161–170 of 280 posts
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.
What's wrong with "large language model"?
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.
> 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 w…
I think part of it is she had excellent PR skills and a dedicated fan base. I was at Google when she quit, working in ML, and hadn't heard of her until the story broke. I remember there were a large number of Memegen posts about it, but no one I spoke with knew about her, so I assumed it was brigading. I think she's since since lost a lot of her allure, especially when she didn't change her mind when the facts about…
Earlier quoted context omitted.
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…
not sure your criticism makes sense though - they did this pre chatgpt. they are talking about the language models of that time. they did not make predictions about the future.
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.
What I look forward to after research like https://arxiv.org/abs/2603.02491 , which demonstrate the necessity of world-modeling capability to achieve satisfactory performance on certain goals, is a refractor the SoTA test suites to demonstrate how much world-modeling is necessary in various task distributions. There have been a few years now of arguments about the level to which transformers do or do not have a world…
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.
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-superpositi…
> 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 w…
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.
What I look forward to after research like https://arxiv.org/abs/2603.02491 , which demonstrate the necessity of world-modeling capability to achieve satisfactory performance on certain goals, is a refractor the SoTA test suites to demonstrate how much world-modeling is necessary in various task distributions. There have been a few years now of arguments about the level to which transformers do or do not have a world…
But the stochastic parrot (LLM) is the world model, isn't it? What's the difference?
I don’t see a problem with the “stochastic parrot” label. It just turns out stochastic parrots are incredibly useful. At a minimum it’s probably more accurate than “AI”.
Bailey: well your version
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That's fundamental to how anything that compresses/understands the world has to work, in the Kolmogeravian sense. That's why people denigrate LLMs as being just "next token predictors" - they're not wrong, but they're missing the point. Because to do that kind of prediction out in the world you have to build up an accurate model of reality - a model that includes yourself! Which is why we and LLMs are self aware. For…
I'm afraid the precise connection you're making isn't totally obvious to me. As far as prediction - I mean sure the cortex and LLMs do prediction, but then so can RNNs or diffusion models or any other generative model. Really any ML architecture is learning to compress its environment in pursuit of modelling. More broadly, the predictive brain model would suggest that all of the brain, not just the neocortex, is dedi…
The whole brain is most definitely not dedicated to prediction, and I don't think "prediction" is a very useful model anyways. You could say the hippocampus is for "prediction" if you really squint, but that's underselling what it's doing. And the basal ganglia operates off of prediction error, but it's more about regulating short timescale feedback loop learning than learning itself - speaking somewhat loosely because it's a particularly ancient structure in the brain and things get muddied.
LLMs obviously don't have functional equivalents to either of those - sure they can remember facts, but they can't intake new facts the way we can, the hippocampus is set up completely differently. But higher reasoning is humans is the neocortex, and that LLMs can do, and there's even structural parallels - LLMs and the neocortex are both implemented in layers, and you can even (with a shit ton of analysis) watch how LLMs walk the geometry across layers. The neocortex needs a lot fewer layers than LLMs, but that's because it's not pure feedforward across layers; the neocortex already does what the "looped LLM" people are trying to do.
> Similarly, if you agree with the Manifold Hypothesis, then all machine learning models operate on manifolds. I agree it's an exciting thought, but then I don't know what would distinguish an LLM from a VAE or SVM in terms of operating over a low-dimensional manifold embedded in high dimensional spaces - maybe just scale?
I view things more mathematically, and in math and physics it frequently turns out that there's really only one solution to a problem, or correct way to model something - and then everything else is just isomorphisms (which may be interesting objects in their own right!).
My interpretation/guess (informed by research, but with a lot of still unanswered questions) is that the manifold is fundamentally the structure that emerges from natural language: natural language has a computational model, just like Church/Turing machines have a computational model. Reimannian manifold is also underselling it a bit, it's also Finsler, and RoPE adds additional structure to that manifold (and when we get to RoPE it wouldn't surprise me if the neocortex and LLMs do diverge significantly in how they compute, but does the structure change? dunno yet).
Basically, I'm fairly confident in what I say about machine learning models that understand natural language; for non-NL machine learning, maybe the same structure emerges with enough complexity, but I haven't really pondered that.
> 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 w…
Google forced its researchers to retract an already submitted paper because it undermined its strategic and commercial story around large language models. The "we just accepted her resignation" is just a lie. Google made harsh demands with opaque reviewers that made vague objections, and then Jeff Dean moved very quickly to get rid of Gebru. Other Google researchers reported that they usually got to work through objections, Gebru got no such opportunity. Google showed that AI labs will not tolerate internal research that seriously criticizes technology central to its business.