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. Most don't seem to be all thay good. The progress in actual model improvement had mostly stalled. If you interact with the latest "raw" models they display all of the issues we've seen in GPT-3.5, just at a smaller rate. The "amazing gamechanger breakthroughs" I read about on social media every week do not seem to lead anywhere. It's all kind of boring, really.
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.
Personally, I think the "stochastic parrot" mental model is far more useful for science, because it primes people for proper testing, skepticism and researching alternatives. If you want useful AI, you want people working on it being skeptical, not credulous.
To me the real question begins only once we have a clear example of a non-trivial scientific discovery that is implicit (IE, not an obvious outcome of reading the literature and talking to the experts) and experimentally verifiable. Once that happens- especially if it is a reproducible process (IE, more discoveries) and it's significant (IE, impacts human life and mind in some profound way)- then the onus very much lies on Bender and her coauthors to explain whether we need more than a sufficiently advanced stochastic parrot.
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.
But the different terms imply different mental models of what LLMs are and can do. If you take two people, one who thinks of them as "artificial intelligence" and one as "stochastic parrots" (with all the implicit context and connotations of the individual words composing them), what mental model would have led to better predictions of LLMs' future circa 2020?
The "stochastic parrots" phrase is very dangerous in that frame. People read far more into what capabilities it implies are (im)possible than the narrow technical description the authors originally argued for. If all they are is spicy autocomplete or pastiche plagiarizers, there's nothing serious to worry about. And when an opposition gets stuck in a trough that mindlessly dismisses their future capabilities out of hand because of a bad mental model, it renders them ineffective at preventing the worst outcomes.
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.
I don’t think this phrase means what people assume when it’s applied to post trained instruct models - which did not exist when the paper was written.
After RL it is not predicting based on samples of the original corpus - but is also chasing a reward function that does require other features.
There has been a lot of subsequent research that really calls many of the statements in this article into question.
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)
What professor Bender is trying to explain here is that they were trying to describe how the LLM’s actually operate, to which point stochastic parrots is a fairly decent term. It is only disparaging if you know absolutely nothing how LLM’s work or you have some strange affixation to chatbots and believing they are far more capable than they actually are.
[1] Coined by Marvin Minsky: https://www.thekurzweillibrary.com/consciousness-is-a-big-su...
For example, consider the term "short wave" radio which refers to wavelengths of at least 10 meters. Today's mobile communications use wavelengths 100x - 10,000x shorter.
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...
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."
From an external standpoint, talking to another human, it's like the other human says one word and then says the next word. That's just how language works. Humans look like "glorified autocomplete" from this perspective.
I mean, looking at the time evolution of the state of the universe, one could say that all of physics and creation is "glorified autocomplete" to posit a next state of the universe given current and past state.
Obviously language and the connection to human thought is more subtle than this; I think we all have a rich inner life. Just from an external perspective we can't observe it; all we can see is the token/phoneme stream. I'm just saying that it's a mistake to try to criticize LLMs on this basis because it's hard to see how the same criticism would not apply to any system (like humans) that generate language.
LLM’s are usually unexpected only when they malfunction and sprout same letter again and again etc - hardly a literary masterpiece. They make very easily recognisable patterns that we can use as helpful tools, but in the end they are devoid of any meaning apart from what we give them. Of course one could say same about art and all language, but I think there still is the fact that we apes somehow recognise each other. And besides, we do know the internal functions that drive the parroting. It is admittedly bit tricky, but in no way as magical as people purport it to be.
You seem to believe, on a more fundamental level, that LLMs are simply not capable of producing text that has deeper connections to itself or represents abstract thoughts. In my opinion, 99% of text written by humans does not show this, just as 99% of text produced by LLMs does not show this, but both have the capability, and I don't believe that LLMs are constrained in such a way that they can never do this.
Exhibit A.
Isn't that pattern matching essentially?