This is an interesting paper and I like this kind of mechanistic interpretability work - but I cannot figure out how the paper title "Transformers know more than they can tell" relates to the actual content. In this case what is it that they know and can't tell?
I believe it's a reference to the paper "Language Models (Mostly) Know What They Know".
There's definitely some link but I'd need to give this paper a good read and refresh on the other to see how strong. But I think your final sentence strengthens my suspicion
Ok, I've read the paper and now I wonder, why did they stop at the most interesting part?
They did all that work to figure out that learning "base conversion" is the difficult thing for transformers. Great! But then why not take that last remaining step to investigate why that specifically is hard for transformers? And how to modify the transformer architecture so that this becomes less hard / more natural / "intuitive" for the network to learn?
Author, here. The paper is about the Collatz sequence, how experiments with a transformer can point at interesting facts about a complex mathematical phenomenon, and how, in supervised math transformers, model predictions and errors can be explained (this part is a follow-up to a similar paper about GCD). From a ML research perspective, the interesting (but surprising) take away is the particular way the long Collatz function is learned: "one loop at a time".
To me, the base conversion is a side quest. We just wanted to rule out this explanation for the model behavior. It may be worth further investigation, but it won't be by us. Another (less important) reason is paper length, if you want to submit to peer reviewed outlets, you need to keep pages under a certain number.
1) Why did you not test the standard Collatz sequence? I would think that including that, as well as testing on Z+, Z+\2Z, and 2Z+, would be a bit more informative (in addition to what you've already done). Even though there's the trivial step it could inform how much memorization the network is doing. You do notice the model learns some shortcuts so I think these could help confirm that and diagnose some of the issues.
2) Is there a specific reason for the cross attention?
Regardless, I think it is an interesting paper (these wouldn't be criteria for rejection were I reviewing your paper btw lol. I'm just curious about your thoughts here and trying to understand better)
FWIW I think the side quest is actually pretty informative here, though I agree it isn't the main point.
It might be a side quest, or it could be an elegant way to frame a category of problems that are resistant to the ways in which transformers can learn; in turn, by solving that structural deficiency in order to enable a model to effectively learn that category of problems, you might empower a new leap in capabilities and power.
We're a handful of breakthroughs before models reach superhuman levels across any and all domains of cognition. It's clear that current architectures aren't going to be the end-all solution, but all we need might simply be a handful of well-posed categorical deficiencies that allow a smooth transition past the current jagged frontiers.
Why release one paper when you can release two? Easier to get citations if you spread your efforts, and if you're lucky, someone needs to reference both of them.
A more serious answer might be that it was simply out of scope of what they set out to do, and they didn't want to fall for scope-creep, which is easier said than done.
I don't question this decision is sometimes (often) driven by the need to increase publication count. (Which, in turn, happens because people find it esaier to count papers than read them.) But there is a counterpoint here, which is that if you write say a 50-pager (not super common but also not unusual in my area, applied math) and spread several interesting results throughout, odds are good many things in the middle will never see the light of day. Of course one can organize the paper in a way to try to mitigate the effects of this, but sometimes it is better and cleaner to break a long paper into shorter pieces that people can actually digest.
Well put. Nobody want salami slices, but nobody wants War and Peace, either (most of the time). Both are problems, even if papers are more often too short than too long.
Not only that but in the academic world 20 papers with 50 citations is worth more than one paper with 1000. Even though the total citation count is the same the former gives you an h-index of 20 (and an i-10 of 20) but the latter only gives you an h-index of 1 (ditto for i-10).
Though truthfully it's hard to say what's better. All can be hacked (a common way to hack citations is to publish surveys. You also just get more by being at a prestigious institution or being prestigious yourself). The metric is really naïve but it's common to use since actual evaluating the merits of individual works is quite time consuming and itself an incredibly noisy process. But hey, publish or perish, am I right?[0]
That's a fantastic example of that that which gets measured gets optimized. The academic world's fascination with this citation metrics is hilarious, it is so reminiscent of programmers optimizing for whatever metric managements has decided is the true measure of programmer productivity. Object code size, lines of code, tickets closed and so on...
It's definitely a toxic part of academia. Honestly if it weren't for that I'd take an academic job over an industry one in a heartbeat.
Some irony is my PhD was in machine learning. Every intro course I now (including mine) discusses reward hacking (aka Goodhart's Law). The irony being that the ML community had dialed this problem up to 11. My peers that optimized this push out 10-20 papers a year. I think that's too many and means most of the papers are low impact. I have similar citation counts to them but lower h-index and they definitely get more prestige for that even though it's harder to publish more frequently in my domain (my experiments take a lot longer). I'm with Higgs though, it's a lazy metric and imo does more harm than good.
cuz you don't sell nonsense in one piece.
it used to be "repeat a lie often enough" ...
now lies are split into pieces ...
you'll see more of all that in the next few years.
but if you wanna stay in awe, at your age and further down the road, don't ask questions like you just asked.
be patient and lean into the split.
brains/minds have been FUBARed. all that remains is buying into the fake, all the way down to faking it when your own children get swooped into it all.
"transformers" "know" and "tell" ... and people's favorite cartoon characters will soon run hedge funds but the rest of the world won't get their piece ... this has all gone too far and to shit for no reason.
I'll take a shot at it. Using collatz as the specific target for investigating the underlying concepts here seems like a big red-herring that's going to generate lots of confused takes. (I guess it was done partly to have access to tons of precomputed training data and partly to generate buzz. The title also seems kind of poorly chosen and/or misleading)
Really the paper is about mechanistic interpretation and a few results that are maybe surprising. First, the input representation details (base) matters a lot. This is perhaps very disappointing if you liked the idea of "let the models work out the details, they see through the surface features to the very core of things". Second, learning was burst'y with discrete steps, not smooth improvement. This may or may not be surprising or disappointing.. it depends how well you think you can predict the stepping.
The model partially solves the problem but fails to learn the correct loop length:
> An investigation of model errors (Section 5) reveals that, whereas large language models commonly “hallucinate” random solutions, our models fail in principled ways. In almost all cases, the models perform the correct calculations for the long Collatz step, but use the wrong loop lengths, by setting them to the longest loop lengths they have learned so far.
That's a bit of an uncharitable summary. In bases 8, 12, 16, 24 and 32 their model achieved 99.7% accuracy. They would never expect it to achieve 100% accuracy. It would be like if you trained a model to predict whether or not a given number is prime. A model that was 100% accurate would defy mathematical knowledge but a model that was 99.7% would certainly be impressive.
In this case, they prove that the model works by categorising inputs into a number of binary classes which just happen to be very good predictors for this otherwise random seeming sequence. I don't know whether or not some of these binary classes are new to mathematics but either way, their technique does show that transformer models can be helpful in uncovering mathematical patterns even in functions that are not continuous.
A pocket calculator that would give the right numbers 99.7% of the time would be fairly useless. The lack of determinism is a problem and there is nothing 'uncharitable' about that interpretation. It is definitely impressive, but it is fundamentally broken, because when you start making chains of things that are 99.7% correct you end up with garbage after very few iterations. That's precisely why digital computers won out over analog ones, the fact that they are deterministic.
Category error. You want 100% accuracy for an impossible problem. This is a famously unsolved conjecture. The only way to get the answer is to fully calculate it. The task was to make a guess and see how well it could do. 99.7 is surprisingly good. If the task was to calculate, the llm could write a python program, just like I would have if asked to calculate the answer.
There is a massive difference between an 'unsolved problem' and a problem solved 'the wrong way'. Yes, 99.7% is surprisingly good. But it did not detect the errors in its own output. And it should have.
Besides, we're all stuck on the 99.7% as if that's the across the board output, but that's a cherry picked result:
"The best models (bases 24, 16 and 32) achieve a near-perfect accuracy of 99.7%, while odd-base models
struggle to get past 80%."
I do think it is a very interesting thing to do with a model and it is impressive that it works at all.
The problem here is deterministic. *It must be for accuracy to even be measured*.
The model isn't trying to solve the Collatz conjecture, it is learning a pretty basic algorithm and then doing this a number of times. The instructions it needs to learn is
if x % 2:
x /= 2
else:
x = x*3 + 1
It also needs to learn to put that in a loop and for that to be a variable, but the algorithm is static.
On the other hand, the Collatz conjecture states that for C(x) (the above algorithm) has a fixed point of 1 for all x (where x \in Z+). Meaning that eventually any input will collapse to the loop 1 -> 4 -> 2 -> 1 (or just terminate at 1). You can probably see we know this is true for at least an infinite set of integers...
Edit: I should note that there is a slight modification to this, though model could get away with learning just this. Their variation limits to odd numbers and not all of them. For example 9 can't be represented by (2^k)m - 1 (but 7 and 15 can). But you can see that there's still a simple algorithm and that the crux is determining the number of iterations. Regardless, this is still deterministic. They didn't use any integers >2^71, which we absolutely know the sequences for and we absolutely know all terminate at 1.
To solve the Collatz Conjecture (and probably win a Fields Metal) you must do one of 2 things.
1) Provide a counter-example
2) Show that this happens for all n, which is an infinite set of numbers, so this strictly cannot be done by demonstration.
Most primality tests aren't 100% accurate either (eg Miller Rabin), they just are "reasonably accurate" while being very fast to compute. You can use them in conjunction to improve your confidence in the result.
Yes, and we know they are inaccurate and we know that if you find a prime that way you can only use it to reject, not confirm so if you think that something is prime you need to check it.
But now imagine that instead of it being a valid reject 0.3% of the time it would also reject valid primes. Now it would be instantly useless because it fails the test for determinism.
It's uncharitable because the comment purports to summarise the entire paper while simply cherry picking the worst result. It would be like if asked how did I do on my test and you said well you got question 1 wrong and then didn't elaborate.
Now I get your point that a function that is 99.7% accurate will eventually always be incorrect but that's not what the comment said.
LLMs are not calculators. If you want a calculator use a calculator. Hell, have your LLM use a calculator.
>That's precisely why digital computers won out over analog ones, the fact that they are deterministic.
I mean, no not really, digital computers are far easier to build and far more multi-purpose (and technically the underlying signals are analog).
Again, if you have a deterministic solution that is 100% correct all the time, use it, it will be cheaper than an LLM. People use LLMs because there are problems that are either not deterministic or the deterministic solution uses more energy than will ever be available in the local part of our universe. Furthermore a lot of AI (not even LLMs) use random noise at particular steps as a means to escape local maxima.
I think they keep coming back to this because a good command of math underlies a vast domain of applications and without a way to do this as part of the reasoning process the reasoning process itself becomes susceptible to corruption.
> LLMs are not calculators. If you want a calculator use a calculator. Hell, have your LLM use a calculator.
If only it were that simple.
> I mean, no not really, digital computers are far easier to build and far more multi-purpose (and technically the underlying signals are analog).
Try building a practical analog computer for a non-trivial problem.
> Again, if you have a deterministic solution that is 100% correct all the time, use it, it will be cheaper than an LLM. People use LLMs because there are problems that are either not deterministic or the deterministic solution uses more energy than will ever be available in the local part of our universe. Furthermore a lot of AI (not even LLMs) use random noise at particular steps as a means to escape local maxima.
No, people use LLMs for anything and one of the weak points in there is that as soon as it requires slightly more complex computation there is a fair chance that the output is nonsense. I've seen this myself in a bunch of non-trivial trials regarding aerodynamic calculations, specifically rotation of airfoils relative to the direction of travel. It tends to go completely off the rails if the problem is non-trivial and the user does not break it down into roughly the same steps as you would if you were to work out the problem by hand (and even then it may subtly mess up).
>A pocket calculator that would give the right numbers 99.7% of the time would be fairly useless.
Well that's great and all, but the vast majority of llm use is not for stuff you can just pluck out a pocket calculator (or run a similarly airtight deterministic algorithm) for, so this is just a moot point.
People really need to let go of this obsession with a perfect general intelligence that never makes errors. It doesn't and has never existed besides in fiction.
yeah it's only correct in 99.7% of all cases, but what if it's also 10'000 times faster? There's a bunch of scenarios where that combination provides a lot of value
Ridiculous counterfactual. The LLM started failing 100% of the time 60! orders of magnitude sooner than the point at which we have checked literally every number.
This is not even to mention the fact that asking a GPU to think about the problem will always be less efficient than just asking that GPU to directly compute the result for closed algorithms like this.
Correctness in software is the first rung of the ladder, optimizing before you have correct output is in almost all cases a complete waste of time. Yes, there are a some scenarios where having a ballpark figure quickly can be useful if you can produce the actual result as well and if you are not going to output complete nonsense the other times but something that approaches the final value. There are a lot of algorithms that do this (for instance: Newton's method for finding square roots).
99.7% of the time good and 0.3% of the time noise is not very useful, especially if there is no confidence indicating that the bad answers are probably incorrect.
Do you think maybe OP would have asked a language model for the answer if they felt like they wanted a language model to give an answer? Or in your mind parent doesn't know about LLMs, and this is your way of introducing them to this completely new concept?
Funny that the "human" answer above took 2 people to be "complete" (i.e. an initial answer, followed by a correction and expansion of concepts), while the LLM one had mostly the same explanation, but complete and in one answer.
Maybe most of us here don't seek just whatever answer to whatever question, but the human connection part of it is important too, that we're speaking with real humans that have real experience with real situations.
Otherwise I'd just be sitting chatting with ChatGPT all day instead of wast...spending all day on HN.
Oh, I agree. What I found funny is the gut reaction of many other readers that downvoted the message (it's greyed out for me at time of writing this comment). Especially given that the user clearly mentioned that it was LLM generated, while also being cheeky with the "transformer" pun, on a ... transformer topic.
I don't know that computers can model arbitrary length sine waves either. At least not in the sense of me being able to input any `x` and get `sin(x)` back out. All computers have finite memory, meaning they can only represent a finite number of numbers, so there is some number `x` above which they can't represent any number.
Neural networks are more limited of course, because there's no way to expand their equivalent of memory, while it's easy to expand a computer's memory.
This is an interesting paper and I like this kind of mechanistic interpretability work - but I cannot figure out how the paper title "Transformers know more than they can tell" relates to the actual content. In this case what is it that they know and can't tell?
I believe it's a reference to the paper "Language Models (Mostly) Know What They Know".
There's definitely some link but I'd need to give this paper a good read and refresh on the other to see how strong. But I think your final sentence strengthens my suspicion
https://arxiv.org/abs/2207.05221
Ok, I've read the paper and now I wonder, why did they stop at the most interesting part?
They did all that work to figure out that learning "base conversion" is the difficult thing for transformers. Great! But then why not take that last remaining step to investigate why that specifically is hard for transformers? And how to modify the transformer architecture so that this becomes less hard / more natural / "intuitive" for the network to learn?
Author, here. The paper is about the Collatz sequence, how experiments with a transformer can point at interesting facts about a complex mathematical phenomenon, and how, in supervised math transformers, model predictions and errors can be explained (this part is a follow-up to a similar paper about GCD). From a ML research perspective, the interesting (but surprising) take away is the particular way the long Collatz function is learned: "one loop at a time".
To me, the base conversion is a side quest. We just wanted to rule out this explanation for the model behavior. It may be worth further investigation, but it won't be by us. Another (less important) reason is paper length, if you want to submit to peer reviewed outlets, you need to keep pages under a certain number.
I'm curious about 2 things.
1) Why did you not test the standard Collatz sequence? I would think that including that, as well as testing on Z+, Z+\2Z, and 2Z+, would be a bit more informative (in addition to what you've already done). Even though there's the trivial step it could inform how much memorization the network is doing. You do notice the model learns some shortcuts so I think these could help confirm that and diagnose some of the issues.
2) Is there a specific reason for the cross attention?
Regardless, I think it is an interesting paper (these wouldn't be criteria for rejection were I reviewing your paper btw lol. I'm just curious about your thoughts here and trying to understand better)
FWIW I think the side quest is actually pretty informative here, though I agree it isn't the main point.
It might be a side quest, or it could be an elegant way to frame a category of problems that are resistant to the ways in which transformers can learn; in turn, by solving that structural deficiency in order to enable a model to effectively learn that category of problems, you might empower a new leap in capabilities and power.
We're a handful of breakthroughs before models reach superhuman levels across any and all domains of cognition. It's clear that current architectures aren't going to be the end-all solution, but all we need might simply be a handful of well-posed categorical deficiencies that allow a smooth transition past the current jagged frontiers.
> We're a handful of breakthroughs before models reach superhuman levels across any and all domains of cognition.
That's a pretty bold claim to make.
Why release one paper when you can release two? Easier to get citations if you spread your efforts, and if you're lucky, someone needs to reference both of them.
A more serious answer might be that it was simply out of scope of what they set out to do, and they didn't want to fall for scope-creep, which is easier said than done.
For interest, this popular pastime goes by several delicious names: https://en.wikipedia.org/wiki/Least_publishable_unit
:-)
I don't question this decision is sometimes (often) driven by the need to increase publication count. (Which, in turn, happens because people find it esaier to count papers than read them.) But there is a counterpoint here, which is that if you write say a 50-pager (not super common but also not unusual in my area, applied math) and spread several interesting results throughout, odds are good many things in the middle will never see the light of day. Of course one can organize the paper in a way to try to mitigate the effects of this, but sometimes it is better and cleaner to break a long paper into shorter pieces that people can actually digest.
Well put. Nobody want salami slices, but nobody wants War and Peace, either (most of the time). Both are problems, even if papers are more often too short than too long.
Not only that but in the academic world 20 papers with 50 citations is worth more than one paper with 1000. Even though the total citation count is the same the former gives you an h-index of 20 (and an i-10 of 20) but the latter only gives you an h-index of 1 (ditto for i-10).
Though truthfully it's hard to say what's better. All can be hacked (a common way to hack citations is to publish surveys. You also just get more by being at a prestigious institution or being prestigious yourself). The metric is really naïve but it's common to use since actual evaluating the merits of individual works is quite time consuming and itself an incredibly noisy process. But hey, publish or perish, am I right?[0]
[0] https://www.sciencealert.com/peter-higgs-says-he-wouldn-t-ha...
That's a fantastic example of that that which gets measured gets optimized. The academic world's fascination with this citation metrics is hilarious, it is so reminiscent of programmers optimizing for whatever metric managements has decided is the true measure of programmer productivity. Object code size, lines of code, tickets closed and so on...
It's definitely a toxic part of academia. Honestly if it weren't for that I'd take an academic job over an industry one in a heartbeat.
Some irony is my PhD was in machine learning. Every intro course I now (including mine) discusses reward hacking (aka Goodhart's Law). The irony being that the ML community had dialed this problem up to 11. My peers that optimized this push out 10-20 papers a year. I think that's too many and means most of the papers are low impact. I have similar citation counts to them but lower h-index and they definitely get more prestige for that even though it's harder to publish more frequently in my domain (my experiments take a lot longer). I'm with Higgs though, it's a lazy metric and imo does more harm than good.
in the academic world 20 papers with 50 citations is worth more than one paper with 1000
It depends. If your goal is to get a job at OpenAI or DeepMind, one famous paper might be better.
Relevant SMBC: https://www.smbc-comics.com/index.php?db=comics&id=1624
cuz you don't sell nonsense in one piece. it used to be "repeat a lie often enough" ... now lies are split into pieces ...
you'll see more of all that in the next few years.
but if you wanna stay in awe, at your age and further down the road, don't ask questions like you just asked.
be patient and lean into the split.
brains/minds have been FUBARed. all that remains is buying into the fake, all the way down to faking it when your own children get swooped into it all.
"transformers" "know" and "tell" ... and people's favorite cartoon characters will soon run hedge funds but the rest of the world won't get their piece ... this has all gone too far and to shit for no reason.
Can someone ELI5 this for a non-mathematician?
I'll take a shot at it. Using collatz as the specific target for investigating the underlying concepts here seems like a big red-herring that's going to generate lots of confused takes. (I guess it was done partly to have access to tons of precomputed training data and partly to generate buzz. The title also seems kind of poorly chosen and/or misleading)
Really the paper is about mechanistic interpretation and a few results that are maybe surprising. First, the input representation details (base) matters a lot. This is perhaps very disappointing if you liked the idea of "let the models work out the details, they see through the surface features to the very core of things". Second, learning was burst'y with discrete steps, not smooth improvement. This may or may not be surprising or disappointing.. it depends how well you think you can predict the stepping.
The model partially solves the problem but fails to learn the correct loop length:
> An investigation of model errors (Section 5) reveals that, whereas large language models commonly “hallucinate” random solutions, our models fail in principled ways. In almost all cases, the models perform the correct calculations for the long Collatz step, but use the wrong loop lengths, by setting them to the longest loop lengths they have learned so far.
The article is saying the model struggles to learn a particular integer function. https://en.wikipedia.org/wiki/Collatz_conjecture
That's a bit of an uncharitable summary. In bases 8, 12, 16, 24 and 32 their model achieved 99.7% accuracy. They would never expect it to achieve 100% accuracy. It would be like if you trained a model to predict whether or not a given number is prime. A model that was 100% accurate would defy mathematical knowledge but a model that was 99.7% would certainly be impressive.
In this case, they prove that the model works by categorising inputs into a number of binary classes which just happen to be very good predictors for this otherwise random seeming sequence. I don't know whether or not some of these binary classes are new to mathematics but either way, their technique does show that transformer models can be helpful in uncovering mathematical patterns even in functions that are not continuous.
A pocket calculator that would give the right numbers 99.7% of the time would be fairly useless. The lack of determinism is a problem and there is nothing 'uncharitable' about that interpretation. It is definitely impressive, but it is fundamentally broken, because when you start making chains of things that are 99.7% correct you end up with garbage after very few iterations. That's precisely why digital computers won out over analog ones, the fact that they are deterministic.
Category error. You want 100% accuracy for an impossible problem. This is a famously unsolved conjecture. The only way to get the answer is to fully calculate it. The task was to make a guess and see how well it could do. 99.7 is surprisingly good. If the task was to calculate, the llm could write a python program, just like I would have if asked to calculate the answer.
There is a massive difference between an 'unsolved problem' and a problem solved 'the wrong way'. Yes, 99.7% is surprisingly good. But it did not detect the errors in its own output. And it should have.
Besides, we're all stuck on the 99.7% as if that's the across the board output, but that's a cherry picked result:
"The best models (bases 24, 16 and 32) achieve a near-perfect accuracy of 99.7%, while odd-base models struggle to get past 80%."
I do think it is a very interesting thing to do with a model and it is impressive that it works at all.
Category error.
The problem here is deterministic. *It must be for accuracy to even be measured*.
The model isn't trying to solve the Collatz conjecture, it is learning a pretty basic algorithm and then doing this a number of times. The instructions it needs to learn is
It also needs to learn to put that in a loop and for that to be a variable, but the algorithm is static.On the other hand, the Collatz conjecture states that for C(x) (the above algorithm) has a fixed point of 1 for all x (where x \in Z+). Meaning that eventually any input will collapse to the loop 1 -> 4 -> 2 -> 1 (or just terminate at 1). You can probably see we know this is true for at least an infinite set of integers...
Edit: I should note that there is a slight modification to this, though model could get away with learning just this. Their variation limits to odd numbers and not all of them. For example 9 can't be represented by (2^k)m - 1 (but 7 and 15 can). But you can see that there's still a simple algorithm and that the crux is determining the number of iterations. Regardless, this is still deterministic. They didn't use any integers >2^71, which we absolutely know the sequences for and we absolutely know all terminate at 1.
To solve the Collatz Conjecture (and probably win a Fields Metal) you must do one of 2 things.
Most primality tests aren't 100% accurate either (eg Miller Rabin), they just are "reasonably accurate" while being very fast to compute. You can use them in conjunction to improve your confidence in the result.
Yes, and we know they are inaccurate and we know that if you find a prime that way you can only use it to reject, not confirm so if you think that something is prime you need to check it.
But now imagine that instead of it being a valid reject 0.3% of the time it would also reject valid primes. Now it would be instantly useless because it fails the test for determinism.
I don't know people are saying it's useful. Just interesting
It's uncharitable because the comment purports to summarise the entire paper while simply cherry picking the worst result. It would be like if asked how did I do on my test and you said well you got question 1 wrong and then didn't elaborate.
Now I get your point that a function that is 99.7% accurate will eventually always be incorrect but that's not what the comment said.
I just tried to get to the heart of the claim based on a skim. Please feel free to refine my summary.
Why do people keep using LLMs as algorithms?
LLMs are not calculators. If you want a calculator use a calculator. Hell, have your LLM use a calculator.
>That's precisely why digital computers won out over analog ones, the fact that they are deterministic.
I mean, no not really, digital computers are far easier to build and far more multi-purpose (and technically the underlying signals are analog).
Again, if you have a deterministic solution that is 100% correct all the time, use it, it will be cheaper than an LLM. People use LLMs because there are problems that are either not deterministic or the deterministic solution uses more energy than will ever be available in the local part of our universe. Furthermore a lot of AI (not even LLMs) use random noise at particular steps as a means to escape local maxima.
> Why do people keep using LLMs as algorithms?
I think they keep coming back to this because a good command of math underlies a vast domain of applications and without a way to do this as part of the reasoning process the reasoning process itself becomes susceptible to corruption.
> LLMs are not calculators. If you want a calculator use a calculator. Hell, have your LLM use a calculator.
If only it were that simple.
> I mean, no not really, digital computers are far easier to build and far more multi-purpose (and technically the underlying signals are analog).
Try building a practical analog computer for a non-trivial problem.
> Again, if you have a deterministic solution that is 100% correct all the time, use it, it will be cheaper than an LLM. People use LLMs because there are problems that are either not deterministic or the deterministic solution uses more energy than will ever be available in the local part of our universe. Furthermore a lot of AI (not even LLMs) use random noise at particular steps as a means to escape local maxima.
No, people use LLMs for anything and one of the weak points in there is that as soon as it requires slightly more complex computation there is a fair chance that the output is nonsense. I've seen this myself in a bunch of non-trivial trials regarding aerodynamic calculations, specifically rotation of airfoils relative to the direction of travel. It tends to go completely off the rails if the problem is non-trivial and the user does not break it down into roughly the same steps as you would if you were to work out the problem by hand (and even then it may subtly mess up).
>A pocket calculator that would give the right numbers 99.7% of the time would be fairly useless.
Well that's great and all, but the vast majority of llm use is not for stuff you can just pluck out a pocket calculator (or run a similarly airtight deterministic algorithm) for, so this is just a moot point.
People really need to let go of this obsession with a perfect general intelligence that never makes errors. It doesn't and has never existed besides in fiction.
yeah it's only correct in 99.7% of all cases, but what if it's also 10'000 times faster? There's a bunch of scenarios where that combination provides a lot of value
Ridiculous counterfactual. The LLM started failing 100% of the time 60! orders of magnitude sooner than the point at which we have checked literally every number.
This is not even to mention the fact that asking a GPU to think about the problem will always be less efficient than just asking that GPU to directly compute the result for closed algorithms like this.
Correctness in software is the first rung of the ladder, optimizing before you have correct output is in almost all cases a complete waste of time. Yes, there are a some scenarios where having a ballpark figure quickly can be useful if you can produce the actual result as well and if you are not going to output complete nonsense the other times but something that approaches the final value. There are a lot of algorithms that do this (for instance: Newton's method for finding square roots).
99.7% of the time good and 0.3% of the time noise is not very useful, especially if there is no confidence indicating that the bad answers are probably incorrect.
[flagged]
Do you think maybe OP would have asked a language model for the answer if they felt like they wanted a language model to give an answer? Or in your mind parent doesn't know about LLMs, and this is your way of introducing them to this completely new concept?
Funny that the "human" answer above took 2 people to be "complete" (i.e. an initial answer, followed by a correction and expansion of concepts), while the LLM one had mostly the same explanation, but complete and in one answer.
Maybe most of us here don't seek just whatever answer to whatever question, but the human connection part of it is important too, that we're speaking with real humans that have real experience with real situations.
Otherwise I'd just be sitting chatting with ChatGPT all day instead of wast...spending all day on HN.
If life is a jobs program, why don't we dig ditches with spoons?
Oh, I agree. What I found funny is the gut reaction of many other readers that downvoted the message (it's greyed out for me at time of writing this comment). Especially given that the user clearly mentioned that it was LLM generated, while also being cheeky with the "transformer" pun, on a ... transformer topic.
Interesting, what about the old proof that neural networks can't model arbitrary length sine waves?
I don't know that computers can model arbitrary length sine waves either. At least not in the sense of me being able to input any `x` and get `sin(x)` back out. All computers have finite memory, meaning they can only represent a finite number of numbers, so there is some number `x` above which they can't represent any number.
Neural networks are more limited of course, because there's no way to expand their equivalent of memory, while it's easy to expand a computer's memory.
Here's the paper for your interest
https://arxiv.org/abs/2006.08195
That proof only applies to fixed architecture feed forward multilayer perceptrons with no recurrence, iirc. Transformers are not that.