5 ms·

All it speaks to is that tokenization is weird and introduces artifacts to LLM performance. Counting letters is a trivial task when you're staring at words on a

All it speaks to is that tokenization is weird and introduces artifacts to LLM performance. Counting letters is a trivial task when you're staring at words on a screen. It's much harder when you're perceiving vectors based on parts of words. The fact that LLMs find certain things easier/harder than humans is completely unsurprising, and there are much more interesting benchmarks to use to compare one LLM to another.

jrflowers 2y ago
This is a good point. While LLMs being incapable of reliably doing a simple task that’s been doable by computers since the punch card days is an important consideration for anyone that might be thinking about using them for anything other than as a toy, this fact is uninteresting because of Reasons
- space_fountain 2y ago
  LLMs can clearly solve problems that computers up to now couldn't. They can't solve all problems and this should definitely be a cautionary note to anyone who wants to use them as an artificial general intelligence, but this take seems no different to someone looking at a punchcard computer and going, it can't even recognize typos or categorize images, what good is this? We've already had human computers who can do everything these can do, and can recognize images and notice typos
  - evilduck 2y ago
    Also humans would revert to explicitly using an algorithm and external storage like a sheet of paper with tally marks or a spreadsheet or even a computer program if you scale the question up to a full sheet of text or a whole book or a collection of books (we probably do it at a single word size too, but it's more intuitive than explicit behavior for most folks when the count sum is around 8 or less). LLMs can't effectively execute algorithms similarly in their context, nor can they memorize new data or facts it was given without providing it tools like function calling or embeddings. If you give LLMs tool calling and storage mechanisms then counting letters in words becomes pretty damn reliable.
  - jrflowers 2y ago
    > going, it can't even recognize typos or categorize images, what good is this? No one said that LLMs aren’t good for anything. I pointed out — in response to another poster downplaying mention of a well-known and undisputed limitation that LLMs often have — that it is valid to consider these well-known and undisputed limitations if one is considering using them for anything other than a toy. It is downright silly to discourage discussion of well-known and undisputed limitations! The only reason for that can only be entirely emotional as there is genuinely nothing tangible to be gained by being steadfast in silence about a fact that isn’t up for debate.
    - space_fountain 2y ago
      I think somehow there were a series of miscommunications. This kind of sub token manipulation task is hard for an LLM for somewhat predictable reasons. Knowing those limitations are important, but don't come up too often in practical circumstances. Outside of contrived examples counting the number of letters in a long word is pretty rare. I took your response to be arguing against a message I'd read to be saying something like the above. Especially when you basically seemed to be saying that limitations like this are important in everything but toy applications. It's uninteresting because it doesn't point towards larger problems with their use in the kind of application they're being used for and are intended for unlike prompts that point to weakness in logic or propensity to hallucinate.
- Nevermark 2y ago
  Try counting the number of your red retina cells that are firing while you look at a painting. Don’t need to be exact as firing is statistical, just give us a good average. Hard? You can’t count? Computers count pixels no problem. So weird you can’t. Dementia? Not an AGI? /h —- This is what is happening. Here are the “Reasons”. In your vision system, the raw information from individual retina signals is munged into a different representation before reaching a level where you have flexible processing. Likewise, in LLMs, letters are munged into tokens before LLMs “see” them. When they sometimes get that “simple” question right, it’s actually a bit of an amazing feat. Given how they are constructed. —- Now try counting R’s as you read at a normal rate, or listen to someone speak. You can’t do that either, during normal processing. When we add spelling to LLMs training examples, they will do it easily. Just as you learned to do it, only after special lessons, after you had already learned to listen and speak. Spelling is its own special practiced skill, in humans and LLMs.
  - jrflowers 2y ago
    > Try counting the number of your red retina cells that are firing during while you look at a painting. This analogy makes sense because everybody could count their red retina cells until a couple years ago when the new painting paradigm arose, and also counting red retinal cells is a good analogy for being able to see simple objects that have always been distinguishable. It is fascinating how tapping the “Do Not Use LLMs For Computation If The Results Need To Be Reliably Better Than A Random Output” sign invites explanations of why that fact is actually Cool and Good
    - Nevermark 2y ago
      Ask anyone who has not specifically learned to spell, to count R’s while you speak. You learned to listen and speak words before you could spell. Imagine if nobody had actually shown you written words? Or they were speaking another dialect but expecting you to count R’s in standard English? LLMs are not trained on words in the form of letters. They process and generate the words in the form of tokens. Pre- and post-processing systems converts letters to tokens and the reverse, without their ability to access that processing. Spelling, for both us and LLMs, requires specific training/lessons. > It is fascinating how tapping the “Do Not Use LLMs For Computation If The Results Need To Be Reliably Better Than A Random Output” sign invites explanations of why that fact is actually Cool and Good Also fascinating: People who hallucinate/confabulate ridiculous straw man rationales for people they disagree with, unaware they are filling in gaps in their knowledge regarding other people’s actual reasoning and the actual subject at hand. So LLM! Such unreliable processing! Perhaps, start posting a reliability disclaimer?
doctorpangloss 2y ago
Counting shit, like cells, peaks in signals, people, inventory, fingers, and votes, is hard, tedious and important to business and life, so I don’t know dude, it seems like a great benchmark to me. Countless posts wasted on denying this simple and obvious fact.
- deleted 2y ago
  [deleted]
- BoorishBears 2y ago
  It's like using a hammer to turn a screw and calling it useless. To envision what a next generation model bound by the same constraints should do, it'd be to recognize that it can't count tokens and use code access to write code that solves the strawberry problem without prompting. Asked to count cells it'd be a model that could write and execute OpenCV tasks. Or to go a step further, be a multimodal model that can synthesize 10000 varations of the target cell, and finetune a model like YOLO on it autonomously. I find arguments that reduce LLMs to "It can't do the simple thing!!!!" come from people unable to apply lateral thinking to how a task can be solved.
  - deleted 2y ago
    [deleted]
  - doctorpangloss 2y ago
    > To envision what a next generation model bound by the same constraints should do, it'd be to recognize that it can't count tokens and use code access to write code that solves the strawberry problem without prompting. The VQA problems I'm describing can be solved seemingly in one case but not combined with counting. Counting is fundamentally challenging for sort of unknown reasons, or perhaps known to the very best labs who are trying to tackle it directly. Another POV is that the stuff you are describing is in some sense so obvious that it has been tried, no?
    - BoorishBears 2y ago
      I don't get what you mean by "unknown reasons", we understand that counting tokens requires a type of introspection transformer models can't do while operating on tokens. What I described is tried, and works, but the models are still not cheap/fast/reliable enough to always do what I described for every query. The difference between what I described and directly asking the model to count is that we know the models can get cheaper, faster, and more reliable at what I described without any earth shattering discoveries Like I don't see any reason why GPT 10 will ever be able to count how many letters there are in the word strawberry without a complete paradigm shift in model building... but going from GPT 3 to GPT 4 we already got a model that can always write the dead simple code required to count it out, and the models that can do so are already getting cheaper and faster every few months without any crazy discoveries.

jrflowers 2y ago

This is a good point. While LLMs being incapable of reliably doing a simple task that’s been doable by computers since the punch card days is an important consideration for anyone that might be thinking about using them for anything other than as a toy, this fact is uninteresting because of Reasons

space_fountain 2y ago
LLMs can clearly solve problems that computers up to now couldn't. They can't solve all problems and this should definitely be a cautionary note to anyone who wants to use them as an artificial general intelligence, but this take seems no different to someone looking at a punchcard computer and going, it can't even recognize typos or categorize images, what good is this? We've already had human computers who can do everything these can do, and can recognize images and notice typos
- evilduck 2y ago
  Also humans would revert to explicitly using an algorithm and external storage like a sheet of paper with tally marks or a spreadsheet or even a computer program if you scale the question up to a full sheet of text or a whole book or a collection of books (we probably do it at a single word size too, but it's more intuitive than explicit behavior for most folks when the count sum is around 8 or less). LLMs can't effectively execute algorithms similarly in their context, nor can they memorize new data or facts it was given without providing it tools like function calling or embeddings. If you give LLMs tool calling and storage mechanisms then counting letters in words becomes pretty damn reliable.
- jrflowers 2y ago
  > going, it can't even recognize typos or categorize images, what good is this? No one said that LLMs aren’t good for anything. I pointed out — in response to another poster downplaying mention of a well-known and undisputed limitation that LLMs often have — that it is valid to consider these well-known and undisputed limitations if one is considering using them for anything other than a toy. It is downright silly to discourage discussion of well-known and undisputed limitations! The only reason for that can only be entirely emotional as there is genuinely nothing tangible to be gained by being steadfast in silence about a fact that isn’t up for debate.
  - space_fountain 2y ago
    I think somehow there were a series of miscommunications. This kind of sub token manipulation task is hard for an LLM for somewhat predictable reasons. Knowing those limitations are important, but don't come up too often in practical circumstances. Outside of contrived examples counting the number of letters in a long word is pretty rare. I took your response to be arguing against a message I'd read to be saying something like the above. Especially when you basically seemed to be saying that limitations like this are important in everything but toy applications. It's uninteresting because it doesn't point towards larger problems with their use in the kind of application they're being used for and are intended for unlike prompts that point to weakness in logic or propensity to hallucinate.
Nevermark 2y ago
Try counting the number of your red retina cells that are firing while you look at a painting. Don’t need to be exact as firing is statistical, just give us a good average. Hard? You can’t count? Computers count pixels no problem. So weird you can’t. Dementia? Not an AGI? /h —- This is what is happening. Here are the “Reasons”. In your vision system, the raw information from individual retina signals is munged into a different representation before reaching a level where you have flexible processing. Likewise, in LLMs, letters are munged into tokens before LLMs “see” them. When they sometimes get that “simple” question right, it’s actually a bit of an amazing feat. Given how they are constructed. —- Now try counting R’s as you read at a normal rate, or listen to someone speak. You can’t do that either, during normal processing. When we add spelling to LLMs training examples, they will do it easily. Just as you learned to do it, only after special lessons, after you had already learned to listen and speak. Spelling is its own special practiced skill, in humans and LLMs.
- jrflowers 2y ago
  > Try counting the number of your red retina cells that are firing during while you look at a painting. This analogy makes sense because everybody could count their red retina cells until a couple years ago when the new painting paradigm arose, and also counting red retinal cells is a good analogy for being able to see simple objects that have always been distinguishable. It is fascinating how tapping the “Do Not Use LLMs For Computation If The Results Need To Be Reliably Better Than A Random Output” sign invites explanations of why that fact is actually Cool and Good
  - Nevermark 2y ago
    Ask anyone who has not specifically learned to spell, to count R’s while you speak. You learned to listen and speak words before you could spell. Imagine if nobody had actually shown you written words? Or they were speaking another dialect but expecting you to count R’s in standard English? LLMs are not trained on words in the form of letters. They process and generate the words in the form of tokens. Pre- and post-processing systems converts letters to tokens and the reverse, without their ability to access that processing. Spelling, for both us and LLMs, requires specific training/lessons. > It is fascinating how tapping the “Do Not Use LLMs For Computation If The Results Need To Be Reliably Better Than A Random Output” sign invites explanations of why that fact is actually Cool and Good Also fascinating: People who hallucinate/confabulate ridiculous straw man rationales for people they disagree with, unaware they are filling in gaps in their knowledge regarding other people’s actual reasoning and the actual subject at hand. So LLM! Such unreliable processing! Perhaps, start posting a reliability disclaimer?

doctorpangloss 2y ago

Counting shit, like cells, peaks in signals, people, inventory, fingers, and votes, is hard, tedious and important to business and life, so I don’t know dude, it seems like a great benchmark to me. Countless posts wasted on denying this simple and obvious fact.

deleted 2y ago
[deleted]
BoorishBears 2y ago
It's like using a hammer to turn a screw and calling it useless. To envision what a next generation model bound by the same constraints should do, it'd be to recognize that it can't count tokens and use code access to write code that solves the strawberry problem without prompting. Asked to count cells it'd be a model that could write and execute OpenCV tasks. Or to go a step further, be a multimodal model that can synthesize 10000 varations of the target cell, and finetune a model like YOLO on it autonomously. I find arguments that reduce LLMs to "It can't do the simple thing!!!!" come from people unable to apply lateral thinking to how a task can be solved.
- deleted 2y ago
  [deleted]
- doctorpangloss 2y ago
  > To envision what a next generation model bound by the same constraints should do, it'd be to recognize that it can't count tokens and use code access to write code that solves the strawberry problem without prompting. The VQA problems I'm describing can be solved seemingly in one case but not combined with counting. Counting is fundamentally challenging for sort of unknown reasons, or perhaps known to the very best labs who are trying to tackle it directly. Another POV is that the stuff you are describing is in some sense so obvious that it has been tried, no?
  - BoorishBears 2y ago
    I don't get what you mean by "unknown reasons", we understand that counting tokens requires a type of introspection transformer models can't do while operating on tokens. What I described is tried, and works, but the models are still not cheap/fast/reliable enough to always do what I described for every query. The difference between what I described and directly asking the model to count is that we know the models can get cheaper, faster, and more reliable at what I described without any earth shattering discoveries Like I don't see any reason why GPT 10 will ever be able to count how many letters there are in the word strawberry without a complete paradigm shift in model building... but going from GPT 3 to GPT 4 we already got a model that can always write the dead simple code required to count it out, and the models that can do so are already getting cheaper and faster every few months without any crazy discoveries.