91–100 of 140 posts
We're rapidly reaching trough of disillusionment with LLMs, and other generative transformer models for that matter. I am happy because it will help a lot of misinformed people understand what is and isn't possible (100+% productivity gains are not).
100% productivity gains on coding tasks are absolutely within the realm of possibility
We're rapidly reaching trough of disillusionment with LLMs, and other generative transformer models for that matter. I am happy because it will help a lot of misinformed people understand what is and isn't possible (100+% productivity gains are not).
100% productivity gains on coding tasks are absolutely within the realm of possibility
Earlier quoted context omitted.
I have nothing against researching this, I think it's important. My main issue is with articles choosing to grab a "conclusion" and imply it extrapolates to larger models, without any support for that. They are going for the catchy title first, fine-print be damned.
Because model size is a trivial parameter, and not a new paradigm. What you're saying is like, you can't extrapolate that long division works on 100 digit numbers because you only worked through it using 7 digit numbers and a few small polynomials.
Sometimes, we go so far as to say there is "emergence" of qualitative differences. But really, this is not necessary (and not proven to actually occur).
What is true is that the performance of LLMs at OOD tasks changes with scale.
So no, it's not the same as solving a math problem.
We're rapidly reaching trough of disillusionment with LLMs, and other generative transformer models for that matter. I am happy because it will help a lot of misinformed people understand what is and isn't possible (100+% productivity gains are not).
100% productivity gains on coding tasks are absolutely within the realm of possibility
Earlier quoted context omitted.
That’s what has been seen in practice though. SOTA LLMs have been shown again and again to solve problems unseen in their data set; and despite their shortcomings they have become extremely useful for a wide variety of tasks.
Even a tiny model for, say, classifying hand-written digits, will correctly classify digits that didn't appear in its training data. (Otherwise it wouldn't be very useful.) That classification is interpolative; the hand-written digit is lands in the space of the training data. Every result is explainable by has having come from training data. That's the null hypothesis. The alternative hypothesis is that it's not exp…
Similarly, LLMs do not invent a new way of reasoning about problems or language. They do, however, apply these to unseen problems.
LLMs are one level of abstraction up, but it's a very interesting level of abstraction.
Earlier quoted context omitted.
That’s what has been seen in practice though. SOTA LLMs have been shown again and again to solve problems unseen in their data set; and despite their shortcomings they have become extremely useful for a wide variety of tasks.
> SOTA LLMs have been shown again and again to solve problems unseen in their data set We have no idea what the training data is though, so you can't say that. > and despite their shortcomings they have become extremely useful for a wide variety of tasks. That seems like a separate question.
O3 pro (but not O3) was successfully able to apply reasoning and math to this domain in interesting ways, much like an expert researcher in these areas would.
Again, the field and the problem is with 100% certainty OOD of the data.
However, the techniques and reasoning methods are of course learned from data. But that's the point, right?
> Without specification, we employ a decoder-only language model GPT2 (Radford et al., 2019) with a configuration of 4 layers, 32 hidden dimensions, and 4 attention heads. Yeah, ok. The research is interesting, warranted, but writing an article about it, and leading with the conclusions gathered from toy models and implying this generalises to production LLMs is useless. We've been here before with small models. Trai…
Doing analysis on small models or small data is perfectly valid if the results extrapolate to large models. Which is why right now we're looking at new research papers that are still listing the same small datasets and comparing to the same small models that papers five years ago did.
There's a mountain of reasons why this makes sense from a cost perspective, and seemingly it does also for quality, too, as the newer models train substantially more cheaply and still outperform the older models.
Naively, this seems like it would be relevant.
Earlier quoted context omitted.
So, you agree with the point that they’re making and you’re mad about it? It’s important to state that the models aren’t doing real reasoning because they are being marketed and sold as if they are. As for your question: ‘So what does "sophisticated simulators of reasoning-like text" even mean here?’ It means CoT interstitial “reasoning” steps produce text that looks like reasoning, but is just a rough approximation,…
"the reasoning often doesn’t line up with the conclusion, or the priors, or reality." My dude, have you ever interacted with human reasoning?
Most of what humans think of as reason is actually "will to power". The capability to use our faculties in a way that produces logical conclusions seems like an evolutionary accident, an off-lable use of the brain's machinery for complex social interaction. Most people never learn to catch themselves doing the former when they intended to engage in the latter, some don't know the difference. Fortunately, the latter provides a means of self-correction, the research here hopes to elucidate whether an LLM based reasoning system has the same property.
In other words, given consistent application of reason I would expect a human to eventually draw logically correct conclusions, decline to answer, rephrase the question, etc. But with an LLM, should I expect a non-determisitic infinite walk though plausible nonsense? I expect reaaoning to converge.
> ... that these "reasoning" models can often produce incoherent, logically unsound answers when questions include irrelevant clauses or deviate even slightly from common templates found in their training data. I have encountered this problem numerous times, now. It really makes me believe that the models do not really understand the topic, even the basics but just try to predict the text. One recent example was me a…
This is correct. There is no understanding, there aren't even concepts. It's just math, it's what we've been doing with words in computers for decades, just faster and faster. They're super useful in some areas, but they're not smart, they don't think.
Earlier quoted context omitted.
100% productivity gains on coding tasks are absolutely within the realm of possibility
And how much of productivity loss due to the insane amount of noise being generated ? (filler ridden reports, emails, videos, podcasts, &c.)