> Applied statistics is a
far more precise descriptor, “but no one wants to use that term, because
it’s not as sexy.”
This really hit me some time back when I was explaining AI to a friend. After about 10 mins of rambling about LLMs and mentioning the attention paper like I knew what I was talking about, it ended with “oh so it’s just a really advanced auto correct”
As an MLE I feel these takes are too reductionist.
You could say the (nearly) same thing about search. And content recommendation. And clustering. And topic modeling. And outlier detection. And spam filtering. And image diffusion. And dimension reduction. And...
There's a lot in common between these things, but there's also a lot cool and different!
For transformers in particular, it's pretty cool that you get some WILD emergent properties simply from scaling up.
So yes, it's just a next token predictor, but I'm just a bundle of nerves and meat. I don't get a lot out of those descriptions.
Some concrete facts about LLMs are explained by their next token predictor nature. Every time it says "wait, that's wrong." instead of generating the correct thing the first time.
I think that's relatively emergent too though! BERT never really did that (at least to my recollection), presumably because its training was never sufficient for it to develop corrective reasoning in a chain of thought.
It's a little different than that. Your bundle of nerves and meat is not static. It changes over time.
To me the heart of the "next token predictor" is that the distributions are static. You can manipulate what you feed into it through context (and a lot of interesting engineering has been applied there through CoT and other techniques to manipulate the prompt). But these models as implemented will never be able try things and learn from mistakes or adapt. They are a set of weights frozen in time. A set of distributions derived from the original data that created them.
That's not quite true though. The fact that most models are in practice non-deterministic has been a huge point of contention.
And there's nothing inherently stopping labs from continuously fine-tuning the weights after every new invocation. It's just a difficult (not to mention expensive) software and ML problem.
Essentially all of economic theory is aimed at explaining, not predicting. The distinction between the two goals [0] is sometimes under-appreciated within the profession, and almost always under-appreciated outside of it.
Most predictive tools in economics and finance have “surprisingly” little economic content; but once you understand the distinction between the two goals, it should be unsurprising that predictive models tend to make few economic assumptions and tend instead to rely on basic statistical techniques. It then shouldn’t be too surprisingly that predicting the future is quite difficult in a context in which the relevant processes are continually seeking an equilibrium that implies unpredictability.
I’m not an economist, but I do a lot of applied financial-economic modeling. State-of-the-art LLMs are really, really terrible at economic intuition. They will hinder, not help, in formulating an economic model, which is mainly a process of coming up with a set of modeling assumptions that lead to a useful (implicitly, tractable) model. However, they’re good at math, so I’ve found them very useful in iterating through different sets of modeling assumptions to identify those that lead somewhere useful. Not having to work out all of the mathematical detail myself, and avoiding getting lost in the weeds in doing so, has accelerated my work immensely.
It wouldn't require perfect accuracy, just rough accuracy and a human to confirm, and it's already more than good enough for that. I do not understand this confusion surrounding modern math.
Here's the thing about economists... The loudest ones don't want to be correct, they want to be influential. The ones who can actually make good predictions work for banks and hedge funds lol.
- Many more mediocre papers written (mediocre ideas, implementation, claude-isms everywhere)
- Much easier to try every possible combination of a regression in order to show the result you want (same for theorists).
The one thing I'm happy about is it's now much easier to extract historical data from old documents from Google Books. Still not perfect, but takes you 95% there. And creating plots and datavis just for quick exploration is super fast.
This really hit me some time back when I was explaining AI to a friend. After about 10 mins of rambling about LLMs and mentioning the attention paper like I knew what I was talking about, it ended with “oh so it’s just a really advanced auto correct”
You could say the (nearly) same thing about search. And content recommendation. And clustering. And topic modeling. And outlier detection. And spam filtering. And image diffusion. And dimension reduction. And...
There's a lot in common between these things, but there's also a lot cool and different!
For transformers in particular, it's pretty cool that you get some WILD emergent properties simply from scaling up.
So yes, it's just a next token predictor, but I'm just a bundle of nerves and meat. I don't get a lot out of those descriptions.
To me the heart of the "next token predictor" is that the distributions are static. You can manipulate what you feed into it through context (and a lot of interesting engineering has been applied there through CoT and other techniques to manipulate the prompt). But these models as implemented will never be able try things and learn from mistakes or adapt. They are a set of weights frozen in time. A set of distributions derived from the original data that created them.
And there's nothing inherently stopping labs from continuously fine-tuning the weights after every new invocation. It's just a difficult (not to mention expensive) software and ML problem.
> They're Made out of Meat
https://web.mit.edu/people/dpolicar/writing/prose/text/think...
Most predictive tools in economics and finance have “surprisingly” little economic content; but once you understand the distinction between the two goals, it should be unsurprising that predictive models tend to make few economic assumptions and tend instead to rely on basic statistical techniques. It then shouldn’t be too surprisingly that predicting the future is quite difficult in a context in which the relevant processes are continually seeking an equilibrium that implies unpredictability.
I’m not an economist, but I do a lot of applied financial-economic modeling. State-of-the-art LLMs are really, really terrible at economic intuition. They will hinder, not help, in formulating an economic model, which is mainly a process of coming up with a set of modeling assumptions that lead to a useful (implicitly, tractable) model. However, they’re good at math, so I’ve found them very useful in iterating through different sets of modeling assumptions to identify those that lead somewhere useful. Not having to work out all of the mathematical detail myself, and avoiding getting lost in the weeds in doing so, has accelerated my work immensely.
0. https://www.stat.berkeley.edu/~aldous/157/Papers/shmueli.pdf
- Many more mediocre papers written (mediocre ideas, implementation, claude-isms everywhere)
- Much easier to try every possible combination of a regression in order to show the result you want (same for theorists).
The one thing I'm happy about is it's now much easier to extract historical data from old documents from Google Books. Still not perfect, but takes you 95% there. And creating plots and datavis just for quick exploration is super fast.