Almost every paper in this vein is not "LLMs are the best in this category of thing, too". There are a couple of fields where LLMs are SOTA for tasks that would otherwise be done by a different algorithm, notably translation, but that's not the majority of these papers.

It's "LLMs can do this category of thing, too". The generality of the architecture is a novel scientific finding in and of itself. Do you remember the last 50 years of AI research where GOFAI systems were universally too fragile to function outside of highly constrained environments, and earlier neural networks were either ineffectual or extremely specialised? The fact that LLMs can function incredibly well (compared to historical AI systems) in diverse scenarios is the scientifically interesting finding.

The reason LLMs are and will be so powerful I think is that they in essence add up the intellectual output of everybody who's output is available online.

Classical AI was about some genius trying to devise the most genius computer inference algorithm. But any single genius can only do so much in limited time and it is difficult to make them work together.

Whereas LLMs are not genius, they are simply using statistics to predict what is the accumulated intellect on the internet. They are simple but they combine the output of millions of minds, which no human genius could ever do.

They are a brain addition machine, rather than super-intelligent by themselves. But when you add together the outputs of many minds you get powerful general purpose output.

Nit: they are powerful because they approximate the intellectual outcome of everyone online.

Applying this to time series forecasting of stock trading would mean you are doing what the rest of the market is doing. At that point, you could have just bought index funds.

Only if what the rest of the market is doing is:

a) publicly available

b) encoded in a format appropriate for training LLMs on

c) similar enough to what index funds are doing

Protip: Buy low, sell high.

Time-series forecasting of random processes is impossible

> They are a brain addition machine

I like that metaphor. They are literally language-gradient addition machines. Language and brains - closely related - but different in that language can add up while brains forever remain separate. Language is an evolutionary process running at higher speed than biological evolution.

This is a really elegantly worded perspective

They likely watched wheel of time recently...

Cool, so they'll be able to project the future time-series of the S&P 500 as well as the average of all the analysts in the world combined? What startup can I buy part of that's most adept at selling that bullshit to investors as SaS to most rapidly separate gamblers from their cash? /s

One comment that made me think on HN a while back is something akin to "The average IQ is between 85 and 115, therefore AI will be akin to friends with IQ's between 85 and 115 giving you advice". So not that useful?

Lower IQ people are likely under represented online.

Accurate information is also over represented because you can be wrong in far more ways than you can be correct. In other words the wisdom of the crowd is in part the 10% of people that know X all match each other while the other 90% is all over the place.

If the AI can distinguish between good and bad reasoning, it can select training material that has a higher average than than the corpus. If it can output data that covers the range, it can generate its own data to bootstrap higher functioning models (like alpha go playing against itself to achieve superhuman go playing skills).

I also don’t think you need to remove the dumb comments from the training material anyway. The AI could be able to simulate either someone of 80 IQ or 180 IQ, producing output consistent with both personas.

It's not so much about IQ but the amount of knowledge in the system.

And ignorant person could have a very high IQ still be very ignorant.

LLMs may not be "smart" but they know a lot.

From my experience with crypto currencies and the intelligent trading bot [0] I would say that transformers will not provide significant benefits when applied to the traditional statistical (numeric) forecasting problems. Such models assume that old events do not affect too much current events.

Yet, there exist problems where even old events retain their strength. An example is where we want to take into account discrete events (tokens in LLM) for predicting stock prices. These events might be explicitly defined (holidays, company announcements, important economic figures etc.) or derived from the data like technical patterns. The strength of transformers are in their ability to ignore the order of events and ignore the distances between them. More precisely, transformers can learn when it is important. In language models, this is used to generate output sequences where semantically equal tokens have completely different order than in the input sequence. Something similar can be done in time series forecasting if we accordingly define "tokens", for example, as technical patterns. Then rising stock prices can be explained (and predicted) not only because of recent numeric behavior but also because "something happened" two weeks ago.

[0] https://github.com/asavinov/intelligent-trading-bot Intelligent Trading Bot: Automatically generating signals and trading based on machine learning and feature engineering

So masked cross attention up to time point t and auto-regressive decoding should in theory be able to model time series. I mean at the high level it captures a Markov process with some contextual memory.