Comment by gbnwl
6 hours ago
Every day I wake up and open HN.
“LLM has made legitimate mathematical discoveries” —> Wow the rate of progress is amazing. Highly upvoted.
“LLM does something not good” -> Does everyone else not realize LLMs are just dumb next token predictors? Highly upvoted.
So tired of this discourse and this site.
The rate of progress can be high and they can also be dumb next token predictors. Not sure why that is hard to understand.
These models can do a lot of things but they also can't do a lot of things. In order to use these models effectively you have to understand that they are next token predictors and how that allows it to do what they do.
Are they useful or not? Will they continue changing the world or not? People who choose one way or the other for describing them typically fall on one side or the other in these questions imo. What do you think? Will these next token predictors change the world or not?
They are useful. They will continue to change the world. They are still next token predictors with all the problems that comes with that.
For them to change the world you have to work with them as next token predictors. Ensure that the next token predictor has enough prediction paths to solve the problems you want and so on. Since when they don't they fail spectacularly. These big companies will continue to add new skills to them, so they will continue to get more useful.
4 replies →
Somewhat.
Yes, but not because they are useful.
It can be a token predictor and still tell me exactly how my life will proceed from now until the indefinite future, or be the most intelligent conversational entity you have ever witnessed.
The issue is of course with using the word "dumb": they are next token predictors, no doubt about it, but whether LLms as a class of system are smart or dumb is entirely unknown and entirely variable in time.
To interact with them effectively you must know how they behave, just like you have to know how humans behave to interact with them effectively. If you disagree, find someone with autism and have a conversation with them.
The "dumb" part comes from how it behaves in contexts where it lacks a lot of data, or where the data is skewed. Since they are tuned to give a prediction anyway and just make something up since sometimes those made up things are useful they will produce dumb results.
So people call them dumb since like dumb people they make strong statements about things they don't understand. And it doesn't matter how much smart things you encode them with, they will keep making strong statements about things they don't understand until they are fundamentally changed.
But since LLM are very smart about things where they have extensive data they can still be used to reliable solve many problems and probably in the future where we understand that better almost completely replace most lawyer and doctors work etc, because a lot of what a frontline doctor or basis lawyer work is very repetitive and can be encoded with billions of examples and decision paths into an expert system framework the LLM will follow.
So people say LLM are dumb since LLM will always keep making dumb statements. This is the same way we call Elon Musk dumb for making a lot of dumb statements, he is a smart guy but he makes dumb statements so her is dumb.
2 replies →
Much of an LLM's capability comes from the structure encoded in its learned representations. The probabilistic outputs are primarily a way of expressing uncertainty and generating fluent text, while compression during training is what forces the model to discover that underlying structure.
> Much of an LLM's capability comes from the structure encoded in its learned representations
And thats encoded as a set of next token predictions. So the way to see how reliably it solves a problem is to look at the chain of predictions, and see where it is unreliable at finding the next spot, or where it always fails and you need to add that link to the dataset to train it.
This isn't magic, today we understand pretty well how to add new skills to LLM, and the better this is understood the faster progress will be.
This also means that if a context doesn't have any good predictions, it will produce a dumb prediction for that context. This results in these bad outcomes, because currently LLM doesn't have a map for where predictions are good or bad.
[dead]
Not sure what your point is? Those things can both be true.
Or should the discourse in a diverse community like HN only reflect the positions you personally hold?
Tell me how a 'nExT toKeN prEdIcTor' can make breakthroughs in math or play a game of chess. These activities aren't pure symbol manipulation, they require actual understanding at some level.
By predicting next tokens
Eh, I'm not going to litigate your claims.
My point is it's silly to whine that HN is a place where multiple points of view on the topic are aired out and discussed.
If you want a personal echo chamber where only your own beliefs are affirmed and anything else is flagged off or downvoted, I'm sure you can go find one or, worst case, vibe code one into existence.
6 replies →
Would be nice to get high karma commenter votes count only ..
Not a dichotomy actually. Highly depends on the task.
Opinions differ. This is not news.
It's almost as if there were many people using this site, and there is no clear consensus on LLMs, so people from various camps upvote interesting stores to support their cause. And people who are still somewhat undecided upvote both, if they present good evidence.
I mean even perennially contentious topics will get this behavior.... some thing about emacs makes the front page, within a day or two there will be a vim post up there. Same with Rust is (good|bad), or if systemd creates an even more awesome tool, the haters will come along and recycle stories about bugs from over a decade ago.
There's a lot of people here. Not all of them read it every hour, and discussions like this among large groups often take a very long time with lots of repetition. Human group dynamics (aka politics) is slow.
> So tired of this discourse and this site.
You're welcome to leave if you don't like it. The site was like this long before you joined, and will like it long after you leave I'm sure.
It's also worth noting, that an awful lot of math discoveries are perfectly in line with dumb next token generators - they are finding a way to formally construct an argument and being surprised when it doesn't work, or surprised at the outcome of the grind. Not all of them are made by brilliant leaps of intuition.