measure the underlying distribution. (not meant to imply that training is random sampling)
The term is not very useful since most humans are stochastic parrots... At least most of the time.
Not suggesting that I don't say stuff on autopilot sometimes but for many people, it's their only mode of operation. They never actually think about anything from first principles. Their whole approach to language is just chaining catchphrases together. It's how a toddler thinks; it seems like many people never moved past that stage of development.
Conversely, that the most prominent proponents of LLMs call them artificial intelligence and then treat them like slaves they're free to abuse ought to be horrifying.
It sometimes feels same as with the models, especially in corporate:
- Lots of Haiku around, many mistakes unless process is very clear
- Some Sonnets, still do mistakes but can adapt
- Some Opus, able to improvise and think outside the box.
But even the Human Opus/Mythos are hilariously wrong sometimes.
Humans are not stochastic parrots. You are 100% wrong about toddlers. This was clearly explained by St. Augustine 1500 years ago:
Did I not, then, as I grew out of infancy, come next to boyhood, or rather did it not come to me and succeed my infancy? My infancy did not go away (for where would it go?). It was simply no longer present; and I was no longer an infant who could not speak, but now a chattering boy. I remember this, and I have since observed how I learned to speak. My elders did not teach me words by rote, as they taught me my letters afterward. But I myself, when I was unable to communicate all I wished to say to whomever I wished by means of whimperings and grunts and various gestures of my limbs (which I used to reinforce my demands), I myself repeated the sounds already stored in my memory by the mind which thou, O my God, hadst given me. When they called some thing by name and pointed it out while they spoke, I saw it and realized that the thing they wished to indicate was called by the name they then uttered. And what they meant was made plain by the gestures of their bodies, by a kind of natural language, common to all nations, which expresses itself through changes of countenance, glances of the eye, gestures and intonations which indicate a disposition and attitude--either to seek or to possess, to reject or to avoid. So it was that by frequently hearing words, in different phrases, I gradually identified the objects which the words stood for and, having formed my mouth to repeat these signs, I was thereby able to express my will. Thus I exchanged with those about me the verbal signs by which we express our wishes and advanced deeper into the stormy fellowship of human life, depending all the while upon the authority of my parents and the behest of my elders.
Humans learn language opportunistically. Toddlers start with a powerful "superchimpanzee" understanding of the real world, and use that to learn words in order to satisfy their needs and desires. Statistical frequency is incidental to what words a toddler learns: what matters is the real-world context. Also note how important it is that infants instinctively understand nonverbal communication.
The most depressing thing about the 2020s AI summer is watching ignorant tech workers use the success of LLMs to launder their own ignorant misanthropy. Your views are many many centuries out of date.
> It argued that large language models (LLMs) generate text by statistically predicting likely sequences of words rather than understanding what they are saying—a process the authors captured with the metaphor of a “stochastic parrot,” a system that repeats patterns without comprehension.
I don't understand what we're setting the record straight on. This is the core point of dispute, and the author just blazes past it to focus on other things. I'm glad to hear "stochastic parrot" isn't intended as an insult, and I agree that it's not right to think of LLMs as a box with a little homunculus inside replying to you. But to me it seems obvious that LLMs are not repeating patterns without comprehension and do understand what they are saying; otherwise they would not be capable of doing things they routinely do.
I think it’s pretty clear that they are repeating without “comprehension” - both mechanistically (as in there is no facility for comprehension in their formulation) and in the ways they fail. The standard rs in strawberry, should I walk or drive to the car wash, etc all play on the fact that they don’t have any real world model or thoughts against which they can judge their output, as do many of the jailbreaks which basically play on the fact that the model has memorized patterns.
There are people who argue semantics, that we can call the pattern matching that LLMs do “understanding”, or the moronic “how do we know that’s isn’t all we do” but for the normal use of comprehension, LLMs at a fundamental level don’t.
> But to me it seems obvious that LLMs are not repeating patterns without comprehension and do understand what they are saying; otherwise they would not be capable of doing things they routinely do.
So this seems obvious to you, and yet to many others, it is equally obvious that LLMs can/could do the things they routinely do without any meaningful sense of "understanding".
This is a facile point. Lisp expert systems transparently don't understand the meaning of any symbols they process, yet with enough developer elbow grease they can do all the same things an LLM can do, with much higher reliability. The fact that LLMs are less transparent than Lisp expert systems (and easier to program) is extremely bad evidence that they understand language. Especially given that AFAICT Opus does not properly understand concepts like "four."
> 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.
I think this metaphor is so strained as to not be useful. I think key here is that the authors say "without any reference to meaning", which is a heavily loaded term, that does definitely apply to parrots, but does not apply when you apply it to immense bodies of text.
Namely that language embeds meaning in language. A sentence being written by a human (as a starting point) is designed to have consistent meaning. While it is possible to write syntactically correct meaningless text, that is not what most of human language has done; the meaning cannot be removed from the text.
This I think is clarifying, from the same paragraph in the text:
> ... the training data never included sharing thoughts with a listener, nor does the machine have the ability to do that.
That's just facially incorrect. The training data is entirely about sharing thoughts with a listener. Else why is the text being written?
I think this is the most measured take I've seen from Bender, and I think it summarizes her most 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 haven't found her essential assertion that LLMs aren't useful and will never be good at anything a lot less compelling. 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.
> 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.
True but also... she wasn't a software engineer putting code in production nor a researcher working no the fundamentals of machine learning negotiating a raise.
She was part of the "Ethical Artificial Intelligence Team" of what was then, and still is now, one of the corporations World wide spending the largest amount of resources precisely on using AI commercially.
> 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-...
It's such a tragedy that they're also extremely solitary animals and die shortly after reproducing the first (and only) time.
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)
Here is what Jeff Dean said about the firing at the time: https://docs.google.com/document/d/1f2kYWDXwhzYnq8ebVtuk9CqQ...
After having used LLMs for some time now, I don't agree with the concept they are just token generators, unless you think that's all humans are too. The way we test in most schools is just picking the right token. We also give them unique problems that they never saw in their training, which is the nature of programming. I realize they are probabilistic token generator models, but I find it harder and harder to accept that somehow there isn't something more going on. I'm not sure whether they are intelligent or not, but for the most part token generation is how you get degrees too. The thing is a parrot just says things it has already heard, it doesn't perform complex reasoning on novel situations and then explain it succinctly.
They are just token generators. It is just that 'just' does a lot of lifting!
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.
I'm sorry but I do tend to feel like this muddies up the discussion on "what this technology really is".
I think "artificial" is actually a pretty good term to describe the output of the models. That output does appear to resemble at least some definition of the word "intelligence" - there is some ability there to do cognition over information that's been provided to them in-context.
What is it to understand, then? If they can work in complex domains and produce coherent output, it would seem to necessitate at least some definition of "understanding" of the corpus, even if that understanding is unlike how a human's brain would understand it.
What else should we call them then? They model language and information in ways that allow them to manipulate it on the fly. They do so 'unnaturally' from a human's point of reference.
I legitimately can't come up with a better term than 'artifical intelligence' -- not to be confused with artificial consciousness, which I don't think exists (yet).
> 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.
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".
What I have been doing in many places—the octopus thought experiment, stochastic parrots, the phrase “synthetic text-extruding machines”—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
> Meanwhile, O, a hyper-intelligent deep-sea octopus who is unable to visit or observe the two islands, discovers a way to tap into the underwater cable and listen in on A and B’s conversations. O knows nothing about English initially, but is very good at detecting statistical patterns. Over time, O learns to predict with great accuracy how B will respond to each of A’s utterances. O also observes that certain words tend to occur in similar contexts, and perhaps learns to generalize across lexical patterns by hypothesizing that they can be used somewhat interchangeably. Nonetheless, Ohas never observed these objects, and thus would not be able to pick out the referent of a word when presented with a set of (physical) alternatives.
This seems kind of obviously wrong at least in the context of coding agents. These models get trained on actual output of the previous version of the model doing its job, often "IRL" on a real computer/project. It's like O is in the conversation for years now and learning from his own interactions between A <-> O <-> B, where A is the human and B is the computer.
The idea O ontologically has never "observed" "these objects" or referents is philosophically strained. Have I observed the moon, or a finger pointing at the moon? Have I observed `sed` more than Fable?
I respect you and parrots, please don’t use parrots as an insult.
Anyone who has spent time with parrots would realise that they can understand the meaning of speech without knowing what the words mean. Then somehow the meaning of word parts, and then you will find them making new words out of other words. Very clever indeed.
So stochastic parrots could indeed be a good description of LLMs. But I think that she meant it as a diminishing term (against the technology) which is pointless. Probably more of a reaction against SV tech bros than more nuanced interpretations.
Five years on, which term do we see as less accurate to describe LLMs? Artificial Intelligence or Stochastic Parrot? I guess it's still an open debate.
I think "(intelligent) language understander" is an apt term. It contains within it the fact that these models are mainly trained on text, and "understand" it beyond a simple token-by-token level (i.e. their latent space maps to more and more complex concepts).
It also separates them from "world understanders" since any understanding they might have about the world comes from text (or images if we include multimodal models). They do not gather experience, memories or other "qualia" that many people (me included) would probably include in a definition of human experience/intelligence.
(fwiw i think artificial intelligence is a good, broad term, but it is both too broad to describe the current sota, and too loaded nowadays to be using in nuanced discussions)
Which frame inspires a more productive research program? Which has better predicted the trajectory of capabilities over the past five years?
>Which frame inspires a more productive research program?
I know what you're implying, but it's not nearly as clever of a comeback as you think.
This question depends on how you define research productivity. There is close to two hundred AI papers published every weekday. Most of them are about GenAI. Despite that the progress in actual model improvement had mostly stalled. If you interact with latest "raw" models they display all of the issues we've seen in GPT-3.5, just at a smaller rate.
The new "hotness" in AI is clearly building more and more elaborate harnesses. This is not at all the direction AI boosters have predicted couple years ago.
There seems to be some confusion between "we can" and "we should" in your comment. Bender (and others) are not discussing the capabilities, but rather (a) the fundamental mechanism(s) (b) the advisability and desirability of deploying systems that use these mechanisms.
> Which has better predicted the trajectory of capabilities over the past five years?
By that standard, parrots, and it's not even close. The framing of intelligence led to an enormous number of predictions that simply haven't been realised: an end to all white collar work, UBI, a total revolution in society, a literal robot god.
People are so desperate to view 'stochastic parrots' as dismissive that they misread the original argument while quickly ignoring all the failed predictions about how AI was going to overturn, save, and destroy everything.
Though, I would point out that where people fall on that seems to correlate very highly with their ability to explain how an attention head works.
Explain it to me
The latter is definitely more colorful, and reflects a parrot's tendency to glom on to patterns. "Not X, but Y" being one of the more infamous ones.
Once in frustration I called a certain frontier model "Sam Altman's Tin Bird" to another agent with memory, and ever since then that other agent refers to ChatGPT as "the tin bird". Definitely a RAG artifact more than an attractor in that case, but I found it amusing.
Spicy autocomplete
What's wrong with "large language model"?
Seems like a lot of people are upset about other people calling both apples and oranges “fruit”.
Its less of open debate would say, and although superposition [1] is interesting, as a way to explain power of some effects, it is clear they are right now closer to Stochastic Parrots than AGI.
Why do I say that? Because you can trivially beat most guardrails, simply by encoding your prompt in base64 for example. :-) Just word matching...no real understanding.
[1] https://chrisclay.substack.com/p/what-is-superposition-in-ne...
> Stochastic Parrot
Nearly all (99%+) people who use this phrase are anti-AI and just looking to show off how much they dislike AI and how clever they can be in insulting it.
So it's a great phrase because in just about every case I can ignore what someone says afterwards.
Similar to "glorified autocomplete."
Pattern matching machines seems more appropriate.
LLMs do not match patterns. They predict one statistically most likely token (only one!) given a history of some N previously known tokens.
> statistically most likely
Isn't that pattern matching essentially?
Is that prediction not based on matching previous patterns, whose frequencies are more or less encoded in the weights?
you're really reaching for no apparent reason. Just move on from pattern matching machines it's not a good mental model for LLMs
afaik before the final sampling, every "next" token has a probability, so theoretically it could select the 10 most likely tokens (based on some kind of sampling algorithm), but you'd end up with exponentially many output-sequences, so nobody does that.
I think the point the poster above was making is that it doesn't predict a phrase or anything like that - just the single next token. So all 10 or 1000 or whatever number of tokens you want are each individually candidates for the single next token, not a sequence of 10 or 100 next tokens. If you wanted to create multiple possible seuqneces, you'd then feed each of the 10 tokens to the network in the initial state, and extract the next token (or 10 next tokens) from that one, than revert back and feed another single one of the 10 tokens, etc.
For humans?
it annoys me how eager people are to hurl the word stochastic as pejorative. Statistics are a great tool for gleaning information from stochastic processes; statistics don't contribute randomness. Random sampling is necessary in order not to bias a sample, it's not used to contribute randomness to the sample but to preserve/measure the underlying distribution. (not meant to imply that training is random sampling)
The term is not very useful since most humans are stochastic parrots... At least most of the time.
Not suggesting that I don't say stuff on autopilot sometimes but for many people, it's their only mode of operation. They never actually think about anything from first principles. Their whole approach to language is just chaining catchphrases together. It's how a toddler thinks; it seems like many people never moved past that stage of development.
Conversely, that the most prominent proponents of LLMs call them artificial intelligence and then treat them like slaves they're free to abuse ought to be horrifying.
It sometimes feels same as with the models, especially in corporate:
- Lots of Haiku around, many mistakes unless process is very clear - Some Sonnets, still do mistakes but can adapt - Some Opus, able to improvise and think outside the box.
But even the Human Opus/Mythos are hilariously wrong sometimes.
Humans are not stochastic parrots. You are 100% wrong about toddlers. This was clearly explained by St. Augustine 1500 years ago:
[https://faculty.georgetown.edu/jod/augustine/conf.pdf]Humans learn language opportunistically. Toddlers start with a powerful "superchimpanzee" understanding of the real world, and use that to learn words in order to satisfy their needs and desires. Statistical frequency is incidental to what words a toddler learns: what matters is the real-world context. Also note how important it is that infants instinctively understand nonverbal communication.
The most depressing thing about the 2020s AI summer is watching ignorant tech workers use the success of LLMs to launder their own ignorant misanthropy. Your views are many many centuries out of date.
i think it actually makes sense, an LLM just imitates human communication, which happens to be useful from time to time.
> most humans are stochastic parrots
There's a lot more happening behind the scenes when a human repeats phrases than what's happening in an LLM.
Sociological phenomenon. The desire to be liked, successful, or popular. The feeling that those phrases brings up.
LLMs are not experiencing any of that. As far as we know, neither is a parrot.
Parrots certainly do experience social needs
'Stochastic parrots' is a great term, but reading it now, it's quite apparent how bad this paper is.
> It argued that large language models (LLMs) generate text by statistically predicting likely sequences of words rather than understanding what they are saying—a process the authors captured with the metaphor of a “stochastic parrot,” a system that repeats patterns without comprehension.
I don't understand what we're setting the record straight on. This is the core point of dispute, and the author just blazes past it to focus on other things. I'm glad to hear "stochastic parrot" isn't intended as an insult, and I agree that it's not right to think of LLMs as a box with a little homunculus inside replying to you. But to me it seems obvious that LLMs are not repeating patterns without comprehension and do understand what they are saying; otherwise they would not be capable of doing things they routinely do.
I think it’s pretty clear that they are repeating without “comprehension” - both mechanistically (as in there is no facility for comprehension in their formulation) and in the ways they fail. The standard rs in strawberry, should I walk or drive to the car wash, etc all play on the fact that they don’t have any real world model or thoughts against which they can judge their output, as do many of the jailbreaks which basically play on the fact that the model has memorized patterns.
There are people who argue semantics, that we can call the pattern matching that LLMs do “understanding”, or the moronic “how do we know that’s isn’t all we do” but for the normal use of comprehension, LLMs at a fundamental level don’t.
> But to me it seems obvious that LLMs are not repeating patterns without comprehension and do understand what they are saying; otherwise they would not be capable of doing things they routinely do.
So this seems obvious to you, and yet to many others, it is equally obvious that LLMs can/could do the things they routinely do without any meaningful sense of "understanding".
This is a facile point. Lisp expert systems transparently don't understand the meaning of any symbols they process, yet with enough developer elbow grease they can do all the same things an LLM can do, with much higher reliability. The fact that LLMs are less transparent than Lisp expert systems (and easier to program) is extremely bad evidence that they understand language. Especially given that AFAICT Opus does not properly understand concepts like "four."
For context, here's the main quote:
> 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.
I think this metaphor is so strained as to not be useful. I think key here is that the authors say "without any reference to meaning", which is a heavily loaded term, that does definitely apply to parrots, but does not apply when you apply it to immense bodies of text.
Namely that language embeds meaning in language. A sentence being written by a human (as a starting point) is designed to have consistent meaning. While it is possible to write syntactically correct meaningless text, that is not what most of human language has done; the meaning cannot be removed from the text.
This I think is clarifying, from the same paragraph in the text:
> ... the training data never included sharing thoughts with a listener, nor does the machine have the ability to do that.
That's just facially incorrect. The training data is entirely about sharing thoughts with a listener. Else why is the text being written?
I think this is the most measured take I've seen from Bender, and I think it summarizes her most 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 haven't found her essential assertion that LLMs aren't useful and will never be good at anything a lot less compelling. 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.